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Table 1 Summary of novel technologies

From: Novel technologies and emerging biomarkers for personalized cancer immunotherapy

Technology Suggestions and potential biomarkers Sample preparation Bioinformatic tools References and recommended reading
Whole exome sequencing for neoantigen discovery • Mutation load for CTLA-4 and PD-1 blockade therapy
• Neoantigen-specific T cell response
DNA from tumor and normal cells EBcall, JointSNVMix, MuTect, SomaticSniper, Strelka, VarScan 2, BIMAS, RNAKPER SYFPEITHI, IDEB, NetMCHpan, TEPITOPEpan, PickPocket, Multipred2, MultiRTA Van Buuren et al., 2014 [248]; Duan et al., 2014 [130]; Snyder et al., 2014 [62]; Snyder et al., 2015 [131]; Rizvi et al., 2015 [104]; Le et al., 2015 [105]; Van Allen et al., 2015 [63]
Gene signature and pattern • MAGE-A3 gene signature
• Chemokine expression in melanoma
• Neoantigen signature
DNA and RNA from tumor, lymph node and PBMCs BRB-ArrayTools, LIMMA, SAM, PAM, Partek, Genomic Suite, GSEA, Ingenuity IPA Quackenbush et al., 2002 [231]; Simon et al., 2013 [249]; Simon et al., 2007 [250]; Subramanian et al., 2005; Smyth et al., 2005; Tusher et al., 2001 [251]; Tibshirani et al., 2002 [252]; Leek et al., 2010 [243]; Gaujoux et al., 2013 [245]; Ulloa-Montoya et al., 2013 [142]; Brown et al., 2014 [148]
Epigenetic-differentiation based immune cell quantification • Immune cell lineage specific epigenetic modification
• Leukocyte ratios in blood and tissue
Genomic DNA from fresh or frozen whole blood, PBMC, lymph node and fresh tissue or FFPE tissue and blood clots HOMER package Motif Finder algorithm, MatInspector (Genomatix), Mendelian randomization Wieczorek et al., 2009 [154]; Sehouli et al., 2011 [155]; Schildknecht et al., 2015 [253]; Steinfelder et al., 2011 [156]; Lavin et al., 2014; Gosselin et al., 2014; Liang et al., 2015
Protein microarray (seromics) • TAA antibody response
• Broad antibody signature
• New antigen discovery
Fresh or frozen serum and plasma Prospector, LIMMA package, PAA package, Spotfire package Gnjatic et al., 2009 [254]; Kwek et al., 2012 [56]; Turewicz et al., 2013 [175]; Graff et al., 2014 [27]
Flow Cytometry and Mass Cytometry • Use best flow practices and recommended flow panels
• Multimers for T cell epitope screening
• TAA-specific T cell response for CTLA-4 blockade therapy
• CD4+ICOS+ T cells for CTLA-4 blockade therapy
• Baseline MDSC for CTLA-4 blockade therapy
Whole blood; Fresh or frozen PBMCs and TILs; Fresh or frozen cells from ascites or pleural effusion Computational algorithm-driven analysis for MDSC, Cytobank, FlowJo, SPADE, PhenoGraph, PCA, viSNE, Citrus, ACCENSE, Isomap, 3D visualization Maecker et al., 2010 [176]; Maecker et al., 2012 [177]; Streitz et al., 2013 [178]; Kvistborg et al., 2012 [255]; Chang et al., 2014 [190]; Yuan et al., 2011 [57]; Carthon et al., 2010 [50]; Kitano et al., 2014 [72]; Levine et al.,2015 [189]
T and B cell receptor deep sequencing • CD3 T cell count
• T Cell clonotype stability for CTLA-4 blockade therapy
• Baseline T cell clonality in tumor in PD-1 blockade therapy
DNA from FFPE; Frozen cells from tumor, lymph node or PBMCs; Fresh or frozen cells from ascites or pleural effusion Shannon Entropy, Morisita’s distance, Estimated TCR gene rearrangements per diploid genomes, Clonality, ImmuneID, Adaptive ImmunoSeq software Cha et al., 2014 [205]; Tumeh et al., 2014 [103]; Howie et al., 2015 [202]
Multicolor IHC staining • CD3 Immune score
• CD8/FOXP3 ratio for tumor necrosis
• PD-L1 expression on tumor in PD-1 blockade therapy
FFPE tissue; Fresh or frozen tissue TissueGnostic system, PerkinElmer system Galon et al., 2006 [10]; Hodi et al., 2008 [54]; Taube et al., 2014 [110]
  1. Abbreviations: PBMC peripheral blood mononuclear cells, TAA tumor associated antigen, MDSC myeloid derived suppressor cells, TILs tumor infiltrating lymphocytes, IHC immunohistochemical staining, TCR T cell receptor, FFPE formalin-fixed, paraffin-embedded, PD-1 programmed cell death-1, PD-L1 programmed cell death ligand −1