Clinical trial methods to discover and validate predictive markers for treatment response in cancer

Confirmatory prediction-driven RCTs in comparative effectiveness settings for cancer treatment

BACKGROUND

Medical advances in the treatment of cancer have allowed the development of multiple approved treatments and prognostic and predictive biomarkers for many types of cancer. Identifying improved treatment strategies among approved treatment options, the study of which is termed comparative effectiveness, using predictive biomarkers is becoming more common. RCTs that incorporate predictive biomarkers into the study design, called prediction-driven RCTs, are needed to rigorously evaluate these treatment strategies. Although researched extensively in the experimental treatment setting, literature is lacking in providing guidance about prediction-driven RCTs in the comparative effectiveness setting.

METHODS

Realistic simulations with time-to-event endpoints are used to compare contrasts of clinical utility and provide examples of simulated prediction-driven RCTs in the comparative effectiveness setting.

RESULTS

Our proposed contrast for clinical utility accurately estimates the true clinical utility in the comparative effectiveness setting while in some scenarios, the contrast used in current literature does not.

DISCUSSION

It is important to properly define contrasts of interest according to the treatment setting. Realistic simulations should be used to choose and evaluate the RCT design(s) able to directly estimate that contrast. In the comparative effectiveness setting, our proposed contrast for clinical utility should be used.

Next Generation Sequencing Reveals High Prevalence of BRCA1 and BRCA2 Variants of Unknown Significance in Early-Onset Breast Cancer in African American Women

BACKGROUND

Variants of unknown significance (VUSs) have been identified in BRCA1 and BRCA2 and account for the majority of all identified sequence alterations. Notably, VUSs occur disproportionately in people of African descent hampering breast cancer (BCa) management and prevention efforts in the population. Our study sought to identify and characterize mutations associated with increased risk of BCa at young age.

METHODS

In our study, the spectrum of mutations in BRCA1 and BRCA2 was enumerated in a cohort of 31 African American women of early age at onset breast cancer, with a family history of breast or cancer in general and/or with triple negative breast cancer. To improve the characterization of the BRCA1 and BRCA2 variants, bioinformatics tools were utilized to predict the potential function of each of the variants.

RESULTS

Using next generation sequencing methods and in silico analysis of variants, a total of 197 BRCA1 and 266 BRCA2 variants comprising 77 unique variants were identified in 31 patients. Of the 77 unique variants, one (1.3%) was a pathogenic frameshift mutation (rs80359304; BRCA2 Met591Ile), 13 (16.9%) were possibly pathogenic, 34 (44.2%) were benign, and 29 (37.7%) were VUSs. Genetic epidemiological approaches were used to determine the association with variant, haplotype, and phenotypes, such as age at diagnosis, family history of cancer and family history of breast cancer. There were 5 BRCA1 SNPs associated with age at diagnosis; rs1799966 (P=.045; Log Additive model), rs16942 (P=.033; Log Additive model), rs1799949 (P=.058; Log Additive model), rs373413425 (P=.040 and .023; Dominant and Log Additive models, respectively) and rs3765640 (P=.033 Log Additive model). Additionally, a haplotype composed of all 5 SNPs was found to be significantly associated with younger age at diagnosis using linear regression modeling (P=.023). Specifically, the haplotype containing all the variant alleles was associated with older age at diagnosis (OR= 5.03 95% CI=.91-9.14).

CONCLUSIONS

Knowing a patient's BRCA mutation status is important for prevention and treatment decision-making. Improving the characterization of mutations will lead to better management, treatment, and BCa prevention efforts in African Americans who are disproportionately affected with aggressive BCa and may inform future precision medicine genomic-based clinical studies.

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.

Predictive biomarker validation in practice: lessons from real trials

BACKGROUND

With the advent of targeted therapies, biomarkers provide a promising means of individualizing therapy through an integrated approach to prediction using the genetic makeup of the disease and the genotype of the patient. Biomarker validation has therefore become a central topic of discussion in the field of medicine, primarily due to the changing landscape of therapies for treatment of a disease and these therapies purported mechanism(s) of action.

PURPOSE

In this report, we discuss the merits and limitations of some of the clinical trial designs for predictive biomarker validation using examples from ongoing or completed clinical trials.

METHODS

The designs are broadly classified as retrospective (i.e., using data from previously well-conducted randomized controlled trials (RCT)) versus prospective (enrichment or targeted, unselected or all-comers, hybrid, and adaptive analysis). We discuss some of these designs in the context of real trials.

