Comprehensive molecular findings in primary malignant melanoma of the esophagus: A multicenter study.
Ling DengHai-Yun WangChun-Fang HuXiao-Yun LiuKuntai JiangJuan-Juan YongXiao-Yan WuKai-Hua GuoFang WangPublished in: Pigment cell & melanoma research (2023)
Primary malignant melanoma of the esophagus (PMME) is an extremely rare but highly aggressive malignancy with a poor prognosis. Due to the scarcity of driver gene alterations, there is a need for more clinical data to comprehensively depict its molecular alterations. This study reviewed 26 PMME cases from three medical centers. Hybrid capture-based targeted sequencing of 295 and 1021 genes was performed in 14 and 12 cases, respectively. We found that PMME patients had a relatively low tumor mutation burden (median, 2.88 mutations per Mb) and were simultaneously accompanied by mutations in genes such as KIT (6/26, 23%), TP53 (6/26, 23%), SF3B1 (4/26, 15%), and NRAS (3/26, 12%). KIT, NRAS, and BRAF were mutually exclusive, and SF3B1 co-occurred with KIT mutation and amplification. The most common pathways affected were the mitogen-activated protein kinases and DNA damage response (DDR) pathways. Stage IV was a risk factor for both progression-free survival (hazard ratio [HR] = 5.14, 95% confidence interval [CI] = 1.32-19.91) and overall survival (OS), HR = 4.33, 95% CI = 1.22-15.30). Treatment with immune-checkpoint inhibitors (ICIs) was an independent factor for favorable OS (HR = 0.10, 95% CI = 0.01-0.91). Overall, PMME is a complex malignancy with diverse gene alterations, especially with harboring DDR alterations for potentially response from ICIs.
Keyphrases
- poor prognosis
- free survival
- genome wide
- genome wide identification
- dna damage response
- end stage renal disease
- long non coding rna
- ejection fraction
- copy number
- chronic kidney disease
- genome wide analysis
- healthcare
- newly diagnosed
- wild type
- dna methylation
- single cell
- electronic health record
- single molecule
- dna repair
- gene expression
- oxidative stress
- cancer therapy
- drug delivery
- deep learning
- risk factors
- dna damage