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.
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.
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.
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.
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.
hematuria; biomarker; risk stratification; Random Forests Classifier; hierarchical clustering; feature selection; urothelial cancer; proteinuria
Considerable attention and an enormous amount of resources have been dedicated to cancer biomarker discovery and validation. However, there are still a limited number of useful biomarkers available for clinical use. An ideal biomarker should be easily assayed with minimally invasive medical procedures but possess high sensitivity and specificity. Commonly used circulating biomarkers are proteins in serum, most of which require labor-intensive analysis hindered by low sensitivity in early tumor detection. Since the deregulation of microRNA (miRNA) is associated with cancer development and progression, profiling of circulating miRNAs has been used in a number of studies to identify novel minimally invasive miRNA biomarkers. In this review, we discuss the origin of the circulating cell-free miRNAs and their carriers in blood. We summarize the clinical use and function of potentially promising miRNA biomarkers in a variety of different cancers, along with their downstream target genes in tumor initiation and development. Additionally, we analyze some technical challenges in applying miRNA biomarkers to clinical practice.
serum; cancer; miRNA; biomarker.
"Virtual libraries" and "digital libraries" have become stock phrases of our times. But what do they really mean? While digital refers to a new form of document encoding and must be approached from that perspective, virtual resonates with aspects that modern philosophy treats with benign neglect at best. The word virtual harbors the notion of potential, and therein lies its hidden strength. Although strong commercial interests try to use the shift to a digital environment to redefine the political economy of knowledge, and thus virtualize libraries into a state of almost complete impotence, all hope is not lost. Librarians of virtualized libraries may well discover that they have re-empowered institutions if they place human interaction at the heart of their operations. In other words, rather than envisioning themselves as knowledge bankers sitting on treasure vaults of knowledge, they should see themselves as "hearts" dynamizing human communities. They should also see themselves as an essential part of these communities, and not as external repositories of knowledge. In this fashion, they will avoid the fate of becoming an oxymoron.
In the past decade, biomarker discovery has become ubiquitous in cancer research. However, despite this interest in biomarker research, few newly-characterized biomarkers have emerged as clinically-used entities. Here, we review the current state of biomarker research in cancer and identify challenges that stall many biomarker discovery efforts. We outline a model for systematic biomarker discovery, exemplified by recent efforts in prostate cancer, in which bioinformatics plays a central role in identifying promising new candidate biomarkers. Finally, we review the role of the National Cancer Institute’s Early Detection Research Network (EDRN) in biomarker studies and the importance of EDRN-led efforts to establish a research standard for more effective biomarker discovery efforts.
biomarker; prostate cancer; bioinformatics; early detection
The search for clinically useful biomarkers has been one of the holy grails of schizophrenia research. This paper will outline the evolving notion of biomarkers and then outline outcomes from a variety of biomarkers discovery strategies. In particular, the impact of high-throughput screening technologies on biomarker discovery will be highlighted and how new or improved technologies may allow the discovery of either diagnostic biomarkers for schizophrenia or biomarkers that will be useful in determining appropriate treatments for people with the disorder. History tells those involved in biomarker research that the discovery and validation of useful biomarkers is a long process and current progress must always be viewed in that light. However, the approval of the first biomarker screen with some value in predicting responsiveness to antipsychotic drugs suggests that biomarkers can be identified and that these biomarkers that will be useful in diagnosing and treating people with schizophrenia.
Biomarkers have been sought after in the field of schizophrenia research for decades. In this paper, we discuss some of the concepts around developing biomarkers in an effort to understand why the use of biomarkers for schizophrenia has not been realized. In particular, we address the following 4 questions. Why would we need a diagnostic biomarker for schizophrenia? How is a biomarker typically defined and how does that influence the discovery of biomarkers in schizophrenia? What is the best use of biomarkers in schizophrenia? Do any biomarkers for schizophrenia currently exist? Thus, while we suggest that no biomarker currently exists for schizophrenia, the heterogeneity associated with schizophrenia will most likely need to be taken into account which will result in multiple biomarkers that identify the multiple underlying pathophysiological processes involved in schizophrenia. Therefore, much additional work will be required prior to obtaining any well-established biomarkers for schizophrenia.
