Go to The Journal of Clinical Investigation
  • About
  • Editors
  • Consulting Editors
  • For authors
  • Publication ethics
  • Publication alerts by email
  • Transfers
  • Advertising
  • Job board
  • Contact
  • Physician-Scientist Development
  • Current issue
  • Past issues
  • By specialty
    • COVID-19
    • Cardiology
    • Immunology
    • Metabolism
    • Nephrology
    • Oncology
    • Pulmonology
    • All ...
  • Videos
  • Collections
    • In-Press Preview
    • Resource and Technical Advances
    • Clinical Research and Public Health
    • Research Letters
    • Editorials
    • Perspectives
    • Physician-Scientist Development
    • Reviews
    • Top read articles

  • Current issue
  • Past issues
  • Specialties
  • In-Press Preview
  • Resource and Technical Advances
  • Clinical Research and Public Health
  • Research Letters
  • Editorials
  • Perspectives
  • Physician-Scientist Development
  • Reviews
  • Top read articles
  • About
  • Editors
  • Consulting Editors
  • For authors
  • Publication ethics
  • Publication alerts by email
  • Transfers
  • Advertising
  • Job board
  • Contact

Usage Information

The genomic landscape of lung cancer in never-smokers from the Women’s Health Initiative
Sitapriya Moorthi, Amy Paguirigan, Pushpa Itagi, Minjeong Ko, Mary Pettinger, Anna C.H. Hoge, Anwesha Nag, Neil A. Patel, Feinan Wu, Cassie Sather, Kevin M. Levine, Matthew P. Fitzgibbon, Aaron R. Thorner, Garnet L. Anderson, Gavin Ha, Alice H. Berger
Sitapriya Moorthi, Amy Paguirigan, Pushpa Itagi, Minjeong Ko, Mary Pettinger, Anna C.H. Hoge, Anwesha Nag, Neil A. Patel, Feinan Wu, Cassie Sather, Kevin M. Levine, Matthew P. Fitzgibbon, Aaron R. Thorner, Garnet L. Anderson, Gavin Ha, Alice H. Berger
View: Text | PDF
Research Article Genetics Therapeutics

The genomic landscape of lung cancer in never-smokers from the Women’s Health Initiative

  • Text
  • PDF
Abstract

Over 200,000 individuals are diagnosed with lung cancer in the United States every year, with a growing proportion of cases, especially lung adenocarcinoma, occurring in individuals who have never smoked. Women over the age of 50 comprise the largest affected demographic. To understand the genomic drivers of lung adenocarcinoma and therapeutic response in this population, we performed whole genome and/or whole exome sequencing on 73 matched lung tumor/normal pairs from postmenopausal women who participated in the Women’s Health Initiative. Somatic copy number alterations showed little variation by smoking status, suggesting that aneuploidy may be a general characteristic of lung cancer regardless of smoke exposure. Similarly, clock-like and APOBEC mutation signatures were prevalent but did not differ in tumors from smokers and never-smokers. However, mutations in both EGFR and KRAS showed unique allelic differences determined by smoking status that are known to alter tumor response to targeted therapy. Mutations in the MYC-network member MGA were more prevalent in tumors from smokers. Fusion events in ALK, RET, and ROS1 were absent, likely due to age-related differences in fusion prevalence. Our work underscores the profound effect of smoking status, age, and sex on the tumor mutational landscape and identifies areas of unmet medical need.

Authors

Sitapriya Moorthi, Amy Paguirigan, Pushpa Itagi, Minjeong Ko, Mary Pettinger, Anna C.H. Hoge, Anwesha Nag, Neil A. Patel, Feinan Wu, Cassie Sather, Kevin M. Levine, Matthew P. Fitzgibbon, Aaron R. Thorner, Garnet L. Anderson, Gavin Ha, Alice H. Berger

×

Usage data is cumulative from January 2025 through January 2026.

Usage JCI PMC
Text version 1,770 431
PDF 288 130
Figure 425 0
Table 55 0
Supplemental data 272 47
Citation downloads 149 0
Totals 2,959 608
Total Views 3,567

Usage information is collected from two different sources: this site (JCI) and Pubmed Central (PMC). JCI information (compiled daily) shows human readership based on methods we employ to screen out robotic usage. PMC information (aggregated monthly) is also similarly screened of robotic usage.

Various methods are used to distinguish robotic usage. For example, Google automatically scans articles to add to its search index and identifies itself as robotic; other services might not clearly identify themselves as robotic, or they are new or unknown as robotic. Because this activity can be misinterpreted as human readership, data may be re-processed periodically to reflect an improved understanding of robotic activity. Because of these factors, readers should consider usage information illustrative but subject to change.

Advertisement

Copyright © 2025 American Society for Clinical Investigation
ISSN 2379-3708

Sign up for email alerts