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Guided chemotherapy based on patient-derived mini-xenograft models improves survival of gallbladder carcinoma patients 被引量:12

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摘要 Background:Gallbladder carcinoma is highly aggressive and resistant to chemotherapy,with no consistent strategy to guide first line chemotherapy.However,patient-derived xenograft(PDX)model has been increasingly used as an effective model for in preclinical study of chemosensitivity.Methods:Mini-PDX model was established using freshly resected primary lesions from 12 patients with gallblad-der to examine the sensitivity with five of the most commonly used chemotherapeutic agents,namely gemcitabine,oxaliplatin,5-fluorouracil,nanoparticle albumin-bound(nab)-paclitaxel,and irinotecan.The results were used to guide the selection of chemotherapeutic agents for adjunctive treatment after the surgery.Kaplan-Meier method was used to compare overall survival(OS)and disease free survival(DFS)with 45 patients who received conventional chemo-therapy with gemcitabine and oxaliplatin.Results:Cell viability assays based on mini-PDX model revealed significant heterogeneities in drug responsiveness.Kaplan-Meier analysis showed that patients in the PDX-guided chemotherapy group had significantly longer median OS(18.6 months;95%CI 15.9-21.3 months)than patients in the conventional chemotherapy group(13.9 months;95%CI 11.7-16.2 months)(P=0.030;HR 3.18;95%CI 1.47-6.91).Patients in the PDX-guided chemotherapy group also had significantly longer median DFS(17.6 months;95%CI 14.5-20.6 months)than patients in the conventional chemotherapy group(12.0 months;95%CI 9.7-14.4 months)(P=0.014;HR 3.37;95%CI 1.67-6.79).Conclusion:The use of mini-PDX model to guide selection of chemotherapeutic regimens could improve the out-come in patients with gallbladder carcinoma.
出处 《Cancer Communications》 SCIE 2018年第1期511-519,共9页 癌症通讯(英文)
基金 This work was supported by the Foundation of Shanghai Shen Kang Hospital Development Center(Nos.16CR2002A and 16CR3028A) National Science Foundation of China(Nos.81472240 and 81773184) Shanghai Outstanding Academic Leaders Plan(2016,JW).
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