GENOMIC LANDSCAPE OF NON-SMALL CELL LUNG CANCER IN SMOKERS AND NEVER SMOKERS

Study design and case descriptions

Tumor and adjacent normal tissue samples for whole genome sequencing were obtained from patients diagnosed with non-small cell lung carcinoma (NSCLC) who underwent definitive surgical resection prior to receiving chemotherapy or radiation at the Alvin J Siteman Cancer Center at Washington University School of Medicine. All samples were subjected to pathology review to establish the histologic diagnosis and tumor cellularity. Only samples with tumor nuclei greater than or equal to 50% of total cellular nuclei in the section were utilized for this study. We identified 17 patients who met all of the above criteria resulting in a cohort comprised of 16 tumors with adenocarcinoma histology and one with large cell carcinoma histology. The median age of patients was 63 years (range 24–77). Five patients included in the study reported no history of tobacco smoking (referred to as “never-smokers” hereafter) and one patient had a very light history of tobacco smoking (10 pack-years) having quit smoking 38 years before developing lung cancer (referred to as “former light smoker” hereafter) (Figure 1). Clinical characteristics including tumor stage, treatment received, and outcome are provided in Supplemental Table S1. Histological images are provided in the supplement (Supplemental Data S1). The study was approved by the Human Research Protection Office (HRPO) at the Washington University School of Medicine.

The initial dataset for this study included whole-genome sequencing (WGS) paired-end sequencing data generated using 17 lung cancer (LUC) tumor-normal pairs, with haploid coverage ranging from 25.03- to 64.49-fold (Supplemental Table S2). Point mutations, small (<30 bp) indels, copy number alterations, and structural variants (SVs) were discovered using various computational approaches (Chen et al., 2009; Larson et al., 2011; Li et al., 2009; McKenna et al., 2010; Ye et al., 2009). Point mutations and indels identified by whole genome sequencing were classified into four tiers as described previously (Mardis et al., 2009) (Supplemental Methods and Supplemental Table S3 & S4); Custom sequence capture arrays were used to validate putative WGS mutations (Supplemental Table S5). Variants of interest identified by whole genome sequencing were extended by recurrence screening in an independent set of 94 primary lung adenocarcinomas (Supplemental Table S6). RNA-seq data were generated for all 17 tumors and a single, matched normal adjacent tissue to LUC9, with between 11,578 to 14,507 genes detected as expressed in each tumor (Supplemental Table S7).

Genomic landscape of lung cancer in relation to tobacco smoking

Substantial differences in the mutational burden, spectrum, and affected genes were found between smokers and never-smokers (Figure 1). Of the 12 samples from tobacco smokers (including the former light smoker), we observed one cancer genome (LUC9) with a significantly higher number of point mutations (tier 1: 1,363) when compared to the other tumor samples (Figure 1). This sample meets our criterion for “hypermutation”, defined as having a total number of tier 1 mutations at least 2 standard deviations greater (1 standard deviation = 329) than the rest of the samples. The total number of point mutations (tiers 1–3) was much higher in tobacco smokers (median 15,659, range 7,424 to 26,202, LUC9 not included) relative to never smokers (median 888, range 842 to 1,268). Similarly, the total number of point mutations involving coding regions (tier 1) also was much higher in smokers (median 209, range 104 to 1363) compared to never smokers (median 18, range 10 to 22). The total number of point mutations in the former light smoker was 403 in tiers 1–3, with only 10 in tier 1 (Supplemental Table S8). Consistent with previous reports (Ding et al., 2008; Lee et al., 2010), lung cancer due to tobacco smoking is associated with significantly higher number of mutations per Mb (mutations per Mb: median 10.5 range 4.9 to 17.6, LUC9 not included) compared to never-smokers with lung cancer (mutations per Mb: median 0.6, range 0.6 to 0.9) and a single former light smoker with lung cancer (0.3 mutations per Mb) in our study. Figure 1 illustrates the different characteristics of mutations in patients according to their smoking status. In particular, C:G→A:T transversions were noted predominantly in tobacco smokers whereas C:G→T:A transitions were the most frequent type of point mutations in never- smokers with lung cancer and the former light smoker, consistent with previously reported studies (Ding et al., 2008; Lee et al., 2010). The mutational spectrum of the single large cell carcinoma sample was not different from those of lung adenocarcinoma associated with tobacco smoking. Overall the number of point mutations in the lung cancer genome appears to be closely related to the patient’s tobacco smoking status and the landscape of the former light smoker genome suggests a possible dose-response relationship between the amount and duration of tobacco smoke exposure and the extent of mutational burden. The hypermutated tumor (LUC9) was found to have point mutations involving several DNA repair genes including PRKDC, TP53, MSH3, POLK, MSH4, FANCM, FBXW7, TOP2B, MLH1, RPA2, BUB1, FANCB and TOP1 (Wood et al., 2001) (http://www.genesilico.pl/index.php/home.html). It is possible that these mutations in DNA repair genes resulted in an impaired ability to repair sustained DNA damage induced by chronic tobacco smoke.

