Abstract 1233: A novel 14-gene signature to predict response to neoadjuvant chemotherapy and endocrine treatment in breast cancer patients

Abstract

Abstract mRNA transcriptomic markers have become a key tool in the classification of breast cancer and deciding treatment regimens for patients. However, limited studies have evaluated markers that might be predictive of response to neoadjuvant treatment (chemotherapy and endocrine treatment). Defining such key markers that are associated with treatment response could offer new insights on the relative molecular mechanisms and provide more tailored treatment regimens to target pathways that might be over-represented. We aimed to identify mRNA expression markers associated with breast cancer patients’ response to neoadjuvant treatment from a pool of studies in which tumor samples were collected at pre-treatment and on-treatment timepoints. We collated 1194 pre- and on-treatment samples (721 unique patients) from 9 publicly available gene expression datasets that met our inclusion criteria. The standardized gene expression values from each study were merged in a global matrix of 1551 genes and 1194 samples. Differential Gene Expression Analysis adjusted for timepoint, pam50 subtype (as predicted by the genefu package in R), treatment and batch effects was conducted. 14 significantly (FDR = 0.05) differentially expressed were identified (CCNA2, RFC3, VRK1, PSMB2, ALDH7A1, RRM2, ADRM1, TSPAN4, ZNF473, RNH1, HDAC1, CDK1, SMC1A and TOR1AIP1) when responders and non-responders were compared, some of which are known drug targets (e.g. PSMB2/carfilzomib). Using the set of identified genes we performed Monte-Carlo consensus clustering on the full set (optimal number of clusters: 6). Clusters were significantly associated with response (p = 2·10-8), timepoint (p < 10-15) and pam50 subtype (p < 10-15), but not treatment (p = 0.35). We then split our data into a training, a validation and a test set (776, 299, 119 samples respectively) and used the 14 genes (alone or in combination with metadata) as predictors to fit three types of machine learning models (lasso-regularized Logistic Regression, Decision Trees and Support Vector Machines). Support Vector Machines demonstrated the best classification performance on the validation set (75% classification accuracy) and achieved accuracy of 80% on the test set. Pathway analysis based on the identified genes revealed enriched nuclear membrane organization, protein deubiquitination, DNA replication and histone modification pathways. Prediction of response to treatment at baseline or mid-treatment can aid in patient stratification in the neoadjuvant setting and separate patients who would benefit from treatment the most and could undergo a less extensive surgical operation from patients who are unlikely to respond and should be scheduled for surgery sooner. This kind of early intervention has the potential to lead to improved patient outcomes and reduced side effects from unnecessary treatment administration. Citation Format: Aristeidis Sionakidis, Jonine D. Figueroa, Timothy I. Cannings. A novel 14-gene signature to predict response to neoadjuvant chemotherapy and endocrine treatment in breast cancer patients [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1233.