SCellBOW: AI-Driven Tumor Risk Stratification from Single-Cell Transcriptomics Using Phenotype Algebra

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Bibliografiske detaljer
Udgivet i:	bioRxiv (Feb 18, 2025)
Hovedforfatter:	Bhattacharya, Namrata
Andre forfattere:	Rockstroh, Anja, Deshpande, Sanket Suhas, Sam Koshy Thomas, Yadav, Anunay, Goswami, Chitrita, Chawla, Smriti, Solomon, Pierre, Fourgeux, Cynthia, Ahuja, Gaurav, Hollier, Brett, Kumar, Himanshu, Roquilly, Antoine, Poschmann, Jeremie, Lehman, Melanie, Nelson, Colleen C, Sengupta, Debarka
Udgivet:	Cold Spring Harbor Laboratory Press
Fag:	Transcriptomes Gene expression Peripheral blood mononuclear cells Leukocytes (mononuclear) Semantics Splenocytes Expression vectors Phenotypes
Online adgang:	Citation/Abstract Full Text - PDF Full text outside of ProQuest
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Beskrivelse
Resumen:	Single-cell RNA-sequencing (scRNA-seq) coupled with robust computational analysis facilitates the characterization of phenotypic heterogeneity within tumors. Current scRNA-seq analysis pipelines are capable of identifying a myriad of malignant and non-malignant cell subtypes from single-cell profiling of tumors. However, given the extent of intra-tumoral heterogeneity, it is challenging to assess the risk associated with individual malignant cell subpopulations, primarily due to the complexity of the cancer phenotype space and the lack of clinical annotations associated with tumor scRNA-seq studies. To this end, we introduce SCellBOW, a scRNA-seq analysis framework inspired by document embedding techniques from the domain of Natural Language Processing (NLP). SCellBOW is a novel computational approach that facilitates effective identification and high-quality visualization of single-cell subpopulations. We compared SCellBOW with existing best practice methods for its ability to precisely represent phenotypically divergent cell types across multiple scRNA-seq datasets, including our in-house generated human splenocyte and matched peripheral blood mononuclear cell (PBMC) dataset. For malignant cells, SCellBOW estimates the relative risk associated with each cluster and stratifies them based on their aggressiveness. This is achieved by simulating how the presence or absence of a specific malignant cell subpopulation influences disease prognosis. Using SCellBOW, we identified a hitherto unknown and pervasive AR−/NElow (androgen-receptor-negative, neuroendocrine-low) malignant subpopulation in metastatic prostate cancer with conspicuously high aggressiveness. Overall, the risk-stratification capabilities of SCellBOW hold promise for formulating tailored therapeutic interventions by identifying clinically relevant tumor subpopulations and their impact on prognosis.Competing Interest StatementThe authors have declared no competing interest.Footnotes* New results added and figures modified.
ISSN:	2692-8205
DOI:	10.1101/2022.12.28.522060
Fuente:	Biological Science Database