A common gene signature across multiple studies relate biomarkers and functional regulation in tolerance to renal allograft
Authors: Daniel Baron, Gérard Ramstein, Mélanie Chesneau, Yann Echasseriau, Annaick Pallier, Chloé Paul, Nicolas Degauque, Maria P. Hernandez-Fuentes, Alberto Sanchez-Fueyo, Kenneth A. Newell, Magali Giral, Jean-Paul Soulillou, Rémi Houlgatte, Sophie Brouard
Journal: Kidney International (2015)

Patients tolerant to a kidney graft display a specific blood cell transcriptional pattern but results from five different studies were inconsistent, raising the question of relevance for future clinical application. To resolve this, we sought to identify a common gene signature, specific functional and cellular components, and discriminating biomarkers for tolerance following kidney transplantation. A meta-analysis of studies identified a robust gene signature involving proliferation of B and CD4 T cells, and inhibition of CD14 monocyte related functions among 96 tolerant samples. This signature was further supported through a cross-validation approach, yielding 92.5% accuracy independent of the study of origin. Experimental validation, performed on new tolerant samples and using a selection of the top-20 biomarkers, returned 91.7% of good classification. Beyond the confirmation of B-cell involvement, our data also indicated participation of other cell subsets in tolerance. Thus, the use of the top 20 biomarkers, mostly centered on B cells, may provide a common and standardized tool towards personalized medicine for the monitoring of tolerant or low-risk patients among kidney allotransplant recipients. These data point to a global preservation of genes favoring the maintenance of a homeostatic and 'healthy' environment in tolerant patients and may contribute to a better understanding of tolerance maintenance mechanisms. Patients tolerant to a kidney graft display a specific blood cell transcriptional pattern but results from five different studies were inconsistent, raising the question of relevance for future clinical application. To resolve this, we sought to identify a common gene signature, specific functional and cellular components, and discriminating biomarkers for tolerance following kidney transplantation. A meta-analysis of studies identified a robust gene signature involving proliferation of B and CD4 T cells, and inhibition of CD14 monocyte related functions among 96 tolerant samples. This signature was further supported through a cross-validation approach, yielding 92.5% accuracy independent of the study of origin. Experimental validation, performed on new tolerant samples and using a selection of the top-20 biomarkers, returned 91.7% of good classification. Beyond the confirmation of B-cell involvement, our data also indicated participation of other cell subsets in tolerance. Thus, the use of the top 20 biomarkers, mostly centered on B cells, may provide a common and standardized tool towards personalized medicine for the monitoring of tolerant or low-risk patients among kidney allotransplant recipients. These data point to a global preservation of genes favoring the maintenance of a homeostatic and 'healthy' environment in tolerant patients and may contribute to a better understanding of tolerance maintenance mechanisms. Transplantation is the treatment of choice for end-stage renal disease. Recent advances in immunosuppression (IS) have improved management of acute rejection and graft survival.1Pascual M. Theruvath T. Kawai T. et al.Strategies to improve long-term outcomes after renal transplantation.N Engl J Med. 2002; 346: 580-590Crossref PubMed Scopus (741) Google Scholar However, due to their toxicity, these drugs have numerous deleterious side effects and only a marginal effect on long-term rejection.2Dantal J. Hourmant M. Cantarovich D. et al.Effect of long-term immunosuppression in kidney-graft recipients on cancer incidence: randomised comparison of two cyclosporin regimens.Lancet. 1998; 351: 623-628Abstract Full Text Full Text PDF PubMed Scopus (623) Google Scholar,3Fishman J.A. Infection in solid-organ transplant recipients.N Engl J Med. 2007; 357: 2601-2614Crossref PubMed Scopus (1382) Google Scholar,4Soulillou J.P. Giral M. Controlling the incidence of infection and malignancy by modifying immunosuppression.Transplantation. 