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Identification of causal genomic alterations is an indispensable step in the implementation of personalized cancer medicine. Analytical methods play a central role in identifying such changes because of the vast amount of data produced by next generation sequencer. Most analytical techniques are designed for the Illumina platform and are therefore suboptimal for analyzing datasets generated by whole exome sequencing (WES) using the Ion Proton System. Accurate identification of somatic mutations requires the characterization of platform-dependent error profiles and genomic properties that affect the accuracy of sequence data as well as platform-oriented optimization of the pipeline. Therefore, we used the Ion Proton System to perform WES of DNAs isolated from tumor and matched control tissues of 1,058 patients with cancer who were treated at the Shizuoka Cancer Center Hospital. Among the initially identified candidate somatic single-nucleotide variants (SNVs), 10,279 were validated by manual inspection of the WES data followed by Sanger sequencing. These validated SNVs were used as an objective standard to determine an optimum cutoff value to improve the pipeline. Using this optimized pipeline analysis, 189,381 SNVs were identified in 1,101 samples. The analytical technique presented here is a useful resource for conducting clinical WES, particularly using semiconductor-based sequencing technology.

The use of next-generation sequencing (NGS) techniques to analyze the genomes of cancer cells has identified numerous genomic alterations, including single-base substitutions, small insertions and deletions, amplification, recombination, and epigenetic modifications. NGS contributes to the clinical management of patients as well as new discoveries that identify the mechanisms of tumorigenesis. Moreover, analysis of gene panels targeting actionable mutations enhances efforts to optimize the selection of chemotherapeutic regimens. However, whole genome sequencing takes several days and costs at least $10,000, depending on sequence coverage. Therefore, laboratories with relatively limited resources must employ a more economical approach. For this purpose, we conducted an integrated nucleotide sequence analysis of a panel of 409-cancer related genes (409-CRG) combined with whole exome sequencing (WES). Analysis of the 409-CRG panel detected low-frequency variants with high sensitivity, and WES identified moderate and high frequency somatic variants as well as germline variants.

Identification of driver genes contributes to the understanding of cancer etiology and is imperative for the development of individualized therapies. Gene amplification is a major event in oncogenesis. Driver genes with tumor-specific amplification-dependent overexpression can be therapeutic targets. In this study, we aimed to identify amplification-dependent driver genes in 1,454 solid tumors, across more than 15 cancer types, by integrative analysis of gene expression and copy number. Amplification-dependent overexpression of 64 known driver oncogenes were found in 587 tumors (40%); genes frequently observed were MYC (25%) and MET (18%) in colorectal cancer; SKP2 (21%) in lung squamous cell carcinoma; HIST1H3B (19%) and MYCN (13%) in liver cancer; KIT (57%) in gastrointestinal stromal tumors; and FOXL2 (12%) in squamous cell carcinoma across tissues. Genomic aberrations in 138 known cancer driver genes and 491 established fusion genes were found in 1,127 tumors (78%). Further analyses of 820 cancer-related genes revealed 16 as potential driver genes, with amplification-dependent overexpression restricted to the remaining 22% of samples (327 tumors) initially undetermined genetic drivers. Among them, AXL, which encodes a receptor tyrosine kinase, was recurrently overexpressed and amplified in sarcomas. Our studies of amplification-dependent overexpression identified potential drug targets in individual tumors.

Project HOPE (High-tech Omics-based Patient Evaluation) has been progressing since its implementation in 2014 using whole-exome sequencing (WES) and gene expression profiling (GEP). With the aim of evaluating immune status in cancer patients, a gene panel consisting of 164 immune response-associated genes (56 antigen-presenting cell and T-cell-associated genes, 34 cytokine- and metabolism-associated genes, 47 TNF and TNF receptor superfamily genes, and 27 regulatory T-cell-associated genes) was established, and its expression and mutation status were investigated using 1,000 cancer patient-derived tumors. Regarding WES, sequencing and variant calling were performed using the Ion Proton system. The average number of single-nucleotide variants (SNVs) detected per sample was 183 ± 507, and the number of hypermutators with more than 500 total SNVs was 51 cases. Regarding GEP, seven immune response-associated genes (VTCN1, IL2RA, ULBP2, TREM1, MSR1, TNFSF9 and TNFRSF12A) were more than 2-fold overexpressed compared with normal tissues in more than 2 organs. Specifically, the positive rate of PD-L1 expression in all patients was 25.8%, and PD-L1 expression was significantly upregulated in hypermutators. The simultaneous analyses of WES and GEP based on immune response-associated genes are very intriguing tools to screen cancer patients suitable for immune checkpoint antibody therapy.

Next-generation DNA sequencing (NGS) of the genomes of cancer cells is contributing to new discoveries that illuminate the mechanisms of tumorigenesis. To this end, the International Cancer Genome Consortium and The Cancer Genome Atlas are investigating novel alterations of genes that will define the pathways and mechanisms of the development and growth of cancers. These efforts contribute to the development of innovative pharmaceuticals as well as to the introduction of genome sequencing as a component of personalized medicine. In particular, chromosomal translocations that fuse coding sequences serve as important pharmaceutical targets and diagnostic markers given their association with tumorigenesis. Although increasing numbers of fusion genes are being discovered using NGS, the methodology used to identify such fusion genes is complicated, expensive, and requires relatively large samples. Here, to address these problems, we describe the design and development of a panel of 491 fusion genes that performed well in the analysis of cultured human cancer cell lines and 600 clinical tumor specimens.