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Record W3212812892 · doi:10.1182/blood-2021-153724

Integrative Genomic Analysis Uncovers Unique Diffuse Large B Cell Lymphoma (DLBCL) Immune Environments and Identifies Associations with Specific Oncogenic Alterations

2021· article· en· W3212812892 on OpenAlexaff
Sravya Tumuluru, James Godfrey, Jovian Yu, Alan Cooper, Xiufen Chen, Gerben Duns, Sonali M. Smith, Christian Steidl, Justin Kline

Bibliographic record

VenueBlood · 2021
Typearticle
Languageen
FieldMedicine
TopicLymphoma Diagnosis and Treatment
Canadian institutionsUniversity of British ColumbiaSpinal Cord Injury BC
Fundersnot available
KeywordsDiffuse large B-cell lymphomaImmune systemBiologyImmune checkpointCD8Cancer researchLymphomaLarge-cell lymphomaChemoimmunotherapyT cellImmunologyImmunotherapy

Abstract

fetched live from OpenAlex

Abstract Introduction: Most patients diagnosed with diffuse large B cell lymphoma (DLBCL) are cured with combination chemoimmunotherapy, but 40% will develop relapsed or refractory (r/r) disease, which is often associated with a poor clinical outcome. PD-1 blockade therapy has been investigated in r/r DLBCL; however, response rates in unselected DLBCL patients are disappointing, highlighting the need for deeper understanding of DLBCL immune landscapes, as well as mechanisms that regulate the immune response to checkpoint blockade therapy (CBT) in this disease. In solid cancers, tumor-cell intrinsic oncogenic signaling strongly influences the immune environment and impacts clinical response to CBT. Despite the recent publication of large-scale genomic datasets in DLBCL, the impact of oncogenic signaling on the immune environment remains to be fully elucidated. In this study, we aimed to characterize immune landscapes associated with DLBCL, as well as the role of lymphoma-intrinsic alterations on shaping the immune environment in this disease. Methods: Using gene set variation analysis (GSVA) in a large cohort of primary DLBCLs (n = ~900), a sample-wise enrichment score was generated for gene sets associated with tumor infiltrating lymphocytes. Gene sets were manually curated to include signatures relating to IFNγ response, T helper cell subsets, CD8 + T cell exhaustion, macrophages, and dendritic cells. A DLBCL cell-of-origin (COO) signature was also included in the GSVA to control for the transcriptional and genomic effects of COO. Samples were hierarchically clustered into related groups. Multispectral immunofluorescence (mIF) for canonical T cell markers was used to confirm GSVA clustering. To mechanistically validate our findings, CRISPR/Cas9 gene editing was used to modulate candidate oncogenes and tumor suppressors genes (TSGs) in the syngeneic A20 murine lymphoma model. Results: GSVA performed on transcriptomes from a large genomic DLBCL dataset revealed four distinct DLBCL immune clusters, termed "ABC hot", "ABC cold", "GCB hot" and "GCB cold", defined by differential expression scores of immune related gene sets (Fig 1A). Concordant with our previous work, DLBCLs with PD-L1 gene amplifications, which are associated with a "T-cell inflamed" tumor microenvironment, were enriched in the "ABC hot" cluster (Fig 1B). Conversely, double hit signature DLBCLs, known to be associated with decreased immune cell infiltration and a GCB COO, were enriched in "GCB cold" DLBCLs (Fig 1C). In an internal cohort of diagnostic DLBCL samples (n = 90) for whom RNA sequencing (RNAseq) and FFPE tissue were available, mIF analysis showed that both "ABC hot" and "GCB hot" DLBCLs had significantly higher ratios of CD8 + T cells to lymphoma cells compared to cold DLBCLs. "ABC hot" DLBCLs also had a significantly higher CD4 + T cell to lymphoma cell ratio (Fig 1D). Importantly, several mutations that correlated with particular DLBCL immune clusters were identified. The "ABC cold" cluster was significantly enriched for loss-of-function (LOF) mutations in TMEM30A and MYD88, whereas LOF mutations in ATM and FOXO1 were commonly observed in "GCB cold" DLBCLs. Finally, LOF mutations in SOCS1 and B2M were significantly enriched in "GCB hot" DLBCLs (Fig 1E, 1F). As LOF SOCS1 mutations were strongly associated with "GCB hot" DLBCLs and are also prevalent in other CBT-sensitive lymphomas, we hypothesized that SOCS1 LOF mutations would enhance lymphoma cell vulnerability to CBT due to increased IFNγ sensitivity resulting from unopposed JAK/STAT activation. To test this hypothesis, we generated Socs1 deficient A20 lymphoma cells. Compared to A20 WT, A20 Socs1-/- cells were characterized by increased pStat1 levels upon IFNγ stimulation (Fig 1G). Interestingly, A20 Socs1-/- tumors showed increased sensitivity to α-PD1 therapy compared to A20 WT in syngeneic hosts. Together, these data suggest that tumor-cell intrinsic JAK/STAT activation via SOCS1 -/- increases lymphoma cell sensitivity to IFNγ and α-PD1 therapy (Fig 1H). Conclusion: We have developed a novel immunogenomic platform to define the role of tumor-cell intrinsic alterations on the immune landscape of DLBCL. Confirmatory studies using in vitro and in vivo models validated the effect of key oncogenes and TSGs on the tumor microenvironment, and suggest these candidate genes may impact response to CBT in DLBCL. Figure 1 Figure 1. Disclosures Smith: Alexion, AstraZeneca Rare Disease: Other: Study investigator; Celgene, Genetech, AbbVie: Consultancy. Steidl: Trillium Therapeutics: Research Funding; Curis Inc.: Consultancy; Epizyme: Research Funding; Seattle Genetics: Consultancy; Bayer: Consultancy; AbbVie: Consultancy; Bristol-Myers Squibb: Research Funding. Kline: Seagen: Membership on an entity's Board of Directors or advisory committees; Morphosys: Consultancy, Membership on an entity's Board of Directors or advisory committees; Kite/Gilead: Speakers Bureau; Karyopharm: Membership on an entity's Board of Directors or advisory committees; Merck: Consultancy, Research Funding; Verastem: Research Funding; SecuraBio: Membership on an entity's Board of Directors or advisory committees; Regeneron: Membership on an entity's Board of Directors or advisory committees.

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 score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.007
GPT teacher head0.217
Teacher spread0.210 · 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
Published2021
Admission routes1
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