Clinical Trial Designs for Prospective Validation of Biomarkers

Sample Size Estimation Using a Partially Clustered Frailty Model for Biomarker-Strategy Designs With Multiple Treatments

Biomarker-guided therapy is a growing area of research in medicine. To optimize the use of biomarkers, several study designs including the biomarker-strategy design (BSD) have been proposed. Unlike traditional designs, the emphasis here is on comparing treatment strategies and not on treatment molecules as such. Patients are assigned to either a biomarker-based strategy (BBS) arm, in which biomarker-positive patients receive an experimental treatment that targets the identified biomarker, or a non-biomarker-based strategy (NBBS) arm, in which patients receive treatment regardless of their biomarker status. We proposed a simulation method based on a partially clustered frailty model (PCFM) as well as an extension of Freidlin formula to estimate the sample size required for BSD with multiple targeted treatments. The sample size was mainly influenced by the heterogeneity of treatment effect, the proportion of biomarker-negative patients, and the randomization ratio. The PCFM is well suited for the data structure and offers an alternative to traditional methodologies.

Application of digital pathology-based advanced analytics of tumour microenvironment organisation to predict prognosis and therapeutic response

In recent years, the application of advanced analytics, especially artificial intelligence (AI), to digital H&E images, and other histological image types, has begun to radically change how histological images are used in the clinic. Alongside the recognition that the tumour microenvironment (TME) has a profound impact on tumour phenotype, the technical development of highly multiplexed immunofluorescence platforms has enhanced the biological complexity that can be captured in the TME with high precision. AI has an increasingly powerful role in the recognition and quantitation of image features and the association of such features with clinically important outcomes, as occurs in distinct stages in conventional machine learning. Deep-learning algorithms are able to elucidate TME patterns inherent in the input data with minimum levels of human intelligence and, hence, have the potential to achieve clinically relevant predictions and discovery of important TME features. Furthermore, the diverse repertoire of deep-learning algorithms able to interrogate TME patterns extends beyond convolutional neural networks to include attention-based models, graph neural networks, and multimodal models. To date, AI models have largely been evaluated retrospectively, outside the well-established rigour of prospective clinical trials, in part because traditional clinical trial methodology may not always be suitable for the assessment of AI technology. However, to enable digital pathology-based advanced analytics to meaningfully impact clinical care, specific measures of 'added benefit' to the current standard of care and validation in a prospective setting are important. This will need to be accompanied by adequate measures of explainability and interpretability. Despite such challenges, the combination of expanding datasets, increased computational power, and the possibility of integration of pre-clinical experimental insights into model development means there is exciting potential for the future progress of these AI applications. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Assessment of the Clinical Utility of Pretreatment DPYD Testing for Patients Receiving Fluoropyrimidine Chemotherapy

PURPOSE

Patients who carry pathogenic variants in DPYD have higher systemic fluoropyrimidine (FP) concentrations and greater risk of severe and fatal FP toxicity. Pretreatment DPYD testing and DPYD-guided FP dosing to reduce toxicity and health care costs is recommended by European clinical oncology guidelines and has been adopted across Europe, but has not been recommended or adopted in the United States. The cochairs of the National Comprehensive Cancer Network Guidelines for colon cancer treatment explained their concerns with recommending pretreatment DPYD testing, particularly the risk that reduced FP doses in DPYD carriers may reduce treatment efficacy.

METHODS

This special article uses previously published frameworks for assessing the clinical utility of cancer biomarker tests, including for germline indicators of toxicity risk, to assess the clinical utility of pretreatment DPYD testing, with a particular focus on the risk of reducing treatment efficacy.

RESULTS

There is no direct evidence of efficacy reduction, and the available indirect evidence demonstrates that DPYD-guided FP dosing results in similar systemic FP exposure and toxicity compared with standard dosing in noncarriers, and is well calibrated to the maximum tolerated dose, strongly suggesting there is minimal risk of efficacy reduction.

CONCLUSION

This article should serve as a call to action for clinicians and clinical guidelines committees in the United States to re-evaluate the clinical utility of pretreatment DPYD testing. If clinical utility has not been demonstrated, further dialogue is needed to clarify what additional evidence is needed and which of the available study designs, also described within this article, would be appropriate. Clinical guideline recommendations for pretreatment DPYD testing would increase clinical adoption and ensure that all patients receive maximally safe and effective FP treatment.

