Cancer-testis (CT) Ags are attractive targets for immunotherapeutic strategies since they are aberrantly expressed in malignant cells and not, or in limited number, in somatic tissues, except germ cells. To identify novel CT genes in multiple myeloma, we used Affymetrix HG-U133 gene expression profiles of 5 testis, 64 primary multiple myeloma cells (MMC), and 24 normal tissue samples. A 5-filter method was developed to keep known CT genes while deleting non-CT genes. Starting from 44,928 probe sets, including probe sets for 18 previously described CT genes, we have obtained 82 genes expressed in MMC and testis and not detected in more than 6 normal tissue samples. This list includes 14 of the 18 known CT genes and 68 novel putative CT genes. Real-time RT-PCR was performed for 34 genes in 12 normal tissue samples, 5 MMC samples, and one sample of five pooled testes. It has validated the CT status of 23 of 34 genes (67%). We found one novel “testis-restricted” gene (TEX14, expression in testis and tumor only), eight “tissue-restricted” (mRNA detected in one or two nongametogenic tissues), and seven “differentially expressed” (mRNA detected in three to six nongametogenic tissues) CT genes. Further studies are warranted to determine the immunogenicity of these novel CT Ag candidates.
The development of successful immunotherapeutic strategies requires the identification and characterization of tumor-associated Ags that will be recognized by the host immune system, leading to tumor rejection. Cancer-testis (CT)3 Ags expressed by germ cells and malignant cells are shared tumor-specific Ags. To date, 47 CT gene families including >90 genes have been identified by either immunological screening methods or expression database analysis (1, 2, 3, 4). Scanlan et al. classified CT genes into four categories according to their expression profiles: 1) “testis restricted” (mRNA detected in testis and tumor samples only), 2) “tissue restricted” (mRNA detected in two or fewer nongametogenic tissues), 3) “differentially expressed” (mRNA detected in three to six nongametogenic tissues), and 4) “ubiquitously expressed” (mRNA detected in more than six nongametogenic tissues) (1). The testis-restricted genes, comprising about one-half of the CT genes, in particular genes belonging to the MAGE, GAGE, and SSX families, are mainly located on chromosome X. Because of their expression being restricted to germ cells and malignant tissues, there should be no deletion of the high-affinity T cell repertoire and, theoretically, a low risk of preexisting immune tolerance. Thus, the testis-restricted CT Ags are used as targets in several cancer vaccination trials (5, 6, 7).
The expression of several CT genes and gene products by malignant plasma cells isolated from patients with multiple myeloma (MM) has been described (8, 9). Among these, MAGE-C1 is the most frequently expressed one (10, 11). NY-ESO-1 and MAGE-A3 protein expression correlate with higher plasma cell proliferation and poor prognosis (11, 12, 13). Using DNA microarray analysis, we reported that 35 CT genes can be detected at the mRNA level in myeloma cells in at least one out of 64 MM patients. Eighteen of these 35 CT genes were expressed in >10% of the patients (11). This study also confirmed the adverse prognosis value of six CT Ags.
CT Ags are immunogenic in MM patients since specific CD8+ T lymphocytes anti-MAGE-A1–4 and LAGE-1 have been detected in peripheral blood of MM patients (14), and at least four testis-restricted CT Ags were the targets of T cell response in patients receiving allotransplants (15). However, the expression of CT proteins is heterogeneous within the tumor-cell population of an individual patient (10, 12) and an immunotherapy strategy targeting only one Ag could lead to the selection of Ag-negative tumor subclones (16). Thus, several Ags should be used to vaccinate a given patient. As we reported that the coexpression of three testis-restricted CT genes is found for 70% of the patients (11), and as the immunogenicity of the proteins encoded by most of these CT genes has not been reported in MM yet, it is of great importance to identify a maximum of CT-like genes that are expressed in primary MM cells (MMC) and, for a given patient, to know the Ag cocktail that is coexpressed in MMC.
