Clinical trials for precision oncology using next-generation sequencing

Biomarker-Driven Oncology Clinical Trials: Key Design Elements, Types, Features, and Practical Considerations

In this precision oncology era, where molecular profiling at the individual patient level becomes increasingly accessible and affordable, more and more clinical trials are now driven by biomarkers, with an overarching objective to optimize and personalize disease management. As compared with the conventional clinical development paradigms, where the key is to evaluate treatment effects in histology-defined populations, the choices of biomarker-driven clinical trial designs and analysis plans require additional considerations that are heavily dependent on the nature of biomarkers (eg, prognostic or predictive, integral or integrated) and the credential of biomarkers' performance and clinical utility. Most recently, another major paradigm change in biomarker-driven trials is to conduct multi-agent and/or multihistology master protocols or platform trials. These trials, although they may enjoy substantial infrastructure and logistical advantages, also face unique operational and conduct challenges. Here we provide a concise overview of design options for both the setting of single-biomarker/single-disease and the setting of multiple-biomarker/multiple-disease types. We focus on explaining the trial design and practical considerations and rationale of when to use which designs, as well as how to incorporate various adaptive design components to provide additional flexibility, enhance logistical efficiency, and optimize resource allocation. Lessons learned from real trials are also presented for illustration.

Next-generation sequencing (NGS) of cell-free circulating tumor DNA and tumor tissue in patients with advanced urothelial cancer: a pilot assessment of concordance

Background

Advances in cancer genome sequencing have led to the development of various next-generation sequencing (NGS) platforms. There is paucity of data regarding concordance of different NGS tests carried out in the same patient.

Methods

Here, we report a pilot analysis of 22 patients with metastatic urinary tract cancer and available NGS data from paired tumor tissue [FoundationOne (F1)] and cell-free circulating tumor DNA (ctDNA) [Guardant360 (G360)].

Results

The median time between the diagnosis of stage IV disease and the first genomic test was 23.5 days (0-767), after a median number of 0 (0-3) prior systemic lines of treatment of advanced disease. Most frequent genomic alterations (GA) were found in the genes TP53 (50.0%), TERT promoter (36.3%); ARID1 (29.5%); FGFR2/3 (20.5%), PIK3CA (20.5%) and ERBB2 (18.2%). While we identified GA in both tests, the overall concordance between the two platforms was only 16.4% (0%-50%), and 17.1% (0%-50%) for those patients (n = 6) with both tests conducted around the same time (median difference = 36 days). On the contrary, in the subgroup of patients (n = 5) with repeated NGS in ctDNA after a median of 1 systemic therapy between the two tests, average concordance was 55.5% (12.1%-100.0%). Tumor tissue mutational burden was significantly associated with number of GA in G360 report (P < 0.001), number of known GA (P = 0.009) and number of variants of unknown significance (VUS) in F1 report (P < 0.001), and with total number of GA (non-VUS and VUS) in F1 report (P < 0.001).

Conclusions

This study suggests a significant discordance between clinically available NGS panels in advanced urothelial cancer, even when collected around the same time. There is a need for better understanding of these two possibly complementary NGS platforms for better integration into clinical practice.

New designs for basket clinical trials in oncology

The established molecular heterogeneity of human cancers has had profound effects on the design of cancer therapeutics. Most cancer drugs are today targeted to molecular alterations present in cancer cells. Tumors of the same primary site, however, often differ with regard to the alterations that they harbor. Consequently, this heterogeneity has required the development of new paradigms for clinical development. In this paper, we review some clinical trial designs finding active use in co-development of therapeutics and predictive biomarkers to inform their use in oncology.

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.

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.

The Bayesian basket design for genomic variant-driven phase II trials

Basket clinical trials are a new category of early clinical trials in which a treatment is evaluated in a population of patients with tumors of various histologic types and primary sites selected for containing specific genomic abnormalities. The objective of such studies is generally to discover histologic types in which the treatment is active. Basket trials are early discovery trials whose results should be confirmed in expanded histology specific cohorts. In this report, we develop a design for planning, monitoring, and analyzing basket trials. A website for using the new design is available at https://brbnci.shinyapps.io/BasketTrials/ and the software is available at GitHub in the "Basket Trials" repository of account brbnci.

