Abstract LB-312: The somatic mutational landscape of ovarian clear cell carcinoma and its precursor lesions
Notice bibliographique
Résumé
Abstract Clear Cell ovarian carcinomas (CCC) represent ∼10% of ovarian carcinomas, with outcomes for high-stage cases significantly worse than the more common high-grade serous form. Response to standard chemotherapies are poor and efforts to improve treatment strategies are confounded by studies grouping ovarian histologies together, as well as a general lack of molecular background data on CCC. CCC frequently occurs in a background of endometriosis. The complete mutational landscape, the molecular basis of the transformation of endometriosis and patterns of clonal evolution in CCC are not understood. We performed whole genome sequencing and gene expression profiling on 19 CCC to uncover candidate somatic alterations (mutations and, copy number aberrations) and measure their effect on transcriptional networks as candidates for driver mutations. We then performed targeted deep sequencing on the primary tumor samples, metastases and, from a subset of cases, adjacent or distant typical and atypical endometriosis. We used statistical modeling approaches to validate mutations, quantify the degree of clonal diversity and trace patterns of selection through oncogenic transformation. Mutations in ARID1A and PIK3CA were by far the most frequent aberrations seen in the cohort (ARID1A: 10/19 cases; PIK3CA: 8/19 cases). The majority of ARID1A mutant cases exhibited bi-allelic loss of function. Two non-ARID1A mutant cases showed alterations in other SWI/SNF complex components. Amongst the 24 most significant candidate drivers impacting expression, five genes (PIK3CA, CTNNB1, TP53, PPP2R1A and KRAS) were known drivers. No association between PIK3CA or ARID1A status with disease stage, genomic instability, or mutation load was observed. Analysis of deep sequencing data suggested the presence of multiple clones in every case. For each case with matching precursor lesions, we observed multiple mutations in at least one such lesion. Cases with ARID1A and PIK3CA mutations always showed evidence of these mutations in their precursor lesions. The proportion of mutations from the primary tumor that were also present in precursor lesions varied widely across the cohort from approximately 10% to nearly 100%. Our data support both ARID1A and PIK3CA mutations as early events in CCC. The pattern of endometriosis transformation could be associated with somatic mutations in all cases. This suggests that candidate tumor-initiating mutations and global- or individually- targetable features should be a focus to improve management of this disease. Finally, we suggest that patterns of mutational conservation across the series of precursor lesions presents an opportunity for early screening of endometriosis tissues as an indicator of transformation potential. Citation Format: Ali Bashashati, Michael Anglesio, Yikan Wang, Gavin Ha, Janine Senz, Winnie Yang, Steve Kalloger, Leah Prentice, Satoshi Yanagida, Clara Salamanca, Galina Soukhatcheva, Anthony Karnezis, Hector Chang, Martin Hirst, Anne-Marie Mes-Mason, Aikou Okamoto, Marco Marra, Blake Gilks, Sohrab Shah, David Huntsman. The somatic mutational landscape of ovarian clear cell carcinoma and its precursor lesions. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr LB-312. doi:10.1158/1538-7445.AM2014-LB-312
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 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| 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 ».