Combined PIK3CA and FGFR Inhibition With Alpelisib and Infigratinib in Patients With PIK3CA-Mutant Solid Tumors, With or Without FGFR Alterations
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Bibliographic record
Abstract
PURPOSE Concurrent PIK3CA mutations and fibroblast growth factor receptor (FGFR) alterations occur in multiple cancer types, including estrogen receptor–positive breast cancer, bladder cancer, and endometrial cancer. In this first-in-human combination trial, we explored safety and preliminary efficacy of combining the PI3Kα selective inhibitor alpelisib with the FGFR1-4 selective inhibitor infigratinib. PATIENTS AND METHODS Patients with PIK3CA-mutant advanced solid tumors, with or without FGFR1-3 alterations, were enrolled in the dose escalation or one of three molecular-defined dose-expansion cohorts. The primary end point was the maximum tolerated dose. Secondary end points included safety, pharmacokinetics, and response. Archival tumor samples were sequenced to explore genomic correlates of response. RESULTS In combination, both agents were escalated to full, single-agent recommended doses (alpelisib, 300 mg per day continuously; infigratinib, 125 mg per day 3 weeks on followed by 1 week off). The toxicity profile of the combination was consistent with the established safety profile of each agent, although 71% of all patients required at least one treatment interruption or dose reduction. Molecularly selected dose expansions in breast cancer and other solid tumors harboring PIK3CA mutations, alone or in combination with FGFR alterations, identified sporadic responses, predominately in tumor types and genotypes previously defined to have sensitivity to these agents. CONCLUSION The combination of alpelisib and infigratinib can be administered at full single-agent doses, although the high rate of dose interruption or reduction suggests long-term tolerability may be challenging. In exploratory signal-seeking cohorts of patients harboring dual PIK3CA and FGFR1-3 alterations, no clear evidence of synergistic activity was observed.
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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 it