Effective incorporation of biomarkers into phase II trials

Augmenting randomized clinical trial data with historical control data: Precision medicine applications

As precision medicine becomes more precise, the sizes of the molecularly targeted subpopulations become increasingly smaller. This can make it challenging to conduct randomized clinical trials of the targeted therapies in a timely manner. To help with this problem of a small patient subpopulation, a study design that is frequently proposed is to conduct a small randomized clinical trial (RCT) with the intent of augmenting the RCT control arm data with historical data from a set of patients who have received the control treatment outside the RCT (historical control data). In particular, strategies have been developed that compare the treatment outcomes across the cohorts of patients treated with the standard (control) treatment to guide the use of the historical data in the analysis; this can lessen the potential well-known biases of using historical controls without any randomization. Using some simple examples and completed studies, we demonstrate in this commentary that these strategies are unlikely to be useful in precision medicine applications.

A Bayesian Decision-Theoretic Design for Simultaneous Biomarker-Based Subgroup Selection and Efficacy Evaluation

The success of drug development of targeted therapy often hinges on an appropriate selection of the sensitive patient population, mostly based on patients' biomarker levels. At the planning stage of a phase II study, although a potential biomarker may have been identified, a threshold value for defining sensitive patient population is often unavailable for adopting many existing biomarker-guided designs. To address this issue, we propose a two-stage design that allows for simultaneous biomarker threshold selection and efficacy evaluation while accommodating situations where the drug is efficacious in the entire patient population. The design uses a Bayesian decision-theoretic approach and incorporates the benefit and cost considerations of the study into a utility function. The operating characteristics of the proposed design under different scenarios are investigated via simulations. We also provide a discussion on the choice of the benefit and cost parameters in practice.

Biomarkers in early-phase trials: fundamental issues

Biomarkers are frequently being included in early-phase clinical trials. This article is meant to introduce clinical investigators to the fundamentals of choosing a biomarker test for use in an early phase trial. Steps to consider are briefly outlined including defining the role of the biomarker in the early phase trial; selecting a fit-for-purpose biomarker test and laboratory; describing the test procedures; carrying out analytical validation testing appropriate for the research objectives and the risk involved in the trial; implementing the test in the trial; and planning for the future. Examples illustrate analytical validation approaches in the context of typical biomarker roles. The importance of collaboration between clinical investigators and laboratory researchers is emphasized.

Statistical Methods for Clinical Trial Designs in the New Era of Cancer Treatment

Recent success of immunotherapy and other targeted therapies in cancer treatment has signaled the advent of precision medicine. Unlike conventional trial designs that aim to find an optimal treatment ignoring inter-patient heterogeneity, clinical trial designs for precision medicine must take into account patients' variability in genes, environments, and lifestyle. This article provides a review of recent research development of clinical trial designs toward this trend.

The Challenge for Development of Valuable Immuno-oncology Biomarkers

The development of immunotherapy is an important breakthrough for the treatment of cancer, with antitumor efficacy observed in a wide variety of tumors. To optimize immunotherapy use, approaches must be developed to identify which patients are likely to achieve benefit. To minimize therapeutic toxicities and costs, understanding the ideal choice and sequencing of the numerous immuno-oncology agents available for individual patients is thus critical, but fraught with challenges. The immune tumor microenvironment (TME) is a unique aspect of the response to immuno-oncology agents and measurement of single biomarkers does not adequately capture these complex interactions. Therefore, multiple potential biomarkers are likely needed. Current candidates in this area include PD-L1 expression, CD8+ tumor-infiltrating lymphocytes, tumor mutation load and neoantigen burden, immune-related gene signatures, and multiplex IHC assays that examine the pharmacodynamic and spatial interactions of the TME. The most fruitful investigations are likely to use several techniques to predict response and interrogate mechanisms of resistance. Immuno-oncology biomarker research must employ validated assays to ask focused research questions utilizing clinically annotated tissue collections and biomarker-focused clinical trial designs to investigate specific endpoints. Real-time input from patients and their advocates into biomarker discovery is necessary to ensure that the investigations pursued will improve both clinical outcomes and quality of life. We herein provide a framework of recommendations to guide the search for immuno-oncology biomarkers of value.

