Abstract Background. Identifying which patients will benefit from individual therapies remains a general challenge in oncology. Predictive biomarkers of drug sensitivity, when known, can increase the rate of success of trials, and allow stratification of patients for targeted combination therapies, as recent shown by the GUIDANCE-01 trial in Large B-Cell Lymphoma. Peripheral T-Cell Lymphomas (PTCL) could particularly benefit from this approach, being heterogeneous diseases against which many therapies are active in unpredictable subsets of patients. A central challenge of biomarker discovery is finding true determinants of drug response, which may be multiple genes, among thousands of molecular features that may accidentally correlate. Here, we aim to mitigate this challenge by combining functional genetic screens and gene expression profiling in diverse PTCL cultures, to enable the systematic discovery of drug sensitivity biomarkers. Methods. To discover genes whose expression directly modifies drug sensitivity, we conducted genome-wide CRISPR screens with PTCL cells under treatment with five therapies with clinical activity in PTCL: Romidepsin, Pralatrexate, Gemcitabine, Oxaliplatin, and Bortezomib. To discover genes whose basal expression significantly correlates with specific drug sensitivities, we measured RNAseq and drug response functions for a diverse panel of 27 PTCL cultures. Genes in the intersection of both experiments are causally related to and correlate with drug responses in PTCL, and have the statistical benefit of detection by independent methods. Results. Functional genetic screens on therapy provided mechanistic insights for each drug of interest, and identified genes whose perturbation affects drug sensitivity. Whereas hundreds of genes nominally correlate with drug sensitivity in cell line panels, for each drug a shortlist of only 5 to 20 genes are both CRISPR hits and correlate with response across PTCL cultures. For each drug tested, multi-gene sensitivity scores constructed from hits stratify PTCL cultures into statistically distinct sensitive and non-sensitive subgroups (Mann Whitney P-Values &amp;lt;0.01). These genes’ relationships to drug mechanisms were often supported by prior studies, including functions in DNA repair, cell cycle regulation, and solute carriers required for cellular entry of specific therapies, such as SLC19A1 for Pralatrexate and SLC43A2 for Oxaliplatin. Conclusions. Combining functional genetic screens and correlative drug sensitivity measurements narrowed the search for predictive biomarkers of drug sensitivity, and may be a general means of refining biomarker hypotheses for validation in clinical datasets. Although chemotherapy sensitivity is commonly polygenic, multi-gene scores based on few mechanistically-related genes were able to significantly distinguish drug sensitive from resistant PTCLs. These approaches provide novel hypotheses for drug-gene relationships that may serve as the basis for molecular subtyping of individual lymphomas for precision regimens. Citation Format: Adam C Palmer, Jacob C Pantazis. Systematic discovery of chemotherapy response biomarkers in Peripheral T Cell Lymphomas [abstract]. In: Proceedings of the Fourth AACR International Meeting on Advances in Malignant Lymphoma: Maximizing the Basic-Translational Interface for Clinical Application; 2024 Jun 19-22; Philadelphia, PA. Philadelphia (PA): AACR; Blood Cancer Discov 2024;5(3_Suppl):Abstract nr PO-031.

Abstract PO-031: Systematic discovery of chemotherapy response biomarkers in Peripheral T Cell Lymphomas