Yoshihiko Suzuki
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Yoshihiko Suzuki has a total of 9 co-authors. Here are the top co-authors by number of shared publications:
- Akihide Tanimoto
Department of Pathology, Kagoshima University Graduate School of Medical and Dental Sciences
1 shared publication - Ikumi Kitazono
Department of Surgical Pathology, Kagoshima University Hospital, 8-35-1 Sakuragaoka, Kagoshima 890-8544, Japan
1 shared publication - Kei Matsuo
Department of Pathology, Kagoshima University Graduate School of Medical and Dental Sciences, 8-35-1 Sakuragaoka, Kagoshima 890-8544, Japan
1 shared publication - Ryo Nakabayashi
Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa 277-8563, Chiba, Japan
1 shared publication - Seiya Yokoyama
Department of Pathology, Kagoshima University Graduate School of Medical and Dental Sciences, 8-35-1 Sakuragaoka, Kagoshima 890-8544, Japan
1 shared publication - Shinichi Morishita
University of Tokyo
1 shared publication
Latest Publications
- Open Access
- 25 March 2026
- Published by MDPI AG in Current Issues in Molecular Biology
- Vol. 48 (4) , 341
Saturation Genome Editing Targeting KRAS Mutations in HCT 116 Colon Carcinoma Cells for Pooled SNV Functional Profiling in Diploid Cancer Model
Evaluating cancer gene mutations is critical for effective therapeutic selection. Although massive parallel sequencing can efficiently detect gene mutations, most are variants of uncertain significance (VUS). Saturation genome editing (SGE) can facilitate VUS analysis by leveraging CRISPR-Cas9 and homology-directed repair to simultaneously introduce abundant gene mutations. Chronic myelogenous leukemia-derived HAP1 cells are widely used in SGE because of their clear genotype–phenotype relationship; however, the sole use of haploid cells limits SGE applicability in cancer research. Therefore, we developed an SGE-based system for evaluating KRAS mutations in diploid HCT 116 colon carcinoma cells. Single-nucleotide variants (SNVs) in KRAS codons A11–V14 were generated using Cas9-based SGE. Massive parallel sequencing revealed increased abundance of KRAS G12 and KRAS G13 SNVs and decreased abundance of the KRAS G12C SNV after KRAS G12C inhibitor treatment in SGE pooled cells. These results indicate that SGE is applicable to diploid HCT 116 cells and useful for evaluating SNV population changes and drug sensitivity. Thus, although haploid HAP1 cells are the primary models for SGE, the successful application of SGE to diploid HCT 116 colon carcinoma cells provides a practical framework for implementing SGE in KRAS-dependent carcinoma cells.
- 25 March 2026
- Published by MDPI AG in Current Issues in Molecular Biology
- Vol. 48 (4) , 341
Open AccessSaturation Genome Editing Targeting KRAS Mutations in HCT 116 Colon Carcinoma Cells for Pooled SNV Functional Profiling in Diploid Cancer Model
Evaluating cancer gene mutations is critical for effective therapeutic selection. Although massive parallel sequencing can efficiently detect gene mutations, most are variants of uncertain significance (VUS). Saturation genome editing (SGE) can facilitate VUS analysis by leveraging CRISPR-Cas9 and homology-directed repair to simultaneously introduce abundant gene mutations. Chronic myelogenous leukemia-derived HAP1 cells are widely used in SGE because of their clear genotype–phenotype relationship; however, the sole use of haploid cells limits SGE applicability in cancer research. Therefore, we developed an SGE-based system for evaluating KRAS mutations in diploid HCT 116 colon carcinoma cells. Single-nucleotide variants (SNVs) in KRAS codons A11–V14 were generated using Cas9-based SGE. Massive parallel sequencing revealed increased abundance of KRAS G12 and KRAS G13 SNVs and decreased abundance of the KRAS G12C SNV after KRAS G12C inhibitor treatment in SGE pooled cells. These results indicate that SGE is applicable to diploid HCT 116 cells and useful for evaluating SNV population changes and drug sensitivity. Thus, although haploid HAP1 cells are the primary models for SGE, the successful application of SGE to diploid HCT 116 colon carcinoma cells provides a practical framework for implementing SGE in KRAS-dependent carcinoma cells.