Seromic profiling of ovarian and pancreatic cancer

Abstract

Autoantibodies, a hallmark of both autoimmunity and cancer, represent an easily accessible surrogate for measuring adaptive immune responses to cancer. Sera can now be assayed for reactivity against thousands of proteins using microarrays, but there is no agreed-upon standard to analyze results. We developed a set of tailored quality control and normalization procedures based on ELISA validation to allow patient comparisons and determination of individual cutoffs for specificity and sensitivity. Sera from 60 patients with pancreatic cancer, 51 patients with ovarian cancer, and 53 age-matched healthy donors were used to assess the binding of IgG antibodies against a panel of >8000 human antigens using protein microarrays and fluorescence detection. The resulting data interpretation led to the definition and ranking of proteins with preferred recognition by the sera from cancer patients in comparison with healthy donors, both by frequency and strength of signal. We found that 202 proteins were preferentially immunogenic in ovarian cancer sera compared to 29 in pancreatic cancer, with few overlaps. Correlates of autoantibody signatures with known tumor expression of corresponding antigens, functional pathways, clinical stage, and outcome were examined. Serological analysis of arrays displaying the complete human proteome (seromics) represents a new era in cancer immunology, opening the way to defining the repertoire of the humoral immune response to cancer.

Conflict of interest statement

The authors declare no conflict of interest.

Figures

Fig. 1.

ELISA with sera from 60 pancreatic cancer patients, 53 healthy donors, and 51 ovarian cancer patients. Results are shown as extrapolated IgG titers against a series of 22 recombinant antigens listed. Three ovarian cancer patient sera were considered not evaluable because of reactivity to more than 50% of antigens tested and were therefore excluded from the graph and from statistics. Results are considered positive if reciprocal titers are >100. Results are representative of at least two repeat assays. LTS, long-term survivor; STS, short-term survivor; IntS, intermediate-term survivor.

Fig. 2.

Flowchart of normalization, validation, and antigen selection strategies for seromics. X = any of the values on the array; Q1 = 25th percentile of all values; Q3 = 75th percentile of all values.

Fig. 3.

Comparison of ELISA and seromics data on panel of antigens using sera with known specificity. Fourteen control sera, plotted along the x axis and known to react with individual antigens shown next to their name, were tested against a series of 30 proteins indicated. In the top panel, reciprocal titers were determined by ELISA from serial dilutions for each serum against each protein, as described in Materials and Methods. Only reciprocal titers greater than 500 are shown in ELISA to allow relevant comparison with seromics data that were generated using a single 1:500 serum dilution. In the lower panel, fold-over-cutoff results of seromics are shown following data transformation and normalization, indicating seroreactivity against the same 30 proteins spotted on ProtoArrays. Trunc, truncated proteins.

Fig. 4.

Three-dimensional heat map representation of seroreactivity against top antigens in sera from ovarian and pancreatic cancer patients and healthy donors, as indicated. Sera are arranged along the y axis, whereas antigens listed in Tables S1 and Tables S2 are arranged along the x axis, with those preferentially immunogenic in ovarian cancer on the left and those preferentially immunogenic in pancreatic cancer on the right, with some overlap. Each peak represents the reactivity of an individual serum to one antigen, expressed as the number of fold-over cutoff, indicating the strength of antibody response. If the ratio to cutoff is greater than 1, the serum is considered to react significantly and peaks appear as yellow. Peaks have graded bars to indicate number of actual folds over cutoff (shown up to 20× over cutoff).

Fig. 5.

Kaplan–Meier analyses of overall survival of cancer patients according to the presence of antibody response to a set of antigens. Detection of antibody response to any of the antigens listed above each graph was measured in ovarian (A and B) and pancreatic (C and D) cancer patients. Significant associations of autoantibody responses with better (A and C) or worse (B and D) clinical outcome were found by comparing differences between curves with the log-rank method. The accession numbers of genes coding for these antigens are listed in Tables S1 and Tables S2.