Validation Of Predictive Biomarkers Research Articles

Immuno-Oncology: The Third Paradigm in Early Drug Development.

Clinical researchers in oncology face the difficulty of developing new drugs for treating cancer patients. This challenge nowadays extends towards new horizons since a high number of drugs are developed in each of the three paradigms: classical cytotoxics, new targeted agents, and emergent immunotherapeutic approaches. Over the last decade, there has been an unstoppable progress in this third paradigm, to the extent that in 2013 immunotherapy was granted the scientific breakthrough of the year. However, the novel mechanisms of action of these immunotherapeutic agents entail a whole new series of concepts, resulting in a number of unresolved questions to which clarification is crucial for their success: establishment of accurate preclinical models able to predict human toxicities, better selection of candidate populations, finding and validation of predictive biomarkers, definition of suitable endpoints, improvements in first-in-human study designs, proposal of more accurate radiological response criteria, management of novel immune-related toxicities and development of combinations based on a biological rationale. In this article, we review the major challenges to overcome in forthcoming years. The final role of immunotherapy in cancer will be determined by our capacity to shed some light on some of these key points.

Current achievements and future perspectives of metronomic chemotherapy.

In recent years, many anticancer drugs have been tested at metronomic dosages for a variety of tumours. Mechanisms of action attributed to metronomic chemotherapy (MCT) include antiangiogenesis, immunomodulation, direct inhibition of tumour growth, effect on tumour initiating cells and the modulation of clonal evolution. An active clinical research, aimed at testing MCT in several cancers, has been conducted over the past 15years. However, because the majority of available results come from earlier phase II studies, mainly performed in the area of breast cancer (BC), it is clear that there are areas still to be investigated. We considered current studies dealing with MCT according to the clinical setting of patients. Despite a certain degree of overlap, we were able to identify four main clinical indications for MCT: refractory disease and frailty of patients, advanced stage disease (requiring first and second-line therapy), early stage disease and maintenance therapy after induction chemotherapy. In addition, a section of this review has been addressed to the combination of MCT with immunotherapy following the growing interest in the reinstatement of immune-surveillance. Crucial questions, such as the definition of optimal schedules of continuously delivered, low-dose chemotherapy and the recognition and validation of predictive biomarkers, need to be further addressed. Moreover, comparisons with the best supportive care are especially lacking and thus urgently awaited to establish the key role of MCT in the care of pretreated and frail patients. Maintenance therapy promises to be one of the most worthwhile developments for MCT. Currently, several combination strategies with standard chemotherapy, target agents or immunotherapy are under investigation but further efforts are needed to fill the gaps of knowledge in this field.

Validation of biomarkers to predict response to immunotherapy in cancer: Volume II — clinical validation and regulatory considerations

There is growing recognition that immunotherapy is likely to significantly improve health outcomes for cancer patients in the coming years. Currently, while a subset of patients experience substantial clinical benefit...

Strategies for power calculations in predictive biomarker studies in survival data.

PurposeBiomarkers and genomic signatures represent potentially predictive tools for precision medicine. Validation of predictive biomarkers in prospective or retrospective studies requires statistical justification of power and sample size. However, the design of these studies is complex and the statistical methods and associated software are limited, especially in survival data. Herein, we address common statistical design issues relevant to these two types of studies and provide guidance and a general template for analysis.MethodsA statistical interaction effect in the Cox proportional hazards model is used to describe predictive biomarkers. The analytic form by Peterson et al. and Lachin is utilized to calculate the statistical power for both prospective and retrospective studies.ResultsWe demonstrate that the common mistake of using only Hazard Ratio's Ratio (HRR) or two hazard ratios (HRs) can mislead power calculations. We establish that the appropriate parameter settings for prospective studies require median survival time (MST) in 4 subgroups (treatment and control in positive biomarker, treatment and control in negative biomarker). For the retrospective study which has fixed survival time and censored status, we develop a strategy to harmonize the hypothesized parameters and the study cohort. Moreover, we provide an easily-adapted R software application to generate a template of statistical plan for predictive biomarker validation so investigators can easily incorporate into their study proposals.ConclusionOur study provides guidance and software to help biostatisticians and clinicians design sound clinical studies for testing predictive biomarkers.