Opportunities for translational epidemiology: the important role of observational studies to advance precision oncology

Within current oncology practice, several genomic applications are being used to inform treatment decisions with molecularly targeted therapies in breast, lung, colorectal, melanoma, and other cancers. This commentary introduces a conceptual framework connecting the full spectrum of biomedical research disciplines, including fundamental laboratory research, clinical trials, and observational studies in the translation of genomic applications into clinical practice. The conceptual framework illustrates the contribution that well-designed observational epidemiologic studies provide to the successful translation of these applications, and characterizes the role observational epidemiology plays in driving the dynamic and iterative bench-to-bedside, and bedside-to-bench translation continuum. We also discuss how the principles of this conceptual model, emphasizing integration of multidisciplinary research, can be applied to the evolving paradigm in "precision oncology" focusing on multiplex tumor sequencing, and we identify opportunities for observational studies to contribute to the successful and efficient translation of this paradigm.Cancer Epidemiol Biomarkers Prev; 24(3); 484-9. ©2015 AACR.

GeneMed: An Informatics Hub for the Coordination of Next-Generation Sequencing Studies that Support Precision Oncology Clinical Trials

We have developed an informatics system, GeneMed, for the National Cancer Institute (NCI) molecular profiling-based assignment of cancer therapy (MPACT) clinical trial (NCT01827384) being conducted in the National Institutes of Health (NIH) Clinical Center. This trial is one of the first to use a randomized design to examine whether assigning treatment based on genomic tumor screening can improve the rate and duration of response in patients with advanced solid tumors. An analytically validated next-generation sequencing (NGS) assay is applied to DNA from patients’ tumors to identify mutations in a panel of genes that are thought likely to affect the utility of targeted therapies available for use in the clinical trial. The patients are randomized to a treatment selected to target a somatic mutation in the tumor or with a control treatment. The GeneMed system streamlines the workflow of the clinical trial and serves as a communications hub among the sequencing lab, the treatment selection team, and clinical personnel. It automates the annotation of the genomic variants identified by sequencing, predicts the functional impact of mutations, identifies the actionable mutations, and facilitates quality control by the molecular characterization lab in the review of variants. The GeneMed system collects baseline information about the patients from the clinic team to determine eligibility for the panel of drugs available. The system performs randomized treatment assignments under the oversight of a supervising treatment selection team and generates a patient report containing detected genomic alterations. NCI is planning to expand the MPACT trial to multiple cancer centers soon. In summary, the GeneMed system has been proven to be an efficient and successful informatics hub for coordinating the reliable application of NGS to precision medicine studies.

Bioinformatics for precision medicine in oncology: principles and application to the SHIVA clinical trial

Precision medicine (PM) requires the delivery of individually adapted medical care based on the genetic characteristics of each patient and his/her tumor. The last decade witnessed the development of high-throughput technologies such as microarrays and next-generation sequencing which paved the way to PM in the field of oncology. While the cost of these technologies decreases, we are facing an exponential increase in the amount of data produced. Our ability to use this information in daily practice relies strongly on the availability of an efficient bioinformatics system that assists in the translation of knowledge from the bench towards molecular targeting and diagnosis. Clinical trials and routine diagnoses constitute different approaches, both requiring a strong bioinformatics environment capable of (i) warranting the integration and the traceability of data, (ii) ensuring the correct processing and analyses of genomic data, and (iii) applying well-defined and reproducible procedures for workflow management and decision-making. To address the issues, a seamless information system was developed at Institut Curie which facilitates the data integration and tracks in real-time the processing of individual samples. Moreover, computational pipelines were developed to identify reliably genomic alterations and mutations from the molecular profiles of each patient. After a rigorous quality control, a meaningful report is delivered to the clinicians and biologists for the therapeutic decision. The complete bioinformatics environment and the key points of its implementation are presented in the context of the SHIVA clinical trial, a multicentric randomized phase II trial comparing targeted therapy based on tumor molecular profiling versus conventional therapy in patients with refractory cancer. The numerous challenges faced in practice during the setting up and the conduct of this trial are discussed as an illustration of PM application.

