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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
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Research Article Genetics Therapeutics

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

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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

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Figure 5

Arm-level copy number alterations identify tumor subtypes unrelated to smoke exposure.

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Arm-level copy number alterations identify tumor subtypes unrelated to s...
(A) Heatmap of unsupervised clustering of arm-level copy number alterations in the WHI cohort using Ward’s minimum variance method for both samples and signatures. The clustering is based on binarized arm-level calls from GISTIC 2.0. Samples were grouped into 3 groups based on broad clusters and copy number patterns. (B) Stacked bar graph showing the percent of never-/smokers and heavy smokers in each copy number group. Fisher’s exact test was used to compare the number of NS/LS in each copy number group compared with the other. (C) Nonsilent TMB in samples split by arm-level copy number group. Mann-Whitney U test. Group I, orange; Group II, blue; and Group III, green. (D) Stacked bar graph showing percent samples in each group with ploidy 2 or ploidy greater than 2. Black bars indicate a ploidy estimate greater than 2, and white bars indicate a ploidy estimate of 2. Fisher’s exact test was used to compare enrichment of ploidy > 2 in each copy number group. ***P < 0.001, *P < 0.05. (E) Fraction genome altered in samples split by arm-level copy number group. One-way ANOVA/Tukey was used to compare significance between the different groups. **** P < 0.0001.

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