Mitochondrial dysfunction in patients with primary congenital insulin resistance

J Clin Invest. doi:10.1172/JCI46405.
Copyright © 2011, The American Society for Clinical Investigation. Alison Sleigh1, Philippa Raymond-Barker2, Kerrie Thackray3, David Porter4, Mensud Hatunic3, Alessandra Vottero5, Christine Burren6, Catherine Mitchell7, Martin McIntyre8, Soren Brage9, T. Adrian Carpenter1, Peter R. Murgatroyd2, Kevin M. Brindle10, Graham J. Kemp11, Stephen O’Rahilly3, Robert K. Semple3 and David B. Savage3

1Wolfson Brain Imaging Centre, University of Cambridge, Cambridge, United Kingdom.
2Wellcome Trust Clinical Research Facility (WTCRF), Addenbrooke’s Hospital, Cambridge, United Kingdom.
3Metabolic Research Laboratories, Institute of Metabolic Science, University of Cambridge, Cambridge, United Kingdom.
4Siemens AG Healthcare Sector, Erlangen, Germany.
5Department of Pediatrics, University of Parma, Parma, Italy.
6University Hospitals Bristol National Health Service Trust, Bristol, United Kingdom.
7Hillingdon Hospital, Hillingdon, United Kingdom.
8Royal Alexandra Hospital, Paisley, United Kingdom.
9Medical Research Council Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, United Kingdom.
10Cancer Research UK Cambridge Research Institute, Li Ka Shing Centre, Cambridge, United Kingdom.
11Department of Musculoskeletal Biology and Magnetic Resonance and Image Analysis Research Centre, University of Liverpool, Liverpool, United Kingdom.

Address correspondence to: D.B. Savage, Metabolic Research Laboratories, Institute of Metabolic Science, University of Cambridge, Addenbrooke’s Hospital, Hills Road, Cambridge CB2 0QQ, United Kingdom. Phone: 44.1223.767.923; Fax: 44.1223.330.598; E-mail: dbs23@medschl.cam.ac.uk.

Authorship note: Robert K. Semple and David B. Savage contributed equally to this work.

Published May 9, 2011
Received for publication January 14, 2011, and accepted in revised form March 23, 2011.

Mitochondrial dysfunction is associated with insulin resistance and type 2 diabetes. It has thus been suggested that primary and/or genetic abnormalities in mitochondrial function may lead to accumulation of toxic lipid species in muscle and elsewhere, impairing insulin action on glucose metabolism. Alternatively, however, defects in insulin signaling may be primary events that result in mitochondrial dysfunction, or there may be a bidirectional relationship between these phenomena. To investigate this, we examined mitochondrial function in patients with genetic defects in insulin receptor (INSR) signaling. We found that phosphocreatine recovery after exercise, a measure of skeletal muscle mitochondrial function in vivo, was significantly slowed in patients with INSR mutations compared with that in healthy age-, fitness-, and BMI-matched controls. These findings suggest that defective insulin signaling may promote mitochondrial dysfunction. Furthermore, consistent with previous studies of mouse models of mitochondrial dysfunction, basal and sleeping metabolic rates were both significantly increased in genetically insulin-resistant patients, perhaps because mitochondrial dysfunction necessitates increased nutrient oxidation in order to maintain cellular energy levels.

Insulin resistance underpins the tight association between the type 2 diabetes and obesity pandemics. Yet despite enormous scientific endeavor, understanding of the molecular pathogenesis of insulin resistance remains incomplete. One of the most characteristic and consistent metabolic features of the obese insulin-resistant state is lipid accumulation in sites other than white adipose tissue, so-called ectopic fat (1). Triglyceride accumulation in the liver and skeletal muscle is strongly associated with insulin resistance in these tissues, and while triglyceride is not itself thought to cause insulin resistance, more reactive lipid species, such as diacylglycerol and ceramide, are believed to impair insulin action (1).

Ectopic fat accumulation presumably reflects a cellular mismatch between the sum of lipid delivery and synthesis and the sum of lipid oxidation and disposal. A combination of recent human and rodent studies have convincingly documented mitochondrial abnormalities in insulin-resistant and diabetic states, highlighting the potential importance of impaired mitochondrial fat oxidation in the accumulation of ectopic fat and giving rise to the suggestion that mitochondrial dysfunction may be the primary defect in prevalent obesity-related insulin resistance (reviewed in ref. 2). These studies include ex vivo morphological and biochemical analyses of tissue samples and/or isolated mitochondria (3–6) as well as noninvasive in vivo magnetic resonance spectroscopy (MRS) measures (7–13). However, although the associations appear robust, the direction of causality remains uncertain (2, 14).

Where human genetic variants are identified that directly produce 1 out of 2 associated phenomena, investigating whether those same genetic variants are also associated with the second phenomenon offers a powerful means of investigating causality in the association. Such Mendelian randomization is now increasingly used in large human epidemiological genetic studies (15); however, the principle is also applicable to rare monogenic disorders. A classic early example of this was the association observed between rare mutations in the LDLR gene, which produce high LDL cholesterol, and premature atherosclerotic disease (16), establishing that high levels of LDL cholesterol are causally linked to atherosclerosis.

Studying patients with mitochondrial genetic defects producing primary mitochondrial dysfunction seems at first sight to offer the potential to conduct a similar Mendelian randomization experiment to test whether mitochondrial dysfunction in humans can produce insulin resistance, but this is likely to be confounded by deleterious effects of generalized mitochondrial dysfunction on ß cell and muscle function (17, 18). However, patients with congenital, severe insulin resistance due to mutations in the insulin receptor (INSR) gene are well described and, though uncommon, afford instead the reciprocal opportunity to test whether primary insulin resistance can produce mitochondrial dysfunction, potentially accounting for some but not necessarily all of their observed association in commoner forms of insulin resistance.

Characteristics of the participants. Seven patients from 4 unrelated families with dominant-negative heterozygous missense mutations (P1236A, A1135E, M1138K, and A1121P) affecting highly conserved residues within the tyrosine kinase domain of the insulin receptor were selected for mitochondrial function studies. Such mutations are well known to produce severe insulin resistance with an autosomal dominant pattern of inheritance (19). All patients had acanthosis nigricans, a cutaneous marker of severe insulin resistance, and the typical biochemical profile of patients with primary insulin receptor defects (“insulin receptoropathies”), including severe hyperinsulinemia and normal triglyceride and HDL cholesterol levels, usually with preserved or elevated serum adiponectin levels (Supplemental Table 1; supplemental material available online with this article; doi: 10.1172/JCI46405DS1). Glycated hemoglobin levels were normal in 4 INSR participants and only minimally elevated in the other 3 (Supplemental Table 1). Intramyocellular lipid levels were similar to those of matched controls in 3 out of 4 INSR mutation carriers (Supplemental Table 1), which is in keeping with the similarly normal hepatic fat levels previously documented in this disorder (20).

As well as being age-, gender-, and BMI-matched (Table 1), control volunteers (n = 12) were deliberately selected for being sedentary, as none of the patients with INSR mutations undertook regular exercise. Fasting biochemical parameters were all within normal limits in the healthy controls (Table 1). All patients and controls wore an Actiheart monitor (CamNtech) and underwent a standard graded exercise calibration test, from which we derived an estimate of maximal oxygen consumption (VO2 max), measured in ml/kg of fat-free mass/min. Results from these estimates were similar in the 2 groups (Table 1).

Table 1 Characteristics of the healthy volunteers and patients with severe insulin resistance due to loss-of-function INSR mutations

Assessment of oxidative phosphorylation function in vivo. The theory of measuring chemical exchange rates using nuclear magnetic resonance magnetization transfer techniques was first introduced by Forsen and Hoffman in 1963 (21). Its application to measure ATP synthesis rates in muscle in vivo has been widely used despite concerns that the measurement will contain a nonoxidative, glycolytic component that could be as large as 80% at rest and that resting ATP turnover may not be the most relevant measure of mitochondrial function (22–27). An alternative approach is to assess the kinetics of replenishment of the phosphocreatine (PCr) pool after exercise, which relies purely on oxidative ATP synthesis (28). This is most conveniently quantified as the PCr recovery half time (t1/2), defined as the time taken for PCr to recover by half the amount it was depleted, which is inversely proportional to functional “mitochondrial capacity.” A recent study in which rats were treated with the mitochondrial complex 1 inhibitor diphenyleneiodonium highlighted reservations about measuring ATP turnover using magnetization (saturation) transfer techniques by documenting a significant slowing of PCr recovery but no change in the rate of ATP synthesis determined from saturation transfer (ST) measurements (26). Given these concerns, we determined both resting ST and postexercise PCr recovery in this study.

