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

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

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.

Original languageEnglish (US)
Article number100417
JournalCell Reports Methods
Volume3
Issue number3
DOIs
StatePublished - Mar 27 2023

Bibliographical note

Funding Information:
The numerical computations were performed on resources provided by UNINETT Sigma2, the National Infrastructure for High Performance Computing and Data Storage in Norway. The project received funding from the UiO:LifeScience initiative through the convergence environment grant PerCaThe. A.K.-L. E.M.M. and A.F. were supported by the center for research-based-innovation BigInsight under grant 237718 of Norges Forskningsråd. J.F. and K.L. were supported by the Fulbright US-Norway Foundation. J.F. K.L. and J.N. were supported by the University of Oslo-University of Minnesota Norwegian Centennial Chair Grant. J.F. was supported by the US National Science Foundation under grant number DMS-2052465. K.L. was supported by the US National Science Foundation under grant number CMMI-1552764. We acknowledge funding from the Research Council of Norway with project numbers 294916, 261936, 309273, and 262652; the Norwegian Cancer Society with project number 182524; and the Norwegian Health Authority South-East with project number 2019096. The authors also acknowledge the Centre for Digital Life Norway for supporting the partner projects PerCaThe and PINpOINT. We thank the Digital Scholarship Center, University of Oslo, for insightful advice on the visual representation and communication of our research findings. Conceptualization, A.K.-L. K.L. A.F. and J.F.; formal analysis, A.K.-L. E.M.M. J.N. E.H. P.A. K.L. A.F. and J.F.; funding acquisition, A.K.-L. K.L. A.F. and J.F.; investigation, E.M.M. D.S.T. M.G. S.M.M. F.S. K.T. and J.M.E.; methodology, A.K.-L. E.M.M. K.L. A.F. and J.F.; resources, S.M.M. F.S. K.T. J.M.E. and A.F.; software, A.K.-L. E.M.M. L.S. J.N. E.H. and P.A.; validation, A.K.-L. E.M.M. D.S.T. M.G. L.S. S.M.M. F.S. K.T. and J.M.E.; visualization, A.K.-L. E.M.M. M.G. K.L. and J.F.; writing – original draft, A.K.-L. E.M.M. K.L. A.F. and J.F.; writing – review & editing, A.K.-L. E.M.M. S.M.M. F.S. K.T. J.M.E. K.L. A.F. and J.F. The authors declare no competing interests.

Funding Information:
The numerical computations were performed on resources provided by UNINETT Sigma2, the National Infrastructure for High Performance Computing and Data Storage in Norway. The project received funding from the UiO :LifeScience initiative through the convergence environment grant PerCaThe. A.K.-L., E.M.M., and A.F. were supported by the center for research-based-innovation BigInsight under grant 237718 of Norges Forskningsråd . J.F. and K.L. were supported by the Fulbright US-Norway Foundation . J.F., K.L., and J.N. were supported by the University of Oslo - University of Minnesota Norwegian Centennial Chair Grant. J.F. was supported by the US National Science Foundation under grant number DMS-2052465 . K.L. was supported by the US National Science Foundation under grant number CMMI-1552764 . We acknowledge funding from the Research Council of Norway with project numbers 294916 , 261936 , 309273 , and 262652 ; the Norwegian Cancer Society with project number 182524 ; and the Norwegian Health Authority South-East with project number 2019096 . The authors also acknowledge the Centre for Digital Life Norway for supporting the partner projects PerCaThe and PINpOINT. We thank the Digital Scholarship Center, University of Oslo, for insightful advice on the visual representation and communication of our research findings.

Publisher Copyright:
© 2023 The Author(s)

Keywords

  • CP: Cancer biology
  • CP: Systems biology
  • deconvolution
  • drug resistance
  • drug screening
  • mechanistic modeling
  • multiple myeloma
  • tumor heterogeneity
  • tumor profiling

PubMed: MeSH publication types

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

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