MétaCan
Menu
Retour à la cohorte
Enregistrement W4310106230 · doi:10.1182/blood-2022-164744

Distinct Molecular Subtypes of Classic Hodgkin Lymphoma Identified By Comprehensive Noninvasive Profiling

2022· article· en· W4310106230 sur OpenAlexaff
Stefan Alig, Mohammad Shahrokh Esfahani, Michael Y. Li, Ragini Adams, Andrea Garofalo, Michael C. Jin, Mari Olsen, Adèle Telenius, Brian J. Sworder, Joseph G. Schroers‐Martin, Daniel A. King, Cédric Rossi, André Schultz, Karan R. Kathuria, Chih Long Liu, Valeria Spina, Lieselot Buedts, Jamie E. Flerlage, Sharon M. Castellino, Ranjana H. Advani, Davide Rossi, Ryan C. Lynch, Olivier Casasnovas, David M. Kurtz, Lianna J. Marks, Michael P. Link, Marc André, Peter Vandenberghe, Christian Steidl, Maximilian Diehn, Ash A. Alizadeh

Notice bibliographique

RevueBlood · 2022
Typearticle
Langueen
DomaineBiochemistry, Genetics and Molecular Biology
ThématiqueCancer Genomics and Diagnostics
Établissements canadiensSpinal Cord Injury BC
Organismes subventionnairesnon disponible
Mots-clésProfiling (computer programming)LymphomaMedicineHodgkin lymphomaComputational biologyPathologyBiologyComputer science

Résumé

récupéré en direct d'OpenAlex

Introduction: The scarcity of malignant Reed-Sternberg cells has hampered comprehensive genomic profiling of classic Hodgkin lymphoma (cHL) as might inform personalized therapeutic strategies. Given that 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 fraction and improve risk stratification. Patients & Methods: We profiled 478 plasma and 26 tumor samples from 304 patients diagnosed with cHL, 98% of whom were enrolled prior to anti-lymphoma therapy. Median age was 29 (range 4-86), 37% had advanced stage (III/IV) disease, and among the subset with early stage (I/II) disease (63%), 91% had unfavorable GHSG risk. We applied CAPP-Seq and Whole Exome Sequencing (WES) to genotype plasma and tumor samples and used 'phased variant enrichment and detection sequencing' (PhasED-Seq) for detection of measurable residual disease (MRD). Whole exome genotypes were generated using a novel gradient boosting model from mutation and cell-free DNA fragmentomic features. We combined mutation calls with genome-wide copy number profiles to define distinct cHL genetic subtypes by lexical clustering through Latent Dirichlet Allocation. To functionally characterize truncating interleukin 4 receptor (IL4R) mutations, we generated a set of recombinant mutant constructs by site directed mutagenesis, and measured phosphorylation levels of IL4R's proximal downstream target STAT6 following ligand stimulation using flow cytometry. Results: Among 16 patients evaluable for paired tumor and blood specimens, analysis of shared mutations detected in both analytes revealed plasma variant allele fractions (AF) to exceed tumor AFs in 75% of cases (Fig A). The average enrichment exceeded 6-fold, demonstrating noninvasive genotyping to be superior to bulk tumor tissue genotyping for most patients. When compared to patients with diffuse large B-cell lymphoma (DLBCL), median plasma AF in cHL were significantly higher (2.3% vs 1.2%, P=0.03), and cHL tumors shed ~2.75x more ctDNA per mL malignant tumor volume (13.8 vs 5.0 haploid genome equivalents (hGE), P<0.0001). We nominate a candidate mechanism driving this striking variation in ctDNA shedding. To comprehensively profile the coding genomic landscape of cHL, we performed plasma WES (360x median coverage) of 119 pretreatment samples with sufficiently high AF allowing us to identify several novel recurrent lesions and to noninvasively define genetically distinct cHL clusters. Among these newly identified recurrent somatic lesions, we identified a novel class of truncating IL4R mutations in ~10% of cHL patients. These IL4R mutations were distinct from those observed in primary mediastinal B-cell lymphoma (PMBL), with cHL mutations typically disrupting IL4R's intracellular immunoreceptor tyrosine-based inhibitory motif (ITIM) domain and conferring cytokine dependent gain of function phenotypes in vitro through enhancement of IL13, but not IL4 signaling (n=48, P<0.05). Strikingly, IL13 expression was substantially higher in cHL tumors than non-Hodgkin lymphomas, and IL13 amplifications (5q31.1) were enriched in IL4R mutant cases (P<0.001), suggesting an underlying autocrine loop. Finally, unlike hotspot IL4R mutations in PMBL, gain-of-function phenotypes of cHL mutations were blockable by antibodies targeting surface IL4R (n=5, P<0.01), which may therefore serve as a precision therapy target. Among 244 treatment-naïve adult patients, pretreatment ctDNA levels predicted progression-free survival (PFS) both as a continuous (HR 2.1, P=0.02) or a dichotomous variable (HR 3.3, P=0.003). Importantly, associations of pretreatment ctDNA levels and outcomes were independent of stage-based and unfavorable risk groups (both P<0.05). Among patients evaluable for MRD, we observed rapid molecular response to therapy, including after ABVD or Bv-AVD. Specifically, MRD negativity rates at C(ycle)1 D(ay)15 and C3D1 were 38% and 90%, respectively. Importantly, ctDNA detection at both C1D15 and C3D1 were prognostic for PFS (P=0.03 and P=0.002, Fig B). Conclusions: Using a noninvasive approach, we overcome known challenges in cHL profiling and describe several molecularly distinct HL subtypes as defined by genotypes, ctDNA levels, and MRD with diagnostic, prognostic, and therapeutic potential. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,001
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,001
Score d'incertitude au seuil0,004

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

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.

Tête enseignante Opus0,008
Tête enseignante GPT0,223
Écart entre enseignants0,215 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations5
Publié2022
Routes d'admission1
Résumé présentoui

Explorer davantage

Même revueBloodMême sujetCancer Genomics and DiagnosticsTravaux en français237 207