All participants completed the MRS scans, apart from 2 INSR subjects who did not undertake the ST measurement due to claustrophobia. The rate of ATP synthesis measured with the ST technique (ST VATP) was similar in both groups (Figure 1, A and B, and Table 1), whereas the rate of PCr recovery after exercise was significantly slower in patients with severe insulin resistance due to INSR mutations than that in healthy controls (Figure 1, C and D, and Table 1). This difference remained significant after correcting for minor differences in VO2 max (Table 1). There was no correlation between ST VATP and the postexercise PCr recovery half time (t1/2) (Spearman’s rho, r = –0.235, P = 0.363).

Figure 1 31P MRS measurements of mitochondrial function. (A) Representative ST spectra, with saturation of the ?-ATP resonance (right, bottom) and corresponding control spectrum (right, top). The 2 spectra are superimposed (left) to show the difference (?) in the Pi resonance. (B) ST VATP in both the controls (white bars; n = 12) and in patients with INSR mutations (black bars; n = 5). (C) Mean fractional PCr recovery curves for controls (gray squares; n = 12) and for patients with INSR mutations (black circles; n = 7). Five spectra were averaged to give a time resolution of 10 seconds for clarity in this figure. The monoexponential fit of the mean recovery rate constant is shown for controls (gray line) and INSR patients (black line). (D) Half time for PCr recovery (t1/2) as measured from the recovery rate after exercise for both controls (white bars; n = 12) and for patients with INSR mutations (black bars; n = 7). In B–D, data are mean ± SEM.

The significantly slowed rate of PCr recovery after exercise in the INSR patients shows that insulin resistance due to a well-defined primary defect in insulin signaling is associated with evidence of mitochondrial dysfunction in vivo. This suggests that the association between mitochondrial dysfunction and insulin resistance reported in prevalent forms of insulin resistance of unknown etiology cannot be assumed to imply that mitochondrial dysfunction causes insulin resistance. Our data are consistent with the mitochondrial dysfunction reported in insulin-deficient patients with type 1 diabetes (29) and several recent murine studies reporting mitochondrial dysfunction in mice with either primary genetic defects in the insulin signaling cascade (30–32) or high-fat feeding–induced insulin resistance, in which the insulin resistance was shown to precede mitochondrial dysfunction (33).

Metabolic rate measurements. Changes in body weight reflect a mismatch between energy intake and energy expenditure. Although human genetic studies increasingly suggest that changes in energy intake are the primary driver of weight changes in most cases in humans, changes in metabolic rate can also lead to weight gain or weight loss. For example, thyrotoxicosis promotes mitochondrial uncoupling and ultimately weight loss (34). As well as being a major determinant of activity-associated energy expenditure, skeletal muscle is also a significant contributor (~20%–30%) to resting energy expenditure (35). In order to determine whether insulin resistance and/or mitochondrial dysfunction might alter metabolic rate, we evaluated basal metabolic rate (BMR) and sleeping metabolic rate (SMR) in our patients with INSR mutations and in the healthy controls. Surprisingly, we found that both the BMR and SMR were significantly increased in patients with INSR mutations when corrected for fat-free mass (Figure 2). While both these independent measures of metabolic rate reflect whole body rather than just skeletal muscle energy metabolism, they suggest that reduced mitochondrial function need not lead to a reduction in resting energy expenditure.

Figure 2 Energy expenditure in patients with loss-of-function INSR mutations. BMR and SMR in healthy controls (white bars; n = 11) and in patients with INSR mutations (black bars; n = 7). Results are expressed per kilogram of fat-free mass (FFM). Data are mean ± SEM.

These human observations contradict the intuitive expectation that defects in mitochondrial energy metabolism will lead to reduced energy expenditure. Nevertheless, the data are consistent with observations in mice with primary defects in mitochondrial function, in which the resultant increase in cellular AMP levels activate AMP kinase, producing a lean insulin-sensitive phenotype (36). The primary determinant of the rate of oxidative phosphorylation is thought to be the homeostatic need to defend the cellular energy charge, so one of a number of possible explanations for these observations is that mitochondrial dysfunction necessitates increased nutrient oxidation in order to maintain cellular energy levels.

Germline dominant-negative defects in the insulin receptor gene may confidently be assumed to produce congenital, severe insulin resistance, and co-inheritance of a primary mitochondrial defect in all the unrelated patients studied is vanishingly unlikely. Thus the association of genetic defects in insulin receptor function with impaired oxidative phosphorylation in vivo may be taken to establish that primary insulin resistance can produce secondary mitochondrial dysfunction. Such an observation in one group with a rare monogenic form of insulin resistance is not necessarily directly transposable to the situation in prevalent obesity-related insulin resistance, which most likely encompasses a heterogeneous group of postreceptor defects. Nevertheless there is currently no evidence that our findings are not generalizable to prevalent insulin resistance, and, even viewed most conservatively, they demonstrate that the association between prevalent insulin resistance and mitochondrial dysfunction must not be assumed to be solely accounted for by a unidirectional effect of primary mitochondrial dysfunction on insulin sensitivity.

Participants. Each participant provided written informed consent, and all studies were conducted in accordance with the principles of the Declaration of Helsinki. Clinical studies were approved by the National Health Service Research Ethics Committee, United Kingdom, and were conducted in the WTCRF.

Patients with severe insulin resistance due to loss-of-function mutations in the INSR gene were identified as part of a long-standing program of research into genetic and acquired forms of severe insulin resistance. Patients with features of Donohue syndrome or Rabson-Mendenhall syndrome were not included, as these patients tend to manifest severe metabolic disturbances, including poorly controlled hyperglycemia, a known cause of mitochondrial dysfunction (37). Instead we recruited patients with normal or near-normal glycated hemoglobin (HBA1C) levels. Healthy age-, gender-, and BMI-matched control volunteers were recruited by advertisement. All were sedentary non-smokers without medical disorders likely to affect energy metabolism and without a family history of diabetes.

Experimental protocol and magnetic resonance studies. See the Supplemental Methods for details regarding these studies.

Biochemical assays. Insulin, leptin, and adiponectin were measured as previously described (20).

Statistics. All statistics were performed in SPSS PASW Statistics 18 (SPSS Inc.). Quantitative data are presented as mean ± SEM. The 2-tailed independent-sample t test was used to compare means between groups, with significance classed as P < 0.05.

View Supplemental data

We thank all the participants and J. Harris, L. McGrath, and the staff of the WTCRF for assistance with clinical studies. This work was supported by grants from the Wellcome Trust (to S. O’Rahilly, R.K. Semple, and D.B. Savage), the UK National Institute for Health Research Cambridge Biomedical Research Centre, the UK Medical Research Council Centre for Obesity and Related Metabolic Diseases, and the Clinical Research Infrastructure Grant.

Conflict of interest: The authors have declared that no conflict of interest exists.

Citation for this article: J Clin Invest doi:10.1172/JCI46405.

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Exhaustion of tumor-specific CD8+ T cells in metastases from melanoma patients

J Clin Invest. doi:10.1172/JCI46102.
Copyright © 2011, The American Society for Clinical Investigation. Lukas Baitsch1, Petra Baumgaertner1, Estelle Devêvre1, Sunil K. Raghav2, Amandine Legat1, Leticia Barba1, Sébastien Wieckowski3, Hanifa Bouzourene3, Bart Deplancke2, Pedro Romero4, Nathalie Rufer1,3 and Daniel E. Speiser1

1Clinical Tumor Immune-Biology Unit, Ludwig Institute for Cancer Research, Lausanne, Switzerland.
2Laboratory of Systems Biology and Genetics, Institute of Bioengineering, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.
3University Hospital Center and University of Lausanne, Lausanne, Switzerland.
4Translational Tumor Immunology Group, Ludwig Institute for Cancer Research, Lausanne, Switzerland.

Address correspondence to: Daniel Speiser, Ludwig Institute for Cancer Research, Hôpital Orthopédique, 05/1552, Av. P.-Decker 4, CH-1011 Lausanne, Switzerland. Phone: 41.21.314.0182; Fax: 41.21.314.7477; E-mail: d.e.speiser@gmail.com.

Published May 9, 2011
Received for publication December 14, 2010, and accepted in revised form March 16, 2011.

In chronic viral infections, CD8+ T cells become functionally deficient and display multiple molecular alterations. In contrast, only little is known of self- and tumor-specific CD8+ T cells from mice and humans. Here we determined molecular profiles of tumor-specific CD8+ T cells from melanoma patients. In peripheral blood from patients vaccinated with CpG and the melanoma antigen Melan-A/MART-1 peptide, we found functional effector T cell populations, with only small but nevertheless significant differences in T cells specific for persistent herpesviruses (EBV and CMV). In contrast, Melan-A/MART-1–specific T cells isolated from metastases from patients with melanoma expressed a large variety of genes associated with T cell exhaustion. The identified exhaustion profile revealed extended molecular alterations. Our data demonstrate a remarkable coexistence of effector cells in circulation and exhausted cells in the tumor environment. Functional T cell impairment is mediated by inhibitory receptors and further molecular pathways, which represent potential targets for cancer therapy.

