In this paper, we describe the first genuine hematopoietic mHag presented by HLA class II (HLA-DQA1*05/B1*02) molecules to CD4+ T cells. This antigen is encoded by a SNP in the B cell lineage-specific CD19 gene. To identify the CD19L-encoded mHag, we developed a powerful genetic strategy, which is nonlaborious and genome-wide applicable to identify a wide range of HLA class I– as well as HLA class II–restricted mHags.
What may be the clinical importance of this novel HLA class II–restricted mHag? Like CD20, CD19 is a B cell lineage-specific molecule, with constitutive expression in acute and chronic B cell lymphoid leukemias, B cell lymphomas, and in a subset of acute myeloid leukemias. The expression of CD19 is rarely down-regulated in B cell malignancies. Furthermore, CD19 is not expressed in pluripotent stem cells (24
). For these reasons, CD19 is widely considered to be an ideal target for immunotherapy of several B cell malignancies. Immunotherapy strategies based on CD19 antibodies (immunotoxins and chimeric TCRs) have been developed and have used both CD4+
T cells as effector cells (25
). Numerous investigators have searched for HLA class I CTL epitopes on the CD19 molecule (27
). Although these approaches may be very beneficial, we think that in an immunotherapy setting, targeting of CD19 with CD4+
T cells also may provide several additional benefits; in our assays, the CD19L
T cells not only mediated potent helper functions to activate DCs and stimulate CD8+
CTLs but also directly functioned as effector cells to lyse HLA-matched CD19L
-positive malignant cells. Thus, immunotherapy with CD19L
CTLs alone or in combination with CD8+
T cells may permit the exploitation of both helper and effector functions whereby more effective and durable antitumor responses may be established. Because CD19 is a B cell–specific molecule, CD19L
-specific therapy may be feasible not only within but also beyond the allo-SCT settings. The CD19L
allele is expressed in 53% of the Caucasian population, and its antigen-presenting molecule HLA-DQA1*05/B1*02 has a frequency of 15.3% in the Caucasian population. Thus, after an HLA-matched SCT, ~2.3% of the donor-recipient pairs will be CD19L
mismatched and eligible for a treatment with CD19L
T cells or vaccination with peptide-loaded or gene-transfected DCs. This percentage can increase up to 8.1% (15.3 × 53%) in an HLA-matched unrelated donor SCT setting or when patients are not transplanted. In all settings, adoptive immunotherapy with ex vivo–generated CD19L
-specific T cells may be feasible, in particular after generation of CD19L
-specific T cells using the so-called TCR transfer approach (15
What may be the impact of our mHag identification strategy? Our novel genetic approach involves a correlation analysis between mHag zygosities and HapMap SNP genotypes to locate the genetic locus of the mHag. Our results demonstrate that this approach is genome-wide applicable and able to precisely map the genetic locus of a wide range of mHags with phenotype frequencies of 10–85%. The basic idea behind our strategy is similar to a recently introduced method, which utilizes genome association scans after performing SNP array analysis on DNA samples pooled from mHag+
). A major advantage of our strategy is that it covers more SNPs. Although the other method can only analyze 65% of the HapMap SNPs after performing 500,000 SNP arrays, we analyze millions of HapMap SNPs covering the whole genome without even performing an SNP array. Furthermore, our strategy is less laborious. In our analyses, the genome-wide identification of the precise LD block of six known mHags required the mHag phenotyping of 27–42 CEPH individuals, whereas the other approach had to phenotype at least 100 individuals to identify the locus of these mHags (28
). Thus, our method can dramatically reduce the work load and the time required to identify mHags, which are the two most important drawbacks of all current mHag identification strategies. The only apparent limitation of our strategy may be its difficulty in identifying mHags with allele frequencies beyond 10–85%. Nonetheless, it should be emphasized that mHags with very low or high phenotype frequencies are of limited immunotherapeutic value. As illustrated in Fig. S3 (available at http://www.jem.org/cgi/content/full/jem.20080713/DC1
), the chance of an mHag mismatch between the recipient and donor is <10%, if the mHag phenotype frequency is beyond 26–78% in the HLA-matched sibling setting or beyond 11–89% in the HLA-matched unrelated donor setting. Because the number of eligible patients will also be limited by HLA restriction, mHag frequencies beyond these limits are of little practical value for clinical application. Thus, our strategy, with sufficient power to identify mHags with frequencies of 10–85%, is actually very suitable for the identification of a vast majority of mHags that are of high value for immunotherapy.
A frequent problem in genetic linkage analyses is phenotyping errors. In our approach, we devoted an extreme attention to avoid such errors because our analyses are executed with a very limited set of CEPH individuals. As phenotype errors will result in the reduction of correlation coefficient, our standard criterion of r2 = 1 to discriminate true-positive hits from false-positive ones cannot be applied for datasets containing phenotype errors. Yet our analyses with datasets containing 10% false-positive phenotypes revealed that the reduction in correlation coefficient can be calculated (Fig. S2) and that these calculated values could be used as threshold values to eliminate false hits without dramatically reducing the power of the analyses. Thus, our approach, with theoretically adjusted threshold values, may still be useful for identification of clinically relevant mHags even when phenotyping errors cannot be excluded. Nonetheless, as the power decreases significantly we still think that the best strategy for success is to use all means to avoid phenotype errors.
Finally, we think that our strategy is universally applicable because there are five ethnic panels with trios genotyped in the Phase III HapMap. Using one of these panels may be sufficient for analysis of a wide range of mHag-specific T cell clones, even if they are obtained from a different population. In fact, using the Caucasoid CEPH panel, we have been able to map the genomic locus of HMSD, ACC-1Y, and ACC-2, mHags which were originally described in the Japanese population. Nonetheless, not all HapMap panels consist of trios, which are required for deducing the zygosity information. Because this may still be a potential limitation for identification of mHags in some ethnic populations, we are currently evaluating the possibility of using unrelated HapMap individuals in our strategy. In conclusion, the first HLA class II–restricted hematopoietic mHag as well as the powerful mHag identification strategy described here can significantly facilitate the application of mHag-based immunotherapy in a broader clinical setting.