Boran Cheng1, Gangling Tong1, Xuan Wu1, Wenwu Cai2, Zhu Li1, Zhongyi Tong2, Lirui He1, Shaokang Yu1, Shubin Wang1. 1. Department of Oncology, Peking University Shenzhen Hospital, Shenzhen, Guangdong Province 518036, People's Republic of China. 2. Department of General Surgery, The Second Xiangya Hospital of Central South University, Changsha, Hunan Province 410011, People's Republic of China.
Abstract
BACKGROUND: Advanced gastric cancer (aGC) has a high global incidence and a high mortality rate and because of its high malignancy and heterogeneity, the existing methods for prognosis are limited, and a new treatment model is necessary. Circulating tumor cells (CTCs) could be considered as a "liquid biopsy" for tumor diagnosis and for monitoring treatment responses and predicting clinical outcome. Clinical studies support the efficacy of programmed cell death 1 (PD-1) immunotherapy in a subset of aGC. METHODS: Cell capture efficiency, as described by the CanPatrol CTC enrichment technique, was validated using artificial blood samples as well as blood samples from 32 aGC patients. Clinicopathologic data of patients were collected from the hospital information system. We used CanPatrol for CTC isolation, classification, and clinical analysis. RESULTS: A cell capture efficiency of >80% was achieved. Significant correlation was observed between CTC enumeration and clinicopathology parameters, including the Lauren classification (r=0.470, P=0.008), perineural invasion (r=0.393, P=0.029), TNM stage (r=0.740, P<0.001), and Ki-67 level (r=0.510, P=0.005). When compared to the traditional methods, monitoring CTC subtypes exhibits higher sensitivity of evaluating the disease status. Enumeration of epithelial CTC subset and its relative abundance in the total CTC pool are highly correlated with clinical efficacy. CTC programmed cell death ligand-1 (PD-L1) could be successfully detected for immunotherapy in addition to PD-L1 immunohistochemistry and microsatellite instability. CONCLUSION: We provide a new method that allows for the simple and effective detection of CTCs in aGC. It has potential clinical applications in monitoring prognosis and guiding future individualized immunotherapy.
BACKGROUND: Advanced gastric cancer (aGC) has a high global incidence and a high mortality rate and because of its high malignancy and heterogeneity, the existing methods for prognosis are limited, and a new treatment model is necessary. Circulating tumor cells (CTCs) could be considered as a "liquid biopsy" for tumor diagnosis and for monitoring treatment responses and predicting clinical outcome. Clinical studies support the efficacy of programmed cell death 1 (PD-1) immunotherapy in a subset of aGC. METHODS: Cell capture efficiency, as described by the CanPatrol CTC enrichment technique, was validated using artificial blood samples as well as blood samples from 32 aGC patients. Clinicopathologic data of patients were collected from the hospital information system. We used CanPatrol for CTC isolation, classification, and clinical analysis. RESULTS: A cell capture efficiency of >80% was achieved. Significant correlation was observed between CTC enumeration and clinicopathology parameters, including the Lauren classification (r=0.470, P=0.008), perineural invasion (r=0.393, P=0.029), TNM stage (r=0.740, P<0.001), and Ki-67 level (r=0.510, P=0.005). When compared to the traditional methods, monitoring CTC subtypes exhibits higher sensitivity of evaluating the disease status. Enumeration of epithelial CTC subset and its relative abundance in the total CTC pool are highly correlated with clinical efficacy. CTC programmed cell death ligand-1 (PD-L1) could be successfully detected for immunotherapy in addition to PD-L1 immunohistochemistry and microsatellite instability. CONCLUSION: We provide a new method that allows for the simple and effective detection of CTCs in aGC. It has potential clinical applications in monitoring prognosis and guiding future individualized immunotherapy.
Authors: Nicole Vincent Jordan; Aditya Bardia; Ben S Wittner; Cyril Benes; Matteo Ligorio; Yu Zheng; Min Yu; Tilak K Sundaresan; Joseph A Licausi; Rushil Desai; Ryan M O'Keefe; Richard Y Ebright; Myriam Boukhali; Srinjoy Sil; Maristela L Onozato; Anthony J Iafrate; Ravi Kapur; Dennis Sgroi; David T Ting; Mehmet Toner; Sridhar Ramaswamy; Wilhelm Haas; Shyamala Maheswaran; Daniel A Haber Journal: Nature Date: 2016-08-24 Impact factor: 49.962
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