Integrating predictive biomarkers and classifiers into oncology clinical development programmes

Maximizing return on socioeconomic investment in phase II proof-of-concept trials

Phase II proof-of-concept (POC) trials play a key role in oncology drug development, determining which therapeutic hypotheses will undergo definitive phase III testing according to predefined Go-No Go (GNG) criteria. The number of possible POC hypotheses likely far exceeds available public or private resources. We propose a design strategy for maximizing return on socioeconomic investment in phase II trials that obtains the greatest knowledge with the minimum patient exposure. We compare efficiency using the benefit-cost ratio, defined to be the risk-adjusted number of truly active drugs correctly identified for phase III development divided by the risk-adjusted total sample size in phase II and III development, for different POC trial sizes, powering schemes, and associated GNG criteria. It is most cost-effective to conduct small POC trials and set the corresponding GNG bars high, so that more POC trials can be conducted under socioeconomic constraints. If δ is the minimum treatment effect size of clinical interest in phase II, the study design with the highest benefit-cost ratio has approximately 5% type I error rate and approximately 20% type II error rate (80% power) for detecting an effect size of approximately 1.5δ. A Go decision to phase III is made when the observed effect size is close to δ. With the phenomenal expansion of our knowledge in molecular biology leading to an unprecedented number of new oncology drug targets, conducting more small POC trials and setting high GNG bars maximize the return on socioeconomic investment in phase II POC trials.

Molecularly targeted cancer therapy: some lessons from the past decade

The tremendous advances achieved in the understanding of cancer biology have delivered unprecedented progress in molecularly targeted cancer therapy in the past decade. The fast growing category of targeted anticancer agents available for clinical use is accompanied by a conceptual revolution in anticancer drug development. Nevertheless, molecularly targeted cancer therapy remains challenged by a high failure rate and an extremely small proportion of patients that can benefit. It is pivotal to take lessons from the past and seek new solutions. This review discusses conceptual progress and remaining challenges in molecularly targeted cancer therapy, and proposes feasible alternatives to increase chances of clinical success in the future.

Considerations for the successful co-development of targeted cancer therapies and companion diagnostics

As diagnostic tests become increasingly important for optimizing the use of drugs to treat cancers, the co-development of a targeted therapy and its companion diagnostic test is becoming more prevalent and necessary. In July 2011, the US Food and Drug Administration released a draft guidance that gave the agency's formal definition of companion diagnostics and introduced a drug-diagnostic co-development process for gaining regulatory approval. Here, we identify areas of drug-diagnostic co-development that were either not covered by the guidance or that would benefit from increased granularity, including how to determine when clinical studies should be limited to biomarker-positive patients, defining the diagnostically selected patient population in which to use a companion diagnostic, and defining and clinically validating a biomarker signature for assays that use more than one biomarker. We propose potential approaches that sponsors could use to deal with these challenges and provide strategies to help guide the future co-development of drugs and diagnostics.

New evidence-based adaptive clinical trial methods for optimally integrating predictive biomarkers into oncology clinical development programs

Predictive biomarkers are important to the future of oncology; they can be used to identify patient populations who will benefit from therapy, increase the value of cancer medicines, and decrease the size and cost of clinical trials while increasing their chance of success. But predictive biomarkers do not always work. When unsuccessful, they add cost, complexity, and time to drug development. This perspective describes phases 2 and 3 development methods that efficiently and adaptively check the ability of a biomarker to predict clinical outcomes. In the end, the biomarker is emphasized to the extent that it can actually predict.

