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Published on: June 30, 2017
Phenotypic deconvolution in heterogeneous cancer cell populations using drug-screening data
Alvaro Köhn-Luque1, Even Moa Myklebust1, Dagim Shiferaw Tadele2,3,4
1Oslo Centre for Biostatistics and Epidemiology, Faculty of Medicine, University of Oslo, 0372 Oslo, Norway.
Abstract:
Tumor heterogeneity is an important driver of treatment failure in cancer since therapies often select for drug-tolerant or drug-resistant cellular subpopulations that drive tumor growth and recurrence. Profiling the drug-response heterogeneity of tumor samples using traditional genomic deconvolution methods has yielded limited results, due in part to the imperfect mapping between genomic variation and functional characteristics. Here, we leverage mechanistic population modeling to develop a statistical framework for profiling phenotypic heterogeneity from standard drug-screen data on bulk tumor samples. This method, called PhenoPop, reliably identifies tumor subpopulations exhibiting differential drug responses and estimates their drug sensitivities and frequencies within the bulk population. We apply PhenoPop to synthetically generated cell populations, mixed cell-line experiments, and multiple myeloma patient samples and demonstrate how it can provide individualized predictions of tumor growth under candidate therapies. This methodology can also be applied to deconvolution problems in a variety of biological settings beyond cancer drug response.
Insights
Tumor heterogeneity drives cancer treatment failure. A new method, PhenoPop, uses population modeling to identify drug-resistant cancer subpopulations from bulk samples, enabling personalized therapy predictions.
Area of Science:
- Oncology
- Computational Biology
- Systems Biology
Background:
- Tumor heterogeneity, characterized by drug-tolerant or resistant subpopulations, is a major cause of cancer treatment failure and recurrence.
- Traditional genomic deconvolution methods struggle to accurately map genomic variations to functional drug responses.
- Understanding phenotypic heterogeneity is crucial for developing effective cancer therapies.
Purpose of the Study:
- To develop a novel statistical framework for profiling phenotypic heterogeneity in bulk tumor samples.
- To identify distinct tumor subpopulations with differential drug responses and estimate their frequencies and sensitivities.
- To enable individualized predictions of tumor growth under various therapeutic strategies.
Main Methods:
- Leveraging mechanistic population modeling to create a statistical framework.
- Applying the PhenoPop method to analyze standard drug-screen data from bulk tumor samples.
- Validating the approach using synthetic cell populations, mixed cell-line experiments, and patient samples.
Main Results:
- PhenoPop reliably identifies tumor subpopulations with varying drug sensitivities.
- The method accurately estimates the frequencies of these subpopulations within bulk samples.
- Individualized predictions of tumor growth under different therapies were demonstrated.
Conclusions:
- PhenoPop offers a powerful tool for dissecting phenotypic heterogeneity in cancer.
- This methodology can guide the development of personalized cancer treatment strategies.
- The approach has broader applicability to deconvolution problems in diverse biological systems.
