Abstract
Lung cancer in never smokers (LCINS) is a common cause of cancer mortality but its genomic landscape is poorly characterized. Here high-coverage whole-genome sequencing of 232 LCINS showed 3 subtypes defined by copy number aberrations. The dominant subtype (piano), which is rare in lung cancer in smokers, features somatic UBA1 mutations, germline AR variants and stem cell-like properties, including low mutational burden, high intratumor heterogeneity, long telomeres, frequent KRAS mutations and slow growth, as suggested by the occurrence of cancer drivers’ progenitor cells many years before tumor diagnosis. The other subtypes are characterized by specific amplifications and EGFR mutations (mezzo-forte) and whole-genome doubling (forte). No strong tobacco smoking signatures were detected, even in cases with exposure to secondhand tobacco smoke. Genes within the receptor tyrosine kinase–Ras pathway had distinct impacts on survival; five genomic alterations independently doubled mortality. These findings create avenues for personalized treatment in LCINS.
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Data availability
The 232 normal and tumor-paired raw data (BAM files) of the WGS datasets have been deposited in the dbGaP under accession no. phs001697.v1.p1. Researchers will need to obtain authorization from the dbGaP to download these data. The RNA-seq raw data (FASTQ files) have been submitted to the Gene Expression Omnibus under accession no. GSE171415. The germline variant dataset from the EAGLE whole-exome sequencing study can be access at the dbGaP with accession no. phs002496.v1.p1. In addition, histological images of these tumors can be found at https://episphere.github.io/svs. Public datasets were used in this study including gnomAD v.2.1.1/ExAC v.0.3.1 (https://gnomad.broadinstitute.org/), 1000 Genomes (phase 3 v.5, https://www.internationalgenome.org/) and dbSNP (v.138, https://www.ncbi.nlm.nih.gov/snp/).
Code availability
The code for the WGS subclonal copy number caller can be found at https://github.com/Wedge-lab/battenberg (v.2.2.8). The code for somatic mutation filtering can be found at https://github.com/xtmgah/Sherlock-Lung. The code for the Dirichlet process-based methods for subclonal reconstruction of tumors can be found at https://github.com/Wedge-lab/dpclust (v.2.2.8). The code for the mutational signature analysis can be found at https://pypi.org/project/sigproextractor/ (SigProfilerExtractor v.0.0.5.77). The code for inferring the order of genomic events can be found at https://github.com/hturner/PlackettLuce (v.0.2-2). The code for the chronological timing analysis can be found at https://gerstung-lab.github.io/PCAWG-11/. The code for P-MACD can be found at https://github.com/NIEHS/P-MACD.
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Acknowledgements
This work has been supported by the Intramural Research Program of the Division of Cancer Epidemiology and Genetics, National Cancer Institute, and the Intramural Research Program of the National Institute of Environmental Health Sciences (project nos. Z01 ES050159 to S.H.W. and Z1AES103266 to D.A.G.), National Institutes of Health (NIH). This project was funded in whole or in part with federal funds from the National Cancer Institute, NIH, under contract nos. 75N91019D00024 and HHSN261201800001I. The content of this publication does not necessarily reflect the views or policies of the U.S. Department of Health and Human Services, nor does mention of trade names, commercial products or organizations imply endorsement by the U.S. Government. The research was supported by the Wellcome Trust Core Award, grant no. 203141/Z/16/Z with funding from the National Institute for Health Research Oxford Biomedical Research Centre. L.B.A. is an Abeloff V scholar and he is personally supported by an Alfred P. Sloan Research Fellowship and a Packard Fellowship for Science and Engineering. Research at the L.B.A. laboratory was supported by a National Institute of Environmental Health Sciences grant no. R01ES032547. The views expressed are those of the authors and not necessarily those of the National Health Service, National Institute for Health Research or Department of Health. The collection of samples from the Institut Universitaire de Cardiologie et de