Abstract 857: Discovering molecular biomarkers through machine learning to assign patients to one of multiple arms for most-likely-to-benefit in an umbrella trial for glioblastoma
Authors: Matthew E. Lee, Sen Peng, Brett Taylor, Shay Ferdosi, Harshil Dhruv, Manmeet Ahluwalia, Karen Fink, Michael Berens
Journal: Cancer Research (2020)

Abstract Glioblastoma Multiforme (GBM) is the most common primary brain tumor, characterized by intra- and inter-tumoral heterogeneity and relentless, lethal progression. Molecular data from next-generation sequencing subclassifies the disease into discrete subtypes, setting the stage for biomarker-driven clinical trials. An umbrella trial tests various treatment arms depending on various biomarker signatures that preferentially assign each new patient into a specific treatment arm. A contingency of an umbrella trial is a suite of distinct, orthogonal molecular biomarkers to classify patients into the likely-most-beneficial arm of the different separate treatment arms. Assigning thresholds to molecular signatures in order to classify a patient as a likely-responder within a single treatment arm is a crucial task. For example, in GBM patients treated with methoxyamine (covalently binds to apurinic/apyrimidinic (AP) DNA damage sites), Gene Set Variation Analysis (GSVA) identifies the differential enrichment in several DNA repair pathways of individuals. The 44 DNA-repair related pathways (or features) make it difficult to define “high” enrichment, as well as which, and to what degree, each feature contributes to the likelihood of a patient's response. Here, we utilize two semi-supervised algorithms, the Transductive Support Vector Machine (TSVM) and Entropy-Regularized Logistic Regression (ERLR). By first training these algorithms in a semi-supervised method using both patient as well as TCGA samples we can then transform the output of GSVA on unlabeled TCGA samples into a labeled data set based on those with the highest certainty of their predicted response. By employing semi-supervised algorithms to self-label the response of GBM Cases in TCGA, a predictive model can be developed which has a larger sample number, thereby reducing uncertainty. Our umbrella trial design currently includes three treatment arms for GBM patients: arsenic trioxide, methoxyamine, and pevonedistat. Each treatment arm contains its own biomarker developed by the above machine learning pipeline based on selected gene mutation status in addition to whole transcriptome data. By conducting a “mock” stratification of GBM patients in TCGA, we identify likely patient responders in a single arm (as low as 9%) to upwards of 45% by expansion to three, independent treatment arms within a single trial. Our utilization of multiple biomarker signatures in an umbrella trial demonstrates how a precision medicine model can be effective in heterogeneous diseases such as GBM. Citation Format: Matthew E. Lee, Sen Peng, Brett Taylor, Shay Ferdosi, Harshil Dhruv, Manmeet Ahluwalia, Karen Fink, Michael Berens. Discovering molecular biomarkers through machine learning to assign patients to one of multiple arms for most-likely-to-benefit in an umbrella trial for glioblastoma [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 857.