ML Models Using Single-Cell Sequencing to Forecast Chemotherapy Resistance in Breast Cancer
Keywords:
Tumour heterogeneity, machine learning, scRNA-seq, single-cell sequencing, breast cancer, chemotherapy resistance, predictive modelling, biomarker identification.Abstract
One of the most common cancers in the world and a major contributor to cancer-related death in women is breast cancer. Chemotherapy is a common therapeutic approach, especially for aggressive and advanced breast cancer. Chemotherapeutic agent resistance, however, continues to be a major clinical problem that frequently leads to treatment failure, illness recurrence, and a bad prognosis for patients. Therefore, enhancing therapeutic outcomes and increasing precision oncology require an understanding of the ability to forecast chemotherapy resistance. Conventional bulk RNA sequencing techniques yield gene expression patterns that are averaged over substantial cell populations, which may mask significant biological differences within tumours.
This restriction makes it impossible to accurately identify heterogeneous tumour cell groups and uncommon resistant clones that could withstand treatment and cause relapse. Single-cell sequencing technologies, on the other hand, provide high-resolution molecular profiling at the individual cell level. Tumour heterogeneity may be thoroughly examined and malignant cells and the tumour microenvironment, including immunological and stromal components, can be characterised using single-cell RNA sequencing (scRNA-seq). Different resistant subpopulations and dynamic cellular changes that lead to chemotherapy failure can be identified using this method.
A potent computational method for deriving significant insights from intricate scRNA-seq datasets is machine learning (ML). ML models can distinguish between resistant and sensitive tumour states, find predictive biomarkers, and discover therapy-induced transcriptional changes by examining high-dimensional transcriptome profiles. Additionally, these models can reveal clonal evolution trajectories, cell state transitions, and circuit activation patterns linked to drug resistance. The integration of scRNA-seq with ML-based prediction frameworks for predicting chemotherapy resistance in breast cancer is the main focus of this study.
It covers popular machine learning algorithms, feature engineering techniques, preprocessing pipelines, and validation tactics needed to guarantee reliable prediction. The study also identifies important biological processes that contribute to resistance, such as the epithelial–mesenchymal transition (EMT), drug efflux transporter activation, increased DNA repair capability, metabolic reprogramming, and immune microenvironment remodeling. All things considered, the combination of scRNA-seq and ML offers a promising approach for the early detection of resistant tumour clones, directing individualized treatment choices, maximizing chemotherapeutic selection, and enhancing long-term outcomes for patients with breast cancer.
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