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Record W4408494026 · doi:10.1101/2025.03.14.643063

Deciphering lung adenocarcinoma evolution and the role of LINE-1 retrotransposition

2025· preprint· en· W4408494026 on OpenAlexaff
Tongwu Zhang, Wei Zhao, Christopher L. Wirth, Marcos Díaz‐Gay, Jinhu Yin, Monia Cecati, Francesca Marchegiani, Phuc H. Hoang, Charles Leduc, Marina K. Baine, William D. Travis, Lynette M. Sholl, Philippe Joubert, Jian Sang, John McElderry, Alyssa Klein, Azhar Khandekar, Caleb Hartman, Jennifer Rosenbaum, Frank J. Colón-Matos, Mona Miraftab, Monjoy Saha, Olivia W. Lee, Kristine Jones, Neil E. Caporaso, Maria Pik Wong, Kin Chung Leung, Chao A. Hsiung, Chih-Yi Chen, Eric S. Edell, Matthew B. Schabath, Sai Yendamuri, Marta Mańczuk, Jolanta Lissowska, Beata Świątkowska, Anush Mukeria, Oxana Shangina, Давид Заридзе, Ivana Holcátová, Dana Mates, Saša Milosavljević, Milan Savić, Yohan Bossé, Bonnie E. Gould Rothberg, David C. Christiani, Valérie Gaborieau, Paul M. Brennan, Geoffrey Liu, Paul L. Hofman, Robert Homer, Soo‐Ryum Yang, Angela Cecilia Pesatori, Dario Consonni, Lixing Yang, Bin Zhu, Jianxin Shi, Kevin M. Brown, Nathaniel Rothman, Stephen J. Chanock, Ludmil B. Alexandrov, Jiyeon Choi, Maurizio Cardelli, Qing Lan, Martin A. Nowak, David C. Wedge, Maria Teresa Landi

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2025
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA modifications and cancer
Canadian institutionsPrincess Margaret Cancer CentreUniversity of TorontoUniversité de MontréalUniversité LavalInstitut universitaire de cardiologie et de pneumologie de QuébecCentre Hospitalier de l’Université de MontréalMcGill University Health Centre
Fundersnot available
KeywordsRetrotransposonLine (geometry)AdenocarcinomaBiologyLungComputational biologyGeneticsEvolutionary biologyInternal medicineMedicineGenomeTransposable elementGeneMathematicsCancer

Abstract

fetched live from OpenAlex

ABSTRACT Understanding lung cancer evolution can identify tools for intercepting its growth. In a landscape analysis of 1024 lung adenocarcinomas (LUAD) with deep whole-genome sequencing integrated with multiomic data, we identified 542 LUAD that displayed diverse clonal architecture. In this group, we observed an interplay between mobile elements, endogenous and exogenous mutational processes, distinct driver genes, and epidemiological features. Our results revealed divergent evolutionary trajectories based on tobacco smoking exposure, ancestry, and sex. LUAD from smokers showed an abundance of tobacco-related C:G>A:T driver mutations in KRAS plus short subclonal diversification. LUAD in never smokers showed early occurrence of copy number alterations and EGFR mutations associated with SBS5 and SBS40a mutational signatures. Tumors harboring EGFR mutations exhibited long latency, particularly in females of European-ancestry (EU_N). In EU_N, EGFR mutations preceded the occurrence of other driver genes, including TP53 and RBM10. Tumors from Asian never smokers showed a short clonal evolution and presented with heterogeneous repetitive patterns for the inferred mutational order. Importantly, we found that the mutational signature ID2 is a marker of a previously unrecognized mechanism for LUAD evolution. Tumors with ID2 showed short latency and high L1 retrotransposon activity linked to L1 promoter demethylation. These tumors exhibited an aggressive phenotype, characterized by increased genomic instability, elevated hypoxia scores, low burden of neoantigens, propensity to develop metastasis, and poor overall survival. Re-activated L1 retrotransposition-induced mutagenesis can contribute to the origin of the mutational signature ID2, including through the regulation of the transcriptional factor ZNF695 , a member of the KZFP family. The complex nature of LUAD evolution creates both challenges and opportunities for screening and treatment plans.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.208
Teacher spread0.202 · 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".

Quick stats

Citations6
Published2025
Admission routes1
Has abstractyes

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