We and others have shown that microarrays are useful tools to detect tumor-associated Ag expression, in particular CTA, in various tumors, including MM (9, 11), renal cell carcinoma (17), breast cancer (18), and melanoma (19). In this study, our aim was to pick up new CT genes that are aberrantly expressed by malignant plasma cells compared with normal plasma cells and a large panel of normal tissues, using known CTA to validate our bioinformatics-based selection. We detected 68 potential novel CT genes fitting these criteria, and the expression of 16 of these was validated by real-time RT-PCR.
Materials and Methods
Patients and cell samples
MMC were purified from 64 consecutive patients with newly diagnosed MM (median age, 59 years). According to Durie-Salmon classification, 11 patients were of stage IA, 11 of stage IIA, 39 of stage IIIA, and 3 of stage IIIB. Twelve patients had IgAκ MM, 7 had IgAλ MM, 24 had IgGκ MM, 10 had IgGλ MM, 6 had Bence-Jones κ MM, 3 had Bence-Jones λ MM, and 2 had nonsecreting MM. Bone marrow samples were obtained from healthy donors and patients after informed consent was given in agreement with French or German laws. Normal bone marrow plasma cells (BMPC) and primary MMC were purified using anti-CD138 MACS microbeads. Briefly, bone marrow aspirates were subjected to density centrifugation. Plasma cells were sorted from mononuclear cells to purity ≥85% using CD138 microbeads and automated magnetic cell sorting (Miltenyi Biotec). Purity was assessed by flow cytometry (FACSCalibur; BD Biosciences) after CD38/CD138 double-staining. Routine smears were assessed by light microscopy. For the isolation of peripheral blood memory B cells (MBC), monocytes, NK cells, and T cells were first removed using anti-CD14, anti-CD16, and anti-CD3 magnetic beads (Dynal Biotech), and MBC were then positively selected using anti-CD27 MACS microbeads (Miltenyi Biotec). XG human myeloma cell lines (HMCL) were obtained and characterized in our laboratory (20, 21, 22, 23). SKMM, OPM2, LP1, and RPMI8226 HMCLs were purchased from the American Type Culture Collection/LGC Promochem.
Preparation of cRNA and microarray hybridization
Testis RNA samples from healthy donors were purchased from CliniSciences. RNA extraction was performed using the RNeasy kit (Qiagen), the SV-total RNA extraction kit (Promega), or TRIzol (Invitrogen) in accordance with the manufacturers’ instructions. RNA was analyzed using an Agilent 2100 bioanalyzer. Labeled cRNA was generated using the small sample labeling protocol II (Affymetrix) and hybridized to HG-U133 A+B GeneChip microarrays (Affymetrix) or HG-U133 2.0 plus arrays according to the manufacturer’s instructions (24). Fluorescence intensities were quantified and analyzed using the GCOS software (Affymetrix). Arrays were scaled to an average intensity of 100. A threshold of 1 was assigned to values ≤1. Gene expression data are deposited in the ArrayExpress public database (www.ebi.ac.uk/microarray-as/ae/, accession no. E-MTAB-81).
