Off-target autophagy inhibition by SHP2 allosteric inhibitors contributes to their antitumor activity in RAS-driven cancers
Bibliographic record
Abstract
Aberrant activation of RAS/MAPK signaling is common in cancer, and efforts to inhibit pathway components have yielded drugs with promising clinical activities. Unfortunately, treatment-provoked adaptive resistance mechanisms inevitably develop, limiting their therapeutic potential. As a central node essential for receptor tyrosine kinase-mediated RAS activation, SHP2 has emerged as an attractive cancer target. Consequently, many SHP2 allosteric inhibitors are now in clinical testing. Here we discovered a previously unrecognized off-target effect associated with SHP2 allosteric inhibitors. We found that these inhibitors accumulate in the lysosome and block autophagic flux in an SHP2-independent manner. We showed that off-target autophagy inhibition by SHP2 allosteric inhibitors contributes to their antitumor activity. We also demonstrated that SHP2 allosteric inhibitors harboring this off-target activity not only suppress oncogenic RAS signaling but also overcome drug resistance such as MAPK rebound and protective autophagy in response to RAS/MAPK pathway blockage. Finally, we exemplified a therapeutic framework that harnesses both the on- and off-target activities of SHP2 allosteric inhibitors for improved treatment of mutant RAS-driven and drug-resistant malignancies such as pancreatic and colorectal cancers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".