Literature DB >> 30409839

Tumour heterogeneity in triplet-paired metastatic tumour tissues in metastatic renal cell carcinoma: concordance analysis of target gene sequencing data.

Sung Han Kim1, Weon Seo Park2, Jinsoo Chung3.   

Abstract

AIMS: The aim of the present study was to determine the concordant correlation in the expression of 88 target genes from triple-paired metastatic tissues in individual patients with metastatic renal carcinoma (mRCC) using a target gene sequencing (TGS) approach.
METHODS: Between 2002 and 2017, a total of 350 triple-paired metastatic tissue samples from 262 patients with mRCC obtained from either nephrectomy or metastasectomy were used for TGS of 88 candidate genes. After quality check, 243 tissue samples from 81 patients were finally applied to TGS. The concordance of triple-paired tissues was analysed with the 88 TGS panels using bioinformatics tools.
RESULTS: Among 81 patients, alterations were observed in 42 (51.9%) for any of the 88 mRCC panel genes; however, no pathogenic gene was detected in 38 (39.5%) . Concordance >95% for altered gene expression among the three tissues was reported in 12 (28.6%) patients, while concordance >95% within two tissues was reported in 30 (71.4%); concordance <50% was reported in the remaining eight patients. Considering several types of genetic alterations, including deletions, insertions, missense and nonsense mutations, and splice variants, genes most frequently detected with genetic alterations in the patients with mRCC were PTEN loss, followed by FLCN, BCR, SMARCA2, AKAP9, MLH1, MYH11, APC and TP53.
CONCLUSIONS: The study provides reference information on the genetic alterations at various organ sites and the multi-heterogeneity of mRCC tissues. The concordance of pathogenic gene alterations within tissues was not high, and approximately half of the patients showed no pathogenic gene alterations at all. © Author(s) (or their employer(s)) 2019. No commercial re-use. See rights and permissions. Published by BMJ.

Entities:  

Keywords:  alteration; concordance; gene sequencing; metastasis; renal cell carcinoma; tissue

Mesh:

Year:  2018        PMID: 30409839     DOI: 10.1136/jclinpath-2018-205456

Source DB:  PubMed          Journal:  J Clin Pathol        ISSN: 0021-9746            Impact factor:   3.411


  1 in total

1.  Embedding covariate adjustments in tree-based automated machine learning for biomedical big data analyses.

Authors:  Elisabetta Manduchi; Weixuan Fu; Joseph D Romano; Stefano Ruberto; Jason H Moore
Journal:  BMC Bioinformatics       Date:  2020-10-01       Impact factor: 3.169

  1 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.