Some information is available regarding the correlation between T-cell response induction as demonstrated by ELISPOT assay and clinical outcome (Table 4). In patients immunized with a polyvalent melanoma vaccine, the induction of specific T-cells reactive with HLA class I binding peptides derived from MAGE-3 and/or MART-1 was found to correlate with prolonged freedom from recurrence (174). In a phase I study with MART-1 peptide in high-risk melanoma patients, T-cell induction against the vaccine peptide, determined by ELISA after two in vitro stimulations but not by ELISPOT assay, was associated with prolonged freedom from relapse (177). In a randomized phase II trial conducted by the same group with gp100 and tyrosinase peptides, IFA +/− IL-12, no correlation between T-cell induction determined by ELISA after one or two in vitro stimulations or by tetramers and relapse-free survival was found (151).
A recent study in which 18 patients with melanoma were vaccinated with peptide-loaded DC showed a good correlation between tumor regression and T-cell response analyzed by ex vivo and recall ELISPOT assay (176). In a phase I clinical trial with ALVAC-CEA-B7, three patients experienced clinically stable disease that correlated with increasing CEA-specific precursor T-cells, as shown by in vitro IFN-γ enzyme-linked immunoassay spot tests (Fig. 4). In a phase II study vaccinating patients with stage IV melanoma with tyrosinase peptides and GM-CSF, specific T-cells were detectable in four of fifteen patients, including the only patient with a mixed response, one of two patients with stable disease, and two patients with prolonged freedom from recurrence (139).
Cytokine flow cytometry or intracellular cytokine cytometry (ICC) is based on direct detection of intracellular cytokine expression with fluorochrome-conjugated anticytokine antibodies after short periods of activation with various stimuli (Fig. 5). Stimulation can be performed with mononuclear cells isolated from PBMC (186), whole blood (187,188), lymph nodes, or other biologic fluids. A total incubation period of 6 hours is optimal for achieving high levels of cytokine-secreting cells for IL-2, IL-4, IFN-γ, and TNF-α, as well as for achieving maximal cytokine staining intensity (188). Cytokine secretion is disrupted during the latter portion of the incubation (usually the last 4 hours) with the addition of drugs that inhibit cytokine secretion such as monensin or brefeldin A (189). The cells are then fixed using paraformaldehyde or similar agents. Permeabilization of cell membranes is achieved using nonionic detergents, followed by intracellular staining using mixtures of antibodies that recognize determinants in fixed and permeabilized cells.
Unstimulated leukocytes normally do not express cytokine. Because background constitutive cytokine expression is rare (usually ≤ 0.05% of CD4 or CD8 T cells), very low frequencies of positive stimulated cells can be detected. In addition, because of the short incubation time in the presence of a secretion inhibitor, bystander effects or cytokine absorption by soluble or cell-surface receptors does not compromise the cytokine immune profiles.
T-cell responses in antigen-specific CFC assays are dominated by memory cells, as determined by phenotyping of cytokine positive cells for markers such as CD45RA versus CD45RO, CD27, CD44, and CD95 (187). CD4+ T cells dominate the response to intact protein antigens, although some CD8+ responses can be detected (187,190,191), particularly with higher antigen doses. On the other hand, optimal peptide epitopes or peptide mixtures can be used as antigens to efficiently induce CD8+ T-cell responses (3,192–195). Figure 6 illustrates CD4+ and CD8+ T cell cytokine responses in normal whole blood cultures responding to whole protein and peptide CMV antigens. Altered in vitro T cell cytokine responses to nominal antigen as a consequence of vaccination or disease status can be measured using this procedure (134,196–203).
Antibodies selected for intracellular staining (e.g. anti-cytokine mAbs) need to possess high affinity and specificity for epitopes that survive the particular fixation and permeabilization conditions used. Altering the fixation and permeabilization conditions might alter the performance and optimal titer of surface and intracellular staining antibodies. Many commonly used monoclonal antibodies to cell surface determinants from a number of vendors can be used with fixed and permeabilized cells (e.g., CD3, CD4, CD8, CD69, and cytokine-specific antibodies). In situations where antibodies to cell-surface markers do not work well under fixation and permeabilization conditions, it is necessary to add a separate surface staining step prior to fixation and permeabilization. Also essential is the use of highly purified fluorochrome conjugates of antibodies used to detect intracellular targets. Particularly, the absence of free fluorochrome and overly-conjugated antibody is important to minimize backgrounds and retain the high avidity of antigen binding in intracellular staining necessary for optimal flow cytometric detection of rare antigen-specific events.
Functional assays in general are subject to higher variation than phenotypic staining, due to assay complexity and the biologic variability associated with in vitro cell activation. Nevertheless recent evaluations have reported an overall coefficient of variation in a CMV-specific CFC assay to be within 5% for intraassay variability (188) and within 20% in a multi-site study (Fig. 7). Interestingly, it was determined that much of the interassay variability could be attributed to differential gating during analysis. For example, CD4dim or CD8dim lymphocytes can often be found as a very minor proportion of the total CD4+ or CD8+ cells. However, these cells tend to be highly enriched for activated cells, since they have undergone down-modulation as a result of recent antigenic stimulation. Thus, inclusion or exclusion of these cells can result in a difference of up to 1.5-fold in the percentage of cytokine-positive cells assessed. With experience, however, consistent gating can certainly be achieved. In the future, automated gating algorithms may also be available to minimize this source of variability.
Cytokine flow cytometry assays are a potentially powerful tool for analyzing antigen-specific T-cell responses in a quantitative manner. Standard functional assays tend to rely on longer stimulation times to amplify specific populations, allowing time for apoptosis and/or proliferation, and thus compromising the ability to quantitate precursor cell frequencies. In addition, the ability to perform CFC assays in whole blood within 8 hours, with minimal hands-on time, make CFC assays highly feasible as clinical monitoring tools. Finally, the multiparametric information obtained from flow cytometry allows for qualitative as well as quantitative information about the nature of immune responses to specific antigens.
