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Published on: January 22, 2013
A Scoping Review of Population Diversity in the Common Genomic Aberrations of Clear Cell Renal Cell Carcinoma
Sean S Kumar1,2,3, Ninad Khandekar4, Komal Dani5
1Macon and Joan Brock Virginia Health Sciences Eastern Virginia Medical School at Old Dominion University, Norfolk, Virginia, USA.
Introduction:
Previous literature has shown that clear cell renal cell carcinoma (ccRCC) is becoming a more prevalent diagnosis and that the incidence and mortality differ both regionally and racially. While the molecular profiles for ccRCC are studied regionally through biopsy and sequencing techniques, the genomic landscape and ccRCC diversity data are not well studied. We conducted a review of the known genomic data on 6 of the most clinically relevant DNA biomarkers in ccRCC: von Hippel-Lindau (vHL), Polybromo-1 (PBRM1), Breast Cancer Gene 1-Associated Protein 1 (BAP1), Histone-Lysine N-Methyltransferase Domain-Containing 2 (SETD2), Mammalian Target of Rapamycin (mTOR), and Lysine-Specific Demethylase 5C (KDM5C). The review compiled genomic diversity data, incidence, and risk factor differences by geographical and racial cohorts.
Methods:
The review methodology was created using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) principles from articles on PubMed and Embase through July 31, 2023, written and published in English, with diagnoses of primary or metastatic ccRCC via cytology or pathology, recorded the incidence of one or more of the 6 biomarkers, explored gene aberration via sequencing, were epidemiological in nature, and/or discussed basic science research, cohort studies, or retrospective studies.
Results:
Aberrations in vHL, PBRM1, and SETD2 driving ccRCC are studied frequently, but the data are heterogeneous, whereas there is a paucity in the data regarding KDM5C, PBRM1, and mTOR mutations.
Conclusion:
Studying the genetic aberrations that frequently occur in different regions gives insight into what current research lacks. When more genomic landscape research arises, precision therapy, risk calculators, and artificial intelligence may help better prognosticate and individualize treatment for those at risk for ccRCC. Provided the scarcity of existing data, and the rising prevalence of ccRCC, more studies must be conducted at the clinical level.
Introduction:
Previous literature has shown that clear cell renal cell carcinoma (ccRCC) is becoming a more prevalent diagnosis and that the incidence and mortality differ both regionally and racially. While the molecular profiles for ccRCC are studied regionally through biopsy and sequencing techniques, the genomic landscape and ccRCC diversity data are not well studied. We conducted a review of the known genomic data on 6 of the most clinically relevant DNA biomarkers in ccRCC: von Hippel-Lindau (vHL), Polybromo-1 (PBRM1), Breast Cancer Gene 1-Associated Protein 1 (BAP1), Histone-Lysine N-Methyltransferase Domain-Containing 2 (SETD2), Mammalian Target of Rapamycin (mTOR), and Lysine-Specific Demethylase 5C (KDM5C). The review compiled genomic diversity data, incidence, and risk factor differences by geographical and racial cohorts.
Methods:
The review methodology was created using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) principles from articles on PubMed and Embase through July 31, 2023, written and published in English, with diagnoses of primary or metastatic ccRCC via cytology or pathology, recorded the incidence of one or more of the 6 biomarkers, explored gene aberration via sequencing, were epidemiological in nature, and/or discussed basic science research, cohort studies, or retrospective studies.
Results:
Aberrations in vHL, PBRM1, and SETD2 driving ccRCC are studied frequently, but the data are heterogeneous, whereas there is a paucity in the data regarding KDM5C, PBRM1, and mTOR mutations.
Conclusion:
Studying the genetic aberrations that frequently occur in different regions gives insight into what current research lacks. When more genomic landscape research arises, precision therapy, risk calculators, and artificial intelligence may help better prognosticate and individualize treatment for those at risk for ccRCC. Provided the scarcity of existing data, and the rising prevalence of ccRCC, more studies must be conducted at the clinical level.
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