Abstract B012: Application of shallow whole genome sequencing to identify therapeutic opportunities in p53abn endometrial cancers
Notice bibliographique
Résumé
Abstract Background: Shallow whole genome sequencing (sWGS) can be used to detect copy number (CN) aberrations. In high-grade serous ovarian (HGSOC) it has been used to identify CN signatures to direct therapy, recognizing two signatures associated with homologous recombination deficiency (HRD). p53abn endometrial carcinomas (EC) represent the most lethal subtype of EC with molecular similarities to HGSOC. Recent data have shown improved outcomes in patients with p53abn EC when adjuvant chemotherapy is used in addition to radiation, however, overall prognosis is still poor and therapeutic advances are urgently needed. Our aim was to apply sWGS with targeted sequencing to assess CN signatures in a cohort of p53abn ECs to identify additional prognostic or predictive stratification. Methods: sWGS was performed on formalin fixed paraffin embedded (FFPE) tissue from 215 p53abn ECs and 188 samples had targeted panel sequencing for 275 cancer related genes. CN variations, common mutations and CN signatures were derived, and associations to clinicopathologic and outcomes data were assessed. Results: 187 p53abn ECs had sufficient quality DNA to generate CN signatures (including 96 serous, 39 endometrioid, 33 carcinosarcoma, 12 clear cell and 7 other), with 5 distinct CN signatures identified. Signature 5 was associated with HRD due to CN loss of BRCA1 and 2 and was found in 35 (19%) of patients. Signatures 3 and 4 were associated with whole genome duplications, with mutations in PIK3CA defining Signature 3. 28% of cases had CCNE1 amplification, and this was enriched with carcinosarcoma histotype (p= 0.0019). 34% of patients had either HER2 gene amplification or HER2 overexpression by immunohistochemistry (IHC), which was seen across all histotypes, and was associated with worse outcomes (p=0.017). The most common mutations beyond TP53 were PIK3CA (32%), PPP2R1A (29%), PIK3R1 (18%), FBXW7 (16%) and PTEN (16%). There was no significant difference in outcomes observed between the CN signatures (35% of patients had recurred and 22% had died from disease at 3 years in total cohort), nor in the distribution of histotypes across signatures. After adjusting for stage, there was no difference in outcomes between histotypes. sWGS in combination with selective sequencing and IHC identified targetable pathways in 84% of patients with p53abn EC (HRD-PARP inhibitors, HER2-anti HER2 therapy, CCNE1-Wee1 inhibitor, PPP2R1A/FBXW7-PKMYT1 inhibitor). Conclusions: sWGS is a relatively inexpensive tool that can be performed on standard FFPE material and identified therapeutic opportunities in 84% of p53abn EC patients. Outcomes were uniformly poor in this cohort of p53abn ECs highlighting the need for improved treatment options beyond surgery and conventional chemoradiation. Further research is needed to determine the efficacy of treatments targeting these identified pathways within p53abn ECs. Citation Format: Amy Jamieson, Juliana Sobral de Barros, Dawn R. Cochrane, Maxwell Douglas, Sameer Shankar, Samuel Leung, Branden Lynch, Janine Senz, Blake Gilks, David G. Huntsman, Jessica N. McAlpine. Application of shallow whole genome sequencing to identify therapeutic opportunities in p53abn endometrial cancers [abstract]. In: Proceedings of the AACR Special Conference on Endometrial Cancer: Transforming Care through Science; 2023 Nov 16-18; Boston, Massachusetts. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(5_Suppl):Abstract nr B012.
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 ».