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Record W2935754753 · doi:10.3389/fonc.2019.00288

Germline Predisposition and Copy Number Alteration in Pre-stage Lung Adenocarcinomas Presenting as Ground-Glass Nodules

2019· article· en· W2935754753 on OpenAlexafffund
Yijiu Ren, Shujun Huang, Chenyang Dai, Dong Xie, Larry Zheng, Huikang Xie, Hui Zheng, Yunlang She, Fangyu Zhou, Yue Wang, Pengpeng Li, Fei Ke, Gening Jiang, Yang Zhang, Bo Su, E. Alejandro Sweet‐Cordero, Nhan L. Tran, Yanan Yang, Jai N. Patel, Christian Rolfo, Gaetano Rocco, Andrés F. Cardona, Alessandro Tuzi, Matteo B. Suter, Ping Yang, Wayne Wenzhong Xu, Chang Chen

Bibliographic record

VenueFrontiers in Oncology · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Genomics and Diagnostics
Canadian institutionsCancerCare ManitobaUniversity of ManitobaResearch Institute in Oncology and Hematology
FundersNational Cancer InstituteShanghai Hospital Development CenterWestern Canada Research GridCancerCare Manitoba Foundation
KeywordsStage (stratigraphy)GermlineAdenocarcinomaLungGermline mutationBiologyCancer researchMedicinePathologyInternal medicineCancerMutationGeneticsPaleontologyGene

Abstract

fetched live from OpenAlex

Objective: Synchronous multiple ground-glass nodules (SM-GGNs) are a distinct entity of lung cancer which is emerging more and more these years in China. The oncogenesis molecular mechanisms of SM-GGNs remain elusive. Methods: We investigated single nucleotide variations (SNV), insertions and deletions (INDEL), somatic copy number variations (CNV), and germline mutations of 69 SM-GGN samples collected from 31 patients using target sequencing (TRS) and whole exome sequencing (WES). Results: In the whole cohort, many known driver mutations were found, including EGFR (21.7%), BRAF (14.5%), and KRAS (6%). However, only one out of the 31 patients had the same somatic missense or truncated events within SM-GGNs, indicating the independent origins for almost all of these SM-GGNs. Many germline mutations with low frequency in Chinese population and genes harboring both germline and somatic variations were discovered in these pre-stage GGNs. These GGNs also bore large segments of copy number gains and/or losses. The CNV segment number tended to be positively correlated with the germline mutations (r = 0.57). The CNV sizes were correlated with the somatic mutations (r = 0.55). A moderate correlation (r = 0.54) was also shown between somatic and germline mutations. Conclusion: Our data suggest that the precancerous unstable CNVs with potentially predisposing genetic backgrounds may foster the onset of driver mutations and the development of independent SM-GGNs during the local stimulation of mutagens.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.103
Threshold uncertainty score0.578

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.005
GPT teacher head0.268
Teacher spread0.264 · 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 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".

Quick stats

Citations23
Published2019
Admission routes2
Has abstractyes

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