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Record W4253096809 · doi:10.1016/j.jtho.2021.01.723

P35.22 WITHDRAWN

2021· article· en· W4253096809 on OpenAlexaff
Lintao Wang, Xiaonan Liu, Xue Yu, Zilong Zhao, Yong Zhang, Yi Bai, Shujun Zhang, Youlong Xu, Pu Zhao, Hong Bao, Xinqiang Wang, Ruifeng Liu, Rui Xu, Jingjing Xiang, Haifeng Jiang, Jiang Yan, Xianglong Wu, Yi Shao, Ji Liang, Qiong Wu, Zulin Zhang, Shuai Lü, Shenglin Ma

Bibliographic record

VenueJournal of Thoracic Oncology · 2021
Typearticle
Languageen
FieldMedicine
TopicCancer Immunotherapy and Biomarkers
Canadian institutionsInstitute of Cancer Research
Fundersnot available
KeywordsMedicine

Abstract

fetched live from OpenAlex

pulmonary lymphoepithelioma-like carcinoma (PLELC), a rare subtype of non-small cell lung cancer (NSCLC).However, the understanding of the treatment for EBV-infected NSCLC was still elusive.Immunotherapy that targets PD-1/PD-L1 has been utilized as a novel clinical treatment in recent years.Here, we focus on the genomic landscapes of lung cancers with EBV-infection and its correlation with PD-L1.Methods: Patients with both PD-L1 expression detection and genomic information were screened in HapLab database.HaploX 605-gene panel sequencing, covering 1.31 MB genome, was performed to analyze the genomic data of patients.PD-L1 expression was detected by immunochemistry.Bioinformatic analysis of genomic mutations and the correlation with the expression of PD-L1 were studied.Results: We analyzed the genomic profiles of 23 EBV-infected NSCLC patients.11 cases of lung squamous-cell carcinoma (LUSC), 4 cases of lung adenocarcinoma (LUAD), 5 cases of lung pulmonary lymphoepithelioma-like carcinoma (PLELC), and 3 unidentified cases were included in this study.Collectively, 93 genome mutations of 67 genes were detected in 23 EBV-infection cases.Top 3 frequently mutated genes were TP53 (27%), CSMD3 (18%) and KMT2D (18%).The EBV-infected patients exhibited a low level of tumor mutation burden (TMB).The median TMB was 1.53 Muts/MB (ranging from 0 to 14.5 Muts/MB).Only 3 of 23 patients (13.0%) harbored the canonical driver mutations in NSCLC.Interestingly, 10/23 patients (43.5%) showed high expression of PD-L1, while 13/23 patients (56.5%) showed low expression.We also assessed the expression of PD-L1 in lung cancers with no EBV-infection (867 cases).Only 118/867 (13.6%) patients without EBV-infection presented high PD-L1 expression, while 749/867 (86.4%) presented low PD-L1 expression.Conclusion: EBV-infection can occur in different kinds of NSCLC, including LUSC, LUAD, and PLELC.TMB and driver mutations of EBV-infected NSCLC were not frequently observed as normal lung cancers, implying a different mechanism leading to EBVinfected lung cancers.Interestingly, EBV-infected NSCLC tended to have a high correlation with the expression of PD-L1.This may give a hint on the application of checkpoint blockade immunotherapy on EBV-infected NSCLC.

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)0.002
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.120
Threshold uncertainty score0.171

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0020.001
Scholarly communication0.0070.002
Open science0.0030.003
Research integrity0.0060.003
Insufficient payload (model declined to judge)0.8800.836

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 head0.024
GPT teacher head0.417
Teacher spread0.393 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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
Published2021
Admission routes1
Has abstractyes

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