Adjuvant treatment decision-making based on conventional clinical/pathological and prognostic single molecular markers or genomic signatures is a therapeutic area in which over-/under-treatment are still key clinical problems even though substantial and continuous improvement of outcome has been achieved over the past decades. Response to therapy is currently not considered in the decision-making procedure.
ADAPT is one of the first new generation (neo)adjuvant trials dealing with individualization of (neo)adjuvant decision-making in early breast cancer and aims to establish early predictive surrogate markers, e.g., Ki-67, for therapy response under a short induction treatment in order to maximally individualize therapy and avoid unnecessary toxicity by ineffective treatment.
The prospective, multi-center, controlled, non-blinded, randomized, investigator initiated phase II/III ADAPT trial has an innovative “umbrella” protocol design. The “umbrella” is common for all patients, consisting of dynamic testing of early therapy response. ADAPT will recruit 4,936 patients according to their respective breast cancer subtype in four distinct sub-trials at 80 trial sites in Germany; 4,000 patients with hormone receptor positive (HR+) and HER2 negative disease will be included in the ADAPT HR+/HER2- sub-trial, where treatment decision is based on risk assessment and therapy response to induction therapy, and 380 patients will be included in ADAPT HER2+/HR+. A further 220 patients will be included in ADAPT HER2+/HR- and 336 patients will be recruited for ADAPT Triple Negative. These three sub-trials focus on identification of early surrogate markers for therapy success in the neoadjuvant setting. Patients will be allocated to the respective sub-trial according to the result of their diagnostic core biopsy, as reported by local/central pathology for HR and HER2 status.
Recent trials, such as the GeparTrio, have shown that response-guided therapy using clinical response may improve outcome. For chemotherapy or HER2-targeted treatment, pathologic complete response in a neoadjuvant setting is an excellent predictor of outcome. For endocrine therapy, response to short induction treatment – as defined by decrease in tumor cell proliferation – strongly correlates with outcome. ADAPT now aims to combine static prognostic and dynamic predictive markers, focusing not just on single therapeutic targets, but also on general markers of proliferation and cell death. Biomarker analysis will help to optimize selection of subtype-specific treatment.
ClinicalTrials.gov: ADAPT Umbrella: NCT01781338; ADAPT HR+/HER2-: NCT01779206; ADAPT HER2+/HR+: NCT01745965; ADAPT HER2+/HR-: NCT01817452; ADAPT TN:NCT01815242.
ADAPT; Biomarker; Early breast cancer; Investigator initiated trial
Breast cancer is a heterogenous disease which shows a great variation in presentation and response to treatment. Currently, the most commonly used prognostic criteria are patient age, tumor size, lymph node status, tumor grade and hormone receptor status. These are however not very accurate. This is partly explained by the fact that they do not demonstrate the inherent genetic variability of breast cancer, which determines the aggressive nature and metastatic potential of the disease. Recent advances in molecular biology have demonstrated that breast cancer is not a single disease. The new diagnostic and prognostic tests based on molecular biology methods have helped identify molecular subtypes of breast cancer that are sensitive to chemotherapy and others that are resistant. This could provide valuable critical information and predict which patients would really benefit from chemo and/or hormonal therapy. Molecular biology will become increasingly important in clinical decision making and as the understanding of molecular processes within cancer cells grow, new targets for therapy will be discovered.
Brest cancer; Cancer cells; Microarray chips
A German working group of 23 breast cancer experts discussed the results from the vote at this year's St. Gallen Consensus Conference on Primary Therapy for Early Breast Cancer (March 11–14, 2009) and came up with some concrete recommendations for day-to-day therapeutic decisions in Germany. Due the fact that the concept of the St. Gallen Consensus Conference merely allows for a minimal consensus, the objective of the working group was to provide practice-related recommendations for day-to-day clinical decisions in Germany. One area of emphasis at St. Gallen was tumor biology as a starting point for reaching individual therapeutic decisions. Intensive discussion was necessary with respect to the clinical relevance of predictive and prognostic factors. A new addition to the area of systemic therapy was a first-ever discussion of the adjuvant administration of bisphosponates and the fact that therapy with trastuzumab in HER2 overexpressing breast cancer has been defined as the standard for neoadjuvant therapy. The value of taxanes as a component of (neo)adjuvant chemotherapy as well as the value of aromatase inhibitors for the endocrine adjuvant treatment of postmenopausal patients were affirmed.
