Who to Treat: A Molecular Signature Approach for Subgroup Identification Scott L. Marshall, Lin Li, Tobias Guennel BioStat Solutions, Inc., 5280 Corporate Drive, Suite C200, Frederick, MD 21703 Delivering actionable results through the power of cutting edge analytics ABSTRACT METHODS The promise of personalized medicine is becoming a reality in the space of oncology with a noticeable shift in the past decade from targeting the largest possible population to targeting cancer subtypes with smaller patient populations or biomarker-defined subgroups of a patient population with enhanced response. Approximately 60% of US drug sales related to anti-cancer drugs are targeted therapies and it is estimated that a similar percentage of drugs in development have a biomarker component. As the emphasis on biomarker strategies across therapeutic areas is on the rise, it is critical that consideration is given to advancing analytics for the discovery and commercialization of biomarkers. • A novel approach for treatment-specific subgroup identification has the ability to aggregate data across assay platforms and estimate patient specific molecular signatures, which then serve as a surrogate “biomarker” for membership in the unobserved underlying treatment-specific subgroup, e.g. biomarker(+) subgroup. This flexible strategy also provides for incorporation of both scientific and business factors, such as confining the search space to a subgroup size that is commercially viable, ultimately resulting in actionable information for use in empirically based decision making. DISCUSSION Consider a working model assuming that the biomarker profile 𝑴 can serve as a surrogate for the subgroup 𝒮 Executive Summary ℎ𝑖 𝑡 = ℎ0 𝑡 exp 𝑍𝑖 𝛽 + 𝑍𝑖 𝑴𝑇𝑖 𝜽 + 𝑿𝑇𝑖 𝜶0 + 𝑴𝑇𝑖 𝜶 Biomarkers Stage 1: • Composite score is defined as 𝛾𝑖 = −𝑴𝑇𝑖 𝜽 for patient 𝑖 • A cutoff 𝜏 for the composite scores is identified through a grid search using a max-chi square approach, and the significance is provided via a parametric bootstrap approach • Subgroup can then be defined as all patients above a cutoff 𝜏, i.e. 𝒮𝜏 = 𝑖: 𝛾𝑖 ≥ 𝜏 • P-value is provided for test of enhanced treatment effect for identified subgroup, and type I error is protected Stage 1: Estimation of Composite Score Business Challenges • Heterogeneous patient populations -> heterogeneous treatment response • Optimizing the drug development process • Differentiation strategy • Treatment response driven by complex biological interactions • Clinically relevant treatment effect in the biomarker(+) group • Need to integrate data from various biological assay platforms • Marketable subgroup size Stage 2: Estimation of Subgroup Defining Threshold Subgroup is identified! 0.6 1.7 2.0 2.7 2.5 2.2 0.3 • • • 2.1 2.6 0.4 1.2 1.1 0.5 Overall Response Rate = 25% Competitor Response Rate = 30% Subgroup Response Rate = 50% Subgroup Size = 20% A subgroup with a competitive advantage exists but it is desired to capture a larger market share -> relax the threshold Transform Challenges into Solutions Patients with MMMS scores Subgroup is identified! 0.6 Stage 2: Subgroup Identification Of particular note, and a significant contribution to the literature, is • A subgroup is estimated to include any subjects with scores above a given threshold Goal: evaluate type I error and power to detect a subgroup with enhanced treatment effect via the MMMS approach Biomarker data was simulated for 25 biomarkers TTE outcomes were generated spiking in a subgroup with enhanced treatment effect in presence/absence of main effect Subgroup is defined by four continuous markers M1,…, M4: 𝑆𝑙𝑖𝑛 = {𝑖: 𝑀𝑖,1 𝑙𝑜𝑔2 + 𝑀𝑖,2 𝑙𝑜𝑔2 + 𝑀𝑖,3 𝑙𝑜𝑔2 + 𝑀𝑖,4 𝑙𝑜𝑔3 > 𝜏} Subgroup hazard ratios: 0.3, 0.4, 0.5, 0.7, 1.0 Subgroup size=30% Sample sizes: 300, 500, 750, 1000 RESULTS Sample Size 300 300 500 500 750 750 1000 1000 Biomarker Main Effect Absent Present Absent Present Absent Present Absent Present Type I Error (%) 5% 10% 20% 3.9 8.2 19.1 3.7 8.4 18.2 4.3 9.3 19.8 5.1 9.5 19.0 5.4 10.1 19.9 4.6 7.5 16.7 4.6 10.1 18.6 4.1 8.6 18.1 • Control of type I error at the nominal level, even in the presence of prognostic factors; • Moderate power (i.e. 60%) is achieved in discovery oriented scenarios, such as a phase 2 clinical trial with the alpha level relaxed at 0.20; • Capability to aggregate information across biological assays Using an estimated patient-specific molecular signature results in generating actionable information that addresses the scientific and business challenges associated with subgroup identification and subsequent patient stratification. Practical Applications and Business Utility • • • • Prospective integration of biomarkers into the study design Stratification of biomarker(+) / biomarker(-) subgroups Enrichment designs Preliminary understanding of potential market share and differentiation strategy Current Extensions and Future Work • • • • General linear model framework Generalized linear model framework (e.g. logistic regression) Estimation of confidence interval for subgroup defining threshold Estimation and correction of bias for treatment effect in biomarker(+) subgroup REFERENCES 0.8 1.8 Li L, Guennel T, Marshall SL, Cheung LWK. (2014) A multi-marker molecular signature approach for treatment-specific subgroup identification with survival outcomes. [Accepted; The Pharmacogenomics Journal] 1.7 2.0 2.7 2.5 2.2 • • • 0.8 1.8 Composite Scores Simulation Study Example Application Patients with MMMS scores Scientific Challenges • A score incorporating information of all biomarkers is derived for each subject (1) Estimates a multi-marker molecular signature (MMMS) via small scale feature selection and (2) Identifies and directly tests for a biomarker driven subgroup with an enhanced treatment effect. where 𝑍𝑖 𝑴𝑖 𝜽 contains interaction terms of the treatment 𝑍𝑖 with each of the biomarker values • Challenges in Patient Stratification • Continuous biomarkers • Categorical biomarker This analytical framework provides a novel combination of elastic net and the maximal chi-square approach in a flexible two stage approach that: 0.3 2.1 2.6 0.4 1.1 1.2 0.5 Overall Response Rate = 25% Competitor Response Rate = 30% Subgroup Response Rate = 40% Subgroup Size = 60%