Gene expression profiling analysis
All gene expression data were normalized with the MAS5 algorithm using the same global scaling and analyzed with our bioinformatics platforms (Remote Analysis of microarray Gene Expression (RAGE), http://rage.montp.inserm.fr; and Amazonia!, http://amazonia.montp.inserm.fr) (25). The Affymetrix call defined as the statistical assignment of a probe set as “present” or “absent” provided a threshold of gene expression. Six percent of the probe sets had a marginal call in normal tissue samples using HG-U133A microarray. The “marginal” call was considered as “present” because we previously found that some genes interrogated with probe sets with a marginal call often display a positive expression by real-time RT-PCR. As discussed below, the decision to consider as present or absent the probe sets with a marginal call did not affect the final results. CT gene expression was assessed in 64 primary MMC, 20 HMCL samples, 8 plasma cell samples from patients with monoclonal gammopathy of undetermined significance (MGUS), 5 normal testis samples, 7 BMPC samples, and 7 MBC samples, using HG-U133 set arrays and in 10 whole bone marrow samples from healthy donors using HG-U133 Plus 2.0 arrays. We also used the gene expression profiling (GEP) from 24 human normal somatic tissues determined with Affymetrix HG-U133A and custom-designed GNFH1 arrays, available from Dr. J. B. Hogenesch’s group on a public database (Ref. 26 and http://biogps.gnf.org/#goto=welcome). For the tissue types containing two to four different samples per tissue (i.e., lung, trachea, heart, prostate, liver, pancreas, kidney, adrenal gland, lymph nodes, thymus, salivary gland, thyroid, pituitary, uterus, tongue, skin, tongue, smooth muscle, psoas muscle, intestine, and appendix) a probe set was stated “not expressed” by the tissue if no present call occurred for two to four samples. When data of more than seven samples of a given tissue were available (i.e., blood, bone marrow, central and peripheral nervous systems) a probe set was stated “expressed” by the tissue if at least three present calls were found. Normal BMPC samples hybridized to the HG-U133 set arrays were considered as the 25th normal tissue sample. Gene expression data of other cancers were obtained from the Oncomine Cancer Microarray database (www.oncomine.org) (27).
Total RNA derived from pooled normal tissues was obtained from Clontech and CliniSciences. The five testis RNA samples used in microarray hybridization were pooled in one testis RNA sample. RNA samples of MMC were those used for the microarray hybridization. We generated cDNA from 500 ng of total RNA using the SuperScript First-Strand Synthesis System for RT-PCR (Invitrogen), according to the manufacturer’s instructions. Real-time PCR was performed with TaqMan gene expression assays and the TaqMan Universal Master Mix from Applied Biosystems using the ABI Prism 7000 sequence detection system. Quantitative PCR analysis was completed using ABI Prism 7000 SDS software. Ct values were collected for GAPDH and the genes of interest during the log phase of the cycle. Gene of interest levels were normalized to GAPDH for each sample (ΔCt = Ct gene of interest − Ct GAPDH) and compared with the values obtained for the testis sample, used as a positive control, using the following formula 100/2ΔΔCt, where 2ΔΔCt = ΔCt unknown − ΔCt-positive control. The testis sample used as a positive control was assigned the arbitrary value of 100. A ΔΔCt value threshold <10 (i.e., less than one-tenth of that in testis) was arbitrarily designed as a negative expression.
Western blot analysis
Cells were lysed in 10 mM Tris-HCl (pH 7.05), 50 mM NaCl, 50 mM NaF, 30 mM sodium pyrophosphate, 1% triton X-100, 5 μM ZnCl2, 100 μM Na3VO4, 1 mM DTT, 20 mM β-glycerophosphate, 20 mM p-nitrophenolphosphate, 20 μg/ml aprotinin, 2.5 μg/ml leupeptin, 0.5 mM PMSF, 0.5 mM benzamidine, 5 μg/ml pepstatin, and 50 nM okadaic acid. Lysates were resolved on 12% SDS-PAGE and transferred to a nitrocellulose membrane (Schleicher and Schuell). Membranes were blocked for 2 h at room temperature in 140 mM NaCl, 3 mM KCl, 25 mM Tris-HCl (pH 7.4), 0.1% Tween 20 (TBS-T), 5% skimmed milk, and then immunoblotted with a mouse anti-MORC (Abnova), a rabbit anti-ARX, anti-ELOVL4 (Abcam), a rabbit anti-TMEFF2 (Proteintech Group), a rabbit anti-CTNNA2 (GeneTex), or with a mouse anti-TMEFF1 (R&D Systems) Ab. As a control for protein loading, we used a mouse monoclonal anti-β-actin Ab (Sigma-Aldrich). The primary Abs were visualized with goat anti-rabbit (Sigma-Aldrich) or goat anti-mouse (Bio-Rad) peroxidase-conjugated Abs by an ECL detection system.