Management of HCC—Standard Approaches and Reports During the Past Year
Local AblationChemoembolizationOther Local Treatment ModalitiesSystemic Treatment
Clinical Trial Design
Basic Science and Biomarkers
Basic and Translational SciencePrognostic and Predictive Markers
Prognostic MarkersPredictive Markers
Accomplishments and Future Directions
Application of the Accomplishments
Biliary Tract Cancers
Overview of the Disease
Management of Biliary Tract Cancers—Current Approaches and Reports During the Past Year
Surgical Resection and Adjuvant TherapySystemic Therapy for Unresectable or Metastatic Biliary Tract Cancer
Cytotoxic ChemotherapyCytotoxic Chemotherapy in Combination With Targeted AgentSingle-Agent Targeted TherapyCombination of Targeted Agents
Colorectal cancer is one of the most common cancers worldwide, and although associated mortality rates in South American countries are generally among the lowest in the world, they are on the rise.
The prognosis of patients diagnosed with metastatic colorectal cancer has improved markedly over the last 12 years, increasing from 5 months with best supportive care to almost 2 years with combination chemotherapy plus bevacizumab. New prognostic and predictive biomarkers have been identified to guide therapy. Prognostic markers indicate patient survival independent of therapy and include disease stage, mutational status, and carcinoembryonic antigen. More recently, predictive markers of treatment outcomes have been identified. The most studied are mutations of the KRAS and BRAF genes, which are associated with resistance to epidermal growth factor receptortargeted therapy.
Tumor blood vessels have a number of structural and functional abnormalities that result in increased tumor vascularity and growth driven by angiogenesis. The anti-vascular endothelial growth factor (VEGF) monoclonal antibody bevacizumab, which binds to and neutralizes VEGF-A, has become a central part of the treatment of metastatic colorectal cancer. The addition of bevacizumab to fluorouracil (5-FU)/leucovorin, irinotecan plus bolus 5-FU/leucovorin, or irinotecan plus infusional 5-FU/leucovorin significantly improves the overall survival of patients with previously untreated metastatic colorectal cancer. In addition, a significant increase in overall survival is seen when bevacizumab is added to oxaliplatin plus infusional 5-FU/leucovorin (FOLFOX) in patients with metastatic colorectal cancer who progressed on a non-bevacizumab-containing regimen.
Although the majority of studies were performed prior to the identification of KRAS and BRAF as predictive biomarkers, subsequent analysis has shown the benefits of bevacizumab occur independently of the mutational status of these genes. In patients who have progressed on a bevacizumab-containing regimen, continuation of bevacizumab is significantly associated with an improved survival based on observational cohort studies. Surgical resection is recommended in patients with metastatic colorectal cancer where complete removal of tumors can be achieved. Perioperative chemotherapy using FOLFOX for 3 months before and 3 months after surgery is associated with a 9% improvement in 3-year survival. The use of chemotherapy in patients initially deemed unresectable has produced resection rates approaching 40%, and the addition of bevacizumab to chemotherapy in this setting is feasible, safe, and effective. In a study of 219 patients, the addition of bevacizumab to FOLFOX was associated with a significant increase in major or complete pathologic response compared with FOLFOX alone.
Improvements in patient survival have changed the treatment paradigm for metastatic colorectal cancer. Newer approaches view treatment not as distinct lines of therapy but as a continuum that includes personalized treatment plans offering maintenance therapy and even drug holidays between aggressive treatment periods. This approach achieves similar efficacy outcomes with reduced toxicity, and investigation of the role of bevacizumab as maintenance therapy is ongoing.
