We amplified RNAs from 63 fine needle aspiration (FNA) samples from 37 s.c. melanoma metastases from 25 patients undergoing immunotherapy for hybridization to a 6108-gene human cDNA chip. By prospectively following the history of the lesions, we could correlate transcript patterns with clinical outcome. Cluster analysis revealed a tight relationship among autologous synchronously sampled tumors compared with unrelated lesions (average Pearson’s r = 0.83 and 0.7, respectively, P < 0.0003). As reported previously, two subgroups of metastatic melanoma lesions were identified that, however, had no predictive correlation with clinical outcome. Ranking of gene expression data from pretreatment samples identified ∼30 genes predictive of clinical response (P < 0.001). Analysis of their annotations denoted that approximately half of them were related to T-cell regulation, suggesting that immune responsiveness might be predetermined by a tumor microenvironment conducive to immune recognition.

Efforts aimed at the discovery of independent predictors of clinical outcome have identified molecular subsets of cancer based on mathematical analyses of their gene expression profiles (1, 2, 3). Subcategories of lymphomas (1) and breast carcinomas (3) with distinct prognostic and/or clinical behavior were recognized. Bittner et al.(2) suggested two biologically distinct molecular profiles of cutaneous melanoma lesions with divergent metastatic potential in vitro but unknown clinical relevance. In addition, one subclass was characterized by the enhanced expression of MART-1, a classic melanoma differentiation antigen. This triggered the hypothesis that perhaps two different disease taxonomies, both classified according to visual methods as melanoma, could be identified by total transcript analysis and might be characterized by different immune responsiveness. We, therefore, wondered whether direct ex vivo documentation of molecular portraits of metastatic melanoma lesions could prospectively identify taxonomically different entities of this disease or subsets related to its natural progression that could, in either case, be of clinical relevance (4). To account for experimental variance attributable to the intrinsic heterogeneity of different tumor deposits and/or the evolving genetic profiles of individual lesions with time, we directly correlated clinical information pertaining to each lesion with its biological profile. Thus, we collected serial FNAs2 (23-gauge) of individual lesions that allowed prospective documentation of their natural history and/or therapeutic outcome. Limitations attributable to the small amount of total RNA obtainable by FNA were circumvented by a recently validated amplification method (5). Because melanoma is a disease predisposed to different forms of immune modulation, in this exploratory study, emphasis was put on the intrinsic biology of individual lesions rather than the specificity of the immunotherapy administered. Thus, FNA samples obtained before conceptually similar but not identical forms of immunotherapy were studied with the goal of identifying candidate predictors of immune responsiveness based on the working hypothesis that common effector pathways may ultimately determine immune rejection of cancer.

FNAs were obtained before treatment in a period spanning from January 5, 1999 through December 16, 1999 from 37 metastases in 25 patients with metastatic melanoma referred to the Surgery Branch, National Cancer Institute for various immunotherapy treatments. The size of each metastasis was serially documented as two perpendicular diameters. The same operator (G. A. O.) performed the FNA by aspirating four quadrants of each lesion. To minimize RNA metabolism and degradation, the material was immediately placed at the bedside in ice-cold RPMI 1640 culture (Biofluids, Rockville, MD) without serum and carried on ice to the laboratory for processing. Over this period, ∼300 FNAs of s.c. melanoma metastases were accrued. From those, thirty-seven s.c. lesions were selected from 25 patients based on quality of material available and relevant clinical outcome (Table 1). A second FNA was obtained, when possible after at least one course of treatment. Paired pre- and posttreatment samples were obtainable in 25 lesions (median follow-up, 11 weeks). Metastases were separated into groups: group 1, cr; group 2, pr (>50% reduction in the product of two perpendicular diameters); group 3, stable disease (<50% reduction and <25% increase); and group 4, no response (>25% increase). All responses lasted >30 days. Thirteen pretreatment FNAs were available from cr lesions. Of those, 4 (P3-a0-cr, P18-a0-cr, P18-b0-cr, and P21-a0-cr) regressed before follow-up FNAs could be obtained. Two pr, 8 sd, and 11 nr pretreatment FNAs were also available.

Microarrays.

Total RNA extracted from FNA and control samples was transcribed in vitro into aRNA and reverse-transcribed into fluorescently labeled cDNA for hybridization to 6108 gene cDNA-based microarrays as described previously (5). Control samples consisted of NHEMs derived from neonatal foreskin and grown in melanocyte culture medium (Clonetics, San Diego, CA). The RCC, the P11-Mel melanoma cell line, and the fibroblast strain (FB) were expanded in standard RPMI 1640 containing 10% human AB serum from FNAs of metastatic renal cell (RCC) and a melanoma metastasis (P11-Mel and FB) cell lines. All three were used for the analysis before they reached the fifth passage in culture. Pooled peripheral blood mononuclear cells from six donors were used to prepare reference aRNA to be cohybridized in all experiments with test aRNA. cDNA targets were labeled with aRNA using Cy3 (green) for reference material and Cy5 (red) for test material. A 16 × 20 × 20 (6400-spot) human cDNA microarray printed at Advanced Technology Center, NCI has 6108 sequence-verified clones representing 5492 unique genes and 537 expressed sequence tag clusters.

Statistical Methods.

All statistical analyses were performed with SPLUS package. The log10-based ratios were normalized by making the median value in an array equal to zero. Pearson correlation coefficients of log-ratios of two expression profiles were used to quantify the similarity between the samples. We visualized relationships among expression profiles by performing average linkage hierarchical clustering and multidimensional scaling analyses (6). In these analyses, we used one-correlation coefficient as the distance between pairs of samples. We performed these analyses both including all genes and including only genes showing high variation in log expression ratios across the entire set of samples. The results were similar, and the latter analyses are shown in this report. The variance of each gene across the entire set of samples was computed, and the median was determined. The genes with high variation were defined as those with variance significantly (P < 0.001 by χ2 test) greater than this median. Two-sample t-statistics and Wilcoxon rank statistics were used to identify the genes that are differentially expressed between two groups (such as cr versus other lesions, melanoma versus melanocytes, and others). We used relatively stringent cutoff levels for significance because of the number of genes being tested. To determine whether the number of differentially expressed genes is higher than expected because of chance, we randomly shuffled the phenotype labels (e.g., cr versus non-cr) and recomputed the two-sample t-statistics for each gene. This analysis was repeated 10,000 times, and the proportions of the random replications that resulted in as many significant genes as seen in the actual data were reported as the significance levels for the number of genes.