Radiogenomics and radiotherapy response modeling

Advances in patient-specific information and biotechnology have contributed to a new era of computational medicine. Radiogenomics has emerged as a new field that investigates the role of genetics in treatment response to radiation therapy. Radiation oncology is currently attempting to embrace these recent advances and add to its rich history by maintaining its prominent role as a quantitative leader in oncologic response modeling. Here, we provide an overview of radiogenomics starting with genotyping, data aggregation, and application of different modeling approaches based on modifying traditional radiobiological methods or application of advanced machine learning techniques. We highlight the current status and potential for this new field to reshape the landscape of outcome modeling in radiotherapy and drive future advances in computational oncology.

Bayesian population finding with biomarkers in a randomized clinical trial

The identification of good predictive biomarkers allows investigators to optimize the target population for a new treatment. We propose a novel utility-based Bayesian population finding (BaPoFi) method to analyze data from a randomized clinical trial with the aim of finding a sensitive patient population. Our approach is based on casting the population finding process as a formal decision problem together with a flexible probability model, Bayesian additive regression trees (BART), to summarize observed data. The proposed method evaluates enhanced treatment effects in patient subpopulations based on counter-factual modeling of responses to new treatment and control for each patient. In extensive simulation studies, we examine the operating characteristics of the proposed method. We compare with a Bayesian regression-based method that implements shrinkage estimates of subgroup-specific treatment effects. For illustration, we apply the proposed method to data from a randomized clinical trial.

Challenges and methodology in the incorporation of biomarkers in cancer clinical trials

Biomarkers can be used to establish more homogeneous groups using the genetic makeup of the tumour to inform the selection of treatment for each individual patient. However, proper preclinical work and stringent validation are needed before taking forward biomarkers into confirmatory studies. Despite the challenges, incorporation of biomarkers into clinical trials could better target appropriate patients, and potentially be lifesaving. The authors conducted a systematic review to describe marker-based and adaptive design methodology for their integration in clinical trials, and to further describe the associated practical challenges. Studies published between 1990 to November 2015 were searched on PubMed. Titles, abstracts and full text articles were reviewed to identify relevant studies. Of the 4438 studies examined, 57 studies were included. The authors conclude that the proposed approaches may readily help researchers to design biomarker trials, but novel approaches are still needed.

Clinical development of new drug-radiotherapy combinations

In countries with the best cancer outcomes, approximately 60% of patients receive radiotherapy as part of their treatment, which is one of the most cost-effective cancer treatments. Notably, around 40% of cancer cures include the use of radiotherapy, either as a single modality or combined with other treatments. Radiotherapy can provide enormous benefit to patients with cancer. In the past decade, significant technical advances, such as image-guided radiotherapy, intensity-modulated radiotherapy, stereotactic radiotherapy, and proton therapy enable higher doses of radiotherapy to be delivered to the tumour with significantly lower doses to normal surrounding tissues. However, apart from the combination of traditional cytotoxic chemotherapy with radiotherapy, little progress has been made in identifying and defining optimal targeted therapy and radiotherapy combinations to improve the efficacy of cancer treatment. The National Cancer Research Institute Clinical and Translational Radiotherapy Research Working Group (CTRad) formed a Joint Working Group with representatives from academia, industry, patient groups and regulatory bodies to address this lack of progress and to publish recommendations for future clinical research. Herein, we highlight the Working Group's consensus recommendations to increase the number of novel drugs being successfully registered in combination with radiotherapy to improve clinical outcomes for patients with cancer.

Molecular and clinical implementations of ovarian cancer mouse avatar models

Innovation in oncology drug development has been hindered by lack of preclinical models that reliably predict clinical activity of novel therapies in cancer patients. Increasing desire for individualize treatment of patients with cancer has led to an increase in the use of patient-derived xenografts (PDX) engrafted into immune-compromised mice for preclinical modeling. Large numbers of tumor-specific PDX models have been established and proved to be powerful tools in pre-clinical testing. A subset of PDXs, referred to as Avatars, establish tumors in an orthotopic and treatment naïve fashion that may represent the most clinical relevant model of individual human cancers. This review will discuss ovarian cancer (OC) PDX models demonstrating the opportunities and limitations of these models in cancer drug development, and describe concepts of clinical trials design in Avatar guided therapy.