Estimating mean local posterior predictive benefit for biomarker-guided treatment strategies

Precision medicine has emerged from the awareness that many human diseases are intrinsically heterogeneous with respect to their pathogenesis and composition among patients as well as dynamic over the course therapy. Its successful application relies on our understanding of distinct molecular profiles and their biomarkers which can be used as targets to devise treatment strategies that exploit current understanding of the biological mechanisms of the disease. Precision medicine present challenges to traditional paradigms of clinical translational, however, for which estimates of population-averaged effects from large randomized trials are used as the basis for demonstrating improvements comparative effectiveness. A general approach for estimating the relative effectiveness of biomarker-guided therapeutic strategies is presented herein. The statistical procedure attempts to define the local benefit of a given biomarker-guided therapeutic strategy in consideration of the treatment response surfaces, selection rule, and inter-cohort balance of prognostic determinants. Theoretical and simulation results are provided. Additionally, the methodology is demonstrated through a proteomic study of lower grade glioma.

Data-Driven Methods for Advancing Precision Oncology

PURPOSE OF REVIEW

This article discusses the advances, methods, challenges, and future directions of data-driven methods in advancing precision oncology for biomedical research, drug discovery, clinical research, and practice.

RECENT FINDINGS

Precision oncology provides individually tailored cancer treatment by considering an individual's genetic makeup, clinical, environmental, social, and lifestyle information. Challenges include voluminous, heterogeneous, and disparate data generated by different technologies with multiple modalities such as Omics, electronic health records, clinical registries and repositories, medical imaging, demographics, wearables, and sensors. Statistical and machine learning methods have been continuously adapting to the ever-increasing size and complexity of data. Precision Oncology supportive analytics have improved turnaround time in biomarker discovery and time-to-application of new and repurposed drugs. Precision oncology additionally seeks to identify target patient populations based on genomic alterations that are sensitive or resistant to conventional or experimental treatments. Predictive models have been developed for cancer progression and survivorship, drug sensitivity and resistance, and identification of the most suitable combination treatments for individual patient scenarios. In the future, clinical decision support systems need to be revamped to better incorporate knowledge from precision oncology, thus enabling clinical practitioners to provide precision cancer care.

SUMMARY

Open Omics datasets, machine learning algorithms, and predictive models have enabled the advancement of precision oncology. Clinical decision support systems with integrated electronic health record and Omics data are needed to provide data-driven recommendations to assist clinicians in disease prevention, early identification, and individualized treatment. Additionally, as cancer is a constantly evolving disorder, clinical decision systems will need to be continually updated based on more recent knowledge and datasets.

Precision oncology: A new era of cancer clinical trials

Traditionally, site of disease and anatomic staging have been used to define patient populations to be studied in individual cancer clinical trials. In the past decade, however, oncology has become increasingly understood on a cellular and molecular level, with many cancer subtypes being described as a function of biomarkers or tumor genetic mutations. With these changes in the science of oncology have come changes to the way we design and perform clinical trials. Increasingly common are trials tailored to detect enhanced efficacy in a patient subpopulation, e.g. patients with a known biomarker value or whose tumors harbor a specific genetic mutation. Here, we provide an overview of traditional and newer biomarker-based trial designs, and highlight lessons learned through implementation of several ongoing and recently completed trials.

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

Biomarker-guided treatment is a rapidly developing area of medicine, where treatment choice is personalised according to one or more of an individual’s biomarker measurements. A number of biomarker-guided trial designs have been proposed in the past decade, including both adaptive and non-adaptive trial designs which test the effectiveness of a biomarker-guided approach to treatment with the aim of improving patient health. A better understanding of them is needed as challenges occur both in terms of trial design and analysis. We have undertaken a comprehensive literature review based on an in-depth search strategy with a view to providing the research community with clarity in definition, methodology and terminology of the various biomarker-guided trial designs (both adaptive and non-adaptive designs) from a total of 211 included papers. In the present paper, we focus on non-adaptive biomarker-guided trial designs for which we have identified five distinct main types mentioned in 100 papers. We have graphically displayed each non-adaptive trial design and provided an in-depth overview of their key characteristics. Substantial variability has been observed in terms of how trial designs are described and particularly in the terminology used by different authors. Our comprehensive review provides guidance for those designing biomarker-guided trials.