Paired value t-statistics and Wilcoxon rank statistics were used to identify the genes that have significant changes between pre- and posttreatment samples in each group. For the paired value t test, the labels of paired pre- and posttreatment samples were switched randomly, and such analysis was also repeated 1000 times to generate permutation-based statistical significance for the number of genes significantly changed between pre- and posttreatment samples (P < 0.001 by t test). To determine whether the association between pairs of synchronous lesions of the same patient is stronger than expected because of chance, we randomly shuffled the patient identifier labels for the synchronized pairs. The average correlation of log-ratios for synchronous pairs in the data set was compared with the distribution of average correlations based on 10,000 sets of randomly paired lesions.

Overview of Melanoma Metastases.

The data set was globally screened by applying a high-stringency filter to minimize labeling or random hybridization bias (Ref. 5; Fig. 1). Clustering algorithms based on the resulting 4293 genes suggested a high degree of relatedness among simultaneously biopsied (synchronous) autologous lesions. Most metastases did not significantly change with time; however, exceptions were noted, as outlined by the color lines in Fig. 1. Two clusters were identified that included 49 and 14 samples, respectively. The larger cluster (cluster I) appeared closely associated with NHEMs. Correlation with clinical information did not segregate the two clusters into biologically distinct categories because metastases from the same patients biopsied at different time points were observed in either group. Instead, the smaller cluster (cluster II) appeared to portrait a late/progression expression profile because its members included an inordinate proportion of later FNAs (16 of 49 pretreatment samples in cluster I versus 11 of 14 in cluster II; Fisher test p2 = 0.003). Serially sampled lesions of some patients demonstrated a shift toward this subset with time, i.e., later samples of FNA pairs shifted in cluster II (P4-b1-pr, P5-b1-sd, P14-a1, cr, P14-b1-cr, and P25-b1-nr). Thus, global transcript analysis failed to identify subsets of melanoma metastases with predictive value with regard to immune responsiveness but rather suggested a progressive drift in time of melanoma metastases away from NHEMs.

Treatment-dependent Adaptation in Expression of Individual Genes.

Because the previous analyses indicated that immune responsiveness could not be predicted by a specific subset of melanoma metastases, we turned our attention to individual gene expression. By comparing individual gene expression in paired pre- and posttreatment samples, we observed that the number of genes differentially expressed in response to immunotherapy was greater in cr lesions. In particular, 17 genes were differentially expressed with a high degree of statistical significance (t test, P < 0.001) in 9 pairs of cr lesions (Fig. 2 A). Because when this large number of tests is performed a low P can simply be obtained by chance, we then performed a permutation analysis to address the frequency in which such a number of significant (P < 0.001) observations would occur by pure chance (see “Materials and Methods”). This was a significantly greater number than would be expected by chance as confirmed by permutation analysis (P < 0.015). This permutation test P gives a general estimate of the significance of the findings based on the complete data set and does not represent a correction for individual Ps. When a similar analysis was performed on 14 pairs of lesions that did not undergo clinical regression, only 6 genes were identified to such degree of significance, a number close to the one expected by chance (P < 0.2). This observation suggests that clinical regression is associated with significant alterations in the transcriptional profile of tumors, whereas lack of response is associated with an indolent intratumor microenvironment rather than a turbulent reaction to a brisk immune response through the adoption of escape mechanisms (7). Analysis of the functional annotations of the genes identified by this analysis depicted a general pattern associated with increased tissue metabolism because it could be expected during tissue destruction and reactive repair mechanism. However, IRF1 up-regulation implicated strong immune stimulatory conditions (see below).

Statistical Ranking of Individual Genes Differentially Expressed in Pretreatment Samples Suggests Classifiers of Immune Responsiveness.

To avoid disturbances attributable to the effect of time and/or treatment, only pretreatment samples were analyzed further to test whether subsets with clinically divergent behavior could be identified. Lesions synchronously sampled before treatment from the same patients were significantly closer to each other than unrelated lesions (average R = 0.83 and 0.7, respectively; permutation test, P < 0.0001). However, clustering of pretreatment samples could not identify clear subsets of melanoma. Nonparametric comparison of individual gene expression between 13 pretreatment cr samples and all other pretreatment lesions (n = 21) identified 14 genes differentially expressed to a P < 0.001 level of significance (Wilcoxon test; Table 2; Fig. 2,B). Similarly, parametric analysis identified 18 genes with levels of expression significantly different at P < 0.001 (t test), mostly overlapping those identified by the Wilcoxon test. Permutation analysis established that the number of differentially expressed genes was significantly greater than that expected by chance (P < 0.05). To further explore the significance of these findings, we compared differences between 13 cr versus 11 nr pretreatment FNAs characterized by most dramatically divergent clinical behavior. Nonparametric analysis identified 13 differentially expressed genes (Wilcoxon P < 0.001) and parametric analysis identified 9 (t test P < 0.001) several overlapping with the previous data set. In total, the combined analysis identified 33 genes of potential interest. Seven genes had no annotated function. Review of available literature suggested that 12 of the remaining 26 genes were associated with immune/inflammatory function (Table 2, boldface entries). This rate was significantly higher than the frequency of genes with known immune regulatory function present in the cDNA chip (10% of 100 randomly selected clones, Fisher test p2 < 0.0001). All of the statistical analyses described here (with the exception of the random sampling of 100 genes) were done a priori and independently of the characterization of the genes. Among the genes preferentially expressed in responding lesions, TIA-1 is a cytolytic granule component responsible for killing of CTL targets (8). EBI3 has a facilitator role in the secretion of IL-12 (9) and is strongly associated with activation of antigen-presenting cells (10). Other genes are associated with transforming growth factor-β-like function (MADH3, INHBA; Ref. 11), IFN (IRF-2 and IF127), or TNF regulation (FIP2). Similarly, JAK-1 and Txk, relatively suppressed in responding lesions, regulate T-cell signaling and differentiation. In addition, IRF-2 expression coincided with the expression of a casein kinase belonging to a family of kinases possible associated with IRFs transcriptional activity (12). Of particular interest was the enhanced expression of IRF2 in immune-responsive lesions. Both IRF1 (up-regulated in responding lesions) and IRF2 may act as functional agonists and can modulate genital wart immune responsiveness through the JAK/STAT pathway (13) and the responsiveness of carcinoid tumors (14) and chronic myeloid leukemia (15) to IFN-α treatment and, in general, immune-mediated tumor suppression (16). The differential expression of immune-regulatory genes between immune-responsive and resistant lesions was not associated with significant differences in density of different immune cell populations, as suggested by various differentiation markers present in the array (Table 2 and Fig. 2 B). In addition, quantitative real-time PCR measurement demonstrated similar expression of lymphoid cell markers, such as T-cell receptor Vβ chain constant domain, CD3 and CD8 in cr versus nr lesions, while confirming the differential expression of immune-regulatory genes (data not shown).

