Abstract A02: Biomarkers of drug response to buparlisib: Results of next-generation sequencing in a phase II trial of advanced endometrial carcinoma
Notice bibliographique
Résumé
Abstract Background: Genetic alterations in the phosphatidylinositol 3-kinase (PI3K) pathway are frequent in endometrial carcinoma (EC; 59% PIK3CA and 66% PTEN alterations; Kandoth C et al., Nature 2013), thus providing a rationale for testing PI3K inhibitors in this tumor type. A Phase II, single-arm clinical trial of buparlisib, an oral pan-PI3K inhibitor that inhibits all four class I PI3K isoforms (α, β, δ, γ), as second-line therapy, was conducted in advanced EC (N=70). All patients (pts) received buparlisib 100 mg/day until disease progression or discontinuation due to an adverse event. The primary objective was objective response rate (RECIST v1.1) in pts with PI3K-activated tumors (PIK3CA and/or PTEN alteration) or in all pts. The activity of single-agent buparlisib was marginal and PI3K pathway activation was not associated with a better outcome in this pt population (Teneriello MG et al., ICACT 2013). Retrospective molecular characterization was undertaken to assess a potential alternate contribution to PI3K pathway activation in EC and the impact on response to treatment. Methods: Next-generation sequencing data were generated for 51 pts (serous n=17, endometrioid n=28, unknown n=6) and targeted coding regions of approximately 400 genes. Using standard bioinformatics tools, short nucleotide variants (SNVs) were called. To generate signatures of PI3K pathway activation, data from the TCGA endometrial cohort (Kandoth C et al., Nature 2013) were first leveraged. Levels of pAKT (pT308 and pS473) and pS6 (pS235 and pS240), proxies for PI3K pathway activation, were modeled using SNVs in the TCGA endometrial cohort. Specifically, elastic net models predicting pAKT and pS6 were optimized using somatic SNVs for endometrioid (6-gene model) and serous (3-gene model) subtypes separately. The TCGA models were then used to create pathway activation scores for each pt based on their molecular profile. Results: No significant relationships were found for pts with the serous subtype, hence the results focus on the endometrioid subtype. Subgroup analysis was performed to assess trends in progression-free survival (PFS) using Cox proportional hazards regression. Pts with the lowest PI3K pathway activation scores, based on the pAKT TCGA model, including pts with PTEN wildtype (n=5), trended towards a lower risk of PFS event compared with pts with moderate or high activation (n=23). In addition, pts with the highest pathway activation scores, based on the pS6 TCGA model, including pts with alterations in mTOR (n=5), trended towards a higher risk of PFS event compared with pts with moderate or low activation (n=23). When combining the two signatures, pts with high pS6 activation (n=5) had a higher risk of PFS event followed by pts with low pS6 and high pAKT scores (n=19) compared with pts with low pS6 and low pAKT scores (n=4). PIK3CA mutations were unrelated to pAKT in the TCGA model for the endometrioid subtype, but PIK3CA-mutated tumors in the TCGA model had slightly higher levels of pS6. Conclusion: In both models, pts with the lowest levels of PI3K pathway activation trended towards a lower risk of PFS event compared with pts with moderate or high levels of PI3K activation. A possible explanation is that the high levels of PI3K pathway activation observed in EC may represent a challenge for single-agent inhibition. Alternatively, for this single-arm study in EC, we can only elucidate the prognostic value of the biomarker and not the contribution of the treatment per se. Further validation with larger sample sizes is warranted to support this hypothesis further. Citation Format: Nadia Solovieff, Angad Pal Singh, Hans Bitter, Markus Riester, Michael Teneriello, Amit Oza, Bradley Monk, Douglas Robinson, Lucia Trandafir, Cristian Massacesi, Emmanuelle di Tomaso, R. Wendel Naumann. Biomarkers of drug response to buparlisib: Results of next-generation sequencing in a phase II trial of advanced endometrial carcinoma. [abstract]. In: Proceedings of the AACR Special Conference: Targeting the PI3K-mTOR Network in Cancer; Sep 14-17, 2014; Philadelphia, PA. Philadelphia (PA): AACR; Mol Cancer Ther 2015;14(7 Suppl):Abstract nr A02.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,003 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
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 ».