Abstract 54: Integrating connectivity mapping resources to prioritize and personalize drug candidates for individual triple negative breast cancer patients.

Abstract Triple negative breast cancer (TNBC) is the most aggressive type of breast cancer with the least therapeutic options. While some patients respond to aggressive standard treatments, many others do not, and recurrence risk for many patients diagnosed with TNBC remains high. With the aim of furthering our understanding of mechanisms of TNBC tumorigenesis and treatment resistance, and to accelerate the development of novel personalized therapeutics on an individual patient level, we analyzed RNA-seq samples collected from eight primary TNBC tumors and their matched PDX models created from patients treated at the Dubin Breast Center. First, we identified differentially expressed up-regulated genes that are uniquely highly expressed in the primary tumors and their matching PDX models while lowly expressed across hundreds of normal human tissues and cell types profiled and harmonized from GTEx, ARCHS4, and Tabula Sapiens. We then examined the effect of knocking out these up-regulated genes in TNBC by querying the DepMap resource. From each patient’s up-regulated genes, we selected those that were most essential to the viability of TNBC cell lines in CCLE and DepMap. To identify small molecule compounds that may down-regulate the expression of individual patient-specific targets, and in turn specifically reduce the viability of the cells within the TNBC tumors, we integrated five Connectivity Mapping resources that measured transcriptomics responses of human cell lines to thousands of approved drugs and other compounds. The Connectivity Mapping resources integrated are the LINCS L1000 dataset (33,571 compounds), Novartis Institutes for BioMedical Research (NIBR) DRUG-seq U2OS MoA Box (4,343 compounds), Gingko Bioworks GDPx1 and GDPx2 (1,353 compounds), and Tahoe-100M (379 compounds). We queried these resources to find consensus FDA approved and pre-clinical compounds that would maximally reduce the expression of the patient-specific identified target genes. This approach is encoded into an online tool called Dr. Gene Budger 2.0 (DGB2), which integrates the Connectivity Mapping resources for the purpose of identifying drugs that maximally increase or decrease the mRNA expression of a single target or a set of genes. With DGB2, we identified candidate therapeutic compounds, individualized for each patient TNBC tumor/PDX model, that are predicted to inhibit tumor growth. Once validated, this approach can be translated to individualize treatment for patients with TNBC, particularly those with disease that is resistant to current standard-of-care treatments. DGB2 is freely available at https://appyters.maayanlab.cloud/#/Drug_Gene_Budger2. Citation Format: Lily D. Taub, Anna Byrd, Daniel J. Clarke, Ido Diamant, Criseyda Martinez, Elisa Port, Hanna Y. Irie, Avi Maayan. Integrating connectivity mapping resources to prioritize and personalize drug candidates for individual triple negative breast cancer patients [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 54.