This explorative study was designed to test the potential of using FNA-derived material to follow, by global transcript analysis, the progression of events occurring in the tumor microenvironment. Our results show that FNA sampling can allow direct documentation of evolving biological processes marking the natural course of a disease or its response to treatment. Subsets of melanoma metastases were identified that could be best explained by temporal changes in the transcriptional pattern of the disease. Because the lesions consisted of a relatively homogeneous collection of s.c. metastases, broader molecular diversity predictive of metastatic potential and tissue localization suggested by others (2) might have been missed.

Contrary to other studies in which global transcript analysis identified clinically relevant subsets of lymphomas and breast carcinomas (1, 3), we did not observe subsets of melanoma predictive of clinical outcome. Individual gene analysis, however, suggested that immune responsiveness may be predetermined and not solely dependent upon the extent of the immune responses elicited by a given treatment. In the same set of data, we have observed that melanoma metastases express a heterogeneous array of cytokines, growth factors, and metalloproteinases (17, 18). Among them, several have chemotactic properties on nucleated blood cells such as BLC, eotaxin, IL-1, IL-8, IL-16, lymphotactin, MCP-1, MCP-3, MCP-4, and RANTES whereas others display potent inflammatory activity such as IL-6, MIP-1α, MIP-1β, MIP-2α (GRO 1/2), and TNF-γ. In addition, the expression profile was found to be surprisingly similar among various cytokines and correlated in most metastases with that of a subset of IFN-responsive elements including IRFs. Thus, it is possible that immune-stimulatory and/or inflammatory stimuli occurring at the tumor site may induce coordinate expression of several immune modulators in some tumors and predispose to immune rejection an otherwise dormant host’s immune system tolerant of poorly immunogenic tumor cells. Our observations yield a novel hypothesis suggesting that responsiveness of melanoma metastases to immunotherapy is predetermined. Because the study was based on a patient population receiving conceptually similar yet heterogeneous therapy, these findings have only exploratory significance, and future studies should address their predictive significance in the context of different therapies.

Fig. 1.

Evolving molecular portraits of metastatic melanoma. Eisen’s hierarchical clustering dendrogram of all samples studied applied to 4293 genes allowed by high-stringency filtering (Cy5:Cy3 ratios with a 3-fold change, signal intensity >500 unless other channel >3000 and 75% of the target pixel >1 SD above background). Control samples included: FB, fibroblast cell line derived from a melanoma metastasis; RCC, renal cancer cell line). The minus sign denotes that the melanocyte strain was cultured without growth factors for 48 h. P11-Mel, melanoma cell line derived from P11-a1 FNA; Ocu Mel, FNA of a hepatic metastasis from an ocular melanoma. FNA samples are color coded and numbered according to the identity of the patient (P). The letter after the number refers to lesion identity, whereas the following number refers to the order in which the FNA of that lesion was obtained (0, pretreatment; 1, first FNA after treatment; 2, second). The blue arrow points to the tight clustering of the melanoma cell line with the lesion from which it originated and underlines the ability of FNA-based global transcript analysis to identify clonal relatedness independently of infiltrating normal cells. Most often autologous FNAs clustered together; exceptions are underlined by colored lines consistent with the color assigned to the patient. In particular, the green line at the bottom of the cluster points shows molecular portrait changes in FNAs from P25 in a period spanning 9 months. P25-b1-nr and P25-a2-nr were excised shortly after the FNAs, and representative H&E stains are shown below for P25-b1-nr. Three different histological phenotypes were identified: epithelioid cell melanoma (60% of the lesion); pleiomorphic transition zone (20%); and chondrosarcomatous metaplasia (20%). This neoplastic metaplasia (19) could not be identified in previous samples from the same or other lesions.

Fig. 1.

Evolving molecular portraits of metastatic melanoma. Eisen’s hierarchical clustering dendrogram of all samples studied applied to 4293 genes allowed by high-stringency filtering (Cy5:Cy3 ratios with a 3-fold change, signal intensity >500 unless other channel >3000 and 75% of the target pixel >1 SD above background). Control samples included: FB, fibroblast cell line derived from a melanoma metastasis; RCC, renal cancer cell line). The minus sign denotes that the melanocyte strain was cultured without growth factors for 48 h. P11-Mel, melanoma cell line derived from P11-a1 FNA; Ocu Mel, FNA of a hepatic metastasis from an ocular melanoma. FNA samples are color coded and numbered according to the identity of the patient (P). The letter after the number refers to lesion identity, whereas the following number refers to the order in which the FNA of that lesion was obtained (0, pretreatment; 1, first FNA after treatment; 2, second). The blue arrow points to the tight clustering of the melanoma cell line with the lesion from which it originated and underlines the ability of FNA-based global transcript analysis to identify clonal relatedness independently of infiltrating normal cells. Most often autologous FNAs clustered together; exceptions are underlined by colored lines consistent with the color assigned to the patient. In particular, the green line at the bottom of the cluster points shows molecular portrait changes in FNAs from P25 in a period spanning 9 months. P25-b1-nr and P25-a2-nr were excised shortly after the FNAs, and representative H&E stains are shown below for P25-b1-nr. Three different histological phenotypes were identified: epithelioid cell melanoma (60% of the lesion); pleiomorphic transition zone (20%); and chondrosarcomatous metaplasia (20%). This neoplastic metaplasia (19) could not be identified in previous samples from the same or other lesions.

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Fig. 2.

Putative predictors of immune responsiveness. A, highest ranking genes differentially expressed in posttreatment FNAs compared with pretreatment FNAs in lesions that regressed with treatment (top panel). The relative expression of gene markers of specific immune cell populations is represented in the bottom panel separated by a white line. B, highest ranking genes that identify pretreatment lesions with potential for clinical regression separated from immune resistant lesions by a yellow vertical line (A). The relative expression of genes markers of specific immune cell populations is represented in the bottom panel separated by a white line. In both pictures, ratios are displayed according to the central method for display using a normalization factor as recommended by Ross et al.(20).

Fig. 2.