The key to optimising our approach in early breast cancer is to individualise care. Each patient has a tumour with innate features that dictate their chance of relapse and their responsiveness to treatment. Often patients with similar clinical and pathological tumours will have markedly different outcomes and responses to adjuvant intervention. These differences are encoded in the tumour genetic profile. Effective biomarkers may replace or complement traditional clinical and histopathological markers in assessing tumour behaviour and risk. Development of high-throughput genomic technologies is enabling the study of gene expression profiles of tumours. Genomic fingerprints may refine prediction of the course of disease and response to adjuvant interventions. This review will focus on the role of multiparameter gene expression analyses in early breast cancer, with regards to prognosis and prediction. The prognostic role of genomic signatures, particularly the Mammaprint and Rotterdam signatures, is evolving. With regard to prediction of outcome, the Oncotype Dx multigene assay is in clinical use in tamoxifen treated patients. Extensive research continues on predictive gene identification for specific chemotherapeutic agents, particularly the anthracyclines, taxanes and alkylating agents.
Current concepts conceive “breast cancer” as a complex disease that comprises several very different types of neoplasms. Nonetheless, breast cancer treatment has considerably improved through early diagnosis, adjuvant chemotherapy, and endocrine treatments. The limited prognostic power of classical classifiers determines considerable over-treatment of women who either do not benefit from, or do not at all need, chemotherapy. Several gene expression based molecular classifiers (signatures) have been developed for a more reliable prognostication. Gene expression profiling identifies profound differences in breast cancers, most probably as a consequence of different cellular origin and different driving mutations and can therefore distinguish the intrinsic propensity to metastasize. Existing signatures have been shown to be useful for treatment decisions, although they have been developed using relatively small sample numbers. Major improvements are expected from the use of large datasets, subtype specific signatures and from the re-introduction of functional information. We show that molecular signatures encounter clear limitations given by the intrinsic probabilistic nature of breast cancer metastasis. Already today, signatures are, however, useful for clinical decisions in specific cases, in particular if the personal inclination of the patient towards different treatment strategies is taken into account.
Breast cancer; Gene expression profiling; Metastasis; Stroma; Angiogenesis
Adjuvant systemic therapy has led to markedly improved outcome in early-stage breast cancer. However, the absolute gains from chemotherapy might be modest in node-negative patients. Adjuvant chemotherapy is the only option for triple-negative breast cancer patients and should be used with trastuzumab in HER2-positive patients. Considering the large group of patients with some degree of endocrine responsiveness, adding chemotherapy according to risk is an option. At present, we guide our therapeutic decisions using clinicopathologic risk classifications like the St. Gallen risk category or Adjuvant! online. A downside of these risk estimations is a low specificity and consequently the risk for overtreatment of a considerable number of patients. To spare patients unnecessary toxicities we need more reliable prognostic factors or tumor markers. From the plethora of tumor markers, only urokinase-type plasminogen activator (uPA)/plasminogen activator inhibitor 1 (PAI-1) and certain multiparameter gene expression assays are recommended by the American Society of Clinical Oncology. These tumor markers are presently investigated in clinical trials in node-negative breast cancer (NNBC-3, MINDACT, TAILORx). These studies will hopefully allow us to quantify the risk of progression in the individual patient and to tailor treatment accordingly. This should lead to a more personalized treatment recommendation.
Breast cancer; Node-negative; Risk category; Adjuvant!; uPA/PAI-1; Gene expression profiling
Biomarkers of breast cancer are necessary for prognosis and prediction to chemotherapy. Prognostic biomarkers provide information regarding outcome irrespective of therapy, while predictive biomarkers provide information regarding response to therapy. Candidate prognostic biomarkers for breast cancers are growth factor receptors, steroid receptors, Ki-67, cyclins, urokinase plasminogen activator, p53, p21, pro- and anti-apoptotic factors, BRCA1 and BRCA2. But currently, the predictive markers are Estrogen and Progesterone receptors responding to endocrine therapy, and HER-2 responding to herceptin. But there are numerous breast cancer cases, where tamoxifen is ineffective even after estrogen receptor positivity. This lead to search of new prognostic and predictive markers and the number of potential markers is constantly increasing due to proteomics and genomics studies. However, most biomarkers individually have poor sensitivity or specificity, or other clinical value. It can be resolved by studying various biomarkers simultaneously, which will help in better prognosis and increasing sensitivity for chemotherapeutic agents. This review is focusing on growth factor receptors, apoptosis markers, signaling cascades, and their correlation with other associated biomarkers in breast cancers. As our knowledge regarding molecular biomarkers for breast cancer increases, prognostic indices will be developed that combine the predictive power of individual molecular biomarkers with specific clinical and pathologic factors. Rigorous comparison of these existing as well as emerging markers with current treatment selection is likely to see an escalation in an era of personalized medicines to ensure the breast cancer patients receive optimal treatment. This will also solve the treatment modalities and complications related to chemotherapeutic regimens.
