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Deconstructing intratumoral heterogeneity through multiomic and multiscale analysis of serial sections
Patrick G Schupp1,2, Samuel J Shelton1, Daniel J Brody1
1Department of Neurological Surgery, University of California, San Francisco, San Francisco,California, USA.
Biorxiv : the Preprint Server for Biology
|August 30, 2023
Summary
Understanding tumor cell diversity is key to fighting cancer resistance. Our new method, Multiomic and Multiscale Analysis (MOMA), precisely maps cancer cell evolution and identifies critical genes like AKR1C3 for better treatment strategies.
Area of Science:
- Oncology
- Genomics
- Computational Biology
Background:
- Intratumoral heterogeneity fuels therapeutic resistance in cancers.
- Understanding clonal evolution and molecular features is crucial for improving patient outcomes.
Approach:
- Developed a statistically motivated strategy, Multiomic and Multiscale Analysis (MOMA), for deconstructing intratumoral heterogeneity.
- Integrated single-nucleotide variants, copy-number variants, and gene expression from deep-sampled IDH-mutant astrocytomas.
- Validated phylogenies, spatial distributions, and transcriptional profiles of distinct malignant clones.
Key Points:
- Identified inaccuracies in common algorithms for cancer cell identification from single-cell transcriptomes.
- Correlated gene expression with tumor purity to discover optimal malignant cell markers.
- Identified AKR1C3 as a core gene consistently expressed by astrocytoma truncal clones, linked to poor outcomes.
Conclusions:
- MOMA offers a robust and flexible strategy for precise deconstruction of intratumoral heterogeneity.
- Clarified core molecular properties of distinct cellular populations within solid tumors.
- Provides a foundation for developing more effective targeted therapies against cancer evolution.