The CFC assay has been used to characterize human T cell responses to a number of infectious disease agents including HIV (197,198,204–206), CMV (134,188,190,193,195,196,199,200,207,213), EBV (214,215), and others (190,216–222). The largest amount of published data using CFC in clinical settings is in the area of HIV-specific immune responses. Pitcher et al. (197) showed that patients with HIV could be stratified into those that maintain a detectable CD4 Th response to HIV antigens by CFC assay and those that do not. All HIV+ nonprogressors tested were in the former group, showing CD4+ Th cell IFN-γ and TNF-α responses to HIV p55 gag of 0.1% or greater. About half of those with progressive disease showed similar CD4+ Th responses, but half did not; likewise with individuals who had been on highly active antiretroviral therapy (HAART) for less than 6 months. Perhaps most interestingly, individuals treated with long-term HAART were uniformly low (< 0.1%) in their CD4 Th response to HIV antigens.
Another potential area of clinical application for CFC assays is in the development of new vaccines, and the refinement of existing vaccines. As part of a recent clinical study of vaccination with a gp120-depleted inactivated HIV immunogen, CD4+ T cell CFC responses to HIV were followed in 18 patients (198). Dramatic increases in the frequency of HIV-specific CD4 cells were demonstrated in 15 of 18 patients after three vaccinations. In another example, varicella immunization has been shown to induce significantly lower CFC responses in older adults than in young adults; and such responses in older adults are not boosted by secondary immunization (190).
Several studies have also demonstrated that CFC assays can be used to detect responses to tumor antigens: MART-1 and tyrosinase epitopes in melanoma (3), MUC-1 mucin in various solid tumors (203), and immunoglobulin idiotype in vaccinated multiple myeloma patients (201). These reports demonstrate that the sensitivity of CFC assays is sufficient to detect positive responses to tumor antigens, although the response frequencies are often lower than with chronic infectious disease antigens. It remains to be seen whether CFC assays are predictive of clinical responses in vaccinated patients with cancer. In part, this is dependent on the use of these assays in studies with a vaccine that has at least some clinical benefit. Because there are currently no Food and Drug Administration-approved cancer vaccines, identifying vaccines that have the most potential for clinical efficacy is of paramount importance.
The ability to quantitate frequencies of functional antigen specific T cells has enabled investigators to assess the relationship between the strength of CD4+ and CD8+ T cell responses and immune protection in a number of disease models. Thus, the response to infection of Lymphocytic Choriomeningitis virus in mice is associated with strong CD4 T cell cytokine responses, which are correlated with long-term memory (223). In a murine model of Leishmania, protective vaccination is associated with the development of IFN-γ-producing CD8 T cells, as measured by CFC (224,225). In a primate model of AIDS, control of viral rebound after structured therapy interruption is strongly correlated with anti-viral CD8 T cell cytokine responses (226). Such responses are also correlated with protection from mucosal challenge after vaccination (227). In humans, susceptibility to CMV-associated end organ disease in HIV-infected individuals was shown to correlate with the loss of CD4+ T-cell IFN-γ responses to CMV antigens (196,199,200). Even though such studies demonstrate correlation of CFC responses with disease outcome, there have been no proven thresholds established for protective immunity as measured by a functional assay in any disease system. In fact, the precise correlates of immunity are still poorly defined for most disease and vaccine systems.
The determination of a positive CFC response is dependent upon the level of background in the negative control sample, and upon the sample size. In fact, a statistical calculation can be made using these variables, such that a given difference between sample and control can be deemed significant with a particular power and confidence level (228). In other words, one obtains greatest sensitivity in the presence of low backgrounds and large sample sizes. For example, a sample size of 25000 events is sufficient to distinguish a 0.1% population as positive over a background of 0.03%, with 90% power and p < 0.05. However, at a background of 0.05%, more than 50000 events would be needed to distinguish the same positive population with the same power and confidence. In practice, achieving backgrounds of less than 0.05% is practical, save for the occasional donor who presents with a significant spontaneous cytokine-secreting population. Thus, it is not impossible to routinely identify positive populations on the order of 0.1% or even slightly less. On the other hand, populations significantly lower, on the order of 0.01%, will not be distinguishable from background, because they approach the average level of spontaneous cytokine-secreting cells in peripheral blood.
One factor that could still compromise the ability to detect significant antigen-specific T-cell responses after vaccination in clinical samples is the suboptimal timing of sample acquisition. This issue is being addressed in experiments examining the dynamics of the tetanus toxoid (TT-)-specific T-cell recall response (229). In these studies, healthy volunteers were vaccinated with TT, and CFC measured the TT-specific T-cell response directly in the peripheral blood after vaccination. The CD4 T-cell response peaked 1 week after vaccination at 0.4% of CD4+ T cells. Remarkably, we also noted a CD8 T-cell response, which peaked at about 0.5% of CD8+ T cells; however, this response peaked as much as 3 weeks later than that of the CD4 response. These data provide a time frame for sampling the peripheral blood to examine antigen-specific T-cell responses to vaccines and support the hypothesis that clinically significant T-cell immunity should be detectable directly from peripheral blood. The detection of spontaneous cytokine secreting cells, in fact, has the lowest limit of detection for any functional assay.
Exploration of the clinical use of CFC assays has been most extensive in HIV disease, where it has been shown to correlate with disease stage (197), and where it has been used to follow vaccine responses (198). Cytokine flow cytometry has also been used to detect responses to cancer vaccines (201,203). The use of CFC with either specific antigens or polyclonal stimuli to assess T cell responsiveness in other disease settings is less well explored. However, the opportunities to enhance patient care by more carefully monitoring the immune system should encourage future studies aimed at validating the use of such monitoring.
Soluble recombinant MHC-peptide tetramers are becoming an increasingly useful tool capable of not only identifying and enumerating antigen-specific T cells, but also of providing functional information when combined with other methodologies. Tetramers may be generated using now well-established procedures (http://www.emory.edu/WHSC/TETRAMER/protocol.html). Briefly, recombinant MHC class I heavy chains that incorporate a short C-terminal substrate peptide for BirA-mediated biotinylation are produced in E. coli, purified from inclusion bodies, folded in the context of synthetic peptides and β2-microglobulin and biotinylated. Based on the specific application, fluorochrome-labeled (FITC, PE, PerCP, etc.) streptavidin is then added to purified (gel filtration and anion exchange) MHC/peptide “monomers” to form soluble tetrameric complexes. Fluorescent MHC/peptide tetramers when incubated with a polyclonal mixture of T cells, under the right conditions, will bind those T cells bearing MHC/peptide-specific TCRs and may be detected by fluorescent imaging systems, such as flow cytometry. While most published information has been gathered using MHC class I-peptide tetramers for CD8+ T cell screening, MHC class II-peptide tetramers have also been developed for the assessment of CD4+ T cell responses (230–232). The ability to monitor CD4+ and CD8+ tumor-specific T-cell responses, particularly in the face of active therapy, will likely prove critical to efforts to define appropriate laboratory surrogates in the cancer setting.