Clinical trial designs incorporating predictive biomarkers

Development of oncologic therapies has traditionally been performed in a sequence of clinical trials intended to assess safety (phase I), preliminary efficacy (phase II), and improvement over the standard of care (phase III) in homogeneous (in terms of tumor type and disease stage) patient populations. As cancer has become increasingly understood on the molecular level, newer "targeted" drugs that inhibit specific cancer cell growth and survival mechanisms have increased the need for new clinical trial designs, wherein pertinent questions on the relationship between patient biomarkers and response to treatment can be answered. Herein, we review the clinical trial design literature from initial to more recently proposed designs for targeted agents or those treatments hypothesized to have enhanced effectiveness within patient subgroups (e.g., those with a certain biomarker value or who harbor a certain genetic tumor mutation). We also describe a number of real clinical trials where biomarker-based designs have been utilized, including a discussion of their respective advantages and challenges. As cancers become further categorized and/or reclassified according to individual patient and tumor features, we anticipate a continued need for novel trial designs to keep pace with the changing frontier of clinical cancer research.

Designing a study to evaluate the benefit of a biomarker for selecting patient treatment

Biomarkers that predict the efficacy of treatment can potentially improve clinical outcomes and decrease medical costs by allowing treatment to be provided only to those most likely to benefit. We consider the design of a randomized clinical trial in which one objective is to evaluate a treatment selection marker. The marker may be measured prospectively or retrospectively using samples collected at baseline. We describe and contrast criteria around which the trial can be designed. An existing approach focuses on determining if there is a statistical interaction between the marker and treatment. We propose three alternative approaches based on estimating clinically relevant measures of improvement in outcomes with use of the marker. Importantly, our approaches accommodate the common scenario in which the marker-based rule for recommending treatment is developed with data from the trial. Sample sizes are calculated for powering a trial to assess these criteria in the context of adjuvant chemotherapy for the treatment of estrogen-receptor-positive, node-positive breast cancer. In this example, we find that larger sample sizes are generally required for assessing clinical impact than for simply evaluating if there is a statistical interaction between marker and treatment. We also find that retrospectively selecting a case-control subset of subjects for marker evaluation can lead to large efficiency gains, especially if cases and controls are matched on treatment assignment.

Evidence of clinical utility: an unmet need in molecular diagnostics for patients with cancer

This article defines and describes best practices for the academic and business community to generate evidence of clinical utility for cancer molecular diagnostic assays. Beyond analytical and clinical validation, successful demonstration of clinical utility involves developing sufficient evidence to demonstrate that a diagnostic test results in an improvement in patient outcomes. This discussion is complementary to theoretical frameworks described in previously published guidance and literature reports by the U.S. Food and Drug Administration, Centers for Disease Control and Prevention, Institute of Medicine, and Center for Medical Technology Policy, among others. These reports are comprehensive and specifically clarify appropriate clinical use, adoption, and payer reimbursement for assay manufacturers, as well as Clinical Laboratory Improvement Amendments-certified laboratories, including those that develop assays (laboratory developed tests). Practical criteria and steps for establishing clinical utility are crucial to subsequent decisions for reimbursement without which high-performing molecular diagnostics will have limited availability to patients with cancer and fail to translate scientific advances into high-quality and cost-effective cancer care. See all articles in this CCR Focus section, "The Precision Medicine Conundrum: Approaches to Companion Diagnostic Co-development."

Pharmacogenetic biomarkers for predicting drug response

Drug response shows significant interpatient variability and evidence that genetics influences outcome of drug therapy has been known for more than five decades. However, the translation of this knowledge to clinical practice remains slow. Using examples from clinical practice six considerations about the implementation of pharmacogenetics (PGx) into routine care are discussed: the need for PGx biomarkers; the sources of genetic variability in drug response; the amount of variability explained by PGx; whether PGx test results are actionable; the level of evidence needed for implementation of PGx and the sources of information regarding interpretation of PGx data.

