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Record W4310112231 · doi:10.1182/blood-2022-165616

Extranodal Marginal Zone Lymphomas Show Recurrent Mutations in DNA Repair Genes, Cancer-Associated Proliferative Signaling and NOTCH1 Signaling Pathways, Regardless of Anatomic Site

2022· article· en· W4310112231 on OpenAlexaffabout
Jennifer R. Chapman‐Fredricks, Devang Thakkar, Juan Pablo Alderuccio, Kikkeri N. Naresh, Sarah L. Ondrejka, Eric D. Hsi, Mina L. Xu, Nathan Paulson, Jean L. Koff, David L. Jaye, Jonathon B. Cohen, Anne Ortved Gang, Rebecca J. Leeman‐Neill, Tushar Dave, Lanie E. Happ, Cassandra Love, Sasan Zandi, Hina Naushad, Emily F. Mason, Abner Louissaint, Haley Martin, Choon Kiat Ong, Raju Pillai, Mette Ølgod Pedersen, C. Cameron Yin, William Choi, Rex Au-Yeung, Marja‐Liisa Karjalainen‐Lindsberg, Amy Chadburn, Vincent Sarno, Matthew McKinney, Payal Sojitra, Andrew G. Evans, Amir Behdad, Carlos Galvez, Chee Leong Cheng, Magdalena Czader, Jiong Yan, Sandeep S. Davé, Izidore S. Lossos

Bibliographic record

VenueBlood · 2022
Typearticle
Languageen
FieldMedicine
TopicLymphoma Diagnosis and Treatment
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsExomeBiologyTranscriptomePopulationHematopathologyGeneticsGeneExome sequencingCancer researchMedicineMutationGene expressionChromosomeCytogenetics

Abstract

fetched live from OpenAlex

Extranodal marginal zone lymphomas (EMZL) are usually indolent B cell lymphomas arising in acquired mucosa-associated lymphoid tissue (MALT) as a result of chronic antigen stimulation from infection or autoimmune processes. EMZLs occurring at different MALT sites have shared and unique genetic events including site-associated chromosomal structural changes and mutations. While large scale genetic analyses of EMZL are lacking, previous small studies have shown that EMZL often show activation of NF-κB through chronic stimulation of the B cell receptor and other cooperating genetic events that include somatic mutations, most frequently in TNFAIP3. Here, we report the largest whole exome sequencing (WES) and copy number (CN) analysis to date in EMZL. Formalin-fixed paraffin-embedded (FFPE) samples of EMZL were collected as part of the Atlas of Blood Cancer Genomes (ABCG) consortium. All cases underwent central pathology review by expert hematopathologists and diagnoses were confirmed as defined by the 2016 WHO classification. We defined global CN gains and losses at log2 fold change value of ±0.3, a value used by many CN tools. DNA and RNA WES were performed using the Illumina platform and DNA and RNA reads aligned to the GRCh38 genome and transcriptome, respectively. Exonic variants were identified and filtered using normal samples and population-based databases to identify putative driver mutations which were then aggregated at the gene level. Mutational analysis was done on samples that passed quality filtering. 225 unique diagnostic specimens were collected from 24 academic hematopathology departments. Cases originated from North America (USA and Canada, 86%), Europe (8%), and Asia (Singapore and China, 6%). 72 tumors were excluded due to inadequate mean coverage related to size of the biopsy or its quality. CN analysis and WES to identify non-silent mutations was performed in 153 unique EMZL including ocular adnexal (31), stomach (26), lung (23), salivary gland (23), and other extranodal locations (50). Average patient age was 63 years (range 53-71 years), female: male ratio of 1.6. Mutation calls identified 718 variants (613 missense mutations and 105 truncating mutations) across 59 putative driver genes detected in ≥5% cases across all the anatomic locations. Among the most commonly mutated genes, we detected genes previously reported to be altered by targeted sequencing in EMZL (e.g., SPEN (12%), TNFAIP3 (A20) (8%), CREBBP (8%), TBL1XR1 (7%), and others) confirming our filtering strategy. A total of 44 focal CN alterations (24 gains and 20 CN losses) were detected for our set of recurrently mutated genes. Global CN gains included chromosome 3 (6%), 6p (7%), 8 (3%), 12 (4%), and 18 (5%) and losses in 21p (10%). Previously described tumor suppressor genes such as TNFAIP3 showed a typical pattern of alterations consisting of CN losses (6q23.3 deletion in 6%) and/or mostly truncating mutations (8%). Combining DNA mutations and CN alterations, the number of genetic lesions per tumor sample in the 59 putative driver genes ranged from 1 to 21, with an average load of 4.9 aberrations per case, consistent with previous genetic studies in other types of MZL lymphoma. The most frequent mutations across all sites were IGLL5 (22%), KMT2C (15%), KMT2D (14%), SPEN (12%), PRDM2 (11%), and NCOR1 (10%). Analysis using Elsevier Pathway Collection, BioPlanet 2019 and Go Biological Process 2021 revealed adjusted statistically significant enrichment of aberrations in genes in NOTCH signaling, DNA repair, cancer-associated sustaining of proliferative signaling, cancer associated histone methylation and positive regulation of transcription, by DNA-templated (GO:0045893) and RNA polymerase II (GO:0045944). Many of the mutated genes were previously not reported in EMZL. Some of the mutations were shared by tumors at different anatomic locations while some were more unique. Mutations in TET2, previous reported as specific for thyroid EMZL, were also detected in ocular adnexa, gastric and salivary gland MZL. Mutations in PTPRD, previously suggested as specific for NMZL, were detected also in EMZL. Overall, this analysis of a large number of samples from different anatomic locations further refines the mutational landscape in EMZL and provides novel clues on pathogenesis. More detailed analyses of mutations, CN variations and RNA expression will be presented at the meeting.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.019
GPT teacher head0.251
Teacher spread0.232 · 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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Citations1
Published2022
Admission routes2
Has abstractyes

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