Rui Mao1, Fan Yang2, Zheng Wang3, Chenxin Xu1, Qian Liu3, Yanjun Liu1,4, Tongtong Zhang4,5. 1. The Center of Gastrointestinal and Minimally Invasive Surgery, Affiliated Hospital of Southwest Jiaotong University, Chengdu, China. 2. Emergency Department, Peking University Third Hospital, School of Medicine, Peking University, Beijing, China. 3. Department of Colorectal Surgery, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China. 4. The Center of Gastrointestinal and Minimally Invasive Surgery, Department of General Surgery, The Third People's Hospital of Chengdu, Chengdu, China. 5. Medical Research Center, The Third People's Hospital of Chengdu, The Affiliated Hospital of Southwest Jiaotong University, The Second Chengdu Hospital Affiliated to Chongqing Medical University, Chengdu, China.
Abstract
BACKGROUND: Some colorectal adenocarcinoma (CRC) patients are susceptible to recurrence, and they rapidly progress to advanced cancer stages and have a poor prognosis. There is an urgent need for efficient screening criteria to identify patients who tend to relapse in order to treat them earlier and more systematically. METHODS: We identified two groups of patients with significantly different outcomes by unsupervised cluster analysis of GSE39582 based on 101 significantly differentially expressed immune genes. To develop an accurate and specific signature based on immune-related genes to predict the recurrence of CRC, a multivariate Cox risk regression model was constructed with a training cohort composed of 519 CRC samples. The model was then validated using 129, 292, and 446 samples in the real-time quantitative reverse transcription PCR (qRT-PCR), test, and validation cohorts, respectively. RESULTS: This classification system can also be used to predict the prognosis in clinical subgroups and patients with different mutation states. Four independent datasets, including qRT-PCR and The Cancer Genome Atlas (TCGA), demonstrated that they can also be used to accurately predict the overall survival of CRC patients. Further analysis suggested that high-risk patients were characterized by worse effects of chemotherapy and immunotherapy, as well as lower immune scores. Ultimately, the signature was identified as an independent prognostic factor. CONCLUSION: The signature can accurately predict recurrence and overall survival in patients with CRC and may serve as a powerful prognostic tool to further optimize cancer immunotherapy.
BACKGROUND: Some colorectal adenocarcinoma (CRC) patients are susceptible to recurrence, and they rapidly progress to advanced cancer stages and have a poor prognosis. There is an urgent need for efficient screening criteria to identify patients who tend to relapse in order to treat them earlier and more systematically. METHODS: We identified two groups of patients with significantly different outcomes by unsupervised cluster analysis of GSE39582 based on 101 significantly differentially expressed immune genes. To develop an accurate and specific signature based on immune-related genes to predict the recurrence of CRC, a multivariate Cox risk regression model was constructed with a training cohort composed of 519 CRC samples. The model was then validated using 129, 292, and 446 samples in the real-time quantitative reverse transcription PCR (qRT-PCR), test, and validation cohorts, respectively. RESULTS: This classification system can also be used to predict the prognosis in clinical subgroups and patients with different mutation states. Four independent datasets, including qRT-PCR and The Cancer Genome Atlas (TCGA), demonstrated that they can also be used to accurately predict the overall survival of CRC patients. Further analysis suggested that high-risk patients were characterized by worse effects of chemotherapy and immunotherapy, as well as lower immune scores. Ultimately, the signature was identified as an independent prognostic factor. CONCLUSION: The signature can accurately predict recurrence and overall survival in patients with CRC and may serve as a powerful prognostic tool to further optimize cancer immunotherapy.
Authors: Murali Janakiram; Jordan M Chinai; Susan Fineberg; Andras Fiser; Cristina Montagna; Ramadevi Medavarapu; Ekaterina Castano; Hyungjun Jeon; Kim C Ohaegbulam; Ruihua Zhao; Aimin Zhao; Steven C Almo; Joseph A Sparano; Xingxing Zang Journal: Clin Cancer Res Date: 2014-12-30 Impact factor: 12.531
Authors: Julie R Brahmer; Scott S Tykodi; Laura Q M Chow; Wen-Jen Hwu; Suzanne L Topalian; Patrick Hwu; Charles G Drake; Luis H Camacho; John Kauh; Kunle Odunsi; Henry C Pitot; Omid Hamid; Shailender Bhatia; Renato Martins; Keith Eaton; Shuming Chen; Theresa M Salay; Suresh Alaparthy; Joseph F Grosso; Alan J Korman; Susan M Parker; Shruti Agrawal; Stacie M Goldberg; Drew M Pardoll; Ashok Gupta; Jon M Wigginton Journal: N Engl J Med Date: 2012-06-02 Impact factor: 91.245
Authors: Ludovic Barault; Céline Charon-Barra; Valérie Jooste; Mathilde Funes de la Vega; Laurent Martin; Patrick Roignot; Patrick Rat; Anne-Marie Bouvier; Pierre Laurent-Puig; Jean Faivre; Caroline Chapusot; Francoise Piard Journal: Cancer Res Date: 2008-10-15 Impact factor: 12.701
Authors: Tsvetelina Pentcheva-Hoang; Tyler R Simpson; Welby Montalvo-Ortiz; James P Allison Journal: Cancer Immunol Res Date: 2014-07-18 Impact factor: 11.151
Authors: Arianna Fumagalli; Koen C Oost; Lennart Kester; Jessica Morgner; Laura Bornes; Lotte Bruens; Lisa Spaargaren; Maria Azkanaz; Tim Schelfhorst; Evelyne Beerling; Maria C Heinz; Daniel Postrach; Danielle Seinstra; Anieta M Sieuwerts; John W M Martens; Stefan van der Elst; Martijn van Baalen; Debajit Bhowmick; Nienke Vrisekoop; Saskia I J Ellenbroek; Saskia J E Suijkerbuijk; Hugo J Snippert; Jacco van Rheenen Journal: Cell Stem Cell Date: 2020-03-12 Impact factor: 24.633