Skip to main content

Table 1 Examples of cancer biomarker assays predictive of response to immunotherapy with different levels of evidence for clinical validity/utility

From: Validation of biomarkers to predict response to immunotherapy in cancer: Volume I — pre-analytical and analytical validation

Biomarker Assay Biomarker Clinical Use Study Type/Level of Evidence References/ Regulatory Clearance
IHC, PD-L1 22C3 pharmDx,
Companion Diagnostic
PD-L1 Predicting response to anti-PD-1 therapy (pembrolizumab) in NSCLC
50 % cut-off
Prospective, Phase III clinical trial KEYNOTE-001 FDA approval [32]
IHC, PD-L1 28-8 pharmDx,
Complementary Test
PD-L1 Informs 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 effect Prospective-retrospective, Phase III clinical trial CheckMate-057 FDA approval [33]
IHC, PD-L1 SP142, Complementary Test PD-L1 Informs about the risk vs. benefit of anti-PD-L1 therapy (atezolizumab) for metastatic urothelial bladder cancer Prospective-retrospective, Phase II clinical trial IMvigor-210 FDA approval [34]
IHC Tumor T cell Infiltrate, PD-L1 with spatial resolution Predictive to anti-PD-1 therapy in melanoma and NSCLC Retrospective, Exploratory analysis Tumeh et al., 2014 [6]; Teng et al. 2015 [35]; Herbst et al., 2014 [36]
Enzyme Linked Immunospot (ELISpot) IFNγ release Post-treatment/monitoring, cancer vaccines Retrospective,
Exploratory analysis
Kenter et al., 2009 [21]; Sheikh et al., 2009 [24]
Multi-parametric Flow Cytometry MDSC, Tregs, ICOS+ CD4 T cells Post-treatment/monitoring, cancer vaccines, Predictive of anti-CTLA-4 therapy in RCC and melanoma Retrospective, Exploratory analysis, Phase I,II trial Walter 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 Cytometry Absolute lymphocyte count (ALC) Predictive of response to anti-CTLA-4 therapy Retrospective, Small cohort, Significant variability among institutions Ku et al., 2010 [174]
Single Cell Network Profiling (SCNP) AraC → cPARP
AraC → CD34
Predictive of response to induction therapy in elderly patients with de novo acute myeloid leukemia Retrospective, Training and validation study establishing clinical utility Cesano et al., 2015 [29]
TCR Sequencing Limited clonality Clonality assessments of tumor-infiltrating lymphocytes,
Predictive to response with anti-CTLA-4 and anti-PD-1 in melanoma
Small cohort
Tumeh et al., 2014 [6]; Cha et al., 2014 [59]
nCounter Gene Expression, NanoString Technologies, Inc. Gene expression profile Predictive of response to anti-PD-1 therapy in melanoma and multiple solid tumors Retrospective, Training and test sets – prospective validation ongoing on different tumor types Ribas 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 load Predictive of response to anti-CTLA-4 therapy in melanoma and anti-PD-1 in NSCLC Retrospective,
Small cohort,
Training and test sets
Snyder et al., 2014 [46]; Rizvi et al., 2015 [47]
NGS/in silico Epitope Prediction MHC class I epitope frequency/specificity Predictive of response to anti-CTLA-4 and anti-PD-1 in melanoma, NSCLC, and CRC Retrospective,
Small cohorts
Snyder 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 Analysis Mismatch-repair status Predictive of response to anti-PD-1 therapy in CRC Phase II study, small cohort Le et al., 2015 [53]