Machine learning-driven exploration of drug therapies for triple-negative breast cancer treatment

Aman Chandra Kaushik; Zhongming Zhao

doi:10.3389/fmolb.2023.1215204

Machine learning-driven exploration of drug therapies for triple-negative breast cancer treatment

Front Mol Biosci. 2023 Aug 4:10:1215204. doi: 10.3389/fmolb.2023.1215204. eCollection 2023.

Authors

Aman Chandra Kaushik¹, Zhongming Zhao^{1

2

3}

Affiliations

¹ Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, United States.
² Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, United States.
³ MD Anderson Cancer Center, UTHealth Graduate School of Biomedical Sciences, Houston, TX, United States.

Abstract

Breast cancer is the second leading cause of cancer death in women among all cancer types. It is highly heterogeneous in nature, which means that the tumors have different morphologies and there is heterogeneity even among people who have the same type of tumor. Several staging and classifying systems have been developed due to the variability of different types of breast cancer. Due to high heterogeneity, personalized treatment has become a new strategy. Out of all breast cancer subtypes, triple-negative breast cancer (TNBC) comprises ∼10%-15%. TNBC refers to the subtype of breast cancer where cells do not express estrogen receptors, progesterone receptors, or human epidermal growth factor receptors (ERs, PRs, and HERs). Tumors in TNBC have a diverse set of genetic markers and prognostic indicators. We scanned the Cancer Cell Line Encyclopedia (CCLE) and Genomics of Drug Sensitivity in Cancer (GDSC) databases for potential drugs using human breast cancer cell lines and drug sensitivity data. Three different machine-learning approaches were used to evaluate the prediction of six effective drugs against the TNBC cell lines. The top biomarkers were then shortlisted on the basis of their involvement in breast cancer and further subjected to testing for radion resistance using data from the Cleveland database. It was observed that Panobinostat, PLX4720, Lapatinib, Nilotinib, Selumetinib, and Tanespimycin were six effective drugs against the TNBC cell lines. We could identify potential derivates that may be used against approved drugs. Only one biomarker (SETD7) was sensitive to all six drugs on the shortlist, while two others (SRARP and YIPF5) were sensitive to both radiation and drugs. Furthermore, we did not find any radioresistance markers for the TNBC. The proposed biomarkers and drug sensitivity analysis will provide potential candidates for future clinical investigation.

Keywords: SETD7; SRARP; YIPF5; drug sensitivity; pharmacogenomics.

Grants and funding

AK received the CPRIT Post-doctoral Fellowship in the Biomedical Informatics, Genomics and Translational Cancer Research Training Program (BIG-TCR) funded by Cancer Prevention and Research Institute of Texas (CPRIT RP210045). ZZ was partially supported by National Institutes of Health grants (R01LM012806).