Phase I Basket Study of Taselisib, an Isoform-Selective PI3K Inhibitor, in Patients with <i>PIK3CA</i> -Mutant Cancers
Bibliographic record
Abstract
Abstract Purpose: Somatic mutations in phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha (PIK3CA), which encodes the p110α catalytic subunit of PI3K, are found in multiple human cancers. While recurrent mutations in PIK3CA helical, regulatory, and kinase domains lead to constitutive PI3K pathway activation, other mutations remain uncharacterized. To further evaluate their clinical actionability, we designed a basket study for patients with PIK3CA-mutant cancers with the isoform-specific PI3K inhibitor taselisib. Patients and Methods: Patients were enrolled on the basis of local PIK3CA mutation testing into one of 11 histology-specific cohorts and treated with taselisib at 6 or 4 mg daily until progression. Tumor DNA from baseline and progression (when available) was sequenced using a next-generation sequencing panel. Exploratory analyses correlating genomic alterations with treatment outcomes were performed. Results: A total of 166 patients with PIK3CA-mutant cancers were enrolled. The confirmed response rate was 9%. Activity varied by tumor type and mutant allele, with confirmed responses observed in head and neck squamous (15.4%), cervical (10%), and other cancers, plus in tumors containing helical domain mutations. Genomic analyses identified mutations potentially associated with resistance to PI3K inhibition upfront (TP53 and PTEN) and postprogression through reactivation of the PI3K pathway (PTEN, STK11, and PIK3R1). Higher rates of dose modification occurred at higher doses of taselisib, indicating a narrow therapeutic index. Conclusions: Taselisib had limited activity in the tumor types tested and is no longer in development. This genome-driven study improves understanding of the activity, limitations, and resistance mechanisms of using PI3K inhibitors as monotherapy to target PIK3CA-mutant tumors.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".