PMCC PMCC

Search tips
Search criteria

Advanced
Results 1-25 (889151)

Clipboard (0)
None

Related Articles

1.  Cancer Screening: A Mathematical Model Relating Secreted Blood Biomarker Levels to Tumor Sizes  
PLoS Medicine  2008;5(8):e170.
Background
Increasing efforts and financial resources are being invested in early cancer detection research. Blood assays detecting tumor biomarkers promise noninvasive and financially reasonable screening for early cancer with high potential of positive impact on patients' survival and quality of life. For novel tumor biomarkers, the actual tumor detection limits are usually unknown and there have been no studies exploring the tumor burden detection limits of blood tumor biomarkers using mathematical models. Therefore, the purpose of this study was to develop a mathematical model relating blood biomarker levels to tumor burden.
Methods and Findings
Using a linear one-compartment model, the steady state between tumor biomarker secretion into and removal out of the intravascular space was calculated. Two conditions were assumed: (1) the compartment (plasma) is well-mixed and kinetically homogenous; (2) the tumor biomarker consists of a protein that is secreted by tumor cells into the extracellular fluid compartment, and a certain percentage of the secreted protein enters the intravascular space at a continuous rate. The model was applied to two pathophysiologic conditions: tumor biomarker is secreted (1) exclusively by the tumor cells or (2) by both tumor cells and healthy normal cells. To test the model, a sensitivity analysis was performed assuming variable conditions of the model parameters. The model parameters were primed on the basis of literature data for two established and well-studied tumor biomarkers (CA125 and prostate-specific antigen [PSA]). Assuming biomarker secretion by tumor cells only and 10% of the secreted tumor biomarker reaching the plasma, the calculated minimally detectable tumor sizes ranged between 0.11 mm3 and 3,610.14 mm3 for CA125 and between 0.21 mm3 and 131.51 mm3 for PSA. When biomarker secretion by healthy cells and tumor cells was assumed, the calculated tumor sizes leading to positive test results ranged between 116.7 mm3 and 1.52 × 106 mm3 for CA125 and between 27 mm3 and 3.45 × 105 mm3 for PSA. One of the limitations of the study is the absence of quantitative data available in the literature on the secreted tumor biomarker amount per cancer cell in intact whole body animal tumor models or in cancer patients. Additionally, the fraction of secreted tumor biomarkers actually reaching the plasma is unknown. Therefore, we used data from published cell culture experiments to estimate tumor cell biomarker secretion rates and assumed a wide range of secretion rates to account for their potential changes due to field effects of the tumor environment.
Conclusions
This study introduced a linear one-compartment mathematical model that allows estimation of minimal detectable tumor sizes based on blood tumor biomarker assays. Assuming physiological data on CA125 and PSA from the literature, the model predicted detection limits of tumors that were in qualitative agreement with the actual clinical performance of both biomarkers. The model may be helpful in future estimation of minimal detectable tumor sizes for novel proteomic biomarker assays if sufficient physiologic data for the biomarker are available. The model may address the potential and limitations of tumor biomarkers, help prioritize biomarkers, and guide investments into early cancer detection research efforts.
Sanjiv Gambhir and colleagues describe a linear one-compartment mathematical model that allows estimation of minimal detectable tumor sizes based on blood tumor biomarker assays.
Editors' Summary
Background.
Cancers—disorganized masses of cells that can occur in any tissue—develop when cells acquire genetic changes that allow them to grow uncontrollably and to spread around the body (metastasize). If a cancer (tumor) is detected when it is small, surgery can often provide a cure. Unfortunately, many cancers (particularly those deep inside the body) are not detected until they are large enough to cause pain or other symptoms by pressing against surrounding tissue. By this time, it may be impossible to remove the original tumor surgically and there may be metastases scattered around the body. In such cases, radiotherapy and chemotherapy can sometimes help, but the outlook for patients whose cancers are detected late is often poor. Consequently, researchers are trying to develop early detection tests for different types of cancer. Many tumors release specific proteins—“cancer biomarkers”—into the blood and the hope is that it might be possible to find sets of blood biomarkers that detect cancers when they are still small and thus save many lives.
Why Was This Study Done?
For most biomarkers, it is not known how the amount of protein detected in the blood relates to tumor size or how sensitive the assays for biomarkers must be to improve patient survival. In this study, the researchers develop a “linear one-compartment” mathematical model to predict how large tumors need to be before blood biomarkers can be used to detect them and test this model using published data on two established cancer biomarkers—CA125 and prostate-specific antigen (PSA). CA125 is used to monitor the progress of patients with ovarian cancer after treatment; ovarian cancer is rarely diagnosed in its early stages and only one-fourth of women with advanced disease survive for 5 y after diagnosis. PSA is used to screen for prostate cancer and has increased the detection of this cancer in its early stages when it is curable.
What Did the Researchers Do and Find?
To develop a model that relates secreted blood biomarker levels to tumor sizes, the researchers assumed that biomarkers mix evenly throughout the patient's blood, that cancer cells secrete biomarkers into the fluid that surrounds them, that 0.1%–20% of these secreted proteins enter the blood at a continuous rate, and that biomarkers are continuously removed from the blood. The researchers then used their model to calculate the smallest tumor sizes that might be detectable with these biomarkers by feeding in existing data on CA125 and on PSA, including assay detection limits and the biomarker secretion rates of cancer cells growing in dishes. When only tumor cells secreted the biomarker and 10% of the secreted biomarker reach the blood, the model predicted that ovarian tumors between 0.11 mm3 (smaller than a grain of salt) and nearly 4,000 mm3 (about the size of a cherry) would be detectable by measuring CA125 blood levels (the range was determined by varying the amount of biomarker secreted by the tumor cells and the assay sensitivity); for prostate cancer, the detectable tumor sizes ranged from similar lower size to about 130 mm3 (pea-sized). However, healthy cells often also secrete small quantities of cancer biomarkers. With this condition incorporated into the model, the estimated detectable tumor sizes (or total tumor burden including metastases) ranged between grape-sized and melon-sized for ovarian cancers and between pea-sized to about grapefruit-sized for prostate cancers.
What Do These Findings Mean?
The accuracy of the calculated tumor sizes provided by the researchers' mathematical model is limited by the lack of data on how tumors behave in the human body and by the many assumptions incorporated into the model. Nevertheless, the model predicts detection limits for ovarian and prostate cancer that broadly mirror the clinical performance of both biomarkers. Somewhat worryingly, the model also indicates that a tumor may have to be very large for blood biomarkers to reveal its presence, a result that could limit the clinical usefulness of biomarkers, especially if they are secreted not only by tumor cells but also by healthy cells. Given this finding, as more information about how biomarkers behave in the human body becomes available, this model (and more complex versions of it) should help researchers decide which biomarkers are likely to improve early cancer detection and patient outcomes.
Additional Information.
Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.0050170.
The US National Cancer Institute provides a brief description of what cancer is and how it develops and a fact sheet on tumor markers; it also provides information on all aspects of ovarian and prostate cancer for patients and professionals, including information on screening and testing (in English and Spanish)
The UK charity Cancerbackup also provides general information about cancer and more specific information about ovarian and prostate cancer, including the use of CA125 and PSA for screening and follow-up
The American Society of Clinical Oncology offers a wide range of information on various cancer types, including online published articles on the current status of cancer diagnosis and management from the educational book developed by the annual meeting faculty and presenters. Registration is mandatory, but information is free
doi:10.1371/journal.pmed.0050170
PMCID: PMC2517618  PMID: 18715113
2.  Biomarker Profiling by Nuclear Magnetic Resonance Spectroscopy for the Prediction of All-Cause Mortality: An Observational Study of 17,345 Persons 
PLoS Medicine  2014;11(2):e1001606.
In this study, Würtz and colleagues conducted high-throughput profiling of blood specimens in two large population-based cohorts in order to identify biomarkers for all-cause mortality and enhance risk prediction. The authors found that biomarker profiling improved prediction of the short-term risk of death from all causes above established risk factors. However, further investigations are needed to clarify the biological mechanisms and the utility of these biomarkers to guide screening and prevention.
Please see later in the article for the Editors' Summary
Background
Early identification of ambulatory persons at high short-term risk of death could benefit targeted prevention. To identify biomarkers for all-cause mortality and enhance risk prediction, we conducted high-throughput profiling of blood specimens in two large population-based cohorts.
Methods and Findings
106 candidate biomarkers were quantified by nuclear magnetic resonance spectroscopy of non-fasting plasma samples from a random subset of the Estonian Biobank (n = 9,842; age range 18–103 y; 508 deaths during a median of 5.4 y of follow-up). Biomarkers for all-cause mortality were examined using stepwise proportional hazards models. Significant biomarkers were validated and incremental predictive utility assessed in a population-based cohort from Finland (n = 7,503; 176 deaths during 5 y of follow-up). Four circulating biomarkers predicted the risk of all-cause mortality among participants from the Estonian Biobank after adjusting for conventional risk factors: alpha-1-acid glycoprotein (hazard ratio [HR] 1.67 per 1–standard deviation increment, 95% CI 1.53–1.82, p = 5×10−31), albumin (HR 0.70, 95% CI 0.65–0.76, p = 2×10−18), very-low-density lipoprotein particle size (HR 0.69, 95% CI 0.62–0.77, p = 3×10−12), and citrate (HR 1.33, 95% CI 1.21–1.45, p = 5×10−10). All four biomarkers were predictive of cardiovascular mortality, as well as death from cancer and other nonvascular diseases. One in five participants in the Estonian Biobank cohort with a biomarker summary score within the highest percentile died during the first year of follow-up, indicating prominent systemic reflections of frailty. The biomarker associations all replicated in the Finnish validation cohort. Including the four biomarkers in a risk prediction score improved risk assessment for 5-y mortality (increase in C-statistics 0.031, p = 0.01; continuous reclassification improvement 26.3%, p = 0.001).
Conclusions
Biomarker associations with cardiovascular, nonvascular, and cancer mortality suggest novel systemic connectivities across seemingly disparate morbidities. The biomarker profiling improved prediction of the short-term risk of death from all causes above established risk factors. Further investigations are needed to clarify the biological mechanisms and the utility of these biomarkers for guiding screening and prevention.
Please see later in the article for the Editors' Summary
Editors' Summary
Background
A biomarker is a biological molecule found in blood, body fluids, or tissues that may signal an abnormal process, a condition, or a disease. The level of a particular biomarker may indicate a patient's risk of disease, or likely response to a treatment. For example, cholesterol levels are measured to assess the risk of heart disease. Most current biomarkers are used to test an individual's risk of developing a specific condition. There are none that accurately assess whether a person is at risk of ill health generally, or likely to die soon from a disease. Early and accurate identification of people who appear healthy but in fact have an underlying serious illness would provide valuable opportunities for preventative treatment.
While most tests measure the levels of a specific biomarker, there are some technologies that allow blood samples to be screened for a wide range of biomarkers. These include nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry. These tools have the potential to be used to screen the general population for a range of different biomarkers.
Why Was This Study Done?
Identifying new biomarkers that provide insight into the risk of death from all causes could be an important step in linking different diseases and assessing patient risk. The authors in this study screened patient samples using NMR spectroscopy for biomarkers that accurately predict the risk of death particularly amongst the general population, rather than amongst people already known to be ill.
What Did the Researchers Do and Find?
The researchers studied two large groups of people, one in Estonia and one in Finland. Both countries have set up health registries that collect and store blood samples and health records over many years. The registries include large numbers of people who are representative of the wider population.
The researchers first tested blood samples from a representative subset of the Estonian group, testing 9,842 samples in total. They looked at 106 different biomarkers in each sample using NMR spectroscopy. They also looked at the health records of this group and found that 508 people died during the follow-up period after the blood sample was taken, the majority from heart disease, cancer, and other diseases. Using statistical analysis, they looked for any links between the levels of different biomarkers in the blood and people's short-term risk of dying. They found that the levels of four biomarkers—plasma albumin, alpha-1-acid glycoprotein, very-low-density lipoprotein (VLDL) particle size, and citrate—appeared to accurately predict short-term risk of death. They repeated this study with the Finnish group, this time with 7,503 individuals (176 of whom died during the five-year follow-up period after giving a blood sample) and found similar results.
The researchers carried out further statistical analyses to take into account other known factors that might have contributed to the risk of life-threatening illness. These included factors such as age, weight, tobacco and alcohol use, cholesterol levels, and pre-existing illness, such as diabetes and cancer. The association between the four biomarkers and short-term risk of death remained the same even when controlling for these other factors.
The analysis also showed that combining the test results for all four biomarkers, to produce a biomarker score, provided a more accurate measure of risk than any of the biomarkers individually. This biomarker score also proved to be the strongest predictor of short-term risk of dying in the Estonian group. Individuals with a biomarker score in the top 20% had a risk of dying within five years that was 19 times greater than that of individuals with a score in the bottom 20% (288 versus 15 deaths).
What Do These Findings Mean?
This study suggests that there are four biomarkers in the blood—alpha-1-acid glycoprotein, albumin, VLDL particle size, and citrate—that can be measured by NMR spectroscopy to assess whether otherwise healthy people are at short-term risk of dying from heart disease, cancer, and other illnesses. However, further validation of these findings is still required, and additional studies should examine the biomarker specificity and associations in settings closer to clinical practice. The combined biomarker score appears to be a more accurate predictor of risk than tests for more commonly known risk factors. Identifying individuals who are at high risk using these biomarkers might help to target preventative medical treatments to those with the greatest need.
However, there are several limitations to this study. As an observational study, it provides evidence of only a correlation between a biomarker score and ill health. It does not identify any underlying causes. Other factors, not detectable by NMR spectroscopy, might be the true cause of serious health problems and would provide a more accurate assessment of risk. Nor does this study identify what kinds of treatment might prove successful in reducing the risks. Therefore, more research is needed to determine whether testing for these biomarkers would provide any clinical benefit.
There were also some technical limitations to the study. NMR spectroscopy does not detect as many biomarkers as mass spectrometry, which might therefore identify further biomarkers for a more accurate risk assessment. In addition, because both study groups were northern European, it is not yet known whether the results would be the same in other ethnic groups or populations with different lifestyles.
In spite of these limitations, the fact that the same four biomarkers are associated with a short-term risk of death from a variety of diseases does suggest that similar underlying mechanisms are taking place. This observation points to some potentially valuable areas of research to understand precisely what's contributing to the increased risk.
Additional Information
Please access these websites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001606
The US National Institute of Environmental Health Sciences has information on biomarkers
The US Food and Drug Administration has a Biomarker Qualification Program to help researchers in identifying and evaluating new biomarkers
Further information on the Estonian Biobank is available
The Computational Medicine Research Team of the University of Oulu and the University of Bristol have a webpage that provides further information on high-throughput biomarker profiling by NMR spectroscopy
doi:10.1371/journal.pmed.1001606
PMCID: PMC3934819  PMID: 24586121
3.  The Preclinical Natural History of Serous Ovarian Cancer: Defining the Target for Early Detection 
PLoS Medicine  2009;6(7):e1000114.
Pat Brown and colleagues carry out a modeling study and define what properties a biomarker-based screening test would require in order to be clinically useful.
Background
Ovarian cancer kills approximately 15,000 women in the United States every year, and more than 140,000 women worldwide. Most deaths from ovarian cancer are caused by tumors of the serous histological type, which are rarely diagnosed before the cancer has spread. Rational design of a potentially life-saving early detection and intervention strategy requires understanding the lesions we must detect in order to prevent lethal progression. Little is known about the natural history of lethal serous ovarian cancers before they become clinically apparent. We can learn about this occult period by studying the unsuspected serous cancers that are discovered in a small fraction of apparently healthy women who undergo prophylactic bilateral salpingo-oophorectomy (PBSO).