Recent positive clinical results in cancer immunotherapy point to the potential of immune-based strategies to provide effective treatment of a variety of cancers. In some patients, the responses to cancer immunotherapy are durable, dramatically extending survival. Extensive research efforts are being made to identify and validate biomarkers that can help identify subsets of cancer patients that will benefit most from these novel immunotherapies. In addition to the clear advantage of such predictive biomarkers, immune biomarkers are playing an important role in the development, clinical evaluation and monitoring of cancer immunotherapies. This Cancer Immunotherapy Resource Document, prepared by the Society for Immunotherapy of Cancer (SITC, formerly the International Society for Biological Therapy of Cancer, iSBTc), provides key references and online resources relevant to the discovery, evaluation and clinical application of immune biomarkers. These key resources were identified by experts in the field who are actively pursuing research in biomarker identification and validation. This organized collection of the most useful references, online resources and tools serves as a compass to guide discovery of biomarkers essential to advancing novel cancer immunotherapies.
Ovarian epithelial carcinoma can be subdivided into separate histological subtypes including clear cell, endometrioid, mucinous, and serous. These carcinoma subtypes may represent distinctive pathways of tumorigenesis and disease development. This distinction could potentially be reflected in the levels of tumor produced factors that enter into the circulation and serve as biomarkers of malignant growth. Here, we analyze levels of circulating biomarkers from a diverse set of patients diagnosed with ovarian carcinoma to identify biomarker trends and relationships associated with distinct carcinoma histotypes and divergent tumorigenic pathways.
We utilize multiplexed bead-based immunoassays to measure serum levels of a diverse array of fifty-eight biomarkers from the sera of patients diagnosed with various histological subtypes of ovarian carcinoma and benign lesions. The biomarkers studied include cancer antigens, oncogenes, cytokines, chemokines, receptors, growth and angiogenic factors, proteases, hormones, and apoptosis and adhesion related molecules. Levels of each biomarker are compared statistically across carcinoma subtypes as well as with benign cases.
A total of 21 serum biomarkers differ significantly between patients diagnosed with ovarian carcinomas and benign cases. Nine of these biomarkers are specific for carcinomas identified as clear cell, endometrioid, or mucinous in histology, while two biomarkers are specific for the serous histology. In a direct comparison of the histology groups, ten biomarkers are found to be subtype specific. Identified biomarkers include traditional and emerging tumor markers, cytokines and receptors, hormones, and adhesion- and metastasis-related proteins.
We demonstrate here that the divergent histology-based tumorigenic pathways proposed for ovarian epithelial carcinomas are associated with distinct profiles of circulating biomarkers. Continued investigation into the relationships between these factors should reveal new insights into the complex mechanisms underlying ovarian epithelial tumorigenesis.
ovarian carcinoma; tumor histology; serum biomarkers; ovarian tumorigenesis
To improve future drug development and patient management for patients with castration-resistant prostate cancer (CRPC), surrogate biomarkers that are linked to relevant outcomes are urgently needed. A biomarker must be measurable, reproducible, linked to relevant clinical outcomes, and demonstrate utility. This is a rapidly evolving area, with recent trials in CRPC incorporating the detection of circulating tumour cells (CTCs), imaging, and patient-reported outcome biomarkers. We discuss the framework for the development of biomarkers for CRPC, including different categories and contexts of use. We also highlight the requirements of analytical validation, the sequence of trials needed for clinical validation and regulatory approval, and the future outlook for imaging and CTC biomarkers.
Although omic-based discovery approaches can provide powerful tools for biomarker identification, several reservations have been raised regarding the clinical applicability of gene expression studies, such as their prohibitive cost. However, the limited availability of antibodies is a key barrier to the development of a lower cost alternative, namely a discrete collection of immunohistochemistry (IHC)-based biomarkers. The aim of this study was to use a systematic approach to generate and screen affinity-purified, mono-specific antibodies targeting progression-related biomarkers, with a view towards developing a clinically applicable IHC-based prognostic biomarker panel for breast cancer.
We examined both in-house and publicly available breast cancer DNA microarray datasets relating to invasion and metastasis, thus identifying a cohort of candidate progression-associated biomarkers. Of these, 18 antibodies were released for extended analysis. Validated antibodies were screened against a tissue microarray (TMA) constructed from a cohort of consecutive breast cancer cases (n = 512) to test the immunohistochemical surrogate signature.
