Abstract 504: A comparative analysis of statistical and machine learning approaches to predict drug resistance based on synergistic genetic alterations

Introduction: This study evaluates the utility of a machine learning approach to predict drug resistance in breast cancer (BC), based on comparison to an automated statistical model, using data from cBioPortal. Methods: Our in silico approach investigated the association between synergistic genetic alterations in BC and survival data with various treatments based on one study (TCGA, PanCancer Atlas 2018). A set of 491 commonly altered genes in cancer (based on TSO500) was analyzed for this study. We programmed a statistical model to automatically select pairs of genes whose alterations (mutations, structural variants/fusions, copy number alterations) correlated with significantly worse five-year overall survival (OS) upon synergistic alterations, compared to one gene alone (p<0.05; HR≥1). The results were analyzed via subgroup analysis of six treatment groups according to cBioPortal (chemotherapy, ancillary, hormone, immunotherapy, radiation, other) to predict potential drug resistance if HR significantly changed based on treatment. We compared our results with a machine learning approach. The model first computed relevancy/attention scores on treatments based on order, duration and type. This allowed the model to consider chronological order and simultaneous treatment administration. We then fed this into a model that returned a classification score (probability of death before 60 months) with ∼90% accuracy, and another model that returned a regression score (expected survival in months) score, with mean squared error<0.02. Results: A statistical brute force approach tested each gene's influence on another, and each treatment category’s influence on a gene pair. Permutations of pairs from the geneset (240590 total) were generated for each treatment; 570 corresponded with significantly worse OS (HR≥1) with chemotherapy, 392 with hormone therapy, 124 with immunotherapy and 614 with radiation therapy. Less than five patients had ancillary or other treatment, preventing us from generating statistically significant results. Permutations were used since the order of genes matters during statistical analysis for alterations to one gene versus synergistic alterations. The machine learning approach yielded treatment rankings for each gene permutation based on the classification score. The latest test demonstrated that most gene permutations correspond with a high classification score with hormone therapy (48%), and most gene permutations correspond with worse total OS with chemotherapy (86%). Rankings show promise that some marked values from statistical analysis are shared with the machine learning model, but further work is needed to improve stability to make accurate predictions for survival outcomes. Conclusion: Our findings suggest the possible utility of an automated approach to predict gene pairs that confer drug resistance in BC. Citation Format: Rishi Nair, Nicholas R. Mistry, Roy Khalife, Anthony M. Magliocco. A comparative analysis of statistical and machine learning approaches to predict drug resistance based on synergistic genetic alterations [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 504.