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Record W4380084290 · doi:10.1002/hon.3163_60

DISTINCT HODGKIN LYMPHOMA SUBTYPES IDENTIFIED BY NONINVASIVE GENOMIC PROFILING

2023· article· en· W4380084290 on OpenAlexaff
Stefan Alig, Mohammad Shahrokh Esfahani, Andrea Garofalo, M. Y. Li, Cédric Rossi, R. M. Adams, Michael S. Binkley, Michael C. Jin, Mari Olsen, Adèle Telenius, Jurik Mutter, Brian J. Sworder, Joseph G. Schroers‐Martin, Daniel A. King, André Schultz, Jan Bögeholz, Sharon Su, Karan R. Kathuria, Xueyuan Kang, C. L. Liu, Valeria Spina, Thomas Tousseyn, Lieselot Buedts, Tim Flerlage, Jamie E. Flerlage, Sharon M. Castellino, Ranjana H. Advani, Davide Rossi, Ryan C. Lynch, Hervé Ghesquières, Olivier Casasnovas, David M. Kurtz, L. J. Marks, Michael P. Link, Marc André, Peter Vandenberghe, Christian Steidl, Maximilian Diehn, Ash A. Alizadeh

Bibliographic record

VenueHematological Oncology · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Genomics and Diagnostics
Canadian institutionsSpinal Cord Injury BC
Fundersnot available
KeywordsGenotypingCopy number analysisGenotypeLymphomaOncologyMedicineBiologyGeneticsCancer researchInternal medicineCopy-number variationGeneGenome

Abstract

fetched live from 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. 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

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.373
Threshold uncertainty score0.760

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.020
GPT teacher head0.288
Teacher spread0.268 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2023
Admission routes1
Has abstractyes

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