CD8+ T cell responses in acute viral diseases have been extensively characterized in mice and humans (1–6). While viruses multiply rapidly during the first week of infection, CD8+ T cells become activated and expand vigorously, reaching a peak of T cell effector function. In parallel with the consequent decline of viral antigen, the majority of CD8+ T cells undergo apoptosis (contraction phase). After pathogen clearance, memory T cells persist for years at low frequencies, ready for accelerated protective immune responses in case of reinfection.

When pathogens are not eliminated, T cells may persist in much larger numbers. They are composed of large numbers of effector cells and low percentages of memory cells. Essentially, there are 2 scenarios of long-term CD8+ T cell activity in viral infection: the first scenario is observed, e.g., in persistent herpesvirus infection (e.g., EBV, CMV) in healthy individuals, where T cells successfully contain the viruses and thus are protective even though they do not eliminate the viruses entirely. The second scenario is associated with viral spread and progressive tissue damage in the presence of large numbers of CD8+ T cells, e.g., in HIV-1, HBV, or HCV infection, and in the murine model of lymphocytic choriomeningitis virus clone 13 (LCMV clone 13) infection. These 2 scenarios are distinguished by a fundamentally different functional competence of CD8+ T cells. In the first scenario, such as in healthy donors infected with EBV or CMV, viral antigen-specific T cells are functionally competent and thus ready for immediate cytokine production and cytotoxicity (7). These cells contribute to rapid reduction of viral load and restoration of health by viral containment to small anatomical compartments (8–10). In contrast, CD8+ T cells in the second scenario (i.e., the failure of viral containment) are functionally impaired (11, 12). The murine infection with LCMV clone 13 is a prototype model of functional T cell impairment, called T cell exhaustion, with progressively reduced production of IL-2, TNF-a, and IFN-?, followed by incapacity to lyse (infected) target cells (13, 14). Analysis of such cells has led to significant discoveries, such as the identification of PD-1, a major inhibitory receptor involved in T cell function (15). Gene expression profiling of murine T cells allowed a global assessment, revealing that T cell exhaustion is associated with numerous molecular alterations, affecting genes regulating chemotaxis, adhesion, coreceptors, migration, metabolism, and energy (2). Hereafter, we call these multiple changes exhaustion profile.

In humans, functional deficits were found in HIV-1–, HCV-, and HBV-specific CD8+ T cells (11, 16, 17), and a recent gene expression study described an exhaustion profile in HIV-1 patients (18). In contrast to virus-specific T cells, only little is known of self- and tumor-specific CD8+ T cells. In humans and mice, it remains to be determined whether functional impairments of bona fide self-antigen–specific T cells represent exhaustion, anergy, or other functional states. In melanoma patients, there are substantial numbers of long-term persisting effector-memory CD8+ T cells, despite failures of immune protection from disease. Circulating human tumor-specific CD8+ T cells may be cytotoxic and produce cytokines in vivo (19–21), indicating that self- and tumor-specific human CD8+ T cells can reach functional competence after potent immunotherapy such as vaccination with peptide, incomplete Freund’s adjuvant (IFA), and CpG (19) or after adoptive transfer (22). In contrast to peripheral blood, T cells from metastasis are functionally deficient, with abnormally low cytokine production and upregulation of the inhibitory receptors PD-1, CTLA-4, and TIM-3 (20, 23–25). Functional deficiency is reversible, since T cells isolated from melanoma tissue can restore IFN-? production after short-term in vitro culture (20). However, it remains to be determined whether this functional impairment involves further molecular pathways, possibly resembling T cell exhaustion or anergy as defined in animal models (2, 26).

The identification of mechanisms responsible for functional impairment of self- and tumor-specific T cells may reveal targets for novel cancer therapies. Human CD8+ T cell responses specific for the melanoma antigen Melan-A/MART-1 represent a model wherein self-specific T cells can be studied in great detail. Furthermore, we took advantage of the strong immunogenicity of vaccination with peptide plus CpG (19). By direct ex vivo analysis, we compared Melan-A/MART-1–specific T cells (hereafter called tumor-specific T cells) with virus-specific T cells by microarray analysis, quantitative PCR (qPCR), and flow cytometry. Recent studies focused on circulating T cells (27), whereas T cells residing in tumor tissue remain poorly characterized. Therefore, we isolated T cells from both peripheral blood and metastases. We found that the former show molecular and functional features of effector cells, similar to circulating CMV-specific T cells, demonstrating that human self- and tumor-specific T cells have the potential to become competent effector cells. In marked contrast, the tumor-specific T cells isolated from metastatic tissue displayed an exhaustion profile, consisting of large numbers of molecular alterations.

Naive and virus-specific T cells show no significant differences between melanoma patients and healthy donors. Recently we demonstrated reproducibility of gene expression profiling of small numbers of (1,000) T cells (28). Applying this technique (Supplemental Figure 1, A–D; supplemental material available online with this article; doi: 10.1172/JCI46102DS1), we analyzed naive and antigen-specific T cells upon sorting of PBMC subsets by flow cytometry. We compared gene expression profiles of naive CD8+ T cells from melanoma patients and healthy donors and found no significant differences (Figure 1A), confirming previous studies (29). For the isolation of antigen-specific cells, we used tetramers and sorted T cells specific for the tumor antigen Melan-A/MART-1, the EBV antigen BMLF1, and the CMV antigen pp65. We compared EBV-specific T cells between healthy donors and patients and did not observe significant differences in gene expression (Figure 1B). In parallel, we found similar phenotypes and similar IFN-? production (Figure 1, C and D). Thus, many CD8+ T cells appeared relatively normal in our patients.

Figure 1 Naive and virus-specific T cells show no significant differences between melanoma patients and healthy donors. (A and B) Volcano plots for all gene probes on the microarray, showing expression differences and P values of naive T cells (healthy donors [HDs] versus patients; A) or EBV-specific T cells (healthy donors versus patients; B). Each point represents 1 gene probe. (C) Lymphocytes were stained with an A2/EBV BMLF1280–288 tetramer together with anti-CD8, anti-CD45RA, and anti-CCR7. The inset shows a dot plot distinguishing the phenotypes among total CD8+ T cells analyzed as controls: naive (N) (CD45RA+CCR7+), central memory (CM) (CD45RA–CCR7+), effector memory (EM) (CD45RA–CCR7–) and effector memory RA+ cells (EMRA) (CD45RA+CCR7–). Bar graph depicts the percentage (mean ± SD) of each phenotype of total CD8+ cells or total EBV tetramer-positive populations from healthy donors or patients. (D) IFN-? production by EBV-specific T cells upon 4-hour stimulation. Whiskers in box plots indicate maximum and minimum values measured. Cross indicates the mean, while line indicates the median.

Gene expression profiling of naive versus nonnaive T cells. Before analyzing tumor-specific T cells, we validated our approach using only 1,000 cells, by searching for the known molecular differences between naive and nonnaive CD8+ T cells (28, 30). We selected genes showing a 3-fold or greater change between naive and nonnaive CD8+ T cells, plus a P value adjusted for the false discovery rate (FDR) of less than 0.05 (Supplemental Figure 2A). With this strategy, we identified 409 upregulated and 364 downregulated genes in naive relative to nonnaive CD8+ T cells (Supplemental Table 1) and found that all naive T cell populations clustered together and apart from all nonnaive T cells (Figure 2A). We selected 8 genes for verification by qPCR. Without exception, they confirmed the microarray results, whereby qPCR detected quantitatively larger differences, owing to the higher sensitivity of qPCR (Supplemental Figure 2B). Additionally, the data for many of the differentially expressed genes (e.g., CCR7, LEF1, SELL, IFNG, GZMB, and HLADR; Supplemental Figure 2C) confirmed well-known differences between naive and nonnaive T cells.

Figure 2 Gene expression of naive and effector T cells from peripheral blood. (A) Two-way hierarchical clustering based on the identified 773 genes, separating all naive from nonnaive T cells. Red indicates overexpression and blue underexpression relative to the mean. Each row represents 1 gene and each column 1 1,000-cell sample from 1 patient or healthy donor. (B) Relative overexpression of GO terms associated with the identified genes, calculated as described in Methods. (C and D) GSEA of publicly available gene sets describing naive and effector T cells. Positions of selected example genes are indicated. Gene sets comprise genes enriched in naive T cells (C) or in effector cells (D). Genes to the left and right of the rank-ordered list are enriched in naive T cells and nonnaive T cells, respectively.

