Precision unveiled: Synergistic genomic landscapes in breast cancer—Integrating single‐cell analysis and decoding drug toxicity for elite prognostication and tailored therapeutics

• Published in: Environmental toxicology 2024-06, Vol.39 (6), p.3448-3472
• Main Authors: Jiang, Chenglu, Zhang, Shengke, Jiang, Lai, Chen, Zipei, Chen, Haiqing, Huang, Jinbang, Tang, Jingyi, Luo, Xiufang, Yang, Guanhu, Liu, Jie, Chi, Hao
• Format: Article
• Language: English
• Subjects: Algorithms; biomarker discovery; Breast cancer; breast neoplasms; breasts; Cancer; Cell interactions; Classification; Classifiers; Decision making; drug toxicity; Drugs; ecotoxicology; Genes; Genetic diversity; Genetic variation; genomics; Glutamine; Immunology; Lymphocytes; Lymphocytes T; Machine learning; Medical treatment; Metabolism; Mutation; Patients; personalized medicine; Robustness; single‐cell analysis; Survival; T-lymphocytes; Therapeutic applications; Toxicity; Transcriptomics; Types
• ISSN: 1520-4081; 1522-7278
• DOI: 10.1002/tox.24205

Background Globally, breast cancer, with diverse subtypes and prognoses, necessitates tailored therapies for enhanced survival rates. A key focus is glutamine metabolism, governed by select genes. This study explored genes associated with T cells and linked them to glutamine metabolism to construct a prognostic staging index for breast cancer patients for more precise medical treatment. Methods Two frameworks, T‐cell related genes (TRG) and glutamine metabolism (GM), stratified breast cancer patients. TRG analysis identified key genes via hdWGCNA and machine learning. T‐cell communication and spatial transcriptomics emphasized TRG's clinical value. GM was defined using Cox analyses and the Lasso algorithm. Scores categorized patients as TRG_high+GM_high (HH), TRG_high+GM_low (HL), TRG_low+GM_high (LH), or TRG_low+GM_low (LL). Similarities between HL and LH birthed a “Mixed” class and the TRG_GM classifier. This classifier illuminated gene variations, immune profiles, mutations, and drug responses. Results Utilizing a composite of two distinct criteria, we devised a typification index termed TRG_GM classifier, which exhibited robust prognostic potential for breast cancer patients. Our analysis elucidated distinct immunological attributes across the classifiers. Moreover, by scrutinizing the genetic variations across groups, we illuminated their unique genetic profiles. Insights into drug sensitivity further underscored avenues for tailored therapeutic interventions. Conclusion Utilizing TRG and GM, a robust TRG_GM classifier was developed, integrating clinical indicators to create an accurate predictive diagnostic map. Analysis of enrichment disparities, immune responses, and mutation patterns across different subtypes yields crucial subtype‐specific characteristics essential for prognostic assessment, clinical decision‐making, and personalized therapies. Further exploration is warranted into multiple fusions between metrics to uncover prognostic presentations across various dimensions.