Biomarker-Guided Adaptive Trial Designs in Phase II and Phase III: A Methodological Review

BACKGROUND

Personalized medicine is a growing area of research which aims to tailor the treatment given to a patient according to one or more personal characteristics. These characteristics can be demographic such as age or gender, or biological such as a genetic or other biomarker. Prior to utilizing a patient's biomarker information in clinical practice, robust testing in terms of analytical validity, clinical validity and clinical utility is necessary. A number of clinical trial designs have been proposed for testing a biomarker's clinical utility, including Phase II and Phase III clinical trials which aim to test the effectiveness of a biomarker-guided approach to treatment; these designs can be broadly classified into adaptive and non-adaptive. While adaptive designs allow planned modifications based on accumulating information during a trial, non-adaptive designs are typically simpler but less flexible.

METHODS AND FINDINGS

We have undertaken a comprehensive review of biomarker-guided adaptive trial designs proposed in the past decade. We have identified eight distinct biomarker-guided adaptive designs and nine variations from 107 studies. Substantial variability has been observed in terms of how trial designs are described and particularly in the terminology used by different authors. We have graphically displayed the current biomarker-guided adaptive trial designs and summarised the characteristics of each design.

CONCLUSIONS

Our in-depth overview provides future researchers with clarity in definition, methodology and terminology for biomarker-guided adaptive trial designs.

Methods: for studying pharmacogenetic profiles of combination chemotherapeutic drugs

INTRODUCTION

Molecular and genetic analysis of tumors and individuals has led to patient-centered therapies, through the discovery and identification of genetic markers predictive of drug efficacy and tolerability. Present therapies often include a combination of synergic drugs, each of them directed against different targets. Therefore, the pharmacogenetic profiling of tumor masses and patients is becoming a challenge, and several questions may arise when planning a translational study.

AREAS COVERED

The review presents the different techniques used to stratify oncology patients and to tailor antineoplastic treatments according to individual pharmacogenetic profiling. The advantages of these methodologies are discussed as well as current limits.

EXPERT OPINION

Facing the rapid technological evolution for genetic analyses, the most pressing issues are the choice of appropriate strategies (i.e., from gene candidate up to next-generation sequencing) and the possibility to replicate study results for their final validation. It is likely that the latter will be the major obstacle in the future. However, the present landscape is opening up new possibilities, overcoming those hurdles that have limited result translation into clinical settings for years.

Targeted therapies and clinical trials in ovarian cancer

Recent advances in molecular profiling have shown that cancers arising from the ovary are phenotypically and genetically heterogeneous. Within histologies, many mutations in druggable targets are uncommon in frequency but mutations leading to activation of specific signal transduction pathways are common. These results support the notion that different targeted agents should be prioritized for testing between and within ovarian cancer histologies. The subsegmentation of ovarian cancers based on molecular features challenge traditional trial designs. Feasibility of accrual and need for data on biological and clinical consequence or target inhibition are leading to trial designs that lump or split patient populations by histology, pathway, gene, and/or mutation. This review summarizes potential therapeutic targets identified from recent molecular profiling studies of ovarian cancers and trial designs to evaluate targeted agents in rare cancer settings.

Biomarker-based Bayesian randomized phase II clinical trial design to identify a sensitive patient subpopulation

