Abstract P4-10-20: Protein quantitation assays for AKT and PTEN to better understand sensitivity and resistance of breast cancer patients to treatment with AKT inhibitor capivasertib
Bibliographic record
Abstract
Abstract Context: The PI3K/AKT/PTEN pathway is frequently aberrantly activated in breast cancer (BC) and involved in resistance to hormonal therapy. Multiple drugs targeting this pathway are approved or in development, including the pan-AKT inhibitor capivasertib (AZD5363). Early phase studies of capivasertib as monotherapy for solid cancers (e.g. NCT01226316, NCT01625286) have used genetic testing to select patients considered most likely to respond based on AKT1, PIK3CA or PTEN mutations. However, not all selected patients responded. Randomised phase 2 studies on Capivasertib as combination treatment showed the importance of context; a more pronounced response in combination with paclitaxel was seen in TNBC patients whose tumors harboured an alteration on AKT1, PIK3CA or PTEN (Schmid et al. ASCO 2018), whereas the same combination in ER+ HER2- advanced/metastatic BC patients did not show a clinical benefit in the overall population nor in the PIK3CA-mutation subgroup (Turner et al. Ann Oncol 2019). The combination with fulvestrant demonstrated an effect in unselected ER+ HER2- advanced BC patients resistant to aromatase inhibitors irrespective of PIK3CA mutations or PTEN IHC status (Jones et al. ASCO 2019). DNA-based assays also have the limitation that potential responders may be missed, e.g., tumors with epigenetic pathway activation but no detectable mutations. Techniques such as IHC and Western Blot (WB) lack the sensitivity, reproducibility and standardization required to precisely quantify these proteins. We hypothesize that precise mass spectrometry (MS)-based methods may facilitate a better understanding of sensitivity and resistance to inhibitors of the PI3K/AKT pathway in future studies. Methods: Protein extracted from tumor tissue lysate is subjected to proteolytic digestion to generate proteotypic peptides used for protein quantitation. Synthetic stable isotope-labeled peptides are added to the digest as an internal standard, after which the target peptides are immuno-enriched with antibodies coupled to magnetic beads. MS analysis is performed by either eluting peptides for measurement with liquid chromatography multiple reaction monitoring tandem MS (LC-MRM-MS/MS) or by spotting beads onto a plate for matrix-assisted laser desorption/ionization MS (MALDI-MS). Calibration curves enable accurate quantitation. A new method for PTEN was applied to 13 breast cancer PDXs. Results were compared to IHC and WB. In a retrospective study, we are applying these methods to test samples from HR+ metastatic breast or gynecological cancer patients (n=25 with PIK3CA-mutations, capivasertib monotherapy; n=26 with PTEN alterations, capivasertib + fulvestrant; n=9 with no PIK3CA, AKT1 or PTEN mutations, not receiving capivasertib). AKT and PTEN quantitation data will be analyzed for a relationship to treatment response as determined by RECIST guidelines (NCT01226316). Results: We validated MS methods to precisely and reproducibly determine AKT and PTEN concentrations in tumor samples. AKT concentrations varied significantly between mutation-positive tumor samples (AKT1: 0.06-0.45, AKT2: 0.05-0.20 fmol/µg total protein). Significant phosphorylation of AKT1-Ser473 or AKT2-Ser474 was observed in 2 out of 17 tested samples. At least 1 mutation-negative sample had a high AKT1 concentration of 0.42 fmol/ug total protein. PTEN concentrations from PDXs were consistent across biological replicates and agreed with IHC and WB. Furthermore, we were able to detect and quantify PTEN from 3 samples that were negative by IHC and/or WB. Conclusion: AKT and PTEN quantitative proteomics assays have the potential to enable more accurate assessment of PI3K/AKT/PTEN pathway activation in tumor tissues from patients treated with inhibitors of this key pathway. Citation Format: Sahar Ibrahim, Constance A Sobsey, Robert Popp, René P. Zahedi, Gerald Batist, Christoph H. Borchers. Protein quantitation assays for AKT and PTEN to better understand sensitivity and resistance of breast cancer patients to treatment with AKT inhibitor capivasertib [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P4-10-20.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".