Methods and Findings
We developed models for the growth, progression, and detection of occult serous cancers on the basis of a comprehensive analysis of published data on serous cancers discovered by PBSO in BRCA1 mutation carriers. Our analysis yielded several critical insights into the early natural history of serous ovarian cancer. First, these cancers spend on average more than 4 y as in situ, stage I, or stage II cancers and approximately 1 y as stage III or IV cancers before they become clinically apparent. Second, for most of the occult period, serous cancers are less than 1 cm in diameter, and not visible on gross examination of the ovaries and Fallopian tubes. Third, the median diameter of a serous ovarian cancer when it progresses to an advanced stage (stage III or IV) is about 3 cm. Fourth, to achieve 50% sensitivity in detecting tumors before they advance to stage III, an annual screen would need to detect tumors of 1.3 cm in diameter; 80% detection sensitivity would require detecting tumors less than 0.4 cm in diameter. Fifth, to achieve a 50% reduction in serous ovarian cancer mortality with an annual screen, a test would need to detect tumors of 0.5 cm in diameter.
Conclusions
Our analysis has formalized essential conditions for successful early detection of serous ovarian cancer. Although the window of opportunity for early detection of these cancers lasts for several years, developing a test sufficiently sensitive and specific to take advantage of that opportunity will be a challenge. We estimated that the tumors we would need to detect to achieve even 50% sensitivity are more than 200 times smaller than the clinically apparent serous cancers typically used to evaluate performance of candidate biomarkers; none of the biomarker assays reported to date comes close to the required level of performance. Overcoming the signal-to-noise problem inherent in detection of tiny tumors will likely require discovery of truly cancer-specific biomarkers or development of novel approaches beyond traditional blood protein biomarkers. While this study was limited to ovarian cancers of serous histological type and to those arising in BRCA1 mutation carriers specifically, we believe that the results are relevant to other hereditary serous cancers and to sporadic ovarian cancers. A similar approach could be applied to other cancers to aid in defining their early natural history and to guide rational design of an early detection strategy.
Please see later in the article for Editors' Summary
Editors' Summary
Background
Every year about 190,000 women develop ovarian cancer and more than 140,000 die from the disease. Ovarian cancer occurs when a cell on the surface of the ovaries (two small organs in the pelvis that produce eggs) or in the Fallopian tubes (which connect the ovaries to the womb) acquires genetic changes (mutations) that allow it to grow uncontrollably and to spread around the body (metastasize). For women whose cancer is diagnosed when it is confined to the site of origin—ovary or Fallopian tube—(stage I disease), the outlook is good; 70%–80% of these women survive for at least 5 y. However, very few ovarian cancers are diagnosed this early. Usually, by the time the cancer causes symptoms (often only vague abdominal pain and mild digestive disturbances), it has spread into the pelvis (stage II disease), into the space around the gut, stomach, and liver (stage III disease), or to distant organs (stage IV disease). Patients with advanced-stage ovarian cancer are treated with surgery and chemotherapy but, despite recent treatment improvements, only 15% of women diagnosed with stage IV disease survive for 5 y.
Why Was This Study Done?
Most deaths from ovarian cancer are caused by serous ovarian cancer, a tumor subtype that is rarely diagnosed before it has spread. Early detection of serous ovarian cancer would save the lives of many women but no one knows what these cancers look like before they spread or how long they grow before they become clinically apparent. Learning about this occult (hidden) period of ovarian cancer development by observing tumors from their birth to late-stage disease is not feasible. However, some aspects of the early natural history of ovarian cancer can be studied by using data collected from healthy women who have had their ovaries and Fallopian tubes removed (prophylactic bilateral salpingo-oophorectomy [PBSO]) because they have inherited a mutated version of the BRCA1 gene that increases their ovarian cancer risk. In a few of these women, unsuspected ovarian cancer is discovered during PBSO. In this study, the researchers identify and analyze the available reports on occult serous ovarian cancer found this way and then develop mathematical models describing the early natural history of ovarian cancer.
What Did the Researchers Do and Find?
The researchers first estimated the time period during which the detection of occult tumors might save lives using the data from these reports. Serous ovarian cancers, they estimated, spend more than 4 y as in situ (a very early stage of cancer development), stage I, or stage II cancers and about 1 y as stage III and IV cancers before they become clinically apparent. Next, the researchers used the data to develop mathematical models for the growth, progression, and diagnosis of serous ovarian cancer (the accuracy of which depends on the assumptions used to build the models and on the quality of the data fed into them). These models indicated that, for most of the occult period, serous cancers had a diameter of less than 1 cm (too small to be detected during surgery or by gross examination of the ovaries or Fallopian tubes) and that more than half of serous cancers had advanced to stage III/IV by the time they measured 3 cm across. Furthermore, to enable the detection of half of serous ovarian cancers before they reached stage III, an annual screening test would need to detect cancers with a diameter of 1.3 cm and to halve deaths from serous ovarian cancer, an annual screening test would need to detect 0.5-cm diameter tumors.
What Do These Findings Mean?
These findings suggest that the time period over which the early detection of serous ovarian cancer would save lives is surprisingly long. More soberingly, the authors find that a test that is sensitive and specific enough to take advantage of this “window of opportunity” would need to detect tumors hundreds of times smaller than clinically apparent serous cancers. So far no ovarian cancer-specific protein or other biomarker has been identified that could be used to develop a test that comes anywhere near this level of performance. Identification of truly ovarian cancer-specific biomarkers or novel strategies will be needed in order to take advantage of the window of opportunity. The stages prior to clinical presentation of other lethal cancers are still very poorly understood. Similar studies of the early natural history of these cancers could help guide the development of rational early detection strategies.
Additional Information
Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1000114.
The US National Cancer Institute provides a brief description of what cancer is and how it develops and information on all aspects of ovarian cancer for patients and professionals. It also provides a fact sheet on BRCA1 mutations and cancer risk (in English and Spanish)
The UK charity Cancerbackup also provides information about all aspects of ovarian cancer
MedlinePlus provides a list of links to additional information about ovarian cancer (in English and Spanish)
The Canary Foundation is a nonprofit organization dedicated to development of effective strategies for early detection of cancers including ovarian cancer.
doi:10.1371/journal.pmed.1000114
PMCID: PMC2711307  PMID: 19636370
4.  Ovarian Carcinoma Subtypes Are Different Diseases: Implications for Biomarker Studies 
PLoS Medicine  2008;5(12):e232.
Background
Although it has long been appreciated that ovarian carcinoma subtypes (serous, clear cell, endometrioid, and mucinous) are associated with different natural histories, most ovarian carcinoma biomarker studies and current treatment protocols for women with this disease are not subtype specific. With the emergence of high-throughput molecular techniques, distinct pathogenetic pathways have been identified in these subtypes. We examined variation in biomarker expression rates between subtypes, and how this influences correlations between biomarker expression and stage at diagnosis or prognosis.
Methods and Findings
In this retrospective study we assessed the protein expression of 21 candidate tissue-based biomarkers (CA125, CRABP-II, EpCam, ER, F-Spondin, HE4, IGF2, K-Cadherin, Ki-67, KISS1, Matriptase, Mesothelin, MIF, MMP7, p21, p53, PAX8, PR, SLPI, TROP2, WT1) in a population-based cohort of 500 ovarian carcinomas that was collected over the period from 1984 to 2000. The expression of 20 of the 21 biomarkers differs significantly between subtypes, but does not vary across stage within each subtype. Survival analyses show that nine of the 21 biomarkers are prognostic indicators in the entire cohort but when analyzed by subtype only three remain prognostic indicators in the high-grade serous and none in the clear cell subtype. For example, tumor proliferation, as assessed by Ki-67 staining, varies markedly between different subtypes and is an unfavourable prognostic marker in the entire cohort (risk ratio [RR] 1.7, 95% confidence interval [CI] 1.2%–2.4%) but is not of prognostic significance within any subtype. Prognostic associations can even show an inverse correlation within the entire cohort, when compared to a specific subtype. For example, WT1 is more frequently expressed in high-grade serous carcinomas, an aggressive subtype, and is an unfavourable prognostic marker within the entire cohort of ovarian carcinomas (RR 1.7, 95% CI 1.2%–2.3%), but is a favourable prognostic marker within the high-grade serous subtype (RR 0.5, 95% CI 0.3%–0.8%).
Conclusions
The association of biomarker expression with survival varies substantially between subtypes, and can easily be overlooked in whole cohort analyses. To avoid this effect, each subtype within a cohort should be analyzed discretely. Ovarian carcinoma subtypes are different diseases, and these differences should be reflected in clinical research study design and ultimately in the management of ovarian carcinoma.
David Huntsman and colleagues describe the associations between biomarker expression patterns and survival in different ovarian cancer subtypes. They suggest that the management of ovarian cancer should reflect differences between these subtypes.
Editors' Summary
Background.
Every year, about 200,000 women develop ovarian cancer and more than 100,000 die from the disease. Ovarian epithelial cancer (carcinoma) occurs when epithelial cells from the ovary or fallopian tube acquire mutations or equivalent changes that allow them to grow uncontrollably within one of the ovaries (two small organs in the pelvis that produce eggs) and acquire the potential to spread around the body (metastasize). While the cancer is confined to the ovaries, cancer specialists call this stage I disease; 70%–80% of women diagnosed with stage I ovarian cancer survive for at least 5 y. However, only a fifth of ovarian cancers are diagnosed at this stage; in the majority of patients the cancer has spread into the pelvis (stage II disease), into the peritoneal cavity (the space around the gut, stomach, and liver; stage III disease), or metastasized to distant organs such as brain (stage IV disease). This peritoneal spread might be associated with often only vague abdominal pain and mild digestive disturbances. Patients with advanced-stage ovarian carcinoma are treated with a combination of surgery and chemotherapy but, despite recent advances in treatment, only 15% of women diagnosed with stage IV disease survive for 5 y.
Why Was This Study Done?
Although it is usually regarded as a single disease, there are actually several distinct subtypes of ovarian carcinoma. These are classified according to their microscopic appearance as high-grade serous, low-grade serous, clear cell, endometrioid, and mucinous ovarian carcinomas. These subtypes develop differently and respond differently to chemotherapy. Yet scientists studying ovarian carcinoma usually regard this cancer as a single entity, and current treatment protocols for the disease are not subtype specific. Might better progress be made toward understanding ovarian carcinoma and toward improving its treatment if each subtype were treated as a separate disease? Why are some tumors confined to the ovary, whereas the majority spread beyond the ovary at time of diagnosis? In this study, the researchers address these questions by asking whether correlations between the expression of “biomarkers” (molecules made by cancer cells that can be used to detect tumors and to monitor treatment effectiveness) and the stage at diagnosis or length of survival can be explained by differential biomarker expression between different subtypes of ovarian carcinoma. They also address the question of whether early stage and late stage ovarian carcinomas are fundamentally different.
What Did the Researchers Do and Find?
The researchers measured the expression of 21 candidate protein biomarkers in 500 ovarian carcinoma samples collected in British Columbia, Canada, between 1984 and 2000. For 20 of the biomarkers, the fraction of tumors expressing the biomarker varied significantly between ovarian carcinoma subtypes. Considering all the tumors together, ten biomarkers had different expression levels in early and late stage tumors. However, when each subtype was considered separately, the expression of none of the biomarkers varied with stage. When the researchers asked whether the expression of any of the biomarkers correlated with survival times, they found that nine biomarkers were unfavorable indicators of outcome when all the tumors were considered together. That is, women whose tumors expressed any of these biomarkers had a higher risk of dying from ovarian cancer than women whose tumors did not express these biomarkers. However, only three biomarkers were unfavorable indicators for high-grade serous carcinomas considered alone and the expression of a biomarker called WT1 in this subtype of ovarian carcinoma is associated with a lower risk of dying. Similarly, expression of the biomarker Ki-67 was an unfavorable prognostic indicator when all the tumors were considered, but was not a prognostic indicator for any individual subtype.
What Do These Findings Mean?
These and other findings indicate that biomarker expression is more strongly associated with ovarian carcinoma subtype than with stage. In other words, biomarker expression is constant from early to late stage, but only within a given subtype. Second, the association of biomarker expression with survival varies between subtypes, hence lumping all subtypes together can yield misleading results. Although these findings need confirming in more tumor samples, they support the view that ovarian carcinoma subtypes are different diseases. In practical terms, therefore, these findings suggest that better ways to detect and treat ovarian cancer are more likely to be found if future biomarker studies and clinical research studies investigate each subtype of ovarian carcinoma separately rather than grouping them all together.
Additional Information.
Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.0050232.
The US National Cancer Institute provides a brief description of what cancer is and how it develops and information on all aspects of ovarian cancer for patients and professionals. It also provides a fact sheet on tumor markers (in English and Spanish)
The UK charity Cancerbackup provides general information about cancer and more specific information about ovarian cancer, including tumor staging
doi:10.1371/journal.pmed.0050232
PMCID: PMC2592352  PMID: 19053170
5.  Evaluating the Quality of Research into a Single Prognostic Biomarker: A Systematic Review and Meta-analysis of 83 Studies of C-Reactive Protein in Stable Coronary Artery Disease 
PLoS Medicine  2010;7(6):e1000286.
In a systematic review and meta-analysis of 83 prognostic studies of C-reactive protein in coronary disease, Hemingway and colleagues find substantial biases, preventing them from drawing clear conclusions relating to the use of this marker in clinical practice.
Background
Systematic evaluations of the quality of research on a single prognostic biomarker are rare. We sought to evaluate the quality of prognostic research evidence for the association of C-reactive protein (CRP) with fatal and nonfatal events among patients with stable coronary disease.
Methods and Findings
We searched MEDLINE (1966 to 2009) and EMBASE (1980 to 2009) and selected prospective studies of patients with stable coronary disease, reporting a relative risk for the association of CRP with death and nonfatal cardiovascular events. We included 83 studies, reporting 61,684 patients and 6,485 outcome events. No study reported a prespecified statistical analysis protocol; only two studies reported the time elapsed (in months or years) between initial presentation of symptomatic coronary disease and inclusion in the study. Studies reported a median of seven items (of 17) from the REMARK reporting guidelines, with no evidence of change over time.
The pooled relative risk for the top versus bottom third of CRP distribution was 1.97 (95% confidence interval [CI] 1.78–2.17), with substantial heterogeneity (I2 = 79.5). Only 13 studies adjusted for conventional risk factors (age, sex, smoking, obesity, diabetes, and low-density lipoprotein [LDL] cholesterol) and these had a relative risk of 1.65 (95% CI 1.39–1.96), I2 = 33.7. Studies reported ten different ways of comparing CRP values, with weaker relative risks for those based on continuous measures. Adjusting for publication bias (for which there was strong evidence, Egger's p<0.001) using a validated method reduced the relative risk to 1.19 (95% CI 1.13–1.25). Only two studies reported a measure of discrimination (c-statistic). In 20 studies the detection rate for subsequent events could be calculated and was 31% for a 10% false positive rate, and the calculated pooled c-statistic was 0.61 (0.57–0.66).
Conclusion
Multiple types of reporting bias, and publication bias, make the magnitude of any independent association between CRP and prognosis among patients with stable coronary disease sufficiently uncertain that no clinical practice recommendations can be made. Publication of prespecified statistical analytic protocols and prospective registration of studies, among other measures, might help improve the quality of prognostic biomarker research.
