Esophageal squamous cell carcinoma (ESCC) is one of the most aggressive cancers and is primarily treated with platinum-based neoadjuvant chemotherapy (NAC). Some ESCCs respond well to NAC. However, biomarkers to predict NAC sensitivity and their response mechanism in ESCC remain unclear. We perform whole-genome sequencing and RNA sequencing analysis of 141 ESCC biopsy specimens before NAC treatment to generate a machine-learning-based diagnostic model to predict NAC reactivity in ESCC and analyzed the association between immunogenomic features and NAC response. Neutrophil infiltration may play an important role in ESCC response to NAC. We also demonstrate that specific copy-number alterations and copy-number signatures in the ESCC genome are significantly associated with NAC response. The interactions between the tumor genome and immune features of ESCC are likely to be a good indicator of therapeutic capability and a therapeutic target for ESCC, and machine learning prediction for NAC response is useful.
Keywords: Chemotherapy response; chemotherapy; copy-number variants; esophageal squamous cell carcinoma; machine learning; neutrophils.
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