Combined PIK3CA and FGFR Inhibition With Alpelisib and Infigratinib in Patients With PIK3CA-Mutant Solid Tumors, With or Without FGFR Alterations
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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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".