The benefits and challenges of incorporating biomarkers into the development of anticancer agents have been increasingly discussed. In many cases, a sensitive subpopulation of patients is determined based on preclinical data and/or by retrospectively analyzing clinical trial data. Prospective exploration of sensitive subpopulations of patients may enable us to efficiently develop definitively effective treatments, resulting in accelerated drug development and a reduction in development costs. We consider the development of a new molecular-targeted treatment in cancer patients. Given preliminary but promising efficacy data observed in a phase I study, it may be worth designing a phase II clinical trial that aims to identify a sensitive subpopulation. In order to achieve this goal, we propose a Bayesian randomized phase II clinical trial design incorporating a biomarker that is measured on a graded scale. We compare two Bayesian methods, one based on subgroup analysis and the other on a regression model, to analyze a time-to-event endpoint such as progression-free survival (PFS) time. The two methods basically estimate Bayesian posterior probabilities of PFS hazard ratios in biomarker subgroups. Extensive simulation studies evaluate these methods' operating characteristics, including the correct identification probabilities of the desired subpopulation under a wide range of clinical scenarios. We also examine the impact of subgroup population proportions on the methods' operating characteristics. Although both methods' performance depends on the distribution of treatment effect and the population proportions across patient subgroups, the regression-based method shows more favorable operating characteristics.

The role of molecular imaging in modern drug development

Drug development represents a highly complex, inefficient and costly process. Over the past decade, the widespread use of nuclear imaging, owing to its functional and molecular nature, has proven to be a determinant in improving the efficiency in selecting the candidate drugs that should either be abandoned or moved forward into clinical trials. This helps not only with the development of safer and effective drugs but also with the shortening of time-to-market. The modern concept and future trends concerning molecular imaging will assumedly be hybrid or multimodality imaging, including combinations between high sensitivity and functional (molecular) modalities with high spatial resolution and morphological techniques.

Statistical design and evaluation of biomarker studies

We review biostatistical aspects of biomarker studies, including design and analysis issues, covering the range of settings required for translational research-from early exploratory studies through clinical trials.

Biomarker-guided repurposing of chemotherapeutic drugs for cancer therapy: a novel strategy in drug development

Cancer is a leading cause of mortality worldwide and matters are only set to worsen as its incidence continues to rise. Traditional approaches to combat cancer include improved prevention, early diagnosis, optimized surgery, development of novel drugs, and honing regimens of existing anti-cancer drugs. Although discovery and development of novel and effective anti-cancer drugs is a major research area, it is well known that oncology drug development is a lengthy process, extremely costly and with high attrition rates. Furthermore, those drugs that do make it through the drug development mill are often quite expensive, laden with severe side-effects and unfortunately, to date, have only demonstrated minimal increases in overall survival. Therefore, a strong interest has emerged to identify approved non-cancer drugs that possess anti-cancer activity, thus shortcutting the development process. This research strategy is commonly known as drug repurposing or drug repositioning and provides a faster path to the clinics. We have developed and implemented a modification of the standard drug repurposing strategy that we review here; rather than investigating target-promiscuous non-cancer drugs for possible anti-cancer activity, we focus on the discovery of novel cancer indications for already approved chemotherapeutic anti-cancer drugs. Clinical implementation of this strategy is normally commenced at clinical phase II trials and includes pre-treated patients. As the response rates to any non-standard chemotherapeutic drug will be relatively low in such a patient cohort it is a pre-requisite that such testing is based on predictive biomarkers. This review describes our strategy of biomarker-guided repurposing of chemotherapeutic drugs for cancer therapy, taking the repurposing of topoisomerase I (Top1) inhibitors and Top1 as a potential predictive biomarker as case in point.

Evaluating many treatments and biomarkers in oncology: a new design

There is a pressing need for more-efficient trial designs for biomarker-stratified clinical trials. We suggest a new approach to trial design that links novel treatment evaluation with the concurrent evaluation of a biomarker within a confirmatory phase II/III trial setting. We describe a new protocol using this approach in advanced colorectal cancer called FOCUS4. The protocol will ultimately answer three research questions for a number of treatments and biomarkers: (1) After a period of first-line chemotherapy, do targeted novel therapies provide signals of activity in different biomarker-defined populations? (2) If so, do these definitively improve outcomes? (3) Is evidence of activity restricted to the biomarker-defined groups? The protocol randomizes novel agents against placebo concurrently across a number of different biomarker-defined population-enriched cohorts: BRAF mutation; activated AKT pathway: PI3K mutation/absolute PTEN loss tumors; KRAS and NRAS mutations; and wild type at all the mentioned genes. Within each biomarker-defined population, the trial uses a multistaged approach with flexibility to adapt in response to planned interim analyses for lack of activity. FOCUS4 is the first test of a protocol that assigns all patients with metastatic colorectal cancer to one of a number of parallel population-enriched, biomarker-stratified randomized trials. Using this approach allows questions regarding efficacy and safety of multiple novel therapies to be answered in a relatively quick and efficient manner, while also allowing for the assessment of biomarkers to help target treatment.

