Abstract B02: DNA repair landscape in High Grade Ovarian Cancer (HGOC) and evolution with neo-adjuvant chemotherapy
Notice bibliographique
Résumé
Abstract Background: HGOC is frequently diagnosed at an advanced stage where NeoAdjuvant Chemotherapy (NACT) provides an essential component of the treatment strategy. HGOC are initially very chemosensitive, putatively due to DNA repair defects. While BRCA mutations explain impaired homologous recombination in 12% to 15% of HGOC, loss of other DNA repair proteins may also be relevant. Unfortunately despite initial response rates of 80%, most invariably relapse. Chemo-resistance mechanisms are poorly understood and previous studies have only focused on the characterization at diagnosis. However given their chemosensitivity, profiling the post-NACT tumor (cleared of sensitive cells and enriched for chemoresistant clones) may be more relevant to uncover mechanisms of platinum resistance. We aimed to evaluate the evaluate the expression of key DNA repair biomarkers in a large series of HGOC tumors obtained at baseline, Post-NACT and at relapse. Methods: TMA was constructed using sequential FFPE tumor samples of high-grade ovarian cancer collected at diagnosis (pre-NACT) (N=143), post-NACT (N=139) and at relapse (N=36) in 170 patients, encompassing 115 patients with sequential tumor samples. Expression of 53BP1, PAR, PARP-1 and ATM were scored by immunohistochemistry using an H-score (0-300) with biomarker negative defined as H-score =< 10. Markers were analysed with a Cox model to predict overall survival (OS) and progression free survival (PFS). Results: At diagnosis, a significant proportion of tumors showed loss of DNA repair proteins: 61% were PAR-negative, and 24%, 15% and 3% negative for ATM, 53BP1 and PARP-1 respectively and a fifth of tumors showed concomitant loss in 2 or more DNA proteins. There was an overall decrease in DNA repair protein expression with NACT, which was significant for PAR (pre-NACT vs post-NACT H-score= 45 vs 19, p=0.0025, N=201) and PARP-1(H-score=208 vs 190; p=0.0149, N=205). In addition paired samples analysis of matched pre- and post-NACT samples showed complete loss of biomarker expression after treatment in a subset (24%, 16%, 6% and 3% for PAR, ATM, TP53BP1 and PARP-1). When comparing post-NACT to relapsed samples, PAR expression increased significantly (mean H-score=19 vs 54, p=0,025). The prognostic value of loss of each DNA repair at diagnosis and after NACT was evaluated. At diagnosis, 53BP1-negative tumors were associated with a significantly worst PFS (HR=3.484; p: 0.0025). Post-NACT, ATM-negative tumors had a significantly worst PFS (HR=1.690; p: 0.0374). As PAR, TP53BP1 and ATM were shown not to be correlated in individual tumors (Pearson correlation), the impact of combined biomarker loss on PFS was evaluated. At diagnosis, double TP53BP1/PAR-negative tumors predicted shorter PFS (PFS: median=17.65 vs 23.23 mths, p:0.0372, HR=0.2237, N=58), while post-NACT double ATM/PAR-negativity was significantly predictive of poor outcome in terms of PFS and OS (PFS: median=17.35 vs 23.77 mths, p:0.0198, HR=0.4759; OS: median=35 vs 50 mths, p:0.0120, HR=0.3602; N=80). Conclusions: We present the first study of change in DNA repair protein expression with NACT. At diagnosis, HGOC is associated with loss of key DNA repair proteins in a significant proportion of patients and NACT can induce significant loss of DNA repair proteins. Combined loss of DNA repair proteins was significantly predictive of survival. Work in ongoing to extend this analysis to a greater panel of DNA repair biomarkers. Citation Format: Aurelie Auguste, Soizick Mesnage, Audrey Le Formal, Elena Cojocaru, Francoise Drusch, Julien Adam, Sebastien Gouy, Enrica Bentivegna, Catherine Lhomme, Patricia Pautier, Catherine Genestie, Alexandra Leary. DNA repair landscape in High Grade Ovarian Cancer (HGOC) and evolution with neo-adjuvant chemotherapy [abstract]. In: Proceedings of the AACR Special Conference on DNA Repair: Tumor Development and Therapeutic Response; 2016 Nov 2-5; Montreal, QC, Canada. Philadelphia (PA): AACR; Mol Cancer Res 2017;15(4_Suppl):Abstract nr B02.
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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».