Selection of candidate CT genes using Affymetrix microarrays
In an attempt to find novel genes showing an expression pattern of known CT genes, that is, expressed in testis and cancer cells but not or poorly expressed in normal tissues, we have defined five consecutive filters making it possible to reduce considerably the probe set number while retaining a maximum of CT genes already known to be expressed in MMC. We used for that the 18 known CT genes expressed in MMC of ≥10% of MM patients identified in our previous study (11). We also used some filters with the Affymetrix-assigned detection call that enables to mix data from different Affymetrix microarrays, avoiding the normalization problem. This call indicates whether a gene is present or absent. As depicted in Fig. 1,A, we selected probe sets displaying simultaneously a present call in at least 6 of 64 MMC samples and in at least 3 of 5 testis samples, leading to a selection of 16,982 probe sets out of the 44,928 probe sets available on the HG-U133 A+B arrays (filter 1). At this step, two known CT genes (MAGE-A1 and XAGE-1) were not retained because their corresponding probe sets were expressed in less than three testis samples. Filter 2 then selected 2559 probe sets with mean signal in MMC samples having a present call at least 2.5-fold higher than the mean signal observed in normal MBC. The 2.5 ratio was chosen because it is the inflection point of the curves delineating the decrease of the total number of probe sets and that of known CT genes according to increasing ratios (Fig. 1,B). This second filter made it possible to focus on genes overexpressed in MMC, while retaining the majority of known CT genes and decreasing the total number of probe sets. Filter 3 consisted of selecting probe sets expressed in <7 normal tissues (≤6 NT) out of 25, as described in Materials and Methods, leaving 341 probe sets. When several probe sets are available for a given gene, it was retained only if all its corresponding probe sets were expressed in less than seven NT. For probe sets located on the HG-U133B array, we could find a corresponding probe set by matching gene names on GNF1H array data from Su et al. for only 353 out of 1098 probe sets (26) (Fig. 1,A). Some poorly working probe sets may give a signal whose height is similar in samples with an absent or a present call. To eliminate such probe sets, we calculated the ratio between the mean signal in MMC samples with a present call and the mean signal in MMC samples with an absent call, and filter 4 removed probe sets with a ratio ≥2.5, yielding 143 probe sets including 14 known CT genes. This 2.5 ratio was the inflection point of the curve drawn in Fig. 1,C, making it possible to delete a maximum of probe sets while keeping all known CT gene probe sets. Finally, we apply a bibliography filter (filter 5) removing genes whose expression in human NT reported in the literature differed from the current results with microarrays. The final list contained 82 probe sets/genes, including 14 out of the starting 18 known CT genes. As indicated in Materials and Methods, the 6% of probe sets with a marginal call were considered as probe sets with a present call. However, considering these probe sets as probe sets with an absent call and running the five filters yielded the same final list of 82 genes. Altogether, these five filters result in a 207-fold enrichment in known CT genes, from the initial 16,982 probe set list to the final 82 probe set list. Genes were assigned into the three CT gene categories, according to their pattern of expression in NT (supplemental Table S1).4 As shown in Fig. 2, we found 16 (19.5%) testis-restricted CT genes, including 10 known CT genes and 6 novel potential CT genes; 31 (37.8%) tissue-restricted CT genes, including 2 known CT genes and 29 novel potential CT genes; and 35 (42.7%) differentially expressed CT genes, including 2 known CT genes and 33 novel potential CT genes. Sixteen genes out of 82 were located on chromosome X, including 11 known and 5 novel putative CT genes (Fig. 2).
