Abstract 4110: Pathway activation driven miRNA depletion highlights cancer signaling vulnerabilities and targeted therapy sensitivity
Authors: Wurm Alexander A. , Brilloff Silke , Carrero Zunamys I. , Drukewitz Stephan , Krüger Alexander , Oster Sandra , Kolovich Sofia , Hüther Melanie , Uhrig Sebastian , Westphal Dana , Meier Friedegund , Pfütze Katrin , Hübschmann Daniel , Horak Peter , Kreutzfeldt Simon , Heining Christoph , Richter Daniela , Fröhling Stefan , Ball Claudia R. , Glimm Hanno 
Published: June 2022

Abstract
Identification of cancer vulnerabilities is pivotal for the success of precision oncology. Comprehensive molecular profiling technologies improved clinical outcome of targeted drugs. However, the complexity of molecular interactions still challenges appropriate data interpretation. Hence, new strategies are needed to support current treatment decisions. One approach would be to consider additional molecular information, for instance microRNA (miRNA) expression patterns. By analyzing various cancer datasets, we observed that inhibition of miRNA expression occurs in a directed manner. Strong cancer driving mutations, such as in KRAS, result in a block of miRNAs inhibiting the cancer driver gene itself, or its associated downstream pathways. As many cancer drivers can activate diverse downstream pathways, we examined whether groups of strongly repressed miRNAs can uncover essential pathways. Therefore, we developed a miRNA depletion based cancer gene dependency model and related drug prediction workflow using miRNA sequencing data from cancer cell lines. We defined a pathway ranking score (PRS) and focused on druggable target genes. To validate our drug prediction workflow, we performed miRNA sequencing in various patient derived 3D cancer models (PDCMs). We predicted top hit pathway dependencies and individual druggable target genes for each culture. Subsequently, samples were used for in vitro drug response assays. Here, samples were significantly more sensitive to predicted targeted drugs, compared to standard of care (SOC) chemotherapy, or non-predicted targeted drugs. Next, we applied our miRNA based drug prediction workflow to four patients and compared three sources for RNA sequencing for each patient: PDCM, primary tumor, and microdissected purified tumor from FFPE tissue. Of note, FFPE purified miRNAs extraction led to comparable results to PDCMs and primary tumors. Finally, we were interested whether our prediction workflow matches with a comprehensive genomic based drug prediction workflow from the MASTER precision oncology trial. Therefore, we sequenced miRNAs from 95 patients and performed subsequent drug prediction. Interestingly, a complete overlap was overserved in only 20% of patients. In 35% of the patients, MASTER drug recommendation was biologically related to our drug prediction, and 40% revealed no match. In 75 out of 91 patients (82%) with available RNA Seq data, at least one of our predicted target genes was noticeably overexpressed on mRNA level. In total, 40% of all with our workflow predicted target genes, and 52% of druggable predicted target genes showed an increase of 1.5 fold or higher on RNA level compared to the entire cohort. In conclusion, unidirectional silenced miRNA expression signatures give further insights into cancer cell pathways and druggable gene dependencies and might be suitable tools for precision oncology.
Citation Format: Alexander A. Wurm, Silke Brilloff, Zunamys I. Carrero, Stephan Drukewitz, Alexander Krüger, Sandra Oster, Sofia Kolovich, Melanie Hüther, Sebastian Uhrig, Dana Westphal, Friedegund Meier, Katrin Pfütze, Daniel Hübschmann, Peter Horak, Simon Kreutzfeldt, Christoph Heining, Daniela Richter, Stefan Fröhling, Claudia R. Ball, Hanno Glimm. Pathway activation driven miRNA depletion highlights cancer signaling vulnerabilities and targeted therapy sensitivity [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 4110.