Impact of genetic dynamics and single-cell heterogeneity on development of nonstandard personalized medicine strategies for cancer

Cancers are heterogeneous and genetically unstable. Current practice of personalized medicine tailors therapy to heterogeneity between cancers of the same organ type. However, it does not yet systematically address heterogeneity at the single-cell level within a single individual's cancer or the dynamic nature of cancer due to genetic and epigenetic change as well as transient functional changes. We have developed a mathematical model of personalized cancer therapy incorporating genetic evolutionary dynamics and single-cell heterogeneity, and have examined simulated clinical outcomes. Analyses of an illustrative case and a virtual clinical trial of over 3 million evaluable "patients" demonstrate that augmented (and sometimes counterintuitive) nonstandard personalized medicine strategies may lead to superior patient outcomes compared with the current personalized medicine approach. Current personalized medicine matches therapy to a tumor molecular profile at diagnosis and at tumor relapse or progression, generally focusing on the average, static, and current properties of the sample. Nonstandard strategies also consider minor subclones, dynamics, and predicted future tumor states. Our methods allow systematic study and evaluation of nonstandard personalized medicine strategies. These findings may, in turn, suggest global adjustments and enhancements to translational oncology research paradigms.

Combining cetuximab with killer lymphocytes synergistically inhibits human cholangiocarcinoma cells in vitro

AIM

We explored the possibility of combining adoptive immunotherapy with cytokine-activated killer (CAK) cells and the epidermal growth factor receptor monoclonal antibody, cetuximab, as a treatment for cholangiocarcinoma.

MATERIALS AND METHODS

CAK cells were cultured with a high-dose of interleukin-2 and anti-CD3 monoclonal antibodies. This cell population contained both activated CD16+/CD56+ (NK) cells and CD3+/NKG2D(high+) T-cells. The effect of CAK cells and cetuximab, alone and in combination, on the viability of human cholangiocarcinoma cells was evaluated.

RESULTS

Culture of CAK cells alone, but not cetuximab alone, exhibited modest cytotoxicity toward cholangiocarcinoma cells. However, combining CAK cells with cetuximab significantly enhanced cytotoxicity. This enhancement was inhibited by the addition of excess human immunoglobulins, suggesting that antibody-dependent cytotoxicity, mediated by activated NK cells in the CAK cell culture was involved in this mechanism.

CONCLUSION

Cetuximab may be used to enhance CAK cell therapeutic activity in patients with cholangiocarcinoma, by potentiating antibody-dependent cellular cytotoxicity.

Benefit–risk management in the age of personalized healthcare

The ongoing developments in personalized healthcare are opening new possibilities for the optimization of the benefit–risk profiles of drugs based on the stratification of patients. Beyond this straightforward application of pharmacogenomics to the optimization of benefit–risk profiles, other approaches, such as metabolomics and systems pharmacology, may contribute to an improved use of medicines in a personalized healthcare setting. These will, however, require fundamental adaptations or new developments in various areas and by all stakeholders, including the pharmaceutical and diagnostics industries, regulators, physicians and patients. Ultimately, personalized healthcare should enable patients to take personal decisions on treatment options based on their preferred benefit–risk profiles of therapeutics.

Personalized medicine and pharmacogenetic biomarkers: progress in molecular oncology testing

In the field of oncology, clinical molecular diagnostics and biomarker discoveries are constantly advancing as the intricate molecular mechanisms that transform a normal cell into an aberrant state in concert with the dysregulation of alternative complementary pathways are increasingly understood. Progress in biomarker technology, coupled with the companion clinical diagnostic laboratory tests, continue to advance this field, where individualized and customized treatment appropriate for each individual patient define the standard of care. Here, we discuss the current commonly used predictive pharmacogenetic biomarkers in clinical oncology molecular testing: BRAF V600E for vemurafenib in melanoma; EML4-ALK for crizotinib and EGFR for erlotinib and gefitinib in non-small-cell lung cancer; KRAS against the use of cetuximab and panitumumab in colorectal cancer; ERBB2 (HER2/neu) for trastuzumab in breast cancer; BCR-ABL for tyrosine kinase inhibitors in chronic myeloid leukemia; and PML/RARα for all-trans-retinoic acid and arsenic trioxide treatment for acute promyelocytic leukemia.