Classification of candidate CT genes according to their expression pattern determined by real-time RT-PCR
We then investigated the pattern of expression of 7 known CT genes and 27 potential CT genes among 12 normal tissues, MMC samples from 5 different patients with MM, and one sample of 5 pooled testes by real time RT-PCR. No detectable expression of six known CT genes (i.e., DDX43, MAGE-A3, MAGE-C1, MAGE-C2, MORC, and SSX1) was observed in each of the 12 normal tissues compared with testis, in agreement with their known testis-restricted status (Fig. 3,A). A slight expression (quantitative PCR ΔΔCt value of 12) of CTAG1B/NY-ESO-1 was detected in the brain. Regarding the novel potential CT genes identified by microarray analyses, real-time RT-PCR was performed for 6 of 6 genes classified in the testis-restricted category, for 18 of 29 genes classified in the tissue-restricted category and not expressed in bone marrow, and for 3 of 33 genes in the differentially expressed category. Among the 27 genes investigated, 6 were poorly expressed in MMC (mean <10) and 2 were actually not expressed in testis. Among the remaining 19 genes, 3 were expressed in MMC and testis but were also expressed in more than 6 NT, thus classified in the “ubiquitously expressed” category (Table I). TEX14 appeared as a novel testis-restricted CT gene (Fig. 3,B). Eight genes were expressed in one to two NT (i.e., CTNNA2, LOC130576, RP1–32F7.2/FAM133A, ANKRD45, ELOVL4, IGSF11, TMEFF1, and TMEFF2), thus classified in the tissue-restricted category (Fig. 3,B), and seven genes (i.e., ARX, FLJ23577, LOC150763, MGC3040, NOL4, PTPN20A/B, and SPAG4) belong to the differentially expressed category since they were expressed in three to six NT (Fig. 3,C). CTNNA2 and FAM133A were expressed only in brain and testis. Table I summarizes real-time RT-PCR data of all known and novel CT genes investigated and provides the level of expression of MMC compared with testis and NT as well as the frequency of expression of each CT gene within the 64 patients. Altogether, by using a primary selection with microarrays and a real-time RT-PCR validation, we found 16 novel potential CT Ags expressed in MM.
Fig. 4 shows the HG-U133 set array signal levels and the Affymetrix call of nine testis and tissue-restricted CT genes in five testis samples, purified plasma cells from 8 patients with MGUS, and MMC from 64 newly diagnosed patients. As reported in Table I, we can observe that the expression of all the novel CT genes is heterogeneous within the MM patient population. FAM133A is the more frequently expressed (53% of patients with MMC having a present call) and TMEFF2 is the less frequently expressed (11%).
Protein study and link with clinical parameters and patients’ survival
As Abs for 6 out of the 68 new putative CT Ags (i.e., MORC, ARX, TMEFF2, TMEFF1, CTNNA2, and ELOVL4) are commercially available, we investigated the protein expression in testis and four myeloma cell lines (two expressing the gene by RT-PCR and two not expressing it). The results were not conclusive because either the protein size in testis had not the expected molecular size or protein expression in myeloma cell lines or testis did not fit with RT-PCR data. Note that these Abs have to date never been validated by academic teams and their specificity should be questioned. We also found no significant link between the expression of these novel potential CT genes with event-free and overall survivals after treatment with high-dose chemotherapy and autologous stem cell transplantation (results not shown).
Expression in other cancers
As most known CT genes are expressed in a broad variety of cancer, we investigated the expression of the 16 microarray and PCR-validated novel CT genes in 41 cancer types, using the database Oncomine, which gathers many published microarray analyses. The novel CT genes were significantly overexpressed in malignant cells compared with normal counterparts of 18 of the 40 cancer types (Table II). Fourteen of the 16 genes were overexpressed in at least one cancer type, other than MM. TMEFF1 and SPAG4 were expressed in six different cancer types.
