Examples of cancer biomarker assays predictive of response to immunotherapy with different levels of evidence for clinical validity/utility

Biomarker AssayBiomarkerClinical UseStudy Type/Level of EvidenceReferences/ Regulatory Clearance
IHC, PD-L1 22C3 pharmDx,Companion DiagnosticPD-L1Predicting response to anti-PD-1 therapy (pembrolizumab) in NSCLC50 % cut-offProspective, Phase III clinical trial KEYNOTE-001FDA approval [32]
IHC, PD-L1 28-8 pharmDx,Complementary TestPD-L1Informs about risk vs. benefit of anti-PD-1 therapy (nivolumab) in non-squamous NSCLC and melanoma- continuous correlation of PD-1 expression with magnitude of treatment effectProspective-retrospective, Phase III clinical trial CheckMate-057FDA approval [33]
IHC, PD-L1 SP142, Complementary TestPD-L1Informs about the risk vs. benefit of anti-PD-L1 therapy (atezolizumab) for metastatic urothelial bladder cancerProspective-retrospective, Phase II clinical trial IMvigor-210FDA approval [34]
IHCTumor T cell Infiltrate, PD-L1 with spatial resolutionPredictive to anti-PD-1 therapy in melanoma and NSCLCRetrospective, Exploratory analysisTumeh et al., 2014 [6]; Teng et al. 2015 [35]; Herbst et al., 2014 [36]
Enzyme Linked Immunospot (ELISpot)IFNγ releasePost-treatment/monitoring, cancer vaccinesRetrospective,Exploratory analysisKenter et al., 2009 [21]; Sheikh et al., 2009 [24]
Multi-parametric Flow CytometryMDSC, Tregs, ICOS+ CD4 T cellsPost-treatment/monitoring, cancer vaccines, Predictive of anti-CTLA-4 therapy in RCC and melanomaRetrospective, Exploratory analysis, Phase I,II trialWalter et al., 2012 [22]; Tarhini et al., 2014 [171]; Di Giacomo et al., 2013 [172]; Hodi et al., 2014 [173]; Martens et al., 2016 [20]
Multi-parametric Flow CytometryAbsolute lymphocyte count (ALC)Predictive of response to anti-CTLA-4 therapyRetrospective, Small cohort, Significant variability among institutionsKu et al., 2010 [174]
Single Cell Network Profiling (SCNP)AraC → cPARPAraC → CD34Predictive of response to induction therapy in elderly patients with de novo acute myeloid leukemiaRetrospective, Training and validation study establishing clinical utilityCesano et al., 2015 [29]
TCR SequencingLimited clonalityClonality assessments of tumor-infiltrating lymphocytes,Predictive to response with anti-CTLA-4 and anti-PD-1 in melanomaRetrospective,Small cohortTumeh et al., 2014 [6]; Cha et al., 2014 [59]
nCounter Gene Expression, NanoString Technologies, Inc.Gene expression profilePredictive of response to anti-PD-1 therapy in melanoma and multiple solid tumorsRetrospective, Training and test sets – prospective validation ongoing on different tumor typesRibas et al., 2015 [61]; Wallden et al., 2016 [175]; Piha-Paul et al., 2016 [176]; Man Chow et al., 2016 [177]
Next Generation Sequencing (NGS)Mutational loadPredictive of response to anti-CTLA-4 therapy in melanoma and anti-PD-1 in NSCLCRetrospective,Small cohort,Training and test setsSnyder et al., 2014 [46]; Rizvi et al., 2015 [47]
NGS/in silico Epitope PredictionMHC class I epitope frequency/specificityPredictive of response to anti-CTLA-4 and anti-PD-1 in melanoma, NSCLC, and CRCRetrospective,Small cohortsSnyder et al., 2014 [46]; Rizvi et al., 2015 [47]; Van Allen et al., 2015 [52]; Van Rooij et al., 2013 [48]
IHC or PCR, Microsatellite Instability AnalysisMismatch-repair statusPredictive of response to anti-PD-1 therapy in CRCPhase II study, small cohortLe et al., 2015 [53]