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Abstract LB-A06: A high-throughput functional screen discovered that endogenous DNA damage underlies mechanisms of selected gene susceptibility factors predisposing to lung cancer

2019· article· en· W2999172547 on OpenAlexaff
Jun Xia, Zhuoyi Song, Xuemei Ji, James McKay, Yohan Bossé, Christopher I. Amos

Bibliographic record

VenueMolecular Cancer Therapeutics · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA modifications and cancer
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsBiologyCandidate geneGenome-wide association studyGeneticsCarcinogenesisGeneLung cancerDNA repairDNA damageGenome instabilityExpression quantitative trait lociCancerSingle-nucleotide polymorphismDNAGenotypeMedicinePathology

Abstract

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Abstract Genome and transcriptome-wide association studies (GWAS and TWAS) have been successful in identifying genetic variants and candidate causal genes associated with lung cancer. However, systematic identification and functional studies of these genes remains a major bottleneck to clinical translation. Endogenous DNA damage has been implicated in genome instability and carcinogenesis, and we have recently discovered large networks of DNA damage-up proteins that are mostly non-DNA repair proteins [Xia J et al., Cell 176 (2019): 127–143], with many more predicted. Here, we validated several lung cancer-associated genes from GWAS and TWAS as DNA damage-up genes, which can potentially explain the cause of gene susceptibility to lung cancer. We performed a comprehensive exome-wide association study and TWAS analysis of the GWAS-nominated loci using expression quantitative trait loci (eQTL) analysis, which revealed many new candidate causal genes for lung cancer. We further tested endogenous DNA damage levels for many of these candidates using high-throughput cell-based assays and discovered that an alteration of the expression levels of multiple genes elevates the endogenous DNA damage, which is an early biomarker for genome instability and cancer. In our recent comprehensive TWAS, we mapped seventeen candidate-causal genes among previously described lung cancer GWAS loci. These include a new susceptibility locus for lung adenocarcinoma with AQP3 as the underlying causal gene and a new susceptibility gene, NEXN, for lung cancer in never-smokers. Additionally, we discovered that IREB2 is the gene most strongly associated with lung cancer on chromosome 15q25. In a separate study, we found that KIAA0930 was overproduced across multiple cancer types, including lung cancer. We used high-throughput cell-based assays and showed that overproduction of AQP3 or KIAA0930 elevated endogenous DNA damage in the lung fibroblast cell line and in kidney epithelial cells, which suggests a pan-cancer phenotype. Additionally, knocking down IREB2 increases spontaneous DNA damage in the lung fibroblast cell line, but this was not observed by knocking down NEXN. We are also implementing unbiased pooled screenings for other functional readouts. We established a functional validation platform that measures endogenous DNA damage for lung cancer susceptibility genes, and more importantly, this functional pipeline can be generalized to other cancers or diseases. Our discovery will potentially annotate many GWAS and TWAS genes as DNA damage-up genes and assign their cancer-driving roles to genome instability. Citation Format: Jun Xia, Zhuoyi Song, Xuemei Ji, James Mckay, Yohan Bossé, Christopher I Amos. A high-throughput functional screen discovered that endogenous DNA damage underlies mechanisms of selected gene susceptibility factors predisposing to lung cancer [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference on Molecular Targets and Cancer Therapeutics; 2019 Oct 26-30; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2019;18(12 Suppl):Abstract nr LB-A06. doi:10.1158/1535-7163.TARG-19-LB-A06

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)0.001
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 score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
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 head0.033
GPT teacher head0.272
Teacher spread0.239 · 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".

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Citations0
Published2019
Admission routes1
Has abstractyes

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