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Abstract LB-196: EGFR-mutant lung adenocarcinomas mutation profiles reveal ARID1A might be a novel tyrosine kinase resistance pathway

2018· article· en· W2887174538 on OpenAlexaff
Xuyuan Gao, Hang Xu, Jcm Ho, Oscar SH Chan, Feng Xu, Junwen Wang, Victor Lee, Vicky Pc Tin, Zhijie Xiao, Siqi Wang, Judy Wai Ping Yam, Maria Pik Wong

Bibliographic record

VenueCancer Research · 2018
Typearticle
Languageen
FieldMedicine
TopicCancer Mechanisms and Therapy
Canadian institutionsSt. Paul's HospitalUniversity of British ColumbiaProvidence Health Care
Fundersnot available
KeywordsCancer researchBiologyAfatinibARID1AEpidermal growth factor receptorT790MGefitinibErlotinibLung cancerFrameshift mutationPTENMutationCancerGeneticsGeneMedicinePI3K/AKT/mTOR pathwaySignal transductionOncology

Abstract

fetched live from OpenAlex

Abstract Lung cancer has the highest mortality across all cancers in the world. Epidermal growth factor receptor (EGFR) is commonly mutated in lung adenocarcinomas from Asian non-smoking females. These tumors usually respond to EGFR tyrosine kinase inhibitors (EGFR-TKI), but the outcomes vary in different patients. Though EGFR T790M, amplification of MET and other mechanisms have been shown to confer EGFR-TKI resistance, the molecular mechanisms in around 30% of patients are still unknown. To uncover novel candidate pathways of EGFR-TKI resistance, we analyzed the whole exome mutation profiles of pre-treatment, EGFR T790M-free lung adenocarcinomas with typical activating mutations (L858R, exon 19 deletions). Amongst 19 non-responding (NR) tumors without significant shrinkage after gefitinib or erlotinib treatment, 67 cancer-related genes showed coding region non-synonymous single nucleotide variations (SNV) or insertion-deletions (indels) but they were not detected in 22 responding tumors. Gene set enrichment analysis (GSEA) showed 14 of the 67 genes were involved in the EGFR and WNT signaling network (false discovery rate q=1.14 e-8 for EGFR and q=4.3 e-9 for WNT), including well-known TKI resistance-related genes PIK3CA, PTEN and NF1. Notably, ARID1A, which encodes a subunit of the chromatin remodeling complex, showed nonsense mutations in 2 NR but not responding tumors, which were validated by Sanger sequencing. More than 40% of ARID1A mutations in lung adenocarcinomas recorded in COSMIC were nonsense mutations or frameshift indels. Loss of function mutations of ARID1A have also been reported in various cancers, together suggesting ARID1A might be a tumor suppressor but its involvement in EGFR mutant lung cancers is unknown. To investigate the potential role of ARID1A, its expression was first studied using Kaplan-Meier plotter. Low expression of ARID1A is associated with significantly better overall survival than high expression level in lung adenocarcinoma patients (p=8.5 e-7). To test whether loss of ARID1A could cause TKI resistance, ARID1A was knocked down by shRNA in HCC827 lung cancer cell line harboring EGFR exon19 deletion. MTT cell viability assay using gefitinib showed increase of IC50. Western blot assay further showed ARID1A knockdown increased phosphorylation of AKT at Ser473. Together, the findings suggest loss of ARID1A could lead to EGFR bypass and promote activation of the downstream PI3K/AKT pathway, leading to EGFR TKI resistance. Overall, our findings suggest EGFR-TKI resistance mutations could be detected before TKI drug selection. Resistant gene mutations present in pre-treatment tumors could help to predict the treatment response. More specifically, our study identified a promising gene list involved in EGFR-TKI resistance, and suggested ARID1A loss could be a novel EGFR-TKI resistance mechanism. Citation Format: Xuyuan Gao, Hang Xu, James CM Ho, Oscar SH Chan, Feng Xu, Junwen Wang, Victor HF Lee, Vicky PC Tin, Zhijie Xiao, Siqi Wang, Judy WP Yam, Maria P Wong. EGFR-mutant lung adenocarcinomas mutation profiles reveal ARID1A might be a novel tyrosine kinase resistance pathway [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr LB-196.

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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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.078
GPT teacher head0.388
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 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".

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Citations1
Published2018
Admission routes1
Has abstractyes

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