A modified MethyLight assay predicts the clinical outcomes of anti-epidermal growth factor receptor treatment in metastatic colorectal cancer.
Kota OuchiShin TakahashiAkira OkitaYasuhiro SakamotoOsamu MutoKenji AmagaiTakaho OkadaHisatsugu OhoriEiji ShinozakiChikashi IshiokaPublished in: Cancer science (2022)
DNA methylation status correlates with clinical outcomes of anti-epidermal growth factor receptor (EGFR) treatment. There is a strong need to develop a simple assay for measuring DNA methylation status for the clinical application of drug selection based on it. In this study, we collected data from 186 patients with metastatic colorectal cancer (mCRC) who had previously received anti-EGFR treatment. We modified MethyLite to develop a novel assay to classify patients as having highly methylated colorectal cancer (HMCC) or low-methylated colorectal cancer (LMCC) based on the methylation status of 16 CpG sites of tumor-derived genomic DNA in the development cohort (n = 30). Clinical outcomes were then compared between the HMCC and LMCC groups in the validation cohort (n = 156). The results showed that HMCC had a significantly worse response rate (4.2% vs 33.3%; P = .004), progression-free survival (median: 2.5 vs 6.6 mo, P < .001, hazard ratio [HR] = 0.22), and overall survival (median: 5.6 vs 15.5 mo, P < .001, HR = 0.23) than did LMCC in patients with RAS wild-type mCRC who were refractory or intolerable to oxaliplatin- and irinotecan-based chemotherapy (n = 101). The DNA methylation status was an independent predictive factor and a more accurate biomarker than was the primary site of anti-EGFR treatment. In conclusion, our novel DNA methylation measurement assay based on MethyLight was simple and useful, suggesting its implementation as a complementary diagnostic tool in a clinical setting.
Keyphrases
- epidermal growth factor receptor
- dna methylation
- tyrosine kinase
- metastatic colorectal cancer
- genome wide
- small cell lung cancer
- advanced non small cell lung cancer
- gene expression
- high throughput
- free survival
- primary care
- end stage renal disease
- machine learning
- squamous cell carcinoma
- newly diagnosed
- chronic kidney disease
- copy number
- high resolution
- prognostic factors
- quality improvement
- big data
- circulating tumor cells