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Integrating Subclonal Response Heterogeneity to Define Cancer Organoid Therapeutic Sensitivity

Authors :
Samantha J. Anderson
Nataliya Volodymyrivna Uboha
Kayla K. Lemmon
Charles P. Heise
Michael F. Bassetti
Katherine A. Johnson
Jeremy D. Kratz
Devon Miller
Sam J. Lubner
Jens C. Eickhoff
Melissa C. Skala
Austin H. Yeung
Evie Carchman
Kristina A. Matkowskyj
Randall J. Kimple
Cristina B. Sanger
Eugene F. Foley
Aishwarya Sunil
Alyssa K. DeZeeuw
Amani A. Gillette
Shujah Rehman
Carley M. Sprackling
Daniel Mulkerin
Linda Clipson
Sean J. McIlwain
Daniel E. Abbott
Lucas C. Zarling
Cheri A. Pasch
Dustin A. Deming
Peter F. Favreau
Elise H. Lawson
Mark E. Burkard
Irene M. Ong
Noelle K. LoConte
Publication Year :
2021
Publisher :
Cold Spring Harbor Laboratory, 2021.

Abstract

Tumor heterogeneity is predicted to confer inferior clinical outcomes, however modeling heterogeneity in a manner that still represents the tumor of origin remains a formidable challenge. Sequencing technologies are limited in their ability to identify rare subclonal populations and predict response to the multitude of available treatments for patients. Patient-derived organotypic cultures have significantly improved the modeling of cancer biology by faithfully representing the molecular features of primary malignant tissues. Patient-derived cancer organoid (PCO) cultures contain numerous individual organoids with the potential to recapitulate heterogeneity, though PCOs are most commonly studied in bulk ignoring any diversity in the molecular profile or treatment response. Here we demonstrate the advantage of evaluating individual PCOs in conjunction with cellular level optical metabolic imaging to characterize the largely ignored heterogeneity within these cultures to predict clinical therapeutic response, identify subclonal populations, and determine patient specific mechanisms of resistance.

Details

Database :
OpenAIRE
Accession number :
edsair.doi...........60f052746f9774871c9acc42f5093d6d
Full Text :
https://doi.org/10.1101/2021.10.15.464556