Making personalized cancer medicine a reality: challenges and opportunities in the development of biomarkers and companion diagnostics

The origins of this article stem from discussions at the American Association for Cancer Research Clinical and Translational Cancer Research Think Tank meeting held in San Francisco in early 2010. This article synthesizes the opinions and issues considered at that meeting, and discusses many of the important events that have since occurred in the field of personalized cancer medicine. Although investigators continue to make progress in better linking individual patient biology with risk determination, diagnosis, prognosis, and treatment selection, the pace of this progress continues to be limited by many of the issues identified in the meeting.

Towards the biomarker-guided rational use of antiangiogenic agents in the treatment of metastatic colorectal cancer

SUMMARY Clinical oncology experience with recently marketed antiangiogenic agents, which inhibit proteins important for tumor angiogenesis, has exposed significant limitations to their efficacy. Bevacizumab, a humanized neutralizing anti-VEGF-A monoclonal antibody, used in combination with cytotoxic chemotherapy for the treatment of metastatic colorectal cancer, represents the best-studied clinical example of targeted antiangiogenic therapy. In this context, bevacizumab provides modestly improved progression-free and overall survival in unselected patient populations via poorly understood mechanisms. Here we review concepts central to the identification and development of biomarkers in order to refine clinical use of bevacizumab in treating colorectal cancer and outline a phenotype-driven strategy for the discovery of high-value candidate biomarkers based on large-scale screening by molecular perturbation.

Predictive markers in early research and companion diagnostic developments in oncology

Predictive biomarkers are discovered and used in oncology research to formulate hypotheses aimed at the identification of patients benefiting from specific therapeutic intervention(s). They pave the way to the development of companion diagnostic tests which are tools readily implemented in the clinic and serve to qualify a patient for treatment with a particular targeted drug or the continued use of a particular drug, thus maximizing the benefit to risk ratio of the medical intervention to the patient. Predictive biomarkers are defined by biological characteristics of the patient's or tumor status that can be measured objectively and correlated with clinical outcome: these can be molecular, cellular or biochemical features. Predictive markers need extensive analytical validation - specific for the tool utilized for their assessment - as well as rigorous clinical qualification in the context of the drug treatment for which they define clinical utility. The process of companion diagnostic development is a highly interdisciplinary and complex one, driven by key crucial milestones and accompanying the same and typical process of a whole drug discovery and development continuum, from marker discovery and validation, assay development, clinical qualification until test approval and commercialization.

Cancer biomarkers: selecting the right drug for the right patient

This Perspective highlights biomarkers that are expressed as a consequence of cancer development and progression. We focus on those biomarkers that are most relevant for identifying patients who are likely to respond to a given therapy, as well as those biomarkers that are most effective for measuring patient response to therapy. These two measures are necessary for selecting the right drug for the right patient, regardless of whether the setting is in drug development or in the post-approval use of the drug for patients with cancer. We also discuss the innovative designs of clinical trials and methodologies that are used to validate and qualify biomarkers for use in specific contexts. Furthermore, we look ahead to the promises and challenges in the field of cancer biomarkers.

Third-generation sequencing techniques and applications to drug discovery

INTRODUCTION

There is an immediate need for functional and molecular studies to decipher differences between disease and 'normal' settings to identify large quantities of validated targets with the highest therapeutic utilities. Furthermore, drug mechanism of action and biomarkers to predict drug efficacy and safety need to be identified for effective design of clinical trials, decreasing attrition rates, regulatory agency approval process and drug repositioning. By expanding the power of genetics and pharmacogenetics studies, next-generation nucleic acid sequencing technologies have started to play an important role in all stages of drug discovery.

AREAS COVERED

This article reviews the first- and second-generation sequencing technologies (SGSTs) and challenges they pose to biomedicine. The article then focuses on the emerging third-generation sequencing technologies (TGSTs), their technological foundations and potential contributions to drug discovery.

EXPERT OPINION

Despite the scientific and commercial success of SGSTs, the goal of rapid, comprehensive and unbiased sequencing of nucleic acids has not been achieved. TGSTs promise to increase sequencing throughput and read lengths, decrease costs, run times and error rates, eliminate biases inherent in SGSTs and offer capabilities beyond nucleic acid sequencing. Such changes will have positive impact on all sequencing applications to drug discovery.