Investigator

Michiaki Hamada

Professor · Waseda University, Faculty of Science and Engineering

MHMichiaki Hamada
Papers(2)
Elucidating Alteratio…Downregulation of HLA…
Collaborators(10)
Yoko YamamotoAnh Quynh DuongKenbun SoneHitoshi IuchiYasushi HirotaKana TamaiAya IshizakaAyako MoriAyumi TaguchiDaisuke Yoshimoto
Institutions(2)
Waseda UniversityNational Cancer Centr…

Papers

Elucidating Alterations in Viral and Human Gene Expression Due to Human Papillomavirus Integration by Using Multimodal RNA Sequencing

Human papillomavirus (HPV) infection is a primary driver of cervical cancer. Integration of HPV into the human genome causes persistent expression of viral oncogenes E6 and E7, which promote carcinogenesis and disrupt host genomic function. However, the impact of integration on host gene expression remains incompletely understood. We used multimodal RNA sequencing, combining total RNA-seq and Cap Analysis of Gene Expression (CAGE), to clarify virus–host interactions after HPV integration. HPV-derived transcripts were detected in 17 of 20 clinical samples. In most specimens, transcriptional start sites (TSSs) showed predominant early promoter usage, and transcript patterns differed with detectable E4 RNA region. Notably, the high RNA expressions of E4 region and viral-human chimeric RNAs were mutually exclusive. Chimeric RNAs were identified in 13 of 17 samples, revealing 16 viral integration sites (ISs). CAGE data revealed two patterns of TSS upregulation centered on the ISs: a two-sided pattern (43.8%) and a one-sided pattern (31.3%). Total RNA-seq showed upregulation of 12 putative cancer-related genes near ISs, including MAGI1-AS1, HAS3, CASC8, BIRC2, and MMP12. These findings indicate that HPV integration drives transcriptional activation near ISs, enhancing expression of adjacent oncogenes. Our study deepens understanding of HPV-induced carcinogenesis and informs precision medicine strategies for cervical cancer.

Downregulation of HLA Class I Expression through HLA-A DNA Methylation Is Associated with Reduced CD8+ T-cell Infiltration in Cervical Cancer

Abstract Human leukocyte antigen class I (HLA-I) is central to tumor immune recognition, but its regulatory mechanisms in cervical cancer remain poorly understood. This study aimed to elucidate the impact of HLA-I regulatory mechanisms on CD8+ T-cell infiltration and identify distinct histotype-specific immune escape strategies across cervical cancer subtypes. Using 98 cervical cancer cases, including squamous cell carcinoma (SCC; n = 53), adenocarcinoma (n = 32), gastric-type adenocarcinoma (GAS; n = 5), small cell carcinoma (Small, n = 4), and mixed histologic types (n = 4), we examined the relationship between CD8+ T-cell infiltration patterns (categorized as infiltrated, excluded, or absent) and HLA-I expression, HLA-A DNA methylation, and HLA-I loss of heterozygosity (LOH). CD8+ T-cell infiltration patterns varied significantly by histologic subtype (P < 0.0001). SCC showed the highest frequency of the infiltrated pattern (73.6%), whereas GAS and Small predominantly displayed an absent pattern. Reduced CD8+ T-cell infiltration correlated with poor survival (P < 0.0001). HLA-I expression mirrored these trends being highest in SCC and lowest in Small and GAS. HLA-A DNA methylation emerged as a key driver of HLA-I downregulation, leading to reduced CD8+ infiltration (P < 0.05). In SCC, both HLA-A methylation and HLA-I LOH contributed to immune evasion; cases lacking these alterations exhibited the highest CD8+ T-cell infiltration levels (P < 0.01). This study identifies distinct HLA-I regulatory mechanisms in cervical cancer, highlighting HLA-A methylation—and particularly HLA-I LOH in SCC—as key drivers of immune evasion. These findings provide a foundation for developing predictive biomarkers and suggest that targeting these specific HLA-I regulatory mechanisms could enhance immunotherapy efficacy.

2Papers
24Collaborators
Lung NeoplasmsCell Line, TumorPapillomavirus InfectionsLymphocytes, Tumor-InfiltratingPrognosisTumor EscapeCaenorhabditis elegans

Positions

2018–

Professor

Waseda University · Faculty of Science and Engineering

2016–

Team Leader

National Institute of Advanced Industrial Science and Technology · Computational Bio Big-Data Open Innovation Laboratory

2014–

Associate Professor

Waseda University · Department of Electrical Engineering and Bioscience

2010–

Associate Professor

University of Tokyo

2002–

Consultant

Mizuho Joho Soken Kabushiki Kaisha

Education

2009

Doctor of Science

Tokyo Institute of Technology

2002

Master of science

Tohoku University · Mathematical Institute

Country

JP

Keywords
bioinformaticscomputational biologynon-coding RNAlncRNAgenomeepigenomeinteractomemachine learningprobabilistic modeldataminingRNA aptamerdeep learning