2001; 72: S89-S93Crossref PubMed Scopus (5) Google Scholar,5Wimmer C.D. Rentsch M. Crispin A. et al.The janus face of immunosuppression - de novo malignancy after renal transplantation: the experience of the Transplantation Center Munich.Kidney Int. 2007; 71: 1271-1278Abstract Full Text Full Text PDF PubMed Scopus (144) Google Scholar Tolerance is thus increasingly regarded as an ideal solution.6Lechler R.I. Garden O.A. Turka L.A. The complementary roles of deletion and regulation in transplantation tolerance.Nat Rev Immunol. 2003; 3: 147-158Crossref PubMed Scopus (213) Google Scholar This situation, mainly corresponding to incompliant cases, has been observed in renal transplantation7Brouard S. Pallier A. Renaudin K. et al.The natural history of clinical operational tolerance after kidney transplantation through twenty-seven cases.Am J Transplant. 2012; 12: 3296-3307Crossref PubMed Scopus (87) Google Scholar,8Roussey-Kesler G. Giral M. Moreau A. et al.Clinical operational tolerance after kidney transplantation.Am J Transplant. 2006; 6: 736-746Crossref PubMed Scopus (154) Google Scholar and current estimates report roughly 100 cases.9Orlando G. Hematti P. Stratta R.J. et al.Clinical operational tolerance after renal transplantation: current status and future challenges.Ann Surg. 2010; 252: 915-928Crossref PubMed Scopus (105) Google Scholar Besides, efforts have been devoted by the European and US transplant community to decipher the regulatory mechanisms and to identify noninvasive biomarkers. These studies could report several gene lists, with evidence converging towards the potential implication of B cells as attested by the identification of numerous related markers,10Braud C. Baeten D. Giral M. et al.Immunosuppressive drug-free operational immune tolerance in human kidney transplant recipients: Part I. Blood gene expression statistical analysis.J Cell Biochem. 2008; 103: 1681-1692Crossref PubMed Scopus (68) Google Scholar,11Brouard S. Mansfield E. Braud C. et al.Identification of a peripheral blood transcriptional biomarker panel associated with operational renal allograft tolerance.Proc Natl Acad Sci USA. 2007; 104: 15448-15453Crossref PubMed Scopus (297) Google Scholar,12Lozano J.J. Pallier A. Martinez-Llordella M. et al.Comparison of transcriptional and blood cell-phenotypic markers between operationally tolerant liver and kidney recipients.Am J Transplant. 2011; 11: 1916-1926Crossref PubMed Scopus (98) Google Scholar,13Newell K.A. Asare A. Kirk A.D. et al.Identification of a B cell signature associated with renal transplant tolerance in humans.J Clin Invest. 2010; 120: 1836-1847Crossref PubMed Scopus (553) Google Scholar,14Sagoo P. Perucha E. Sawitzki B. et al.Development of a cross-platform biomarker signature to detect renal transplant tolerance in humans.J Clin Invest. 2010; 120: 1848-1861Crossref PubMed Scopus (436) Google Scholar a unique differentiation profile,15Chesneau M. Pallier A. Braza F. et al.Unique B cell differentiation profile in tolerant kidney transplant patients.Am J Transplant. 2014; 14: 144-155Crossref PubMed Scopus (114) Google Scholar and an increasing number of several subtypes in the blood of these patients.13Newell K.A. Asare A. Kirk A.D. et al.Identification of a B cell signature associated with renal transplant tolerance in humans.J Clin Invest. 2010; 120: 1836-1847Crossref PubMed Scopus (553) Google Scholar,14Sagoo P. Perucha E. Sawitzki B. et al.Development of a cross-platform biomarker signature to detect renal transplant tolerance in humans.J Clin Invest. 2010; 120: 1848-1861Crossref PubMed Scopus (436) Google Scholar,16Louis S. Braudeau C. Giral M. et al.Contrasting CD25hiCD4+T cells/FOXP3 patterns in chronic rejection and operational drug-free tolerance.Transplantation. 