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Abstract SY26-02: <i>Sherlock-Lung</i>: Tracing lung cancer mutational processes in never smokers

2019· article· en· W4249406782 on OpenAlexaff
Maria Teresa Landi, Tongwu Zhang, Montserrat García‐Closas, Yohan Bossé, Jianxin Shi, Bin Zhu, Qing Lan, Nathaniel Rothman, Jiyeon Choi, Neil E. Caporaso, Wei Zhao, Chongyi Chen, Eytan Ruppin, Jennifer Rosenbaum, Dmitry A. Gordenin, Sai Yendamuri, Chris Amos, Paul Brennan, David C. Christiani, Geoffrey Liu, Bonnie Gould Rothberg, Matthew B. Schabath, Han Liang, Ludmil B. Alexandrov, David E. Wedge, Stephen J. Chanock

Bibliographic record

VenueCancer Research · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Genomics and Diagnostics
Canadian institutionsUniversity of TorontoUniversité LavalInstitut universitaire de cardiologie et de pneumologie de Québec
Fundersnot available
KeywordsLung cancerMedicineCancerCarcinogenesisAdenocarcinomaOncologyEtiologyLungInternal medicineBioinformaticsBiology

Abstract

fetched live from OpenAlex

Abstract Globally, about 2 million people are diagnosed with lung cancer annually, making it the most common type of cancer in the world. In addition, lung cancer is the leading cause of cancer death among both men and women, accounting for ~25% of all cancer deaths in the United States and ~19% of cancer deaths worldwide. Although tobacco smoking is the most common cause of lung cancer, it is estimated that 10-25% of all patients diagnosed with lung cancer worldwide are never smokers, with higher proportions in Asians and women. Compared to former and current smokers with lung cancer, lung cancers in never smokers tend to present at an earlier age and more frequently have adenocarcinoma histology. Although a few risk factors are known to contribute to the etiology of lung cancer in never smokers, a large fraction of cancer cases cannot be explained by established environmental and genetic risk factors, highlighting the need for research in this area. One promising approach to identify the etiological factors involved in lung tumorigenesis in never smokers is based on the study of the “mutational signatures” that the exogenous and endogenous processes leave on the tumor tissue and surrounding areas. Somatic mutations accumulate throughout individuals’ lifetime, as a result of the balance between DNA damage and DNA repair. Mutational sources can change over time and mutational signatures can provide information on the tumor evolutionary trajectory, tracking activities of mutational processes. We designed “Sherlock-Lung” to identify mutational signatures in lung cancers from never smokers and order them based on their clonal and subclonal localization to have an insight on the factors involved in the initiation of the tumors. Prevention and treatment strategies are likely to be more effective when targeting the tumor initiating factors rather than those responsible for the tumor progression. Here, we present the Sherlock-Lung study design, which includes a combination of lung cancer cases from populations highly exposed to lung cancer risk factors and populations with unknown exposures. We also present preliminary data from this study and what we have learned from whole genome sequencing analysis of the mutational landscape of lung cancers from smokers and nonsmokers and from different histological subtypes. For example, although the mutational signature associated with tobacco smoking is largely clonal in all lung cancers from smokers, consistent with an initiating role of tobacco smoking in lung tumorigenesis, the mutational signatures distribution varies across histological subtypes. Moreover, the frequency of mutations in major cancer driver genes varies dramatically between smokers and never smokers, and mutational signatures distribution appears to differ between tumors with and without specific driver genes. In never smokers, without the dominant mutational source of tobacco smoking, other signatures emerged, e.g., that associated with reactive oxygen species. Although this approach has already revealed important information on the etiological processes involved in tumorigenesis of lung cancer and other cancer types, there is a growing list of open questions and challenges that need to be addressed. In population studies, careful exposure assessment to identify and validate new mutational signatures and the inclusion of study subjects from different geographical locations and ethnicities are critical. Citation Format: Maria Teresa Landi, Tongwu Zhang, Montserrat Garcia-Closas, Yohan Bossé, Jianxin Shi, Bin Zhu, Qing Lan, Nathaniel Rothman, Jiyeon Choi, Neil E. Caporaso, Wei Zhao, Chongyi Chen, Eytan Ruppin, Jennifer Rosenbaum, Dmitry Gordenin, Sai Yendamuri, Chris Amos, Paul Brennan, David C. Christiani, Geoff Liu, Bonnie Gould Rothberg, Matthew Schabath, Han Liang, Ludmil Alexandrov, David Wedge, Stephen Chanock. Sherlock-Lung: Tracing lung cancer mutational processes in never smokers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr SY26-02.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
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 science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.003

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 head0.026
GPT teacher head0.373
Teacher spread0.346 · 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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Citations0
Published2019
Admission routes1
Has abstractyes

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