Genomic landscape of colorectal cancer in Japan: clinical implications of comprehensive genomic sequencing for precision medicine

Abstract

Background: Comprehensive genomic sequencing (CGS) has the potential to revolutionize precision medicine for cancer patients across the globe. However, to date large-scale genomic sequencing of cancer patients has been limited to Western populations. In order to understand possible ethnic and geographic differences and to explore the broader application of CGS to other populations, we sequenced a panel of 415 important cancer genes to characterize clinically actionable genomic driver events in 201 Japanese patients with colorectal cancer (CRC).

Methods: Using next-generation sequencing methods, we examined all exons of 415 known cancer genes in Japanese CRC patients (n = 201) and evaluated for concordance among independent data obtained from US patients with CRC (n = 108) and from The Cancer Genome Atlas-CRC whole exome sequencing (WES) database (n = 224). Mutation data from non-hypermutated Japanese CRC patients were extracted and clustered by gene mutation patterns. Two different sets of genes from the 415-gene panel were used for clustering: 61 genes with frequent alteration in CRC and 26 genes that are clinically actionable in CRC.

Results: The 415-gene panel is able to identify all of the critical mutations in tumor samples as well as WES, including identifying hypermutated tumors. Although the overall mutation spectrum of the Japanese patients is similar to that of the Western population, we found significant differences in the frequencies of mutations in ERBB2 and BRAF. We show that the 415-gene panel identifies a number of clinically actionable mutations in KRAS, NRAS, and BRAF that are not detected by hot-spot testing. We also discovered that 26% of cases have mutations in genes involved in DNA double-strand break repair pathway. Unsupervised clustering revealed that a panel of 26 genes can be used to classify the patients into eight different categories, each of which can optimally be treated with a particular combination therapy.

Conclusions: Use of a panel of 415 genes can reliably identify all of the critical mutations in CRC patients and this information of CGS can be used to determine the most optimal treatment for patients of all ethnicities.

Keywords: Actionable driver mutation; Colorectal cancer; Comprehensive genomic sequencing; Ethnicity; Hypermutation; Japanese; Precision medicine.

Fig. 1

Genetic aberrations across common oncogenic pathways in CRC. Japanese patients (a) and US patients (b) were evaluated for gene alterations in the key cancer pathways. Amplification (red), deletion (blue), missense point mutations (green), or frameshift mutations (brown). Altered cases are defined as the total number of unique samples with a genetic aberration in each pathway. c Percent of patients with a variation for each given gene. Statistical significance was determined using Fisher’s exact test. d J-CRC, US-CRC, and TCGA sample data were evaluated for gene alterations in the dsDNA break repair pathway in the 415-gene panel. e Percent of patients with a variation for each given gene. Statistical significance was determined using Fisher’s exact test

Fig. 2

Mutation rates in Japanese and US CRC patients. Mutation rates from Japanese patients (a) and US patients (b) were determined by the number of non-synonymous SNVs in the 415-gene panel. Hypermutated and non-hypermutated cancers separated by the dashed line. Red, MMR-deficient; gray, MMR-intact; white, no data. c Data from TCGA CRC cases (green) were downsampled to the content of the 415-gene CGS platform (blue; non-synonymous SNPs). Correlation between mutation rates determined by CGS and WES (insert). d ROC analysis using the 415-gene CGS platform, WES, and random sets of 400, 300, 200, 100, and 50 genes as predictors of hypermutated samples (TCGA dataset). e Aggregated mutational signature profiles for hypermutated (top) and non-hypermutated cases (bottom). The pie charts represent inferred contribution of COSMIC signatures to corresponding profiles. f Mutations in BRAF for Japanese patients (n = 201), US patients (n = 108), and TCGA samples (n = 224) were aligned to protein domains. The number of mutations at each given amino acid were plotted in corresponding pie graphs. As shown, BRAF V600E was the highest frequency mutations in each protein. Patient samples were further plotted by mutation status: (g) BRAF-hypermutated, (h) BRAF-non-hypermutated

Fig. 3

Cluster of 26-gene co-mutation patterns. Cluster analysis was performed on non-hypermutated Japanese CRC samples (n = 184 tumors) by using Euclidean distance and Ward’s clustering method and co-mutation patterns of the 26-gene subset with statistical analysis are shown. Mutation rate in each group is shown as a bar graph in the middle panel. Group-based mean values for age and tumor diameter are shown (left) with cluster colors and fraction for clinical information (right). Dark bars indicate significant difference (p < 0.05, two-tailed Fisher’s exact test) to the distribution of all other non-hypermutated donors, light bars are non-significant (*p < 0.05, **p < 0.01). Chemo chemotherapy; Cmab Cetuximab; Pmab Panitumumab; Bmab Bevacizumab. ^†Combination therapy with other inhibitors (e.g. anti-EGFR, MEK inhibitors) will be recommended

Fig. 4

Clinical outcomes of Stage IV patients treated with anti-EGFR therapies. a Waterfall plot for 33 patients with Stage IV CRC after anti-EGFR targeted therapy in addition to cytotoxic chemotherapy. The vertical axis shows the best calculated responses on the basis of measurable lesions in each individual patient. b Swimmers plot for 39 patients with Stage IV CRC treated with anti-EGFR therapies. The horizontal axis shows progression-free survival for each patient. c, d Kaplan–Meier survival estimates according to genomic subgroups. c Progression-free survival was analyzed in 39 patients with Stage IV CRC treated with anti-EGFR therapies. The patients were divided to “All WT (wild type)” (Cluster 1; n = 15) or “Mutated” (Clusters 2–8; n = 24) based on the cluster analysis with targeted therapy-related 26 genes. d Progression-free survival was analyzed for 36 patients with Stage IV CRC treated with anti-EGFR therapies based on subgroups (All WT, cluster 1; RNF and BRAF, cluster 4; PTEN, cluster 5; RAS, cluster 6) by clustering with the 26 genes