JCO precision oncology · 2026
Integrating DNA Mutations and RNA Expression of Cancer Driver Genes in Asian Rare Cancers: A Pan-Cancer Analysis.
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de Almeida Kathleen Yasmin, Imasa Marcelo Severino B, Voon Pei Jye, Soo Hoo Hwoei Fen, Chen Tom Wei-Wu, Yusak Suhana, Ramachandran Rangasamy, Abu Bakar Najihah, Chen Ming-Huang, Yatabe Yasushi, Nakamura Kenichi, Okuma Hitomi Sumiyoshi, Yonemori Kan
Аннотация
PURPOSE: Rare cancers are molecularly heterogeneous and lack robust clinical evidence to guide treatment. Whether cancer driver gene expression is governed by tumor lineage or specific somatic alterations-and whether this distinction carries clinical relevance-remains poorly characterized, particularly in Asian populations. This study aimed to determine the relative contributions of tumor lineage and DNA mutations to RNA expression of cancer driver genes and to evaluate the clinical implications of genomic alteration status in Asian patients with rare cancers.
METHODS: DNA and RNA were extracted from formalin-fixed paraffin-embedded tumor samples obtained through the MASTER KEY Asia research network. Next-generation sequencing was performed to evaluate genomic alterations and gene expression profiles. RNA expression of cancer driver genes was quantified as TPM Z scores. Elastic net regression with 10-fold cross-validation assessed the relative contributions of tumor lineage and DNA mutations to RNA expression, and findings were independently replicated in another cohort.
RESULTS: Integrated DNA and RNA data were available for 128 patients. Tumor lineage was the predominant determinant of RNA expression in 17 of 27 cancer driver genes (63%), whereas ERBB2 and MDM2 demonstrated genomic-predominant regulation, replicated across both cohorts. Alterations in ERBB2 or ARID1A were associated with a lower frequency of disease progression compared with patients without either alteration (P = .01), independently of ERBB2 RNA expression. Functional subclassification of TP53 revealed that gain-of-function mutations were associated with higher rates of disease progression (P = .007) and shorter progression-free survival than loss-of-function variants (hazard ratio, 2.355 [95% CI, 1.086 to 5.105]; P = .029). Tumor mutational burden was not associated with treatment response.
CONCLUSION: These findings demonstrate that genomic alteration status, including TP53 functional subclassification, provides clinically meaningful information beyond transcriptional profiling alone, supporting integrative genomics and transcriptomics analysis for precision oncology in rare cancers.
METHODS: DNA and RNA were extracted from formalin-fixed paraffin-embedded tumor samples obtained through the MASTER KEY Asia research network. Next-generation sequencing was performed to evaluate genomic alterations and gene expression profiles. RNA expression of cancer driver genes was quantified as TPM Z scores. Elastic net regression with 10-fold cross-validation assessed the relative contributions of tumor lineage and DNA mutations to RNA expression, and findings were independently replicated in another cohort.
RESULTS: Integrated DNA and RNA data were available for 128 patients. Tumor lineage was the predominant determinant of RNA expression in 17 of 27 cancer driver genes (63%), whereas ERBB2 and MDM2 demonstrated genomic-predominant regulation, replicated across both cohorts. Alterations in ERBB2 or ARID1A were associated with a lower frequency of disease progression compared with patients without either alteration (P = .01), independently of ERBB2 RNA expression. Functional subclassification of TP53 revealed that gain-of-function mutations were associated with higher rates of disease progression (P = .007) and shorter progression-free survival than loss-of-function variants (hazard ratio, 2.355 [95% CI, 1.086 to 5.105]; P = .029). Tumor mutational burden was not associated with treatment response.
CONCLUSION: These findings demonstrate that genomic alteration status, including TP53 functional subclassification, provides clinically meaningful information beyond transcriptional profiling alone, supporting integrative genomics and transcriptomics analysis for precision oncology in rare cancers.