2006; 81: 398-407Crossref PubMed Scopus (237) Google Scholar,17Pallier A. Hillion S. Danger R. et al.Patients with drug-free long-term graft function display increased numbers of peripheral B cells with a memory and inhibitory phenotype.Kidney Int. 2010; 78: 503-513Abstract Full Text Full Text PDF PubMed Scopus (215) Google Scholar Although informative, these lists poorly overlapped, raising question about the pertinence of the results for future clinical application. However, as recently exemplified by the identification of rejection markers across multiple transplanted organs,18Khatri P. Roedder S. Kimura N. et al.A common rejection module (CRM) for acute rejection across multiple organs identifies novel therapeutics for organ transplantation.J Exp Med. 2013; 210: 2205-2221Crossref PubMed Scopus (166) Google Scholar comparing and integrating data from several studies by meta-analysis19Cahan P. Rovegno F. Mooney D. et al.Meta-analysis of microarray results: challenges, opportunities, and recommendations for standardization.Gene. 2007; 401: 12-18Crossref PubMed Scopus (103) Google Scholar is an ideal solution to increase reliability and consistency in the results and conclusions. Hence, when applied to the five existing tolerance-related studies,10Braud C. Baeten D. Giral M. et al.Immunosuppressive drug-free operational immune tolerance in human kidney transplant recipients: Part I. Blood gene expression statistical analysis.J Cell Biochem. 2008; 103: 1681-1692Crossref PubMed Scopus (68) Google Scholar,11Brouard S. Mansfield E. Braud C. et al.Identification of a peripheral blood transcriptional biomarker panel associated with operational renal allograft tolerance.Proc Natl Acad Sci USA. 2007; 104: 15448-15453Crossref PubMed Scopus (297) Google Scholar,12Lozano J.J. Pallier A. Martinez-Llordella M. et al.Comparison of transcriptional and blood cell-phenotypic markers between operationally tolerant liver and kidney recipients.Am J Transplant. 2011; 11: 1916-1926Crossref PubMed Scopus (98) Google Scholar,13Newell K.A. Asare A. Kirk A.D. et al.Identification of a B cell signature associated with renal transplant tolerance in humans.J Clin Invest. 2010; 120: 1836-1847Crossref PubMed Scopus (553) Google Scholar,14Sagoo P. Perucha E. Sawitzki B. et al.Development of a cross-platform biomarker signature to detect renal transplant tolerance in humans.J Clin Invest. 2010; 120: 1848-1861Crossref PubMed Scopus (436) Google Scholar such meta-analysis may also enable to reconcile data. In this context, the first objective of our work was to define a robust gene signature20Alizadeh A.A. Eisen M.B. Davis R.E. et al.Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling.Nature. 2000; 403: 503-511Crossref PubMed Scopus (7996) Google Scholar indicative of tolerance. The second objective was to define specific functional and cellular components supported by the signature, as previously defined,20Alizadeh A.A. Eisen M.B. Davis R.E. et al.Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling.Nature. 2000; 403: 503-511Crossref PubMed Scopus (7996) Google Scholar and that may bring clues for the understanding of peripheral regulatory mechanisms. The third objective was to validate a subset of markers that could be further used in clinics for the stratification of kidney recipients with a low risk of rejection. A k-means clustering identified 10 clusters (K1 to K10) in each study (Supplementary Figure SI1 online) with clear functional annotations (Figure 1a). A total of 19 clusters from the 50 were found to be significant (Supplementary Figures SI2 and SI3 online) between tolerant (TOL) and stable (STA), with at least one of the two tests (Student and/or Fisher, P<0.01). These clusters had relevant but limited similarities (Figure 1b, Supplementary Figure SI2 online). From the 19 clusters (Figure 1c), most of the differential genes (81% in average) in one study did not replicate in other studies (Supplementary Figure SI2 online). Only 0.08% (14 genes: APOM, ARHGAP17, AURKB, IGBP1, IL10RA, IL15RA, IL1RL1, INSM1, IRF4, MAOA, MICB, SMAD3, TK1, and YPEL2) was in fact commonly identified across the five studies (Supplementary Figures SI3 and SI4 online). Two of them (TK1 and IRF4) were present either in the 49-gene footprint from Brouard11Brouard S. Mansfield E. Braud C. et al.Identification of a peripheral blood transcriptional biomarker panel associated with operational renal allograft tolerance.Proc Natl Acad Sci USA. 2007; 104: 15448-15453Crossref PubMed Scopus (297) Google Scholar or in the top 30 genes from Newell13Newell K.A. Asare A. Kirk A.D. et al.Identification of a B cell signature associated with renal transplant tolerance in humans.J Clin Invest. 