<i>PIK3CA</i>copy-number gain and inhibitors of the PI3K/AKT/mTOR pathway in triple-negative breast cancer
Bibliographic record
Abstract
As wider insights are gained on the molecular landscape of triple-negative breast cancer (TNBC), novel targeted therapeutic strategies might become an option in this setting as well. Activating mutations of PIK3CA represent the second most common alteration in TNBC after the TP53 mutation, with a prevalence of ∼10%–15%. Considering the well-established predictive role of PIK3CA mutations for response to agents targeting the PI3K/AKT/mTOR pathway, several clinical trials are currently evaluating these drugs in patients with advanced TNBC. However, much less is known regarding the actionability of PIK3CA copy-number gains, which represent a thoroughly common molecular alteration in TNBC, with a prevalence estimated at 6%–20%, and are listed as “likely gain-of-function” alterations in the OncoKB database. In the present paper, we describe two clinical cases in which patients harboring PIK3CA -amplified TNBC received a targeted treatment with the mTOR-inhibitor everolimus and the PI3K-inhibitor alpelisib, respectively, with evidence of disease response on 18F-FDG positron-emission tomography (PET) imaging. Hence, we discuss the evidence presently available regarding a possible predictive value of PIK3CA amplification for response to targeted treatment strategies, suggesting that this molecular alteration might represent an intriguing biomarker in this sense. Considering that few of the currently active clinical trials assessing agents targeting the PI3K/AKT/mTOR pathway in TNBC select patients based on tumor molecular characterization, and none of these based on PIK3CA copy-number status, we urge for the introduction of PIK3CA amplification as a criterion for patient selection in future clinical trials in this setting.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".