prognostic markers; estrogen; growth factor receptor; apoptosis; chemotherapy; mitogen-activated protein kinase
Despite the lifetimes that increased in breast cancers due to the the early screening programs and new therapeutic strategies, many cases still are being lost due to the metastatic relapses. For this reason, new approaches such as the proteomic techniques have currently become the prime objectives of breast cancer researches. Various omic-based techniques have been applied with increasing success to the molecular characterisation of breast tumours, which have resulted in a more detailed classification scheme and have produced clinical diagnostic tests that have been applied to both the prognosis and the prediction of outcome to the treatment. Implementation of the proteomics-based techniques is also seen as crucial if we are to develop a systems biology approach in the discovery of biomarkers of the early diagnosis, prognosis and prediction of the outcome of the breast cancer therapies. In this review, we discuss the studies that have been conducted thus far, for the discovery of diagnostic, prognostic and predictive biomarkers, and evaluate the potential of the discriminating proteins identified in this research for clinical use as breast cancer biomarkers.
Breast cancer; early diagnosis; prognostic markers; proteomic techniques; micro array techniques; mass spectrometry; surface enhanced laser desorption ionisation; matrix-assisted laser desorption ionisation.
Breast cancer is a leading cause of cancer-related deaths in women worldwide. The clinical course of this disease is highly variable and clinicians continuously search for prognostic parameters that can accurately predict prognosis, and indicate a suitable adjuvant therapy for each patient. Amplification of the two oncogenes HER-2/neu and c-myc and inactivation of the tumor suppressor gene p53 are frequently encountered in breast carcinomas. The purpose of this study was to use the fluorescence in situ hybridization (FISH) for the assessment of HER-2/neu and c-myc amplification and p53 inactivation and to relate these molecular markers with the commonly used clinical and pathological factors. The study was conducted on 34 tissue samples obtained from 33 females and 1 male with breast carcinomas and 17 samples obtained from 16 females and 1 male with benign breast lesions. Results revealed that the level of HER-2/neu, c-myc and p53 in the malignant group was significantly increased as compared to the benign group. On relating the level of the molecular markers to clinicopathological factors, p53 was significantly associated with increased patient’s age. The sensitivity of the investigated markers significantly increased with larger tumor size. Concerning tumor grade, HER-2/neu and p53 showed a significant increase in low-grade tumors whereas c-myc showed a highly significant increase in high-grade tumors. With regard to disease staging, HER-2/neu and c-myc were the only markers that showed significant increase at late stages of disease. p53 and HER-2/neu were significantly associated with positive lymph nodal status. A significant correlation was obtained between the levels of the three biomarkers to each other. Conclusively, the combination of HER-2/neu, c-myc and p53 can stratify patients into different risk groups.
breast cancer; fluorescent in situ hybridization (FISH); genetic alterations; HER-2/neu; p53; c-myc
The use of biomarkers ensures breast cancer patients receive optimal treatment. Established biomarkers such as estrogen receptor (ER) and progesterone receptor (PR) have been playing significant roles in the selection and management of patients for endocrine therapy. HER2 is a strong predictor of response to trastuzumab. Recently, the roles of ER as a negative and HER2 as a positive indicator for chemotherapy have been established. Ki67 has traditionally been recognized as a poor prognostic factor, but recent studies suggest that measurement of Ki67-positive cells during treatment will more effectively predict treatment efficacy for both anti-hormonal and chemotherapy. p53 mutations are found in 20–35% of human breast cancers and are associated with aggressive disease with poor clinical outcome when the DNA-binding domain is mutated. The utility of cyclin D1 as a predictor of breast cancer prognosis is controversial, but cyclin D1b overexpression is associated with poor prognosis. Likewise, overexpression of the low molecular weight form of cyclin E1 protein predicts poor prognosis. Breast cancers from BRCA1/2 carriers often show high nuclear grades, negativity to ER/PR/HER2, and p53 mutations, and thus, are associated with poor prognosis. The prognostic values of other molecular markers, such as p14ARF, TBX2/3, VEGF in breast cancer are also discussed. Careful evaluation of these biomarkers with current treatment modality is required to determine whether their measurement or monitoring offer significant clinical benefits.
