Abstract P1-10-19: Tyrosine aminoacyl-tRNA synthetase sensitizes breast cancer to chemotherapy througha necroptosis-mediated mechanism: High-throughput proteomics and machine learningalgorithm based feature selection analyses
Authors: Ryu Han Suk 
Published: March 2020

Although more than 70% of entire patients are currently receiving the chemotherapy regimen, pathologic complete response (pCR) rate is still low. Therefore, the need for a marker predictive of response to a particular cytotoxic regimen is becoming all the more necessary to optimize therapeutic efficacy and to avoid unnecessary complications. We performed quantitative proteomics in FFPE biopsy breast cancer samples consist of non-responsive and responsive groups to chemotherapy. To define the best classifier to evaluate the predictive power of signatures, we employed four different machine learning algorithms and performed repeated cross-validation on the training set to classify samples between pCR and non-pCR groups. The candidate proteins selected from the machine-learning algorithms were subsequently validated by immunohistochemistry of 123 cases of independent needle biopsy FFPE samples. The further experimental model of breast cancer was performed to identify the molecular mechanism of how the biomarker potentiates sensitivity of multiple breast cancer cells to chemotherapy response and synergistic effect with a combination of necroptotic mimetics. The filtered dataset was subjected to statistical analysis using Student’s t-test (p-value &lt; 0.05), which resulted in 539 proteins with differential abundances. To identify the most meaningful changes in two conditions, the volcano plot was used and 13 proteins were prioritized. We searched for biological process in the Gene Ontology (GO) enrichment analysis in each proteomic cluster. Several immune responses process, apoptotic process, DNA replication process, and aminoacylation for protein translation process primarily were represented in the group with complete remission. On the other hand, cell adhesion process, cytoskeleton organization process, vesicle organization process and Golgi organization process overrepresented in breast cancer which showed poor responses to the therapy. The machine learning approaches demonstrated the highest AUC value, 0.978 (sensitivity 1.0 and specificity 0.714) with a combination of 11 proteins. For the accuracy prediction, four different algorithms demonstrated evenly high accuracy rates from 0.85 to 0.95 with a combination of the selected seven candidates. They were evaluated in the subsequent steps of verification using immunohistochemistry (IHC) in 123 patient cohorts. The predictive relevance of individual proteins, YARS to distinguish CR from nCR was AUC of 0.749 for all cases. The overexpression of YARS induced chemotherapeutic sensitivity in hormone receptor-positive breast cancer cell lines such as T47D, which was confirmed with multiple molecular biology-driven assays. The further experimental model of breast cancer demonstrated that YARS induced tumor necroptosis which enhanced chemotherapy response and was synergized with a combination of SMAC mimetics that is a potent necroptosis inducer. In summary, our discovery data provide a global molecular landscape for determining chemotherapy response in breast cancer at the proteome level. Furthermore, we provided a novel protein candidate to predict chemotherapy response and identify a novel protein YARS in combination with Smac mimetics synergistically induced necroptosis which produces more cell death in response to combined chemotherapy in breast cancer.

Citation Format: Han Suk Ryu. Tyrosine aminoacyl-tRNA synthetase sensitizes breast cancer to chemotherapy througha necroptosis-mediated mechanism: High-throughput proteomics and machine learningalgorithm based feature selection analyses [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P1-10-19.