Evidence map›Paper›PMID 38826317›Full record

ArticlebioRxiv : the preprint server for biology2024

Merging Metabolic Modeling and Imaging for Screening Therapeutic Targets in Colorectal Cancer.

Niki Tavakoli, Emma J Fong, Abigail Coleman, Yu-Kai Huang, Mathias Bigger, Michael E Doche, Seungil Kim, Heinz-Josef Lenz, Nicholas A Graham, Paul Macklin and 2 more

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in bioRxiv : the preprint server for biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

12 authors.

Niki TavakoliAlfred E. Mann Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, 90089, USA.
Emma J FongEllison Institute of Technology, Los Angeles, CA, 90064, USA.
Abigail ColemanEllison Institute of Technology, Los Angeles, CA, 90064, USA.
Yu-Kai HuangEllison Institute of Technology, Los Angeles, CA, 90064, USA.
Mathias BiggerEllison Institute of Technology, Los Angeles, CA, 90064, USA.
Michael E DocheEllison Institute of Technology, Los Angeles, CA, 90064, USA.
Seungil KimEllison Institute of Technology, Los Angeles, CA, 90064, USA.
Heinz-Josef LenzDivision of Medical Oncology, Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA, 90033, USA.
Nicholas A GrahamMork Family Department of Chemical Engineering and Materials Science, University of Southern California, Los Angeles, CA, 90089, USA.ORCID 0000-0002-6811-1941
Paul MacklinDepartment of Intelligent Systems Engineering, Indiana University, Bloomington, IN, 46202, USA.ORCID 0000-0002-9925-0151
Stacey D FinleyAlfred E. Mann Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, 90089, USA.ORCID 0000-0001-6901-3692
Shannon M MumenthalerAlfred E. Mann Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, 90089, USA.ORCID 0000-0003-2687-3769

Funding

USC/NORRIS COMPREHENSIVE CANCER CENTER (CORE) SUPPORTP30CA014089 · NCI · UNIVERSITY OF SOUTHERN CALIFORNIA · PI Fumito Ito · 1985 to 2026
$181.4M
Multiscale systems biology modeling to exploit tumor-stromal metabolic crosstalk in colorectal cancerU01CA232137 · NCI · UNIVERSITY OF SOUTHERN CALIFORNIA · PI FINLEY, STACEY DELERIA, MACKLIN, PAUL T · 2018 to 2022
$2.8M
NCI NIH HHS P30 CA014089NCI NIH HHS U01 CA232137
6 · The paper itself

Abstract

Cancer-associated fibroblasts (CAFs) play a key role in metabolic reprogramming and are well-established contributors to drug resistance in colorectal cancer (CRC). To exploit this metabolic crosstalk, we integrated a systems biology approach that identified key metabolic targets in a data-driven method and validated them experimentally. This process involved a novel machine learning-based method to computationally screen, in a high-throughput manner, the effects of enzyme perturbations predicted by a computational model of CRC metabolism. This approach reveals the network-wide effects of metabolic perturbations. Our results highlighted hexokinase (HK) as a crucial target, which subsequently became our focus for experimental validation using patient-derived tumor organoids (PDTOs). Through metabolic imaging and viability assays, we found that PDTOs cultured in CAF-conditioned media exhibited increased sensitivity to HK inhibition, confirming the model predictions. Our approach emphasizes the critical role of integrating computational and experimental techniques in exploring and exploiting CRC-CAF crosstalk.

Indexed as

cancer associated fibroblastcolorectal cancerfluorescence lifetime imaging microscopymetabolic modelingtumor microenvironment

Identifiers

PMID38826317
PMCPMC11142224

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.