Criteria for the use of omics-based predictors in clinical trials: explanation and elaboration

High-throughput 'omics' technologies that generate molecular profiles for biospecimens have been extensively used in preclinical studies to reveal molecular subtypes and elucidate the biological mechanisms of disease, and in retrospective studies on clinical specimens to develop mathematical models to predict clinical endpoints. Nevertheless, the translation of these technologies into clinical tests that are useful for guiding management decisions for patients has been relatively slow. It can be difficult to determine when the body of evidence for an omics-based test is sufficiently comprehensive and reliable to support claims that it is ready for clinical use, or even that it is ready for definitive evaluation in a clinical trial in which it may be used to direct patient therapy. Reasons for this difficulty include the exploratory and retrospective nature of many of these studies, the complexity of these assays and their application to clinical specimens, and the many potential pitfalls inherent in the development of mathematical predictor models from the very high-dimensional data generated by these omics technologies. Here we present a checklist of criteria to consider when evaluating the body of evidence supporting the clinical use of a predictor to guide patient therapy. Included are issues pertaining to specimen and assay requirements, the soundness of the process for developing predictor models, expectations regarding clinical study design and conduct, and attention to regulatory, ethical, and legal issues. The proposed checklist should serve as a useful guide to investigators preparing proposals for studies involving the use of omics-based tests. The US National Cancer Institute plans to refer to these guidelines for review of proposals for studies involving omics tests, and it is hoped that other sponsors will adopt the checklist as well.

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.

Design of oncology clinical trials: a review

Cancer is a disease that occurs due to the uncontrolled multiplication of cells that invade nearby tissues and can spread to other parts of the body. An increased incidence of cancer in the world has led to an increase in oncology research and in the number of oncology trials. Well designed oncology clinical trials are a key part of developing effective anti-cancer drugs. This review focuses on statistical considerations in the design and analysis of oncology clinical trials.

Discovery and validation of graft-versus-host disease biomarkers

Allogeneic hematopoietic stem cell transplantation (allo-HSCT) is the most effective tumor immunotherapy available. Although allo-HSCT provides beneficial graft-versus-tumor effects, acute GVHD (aGVHD) is the primary source of morbidity and mortality after HSCT. Diagnosis of aGVHD is typically based on clinical symptoms in one or more of the main target organs (skin, liver, gastrointestinal tract) and confirmed by biopsy. However, currently available diagnostic and staging tools often fail to identify patients at higher risk of GVHD progression, unresponsiveness to therapy, or death. In addition, there are shortcomings in the prediction of GVHD before clinical signs develop, indicating the urgent need for noninvasive and reliable laboratory tests. Through the continuing evolution of proteomics technologies seen in recent years, plasma biomarkers have been identified and validated as promising diagnostic tools for GVHD and prognostic tools for nonrelapse mortality. These biomarkers may facilitate timely and selective therapeutic intervention but should be more widely validated and incorporated into a new grading system for risk stratification of patients and better-customized treatment. This review identifies biomarkers for detecting GVHD, summarizes current information on aGVHD biomarkers, proposes future prospects for the blinded evaluation of these biomarkers, and discusses the need for biomarkers of chronic GVHD.

Overcoming implementation challenges of personalized cancer therapy

Personalized cancer therapy is based on the precept that detailed molecular characterization of the patient's tumour and its microenvironment will enable tailored therapies to improve outcomes and decrease toxicity. The goal of personalized therapy is to target aberrations that drive tumour growth and survival, by administering the right drug combination for the right person. This is becoming increasingly achievable with advances in high-throughput technologies to characterize tumours and the expanding repertoire of molecularly targeted therapies. However, there are numerous challenges that need to be surpassed before delivering on the promise of personalized cancer therapy. These include tumour heterogeneity and molecular evolution, costs and potential morbidity of biopsies, lack of effective drugs against most genomic aberrations, technical limitations of molecular tests, and reimbursement and regulatory hurdles. Critically, the 'hype' surrounding personalized cancer therapy must be tempered with realistic expectations, which, today, encompass increased survival times for only a portion of patients.

