Proteomics-driven multi-omics classification of triple negative breast cancer
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| Year of publication | 2025 |
| Type | Conference abstract |
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| Description | Triple-negative breast cancer (TNBC) is an aggressive subtype of breast cancer lacking estrogen, progesterone, and HER2 receptors. It makes up 15% of all breast cancer cases and often affects women under 40. Due to its poor prognosis and the absence of targetable receptors, chemotherapy is the primary treatment. TNBC has been categorized into about four subgroups based on gene expression profiles, but we believe that next-generation proteomics could provide a more accurate classification, as proteins are the key molecular effectors in cells. This study investigated a well-characterized cohort of 105 fresh-frozen triple-negative breast cancer (TNBC) tissues obtained from the Masaryk Memorial Cancer Institute. Employing whole-exome sequencing (WES) and RNA sequencing via Illumina NovaSeq, alongside data-independent acquisition-based proteomics, we generated high-quality multi-omics data for 96 samples. The integration of RNA-Seq and proteomics datasets led to the identification of 1223 transcript-protein pairs exhibiting strong correlations. Utilizing these 1223 proteins, we classified the TNBC samples into 7 distinct clusters. For TNBC subtypes discrimination, we developed a classifier using the Random Forest algorithm containing 45 proteins with an overall accuracy of 78.6% when assessed using Leave-One-Out Cross-Validation. Furthermore, to elucidate the molecular underpinnings of these clusters, we conducted gene set enrichment analysis (GSEA) utilizing both proteomics and RNA-seq datasets. Top candidates were selected based on GSEA outcomes, as well as progression-free survival and overall survival analyses. Functional validation of top candidates is currently in process. The pivotal aim of this project is to pinpoint therapeutic targets that could enhance treatment strategies for TNBC. Acknowledgement Supported by the Ministry of Health of the Czech Republic in cooperation with the Czech Health Research Council under project No. NU22-08-00230. Supported by the project National Institute for Cancer Research (Programme EXCELES, ID Project No. LX22NPO5102)—Funded by the European Union—Next Generation EU. |
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