SCellBOW: AI-Driven Tumor Risk Stratification from Single-Cell Transcriptomics Using Phenotype Algebra Article Swipe
N Bhattacharya
,
Anja Rockstroh
,
Sanket Suhas Deshpande
,
Sam Koshy Thomas
,
Anunay Yadav
,
Chitrita Goswami
,
Smriti Chawla
,
Pierre Solomon
,
Cynthia Fourgeux
,
Gaurav Ahuja
,
Brett G. Hollier
,
Himanshu Kumar
,
Antoine Roquilly
,
Jérémie Poschmann
,
Melanie Lehman
,
Colleen C. Nelson
,
Debarka Sengupta
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.7554/elife.98469.2
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.7554/elife.98469.2
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 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 tumor 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 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.
Related Topics
Concepts
Risk stratification
Phenotype
Stratification (seeds)
Transcriptome
Computer science
Computational biology
Biology
Internal medicine
Medicine
Genetics
Gene
Gene expression
Germination
Botany
Dormancy
Seed dormancy
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.7554/elife.98469.2
- OA Status
- gold
- References
- 94
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4409736821
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4409736821Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.7554/elife.98469.2Digital Object Identifier
- Title
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SCellBOW: AI-Driven Tumor Risk Stratification from Single-Cell Transcriptomics Using Phenotype AlgebraWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-04-22Full publication date if available
- Authors
-
N Bhattacharya, Anja Rockstroh, Sanket Suhas Deshpande, Sam Koshy Thomas, Anunay Yadav, Chitrita Goswami, Smriti Chawla, Pierre Solomon, Cynthia Fourgeux, Gaurav Ahuja, Brett G. Hollier, Himanshu Kumar, Antoine Roquilly, Jérémie Poschmann, Melanie Lehman, Colleen C. Nelson, Debarka SenguptaList of authors in order
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https://doi.org/10.7554/elife.98469.2Publisher landing page
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://doi.org/10.7554/elife.98469.2Direct OA link when available
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Risk stratification, Phenotype, Stratification (seeds), Transcriptome, Computer science, Computational biology, Biology, Internal medicine, Medicine, Genetics, Gene, Gene expression, Germination, Botany, Dormancy, Seed dormancyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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94Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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