Please see later in the article for the Editors' Summary
Editors' Summary
Background
Coronary artery disease is the leading cause of death among adults in developed countries. With age, fatty deposits called atherosclerotic plaques coat the walls of the arteries, the vessels that carry blood to the body's organs. Because they narrow the arteries, atherosclerotic plaques restrict blood flow. If plaques form in the arteries that feed the heart, the result is coronary artery disease, the symptoms of which include shortness of breath and chest pains (angina). If these symptoms only occur during exertion, the condition is called stable coronary artery disease. Coronary artery disease can cause potentially fatal heart attacks (myocardial infarctions). A heart attack occurs when a plaque ruptures and a blood clot completely blocks the artery, thereby killing part of the heart. Smoking, high blood pressure, high blood levels of cholesterol (a type of fat), diabetes, and being overweight are risk factors for coronary artery disease. Treatments for the condition include lifestyle changes and medications that lower blood pressure and blood cholesterol. Narrowed arteries can also be widened using a device called a stent or surgically bypassed.
Why Was This Study Done?
Clinicians can predict whether a patient with coronary artery disease is likely to have a heart attack by considering their risk factors. They then use this “prognosis” to help them manage the patient. To provide further help for clinicians, researchers are trying to identify prognostic biomarkers (molecules whose blood levels indicate how a disease might develop) for coronary artery disease. However, before a biomarker can be used clinically, it must be properly validated and there are concerns that there is insufficient high quality evidence to validate many biomarkers. In this systematic review and meta-analysis, the researchers ask whether the evidence for an association between blood levels of C-reactive protein (CRP, an inflammatory protein) and subsequent fatal and nonfatal events affecting the heart and circulation (cardiovascular events) among patients with stable coronary artery disease supports the routine measurement of CRP as recommended in clinical practice guidelines. A systematic review uses predefined criteria to identify all the research on a given topic; a meta-analysis is a statistical method for combining the results of several studies.
What Did the Researchers Do and Find?
The researchers identified 83 studies that investigated the association between CRP levels measured in people with coronary artery disease and subsequent cardiovascular events. Their examination of these studies revealed numerous reporting and publication short-comings. For example, none of the studies reported a prespecified statistical analysis protocol, yet analyses should be prespecified to avoid the choice of analytical method biasing the study's results. Furthermore, on average, the studies only reported seven of the 17 recommended items in the REMARK reporting guidelines, which were designed to improve the reporting quality of tumor biomarker prognostic studies. The meta-analysis revealed that patients with a CRP level in the top third of the distribution were nearly twice as likely to have a cardiovascular event as patients with a CRP in the bottom third of the distribution (a relative risk of 1.97). However, the outcomes varied considerably between studies (heterogeneity) and there was strong evidence for publication bias—most published studies were small and smaller studies were more likely to report higher relative risks. Adjustment for publication bias reduced the relative risk associated with high CRP levels to 1.19. Finally, nearly all the studies failed to calculate whether CRP measurements discriminated between patients likely and unlikely to have a subsequent cardiovascular event.
What Do These Findings Mean?
These findings suggest that, because of multiple types of reporting and publication bias, the size of the association between CRP levels and prognosis among patients with stable coronary artery disease is extremely uncertain. They also suggest that CRP measurements are unlikely to add anything to the prognostic discrimination achieved by considering blood pressure and other standard clinical factors among this patient group. Thus, the researchers suggest, the recommendation that CRP measurements should be used in the management of patients with stable coronary artery disease ought to be removed from clinical practice guidelines. More generally, these findings increase concerns about the quality of research into prognostic biomarkers and highlight areas that need to be changed, the most fundamental of which is the need to preregister studies on prognostic biomarkers and their analytic protocols.
Additional Information
Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1000286.
The MedlinePlus Encyclopedia has pages on coronary artery disease and C-reactive protein (in English and Spanish)
MedlinePlus provides links to other sources of information on heart disease
The American Heart Association provides information for patients and caregivers on all aspects of cardiovascular disease, including information on the role of C-reactive protein in heart disease
Information is available from the British Heart Foundation on heart disease and keeping the heart healthy
Wikipedia has pages on biomarkers and on C-reactive protein (note that Wikipedia is a free online encyclopedia that anyone can edit; available in several languages)
The EQUATOR network is a resource center for good reporting of health research studies
doi:10.1371/journal.pmed.1000286
PMCID: PMC2879408  PMID: 20532236
6.  Cancer Biomarkers: Can We Turn Recent Failures into Success? 
Disease biomarkers are used widely in medicine. But very few biomarkers are useful for cancer diagnosis and monitoring. Over the past 15 years, major investments have been made to discover and validate cancer biomarkers. Despite such investments, no new major cancer biomarkers have been approved for clinical use for at least 25 years. In the last decade, many reports have described new cancer biomarkers that promised to revolutionize the diagnosis of cancer and the management of cancer patients. However, many initially promising biomarkers have not been validated for clinical use. In this commentary, a plethora of parameters before sample analysis, during sample analysis, and after sample analysis that can complicate biomarker discovery and validation and lead to “false discovery” are discussed. Several examples of biomarker discoveries that were published in high-profile journals are also presented, as well as why they were not validated and the lessons learned from these false discoveries, so that similar mistakes can be avoided in the future.
doi:10.1093/jnci/djq306
PMCID: PMC2950166  PMID: 20705936
7.  Evaluation of a Minimally Invasive Cell Sampling Device Coupled with Assessment of Trefoil Factor 3 Expression for Diagnosing Barrett's Esophagus: A Multi-Center Case–Control Study 
PLoS Medicine  2015;12(1):e1001780.
Background
Barrett's esophagus (BE) is a commonly undiagnosed condition that predisposes to esophageal adenocarcinoma. Routine endoscopic screening for BE is not recommended because of the burden this would impose on the health care system. The objective of this study was to determine whether a novel approach using a minimally invasive cell sampling device, the Cytosponge, coupled with immunohistochemical staining for the biomarker Trefoil Factor 3 (TFF3), could be used to identify patients who warrant endoscopy to diagnose BE.
Methods and Findings
A case–control study was performed across 11 UK hospitals between July 2011 and December 2013. In total, 1,110 individuals comprising 463 controls with dyspepsia and reflux symptoms and 647 BE cases swallowed a Cytosponge prior to endoscopy. The primary outcome measures were to evaluate the safety, acceptability, and accuracy of the Cytosponge-TFF3 test compared with endoscopy and biopsy.
In all, 1,042 (93.9%) patients successfully swallowed the Cytosponge, and no serious adverse events were attributed to the device. The Cytosponge was rated favorably, using a visual analogue scale, compared with endoscopy (p < 0.001), and patients who were not sedated for endoscopy were more likely to rate the Cytosponge higher than endoscopy (Mann-Whitney test, p < 0.001). The overall sensitivity of the test was 79.9% (95% CI 76.4%–83.0%), increasing to 87.2% (95% CI 83.0%–90.6%) for patients with ≥3 cm of circumferential BE, known to confer a higher cancer risk. The sensitivity increased to 89.7% (95% CI 82.3%–94.8%) in 107 patients who swallowed the device twice during the study course. There was no loss of sensitivity in patients with dysplasia. The specificity for diagnosing BE was 92.4% (95% CI 89.5%–94.7%). The case–control design of the study means that the results are not generalizable to a primary care population. Another limitation is that the acceptability data were limited to a single measure.
Conclusions
The Cytosponge-TFF3 test is safe and acceptable, and has accuracy comparable to other screening tests. This test may be a simple and inexpensive approach to identify patients with reflux symptoms who warrant endoscopy to diagnose BE.
Editors' Summary
Background
Barrett's esophagus is a condition in which the cells lining the esophagus (the tube that transports food from the mouth to the stomach) change and begin to resemble the cells lining the intestines. Although some people with Barrett's esophagus complain of burning indigestion or acid reflux from the stomach into the esophagus, many people have no symptoms or do not seek medical advice, so the condition often remains undiagnosed. Long-term acid reflux (gastroesophageal reflux disease), obesity, and being male are all risk factors for Barrett's esophagus, but the condition's exact cause is unclear. Importantly, people with Barrett's esophagus are more likely to develop esophageal cancer than people with a normal esophagus, especially if a long length (segment) of the esophagus is affected or if the esophagus contains abnormally growing “dysplastic” cells. Although esophageal cancer is rare in the general population, 1%–5% of people with Barrett's esophagus develop this type of cancer; about half of people diagnosed with esophageal cancer die within a year of diagnosis.
Why Was This Study Done?
Early detection and treatment of esophageal cancer increases an affected individual's chances of survival. Thus, experts recommend that people with multiple risk factors for Barrett's esophagus undergo endoscopic screening—a procedure that uses a small camera attached to a long flexible tube to look for esophageal abnormalities. Once diagnosed, patients with Barrett's esophagus generally enter an endoscopic surveillance program so that dysplastic cells can be identified as soon as they appear and removed using endoscopic surgery or “radiofrequency ablation” to prevent cancer development. However, although endoscopic screening of everyone with reflux symptoms for Barrett's esophagus could potentially reduce deaths from esophageal cancer, such screening is not affordable for most health care systems. In this case–control study, the researchers investigate whether a cell sampling device called the Cytosponge coupled with immunohistochemical staining for Trefoil Factor 3 (TFF3, a biomarker of Barrett's esophagus) can be used to identify individuals who warrant endoscopic investigation. A case–control study compares the characteristics of patients with and without a specific disease. The Cytosponge is a small capsule-encased sponge that is attached to a string. The capsule rapidly dissolves in the stomach after being swallowed, and the sponge collects esophageal cells for TFF3 staining when it is retrieved by pulling on the string.
What Did the Researchers Do and Find?
The researchers enrolled 463 individuals attending 11 UK hospitals for investigational endoscopy for dyspepsia and reflux symptoms as controls, and 647 patients with Barrett's esophagus who were attending hospital for monitoring endoscopy. Before undergoing endoscopy, the study participants swallowed a Cytosponge so that the researchers could evaluate the safety, acceptability, and accuracy of the Cytosponge-TFF3 test for the diagnosis of Barrett's esophagus compared with endoscopy. Nearly 94% of the participants swallowed the Cytosponge successfully, there were no adverse effects attributed to the device, and those participants that swallowed the device generally rated the experience as acceptable. The overall sensitivity of the Cytosponge-TFF3 test (its ability to detect true positives) was 79.9%. That is, 79.9% of the individuals with endoscopically diagnosed Barrett's esophagus were identified as having the condition using the new test. The sensitivity of the test was greater among patients who had a longer length of affected esophagus and importantly was not reduced in patients with dysplasia. Compared to endoscopy, the specificity of the Cytosponge-TFF3 test (its ability to detect true negatives) was 92.4%. That is, 92.4% of people unaffected by Barrett's esophagus were correctly identified as being unaffected.
What Do These Findings Mean?
The case–control design of this study means that its results are not generalizable to a primary care population. Also, the study used only a single measure of the acceptability of the Cytosponge-TFF3 test, Nevertheless, these findings indicate that this minimally invasive test for Barrett's esophagus is safe and acceptable, and that its accuracy is similar to that of colorectal cancer and cervical cancer screening tests. The Cytosponge-TFF3 test might, therefore, provide a simple, inexpensive way to identify those patients with reflux symptoms who warrant endoscopy to diagnose Barrett's esophagus, although randomized controlled trials of the test are needed before its routine clinical implementation. Moreover, because most people with Barrett's esophagus never develop esophageal cancer, additional biomarkers ideally need to be added to the test before its routine implementation to identify those individuals who have the greatest risk of esophageal cancer, and thereby avoid overtreatment of Barrett's esophagus.
Additional Information
Please access these websites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001780.
The US National Institute of Diabetes and Digestive and Kidney Diseases provides detailed information about Barrett's esophagus and gastroesophageal reflux disease
The US National Cancer Institute provides information for patients and health professionals about esophageal cancer (in English and Spanish)
Cancer Research UK (a non-profit organization) provides detailed information about Barrett's esophagus (including a video about having the Cytosponge test and further information about this study, the BEST2 Study) and about esophageal cancer
The UK National Health Service Choices website has pages on the complications of gastroesophageal reflux and on esophageal cancer (including a real story)
Heartburn Cancer Awareness Support is a non-profit organization that aims to improve public awareness and provides support for people affected by Barrett's esophagus; the organization's website explains the range of initiatives to promote education and awareness as well as highlighting personal stories of those affected by Barrett's esophagus and esophageal cancer
The British Society of Gastroenterology has published guidelines on the diagnosis and management of Barrett's esophagus
The UK National Institute for Health and Care Excellence has published guidelines for gastroesophageal reflux
The Barrett's Esophagus Campaign is a UK-based non-profit organization that supports research into the condition and provides support for people affected by Barrett's esophagus; its website includes personal stories about the condition
In a multi-center case-control study, Rebecca Fitzgerald and colleagues examine whether a minimally invasive cell sampling device could be used to identify patients who warrant endoscopy to diagnose Barrett's esophagus.
doi:10.1371/journal.pmed.1001780
PMCID: PMC4310596  PMID: 25634542
8.  Inflammatory and Coagulation Biomarkers and Mortality in Patients with HIV Infection 
PLoS Medicine  2008;5(10):e203.
Background
In the Strategies for Management of Anti-Retroviral Therapy trial, all-cause mortality was higher for participants randomized to intermittent, CD4-guided antiretroviral treatment (ART) (drug conservation [DC]) than continuous ART (viral suppression [VS]).
We hypothesized that increased HIV-RNA levels following ART interruption induced activation of tissue factor pathways, thrombosis, and fibrinolysis.
Methods and Findings
Stored samples were used to measure six biomarkers: high sensitivity C-reactive protein (hsCRP), interleukin-6 (IL-6), amyloid A, amyloid P, D-dimer, and prothrombin fragment 1+2. Two studies were conducted: (1) a nested case–control study for studying biomarker associations with mortality, and (2) a study to compare DC and VS participants for biomarker changes. For (1), markers were determined at study entry and before death (latest level) for 85 deaths and for two controls (n = 170) matched on country, age, sex, and date of randomization. Odds ratios (ORs) were estimated with logistic regression. For each biomarker, each of the three upper quartiles was compared to the lowest quartile. For (2), the biomarkers were assessed for 249 DC and 250 VS participants at study entry and 1 mo following randomization. Higher levels of hsCRP, IL-6, and D-dimer at study entry were significantly associated with an increased risk of all-cause mortality. Unadjusted ORs (highest versus lowest quartile) were 2.0 (95% confidence interval [CI], 1.0–4.1; p = 0.05), 8.3 (95% CI, 3.3–20.8; p < 0.0001), and 12.4 (95% CI, 4.2–37.0; p < 0.0001), respectively. Associations were significant after adjustment, when the DC and VS groups were analyzed separately, and when latest levels were assessed. IL-6 and D-dimer increased at 1 mo by 30% and 16% in the DC group and by 0% and 5% in the VS group (p < 0.0001 for treatment difference for both biomarkers); increases in the DC group were related to HIV-RNA levels at 1 mo (p < 0.0001). In an expanded case–control analysis (four controls per case), the OR (DC/VS) for mortality was reduced from 1.8 (95% CI, 1.1–3.1; p = 0.02) to 1.5 (95% CI, 0.8–2.8) and 1.4 (95% CI, 0.8–2.5) after adjustment for latest levels of IL-6 and D-dimer, respectively.
Conclusions
IL-6 and D-dimer were strongly related to all-cause mortality. Interrupting ART may further increase the risk of death by raising IL-6 and D-dimer levels. Therapies that reduce the inflammatory response to HIV and decrease IL-6 and D-dimer levels may warrant investigation.