We assessed biological classification of the 773 differentially expressed genes by assigning them to 9 Gene Ontology (GO) terms and then determined whether any of these GO terms were overrepresented in our list compared with the predicted frequency in a random gene list. Not surprisingly, we found about twice as many immune response genes as the number predicted from a random gene test (Figure 2B). Additionally, the GO terms for translation, cell death, and apoptosis were overrepresented in nonnaive cells, whereas genes involved in DNA repair were underrepresented.

In 2005, Willinger et al. made a thorough gene expression analysis of human CD8+ T cells from healthy donors without distinction of antigen specificity (31). They determined large differences between naive and total effector cells, providing gene sets characteristic for the distinction of the 2 populations. From these data, we used 2 gene sets, one which is up- and one which is downregulated in effector CD8+ T cells. Furthermore, in 2007, Wherry et al. defined gene sets that were up- or downregulated in antigen-specific memory, effector, and exhausted CD8+ T cells from LCMV-infected mice (2). While the gene sets from Willinger et al. described long-term effects of effector differentiation (analysis of total human CD8+ T cell subsets in steady state), the gene sets from Wherry et al. described shorter-term changes of gene expression (model of acute and chronic infection). With Gene Set Enrichment Analysis (GSEA), we determined whether gene sets were enriched in our rank-ordered list of differentially expressed genes. Our naive T cells showed upregulation of the 2 gene sets downregulated in effector cells as identified by Wherry et al. (ref. 2 and Figure 2C) and by Willinger et al. (ref. 31 and Figure 2C). Conversely, the gene sets enriched in our nonnaive T cells were those upregulated in effector cells as defined by Wherry et al. (Figure 2D) and by Willinger et al. (Figure 2D). Together, these data confirm the reproducibility of microarray analysis of highly purified cells, validating our approach of ex vivo analysis of antigen-specific T cells with small cell numbers.

Different gene expression profiles between circulating tumor- and virus-specific T cells. A major aim of our study was to determine whether tumor-specific CD8+ T cells were similar to or different from virus-specific T cells. By applying the same selection criteria as above (i.e., fold change = 3, adjusted P < 0.05), we found 390 genes that were differentially expressed between tumor- and EBV-specific T cells (259 upregulated and 131 downregulated) (Figure 3A and Supplemental Table 2), while only 184 genes (72 upregulated and 112 downregulated) were differentially expressed when compared with CMV-specific T cells (Figure 3B and Supplemental Table 3). Therefore, the differences between CMV- and tumor-specific T cells were smaller than between EBV- and tumor-specific T cells. A 2-way hierarchical clustering with these probes showed clear distinction between tumor- and EBV-specific (Figure 3C) and tumor- and CMV-specific T cell populations (Figure 3D) from the individual patients and healthy donors despite the high genetic heterogeneity between individuals and the similarity of surface markers of these T cell populations (Supplemental Figure 3A). Microarray data were confirmed through the analysis of a series of genes by qPCR, among them several inhibitory receptors (Figure 3, E and G). As compared with both EBV- and CMV-specific cells, TIM3 and CTLA4 were upregulated in tumor-specific T cells, while CD160 was upregulated in virus-specific T cells (Figure 3, E and G). 2B4 was upregulated in CMV-specific T cells. Interestingly, as compared with EBV-specific T cells, tumor-specific T cells expressed more mRNA encoding granzyme B (GZMB) and granulysin (GNLY), but less XCL1 (lymphotactin). XCL1 was also upregulated in CMV-specific T cells. Finally, we performed a GO term analysis and found that the differences between tumor- and the 2 virus-specific T cell populations were smaller (Figure 3, F and H) than the differences of naive versus nonnaive T cells (Figure 2B). Remarkably, immune response genes were not specifically overrepresented relative to a random gene list, suggesting overall similar expression of immune genes in effector T cells specific for EBV, CMV, and Melan-A/MART-1, despite the differences found for inhibitory receptors.

Figure 3 Gene expression of circulating CD8+ T cells depending on antigen specificity. (A and B) Volcano plots for all gene probes, showing differential expression and P values of the comparison of tumor- versus EBV-specific T cells (A) or tumor- versus CMV-specific T cells (B); diagramming is similar to that in Figure 1. (C and D) Two-way hierarchical clustering based on the identified gene probes separating all tumor-specific T cells from EBV- (C, 405 gene probes corresponding to 390 genes) and from CMV-specific T cells (D, 187 gene probes corresponding to 184 genes). Red indicates overexpression and blue underexpression relative to the mean. Each row represents 1 gene and each column 1 1,000-cell sample from 1 patient (tumor-, EBV- and CMV-specific cells) or healthy donor (EBV-specific cells, n = 4). (E and G) Log fold changes between tumor- and EBV- (E) or tumor- and CMV-specific T cells (G) of data from microarrays (blue bars) and qPCR (red bars). Positive and negative values indicate overexpression in tumor- and in virus-specific T cells, respectively. Data are represented as mean ± SEM. (F and H) Relative overexpression of GO terms associated with the identified 390 genes (Melan-A/MART-1 versus EBV; F) or with the identified 184 genes (Melan-A/MART-1 versus CMV; H), calculated as described in Methods.

The gene expression profile of circulating tumor-specific CD8+ T cells corresponds to late-differentiated effector cells. EBV- and CMV-specific T cells are recognized as prototypes of early- and late-differentiated effector cells, respectively (7). This distinction fits with the phenotypes of these 2 populations (Supplemental Figure 3A). We created rank-ordered gene lists to compare tumor-specific with the 2 virus-specific CD8+ T cell populations. The gene sets defined as upregulated in effector cells by Wherry et al. (ref. 2 and Figure 4A) and Willinger et al. (ref. 31 and Figure 4A) were enriched in tumor-specific cells, as compared with their EBV-specific counterparts. In contrast, the only gene set enriched in EBV-specific T cells compared with tumor-specific T cells was the small gene set containing genes specifically upregulated in memory cells when compared with naive CD8+ T cells as defined by Wherry et al. (Figure 4B). This is likely due to the lower degree of effector differentiation of EBV-specific T cells (which are nevertheless predominantly effector rather than memory cells; Supplemental Figure 3A). When we compared tumor- with CMV-specific CD8+ T cells, we could not find enrichment for any gene set (Figure 4C), confirming the late differentiation stage of tumor-specific T cells. To verify the differential expression of granzyme B and perforin ex vivo on the protein level, we performed intracellular staining. As expected, the tumor- and CMV-specific CD8+ T cells expressed more granzyme B and perforin than the EBV-specific CD8+ T cells (Figure 4D). However, all 3 antigen-experienced T cells produced high levels of IFN-? after 4 hours triggering with peptide-loaded T2 cells (Supplemental Figure 3B). Together, our results demonstrate that tumor- and CMV-specific CD8+ T cells resembled each other closely, while EBV-specific CD8+ T cells were in earlier stages of effector differentiation.

Figure 4 Circulating tumor-specific T cells are late-differentiated effector cells, resembling CMV-specific T cells. (A) Gene set enrichment of genes describing effector cells (see Figure 2D). Genes to the left and right of the rank-ordered list are enriched in tumor- and EBV-specific T cells, respectively. (B) Gene set enrichment of genes describing memory cells (2). Genes to the left and right of the rank-ordered list are enriched in tumor- and EBV-specific T cells, respectively. (C) No differences were found between Melan-A/MART-1– and CMV-specific T cells, demonstrated by a gene set defining effector cell–related genes (31). (D) Intracellular staining of naive and antigen-specific T cells. Top panels show 1 representative example; below are the combined results of all samples (EBV and CMV, n = 5; Melan-A, n = 15; naive, n = 25). Data of EBV- and CMV-specific T cells are from healthy donors, while data of tumor-specific T cells are from patients. ***P < 0.001. Whiskers in box plots indicate maximum and minimum values measured. Line indicates the median.

In contrast to circulating T cells, tumor-specific T cells from tumor-infiltrated lymph nodes show an exhaustion profile. Previous studies indicated that functional impairment of tumor-specific T cells may occur primarily in situ (20, 24), which was also the case after strong systemic T cell activation by CpG-based vaccination (25). Therefore, we established a clinical investigation protocol to recover large numbers of live cells from tumor-infiltrated lymph nodes (TILN). This enabled us to perform functional studies and gene expression analysis ex vivo from tumor-specific T cells from TILN, in comparison with circulating T cells. Tumor-specific T cells from metastases showed highly insufficient IFN-? production upon 4-hour peptide triggering (Figure 5A), as published previously (20, 24). Microarray analysis allowed the identification of 332 genes (201 up- and 131 downregulated in TILN; Supplemental Table 4) that were differentially expressed between tumor-specific CD8+ T cells from PBMC versus TILN, using the same criteria as before (Figure 5B). Hierarchical clustering using these genes divided the 13 samples into 2 groups only, one for blood and the other for TILN-derived tumor-specific T cells (Figure 5C). qPCR performed for a selection of genes allowed proper validation (Figure 5D). Among the genes upregulated in tumor-specific cells from TILN were the lymph node retention receptor CRTAM, the chemokines XCL1 and XCL2, the activation marker TNFRSF9, and the inhibitory receptor CTLA4. CXCL13, a B cell chemoattractant usually found in the B cell compartment of lymph nodes, was one of the most highly overexpressed genes. Among the genes downregulated in TILN cells were the cell-growth–regulating protein LYAR and the inhibitory receptor KLRG1. When classifying the differentially expressed genes into broad GO terms, we found that genes involved in cell death and apoptosis and in the immune response were overrepresented compared with a randomly selected gene list (Figure 5E). To obtain a more general overview of the differences of tumor-specific CD8+ T cells from blood versus TILN, we studied gene sets specific for effector cells, naive cells, memory cells, and exhausted cells, as described above. Remarkably, the gene set described for exhausted T cells (2) was significantly enriched in tumor-specific cells from TILN, in contrast with the gene sets characteristic for naive, memory, and effector T cells (Figure 5F). These large-scale data demonstrate an impressive exhaustion profile, with extended molecular alterations of multiple pathways in tumor-specific CD8+ T cells from metastases.