Biomarkers for hepatocellular carcinoma

Hepatocellular carcinoma (HCC) ranks high among the most common and fatal cancers in the world. HCC develops from chronic liver diseases, especially from hepatitis C virus-related and hepatitis B virus (HBV)-related liver diseases. In this sense, useful biomarkers for HCC detection for the patients at risk of HCC are quite important. Recently, new therapies for HCC have been developed, and the prognosis of the patients has improved. However, considering the recurrence rate of HCC after treatment is very high, biomarkers that detect recurrence at an early stage are also required. In addition, since new drugs such as multikinase inhibitors have been introduced to the clinical scene, surrogate biomarkers to predict the effectiveness of treatment will be required in the near future. So far, many biomarkers for HCC have been developed, and their clinical usefulness has been assessed. As a result, several biomarkers for HCC are widely used. However, investigations to discover more useful biomarkers that fit in clinical settings are under way. In this review article, biomarkers for HCC are overviewed to examine their clinical usefulness.

Circulating endothelial cells and their apoptotic fraction are mutually independent predictive biomarkers in Bevacizumab-based treatment for advanced colorectal cancer

BACKGROUND

Bevacizumab has shown consistent clinical efficacy in metastatic colorectal cancer (mCRC), but some patients respond better than others. Thus, it is crucial to identify biomarkers that permit the recognition of potentially responsive subjects and to spare toxicity in those who are unlikely benefit from treatment.

METHODS

In 24 mCRC patients undergoing Bevacizumab-based first-line treatment, we assessed by multiparameter flow cytometry changes in circulating endothelial cell (CEC) number, their apoptotic fraction (APO-CEC) and their mutual relationship. Data were compared with those from a group of 21 healthy subjects.

RESULTS

CECs and APO-CECs were higher in patients versus controls (p = 0.01 and p > 0.05, respectively). The increase in CECs at the 3rd cycle in complete response (CR) patients was statistically significant (p = 0.048). A better progression-free survival was evidenced in patients that showed an increase in CECs at the 6th cycle (p = 0.009). Regarding the changes in CECs and APO-CECs, a strong correlation was evidenced, at baseline, both in the global population (0.002; r: 0.53) and in the CR subgroup (p: 0.02; r: 0.77). In the partial response + stable and progression disease (SD + PD) subgroup, this correlation was highly significant at the 6th cycle (p: 0.001; r: 0.83).

CONCLUSIONS

We confirmed the predictive role of an increase in CECs in mCRC patients treated with Bevacizumab-based therapy and showed that modifications in CECs and APO-CECs are independent factors. This underlines the relevance of a simultaneous quantitative and functional evaluation of these biomarkers in view of their possible diagnostic utility.

Predictive biomarkers in colorectal cancer: usage, validation, and design in clinical trials

As cancer treatment development has shifted its attention to targeted therapies, it is becoming increasingly important to provide tools for selecting the right treatment for an individual patient to achieve optimal clinical benefit. Biomarkers, identified and studied in the process of understanding the nature of the disease at the molecular pathogenesis level, have been increasingly recognized as a critical aspect in more accurate diagnosis, prognosis assessment, and therapeutic targeting. Predictive biomarkers, which can aid treatment decisions, require extensive data for validation. In this article, we discuss the definition, clinical usages, and more extensively the clinical trial designs for the validation of predictive biomarkers. Predictive biomarker validation methods can be broadly grouped into retrospective and prospective designs. Retrospective validation utilizes data from previously conducted prospective randomized controlled trials. Prospective designs include enrichment designs, treatment-by-marker interaction designs, marker-based strategy designs, and adaptive designs. We discuss each design with examples and provide comparisons of the advantages and disadvantages among the different designs. We conclude that the combination of scientific, clinical, statistical, ethical, and practical considerations provides guidance for the choice of the clinical trial design for validation of each proposed predictive biomarker.

