Combining adaptive and innate immunity induction modes, the repertoire of immunoglobulin M (IgM) can reflect changes in the internal environment including malignancies. Previously, it was shown that a mimotope library reflecting the public IgM repertoire of healthy donors (IgM IgOme) can be mined for efficient probes of tumor biomarker antibody reactivities. To better explore the interpretability of this approach for IgM, solid tumor-related profiles of IgM reactivities to linear epitopes of actual tumor antigens and viral epitopes were studied. The probes were designed as oriented planar microarrays of 4526 peptide sequences (as overlapping 15-mers) derived from 24 tumor-associated antigens and 209 cancer-related B cell epitopes from 30 viral antigens. The IgM reactivity in sera from 21 patients with glioblastoma multiforme, brain metastases of other tumors, and non-tumor-bearing neurosurgery patients was thus probed in a proof-of-principle study. A graph representation of the binding data was developed, which mapped the cross-reactivity of the mixture of IgM (poly)specificities, delineating different antibody footprints in the features of the graph—neighborhoods and cliques. The reactivity graph mapped the major features of the IgM repertoire such as the magnitude of the reactivity (titer) and major cross-reactivities, which correlated with blood group reactivity, non-self recognition, and even idiotypic specificities. A correlation between an aspect of this image of the IgM IgOme, namely, small cliques reflecting rare self-reactivities and the capacity of subsets of the epitopes to separate the diagnostic groups studied was found. In this way, the graph representation helped the feature selection in its filtering step and provided reduced feature sets, which, after recursive feature elimination, produced a classifier containing 51 peptide reactivities separating the three diagnostic groups with an unexpected efficiency. Thus, IgM IgOme approaches to repertoire studies is greatly augmented when self/viral antigens are used and the data are represented as a reactivity graph. This approach is most general, and if it is applicable to tumors in immunologically privileged sites, it can be applied to any solid tumors, for instance, breast or lung cancer.

The immune system can be used as a biosensor of the internal environment. Changes in the reactivities of the antibody repertoire can be used as a readout for a wide range of disturbances including various inflammatory conditions and malignant tumors. Extending our previous work based on IgM mimotope libraries, here we report our studies on the interpretability of profiles of IgM reactivities to a library of natural 15-mer peptides derived from 20 tumor associated antigens and 193 linear B cell epitopes involved in tumor pathogenesis. Sera from 21 patients with glioblastoma multiforme (GBM, n=10), brain metastases of other tumors (n=5) and non-tumor bearing neurosurgery patients (n=6) were used to probe their IgM reactivity with an array of 4526 peptide sequences. Using feature selection algorithms, we were able to extract profiles that separated well the three diagnostic groups with accuracy of up to 0.9. A key feature of the profiles extracted was their size (138 peptides for differentiating GBM and 340 – for tumor bearing patients) and origin from practically all tested antigens. Comparable numbers of reactivities were gained or lost in tumor bearing patients. A minimal set of the most significant 41 reactivities from 16 antigens contained disproportionately large number of epitopes from stromelysine-3 and erbB2 receptor with some of the reactivities gained and other lost in cancer patients. Epitopes from human papilloma virus 16 and HTLV-1 were included too. Some of the reactivities were readily interpretable both as antigen source and structural context (signal peptides). The interpretation of the rest requires further confirmatory studies. Thus, a set of natural peptides from tumor antigens readily provides profile of interpretable IgM reactivities which can serve as classifiers for clinically relevant patient stratification.