Xingxing Gao1,2,3, Hechen Huang1,2,3, Yubo Wang1,2,3, Caixu Pan1,2,3, Shengyong Yin1,2,3, Lin Zhou1,2,3, Shusen Zheng1,2,3. 1. Division of Hepatobiliary and Pancreatic Surgery, Department of Surgery, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China. 2. NHC Key Laboratory of Combined Multi-organ Transplantation, Hangzhou, China. 3. Key Laboratory of Organ Transplantation, Research Center for Diagnosis and Treatment of Hepatobiliary Diseases, Hangzhou, China.
Abstract
PURPOSE: The tumor microenvironment (TME) plays a critical role in the pathogenesis of hepatocellular carcinoma (HCC). However, underlying compositions and functions that drive the establishment and maintenance of the TME classifications are less-well understood. METHODS: A total of 766 HCC patients from three public cohorts were clustered into four immune-related subclasses based on 13 TME signatures (11 immune-related cells and 2 immune-related pathways) calculated by MCP-counter. After analyzing the landscapes of functional annotation, methylation, somatic mutation, and clinical characteristics, we built a TME-based Support Vector Machine of 365 patients (discovery phase) and 401 patients (validation phase). We applied this SVM model on another two independent cohorts of patients who received sorafenib/pembrolizumab treatment. RESULTS: About 33% of patients displayed an immune desert pattern. The other subclasses were different in abundance of tumor infiltrating cells. The Immunogenic subclass (17%) associated with the best prognosis presented a massive T cell infiltration and an activation of immune checkpoint pathway. The 13 TME signatures showed a good potential to predict the TME classification (average AUC = 88%). Molecular characteristics of immunohistochemistry from Zhejiang cohort supported our SVM classification. The optimum response to pembrolizumab (78%) and sorafenib (81%) was observed in patients belonging to the Immunogenic subclass. CONCLUSIONS: The HCC patients from distinct immune subclass showed significant differences in clinical prognosis and response to personalized treatment. Based on tumor transcriptome data, our workflow can help to predict the clinical outcomes and to find appropriate treatment strategies for HCC patients.
PURPOSE: The tumor microenvironment (TME) plays a critical role in the pathogenesis of hepatocellular carcinoma (HCC). However, underlying compositions and functions that drive the establishment and maintenance of the TME classifications are less-well understood. METHODS: A total of 766 HCC patients from three public cohorts were clustered into four immune-related subclasses based on 13 TME signatures (11 immune-related cells and 2 immune-related pathways) calculated by MCP-counter. After analyzing the landscapes of functional annotation, methylation, somatic mutation, and clinical characteristics, we built a TME-based Support Vector Machine of 365 patients (discovery phase) and 401 patients (validation phase). We applied this SVM model on another two independent cohorts of patients who received sorafenib/pembrolizumab treatment. RESULTS: About 33% of patients displayed an immune desert pattern. The other subclasses were different in abundance of tumor infiltrating cells. The Immunogenic subclass (17%) associated with the best prognosis presented a massive T cell infiltration and an activation of immune checkpoint pathway. The 13 TME signatures showed a good potential to predict the TME classification (average AUC = 88%). Molecular characteristics of immunohistochemistry from Zhejiang cohort supported our SVM classification. The optimum response to pembrolizumab (78%) and sorafenib (81%) was observed in patients belonging to the Immunogenic subclass. CONCLUSIONS: The HCC patients from distinct immune subclass showed significant differences in clinical prognosis and response to personalized treatment. Based on tumor transcriptome data, our workflow can help to predict the clinical outcomes and to find appropriate treatment strategies for HCC patients.
Authors: Derek Y Chiang; Augusto Villanueva; Yujin Hoshida; Judit Peix; Philippa Newell; Beatriz Minguez; Amanda C LeBlanc; Diana J Donovan; Swan N Thung; Manel Solé; Victoria Tovar; Clara Alsinet; Alex H Ramos; Jordi Barretina; Sasan Roayaie; Myron Schwartz; Samuel Waxman; Jordi Bruix; Vincenzo Mazzaferro; Azra H Ligon; Vesna Najfeld; Scott L Friedman; William R Sellers; Matthew Meyerson; Josep M Llovet Journal: Cancer Res Date: 2008-08-15 Impact factor: 12.701
Authors: Abdullah Al Emran; Aniruddha Chatterjee; Euan J Rodger; Jessamy C Tiffen; Stuart J Gallagher; Michael R Eccles; Peter Hersey Journal: Trends Immunol Date: 2019-03-07 Impact factor: 16.687
Authors: Al B Benson; Michael I D'Angelica; Daniel E Abbott; Thomas A Abrams; Steven R Alberts; Daniel Anaya Saenz; Chandrakanth Are; Daniel B Brown; Daniel T Chang; Anne M Covey; William Hawkins; Renuka Iyer; Rojymon Jacob; Andrea Karachristos; R Kate Kelley; Robin Kim; Manisha Palta; James O Park; Vaibhav Sahai; Tracey Schefter; Carl Schmidt; Jason K Sicklick; Gagandeep Singh; Davendra Sohal; Stacey Stein; G Gary Tian; Jean-Nicolas Vauthey; Alan P Venook; Andrew X Zhu; Karin G Hoffmann; Susan Darlow Journal: J Natl Compr Canc Netw Date: 2017-05 Impact factor: 11.908
Authors: Yujin Hoshida; Sebastian M B Nijman; Masahiro Kobayashi; Jennifer A Chan; Jean-Philippe Brunet; Derek Y Chiang; Augusto Villanueva; Philippa Newell; Kenji Ikeda; Masaji Hashimoto; Goro Watanabe; Stacey Gabriel; Scott L Friedman; Hiromitsu Kumada; Josep M Llovet; Todd R Golub Journal: Cancer Res Date: 2009-09-01 Impact factor: 12.701