Designing phase II trials in cancer: a systematic review and guidance

Background:

Literature reviews of cancer trials have highlighted the need for better understanding of phase II statistical designs. Understanding the key elements associated with phase II design and knowledge of available statistical designs is necessary to enable appropriate phase II trial design.

Methods:

A systematic literature review was performed to identify phase II trial designs applicable to oncology trials. The results of the review were used to create a library of currently available designs, and to develop a structured approach to phase II trial design outlining key points for consideration.

Results:

A total of 122 papers describing new or adapted phase II trial designs were obtained. These were categorised into nine levels, reflecting the practicalities of implementation, and form a library of phase II designs. Key design elements were identified by data extraction. Along with detailed description of the key elements and the library of designs, a structured thought process was developed to form a guidance document for choice of phase II oncology trial design.

Conclusion:

The guidance offers researchers a structured and systematic approach to identifying phase II trial designs, highlighting key issues to be considered by both clinicians and statisticians and encouraging an interactive approach to more informed trial design.

Integrating biomarkers in clinical trials

Biomarkers have a growing role in clinical trials. With the advent of the targeted therapy era, molecular biomarkers in particular are becoming increasingly important within both clinical research and clinical practice. This article focuses on biomarkers that anticipate the prognosis of individual patients ('prognostic' biomarkers) and on biomarkers that predict how individual patients will respond to specific treatments ('predictive' biomarkers, also called 'effect modifiers'). Specific Phase II and III clinical trial designs are discussed in detail for their ability to validate the biomarker and/or to establish the effect of an experimental therapy in patient populations defined by the presence or absence of the biomarker. Contemporary biomarker-based clinical trials in oncology are used as examples.

Unique characteristics of adolescent and young adult acute lymphoblastic leukemia, breast cancer, and colon cancer

Each year in the United States, nearly 70 000 individuals between the ages of 15 and 40 years are diagnosed with cancer. Although overall cancer survival rates among pediatric and older adult patients have increased in recent decades, there has been little improvement in survival of adolescent and young adult (AYA) cancer patients since 1975 when collected data became adequate to evaluate this issue. In 2006, the AYA Oncology Progress Review Group made recommendations for addressing the needs of this population that were later implemented by the LIVESTRONG Young Adult Alliance. One of their overriding questions was whether the cancers seen in AYA patients were biologically different than the same cancers in adult and/or pediatric patients. On June 9-10, 2009, the National Cancer Institute (NCI) and the Lance Armstrong Foundation (LAF) convened a workshop in Bethesda, MD, entitled "Unique Characteristics of AYA Cancers: Focus on Acute Lymphocytic Leukemia (ALL), Breast Cancer and Colon Cancer" that aimed to examine the current state of basic and translational research on these cancers and to discuss the next steps to improve their prognosis and treatment.

Translational research in oncology: key bottlenecks and new paradigms

Translational research is about transforming progress in basic research into products that benefit patients. Here I discuss some of the key obstacles to effective translational research in oncology that have previously received limited attention. Basic research often does not go far enough for straightforward clinical translation, and long-term, high-risk endeavours to fill these key gaps have not been adequately addressed either by industry or by the culture of investigator-initiated research. These key gaps include the identification of causative oncogenic mutations and new approaches to regulating currently undruggable targets such as tumour suppressor genes. Even where an inhibitor of a key target has been identified, new approaches to clinical development are needed. The current approach of treating broad populations of patients based primarily on primary cancer site is not well suited to the development of molecularly targeted drugs. Although developing drugs with predictive diagnostics makes drug development more complex, it can improve the success rate of development, as well as provide benefit to patients and the economics of healthcare. I review here some prospective Phase III designs that have been developed for transition from the era of correlative science to one of reliable predictive and personalised oncology.

Fit-for-purpose biomarker method validation for application in clinical trials of anticancer drugs

Clinical development of new anticancer drugs can be compromised by a lack of qualified biomarkers. An indispensable component to successful biomarker qualification is assay validation, which is also a regulatory requirement. In order to foster flexible yet rigorous biomarker method validation, the fit-for-purpose approach has recently been developed. This minireview focuses on many of the basic issues surrounding validation of biomarker assays utilised in clinical trials. It also provides an overview on strategies to validate each of the five categories that define the majority of biomarker assays.

