Abstract A028: Quantifying resistance to targeted anticancer therapies at scale with duplex sequencing

Abstract

Abstract Drug resistance to targeted anticancer therapeutics is one of the most researched problems in cancer genetics. A fundamental limiting factor facing most drug resistance studies is that resistance is studied retrospectively, i.e., mutants are characterized for their drug resistance only once they are seen in the clinic. Deep mutational scanning (DMS) is a field of functional genomics that can enable a-priori identification of resistant mutants- long before they are seen in the clinic. Quantifying resistance prospectively can guide clinical treatment decision-making and drug discovery efforts. In order to guide clinical practice, a DMS screen would need to have the sensitivity to measure drug resistance well and be able to relate screen measurements to clinical pharmacology. However, current DMS screens are not adopted for drug resistance and face unique technical challenges that limit the overall interpretability of screen data. Here we demonstrate the first scalable duplex sequencing DMS screen and use it to study Imatinib resistance in BCR-ABL. We first studied the pooled growth of 20 known Imatinib resistant mutants at low allele frequencies. We observed significant depletion artifacts and dose-response variations when measuring mutant outgrowth using allele frequency alone. To correct for these artifacts, we developed an inference-based approach that identifies and addresses depletion and dose variation artifacts that are commonly seen in drug resistance screens. The described measurement methodology enables precise and accurate tracking of low allele-frequency mutants. We also describe the first error-corrected duplex sequencing workflow that can achieve sequencing depths of 3 × 105, which is 20x higher than commercially available duplex sequencing kits. Sequencing at these depths enabled us to detect and measure 96.7% of all possible amino acid substitutions in the ABL kinase (n=4,788 mutants). We used our measurements to characterize all treatment-emergent variants of uncertain drug resistance in CML patients. We find that 22% of the rare variants seen in Imatinib refractory CML patients drive imatinib resistance, while the rest are likely bystanders of another resistance mechanism. Furthermore, because we can relate our measurements to clinical pharmacology, we identify variants that are good candidates for dose escalation from 400mg to 800mg daily dose of imatinib. Here, we demonstrate the first scalable duplex sequencing DMS screen that achieves high levels of sensitivity and specificity in measuring mutant drug resistance. We demonstrate the utility of this workflow by characterizing all variants of uncertain drug resistance in the ABL kinase. Addressing technical key barriers that face DMS screens of drug resistance enables us to make recommendations for treatment decisions on variants of unknown drug resistance. This approach can be used by pharmaceutical companies and geneticists to prospectively identify drug resistant variants to investigational drugs in candidate druggable biomarkers. Citation Format: Haider Inam, Josh Reynolds, Marta Tomaszkiewicz, Scott Leighow, Yiyun Rao, Ivan Sokirniy, Justin Pritchard. Quantifying resistance to targeted anticancer therapies at scale with duplex sequencing [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Translating Cancer Evolution and Data Science: The Next Frontier; 2023 Dec 3-6; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(3 Suppl_2):Abstract nr A028.