Immunosignature system for diagnosis of cancer

Although the search for disease biomarkers continues, the clinical return has thus far been disappointing. The complexity of the body’s response to disease makes it difficult to represent this response with only a few biomarkers, particularly when many are present at low levels. An alternative to the typical reductionist biomarker paradigm is an assay we call an “immunosignature.” This approach leverages the response of antibodies to disease-related changes, as well as the inherent signal amplification associated with antigen-stimulated B-cell proliferation. To perform an immunosignature assay, the antibodies in diluted blood are incubated with a microarray of thousands of random sequence peptides. The pattern of binding to these peptides is the immunosignature. Because the peptide sequences are completely random, the assay is effectively disease-agnostic, potentially providing a comprehensive diagnostic on multiple diseases simultaneously. To explore the ability of an immunosignature to detect and identify multiple diseases simultaneously, 20 samples from each of five cancer cohorts collected from multiple sites and 20 noncancer samples (120 total) were used as a training set to develop a reference immunosignature. A blinded evaluation of 120 blinded samples covering the same diseases gave 95% classification accuracy. To investigate the breadth of the approach and test sensitivity to biological diversity further, immunosignatures of >1,500 historical samples comprising 14 different diseases were examined by training with 75% of the samples and testing the remaining 25%. The average accuracy was >98%. These results demonstrate the potential power of the immunosignature approach in the accurate, simultaneous classification of disease.Cancer is the most likely disease for which an early diagnostic would be immediately beneficial. Unfortunately, finding specific biomarkers, especially for cancer, has been complicated by the fact that biological molecules (RNA, DNA, proteins, or peptides) that are uniquely released by a small tumor into the bloodstream are extremely dilute. Classical biomarker assays are based on one-to-one molecular recognition events to detect one or a few specific analytes that are often measured by antibody–protein interactions. There are three fundamental limitations with this approach, all of which are confounded by the dilution problem alluded to above. The first is that the cross-reactivity of such interactions poses a formidable problem in distinguishing diseases. Biology’s promiscuous use of a limited number of homologous sequences, folds, and domains makes specificity difficult. The second is that diseases such as cancer are themselves heterogeneous, and individual response to disease, at a molecular level, can vary considerably. It is unlikely that this level of complexity can be quantitatively assessed by one or a few specific proteins or metabolites in a way that supports robust diagnosis. Third, many of the biomarkers that have been proposed are of low stability or require substantial preassay purification or preparation; these aspects introduce substantial variation into the measured values (1, 2). As a result, although considerable effort has been put into the development of biomarkers, only a small fraction of candidates make it to clinical practice, and the utility of those that are used is sometimes only modest (3–5). Here, we explore the ability of the immunosignature technology to address the ideal of a simple, comprehensive diagnostic for multiple cancers.An “immunosignature” is the pattern obtained when circulating antibodies in blood are allowed to bind to a large microarray of randomized-sequence peptides affixed to a solid surface (6). Cancers generate neoantigens by virtue of their mutagenic nature, and they tend to release native proteins and biomolecules not normally encountered by the immune system (7–9). These behaviors can elicit an immune response (6, 10, 11). By virtue of the tremendous amplification afforded by B-cell replication (12), the signal elicited by the disease-specific antigens is massively amplified. In fact, a key aspect of the immunosignature assay is that the blood is greatly diluted before application to the array, such that only the antibodies that have been sufficiently amplified give distinct signals (13).Another somewhat counterintuitive aspect of the method is that the peptide sequences used on the microarray are purposefully not chosen to represent the natural antigens of the antibodies produced in response to disease. In fact, in the arrays of 10,000 peptides used in this study, the peptide sequences were generated with a random number generator. This enables the same microarray to be used for diagnosis of any disease. Despite using random-sequence peptides, monoclonal antibodies generated from a wide variety of antigens show specific patterns of binding on these arrays, to both cognate and noncognate sequences (14, 15). Many of the peptides bound by a monoclonal antibody against a known linear epitope have no obvious sequence similarity to that epitope. Most of the peptides thus identified have demonstrated low affinity in solution for the antibody but are retained on the arrays due to avidity created by close spacing of individual peptides (15).An immunosignature of an individual consists of an overlay of the patterns from the binding signals of many of the most prominent circulating antibodies. Some of the binding signals are present in most individuals (whether sick or healthy), and some are unique to an individual, but if the individual has a disease such as a cancer, a subset of the binding signals will be due to disease-associated antigens that are common to most individuals with the disease (16). An important aspect of this approach is that it senses essentially all antibodies raised to the disease and detects each of the antibodies as separable binding patterns composed of unique molecular recognition elements. This differs from, for example, an ELISA, which might sum the contributions of many different antibodies using a single protein, cell, or virus capsid. Again, from a statistical perspective, the high dimensionality of this readout affords much more specificity than could be obtained from a set of cognate sequences or from an array of the native antigens themselves.Not only does the use of highly dilute blood and random peptide sequences in the immunosignature assay paradoxically give rise to improved sensitivity and specificity but these aspects of the assay also result in several other unique benefits of the immunosignature approach. Because of the dilution (1:500 in these studies), blood proteins other than antibodies do not significantly bind to the arrays, meaning that there is no sample preparation involved other than dilution (17). The dilution ensures the assay is sample-sparing. Finally, the assay is disease-agnostic. The arrays can be used for the simultaneous detection and identification of multiple diseases.It is simultaneous detection and identification of multiple diseases with a single assay that underlies the true potential of this approach as a disruptive force in healthcare. This, combined with the fact that serum antibodies are robust to handling (17, 18) such that a drop of blood can be sent dried on filter paper through the mail (17), should enable frequent, inexpensive monitoring for many different diseases. The goal of the current work is to test the multidisease aspect of immunosignatures rigorously. Although the approach has previously been used to discriminate various subtypes of brain cancer (19), it has not yet demonstrated multiplexed cancer diagnosis. Here, we perform a blinded train/test validation study wherein a group of 120 individuals with five different cancers from various geographic regions was used as a training set to define a multicancer signature. The signature predicted the disease status of a test cohort of equal size and composition. To explore the ability of the approach to discriminate between an even larger set of diseases, 1,516 different individuals spanning 14 different disease cohorts plus a diverse cohort of healthy controls were assayed and the ability to distinguish between these diseases was evaluated.