Putative predictors of immune responsiveness. A, highest ranking genes differentially expressed in posttreatment FNAs compared with pretreatment FNAs in lesions that regressed with treatment (top panel). The relative expression of gene markers of specific immune cell populations is represented in the bottom panel separated by a white line. B, highest ranking genes that identify pretreatment lesions with potential for clinical regression separated from immune resistant lesions by a yellow vertical line (A). The relative expression of genes markers of specific immune cell populations is represented in the bottom panel separated by a white line. In both pictures, ratios are displayed according to the central method for display using a normalization factor as recommended by Ross et al.(20).

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2

The abbreviations used are: FNA, fine needle aspiration; cr, complete regression; pr, partial regression; sd, stable disease; nr, no response; aRNA, antisense RNA; NHEM, normal human epithelial melanocyte; RCC, renal cell carcinoma; TNF, tumor necrosis factor; IRF, IFN regulatory factor; IL, interleukin; MCP, monocyte chemotactic protein; MIP, macrophage inflammatory protein.

Table 1

Metastases that regressed completely in response to therapy

The number following P refers to patient’s identity. The following letter (a, b, …) identifies individual metastasis. The following number codes the relationship between FNA and treatment (0, pretreatment; 1, first and 2, second biopsy after treatment. Size of tumor, two ortogonal diameters. Time of FNA in relation to treatment (pre versus post). Treatment type, melanoma antigen targeted by the immunization. Systemic IL-2 corresponds to 720,000 IU/kg every 8 h to limit toxicity.

Case no.Sex/AgeFNA siteDate of FNASize of metastasisTime of FNATreatment typeSystemic IL-2 administrationDate of last follow-upSize at last follow-upSource total RNA (μg)aRNA yield (μg)
P1-a0-cr M/34 R Groin 10/7/1999 1 × 1 Pre MART-1/GP yes 2/29/2000 87 
P1-a1-cr M/34 R Groin 12/2/1999 1.5 × 1.5 Post MART-1/GP yes 2/29/2000 48 
P2-a0-cr M/52 L Parotid 6/22/1999 1.5 × 1.5 Pre TRP-1 yes 10/15/1999 17 149 
P2-a1-cr M/52 L Parotid 8/6/1999 2 × 2 Post TRP-1 yes 10/15/1999 14 140 
P3-a0-cr M/31 L Axilla 2/5/1999 2 × 2 Pre TRP-1 yes 4/1/1999 10 73 
P6-a0-cr M/49 L Lat Knee 2/23/1999 2.5 × 2.5 Pre GP yes 7/29/1999 125 
P6-a1-cr M/49 L Lat Knee 6/29/1999 3 × 2.5 Post GP yes 7/29/1999 145 
P6-b0-cr M/49 L Med Knee 2/23/1999 1.5 × 1.5 Pre GP yes 7/29/2000 70 
P6-b1-cr M/49 L Med Knee 6/29/1999 1 × 1 Post GP yes 7/29/2000 195 
P10-a0-cr M/67 L Neck 3/17/1999 2 × 2 Pre GP no 8/31/1999 27 103 
P10-a1-cr M/67 L Neck 7/1/1999 2 × 3 Post GP no 8/31/1999 96 
P14-a0-cr M/49 R Face Inf 4/6/1999 1 × 2 Pre GP yes 8/11/1999 115 
P14-a1-cr M/49 R Face Inf 7/1/1999 2 × 2 Post GP yes 8/11/1999 77 
P14-b0-cr M/49 R Face Sup 3/19/1999 2 × 2 Pre GP yes 8/11/1999 92 
P14-b1-cr M/49 R Face Sup 7/1/1999 2 × 2 Post GP yes 8/11/1999 20 
P16-a0-cr M/62 R Chest 4/27/1999 1 × 1.5 Pre MART-1 yes 9/1/1999 134 
P16-a1-cr M/62 R Chest 6/10/1999 1.5 × 1.5 Post MART-1 yes 9/1/1999 12 143 
P18-a0-cr F/65 L Neck 6/8/1999 2 × 2 Pre GP yes 8/31/1999 51 
P18-b0-cr F/65 R Neck 6/8/1999 3 × 3 Pre GP yes 8/31/1999 37 
P21-a0-cr F/35 L Temple 9/21/1999 1 × 1.5 Pre GP (cells) no 11/3/1999 90 11 
P23-a0-cr M/53 R Thigh Dist 6/22/1999 2.5 × 2.5 Pre MART-1 yes 1/6/2000 52 
P23-a1-cr M/53 R Thigh Low 9/30/1999 1 × 1.5 Post MART-1 yes 1/6/2000 84 
P23-b1-cr M/53 R Thigh Prox 9/30/1999 1.5 × 2 Post MART-1 yes 1/6/2000 
            
Metastases that regressed partially in response to therapy            
P4-b0-pr M/67 R Knee 3/11/1999 1 × 1 Pre GP yes 7/7/1999 0.5 × 0.5 23 147 
P4-b1-pr M/67 R Knee 6/22/1999 0.5 × 0.5 Post GP yes 7/7/1999 0.5 × 0.5 51 16 
P20-a0-pr M/54 R Groin 7/27/1999 4 × 4 Pre MART-1/GP no 9/7/1999 2 × 3 43 41 
P20-a1-pr M/54 R Groin 9/7/1999 2 × 3 Post MART-1/GP no 9/7/1999 2 × 3 25 
            
Metastases that remained stable during the observation period            
P4-a0-sd M/67 R Prox Leg 3/11/1999 2 × 2 Pre GP yes 7/7/1999 1.5 × 1.5 116 
P4-a1-sd M/67 R Prox Leg 6/22/1999 1.5 × 1.5 Post GP yes 7/7/1999 1.5 × 1.5 92 
P5-a0-sd F/58 L Groin 7/28/1999 1 × 1 Pre GP-T cells no 10/14/1999 1.5 × 1.0 118 
P5-a1-sd F/58 L Groin 8/23/1999 1.5 × 1.5 Post GP-T cells no 10/14/1999 1.5 × 1.0 33 
P5-b0-sd F/58 L Leg 7/28/1999 1 × 2 Pre GP-T cells no 10/14/1999 2 × 2 150 
P5-b1-sd F/58 L Leg 8/23/1999 2 × 2 Post GP-T cells no 10/14/1999 2 × 2 85 
P8-b0-sd M/74 R Arm 6/29/1999 3 × 3 Pre GP no 8/17/1999 3 × 3.5 147 
P8-b1-sd M/74 R Arm 8/17/1999 3 × 3.5 Post GP no 8/17/1999 3 × 3.5 14 166 
P13-a0-sd M/58 R Arm Inf 9/9/1999 2 × 2 Pre GP no 12/7/1999 2 × 2.5 101 
P13-a1-sd M/58 R Arm Inf 12/7/1999 2 × 2.5 Post GP no 12/7/1999 2 × 2.5 10 115 
P13-b0-sd M/58 R Arm Sup 9/9/1999 2 × 2 Pre GP no 12/7/1999 2.5 × 3.5 18 138 
P21-b0-sd F/35 R Occipital 9/21/1999 2.5 × 3 Pre GP (cells) no 11/3/1999 3 × 3.5 30 124 
P21-b1-sd F/35 R Occipital 11/3/1999 3 × 3.5 Post GP (cells) no 11/3/1999 3 × 3.5 37 102 
P22-a0-sd M/32 R Leg 4/29/1999 2 × 2 Pre MART-1/GP no 7/1/1999 2 × 2 62 
            
