Abstract 4894: Multi-omic Approach for Drug Repurposing in the Poor Prognosis CMS4 Subtype of Colon Cancer

Abstract Introduction: Colon cancer (CC) is one of the deadliest malignancy in developing countries. Key drivers considered for the clinical management are RAS, BRAF alterations together with TNM staging and microsatellite status (MS). With the advancement of sequencing technologies and bioinformatic approaches, various molecular classifications of CC have been proposed. Guinney et al. proposed a transcriptomic-based molecular subtyping, the so-called four Consensus Molecular Subtypes (CMS). CMS4 patients have the worst prognosis. Biologically, CMS4, also known as mesenchymal, shows high expression of genes related to EMT, matrix remodelling, TGFb signaling, and inflammatory-related system and a peculiar enrichment in stromal cells. Thus, in the present study we are focusing on CMS4 subgroup in order to identify drugs to be repurposed in such a clinical setting through a multi-omic approach. Methods: Using TCGAbiolinks R package, we retrieved STAR Counts transcriptome profiling data, for COAD and READ. CMSclassifier was used to determine the CMS of the samples. We separated the coding genes from the long noncoding RNAs (lncRNAs) using annotation databases generated from Ensembl. We estimated the fraction of cell populations throughout the CIBERSORTx. Features selection approaches was performed both for coding genes and lncRNA. Data normalization has been carried out through variance stabilizing transformation for read count data. To identify CMS and potential biomarkers, we adopted mixOmics N-integration method. Our independent validation cohort consists of 100 patients, locally enrolled, upon local Ethical Committee approval. Features contributing to CMS4 subtype have been extracted and use to construct drug-gene interaction network. Results: To set-up the classification model the DIABLO approach was used, reaching a mean AUCROC of 0.9022. Contributing features related CMS4 subtypes included nine coding genes, six lncRNAs and, regarding CibersortX deconvolution, Macrophage M0 and M2.The drug-gene interaction network includes six subnetworks centered to ALOX5, KCNMA1, AQP9, TGFB3, THBS4 and DPYSL3. The role of TGFb pathway was confirmed, considering the contribution to classification. Interestingly, we found interactions with Non-Steroid Anti-Inflammatory Drugs (NSAIDs), such as Mesalazine. Moreover, THBSA gene, involved in cell-to-cell, cell-to-matrix and stromal response, could also be targeted. Conclusions: The results of our drug repositioning approach have been biologically corroborated considering, as mentioned above, the peculiar enrichment of CMS4 subtype for TGFb pathway and stromal cells. The chance to modulate the macrophagic activity in the tumor microenvironment through NSAIDs could be evaluated. Experimental validation on patient-derived organoids (PDOs), related to samples included to local validation cohort, is ongoing. Citation Format: Tommaso M. Marvulli, Debora Traversa, Roberta Di Fonte, Leonarda Maurmo, Amalia Azzariti, Letizia Porcelli, Claudio A. Coppola, Concetta Saponaro, Eliseo Mattioli, Francesco A. Zito, Rossella Fasano, Simona Serratì, Davide Quaresmini, Oronzo Brunetti, Stefania Tommasi, Raffaella Massafra, Simona De Summa. Multi-omic approach for drug repurposing in the poor prognosis CMS4 subtype of colon cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4894.