Figure 5 Exhaustion profile of tumor-specific T cells in situ. (A) IFN-? production by tumor-specific T cells from the circulation (blood; n = 6) or TILN (n = 8) after 4-hour antigen stimulation. Whiskers in box plots indicate maximum and minimum values measured. Cross indicates the mean, while line indicates the median. **P < 0.01. (B) Differential gene expression by tumor-specific T cells isolated from blood versus TILN, as illustrated by a volcano plot for all gene probes. (C) Two-way hierarchical clustering based on the identified 346 genes separating all blood-derived tumor-specific T cells from their TILN counterparts. Red indicates overexpression and blue underexpression relative to the mean. Each row represents 1 gene and each column 1 1,000-cell sample from 1 patient. (D) Log fold change between tumor-specific T cells from blood versus TILN; data from microarrays (blue bars) and qPCR (red bars). Positive and negative values indicate overexpression in tumor-specific T cells from TILN and from blood, respectively. Mean ± SEM. (E) Relative overexpression of GO terms associated with the identified genes, calculated as described in Methods. (F) Enrichment of the gene set described for exhausted T cells (2) in TILN-derived tumor-specific T cells, relative to their blood-derived counterparts. The positions of inhibitory receptors found in this gene set on the rank-ordered gene list are indicated. A position to the left indicates enrichment in TILN-derived cells, a position to the right enrichment in blood-derived cells.

Enhanced expression of inhibitory receptors, such as CTLA4 and LAG3, was observed in T cell exhaustion (2, 23, 32–34). Interestingly, their expression was enriched in TILN cells, with the notable exceptions of PTGER2 and KLRG1 (Figure 5F). However, KLRG1 was described as more strongly expressed in functionally competent effector cells than in exhausted T cells (2), compatible with our data. The absolute expression values of selected inhibitory receptors are detailed in Table 1. Although it seems likely that the tumor microenvironment plays a role, the reasons for the observed enhanced expression of inhibitory receptors remain to be elucidated.

Table 1 Expression of selected inhibitory receptors by tumor-specific T cells

Differential protein expression of multiple inhibitory receptors by tumor- and virus-specific CD8+ T cells. To determine expression of inhibitory receptors at the protein level, we produced tetramers labeled with (multiple) different fluorochromes and used them in combination with several monoclonal antibodies (multi-tetramer staining; Figure 6A). Compatible with mRNA data, CD160 and 2B4 were more frequently expressed by both EBV- and CMV-specific T cells than by tumor-specific T cells from peripheral blood (Figure 6B), in agreement with a study reporting that most CD160+ cells coexpressed 2B4 (35). In contrast, circulating tumor-specific T cells expressed more TIM-3 and more PD-1 than the 2 virus-specific T cell populations (Figure 6B), in line with 2 recent reports of TIM-3+PD-1+ cells among tumor-specific T cells (23, 36). Large percentages of PD-1+ tumor-specific T cells coexpressed TIM-3 and/or KLRG-1. Similar results were obtained when we analyzed the mean fluorescence intensity (Supplemental Figure 4). Our technique allowed analyzing simultaneous coexpression of multiple inhibitory receptors, for CD160, KLRG-1, PD-1, and TIM-3, or for 2B4, LAG-3, and CTLA-4. We found a pronounced increase in inhibitory receptor coexpression from naive to central memory, effector memory, and effector memory RA+ cells (data not shown). On antigen-specific T cells, there were various combinations of inhibitory receptors. Melan-A–specific T cells from TILN expressed more CTLA-4, LAG-3, and TIM-3, but less KRLG-1 than their counterparts from peripheral blood (Figure 6C), confirming the results obtained by the microarray analysis. These data reveal a high level of heterogeneity, with multiple antigen-specific T cell subpopulations expressing different combinations of inhibitory receptors. It is likely that many of these subpopulations are effector memory cells and effector memory RA+, as they make up the vast majority of Melan-A–specific T cells (Supplemental Figure 3A). Naive and central memory cells were infrequent, but may nevertheless contribute to this heterogeneity. Furthermore, extended studies are necessary to determine the functional impact of coexpressed inhibitory receptors. Finally, the marked differences between tumor-, CMV-, and EBV-specific T cells suggest different roles of inhibitory receptors in viral infection versus cancer.

Figure 6 Multi-tetramer staining assessing coexpression of inhibitory receptors. (A) Staining with tetramers binding to EBV- (PE–Texas Red), Melan-A/MART-1– (APC–eFluor 780), or CMV- (PE–Texas Red and APC–eFluor 780) specific T cells (labeling tetramers with 2 instead of 1 fluorochrome identifies larger numbers of epitope-specific T cell populations than the number of fluorescence channels used). T cells were analyzed for coexpression of 7 inhibitory receptors: KLRG-1 (Alexa Fluor 488), TIM-3 (PE), PD-1 (PerCP-eFluor710), and CD160 (Alexa Fluor 647), or LAG-3 (FITC), 2B4 (PE-Cy5.5), and CTLA-4 (APC). (B) Expression of 7 different inhibitory receptors. Histograms of a representative sample are gated on CD8+ tetramer+ cells. Box plots summarize the data of all patients analyzed (EBV, n = 16; CMV, n = 6; Melan-A blood, n = 10, except for CTLA-4, n = 3; Melan-A TILN, n = 8–9). Whiskers in box plots indicate the maximum and minimum values measured. Cross indicates the mean, while line indicates the median. *P < 0.05; #P < 0.01; §P < 0.001. (C) Coexpression of 0 to 4 and 0 to 3 inhibitory receptors was analyzed with SPICE (48).

In peripheral blood, tumor-specific T cells induced by vaccination showed an effector cell profile (Supplemental Figure 5), similar to CMV-specific T cells and similar to the murine counterpart of CD8+ T cells in acute LCMV Armstrong infection (2). Differentiation of EBV-specific CD8+ T cells was less pronounced, but they nevertheless resembled effector cells. In contrast to these 4 effector cell populations, tumor-specific T cells in situ displayed an exhaustion profile, with significant similarity to murine T cells in chronic infection with LCMV clone 13 (2).

Tumor-infiltrating T cells are functionally deficient (20, 23, 24, 34), which is likely coresponsible for the limited efficacy of immunotherapy. However, the underlying mechanisms remain poorly characterized, in contrast with chronic infectious diseases (1, 2). Our finding of T cell exhaustion in melanoma metastases results from what we believe is the first comprehensive molecular characterization of self- and tumor-specific T cells, providing explanations for their functional impairment. Tumor-specific T cells from metastases showed considerable molecular alterations, with surprisingly strong overexpression of many genes regulating various cell functions. This included genes involved in immune responses, cell death and apoptosis, and cell cycle and DNA repair. Thus, the data point to enhanced immune activation and apoptosis, and problems in maintaining DNA integrity and sustaining cell cycling in T cells of metastases.

We did not find significant correlations between our T cell data and clinical results (e.g., patient survival). However, phase I studies such as the present trial of immunotherapy are not suited for clinical outcome analysis. Rather, they are designed for providing enhanced biological insight. Indeed, we identified specific molecular alterations potentially representing molecular targets for improved therapy. Nevertheless, further studies are required to determine which of these targets are most promising for evaluation in large-scale phase III clinical trials.

Based on the available evidence for functional T cell impairment in HIV-1, HBV, and HCV infections (11, 16–18, 37), it will be useful to perform comparative molecular profiling of T cells in different infections and malignancies in order to identify similarities and differences, providing the rational basis for therapy optimization. Very recently, HIV-1–specific T cells have been profiled, with identification of T cell exhaustion and BATF upregulation by PD-1 in patients failing to control HIV infection (18). Even though we did not find enhanced BATF expression in tumor-specific T cells from TILN, we observed similarities in gene expression signatures and upregulation of multiple inhibitory receptors on tumor-specific T cells also at the protein level.