2010; 120: 1836-1847Crossref PubMed Scopus (553) Google Scholar (Supplementary Figure SI5 online). These 14 genes did not accurately discriminate TOL from STA (Supplementary Figure SI6 online). Altogether, these data show that comparison of gene lists identified a limited consensus and was not enough to derive a robust meta-signature. Download .xls (3.92 MB) Help with xls files Supplementary Figure S1 Download .xls (.14 MB) Help with xls files Supplementary Figure S2 Download .xls (2.23 MB) Help with xls files Supplementary Figure S3 Download .xls (.03 MB) Help with xls files Supplementary Figure S4 Download .xls (.09 MB) Help with xls files Supplementary Figure S5 Download .doc (.08 MB) Help with doc files Supplementary Figure S6 Download .xls (2.82 MB) Help with xls files Supplementary Figure S7 To ensure comparability across studies, the 1846 common genes were retained. This selection is highly skewed towards immune functions (Supplementary Figure SI7 online); however, it covers as high as 65% of the biological functions from the gene ontology. After standardization, data sets were merged and analyzed as a single corpus of data (Figure 2a) comprising 596 samples and available in the Gene Expression Omnibus repository (no. GSE49198). Hierarchical clustering highlighted clear profiles correlated to sample groups (Figure 2a and b). To identify the ones associated with tolerance, we further analyzed the 10-K-means clusters (Figure 2c), K1 (224 genes) linked to proliferation, K2 (183 genes) to endocytosis, and K10 (188 genes) to lymphocyte (B and T) activation and differentiation (Figure 2d, Supplementary Figures SI8 and SI9 online) were found to be the most differential (P<0.00001) between TOL (n=96) and STA (n=343). They thus defined a highly discriminative (P=5.05E-15) 595-gene signature as measured by Fisher's exact test on the contingencies of the sample tree (Figure 2d, Supplementary Figure SI8 online). It comprised eight (APOM, AURKB, IGBP1, IL10RA, IL1RL1, IRF4, SMAD3, and TK1) out of the 14-consensus genes from gene list comparison (Supplementary Figure SI4 online) and also 13 B-cell molecules (AFF3, BLK, BLNK, CD22, CD79B, FCER2, FCRL2, ID3, IGKC, IGLL1, MS4A1, MZB1 and TCL1A) belonging to the top-ranked gene lists (Supplementary Figure SI5 online) from the different studies.10Braud C. Baeten D. Giral M. et al.Immunosuppressive drug-free operational immune tolerance in human kidney transplant recipients: Part I. Blood gene expression statistical analysis.J Cell Biochem. 2008; 103: 1681-1692Crossref PubMed Scopus (68) Google Scholar,11Brouard S. Mansfield E. Braud C. et al.Identification of a peripheral blood transcriptional biomarker panel associated with operational renal allograft tolerance.Proc Natl Acad Sci USA. 2007; 104: 15448-15453Crossref PubMed Scopus (297) Google Scholar,12Lozano J.J. Pallier A. Martinez-Llordella M. et al.Comparison of transcriptional and blood cell-phenotypic markers between operationally tolerant liver and kidney recipients.Am J Transplant. 2011; 11: 1916-1926Crossref PubMed Scopus (98) Google Scholar,13Newell K.A. Asare A. Kirk A.D. et al.Identification of a B cell signature associated with renal transplant tolerance in humans.J Clin Invest. 2010; 120: 1836-1847Crossref PubMed Scopus (553) Google Scholar,14Sagoo P. Perucha E. Sawitzki B. et al.Development of a cross-platform biomarker signature to detect renal transplant tolerance in humans.J Clin Invest. 2010; 120: 1848-1861Crossref PubMed Scopus (436) Google Scholar Altogether, these data show that integrative meta-analysis can identify a robust gene meta-signature of tolerance. Download .xls (.18 MB) Help with xls files Supplementary Figure S8 Download .xls (.78 MB) Help with xls files Supplementary Figure S9 Download .xls (.25 MB) Help with xls files Supplementary Figure S10 Download .xls (.03 MB) Help with xls files Supplementary Figure S11 Download .doc (.06 MB) Help with doc files Supplementary Figure S12 A screening of the compendium of gene sets retained the 100 best hits for each cluster (K1, K2, and K10; Figure 3, Supplementary Figure SI10 online). Although significant (P<0.00001), these overlaps were partial (mean coverage=8.89±4.82%) but displayed good functional congruence (Figure 3a): the 100 best hits converged to cell proliferation and cell adhesion for K1, inflammatory response for K2 and lymphocyte activation for K10. A similar analysis with a collection of blood transcriptional modules (Supplementary Figure SI11 online)21Chaussabel D. Quinn C. Shen J. et al.A modular analysis framework for blood genomics studies: application to systemic lupus erythematosus.Immunity. 