In practice, each new tetramer must be tested for specificity and titered before use. It is important to use tetramers at optimal concentrations to maximize the signal-to-noise ratio determined via flow cytometry, particularly when looking for rare T cell populations (< 0.1%). In general, the consensus is that the lower limit of detection for currently used tetramer-based assays is approximately 1/8000–1/10000 (i.e., 0.01%–0.0125%) (221,233–236). In most cases, quality-control requires the generation of specific T cell lines or clones to validate the specificity and high signal-to-noise ratio required for optimal performance of tetramer-based analyses. Because MHC/peptide tetramers are noncovalent complexes (i.e., MHC heavy chain, β2-microglobulin, peptide, and streptavidin), they “degrade” at variable rates that appear to depend in large part on MHC-peptide affinity (Lee P, personal communication, November, 2001). Hence, a given lot of MHC-peptide tetramer may be stable anywhere from 2 years (for most viral peptide antigens) to as little as 3 months (for certain low-affinity “self ” peptides), necessitating periodic retesting of the reagent's efficacy by retitration analyses on a regular (every 6–8 weeks) basis.
There are a number of technical factors that may impact on the quality of tetramer data. In addition to the inherent instability of the peptide-MHC interaction, the concentration of cells and tetramer used, timing and temperature used for staining, the counterstain (fluorochrome labeled antiCD8, antiCD3, etc.) implemented in multiparameter analyses, and contaminant non-T-cell populations present in bulk populations may dictate the interpretation of results.
Typically, at least one million PBMCs are used per staining condition and as many events as possible (105–106) are collected for analysis. Staining proceeds using tetramers (often PE-conjugated) at their “optimal” quality-controlled concentrations, generally for 15–30 minutes at room temperature, together with, or followed by, addition of anti-CD8-FITC (and anti-CD4/14/19-Cy5PE, optional probes for “negative” selection of events). Cells are then washed extensively (2–3 times). Propidium iodide (PI) may be added before FACS analysis (optional) to exclude dead cells. Multiparameter analyses are generally performed using flow cytometry. Lymphocytes are gated based on their forward and side-scatter, dead and “sticky” cells are excluded based on PI staining and/or with “dump” (negative selecting) antibodies, and the remaining cells are assessed for CD8 verus tetramer staining status. As the percentage of CD8+ T cells in total PBMC can vary widely between samples, tetramer-positive events are generally “normalized” as a percentage of total CD8+ T cells or data may be reported as the absolute number of tetramer+/CD8+ cells per quanta (i.e., μl-mL) of donor blood. Only cell populations that are clustered well and display a clear separation from the CD8+/tetramer-negative T-cell population in two parameter analyses are considered to be “real” events for “higher” avidity T-cell populations. In contrast to pathogen-specific T cell systems, however, cancer-reactive T cells may be of lower overall avidity perhaps due to tolerance mechanisms invoked against “self ” epitopes that tumor cells frequently present in their MHC complexes. As a result, some caution should be taken so as not to discard events at or near the level of background as “noise,” since they may indeed be informative of “low” avidity antitumor T cells. As described below, the specificity of tetramer staining of T cells may be confirmed by inclusion of “competitors” such as antiCD3 antibodies (127).
The concentration of tetramers used to stain cells is critical in determining the optimal signal-to-noise range for a given probe. Importantly, the percentage and fluorescence intensity of tetramer-positive events appear to increase in proportion to tetramer concentration, until a plateau is reached. Thus, staining with suboptimal tetramer concentrations may lead to an inaccurate assessment of specific T-cell frequencies. This is particularly important when making comparisons between samples or across multiple tetramers.
Based on an expanding literature, the staining temperature appears to significantly impact the degree of specific tetramer staining (235–237). Tetramer staining of T cells on ice (or at 4°C) appears to allow for significant formation of low avidity (frequently cross-reactive) interactions, thus decreasing the signal-to-noise ratio. In contrast, tetramer staining performed at room temperature (23°C) or 37°C, appears to favor predominantly higher avidity tetramer-TCR interactions, thereby reducing background staining of low avidity (specific or crossreactive) T cells.
Another important variable is the specific “counterstaining” antiCD8 (or CD4) antibody used in multiparameter analyses. Several reports have suggested that tetramer staining may be affected by the concentration of antiCD8 (or CD3) antibodies used, and even by the particular clone of antibody used (234,235,238,239). Indeed, depending on the CD8 determinant recognized by the antibody, antiCD8 counterstaining might block, have little impact on, or even augment the intensity of MHC/peptide tetramer staining of T cells (236,238,239). In careful titration experiments, the percentage of registered tetramer-positive events may vary depending on the concentration and type of antiCD8 antibodies concentration. Clearly, this provides another major area of variability when comparing data obtained between samples tested at unrelated laboratories using nonidentical reagents and protocols. The recent development of MHC/peptide tetramers based on mutant class I heavy chains that fail to bind to the CD8 coreceptor (Gammon S, personal communication, November, 2001) may reduce “noise” affiliated with low-avidity or cross-reactive T cells and should provide a clearer resolution for at least higher avidity (CD8-independent) specific T cell clonotypes in a bulk population.
As mentioned above, exclusion of certain non-T cell types is also important to the resolution of tetramer-based analyses. Certain cells (such as monocytes) appear to “stick” to tetramer reagents, and can significantly increase the population “background” staining observed in flow cytometry testing. To reduce the severity of this practical problem, one typically includes antibodies to markers not present on cells of interest to exclude these cells in data interpretation.