Abstract 395: Patient stratification and drug combination strategy based on drug response and genomic information from PDX clinical trials (PCTs)

Abstract Cancer is a heterogeneous disease with various molecular lesions and drug response profiles within the same tumor type. Patient stratification in clinical trials based on molecular features has contributed to recent success of several targeted cancer drugs on molecular aberrations such as BCR-ABL translocation in CML, Her2 amplification in breast and gastric cancers, EGFR mutations and ALK fusions in lung cancer and BRAF mutation in melanoma. However, in most cancers, the molecular features which can be used for patient stratification are not as simple as a single genetic aberration. Multiple drug resistance mechanisms caused by various mutations in cancer signaling pathways can also increase the uncertainty of clinical outcomes. To increase the chance of success in human clinical studies, patient-derived xenograft (PDX) clinical trials (PCTs) have increasingly been used for predictive biomarker validation, resistance mechanism investigation and combination therapy selection. PDX tumor models have been demonstrated to have high correlations with human patients in tumor pathology, molecular characteristics and drug responses. Large scale PCTs have also shown consistency in results when compared to related human clinical trials. At GenenDesign, we have established over 1000 PDX tumor models and more than 100 resistance models against various cancer drugs. Many of these PDX models have been characterized at RNA/Exome sequence, gene expression, gene copy number and hot-spot mutation levels. We carried out our PDX clinical trials by testing multiple approved drugs and clinical drug candidates such as targeted inhibitors against FGFRs, c-Met/ALK, HER2, EGFR, cell cycle regulators, Ras/Raf pathway, PI3K/Akt pathway, as well as chemotherapy drugs in biomarker-driven multi-drug multi-arm expanded PDX clinical trials. So far, we have accumulated more than 3000 efficacy data sets and associated PD samples. Analysis of drug response and associated genomic information from PDX clinical trials yielded rich information for predictive biomarker identification and validation. At the same time, many potential resistance mechanisms were also revealed. These information can make human clinical trial better prepared, more efficient and focused. More importantly, testing of a targeted drug with multiple chemotherapies in the same models can also provide guidance on future combination selection strategy. Citation Format: Jingjing Jiang, Tengfei Yu, Ying Yan, Wei Du, Tingting Tan, Xuqin Yang, Jiali Gu, Xin K. Ye, Zhenyu Gu. Patient stratification and drug combination strategy based on drug response and genomic information from PDX clinical trials (PCTs). [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 395.

Reviewing the evidence supporting predictive biomarkers in European medicines agency indications and contraindications using visual plots

Objectives Predictive biomarkers can be used to identify most suitable treatments for patients. To maximise patient benefit valid predictive biomarkers need to be used and several trial designs have been proposed to achieve this. We aimed to review the evidence considered sufficient by the European Medicines Agency (EMA) for inclusion of a predictive biomarker in a drug indication or contraindication and this required development of a methodology to summarise key information.

Personalized tumor therapy for gastrointestinal tumors

Personalized tumor therapy or more precisely tumor-specific therapy has now become established in routine clinical oncology. Particularly for solid tumors, the emerging knowledge about pathways and signaling networks allows the establishment of new targeted therapies and hopefully a continuous improvement in patient care. Targeted therapies are now established as the standard of care as single agents or in combination with chemotherapy. Using targeted therapies, substantial improvements regarding tumor response, median progression-free survival and median overall survival can be achieved for selected gastrointestinal tumors. However, these strategies also have novel side effects that need to be addressed. Importantly, valid (positive) predictive biomarkers are needed for a rational use of these novel compounds. This review article presents the current state of targeted tumor therapy for the diagnostics and therapy of gastrointestinal tumors and provides assistance in integrating the current knowledge into the routine clinical context.

Discovery and Validation of Predictive Biomarkers of Survival for Non-small Cell Lung Cancer Patients Undergoing Radical Radiotherapy: Two Proteins With Predictive Value

Lung cancer is the most frequent cause of cancer-related death world-wide. Radiotherapy alone or in conjunction with chemotherapy is the standard treatment for locally advanced non-small cell lung cancer (NSCLC). Currently there is no predictive marker with clinical utility to guide treatment decisions in NSCLC patients undergoing radiotherapy. Identification of such markers would allow treatment options to be considered for more effective therapy. To enable the identification of appropriate protein biomarkers, plasma samples were collected from patients with non-small cell lung cancer before and during radiotherapy for longitudinal comparison following a protocol that carries sufficient power for effective discovery proteomics. Plasma samples from patients pre- and during radiotherapy who had survived >18mo were compared to the same time points from patients who survived <14mo using an 8 channel isobaric tagging tandem mass spectrometry discovery proteomics platform. Over 650 proteins were detected and relatively quantified. Proteins which showed a change during radiotherapy were selected for validation using an orthogonal antibody-based approach. Two of these proteins were verified in a separate patient cohort: values of CRP and LRG1 combined gave a highly significant indication of extended survival post one week of radiotherapy treatment.

