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CD28 Mutations in Peripheral T-Cell Lymphomagenesis and Progression

2014· article· en· W1453424702 on OpenAlexaff
Joseph Rohr, Shuangping Guo, Dongdong Hu, Alyssa Bouska, Randy D. Gascoyne, Andreas Rosenwald, Peter D. Simone, Weiwei Zhang, Wenming Xiao, Chao Wang, Kai Fu, Timothy C. Greiner, Dennis D. Weisenburger, Julie M. Vose, Louis M. Staudt, Françoise Berger, Simon Davis, Timothy W. McKeithan, Javeed Iqbal, Wing-Chung Chan

Bibliographic record

VenueBlood · 2014
Typearticle
Languageen
FieldMedicine
TopicLymphoma Diagnosis and Treatment
Canadian institutionsBC Cancer Agency
Fundersnot available
KeywordsCancer researchRHOABiologyLymphomaMutationT-cell lymphomaT cellMolecular biologyGeneticsImmunologyGeneSignal transductionImmune system

Abstract

fetched live from OpenAlex

Abstract Peripheral T-cell lymphomas (PTCLs) are a heterogeneous group of diseases with poor prognosis and no standard therapy due to widely varying responses to treatments, thus new therapeutic targets need to be identified. Gene expression profiling (GEP) has helped to separate classes of these diseases to predict survival. Recent genetic studies on the two most common subtypes of PTCL in the Western world, angioimmunoblastic T-cell lymphoma (AITL) and PTCL, not otherwise specified (PTCL-NOS), have revealed recurrent mutations in the epigenetic modifiers TET2, IDH2, and DNMT3A; in the small GTPase RHOA; and rarely in the T-cell receptor (TCR) adaptor protein FYN. Because TCR stimulation is necessary for normal T-cell expansion, activating mutations in the stimulation (CD3/TCR) and costimulation (CD28) pathways could be involved in malignant lymphopoiesis. We performed whole-transcriptome sequencing on a small set of primary PTCL cases and found mutations in CD28, including an aspartate 124 mutation within a fusion transcript between CD28 and family member ICOS as well as a threonine 195 mutation. On targeted re-sequencing of 92 PTCL cases, we found two AITL cases and one ALK- anaplastic large cell lymphoma case with an aspartate 124 mutation (variant frequencies [VF]: 1.03% to 5.90%). We also found mutations at threonine 195 (two AITL, one PTCL-NOS, Tbx21 subtype; VF: 2.90% to 12.30%). Additionally, we found two recurrent mutations with low variant frequencies (<2.0%). We also determined each targeted sequencing sample’s RHOA mutation status; high-VF CD28 mutations also had RHOA glycine 17 mutations, almost all of which were to valine (G17V) consistent with previous reports of RHOA mutations in PTCLs. Surface Plasmon Resonance (SPR) analysis of the two highest-VF mutated residues D124 and T195 in CD28 show increased affinities of CD28 mutants for partners. The D124 to valine mutation (D124V) within the extracellular ligand binding region increases affinity for CD86. The T195 to proline mutation (T195P) increases binding of GRB2 and GADS/GRAP2 to the CD28 cytoplasmic tail at the intracellular SH2-binding domain. Molecular modeling of these mutations offers possible explanations for these differential affinities, including potential increased electrostatic binding at the extracellular interface and binding partner side-chain stabilization intracellularly. Further, these mutations have a significantly increased capacity to activate NF-κB upon ligation of CD80 or CD86 and anti-CD3 stimulation. Mutations in CD28 often occur with low frequency in PTCL tumor samples; given this low frequency, the mutations are likely not a primary mechanism of lymphomagenesis. It is possible that over-activation of CD28 represents a late-stage event in clonal evolution, allowing a tumor subclone to further grow and proliferate. These CD28-mutant clones may play a role in later disease course, in refractory disease, or in post-therapeutic relapse. Disclosures Fu: Nanostring: The author is a potential inventor on a patent application using Nanostring technology for the Lymph2Cx assay, which has been licensed from the NIH by Nanostring Patents & Royalties.

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)0.000
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
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.008
GPT teacher head0.251
Teacher spread0.244 · 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".

Quick stats

Citations9
Published2014
Admission routes1
Has abstractyes

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