Trial Registration: ClinicalTrials.gov (NCT00027352).
Analyzing biomarker data from participants in a previous randomized controlled trial of continuous versus interrupted HIV treatment (the SMART trial), James Neaton and colleagues find that mortality was related to IL-6 and fibrin D-dimers.
Editors' Summary
Background.
Globally, more than 30 million people are infected with the human immunodeficiency virus (HIV), the virus that causes acquired immunodeficiency syndrome (AIDS). HIV infects and destroys immune system cells (including CD4 cells, a type of lymphocyte). The first stage of HIV infection can involve a short flu-like illness but in the second stage, which can last many years, HIV replicates in the lymph glands (small immune system organs throughout the body) without causing any symptoms. Eventually, however, the immune system becomes so damaged that HIV-infected individuals begin to succumb to “opportunistic” infections (for example, bacterial pneumonia) and cancers (in particular, Karposi sarcoma) that the immune system would normally prevent. AIDS itself is characterized by one or more severe opportunistic infections or cancers (so-called AIDS-related diseases) and by a low blood CD4 cell count. HIV infections cannot be cured but antiretroviral therapy (ART)—combinations of powerful antiretroviral drugs—can keep them in check, so many HIV-positive people now have substantially improved life expectancy.
Why Was This Study Done?
Unfortunately, the effectiveness of ART sometimes wanes over time and prolonged ART can cause unpleasant side effects. Consequently, alternative ART regimens are continually being tested in clinical trials. In the Strategies for Management of Anti-Retroviral Therapy (SMART) trial, for example, HIV-positive patients received either continuous ART (the viral suppression or VS arm), or ART only when their CD4 cell counts were below 250 cells/mm3 (the drug conservation or DC arm; the normal adult CD4 cell count is about 1,000 cells/mm3). Unexpectedly, more people died in the DC arm than in the VS arm from non-AIDS diseases (including heart and circulation problems), a result that led to the trial being stopped early. One possible explanation for these excess deaths is that increased HIV levels following ART interruption might have induced an inflammatory response (a non-specific immune response that occurs with infection or wounding) and/or a hypercoagulable state (a condition in which blood clots form inside undamaged blood vessels) and that these changes increased the risk of death from non-AIDS diseases. In this study, the researchers test this hypothesis.
What Did the Researchers Do and Find?
The researchers measured the levels of proteins that indicate the presence of inflammation or increased coagulation (biomarkers) in stored blood samples from the 85 people who died during the SMART trial (55 and 30 of the participants assigned to receive DC and VS, respectively) and from 170 survivors who served as comparison (control) participants. (Two control participants were “matched” to each participant who had died (cases). In this “case-control” study, an increased risk of death was associated with higher levels at study entry of the inflammation biomarkers high-sensitivity C-reactive protein (hsCRP) and interleukin 6 (IL-6) and of the coagulation biomarker D-dimer. The risk of death among people with hsCRP values in the highest quarter of measured values was twice that among people with hsCRP values in the lowest quarter (this is expressed as an odds ratio of 2). For IL-6 and D-dimer, the equivalent odds ratios were 8.3 and 12.4, respectively. Furthermore, increases in hsCRP, IL-6 and D-dimer after study entry were associated with an increased risk of death. The researchers also measured blood levels of the same biomarkers in 250 randomly chosen patients from each of the two treatment arms. IL-6 levels increased by 30% over the first month of the trial in the DC arm but were unchanged in the VS arm. Over the same period, D-dimer levels increased by 16% and 5% in the DC and VS arms, respectively. Increases in both markers in the DC arm were related to HIV RNA levels after one month.
What Do These Findings Mean?
Taken together, these findings suggest that HIV-induced activation of inflammation and coagulation increases the risk of death among HIV-positive patients and that interrupting ART further increases this risk, possibly by increasing IL-6 and D-dimer levels. Because only a small number of people died in this study, the relationship between these biomarkers and death and illness among treated and untreated HIV-positive individuals needs to be confirmed in further studies. However, these findings suggest that the development of therapies that reduce the effect that HIV replication has on inflammation and blood coagulation, or that reduce IL-6 and D-dimer levels, might extend the life-expectancy of HIV-positive people.
Additional Information.
Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.0050203.
Information is available from the US National Institute of Allergy and Infectious Diseases on HIV infection and AIDS and about the SMART trial
HIV InSite has comprehensive information on all aspects of HIV/AIDS
Information is also available from Avert, an international AIDS charity, on HIV/AIDS
More information about the SMART trial is available on ClinicalTrials.gov, a database of clinical trials maintained by the US National Institutes of Health
doi:10.1371/journal.pmed.0050203
PMCID: PMC2570418  PMID: 18942885
9.  Role of DNA Methylation and Epigenetic Silencing of HAND2 in Endometrial Cancer Development 
PLoS Medicine  2013;10(11):e1001551.
TB filled in by Laureen
Please see later in the article for the Editors' Summary
Background
Endometrial cancer incidence is continuing to rise in the wake of the current ageing and obesity epidemics. Much of the risk for endometrial cancer development is influenced by the environment and lifestyle. Accumulating evidence suggests that the epigenome serves as the interface between the genome and the environment and that hypermethylation of stem cell polycomb group target genes is an epigenetic hallmark of cancer. The objective of this study was to determine the functional role of epigenetic factors in endometrial cancer development.
Methods and Findings
Epigenome-wide methylation analysis of >27,000 CpG sites in endometrial cancer tissue samples (n = 64) and control samples (n = 23) revealed that HAND2 (a gene encoding a transcription factor expressed in the endometrial stroma) is one of the most commonly hypermethylated and silenced genes in endometrial cancer. A novel integrative epigenome-transcriptome-interactome analysis further revealed that HAND2 is the hub of the most highly ranked differential methylation hotspot in endometrial cancer. These findings were validated using candidate gene methylation analysis in multiple clinical sample sets of tissue samples from a total of 272 additional women. Increased HAND2 methylation was a feature of premalignant endometrial lesions and was seen to parallel a decrease in RNA and protein levels. Furthermore, women with high endometrial HAND2 methylation in their premalignant lesions were less likely to respond to progesterone treatment. HAND2 methylation analysis of endometrial secretions collected using high vaginal swabs taken from women with postmenopausal bleeding specifically identified those patients with early stage endometrial cancer with both high sensitivity and high specificity (receiver operating characteristics area under the curve = 0.91 for stage 1A and 0.97 for higher than stage 1A). Finally, mice harbouring a Hand2 knock-out specifically in their endometrium were shown to develop precancerous endometrial lesions with increasing age, and these lesions also demonstrated a lack of PTEN expression.
Conclusions
HAND2 methylation is a common and crucial molecular alteration in endometrial cancer that could potentially be employed as a biomarker for early detection of endometrial cancer and as a predictor of treatment response. The true clinical utility of HAND2 DNA methylation, however, requires further validation in prospective studies.
Please see later in the article for the Editors' Summary
Editors' Summary
Background
Cancer, which is responsible for 13% of global deaths, can develop anywhere in the body, but all cancers are characterized by uncontrolled cell growth and reduced cellular differentiation (the process by which unspecialized cells such as “stem” cells become specialized during development, tissue repair, and normal cell turnover). Genetic alterations—changes in the sequence of nucleotides (DNA's building blocks) in specific genes—are required for this cellular transformation and subsequent cancer development (carcinogenesis). However, recent evidence suggests that epigenetic modifications—reversible, heritable changes in gene function that occur in the absence of nucleotide sequence changes—may also be involved in carcinogenesis. For example, the addition of methyl groups to a set of genes called stem cell polycomb group target genes (PCGTs; polycomb genes control the expression of their target genes by modifying their DNA or associated proteins) is one of the earliest molecular changes in human cancer development, and increasing evidence suggests that hypermethylation of PCGTs is an epigenetic hallmark of cancer.
Why Was This Study Done?
The methylation of PCGTs, which is triggered by age and by environmental factors that are associated with cancer development, reduces cellular differentiation and leads to the accumulation of undifferentiated cells that are susceptible to cancer development. It is unclear, however, whether epigenetic modifications have a causal role in carcinogenesis. Here, the researchers investigate the involvement of epigenetic factors in the development of endometrial (womb) cancer. The risk of endometrial cancer (which affects nearly 50,000 women annually in the United States) is largely determined by environmental and lifestyle factors. Specifically, the risk of this cancer is increased in women in whom estrogen (a hormone that drives cell proliferation in the endometrium) is functionally dominant over progesterone (a hormone that inhibits endometrial proliferation and causes cell differentiation); obese women and women who have taken estrogen-only hormone replacement therapies fall into this category. Thus, endometrial cancer is an ideal model in which to study whether epigenetic mechanisms underlie carcinogenesis.
What Did the Researchers Do and Find?
The researchers collected data on genome-wide DNA methylation at cytosine- and guanine-rich sites in endometrial cancers and normal endometrium and integrated this information with the human interactome and transcriptome (all the physical interactions between proteins and all the genes expressed, respectively, in a cell) using an algorithm called Functional Epigenetic Modules (FEM). This analysis identified HAND2 as the hub of the most highly ranked differential methylation hotspot in endometrial cancer. HAND2 is a progesterone-regulated stem cell PCGT. It encodes a transcription factor that is expressed in the endometrial stroma (the connective tissue that lies below the epithelial cells in which most endometrial cancers develop) and that suppresses the production of the growth factors that mediate the growth-inducing effects of estrogen on the endometrial epithelium. The researchers hypothesized, therefore, that epigenetic deregulation of HAND2 could be a key step in endometrial cancer development. In support of this hypothesis, the researchers report that HAND2 methylation was increased in premalignant endometrial lesions (cancer-prone, abnormal-looking tissue) compared to normal endometrium, and was associated with suppression of HAND2 expression. Moreover, a high level of endometrial HAND2 methylation in premalignant lesions predicted a poor response to progesterone treatment (which stops the growth of some endometrial cancers), and analysis of HAND2 methylation in endometrial secretions collected from women with postmenopausal bleeding (a symptom of endometrial cancer) accurately identified individuals with early stage endometrial cancer. Finally, mice in which the Hand2 gene was specifically deleted in the endometrium developed precancerous endometrial lesions with age.
What Do These Findings Mean?
These and other findings identify HAND2 methylation as a common, key molecular alteration in endometrial cancer. These findings need to be confirmed in more women, and studies are needed to determine the immediate molecular and cellular consequences of HAND2 silencing in endometrial stromal cells. Nevertheless, these results suggest that HAND2 methylation could potentially be used as a biomarker for the early detection of endometrial cancer and for predicting treatment response. More generally, these findings support the idea that methylation of HAND2 (and, by extension, the methylation of other PCGTs) is not a passive epigenetic feature of cancer but is functionally involved in cancer development, and provide a framework for identifying other genes that are epigenetically regulated and functionally important in carcinogenesis.
Additional Information
Please access these websites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001551
The US National Cancer Institute provides information on all aspects of cancer and has detailed information about endometrial cancer for patients and professionals (in English and Spanish)
The not-for-profit organization American Cancer Society provides information on cancer and how it develops and specific information on endometrial cancer (in several languages)
The UK National Health Service Choices website includes an introduction to cancer, a page on endometrial cancer, and a personal story about endometrial cancer
The not-for-profit organization Cancer Research UK provides general information about cancer and specific information about endometrial cancer
Wikipedia has a page on cancer epigenetics (note: Wikipedia is a free online encyclopedia that anyone can edit; available in several languages)
The Eve Appeal charity that supported this research provides useful information on gynecological cancers
doi:10.1371/journal.pmed.1001551
PMCID: PMC3825654  PMID: 24265601
10.  Targeted Nanoparticles for Imaging Incipient Pancreatic Ductal Adenocarcinoma  
PLoS Medicine  2008;5(4):e85.
Background
Pancreatic ductal adenocarcinoma (PDAC) carries an extremely poor prognosis, typically presenting with metastasis at the time of diagnosis and exhibiting profound resistance to existing therapies. The development of molecular markers and imaging probes for incipient PDAC would enable earlier detection and guide the development of interventive therapies. Here we sought to identify novel molecular markers and to test their potential as targeted imaging agents.
Methods and Findings
Here, a phage display approach was used in a mouse model of PDAC to screen for peptides that specifically bind to cell surface antigens on PDAC cells. These screens yielded a motif that distinguishes PDAC cells from normal pancreatic duct cells in vitro, which, upon proteomics analysis, identified plectin-1 as a novel biomarker of PDAC. To assess their utility for in vivo imaging, the plectin-1 targeted peptides (PTP) were conjugated to magnetofluorescent nanoparticles. In conjunction with intravital confocal microscopy and MRI, these nanoparticles enabled detection of small PDAC and precursor lesions in engineered mouse models.
Conclusions
Our approach exploited a well-defined model of PDAC, enabling rapid identification and validation of PTP. The developed specific imaging probe, along with the discovery of plectin-1 as a novel biomarker, may have clinical utility in the diagnosis and management of PDAC in humans.
Kimberly Kelly and colleagues describe the discovery of plectin-1 as a novel biomarker for pancreatic ductal adenocarcinoma and the subsequent development of a specific imaging probe using this marker.
Editors' Summary
Background.
Pancreatic cancer is a leading cause of cancer-related death in the US. Like all cancers, it occurs when cells begin to grow uncontrollably and to move around the body (metastasize) because of changes (mutations) in their genes. If pancreatic cancer is found early, surgical removal of the tumor can sometimes provide a cure. Unfortunately, this cancer rarely causes any symptoms in its early stages and the symptoms it does eventually cause—jaundice, abdominal and back pain, and weight loss—are also seen in other illnesses. In addition, even though magnetic resonance imaging (MRI) or other noninvasive imaging techniques can be used to look at the pancreas, by the time tumors are large enough to show up on MRI scans, they have often already spread. Consequently, in most patients, pancreatic cancer is advanced by the time a diagnosis is made, hence surgery is no longer useful. These patients are given radiotherapy and chemotherapy but these treatments are rarely curative and most patients die within a year of diagnosis.
Why Was This Study Done?
If more pancreatic cancers could be found before they had metastasized, it should extend the life expectancy of patients with this type of cancer. An early detection method would be particularly useful for monitoring people at high risk of developing pancreatic cancer. These include people with certain inherited cancer syndromes, pancreatitis (inflammation of the pancreas), and diabetes. Because cancer cells have many mutations, they express different proteins on their cell surface from normal cells. If these proteins could be identified, it might be possible to develop an “imaging probe”—a molecule that binds to a protein found only on cancer cells and that can be detected with MRI, for example—for early detection of pancreatic cancer. In this study, the researchers use a technique called “phage display” to identify several peptides (short sequences of amino acids, the constituent parts of proteins) that specifically bind to pancreatic cancer cells early in their development. They then investigate the possibility of developing an imaging probe from one of these peptides.
What Did the Researchers Do and Find?
The researchers isolated early pancreatic cancer cells from a mouse model of human pancreatic ductal adenocarcinoma (PDAC; the commonest type of pancreatic cancer). Then, by mixing together these cells and normal mouse pancreatic cells with a library of phage clones (phages are viruses that infect bacteria; a clone is a group of genetically identical organisms), each engineered in the laboratory to express a random seven amino-acid peptide, they identified one clone, clone 27, that bound to the mouse tumor cells but not to normal cells. Clone 27 also showed up in the cancer cells in samples of mouse pancreatic intraepithelial neoplasias (PanINs; precursors to pancreatic cancer), mouse PDACs, and human PDACs.