Somatically mutated genes in lung cancer

Tumor heterogeneity analysis using deep digital sequencing data

By performing targeted sequencing with high read coverage (mean depth of 381 reads) to validate variants detected by WGS, we were able to accurately estimate the variant allele frequencies (VAFs) for somatic mutations identified in each tumor sample. Based on the VAF distribution, we were able to estimate the number and size of the clonal populations in each tumor sample. Recent studies have shown the importance of clonal evolution in tumor progression and development of metastasis (Ding et al., 2012; Gerlinger et al., 2012) Using mutations from copy number neutral regions, we found that 10 tumors had a multi-clonal signature while seven tumors were largely monoclonal (Table 1 and Figure 2). We did not find any correlation between smoking status and tumor clonality. Based on the VAFs of mutations, we were able to identify mutations that were present in the founding clone and/or the subclone(s). All EFGR and KRAS mutations validated in our cohort were present in the founder clones of the associated tumor samples (for example, the EGFR mutation in LUC15 at 19% VAF, Figure 2D, and the KRAS mutation in LUC10 at 48% VAF, Figure 2F). The clonal distributions of other mutations involving genes such as HGF were varied between samples. In the LUC9 tumor in particular, which exhibits two distinct mutation clusters at median VAFs 41.1% and 20.4%, an HGF mutation exists in both subclones (Figure 2E). We extended the subclonality analyses to copy number alterations (in particular, deletions) for LUC9, using an algorithm that compare the observed read counts with the expected diploid read counts in the affected intervals. We found a biclonal pattern in the deletions similar to what we observed with SNV analysis described above (Supplemental Table S14, Supplemental Figure S1). In LUC10, an HGF mutation exists in the secondary clone at 17% VAF (Figure 2F). It is likely that EGFR and KRAS mutations are initiating events for lung cancer and other mutations such as HGF mutations are acquired later and perhaps are important for tumor maintenance and progression.

Of the 10 tumor samples that had bi-clonal population, 8 had one or more potentially targetable mutations in the subclone (Supplemental Table S15). These 8 samples also had at least one additional targetable mutation in the dominant clone. It is conceivable that future studies will need to focus on drug therapies affecting critical genes or key pathways not only in the dominant clone but also in the subclone. A treatment strategy focused mainly on the dominant clones could potentially fail owing to emergence of subclones that are not originally targeted for therapy.

Structural variants identified by whole genome and transcriptome sequencing

Among the validated 173 somatic rearrangements detected by whole genome sequencing data were 59 inter-chromosomal translocations, 7 tandem duplications, 74 deletions, and 33 inversions (Supplemental Table S16). The majority of the inter-chromosomal events were clustered in four samples: three from smokers and one from a never-smoker (Supplemental Data S2). The never-smoker (LUC7) tumor genome is characterized by widespread chromosomal disruption consistent with chromothripsis (Stephens et al. 2011). We identified 15 validated inter-chromosomal translocation events between chromosome 5 and other chromosomes across the LUC7 tumor genome, with most events connecting the distal end of chromosome 5q with various locations on chromosomes 10, 12, 17, and 20. Copy number alterations often co-occur with translocation breakpoints, consistent with previously described chromothripsis events. We did not identify any TP53 mutations in this tumor though mutations involving the TP53 gene have been reported to be associated with chromothripsis (Rausch et al., 2012).

