Examination of mutation signature and mutation spectrum as predictors of response to immune checkpoint blockade therapy.

Abstract

2537 Background: In recent years, there has been promising progress in the use of immune checkpoint blockade (ICB) as a treatment for various cancer types such as lung, kidney, bladder, skin, colon, and breast cancer. In order for this modality to have increased success, more precise selection tools are needed to predict which patients will benefit from treatment. Tumor Mutational burden (TMB) has been implicated as a biomarker for ICB response due to the increased tumor immunogenicity present in high TMB samples. However, we hypothesize that the quality of mutations may also have an impact in determining immune response, rather than just the quantity of mutations alone. Methods: A retrospective analysis of 2041 patients across multiple solid tumor types was conducted using an open-access, open-source cancer genomics database (Gao, Sci Signal, 2013), with the goal of assessing the influence of mutational signature on patient response to ICB (including CTLA-4, PD-1, and PDL-1). Patient demographics, treatment variables/outcomes, and tumor variables, including mutation spectrum, and mutation count, were evaluated. A paired two sample test for means was used to analyze data with p < 0.05 considered statistically significant. Response outcomes were determined per RECIST v 1.1. Results: Our data demonstrate that mutational spectrum profiles show distinct patterns when considering response to ICB, independent of TMB. Patients with breast invasive ductal carcinoma who responded to ICB had less C > A mutations ( p = 0.020) and more C > T mutations ( p = 0.017) compared to non-responders. Lung adenocarcinoma responders had less C > T mutations ( p = 0.003) and more C > A mutations ( p = 0.0003) compared to non-responders. Furthermore, both head and neck squamous cell carcinoma and glioblastoma multiforme responders had more T > A mutations ( p = 0.047 and p = 0.011, respectively) than non-responders. However, in other cancer types studied (urothelial, colon, renal, lung squamous, and melanoma), there were no obvious trends to distinguish responders from non-responders. Preferentially mutated gene targets were also considered for all 10 cancer types (including CARD11, SMARCA4, GLI1, PIK3R1, PTPRT, among others), many of which were also tied to the unique mutational signatures observed and which may be separately identified as novel ICB therapy targets. Conclusions: We found that specific mutation type may impact response to ICB in at least 4 cancer types, including breast, lung adenocarcinoma, head and neck squamous cell carcinoma and glioblastoma multiforme. Of note, this trend was not found in other cancer types which are typically found to have higher TMB. Moving away from solely considering the magnitude of mutations in tumor samples and towards identifying specific mutational signatures may aid in providing necessary specificity for selecting patients for ICB cancer therapy.