Topoisomerase-1 gene copy aberrations are frequent in patients with breast cancer.

Topoisomerase-1 (Top1) targeting drugs have shown promising efficacy in patients with metastatic breast cancer (BC). However, these drugs are rather toxic calling for development and validation of predictive biomarkers to increase the therapeutic index. As these drugs are targeting the Top1 protein and since no validated anti-Top1 antibodies for immunohistochemistry have been reported, we raised the hypothesis that TOP1 gene amplifications may serve as a proxy for the Top1 protein and thereby a biomarker of response to treatment with Top1 inhibitors in BC. The aim was to determine the prevalence of TOP1 gene copy gain in BC. The prevalence of TOP1 gene copy gain was investigated by fluorescence in situ hybridization with a TOP1/CEN-20 probemix in normal breast tissue (N = 100) and in tissue from patients with metastatic BC in a discovery (N = 100) and a validation cohort (N = 205). As amplification of 20q including CEN-20 is common in BC a TOP1/CEN-2 probemix was applied to the validation cohort. More than 30% of the patients had gene copy numbers of ≥ 4 and ∼20% of the patients had TOP1/CEN-20 ratios ≥ 1.5. The CEN-2 probe did not add any information. Gain of the TOP1 gene appears to be common in BC making the gene a potential biomarker for response to treatment with Top1 inhibitors. As 20q amplification is a common finding in BC and as no other suitable reference gene has yet been identified, TOP1 copy number may be a more valid method of detecting gain than using a gene/centromere ratio.

Epidermal growth factor receptor-directed monoclonal antibodies in nonsmall cell lung cancer: an update.

The epidermal growth factor receptor (EGFR) is overexpressed in many nonsmall cell lung cancers (NSCLCs). Blockade of EGFR by monoclonal antibodies has been studied as a strategy to improve the outcome of first-line chemotherapy in patients with NSCLC. The present review updates the findings from phase III trials. Cetuximab improved survival when combined with first-line chemotherapy and this benefit was limited to patients with high EGFR expression in their tumors. A Southwest Oncology Group study currently prospectively evaluates the predictive biomarkers for cetuximab. In the SQUIRE phase III trial, necitumumab added to cisplatin and gemcitabine increased the survival in patients with advanced squamous cell NSCLC. The INSPIRE trial studied chemotherapy with and without necitumumab in patients with nonsquamous NSCLC but was prematurely halted because of increased thromboembolic events with chemotherapy and necitumumab. EGFR monoclonal antibodies improved the outcome including survival in selected patients with advanced NSCLC. Prospective validation of predictive biomarkers is ongoing.

Efficient, Adaptive Clinical Validation of Predictive Biomarkers in Cancer Therapeutic Development.

Predictive biomarkers, defined as biomarkers that can be used to identify patient populations who will optimally benefit from therapy, are an important part of the future of oncology. They have the potential to reduce the size and cost of clinical development programs for oncology therapy, while increasing their probability of success and the ultimate value of cancer medicines. But predictive biomarkers do not always work, and under these circumstances they add cost, complexity, and time to drug development. This chapter describes Phase 2 and 3 development methods which efficiently and adaptively evaluate the ability of the biomarker to predict clinical outcomes. In the end, the biomarker is emphasized to the extent that it is actually predictive. This allows clinical cancer drug developers to manage uncertainty in the validity of biomarkers, leading to maximal value for predictive biomarkers and their associated oncology therapies.

Genetic biomarkers in epilepsy.

