Genomics of NSCLC patients both affirm PD-L1 expression and predict their clinical responses to anti-PD-1 immunotherapy

[11] Carbone DP(2014)Predictive correlates of response to the anti-PD-L1 antibody MPDL3280A in cancer patients Nature 515 563-55

[12] Carbognin L(2016)Biomarkers for PD-1/PD-L1 blockade therapy in non-small-cell lung cancer: is PD-L1 expression a good marker for patient selection? Clinical Lung Cancer 17 350-660

[13] Pilotto S(2016)PD-L1 expression as a predictive biomarker: is absence of proof the same as proof of absence? JAMA Oncology. 2 54-128

[14] Milella M(2016)The “cancer immunogram” Science 352 658-249

[15] Vaccaro V(2015)Mutational landscape determines sensitivity to PD-1 blockade in non-small cell lung cancer Science 348 124-30181

[16] Brunelli M(2014)Benchmarking mutation effect prediction algorithms using functionally validated cancer-related missense mutations Genome Biol 15 484-160

[17] Calio A(2011)Predicting the functional impact of protein mutations: application to cancer genomics Nucleic Acids Res 39 e118-111

[18] Cuppone F(2010)A method and server for predicting damaging missense mutations Nat Methods 7 248-1522

[19] Sperduti I(2012)Predicting the functional effect of amino acid substitutions and indels PLoS One 7 e46688-3590

[20] Giannarelli D(1999)Quantification of short term signaling by the epidermal growth factor receptor J Biol Chem 274 30169-778

[11] Carbone DP(2014)Predictive correlates of response to the anti-PD-L1 antibody MPDL3280A in cancer patients Nature 515 563-55

[12] Carbognin L(2016)Biomarkers for PD-1/PD-L1 blockade therapy in non-small-cell lung cancer: is PD-L1 expression a good marker for patient selection? Clinical Lung Cancer 17 350-660

[13] Pilotto S(2016)PD-L1 expression as a predictive biomarker: is absence of proof the same as proof of absence? JAMA Oncology. 2 54-128

[14] Milella M(2016)The “cancer immunogram” Science 352 658-249

[15] Vaccaro V(2015)Mutational landscape determines sensitivity to PD-1 blockade in non-small cell lung cancer Science 348 124-30181

[16] Brunelli M(2014)Benchmarking mutation effect prediction algorithms using functionally validated cancer-related missense mutations Genome Biol 15 484-160

[17] Calio A(2011)Predicting the functional impact of protein mutations: application to cancer genomics Nucleic Acids Res 39 e118-111

[18] Cuppone F(2010)A method and server for predicting damaging missense mutations Nat Methods 7 248-1522

[19] Sperduti I(2012)Predicting the functional effect of amino acid substitutions and indels PLoS One 7 e46688-3590

[20] Giannarelli D(1999)Quantification of short term signaling by the epidermal growth factor receptor J Biol Chem 274 30169-778