Drug combination prioritization for cancer treatment using single-cell RNA-seq based transfer learning

Abstract

Precision oncology seeks to match patients to the optimal pharmacological regimen; yet, due to tumor heterogeneity, this is challenging. Numerous studies have been conducted to produce clinically relevant pharmacological response forecasts by integrating modern machine learning algorithms and several data types. Insufficient patient numbers and lack of knowledge of the molecular targets for each drug under study limit their use. As a proof of concept, we use single-cell RNA-seq based transfer learning to contextualize patients' tumor cells in terms of their more similar cell lines with known susceptibility to drug combinations. Our objective is to maximize the translational potential of in-vitro assays for identifying synergistic drug combinations and prioritizing them for clinical use. Consistent findings in a cohort of breast cancer patients corroborated our understanding of the disease's molecular subtypes. To aid in creating personalized treatments and data-driven clinical trials, we identified the most prevalent cell lines and prioritized synergistic combinations based on tumor compositions at various resolution levels.

Single-cell gene set enrichment analysis and transfer learning for functional annotation of scRNA-seq data

“…For example, we have already shown that we can use breast cancer cell lines for automatic cancer subtype classification starting from the single-cell transcriptomic dataset of patient biopsies ( 7 ). Recently, it has also been shown that with transfer learning, we can use the knowledge of sensitive drugs for each cell line to predict the patient's treatment once the patient’s cells were confidently mapped on the reference atlas ( 78 ). Here, we demonstrated our method has high accuracy in cell mapping even when we profile cells’ transcriptomes after several culture passages in a different batch or with a different sequencing technique.…”

Single-cell gene set enrichment analysis and transfer learning for functional annotation of scRNA-seq data

“…For example, we have already shown that we can use breast cancer cell lines for automatic cancer subtype classification starting from the single cell transcriptomic dataset of patient biopsies (7). Recently, it has also been shown that with transfer learning, we can use the knowledge of sensitive drugs for each cell line to predict the patient's treatment once the patient's cells were confidently mapped on the reference atlas (73). Here, we demonstrated our method has high accuracy in cell mapping even when we profile cells' transcriptomes after several culture passages in a different batch or with a different sequencing technique.…”

Population-level integration of single-cell datasets enables multi-scale analysis across samples

“…Algorithms for efficient use of reference atlases are known as single-cell reference mapping methods [19][20][21], which build upon data integration algorithms to rapidly update an existing reference atlas by integrating a new query dataset. Transferring information from the reference atlas to the query enables efficient cell-type annotation of the query cells, automatic identification of novel (sub) populations [20,22] and unseen disease or treatment affected cells [4,22,23].…”