While early studies of substituted “heteroclytic” MHC/peptide tetramers (particularly the HLA-A2.1/MART-1 26–35 tetramer) indicated arguably “high” frequencies of “specific” peripheral blood CD8+ T cells in HLA-A2.1+ donors (240), this result appears to represent an exception, rather than a rule for most tetramer-based testing in the cancer setting. In general, MHC/tumor peptide tetramers detect only low background frequency events (< 0.01%) when used to stain freshly isolated normal donor peripheral blood T cells (157,235,237). In marked contrast, tumor-specific T cells can be frequently detected directly from the blood of patients with cancer ( and Storkus et al., unpublished data), and these frequencies can be significantly enhanced as a result of patient vaccination (151,159,184,237,241,242).
What is the clinical relevance of this assay? At present, it is too early to say, given the limited number of clinical trial reports using MHC-peptide tetramers for immune monitoring. Even though it is clear that tetramer-based analyses can detect and quantify pre-versus posttherapy changes in specific peripheral T-cell frequencies, this differential only infrequently correlated with clinical outcome in cancer-vaccine trials (4,151). However, there are also exceptions (228), which clearly support the more extensive and systematic evaluation of “optimized” tetramer-based immune monitoring. Based on our current understanding, it would be expected that clinical responders derive from the cohort of patients that display increases in their tetramer+ T-cell frequencies after treatment; however, the mere circulation of high frequencies of tumor-reactive T cells does not guarantee tumor regression (4,6,242). There is clear promise for the application of tetramers as a “front-line” clinical immune monitoring system; however, many more prospective trials employing these assays will need to be performed to determine the clinical relevance of tetramer-based analyses.
Two points should be emphasized. First, MHC-peptide tetramers stably bind to TCR exhibiting a certain minimal avidity. Hence, functional and potentially clinically important T cells may be missed in these assays, depending on the staining conditions (temperature, concentration of tetramers, anti-CD8 antibodies, etc.). Indeed, there have been reported cases where epitope-specific T cells fail to be effectively imaged using the appropriate MHC/peptide tetramers (Gajewski T, personal communication, November, 2001). Secondly, many clinically important peptide epitopes may bind MHC with sufficiently low affinity that precludes the technical production of tetramer probes (ineffective folding, etc.) (243). Under these conditions, TCR cross-reactive peptide “super”-agonists must be pursued to construct stable tetramers in order that effective screening might be accomplished.
Major histocompatability complex-peptide tetramers may ultimately serve as the foundation for a legitimate laboratory monitoring system for T-cell responses in patients with cancer; however, current enthusiasm for this possibility must be moderated by the lack of stringent validation in a large number of clinical trials. At the current time, MHC-peptide tetramers must be considered a highly promising research tool with clinical intent. The ability to produce an “off-the-shelf ” probe, combined with the high-throughput and sensitivity of flow cytometry, clearly makes this assay system attractive for potential application in future trials involving large patient populations. Based on anticipated inter-assay variability at the current time, particularly in multiparameter analyses, this monitoring system may be best applied by a central screening laboratory supporting the single-or multi-site performance of immunotherapeutic approaches.
The qRT-PCR assay is based on the principle that amplification of cDNA by the polymerase chain reaction follows a strict mathematical equation whereby with each cycle of amplification two copies are made from each individual. Thus, the amount of cDNA amplified after a given number of cycles will be directly proportional to the log2 of the starting amount of template. It follows that, if the amount of amplified cDNA throughout various amplification cycles could be quantitated, the starting amount of template could be extrapolated. This quantitation is achieved with a gene-specific nucleotide probe complementary to a region of DNA nested between the PCR primers. This probe is labeled with a fluorochrome and also with a quencher that can absorb fluorescence. During amplification the probe is removed from the DNA strand and degraded by the 5´-3´ exonuclease activity of Taq DNA polymerase and the fluorochrome is separated from the quencher yielding one unit of fluorescence for each cycle of amplification. By recording incremental fluorescence at each PCR cycle it is, therefore, possible to calculate the starting amount of cDNA template. In particular, by titrating known amounts of the relevant cDNA used as templates a standard curve can be constructed that allows quantitation of the number of cDNA and, indirectly, RNA copies in a given specimen (244). The use of a recombinant standard is strongly recommended because this gives absolute information about a given transcript copy number and, therefore, simplifies comparisons among different laboratories. Thus, by qRT-PCR it is possible to gather quantitative information about gene expression in any given specimen assuming that the tissues were handled properly so as to preserve the quality and quantity of RNA.
Among the technical variables to be considered in the interpretation and standardization of qRT-PCR results are: first, the selection of a reference gene against which to normalize the test results and, second, the strategy adopted for discriminating a positive from a negative result. Even though β-actin or other classic “house-keeping” proteins may be suitable reference genes for the study of cancer tissues, they may not be appropriate for studying the response of specific T-cell subsets to immune stimulation because they do not take into account variations in frequency of the cells targeted by the stimulus. For example, if the targets of the stimulus are CD8+ T cells, possible variations in the numbers relative to other cell subsets in a given population (i.e., PBMC) could be normalized using CD8 mRNA as reference that is likely not to be sensitive to the stimulus applied within the time frame of the assay. The selection of the normalizing factor and knowledge of its kinetics should be tailored according to the particular experimental conditions.
The other point still debated is the definition of a “positive result”. Kammula et al. considered a positive result to be a 2–3-fold increase above the mean expression of test genes in a relatively large number of irrelevant specimens. This was found to be > 5 standard deviations above the mean expression in irrelevant specimens (5). Independent from its definition, most laboratories agree that a positive result should be confirmed by reproducing the same experiment at least once. Other technical issues related to the application of qRT-PCR to the field of immunogenetics and immune monitoring have been recently discussed (245).