The identification of valid biomarkers for outcome prediction of diseases and improvement of drug response, as well as avoidance of side effects is an emerging field of interest in medicine. The concept of individualized therapy is becoming increasingly important in the treatment of patients with epilepsy, as predictive markers for disease prognosis and treatment outcome are still limited. Currently, the clinical decision process for selection of an antiepileptic drug (AED) is predominately based on the patient's epileptic syndrome and side effect profiles of the AEDs, but not on effectiveness data. Although standard dosages of AEDs are used, supplemented, in part, by therapeutic monitoring, the response of an individual patient to a specific AED is generally unpredictable, and the standard care of patients in antiepileptic treatment is more or less based on trial and error. Therefore, there is an urgent need for valid predictive biomarkers to guide patient-tailored individualized treatment strategies in epilepsy, a research area that is still in its infancy. This review focuses on genomic factors as part of an individual concept for AED therapy summarizing examples that influence the prognosis of the disease and the response to AEDs, including side effects.

Statistical issues in the validation of prognostic, predictive, and surrogate biomarkers

Biomarkers have many distinct purposes, and depending on their intended use, the validation process varies substantially. The goal of this article is to provide an introduction to the topic of biomarkers, and then to discuss three specific types of biomarkers, namely, prognostic, predictive, and surrogate. A principle challenge for biomarker validation from a statistical perspective is the issue of multiplicity. In general, the solution to this multiplicity challenge is well known to statisticians: pre-specification and replication. Critical requirements for prognostic marker validation include uniform treatment, complete follow-up, unbiased case selection, and complete ascertainment of the many possible confounders that exist in the context of an observational sample. In the case of predictive biomarker validation, observational data are clearly inadequate and randomized controlled trials are mandatory. Within the context of randomization, strategies for predictive marker validation can be grouped into two categories: retrospective versus prospective validation. The critical validation criteria for a surrogate endpoint is to ensure that if a trial uses a surrogate endpoint, the trial will result in the same inferences as if the trial had observed the true endpoint. The field of surrogate endpoint validation has now moved to the multi-trial or meta-analytic setting as the preferred method. Biomarkers are a highly active research area. For all biomarker developmental and validation studies, the importance of fundamental statistical concepts remains the following: pre-specification of hypotheses, randomization, and replication. Further statistical methodology research in this area is clearly needed as we move forward.

A review of phase II trial designs for initial marker validation

Phase II clinical trials aim to identify promising experimental regimens for further testing in phase III trials. In this review article, we focus on phase II designs for initial predictive biomarker validation to determine if a drug should be developed for an unselected patient population or for a biomarker-defined patient subset only. Several prospective designs for biomarker-directed therapy have been proposed, differing primarily in the study population, or randomization scheme, or both. The design choice is driven by scientific rationale, marker prevalence, strength of preliminary evidence, assay performance, and turn-around times for marker assessment. The enrichment design is most appropriate when compelling preliminary evidence suggests treatment benefit in only certain marker-defined subgroups, the all-comers design is useful when preliminary evidence regarding treatment effects in marker subgroups is unclear, and adaptive designs have the most potential in the setting of multiple treatment options and multiple marker-defined subgroups. We recently proposed a 2-stage phase II design that has the option for direct assignment (i.e., stop randomization and assign all patients to the experimental arm in stage 2) based on interim analysis (IA) results. This design not only recognizes the need for randomization but also acknowledges the possibility of promising but inconclusive results after pre-planned IA. Simulation studies demonstrated that the direct assignment-option design has minimal power loss, marginal increase in type I error rates, and reasonable robustness to population shift effects. Systematic evaluation and implementation of these design strategies in the phase II setting are essential for accelerating the clinical validation of biomarker guided-therapy.

75P - Expression of XIST and 53BP1 as Predictive Markers in BRCA1 Deficient Breast Cancer

The identification and validation of predictive biomarkers has often been hampered by issues in retrospective clinical study design, for example missing control groups and small or biased patient series. Despite a wealth of preclinical mechanistic insights and finished randomized controlled trials (RCT), translation to the clinic is infrequent. XIST and 53BP1 are two genes that were previously identified in preclinical studies to predict resistance to therapy targeting BRCA1 deficiency. We studied the expression of these two genes in the context of an RCT randomizing between conventional (5 cycles fluorouracil, epirubicin, cyclophosphamide (FEC)) and BRCA1 defect targeting chemotherapy (4 cycles FEC + 1 cycle of high dose carboplatin, thiotepa, cyclophosphamide).

