Anti-Glycan Autoantibody Signatures of Malignancy Status: Applications of Printed Glycan Array in Cancer Diagnostics and Discovery of Therapeutic Targets
Authors: Margaret Huflejt
Journal: Clinical Cancer Research (2007)

ED04-02   Cancer continues to be one of the major causes of mortality in the world. It is estimated by the American Cancer Society that over 550,000 Americans will die of cancer yearly, and that 8.9 million Americans have a history of cancer. Over 1.3 million new cases are annually diagnosed in the U.S. When cancer is diagnosed at an early stage, however, current therapies have generated remarkable results. In contrast, very few therapies have shown success at treating late-stage cancers. Therefore, there is a very urgent clinical need for reliable, non-invasive and affordable screening tests for cancer and cancer risk. Preferably, such screening tests would be serum-based to be readily available to broad populations and particularly to high-risk populations. Ideally, such tests would also be able to identify the cancer type. In search for cancer biomarkers, it has become apparent that the identification of truly informative diagnostic molecules must parallel the progress in understanding the complex biology of malignant transformation and the entire tumor environment. That would not only allow reliable detection of cancer, but would also drive the development of targeted therapeutics and “personalized” medicine.   Background and Principles  All eukaryotic cell types are coated with complex carbohydrates bound to all proteins and majority of lipids forming cellular membranes. Aberrations in this otherwise well programmed glycosylation occur very early during malignant transformation and result in the appearance of specific glycans known as Tumor Associated Carbohydrate Antigens (TACAs) on cell surfaces and in serum components. TACAs are known ligands for galectins, selectins and other carbohydrate-binding and tumour-promoting proteins. Differences in serum glycan profiles between healthy individuals and cancer patients have been well demonstrated. TACAs are also known to trigger an immune response resulting in generation of anti-glycans autoantibodies, or AGAs. It is becoming evident that the immune response against the cell surface glycolipids and glycoproteins predominantly targets their carbohydrate moiety. Since our initial finding that multiple antibodies against TACAs present in human sera can be simultaneously detected-- or profiled-- using a printed glycan array (PGA), our research has been focused on developing this new array platform for clinical cancer diagnostics.  We hypothesized that immune activity against certain TACAs is a natural defense mechanism targeting malignant or pre-malignant cells “labeled” by the presence of these TACAs on their surface, and that the identification of such autoantibodies could provide a screening tool for early detection of malignancy and assessment of prognosis. We further hypothesized that the dynamics of malignant transformation depends on a fine balance between the ability of the immune system to recognize malignancy-signaling aberrant glycosylation on cellular surfaces and the expression of tumor-promoting galectins binding to the TACAs and blocking the immune recognition of malignant / pre-malignant cells.   Materials, Methods, and Computational Data Evaluation  Our current glycan library contains over 200 glycans, out of which more than 170 are known TACAs and closely related structures including T-, sialylated T- and Tn antigens, Lewis structures, Type 2 oligosaccharides, polylactosamines, gangliosides, B-1,6 branched glycans and their sulfated, fucosylated, sialylated, and acetylated derivatives. Blood group antigens and other oligosaccharides found on “healthy” cell membranes are also included. This entire glycan library is simultaneously arrayed on glass slides into a set of subgrids representing four different glycans concentrations: 0.5; 2; 10 and 50 uM to allow collection of information about antibody binding affinities, and in four replicates for each glycan/concentration, to allow statistical evaluation of signal intensities of bound antibodies. The assay is currently using 16 microliters of serum (1:25 dilution in PBS/BSA/Tween-20), and binding of anti-glycan IgG, IgM and IgA antibodies is visualized with biotinylated secondary antibodies and streptavidin-Alexa555. The fluorescence intensity of AGAs bound to TACAs is quantified with BioDiscovery/ImaGene software. [ ]  The data evaluation process consists of univariate feature selection and ranking which includes Student t-test, non-parametric Wilcoxon test (Mann-Whitney U test) and the area under the Receiver Operating Characteristic (ROC) curve. The glycans are ranked according to their p-values associated with the significance of difference of means/medians between the study subpopulations. The multivariate Hotelling’s T-squared test is then used to evaluate the significance of the groups of glycans obtained by the individual ranking. The achieved significance level (ASL) is determined by permutation bootstrap with the plug-in statistic based on Hotelling’s p-values. The visualization of clusters of subpopulations is provided with 2D and 3D scatter plots obtained with Fisher linear discriminant (FLD) and support vector machine (SVM) projections of the subspace of highest ranked glycans to two and three canonical dimensions. [ ]   Results and Discussion  In search for immuno-signatures of cancer and cancer risk, we have performed several studies in which we have examined immunoprofiles of AGAs in well-characterized populations of patients with different stages of breast, ovarian and lung malignancies, and in age-matched healthy individuals. We discovered an unexpected diversity of carbohydrate-binding antibodies in populations of both healthy donors and cancer patients, and identified novel glycan structures not known previously as targets of immune response. Mathematical analyses of AGA repertoire delivered putative “cancer signatures” that uniquely define specific cancer types. Moreover, AGA immunosignatures were distinct for the given cancer stages, thus demonstrating changes in cell surface glycosylation that accompany malignant transformation in tumor environment- as reflected by immune response. For example, one of our trials included an exploratory pilot study of 20 healthy donors and 38 patients with HER-2-amplified, stage II-III breast cancer who were all enrolled in a prospective randomized trial of primary chemotherapy with (n=28) or without (n=10) trastuzumab and treated between 2002 and 2004 at the UT M. D. Anderson Cancer Center. Profiling of AGAs has allowed identification of two sets of putative biomarker signatures discriminating (1) breast cancer patients from healthy donors (ASL = 0.015), and (2) breast cancer patients treated with trastuzumab who reached pathological complete response (pCR) vs. incomplete response (ASL = 0.037). The obtained ASL values suggest strong (1) and reasonably strong (2) evidence against the null hypothesis that the two subpopulations were sampled from the same distributions. We also observed differences in pre-treatment AGA profiles of patients who achieved pCR to neoadjuvant chemotherapy with trastuzumab compared to those who did not. [ ]  In another study we evaluated a population of patients with metastatic breast cancer (MBC) against a population of healthy donors. A putative MBC signature obtained in this trial was different from the AGA signature associated with earlier stages of breast cancer. Furthermore, within the MBC patient population itself, we observed AGA features that correlated with the presence of circulating tumour cells (CTCs) and were distinct from the putative breast cancer signature of the entire MBC study population, thus demonstrating that AGA-based serum analysis can serve to improve diagnostics and prognosis assessment in breast cancer. We later detected nearly identical MBC signature based on our evaluation of MBC patient population from the NCI / EDRN serum bank based in Duke University. Larger populations of patients with ovarian and lung cancers are currently evaluated in ongoing studies. [ ]  In conclusion, we demonstrated that the PGA is a sensitive tool for evaluation of serum AGAs. Individual AGA patterns are different between cancer patients and healthy individuals. The information on AGA-based immuno-signatures will likely lead to diagnostic approaches for cancer detection at much earlier, more treatable stages and to the development of personalized immunotherapeutic strategies for management of health status of patients with cancer.