Since this method is dependent on logarithmic amplification, qRT-PCR allows detection of minimal amounts of RNA in small samples. In addition, by being a sequence-based method, it allows the study of the expression of any gene for whom the sequence is known independent of the availability of antibodies or other markers specific for individual gene products. Therefore, qRT-PCR can be considered the method of choice for the rapid and reproducible measurement of gene expression in small samples (246,247). In addition to its sensitivity, qRT-PCR also provides flexibility of analysis since cDNA is quite stable and it is possible to preserve clinical material almost indefinitely for the future analysis of expression of genes whose relevance to the clinical situation was not known at the time of the original analysis. A further improvement in the sensitivity of this method came from the recent development of an mRNA amplification method that maintains the proportional expression of various genes within a given sample (248). The addition of this preliminary step allows analysis of the expression of a practically infinite number of genes present in any tissue sample without causing significant distortion of their relative expression (249). This improvement has rendered qRT-PCR an extremely valuable tool for the validation of gene expression estimates derived from cDNA arrays (250).
Compared with other methods, qRT-PCR has some specific disadvantages. One important limitation of this method is the lack of discrimination among cell subsets present in a given sample responsible for differential gene expression. Theoretically, this could be overcome under special conditions. For instance, by using purified cell specimens or micro-dissected material (251), it is possible to improve the cellular specificity of the analysis. Unfortunately, this is not always feasible or practical and, therefore, other strategies need to be implemented to address this problem. Kammula et al. have suggested the use of cell specific reference genes to normalize the calculated expression of a gene believed to be expressed only by a particular cell population (5). For example, peripheral blood mononuclear cells (PBMC) could be directly stimulated ex vivo with the same peptide used for vaccination, allowing antigen presentation to occur among the sample cells. Since the peptide in question has only HLA class I binding characteristics it could be postulated that the main affect of such stimulation would occur on CD8 expressing cytotoxic T cells. Upon cognate stimulation, cytokine (i.e., IFN-γ) transcript expression is theoretically induced only in the vaccine-induced T cells. Thus, IFN-γ expression could be estimated as a proportion of copies of cytokine messenger RNA over that of general house-keeping genes such as β-actin but also normalized according to the abundance of expression of CD8 messenger RNA more directly proportional to the frequency of the cells targeted by the test in this particular case (5). This complex strategy gives an approximate estimation of gene expression for a cell population but does not yield information about gene expression in individual cells within the population. For instance, Neilsen et al. noted that only a small percent of vaccine-induced T cells (as enumerated by tetrameric HLA/epitope complexes) produce IFN-γ upon cognate stimulation (as demonstrated by intracellular FACS staining for cytokine expression) (6). This detail could not have been obtained using qRT-PCR. This limitation may have particular significance in some studies where such level of discrimination is relevant, whereas in other monitoring circumstances it might not be as important. In particular, qRT-PCR may be useful when semi-quantitative analysis of gene expression is sufficient but a broad range of genes need to be analyzed for whom no antibodies are available for surface or intra-cellular staining. An important issue to be taken into account is the kinetics of expression of different transcripts in response to a given stimulation. For example, Kammula et al. noted that IFN-γ, GM-CSF, and IL-2 have very similar kinetics of expression with a peak transcript level by qRT-PCR approximately 3 hours after stimulation. This, however, does not apply to other genes such as TNF-α whose peak expression occurs at a later time point (5). Thus, the kinetics of expression of various genes (whether test genes or putative housekeeping genes) needs to be assessed in relevant experimental conditions before the test is applied.
Another major limitation of qRT-PCR is that it does directly measure the expression of proteins but of mRNA encoding proteins. This problem may not be significant in certain situations where the expression of a given gene is used more as a marker of cellular activation in response to a given stimulus rather than for estimation of its down-stream effects. For instance, Kammula et al have noted that several gene transcripts are rapidly and specifically upregulated on cognate stimulation of T cells (5). Such genes include not only cytokines such as IFN-γ, GM-CSF, TNF-α, IL-2, etc., but also surface markers such as CD25 and CD69. Although estimation of messenger RNA expression does not guarantee expression of the corresponding proteins, it yields accurate information about the level of responsiveness of a given cell population to a cognate stimulus through transcriptional activation of the responsive gene.
The qRT-PCR technique was originally used to measure virus loads in patients for diagnosis in different diseases such as CMV, EBV, and HBV (252–254) and for monitoring viral infections in transplanted patients (255,256). Others have used qRT-PCR in follow-up examinations for treating patients with hematological cancers (257). Still others have looked for evidence of micro-metastases in blood, lymph nodes and bone marrow, by measuring the expression level of cancer specific markers (258). However, little information, is available in the literature about the utilization of qRT-PCR for immune monitoring, as this methodology as been only recently applied to this field. Although various authors have applied this technology to the analysis of immune related markers in vivo in small samples, most of these studies were not specifically related to immune monitoring of vaccinated patients (246,247,259–261). Investigators at the National Cancer Institute Surgery Branch identified T-cell reactivity toward epitopes used for active-specific vaccination of melanoma patients by measuring IFN-γ transcript levels in PBMC obtained before and after treatment and stimulated ex vivo with the vaccine-relevant epitope (5). Evidence of vaccine induced sensitization of circulating lymphocytes obtained with qRT-PCR correlated with results obtained with classic in vitro sensitization methods (244) as well as T-cell phenotyping with tetrameric HLA/epitope complexes and intra-cellular cytokine detection by FACS analysis (6,241). A significant advantage to the use of qRT-PCR for immune monitoring is its flexibility. In addition to monitoring immune reactivity against individual tumor-associated peptides restricted by specific HLA molecules, qRT-PCR can also be applied to analyze immune reactivity against whole proteins, mixtures of proteins or even whole tumor cells without knowledge of the relevant peptides or restriction elements. For instance, to monitor reactivities raised in patients with cancer of various HLA types by immunization against a tumor-associated protein, autologous antigen presenting cells manipulated to transiently express the protein of interest have been used to stimulate cytokine mRNA production from PBL collected from patients before, during and after therapy (Topalian et al., manuscript in preparation).
An important application of qRT-PCR in our experience has been the analysis of tumor specimens obtained from fine-needle aspirates. With this strategy, Morcellun measured dynamic changes in expression of tumor antigens (249), cytokines and other immune cell specific markers (262) during immunization. Although some of these markers could have been assessed by immunohistochemistry, for others there was no available antibody. In addition, the limited amount of material obtainable with fine needle aspiration would not have allowed the preparation of a sufficient number of cytology slides to study more than a few markers, while the RNA extracted from individual fine needle aspirates and amplified according to Wang's method (248) allowed the study of a virtually unlimited number of genes (263).