Advanced-stage pancreatic cancer: therapy options

Pancreatic ductal adenocarcinoma is one of the most aggressive cancers, and surgical resection is a requirement for a potential cure. However, the majority of patients are diagnosed with advanced-stage disease, either metastatic (50%) or locally advanced cancer (30%). Although palliative chemotherapy is the standard of care for patients with metastatic disease, management of locally advanced adenocarcinoma is controversial. Several treatment options, including extended surgical resections, neoadjuvant therapy with subsequent resections, as well as palliative radiotherapy and/or chemotherapy, should be considered. However, there is little evidence available to support treatment options for locally advanced disease. As valid predictive biomarkers for stratification of therapy are not available today, future trials need to define the role of the different treatment options. This Review summarizes the current evidence and discusses available treatment options for both locally advanced and metastatic pancreatic adenocarcinoma.

Abstract LB-219: Neoadjuvant multidisciplinary phase II study (BRANCH) of an early bevacizumab schedule plus chemo-radiation therapy in rectal cancer: efficacy, safety, and biomarkers.

Abstract BACKGROUND: The benefits of combining bevacizumab (BEV) plus chemotherapy have thus far been rather modest, stimulating interest in developing novel effective combination schedule as well as valid predictive biomarkers. Several evidences support the hypothesis that BEV can normalize the abnormal tumor vasculature, resulting in more efficient delivery of drugs and oxygen to cancer cells and that this effect seems to be transient and with a relatively narrow therapeutic window. In BRANCH study (NCT01481545) we assess the safety and efficacy of an experimental schedule of early (4 days before) BEV added to neoadjuvant chemotherapy (CT) and radiotherapy (RT) in poor-risk locally advanced rectal cancer (pLARC) patients (pts) and explore the potential of circulating endothelial cells (CECs) and tumor lesion glycolysis (TLG) as surrogate markers of pathological response. PATIENTS AND METHODS: 46 pts (cT4, cN+, cT3≤ 5 cm from the anal verge and/or positive circumferential margin, M1 resectable) received 3 biweekly courses of oxaliplatin (100 mg/m2)/raltitrexed (2.5 mg/m2) on day 1, and 5-FU (800 mg/m2)/folinic acid (250 mg/m2) on day 2 during pelvic RT (45 Gy). BEV (5 mg/kg) was given biweekly 4 days before beginning of CT/RT for 2 courses. Toxicity was graded with NCI-CTC v.3. Pathological response was defined using a modified Mandard tumor regression grade (TRG). According to the Simon's two-stage design, assuming an hypothesis of a 50% TRG1 (complete tumor regression) (α error=0.05, β error=0.20), at least 6/16 TRG1 should be obtained to continue accrual to 46 pts. CECs were quantified at baseline (BL) before BEV, at several time points thereafter during treatment and before surgery, by flow cytometry. TLG, was evaluated at BL, on day 10 and before surgery, by FDG-PET. Statistical analysis was by Mann-Whitney test. RESULTS: TRG1 required by statistical design was reached in the first 16 pts and in the final 46 pts: 23 TRG1 (50%), 14 TRG2 (30%) and 8 TRG3-4 (17%). One pt refused surgery. Grade ¾ neutropenia was the most common adverse event (13/46 pts, 28%). TLG reduction on day 10 vs BL was significantly higher in responders TRG1-2 compared to non-responders TRG3-4 pts (median -72%, range -90%+31% vs -38%, range -45%+25%; p &amp;lt; 0.05). Median CECs at BL were higher in TRG1-2 vs TRG3-4 pts (median 0.22/µl, range 0-3.98 vs 0/µl, range 0-0.174; p=0.009). Moreover, in TRG1-2 pts CECs were significantly reduced on day 10 vs BL (median 0.014/µl, range 0-2.29; p=0.002). This pattern was not seen in TRG3-4 pts with a tendency toward increased levels (median 0.316/µl, range 0-2.64; p=0.097). CONCLUSIONS: Early schedule of BEV plus CT and RT appears safe and active yielding high rate of TRG1 and TRG2 responses in pLARC. Early FDG-PET and CECs evaluation emerged as potential biomarkers for treatment selection to be incorporated in design of future studies with this regimen. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr LB-219. doi:1538-7445.AM2012-LB-219

Abstract 854: Patient stratification strategies for NVP-BGJ398, a selective pan-FGFR inhibitor in phase I clinical trials

