Phenotypic deconvolution in heterogeneous cancer cell populations using drug-screening data

Alvaro Köhn-Luque; Even Moa Myklebust; Dagim Shiferaw Tadele; Mariaserena Giliberto; Leonard Schmiester; Jasmine Noory; Elise Harivel; Polina Arsenteva; Shannon M Mumenthaler; Fredrik Schjesvold; Kjetil Taskén; Jorrit M Enserink; Kevin Leder; Arnoldo Frigessi; Jasmine Foo

doi:10.1016/j.crmeth.2023.100417

Phenotypic deconvolution in heterogeneous cancer cell populations using drug-screening data

Cell Rep Methods. 2023 Mar 6;3(3):100417. doi: 10.1016/j.crmeth.2023.100417. eCollection 2023 Mar 27.

Authors

Alvaro Köhn-Luque¹, Even Moa Myklebust¹, Dagim Shiferaw Tadele^{2

3

4}, Mariaserena Giliberto^{5

6}, Leonard Schmiester¹, Jasmine Noory⁷, Elise Harivel^{1

8}, Polina Arsenteva^{1

9}, Shannon M Mumenthaler^{10

11

12}, Fredrik Schjesvold^{6

13}, Kjetil Taskén^{5

6}, Jorrit M Enserink^{2

3

14}, Kevin Leder¹⁵, Arnoldo Frigessi^{1

16}, Jasmine Foo⁷

Affiliations

¹ Oslo Centre for Biostatistics and Epidemiology, Faculty of Medicine, University of Oslo, 0372 Oslo, Norway.
² Department of Molecular Cell Biology, Institute for Cancer Research, Oslo University Hospital, 0379 Oslo, Norway.
³ Centre for Cancer Cell Reprogramming, Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, 0318 Oslo, Norway.
⁴ Translational Hematology and Oncology Research, Cleveland Clinic, Cleveland, OH 44131, USA.
⁵ Department of Cancer Immunology, Institute for Cancer Research, Oslo University Hospital, 0310 Oslo, Norway.
⁶ KG Jebsen Center for B-Cell Malignancies, Institute for Clinical Medicine, University of Oslo, 0450 Oslo, Norway.
⁷ Institute for Mathematics and its Applications, School of Mathematics, University of Minnesota, Minneapolis, MN 55455, USA.
⁸ ENSTA, Institut Polytechnique de Paris, Palaiseau, 91120 Paris, France.
⁹ Institut de Matématiques de Bourgogne, Universite de Bourgogne, Dijon Cedex, 21078 Dijon, France.
¹⁰ Lawrence J. Ellison Institute for Transformative Medicine, Los Angeles, CA 90064, USA.
¹¹ Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA 90089, USA.
¹² Department of Oncology, Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA.
¹³ Oslo Myeloma Center, Department of Hematology, Oslo University Hospital, 0450 Oslo, Norway.
¹⁴ Section for Biochemistry and Molecular Biology, Faculty of Mathematics and Natural Sciences, University of Oslo, 0037 Oslo, Norway.
¹⁵ College of Science and Engineering, University of Minnesota, Minneapolis, MN 55455, USA.
¹⁶ Oslo Centre for Biostatistics and Epidemiology, Oslo University Hospital, 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.

Keywords: deconvolution; drug resistance; drug screening; mechanistic modeling; multiple myeloma; tumor heterogeneity; tumor profiling.

Publication types

Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

Antineoplastic Agents* / pharmacology
Cell Line
Early Detection of Cancer
Genomics
Humans
Neoplasms* / drug therapy

Substances

Antineoplastic Agents