Randomized reverse marker strategy design for prospective biomarker validation

We describe a novel study design for validating marker-based treatment strategies meant to select among possible therapeutic options using a biologic marker. Studying existing designs in realistic scenarios, we demonstrate that this design is more than four times more efficient for testing the interaction between a marker and its intended treatment. Our analysis employs a simple parametric framework that uncovers systematic biases in currently proposed designs and suggests how they may be accommodated or enumerated. In the context of markers for choosing a treatment for recurrent ovarian cancer, our proposal requires sample sizes on the order of recently completed phases II and III studies making validation studies for this clinical decision scenario viable.

Re-overcoming barriers in translating biomarkers to clinical practice

IMPORTANCE OF THE FIELD

Recently, there has been growing evidence for the concept of personalized medicine as the implementation of genomic and molecular information in the delivery of healthcare. In parallel, the identification of biomarkers has become of enormous significance as a prerequisite for individualized intervention regimens.

AREAS COVERED IN THIS REVIEW

Biomarkers are developed to improve prevention, diagnosis or therapeutic outcome of a given disease. Although each application reveals distinct developmental strategies, evidence-based approval of new biomarkers is important for the success of new drugs, diagnostic tests or recommendations in preventive medicine. Current hurdles to bringing biomarkers into clinical practice are reviewed, thereby focusing on adequate approaches to overcome these limitations in the future.

WHAT THE READER WILL GAIN

The reader will get an introduction to strategies resolving actual barriers in clinical biomarker development.

TAKE HOME MESSAGE

The identification of evidence-based biomarkers is crucial for the success of individualized therapeutic approaches. Developmental strategies have to be adapted to clinical need, thereby focusing on biomarker validation in clinical settings as well as on the establishment of standardized biomarker test systems for routine application. Consortia have been established bringing together representatives of government, academia and industry to improve future biomarker development.

Assessing treatment-selection markers using a potential outcomes framework

Treatment-selection markers are biological molecules or patient characteristics associated with one's response to treatment. They can be used to predict treatment effects for individual subjects and subsequently help deliver treatment to those most likely to benefit from it. Statistical tools are needed to evaluate a marker's capacity to help with treatment selection. The commonly adopted criterion for a good treatment-selection marker has been the interaction between marker and treatment. While a strong interaction is important, it is, however, not sufficient for good marker performance. In this article, we develop novel measures for assessing a continuous treatment-selection marker, based on a potential outcomes framework. Under a set of assumptions, we derive the optimal decision rule based on the marker to classify individuals according to treatment benefit, and characterize the marker's performance using the corresponding classification accuracy as well as the overall distribution of the classifier. We develop a constrained maximum-likelihood method for estimation and testing in a randomized trial setting. Simulation studies are conducted to demonstrate the performance of our methods. Finally, we illustrate the methods using an HIV vaccine trial where we explore the value of the level of preexisting immunity to adenovirus serotype 5 for predicting a vaccine-induced increase in the risk of HIV acquisition.

A 2-stage phase II design with direct assignment option in stage II for initial marker validation

Biomarkers are critical to targeted therapies, as they may identify patients more likely to benefit from a treatment. Several prospective designs for biomarker-directed therapy have been previously proposed, differing primarily in the study population, randomization scheme, or both. Recognizing the need for randomization, yet acknowledging the possibility of promising but inconclusive results after a stage I cohort of randomized patients, we propose a 2-stage phase II design on marker-positive patients that allows for direct assignment in a stage II cohort. In stage I, marker-positive patients are equally randomized to receive experimental treatment or control. Stage II has the option to adopt "direct assignment" whereby all patients receive experimental treatment. Through simulation, we studied the power and type I error rate of our design compared with a balanced randomized two-stage design, and conducted sensitivity analyses to study the effect of timing of stage I analysis, population shift effects, and unbalanced randomization. Our proposed design has minimal loss in power (<1.8%) and increased type I error rate (<2.1%) compared with a balanced randomized design. The maximum increase in type I error rate in the presence of a population shift was between 3.1% and 5%, and the loss in power across possible timings of stage I analysis was less than 1.2%. Our proposed design has desirable statistical properties with potential appeal in practice. The direct assignment option, if adopted, provides for an "extended confirmation phase" as an alternative to stopping the trial early for evidence of efficacy in stage I.

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.

