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
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| 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) | 0,000 | 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 tête enseignante, 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 ».