Metastases that did not respond to therapy            
P7-a0-nr F/76 L Breast 6/10/1999 1 × 2 Pre GP no 7/27/1999 2 × 2 116 
P8-a0-nr M/74 Abdomen 6/29/1999 4 × 6 Pre GP no 8/17/1999 7 × 7 65 
P8-a1-nr M/74 Abdomen 8/17/1999 7 × 7 Post GP no 8/17/1999 7 × 7 14 57 
P9-a0-nr F/59 L Scalp 1/5/1999 1 × 1 Pre GP yes 3/1/9399 3 × 3 241 
P11-a0-nr M/48 R Neck 5/27/1999 2.5 × 3 Pre GP yes 10/14/1999 9 × 10 41 
P11-a1-nr M/48 R Neck 7/13/1999 6 × 10 Post GP yes 10/14/1999 9 × 10 56 149 
P12-a0-nr F/58 R Thigh Dist 5/27/1999 1 × 1 Pre GP yes 9/3/1999 2 × 2 102 
P12-a1-nr F/58 R Thigh Dist 9/3/1999 2 × 2 Post GP yes 9/3/1999 2 × 2 98 
P12-b0-nr F/58 Epigastric 5/27/1999 1.5 × 1.5 Pre GP yes 9/3/1999 4 × 4 129 
P12-b1-nr F/58 Epigastric 9/3/1999 4 × 4 Post GP yes 9/3/1999 4 × 4 14 102 
P12-c0-nr F/58 R Thigh Prox 5/27/1999 3 × 3 Pre GP yes 12/15/1999 11.5 × 13 13 149 
P12-c1-nr F/58 R Thigh Prox 9/3/1999 10 × 13 Post GP yes 12/15/1999 11.5 × 13 13 128 
P15-a1-nr M/55 L Chest 1/21/1999 1 × 1 Post GP yes 3/4/1999 2 × 2 16 
P17-a0-nr M/52 L Supraclav 6/15/1999 1 × 2 Pre GP yes 9/30/1999 3 × 3.5 149 
P17-a1-nr M/52 L Supraclav 9/30/1999 3.0 × 3.5 Post GP yes 9/30/1999 3 × 3.5 111 
P19-a0-nr M/50 T Chest 3/11/1999 3 × 4 Pre GP yes 5/28/1999 8 × 8 16 
P24-a0-nr F/32 R Back 6/28/1999 2 × 2 Pre none yes 8/31/1999 4 × 5 19 113 
P24-a1-nr F/32 R Back 8/31/1999 4 × 4 Post none yes 8/31/1999 4 × 5 28 202 
P25-a1-nr F/51 L Abdomen 8/24/1999 2 × 2.5 Post none yes 12/16/1999 4 × 5 77 
P25-a2-nr F/51 L Abdomen 12/16/1999 4 × 5 Post none yes 12/16/1999 4 × 5 91 
P25-b0-nr F/51 R Abdomen 3/30/1999 2 × 3 Pre none yes 12/16/1999 6 × 8 118 
P25-b1-nr F/51 R Abdomen 12/16/1999 6 × 8 Post none yes 12/16/1999 6 × 8 27 130 
Case no.Sex/AgeFNA siteDate of FNASize of metastasisTime of FNATreatment typeSystemic IL-2 administrationDate of last follow-upSize at last follow-upSource total RNA (μg)aRNA yield (μg)
P1-a0-cr M/34 R Groin 10/7/1999 1 × 1 Pre MART-1/GP yes 2/29/2000 87 
P1-a1-cr M/34 R Groin 12/2/1999 1.5 × 1.5 Post MART-1/GP yes 2/29/2000 48 
P2-a0-cr M/52 L Parotid 6/22/1999 1.5 × 1.5 Pre TRP-1 yes 10/15/1999 17 149 
P2-a1-cr M/52 L Parotid 8/6/1999 2 × 2 Post TRP-1 yes 10/15/1999 14 140 
P3-a0-cr M/31 L Axilla 2/5/1999 2 × 2 Pre TRP-1 yes 4/1/1999 10 73 
P6-a0-cr M/49 L Lat Knee 2/23/1999 2.5 × 2.5 Pre GP yes 7/29/1999 125 
P6-a1-cr M/49 L Lat Knee 6/29/1999 3 × 2.5 Post GP yes 7/29/1999 145 
P6-b0-cr M/49 L Med Knee 2/23/1999 1.5 × 1.5 Pre GP yes 7/29/2000 70 
P6-b1-cr M/49 L Med Knee 6/29/1999 1 × 1 Post GP yes 7/29/2000 195 
P10-a0-cr M/67 L Neck 3/17/1999 2 × 2 Pre GP no 8/31/1999 27 103 
P10-a1-cr M/67 L Neck 7/1/1999 2 × 3 Post GP no 8/31/1999 96 
P14-a0-cr M/49 R Face Inf 4/6/1999 1 × 2 Pre GP yes 8/11/1999 115 
P14-a1-cr M/49 R Face Inf 7/1/1999 2 × 2 Post GP yes 8/11/1999 77 
P14-b0-cr M/49 R Face Sup 3/19/1999 2 × 2 Pre GP yes 8/11/1999 92 
P14-b1-cr M/49 R Face Sup 7/1/1999 2 × 2 Post GP yes 8/11/1999 20 
P16-a0-cr M/62 R Chest 4/27/1999 1 × 1.5 Pre MART-1 yes 9/1/1999 134 
P16-a1-cr M/62 R Chest 6/10/1999 1.5 × 1.5 Post MART-1 yes 9/1/1999 12 143 
P18-a0-cr F/65 L Neck 6/8/1999 2 × 2 Pre GP yes 8/31/1999 51 
P18-b0-cr F/65 R Neck 6/8/1999 3 × 3 Pre GP yes 8/31/1999 37 
P21-a0-cr F/35 L Temple 9/21/1999 1 × 1.5 Pre GP (cells) no 11/3/1999 90 11 
P23-a0-cr M/53 R Thigh Dist 6/22/1999 2.5 × 2.5 Pre MART-1 yes 1/6/2000 52 
P23-a1-cr M/53 R Thigh Low 9/30/1999 1 × 1.5 Post MART-1 yes 1/6/2000 84 
P23-b1-cr M/53 R Thigh Prox 9/30/1999 1.5 × 2 Post MART-1 yes 1/6/2000 
            