Antibody screening revealed 3 candidate prognostic markers: the cell cycle regulator, Anillin (ANLN); the mitogen-activated protein kinase, PDZ-Binding Kinase (PBK); and the estrogen response gene, PDZ-Domain Containing 1 (PDZK1). Increased expression of ANLN and PBK was associated with poor prognosis, whilst increased expression of PDZK1 was associated with good prognosis. A 3-marker signature comprised of high PBK, high ANLN and low PDZK1 expression was associated with decreased recurrence-free survival (p < 0.001) and breast cancer-specific survival (BCSS) (p < 0.001). This novel signature was associated with high tumour grade (p < 0.001), positive nodal status (p = 0.029), ER-negativity (p = 0.006), Her2-positivity (p = 0.036) and high Ki67 status (p < 0.001). However, multivariate Cox regression demonstrated that the signature was not a significant predictor of BCSS (HR = 6.38; 95% CI = 0.79-51.26, p = 0.082).
We have developed a comprehensive biomarker pathway that extends from discovery through to validation on a TMA platform. This proof-of-concept study has resulted in the identification of a novel 3-protein prognostic panel. Additional biochemical markers, interrogated using this high-throughput platform, may further augment the prognostic accuracy of this panel to a point that may allow implementation into routine clinical practice.
Prognostic biomarkers; Tissue microarray; Breast cancer; Antibody screening; Antibody validation
To frame the general process of biomarker discovery and development, and to describe a proposal for the development of a multi-biomarker based risk model for pediatric septic shock.
Narrative literature review and author generated data.
Biomarkers can be grouped into four broad classes, based on the intended function: diagnostic, monitoring, surrogate, and stratification. Biomarker discovery and development requires a rigorous process, which is frequently not well followed in the critical care medicine literature. Very few biomarkers have successfully transitioned from the candidate stage to the true biomarker stage. There is great interest in developing diagnostic and stratification biomarkers for sepsis. Procalcitonin is currently the most promising diagnostic biomarker for sepsis. Recent evidence suggests that interleukin-8 can be used to stratify children with septic shock having a high likelihood of survival with standard care. Currently, there is a multi-institutional effort to develop a multi-biomarker based sepsis risk model intended to predict outcome and illness severity for individual children with septic shock.
Biomarker discovery and development is an important portion of the pediatric critical care medicine translational research agenda. This effort will require collaboration across multiple institutions and investigators. Rigorous conduct of biomarker-focused research holds the promise of transforming our ability to care for individual patients and our ability to conduct clinical trials in a more effective manner.
There have been some successes in qualifying biomarkers and applying them to drug development and clinical treatment of various diseases. A recent success is illustrated by a collaborative effort among the US Food and Drug Administration, the European Medicines Agency, and the pharmaceutical industry to provide a set of seven preclinical kidney toxicity biomarkers for drug development. Other successes include, but are not limited to, clinical biomarkers for cancer treatment and clinical management of heart transplant patients. The value of fully qualified surrogate endpoints in facilitating successful drug development is undisputed, especially for diseases in which the traditional clinical outcome can only be assessed in large, multi-year trials. Emerging biomarkers, including chemical genomic or imaging biomarkers, and measurement of circulating tumor cells hold great promise for early diagnosis of disease and as prognostic tests for managing treatment of chronic diseases such as osteoarthritis, Alzheimer disease, cardiovascular disease, and cancer. To advance the success of treating and managing these diseases, efforts are needed to establish the temporal relationship between changes in inflammatory or imaging biomarkers with the progression of the chronic disease, and in the case of cancer, between the extent of circulating cancer cells and tumor progression or remission.
biomarkers; diagnostic; diseases; gene expression; imaging
Thousands of articles describing biomarkers predictive of treatment and prognostic of survival in cancer have been published, yet only a handful of biomarkers are currently used routinely in the clinic. Biomarkers need to be analytically standardized, validated, and clinically useful. This review will address the challenges and ways in which we can improve our discovery and translation of prospective biomarkers from the lab into validated diagnostic tests with a specific focus on patients diagnosed with glioblastoma and MGMT promoter methylation status. There has been long-held enthusiasm to use MGMT promoter methylation as a predictive biomarker for patients treated with the alkylating agent, temozolomide; however in the majority of centers around the world, this has not yet transpired.
biomarkers; treatment response; glioblastoma; MGMT; tumor heterogeneity
Biomarkers are pivotal for cancer detection, diagnosis, prognosis and therapeutic monitoring. However, currently available cancer biomarkers have the disadvantage of lacking specificity and/or sensitivity. Developing effective cancer biomarkers becomes a pressing and permanent need. The cancer secretome, the totality of proteins released by cancer cells or tissues, provides useful tools for the discovery of novel biomarkers. The focus of this article is to review the recent advances in cancer secretome analysis. We aim to elaborate the approaches currently employed for cancer secretome studies, as well as its applications in the identification of biomarkers and the clarification of carcinogenesis mechanisms. Challenges encountered in this newly emerging field, including sample preparation, in vivo secretome analysis and biomarker validation, are also discussed. Further improvements on strategies and technologies will continue to drive forward cancer secretome research and enable development of a wealth of clinically valuable cancer biomarkers.