RESULTS

Well-designed retrospective analysis of prospective RCT can bring forward effective treatments to marker defined subgroup of patients in a timely manner. An example is the KRAS gene status in colorectal cancer - the benefit from cetuximab and panitumumab was demonstrated to be restricted to patients with wild type status based on prospectively specified analyses using data from previously conducted RCTs. Prospective enrichment designs are appropriate when compelling preliminary evidence suggests that not all patients will benefit from the study treatment under consideration; however, this may sometimes leave questions unanswered. An example is the established benefit of trastuzumab as adjuvant therapy for breast cancer; a clear definition of HER2-positivity and the assay reproducibility have, however, remained unanswered. An all-comers design is optimal where preliminary evidence regarding treatment benefit and assay reproducibility is uncertain (e.g., EGFR expression and tyrosine kinase inhibitors in lung cancer), or to identify the most effective therapy from a panel of regimens (e.g., chemotherapy options in breast cancer).

LIMITATIONS

The designs discussed here rest on the assumption that the technical feasibility, assay performance metrics, and the logistics of specimen collection are well established and that initial results demonstrate promise with regard to the predictive ability of the marker(s).

CONCLUSIONS

The choice of a clinical trial design is driven by a combination of scientific, clinical, statistical, and ethical considerations. There is no one size fits all solution to predictive biomarker validation.

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.

Clinical trials design and treatment tailoring: General principles applied to breast cancer research

Nowadays tailored therapy tends to replace standard cancer treatment approaches. Tailoring treatment is possible thanks to clinical trials results that identified subgroups of patients benefiting most from some treatments. Treatment can be tailored on the basis of specific clinical characteristics of the population or on the basis of predictive or prognostic markers. Finally treatment can be tailored for specific molecular targets. This evolution in cancer treatment has triggered the development of innovative trial designs to validate these new hypotheses. The real challenge of the next coming years resides in recruiting large number of patients from specific subgroups to validate tailored therapies.

Expression analysis of mRNA in formalin-fixed, paraffin-embedded archival tissues by mRNA in situ hybridization

Gene expression in diseased tissues can indicate the contribution to a disease process and potentially guide therapeutic decision-making. Archival tissues with associated clinical outcome may be useful to discover or validate the role of a candidate gene in a disease process or the response to therapy. Such archival tissues are commonly formalin-fixed and paraffin-embedded, restricting the methods available for gene expression analysis. Obviously, the detection of proteins in tissues requires adaptation for each protein and the detection of secreted proteins can prove difficult or of reduced value since the protein detected may not reflect the total amount produced. Thus, we describe here a reliable method for the detection of mRNA in archival tissues. The method for mRNA in situ hybridization (ISH) was adapted by us for >15 different genes and applied to several hundred tissue microarrays (TMAs) and full sections generating >10,000 expression data points. We also discuss the utility of TMAs to simultaneously analyze several hundred tissue samples on one slide to minimize variability and preserve valuable tissue samples. Experimental protocols are provided that can be implemented without major hurdles in a typical molecular pathology laboratory and we discuss quantitative analysis as well as advantages and limitations of ISH with a special focus on secreted proteins. We conclude that ISH is a reliable and cost effective approach to gene expression analysis in archival tissues that is amenable to screening of series of tissues or of genes of interest.

Translational research in breast cancer

Translational research (TR) involves both the development of novel diagnostics and novel therapeutics. These two major developmental areas are often associated with each other and these associations often bring new paradigms in the management of cancer patients. For example, the development of trastuzumab-based treatments has been conducted in harmony with the development of new methodologies to assess the expression of the Her-2 gene or protein, and from this, a therapeutic modality was established for breast cancer patients as a novel and individualized treatment system. TR covers a broad spectrum, from diagnosis to treatment, and it seems to act as a catalyst for developing novel paradigms. Therefore, it is crucial to conduct TR in clinical trials, in particular, prospective clinical trials. In this regard, TR can accelerate the development of new methodologies and increase trial efficiency. In this review, we describe the importance of TR, particularly that related to novel therapeutics.

Clinical Trial Designs for Prospective Validation of Biomarkers

Traditionally, anatomic staging systems have been used to determine predictions of individual patient outcome and, to a lesser extent, guide the choice of treatment in patients with cancer. With new targeted therapies, the role of biomarkers is increasingly promising, suggesting an integrated approach using the genetic make-up 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 in differential diagnosis (identifying individuals who are likely to respond to specific drugs). To be clinically useful, a marker must favorably affect clinical outcomes such as decreased toxicity, increased overall and/or disease-free survival, or improved quality of life. This paper focuses on possible clinical trial designs for assessing the utility of a predictive marker(s) for toxicity or clinical efficacy. We consider the scenario of single and multiple markers as well as present alternative solutions based on the prevalence of a marker. Our designs rest on the assumption that the methods for assessment of the biomarker are established and the initial results show promise with regard to the predictive ability of a marker. Additional research is clearly warranted to achieve the goal of 'predictive oncology'.