Abstract PR012: Targeted sequencing of 2,138 bone and soft tissue sarcomas reveals commonly altered pathways, subtype-independent genetic clusters, and potential therapeutic targets
Notice bibliographique
Résumé
Abstract A hallmark of sarcomas is genetic, biologic, and clinical heterogeneity, which is a challenge for preclinical development of therapeutic targets, clinical trial design, and patient care. Because sarcomas comprise over 100 subtypes, a broad comparative genetic analysis of subtypes has been lacking and sarcoma genetic studies have by necessity focused on one or a few subtypes. We will report the results of a comprehensive analysis of somatic genetic alterations detected by a targeted sequencing platform in a single institution cohort of 2,138 patient-specific samples of soft tissue and bone sarcoma representing 45 distinct pathological entities. We determined subtype-specific mutation and copy number (arm and gene level) events with a focus on pathways in which there are therapeutic targets under clinical or preclinical investigation. The most commonly altered pathways are cell cycle control, TP53, PI3K, receptor tyrosine kinase/RAS, and epigenetic regulators (e.g. >40% of uterine leiomyosarcoma [n=165], undifferentiated pleomorphic sarcoma [UPS, n=145] and pleomorphic liposarcoma [n=22]). Subtype-specific associations in rare subtypes include TERT amplification in 41% of intimal sarcoma (n=18) and alterations in chromatin remodeling and histone modifying complexes in uterine adenosarcoma (43% and 36%, respectively; n=14). Epigenetic pathway alterations in common sarcomas were also noted, including an increased frequency of oncogenic alterations in epigenetic regulators in dedifferentiated liposarcoma (DDLS; n=167) (25%) vs. well differentiated liposarcoma (8%; n=48). We evaluated rates of whole genome doubling (WGD), which in subtypes such as UPS and osteosarcoma (n=129) were >45%, which is more frequent than many carcinomas. WGD associated with worse overall survival in metastatic (p=0.042) but not primary samples (p=0.391). Among specific subtypes, WGD was associated with worse overall survival in metastatic UPS (p=0.022). Unsupervised clustering of samples based on genetic features revealed that in some cases sarcomas are more similar to individual tumors of distinct histology than to other sarcomas of the same subtype. For example, UPS and soft tissue leiomyosarcoma (n=125) segregated into multiple genetic clusters (high entropy) compared to others with low entropy (DDLS), which could have implications for future clinical trial interpretation and/or design. We also determined the subtype-specific rate of tumor mutation burden, which was overall low compared to many carcinomas, but notably varied between and within subtypes, with some having a long tail in the upper range of the distribution. Lastly, we assessed targetable alterations on a subtype-specific basis. Together with other ‘multiomic’ approaches, we anticipate this work will motivate preclinical studies of subtype-specific sarcoma biology and potential therapeutic targets and will inform efforts to interpret outcomes in sarcoma clinical trials with respect to underlying genetic subtypes. Citation Format: Benjamin A. Nacev, Francisco Sanchez-Vega, Shaleigh Smith, Cristina Antonescu, Evan Rosenbaum, Hongyu Shi, Cerise Tang, Nicholas Socci, Satshil Rana, Rodrigo Gularte-Merida, Ahmet Zehir, Mrinal Gounder, Timothy Bowler, Anisha Luthra, Bhumika Jadeja, Azusa Okada, Jonathan Strong, Jake Stoller, Jason Chan, Ping Chi, Sandra D'Angelo, Mark Dickson, Ciara Kelly, Mary Louise Keohan, Sujana Movva, Katherine Thornton, Paul Meyers, Leonard Wexler, Emily Slotkin, Julia Glade Bender, Neerav Shukla, Martee Hensley, John Healey, Michael La Quaglia, Kaled Alektiar, Aimee Crago, Sam Yoon, Brian Untch, Sarah Chiang, Narasimhan Agaram, Meera Hameed, Michael Berger, David Solit, Nikolaus Schultz, Marc Ladanyi, Samuel Singer, William Tap. Targeted sequencing of 2,138 bone and soft tissue sarcomas reveals commonly altered pathways, subtype-independent genetic clusters, and potential therapeutic targets [abstract]. In: Proceedings of the AACR Special Conference: Sarcomas; 2022 May 9-12; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2022;28(18_Suppl):Abstract nr PR012.
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) | 0,002 | 0,001 |
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 ».