High-throughput phenotypic profiling of patient-derived colon cancer organoids to reveal a chemoresistant, AKT-driven tumor subpopulation and treatment strategy.

J Julia Michela Morris (University of Arizona Cancer Center, Tucson, AZ) P Pearl Wichaidit (University of Arizona Cancer Center, Tucson, AZ) K Katharine Johnson (University of Arizona Cancer Center, Tucson, AZ) L Lauren Riede (University of Arizona Cancer Center, Tucson, AZ) R Reeba Varghese (University of Arizona Cancer Center, Tucson, AZ) D Dante Bellomo (University of Arizona Cancer Center, Tucson, AZ) B Baris Kerimoglu (University of Arizona Cancer Center, Tucson, AZ) M Mary Yagle (University of Arizona Cancer Center, Tucson, AZ) M Megha Padi (Bioinformatics Shared Resource, Arizona Cancer Center, University of Arizona) K Kelvin Pond (University of Arizona Cancer Center, Tucson, AZ) N Nathan Ellis (University of Arizona Cancer Center, Tucson, AZ) A Aaron J. Scott (University of Arizona Cancer Center, Tucson, AZ) C Curtis Andrew Thorne (University of Arizona Cancer Center, Tucson, AZ)

Abstract

e15073 Background: Colorectal cancer (CRC) remains a significant clinical challenge due to its intrinsic heterogeneity, the paucity of personalized treatment options, and the commonality of chemoresistance. Methods: To better understand drug response dynamics at the single-cell level, we have developed a high-throughput, image-based phenotypic profiling pipeline using CRC patient-derived organoid (PDO) monolayer cultures. Diverse CRC PDOs representing multiple Consensus Molecular Subtypes were treated with a combination of 5-Fluorouracil and Oxaliplatin (FOX), imaged utilizing a biomarker panel identified using transcriptomic data, and analyzed using our newly developed phenotypic profiling pipeline. To further elucidate individual tumor response in vivo, we have also developed protocols to implant our PDOs into mice to create Patient Derived Xenograft (PDX) models. Results: Using our new analysis pipeline, we have identified a subpopulation of CRC cells that, after acute exposure to FOX treatment, exhibit elevated AKT signaling and increased expression of cancer stem cell markers. We observe this subpopulation across multiple PDOs, including across distinct CRC molecular subtypes, suggesting that chemotherapy itself may contribute to the enrichment of drug-resistant phenotypes driven by PI3K/AKT survival signaling. Targeting this survival phenotype, we also demonstrate that transient pre-treatment with the clinically utilized PI3K/mTOR dual inhibitor Dactolisib effectively sensitizes CRC PDOs to FOX, synergizing with this standard-of-care chemotherapy regimen and reducing the fraction of chemoresistant cells. Synergy score profiling revealed that this combination was broadly effective across diverse PDOs, with the strongest response noted in the organoid exhibiting the highest baseline AKT signaling. Preliminary data in our PDX models suggest this combination is synergistic in vivo as well. Conclusions: Our findings highlight the power of personalized organoid-based phenotypic profiling for dissecting molecular mechanisms of therapeutic resistance and support the rationale for transient PI3K/mTOR inhibition as a strategy to improve CRC treatments and outcomes.

Article Details

Volume / Issue Vol. 44, Issue 16_suppl
Published June 01, 2026
ISSN 0732-183X
Publisher Lippincott Williams & Wilkins

Journal Info

Journal of Clinical Oncology

Lippincott Williams & Wilkins

ISSN: 0732-183X Health Sciences

Authors (13)

J

Julia Michela Morris

University of Arizona Cancer Center, Tucson, AZ

P

Pearl Wichaidit

University of Arizona Cancer Center, Tucson, AZ

K

Katharine Johnson

University of Arizona Cancer Center, Tucson, AZ

L

Lauren Riede

University of Arizona Cancer Center, Tucson, AZ

R

Reeba Varghese

University of Arizona Cancer Center, Tucson, AZ

D

Dante Bellomo

University of Arizona Cancer Center, Tucson, AZ

B

Baris Kerimoglu

University of Arizona Cancer Center, Tucson, AZ

M

Mary Yagle

University of Arizona Cancer Center, Tucson, AZ

M

Megha Padi

Bioinformatics Shared Resource, Arizona Cancer Center, University of Arizona

K

Kelvin Pond

University of Arizona Cancer Center, Tucson, AZ

N

Nathan Ellis

University of Arizona Cancer Center, Tucson, AZ

A

Aaron J. Scott

University of Arizona Cancer Center, Tucson, AZ

C

Curtis Andrew Thorne

University of Arizona Cancer Center, Tucson, AZ