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Association of common inherited <i>NOD2</i> mutations with exceptional response to immune checkpoint inhibitors.

2023· article· en· W4379283073 on OpenAlexaff
Megan Barnet, Etienne Masle‐Farquhar, Amanda J. Russell, Deborah L. Burnett, Katherine Jackson, Robert Brink, Gillian Z. Heller, Cindy Yang, Stephenie D. Prokopec, Andreas Behren, Ian D. Davis, Geoffrey Peters, Michael Boyer, Adnan Nagrial, Bo Gao, Prunella Blinman, Steven Kao, Jonathan Cebon, Christopher C. Goodnow

Bibliographic record

VenueJournal of Clinical Oncology · 2023
Typearticle
Languageen
FieldMedicine
TopicCancer Immunotherapy and Biomarkers
Canadian institutionsPrincess Margaret Cancer Centre
Fundersnot available
KeywordsMedicineNOD2Lung cancerOncologyInternal medicineImmune systemMelanomaColorectal cancerCohortImmunologyCancerCancer researchInnate immune system

Abstract

fetched live from OpenAlex

2514 Background: Melanomas and lung cancers usually display a large burden of somatically mutated proteins, yet only a subset of individuals can be induced to mount a therapeutic immune response against tumours. Inherited mutations in immune regulatory genes may be one factor predisposing to a heightened immune response with addition of an immune stimulus. Methods: Germline whole genome sequencing (WGS) was performed in a patient with exceptional response (ExR) to targeted radiotherapy for metastatic melanoma (complete abscopal response). A target variant gene was identified, and interrogated within a prospectively recruited cohort of patients with ExR or non-response to anti-PD1 for non-small cell lung cancer (NSCLC). Results were experimentally tested in mice and validated in an independent mixed cancer clinical cohort. Results: Compound heterozygous variants in NOD2 were found within the patient with abscopal response (expected genotype frequency 1.3 in 10,000), whose cancer regression occurred concurrently with a flare of pre-existing Crohn’s colitis (a NOD2-associated condition). Patients treated with anti-PD1 for NSCLC were prospectively recruited (n=144). Individuals with ExR to PD1 were selected, defined by progression-free survival (PFS) ≥2 years and ≥1 CTCAE grade 2 or higher immune-related adverse event (n=40). Median follow-up was 41 months (median PFS not reached). Patients with best response of progressive disease were selected for comparison (n=18, median PFS 2.76m, 95% CI, 2.1-3.81). WGS from blood was analysed for all human NOD2 variants known to impair NOD2 signalling. Of ExR patients, 25% carried 1 or more functional NOD2 variants, totalling 11 of 80 alleles (13.8%); more than twice the expected allele frequency (6.58%, p=0.0199). Within non-responders, 1 patient carried a variant NOD2 allele (1 of 36 alleles, 2.78%). The association between NOD2 loss-of-function and heightened immune response to anti-PD1 was experimentally tested in Nod2-null C57BL/6J ( Nod2fs) mice. Nod2fs mice transplanted with a syngeneic cell line and treated with anti-PD1 showed greater tumour response compared with Nod2wild-type littermates (60-day OS 41.67% vs 0%, p<0.01). Flow cytometry of tumours showed greater proportion of (CD44hi CD62Llow) CD8+ and CD4+ effector memory differentiated cells within Nod2fs versus Nod2wild-type animals (p<0.05). Results were validated within an independent clinical cohort (n=105). Overall response rate to PD1 in patients with mutant vs wild-type NOD2 was 50% versus 15% (p=0.03). Conclusions: These results provide four complementary lines of evidence that common inherited defects in the immune regulatory gene NOD2 promote exceptional immune response following acute triggers to control cancer. NOD2 may be a biomarker for treatment stratification or a therapeutic target to enhance anti-PD1 response.

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 score0.010

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.074
GPT teacher head0.440
Teacher spread0.367 · 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".

Quick stats

Citations1
Published2023
Admission routes1
Has abstractyes

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