Metastases that regressed partially in response to therapy            
P4-b0-pr M/67 R Knee 3/11/1999 1 × 1 Pre GP yes 7/7/1999 0.5 × 0.5 23 147 
P4-b1-pr M/67 R Knee 6/22/1999 0.5 × 0.5 Post GP yes 7/7/1999 0.5 × 0.5 51 16 
P20-a0-pr M/54 R Groin 7/27/1999 4 × 4 Pre MART-1/GP no 9/7/1999 2 × 3 43 41 
P20-a1-pr M/54 R Groin 9/7/1999 2 × 3 Post MART-1/GP no 9/7/1999 2 × 3 25 
            
Metastases that remained stable during the observation period            
P4-a0-sd M/67 R Prox Leg 3/11/1999 2 × 2 Pre GP yes 7/7/1999 1.5 × 1.5 116 
P4-a1-sd M/67 R Prox Leg 6/22/1999 1.5 × 1.5 Post GP yes 7/7/1999 1.5 × 1.5 92 
P5-a0-sd F/58 L Groin 7/28/1999 1 × 1 Pre GP-T cells no 10/14/1999 1.5 × 1.0 118 
P5-a1-sd F/58 L Groin 8/23/1999 1.5 × 1.5 Post GP-T cells no 10/14/1999 1.5 × 1.0 33 
P5-b0-sd F/58 L Leg 7/28/1999 1 × 2 Pre GP-T cells no 10/14/1999 2 × 2 150 
P5-b1-sd F/58 L Leg 8/23/1999 2 × 2 Post GP-T cells no 10/14/1999 2 × 2 85 
P8-b0-sd M/74 R Arm 6/29/1999 3 × 3 Pre GP no 8/17/1999 3 × 3.5 147 
P8-b1-sd M/74 R Arm 8/17/1999 3 × 3.5 Post GP no 8/17/1999 3 × 3.5 14 166 
P13-a0-sd M/58 R Arm Inf 9/9/1999 2 × 2 Pre GP no 12/7/1999 2 × 2.5 101 
P13-a1-sd M/58 R Arm Inf 12/7/1999 2 × 2.5 Post GP no 12/7/1999 2 × 2.5 10 115 
P13-b0-sd M/58 R Arm Sup 9/9/1999 2 × 2 Pre GP no 12/7/1999 2.5 × 3.5 18 138 
P21-b0-sd F/35 R Occipital 9/21/1999 2.5 × 3 Pre GP (cells) no 11/3/1999 3 × 3.5 30 124 
P21-b1-sd F/35 R Occipital 11/3/1999 3 × 3.5 Post GP (cells) no 11/3/1999 3 × 3.5 37 102 
P22-a0-sd M/32 R Leg 4/29/1999 2 × 2 Pre MART-1/GP no 7/1/1999 2 × 2 62 
            
Metastases that did not respond to therapy            
P7-a0-nr F/76 L Breast 6/10/1999 1 × 2 Pre GP no 7/27/1999 2 × 2 116 
P8-a0-nr M/74 Abdomen 6/29/1999 4 × 6 Pre GP no 8/17/1999 7 × 7 65 
P8-a1-nr M/74 Abdomen 8/17/1999 7 × 7 Post GP no 8/17/1999 7 × 7 14 57 
P9-a0-nr F/59 L Scalp 1/5/1999 1 × 1 Pre GP yes 3/1/9399 3 × 3 241 
P11-a0-nr M/48 R Neck 5/27/1999 2.5 × 3 Pre GP yes 10/14/1999 9 × 10 41 
P11-a1-nr M/48 R Neck 7/13/1999 6 × 10 Post GP yes 10/14/1999 9 × 10 56 149 
P12-a0-nr F/58 R Thigh Dist 5/27/1999 1 × 1 Pre GP yes 9/3/1999 2 × 2 102 
P12-a1-nr F/58 R Thigh Dist 9/3/1999 2 × 2 Post GP yes 9/3/1999 2 × 2 98 
P12-b0-nr F/58 Epigastric 5/27/1999 1.5 × 1.5 Pre GP yes 9/3/1999 4 × 4 129 
P12-b1-nr F/58 Epigastric 9/3/1999 4 × 4 Post GP yes 9/3/1999 4 × 4 14 102 
P12-c0-nr F/58 R Thigh Prox 5/27/1999 3 × 3 Pre GP yes 12/15/1999 11.5 × 13 13 149 
P12-c1-nr F/58 R Thigh Prox 9/3/1999 10 × 13 Post GP yes 12/15/1999 11.5 × 13 13 128 
P15-a1-nr M/55 L Chest 1/21/1999 1 × 1 Post GP yes 3/4/1999 2 × 2 16 
P17-a0-nr M/52 L Supraclav 6/15/1999 1 × 2 Pre GP yes 9/30/1999 3 × 3.5 149 
P17-a1-nr M/52 L Supraclav 9/30/1999 3.0 × 3.5 Post GP yes 9/30/1999 3 × 3.5 111 
P19-a0-nr M/50 T Chest 3/11/1999 3 × 4 Pre GP yes 5/28/1999 8 × 8 16 
P24-a0-nr F/32 R Back 6/28/1999 2 × 2 Pre none yes 8/31/1999 4 × 5 19 113 
P24-a1-nr F/32 R Back 8/31/1999 4 × 4 Post none yes 8/31/1999 4 × 5 28 202 
P25-a1-nr F/51 L Abdomen 8/24/1999 2 × 2.5 Post none yes 12/16/1999 4 × 5 77 
P25-a2-nr F/51 L Abdomen 12/16/1999 4 × 5 Post none yes 12/16/1999 4 × 5 91 
P25-b0-nr F/51 R Abdomen 3/30/1999 2 × 3 Pre none yes 12/16/1999 6 × 8 118 
P25-b1-nr F/51 R Abdomen 12/16/1999 6 × 8 Post none yes 12/16/1999 6 × 8 27 130 
Table 2