Besides analysis of tumor-specific T cells after vaccination, it would be interesting to profile spontaneously arising T cell responses and naive tumor-specific T cells from patients and healthy donors, with the aim of identifying disease mechanisms responsible for altered T cell function. We expect that tumor-specific T cells from healthy donors would show an expression profile similar to total naive CD8+ T cells, while spontaneously responding T cells may show some degree of effector cell differentiation. However, such studies are technically challenging, since tumor-specific T cells in healthy donors and early stages of cancer are rare and difficult to isolate for ex vivo analysis. Therefore, laboratory techniques must be optimized for comprehensive characterization of even smaller cell numbers, ultimately down to the single cell level.

T cell tolerance to self and tumor antigens is assured by negative selection in the thymus and through anergy induction and T cell deletion in the periphery. Anergy has been characterized in at least 9 different experimental settings, most of them in vitro models and/or CD4+ T cell models (26). Unfortunately, no comprehensive gene expression data are available. Therefore we could not systematically evaluate anergy in our study. Nevertheless, we made an attempt by evaluating 29 anergy-related genes described in a model of ionomycin-induced anergy and a model of deletional tolerance (38, 39). We found that some of these genes (e.g., CBLB and CTLA4) were enriched in tumor-specific T cells from metastases (Supplemental Table 4), but most of the described genes (e.g., ITCH, EGR2, and DGKZ) were not enriched (not shown).

Our study was performed in patients with advanced stage III–IV melanoma. It has been hypothesized that late cancer stages may be associated with T cell exhaustion (1), whereas anergy and tolerance would be induced already at early stages of tumorigenesis (40, 41). Possibly, self- and tumor-specific T cells may show discrete alterations already at the naive stage and/or after spontaneous activation. Perhaps anergy mechanisms are functional even at later disease stages. The elucidation of these points requires further methodological progress. For the time being, our data support the conclusion that exhaustion likely contributes to the functional deficiencies, but does not rule out the involvement of further mechanisms such as anergy or self tolerance.

In circulating tumor-specific T cells, we found effector cell signatures compatible with their ample production of granzyme B and perforin (Figure 4) and efficient expression of IFN-? upon 4-hour triggering with antigen (Supplemental Figure 3B and refs. 20, 21). Due to the high efficacy of CpG 7909 as adjuvant, the circulating tumor-specific T cells studied here were more strongly activated (19) than in most other cancer vaccine studies with their lower frequencies and less pronounced effector cell differentiation. Thus, our data of circulating cells are not representative for the latter, but nevertheless demonstrate that self- and tumor-specific T cells have the potential to become effector cells. Despite the high efficacy of the adjuvant used, we could not observe significant bystander effects on circulating T cells with specificities other than for the vaccine (Figure 1).

One could argue that vaccination should have activated the tumor-specific T cells to an even higher degree than CMV-specific T cells in healthy donors (which was actually the case in some of our melanoma patients; our unpublished observations). Protection from latent CMV is likely less demanding for T cells than protection from acute viral disease. Possibly, even more strongly activated T cells may be required for protection from cancer progression. Indeed, adoptive transfer therapy has shown that tumor-specific T cells at much higher frequency and strong activation can eliminate large melanoma metastases (22). Molecular profiling of these cells in comparison with T cells during acute viral infections may reveal eventual differences from our data. Alternatively, therapeutic success and protection from disease may be primarily achieved due to high numbers of T cells with molecular properties similar to those described here. However, patients with acute viral infections are rarely accessible for clinicians and researchers. Moreover, antitumor vaccines rarely induce T cell responses comparable to acute viral infections. In contrast to vaccines consisting of synthetic molecules and inactivated pathogens, live vaccines (essentially vaccinia virus and yellow fever vaccine) can induce high T cell frequencies (42, 43) and efficient protection. Comprehensive profiling of these T cells is feasible and may likely contribute to identifying protective mechanisms of human T cells.

Differentiation from naive to effector T cells introduces large changes in expression of not only immune response genes, but also of genes involved in translation, in cell death and apoptosis, and in cell migration. These changes result in increased production of effector molecules, migration to pathologic tissue, and cell survival. Based on GO terms, we compared EBV- with circulating tumor-specific T cells and found that the latter overexpressed genes involved in translation, cell death, and apoptosis, likely reflecting the fact that the tumor-specific T cells were more advanced in effector cell differentiation. Compared with CMV-specific T cells, circulating tumor-specific T cells expressed slightly more genes related to transcription, but fewer genes involved in cell migration. Despite these distinctions, the 3 effector cell populations from peripheral blood were relatively similar.

Inhibitory receptors were prominent among the differentially expressed genes. This group of genes is attracting increasing attention, also because of its importance in T cell exhaustion and therapeutic potential (2). Nevertheless, the circulating tumor-specific T cells expressed granzyme B and perforin at high levels and were functionally competent (19). Apparently, effector cells can express inhibitory receptors but nevertheless maintain functional competence. Our study demonstrates coexistence of functional cells in circulation and exhausted cells in metastases. We have preliminary data indicating that this may occur even within individual T cell clonotypes (our unpublished observations). It appears that migration of T cells into the tumor tissue is associated with downregulation of cytokine production and exhaustion as a consequence of encountering inhibitory receptor ligands expressed in the tumor tissue, in conjunction with antigen recognition. Thus, exhaustion of T cells in metastases but not in peripheral blood may be linked to the frequent and strong expression of these ligands in the tumor microenvironment. This interpretation is compatible with our earlier findings that the functional deficiency of tumor-residing T cells is readily reversible, since T cells from metastases regain function after 1 to 2 days culture in vitro (20, 44).

HCV-specific T cells may coexpress up to 4 of the inhibitory receptors KLRG1, 2B4, CD160, and PD-1, correlating with CD127 downregulation and functional impairment (32). Even though many tumor-specific T cells expressed KLRG1, 2B4, PD-1, and TIM-3, they did not express CD160. This difference may be functionally relevant for HCV- versus tumor-specific T cells. Moreover, expression of inhibitory receptors was more abundant in T cells from TILN as opposed to blood. The differential coexpression of multiple inhibitory receptors in viral infection versus cancer, and depending on antigen specificity/differentiation status and anatomical localization, suggests that the functional regulation of antigen-specific T cells is more complex than previously thought.

In summary, our study provides comprehensive molecular profiles of human CD8+ T cells. Although tumor-specific T cells can acquire substantial effector cell properties, they display an exhaustion profile in metastases. With modern technologies applied to small cell numbers, it becomes increasingly possible to determine whether functional impairment and molecular exhaustion of tumor-specific T cells are due to their specificity for self antigen, and/or immune suppression in situ.

Healthy donors, melanoma patients, lymphocyte isolation, and flow cytometry. Blood from 4 A2+ healthy donors was obtained from the university blood transfusion center of Lausanne, Switzerland. Peripheral blood and surgery specimens were obtained from A*0201+ patients with stage III/IV metastatic melanoma. Patients had received multiple monthly low-dose vaccinations s.c. with 100 µg Melan-A/MART-1 peptide and CpG (500 µg PF-3512676/7909; provided by Pfizer/Coley Pharmaceutical Group), emulsified in IFA (300–600 µl Montanide ISA-51; provided by Seppic) as described previously (19). Analysis of circulating tumor-specific T cells was done after 11 ± 5 monthly vaccinations; the last was at a mean of 96 days before blood withdrawal. Tumor-specific T cells from TILN were prepared after finely mincing surgery specimens, which were obtained after 7 ± 2 monthly vaccinations, the last at a mean of 79 days before surgery. Vaccinations were done in the context of Ludwig Institute for Cancer Research trials (19, 45) and approved by the Ludwig Institute for Cancer Research protocol review committee as well as by the medical and ethical committees of the University Hospital (Lausanne). Blood and tissue were obtained upon informed patient consent, and the study was performed according to the relevant regulatory standards. Mononuclear cells were purified by density gradient using Lymphoprep (Axis-Shield) and immediately cryopreserved in RPMI 1640 supplemented with 40% FCS and 10% DMSO.