The peptide in clone 27, the researchers report, binds to plectin-1, a protein present both inside and on the membrane of human and mouse PDAC cells but only on the inside of normal pancreatic cells. Finally, the researchers attached this plectin-1–targeted peptide (PTP) to a nanoparticles that was both magnetic and fluorescent (PTP-NP) and used special microscopy (which detects the fluorescent part of this very small particle) and MRI (which detects its magnetic portion) to show that this potential imaging probe was found in areas of PDAC (but not in normal pancreatic tissue) in the mouse model of human PDAC.
What Do These Findings Mean?
These findings identify PTP as a peptide that can distinguish normal pancreatic cells from pancreatic cancer cells. The discovery that plectin-1 (a cytoskeletal component) is abnormally expressed on the cell surface of PDACs provides new information about the development of pancreatic cancer that could eventually lead to new ways to treat this disease. These findings also show that PTP can be used to generate a nanoparticle-based imaging agent that can detect PDAC within a normal pancreas. These results need to be confirmed in people—results obtained in mouse models do not always reflect what happens in people. Nevertheless, they suggest that PTP-NPs might allow the noninvasive detection of early tumors in people at high risk of developing pancreatic cancer, an advance that could extend their lives by identifying tumors earlier, when they can be removed surgically.
Additional Information.
Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.0050085.
• The Panreatic Cancer Action Network and the Lustgarten Foundation for Pancreatic Cancer Research provide information, support, and advocacy for patients, families, and healthcare professionals
• The MedlinePlus Encyclopedia has a page on pancreatic cancer (in English and Spanish). Links to further information are provided by MedlinePlus
• The US National Cancer Institute has information about pancreatic cancer for patients and health professionals (in English and Spanish)
• The UK charity Cancerbackup also provides information for patients about pancreatic cancer
doi:10.1371/journal.pmed.0050085
PMCID: PMC2292750  PMID: 18416599
11.  Nuclear Receptor Expression Defines a Set of Prognostic Biomarkers for Lung Cancer 
PLoS Medicine  2010;7(12):e1000378.
David Mangelsdorf and colleagues show that nuclear receptor expression is strongly associated with clinical outcomes of lung cancer patients, and this expression profile is a potential prognostic signature for lung cancer patient survival time, particularly for individuals with early stage disease.
Background
The identification of prognostic tumor biomarkers that also would have potential as therapeutic targets, particularly in patients with early stage disease, has been a long sought-after goal in the management and treatment of lung cancer. The nuclear receptor (NR) superfamily, which is composed of 48 transcription factors that govern complex physiologic and pathophysiologic processes, could represent a unique subset of these biomarkers. In fact, many members of this family are the targets of already identified selective receptor modulators, providing a direct link between individual tumor NR quantitation and selection of therapy. The goal of this study, which begins this overall strategy, was to investigate the association between mRNA expression of the NR superfamily and the clinical outcome for patients with lung cancer, and to test whether a tumor NR gene signature provided useful information (over available clinical data) for patients with lung cancer.
Methods and Findings
Using quantitative real-time PCR to study NR expression in 30 microdissected non-small-cell lung cancers (NSCLCs) and their pair-matched normal lung epithelium, we found great variability in NR expression among patients' tumor and non-involved lung epithelium, found a strong association between NR expression and clinical outcome, and identified an NR gene signature from both normal and tumor tissues that predicted patient survival time and disease recurrence. The NR signature derived from the initial 30 NSCLC samples was validated in two independent microarray datasets derived from 442 and 117 resected lung adenocarcinomas. The NR gene signature was also validated in 130 squamous cell carcinomas. The prognostic signature in tumors could be distilled to expression of two NRs, short heterodimer partner and progesterone receptor, as single gene predictors of NSCLC patient survival time, including for patients with stage I disease. Of equal interest, the studies of microdissected histologically normal epithelium and matched tumors identified expression in normal (but not tumor) epithelium of NGFIB3 and mineralocorticoid receptor as single gene predictors of good prognosis.
Conclusions
NR expression is strongly associated with clinical outcomes for patients with lung cancer, and this expression profile provides a unique prognostic signature for lung cancer patient survival time, particularly for those with early stage disease. This study highlights the potential use of NRs as a rational set of therapeutically tractable genes as theragnostic biomarkers, and specifically identifies short heterodimer partner and progesterone receptor in tumors, and NGFIB3 and MR in non-neoplastic lung epithelium, for future detailed translational study in lung cancer.
Please see later in the article for the Editors' Summary
Editors' Summary
Background
Lung cancer, the most common cause of cancer-related death, kills 1.3 million people annually. Most lung cancers are “non-small-cell lung cancers” (NSCLCs), and most are caused by smoking. Exposure to chemicals in smoke causes changes in the genes of the cells lining the lungs that allow the cells to grow uncontrollably and to move around the body. How NSCLC is treated and responds to treatment depends on its “stage.” Stage I tumors, which are small and confined to the lung, are removed surgically, although chemotherapy is also sometimes given. Stage II tumors have spread to nearby lymph nodes and are treated with surgery and chemotherapy, as are some stage III tumors. However, because cancer cells in stage III tumors can be present throughout the chest, surgery is not always possible. For such cases, and for stage IV NSCLC, where the tumor has spread around the body, patients are treated with chemotherapy alone. About 70% of patients with stage I and II NSCLC but only 2% of patients with stage IV NSCLC survive for five years after diagnosis; more than 50% of patients have stage IV NSCLC at diagnosis.
Why Was This Study Done?
Patient responses to treatment vary considerably. Oncologists (doctors who treat cancer) would like to know which patients have a good prognosis (are likely to do well) to help them individualize their treatment. Consequently, the search is on for “prognostic tumor biomarkers,” molecules made by cancer cells that can be used to predict likely clinical outcomes. Such biomarkers, which may also be potential therapeutic targets, can be identified by analyzing the overall pattern of gene expression in a panel of tumors using a technique called microarray analysis and looking for associations between the expression of sets of genes and clinical outcomes. In this study, the researchers take a more directed approach to identifying prognostic biomarkers by investigating the association between the expression of the genes encoding nuclear receptors (NRs) and clinical outcome in patients with lung cancer. The NR superfamily contains 48 transcription factors (proteins that control the expression of other genes) that respond to several hormones and to diet-derived fats. NRs control many biological processes and are targets for several successful drugs, including some used to treat cancer.
What Did the Researchers Do and Find?
The researchers analyzed the expression of NR mRNAs using “quantitative real-time PCR” in 30 microdissected NSCLCs and in matched normal lung tissue samples (mRNA is the blueprint for protein production). They then used an approach called standard classification and regression tree analysis to build a prognostic model for NSCLC based on the expression data. This model predicted both survival time and disease recurrence among the patients from whom the tumors had been taken. The researchers validated their prognostic model in two large independent lung adenocarcinoma microarray datasets and in a squamous cell carcinoma dataset (adenocarcinomas and squamous cell carcinomas are two major NSCLC subtypes). Finally, they explored the roles of specific NRs in the prediction model. This analysis revealed that the ability of the NR signature in tumors to predict outcomes was mainly due to the expression of two NRs—the short heterodimer partner (SHP) and the progesterone receptor (PR). Expression of either gene could be used as a single gene predictor of the survival time of patients, including those with stage I disease. Similarly, the expression of either nerve growth factor induced gene B3 (NGFIB3) or mineralocorticoid receptor (MR) in normal tissue was a single gene predictor of a good prognosis.
What Do These Findings Mean?
These findings indicate that the expression of NR mRNA is strongly associated with clinical outcomes in patients with NSCLC. Furthermore, they identify a prognostic NR expression signature that provides information on the survival time of patients, including those with early stage disease. The signature needs to be confirmed in more patients before it can be used clinically, and researchers would like to establish whether changes in mRNA expression are reflected in changes in protein expression if NRs are to be targeted therapeutically. Nevertheless, these findings highlight the potential use of NRs as prognostic tumor biomarkers. Furthermore, they identify SHP and PR in tumors and two NRs in normal lung tissue as molecules that might provide new targets for the treatment of lung cancer and new insights into the early diagnosis, pathogenesis, and chemoprevention of lung cancer.
Additional Information
Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1000378.
The Nuclear Receptor Signaling Atlas (NURSA) is consortium of scientists sponsored by the US National Institutes of Health that provides scientific reagents, datasets, and educational material on nuclear receptors and their co-regulators to the scientific community through a Web-based portal
The Cancer Prevention and Research Institute of Texas (CPRIT) provides information and resources to anyone interested in the prevention and treatment of lung and other cancers
The US National Cancer Institute provides detailed information for patients and professionals about all aspects of lung cancer, including information on non-small-cell carcinoma and on tumor markers (in English and Spanish)
Cancer Research UK also provides information about lung cancer and information on how cancer starts
MedlinePlus has links to other resources about lung cancer (in English and Spanish)
Wikipedia has a page on nuclear receptors (note that Wikipedia is a free online encyclopedia that anyone can edit; available in several languages)
doi:10.1371/journal.pmed.1000378
PMCID: PMC3001894  PMID: 21179495
12.  Better Cancer Biomarker Discovery Through Better Study Design 
European journal of clinical investigation  2012;42(12):10.1111/j.1365-2362.2012.02727.x.
Background
High through-put laboratory technologies coupled with sophisticated bioinformatics algorithms have tremendous potential for discovering novel biomarkers, or profiles of biomarkers, that could serve as predictors of disease risk, response to treatment or prognosis. We discuss methodological issues in wedding high through-put approaches for biomarker discovery with the case-control study designs typically used in biomarker discovery studies, especially focusing on nested case-control designs.
Methods
We review principles for nested case-control study design in relation to biomarker discovery studies and describe how the efficiency of biomarker discovery can be effected by study design choices. We develop a simulated prostate cancer cohort data set and a series of biomarker discovery case-control studies nested within the cohort to illustrate how study design choices can influence biomarker discovery process.
Result
Common elements of nested case-control design, incidence density sampling and matching of controls to cases, are not typically factored correctly into biomarker discovery analyses, inducing bias in the discovery process. We illustrate how incidence density sampling and matching of controls to cases reduces the apparent specificity of truly valid biomarkers “discovered” in a nested case-control study. We also propose and demonstrate a new case-control matching protocol, we call “anti-matching”, that improves the efficiency of biomarker discovery studies.
Conclusions
For a valid, but as yet undiscovered, biomarker(s) disjunctions between correctly designed epidemiologic studies and the practice of biomarker discovery reduce the likelihood that true biomarker(s) will be discovered and increases the false positive discovery rate.
doi:10.1111/j.1365-2362.2012.02727.x
PMCID: PMC3828645  PMID: 22998109
High Through-put; Biomarker Discovery; Epidemiology; case-control
13.  A Mouse to Human Search for Plasma Proteome Changes Associated with Pancreatic Tumor Development 
PLoS Medicine  2008;5(6):e123.
Background
The complexity and heterogeneity of the human plasma proteome have presented significant challenges in the identification of protein changes associated with tumor development. Refined genetically engineered mouse (GEM) models of human cancer have been shown to faithfully recapitulate the molecular, biological, and clinical features of human disease. Here, we sought to exploit the merits of a well-characterized GEM model of pancreatic cancer to determine whether proteomics technologies allow identification of protein changes associated with tumor development and whether such changes are relevant to human pancreatic cancer.
Methods and Findings
Plasma was sampled from mice at early and advanced stages of tumor development and from matched controls. Using a proteomic approach based on extensive protein fractionation, we confidently identified 1,442 proteins that were distributed across seven orders of magnitude of abundance in plasma. Analysis of proteins chosen on the basis of increased levels in plasma from tumor-bearing mice and corroborating protein or RNA expression in tissue documented concordance in the blood from 30 newly diagnosed patients with pancreatic cancer relative to 30 control specimens. A panel of five proteins selected on the basis of their increased level at an early stage of tumor development in the mouse was tested in a blinded study in 26 humans from the CARET (Carotene and Retinol Efficacy Trial) cohort. The panel discriminated pancreatic cancer cases from matched controls in blood specimens obtained between 7 and 13 mo prior to the development of symptoms and clinical diagnosis of pancreatic cancer.
Conclusions
Our findings indicate that GEM models of cancer, in combination with in-depth proteomic analysis, provide a useful strategy to identify candidate markers applicable to human cancer with potential utility for early detection.
Samir Hanash and colleagues identify proteins that are increased at an early stage of pancreatic tumor development in a mouse model and may be a useful tool in detecting early tumors in humans.
Editors' Summary
Background.
Cancers are life-threatening, disorganized masses of cells that can occur anywhere in the human body. They develop when cells acquire genetic changes that allow them to grow uncontrollably and to spread around the body (metastasize). If a cancer is detected when it is still small and has not metastasized, surgery can often provide a cure. Unfortunately, many cancers are detected only when they are large enough to press against surrounding tissues and cause pain or other symptoms. By this time, surgical removal of the original (primary) tumor may be impossible and there may be secondary cancers scattered around the body. In such cases, radiotherapy and chemotherapy can sometimes help, but the outlook for patients whose cancers are detected late is often poor. One cancer type for which late detection is a particular problem is pancreatic adenocarcinoma. This cancer rarely causes any symptoms in its early stages. Furthermore, the symptoms it eventually causes—jaundice, abdominal and back pain, and weight loss—are seen in many other illnesses. Consequently, pancreatic cancer has usually spread before it is diagnosed, and most patients die within a year of their diagnosis.
Why Was This Study Done?
If a test could be developed to detect pancreatic cancer in its early stages, the lives of many patients might be extended. Tumors often release specific proteins—“cancer biomarkers”—into the blood, a bodily fluid that can be easily sampled. If a protein released into the blood by pancreatic cancer cells could be identified, it might be possible to develop a noninvasive screening test for this deadly cancer. In this study, the researchers use a “proteomic” approach to identify potential biomarkers for early pancreatic cancer. Proteomics is the study of the patterns of proteins made by an organism, tissue, or cell and of the changes in these patterns that are associated with various diseases.
What Did the Researchers Do and Find?
The researchers started their search for pancreatic cancer biomarkers by studying the plasma proteome (the proteins in the fluid portion of blood) of mice genetically engineered to develop cancers that closely resemble human pancreatic tumors. Through the use of two techniques called high-resolution mass spectrometry and acrylamide isotopic labeling, the researchers identified 165 proteins that were present in larger amounts in plasma collected from mice with early and/or advanced pancreatic cancer than in plasma from control mice. Then, to test whether any of these protein changes were relevant to human pancreatic cancer, the researchers analyzed blood samples collected from patients with pancreatic cancer. These samples, they report, contained larger amounts of some of these proteins than blood collected from patients with chronic pancreatitis, a condition that has similar symptoms to pancreatic cancer. Finally, using blood samples collected during a clinical trial, the Carotene and Retinol Efficacy Trial (a cancer-prevention study), the researchers showed that the measurement of five of the proteins present in increased amounts at an early stage of tumor development in the mouse model discriminated between people with pancreatic cancer and matched controls up to 13 months before cancer diagnosis.
What Do These Findings Mean?
These findings suggest that in-depth proteomic analysis of genetically engineered mouse models of human cancer might be an effective way to identify biomarkers suitable for the early detection of human cancers. Previous attempts to identify such biomarkers using human samples have been hampered by the many noncancer-related differences in plasma proteins that exist between individuals and by problems in obtaining samples from patients with early cancer. The use of a mouse model of human cancer, these findings indicate, can circumvent both of these problems. More specifically, these findings identify a panel of proteins that might allow earlier detection of pancreatic cancer and that might, therefore, extend the life of some patients who develop this cancer. However, before a routine screening test becomes available, additional markers will need to be identified and extensive validation studies in larger groups of patients will have to be completed.