Classifiers of immune responsiveness

Clone IDAbbrev.TitleWilcoxont testPutative functionaWilcoxont testRelative expressionb
Genes that discriminate between pretreatment melanoma lesions that regressed completely or did not (13 versus 21)13 cr vs. 11 nr
195458 EST EST 0.00004 0.00003 Unknown 0.00040 0.00031 Suppressed 
768496 EBI3 EBI3 0.00004 0.00051 IL-12 facilitator 0.00005 0.00138 Enhanced 
269815 INHBA Inhibin, βA 0.00008 0.00013 TGF-β family 0.00312 0.00344 Enhanced 
280752 RBL2 Retinoblastoma-like 2 (p130) 0.00018 0.00010 Putative tumor suppressor 0.02035 0.00500 Suppressed 
811116 EST EST 0.00029 0.00022 Unknown 0.00479 0.00520 Enhanced 
504469 ODF2 Outer dense fibre of sperm tails 2 0.00040 0.00019 Cell migration 0.00885 0.00606 Enhanced 
588637 ACTG1 Actin-cytoskeleton γ actin 0.00040 0.00022 Cell migration 0.00389 0.00184 Suppressed 
323917 EHD1 EH domain containing 1 0.00044 0.00554 Ligand-induced endocytosis 0.00056 0.00397 Enhanced 
365098 BNIP3L BCL2/adenovirus E1B 19kD-interacting protein 3-like 0.00047 0.00027 Putative tumor suppressor 0.00092 0.00138 Suppressed 
814095 LTA4H Leukotriene A4 hydrolase 0.00047 0.00146 Immediate hypersensitivity 0.00385 0.00743 Suppressed 
321739 IRF2 IFN-γ regulatory factor 2 0.00055 0.00019 IFN expression regulation 0.00727 0.00289 Enhanced 
109316 Serpina3 α1-antichymotrypsin 0.00055 0.00872 Proteinase inhibitor 0.01075 NSc Enhanced 
1457955 EST EST 0.00076 0.00019 Unknown 0.00385 0.00175 Suppressed 
51463 BIRC1 Neuronal apoptosis inhibitory protein 0.00095 0.00129 Apoptosis regulator 0.00562 0.01252 Suppressed 
323371 APP Amyloid β (A4) precursor protein 0.00100 0.00099 Intracellular signaling 0.00594 0.00402 Enhanced 
142259 FIP2 Tumor necrosis factor α-inducible cellular protein 0.00118 0.00041 TNF pathway 0.00727 0.00441 Suppressed 
854138 CSNK1E Casein kinase 1, epsilon 0.00118 0.01792 DNA replication repair 0.00030 0.00258 Enhanced 
322160 PTEN MMAC1-PTEN-Tumor suppressor gene 0.00118 0.00067 Putative tumor suppressor 0.00482 0.00338 Suppressed 
796297 KIAA1605 KIAA1605 0.00136 0.00051 Unknown 0.00092 0.00055 Enhanced 
345935 MADH MADH3 0.00157 0.00044 TGF-β response regulator 0.00053 0.00030 Enhanced 
588915 IFI27 IFN, α-inducible protein 27 0.00176 0.01109 IFN-induced tumor suppressor 0.00098 NS Suppressed 
344080 SHMT2 Serine hydroxymethyltransferase mitochondrial precursor 0.00206 0.00097 Cell metabolism 0.00474 0.02665 Enhanced 
240109 FLJ10632 Homo sapiens cDNA FLJ10632 0.00216 0.00065 Unknown 0.01587 0.01430 Suppressed 
725284 PHKG2 Phosphorylase kinase, gamma 2 0.00238 0.00301 Kinase 0.00219 0.00091 Enhanced 
240099 EST EST 0.00327 0.01459 Unknown 0.00086 0.00514 Suppressed 
324210 SR-BP1 Sigma receptor (SR31747 binding protein 1) 0.00354 0.00271 Cell proliferation regulation 0.00197 0.00085 Enhanced 
43884 PPIF Peptidylprolyl isomerase F (cyclophilin F) 0.00402 0.00099 Cell metabolism NS 0.04096 Enhanced 
563423 JAK1 JAK1 0.00830 0.01792 IL-2 receptor regulation 0.00008 0.00007 Suppressed 
136218 TIA1 TIA1 cytotoxic granule-associated RNA-binding protein 0.01111 0.00642 CTL-mediated cytolysis 0.00056 0.00168 Enhanced 
1103633 KIAA0515 KIAA0515 0.02179 0.03866 Unknown 0.00053 NS Suppressed 
322961 CAPZB Capping protein (actin filament) muscle Z-line, beta 0.03756 0.02295 Cell migration 0.00055 0.00027 Enhanced 
221846 CHES1 Checkpoint suppressor 1 0.04612 0.04285 DNA-damage checkpoint 0.00154 0.00071 Suppressed 
148421 TXK TXK tyrosine kinase NS NS T-cell regulation 0.00156 0.00012 Suppressed 
Abundance of immune cell marker genes in pretreatment melanoma lesions that regressed completely or did not (13 versus 21)      13 cr vs. 11 nr   
377560 CD3 delta CD3 δ ND 0.46798 T-cell marker ND 0.50000 Equal 
306841 TCR T cell receptor beta locus ND 0.68111 T-cell marker ND 0.75879 Equal 
771258 CD8 alpha CD8 α chain ND 0.85709 Cytotoxic T-cell marker ND 0.39163 Equal 
86189 CD4 CD4 (p55) ND 0.09511 Helper T-cell marker ND 0.34623 Equal 
282679 CD16 CD16-Fcγ receptor IIIa ND 0.62066 NK cell marker ND 0.95314 Equal 
154015 CD11A CD11A-Integrin, α L-LFA-1 α chain ND 0.84775 Leukocyte-associated marker ND 0.80301 Equal 
435434 CD14 CD14 ND 0.42577 Macrophage marker ND 0.56275 Equal 
50214 CD86 CD86, B72, CD28/CTLA-4 ligand ND 0.37608 Macrophage/B-cell marker ND 0.97991 Equal 
564503 CD83 CD83-B-G antigen IgV domain homolog-HB15 ND 0.38766 Activated B cells and dendritic cells ND 0.69852 Equal 
Clone IDAbbrev.TitleWilcoxont testPutative functionaWilcoxont testRelative expressionb
Genes that discriminate between pretreatment melanoma lesions that regressed completely or did not (13 versus 21)13 cr vs. 11 nr