In conclusion, the qRT-PCR represents a useful tool for the monitoring of patients with cancer undergoing immune manipulation. This tool offers unique advantages and should be considered as part of a repertoire used to design a comprehensive immune monitoring strategy.
Flanagan et al. compared responses to multiple malarial peptides using ex vivo ELISPOT, cultured ELISPOT, and lymphoproliferation assays (156). All three assays demonstrated immune responses; however, different peptide recognition patterns were observed in the three assays with little correlation between them. There was a trend for the lymphoproliferation, but not the ELISPOT data to correlate with antibody responses. Thus, ELISPOT and lymphoproliferation assays appear to measure different aspects of the immune response.
Newer assays of T-cell specificity and function have been introduced that have distinct advantages over proliferation and cytotoxicity assays. Immunofluorescent staining with MHC-peptide tetramers is a simple and rapid method for enumerating T cells specific for single epitopes. However, it is not a functional test, as it reveals only the specificity of a population of T cells as determined by their ability to recognize a peptide bound to a particular MHC molecule. This limits the clinical usefulness of tetramers as monitoring tools, because they are restricted by epitope and by MHC allele, and because specificity does not always correlate with function especially in patients with advanced malignancies (3,185,269).
Enzyme-linked immunospot and CFC assays are similar in that they both measure the production of cytokines by individual T cells as a surrogate for function. They differ in the cell processing requirements for assay set-up (CFC can be performed on whole blood; ELISPOT requires isolation of PBMC or even CD4-or CD8-depleted PBMC). These assays also differ in their turnaround time (8 hours for CFC and 24–48 hours for ELISPOT). Finally, the detection systems of the two assays differ (flow cytometry for CFC versus microscopy for ELISPOT). Enzyme-linked immunospot assays can achieve low limits of detection, up to 1 in 300000 in one report (270). If spontaneously activated cytokine-producing cells are in fact present at 0.01%–0.02% (1 in 5000–1 in 10000), such limits of detection should not be possible in an assay that measures cytokine production. However, it is possible that such spontaneous cytokine secretion is transient in nature and is therefore detected in CFC but not detected in the longer duration ELISPOT assays. If the above assumptions are true, in the absence of background-reducing strategies for CFC assays, ELISPOT could represent a potentially more sensitive assay than CFC. However, a lower limit of detection is not useful if the assay underestimates the positive responses to an antigen.
In comparative studies, the frequencies of cytokine-positive cells obtained by CFC have been several-fold higher than those obtained by ELISPOT (214,271). Using T-cell clones, peptide-reactive T-cells found to be positive by intracytoplasmatic staining were also detected by ELISPOT, and the lower detection limit was moderately in favor of the ELISPOT approach (155). In one study, PBMC samples from six subjects were analyzed for the frequency of FLU-reactive CD8+ T-cells by flow cytometry detecting either intracellular IFN-γ (IC-FC) or secreted IFN-γ (S-FC) and by IFN-γ ELISPOT assay. The frequency of FLU peptide-reactive T-cells determined by IC-FC and ELISPOT assay showed a high interassay reproducibility and a close correlation between both assays. Little or no IFN-γ production was observed in unstimulated PBMC samples using intracellular IFN-γ FC or ELISPOT assay. In contrast, using S-FC, a high number of IFN-γ-secreting CD8+ T-cells were detected in unstimulated PBMC. The frequency of FLU-reactive CD8+ T-cells determined by S-FC did not correlate with those detected by IC-FC or ELISPOT assay (272).
A number of studies have extensively compared tetramer and cytokine ELISPOT analyses (265–268,273). Typically, tetramer analyses have provided frequency estimates that exceed those detected by ELISPOT. Even though this may, in part, be a result of issues of “higher backgrounds” associated with certain MHC-peptide tetramers, it may also be a result of the fact that cytokine ELISPOT assays detect functional memory/effector T cells that responded to cognate antigen. Because this population of “primed” T cells represents a subset of all T cells bearing TCR that can bind a MHC-peptide complex, while MHC-peptide tetramers can detect “nonresponsive” (i.e., naïve, anergic, or hyporesponsive”) and “functional” T cells, it is not surprising that tetramer assays provide a higher estimate. When combined with a functional readout (intracellular or membrane captured cytokines) in multiparameter assessments, however, tetramer estimates fall more in line with the results of LDA and ELISPOT assays. A study by Rubio-Godoy et al. (273) showed that a tyrosinase 368–376 peptide-specific T-cell clone detected by IFN-γ ELISPOT in patients with melanoma was not detectable by staining with the corresponding A2/peptide multimers, but was cytolytic. This was explained by a faster TCR/pMHC complex dissociation rate of this clone.
Cytokine flow cytometry frequencies, in the absence of anergic cells, tend to be similar to those obtained by MHC tetramer analysis (266,274).
In summary, each assay has advantages and disadvantages. The recommendations agreed on for monitoring of cancer-vaccine trials during the Workshop are listed in Table 6. These recommendations take into account the principles of the assays as well as the current state of development of individual assays towards standardization and validation. The latter is expected to change over the coming years. These recommendations are further limited by the paucity of vaccine trials that have systematically used two or more immune monitoring assays, an approach that would facilitate comparison of results. This situation also is likely to change in the near future. In principle, three situations with cancer vaccines have to be considered separately when devising a suitable immune-monitoring plan: (1) vaccinations aiming to induce a response against defined CD8 T cell epitopes, (2) vaccinations aiming to induce a response against a specific protein, and (3) vaccinations with antigens that are at least partially undefined.
The immune monitoring recommendations for these three situations are discussed separately below.
Before including a patient in a vaccine trial, the ability of the individual patient to mount an immune response should be assessed. This could be accomplished with assays measuring general T-cell function such at ζ chain expression (see “T-Cell Receptor Dysfunction in Cancer”). While providing potentially useful information, these assays are at present insufficiently standardized and validated to be used as eligibility criteria for vaccine trials. Additional prospective assessments of the ability of these assays to predict for the robustness of an immune response to a cancer vaccine need to first be performed.