We also analyzed the tumor genomes for novel fusion genes, an area of great interest therapeutically with the recent discovery of novel fusions involving kinase genes ALK, ROS and RET in NSCLC (Takeuchi et al., 2012). With combined whole genome and transcriptome sequencing, we were able to systematically identify and validate fusion genes. Three different algorithms, ChimeraScan (Iyer et al., 2011), defuse (McPherson et al., 2011) and BreakFusion (Chen et al., 2012), were used to identify fusion genes from the transcriptome sequencing data. High confidence fusions then were orthogonally validated by analysis of the whole genome DNA sequencing data. Based on this analysis, we identified 14 high-confidence fusions (Supplemental Table S17 and Supplemental Methods), including an in-frame novel fusion KDELR2-ROS1 in LUC11 and an EML4-ALK fusion in LUC16. Even though ROS1 kinase fusions have been previously reported in patients diagnosed with NSCLC and cholangiocarcinoma (Bergethon et al., 2012; Gu et al., 2011; Rikova et al., 2007), we identified a novel 5’ partner (KDELR2) in our never-smoker sample. A variety of genes have been reported to be 5’ partners in ROS1 fusions and it is not known whether the 5’ partner plays a role in the oncogenic activity of the fusion kinase (Rikova et al., 2007; Takeuchi et al., 2012). Apart from fusion kinases, an in-frame fusion was detected between the RASSF1A (RAS association domain family protein 1) and TTYH2 (Tweety, Drosophila Homolog of 2) genes. Another novel fusion consisted of a transcription factor in the 3’ end; FZR1-NFIC. NFIC (nuclear factor I/C) is a dimeric DNA-binding protein and functions as a cellular transcription factor. FZR1 in association with the APC gene is involved in the regulation of mitosis and meiosis.

Integrated analyses of the whole genome and transcriptome data

One of the major strengths of our study is the integration of whole genome and transcriptome sequencing. Starting with 3,726 tier 1 variants (point mutations only) from all samples identified by WGS, we characterized the expression of each gene by digital (NGS-based) RNA-sequencing (Supplemental Methods). The median read coverage from RNA sequencing for all tier 1 variant positions was 24×, but in expressed genes, the median read coverage reached, 129×. We observed significant concordance in variant identification between genome and transcriptome sequencing. Transcriptome sequencing confirmed the presence of 40% of the variants identified by WGS (at least one RNA-seq read) despite the observation that 34% of variants identified in WGS data were from a non-expressed allele and 3% of variants from highly expressed genes were not sufficiently covered at the variant positions. We utilized the RNA-seq data to further classify variants into four categories according to their expression patterns: expressed, mutant-biased, wild type-biased, and silent gene (Figure 3A, 3B, Supplemental Table S18 and Supplemental Methods). The genomes of lung cancer from never-smokers had a higher proportion of expressed variants (49.4%) than tobacco smokers (29.1% or 27.0% if the hyper-mutated LUC9 is excluded). The number of expressed variants that are biased towards the mutant allele is a small proportion of all variants (9.6%) (Figure 3B). For these variants, the mutant allele had a significantly higher variant allele frequency (> 20% higher) in the RNA compared to the DNA. Notably, a few genes (KRAS, TP53, GTF3C1, PLEKHA6, and SGOL2) showed mutant biased over-expression relative to the wild type allele in more than one sample. For example, KRAS mutations were detected in five of the 17 samples and in all of these the mutant allele was preferentially expressed (Supplemental Table S19). We did not identify copy number amplification in the mutant-biased expression of the KRAS gene. KRAS and TP53 were highly expressed (above the 75th percentile) in all 17 cases and eight of nine KRAS/TP53 mutations occurred in smokers. KRAS and TP53 mutations were mutually exclusive in our 17 cases (Figure 3C) although previous studies showed they could be present in the same samples (Ding et al., 2008). While the VAFs observed in WGS and RNA-seq are generally correlated (Figure 3D), rare cases such as KRAS and TP53 deviate from the expected VAF considerably. The mechanism underlying the observed difference in VAFs at the DNA and RNA level for these genes remains unknown.

Interestingly, we observed a lower mutation frequency in tier 1 than in tiers 2 and 3 for all 17 cases and the average ratios of tier1 vs. tiers 2–3 frequencies are 0.628 and 0.700 for never smokers and smokers, respectively (Figure 4A) (the former light smoker, LUC1, was not included in the calculation, nor was the tier 3 mutation rate for the hypermutated sample, LUC9). This observation is statistically significant (p = 1.526 × 10⁻⁵), suggesting that selection pressure and transcription-coupled repair for coding mutations might be responsible for the reduced mutation rate in tier 1. Our result is consistent with the genome-wide analyses of mutation frequencies in a melanoma cell line (Pleasance et al., 2010a) and a lung cancer cell line from a smoker (Pleasance et al., 2010b)). Further, we investigated the relationship between mutation frequency and gene expression level and found that highly expressed genes (FPKM >15) have less than 4 mutations per Mbp while genes that are not expressed (FPKM = 0) have close to 14 mutations per Mbp. Thus, our analysis revealed a negative correlation between gene expression level and mutation frequency in lung cancers (correlation coefficient = -0.49, p = 0.1804), consistent with transcription-coupled repair mechanism (Figure 4C).