Biomarkers that can be used in combination with established screening tests to reduce false positive rates are in considerable demand. In this article, we present methods for evaluating the diagnostic performance of combination tests that require positivity on a biomarker test in addition to a standard screening test. These methods rely on relative true and false positive rates to measure the loss in sensitivity and gain in specificity associated with the combination relative to the standard test. Inference about the relative rates follows from noting their interpretation as conditional probabilities. These methods are extended to evaluate combinations with continuous biomarker tests by introducing a new statistical entity, the relative receiver operating characteristic (rROC) curve. The rROC curve plots the relative true positive rate versus the relative false positive rate as the biomarker threshold for positivity varies. Inference can be made by applying existing ROC methodology. We illustrate the methods with two examples: a breast cancer biomarker study proposed by the Early Detection Research Network (EDRN) and a prostate cancer case-control study examining the ability of free prostate-specific antigen (PSA) to improve the specificity of the standard PSA test.
Diagnostic tests; Relative accuracy; ROC curve; Specificity; Study design
The development of a new breast cancer biomarker for early detection is a process that begins with biomarker discovery, followed by a rigorous definition and evaluation of the whole process of biomarker determination (analytical validation). It terminates with the assessment of the impact of the biomarker on clinical practice (clinical validation). A 4-phase scheme for the analytical validation process of the biomarkers for early diagnosis has recently been proposed with the aim of covering the need for standardized operating procedures as well as the need for monitoring and maintaining their quality. As far as clinical validation of biomarkers for early diagnosis is concerned, however, a well established phased approach exists, and guidelines are available for both planning studies and reporting results. Although analytical and clinical validation should be logically linked, often this is not the case in real-word practice, especially in the early phases of biomarker development. This is also the case with breast cancer biomarkers for early detection.
Biomarkers for early cancer detection; Breast cancer; Analytical validation; Clinical validation
The exceptional high mortality of lung cancer can be instigated to a high degree by late diagnosis. Despite the plethora of studies on potential molecular biomarkers for lung cancer diagnosis, very few have reached clinical implementation. In this study we developed a panel of DNA methylation biomarkers and validated their diagnostic efficiency in bronchial washings from a large retrospective cohort. Candidate targets from previous high-throughput approaches were examined by Pyrosequencing in an independent set of 48 lung tumor/normal paired. Ten promoters were selected and quantitative methylation-specific PCR (qMSP) assays were developed and used to screen 655 bronchial washings (BWs) from the Liverpool Lung Project (LLP) subjects divided into training (194 cases and 214 Controls) and validation (139 cases and 109 controls) sets. Three statistical models were employed to select the optimal panel of markers and evaluate the performance of the discriminatory algorithms. The final logit regression model incorporated hypermethylation at p16, TERT, WT1 and RASSF1.The performance of this 4-gene methylation signature in the validation set demonstrated 82% sensitivity and 91% specificity. In comparison, cytology alone in this set provided 43% sensitivity at 100% specificity. The diagnostic efficiency of the panel did not show any biases with age, gender, smoking and the presence of a non-lung neoplasm. However, sensitivity was predictably higher in central (squamous and small cell) than peripheral (adenocarcinomas) tumors, as well as in stage 2 or greater tumors.These findings clearly demonstrate the impact of DNA methylation-based assays in the diagnosis of cytologically occult lung neoplasms. A prospective trial is currently imminent in the LLP study to provide data on the enhancement of diagnostic accuracy in a clinical setting, including by additional markers.