Multi-marker Solid Tumor Panels Using Next-generation Sequencing to Direct Molecularly Targeted Therapies

In contemporary oncology practices there is an increasing emphasis on concurrent evaluation of multiple genomic alterations within the biological pathways driving tumorigenesis. At the foundation of this paradigm shift are several commercially available tumor panels using next-generation sequencing to develop a more complete molecular blueprint of the tumor. Ideally, these would be used to identify clinically actionable variants that can be matched with available molecularly targeted therapy, regardless of the tumor site or histology. Currently, there is little information available on the post-analytic processes unique to next-generation sequencing platforms used by the companies offering these tests. Additionally, evidence of clinical validity showing an association between the genetic markers curated in these tests with treatment response to approved molecularly targeted therapies is lacking across all solid-tumor types. To date, there is no published data of improved outcomes when using the commercially available tests to guide treatment decisions. The uniqueness of these tests from other genomic applications used to guide clinical treatment decisions lie in the sequencing platforms used to generate large amounts of genomic data, which have their own related issues regarding analytic and clinical validity, necessary precursors to the evaluation of clinical utility. The generation and interpretation of these data will require new evidentiary standards for establishing not only clinical utility, but also analytical and clinical validity for this emerging paradigm in oncology practice.

Pharmacogenomics in oncology care

Cancer pharmacogenomics have contributed a number of important discoveries to current cancer treatment, changing the paradigm of treatment decisions. Both somatic and germline mutations are utilized to better understand the underlying biology of cancer growth and treatment response. The level of evidence required to fully translate pharmacogenomic discoveries into the clinic has relied heavily on randomized control trials. In this review, the use of observational studies, as well as, the use of adaptive trials and next generation sequencing to develop the required level of evidence for clinical implementation are discussed.

Drug-diagnostics co-development in oncology

Developments in genomics are providing a biological basis for the heterogeneity of clinical course and response to treatment that have long been apparent to clinicians. The ability to molecularly characterize human diseases presents new opportunities to develop more effective treatments and new challenges for the design and analysis of clinical trials. In oncology, treatment of broad populations with regimens that benefit a minority of patients is less economically sustainable with expensive molecularly targeted therapeutics. The established molecular heterogeneity of human diseases requires the development of new paradigms for the design and analysis of randomized clinical trials as a reliable basis for predictive medicine. We review prospective designs for the development of new therapeutics and predictive biomarkers to inform their use. We cover designs for a wide range of settings. At one extreme is the development of a new drug with a single candidate biomarker and strong biological evidence that marker negative patients are unlikely to benefit from the new drug. At the other extreme are phase III clinical trials involving both genome-wide discovery of a predictive classifier and internal validation of that classifier. We have outlined a prediction based approach to the analysis of randomized clinical trials that both preserves the type I error and provides a reliable internally validated basis for predicting which patients are most likely or unlikely to benefit from a new regimen.

Multiomics medicine in oncology: assessing effectiveness, cost–effectiveness and future research priorities for the molecularly unique individual

The development of genomic technologies has ushered in the era of pharmacogenomics. However, discoveries and clinical use of targeted therapies are still in their infancy. A focus on monogenic pharmacogenetic traits may contribute to this lack of progress. Variation in drug response is likely a complex paradigm involving not only genomic factors but proteomic, metabolomic and epigenomic influences. The incorporation of these omics elements into pharmaceutical development and clinical decision-making will ultimately require the use of methods to determine clinical and economic value. Current methodologies and guidelines for determining clinical effectiveness and cost–effectiveness may have limited applicability to the increasingly personalized nature of omics treatment strategies. Using examples from oncology, this article argues for the adaptation and tailoring of three existing methods for ensuring development and clinical use of multiomics-guided therapies that are effective, safe and offer value for money.