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
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,003 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,010 | 0,003 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».