Specimen quality shapes the actionable genomic landscape in comprehensive cancer genomic profiling
4 affiliations
- 1Department of Clinical and Molecular Genetics, Hiroshima University Hospital, Hiroshima University, Hiroshima, Japan
- 2Department of Pharmaceutical Services, Hiroshima University Hospital, Hiroshima, Japan
- 3Department of Anatomical Pathology, Hiroshima University Hospital, Hiroshima, Japan
- 4Department of Gastroenterology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
Abstract
Comprehensive genomic profiling (CGP) is widely used to identify actionable alterations and guide precision oncology, yet only a minority of tested patients receive genome-matched therapies, underscoring a gap between genomic findings and clinical benefit. We hypothesized that this gap may partly reflect variation in the reliability and interpretability of genomic information generated from specimens of different quality and by different assay modalities. To examine this possibility, we performed a retrospective multicenter analysis of 2002 CGP tests conducted between 2019 and 2025 across 13 institutions in Japan. Detection of short variants, copy number alterations (CNAs), structural variants, and genomic signatures, including microsatellite instability, tumor mutational burden, and homologous recombination deficiency signature, was compared among FoundationOne CDx specimens classified as pass (F1-pass) or qualified (F1-qual) and liquid-based CGP (liq-CGP). Short variant detection remained largely preserved in F1-qual specimens, whereas CNA and genomic signature detection were substantially reduced. In pancreatic adenocarcinoma, KRAS variants were detected in 93% of F1-pass, 88% of F1-qual, and 57% of liq-CGP cases. These differences affected the proportion of patients offered genome-matched therapies. Machine learning models predicted QC status with area-under-the-curve values of 0.72–0.78. Our findings support QC-aware CGP selection in routine precision oncology.
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BibTeX
@article{nakahara2026specimen,
title = {{Specimen quality shapes the actionable genomic landscape in comprehensive cancer genomic profiling}},
author = {Hikaru Nakahara and Hiroaki Niitsu and Masanori Motonaga and Hiroaki Matsuo and Koji Arihiro and C.Nelson Hayes and Takao Hinoi},
journal = {NPJ Genomic Medicine},
year = {2026},
month = jul,
doi = {10.1038/s41525-026-00595-8},
url = {https://doi.org/10.1038/s41525-026-00595-8},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - JOUR
TI - Specimen quality shapes the actionable genomic landscape in comprehensive cancer genomic profiling
AU - Hikaru Nakahara
AU - Hiroaki Niitsu
AU - Masanori Motonaga
AU - Hiroaki Matsuo
AU - Koji Arihiro
AU - C.Nelson Hayes
AU - Takao Hinoi
PY - 2026
DA - 2026/07/11
JO - NPJ Genomic Medicine
DO - 10.1038/s41525-026-00595-8
UR - https://doi.org/10.1038/s41525-026-00595-8
AB - Comprehensive genomic profiling (CGP) is widely used to identify actionable alterations and guide precision oncology, yet only a minority of tested patients receive genome-matched therapies, underscoring a gap between genomic findings and clinical benefit. We hypothesized that this gap may partly reflect variation in the reliability and interpretability of genomic information generated from specimens of different quality and by different assay modalities. To examine this possibility, we performed a retrospective multicenter analysis of 2002 CGP tests conducted between 2019 and 2025 across 13 institutions in Japan. Detection of short variants, copy number alterations (CNAs), structural variants, and genomic signatures, including microsatellite instability, tumor mutational burden, and homologous recombination deficiency signature, was compared among FoundationOne CDx specimens classified as pass (F1-pass) or qualified (F1-qual) and liquid-based CGP (liq-CGP). Short variant detection remained largely preserved in F1-qual specimens, whereas CNA and genomic signature detection were substantially reduced. In pancreatic adenocarcinoma, KRAS variants were detected in 93% of F1-pass, 88% of F1-qual, and 57% of liq-CGP cases. These differences affected the proportion of patients offered genome-matched therapies. Machine learning models predicted QC status with area-under-the-curve values of 0.72–0.78. Our findings support QC-aware CGP selection in routine precision oncology.
SN - 2056-7944
ER -