RTK-Dependent Inducible Degradation of Mutant PI3Kα Drives GDC-0077 (Inavolisib) Efficacy
Bibliographic record
Abstract
Abstract PIK3CA is one of the most frequently mutated oncogenes; the p110a protein it encodes plays a central role in tumor cell proliferation. Small-molecule inhibitors targeting the PI3K p110a catalytic subunit have entered clinical trials, with early-phase GDC-0077 studies showing antitumor activity and a manageable safety profile in patients with PIK3CA-mutant breast cancer. However, preclinical studies have shown that PI3K pathway inhibition releases negative feedback and activates receptor tyrosine kinase signaling, reengaging the pathway and attenuating drug activity. Here we discover that GDC-0077 and taselisib more potently inhibit mutant PI3K pathway signaling and cell viability through unique HER2-dependent mutant p110a degradation. Both are more effective than other PI3K inhibitors at maintaining prolonged pathway suppression. This study establishes a new strategy for identifying inhibitors that specifically target mutant tumors by selective degradation of the mutant oncoprotein and provide a strong rationale for pursuing PI3Kα degraders in patients with HER2-positive breast cancer. Significance: The PI3K inhibitors GDC-0077 and taselisib have a unique mechanism of action; both inhibitors lead to degradation of mutant p110a protein. The inhibitors that have the ability to trigger specific degradation of mutant p110a without significant change in wild-type p110a protein may result in improved therapeutic index in PIK3CA-mutant tumors. See related commentary by Vanhaesebroeck et al., p. 20. This article is highlighted in the In This Issue feature, p. 1
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".