2008; 29: 150-164Abstract Full Text Full Text PDF PubMed Scopus (490) Google Scholar significantly (P<0.001) linked K2 genes to pro-inflammatory-related monocyte modules, whereas K10 genes were linked to B-cell and T-cell modules. Particular results (Figure 3b) from different studies22Fulcher J.A. Hashimi S.T. Levroney E.L. et al.Galectin-1-matured human monocyte-derived dendritic cells have enhanced migration through extracellular matrix.J Immunol. 2006; 177: 216-226Crossref PubMed Scopus (96) Google Scholar,23Haddad R. Guardiola P. Izac B. et al.Molecular characterization of early human T/NK and B-lymphoid progenitor cells in umbilical cord blood.Blood. 2004; 104: 3918-3926Crossref PubMed Scopus (113) Google Scholar,24Hoffmann R. Seidl T. Neeb M. et al.Changes in gene expression profiles in developing B cells of murine bone marrow.Genome Res. 2002; 12: 98-111Crossref PubMed Scopus (78) Google Scholar,25Jourdan M. Caraux A. De V.J. et al.An in vitro model of differentiation of memory B cells into plasmablasts and plasma cells including detailed phenotypic and molecular characterization.Blood. 2009; 114: 5173-5181Crossref PubMed Scopus (192) Google Scholar,26Lee M.S. Hanspers K. Barker C.S. et al.Gene expression profiles during human CD4+ T cell differentiation.Int Immunol. 2004; 16: 1109-1124Crossref PubMed Scopus (66) Google Scholar,27Luckey C.J. Bhattacharya D. Goldrath A.W. et al.Memory T and memory B cells share a transcriptional program of self-renewal with long-term hematopoietic stem cells.Proc Natl Acad Sci USA. 2006; 103: 3304-3309Crossref PubMed Scopus (207) Google Scholar illustrate the link to T CD4 (panel 1)26Lee M.S. Hanspers K. Barker C.S. et al.Gene expression profiles during human CD4+ T cell differentiation.Int Immunol. 2004; 16: 1109-1124Crossref PubMed Scopus (66) Google Scholar and especially B (panels 2–4 and 6)23Haddad R. Guardiola P. Izac B. et al.Molecular characterization of early human T/NK and B-lymphoid progenitor cells in umbilical cord blood.Blood. 2004; 104: 3918-3926Crossref PubMed Scopus (113) Google Scholar,24Hoffmann R. Seidl T. Neeb M. et al.Changes in gene expression profiles in developing B cells of murine bone marrow.Genome Res. 2002; 12: 98-111Crossref PubMed Scopus (78) Google Scholar,25Jourdan M. Caraux A. De V.J. et al.An in vitro model of differentiation of memory B cells into plasmablasts and plasma cells including detailed phenotypic and molecular characterization.Blood. 2009; 114: 5173-5181Crossref PubMed Scopus (192) Google Scholar,27Luckey C.J. Bhattacharya D. Goldrath A.W. et al.Memory T and memory B cells share a transcriptional program of self-renewal with long-term hematopoietic stem cells.Proc Natl Acad Sci USA. 2006; 103: 3304-3309Crossref PubMed Scopus (207) Google Scholar lymphocytes at various differentiation stages. For instance (panels 1 and 4),24Hoffmann R. Seidl T. Neeb M. et al.Changes in gene expression profiles in developing B cells of murine bone marrow.Genome Res. 2002; 12: 98-111Crossref PubMed Scopus (78) Google Scholar,26Lee M.S. Hanspers K. Barker C.S. et al.Gene expression profiles during human CD4+ T cell differentiation.Int Immunol. 2004; 16: 1109-1124Crossref PubMed Scopus (66) Google Scholar K1 was remarkably enriched (P<1.00E-16) in early proliferating T- and B-cell markers. Conversely, markers of differentiated cell subsets were significantly linked to K10: P=1.01E-13 for T CD4+26Lee M.S. Hanspers K. Barker C.S. et al.Gene expression profiles during human CD4+ T cell differentiation.Int Immunol. 2004; 16: 1109-1124Crossref PubMed Scopus (66) Google Scholar and P=7.55E-09 for B cells.24Hoffmann R. Seidl T. Neeb M. et al.Changes in gene expression profiles in developing B cells of murine bone marrow.Genome Res. 2002; 12: 98-111Crossref PubMed Scopus (78) Google Scholar Download .xls (.08 MB) Help with xls files Supplementary Figure S13 Download .ppt (.09 MB) Help with ppt files Supplementary Figure S14 The TOL signature (K1, K2, and K10; Figure 4a) was compared with clusters from various tissue (Figure 4b) and blood cell (Figure 4c) samples. This comparison identified K1 as a proliferation cluster (for example, AURKB, CCNB2, CDC20, CHEK1, NEK2, and PLK4) gathering 67% (P=6.31E-13) and 