mTORC1-Driven Protein Translation Correlates with Clinical Benefit of Capivasertib within a Genetically Preselected Cohort of <i>PIK3CA</i> -Altered Tumors
Bibliographic record
Abstract
Capivasertib is a potent selective inhibitor of AKT. It was recently FDA approved in combination with fulvestrant to treat HR+, HER2-negative breast cancers with certain genetic alteration(s) activating the PI3K pathway. In phase I trials, heavily pretreated patients with tumors selected for activating PI3K pathway mutations treated with capivasertib monotherapy demonstrated objective response rates of <30%. We investigated the proteomic profile associated with capivasertib response in genetically preselected patients and cancer cell lines. We analyzed samples from 16 PIK3CA-mutated patient tumors collected prior to capivasertib monotherapy in the phase I trial. PI3K pathway proteins were precisely quantified with immuno-Matrix-Assisted Laser Desorption/Ionization-mass spectrometry (iMALDI-MS). Global proteomic profiles were also obtained. Patients were classified according to response to capivasertib monotherapy: "clinical benefit (CB)" (≥12 weeks without progression, n = 7) or "no clinical benefit (NCB)" (progression in <12 weeks, n = 9). Proteins that differed between the patient groups were subsequently quantified in AKT1- or PIK3CA-altered breast cancer cell lines with varying capivasertib sensitivity. The measured concentrations of AKT1 and AKT2 varied among the PIK3CA-mutated tumors but did not differ between the CB and NCB groups. However, analysis of the global proteome data showed that translational activity was higher in tumors of the NCB vs. CB group. When reproducibly quantified by validated LC-MRM-MS assays, the same proteins of interest similarly distinguished between capivasertib-sensitive versus -resistant cell lines. The results provide further evidence that increased mTORC1-driven translation functions as a mechanism of resistance to capivasertib monotherapy. Protein concentrations may offer additional insights for patient selection for capivasertib, even among genetically preselected patients. SIGNIFICANCE: Capivasertib's first-in-class FDA approval demonstrates its promise, yet there remains an opportunity to optimize its use. Our results provide new evidence that proteomics can stratify genetically preselected patients on clinical benefit. Characterization of the same profile in cell lines furnishes additional validation. Among PIK3CA-altered tumors, increased mTORC1-driven translation appears to confer intrinsic resistance. Assessing mTORC1 activation could therefore prove a useful complement to the existing genetic selection strategy for capivasertib.
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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 | 0.001 | 0.000 |
| 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 | 0.001 | 0.000 |
| 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".