Back

Driving proteomic imbalance in malignancy provokes proteomic catastrophe and confers tumor suppression

Ram, B. M.; Shriwas, O.; Xu, M.; Chuang, K.-H.; Dai, C.

2026-03-06 cancer biology
10.1101/2024.05.24.595838 bioRxiv
Show abstract

Unlike genomic instability, the implications of proteomic instability in cancer remain ambiguous. By governing the proteotoxic stress response, heat shock factor 1 (HSF1) sustains proteomic stability upon environmental insults. Apart from its importance to stress resistance and survival, HSF1 is emerging as a powerful oncogenic enabler. In the Neurofibromatosis type I (NF1)-deficient malignant peripheral nerve sheath tumor (MPNST) cells, HSF1 depletion triggered protein polyubiquitination, aggregation, and even tumor-suppressive amyloidogenesis. In contrast, HSF1 is dispensable for the proteome of non-transformed human Schwann cells. Mechanistically, HSF1 defends the essential mitochondrial chaperone HSP60 against the direct assault from soluble amyloid oligomers. To survive and adapt to compromised protein quality, owing to HSF1 deficiency, MPNST cells mobilized JNK to repress mTORC1 and protein translation, thereby attenuating protein quantity to alleviate proteomic imbalance. mTORC1 stimulation, via either pharmacological JNK blockade, genetic TSC2 depletion, or leucine supplementation, markedly aggravated the proteomic imbalance elicited by HSF1 deficiency. This catastrophic imbalance instigated pronounced cell death partly through unchecked amyloidogenesis, thereby exerting tumor suppression in both MPNST and melanoma models in vivo. Thus, HSF1 safeguards the cancer proteome to enable the oncogenic potential of mTORC1. This proof-of-principle study highlights provoking proteomic catastrophe as a next-generation therapeutic concept for combating malignancy.

Matching journals

The top 8 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.