TY - JOUR
T1 - A pan-cancer approach to predict responsiveness to immune checkpoint inhibitors by machine learning
AU - Polano, Maurizio
AU - Chierici, Marco
AU - Dal Bo, Michele
AU - Gentilini, Davide
AU - Di Cintio, Federica
AU - Baboci, Lorena
AU - Gibbs, David L.
AU - Furlanello, Cesare
AU - Toffoli, Giuseppe
PY - 2019/10
Y1 - 2019/10
N2 - Immunotherapy by using immune checkpoint inhibitors (ICI) has dramatically improved the treatment options in various cancers, increasing survival rates for treated patients. Nevertheless, there are heterogeneous response rates to ICI among different cancer types, and even in the context of patients affected by a specific cancer. Thus, it becomes crucial to identify factors that predict the response to immunotherapeutic approaches. A comprehensive investigation of the mutational and immunological aspects of the tumor can be useful to obtain a robust prediction. By performing a pan-cancer analysis on gene expression data from the Cancer Genome Atlas (TCGA, 8055 cases and 29 cancer types), we set up and validated a machine learning approach to predict the potential for positive response to ICI. Support vector machines (SVM) and extreme gradient boosting (XGboost) models were developed with a 10×5-fold cross-validation schema on 80% of TCGA cases to predict ICI responsiveness defined by a score combining tumor mutational burden and TGF-β signaling. On the remaining 20% validation subset, our SVM model scored 0.88 accuracy and 0.27 Matthews Correlation Coefficient. The proposed machine learning approach could be useful to predict the putative response to ICI treatment by expression data of primary tumors.
AB - Immunotherapy by using immune checkpoint inhibitors (ICI) has dramatically improved the treatment options in various cancers, increasing survival rates for treated patients. Nevertheless, there are heterogeneous response rates to ICI among different cancer types, and even in the context of patients affected by a specific cancer. Thus, it becomes crucial to identify factors that predict the response to immunotherapeutic approaches. A comprehensive investigation of the mutational and immunological aspects of the tumor can be useful to obtain a robust prediction. By performing a pan-cancer analysis on gene expression data from the Cancer Genome Atlas (TCGA, 8055 cases and 29 cancer types), we set up and validated a machine learning approach to predict the potential for positive response to ICI. Support vector machines (SVM) and extreme gradient boosting (XGboost) models were developed with a 10×5-fold cross-validation schema on 80% of TCGA cases to predict ICI responsiveness defined by a score combining tumor mutational burden and TGF-β signaling. On the remaining 20% validation subset, our SVM model scored 0.88 accuracy and 0.27 Matthews Correlation Coefficient. The proposed machine learning approach could be useful to predict the putative response to ICI treatment by expression data of primary tumors.
KW - Immune checkpoint inhibitor
KW - Immunology-pancancer
KW - Machine learning
UR - http://www.scopus.com/inward/record.url?scp=85073712701&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85073712701&partnerID=8YFLogxK
U2 - 10.3390/cancers11101562
DO - 10.3390/cancers11101562
M3 - Article
AN - SCOPUS:85073712701
VL - 11
JO - Cancers
JF - Cancers
SN - 2072-6694
IS - 10
M1 - 1562
ER -