DISTINCT HODGKIN LYMPHOMA SUBTYPES IDENTIFIED BY NONINVASIVE GENOMIC PROFILING
Notice bibliographique
Résumé
Introduction: The scarcity of malignant Reed-Sternberg cells has hampered comprehensive genomic profiling of classic Hodgkin lymphoma (cHL) as might inform personalized therapeutic strategies. Since profiling of circulating tumor DNA (ctDNA) has shown utility in non-Hodgkin lymphoma genotyping and risk stratification, we employed a noninvasive approach in cHL to overcome challenges imposed by low tumor fractions. Methods: We profiled baseline plasma samples from 366 patients diagnosed with cHL, 99% of whom were enrolled prior to anti-lymphoma therapy. Median age was 32 (range 4–88), 48% had advanced stage (III/IV) disease, and among the subset with early stage (I/II) disease (52%), 91% had unfavorable GHSG risk. We applied CAPP-Seq and Whole Exome Sequencing (WES) to explore noninvasive genotypes. Whole exome genotypes were generated using a novel gradient boosting model from mutation and cfDNA fragmentomic features. Distinct cHL genetic subtypes were identified by lexical clustering through Latent Dirichlet Allocation. Results: We first profiled all pretreatment samples using a 576-kb capture panel targeting genes recurrently mutated in cHL and other B-cell lymphomas. 293 patients (80% of cases) were evaluable for noninvasive genotyping and clustering analyses. We additionally used WES to profile a subset of patients (n = 119; 41%) enriched for samples with higher plasma allelic fractions. We then integrated somatic copy-number aberrations (SCNAs) with non-silent somatic mutation calls as weighted features to discover 2 dominant genetic subtypes. Cluster H1 comprised ∼68% of cases and was dominated by somatic mutations in genes canonically involved in NFκB, JAK/STAT, and PI3K signaling. Conversely, cluster H2 (∼32% of cases) was characterized by a variety of SCNA events as well as mutations in TP53, KMT2D, and BCL2 (Figure A). H1 tumors had a significantly higher somatic mutational burden, while H2 tumors had a larger fraction of their genome affected by SCNAs (both p < 0.001, Figure B,C). Patients with H2 subtype demonstrated the known bimodal age distribution of cHL with an early peak in the 20s and a second peak at >60 years. In contrast, H1 tumors predominantly occurred in younger patients (p = 0.02, Figure D). Patients with an H2 genotype were predominantly male (p = 0.007), enriched for EBV positive tumors (p < 0.0001, Figure E) and mixed cellularity subtype (p = 0.01, Figure F). Importantly, patients with the H2 subtype had inferior clinical outcomes (p < 0.01, Figure G) independent of high ctDNA levels (Hazard ratio 2.0, p < 0.05). Exploration of transcriptional differences between genetic subtypes using invasive and noninvasive methods are under way and will be presented at the meeting. The research was funded by: National Cancer Institute (R01CA257655 and R01CA233975). Keywords: genomics, epigenomics, and other-omics, Hodgkin lymphoma, liquid biopsy Conflicts of interests pertinent to the abstract S. K. Alig Honoraria: Takeda Pharmaceuticals M. Shahrokh Esfahani Consultant or advisory role: Foresight Diagnostics C. Rossi Educational grants: Kite, Abbvie B. J. Sworder Consultant or advisory role: Foresight Diagnostics A. Schultz Employment or leadership position: Foresight Diagnostics J. E. Flerlage Research funding: Seattle Genetics R. Advani Consultant or advisory role: ADC Therapeutics, BMS, Daiichi Sankyo, Epizyme, Gilead, Incyte, Merck, Roche, Sanofi Research funding: ADC Therapeutics, Cyteir, Daiichi Sankyo, Gilead, Merck, Regeneron, Roche, Seattle Genetics D. Rossi Consultant or advisory role: AstraZeneca, Janssen, AbbVie, Gilead, MSD, BMS, BeiGene Honoraria: AstraZeneca, Janssen, AbbVie, Gilead, BMS, BeiGene Research funding: AstraZeneca, Janssen, Gilead, BeiGene Educational grants: AstraZeneca, Janssen, BMS, BeiGene R. Lynch Consultant or advisory role: Cancer Study Group Research funding: TG Therapeutics, Incyte, Bayer, Cyteir, Genentech, SeaGen, Rapt D. M. Kurtz Consultant or advisory role: Roche, Adaptive Biotechnologies, Genentech, Foresight Diagnostics Stock ownership: Foresight Diagnostics L. J. Marks Honoraria: Abbvie M. P. Link Research funding: Seagen, LLC P. Vandenberghe Honoraria: Novartis, Miltenyi Biotec, Johnson & Johnson, Becton Dickinson, Kite, BMS/Celgene Research funding: Johnson & Johnson C. Steidl Consultant or advisory role: Abbvie, Bayer, Bristol Myers Squibb, Curis Inc, Roche, Seattle Genetics Research funding: Epizyme, Trillium Therapeutics M. Diehn Consultant or advisory role: Foresight Diagnostics Stock ownership: Foresight Diagnostics A. A. Alizadeh Employment or leadership position: Foresight Diagnostics Consultant or advisory role: Adaptive Biotechnologies, Genentech, Karyopharm, Foresight Diagnostics, BMS, Roche, Gilead, Cibermed Stock ownership: Syncopation, Foresight Diagnostics, Gilead, Cibermed Research funding: BMS
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 | 0,001 | 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 ».