For microarray analysis, 1,000 cells from each sample were sorted using a Vantage SE directly into lysis and storage buffer provided by Miltenyi Biotec as shown in Supplemental Figure 1. CD8+ T cells were enriched using magnetic bead sorting (Miltenyi Biotec). Cells were stained on ice and diluted at one million cells/ml. Cells were stained with CD8-specific antibody, the dead cell marker DAPI, and either with lineage markers (CD4, CD14, CD16, CD19) together with A2/EBV BMLF1280–288 (GLCTLVAML), A2/CMV pp65495–503 (NLVPMVATV), or A2/Melan-A/MART-126–35A27L (ELAGIGILTV) tetramers binding to high- and low-affinity T cell receptors (46) or with CD45RA-, CCR7-, CD28-, and CD27-specific antibodies. Naive T cells were defined as CD8+CD45RA+CCR7+CD27+CD28+. The sorting strategy is shown in Supplemental Figure 1. Manipulations were done at 4°C, avoiding gene expression alteration due to staining and sorting. Sorting purity was high, as determined by analyzing aliquots before and after FACS sorting. Representative examples are shown in Supplemental Figure 1, B–D. Among CD8+ T cells, percentages for A2/EBV tetramer+ cells were 1.00 ± 0.89 (4 healthy donors and 12 patients); for A2/CMV tetramer+ cells, 1.53 ± 1.08 (7 patients); and for A2/Melan-A/MART-1 tetramer+ cells, 1.43 ± 1.31 in blood (11 patients) and 3.35 ± 3.35 in TILN (7 patients). After sorting, lysed cells were incubated for 10 minutes at 45°C and then directly frozen at –80°C.

Intracellular antibody staining was performed as previously described (27). In brief, cells from the CD8+ fraction were first stained with PE-labeled tetramers, followed by anti–CD8–Pacific Blue antibody. After washing in PBS, cells were incubated with LIVE/DEAD-Fixable-Aqua (Invitrogen) for dead cell exclusion, and fixed at room temperature (RT) during 30 minutes (1% formaldehyde buffer). Cells were washed and stained with mAbs anti–perforin-FITC or anti–granzyme B–FITC (BD) in FACS buffer with 0.1% saponin for 30 minutes at 4°C. For the staining of IFN-?, CD8+ cells were stimulated with peptide-loaded T2 cells for 4 hours in the presence of Brefeldin-A (Sigma-Aldrich) prior to antibody staining with anti–IFN-?–PE-Cy7 (BD Pharmingen). Data of IFN-?–production from tumor-specific T cells were previously published (20).

For antibody staining of multiple inhibitory receptors, samples were purified and enriched as described above and then stained using tetramers detecting the same EBV, CMV, or Melan-A/MART-1 epitopes as described above. Melan-A–specific tetramers were labeled with APC–eFluor 780 (eBioscience), EBV-specific tetramers were labeled with PE–Texas Red (BD Pharmingen), and CMV-specific tetramers were labeled with both APC–eFluor 780 and PE–Texas Red, allowing for individual analysis of T cells specific for the 3 epitopes in a single sample (multi-tetramer staining technique; ref. 47). After 45 minutes at 4°C, cells were washed and surface staining was performed for CD8, CCR7, CD45RA and (a) LAG-3 (Alexis Biochemicals) and 2B4 (BioLegend) or (b) KLRG-1 (gift from H.-P. Pircher, Department of Immunology, University of Freiburg, Freiburg, Germany), TIM-3 (R&D Systems), PD-1 (eBioscience), and CD160 (eBioscience). Samples (a) were fixed at room temperature for 30 minutes (1% formaldehyde buffer) and then stained for CTLA-4 (BD Biosciences — Pharmingen) in FACS buffer with 0.1% saponin for 30 minutes at 4°C. LIVE/DEAD-Fixable-Aqua (Invitrogen) was used as a dead cell exclusion marker, and appropriate isotype controls were used to define negative populations. Data were acquired on a Gallios Flow Cytometer (Beckman Coulter) and analyzed using FlowJo 9.1 (TreeStar). Analysis of coexpression of inhibitory receptors used SPICE version 5.1 (48).

Microarray and qPCR. Gene expression profiling was done in 2 experiments. The first experiment included samples from blood-derived naive, EBV-, and tumor-specific T cells. The second experiment included tumor-specific T cells from blood and metastasis, and CMV-specific T cells from blood. Frozen samples were sent to Miltenyi Biotec and processed according to the vendor-recommended protocol for gene expression analysis. Samples were hybridized to Agilent Whole Human Genome Oligo Microarrays 4x44K and scanned using the Agilent microarray scanner system (Agilent). The Agilent Feature Extraction Software was used for readout and processing of image files. Background correction, filtering of data, and quantile normalization were done using the Agi4x44PreProcess software package as described in the package manual. The Limma software package was used to identify the differentially expressed genes and creation of rank-ordered lists. We analyzed eventual contaminations from B cells, monocytes, and dendritic cells, and found that expression levels of IGHG1, CD19, TLRs, and CD1 were between 0.28% and 2.72% of the respective expression of CD3E, confirming the high purity of our samples. We also evaluated intra-group variability possibly leading to high background. For this, we randomly split the data from 13 naive CD8+ T cell samples into 3 pairs of 2 groups of 6 and 7 samples each and analyzed differences between the groups. We found that none of the gene probes were different in any of the pairings, demonstrating that the background was low (data not shown). For nonnaive cells (Figure 2), the data from EBV- and tumor-specific CD8+ T cells were pooled. Genes were assigned to broad GO terms using the GO Term Mapper ( http://go.princeton.edu/cgi-bin/GOTermMapper), yielding both the percentage of submitted genes attributed to a given GO term versus the percentage of all annotated genes attributed to that GO term. Relative overrepresentation was calculated by dividing the percentage of submitted genes attributed to a GO term by the percentage of all available genes annotated with this GO term. Rank-ordered gene lists (ranked according to the B value) were analyzed with GSEA ( www.broadinstitute.org/gsea; ref. 49). Enrichment was considered significant if P was less than 0.05 and FDR was less than 0.25 as suggested in the online tool.

qPCR was performed to validate the enriched genes observed in microarray experiments. Custom-ordered oligos (Microsynth) were designed using the online tool from Universal Roche Library Assay Design Centre (Supplemental Table 5). Reaction mix used was Power Sybr Green Master Mix (Applied Biosystems), and amplification was monitored with Applied Biosystems 7900HT Fast Real-Time PCR System (15-minute enzyme activation and 40 cycles of 15 seconds 95°C, 1 minute 60°C). A Hamilton Liquid Handling Robotic System was used to assemble the 384-well plates. Amplified cDNA samples used for microarray analysis were diluted (1:50) and used for qPCR after confirming the linear and single product amplification by the primers. Samples were measured in triplicate. GAPDH was used as a housekeeping gene to calculate relative expression values.

Statistics. For quantitative comparisons, Student’s t test (2-sample 2-tailed comparison) or 1-way ANOVA with Tukey post-test (multiple-sample comparison) was performed with Prism 5.0; P < 0.05 was considered as significant. P values and FDRs for GSEA were calculated with 1,000 permutations in the online tool. Microarray analysis was done with relatively restrictive criteria, i.e., by considering gene probes as significant if the P value, corrected for a FDR of 0.05, was P = 0.05 and the fold change was = 3.

Accession numbers. The gene-expression data described in this paper have been deposited in the NCBI Gene Expression Omnibus and are accessible through the GEO accession number GSE24536.

View Supplemental data

We are obliged to the patients for their dedicated collaboration. We gratefully acknowledge M. Delorenzi, F. Schütz, H.-P. Pircher, M. Etzrodt, M. Pittet, M. Matter, O. Michielin, L.J. Old, J. O’Donnell-Tormey, E.W. Hoffman, and A. Krieg for essential contributions; D. Zehn, P. Ohashi, H.R. MacDonald, J. Skipper, and H.F. Oettgen for support; and P. Schneider, L. Derre, M. Braun, C. Christiansen-Jucht, C. Jandus, J.-P. Rivals, T. Lövgren, and M. Iancu for collaboration and advice. We thank P. Guillaume and I. Luescher for tetramers, and Pfizer and Coley Pharmaceutical Group (USA) for providing CpG 7909 (PF-3512676). This work was supported by the Ludwig Institute for Cancer Research, the Cancer Research Institute (USA), the Cancer Vaccine Collaborative, Atlantic Philanthropies (USA), the Wilhelm Sander-Foundation (Germany), the Swiss Cancer League (grant 02279-08-2008), the Swiss National Science Foundation, and the Swiss National Center of Competence in Research (NCCR) Molecular Oncology.

Conflict of interest: The authors have declared that no conflict of interest exists.

Citation for this article: J Clin Invest doi:10.1172/JCI46102.

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The Way Doctors Treat Patients With Cancer And Autoimmune Diseases Could Change Following New Discovery

Researchers in the Faculty of Medicine & Dentistry at the University of Alberta have made an important discovery that provides a new understanding of how our immune system "learns" not to attack our own body, and this could affect the way doctors treat patients with autoimmune diseases and cancer.

When patients undergo chemotherapy for cancer or as part of experimental therapies to treat autoimmune diseases such as diabetes and lupus, the treatment kills the patients' white blood cells. What can be done afterwards, is to give these patients blood stem cells through transplantation. Stem cells are taken from patients then injected back into them - with the theory being that the patients' immune system won't attack their own cells, and the stem cells can get to work healing their bodies.