Additional Information.
Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.0050123.
The MedlinePlus Encyclopedia has a page on pancreatic cancer (in English and Spanish). Links to further information are provided by MedlinePlus
The US National Cancer Institute has information about pancreatic cancer for patients and health professionals (in English and Spanish)
The UK charity Cancerbackup also provides information for patients about pancreatic cancer
The Clinical Proteomic Technologies for Cancer Initiative (a US National Cancer Institute initiative) provides a tutorial about proteomics and cancer and information on the Mouse Proteomic Technologies Initiative
doi:10.1371/journal.pmed.0050123
PMCID: PMC2504036  PMID: 18547137
14.  Metabolic Profiling of CSF: Evidence That Early Intervention May Impact on Disease Progression and Outcome in Schizophrenia 
PLoS Medicine  2006;3(8):e327.
Background
The identification of schizophrenia biomarkers is a crucial step towards improving current diagnosis, developing new presymptomatic treatments, identifying high-risk individuals and disease subgroups, and assessing the efficacy of preventative interventions at a rate that is not currently possible.
Methods and Findings
1H nuclear magnetic resonance spectroscopy in conjunction with computerized pattern recognition analysis were employed to investigate metabolic profiles of a total of 152 cerebrospinal fluid (CSF) samples from drug-naïve or minimally treated patients with first-onset paranoid schizophrenia (referred to as “schizophrenia” in the following text) and healthy controls. Partial least square discriminant analysis showed a highly significant separation of patients with first-onset schizophrenia away from healthy controls. Short-term treatment with antipsychotic medication resulted in a normalization of the disease signature in over half the patients, well before overt clinical improvement. No normalization was observed in patients in which treatment had not been initiated at first presentation, providing the first molecular evidence for the importance of early intervention for psychotic disorders. Furthermore, the alterations identified in drug-naïve patients could be validated in a test sample set achieving a sensitivity and specificity of 82% and 85%, respectively.
Conclusions
Our findings suggest brain-specific alterations in glucoregulatory processes in the CSF of drug-naïve patients with first-onset schizophrenia, implying that these abnormalities are intrinsic to the disease, rather than a side effect of antipsychotic medication. Short-term treatment with atypical antipsychotic medication resulted in a normalization of the CSF disease signature in half the patients well before a clinical improvement would be expected. Furthermore, our results suggest that the initiation of antipsychotic treatment during a first psychotic episode may influence treatment response and/or outcome.
Metabolic profiling of the cerebrospinal fluid shows differences between healthy controls and patients with first-onset schizophrenia. Early treatment appears to rapidly normalize the profiles in some of the patients.
Editors' Summary
Background.
Biological markers, or “biomarkers,” are combinations of molecules that are present in certain diseases. Scientists are interested in discovering new biomarkers because they could be useful for diagnosis of those diseases. The presence of such biomarkers might in some cases even precede the development of disease symptoms, which could help in early diagnosis, treatment, and maybe even prevention. Schizophrenia is a disease for which no “objective” biological test exists, and scientists are trying to find biomarkers that would help with diagnosis. The current diagnosis of schizophrenia is based on the symptoms experienced and reported by the patient, in combination with signs observed by a psychiatrist, clinical psychologist, or other clinician.
Why Was This Study Done?
This study was done to search for biomarkers for schizophrenia. The researchers studied the metabolic state of patients and healthy volunteers (controls). In other words, they focused on the small molecules present in cells, tissues, or body fluids. The metabolic state reflects what has been encoded by a person's genes and modified by environmental factors. Focusing on the metabolic state makes sense for a disease like schizophrenia, since many different genetic and environmental factors are thought to be responsible for causing it.
What Did the Researchers Do and Find?
The researchers studied the metabolic state of 82 patients with schizophrenia and 70 healthy controls by studying the levels of different molecules present in their cerebrospinal fluid (the clear body fluid that surrounds the brain and the spinal cord). Of the patients, 54 had just been diagnosed with schizophrenia (or a similar illness called brief psychotic disorder) and had not yet taken any medications to treat schizophrenia (so-called antipsychotic medication). The remaining patients were undergoing treatment with a range of antipsychotic drugs. The researchers found different levels of certain molecules in the spinal fluid of newly diagnosed patients who had never taken schizophrenia drugs compared with healthy individuals of the same ages. These molecules might therefore turn out to be useful biomarkers for schizophrenia. The differences between patients and controls suggested that the metabolism of several substances—including glucose and acetate—might be altered in the brains of patients with schizophrenia or brief psychotic disorder. The researchers also found that the levels of these molecules in some of the patients with newly diagnosed schizophrenia who were given medication became similar to the levels in the control individuals.
What Do These Findings Mean?
These results are encouraging because they suggest that studying “metabolic profiles” might lead to finding a set of biomarkers that could reliably help in early diagnosis of schizophrenia. Such biomarkers might possibly also help in monitoring patients' responses to drug treatment. However, as acknowledged by the study's authors and emphasized by Rima Kaddhurah Daouk in an accompanying Perspective, these early results need to be tested in larger studies and confirmed before their clinical relevance will be known. It will be important for such follow-up studies to involve patients with other psychiatric diseases (not just schizophrenia), to see whether the biomarkers are specific to schizophrenia or whether they indicate a broader range of psychiatric diseases.
Additional Information.
Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.0030327.
National Institutes of Mental Health pages on schizophrenia
The National Alliance for Research on Schizophrenia and Depression
The National Alliance for the Mentally Ill
The Schizophrenia Society of Canada
Wikipedia page on schizophrenia (note: Wikipedia is an online encyclopedia that anyone can edit)
doi:10.1371/journal.pmed.0030327
PMCID: PMC1551919  PMID: 16933966
15.  Circulating Mitochondrial DNA in Patients in the ICU as a Marker of Mortality: Derivation and Validation 
PLoS Medicine  2013;10(12):e1001577.
In this paper, Choi and colleagues analyzed levels of mitochondrial DNA in two prospective observational cohort studies and found that increased mtDNA levels are associated with ICU mortality, and improve risk prediction in medical ICU patients. The data suggests that mtDNA could serve as a viable plasma biomarker in MICU patients.
Background
Mitochondrial DNA (mtDNA) is a critical activator of inflammation and the innate immune system. However, mtDNA level has not been tested for its role as a biomarker in the intensive care unit (ICU). We hypothesized that circulating cell-free mtDNA levels would be associated with mortality and improve risk prediction in ICU patients.
Methods and Findings
Analyses of mtDNA levels were performed on blood samples obtained from two prospective observational cohort studies of ICU patients (the Brigham and Women's Hospital Registry of Critical Illness [BWH RoCI, n = 200] and Molecular Epidemiology of Acute Respiratory Distress Syndrome [ME ARDS, n = 243]). mtDNA levels in plasma were assessed by measuring the copy number of the NADH dehydrogenase 1 gene using quantitative real-time PCR. Medical ICU patients with an elevated mtDNA level (≥3,200 copies/µl plasma) had increased odds of dying within 28 d of ICU admission in both the BWH RoCI (odds ratio [OR] 7.5, 95% CI 3.6–15.8, p = 1×10−7) and ME ARDS (OR 8.4, 95% CI 2.9–24.2, p = 9×10−5) cohorts, while no evidence for association was noted in non-medical ICU patients. The addition of an elevated mtDNA level improved the net reclassification index (NRI) of 28-d mortality among medical ICU patients when added to clinical models in both the BWH RoCI (NRI 79%, standard error 14%, p<1×10−4) and ME ARDS (NRI 55%, standard error 20%, p = 0.007) cohorts. In the BWH RoCI cohort, those with an elevated mtDNA level had an increased risk of death, even in analyses limited to patients with sepsis or acute respiratory distress syndrome. Study limitations include the lack of data elucidating the concise pathological roles of mtDNA in the patients, and the limited numbers of measurements for some of biomarkers.
Conclusions
Increased mtDNA levels are associated with ICU mortality, and inclusion of mtDNA level improves risk prediction in medical ICU patients. Our data suggest that mtDNA could serve as a viable plasma biomarker in medical ICU patients.
Please see later in the article for the Editors' Summary
Editors' Summary
Background
Intensive care units (ICUs, also known as critical care units) are specialist hospital wards that provide care for people with life-threatening injuries and illnesses. In the US alone, more than 5 million people are admitted to ICUs every year. Different types of ICUs treat different types of problems. Medical ICUs treat patients who, for example, have been poisoned or who have a serious infection such as sepsis (blood poisoning) or severe pneumonia (inflammation of the lungs); trauma ICUs treat patients who have sustained a major injury; cardiac ICUs treat patients who have heart problems; and surgical ICUs treat complications arising from operations. Patients admitted to ICUs require constant medical attention and support from a team of specially trained nurses and physicians to prevent organ injury and to keep their bodies functioning. Monitors, intravenous tubes (to supply essential fluids, nutrients, and drugs), breathing machines, catheters (to drain urine), and other equipment also help to keep ICU patients alive.
Why Was This Study Done?
Although many patients admitted to ICUs recover, others do not. ICU specialists use scoring systems (algorithms) based on clinical signs and physiological measurements to predict their patients' likely outcomes. For example, the APACHE II scoring system uses information on heart and breathing rates, temperature, levels of salts in the blood, and other signs and physiological measurements collected during the first 24 hours in the ICU to predict the patient's risk of death. Existing scoring systems are not perfect, however, and “biomarkers” (molecules in bodily fluids that provide information about a disease state) are needed to improve risk prediction for ICU patients. Here, the researchers investigate whether levels of circulating cell-free mitochondrial DNA (mtDNA) are associated with ICU deaths and whether these levels can be used as a biomarker to improve risk prediction in ICU patients. Mitochondria are cellular structures that produce energy. Levels of mtDNA in the plasma (the liquid part of blood) increase in response to trauma and infection. Moreover, mtDNA activates molecular processes that lead to inflammation and organ injury.
What Did the Researchers Do and Find?
The researchers measured mtDNA levels in the plasma of patients enrolled in two prospective observational cohort studies that monitored the outcomes of ICU patients. In the Brigham and Women's Hospital Registry of Critical Illness study, blood was taken from 200 patients within 24 hours of admission into the hospital's medical ICU. In the Molecular Epidemiology of Acute Respiratory Distress Syndrome study (acute respiratory distress syndrome is a life-threatening inflammatory reaction to lung damage or infection), blood was taken from 243 patients within 48 hours of admission into medical and non-medical ICUs at two other US hospitals. Patients admitted to medical ICUs with a raised mtDNA level (3,200 or more copies of a specific mitochondrial gene per microliter of plasma) had a 7- to 8-fold increased risk of dying within 28 days of admission compared to patients with mtDNA levels of less than 3,200 copies/µl plasma. There was no evidence of an association between raised mtDNA levels and death among patients admitted to non-medical ICUs. The addition of an elevated mtDNA level to a clinical model for risk prediction that included the APACHE II score and biomarkers that are already used to predict ICU outcomes improved the net reclassification index (an indicator of the improvement in risk prediction algorithms offered by new biomarkers) of 28-day mortality among medical ICU patients in both studies.
What Do These Findings Mean?
These findings indicate that raised mtDNA plasma levels are associated with death in medical ICUs and show that, among patients in medical ICUs, measurement of mtDNA plasma levels can improve the prediction of the risk of death from the APACHE II scoring system, even when commonly measured biomarkers are taken into account. These findings do not indicate whether circulating cell-free mtDNA increased because of the underlying severity of illness or whether mtDNA actively contributes to the disease process in medical ICU patients. Moreover, they do not provide any evidence that raised mtDNA levels are associated with an increased risk of death among non-medical (mainly surgical) ICU patients. These findings need to be confirmed in additional patients, but given the relative ease and rapidity of mtDNA measurement, the determination of circulating cell-free mtDNA levels could be a valuable addition to the assessment of patients admitted to medical ICUs.
Additional Information
Please access these websites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001577.
The UK National Health Service Choices website provides information about intensive care
The Society of Critical Care Medicine provides information for professionals, families, and patients about all aspects of intensive care
MedlinePlus provides links to other resources about intensive care (in English and Spanish)
The UK charity ICUsteps supports patients and their families through recovery from critical illness; its booklet Intensive Care: A Guide for Patients and Families is available in English and ten other languages; its website includes patient experiences and relative experiences of treatment in ICUs
Wikipedia has a page on ICU scoring systems (note that Wikipedia is a free online encyclopedia that anyone can edit; available in several languages)
doi:10.1371/journal.pmed.1001577
PMCID: PMC3876981  PMID: 24391478
16.  Whole Blood Gene Expression Profiles to Assess Pathogenesis and Disease Severity in Infants with Respiratory Syncytial Virus Infection 
PLoS Medicine  2013;10(11):e1001549.
In this study, Mejias and colleagues found that specific blood RNA profiles of infants with RSV LRTI allowed for specific diagnosis, better understanding of disease pathogenesis, and better assessment of disease severity.
Please see later in the article for the Editors' Summary
Background
Respiratory syncytial virus (RSV) is the leading cause of viral lower respiratory tract infection (LRTI) and hospitalization in infants. Mostly because of the incomplete understanding of the disease pathogenesis, there is no licensed vaccine, and treatment remains symptomatic. We analyzed whole blood transcriptional profiles to characterize the global host immune response to acute RSV LRTI in infants, to characterize its specificity compared with influenza and human rhinovirus (HRV) LRTI, and to identify biomarkers that can objectively assess RSV disease severity.
Methods and Findings
This was a prospective observational study over six respiratory seasons including a cohort of infants hospitalized with RSV (n = 135), HRV (n = 30), and influenza (n = 16) LRTI, and healthy age- and sex-matched controls (n = 39). A specific RSV transcriptional profile was identified in whole blood (training cohort, n = 45 infants; Dallas, Texas, US) and validated in three different cohorts (test cohort, n = 46, Dallas, Texas, US; validation cohort A, n = 16, Turku, Finland; validation cohort B, n = 28, Columbus, Ohio, US) with high sensitivity (94% [95% CI 87%–98%]) and specificity (98% [95% CI 88%–99%]). It classified infants with RSV LRTI versus HRV or influenza LRTI with 95% accuracy. The immune dysregulation induced by RSV (overexpression of neutrophil, inflammation, and interferon genes, and suppression of T and B cell genes) persisted beyond the acute disease, and immune dysregulation was greatly impaired in younger infants (<6 mo). We identified a genomic score that significantly correlated with outcomes of care including a clinical disease severity score and, more importantly, length of hospitalization and duration of supplemental O2.
Conclusions
Blood RNA profiles of infants with RSV LRTI allow specific diagnosis, better understanding of disease pathogenesis, and assessment of disease severity. This study opens new avenues for biomarker discovery and identification of potential therapeutic or preventive targets, and demonstrates that large microarray datasets can be translated into a biologically meaningful context and applied to the clinical setting.
Please see later in the article for the Editors' Summary
Editors' Summary
Background
Lower respiratory tract infections (LRTIs)—bacterial and viral infections of the lungs and airways (the tubes that take oxygen-rich air to the lungs)—are major causes of illness and death in children worldwide. Pneumonia (infection of the lungs) alone is responsible for 14% of all child deaths. The leading cause of viral LTRIs in children is respiratory syncytial virus (RSV), which is readily transmitted from person to person by direct contact with nasal fluids or airborne droplets. Almost all children have an RSV infection before their second birthday, but most have only minor symptoms similar to those of a common cold and are cared for at home. Unfortunately, some children develop more serious conditions when they become infected with RSV, such as pneumonia or bronchiolitis (swelling and mucus build-up in the bronchioles, the smallest air passages in the lungs). These children have to be admitted to the hospital for supportive care—there is no specific treatment for RSV infection—such as the provision of supplemental oxygen.
