PI3Kα Inhibitor and Degrader Inavolisib Can Co-opt FGFR2 to Enhance Responses in Patients with <i>PIK3CA</i> -Mutated Solid Tumors and in Preclinical Models
Bibliographic record
Abstract
PURPOSE: PIK3CA mutations frequently drive solid tumors, particularly hormone receptor-positive breast cancer. Inavolisib, an ATP-competitive p110α inhibitor, also promotes the degradation of mutated p110α. PI3K inhibitors have generally shown modest single-agent activity and have safety concerns. PATIENTS AND METHODS: A first-in-human phase 1 study (NCT03006172) evaluated oral inavolisib in patients with PIK3CA-mutated solid tumors to determine the maximum tolerated dose and safety. Correlative analyses included ctDNA. Preclinical studies in cell lines and xenografts elucidated the role of FGFR2. RESULTS: The maximum tolerated dose was 9 mg daily, with a manageable safety profile (e.g., hyperglycemia and diarrhea). Inavolisib showed linear pharmacokinetics, consistent pharmacodynamic modulation, and antitumor activity in hormone receptor-positive PIK3CA-mutated breast cancer (26% objective response rate and 45% clinical benefit rate). FGFR2 hotspot mutations in ctDNA were strongly associated with clinical benefit. Preclinically, oncogenic FGFR2 signaling enhanced inavolisib sensitivity by engaging HER3, RAS, and p85β, that facilitated mutated p110α degradation, surpassing nondegrading inhibitors. Combination therapy with FGFR2 inhibitors showed synergy and delayed resistance. CONCLUSIONS: These findings highlight a novel cooperativity between FGFR2 and p110α that boosts the effectiveness of inavolisib. The data support advancing precision oncology beyond single biomarkers to complex algorithms utilizing co-occurring alterations, suggesting that combining inavolisib with FGFR2 inhibitors may offer enhanced and more durable responses in PIK3CA/FGFR2-altered tumors.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".