Although the field of mass spectrometry-based proteomics is still in its infancy, recent developments in targeted proteomic techniques have left the field poised to impact the clinical protein biomarker pipeline now more than at any other time in history. For proteomics to meet its potential for finding biomarkers, clinicians, statisticians, epidemiologists and chemists must work together in an interdisciplinary approach. These interdisciplinary efforts will have the greatest chance for success if participants from each discipline have a basic working knowledge of the other disciplines. To that end, the purpose of this review is to provide a nontechnical overview of the emerging/evolving roles that mass spectrometry (especially targeted modes of mass spectrometry) can play in the biomarker pipeline, in hope of making the technology more accessible to the broader community for biomarker discovery efforts. Additionally, the technologies discussed are broadly applicable to proteomic studies, and are not restricted to biomarker discovery.
targeted proteomics; multiple reaction monitoring; selected reaction monitoring; biomarker; mass spectrometry
To critically review and illustrate current methodologic and statistical considerations for bladder cancer biomarker discovery and evaluation.
Original, review, and methodological articles, and editorials were reviewed and summarized.
Biomarkers may be useful at multiple stages of bladder cancer management: early detection, diagnosis, staging, prognosis, and treatment; however, few novel biomarkers are currently used in clinical practice. The reasons for this disjunction are manifold and reflect the long and difficult pathway from candidate biomarker discovery to clinical assay, and the lack of coherent and comprehensive processes (pipelines) for biomarker development. Conceptually, the development of new biomarkers should be a process that is similar to therapeutic drug evaluation - a highly regulated process with carefully regulated phases from discovery to human applications. In a further effort to address the pervasive problem of inadequacies in the design, analysis, and reporting of biomarker prognostic studies, a set of reporting recommendations are discussed. For example, biomarkers should provide unique information that adds to known clinical and pathologic information. Conventional multivariable analyses are not sufficient to demonstrate improved prediction of outcomes. Predictive models, including or excluding any new putative biomarker, needs to show clinically significant improvement of performance in order to claim any real benefit. Towards this end, proper model building, avoidance of overfitting, and external validation are crucial. In addition, it is important to choose appropriate performance measures dependent on outcome and prediction type and to avoid use of cut-points. Biomarkers providing a continuous score provide potentially more useful information than cut-points since risk fits a continuum model. Combination of complementary and independent biomarkers is likely to better capture the biologic potential of a tumor than any single biomarker. Finally, methods that incorporate clinical consequences such as decision curve analysis are crucial to the evaluation of biomarkers.
Attention to sound design and statistical practice should be delivered as early as possible and will help maximize the promise of biomarkers for patient care. Studies should include a measure of predictive accuracy and clinical decision-analysis. External validation using data from an independent cohort provides the strongest evidence that a model is valid. There is a need for adequately assessed clinical biomarkers in bladder cancer.
biomarker; diagnosis; prognosis; treatment; nomogram; decision-analysis; bladder cancer; statistics; statistical analysis
T cell therapy represents an emerging and promising modality for the treatment of both infectious disease and cancer. Data from recent clinical trials have highlighted the potential for this therapeutic modality to effect potent anti-tumor activity. Biomarkers, operationally defined as biological parameters measured from patients that provide information about treatment impact, play a central role in the development of novel therapeutic agents. In the absence of information about primary clinical endpoints, biomarkers can provide critical insights that allow investigators to guide the clinical development of the candidate product. In the context of cell therapy trials, the definition of biomarkers can be extended to include a description of parameters of the cell product that are important for product bioactivity.
This review will focus on biomarker studies as they relate to T cell therapy trials, and more specifically: i. An overview and description of categories and classes of biomarkers that are specifically relevant to T cell therapy trials, and ii. Insights into future directions and challenges for the appropriate development of biomarkers to evaluate both product bioactivity and treatment efficacy of T cell therapy trials.
With the arrival of the postgenomic era, there is increasing interest in the discovery of biomarkers for the accurate diagnosis, prognosis, and early detection of cancer. Blood-borne cancer markers are favored by clinicians, because blood samples can be obtained and analyzed with relative ease. We have used a combined mining strategy based on an integrated cancer microarray platform, Oncomine, and the biomarker module of the Ingenuity Pathways Analysis (IPA) program to identify potential blood-based markers for six common human cancer types.
In the Oncomine platform, the genes overexpressed in cancer tissues relative to their corresponding normal tissues were filtered by Gene Ontology keywords, with the extracellular environment stipulated and a corrected Q value (false discovery rate) cut-off implemented. The identified genes were imported to the IPA biomarker module to separate out those genes encoding putative secreted or cell-surface proteins as blood-borne (blood/serum/plasma) cancer markers. The filtered potential indicators were ranked and prioritized according to normalized absolute Student t values. The retrieval of numerous marker genes that are already clinically useful or under active investigation confirmed the effectiveness of our mining strategy. To identify the biomarkers that are unique for each cancer type, the upregulated marker genes that are in common between each two tumor types across the six human tumors were also analyzed by the IPA biomarker comparison function.