Considerations on implementing diagnostic markers into clinical decision making in bladder cancer

Bladder cancer is a common disease that is often detected late and has a high rate of recurrence and progression. Cystoscopy is the main tool in detection and surveillance of bladder cancer but is invasive and can miss some cancers. Cytology is frequently utilized but suffers from a poor sensitivity. There are several commercially available urine-based tumor markers currently available but their use is not recommended by guideline panels. Markers such as the Urovysion FISH assay and the NMP22 BladderChek test are approved for surveillance and detection in patients with hematuria. The added benefit of these markers and other commercially available markers (e.g. Ucyt+, BTA stat) has not been well investigated though it appears these markers are insufficiently sensitive to replace cystoscopy. Additional studies are needed to determine the clinical scenarios where bladder markers are best utilized (screening, surveillance, early detection, evaluating cytologic atypia) and what impact they should have on clinical decision making. Furthermore, a variety of issues and barriers can affect the movement of clinical tests from research to clinical practice. This article addresses some of the challenges facing research and medical communities in the delivery and integration of markers for bladder cancer diagnosis. Moreover, we attempt to outline criteria for the clinical utility of new bladder cancer diagnostic markers.

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.

A Bayesian adaptive design with biomarkers for targeted therapies

BACKGROUND

Targeted therapies are becoming increasingly important for the treatment of various diseases. Biomarkers are a critical component of a targeted therapy as they can be used to identify patients who are more likely to benefit from a treatment. Targeted therapies, however, have created major challenges in the design, conduct, and analysis of clinical trials. In traditional clinical trials, treatment effects for various biomarkers are typically evaluated in an exploratory fashion and only limited information about the predictive values of biomarkers obtained.

PURPOSE

New study designs are required, which effectively evaluate both the diagnostic and the therapeutic implication of biomarkers.

METHODS

The Bayesian approach provides a useful framework for optimizing the clinical trial design by directly integrating information about biomarkers and clinical outcomes as they become available. We propose a Bayesian covariate-adjusted response-adaptive randomization design, which utilizes individual biomarker profiles and patient's clinical outcomes as they become available during the course of the trial, to assign the most efficacious treatment to individual patients. Predictive biomarker subgroups are determined adaptively using a partial least squares regression approach.

RESULTS

A series of simulation studies were conducted to examine the operating characteristics of the proposed study design. The simulation studies show that the proposed design efficiently identifies patients who benefit most from a targeted therapy and that there are substantial savings in the sample size requirements when compared to alternative designs.

LIMITATIONS

The design does not control for the type I error in the traditional sense and a positive result should be confirmed by conducting an independent phase III study focusing on the selected biomarker profile groups.

CONCLUSIONS

We conclude that the proposed design may serve a useful role in the early efficacy phase of targeted therapy development.

Moving from correlative science to predictive oncology

Many diagnostic entities traditionally viewed as individual diseases are heterogeneous in molecular pathogenesis and treatment responsiveness. This results in treatment of many patients with ineffective drugs, the conduct of large clinical trials to identify small average treatment benefits for heterogeneous groups of patients. In oncology, genomic technologies provide powerful tools for identification of patients who require systemic treatment and for selecting the most appropriate drug. Development of drugs with companion diagnostics, however, increases the complexity of clinical development and requires new approaches to the design and analysis of clinical trials. Adapting to the fundamental importance of tumor genomics will require paradigm changes for clinical and statistical investigators in academia, industry and government. In this paper we attempt to address some of these issues and to comment specifically on the design of clinical studies for evaluating the clinical utility and robustness of prognostic and predictive biomarkers.

The efficiency of clinical trial designs for predictive biomarker validation

BACKGROUND

The rapid advance of molecular genetic technology and of molecular diagnostics companies have set the stage for a new era in personalized treatments. Biomarkers such as gene expressions may be integrated into the anatomically based tumor-node-metastasis staging system to provide information for risk stratification and treatment selection. With the assumption that preliminary results show evidence that a biomarker has predictive value, the marker-based designs are geared to assess the purported predictive value in a clinical trial.

PURPOSE

In this article, we compared the efficiency of the traditional design, which does not involve a biomarker, to several alternative designs in terms of the sample size required in each trial.

METHODS

We first derived the variance formulas for the two-sample t-tests under the various designs when the biomarker assay is imperfect, and then conducted numerical and simulation studies to evaluate the performance of the various designs.

