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Record W3194415632 · doi:10.1136/gutjnl-2021-324420

Epigenetic promoter alterations in GI tumour immune-editing and resistance to immune checkpoint inhibition

2021· article· en· W3194415632 on OpenAlexaff
Raghav Sundar, Kie Kyon Huang, Vikrant Kumar, Kalpana Ramnarayanan, Deniz Demircioğlu, Zhisheng Her, Xuewen Ong, Zul Fazreen Bin Adam Isa, Manjie Xing, Angie Lay-Keng Tan, David Wai Meng Tai, Su Pin Choo, Weiwei Zhai, Jia Qi Lim, Meghna Das Thakur, Luciana Molinero, Edward Cha, Marcella Fassò, Monica Niger, Filippo Pietrantonio, Jeeyun Lee, Anand D. Jeyasekharan, Aditi Qamra, Radhika Patnala, Arne Fabritius, Mark De Simone, Joe Yeong, Cedric Chuan Young Ng, Sun Young Rha, Yukiya Narita, Kei Muro, Yu Amanda Guo, Anders J. Skanderup, Jimmy Bok Yan So, Wei Peng Yong, Qingfeng Chen, Jonathan Göke, Patrick Tan

Bibliographic record

VenueGut · 2021
Typearticle
Languageen
FieldMedicine
TopicCancer Immunotherapy and Biomarkers
Canadian institutionsRoche (Canada)University Health Network
FundersNational Medical Research CouncilAssociazione Italiana per la Ricerca sul CancroNational Research Foundation SingaporeDuke-NUS Medical School
KeywordsImmune systemImmunotherapyCancer researchTumor microenvironmentImmune checkpointBiologyCancerEpigeneticsCancer immunotherapyImmunologyMedicineGeneGenetics

Abstract

fetched live from OpenAlex

Objectives Epigenomic alterations in cancer interact with the immune microenvironment to dictate tumour evolution and therapeutic response. We aimed to study the regulation of the tumour immune microenvironment through epigenetic alternate promoter use in gastric cancer and to expand our findings to other gastrointestinal tumours. Design Alternate promoter burden (APB) was quantified using a novel bioinformatic algorithm ( proActiv ) to infer promoter activity from short-read RNA sequencing and samples categorised into APB high , APB int and APB low. Single-cell RNA sequencing was performed to analyse the intratumour immune microenvironment. A humanised mouse cancer in vivo model was used to explore dynamic temporal interactions between tumour kinetics, alternate promoter usage and the human immune system. Multiple cohorts of gastrointestinal tumours treated with immunotherapy were assessed for correlation between APB and treatment outcomes. Results APB high gastric cancer tumours expressed decreased levels of T-cell cytolytic activity and exhibited signatures of immune depletion. Single-cell RNAsequencing analysis confirmed distinct immunological populations and lower T-cell proportions in APB high tumours. Functional in vivo studies using ‘humanised mice’ harbouring an active human immune system revealed distinct temporal relationships between APB and tumour growth, with APB high tumours having almost no human T-cell infiltration. Analysis of immunotherapy-treated patients with GI cancer confirmed resistance of APB high tumours to immune checkpoint inhibition. APB high gastric cancer exhibited significantly poorer progression-free survival compared with APB low (median 55 days vs 121 days, HR 0.40, 95% CI 0.18 to 0.93, p=0.032). Conclusion These findings demonstrate an association between alternate promoter use and the tumour microenvironment, leading to immune evasion and immunotherapy resistance.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.014
GPT teacher head0.267
Teacher spread0.252 · 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 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

Citations40
Published2021
Admission routes1
Has abstractyes

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