We present herein a new five-filter method to find novel potential CT Ags expressed in human myeloma cells based on the GEP of testis, MMC, and normal tissues. To use publicly available data for normal tissues and to bypass the normalization issue, we developed filters using the Affymetrix call, making it possible to compare data from different hybridization processes or different microarray types. MM is a heterogeneous disease with at least seven molecular entities defined by GEP (28). To avoid loss of information due to this MM heterogeneity, we selected MMC samples with a present call for each probe set to compare Affymetrix signals in MMC to those in normal MBC (R1). The filter criteria were defined to keep a maximum of known CT genes while deleting a maximum of non-CT genes, which provided a 207-fold enrichment of known CT genes and yields to 68 novel putative CT genes shared by MMC and testis. We are aware that we likely have missed some new potential CT genes. As our GEP data are publicly available (accession no. E-MTAB-81), the present study may encourage the design of new filters to pick up additional CT genes. Additionally, as indicated in Table II, this five-filter method can be easily extended to other cancer diseases.
This microarray-based method is a first step to pick up rapidly new CT genes, and real-time RT-PCR is mandatory to check the information. First, real-time RT-PCR allowed confirmation of the pattern of expression of seven known CT genes among 13 normal tissues (including testis) and MMC samples and their testis-restricted status. Second, real-time RT-PCR confirmed the CT status of 16 novel CT genes out of the 27 tested (60%). This validation highlights the interest of the five-filter method to rapidly select new putative CT genes. TEX14 is a testis restricted protein localized to germ cell intercellular bridges that is involved in spermatogenesis and fertility (29). CTNNA2 for catenin α-2 is a structural component of cytoskeleton associated with cadherin proteins and is involved in cell adhesion. In particular, it stabilizes synaptic contacts (30). ELOVL4, for elongation of very long chain fatty acids-like 4, is a G protein expressed in retina and particularly in the endoplasmic reticulum of photoreceptor cells (31). A 5-bp deletion in the ELOVL4 gene causes macular degeneration (32). IGSF11 was previously described as expressed in testis and brain only (33). We confirmed herein its expression in testis and brain but showed that IGSF11 is also expressed in adrenal gland. This gene is up-regulated in gastrointestinal and hepatocellular carcinomas, encodes a receptor essential for gastric cancer cell growth (34, 35), and has been described as a novel immunogenic target since a natural HLA-A2-restricted T cell epitope was identified and a subsequent anchor-modified peptide was synthesized (35). TMEFF1 and TMEFF2, for transmembrane protein with EGF-like and two follistatin-like domains 1 and 2, respectively, encode putative growth factors predominantly expressed in the brain (36, 37). Soluble TMEFF2 is an activating ERBB4 ligand (38) and is part of the neuregulin signaling pathway, which contributes to the pathogenesis of MM (39). TMEFF2 has been characterized as a survival factor for hippocampal and mesencephalic neurons (36) but is methylated in colorectal cancer (40) and down-regulated in androgen-independent prostate cancer (41). TMEFF1 inhibits the proliferation of brain cancer cell lines (37). Thus, TMEFF1 may be a tumor suppressor gene.
PTPN20 is a nuclear phosphatase whose mRNA was previously detected among normal tissues in testis only, but it is overexpressed in cancer cell lines (42). Its biological role is not elucidated yet. ARX, for Aristaless-related homeobox, is a transcription factor playing a crucial role in development with multiple transcript isoforms (43). Mutations in this gene cause X-linked mental retardation syndromes (44, 45). SPAG4, for sperm-associated Ag 4, binds ODF proteins in sperm tails involved in spermatozoid motility (46). Its expression has already been reported in malignant cells of different cancers, and the authors suggested that SPAG4 could be a novel cancer marker (47).
ANKRD45, FAM133A, NOL4, LOC130576, FLJ23577, LOC150763, and MGC3040 encode for putative proteins with unknown functions.
For these 16 microarray- and PCR-validated CT genes, it will be important to check for protein expression. This is also the case for some known CT genes for which protein expression has never been evidenced, such as MORC or DDX43. One of the main difficulties is the lack of validated Abs. Indeed, we failed to obtain conclusive results in Western blot assays with commercially available Abs directed against ARX, CTNNA2, ELOVL4, TMEFF1, and TMEFF2 because of their poor specificity. No Abs directed against the remaining proteins could be found. Of note, these commercially available Abs have not been validated by academic teams yet.
