Back

Engaging the host adaptive response to nutrient limitation delays cancer cachexia

Gomez-Jenkins, M.; Das, T.; Ibrahim, M.; Fard, D.; Lopez-Darwin, S.; Raeber, J.; Powers, J.; Hu, Z.; Sarkar, H.; CararoLopes, E.; Sawant, A.; Reyes-Castellanos, G.; Lu, W.; Qiang, H.; Sen, A.; Kwon, Y.-Y.; Wadhera, S.; Armughan, H.; Shi, S.; Rabinowitz, J. D.; Hui, S.; Anthony, T. G.; Skinnider, M.; Raphael, B.; Goncalves, M. D.; Janowitz, T.; White, E.

2026-10-09 cancer biology
10.64898/2026.10.08.757672 bioRxiv
Show abstract

Cancer cachexia is a debilitating wasting syndrome associated with involuntary weight loss, reduced appetite and food intake, adipose and muscle atrophy, and systemic inflammation with induction of a myriad of candidate cachexia factors. Using a multi-omic approach in a genetically engineered mouse model of lung cancer cachexia, we found that early circulating metabolic alterations preceded overt weight loss. Although both cachexia and fasting are characterized by a reduction in food intake, we show that the systemic metabolic changes in cachexia are distinct, indicating a lack of induction in the normal protective response to negative energy balance during cachexia. Cancer cachexia causes profound hepatic reprogramming, and activation of inflammatory programs, alongside muscle atrophy and elevation of circulating cytokines. However, targeting individual candidate cachectic factors such as IL-6, TNF-, GDF15, CCL2, LIF, and IL17A failed to rescue weight loss or prolong survival. In contrast, caloric restriction initiated prior to tumor induction elicited a program of protection during tumor progression that attenuated multi-tissue inflammatory and catabolic transcriptional responses, preserved lean mass, and prolonged survival. Together, these findings suggest that metabolic preconditioning broadly dampens systemic inflammation and delays the onset and progression of cancer cachexia.

Matching journals

The top 7 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.