195458 EST EST 0.00004 0.00003 Unknown 0.00040 0.00031 Suppressed 
768496 EBI3 EBI3 0.00004 0.00051 IL-12 facilitator 0.00005 0.00138 Enhanced 
269815 INHBA Inhibin, βA 0.00008 0.00013 TGF-β family 0.00312 0.00344 Enhanced 
280752 RBL2 Retinoblastoma-like 2 (p130) 0.00018 0.00010 Putative tumor suppressor 0.02035 0.00500 Suppressed 
811116 EST EST 0.00029 0.00022 Unknown 0.00479 0.00520 Enhanced 
504469 ODF2 Outer dense fibre of sperm tails 2 0.00040 0.00019 Cell migration 0.00885 0.00606 Enhanced 
588637 ACTG1 Actin-cytoskeleton γ actin 0.00040 0.00022 Cell migration 0.00389 0.00184 Suppressed 
323917 EHD1 EH domain containing 1 0.00044 0.00554 Ligand-induced endocytosis 0.00056 0.00397 Enhanced 
365098 BNIP3L BCL2/adenovirus E1B 19kD-interacting protein 3-like 0.00047 0.00027 Putative tumor suppressor 0.00092 0.00138 Suppressed 
814095 LTA4H Leukotriene A4 hydrolase 0.00047 0.00146 Immediate hypersensitivity 0.00385 0.00743 Suppressed 
321739 IRF2 IFN-γ regulatory factor 2 0.00055 0.00019 IFN expression regulation 0.00727 0.00289 Enhanced 
109316 Serpina3 α1-antichymotrypsin 0.00055 0.00872 Proteinase inhibitor 0.01075 NSc Enhanced 
1457955 EST EST 0.00076 0.00019 Unknown 0.00385 0.00175 Suppressed 
51463 BIRC1 Neuronal apoptosis inhibitory protein 0.00095 0.00129 Apoptosis regulator 0.00562 0.01252 Suppressed 
323371 APP Amyloid β (A4) precursor protein 0.00100 0.00099 Intracellular signaling 0.00594 0.00402 Enhanced 
142259 FIP2 Tumor necrosis factor α-inducible cellular protein 0.00118 0.00041 TNF pathway 0.00727 0.00441 Suppressed 
854138 CSNK1E Casein kinase 1, epsilon 0.00118 0.01792 DNA replication repair 0.00030 0.00258 Enhanced 
322160 PTEN MMAC1-PTEN-Tumor suppressor gene 0.00118 0.00067 Putative tumor suppressor 0.00482 0.00338 Suppressed 
796297 KIAA1605 KIAA1605 0.00136 0.00051 Unknown 0.00092 0.00055 Enhanced 
345935 MADH MADH3 0.00157 0.00044 TGF-β response regulator 0.00053 0.00030 Enhanced 
588915 IFI27 IFN, α-inducible protein 27 0.00176 0.01109 IFN-induced tumor suppressor 0.00098 NS Suppressed 
344080 SHMT2 Serine hydroxymethyltransferase mitochondrial precursor 0.00206 0.00097 Cell metabolism 0.00474 0.02665 Enhanced 
240109 FLJ10632 Homo sapiens cDNA FLJ10632 0.00216 0.00065 Unknown 0.01587 0.01430 Suppressed 
725284 PHKG2 Phosphorylase kinase, gamma 2 0.00238 0.00301 Kinase 0.00219 0.00091 Enhanced 
240099 EST EST 0.00327 0.01459 Unknown 0.00086 0.00514 Suppressed 
324210 SR-BP1 Sigma receptor (SR31747 binding protein 1) 0.00354 0.00271 Cell proliferation regulation 0.00197 0.00085 Enhanced 
43884 PPIF Peptidylprolyl isomerase F (cyclophilin F) 0.00402 0.00099 Cell metabolism NS 0.04096 Enhanced 
563423 JAK1 JAK1 0.00830 0.01792 IL-2 receptor regulation 0.00008 0.00007 Suppressed 
136218 TIA1 TIA1 cytotoxic granule-associated RNA-binding protein 0.01111 0.00642 CTL-mediated cytolysis 0.00056 0.00168 Enhanced 
1103633 KIAA0515 KIAA0515 0.02179 0.03866 Unknown 0.00053 NS Suppressed 
322961 CAPZB Capping protein (actin filament) muscle Z-line, beta 0.03756 0.02295 Cell migration 0.00055 0.00027 Enhanced 
221846 CHES1 Checkpoint suppressor 1 0.04612 0.04285 DNA-damage checkpoint 0.00154 0.00071 Suppressed 
148421 TXK TXK tyrosine kinase NS NS T-cell regulation 0.00156 0.00012 Suppressed 
Abundance of immune cell marker genes in pretreatment melanoma lesions that regressed completely or did not (13 versus 21)      13 cr vs. 11 nr   
377560 CD3 delta CD3 δ ND 0.46798 T-cell marker ND 0.50000 Equal 
306841 TCR T cell receptor beta locus ND 0.68111 T-cell marker ND 0.75879 Equal 
771258 CD8 alpha CD8 α chain ND 0.85709 Cytotoxic T-cell marker ND 0.39163 Equal 
86189 CD4 CD4 (p55) ND 0.09511 Helper T-cell marker ND 0.34623 Equal 
282679 CD16 CD16-Fcγ receptor IIIa ND 0.62066 NK cell marker ND 0.95314 Equal 
154015 CD11A CD11A-Integrin, α L-LFA-1 α chain ND 0.84775 Leukocyte-associated marker ND 0.80301 Equal 
435434 CD14 CD14 ND 0.42577 Macrophage marker ND 0.56275 Equal 
50214 CD86 CD86, B72, CD28/CTLA-4 ligand ND 0.37608 Macrophage/B-cell marker ND 0.97991 Equal 
564503 CD83 CD83-B-G antigen IgV domain homolog-HB15 ND 0.38766 Activated B cells and dendritic cells ND 0.69852 Equal 
a

Boldface entries show genes associated with immune/inflammatory function.

b

Relative expression of individual genes in responding compared with nonresponding lesions.

c

NS, not significant; ND, not done.

We acknowledge Douglas E. Kesselring for the expert preparation of the visual graphics.

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