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

Masucci, Giuseppe V.; Cesano, Alessandra; Hawtin, Rachael; Janetzki, Sylvia; Zhang, Jenny; Kirsch, Ilan; Dobbin, Kevin K.; Alvarez, John; Robbins, Paul B.; Selvan, Senthamil R.; Streicher, Howard Z.; Butterfield, Lisa H.; Thurin, Magdalena

doi:10.1186/s40425-016-0178-1

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	Retrospective, 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]

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