Once new CT genes have been identified, the immunogenicity of the proteins they encoded for should be carefully questioned and demonstrated depending on the tissue expression of these CT genes. Indeed, “non-self” proteins are recognized by high avidity T cells retained during central thymic selection. The most promising CT gene is TEX14, which is expressed only in germ cells. CT genes expressed only in CNS are also of interest since this tissue can be considered as a relatively “immune privilege” site (48). However, the issue of immunologic privilege of the brain is not clear in inflammatory conditions, as brain tumors appear to be targeted in some cases by immunotherapy. For CT genes with a lack of overexpression in MMC relative to normal tissues such as lung, stomach, or liver, high-avidity T cells that recognize these Ags should be deleted by thymic and peripheral tolerance mechanisms (49, 50). However, low-avidity T cells specific for tissue-restricted Ags may escape thymic negative selection and are able to induce autoimmunity (51). Of note, we and others have shown the persistence of a repertoire to widely distributed self proteins as H chain immunoglobulins (52), the HM1.24 B cell Ag (53), or melanocyte proteins (54). So far, none of the 16 novel putative Ags, except for IGSF11, has been shown to be immunogenic. This is also the case for some previously discovered CT genes that have been categorized in the CT gene list only on the basis on their restricted expression pattern (1). The immunogenicity of a protein can be evidenced in humans either by detecting serum Abs recognizing the given protein and/or by detecting specific cellular immunity, that is, CD4 and CD8 T cells recognizing protein-derived peptides. We are now focusing on this topic for six testis-restricted CT genes and also for MORC and MAGE-C1. Putative HLA-A2-restricted 9-mer peptides were designed using the SYFPEITHI and BIMAS softwares (55, 56); their affinity has been validated in a biological HLA-A2 binding assay (unpublished data) and we are now trying to obtain peptide-specific T cells. However, one major issue is to demonstrate that MMC can efficiently process and present the immunogenic peptides and be killed by peptide-specific T cells. The use of HLA-A2-positive HMCL as targets in cytotoxicity assays will ensure that the peptide-directed T cells are able to recognize endogenous processed Ags, naturally presented in MHC molecules of tumor cells.
Ninety-three CT genes were previously identified (www.cancerimmunity.org/CTdatabase) and this study adds 16 microarray- and PCR-validated new putative CT genes that are expressed in multiple myeloma but also in various cancers. This suggests but does not prove that the majority of CT genes, classified in the four categories defined by Scanlan et al. (1), have been identified. It would be possible to rapidly answer this question, identifying most CT genes expressed in various cancers, because the current five-filter method, avoiding the normalization issue, could be easily applied to other cancers provided that gene expression of malignant cells and their normal counterpart is available.
The authors have no financial conflicts of interest.
The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.
This work was supported by grants from the Ligue Nationale Contre le Cancer (équipe labellisée 2009), Paris, France, from Institut National du Cancer (no. R07001FN), and from the Myeloma Stem Cell Network European Grant (no. E06005FF), the Hopp-Foundation, Germany, the University of Heidelberg, Heidelberg, Germany, the National Centre for Tumor Diseases, Heidelberg, Germany, and the Tumorzentrum Heidelberg/Mannheim, Germany.
M.C. designed research, performed the experiments, and wrote the paper; D.H., M.H., and H.G. collected bone marrow samples and clinical data; G.R. performed quantitative PCR experiments; T.R. developed bioinformatics tools to analyze the data; D.H. and M.H. participated in the writing of the paper; and B.K. is the senior investigator who designed research and wrote the paper.
Abbreviations used in this paper: CT, cancer-testis; BMPC, bone marrow plasma cell; GEP, gene expression profiling; MBC, memory B cell; HMCL, human myeloma cell line; MGUS, monoclonal gammopathy of undetermined significance; MM, multiple myeloma; MMC, multiple myeloma cell; NT, normal tissue.
The online version of this article contains supplemental material.