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Record W2991549326 · doi:10.1182/blood-2019-127375

Mutational Landscape of Grey Zone Lymphoma

2019· article· en· W2991549326 on OpenAlexaff
Clémentine Sarkozy, Stacy Hung, Katsuyoshi Takata, Elizabeth A. Chavez, Tomohiro Aoki, Gerben Duns, Graham W. Slack, Adèle Telenius, Tomoko Miyata‐Takata, Elena Viganò, Thierry Jo Molina, Diane Damotte, Susana Ben‐Neriah, Christiane Copie‐Bergman, Camille Laurent, Anja Mottok, Gilles Salles, Kerry J. Savage, David W. Scott, Alexandra Traverse‐Glehen, Christian Steidl

Bibliographic record

VenueBlood · 2019
Typearticle
Languageen
FieldMedicine
TopicLymphoma Diagnosis and Treatment
Canadian institutionsBC Cancer AgencySpinal Cord Injury BC
Fundersnot available
KeywordsExome sequencingExomeLymphomaIndelBiologyPathologyMedicineCancer researchGeneticsGeneSingle-nucleotide polymorphismMutation

Abstract

fetched live from OpenAlex

Introduction: Grey zone lymphoma (GZL), a B-cell lymphoma with features intermediate between large B-cell lymphoma (LBCL) and classical Hodgkin lymphoma (cHL), is a rare and poorly defined entity. To decipher its mutational landscape and discover new therapeutic targets, we performed exome sequencing of 31 GZL cases. Methods: GZL cases from the LYSA group (N=139) and BC Cancer (N=30) were centrally reviewed and classified as previously published (Sarkozy et al, Am J Surg Pathol 2019). Whole-exome sequencing was performed on 31 cases with available fresh frozen tissue, using laser micro-dissection (LMD, MMI technology) to enrich for tumor cells and obtain matching normal DNA from microenvironment cells. DNA was extracted (Agencourt® DNAdvance kit) and genomic libraries were constructed with the Ovation ultra-low kit (Nugen®). Exome capture was performed using Agilent SureSelectXT V6+UTR followed by paired-end sequencing (NextSeq®). Somatic nucleotide variants (SNVs) and indels were identified using VarScan, Strelka and Mutect. Parameters affecting the sensitivity and specificity of variant calling were optimized using 7 "gold standard" cases for which DNA from peripheral blood cells was additionally available. Possible oncogenic drivers were identified based on rate of recurrence, MutSigCV and literature review. Results: Among the 31 GZL cases, the median age was 41 y (14-83) with a sex ratio of 15M:16F; 21 cases had mediastinal involvement, including 15 within the thymic area; EBER in-situ hybridization (ISH) was positive in 8 cases. Seven (23%) cases were classified as group-0 (cHL morphology with 100% CD20 expression), 22 (71%) with an intermediate morphology as group-1 (N=9, cHL-like morphology) or group-2 (N=13, LBCL-like morphology) and 2 (6%) as group-3 (LBCL with 100% of CD30 expression). The mean coverage was 96X (42-203) for tumor samples. One case was excluded due to failure in the LMD process. Among the 30 cases, 6628 variants across 4826 genes were found, including 2903 coding mutations (325 indels and 2808 SNVs, mean of 104/sample, range: 15-678), 721 affecting the 5' UTR and 2774 the 3' UTR. A total of 152 genes were identified as being potential oncogenic drivers, with a mean of 11 mutated genes per case (range 2-36). The most recurrently mutated genes were SOCS1 (33%), B2M (23%), GNA13 (20%), LRRN3 (17%), and ZNF217, NCOR1, ITPKB, IRF2BP2, CSF2RB, and CSMD3 (13% each). The epigenetic SWI/SNF and transcription regulation pathway (including NCOR1/2, ARID1A, KMT2D, KMT2A) was affected in 73% of the cases, JAK/STAT in 70% and NF-kB in 19%. As assessed by CNVkit and GISTIC, the most recurrent gains/amplifications identified were in 9p24.1 (JAK2, CD274, PDCD2LG2; 69%) and 2p16.1 (REL, BCL11A; 62%), and losses in 11q14.3 (ATM; 48%) and 12q24.33 (NCOR2; 48%). Based on mutational signature analysis, individual base substitutions were linked to mutagenic processes, with the highest contributions associated with aging (29%) and defective DNA mismatch repair (27%); moreover, mutations attributable to AID/APOBEC activity (5%), were found to be significantly enriched in EBV- vs. EBV+ cases (p = 0.013). EBV+ cases had fewer total variants (mean 98 vs 258, p=0.08) and potential oncogenic variants (mean 7 vs 15, p=0.03) compared to EBV- cases. EBV+ cases also lacked mutations in the NF-kB pathway and MHC-class I components (B2M and HLA-B: 0% vs 43% in EBV-, p=0.06) but had mutations in STAT3, DHX58, ACTB and ATP13A4 (6/7 cases) not present in the 23 EBV- cases. LRRN3 and GNA13 mutations were significantly associated with thymic area involvement (40% vs 0%, p=0.01). Furthermore, fluorescence-ISH indicated that 20% (1/5) of EBV+ cases had a rearrangement in the CIITA locus (16p13.13) vs 53% (9/17) in EBV- cases. Patients with an intermediate morphology had more oncogenic variants than those in group 0 and 3 (mean of 15 vs 6 variants/case, p=0.01 affecting 12 vs 5 genes, p=0.004). Finally, NCOR1 (N=4) and NCOR2 (N=2) mutations were exclusively found in cases with intermediate morphology (23% vs 0% for those with group 0 or 3 morphology). Conclusion: These data suggest that GZL is a highly heterogenous disease harboring somatic driver events shared with PMBCL and HL. We also discovered novel gene mutations pointing to the importance of previously unrecognized pathways in the pathogenesis of GZL. The distinct mutational pattern in EBV+ GZL suggests divergent evolutionary trajectories. Disclosures Sarkozy: Takeda: Research Funding. Salles:Merck: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Novartis, Servier, AbbVie, Karyopharm, Kite, MorphoSys: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Educational events; Autolus: Consultancy, Membership on an entity's Board of Directors or advisory committees; Takeda: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Educational events; Epizyme: Consultancy, Honoraria; BMS: Honoraria; Amgen: Honoraria, Other: Educational events; Roche, Janssen, Gilead, Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Educational events. Savage:BMS, Merck, Novartis, Verastem, Abbvie, Servier, and Seattle Genetics: Consultancy, Honoraria; Seattle Genetics, Inc.: Consultancy, Honoraria, Research Funding. Scott:Celgene: Consultancy; Roche/Genentech: Research Funding; Janssen: Consultancy, Research Funding; NanoString: Patents & Royalties: Named inventor on a patent licensed to NanoSting [Institution], Research Funding. Steidl:Juno Therapeutics: Consultancy; Tioma: Research Funding; Roche: Consultancy; Bristol-Myers Squibb: Research Funding; Nanostring: Patents & Royalties: Filed patent on behalf of BC Cancer; Seattle Genetics: Consultancy; Bayer: Consultancy.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.008
GPT teacher head0.229
Teacher spread0.222 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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".

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Citations6
Published2019
Admission routes1
Has abstractyes

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