breast cancer; prognosis; molecular marker; Ki67; ER; PR; HER2; cyclin D1; cyclin E; p53; ARF; TBX2/3; BRCA1/2; VEGF
Breast cancer comprises a collection of diseases with distinctive clinical, histopathological, and molecular features. Importantly, tumors with similar histological features may display disparate clinical behaviors. Gene expression profiling using microarray technologies has improved our understanding of breast cancer biology and has led to the development of a breast cancer molecular taxonomy and of multigene 'signatures' to predict outcome and response to systemic therapies. The use of these prognostic and predictive signatures in routine clinical decision-making remains controversial. Here, we review the clinical relevance of microarray-based profiling of breast cancer and discuss its impact on patient management.
Triple-negative breast cancer is a subtype of breast cancer with aggressive tumor behavior and distinct disease etiology. Due to the lack of an effective targeted medicine, treatment options for triple-negative breast cancer are few and recurrence rates are high. Although various multi-gene prognostic markers have been proposed for the prediction of breast cancer outcome, most of them were proven clinically useful only for estrogen receptor-positive breast cancers. Reliable identification of triple-negative patients with a favorable prognosis is not yet possible.
Clinicopathological information and microarray data from 157 invasive breast carcinomas were collected at National Taiwan University Hospital from 1995 to 2008. Gene expression data of 51 triple-negative and 106 luminal breast cancers were generated by oligonucleotide microarrays. Hierarchical clustering analysis revealed that the majority (94%) of triple-negative breast cancers were tightly clustered together carrying strong basal-like characteristics. A 45-gene prognostic signature giving 98% predictive accuracy in distant recurrence of our triple-negative patients was determined using the receiver operating characteristic analysis and leave-one-out cross validation. External validation of the prognostic signature in an independent microarray dataset of 59 early-stage triple-negative patients also obtained statistical significance (hazard ratio 2.29, 95% confidence interval (CI) 1.04–5.06, Cox P = 0.04), outperforming five other published breast cancer prognostic signatures. The 45-gene signature identified in this study revealed that TGF-β signaling of immune/inflammatory regulation may play an important role in distant metastatic invasion of triple-negative breast cancer.
Gene expression data and recurrence information of triple-negative breast cancer were collected and analyzed in this study. A novel set of 45-gene signature was found to be statistically predictive in disease recurrence of triple-negative breast cancer. The 45-gene signature, if further validated, may be a clinically useful tool in risk assessment of distant recurrence for early-stage triple-negative patients.
Individualized cancer treatment (e.g. targeted therapy) based on molecular alterations has emerged as an important strategy to improve the current standard-of-care chemotherapy. A large number of studies have demonstrated the importance of biomarkers not only in predicting prognosis but more importantly in predicting the response towards therapies. For example, amplification or mutation status of the two biomarkers HER2 (human epidermal growth factor 2) and BRCA (breast cancer) can be used to decide on a specific targeted therapy in breast cancer. However, no biomarkers with a similar clinical impact have been identified in pancreatic ductal adenocarcinoma. Although many genome-wide and proteome-based high-throughput studies have identified candidate genes or proteins as promising biomarkers, none of them were eventually transferred into the clinical setting. Notably, the most reliable markers for predicting prognosis are still the tumor stage and grade and biomarkers for therapy response remain undefined. One reason lies in the lack of systemic approaches to analyze the complexity of dominating cancer pathways and the impact of such signal complexity on prognosis and therapy response.