RESULTS

Based on numerical and simulation studies, we conclude that the marker-based strategy designs are less efficient than the traditional design in general. Since the biomarker assay is imperfect in a realistic setting, the estimated sample size for each alternative design is influenced by the sensitivity and specificity of the assay and the prevalence of the biomarker in the population of interest as well as the parameters involved in a standard sample size calculation.

LIMITATIONS

Due to limitations of a simulation study, it is not clear whether our results can be generalized to other parameter settings that are different from the ones used in the simulation study.

CONCLUSIONS

The marker-based strategy designs are less efficient than the traditional design in general. If there is no treatment effect among marker-negative patients, it is still feasible to use the marker-based strategy design I if the assay sensitivity is high. If the treatment effect among marker-negative patients is half of the effect among marker-positive patients, the marker prevalence must be relatively high and the sensitivity of the assay must be very high for the marker-based strategy design I to approximate the efficiency of the traditional design. The efficiency of the marker-based strategy design II relative to the traditional design is low in all scenarios considered under the current study.

Randomized clinical trials with biomarkers: design issues

Clinical biomarker tests that aid in making treatment decisions will play an important role in achieving personalized medicine for cancer patients. Definitive evaluation of the clinical utility of these biomarkers requires conducting large randomized clinical trials (RCTs). Efficient RCT design is therefore crucial for timely introduction of these medical advances into clinical practice, and a variety of designs have been proposed for this purpose. To guide design and interpretation of RCTs evaluating biomarkers, we present an in-depth comparison of advantages and disadvantages of the commonly used designs. Key aspects of the discussion include efficiency comparisons and special interim monitoring issues that arise because of the complexity of these RCTs. Important ongoing and completed trials are used as examples. We conclude that, in most settings, randomized biomarker-stratified designs (ie, designs that use the biomarker to guide analysis but not treatment assignment) should be used to obtain a rigorous assessment of biomarker clinical utility.

Clinical trial designs for evaluating the medical utility of prognostic and predictive biomarkers in oncology

Physicians need improved tools for selecting treatments for individual patients. Many diagnostic entities hat were traditionally viewed as individual diseases are heterogeneous in their molecular pathogenesis and treatment responsiveness. This results in the treatment of many patients with ineffective drugs, incursion of substantial medical costs for the treatment of patients who do not benefit and the conducting of large clinical trials to identify small, average treatment benefits for heterogeneous groups of patients. In oncology, new genomic technologies provide powerful tools for the selection of patients who require systemic treatment and are most (or least) likely to benefit from a molecularly targeted therapeutic. In the large amount of literature on biomarkers, there is considerable uncertainty and confusion regarding the specifics involved in the development and evaluation of prognostic and predictive biomarker diagnostics. There is a lack of appreciation that the development of drugs with companion diagnostics increases the complexity of clinical development. Adapting to the fundamental importance of tumor heterogeneity and achieving the benefits of personalized oncology for patients and healthcare costs will require paradigm changes for clinical and statistical investigators in academia, industry and regulatory agencies. In this review, I attempt to address some of these issues and provide guidance on the design of clinical trials for evaluating the clinical utility and robustness of prognostic and predictive biomarkers.

Clinical trial designs for predictive biomarker validation: theoretical considerations and practical challenges

PURPOSE

Biomarkers can add substantial value to current medical practice by providing an integrated approach to prediction using the genetic makeup of the tumor and the genotype of the patient to guide patient-specific treatment selection. We discuss and evaluate various clinical trial designs for the validation of biomarker-guided therapy.

METHODS

Designs for predictive marker validation are broadly classified as retrospective (ie, using data from previously well-conducted randomized controlled trials [RCTs]) versus prospective (enrichment, unselected, hybrid, or adaptive analysis). We discuss the salient features of each design in the context of real trials.

