Title Genomic analysis of small renal masses reveals mutations linked with renal cell carcinoma and fast-growing tumors /
Authors Vaicekauskaitė, Ieva ; Žalimas, Algirdas ; Sabaliauskaitė, Rasa ; Žukauskaitė, Kristina ; Trakymas, Mantas ; Ušinskienė, Jurgita ; Ulys, Albertas ; Jarmalaitė, Sonata
DOI 10.1007/s00432-025-06162-5
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Is Part of Journal of cancer research and clinical oncology.. Springer. 2025, vol. 151, iss. 3, art. no. 118, p. [1-11].. ISSN 0171-5216. eISSN 1432-1335
Keywords [eng] kidney tumor ; next generation sequencing ; renal cell carcinoma ; small renal mass ; somatic mutations
Abstract [eng] Purpose: Small renal masses (SRMs) SRMs are a heterogeneous group of small kidney lesions. Currently, the genomic landscape of SRMs is understudied, and clinically relevant tools for malignancy detection and fast tumor growth prediction are lacking. The aim of the study was to evaluate whether mutations in SRMs are associated with increased risk of renal cell carcinoma (RCC) or aggressive tumors. Methods: In this pilot study, 52 patients with SRMs were divided based on tumor histology into RCC and benign tumors, while RCC cases were divided into fast-growing and slow-growing tumor groups. Tissue biopsy samples evaluated for mutations in 51 cancer hotspot genes using next generation sequencing and qPCR. Non-benign mutations were tested for associations with RCC and clinical features. Receiver operating curve analysis used for evaluation of mutation biomarker models prediction of RCC and fast-growing tumors. Results: 75% of SRMs harbored non-synonymous alterations in 16/51 genes. 38.5% of detected mutations were listed in ClinVar and correlated with smaller SRM volume (p = 0.023). KRAS, VHL, HNF1A, TP53, and ATM mutations were predominantly detected in RCC rather than benign SRMs (p = 0.046). SRMs with pathogenic mutations were at three times higher risk of being RCC and four times higher risk of fast growth. Conclusion: Genomic biomarkers may improve risk stratification and management of patients with SRMs, however a more extensive genomic analysis of SRMs is still needed.
Published Springer
Type Journal article
Language English
Publication date 2025
CC license CC license description