Background:KRAS mutations are the most common oncogenic drivers in cancer. While first-generation KRAS inhibitors (KRASi) represent a therapeutic advancement, objective response rates and therapeutic durability remain suboptimal, with the majority of patients progressing on treatment. The mechanisms of resistance to KRASi therapy and clinical strategies for follow-on treatments are not well-defined.

Methods:72 biopsies from non-small cell lung cancer (NSCLC) patients with known time on treatment with sotorasib were analyzed by RNA-sequencing and the krasID biomarker model. Individual biological module scores served as input features for a classification model. Tuning and model refinement was performed with nested cross-validation. 5 NSCLC patients with pre- and post-sotorasib biopsies were evaluated for longitudinal changes in tumor biology as assessed by krasID modules.

Results:This study comprised longitudinal evaluation of real-world NSCLC patients treated with sotorasib, a first-generation KRASi, analyzed with a RNA- and machine learning-based patient classifier, Genialis™ krasID. The krasID classifier stratified patients by scoring a number of biological signatures (“modules”) relevant to KRAS-mediated oncogenesis, and predicted therapy responses by algorithmic integration of these signatures. For KRASi-specific stratification, the classifier achieved an AUROC of 0.81 and specificity of 0.86 in predicting KRASi response. Beyond predicting initial drug response, we investigated the functionality of the biomarker to inform whether patients remained on sotorasib treatment at 6 months (AUROC > 0.8, see Table), a time point aligning with the median progression-free survival of 5.6 months from CodeBreaK 200 trial. The response-duration classifier proved capable of predicting the time to disease progression, an important clinical parameter considering that approximately 2/3 of patients progress on sotorasib by 6 months. Analysis of pre- and post-treatment biopsies in a subset of these patients implicated shared and unique resistance mechanisms. krasID identified certain patients with compensatory resistance mechanisms outside the MAPK/KRAS signaling cascade who nevertheless continued to show molecular benefit to sotorasib. Patient tolerable combination therapies targeting secondary resistance mechanisms, alongside continued sotorasib treatment, may improve outcomes compared to discontinuation of KRASi.

Conclusions:These findings position krasID as a next-generation biomarker for identifying KRASi responders, predicting treatment duration, and guiding personalized follow-on therapies for KRAS-mutated cancers.

Performance of time on sotorasib classifier

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