The assays that are principally suitable for this purpose include ELISPOT, CFC, tetramer, qRT-PCR, and LDA assays. The recommendation from the Immune Monitoring Workshop is to use, whenever possible, a combination of two of the following assays: ELISPOT, CFC, and tetramer assays.
The ELISPOT assay is currently the best characterized. One inter- and numerous intralaboratory comparative studies have been published, as detailed above. Furthermore, the ELISPOT assay has the highest reported sensitivity to detect a T-cell response against most tumor antigens, although this may not be the case with T cells producing low levels of cytokines (Keilholz U, unpublished data). Reliable analysis is relatively easy to perform, but requires constant care because several critical reagents can vary from batch-to-batch. Furthermore, an automated plate reader is required for objective analysis.
The CFC assay exploits the same T-cell properties as the ELISPOT assay, however considerably fewer studies analyzing T-cell responses to tumor antigens are published with the CFC. Most of the current experience with the CFC assay is with viral diseases, which may not be relevant to many of the less immunogenic tumor-derived self-antigens. Cytokine flow cytometry commonly employs multicolor staining. Therefore there is a need for a dedicated and specially trained person to perform the flow analysis, because gating of cells during event acquisition and setting of markers to discriminate negative and positive staining requires experience and a high level of expertise. Theoretically, intelligent interactive software solutions could be developed to ease this process and reduce operator variability.
The tetramer assay is currently less well standardized and is less sensitive than the ELISPOT assay and probably the CFC. For each T-cell epitope an individual tetramer has to be produced and characterized. However, once a specific tetramer is available, the assay has the advantage over ELISPOT and CFC assays that it allows the detection of specific T-cells regardless of their ability to produce cytokines. For the same reason, however, the tetramer assay is unable to distinguish between functional and dysfunctional T cells, or between T cells producing Th1 or Th2 type cytokines. Therefore, it is currently recommended that the tetramer assay be used in conjunction with one of the two functional cellular assays (ELISPOT or CFC).
The qRT-PCR assays are also of great interest because they require the least in-vitro manipulation. Two hours of stimulation are sufficient to elicit cytokine transcription, but not cytokine release, making intercellular variability during the incubation period unlikely. Furthermore, qRT-PCR is very flexible. Once the cDNA is generated a large number of different cytokines can be tested using the material obtained from a single experiment. A major disadvantage of the qRT-PCR assay is that it is not a single cell assay and therefore cannot quantitate T cell frequencies or characterize specific T cells. Because of the lack of standardization, qRT-PCR assays cannot currently be recommended as a sole assay for monitoring of T cells in peripheral blood. On the other hand, the use of the qRT-PCR assay for assessment of tissue samples is of special interest, since the number of cells recovered from tissue is usually insufficient to allow the performance of ELISPOT or CFC assays. Consequently, the combination of qRT-PCR to assess for reactive cells and peptide specific cell quantification via tetramer staining may ultimately prove to be extremely valuable for in situ analysis of tissue specimens.
The disadvantage of the LDA is the requirement of several rounds of in vitro restimulation resulting in quantitative and functional biases. Also the amount of work required to perform the analysis represents a major practical limitation. For these reasons, the LDA is not recommended for trial monitoring.
In summary, if single epitope-specific T cells are to be monitored in peripheral blood, the ELISPOT assay is recommended as a sensitive assay to detect functional T cells. If possible, all samples should be further analyzed with a second assay, either CFC or tetramers. The CFC assay would be a confirmatory assay, and the tetramer assay would allow detection of the antigen specific T-cells that didn't produce the cytokine used in the read-out of the ELISPOT assay. Cytokine flow cytometry as well as tetramer analyses allow, in cases where there is a T-cell response of sufficient magnitude, detailed further characterization of epitope specific T cells (see “Current Developments”).
In protein vaccine trials, antibody responses, CD4 T-cell responses, and CD8 T-cell responses may be induced, signifying a higher level of complexity. The role of each of these components for tumor rejection is currently unknown and may vary from one protein to another. Therefore, in protein vaccine trials, all three types of immune responses are of interest. For analyzing antibody responses, the specific recommendations given in the “Antibody Assays” section of this work are applicable. Assessment of CD4 responses is usually performed with proliferation assays, with the limitations discussed in the section describing LDA (see “Limiting Dilution Analysis”). Variations of the ELISPOT and the CFC assay have been developed to enable assessment of CD4 responses and will likely replace the proliferation assay in the future. Vaccination with proteins may also induce (albeit usually with low efficacy) CD8 responses, which can be monitored in a way analogous to what was recommended for monitoring of responses against defined CD8 T-cell epitopes. No further recommendations were made during the Workshop because of the limited availability of published information on immune monitoring for clinical trials utilizing protein vaccines.
In case of vaccination with modified tumor cells, with tumor-cell lysates, or with DC-tumor cell fusions, a variety of antigens may differentially induce immune responses, including antibody responses, CD4, and CD8 T-cell responses. Under these circumstances the principles detailed under the “Current Recommendations for Trial Monitoring” section apply, with the vaccine preparation used as the target material rather than a specific protein. To dissect the immune response to complex vaccines, efforts should be undertaken to characterize at least a limited number of antigens in the vaccine preparation as a way of monitoring the specific components of the immune response. Furthermore, it should be noted that under the conditions described in the respective sections the ELISPOT and CFC assays might also work with whole cellular targets.
The workshop and this report are oriented toward the quantitatation of CD8 T-cell responses towards tumor antigens presented by MHC class I. In addition to the quantitation of a T-cell response, further characteristics of antigen-specific T cells are important, namely their level of functional differentiation, including lymph node homing, proliferative capacity and lytic effector function, and their expression of receptors governing T-cell migration into specific peripheral tissues. Reagents to investigate these parameters are becoming increasingly available and are likely to be incorporated into future immune monitoring strategies.
