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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 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.

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.