Why Was This Study Done?
The lack of a treatment (and of a vaccine) for RSV is largely due to our incomplete understanding of the cellular events and reactions, including the host immune response, that occur during the development of an RSV infection (disease pathogenesis). Moreover, based on physical examination and available diagnostic tools, it is impossible to predict which children infected with RSV will develop a serious condition that requires hospitalization and which ones can be safely nursed at home. Here, the researchers use microarrays to analyze the global host response to acute RSV LTRI in infants, to define gene expression patterns that are specific to RSV infection rather than infection with other common respiratory viruses, and to identify biomarkers that indicate the severity of RSV infection. “Microarray” analysis allows researchers to examine gene expression patterns (“RNA transcriptional profiles”) in, for example, whole blood; a biomarker is a molecule whose level in bodily fluids or tissues indicates how a disease might develop and helps with patient classification.
What Did the Researchers Do and Find?
The researchers compared the RNA transcriptional profile in whole blood taken from children less than two years old hospitalized with RSV, human rhinovirus, or influenza virus infection (rhinovirus and influenza are two additional viral causes of LRTI), and from healthy infants. Using “statistical group comparisons,” they identified more than 2,000 transcripts that were differentially expressed in blood from 45 infants with RSV infection and from 14 healthy matched controls. Genes related to interferon function (interferons are released by host cells in response to the presence of disease-causing organisms) and neutrophil function (neutrophils are immune system cells that, like interferons, are involved in the innate immune response, the body's first line of defense against infection) were among the most overexpressed genes in infants infected with RSV. Genes regulating T and B cells (components of the adaptive immune response, the body's second-line of defense against infection) were among the most underexpressed genes. This specific transcriptional profile, which was validated in three additional groups of infants, accurately distinguished between infants infected with RSV and those infected with human rhinovirus or influenza virus. Finally, a “molecular distance to health” score (a numerical score that quantifies the transcriptional perturbation associated with an illness) was correlated with the clinical disease severity score of the study participants, with how long they needed supplemental oxygen, and with their duration of hospitalization.
What Do These Findings Mean?
These findings suggest that it might be possible to use whole blood RNA transcriptional profiles to distinguish between infants infected with RSV and those with other viruses that commonly cause LRTI. Moreover, if these findings can be replicated in more patients (including non-hospitalized children), gene expression profiling might provide a strategy for triaging patients with RSV infections when they first present to an emergency department and for monitoring clinical changes during the course of the infection, particularly given the development of molecular tools that might soon enable the “real time” acquisition of transcriptional profiles in the clinical setting. Finally, although certain aspects of the study design limit the accuracy and generalizability of the study's findings, these data provide new insights into the pathogenesis of RSV infection and open new avenues for the discovery of biomarkers for RSV infection and for the identification of therapeutic and preventative targets.
Additional Information
Please access these websites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001549.
This study is further discussed in a PLOS Medicine Perspective by Peter Openshaw
The US Centers for Disease Control and Prevention provides information about RSV infection
The US National Heart, Lung, and Blood Institute provides information about the respiratory system and about RSV infections
The UK National Health Service Choices website provides information about bronchiolitis
The British Lung Foundation also provides information on RSV and on bronchiolitis
MedlinePlus provides links to other resources about RSV infections and about pneumonia (in English and Spanish); the MedlinePlus encyclopedia has a page on bronchiolitis (in English and Spanish)
PATH is an international non-profit organization investigating new RSV vaccines
doi:10.1371/journal.pmed.1001549
PMCID: PMC3825655  PMID: 24265599
17.  Microenvironmental Influences on Metastasis Suppressor Expression and Function during a Metastatic Cell’s Journey 
Cancer Microenvironment  2014;7(3):117-131.
Metastasis is the process of primary tumor cells breaking away and colonizing distant secondary sites. In order for a tumor cell growing in one microenvironment to travel to, and flourish in, a secondary environment, it must survive a series of events termed the metastatic cascade. Before departing the primary tumor, cells acquire genetic and epigenetic changes that endow them with properties not usually associated with related normal differentiated cells. Those cells also induce a subset of bone marrow-derived stem cells to mobilize and establish pre-metastatic niches [1]. Many tumor cells undergo epithelial-to-mesenchymal transition (EMT), where they transiently acquire morphologic changes, reduced requirements for cell-cell contact and become more invasive [2]. Invasive tumor cells eventually enter the circulatory (hematogenous) or lymphatic systems or travel across body cavities. In transit, tumor cells must resist anoikis, survive sheer forces and evade detection by the immune system. For blood-borne metastases, surviving cells then arrest or adhere to endothelial linings before either proliferating or extravasating. Eventually, tumor cells complete the process by proliferating to form a macroscopic mass [3].
Up to 90 % of all cancer related morbidity and mortality can be attributed to metastasis. Surgery manages to ablate most primary tumors, especially when combined with chemotherapy and radiation. But if cells have disseminated, survival rates drop precipitously. While multiple parameters of the primary tumor are predictive of local or distant relapse, biopsies remain an imperfect science. The introduction of molecular and other biomarkers [4, 5] continue to improve the accuracy of prognosis. However, the invasive procedure introduces new complications for the patient. Likewise, the heterogeneity of any tumor population [3, 6, 7] means that sampling error (i.e., since it is impractical to examine the entire tumor) necessitates further improvements.
In the case of breast cancer, for example, women diagnosed with stage I diseases (i.e., no evidence of invasion through a basement membrane) still have a ~30 % likelihood of developing distant metastases [8]. Many physicians and patients opt for additional chemotherapy in order to “mop up“ cells that have disseminated and have the potential to grow into macroscopic metastases. This means that ~ 70 % of patients receive unnecessary therapy, which has undesirable side effects. Therefore, improving prognostic capability is highly desirable.
Recent advances allow profiling of primary tumor DNA sequences and gene expression patterns to define a so-called metastatic signature [9–11], which can be predictive of patient outcome. However, the genetic changes that a tumor cell must undergo to survive the initial events of the metastatic cascade and colonize a second location belie a plasticity that may not be adequately captured in a sampling of heterogeneous tumors. In order to tailor or personalize patient treatments, a more accurate assessment of the genetic profile in the metastases is needed. Biopsy of each individual metastasis is not practical, safe, nor particularly cost-effective. In recent years, there has been a resurrection of the notion to do a ‘liquid biopsy,’ which essentially involves sampling of circulating tumor cells (CTC) and/or cell free nucleic acids (cfDNA, including microRNA (miRNA)) present in blood and lymph [12–16].
The rationale for liquid biopsy is that tumors shed cells and/or genetic fragments into the circulation, theoretically making the blood representative of not only the primary tumor but also distant metastases. Logically, one would predict that the proportion of CTC and/or cfDNA would be proportionate to the likelihood of developing metastases [14]. While a linear relationship does not exist, the information within CTC or cfDNA is beginning to show great promise for enabling a global snapshot of the disease. However, the CTC and cfDNA are present at extremely low levels. Nonetheless, newer technologies capture enough material to enrich and sequence the patient’s DNA or quantification of some biomarkers.
Among the biomarkers showing great promise are metastasis suppressors which, by definition, block a tumor cell’s ability to complete the metastatic process without prohibiting primary tumor growth [17]. Since the discovery of the first metastasis suppressor, Nm23, more than 30 have been functionally characterized. They function at various stages of the metastatic cascade, but their mechanisms of action, for the most part, remain ill-defined. Deciphering the molecular interactions of functional metastasis suppressors may provide insights for targeted therapies when these regulators cease to function and result in metastatic disease.
In this brief review, we summarize what is known about the various metastasis suppressors and their functions at individual steps of the metastatic cascade (Table 1). Some of the subdivisions are rather arbitrary in nature, since many metastasis suppressors affect more than one step in the metastatic cascade. Nonetheless what emerges is a realization that metastasis suppressors are intimately associated with the microenvironments in which cancer cells find themselves [18].
doi:10.1007/s12307-014-0148-4
PMCID: PMC4275500  PMID: 24938990
BRMS1; CD44; CRMP4; DCC; DLC1; GSN; LIFR; LSD1; MTBP; OGR1; RKIP; SSeCKS; Stefin A; RhoGDI2; RRM1; Caspase 8; Gas1. KAI1; Regulatory RNA; miRNA; KISS1; NDRG1; NME1; MKK4; MKK7; p38; CADM1; TSLC1; FXR; Invasion; Motility; Metastasis suppressor; Colonization; Cell-free DNA; Circulating tumor cell; CTC; DTC; cfDNA
18.  Collectives of diagnostic biomarkers identify high-risk subpopulations of hematuria patients: exploiting heterogeneity in large-scale biomarker data 
BMC Medicine  2013;11:12.
Background
Ineffective risk stratification can delay diagnosis of serious disease in patients with hematuria. We applied a systems biology approach to analyze clinical, demographic and biomarker measurements (n = 29) collected from 157 hematuric patients: 80 urothelial cancer (UC) and 77 controls with confounding pathologies.
Methods
On the basis of biomarkers, we conducted agglomerative hierarchical clustering to identify patient and biomarker clusters. We then explored the relationship between the patient clusters and clinical characteristics using Chi-square analyses. We determined classification errors and areas under the receiver operating curve of Random Forest Classifiers (RFC) for patient subpopulations using the biomarker clusters to reduce the dimensionality of the data.
Results
Agglomerative clustering identified five patient clusters and seven biomarker clusters. Final diagnoses categories were non-randomly distributed across the five patient clusters. In addition, two of the patient clusters were enriched with patients with 'low cancer-risk' characteristics. The biomarkers which contributed to the diagnostic classifiers for these two patient clusters were similar. In contrast, three of the patient clusters were significantly enriched with patients harboring 'high cancer-risk" characteristics including proteinuria, aggressive pathological stage and grade, and malignant cytology. Patients in these three clusters included controls, that is, patients with other serious disease and patients with cancers other than UC. Biomarkers which contributed to the diagnostic classifiers for the largest 'high cancer- risk' cluster were different than those contributing to the classifiers for the 'low cancer-risk' clusters. Biomarkers which contributed to subpopulations that were split according to smoking status, gender and medication were different.
Conclusions
The systems biology approach applied in this study allowed the hematuric patients to cluster naturally on the basis of the heterogeneity within their biomarker data, into five distinct risk subpopulations. Our findings highlight an approach with the promise to unlock the potential of biomarkers. This will be especially valuable in the field of diagnostic bladder cancer where biomarkers are urgently required. Clinicians could interpret risk classification scores in the context of clinical parameters at the time of triage. This could reduce cystoscopies and enable priority diagnosis of aggressive diseases, leading to improved patient outcomes at reduced costs.
doi:10.1186/1741-7015-11-12
PMCID: PMC3570289  PMID: 23327460
hematuria; biomarker; risk stratification; Random Forests Classifier; hierarchical clustering; feature selection; urothelial cancer; proteinuria
19.  Standardization of Diagnostic Biomarker Concentrations in Urine: The Hematuria Caveat 
PLoS ONE  2012;7(12):e53354.
Sensitive and specific urinary biomarkers can improve patient outcomes in many diseases through informing early diagnosis. Unfortunately, to date, the accuracy and translation of diagnostic urinary biomarkers into clinical practice has been disappointing. We believe this may be due to inappropriate standardization of diagnostic urinary biomarkers. Our objective was therefore to characterize the effects of standardizing urinary levels of IL-6, IL-8, and VEGF using the commonly applied standards namely urinary creatinine, osmolarity and protein. First, we report results based on the biomarker levels measured in 120 hematuric patients, 80 with pathologically confirmed bladder cancer, 27 with confounding pathologies and 13 in whom no underlying cause for their hematuria was identified, designated “no diagnosis”. Protein levels were related to final diagnostic categories (p = 0.022, ANOVA). Osmolarity (mean = 529 mOsm; median = 528 mOsm) was normally distributed, while creatinine (mean = 10163 µmol/l, median = 9350 µmol/l) and protein (0.3297, 0.1155 mg/ml) distributions were not. When we compared AUROCs for IL-6, IL-8 and VEGF levels, we found that protein standardized levels consistently resulted in the lowest AUROCs. The latter suggests that protein standardization attenuates the “true” differences in biomarker levels across controls and bladder cancer samples. Second, in 72 hematuric patients; 48 bladder cancer and 24 controls, in whom urine samples had been collected on recruitment and at follow-up (median = 11 (1 to 20 months)), we demonstrate that protein levels were approximately 24% lower at follow-up (Bland Altman plots). There was an association between differences in individual biomarkers and differences in protein levels over time, particularly in control patients. Collectively, our findings identify caveats intrinsic to the common practice of protein standardization in biomarker discovery studies conducted on urine, particularly in patients with hematuria.
doi:10.1371/journal.pone.0053354
PMCID: PMC3534058  PMID: 23300915
20.  Novel diagnostic biomarkers for prostate cancer 
Journal of Cancer  2010;1:150-177.
Prostate cancer is the most frequently diagnosed malignancy in American men, and a more aggressive form of the disease is particularly prevalent among African Americans. The therapeutic success rate for prostate cancer can be tremendously improved if the disease is diagnosed early. Thus, a successful therapy for this disease depends heavily on the clinical indicators (biomarkers) for early detection of the presence and progression of the disease, as well as the prediction after the clinical intervention. However, the current clinical biomarkers for prostate cancer are not ideal as there remains a lack of reliable biomarkers that can specifically distinguish between those patients who should be treated adequately to stop the aggressive form of the disease and those who should avoid overtreatment of the indolent form.
A biomarker is a characteristic that is objectively measured and evaluated as an indicator of normal biologic processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention. A biomarker reveals further information to presently existing clinical and pathological analysis. It facilitates screening and detecting the cancer, monitoring the progression of the disease, and predicting the prognosis and survival after clinical intervention. A biomarker can also be used to evaluate the process of drug development, and, optimally, to improve the efficacy and safety of cancer treatment by enabling physicians to tailor treatment for individual patients. The form of the prostate cancer biomarkers can vary from metabolites and chemical products present in body fluid to genes and proteins in the prostate tissues.
Current advances in molecular techniques have provided new tools facilitating the discovery of new biomarkers for prostate cancer. These emerging biomarkers will be beneficial and critical in developing new and clinically reliable indicators that will have a high specificity for the diagnosis and prognosis of prostate cancer. The purpose of this review is to examine the current status of prostate cancer biomarkers, with special emphasis on emerging markers, by evaluating their diagnostic and prognostic potentials. Both genes and proteins that reveal loss, mutation, or variation in expression between normal prostate and cancerous prostate tissues will be covered in this article. Along with the discovery of prostate cancer biomarkers, we will describe the criteria used when selecting potential biomarkers for further development towards clinical use. In addition, we will address how to appraise and validate candidate markers for prostate cancer and some relevant issues involved in these processes. We will also discuss the new concept of the biomarkers, existing challenges, and perspectives of biomarker development.
PMCID: PMC2962426  PMID: 20975847
diagnostic biomarkers; prostate cancer
21.  Predicting Survival within the Lung Cancer Histopathological Hierarchy Using a Multi-Scale Genomic Model of Development 
PLoS Medicine  2006;3(7):e232.
Background
The histopathologic heterogeneity of lung cancer remains a significant confounding factor in its diagnosis and prognosis—spurring numerous recent efforts to find a molecular classification of the disease that has clinical relevance.