Pancreatic cancer; Diagnostic markers; Biomarkers; Targeted therapy; Prognosis; Pathways
Expression profiling and biomarker(s) discovery aim to provide means for tumour diagnosis, classification, therapy response and prognosis. The identification of novel markers could potentially lead to the building of robust early detection strategies and personalized, effective breast cancer therapies that would improve patient outcome. Recent evidence supports the hypothesis that genomic expression profiling using microarray analysis is a reliable method for breast cancer classification and prognostication. However, genes clearly do not act by themselves, or indeed they do not have catalytic or signalling capabilities. Hence, genetic biomarker information alone cannot perfectly predict cancer and its response to treatment. Genes clearly exert their effect after transcription through translation into active proteins. Consequently, postgenomic projects correlating protein expression profiles with tumour classification have led to some established biomarkers. In this regard, these biomarkers associate with disease prediction and can be associated with treatment response. Recently, Brozokova and colleagues demonstrated that surface-enhanced laser desorption ionization time of flight mass spectrometry (SELDI-TOF MS) profiling of breast cancer tissue proteomes can potentially expand the biomarker repertoire and our knowledge of breast cancer behaviour.
Accurate prognosis of breast cancer can spare a significant number of breast cancer patients from receiving unnecessary adjuvant systemic treatment and its related expensive medical costs. Recent studies have demonstrated the potential value of gene expression signatures in assessing the risk of post-surgical disease recurrence. However, these studies all attempt to develop genetic marker-based prognostic systems to replace the existing clinical criteria, while ignoring the rich information contained in established clinical markers. Given the complexity of breast cancer prognosis, a more practical strategy would be to utilize both clinical and genetic marker information that may be complementary.
A computational study is performed on publicly available microarray data, which has spawned a 70-gene prognostic signature. The recently proposed I-RELIEF algorithm is used to identify a hybrid signature through the combination of both genetic and clinical markers. A rigorous experimental protocol is used to estimate the prognostic performance of the hybrid signature and other prognostic approaches. Survival data analyses is performed to compare different prognostic approaches.
The hybrid signature performs significantly better than other methods, including the 70-gene signature, clinical makers alone and the St. Gallen consensus criterion. At the 90% sensitivity level, the hybrid signature achieves 67% specificity, as compared to 47% for the 70-gene signature and 48% for the clinical makers. The odds ratio of the hybrid signature for developing distant meta-stases within five years between the patients with a good prognosis signature and the patients with a bad prognosis is 21.0 (95% CI: 6.5–68.3), far higher than either genetic or clinical markers alone.
Uncontrolled proliferation is a hallmark of cancer. In breast cancer, immunohistochemical assessment of the proportion of cells staining for the nuclear antigen Ki67 has become the most widely used method for comparing proliferation between tumor samples. Potential uses include prognosis, prediction of relative responsiveness or resistance to chemotherapy or endocrine therapy, estimation of residual risk in patients on standard therapy and as a dynamic biomarker of treatment efficacy in samples taken before, during, and after neoadjuvant therapy, particularly neoadjuvant endocrine therapy. Increasingly, Ki67 is measured in these scenarios for clinical research, including as a primary efficacy endpoint for clinical trials, and sometimes for clinical management. At present, the enormous variation in analytical practice markedly limits the value of Ki67 in each of these contexts. On March 12, 2010, an international panel of investigators with substantial expertise in the assessment of Ki67 and in the development of biomarker guidelines was convened in London by the cochairs of the Breast International Group and North American Breast Cancer Group Biomarker Working Party to consider evidence for potential applications. Comprehensive recommendations on preanalytical and analytical assessment, and interpretation and scoring of Ki67 were formulated based on current evidence. These recommendations are geared toward achieving a harmonized methodology, create greater between-laboratory and between-study comparability, and allow earlier valid applications of this marker in clinical practice.
Tissue polypeptide specific antigen (TPS) measures an antigenic determinant associated with human cytokeratin 18. TPS is a marker of tumor cell activity in contrast to markers related to tumor burden. The value of detecting circulating TPS lies in the early detection of recurrence by serial determinations and in the rapid assessment of the efficacy of the treatment. Pretreatment levels of TPS in patients with metastatic breast cancer are related with prognosis. Decreasing TPS levels during therapy monitoring indicate response and a fast response is correlated to favourable prognosis. Increasing TPS levels, in the presence of clinically stable disease or partial remission, predict disease progression with a considerable lead-time. Improved effectiveness in breast cancer management can be seen when TPS is used in combination with CA 15-3. When tumor marker determinations are applied in a proper way in the appropriate situation, the results can assist the oncologist. Thus monitoring of therapy in patients with metastatic breast cancer should be based upon serial TPS and CA 15-3 determinations in serum. The use of tumor marker determinations in the early follow-up interval following surgery to detect early tumor recurrence may be simpler, more sensitive and less expensive than imaging methods.
