Abstract A32: Identifying actionable pathway malfunction scores with ML algorithm for omics data

Abstract

Background: Driver mutations are traditionally considered as actionable biomarkers for targeted drugs, but the resistance and relapse effects often occur even when these events are precisely discovered. At the same time, primary DNA mutations can be only the triggers for cell malignancy and further development of the tumor occurs due to following pathways imbalance, which may be reflected in gene expression. The goal is to detect preaffected pathways that are most close to the oncogenic affected state, so during the treatment strategy planning we could consider these pathways as the next potential targets after nonresponse or relapse. Methods: We took the data from TCGA Pan-Cancer Atlas on whole-exome sequencing and RNA expression for 33 cancers. Mutations were filtered based on their pathogenicity (1,2). The training set included data on mutations and corresponded RNA levels of 1821 cancer pathways-related genes (3). ML method-logistic regression, with 5-fold cross-validation with a test set, was realized on Python 3.7. Results: Using gene expression data, 9 most common actionable events were predicted: oncogenic mutations affecting Ras, Raf, Ras/Raf/MEK, PI3K, CDK protein families, amplifications of EGFR, ERBB2, CDK4 genes, with an accuracy of 80% - 93%. Results were the probabilities of events: range 10-30% occurrence is shown. Discussion: We considered the obtained molecular events probabilities as the scores of corresponding pathways’ malfunctions. For some molecular events, more than one-third of patients has >10% affected (unbalanced) pathway state. This approach after validation can be used in clinical research practice for patient cohorts risk stratification, or as additional reinforcement for drug companion tests. References: 1. COSMIC; 2. Chakravarty et al., 2017a; 3. KEGG. Citation Format: Dmitrii Chebanov, Nadezhda Tatevosova, Irina Mikhaylova. Identifying actionable pathway malfunction scores with ML algorithm for omics data [abstract]. In: Proceedings of the AACR Special Conference on the Microbiome, Viruses, and Cancer; 2020 Feb 21-24; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2020;80(8 Suppl):Abstract nr A32.