Predicting clinical response to anticancer drugs using an ex vivo platform that captures tumour heterogeneity.
Predicting clinical response to anticancer drugs using an ex vivo platform that captures tumour heterogeneity.
复制标题
DOI：
10.1038/ncomms7169
复制
发表时间：
2015-02-27
影响因子：
16.6
通讯作者：
Majumder, Pradip K.
中科院分区：
文献类型：
--
作者：
Majumder, Biswanath;Baraneedharan, Ulaganathan;Thiyagarajan, Saravanan;Radhakrishnan, Padhma;Narasimhan, Harikrishna;Dhandapani, Muthu;Brijwani, Nilesh;Pinto, Dency D.;Prasath, Arun;Shanthappa, Basavaraja U.;Thayakumar, Allen;Surendran, Rajagopalan;Babu, Govind K.;Shenoy, Ashok M.;Kuriakose, Moni A.;Bergthold, Guillaume;Horowitz, Peleg;Loda, Massimo;Beroukhim, Rameen;Agarwal, Shivani;Sengupta, Shiladitya;Sundaram, Mallikarjun;Majumder, Pradip K.
预测抗癌药物的临床反应仍然是癌症治疗中的一大挑战。新出现的报告表明，肿瘤微环境和异质性可能会限制目前生物标记物指导的化疗策略的预测能力。在这里，我们报告了个性化的肿瘤生态系统的工程，该工程在上下文中保存了肿瘤的异质性，并使用在定义的肿瘤分级匹配的基质支架和自体患者血清中维持的肿瘤外植体来复制肿瘤微环境。从109名患者中设计的肿瘤生态系统对抗癌药物的功能反应以及相应的临床结果被用于训练机器学习算法；然后将学习到的模型应用于由55名患者组成的独立验证组的临床反应预测，其中我们实现了100%的预测灵敏度，同时将特异度保持在所需的高范围内。肿瘤生态系统和算法一起被称为CANScript技术，可以成为实现个性化医疗的强大平台。抗癌治疗的疗效因患者而异，因此需要个性化的治疗方法。在这里，作者表明，通过在患者匹配的微环境中进行肿瘤外植体培养，结合机器学习算法，可以预测对化疗的反应性。
Predicting clinical response to anticancer drugs remains a major challenge in cancer treatment. Emerging reports indicate that the tumour microenvironment and heterogeneity can limit the predictive power of current biomarker-guided strategies for chemotherapy. Here we report the engineering of personalized tumour ecosystems that contextually conserve the tumour heterogeneity, and phenocopy the tumour microenvironment using tumour explants maintained in defined tumour grade-matched matrix support and autologous patient serum. The functional response of tumour ecosystems, engineered from 109 patients, to anticancer drugs, together with the corresponding clinical outcomes, is used to train a machine learning algorithm; the learned model is then applied to predict the clinical response in an independent validation group of 55 patients, where we achieve 100% sensitivity in predictions while keeping specificity in a desired high range. The tumour ecosystem and algorithm, together termed the CANScript technology, can emerge as a powerful platform for enabling personalized medicine. Efficacy of anticancer treatments vary across patients, imposing a need for personalized approaches. Here the authors show that responsiveness to chemotherapy can be predicted using tumour explant cultures in a patient-matched microenvironment, coupled with a machine-learning algorithm.
登录
查看更多内容
影响因子：
11.5
作者：
Akervall, J;Xiang, G;Teh, BT
通讯作者：
Teh, BT
影响因子：
45.3
作者：
Liedtke, Cornelia;Mazouni, Chafika;Pusztai, Lajos
通讯作者：
Pusztai, Lajos
影响因子：
64.8
作者：
Garnett, Mathew J.;Edelman, Elena J.;Heidorn, Sonja J.;Greenman, Chris D.;Dastur, Anahita;Lau, King Wai;Greninger, Patricia;Thompson, I. Richard;Luo, Xi;Soares, Jorge;Liu, Qingsong;Iorio, Francesco;Surdez, Didier;Chen, Li;Milano, Randy J.;Bignell, Graham R.;Tam, Ah T.;Davies, Helen;Stevenson, Jesse A.;Barthorpe, Syd;Lutz, Stephen R.;Kogera, Fiona;Lawrence, Karl;McLaren-Douglas, Anne;Mitropoulos, Xeni;Mironenko, Tatiana;Thi, Helen;Richardson, Laura;Zhou, Wenjun;Jewitt, Frances;Zhang, Tinghu;O'Brien, Patrick;Boisvert, Jessica L.;Price, Stacey;Hur, Wooyoung;Yang, Wanjuan;Deng, Xianming;Butler, Adam;Choi, Hwan Geun;Chang, JaeWon;Baselga, Jose;Stamenkovic, Ivan;Engelman, Jeffrey A.;Sharma, Sreenath V.;Delattre, Olivier;Saez-Rodriguez, Julio;Gray, Nathanael S.;Settleman, Jeffrey;Futreal, P. Andrew;Haber, Daniel A.;Stratton, Michael R.;Ramaswamy, Sridhar;McDermott, Ultan;Benes, Cyril H.
通讯作者：
Benes, Cyril H.
影响因子：
3.4
作者：
Matzke, Melissa M.;Brown, Joseph N.;Gritsenko, Marina A.;Metz, Thomas O.;Pounds, Joel G.;Rodland, Karin D.;Shukla, Anil K.;Smith, Richard D.;Waters, Katrina M.;McDermott, Jason E.;Webb-Robertson, Bobbie-Jo
通讯作者：
Webb-Robertson, Bobbie-Jo
影响因子：
82.9
作者：
Majumder, PK;Febbo, PG;Sellers, WR
通讯作者：
Sellers, WR