Tumor markers; TPS; Breast cancer management
Breast cancer is a heterogeneous disease that is not totally eradicated by current therapies. The classification of breast tumors into distinct molecular subtypes by gene profiling and immunodetection of surrogate markers has proven useful for tumor prognosis and prediction of effective targeted treatments. The challenge now is to identify molecular biomarkers that may be of functional relevance for personalized therapy of breast tumors with poor outcome that do not respond to available treatments. The Mitochondrial Tumor Suppressor (MTUS1) gene is an interesting candidate whose expression is reduced in colon, pancreas, ovary and oral cancers. The present study investigates the expression and functional effects of MTUS1 gene products in breast cancer.
Methods and Findings
By means of gene array analysis, real-time RT-PCR and immunohistochemistry, we show here that MTUS1/ATIP3 is significantly down-regulated in a series of 151 infiltrating breast cancer carcinomas as compared to normal breast tissue. Low levels of ATIP3 correlate with high grade of the tumor and the occurrence of distant metastasis. ATIP3 levels are also significantly reduced in triple negative (ER- PR- HER2-) breast carcinomas, a subgroup of highly proliferative tumors with poor outcome and no available targeted therapy. Functional studies indicate that silencing ATIP3 expression by siRNA increases breast cancer cell proliferation. Conversely, restoring endogenous levels of ATIP3 expression leads to reduced cancer cell proliferation, clonogenicity, anchorage-independent growth, and reduces the incidence and size of xenografts grown in vivo. We provide evidence that ATIP3 associates with the microtubule cytoskeleton and localizes at the centrosomes, mitotic spindle and intercellular bridge during cell division. Accordingly, live cell imaging indicates that ATIP3 expression alters the progression of cell division by promoting prolonged metaphase, thereby leading to a reduced number of cells ungergoing active mitosis.
Our results identify for the first time ATIP3 as a novel microtubule-associated protein whose expression is significantly reduced in highly proliferative breast carcinomas of poor clinical outcome. ATIP3 re-expression limits tumor cell proliferation in vitro and in vivo, suggesting that this protein may represent a novel useful biomarker and an interesting candidate for future targeted therapies of aggressive breast cancer.
To improve the treatment of patients with colorectal cancer, efforts must be directed toward the identification of patients likely to respond to a specific therapy, those who will experience severe toxicities, and those who will benefit from chemotherapy in the adjuvant setting. In recent years, studies on a global scale have attempted to define subsets of biochemical markers that may predict response to treatment (evaluated through clinical response, toxicity, and time to disease progression), and prognostic markers, which are equally important in determining how aggressive the disease is (generally evaluated in terms of overall survival), and the likelihood of disease recurrence after surgery. The science of pharmacogenomics is emerging as a useful molecular tool to investigate the disparity in drug efficacy by analyzing variations such as genetic polymorphisms in drug targets, metabolizing enzymes, transporters, and influential receptors. Consequently, the identification of accurate and validated predictive and prognostic markers combined with an increasing arsenal of therapeutic agents will improve the clinician’s ability to tailor effective therapy to the molecular profile of the patient while minimizing life-threatening toxicities. This paper describes markers under study in the setting of colorectal cancer, including loss of heterozygosity of 18q and microsatellite instability, polymorphisms in thymidylate synthase, IL-8, CXCR-2, vascular endothelial growth factor, epidermal growth factor and their receptors, and K-ras mutations.
Patients with early-stage breast cancer, treated with endocrine therapy, have approximately 90% 5-year disease-free survival. However, for patients at higher risk of relapse despite endocrine therapy, additional adjuvant therapy, such as chemotherapy, may be indicated. The challenge is to prospectively identify such patients. The Mammostrat® test uses five immunohistochemical markers to stratify patients on tamoxifen therapy into risk groups to inform treatment decisions. We tested the efficacy of this panel in a mixed population of cases treated in a single center with breast-conserving surgery and long-term follow-up.
