Abstract
BACKGROUND: Lung cancer is the most aggressive cancer, resulting in one-quarter of all cancer-related deaths, and its metastatic spread accounts for >70% of these deaths, especially metastasis to the brain. Metastasis-associated mutations are important biomarkers for metastasis prediction and outcome improvement. METHODS: In this study, we applied whole-exome sequencing (WES) to identify potential metastasis-related mutations in 12 paired lung cancer and brain metastasis samples. RESULTS: We identified 1,702 single nucleotide variants (SNVs) and 6,131 mutation events among 1,220 genes. Furthermore, we identified several lung cancer metastases associated genes (KMT2C, AHNAK2). A mean of 3.1 driver gene mutation events per tumor with the dN/dS (non-synonymous substitution rate/synonymous substitution rate) of 2.13 indicating a significant enrichment for cancer driver gene mutations. Mutation spectrum analysis found lung-brain metastasis samples have a more similar Ti/Tv (transition/transversion) profile with brain cancer in which C to T transitions are more frequent while lung cancer has more C to A transversion. We also found the most important tumor onset and metastasis pathways, such as chronic myeloid leukemia, ErbB signaling pathway, and glioma pathway. Finally, we identified a significant survival associated mutation gene ERF in both The Cancer Genome Atlas (TCGA) (P=0.01) and our dataset (P=0.012). CONCLUSIONS: In summary, we conducted a pairwise lung-brain metastasis based exome-wide sequencing and identified some novel metastasis-related mutations which provided potential biomarkers for prognosis and targeted therapeutics.