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Enhancing Next-Generation Sequencing-Guided Cancer Care Through Cognitive Computing
Nirali M Patel1,2, Vanessa V Michelini3, Jeff M Snell1,4
1Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.
The Oncologist
|November 22, 2017
Summary
Cognitive computing with Watson for Genomics (WfG) identified more actionable genomic events for cancer patients than human molecular tumor boards (MTBs). This AI-powered approach offers faster, comprehensive analysis for precision medicine and clinical trial matching.
Area of Science:
- Oncology
- Genomics
- Bioinformatics
Background:
- Next-generation sequencing (NGS) generates vast genomic data, posing challenges for cancer therapy guidance.
- Human molecular tumor boards (MTBs) manually curate this data, including new therapies and clinical trials, which is time-consuming.
- The rapid evolution of genomic data interpretation necessitates more efficient analysis methods.
Purpose of the Study:
- To evaluate the utility of cognitive computing, specifically Watson for Genomics (WfG), in analyzing genomic data compared to traditional human MTBs.
- To determine if WfG can identify actionable genomic events missed by human curation.
- To assess the potential of WfG in improving patient care through faster and more comprehensive genomic data analysis.
Main Methods:
- A retrospective analysis of 1,018 patient cases previously sequenced and analyzed by a human MTB at UNC.
- Application of Watson for Genomics (WfG), a cognitive computing technology, to the same patient cases for genomic analysis.
- Comparison of WfG-identified actionable genomic events against those identified by the human MTB.
Main Results:
- WfG identified additional actionable genomic events in 323 (32%) patients not found by traditional MTB curation.
- A significant portion of these newly identified events qualified patients for biomarker-selected clinical trials.
- WfG analysis identified relevant clinical trial opportunities within a month prior to its analysis in nearly a quarter of these patients.
- Automated WfG analysis was completed in under 3 minutes per case.
Conclusions:
- The interpretation and actionability of somatic NGS results evolve too rapidly for sole reliance on human curation.
- Cognitive computing-powered MTBs can enhance patient care by providing rapid, comprehensive genomic data analysis.
- This approach facilitates timely identification of clinical trial eligibility, advancing precision medicine.
- WfG offers a potentially faster, cost-effective solution for genomic data interpretation and clinical trial matching.
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