A formalin-fixed paraffin-embedded (FFPE)-based prognostic signature to predict metastasis in clinically low risk stage I/II microsatellite stable colorectal cancer.
Approximately 20% early-stage (I/II) colorectal cancer (CRC) patients develop metastases despite curative surgery. We aim to develop a formalin-fixed and paraffin-embedded (FFPE)-based predictor of metastases in early-stage, clinically-defined low risk, microsatellite-stable (MSS) CRC patients. We considered genome-wide mRNA and miRNA expression and mutation status of 20 genes assayed in 150 fresh-frozen tumours with known metastasis status. We selected 193 genes for further analysis using NanoString nCounter arrays on corresponding FFPE tumours. Neither mutation status nor miRNA expression improved the estimated prediction. The final predictor, ColoMet19, based on the top 19 genes' mRNA levels trained by Random Forest machine-learning strategy, had an estimated positive-predictive-value (PPV) of 0.66. We tested ColoMet19 on an independent test-set of 131 tumours and obtained a population-adjusted PPV of 0.67 indicating that early-stage CRC patients who tested positive have a 67% risk of developing metastases, substantially higher than the metastasis risk of 40% for node-positive (Stage III) patients who are generally treated with chemotherapy. Predicted-positive patients also had poorer metastasis-free survival (hazard ratios [HR] = 1.92, design-set; HR = 2.05, test-set). Thus, early-stage CRC patients who test positive may be considered for adjuvant therapy after surgery.
Duke Scholars
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- Transcriptome
- Tissue Fixation
- Time Factors
- Risk Factors
- Risk Assessment
- Reproducibility of Results
- ROC Curve
- RNA, Messenger
- Proportional Hazards Models
- Predictive Value of Tests
Citation
Published In
DOI
EISSN
Publication Date
Volume
Start / End Page
Location
Related Subject Headings
- Transcriptome
- Tissue Fixation
- Time Factors
- Risk Factors
- Risk Assessment
- Reproducibility of Results
- ROC Curve
- RNA, Messenger
- Proportional Hazards Models
- Predictive Value of Tests