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Record W2986177105 · doi:10.1182/blood-2019-127800

Characterization of the "Immune Evasion" Phenotype of Richter Syndrome and the Implications for Immune-Checkpoint Inhibitor Therapy

2019· article· en· W2986177105 on OpenAlexaff
Clare Gould, Jennifer Lickiss, Yamuna Kankanige, Satwica Yerneni, John Markham, Costas K. Yannakou, Diego Villa, Graham W. Slack, Collin K. Chin, Constantine S. Tam, Stephen Lade, Niles Nelson, Paul J. Neeson, John F. Seymour, Michael Dickinson, David Westerman, Piers Blombery

Bibliographic record

VenueBlood · 2019
Typearticle
Languageen
FieldMedicine
TopicChronic Lymphocytic Leukemia Research
Canadian institutionsSpinal Cord Injury BCUniversity of British Columbia
Fundersnot available
KeywordsImmune checkpointIbrutinibImmune systemImmunologyMedicineChronic lymphocytic leukemiaOncologyImmunotherapyCancer researchInternal medicineBiologyLeukemia

Abstract

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Richter syndrome (RS) is the transformation of chronic lymphocytic leukemia (CLL) to a high-grade B-cell lymphoma and is associated with an aggressive clinical course and poor prognosis. Conventional treatment options for RS are generally associated with low response rates and limited durability making this entity an area of significant unmet therapeutic need. Immune checkpoint inhibitor therapy has shown promise in the treatment of some aggressive lymphoma subtypes. In RS, modest benefits have been reported in small phase two trials of anti-PD-1 monotherapy and in combination with ibrutinib, however larger scale studies are lacking (Ding et al Blood, 2017; Jain et al Blood, 2016). We sought to characterise the immune-evasion phenotype of RS focussing on potential genetic biomarkers which may inform the selection of patients who are most likely to benefit from immune-directed therapies. We first assessed the gene expression of immune-checkpoint molecules given their potential clinical relevance and ability to be targeted by available therapeutic agents. Given immunohistochemical (IHC) assessment of immune-checkpoint molecules is recognized to be associated with high inter-observer variability and there is a high correlation between gene expression of immune-checkpoint molecules and IHC, we performed gene expression quantification using the Nanostring nCounter Human Immunology V2 panel (Nanostring Technologies, USA). Nanostring analysis was performed on samples from 17 patients with histologically confirmed RS (DLBCL subtype) and compared to 73 cases of de novo (non-transformed) DLBCL. Significant differences in the gene expression of checkpoint molecules was observed between RS and DLBCL biopsies, including higher expression of LAG3, PD1 and TIGIT in RS (p=0.0001, logFC 1.9; p=0.0017, logFC 1.1 and p=0.0437, logFC 0.7 vs DLBCL, respectively). PD-L2 and TIM3 gene expression were both significantly lower in RS compared to DLBCL (p = 0.0059, logFC 0.8; p = 0.012, logFC 0.8). PDL1 and CTLA4 gene expression did not significantly differ between RS and DLBCL. We next assessed the gene expression of T- and NK- cell markers (including CD3, CD4, CD8, FOXP3 and CD56) and the ratios of these markers to malignant B-cells (CD19). We observed no significant difference between RS and DLBCL, consistent with a similar relative quantity of immune cell infiltration between the two entities. Significantly higher gene expression of CD39, a marker of CD8+ T-cell exhaustion, was observed in RS than DLBCL (p = 0.031; logFC 0.5). Additional immune-related genes were next assessed, including those involved in antigen presentation (e.g. B2M, HLA molecules, TAP), immunosuppressive cytokine generation (e.g. ARG1, IDO1) and apoptosis resistance (e.g. FAS) which showed no significant differences in expression between RS and DLBCL. To assess whether these findings were consistent across other transformed lymphoma subtypes, we compared RS to a cohort of transformed follicular lymphoma (tFL, n=16) and transformed marginal zone lymphoma (tMZL, n=25). LAG3 expression was significantly higher in RS compared to both tFL and tMZL (p=0.0002, logFC 2.7; p=0.019, logFC 1.7). PD1 expression was also significantly higher in RS than tFL but not tMZL (p=0.0045, logFC 1.7; p=0.39, logFC 0.4). Given the established association of copy number amplifications involving immune checkpoint molecules (e.g. PD-L1/PD-L2 on 9p24.1) representing a potential predictive biomarker of response in other lymphomas, we performed hybridization-based NGS with whole genome copy number assessment to evaluate immune checkpoint gene loci in the three cohorts. No significant focal amplifications were detected in RS samples with overexpressed immune-checkpoint molecules. In contrast, three patients in the DLBCL/transformed cohort had focal copy number amplifications involving PD-L1. No copy number amplification of LAG3 was observed in either RS or DLBCL. In summary, we have observed significantly increased gene expression of LAG3, PD1 and TIGIT in RS compared to de novo DLBCL. Combined with increased gene expression of the exhausted cytotoxic T-cell marker CD39, these data provide a strong biological rationale for pursuing LAG3 inhibition either alone or in combination with other immune checkpoint blockade to enhance anti-tumour T cell responses in this difficult-to-treat entity. CG/JL/YK co-first authors Disclosures Gould: NovoNordisk: Other: Travel funding - domestic flights to attend education, May 2018. Villa:Roche, Abbvie, Celgene, Seattle Genetics, Lundbeck, AstraZeneca, Nanostring, Janssen, Gilead: Consultancy, Honoraria. Tam:Abbvie, Janssen: Research Funding; Abbvie, Janssen, Beigene, Roche, Novartis: Honoraria. Neeson:Roche Genetech: Research Funding; Allergan: Research Funding; Juno/Celgene: Research Funding; Compugen: Research Funding; BMS: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding. Seymour:Roche: Consultancy, Research Funding, Speakers Bureau; Takeda: Consultancy; AbbVie: Consultancy, Honoraria, Research Funding, Speakers Bureau; Acerta: Consultancy; Celgene: Consultancy, Research Funding, Speakers Bureau; Janssen: Consultancy, Research Funding. Dickinson:Janssen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Takeda: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; F. Hoffmann-La Roche Ltd: Consultancy, Honoraria, Research Funding, Speakers Bureau; Novartis: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Merck Sharpe and Dohme: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Celgene: Consultancy, Honoraria, Research Funding, Speakers Bureau; GlaxoSmithKline: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding. Blombery:Invivoscribe: Honoraria; Novartis: Consultancy; Janssen: Honoraria.

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 head0.016
GPT teacher head0.265
Teacher spread0.250 · 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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Citations0
Published2019
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