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. 2012 Nov;22(11):2109-19.
doi: 10.1101/gr.145144.112. Epub 2012 Sep 13.

The transcriptional landscape and mutational profile of lung adenocarcinoma

Jeong-Sun Seo  1 , Young Seok Ju, Won-Chul Lee, Jong-Yeon Shin, June Koo Lee, Thomas Bleazard, Junho Lee, Yoo Jin Jung, Jung-Oh Kim, Jung-Young Shin, Saet-Byeol Yu, Jihye Kim, Eung-Ryoung Lee, Chang-Hyun Kang, In-Kyu Park, Hwanseok Rhee, Se-Hoon Lee, Jong-Il Kim, Jin-Hyoung Kang, Young Tae Kim
Affiliations

The transcriptional landscape and mutational profile of lung adenocarcinoma

Jeong-Sun Seo et al. Genome Res. 2012 Nov.

Abstract

All cancers harbor molecular alterations in their genomes. The transcriptional consequences of these somatic mutations have not yet been comprehensively explored in lung cancer. Here we present the first large scale RNA sequencing study of lung adenocarcinoma, demonstrating its power to identify somatic point mutations as well as transcriptional variants such as gene fusions, alternative splicing events, and expression outliers. Our results reveal the genetic basis of 200 lung adenocarcinomas in Koreans including deep characterization of 87 surgical specimens by transcriptome sequencing. We identified driver somatic mutations in cancer genes including EGFR, KRAS, NRAS, BRAF, PIK3CA, MET, and CTNNB1. Candidates for novel driver mutations were also identified in genes newly implicated in lung adenocarcinoma such as LMTK2, ARID1A, NOTCH2, and SMARCA4. We found 45 fusion genes, eight of which were chimeric tyrosine kinases involving ALK, RET, ROS1, FGFR2, AXL, and PDGFRA. Among 17 recurrent alternative splicing events, we identified exon 14 skipping in the proto-oncogene MET as highly likely to be a cancer driver. The number of somatic mutations and expression outliers varied markedly between individual cancers and was strongly correlated with smoking history of patients. We identified genomic blocks within which gene expression levels were consistently increased or decreased that could be explained by copy number alterations in samples. We also found an association between lymph node metastasis and somatic mutations in TP53. These findings broaden our understanding of lung adenocarcinoma and may also lead to new diagnostic and therapeutic approaches.

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Figures

Figure 1.
Figure 1.
The transcriptional landscape and mutational profile of 87 lung adenocarcinomas. Each column characterizes the signature of cancer in one patient. Patients are classified into smokers and never-smokers. Each row represents a selected gene of interest, including known driver genes of lung adenocarcinoma, genes observed to be frequently mutated (≥3 cancers), and protein kinase genes involved in fusion and exon-skipping (ES) events. Cells are colored to indicate discovery of somatic mutations (red), gene overexpression (blue), both (purple), and fusion and ES events (green) in cancer tissue. Patients with lymph node metastasis are indicated with “M” on the bottom row.
Figure 2.
Figure 2.
Graphical representation of 45 fusion genes identified from transcriptome sequencing of 87 lung adenocarcinomas. Protein kinase-containing fusion genes are indicated with red lines joining the two genomic loci, while other fusions are indicated by blue lines. The protein kinase genes and their fusion partners are labeled in red and green, respectively (outer layer).
Figure 3.
Figure 3.
Fusion genes and alternative splicing events revealed by RNA sequencing. (A) Schematic figures of the domain structures of novel protein kinase fusion genes. (B) Exon 14 skipping in MET proto-oncogene demonstrated by read depth across gene model. (TM) Trans-membrane domain.
Figure 4.
Figure 4.
Mutational and transcriptional variation in cancer between never-smokers and smokers. (A) The number of somatic mutations (nonsynonymous single nucleotide and short-indel mutations) in the cancer tissue of each patient. Patients are classified into never-smokers and smokers, and further sorted by mutation count. (Inset) Box plot of somatic mutation counts for never-smokers and smokers. The two groups are significantly different (P = 0.001079). (B) The proportion of the six possible nonsynonymous substitutions found within smokers and never-smokers. The two groups were significantly different with respect to transversions C > A and T > G (**, P < 0.001) and transversion T > A (*, P < 0.01). (C) The number of cancer-outlier genes (COGs; extremely high-expressed genes in a subset of cancer specimens; see Methods for details) in each cancer tissue. Patients are sorted as above. (Inset) Box plot showing that lung adenocarcinoma in smokers contains more cancer-outlier genes. (D) Gene expression within cancer tissues against average expression in normal tissue. Scatter plots for patients LC_S33 (a never-smoker patient) and LC_S51 (a smoker patient) are shown, providing an example of the variation in gene expression perturbation. Selected genes of interest are labeled. Genes were categorized as “Cancer-up” where generally overexpressed and “Cancer-down” where generally underexpressed in lung cancer compared with paired-normal tissue by hierarchical clustering (see Supplemental Material).
Figure 5.
Figure 5.
JRBs identified from gene expression signatures. (A) Large JRBs observed on chromosome 5 in one cancer sample (patient LC_S51) and its high correlation with CGH array results. (Top row) Relative expression levels of the genes on chromosome 5 (gray dots), their moving averages (red line), and detected JRBs (red horizontal bars). (Middle row) CGH array results for patient LC_S51. Log2 ratio of probes (blue dots) and identified copy number alterations (blue horizontal bars). (Bottom row) Karyogram of chromosome 5. (B) Correlation between JRBs and CGH array data for three cancer specimens. The x-axis represents the averaged Z-scores of JRB and the y-axis indicates the averaged CGH array log2 ratios for the genomic area. (C) The genomic location of JRBs and number of cancer tissues involved. Increased- and decreased-expression JRBs are shown in blue and red bars, respectively.
Figure 6.
Figure 6.
A summary of the mutational profiles of 200 lung adenocarcinomas. Pie chart shows the distribution of driver mutations identified in 200 lung adenocarcinoma patients in this study.
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