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Record W4396943617 · doi:10.1126/sciadv.adj5428

N-MYC impairs innate immune signaling in high-grade serous ovarian carcinoma

2024· article· en· W4396943617 on OpenAlexaff
Alex Miranda, Swetansu Pattnaik, Phineas T. Hamilton, M. Fuß, Shreena Kalaria, Céline M. Laumont, Julian Smazynski, Mónica Mesa, Allyson C. Banville, Xinpei Jiang, Russell W. Jenkins, Israel Cañadas, Brad H. Nelson

Bibliographic record

VenueScience Advances · 2024
Typearticle
Languageen
FieldImmunology and Microbiology
Topicinterferon and immune responses
Canadian institutionsUniversity of VictoriaUniversity of British Columbia
Fundersnot available
KeywordsInnate immune systemCancer researchBiologyImmune systemOvarian cancerChemokineSignal transductionImmunotherapyInterferonCancerImmunologyCell biology

Abstract

fetched live from OpenAlex

High-grade serous ovarian cancer (HGSC) is a challenging disease, especially for patients with immunologically “cold” tumors devoid of tumor-infiltrating lymphocytes (TILs). We found that HGSC exhibits among the highest levels of MYCN expression and transcriptional signature across human cancers, which is strongly linked to diminished features of antitumor immunity. N-MYC repressed basal and induced IFN type I signaling in HGSC cell lines, leading to decreased chemokine expression and T cell chemoattraction. N-MYC inhibited the induction of IFN type I by suppressing tumor cell–intrinsic STING signaling via reduced STING oligomerization, and by blunting RIG-I–like receptor signaling through inhibition of MAVS aggregation and localization in the mitochondria. Single-cell RNA sequencing of human clinical HGSC samples revealed a strong negative association between cancer cell–intrinsic MYCN transcriptional program and type I IFN signaling. Thus, N-MYC inhibits tumor cell–intrinsic innate immune signaling in HGSC, making it a compelling target for immunotherapy of cold tumors.

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)0.001
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 score0.042
Threshold uncertainty score0.655

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.010
GPT teacher head0.268
Teacher spread0.258 · 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".

Quick stats

Citations17
Published2024
Admission routes1
Has abstractyes

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