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Record W2985949593 · doi:10.1182/blood-2019-127399

Recurrent Patterns of Clonal Evolution in Relapsed-Refractory DLBCL Following Treatment with R-CHOP

2019· article· en· W2985949593 on OpenAlexaff
Christopher Rushton, Sarah E. Arthur, Miguel Alcaide, Matthew C. Cheung, Nicole Thomas, Laura K. Hilton, 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, Kirstie L.S. Cleary, Mark S. Cragg, David W. Scott, Nathalie A. Johnson, Ryan D. Morin

Bibliographic record

VenueBlood · 2019
Typearticle
Languageen
FieldMedicine
TopicLymphoma Diagnosis and Treatment
Canadian institutionsJewish General HospitalSpinal Cord Injury BCMcGill UniversityQueen's UniversitySimon Fraser University
Fundersnot available
KeywordsDiffuse large B-cell lymphomaOncologyLymphomaInternal medicineExome sequencingFollicular lymphomaExomeMedicineCancer researchMutationBiologyGeneGenetics

Abstract

fetched live from OpenAlex

Introduction Diffuse large B-cell lymphoma (DLBCL) is the most common subtype of non-Hodgkin lymphoma and is characterized by its genetic and clinical heterogeneity. Patients can develop DLBCL de novo or as a transformation from other lymphoid malignancies, most commonly follicular lymphoma. For patients with relapsed/refractory DLBCL (rrDLBCL), prognosis is extremely poor with 2-year overall survival of 20-40%. While numerous treatments are under investigation to improve patient outcomes, the success of these treatments has been limited as the genetic mechanisms underpinning treatment resistance are largely unknown. Identifying genomic alterations which contribute to relapse may improve salvage therapy for patients with rrDLBCL or allow patients to be stratified prior to frontline treatment. Methods To identify genomic alterations which contribute to R-CHOP resistance, we previously collected samples from patients enrolled in four clinical trials exploring candidate salvage therapies for patients with rrDLBCL as well as a retrospective rrDLBCL cohort, totalling 193 cases (133 de-novo DLBCL, 60 transformed). Plasma samples were collected from each patient upon relapse along with diagnostic tissue biopsies where available. A combination of exome sequencing and target-panel sequencing of lymphoma associated genes was performed on circulating tumour DNA and tissue biopsies (if available). Mutations implicated in R-CHOP resistance were identified through two complimentary strategies. First, the mutation frequency of recurrently mutated genes across de novo rrDLBCL samples was compared to a cohort of unrelated diagnostic DLBCL cases (n=1691) to identify genes enriched for mutations. Second, the genomic landscape and tumor clonal structure was compared prior to and following R-CHOP to identify mutations in each patient that underwent clonal expansion following therapy. Anti-CD20 antibody binding affinity of MS4A1 mutants was evaluated using flow cytometry on transfected CHO-S cells. Results We have identified five genes enriched for mutations in our rrDLBCL cohort relative to diagnostic DLBCL: KMT2D (Mutated in 49%, Q=0.0385), TP53 (47%, Q=1.07x10-9), FOXO1 (11%, Q=0.0727), NFKBIE (11%, Q=0.0385), and MS4A1 (8%, Q=0.0522). Consistent with its characterization as a poor prognostic marker, mutations in TP53 were typically present at diagnosis and remained stable following R-CHOP therapy for both de novo and transformed DLBCL (23/27 cases, 85%). Recurrent mutations affecting Arg248 of TP53 (6.8%, Q=0.0413) were also clonally stable and have previously been associated with poor overall survival across several cancer types. The histone methyltransferase KMT2D is dominated by nonsense and frameshift mutations which were stable or underwent clonal expansion following R-CHOP (17/19, 89%). Recurrent missense mutations in MS4A1 targeted the small loop and adjacent transmembrane domains of CD20, including several patients with a Tyr86 mutation. Transfected cells carrying Tyr86Cys or Leu66Arg mutations were not bound by rituximab or other anti-CD20 antibodies including obinituzumab and ofatumumab. Subclonal populations containing MS4A1 mutations underwent clonal expansion (6 cases) or were stable (1 case) following treatment, including one case with multiple MS4A1 mutations in distinct subclonal populations which both underwent clonal expansion. In another unique case, a series of ctDNA samples were available prior to and following R-CHOP and salvage therapy, where we again observed convergent evolution of two mutually exclusive clonal subpopulations containing MS4A1 mutations. The first subpopulation underwent clonal expansion following frontline therapy but was extinguished following salvage therapy, while the other subpopulation underwent clonal expansion following salvage therapy and harboured a transmembrane domain mutation. Conclusion Mutations in TP53 and truncating mutations in KMT2D are generally present prior to treatment and will be investigated as biomarkers of treatment failure. Additional mutations are not always present at diagnosis, but their emergence can be detected in ctDNA and relapsed tissue, specifically mutations in MS4A1. As mutations in MS4A1 attenuate rituximab binding and are recurrently associated with clonal expansion, they likely impart a selective advantage and lead to resistance against anti-CD20 antibodies. Disclosures Michaud: Epizyme: Employment. Daigle:Epizyme: Employment. Jain:Kite/Gilead: Consultancy. Kuruvilla:Roche: Honoraria; Astra Zeneca: Honoraria; Novartis: Honoraria; Merck: Honoraria; Karyopharm: Honoraria; Gilead: Honoraria; Celgene: Honoraria; BMS: Honoraria; Amgen: Honoraria; Seattle Genetics: Consultancy; Roche: Consultancy; Merck: Consultancy; Karyopharm: Consultancy; Gilead: Consultancy; Janssen: Research Funding; Roche: Research Funding; BMS: Consultancy; Abbvie: Consultancy; Seattle Genetics: Honoraria; Janssen: Honoraria. Assouline:Abbvie: Consultancy, Honoraria; Janssen: Consultancy, Honoraria, Speakers Bureau; F. Hoffmann-La Roche Ltd: Consultancy, Honoraria; Pfizer: Consultancy, Honoraria, Speakers Bureau. Scott:NanoString: Patents & Royalties: Named inventor on a patent licensed to NanoSting [Institution], Research Funding; Celgene: Consultancy; Roche/Genentech: Research Funding; Janssen: Consultancy, Research Funding. Johnson:BD Biosciences: Other: Provided a significant proportion of the antibodies used in this project free of cost.; Merck: Consultancy, Honoraria; BMS: Consultancy, Honoraria; Abbvie: Consultancy, Employment, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Roche: Consultancy, Employment, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel fees, gifts, and others, Research Funding; Seattle Genetics: Honoraria; Lundbeck: Employment, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel fees, gifts, and others, Research Funding.

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 score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.010
GPT teacher head0.242
Teacher spread0.233 · 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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Citations3
Published2019
Admission routes1
Has abstractyes

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