Somatically altered pathways

PathScan (Wendl et al., 2011) analysis was performed to identify significant clusters of point mutations involving genes in annotated KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways. The analysis identified 50 pathways with statistically significant (p < 0.05) enrichment of mutations. (Supplemental Table S20 and Supplemental Methods) We subsequently incorporated information regarding indels, copy number variations, and mRNA expression level changes involving individual genes in the significant pathways. Based on this analysis we identified several pathways that are affected in lung cancer, including JAK/STAT pathway (Figure 5A).

Genes involved in extra cellular matrix (ECM) interaction, focal or cell adhesion and cell cycle pathways were significantly enriched in lung cancer. ECM interaction and cell adhesion genes play important roles in morphogenesis, maintenance of cellular and tissue structure, cell migration and proliferation. Similarly, there was significant enrichment of genes involved in cell cycle including the PRKDC gene that was recurrently mutated in three patient samples. In addition, there were mutations involving the cyclins, CCNA1 and CCNB3 that are essential for activation of cyclin-dependent kinases (CDKs) and progression of cell cycle. We also identified significant enrichment of mutations in the JAK-STAT (p = 0.04) pathway in our sample set. Janus kinases are a family of tyrosine kinases that are involved in cytokine receptor signaling and JAK2 in particular mediates signaling for class II cytokine receptors, cytokine receptors that utilize the γ_c receptor subunit, and receptors that utilize the gp130 subunit (Rodig et al., 1998). The gain of function V617F mutation in the pseudokinase domain of the JAK2 gene leads to constitutive activation of the kinase domain and is associated with uncontrolled hematopoietic cell proliferation in various myeloproliferative neoplasms (Kralovics et al., 2005). However, it is not known whether mutations involving JAK2 play a significant role in solid epithelial tumors particularly in lung cancer. Recently JAK2 (V617F) mutations were reported in a small proportion (1%) of patients with lung cancer (Lipson et al., 2012). In our sample set we identified two patients with missense mutations (M532V & V615L) in the protein kinase 1 domain of the JAK2 gene. The detection of recurrent mutations in the protein kinase domain of the JAK2 gene as well as mutations in in other genes involved in the JAK-STAT pathway (JAK3 and STAT1), indicate that activation of this pathway may be oncogenic in a subset of patients with lung cancer (Figure 5A and 5B). These findings assume further importance with the development of drugs that effectively target activating mutations in the JAK2 gene. In addition, we identified that several other pathways were significantly affected in lung cancer including G protein coupled receptor, ion channels, chemokine signaling, calcium signaling pathways, immune modulation and ErbB signaling.

Therapeutic targets

The use of whole cancer genome sequencing and/or transcriptome sequencing to identify therapeutic targets has been recently reported (Jones et al., 2010b). Apart from previously characterized activating mutations in the tyrosine kinase domain of the EGFR gene, we identified potential therapeutic targets including point mutations in the HGF, MET, JAK2, EPHA3 genes and fusions including KDELR2-ROS1 and EML4-ALK. In an effort to comprehensively identify therapeutic targets in lung cancer, we matched gene alterations including point mutations, copy number amplifications, and high gene expression levels with novel compounds that are currently being evaluated for the treatment of lung cancer (Somaiah and Simon, 2011) (Figure 6). As a result, we identified 54 genes with potentially druggable alterations in our 17 lung cancer patients with several novel therapeutic targets including tyrosine kinases (JAK, BRAF, PIK3CG, IGF1R, MET, RET & FGFR1), heat shock protein (HSP90AA1) and histone deacetylases (HDAC1, HDAC2, HDAC6, HDAC9). A median of 11 (range 7–17) potentially druggable targets was found for each patient. Novel and recurrent druggable point mutations included PRKCB2, MET, JAK2, HGF, and ERBB4, in addition to previously well-known targets such as, KRAS, EGFR and BRAF. These findings clearly illustrate there are several novel potential therapeutic targets in patients with lung cancer that require further exploration.

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