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Record W4282939680 · doi:10.1158/1538-7445.am2022-6108

Abstract 6108: <i>In vivo</i> CRISPR screens identified dual function of MEN1-MLL1 in regulating tumor-microenvironment interactions

2022· article· en· W4282939680 on OpenAlexaff
Peiran Su, Yin Liu, Ming‐Sound Tsao, Housheng Hansen He

Bibliographic record

VenueCancer Research · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Genomics and Diagnostics
Canadian institutionsUniversity Health NetworkUniversity of Toronto
Fundersnot available
KeywordsBiologyEpigeneticsCancer researchIn vivoCRISPRTumor microenvironmentChromatinGene knockoutCancerCell biologyGeneticsGene

Abstract

fetched live from OpenAlex

Abstract INTRODUCTION: CRISPR functional genomic screens have been widely adopted to identify essential genes and potential drug targets in cell line models. However, it is well known that cell line models studied in vitro do not fully capture the biology in patient tumors due to the lack of the tumor microenvironment. The primary objective of this study was to perform functional genomic screens in in vivo models to identify clinically relevant epigenetic vulnerabilities. METHODS: We designed an EpiDrug sgRNA library that target 357 epigenetic regulators in human, and applied this targeted sgRNA library for essentiality screen in 2D cell culture and 3D xenograft models. We subsequently selected targets that can only be identified in vivo for validation. We also investigated the underlying mechanism of the in vivo specific phenotype caused by target knockouts. Finally, we explored the clinical significance of treating selected target with a small molecule inhibitor. RESULTS: We identified MEN1 as the top hit that confers differential essentialities between in vitro and in vivo models. Knockout of MEN1 has no impact on cell proliferation in 2D cell culture but profoundly promotes tumor growth in human lung and colorectal adenocarcinoma cell line derived xenograft models. In syngeneic colon cancer model CT26, knockout of Men1 resulted in faster tumor growth in immune deficient mice, but reduced tumor growth in immune competent mice. Mechanistically, knockout of MEN1/Men1 can cause the redistribution of its interaction partner MLL1/Mll1, a histone methyl transferase, to repeat regions in chromatin and stimulate the expression of double stranded RNA. This resulted in viral mimicry response, which activates a group of genes involved in cytokine-cytokine receptor interaction. Single-cell RNA-seq and CyTOF analysis revealed that activation of the cytokines induces tumor promoting neutrophil and tumor suppressing CD8+ T cell infiltration in immunodeficient and immunocompetent mice, respectively. Using patient data from TCGA, we identified that the expression level of MEN1 is negatively correlated with neutrophil and CD8+ T cell tumor infiltration in most cancer types including lung and colorectal adenocarcinoma. A small molecular inhibitor targeting Men1-Mll1 interaction dramatically reduced CT26 tumor growth in immunocompetent mice, but its phenotype was reversed by CD8 neutralizing antibody. Finally, we demonstrated the strong synergy of the inhibitor with anti-PD-L1 antibody. CONCLUSION: Our study demonstrated the utility of in vivo CRISPR screen in identifying therapeutic targets that modulate tumor-microenvironment interactions, and identified MEN1 as a promising therapeutic targets alone or in combination with immunotherapy. Citation Format: Peiran Su, Yin Liu, Ming-Sound Tsao, Housheng H. He. In vivo CRISPR screens identified dual function of MEN1-MLL1 in regulating tumor-microenvironment interactions [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 6108.

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)0.000
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 score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.036
GPT teacher head0.345
Teacher spread0.309 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

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