Pneumologie de Québec (IUCPQ), Université Laval was supported by the IUCPQ Foundation. The GR Program 2010-2316264 supported L.A.M. for sample collection by the Istituto di Ricovero e Cura a Carattere Scientifico Fondazione Casa Sollievo della Sofferenza. A.L.M. is supported by a Damon Runyon Cancer Research Foundation postdoctoral fellowship (no. DRG:2368-19) and a Postdoctoral Enrichment Program Award from the Burroughs Wellcome Fund (no. 1019903). C.F.K. is supported in part by grant no. R35HL150876-01, the Thoracic Foundation, Ellison Foundation, American Lung Association (no. LCD-619492) and the Harvard Stem Cell Institute. N.L-B. acknowledges funding from the European Research Council (consolidator grant no. 682398). P.H. is supported in part by the Association pour la Recherche contre le Cancer (CANC’AIR GENExposomics project). This work has been supported in part by the Tissue Core at the H. Lee Moffitt Cancer Center & Research Institute, a comprehensive cancer center designated by the National Cancer Institute and funded in part by a Moffitt Cancer Center Support Grant (no. P30-CA076292). B.E.G.R. is supported by NIH grant nos. 1P50 CA196530-01 and NIH 1K08 CA151645-01. We thank the Sherlock-Lung study scientific advisory board (M. Meyerson, J. Samet, M. Spitz, R. Summers, M. Thun and W. Travis) for their support. We also thank Y. Rubanova from Toronto University for her help with the TrackSig analysis. We thank the staff at the IUCPQ Université Laval Biobank, Nice Biobank Centre de Ressources Biologiques, Yale University and Moffitt Cancer Center & Research Institute for their valuable assistance in collecting samples and corresponding clinical data. This work utilized the computational resources of the NIH high-performance computational capabilities Biowulf cluster (http://hpc.nih.gov).
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Contributions
M.T.L. and T.Z. conceptualized the study. T.Z., D.C.W., J. Shi., B.Z., N.A-P., N.L-B., B.Z., S.H.W., Y.P., H.C., T.R., D.R.S., D.A.G., L.B.A. and M.T.L. devised the methodology. T.Z., N.A-P., W.Z., P.H.H., R.L., K.H.-H., A.G.-P., F.M.-J., A.C., I.P., J. Sang, J. Shi, J.K., N.S., L.J.K., S.M.A.I., B.O., A.K., A.L.M. and C.F.K. carried out the formal analysis. A.H., N.C., J.C., D.H. and K.M.B. carried out the laboratory work. M.O., S.M.L., M.D., P.L., P.M.S.B. and J.S.A. carried out the pathology work. P.J., Y.B., P.H., D.C., A.C.P., L.A.M., B.E.G.R., M.L.P., M.C., M.B.S., N.E.C., M.L. and S.J.C. managed the resources. M.K., L.M. and J.R. curated the data. T.Z. and M.T.L. wrote the original draft. D.C.W., S.J.C., Y.B., Q.L., N.R., M.G-C., D.A.G., L.B.A., N.L-B., B.Z., J. Sang, J. Shi, T.Z., P.H.H. and M.T.L. reviewed and edited the draft. All authors carried out the data visualization. M.T.L. supervised the study.
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Extended data
Extended Data Fig. 1 Genomic alterations of RTK-RAS pathway in Sherlock-Lung.
a, Oncoplot showing mutual exclusivity of genes within the RTK-RAS pathway, which were used to define the RTK-RAS status. The bottom bar shows tumor histological types. b, Comparison of genomic features between RTK-RAS negative and positive tumors. Left four panels: tumor mutational burden, percentage of genome with SCNAs, SV burden and T/N TL ratio. P-values are calculated using the two-sided Mann-Whitney U test; Middle three panels: enrichments for Kataegis events, WGD events, and BRCA2 LOH. P-values and OR are calculated using Fisher’s exact test (two-sided); Right panel: Contributions of each SBS signature.
Extended Data Fig. 2 Genomic alterations of TP53 pathway in Sherlock-Lung.
a, Oncoplot showing the mutual exclusivity between TP53 mutations and MDM2 amplification, which was used to define the TP53 proficient and deficient groups. The bottom bar shows tumor histological types. b, Comparison of genomic features between TP53-proficient and TP53-deficient tumors. Left three panels: tumor mutation burden, percentage of genome with SCNA and SV burden. P-values are calculated using the two-sided Mann-Whitney U test. Middle four panels: enrichments for BRCA1 LOH, Kataegis events, WGD events, and HLA LOH. P-values and OR are calculated using Fisher’s exact test (two-sided). Right panel: Contributions of each SBS signature.
