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Updated: Mar 12, 2026

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Next-Generation Sequencing and Result Interpretation in Clinical Oncology: Challenges of Personalized Cancer Therapy

Yekaterina B Khotskaya1, Gordon B Mills1,2, Kenna R Mills Shaw1

  • 1Sheikh Khalifa Bin Zayed Al Nahyan Institute for Personalized Cancer Therapy.

Annual Review of Medicine
|November 5, 2016
PubMed

Insights

Next-generation sequencing (NGS) advances cancer care, but interpreting tumor genetic variants is challenging. Multidisciplinary teams are crucial for identifying actionable mutations to personalize cancer therapy.

Area of Science:

  • Genomic Medicine
  • Oncology
  • Bioinformatics

Background:

  • Next-generation sequencing (NGS) technologies, including whole-exome and whole-genome sequencing, are rapidly advancing.
  • Clinical successes with targeted therapeutics and biomarker-guided trials are driving further NGS innovation.
  • Effective integration of NGS into clinical oncology is hindered by data interpretation challenges.

Purpose of the Study:

  • To highlight the challenges in interpreting next-generation sequencing data in oncology.
  • To emphasize the need for multidisciplinary decision-support teams in clinical practice.
  • To underscore the importance of identifying actionable mutations for personalized cancer therapy.

Main Methods:

  • Analysis of somatic variants from patient tumor sequencing.
  • Distinguishing between 'driver' and 'passenger' mutations.
  • Review of clinical decision-making processes in personalized oncology.

Main Results:

  • Tumor sequencing typically identifies 30-65 somatic variants per patient.
  • The majority of these variants are non-actionable 'passenger' mutations.
  • Identifying 'driver' mutations requires expert interpretation.

Conclusions:

  • Interpreting NGS data is critical for effective clinical application in oncology.
  • Multidisciplinary teams are essential for determining mutation actionability.
  • Personalized cancer therapy relies on identifying and targeting driver mutations based on genomic data.
Keywords:
decision supportdriver mutationspassenger mutationsvariant actionability

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