The upregulated marker genes shared among the six cancer types may serve as a molecular tool to complement histopathologic examination, and the combination of the commonly upregulated and unique biomarkers may serve as differentiating markers for a specific cancer. This approach will be increasingly useful to discover diagnostic signatures as the mass of microarray data continues to grow in the ‘omics’ era.
One of the new roles for enzymes in personalized medicine builds on a rational approach to cancer biomarker discovery using enzyme-associated aberrant glycosylation. A hallmark of cancer, aberrant glycosylation is associated with differential expressions of enzymes such as glycosyltransferase and glycosidases. The aberrant expressions of the enzymes in turn cause cancer cells to produce glycoproteins with specific cancer-associated aberrations in glycan structures.
In this review we provide examples of cancer biomarker discovery using aberrant glycosylation in three areas. First, changes in glycosylation machinery such as glycosyltransferases/glycosidases could be used as cancer biomarkers. Second, most of the clinically useful cancer biomarkers are glycoproteins. Discovery of specific cancer-associated aberrations in glycan structures of these existing biomarkers could improve their cancer specificity, such as the discovery of AFP-L3, fucosylated glycoforms of AFP. Third, cancer-associated aberrations in glycan structures provide a compelling rationale for discovering new biomarkers using glycomic and glycoproteomic technologies.
As a hallmark of cancer, aberrant glycosylation allows for the rational design of biomarker discovery efforts. But more important, we need to translate these biomarkers from discovery to clinical diagnostics using good strategies, such as the lessons learned from translating the biomarkers discovered using proteomic technologies to OVA 1, the first FDA-cleared In Vitro Diagnostic Multivariate Index Assay (IVDMIA). These lessons, providing important guidance in current efforts in biomarker discovery and translation, are applicable to the discovery of aberrant glycosylation associated with enzymes as cancer biomarkers as well.
Enzyme; Aberrant Glycosylation; Cancer Biomarkers; Glycosyltransferases; Glycoprotein; Glycan
Most clinical blood biomarkers lack the necessary sensitivity and specificity to reliably detect cancer at an early stage, when it is best treatable. It is not yet clear how early a clinical blood assay can be used to detect cancer, or how biomarker-based strategies can be improved to enable earlier detection of smaller tumors. To address these issues, we developed a mathematical model describing dynamic plasma biomarker kinetics in relation to the growth of a tumor, beginning with a single cancer cell. To exemplify a realistic scenario in which biomarker is shed by both cancerous and non-cancerous cells, we primed the model on ovarian tumor growth and CA125 shedding data, for which tumor growth parameters and shedding rates are readily available in published literature. We found that a tumor could grow unnoticed for over 10.1 years and reach a volume of (20.44 mm)3 before becoming detectable by current clinical blood assays. Model parameters were perturbed over log-orders of magnitude to quantify ideal shedding rates and identify other blood-based strategies required for early sub-millimeter tumor detectability. Detection times we estimated are consistent with recently published tumor progression timelines based on clinical genomic sequencing data for several cancers. In this study, we rigorously showed that shedding rates of current clinical blood biomarkers are likely 104-fold too low to enable detection of a developing tumor within the first decade of tumor growth. The model presented here can be extended to virtually any solid cancer and associated biomarkers.
Purpose of review
Patients with locally “advanced” or muscle invasive bladder cancer have higher mortality rates than patients with non-muscle invasive (“superficial”) bladder cancer. Biomarkers can stratify clinical outcomes and thus promise to more accurately prognosticate and thus help assign patients to the appropriate treatments. The aim of this review is to summarize biomarker developments in the past year and to discuss their implications in prognosis and treatment selection in locally advanced bladder cancer.
Prognostic biomarkers for bladder cancer are identified at the DNA, RNA and/or protein levels. Some are new markers; others are established markers with new roles in bladder cancer. Markers can report on the risk of disease recurrence or metastasis, or treatment responsiveness and thus are useful in determining “who to treat” and “what to treat with”.
The list of biomarkers for prognosis and treatment selection for advanced bladder cancer is growing. For most, their clinical relevance is unclear due to their lack of validation in external datasets. MicroRNAs and new techniques including next-generation sequencing offer additional opportunities for biomarker discovery, validation, and clinical applications.
biomarker; prognosis; treatment selection; advanced bladder cancer