Extended Data Fig. 3 Recurrence of SV breakpoints in Sherlock-Lung.
The frequencies of chromosomal breakpoints are calculated using 5 Mb as a window across the whole genome.
Extended Data Fig. 4 Summary of genomic features in LCINS based on different SCNA clusters.
Panels from top to bottom describe: 1) most frequently mutated or potential driver genes; 2) oncogenic fusions; 3) somatic mutations in surfactant associated genes; 4) significant focal SCNAs; 5) significant arm-level SCNAs; 6) genes with rare germline mutations; 7) and 8) other genomic features. The numbers on the right panel show the overall frequency (1-7) or median values (8). NRPCC: the number of reads per clonal copy.
Extended Data Fig. 5 Genes with signals of positive selection in Sherlock-Lung.
a, The scatter plot showing significantly mutated genes according to IntOGen q-value <0.05 (y-axis) and mutational frequency in the cohort (x-axis). Genes are colored according to their inferred mode of action in tumorigenesis. b, Recurrent non-synonymous driver mutations (in ≥2 patients).
Extended Data Fig. 6 Dominant endogenous processes in Sherlock-Lung.
a, Density plot of cosine similarity between original mutational profile and reconstructed mutational profile using reference signatures from (top to bottom): 65 COSMIC SBS signatures, 22 COSMIC SBS signatures for endogenous processes, 53 MutaGene SBS signatures of environmental exposures, and a combined set of signatures including the 22 endogenous and 53 environmental exposure signatures. b, Comparison of the cosine similarity between the original mutational profiles and reconstructed mutational profiles using endogenous and exogenous signatures (similar to a). Each dot represents one sample. The size and color represent the total number of mutations and tumor histological type, respectively.
Extended Data Fig. 7 Association between T/N TL ratio and somatic alterations in Sherlock-Lung.
a, Distribution of mean telomere lengths (TL) in Sherlock-Lung (dark blue, overall and by histological type), TCGA LUAD (green, overall and by smoking status) and TCGA other cancer types (Grey). Total sample numbers for each type are shown at the top. Error bars, 95% CIs from linear mixed model. b, Scatterplot showing association between T/N TL ratio and somatic alterations. Association P-values (two-sided t-test; FDR adjusted using Benjamini-Hochberg method) are shown on the y-axis. Genomic alterations with FDR <=0.1 or T/N TL ratio >1.1 or <0.9 are labeled and further highlighted in red when significant (FDR=0.05; horizontal dashed line). c, The proportion of each SCNA cluster among the group of tumors with somatic alterations significantly associated with shorten T/N TL including Chr22q Loss, Chr9p/q Loss or HLA LOH.
Extended Data Fig. 8 Homologous recombination deficiency (HRD) in Sherlock-Lung.
a, HRDetect scores of Sherlock-Lung samples. HRD-high: >0.7, HRD-low: < 0.005. b, Comparison of the number of total indels, microhomology deletions, SVs, and SNVs between samples with HRDetect score below 0.7 (group N) and above 0.7 (group Y). P-values are calculated using the two-sided Mann-Whitney U test. For box plots, center lines show the medians; box limits indicate the 25th and 75th percentiles; whiskers extend 1.5 times the interquartile range from the 25th and 75th percentiles.
Extended Data Fig. 9 Genomic alterations in HRD associated genes in Sherlock-Lung.
a, Oncoplot of genomic alterations in HRD associated genes, including germline mutations, somatic mutations and LOH. Samples with biallelic alterations are represented by bars with two different colors. The bottom bar shows tumor histological types. b, Boxplots of HRDetect scores (top) and SBS mutation loads (bottom) in tumors with and without LOH of six HR associated genes. FDR are calculated using the two-sided Mann-Whitney U test with multiple testing correction based on the Benjamini & Hochberg method. For box plots, center lines show the medians; box limits indicate the 25th and 75th percentiles; whiskers extend 1.5 times the interquartile range from the 25th and 75th percentiles.
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Supplementary Notes, Methods and Figs. 1–38.
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Zhang, T., Joubert, P., Ansari-Pour, N. et al. Genomic and evolutionary classification of lung cancer in never smokers. Nat Genet 53, 1348–1359 (2021). https://doi.org/10.1038/s41588-021-00920-0
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DOI: https://doi.org/10.1038/s41588-021-00920-0
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