Title: Identification of Src Family Kinases as potential therapeutic targets for chemotherapy-resistant triple negative breast cancer

Accession Number: GSE204825

Link to Dataset: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE204825

Repository: Gene Expression Omnibus (GEO)

Data Type(s): Gene Expression

Experiment Type(s): Expression profiling by high throughput sequencing

Organism(s): Homo sapiens

Summary: Neoadjuvant chemotherapy (NAC) remains the cornerstone of treatment for triple negative breast cancer (TNBC) with the goal of complete eradication of disease. However, for patients with residual disease after NAC, recurrence and mortality rates are high and identification of novel therapeutic targets are urgently needed. Here we quantified tyrosine phosphorylation (pTyr) mediated signaling networks in chemotherapy sensitive (CS) and resistant (CR) TNBC patient derived xenografts (PDX) to gain novel therapeutic insights. We identified Src Family Kinases (SFKs) as potential therapeutic targets in CR TNBC PDXs. Treatment with dasatinib, an FDA approved SFK inhibitor, led to inhibition of tumor growth in vivo. Further analysis of post-treatment PDXs revealed multiple mechanisms of actions of the drug confirming the multi-target inhibition of dasatinib. Direct analysis of pTyr in tumor specimens from TNBC patients suggest a low prevalence of SFK driven tumors in the clinic which may explain why clinical trials evaluating dasatinib failed in unselected breast cancer patients. Taken together, these results underscore the importance of pTyr characterization of tumors in identifying new targets as well as stratifying patients based on their activated signaling networks for therapeutic options. Our data also suggest that treatment with an SFK inhibitor may benefit a subset of TNBC patients with CR disease.

Publication(s) associated with this dataset:

• Kohale I, Yu J, Zhuang Y, Fan X, Reddy RJ, Sinnwell J, Kalari KR, Boughey J, Carter JM, Goetz MP, Wang L, White FM. 2022. Identification of Src family kinases as potential therapeutic targets for chemotherapy-resistant triple negative breast cancer. Cancers (Basel) 14(17):4220. doi:10.3390/cancers14174220 PMID:36077757 PMCID:PMC9454481

Project(s) associated with this dataset:

• Massachusetts Institute of Technology: High Resolution Mutation Spectra and Multi-Omics for Deducing Etiology and Predicting Disease