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Record W2985936603 · doi:10.1182/blood-2019-124235

Molecular Correlates of Central Nervous System Relapse in Diffuse Large B-Cell Lymphoma

2019· article· en· W2985936603 on OpenAlexaff
Robert Kridel, Keren Isaev, Daisuke Ennishi, Brian Skinnider, Karen Mungall, Andrew J. Mungall, Mehran Bakhtiari, Rosemarie Tremblay‐LeMay, Barbara Meissner, Susana Ben‐Neriah, Merrill Boyle, Diego Villa, Marco A. Marra, Christian Steidl, Randy D. Gascoyne, Kerry J. Savage, David W. Scott

Bibliographic record

VenueBlood · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
Topic14-3-3 protein interactions
Canadian institutionsUniversity Health NetworkSpinal Cord Injury BCCanada's Michael Smith Genome Sciences CentrePrincess Margaret Cancer Centre
Fundersnot available
KeywordsDiffuse large B-cell lymphomaMedicineOncologyInternal medicineInternational Prognostic IndexLymphomaGene expression profilingPathologyGene expressionBiologyGene

Abstract

fetched live from OpenAlex

Introduction: Central nervous system (CNS) relapse is a rare phenomenon in diffuse large B-cell lymphoma (DLBCL), occurring in less than 5% of all patients, but is associated with disproportionate morbidity and mortality. Indeed, the median survival of patients diagnosed with CNS relapse is as low as 2-4 months. Individual risk factors for CNS relapse are well established, and include clinical parameters such as stage, number/type of extranodal sites and elevated lactate dehydrogenase. These and other clinical risk factors have been integrated into a risk score that is reproducible and easy to calculate (CNS International Prognostic Index). Moreover, molecular attributes such as double-hit translocation status, MYC/BCL2 dual protein expression and the activated B-cell-like subtype have been associated with a higher risk of CNS relapse. However, while experts recommend prophylactic interventions for high-risk patients, the major shortcoming of available risk tools is their limited sensitivity. Herein, we evaluated whether gene expression and/or mutational profiles can identify those patients that will ultimately experience CNS relapse, and whether intratumoral heterogeneity impedes accurate prognostication. Methods: We accrued diagnostic FFPET samples from 230 newly diagnosed DLBCL patients, selected to fall into 3 clinical groups: 1) cases with CNS relapse/CNS involvement at diagnosis (n=58); 2) cases with systemic relapse but without CNS relapse (n=64) and 3) cases without any relapse (n=108). These 230 samples were subjected to microarray-based gene expression profiling and differential gene expression analysis. Pathway analysis was performed using Gene Set Enrichment Analysis on ranked gene lists. We assembled a partially overlapping dataset with mutation data of 45 genes in 213 diagnostic samples (n=65 with CNS relapse, 62 with systemic relapse and 86 without relapse). Lastly, we performed exome sequencing in 5 pairs (peripheral and CNS parenchymal tumors) of patients with CNS relapse or CNS involvement at diagnosis, and reconstructed clonal phylogenies using PyClone. Results: Focusing on gene expression data at first, we did not observe significant differential expression between CNS relapse and non-relapse cases at the individual gene level. This was in contrast to the comparison between systemic relapse vs. non-relapse cases where 368 genes were differentially expressed (adjusted P<0.05). In terms of pathway analysis, minimal gene set enrichment was seen in CNS relapse cases, whereas functional annotations such as translation, ribosome biogenesis and MYC targets were significantly enriched in cases with systemic relapse. In keeping with these observations, the percentage of cases that were positive for the recently published double hit signature was highest in cases with systemic relapse (64% vs. 39% in CNS relapse cases and 27% in cases without relapse, P=0.012). However, CNS relapsing cases were defined by down-regulation of numerous immune signatures (e.g. interferon and multiple T cell signatures), suggesting that an intact immune response may have a protective effect on CNS relapse. Considering mutation data, we found that TP53 and SGK were most commonly mutated in systemic relapse cases, while TNFRSF14 and KTM2D were most commonly mutated in non-relapse cases (all adjusted P<0.05). The only gene mutation with a borderline significant trend for enrichment in CNS relapse cases was MYD88 (adjusted P=0.05). We then performed exome sequencing of 5 tumor pairs. A subset of high-confidence somatic variants and tumor purity were used as input for PyClone to infer clonal population structures. In all pairs, we documented the existence of common ancestral mutations, as well as significant clonal divergence, with CNS-exclusive mutations not identified in diagnostic specimens. Conclusion: In summary, we have documented that CNS and systemic relapse result from distinct biological processes that, in part, may be associated with the underlying taxonomy of DLBCL. Our findings further show that CNS relapse results from the dissemination of sub-clones that may not be readily sampled at the time of diagnosis, and that intratumoral heterogeneity may limit our ability to predict CNS relapse. Large-scale, integrative analyses and in-depth characterization of clonal trajectories hold the promise to increase our ability to predict dissemination of DLBCL into the CNS. Disclosures Kridel: Gilead Sciences: Research Funding. Villa:Roche, Abbvie, Celgene, Seattle Genetics, Lundbeck, AstraZeneca, Nanostring, Janssen, Gilead: Consultancy, Honoraria. Steidl:Nanostring: Patents & Royalties: Filed patent on behalf of BC Cancer; Juno Therapeutics: Consultancy; Bristol-Myers Squibb: Research Funding; Roche: Consultancy; Tioma: Research Funding; Bayer: Consultancy; Seattle Genetics: Consultancy. Savage:BMS, Merck, Novartis, Verastem, Abbvie, Servier, and Seattle Genetics: Consultancy, Honoraria; Seattle Genetics, Inc.: Consultancy, Honoraria, Research Funding. Scott:Celgene: Consultancy; Roche/Genentech: Research Funding; NanoString: Patents & Royalties: Named inventor on a patent licensed to NanoSting [Institution], Research Funding; Janssen: Consultancy, 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 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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.037
Threshold uncertainty score0.542

Codex and Gemma teacher scores by category

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

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 head0.003
GPT teacher head0.192
Teacher spread0.189 · 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 designBench or experimental
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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Citations0
Published2019
Admission routes1
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