Tissue microarrays from a consecutive series (1981 to 1998) of 1,812 women managed by wide local excision and postoperative radiotherapy were collected following appropriate ethical review. Of 1,390 cases stained, 197 received no adjuvant hormonal or chemotherapy, 1,044 received tamoxifen only, and 149 received a combination of hormonal therapy and chemotherapy. Median age at diagnosis was 57, 71% were postmenopausal, 23.9% were node-positive and median tumor size was 1.5 cm. Samples were stained using triplicate 0.6 mm2 tissue microarray cores, and positivity for p53, HTF9C, CEACAM5, NDRG1 and SLC7A5 was assessed. Each case was assigned a Mammostrat® risk score, and distant recurrence-free survival (DRFS), relapse-free survival (RFS) and overall survival (OS) were analyzed by marker positivity and risk score.
Increased Mammostrat® scores were significantly associated with reduced DRFS, RFS and OS in estrogen receptor (ER)-positive breast cancer (P < 0.00001). In multivariate analyses the risk score was independent of conventional risk factors for DRFS, RFS and OS (P < 0.05). In node-negative, tamoxifen-treated patients, 10-year recurrence rates were 7.6 ± 1.5% in the low-risk group versus 20.0 ± 4.4% in the high-risk group. Further, exploratory analyses revealed associations with outcome in both ER-negative and untreated patients.
This is the fifth independent study providing evidence that Mammostrat® can act as an independent prognostic tool for ER-positive, tamoxifen-treated breast cancer. In addition, this study revealed for the first time a possible association with outcome regardless of node status and ER-negative tumors. When viewed in the context of previous results, these data provide further support for this antibody panel as an aid to patient management in early-stage breast cancer.
Early breast cancer treatment is based on a multimodality approach with the application of clinical and histological prognostic factors to determine locoregional and systemic treatments. The entire scientific community is strongly involved in the management of this disease: radiologists for screening and early diagnosis, gynecologists, surgical oncologists and radiation oncologists for locoregional treatment, pathologists and biologists for personalized characterization, genetic counselors for BRCA mutation history and medical oncologists for systemic therapies.
Recently, new biological tools have established various prognostic subsets of breast cancer and developed predictive markers for miscellaneous treatments.
The aim of this article is to highlight the contribution of biological tools in the locoregional management of early breast cancer.
Despite significant advances in the treatment of primary cancer, the ability to predict the metastatic behavior of a patient’s cancer, as well as to detect and eradicate such recurrences, remain major clinical challenges in oncology. While many potential molecular biomarkers have been identified and tested previously, none have greatly improved the accuracy of specimen evaluation over routine histopathological criteria and they predict individual outcomes poorly. However, the recent introduction of high-throughput microarray technology has opened new avenues in genomic investigation of cancer, and through application in tissue-based studies and appropriate animal models, has facilitated the identification of gene expression signatures that are associated with the lethal progression of breast cancer. The use of these approaches has the potential to greatly impact our knowledge of tumor biology, to provide efficient biomarkers, and enable development towards customized prognostication and therapies for the individual.
The accurate estimation of outcome in patients with malignant disease is an essential component of the optimal treatment, decision-making and patient counseling processes. The prognosis and disease outcome of breast cancer patients can differ according to geographic and ethnic factors. To our knowledge, to date these factors have never been validated in a homogenous loco-regional patient population, with the aim of achieving accurate predictions of outcome for individual patients. To clarify this topic, we created a new comprehensive prognostic and predictive model for Taiwanese breast cancer patients based on a range of patient-related and various clinical and pathological-related variables.
Demographic, clinical, and pathological data were analyzed from 1 137 patients with breast cancer who underwent surgical intervention. A survival prediction model was used to allow analysis of the optimal combination of variables.
The area under the receiver operating characteristic (ROC) curve, as applied to an independent validation data set, was used as the measure of accuracy. Results were compared by comparing the area under the ROC curve.
our model building exercise of mortality risk was able to predict disease outcome for individual patients with breast cancer. This model could represent a highly accurate prognostic tool for Taiwanese breast cancer patients.