But U of A medical researchers Govindarajan Thangavelu, Colin Anderson and their collaborators discovered that if a particular molecule is not working properly in T-cells, the body will attack itself. This is significant for stem-cell transplantation treatment because it means the immune systems of the patients could consider their own cells "foreign" and initiate an attack.

"So your own cells would be killing you," says Thangavelu, a PhD student specializing in immunology, who was the first author in the research study, which was recently published in the peer-reviewed Journal of Autoimmunity. "What we found is if this molecule is absent in T-cells, if the pathway isn't intact, it will cause severe autoimmunity to the subject's own body. In essence, subjects become allergic to their own cells."

Anderson, an associate professor with the Alberta Diabetes Institute and Principal Investigator added: "The ability of our immune system to attack dangerous microbes while not attacking our own cells or tissues is a delicate balance. Restarting the immune system after wiping it out in patients with autoimmune diseases or cancer requires re-establishing this appropriate balance. We discovered that a particular immune system molecule is critical to prevent the immune system from attacking our own cells or tissues when the immune system is restarted. If that molecule is missing, the immune system will wreak havoc on the body."

T-cells are supposed to protect people and animals from things invading their bodies. But this research demonstrates if these cells become unregulated because they are missing a molecule, it can lead to autoimmunity - particularly dangerous in scenarios where patients have lost white blood cells when they are being treated for autoimmune diseases or cancer.

Thangavelu has won awards for this research. He was invited to present his work at an international conference of immunology in Japan last year. He has also travelled to the United Kingdom to talk about his findings with the medical community.

This research was funded by: the Juvenile Diabetes Research Foundation, the Canadian Institutes of Health Research, Alberta Innovates-Health Solutions and the Alberta Diabetes Institute.

Source:
Raquel Maurier
University of Alberta Faculty of Medicine & Dentistry

Patient's Own Cells May Hold Therapeutic Promise After Reprogramming, Gene Correction

Scientists from the Morgridge Institute for Research, the University of Wisconsin-Madison, the University of California and the WiCell Research Institute moved gene therapy one step closer to clinical reality by determining that the process of correcting a genetic defect does not substantially increase the number of potentially cancer-causing mutations in induced pluripotent stem cells.

Their work, scheduled for publication the week of April 4 in the online edition of the journal Proceedings of the National Academy of Sciences and funded by a Wynn-Gund Translational Award from the Foundation Fighting Blindness, suggests that human induced pluripotent stem cells altered to correct a genetic defect may be cultured into subsequent generations of cells that remain free of the initial disease. However, although the gene correction itself does not increase the instability or the number of observed mutations in the cells, the study reinforced other recent findings that induced pluripotent stem cells themselves carry a significant number of genetic mutations.

"This study showed that the process of gene correction is compatible with therapeutic use," says Sara Howden, primary author of the study, who serves as a postdoctoral research associate in James Thomson's lab at the Morgridge Institute for Research. "It also was the first to demonstrate that correction of a defective gene in patient-derived cells via homologous recombination is possible."

Like human embryonic stem cells, induced pluripotent stem cells can become any of the 220 mature cell types in the human body. Induced pluripotent stem cells are created when skin or other mature cells are reprogrammed to a pluripotent state through exposure to select combinations of genes or proteins.

Since they can be derived from a patient's own cells, induced pluripotent stem cells may offer some clinical advantages over human embryonic stem cells by avoiding problems with rejection. However, scientists are still working to understand subtle differences between human embryonic and induced pluripotent stem cells, including a higher rate of genetic mutations among the induced pluripotent cells and evidence that the cells may retain some "memory" of their previous lineage.

Gene therapy using induced pluripotent stem cells holds promise for treating many inherited and acquired diseases such as Huntington's disease, degenerative retinal disease or diabetes. The patient in this study suffers from a degenerative eye disease known as gyrate atrophy, which is characterized by progressive loss of visual acuity and night vision leading to eventual blindness. While diseases such as genetic retinal disorders and diabetes offer attractive targets for induced pluripotent stem cell-based transplant therapies, concerns have been raised over the commonly occurring mutations in the cells and their potential to become cancerous.

Howden says that because gene targeting to correct specific genetic defects typically requires an extended culture period beyond initial induced pluripotent stem cell generation, researchers have been interested to learn whether the process would increase the number of mutations in the cells. The team set out to determine if it was possible to correct defects without introducing a level of mutations that would be incompatible with clinical applications.

In the study, the researchers used a technique called episomal reprogramming to generate the induced pluripotent stem cells. In contrast to techniques that use retroviruses, episomal reprogramming doesn't involve inserting DNA into the genome. This technique allowed them to produce cells that were free of potentially harmful transgene sequences.

The scientists then corrected the actual retinal disease-causing gene defect using a technique called homologous recombination. The stem cells were extensively "characterized" or studied before and after the process to assess whether they developed significant additional mutations or variations. The results showed that the culture conditions required to correct a genetic defect did not substantially increase the number of mutations.

"By showing that the process of correcting a genetic defect in patient-derived induced pluripotent cells is compatible with therapeutic use, we eliminated one barrier to gene therapy based on these cells," Howden says. "There is still much work to be done."

David Gamm, an author of the study and an assistant professor with the Department of Ophthalmology and the Waisman Center Stem Cell Research Program, says the ability to correct gene defects in a patient's own induced pluripotent stem cells should increase the appeal of stem cell technology to researchers striving to improve vision in patients with inherited blinding disorders.

"Although further development certainly is needed before such techniques may reach the clinical trial stage, our findings offer reason for continued hope," Gamm says. "Dr. Howden and our collaborative group have overcome an important hurdle which, when considered in the context of other recent developments, may lead to personalized stem cell therapies that benefit people with genetic visual disorders."

Source: University of Wisconsin-Madison

 

Stem Cell Hope For Patients With Aggressive MS

Main Category: Multiple Sclerosis
Replacing deliberately destroyed bone marrow with the patient's own stem cells may help stabilize aggressive forms of multiple sclerosis (MS), according to a pilot study in Greece that was published recently in the journal Neurology.

The researchers said their study, which followed 35 patients for an average of 11 years after transplant, proves the method, called hemopoietic stem cell transplantation, is feasible and that clinical trials should now be done to see if it offers an effective alternative to current treatments.

Study author Dr Vasilios Kimiskidis, of Aristotle University of Thessaloniki Medical School in Thessaloniki, and colleagues found that for 25% of patients who received the treatment, their MS was no worse 15 years later, as would normally be expected.

Kimiskidis told the press that bearing in mind clinical trials are now needed, "our feeling is that stem cell transplants may benefit people with rapidly progressive MS".

"This is not a therapy for the general population of people with MS but should be reserved for aggressive cases that are still in the inflammatory phase of the disease" said Kimiskidis.

Multiple sclerosis (MS) is a disease where the body's own immune system attacks myelin, the fatty covering that insulates nerve cell fibers in the brain and spinal cord, resulting in slower nerve signals.

According to the National Institutes of Health, no one knows exactly how many people have MS, but some estimates put the number of Americans with the disease at between approximately 250,000 to 350,000, and the number of people worldwide at over 2 million.

The treatment used in this study, which started in 1995, took bone marrow stem cells from the patients' own bodies and transplanted them back after using a course of chemotherapy to wipe out the immune cells in the bone marrow, including those thought to be attacking the central nervous system.

The purified stem cells are thought to "reboot" the immune system.

The patients in the study had rapidly progressive MS and had tried a number of other treatments with little or no effect.

All were severely disabled with the disease. Their average score on the multiple sclerosis severity scale, a scale of disease activity where 0 is a normal neurological exam and 10 means death due to MS, was 6, which means able to walk with a cane or crutch (a score of 7 usually means the patient is wheelchair-bound). All patients had worsened by at least one point on the scale in the year before receiving the transplant.

The study results showed that after the transplant, the chance of patients having no worsening of the disease for 15 years was 25%. The chance was higher, at 44%, for those who had active brain lesions at the time of the transplant.

For 16 people, symptoms improved by an average of one point on the multiple sclerosis severity scale, after the transplant, and the improvements lasted for an average of 2 years. They also experienced a reduction in number and size of brain lesions.

However, two of the patients died as a result of complications linked to the transplant: one died 2 months and the other died 2.5 years after transplant.

"Long-term results of stem cell transplantation for MS: A single-center experience."
A. Fassas, V.K. Kimiskidis, I. Sakellari, K. Kapinas, A. Anagnostopoulos, V. Tsimourtou, K. Sotirakoglou, and A. Kazis.
Neurology, 22 March 2011 76:1066-1070
DOI: 10.1212/WNL.0b013e318211c537

Additional source: American Academy of Neurology (21 Mar 2011).

Written by: Catharine Paddock, PhD
Copyright: Medical News Today