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Molecular Subgroups of Peripheral T-Cell Lymphoma Evolve By Distinct Genetic Pathways

2016· article· en· W2765240381 on OpenAlexaff
Tayla B. Heavican, Jiayu Yu, Alyssa Bouska, Timothy C. Greiner, Cynthia M. Lachel, Chao Wang, Bhavana J. Davé, Catalina Amador, Kai Fu, Julie M. Vose, Dennis D. Weisenburger, Randy D. Gascoyne, Sylvia Hartmann, Martin Bjerregård Pedersen, Ryan A. Wilcox, Bin Tean Teh, Soon Thye Lim, Choon Kiat Ong, Masao Seto, Françoise Berger, Andreas Rosenwald, German Ott, Elı́as Campo, Lisa M. Rimsza, Elaine S. Jaffe, Rita M. Braziel, Francesco d’Amore, Giorgio Inghirami, Francesco Bertoni, Louis M. Staudt, Timothy W. McKeithan, Stefano Pileri, Wing C. Chan, Javeed Iqbal

Bibliographic record

VenueBlood · 2016
Typearticle
Languageen
FieldMedicine
TopicLymphoma Diagnosis and Treatment
Canadian institutionsBC Cancer Agency
Fundersnot available
KeywordsPeripheral T-cell lymphomaSNP arrayLymphomaSNPComparative genomic hybridizationBiologyCopy-number variationSingle-nucleotide polymorphismCancer researchGeneGeneticsMedicineOncologyChromosomeT cellGenotypeImmunology

Abstract

fetched live from OpenAlex

Abstract Peripheral T-cell lymphoma (PTCL) is a group of clinically and pathologically heterogeneous non-Hodgkin lymphomas (NHL). Using gene expression profiling (GEP), we have defined molecular classifiers for PTCL subtypes reflecting their pathobiology and oncogenic pathways (Iqbal et al. 2014). We have also shown associations of specific mutations with the molecular subgroups (Wang et al. 2015). Although genomic information is increasing, the pathogenetic mechanisms of PTCLs remain largely unknown. Therefore, we analyzed copy number variation (CNV) and GEP to identify unique genetic abnormalities in the defined PTCL molecular subgroups. CNV data were generated on fresh frozen or formalin-fixed paraffin-embedded genomic DNA (n=114) on 3 Affymetrix platforms (SNP 6.0, 250K SNP, and OncoScan). Two published cohorts (PTCL-NOS, Hartmann et al. 2010; ALCL, Boi et al. 2013) were included for validation. The gene expression analysis, morphological review and clinical characteristics of these cases have been included in previous studies (Iqbal et al. 2010, 2014). Angioimmunoblastic T-cell lymphoma (AITL) represents 20% of all PTCL cases. The most recurrent CNV in AITL was chromosome (chr) 5 gain (39%), followed by chr 21 gain (21%). Interestingly, chr 21 gain co-occurred with chr 5 gain (p=0.003). No recurrent losses (≥20%) were identified among these cases. Molecularly re-classified AITL cases from morphologically classified PTCL-NOS cases showed concordant results with bonafide AITL cases. Of the commonly mutated genes, DNMT3A, IDH2, RHOA and TET2, only IDH2R172Kshowed a significant association (p=0.012) with chr 5 gain. GEP showed enrichment of gene signatures associated with oxidative phosphorylation (PGC-1α target genes) in cases with chr 5 gain. PTCL, not otherwise specified (PTCL-NOS) is the most common PTCL subtype and cannot be further sub-classified using conventional approaches; however, we have identified 2 molecular subgroups within PTCL-NOS, the GATA3 and TBX21 subgroups which are related to 2 distinct T-helper subsets (Iqbal et al. 2014), by employing GEP. Consistent with earlier observations (Hartmann et al. 2010), PTCL-NOS showed remarkably varied CNVs with nearly 50% of cases showing high CNV frequencies. When correlated with molecular subgroups, distinctive CNVs were observed in the molecular GATA3 and TBX21 subgroups. The GATA3 subgroup displayed a large assortment of CNVs. Complete or partial gain of chr 7 (57%) was the most recurrent gain in these cases. Losses affecting 17p, 10q and 9p21, encompassing tumor suppressors such as TP53 (57%), PTEN (43%) and CDKN2A (43%), were frequent in the GATA3 subgroup. The TBX21 subgroup had significantly fewer CNVs, as none were recurring (≥20%); but gains of 5p or 11p were observed in 14%. Additionally, PTCL-NOS cases with ≥10% abnormal genome had significantly poorer overall survival (p=0.012) compared to those with fewer abnormalities. This finding validates the GEP molecularly defined subgroups, as the GATA3 subgroup displayed more CNVs and has been associated with a worse prognosis compared to the TBX21 subgroup (Iqbal et al. 2014). We were able to distinguish CNVs characteristic of the different entities, including the co-occurrence of chr 5 and 21 gains specific in AITL. Gain of 1q (complete or partial) was identified in the GATA3 subgroup of PTCL-NOS and anaplastic lymphoma kinase (ALK) (-) ALCL with equal frequencies (~ 36%), but only 16% in ALK(+) ALCL. Complete or partial gain of chr 7 was also observed in ALCL, but at a considerably lower frequency than in the GATA3 subgroup. Additionally, gain of chr 18 or regions of 17q, and loss of 5q or regions on both arms of chr 9, were more frequent in the GATA3 subgroup compared to other entities. The TBX21 subgroup was primarily differentiated from the GATA3 subgroup by presence of fewer CNVs. Our analysis provides a framework for future investigations into the molecular pathogenesis of PTCL, and highlights potential candidate oncogenes and tumor suppressors deregulated by copy number aberrations. Comparative analysis revealed that certain chromosomal abnormalities are entity-specific. AITL cases with IDH2R172K also had trisomy 5 suggesting that these oncogenic events cooperate in malignant transformation. Thus, the complexity of PTCL is finally becoming clearer with the integration of high resolution molecular techniques for global genomic analysis. Disclosures No relevant conflicts of interest to declare.

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 score0.001
Threshold uncertainty score0.005

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)0.0010.000

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 head0.007
GPT teacher head0.201
Teacher spread0.194 · 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
Published2016
Admission routes1
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