Mucinous adenocarcinoma is a pharmacogenomically distinct subtype of colorectal cancer.
Ian S ReynoldsEmer O'ConnellMichael FichtnerDeborah A McNamaraElaine W KayJochen H M PrehnSimon J FurneyJohn P BurkePublished in: The pharmacogenomics journal (2019)
Mucinous colorectal cancer is a unique histological subtype that is known to respond poorly to cytotoxic chemotherapy and radiotherapy. There are a number of genes known to be associated with resistance to 5-fluorouracil (5-FU), oxaliplatin, and irinotecan. The aim of this study was to compare the somatic mutation frequency and copy number variation (CNV) in these genes between mucinous and non-mucinous colorectal cancer. A systematic search of PubMed was performed to identify papers investigating drug resistance in colorectal cancer. From this review, a list of 26 drug-resistance-associated genes was compiled. Using patient data from The Cancer Genome Atlas (TCGA), the somatic mutation rate and CNV was compared between patients with mucinous and non-mucinous colorectal cancer. Statistical analysis was carried out using GraphPad PRISM® version 5.00. Data were available on 531 patients (464 non-mucinous, 67 mucinous). A statistically significant difference in the somatic mutation rate between the two cohorts was identified in the TYMP (p = 0.0179), ATP7B (p = 0.0465), SRPK1 (p = 0.0135), ABCB1 (p = 0.0423), and ABCG2 (p = 0.0102) genes. A statistically significant difference in CNV was identified between the two cohorts in the GSTP1 (p = 0.0405), CCS (p = 0.0063), and TOP1 (p = 0.0048) genes. Differences in somatic mutation rate and CNV in genes associated with resistance to 5-FU, oxaliplatin, and irinotecan may partly account for the pattern of resistance observed in mucinous colorectal cancers. These genetic alterations may prove useful when deciding on a personalized approach to chemotherapy and may also represent potential therapeutic targets going forward.
Keyphrases
- low grade
- copy number
- genome wide
- mitochondrial dna
- high grade
- bioinformatics analysis
- locally advanced
- genome wide identification
- end stage renal disease
- chronic kidney disease
- early stage
- ejection fraction
- big data
- gene expression
- radiation therapy
- deep learning
- papillary thyroid
- case report
- artificial intelligence
- childhood cancer