For functional T-cell differentiation, three CD8 T-cell subsets can be distinguished by virtue of expression of the lymph node homing receptor CCR7 and the CD45 isoform RA as proposed in a model by Sallusto and Lancavecchia (275). Naive T cells are typically CCR7+CD45RA+, central memory T cells are CCR7+CD45RA−, and peripheral memory T cells are CCR7-CD45RA−. According to this model, naive T cells home to T-cell areas of lymph nodes and require antigen priming by DC to become functionally active. The central memory T cells are capable of directly migrating into inflamed tissue and proliferating upon antigen exposure, but lack immediate effector function. Peripheral memory T cells also can migrate directly into inflamed tissue and can then respond directly to antigen exposure with production of IFN-γ and IL-4. A fourth T-cell subset, termed terminally differentiated lytic effector T cells, has recently been added to this model (276,277). These T cells are CCR7−CD45RA+, are able to migrate into inflamed tissues, and are capable of efficiently lysing target cells without further stimulation. The classification of T-cell subsets according to their level of functional differentiation may provide very useful information and may differ from one vaccination protocol to the other and even from one antigen to the other. The respective antibodies necessary to perform these subset analyses are available and can easily be incorporated into CFC and tetramer assays.
A far less well-defined topic is the expression of homing and chemokine receptors on T cells. The degree of expression of these receptors appears to determine tissue migration (278). This expression of homing and chemokine receptors on T cells may be of great importance. Because immune monitoring is usually performed using peripheral blood T cells, determining their capacity to specifically extravasate into other compartments may be critical to understanding the clinical efficacy of a particular vaccine.
For the future, the organizers and authors hope that these Workshop proceedings will help provide a uniform language and a common experimental approach for clinical and translational research in the field of cancer vaccines. The Society for Biological Therapy is dedicated to providing forums for discussion of further advances in this area and is planning to organize a second Immune Monitoring Workshop in the future.
Immune Monitoring Workshop Participants by Breakout Session Group
Flow Cytometry for Cytokine Secretion:
Herbert Kim Lyerly, M.D. (Co-Chair), Vernon Maino, Ph.D. (Co-Chair), Paul Chapman, M.D., Thomas Davis, M.D., Robert Dillman, M.D., Susan Doleman, Susan Hand, Ph.D., Eddy Hsueh, M.D., Michael Lotze, M.D., James Mier, M.D., John Neefe, M.D., Sattva Neelapu, M.D., Craig Slingluff, Jr., M.D., Paul Sondel, M.D., Ph.D., Edwin Walker, Ph.D., Louis Weiner, M.D., and Jon Wigginton, M.D.
Enzyme-Linked Immunospot/Enzyme-Linked Immunosorbent Assay:
Carmen Scheibenbogen, M.D. (Co-Chair), Ulrich Keilholz, M.D. (Co-Chair), Jeffrey Schlom, Ph.D. (Co-Chair), Jean-Claude Bystryn, M.D., Carter Cliff, John Dunne, Ph.D., Lawrence Fox, M.D., Ph.D., Frank Haluska, M.D., Ph.D., Stephen Hodi, M.D., Lori Jones, Ph.D., Howard Kaufman, M.D., Janet Lathey, Ph.D., Jonathan Lewis, M.D., Ph.D., Philip Livingston, M.D., Cristina Musselli, M.D., Ph.D., Laurie Stephen, Ph.D., Crystal Sung, Ted Trimble, M.D., Theresa Whiteside, Ph.D., and Robert Wiltrout, Ph.D.
Real Time Polymerase Chain Reaction:
Francesco Marincola, M.D. (Co-Chair), Kathleen Beach, M.D., David Essayan, M.D., Jared Gollob, M.D., Elizabeth Jaffee, M.D., Reiner Laus, M.D., Mike Perricone, Ph.D., Knut Sturmhoefel, Ph.D., Suzanne Topalian, M.D., Pierre Triozzi, M.D., Nancy Valente, M.D., and Frank Valone, M.D.
T-Cell Receptor Function:
James Finke, Ph.D. (Co-Chair), Dmitry Gabrilovich, M.D., Ph.D. (Co-Chair), W. Martin Kast, Ph.D. (Co-Chair), Keith Douglas, Jeff Edelson, M.D., Stephen Fields, Ph.D., Oscar Kashala, M.D., Ph.D., Stephanie Kenis, Samir Khleif, M.D., Robert Martell, M.D., Ph.D., Augusto Ochoa, M.D., Nicholas Restifo, M.D., Steven Rosenberg, M.D., Ph.D., Scott Saxman, Ph.D., and Peter Wettstein, Ph.D.
T-Helper and Antibody Assays/Limiting Dilution Analysis:
John Kirkwood, M.D. (Co-Chair), Nora (Mary) Disis, M.D. (Co-Chair), Mark Albertini, M.D., Priscilla Ayers, Neil Berinstein, M.D., Soldano Ferrone, M.D., Ph.D., Bernard Fox, Ph.D., James Mulé, Ph.D., Rathinam Selvan, Ph.D., Vernon Sondak, M.D., Michael Vasconcelles, M.D., and Hassan Zarour, M.D.
Peter P. Lee, M.D. (Co-Chair), Walter Storkus, Ph.D. (Co-Chair), Thomas Gajewski, M.D., Ph.D., Susan Gammon, Ph.D., MBA, Cheryl Guyre, Peter Hersey, M.D., Ph.D., Barb Hickingbottom, Tina Kuus-Reichel, Ph.D., Ping Law, Ph.D., Bill Rees, Jeffrey Sosman, M.D., John Thompson, M.D.
List of Authors for Specific Sections Lecture Summaries
Mechanisms of T-Cell Dysfunction:
James H. Finke, Charlie Tannenbaum, Patricia Rayman, Amy Richmond, Eric His, and Ronald Bukowski
Defective Dendritic Cell Differentiation in Cancer:
Sergei Kusmartsev and Dmitry I. Gabrilovich
Breakout Session Reports
Measuring T-Cell Receptor Function in Cancer:
W. Martin Kast, Dmitry I. Gabrilovich, and James H. Finke
Antibody Assays and Limiting Dilution Analysis:
Mary L. Disis and John M. Kirkwood
Enzyme-Linked Immunospot Assays:
Carmen Scheibenbogen and Jeff Schlom
Cytokine Flow Cytometry:
Vernon C. Maino, Holden T. Maecker, Paul J. Mosca, and Herbert Kim Lyerly
Peter P. Lee and Walter Storkus
Real Time Polymerase Chain Reaction Assays:
Francesco M. Marincola and Suzanne Topalian
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