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Record W2949969572 · doi:10.1002/hon.4_2629

IDENTIFYING MUTATIONS ENRICHED IN RELAPSED‐REFRACTORY DLBCL TO DERIVE GENETIC FACTORS UNDERLYING TREATMENT RESISTANCE

2019· article· en· W2949969572 on OpenAlexaff
Christopher Rushton, Miguel Alcaide, Matthew C. Cheung, Nicole Thomas, Sarah E. Arthur, Neil R. Michaud, Scott R. Daigle, Jordan Davidson, Kevin Bushell, Stephen Yu, Michael D. Jain, Lois E. Shepherd, Michael Crump, Koren K. Mann, John Kuruvilla, Sarit Assouline, Nathalie A. Johnson, Daniel Scott, Ryan D. Morin

Bibliographic record

VenueHematological Oncology · 2019
Typearticle
Languageen
FieldMedicine
TopicLymphoma Diagnosis and Treatment
Canadian institutionsCanada's Michael Smith Genome Sciences CentreMcGill UniversityQueen's UniversityGenome British ColumbiaPrincess Margaret Cancer CentreSimon Fraser University
Fundersnot available
KeywordsOncologyDiffuse large B-cell lymphomaInternal medicineExome sequencingPopulationExomeMedicineLymphomaGermline mutationCohortMutationBiologyGeneGenetics

Abstract

fetched live from OpenAlex

Introduction: Diffuse Large B-cell Lymphoma (DLBCL) the most common subtype of Non-Hodgkin Lymphoma and is characterized by genetic and clinical heterogeneity. In relapsed/refractory DLBCL (rrDLBCL) cases, where frontline treatment is unsuccessful, patient prognosis is extremely poor, with 2-year overall survival of 20-40%. While numerous treatments are under investigation to improve patient outcomes, their success has been limited as the genetic mechanisms underpinning treatment resistance are largely unknown. Identifying genomic alterations associated with relapse may open new treatment avenues and allow the stratification of patients into subgroups based upon relevant mutations. Methods: We have collected samples from 134 patients enrolled in three clinical trials (LY17, Obinituzumab-GDP, QCROC2) exploring candidate treatment options for rrDLBCL. For each patient enrolled, blood plasma samples were collected prior to and at several time points following candidate treatment. A combination of exome sequencing and target panel sequencing of lymphoma-associated genes was performed on cell-free DNA extracted from plasma samples and tissue biopsies (where available) obtained upon trial enrollment (relapse). Somatic mutations were identified using Strelka2, and clonal population structure was inferred using PyClone. Mutation prevalence was compared to a large unselected cohort of diagnostic DLBCLs to identify genes enriched for mutations. Results: Patients with rrDLBCL were enriched for mutations in 5 genes; TP53 (Q=6.74x10-5), IL4R (Q=0.00391), HVCN1(Q=0.0729), RB1 (Q=0.0127) and MS4A1 (Q=0.0522), with TP53 mutations previously associated with rrDLBCL. Mutations in IL4Rmay lead to constitutively active JAK/STAT signalling and inferior overall survival in DLBCL. HVCN1 encodes a voltage-gated proton channel which modulates the B-Cell Receptor (BCR), and truncated HVCN1 isoforms have been shown to enhance BCR signaling. MS4A1 encodes CD20, the target of the monoclonal antibody Rituximab, a cornerstone of frontline DLBCL treatment. In several patients, clonal subpopulations with MS4A1 mutations underwent clonal selection following treatment. These mutations are predicted to either truncate CD20, or destabilize a common transmembrane helix, with 4/15 patients containing mutations affecting Tyrosine 86. We also observed recurrent in-frame deletions targeting S1680 of CREBBP, and although CREBBP mutations are associated with treatment resistance in other cancers, the functional effect of this deletion has not been characterized. Conclusions: DLBCL patients with mutations in relapse-enriched genes are at a higher risk of treatment failure. Mutations in these genes, specifically hotspot deletions, may have power as biomarkers to identify patients at a high risk of relapse and could inform on the mechanism of acquired resistance to components of R-CHOP. Keywords: CD20; diffuse large B-cell lymphoma (DLBCL); R-CHOP. Disclosures: Michaud, N: Employment Leadership Position: Epizyme. Daigle, S: Employment Leadership Position: Epizyme.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.726

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0000.001

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 head0.085
GPT teacher head0.371
Teacher spread0.287 · 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 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

Citations2
Published2019
Admission routes1
Has abstractyes

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