Dissecting cell type-specific metabolism in pancreatic ductal adenocarcinoma
Abstract
Tumors are composed of many different cell types including cancer cells, fibroblasts, and immune cells. Dissecting functional metabolic differences between cell types within a mixed population can be challenging due to the rapid turnover of metabolites relative to the time needed to isolate cells. To overcome this challenge, we traced isotope-labeled nutrients into macromolecules that turn over more slowly than metabolites. This approach was used to assess differences between cancer cell and fibroblast metabolism in murine pancreatic cancer organoid-fibroblast co-cultures and tumors. Pancreatic cancer cells exhibited increased pyruvate carboxylation relative to fibroblasts, and this flux depended on both pyruvate carboxylase and malic enzyme 1 activity. Consequently, expression of both enzymes in cancer cells was necessary for organoid and tumor growth, demonstrating that dissecting the metabolism of specific cell populations within heterogeneous systems can identify dependencies that may not be evident from studying isolated cells in culture or bulk tissue.
Data availability
All data generated or analyzed during this study are included in the manuscript and supporting files.
Article and author information
Author details
Funding
Damon Runyon Cancer Research Foundation (DRG-2241-15)
- Allison N Lau
National Cancer Institute (U54CA163109)
- Vasilena Gocheva
Human Frontiers Science Program (LT000195/2015-L)
- Giulia Biffi
EMBO (ALTF 1203-2014)
- Giulia Biffi
Howard Hughes Medical Institute
- Tyler Jacks
- Matthew G Vander Heiden
MRC (CSF MR/P008801/1)
- Nicholas J Matheson
NHSBT (WPA15-02)
- Nicholas J Matheson
NIHR Cambridge BRC
- Nicholas J Matheson
National Institutes of Health (R01CA211184)
- Omer Yilmaz
National Institutes of Health (R01CA034992)
- Omer Yilmaz
Lustgarten Foundation
- Matthew G Vander Heiden
Damon Runyon Cancer Research Foundation (DRG-2367-19)
- Sharanya Sivanand
Stand Up To Cancer
- Matthew G Vander Heiden
MIT Center for Precision Cancer Medicine
- Matthew G Vander Heiden
Ludwig Center at MIT
- Tyler Jacks
- Matthew G Vander Heiden
Emerald Foundation
- Matthew G Vander Heiden
National Cancer Institute (R01CA168653)
- Matthew G Vander Heiden
National Cancer Institute (R01CA201276)
- Matthew G Vander Heiden
National Cancer Institute (R35CA242379)
- Matthew G Vander Heiden
National Cancer Institute (P30CA14051)
- Matthew G Vander Heiden
Damon Runyon Cancer Research Foundation (DRG-2299-17)
- Evan C Lien
National Cancer Institute (K99CA234221)
- Allison N Lau
National Institutes of Health (T32GM007287)
- Zhaoqi Li
- Kiera M Sapp
Jane Coffin Childs Memorial Fund for Medical Research
- Alicia M Darnell
- Vasilena Gocheva
Swedish Foundation for Strategic Research
- Raphael Ferreira
Knut and Alice Wallenberg Foundation
- Raphael Ferreira
Barbro Osher Pro Suecia Foundation
- Raphael Ferreira
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Ethics
Animal experimentation: All animal studies were approved by the MIT Committee on Animal Care under protocol #0119-001-22.
Reviewing Editor
- Ralph DeBerardinis, UT Southwestern Medical Center, United States
Publication history
- Received: March 9, 2020
- Accepted: July 9, 2020
- Accepted Manuscript published: July 10, 2020 (version 1)
- Version of Record published: August 5, 2020 (version 2)
Copyright
© 2020, Lau et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
Metrics
-
- 5,537
- Page views
-
- 1,037
- Downloads
-
- 29
- Citations
Article citation count generated by polling the highest count across the following sources: Scopus, Crossref, PubMed Central.
Download links
Downloads (link to download the article as PDF)
Open citations (links to open the citations from this article in various online reference manager services)
Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)
Further reading
-
- Cancer Biology
- Chromosomes and Gene Expression
The transformation of normal to malignant cells is accompanied by substantial changes in gene expression programs through diverse mechanisms. Here, we examined the changes in the landscape of transcription start sites and alternative promoter (AP) usage and their impact on the translatome in TCL1-driven chronic lymphocytic leukemia (CLL). Our findings revealed a marked elevation of APs in CLL B cells from Eµ-Tcl1 transgenic mice, which are particularly enriched with intra-genic promoters that generate N-terminally truncated or modified proteins. Intra-genic promoter activation is mediated by (1) loss of function of ‘closed chromatin’ epigenetic regulators due to the generation of inactive N-terminally modified isoforms or reduced expression; (2) upregulation of transcription factors, including c-Myc, targeting the intra-genic promoters and their associated enhancers. Exogenous expression of Tcl1 in MEFs is sufficient to induce intra-genic promoters of epigenetic regulators and promote c-Myc expression. We further found a dramatic translation downregulation of transcripts bearing CNY cap-proximal trinucleotides, reminiscent of cells undergoing metabolic stress. These findings uncovered the role of Tcl1 oncogenic function in altering promoter usage and mRNA translation in leukemogenesis.
-
- Cancer Biology
The median-effect equation has been widely used to describe the dose-response relationship and identify compounds that activate or inhibit specific disease targets in contemporary drug discovery. However, the experimental data often contain extreme responses, which may significantly impair the estimation accuracy and impede valid quantitative assessment in the standard estimation procedure. To improve the quantitative estimation of the dose-response relationship, we introduce a novel approach based on robust beta regression. Substantive simulation studies under various scenarios demonstrate solid evidence that the proposed approach consistently provides robust estimation for the median-effect equation, particularly when there are extreme outcome observations. Moreover, simulation studies illustrate that the proposed approach also provides a narrower confidence interval, suggesting a higher power in statistical testing. Finally, to efficiently and conveniently perform common lab data analyses, we develop a freely accessible web-based analytic tool to facilitate the quantitative implementation of the proposed approach for the scientific community.