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587 Inducing mismatch repair deficiency in combination with anti-CTLA4 therapy is highly effective against non-immunogenic neuroblastoma tumors

2021· article· en· W3212221590 on OpenAlexaff
Mikal El‐Hajjar, Lara Gerhardt, Mithunah Krishnamoorthy, René Figueredo, Xiufen Zheng, James Koropatnick, Saman Maleki

Bibliographic record

VenueRegular and Young Investigator Award Abstracts · 2021
Typearticle
Languageen
FieldMedicine
TopicNeuroblastoma Research and Treatments
Canadian institutionsWestern University
Fundersnot available
KeywordsNeuroblastomaCancer researchImmunotherapyMedicineMelanomaDNA mismatch repairTumor-infiltrating lymphocytesFlow cytometryNivolumabAntibodyMLH1CancerOncologyImmunologyInternal medicineBiologyCell cultureColorectal cancer

Abstract

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<h3>Background</h3> Despite rigorous multimodal therapy, recurrence is common among high-risk neuroblastoma patients. Neuroblastoma is a poorly immunogenic tumor with low tumor mutational burden (TMB). Currently, immunotherapy with immune checkpoint inhibitors (ICIs) are not approved for neuroblastoma. Novel strategies to sensitize neuroblastoma to ICIs are urgently needed. We have induced mismatch repair (MMR) deficiency in mouse neuro-2a tumors and show that these tumors become highly immunogenic and responsive to anti-CTLA4 but not anti-PD1 therapy. <h3>Methods</h3> The MMR gene <i>Mlh1</i> was knocked out of neuro-2a and B16F10 cells, and clones were selected. MMR-deficient (dMMR) and -proficient (pMMR) neuroblastoma tumors were grown in immunocompetent and immunodeficient animals. Tumors were harvested, and tumor-infiltrating lymphocytes (TILs) were analyzed by flow cytometry. Neuro-2a tumor-bearing mice were treated with anti-PD1, anti-CTLA4, or a combination of both antibodies. NK cells were depleted in mice treated with anti-CTLA4 using the anti-asialo GM1 antibody to examine their role in the efficacy of anti-CTLA4 treatment. dMMR B16F10 melanoma tumor-bearing mice were treated with anti-PD1 followed by the analysis of TILs. Publicly available TARGET and TCGA databases were mined to examine the effect of T-cell infiltration on neuroblastoma and melanoma patient‘s survival, respectively. <h3>Results</h3> We show that high-risk neuroblastoma and melanoma patients with tumors containing high levels of T-cell and memory T-cell-related genes have improved survival. Additionally, inducing MMR deficiency in neuro-2a cells renders these tumors immunogenic, with high T-cell infiltration, and inhibits tumor growth in mice in an immune-dependent manner. We also show that dMMR neuroblastoma tumors are highly sensitive to anti-CTLA4 but not anti-PD1 treatment. Anti-CTLA4 therapy cured most tumor-bearing mice, inducing immune memory, epitope spreading, and increased tumor-specific T-cells in cured animals. Notably, the effect of anti-CTLA4 therapy was independent of NK cells. In neuroblastoma tumors with induced MMR deficiency, anti-PD1 treatment antagonized anti-CTLA4 by upregulating inhibitory molecules on T-cells and increasing the level of dysfunctional TILs. Interestingly, anti-PD1 therapy was effective against high TMB-background melanoma tumors with induced MMR deficiency. Our data suggests that this effect relied on lowering T-cell inhibitory molecules rather than increasing T-cell infiltration into tumors. <h3>Conclusions</h3> Inducing MMR deficiency in low and high TMB-background tumors leads to distinct responses to anti-PD1 therapy, with low levels of T-cell exhaustion being a positive indicator of response. Anti-CTLA4 therapy in combination with induced MMR deficiency is a novel strategy for treating low TMB-background high-risk neuroblastoma. <h3>Ethics Approval</h3> This study was approved by the Animal Care Committee at Western University; approval number 2017-030.

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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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.396
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.011
GPT teacher head0.244
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 teacher head, not a consensus.

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
Published2021
Admission routes1
Has abstractyes

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