RESULTS

Well-designed retrospective analysis from well-conducted prospective RCTs can bring forward effective treatments to marker-defined subgroups of patients in a timely manner (eg, KRAS and colorectal cancer). Enrichment designs are appropriate when preliminary evidence suggest that patients with or without that marker profile do not benefit from the treatments in question; however, this may sometimes leave questions unanswered (eg, trastuzumab and breast cancer). An unselected design is optimal where preliminary evidence regarding treatment benefit and assay reproducibility is uncertain (eg, epidermal growth factor receptor and lung cancer). Hybrid designs are appropriate when preliminary evidence demonstrate the efficacy of certain treatments for a marker-defined subgroup, making it unethical to randomly assign patients with that marker status to other treatments (eg, multigene assay and breast cancer). Adaptive analysis designs allow for prespecified marker-defined subgroup analyses of data from an RCT.

CONCLUSION

The implementation of these design strategies will lead to a more rapid clinical validation of biomarker-guided therapy.

Clinical trial designs for predictive biomarker validation: one size does not fit all

Traditionally, anatomic staging systems have been used to provide predictions of individual patient outcome and, to a lesser extent, guide the choice of treatment in cancer patients. With targeted therapies, biomarkers have the potential for providing added value through an integrated approach to prediction using the genetic makeup of the tumor and the genotype of the patient for treatment selection and patient management. Specifically, biomarkers can aid in patient stratification (risk assessment), treatment response identification (surrogate markers), or differential diagnosis (identifying individuals who are likely to respond to specific drugs). In this study, we explore two major topics in relation to the design of clinical trials for predictive marker validation. First, we discuss the appropriateness of an enrichment (i.e., targeted) vs. an unselected design through case studies focusing on the clinical question(s) at hand, the strength of the preliminary evidence, and assay reproducibility. Second, we evaluate the efficiency (total number of events and sample size) of two unselected predictive marker designs for validation of a marker under a wide range of clinically relevant scenarios, exploring the impact of the prevalence of the marker and the hazard ratios for the treatment comparisons. The review and evaluation of these designs represents an essential step toward the goal of personalized medicine because we explicitly seek to explore and evaluate the methodology for the clinical validation of biomarker guided therapy.

Genomic advances and their impact on clinical trial design

Medical treatment for patients has historically been based on two primary elements: the expected outcome for the patient, and the ability of treatment to improve the expected outcome. The advance in genomic technologies has the potential to change this paradigm and add substantial value to current medical practice by providing an integrated approach to guide patient-specific treatment selection using the genetic make-up of the disease and the genotype of the patient. Specifically, genomic signatures can aid in patient stratification (risk assessment), treatment response identification (surrogate markers), and/or in differential diagnosis (identifying who is likely to respond to which drug(s)). Several critical issues, including scientific rationale, clinical trial design, marker assessment methods, cost and feasibility have to be carefully considered in the validation of biomarkers through clinical research before they can be routinely integrated into clinical practice. Here, we highlight the impact of genomic advances on various aspects of clinical trial design.

A critical appraisal of logistic regression-based nomograms, artificial neural networks, classification and regression-tree models, look-up tables and risk-group stratification models for prostate cancer

OBJECTIVE

To evaluate several methods of predicting prostate cancer-related outcomes, i.e. nomograms, look-up tables, artificial neural networks (ANN), classification and regression tree (CART) analyses and risk-group stratification (RGS) models, all of which represent valid alternatives.

METHODS

We present four direct comparisons, where a nomogram was compared to either an ANN, a look-up table, a CART model or a RGS model. In all comparisons we assessed the predictive accuracy and performance characteristics of both models.

RESULTS

Nomograms have several advantages over ANN, look-up tables, CART and RGS models, the most fundamental being a higher predictive accuracy and better performance characteristics.

CONCLUSION

These results suggest that nomograms are more accurate and have better performance characteristics than their alternatives. However, ANN, look-up tables, CART analyses and RGS models all rely on methodologically sound and valid alternatives, which should not be abandoned.

New target-based agents involve new clinical trial designs

Clinical cancer investigation is performed through clinical trials. Development and measurement of clinical efficacy of new target-based agents differs from classic cytotoxic drugs. Whereas the aim of chemotherapy drugs is to destroy tumoral cells, new agents try to inhibit cell profileration without a clear tumor shrinkage. The main endpoint for phase I trials is to determine the optimal biological response with the least toxicity; oncopharmacogenomic studies must be performed in tumoral biopsies to assess the target inhibition. Time to progression and biological activity are the endpoints for phase II studies. Finally, phase III trials will determine overall survival.