Abstract The mammalian fibroblast growth factor/fibroblast growth factor receptor (FGF/FGFR) family comprises 22 FGF ligands, which exert their action through 4 highly conserved transmembrane tyrosine kinase receptors (FGFR1, FGFR2, FGFR3 and FGFR4). This highly complex signaling network regulates fundamental developmental pathways and physiological processes in the adult organism. A growing body of evidence linking deregulation of the FGF/FGFR system, as a consequence of genetic alterations, to the pathogenesis of cancer makes these receptors attractive targets for therapeutic intervention. To this purpose we have developed NVP-BGJ398, an orally bioavailable, selective pan-FGFR kinase inhibitor which is currently in clinical Phase I dose escalation trial. In order to preclinically identify and validate patient stratification biomarkers that accurately predict for response to NVP-BGJ398 in the clinic, we have utilized the Cancer Cell Line Encyclopedia (CCLE). The CCLE is a collection of almost 1000 cancer cell lines representing multiple tumor types that, in a collaborative effort between The Novartis Institutes for BioMedical Research and the Broad Institute, has been comprehensively annotated in terms of genome-scale mRNA expression, gene copy number alterations and gene mutations (http:/www.broadinstitute.org/ccle). In addition, over half of these cell lines were subjected to high-throughput cell viability assays in the presence NVP-BGJ398, with the aim of generating cell line sensitivity data. Here we show the validation of predictive biomarkers of response in primary human tumor xenografts, revealed through the analysis of the CCLE, as well as the identification of potentially new indications for NVP-BGJ398. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 854. doi:1538-7445.AM2012-854

Möglichkeiten und Grenzen der stratifizierenden Medizin am Beispiel von prädiktiven Biomarkern und „zielgerichteten“ medikamentösen Therapien in der Onkologie

Research over the past two decades has untangled the molecular heterogeneity of cancer at the cellular level. The molecular techniques that have been developed in the past 5 to 10 years are dramatically improving our understanding of the genomic aberrations that underlie the malignant transformation of normal cells. At the same time, however, advances in our understanding of the genetic basis of solid tumours and hematologic malignancies have revealed how complex cancer is, and consequently how much more challenging it is in many of the more common cancers to identify the right drug for the right patient in the adequate dose at the right time. Given the slow pace of translation from genome science to individualised medicine in oncology, this expert review, mainly taking into account recently published perspective articles and reviews, describes the molecular heterogeneity of cancer, the difficulties in developing novel molecularly targeted agents, and the need for developing biomarkers to optimise drug development and clinical use. Distinct types of biomarkers, breakthroughs as well as disappointments in the clinical implementation of biomarkers and targeted therapeutics into clinical practice are discussed by focusing on four targeted anti-cancer drugs. Finally, as clinical biomarker tests that predict response to particular therapies will play an important role in achieving stratified medicine in the near future, this article will conclude by giving some recommendations for effective biomarker evaluation and clinical trial designs for predictive biomarker validation.

Predictive biomarkers with potential of converting conventional chemotherapy to targeted therapy in patients with metastatic colorectal cancer

The availability of systemic chemotherapy regimens for the treatment of patients with metastatic colorectal cancer (mCRC) is based on the results from large prospective, randomized studies. The main chemotherapeutic drugs used in treatment of mCRC are the fluoropyrimidines (5-fluorouracil (5-FU); capecitabine) in combination with either oxaliplatin (FOLFOX) or irinotecan (FOLFIRI). The objective response rate to either combination is approximately 50%, where no significant differences with regard to progression free survival or overall survival have been observed. Interestingly, a number of preclinical and clinical studies have indicated lack of full cross resistance between oxaliplatin based and irinotecan based treatment. Therefore, it is possible that certain mCRC patient subpopulations would benefit more from one drug combination rather than the other. To address this clinical problem there has been much focus on development and validation of predictive biomarkers for these three drugs. Here, we present a thorough review on the current status of predictive biomarkers for 5-FU, oxaliplatin and irinotecan treatment of mCRC patients. The overall conclusions were as follows: Several promising biomarker candidates were identified, notably thymidylate synthase for 5-FU, topoisomerase I for irinotecan and ERCC1 for oxaliplatin. However, these candidates warrant further analysis, where assay performance and clinical trial design should be in focus.