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Updated: May 4, 2026

Integration of Wet and Dry Bench Processes Optimizes Targeted Next-generation Sequencing of Low-quality and Low-quantity Tumor Biopsies
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From molecular understanding to clinical advances

Chung-Han Lee1, Robert J Motzer1

  • 1Genito-urinary Oncology Service, Department of Medicine, Memorial Sloan-Kettering Cancer Center, 1275 York Avenue, New York, NY 10021, USA.

Nature Reviews. Urology
|December 25, 2013
PubMed

Insights

Big data and computational biology identified key mutations and drug targets for clear cell renal cell carcinoma. Ongoing research into cancer

Area of Science:

  • Oncology
  • Genomics
  • Bioinformatics

Background:

  • Clear cell renal cell carcinoma (ccRCC) is the most common subtype of kidney cancer.
  • Advances in big data analytics and computational biology have revolutionized our understanding of cancer genomics.

Purpose of the Study:

  • To highlight the significant mutations and drug targets identified in ccRCC through big data analysis.
  • To emphasize the potential for future clinical benefits derived from molecular insights.

Main Methods:

  • Utilized big data analytics to process large-scale genomic datasets.
  • Applied computational biology approaches to identify actionable mutations and drug targets.

Main Results:

  • Identified several potential actionable mutations in clear cell renal cell carcinoma.
  • Discovered promising drug targets for ccRCC treatment.

Conclusions:

  • Big data and computational biology are powerful tools for advancing ccRCC research.
  • Continued exploration of molecular underpinnings will lead to improved clinical outcomes.

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