Spatiotemporal metabolic networks in a preclinical model of pancreatic cancer and associated cachexia.
13 affiliations
- 1Division of Endocrinology, Boston Children's Hospital, Boston, MA, USA.
- 2Department of Pediatrics, Harvard Medical School, Boston, MA, USA.
- 3Metabolomics Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- 4Department of Pathology, Massachusetts General Hospital, Boston, MA, USA.
- 5Department of Pathology, Harvard Medical School, Boston, MA, USA.
- 6Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
- 7Division of Endocrinology, Boston Children's Hospital, Boston, MA, USA. [email protected].
- 8Department of Pediatrics, Harvard Medical School, Boston, MA, USA. [email protected].
- 9Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA. [email protected].
- 10The Ohio State University Comprehensive Cancer Center & Department of Molecular Medicine and Therapeutics, The Ohio State University College of Medicine, Columbus, OH, USA. [email protected].
- 11Division of Endocrinology, Boston Children's Hospital, Boston, MA, USA. [email protected].
- 12Department of Pediatrics, Harvard Medical School, Boston, MA, USA. [email protected].
- 13Broad Institute of MIT and Harvard, Cambridge, MA, USA. [email protected].
Abstract
Cachexia or body wasting is a significant contributing factor to the morbidity and mortality of pancreatic cancer. Despite an unmet need, its metabolic underpinnings remain poorly understood. Here, we characterize a mouse model that recaptures the longitudinal progression of human cachexia during pancreatic cancer development. We profile the metabolomes of pre-, early- and late-stage cancer-associated cachexia in male and female pancreas, its interstitial fluid (IF), plasma, liver, adipose tissue and skeletal muscle. We find that each tissue has a unique metabolome and trajectory across stages and reveal a marked lipid enrichment in the tumor IF. Using mathematical modeling, we identify metabolites participating in cross-tissue networks and in vivo validate a lactate-glucose conversion. We find systemic metabolic changes prior to weight loss and use feature selection algorithms to identify early detection markers. Our study provides a resource for system-wide metabolic evolution of pancreatic cancer and associated cachexia, highlighting the potential for timely diagnostics and interventions.
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BibTeX
@article{majem2026spatiotemporal,
title = {{Spatiotemporal metabolic networks in a preclinical model of pancreatic cancer and associated cachexia.}},
author = {Blanca Majem and Min-Sik Lee and Kyung Cheul Shin and Insia Naqvi and Courtney Dennis and Lucas Dailey and Sarah Jeanfavre and Mari Mino-Kenudson and Joao B Xavier and Clary B Clish and Deepti Mathur and Nada Y Kalaany},
journal = {Nature communications},
year = {2026},
month = sep,
doi = {10.1038/s41467-026-77745-0},
url = {https://europepmc.org/article/MED/42897170},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - Spatiotemporal metabolic networks in a preclinical model of pancreatic cancer and associated cachexia.
AU - Blanca Majem
AU - Min-Sik Lee
AU - Kyung Cheul Shin
AU - Insia Naqvi
AU - Courtney Dennis
AU - Lucas Dailey
AU - Sarah Jeanfavre
AU - Mari Mino-Kenudson
AU - Joao B Xavier
AU - Clary B Clish
AU - Deepti Mathur
AU - Nada Y Kalaany
PY - 2026
DA - 2026/09/18
JO - Nature communications
DO - 10.1038/s41467-026-77745-0
UR - https://europepmc.org/article/MED/42897170
AB - Cachexia or body wasting is a significant contributing factor to the morbidity and mortality of pancreatic cancer. Despite an unmet need, its metabolic underpinnings remain poorly understood. Here, we characterize a mouse model that recaptures the longitudinal progression of human cachexia during pancreatic cancer development. We profile the metabolomes of pre-, early- and late-stage cancer-associated cachexia in male and female pancreas, its interstitial fluid (IF), plasma, liver, adipose tissue and skeletal muscle. We find that each tissue has a unique metabolome and trajectory across stages and reveal a marked lipid enrichment in the tumor IF. Using mathematical modeling, we identify metabolites participating in cross-tissue networks and in vivo validate a lactate-glucose conversion. We find systemic metabolic changes prior to weight loss and use feature selection algorithms to identify early detection markers. Our study provides a resource for system-wide metabolic evolution of pancreatic cancer and associated cachexia, highlighting the potential for timely diagnostics and interventions.
SN - 2041-1723
ER -