Transcriptome amplification and fusion gene detection using ultra-trace cells derived from cytology specimens

Tumours are complex and despite scientific breakthroughs, there is much to be elucidated about cancer and its mechanisms. By personalising treatment, outcomes can be improved and genetic analysis is key to this as it allows genetic alterations to be identified and treatment to be precisely targeted. Dr Kenji Amemiya is a cytotechnologist and Chief Clinical Laboratory Technician at the Yamanashi Prefectural Central Hospital Genome Analysis Center (GAC). He is working to better understand the complex inner landscape of cancer and therefore improve cancer detection, diagnosis and treatment. Formalin‐fixed paraffin‐embedded (FFPE) specimens are typically used for genetic testing but there are drawbacks to this, including the FFPE DNA becoming damaged during the fixation process with formalin, resulting in low quality and quantity of DNA. Amemiya is collaborating closely with Chairman of the GAC, Professor Emeritus Masao Omata. They are interested in the use of cytology samples instead of tissue samples in genome analysis, as a less invasive method. Survival rates are increased when genomic analysis is utilised in non-small cell lung cancer (NSCLC). In one project, Amemiya, Omata and the team explored the potential of performing NGS analysis with archived cytological specimens (ACSs). They utilised an integrated NGS platform called the Oncomine Dx Target Test Multi-CDx system to compare the quantity and quality of extracted nucleic acids, the sequencing quality control (QC) and the detected variants between ACSs and FFPE tissues. They found that the RNA integrity was higher in ACSs and there was no difference in sequencing results, compared with FFPE tissues.

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Published
2024-10-28
DOI
https://doi.org/10.21820/23987073.2024.2.41
Primary Topic
Gene expression and cancer classification
Type
article
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article

Transcriptome amplification and fusion gene detection using ultra-trace cells derived from cytology specimens

Kenji Amemiya
Impact
Gene expression and cancer classification
article

Transcriptome amplification and fusion gene detection using ultra-trace cells derived from cytology specimens

Kenji Amemiya
article en

Abstract

Tumours are complex and despite scientific breakthroughs, there is much to be elucidated about cancer and its mechanisms. By personalising treatment, outcomes can be improved and genetic analysis is key to this as it allows genetic alterations to be identified and treatment to be precisely targeted. Dr Kenji Amemiya is a cytotechnologist and Chief Clinical Laboratory Technician at the Yamanashi Prefectural Central Hospital Genome Analysis Center (GAC). He is working to better understand the complex inner landscape of cancer and therefore improve cancer detection, diagnosis and treatment. Formalin‐fixed paraffin‐embedded (FFPE) specimens are typically used for genetic testing but there are drawbacks to this, including the FFPE DNA becoming damaged during the fixation process with formalin, resulting in low quality and quantity of DNA. Amemiya is collaborating closely with Chairman of the GAC, Professor Emeritus Masao Omata. They are interested in the use of cytology samples instead of tissue samples in genome analysis, as a less invasive method. Survival rates are increased when genomic analysis is utilised in non-small cell lung cancer (NSCLC). In one project, Amemiya, Omata and the team explored the potential of performing NGS analysis with archived cytological specimens (ACSs). They utilised an integrated NGS platform called the Oncomine Dx Target Test Multi-CDx system to compare the quantity and quality of extracted nucleic acids, the sequencing quality control (QC) and the detected variants between ACSs and FFPE tissues. They found that the RNA integrity was higher in ACSs and there was no difference in sequencing results, compared with FFPE tissues.

ImpactVol. 2024(2)
Yamanashi Prefectural Central Hospital (JP)
Good health and well-being
Openalex Percentile: Top 82%
Gene expression and cancer classification
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