bioRxivBioinformaticsPreprint
A single-cell atlas linking intratumoral states to therapeutic vulnerabilities across cancers
María González-BermejoiD, Laura Serrano-RoniD, Santiago García‐MartíniD, Óscar Lapuente-SantanaiD, Ignacio Sanz-PortilloiD, Pablo González-MartíneziD, Gonzalo Goméz-LópeziD, Fátima Al‐ShahrouriD
Abstract
Intratumoral heterogeneity (ITH) is a major determinant of therapeutic failure, yet its impact on drug response across cancers remains incompletely understood. Here, we present the Therapeutic Cancer Cell Atlas (TCCA), a pan-cancer single-cell resource integrating [~]1.8 million transcriptomes from 537 patients and 183 cancer cell lines spanning 34 tumor types. By combining single-cell transcriptomics with copy-number alteration inference and computational drug-response prediction, we systematically map therapeutic heterogeneity at subclonal resolution across cancers. Using this framework, we identify ten recurrent therapeutic clusters that capture conserved and context-specific drug vulnerabilities across tumor lineages. Notably, therapeutic heterogeneity is largely decoupled from genomic and transcriptomic diversity and instead arises from distinct functional transcriptional programs and tumor microenvironment (TME) states. Integration with transcriptional metaprograms and TME archetypes reveals how stress responses, proliferative states, lineage programs, and immune context shape drug sensitivity beyond tissue of origin. We further demonstrate the translational relevance of TCCA by linking therapeutic clusters to patient outcomes and validating predicted vulnerabilities using pharmacogenomic datasets, including clinically actionable examples in aggressive tumor subtypes. Together, TCCA provides a multidimensional atlas connecting subclonal states, microenvironmental context, and drug response, offering a scalable framework to guide therapeutic prioritization, drug repurposing, and combination strategies in precision oncology.
- organism: human
- assay: scRNA-seq
- research focus: clustering
- research focus: differential expression
- research focus: drug response / drug discovery
- approach: statistical model
- approach: machine learning (classical)
- Citations
- 0
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- A Pan-Cancer Single-Cell Compendium of Intratumoural Heterogeneity.
Ido Nofech-Mozes, Ashton Cook, … Sagi Abelson·bioRxiv·Jan 7, 2026
- Predicting heterogeneity in clone-specific therapeutic vulnerabilities using single-cell transcriptomic signatures
Chayaporn Suphavilai, Shumei Chia, … Niranjan Nagarajan·Genome MedicinePeer-reviewed·Dec 1, 2021
- Mechanistic Decoding of Pharmacodynamic Intratumoral Heterogeneity: A Pan-Cancer Regulatory Framework
Zhixiang Ren, Yiming Ren, … Huan Xu·bioRxiv·Oct 5, 2026
- A single-cell atlas of breast cancer cell lines to study tumour heterogeneity and drug response
Gennaro Gambardella, Gaetano Viscido, … Diego di Bernardo·Nature CommunicationsPeer-reviewed·Mar 31, 2022
- Spatial Transcriptomics in Breast Cancer Reveals Tumour Microenvironment-Driven Drug Responses and Clonal Therapeutic Heterogeneity
María José Jiménez‐Santos, Santiago García‐Martín, … Fátima Al‐Shahrour·NAR CancerPeer-reviewed·Oct 3, 2024
- Inferring therapeutic vulnerability within tumors through integration of pan-cancer cell line and single-cell transcriptomic profiles
Weijie Zhang, Danielle Maeser, … R. Stephanie Huang·bioRxiv·Oct 29, 2023