73% (P=1.04E-13), respectively, of the genes from proliferating tissues (for example, testis and skin; Figure 4b) or cells (early blood precursors; Figure 4c). K2 and K10 gathered 90% (P=2.30E-20) of immune tissue markers (for example, bone marrow, thymus, spleen, and lymph nodes; Figure 4b) showing their immunological specificity. K2 contained 82% (P=3.44E-85) of the granulocyte/monocyte lineage markers (Figure 4c) and corresponded to a CD14 monocyte cluster (for example, CD14, CD163, CD68, ITGAM, ITGB2, and PECAM1; Figure 4d). K10 contained 86% (P=5.12E-40) of the T lineage makers (Figure 4c) that were preferentially expressed in naive and differentiated CD4 subsets (for example, CD247, CD28, CD48, CD5, LAT, and MAL; Figure 4d). It also harbored 71% (P=9.31E-10) of the B lineage markers (Figure 4c) expressed in naive and differentiated subsets (for example, BLNK, CD22, CD40, CD79B, FCER2, and MS4A1; Figure 4d). Altogether, these data suggest the involvement of different cell subsets in tolerance, as attested by the specific expansion and differentiation of B and CD4 T cells, and the inhibition of monocyte functions. To assess the reproducibility of the signature with external data, similar trends of expression were searched in a collection of data sets using co-expression (Figure 5a) and rank (Figure 5b)-based approaches. A clustering analysis showed that recurrent partners of co-expression fell into six functionally relevant meta-clusters (Figure 5a). The two most significant meta-clusters (M3 and M6) gathered 169 (P=1.1E-84) and 62 (P=1.1E-55) genes that were involved in immune and cell cycle functions. In the second analysis (Figure 5b), 215 distinct pairs of samples significantly (area under curve<0.8; Q-value<10-11) harbored the same pattern in terms of rank differences. A text-mining analysis revealed that 70% (P=3.40E-14) of the matched studies were related to blood (Supplementary Figure SI12 online) or other cell subsets (mononuclear cells: P=3.45E-10; lymphocytes: P=2.88E-07; B cells: P=6.29E-03). Download .ppt (.16 MB) Help with ppt files Supplementary Figure S15 Altogether, these data show that genes from the signature are recurrently associated with the same functionally related neighbors of co-expression and display a TOL-like pattern in numerous blood cell–related studies. To statistically validate the signature, a cross-validation (Figure 6a) and an experimental validation (Figure 6b) were performed. A full sixfold cross-validation was performed on the set of 1846 genes (Figure 6a). Each initial data set was used as an external validation set, whereas the five others served as training sets for the selection of the top discriminating genes (panel 1). An optimal selection of top-200 genes (Supplementary Figure SI13 online), conserved at 76% across iterations, yielded good mean classification performances (Supplementary Figure SI6 online): 92.5% accuracy, 76.3% sensitivity, 98.2% specificity, 91.1% positive predictive value, and 91.0% negative predictive value. These performances were not compromised regardless of the data set used for test (panel 2). Download .ppt (.08 MB) Help with ppt files Supplementary Figure S16 Download .doc (.04 MB) Help with doc files Supplementary Figure S17 Second, a limited selection of the top-20 markers, mostly centered on B cells and overexpressed in TOL (Supplementary Figure SI14 online), was sufficient to accurately discriminate TOL from STA (Supplementary Figure SI6 online). One of these markers, IRF4, was also identified by gene list comparison (Supplementary Figure SI4 online). Their quantitative measurement by real-time PCR (independent technique) on a new collection of 67 samples (Figure 6b, Supplementary Figure SI15 online) yielded strong discrimination of TOL and STA (P=4.39E-9): 16 markers (80%) were indeed altered (P<0.05) in the same sense. Leave-one-out prediction yielded excellent reproducibility (100% recall) on the 12 new time samples from already analyzed TOL cases and good external validation on the six new TOL cases (83.3% recall). Good classification (91.7%) was achieved (94.4% sensitivity, 90% specificity, 85% positive predictive value, and 96.4% negative predictive value), with one TOL and three STA being misclassified (Supplementary Figure SI16 online). Download .ppt (.21 MB) Help with ppt files Supplementary Figure S18