Phase II clinical trials in oncology: are we hitting the target?

The number of novel and molecularly targeted agents in the last decade that need screening for preliminary efficacy in Phase II trials has increased. Many of these agents have a cytostatic mode of action that is difficult to assess using traditional Phase II designs. These new agents require detailed evaluation to optimize their dosing, to evaluate their effects on their target and to define early markers that predict for a definitive benefit. This review focuses on the options for Phase II trial designs. The different end points, single versus multiarm and randomized designs, the use of biomarkers and Bayesian approaches are also reviewed. The final design chosen will depend on the characteristics and circumstances of each individual study.

Endosialin: from vascular target to biomarker for human sarcomas

Biomarkers have been the focus of investigations to diagnose disease, track response to therapy and predict prognosis. Meanwhile, the identification of new targets for therapeutic intervention is an ongoing quest in the field of oncology. The recognition of endosialin as an antigen that is selectively overexpressed in human tumor tissues offers new strategies for treating cancer. Not only do the tumor vasculature and stromal compartments upregulate endosialin but, importantly, the malignant cells of sarcomas strongly express endosialin as well. A diagnostic assay that measures the intensity of endosialin expression in malignant tissues would assist in selecting patients that could benefit from an antiendosialin therapy. Thus, endosialin holds potential value both as a therapeutic target and as a biomarker for certain human cancers.

The design of phase II clinical trials testing cancer therapeutics: consensus recommendations from the clinical trial design task force of the national cancer institute investigational drug steering committee

The optimal design of phase II studies continues to be the subject of vigorous debate, especially studies of newer molecularly targeted agents. The observations that many new therapeutics "fail" in definitive phase III studies, coupled with the numbers of new agents to be tested as well as the increasing costs and complexity of clinical trials, further emphasize the critical importance of robust and efficient phase II design. The Clinical Trial Design Task Force (CTD-TF) of the National Cancer Institute (NCI) Investigational Drug Steering Committee (IDSC) has published a series of discussion papers on phase II trial design in Clinical Cancer Research. The IDSC has developed formal recommendations about aspects of phase II trial design that are the subject of frequent debate, such as endpoints (response versus progression-free survival), randomization (single-arm designs versus randomization), inclusion of biomarkers, biomarker-based patient enrichment strategies, and statistical design (e.g., two-stage designs versus multiple-group adaptive designs). Although these recommendations in general encourage the use of progression-free survival as the primary endpoint, randomization, inclusion of biomarkers, and incorporation of newer designs, we acknowledge that objective response as an endpoint and single-arm designs remain relevant in certain situations. The design of any clinical trial should always be carefully evaluated and justified based on characteristic specific to the situation.

Comparison of error rates in single-arm versus randomized phase II cancer clinical trials

PURPOSE To improve the understanding of the appropriate design of phase II oncology clinical trials, we compared error rates in single-arm, historically controlled and randomized, concurrently controlled designs. PATIENTS AND METHODS We simulated error rates of both designs separately from individual patient data from a large colorectal cancer phase III trials and statistical models, which take into account random and systematic variation in historical control data. RESULTS In single-arm trials, false-positive error rates (type I error) were 2 to 4 times those projected when modest drift or patient selection effects (eg, 5% absolute shift in control response rate) were included in statistical models. The power of single-arm designs simulated using actual data was highly sensitive to the fraction of patients from treatment centers with high versus low patient volumes, the presence of patient selection effects or temporal drift in response rates, and random small-sample variation in historical controls. Increasing sample size did not correct the over optimism of single-arm studies. Randomized two-arm design conformed to planned error rates. CONCLUSION Variability in historical control success rates, outcome drifts in patient populations over time, and/or patient selection effects can result in inaccurate false-positive and false-negative error rates in single-arm designs, but leave performance of the randomized two-arm design largely unaffected at the cost of 2 to 4 times the sample size compared with single-arm designs. Given a large enough patient pool, the randomized phase II designs provide a more accurate decision for screening agents before phase III testing.