Identifying genetic factors of response and resistance to CDK4/6 inhibitors in metastatic HR+/HER2-breast cancer using real-world data.

Abstract

1064 Background: CDK4/6 inhibitors plus endocrine therapy are approved for treatment of HR+/HER2- metastatic breast cancer (MBC) and have shown to provide a significant progression free survival benefit over endocrine therapy alone. But not all patients benefit from this treatment and some develop resistance over time. The molecular mechanisms governing this resistance are poorly understood. We have developed a real world dataset that includes data elements from structured EMR tables as well as deeply curated unstructured data from BC patients (ConcertAI Genome360 BC Dataset) who have been treated with CDK4/6 inhibitors and have undergone DNA sequencing to identify somatic mutations. We have leveraged this linked clinical-genomics dataset to identify genetic drivers of resistance and response to CDK4/6 inhibitors. Methods: This retrospective study uses the Genome360 BC Dataset (N = 1249). The patient’s eligibility to be included in this study (N = 456) was HR+/HER2- MBC patients with age > 18 years treated with at least one of the CDK4/6 inhibitors and have response data based on RECIST criteria (responders = 231, non-responders = 225). For each patient in both cohorts, all pathogenic gene mutations and copy number changes were identified and enrichment analysis was performed. Biomarkers with Z value > 1.96 (p value < 0.05) were considered for further analysis. Pathway analysis was performed using these biomarkers and the CDK4/6 pathway to identify pathways and genes that can potentially be targeted to overcome resistance based on the mutational landscape of the patients receiving therapy. Results: We identified 7 potential segments (similar groups of genes) which predicted response or resistance to CDK4/6 inhibitors. Here we present data on 3 such segments which are closely related. Loss of function mutations in RB1 were enriched in the non-responder population (Z value = 2.33; p value = 0.026; N = 31). This is consistent with previously reported findings. In addition, amplifications and gain of function mutations in MYC and associated genes were also significantly enriched in the non-responder population (Z value = 2.71; P value = 0.01; N = 44). Interestingly, loss of function mutations in TSC1/2 genes which are downstream of MYC were predictors of good response to CDK4/6 inhibitors (Z value = 2.19; P value = 0.036; N = 30), strengthening the role of the parallel MYC signaling pathway in resistance to CDK4/6 inhibitors. Conclusions: Using our Genome360 BC Dataset, we have identified genetic markers affecting response to CDK4/6 inhibitors. In addition to the known role of RB1 in resistance to CDK4/6 inhibitors, the MYC signaling pathway emerged as a strong candidate. Based on these results, patients with mutations in these pathways may benefit from addition of mTOR or PKL1 inhibitors to CDK4/6 inhibitors to overcome resistance and prolong their effect.