Methods and Findings
Molecular profiles of tumors from 186 patients representing four different lung cancer subtypes (and 17 normal lung tissue samples) were compared with a mouse lung development model using principal component analysis in both temporal and genomic domains. An algorithm for the classification of lung cancers using a multi-scale developmental framework was developed. Kaplan–Meier survival analysis was conducted for lung adenocarcinoma patient subgroups identified via their developmental association. We found multi-scale genomic similarities between four human lung cancer subtypes and the developing mouse lung that are prognostically meaningful. Significant association was observed between the localization of human lung cancer cases along the principal mouse lung development trajectory and the corresponding patient survival rate at three distinct levels of classical histopathologic resolution: among different lung cancer subtypes, among patients within the adenocarcinoma subtype, and within the stage I adenocarcinoma subclass. The earlier the genomic association between a human tumor profile and the mouse lung development sequence, the poorer the patient's prognosis. Furthermore, decomposing this principal lung development trajectory identified a gene set that was significantly enriched for pyrimidine metabolism and cell-adhesion functions specific to lung development and oncogenesis.
Conclusions
From a multi-scale disease modeling perspective, the molecular dynamics of murine lung development provide an effective framework that is not only data driven but also informed by the biology of development for elucidating the mechanisms of human lung cancer biology and its clinical outcome.
Editors' Summary
Background.
Lung cancer causes the most deaths from cancer worldwide—around a quarter of all cancer deaths—and the number of deaths is rising each year. There are a number of different types of the disease, whose names come from early descriptions of the cancer cells when seen under the microscope: carcinoid, small cell, and non–small cell, which make up 2%, 13%, and 86% of lung cancers, respectively. To make things more complicated, each of these cancer types can be subdivided further. It is important to distinguish the different types of cancer because they differ in their rates of growth and how they respond to treatment; for example, small cell lung cancer is the most rapidly progressing type of lung cancer. But although these current classifications of cancers are useful, researchers believe that if the underlying molecular changes in these cancers could be discovered then a more accurate way of classifying cancers, and hence predicting outcome and response to treatment, might be possible.
Why Was This Study Done?
Previous work has suggested that some cancers come from very immature cells, that is, cells that are present in the early stages of an animal's development from an embryo in the womb to an adult animal. Many animals have been closely studied so as to understand how they develop; the best studied model that is also relevant to human disease is the mouse, and researchers have previously studied lung development in mice in detail. This group of researchers wanted to see if there was any relation between the activity (known as expression) of mouse genes during the development of the lung and the expression of genes in human lung cancers, particularly whether they could use gene expression to try to predict the outcome of lung cancer in patients.
What Did the Researchers Do and Find?
They compared the gene expression in lung cancer samples from 186 patients with four different types of lung cancer (and in 17 normal lung tissue samples) to the gene expression found in normal mice during development. They found similarities between expression patterns in the lung cancer subtypes and the developing mouse lung, and that these similarities explain some of the different outcomes for the patients. In general, they found that when the gene expression in the human cancer was similar to that of very immature mouse lung cells, patients had a poor prognosis. When the gene expression in the human cancer was more similar to mature mouse lung cells, the prognosis was better. However, the researchers found that carcinoid tumors had rather different expression profiles compared to the other tumors.
  The researchers were also able to discover some specific gene types that seemed to have particularly strong associations between mouse development and the human cancers. Two of these gene types were ones that are involved in building and breaking down DNA itself, and ones involved in how cells stick together. This latter group of genes is thought to be involved in how cancers spread.
What Do These Findings Mean?
These results provide a new way of thinking about how to classify lung cancers, and also point to a few groups of genes that may be particularly important in the development of the tumor. However, before these results are used in any clinical assessment, further work will need to be done to work out whether they are true for other groups of patients.
Additional Information.
Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.0030232.
•  MedlinePlus has information from the United States National Library of Medicine and other government agencies and health-related organizations [MedlinePlus]
•  National Institute on Aging is also a good place to start looking for information [National Institute for Aging]
•  [The National Cancer Institute] and Lung Cancer Online [ Lung Cancer Online] have a wide range of information on lung cancer
Comparison of gene expression patterns in patients with lung cancer and in mouse lung development showed that those tumors associated with earlier mouse lung development had a poorer prognosis.
doi:10.1371/journal.pmed.0030232
PMCID: PMC1483910  PMID: 16800721
22.  Discovery and Preclinical Validation of Salivary Transcriptomic and Proteomic Biomarkers for the Non-Invasive Detection of Breast Cancer 
PLoS ONE  2010;5(12):e15573.
Background
A sensitive assay to identify biomarkers using non-invasively collected clinical specimens is ideal for breast cancer detection. While there are other studies showing disease biomarkers in saliva for breast cancer, our study tests the hypothesis that there are breast cancer discriminatory biomarkers in saliva using de novo discovery and validation approaches. This is the first study of this kind and no other study has engaged a de novo biomarker discovery approach in saliva for breast cancer detection. In this study, a case-control discovery and independent preclinical validations were conducted to evaluate the performance and translational utilities of salivary transcriptomic and proteomic biomarkers for breast cancer detection.
Methodology/Principal Findings
Salivary transcriptomes and proteomes of 10 breast cancer patients and 10 matched controls were profiled using Affymetrix HG-U133-Plus-2.0 Array and two-dimensional difference gel electrophoresis (2D-DIGE), respectively. Preclinical validations were performed to evaluate the discovered biomarkers in an independent sample cohort of 30 breast cancer patients and 63 controls using RT-qPCR (transcriptomic biomarkers) and quantitative protein immunoblot (proteomic biomarkers). Transcriptomic and proteomic profiling revealed significant variations in salivary molecular biomarkers between breast cancer patients and matched controls. Eight mRNA biomarkers and one protein biomarker, which were not affected by the confounding factors, were pre-validated, yielding an accuracy of 92% (83% sensitive, 97% specific) on the preclinical validation sample set.
Conclusions
Our findings support that transcriptomic and proteomic signatures in saliva can serve as biomarkers for the non-invasive detection of breast cancer. The salivary biomarkers possess discriminatory power for the detection of breast cancer, with high specificity and sensitivity, which paves the way for prediction model validation study followed by pivotal clinical validation.
doi:10.1371/journal.pone.0015573
PMCID: PMC3013113  PMID: 21217834
23.  Computational Biomarker Pipeline from Discovery to Clinical Implementation: Plasma Proteomic Biomarkers for Cardiac Transplantation 
PLoS Computational Biology  2013;9(4):e1002963.
Recent technical advances in the field of quantitative proteomics have stimulated a large number of biomarker discovery studies of various diseases, providing avenues for new treatments and diagnostics. However, inherent challenges have limited the successful translation of candidate biomarkers into clinical use, thus highlighting the need for a robust analytical methodology to transition from biomarker discovery to clinical implementation. We have developed an end-to-end computational proteomic pipeline for biomarkers studies. At the discovery stage, the pipeline emphasizes different aspects of experimental design, appropriate statistical methodologies, and quality assessment of results. At the validation stage, the pipeline focuses on the migration of the results to a platform appropriate for external validation, and the development of a classifier score based on corroborated protein biomarkers. At the last stage towards clinical implementation, the main aims are to develop and validate an assay suitable for clinical deployment, and to calibrate the biomarker classifier using the developed assay. The proposed pipeline was applied to a biomarker study in cardiac transplantation aimed at developing a minimally invasive clinical test to monitor acute rejection. Starting with an untargeted screening of the human plasma proteome, five candidate biomarker proteins were identified. Rejection-regulated proteins reflect cellular and humoral immune responses, acute phase inflammatory pathways, and lipid metabolism biological processes. A multiplex multiple reaction monitoring mass-spectrometry (MRM-MS) assay was developed for the five candidate biomarkers and validated by enzyme-linked immune-sorbent (ELISA) and immunonephelometric assays (INA). A classifier score based on corroborated proteins demonstrated that the developed MRM-MS assay provides an appropriate methodology for an external validation, which is still in progress. Plasma proteomic biomarkers of acute cardiac rejection may offer a relevant post-transplant monitoring tool to effectively guide clinical care. The proposed computational pipeline is highly applicable to a wide range of biomarker proteomic studies.
Author Summary
Novel proteomic technology has led to the generation of vast amounts of biological data and the identification of numerous potential biomarkers. However, computational approaches to translate this information into knowledge capable of impacting clinical care have been lagging. We propose a computational proteomic pipeline for biomarker studies that is founded on the combination of advanced statistical methodologies. We demonstrate our approach through the analysis of data obtained from heart transplant patients. Heart transplantation is the gold standard treatment for patients with end-stage heart failure, but is complicated by episodes of immune rejection that can adversely impact patient outcomes. Current rejection monitoring approaches are highly invasive, requiring a biopsy of the heart. This work aims to reduce the need for biopsies, and demonstrate the power and utility of computational approaches in proteomic biomarker discovery. Our work utilizes novel high-throughput proteomic technology combined with advanced statistical techniques to identify blood markers that guide the decision as to whether a biopsy is warranted, reduce the number of unnecessary biopsies, and ultimately diagnose the presence of rejection in heart transplant patients. Additionally, the proposed computational methodologies can be applied to a range of proteomic biomarker studies of various diseases and conditions.
doi:10.1371/journal.pcbi.1002963
PMCID: PMC3617196  PMID: 23592955
24.  Disease Biomarkers in Cerebrospinal Fluid of Patients with First-Onset Psychosis 
PLoS Medicine  2006;3(11):e428.
Background
Psychosis is a severe mental condition that is characterized by a loss of contact with reality and is typically associated with hallucinations and delusional beliefs. There are numerous psychiatric conditions that present with psychotic symptoms, most importantly schizophrenia, bipolar affective disorder, and some forms of severe depression referred to as psychotic depression. The pathological mechanisms resulting in psychotic symptoms are not understood, nor is it understood whether the various psychotic illnesses are the result of similar biochemical disturbances. The identification of biological markers (so-called biomarkers) of psychosis is a fundamental step towards a better understanding of the pathogenesis of psychosis and holds the potential for more objective testing methods.
Methods and Findings
Surface-enhanced laser desorption ionization mass spectrometry was employed to profile proteins and peptides in a total of 179 cerebrospinal fluid samples (58 schizophrenia patients, 16 patients with depression, five patients with obsessive-compulsive disorder, ten patients with Alzheimer disease, and 90 controls). Our results show a highly significant differential distribution of samples from healthy volunteers away from drug-naïve patients with first-onset paranoid schizophrenia. The key alterations were the up-regulation of a 40-amino acid VGF-derived peptide, the down-regulation of transthyretin at ~4 kDa, and a peptide cluster at ~6,800–7,300 Da (which is likely to be influenced by the doubly charged ions of the transthyretin protein cluster). These schizophrenia-specific protein/peptide changes were replicated in an independent sample set. Both experiments achieved a specificity of 95% and a sensitivity of 80% or 88% in the initial study and in a subsequent validation study, respectively.
Conclusions
Our results suggest that the application of modern proteomics techniques, particularly mass spectrometric approaches, holds the potential to advance the understanding of the biochemical basis of psychiatric disorders and may in turn allow for the development of diagnostics and improved therapeutics. Further studies are required to validate the clinical effectiveness and disease specificity of the identified biomarkers.
Protein profiles from 179 cerebrospinal fluid samples yield differences between patients with psychotic disorders and healthy volunteers, suggesting that such biomarkers could assist in the early diagnosis of mental illness.
Editors' Summary
Background.
Psychosis is an abnormal mental state characterized by loss of contact with reality, often associated with hallucinations, delusions, personality changes, and disorganized thinking. Psychotic symptoms occur in several psychiatric disorders, including schizophrenia, bipolar disorder, and psychotic depression. It is not clear what the underlying biological abnormalities in the brain are, and whether they are the same for the different psychotic illnesses. The hope is that recent advances in brain imaging and systematic characterization of genetic activity and protein composition in the brain might help to shed light on mental diseases, eventually leading to better diagnosis, treatment, and possibly even prevention.
Why Was This Study Done?
This study was carried out in order to search for biomarkers for psychosis and schizophrenia by comparing the protein composition in the cerebrospinal fluid (the clear body fluid that surrounds the brain and the spinal cord) of patients with different psychotic disorders and normal individuals who served as controls.
What Did the Researchers Do and Find?
The researchers used a technique called surface-enhanced laser desorption ionization mass spectrometry, which allows a comprehensive analysis of the protein composition of a particular sample, on a total of 179 cerebrospinal fluid samples. The samples came from 90 individuals without mental illness who served as controls, 58 people with schizophrenia who were very recently diagnosed and had not yet taken any medication, 16 patients with depression, five patients with obsessive-compulsive disorder, and ten patients with Alzheimer disease. All of the patients gave their informed consent to participate in the study. The researchers found that samples from treatment-naïve schizophrenic patients had a number of characteristic changes compared with samples from control individuals, and that those changes were not found in the patients with other mental illnesses. The researchers then wanted to test whether they would see the same pattern in a separate set of patients with schizophrenia versus controls, which turned out to be the case. Two of the changes in the cerebrospinal fluid that were associated with schizophrenia, namely higher levels of parts of a protein called VGF and lower levels of a protein called transthyretin, were also found in post-mortem brain samples of patients with schizophrenia compared with samples from controls. Lower levels of transthyretin were also found in serum (blood) of first-onset drug naïve schizophrenia patients.
What Do These Findings Mean?
These results suggest that this approach has the potential to find biomarkers for psychosis and, possibly, schizophrenia that might help in the understanding of the molecular basis for these conditions. If shown, in future studies, to be directly involved in causing the disease symptoms, they would be important targets for treatment and prevention efforts, and might also be useful for diagnostic purposes. Overall, there are promising examples, such as this study, suggesting that new molecular techniques can yield fresh insights into psychiatric illnesses such as schizophrenia and other psychotic disorders. Additional studies are needed to confirm the findings presented here and to address many open questions, and would seem well justified given these results.
Additional Information.
Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.0030428.
MedlinePlus entries on psychosis and schizophrenia
The National Alliance for Research on Schizophrenia and Depression
The National Alliance for the Mentally Ill
The Schizophrenia Society of Canada
Wikipedia entries on psychosis and schizophrenia (note that Wikipedia is an online encyclopedia that anyone can edit)
doi:10.1371/journal.pmed.0030428
PMCID: PMC1630717  PMID: 17090210
25.  Biomarker Validation: Common Data Analysis Concerns 
The Oncologist  2014;19(8):886-891.
Statistical concerns such as confounding and multiplicity, for which solutions have existed for years, are common in biomarker validation studies; however, published validation studies may not address these issues. By not only raising the issues but also describing possible solutions, this discussion may help decrease false discovery and enhance the reproducibility of validation study findings.
Biomarker validation, like any other confirmatory process based on statistical methodology, must discern associations that occur by chance from those reflecting true biological relationships. Validity of a biomarker is established by authenticating its correlation with clinical outcome. Validated biomarkers can lead to targeted therapy, improve clinical diagnosis, and serve as useful prognostic and predictive factors of clinical outcome. Statistical concerns such as confounding and multiplicity are common in biomarker validation studies. This article discusses four major areas of concern in the biomarker validation process and some of the proposed solutions. Because present-day statistical packages enable the researcher to address these common concerns, the purpose of this discussion is to raise awareness of these statistical issues in the hope of improving the reproducibility of validation study findings.
doi:10.1634/theoncologist.2014-0061
PMCID: PMC4122484  PMID: 25001264
Biomarker; Selection bias; Confounding factors; Validation studies

Results 1-25 (889151)