Functional drug susceptibility testing using single-cell mass predicts treatment outcome in patient-derived cancer neurosphere models

Max A. Stockslager; Seth Malinowski; Mehdi Touat; Jennifer C. Yoon; Jack Geduldig; Mahnoor Mirza; Annette S. Kim; Patrick Y. Wen; Kin-Hoe Chow; Keith L. Ligon; Scott R. Manalis

Cell Reports (Oct 2021)

Functional drug susceptibility testing using single-cell mass predicts treatment outcome in patient-derived cancer neurosphere models

Max A. Stockslager,
Seth Malinowski,
Mehdi Touat,
Jennifer C. Yoon,
Jack Geduldig,
Mahnoor Mirza,
Annette S. Kim,
Patrick Y. Wen,
Kin-Hoe Chow,
Keith L. Ligon,
Scott R. Manalis

Affiliations

Max A. Stockslager: Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA; Koch Institute for Integrative Cancer Research, Cambridge, MA, USA
Seth Malinowski: Department of Oncologic Pathology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
Mehdi Touat: Department of Oncologic Pathology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA; Broad Institute of Harvard and MIT, Cambridge, MA, USA; Sorbonne Université, Inserm, CNRS, UMR S 1127, Institut du Cerveau et de la Moelle épinière, ICM, AP-HP, Hôpitaux Universitaires La Pitié Salpêtrière - Charles Foix, Service de Neurologie 2-Mazarin, Paris, France
Jennifer C. Yoon: Koch Institute for Integrative Cancer Research, Cambridge, MA, USA
Jack Geduldig: Department of Oncologic Pathology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
Mahnoor Mirza: Koch Institute for Integrative Cancer Research, Cambridge, MA, USA
Annette S. Kim: Department of Pathology, Brigham & Women’s Hospital, Harvard Medical School, Boston, MA, USA
Patrick Y. Wen: Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA USA
Kin-Hoe Chow: Center for Patient-Derived Models, Dana-Farber Cancer Institute, Boston, MA, USA
Keith L. Ligon: Department of Oncologic Pathology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA; Broad Institute of Harvard and MIT, Cambridge, MA, USA; Department of Pathology, Brigham & Women’s Hospital, Harvard Medical School, Boston, MA, USA; Center for Patient-Derived Models, Dana-Farber Cancer Institute, Boston, MA, USA; Department of Pathology, Boston Children’s Hospital, Harvard Medical School, Boston, MA, USA; Corresponding author
Scott R. Manalis: Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA; Koch Institute for Integrative Cancer Research, Cambridge, MA, USA; Broad Institute of Harvard and MIT, Cambridge, MA, USA; Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA; Corresponding author

Journal volume & issue: Vol. 37, no. 1
p. 109788

Abstract

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Summary: Functional precision medicine aims to match individual cancer patients to optimal treatment through ex vivo drug susceptibility testing on patient-derived cells. However, few functional diagnostic assays have been validated against patient outcomes at scale because of limitations of such assays. Here, we describe a high-throughput assay that detects subtle changes in the mass of individual drug-treated cancer cells as a surrogate biomarker for patient treatment response. To validate this approach, we determined ex vivo response to temozolomide in a retrospective cohort of 69 glioblastoma patient-derived neurosphere models with matched patient survival and genomics. Temozolomide-induced changes in cell mass distributions predict patient overall survival similarly to O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation and may aid in predictions in gliomas with mismatch-repair variants of unknown significance, where MGMT is not predictive. Our findings suggest cell mass is a promising functional biomarker for cancers and drugs that lack genomic biomarkers.

Published in Cell Reports

ISSN: 2211-1247 (Online)
Publisher: Elsevier
Country of publisher: Netherlands
LCC subjects: Science: Biology (General)
Website: http://www.cell.com/cell-reports/home

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