Diagnostic markers of ovarian cancer by high-throughput antigen cloning and detection on arrays

MChip: a tool for influenza surveillance

The design and characterization of a low-density microarray for subtyping influenza A is presented. The microarray consisted of 15 distinct oligonucleotides designed to target only the matrix gene segment of influenza A. An artificial neural network was utilized to automate microarray image interpretation. The neural network was trained to recognize fluorescence image patterns for 68 known influenza viruses and subsequently used to identify 53 unknowns in a blind study that included 39 human patient samples and 14 negative control samples. The assay exhibited a clinical sensitivity of 95% and clinical specificity of 92%.

Risk-based and diagnostics-linked personalized medicine for cancer

Personalized medicine is gaining momentum in healthcare by allowing physicians to determine an individual’s predisposition to different diseases and to better diagnose and treat those diseases. Personalized medicine is frequently described as the right drug for the right patient at the right time; that is, a physician will prescribe treatment based on the individual characteristics of each patient such that the patient is likely to respond to a particular treatment with minimal adverse events. The heterogeneity of cancer makes the implementation of personalized medicine a necessity.

Update on ovarian cancer screening

PURPOSE OF REVIEW

Epithelial ovarian cancer is usually diagnosed in the advanced stage and carries a poor prognosis. When detected at an early stage, the 5-year survival rate is 90%. Despite the availability of various diagnostic tools for ovarian cancer screening, high levels of sensitivity and specificity are not achievable. There is therefore an ongoing need to identify new screening tests and strategies that should be readily available, relatively noninvasive, and achieve high sensitivity, specificity, and positive and negative predictive values.

RECENT FINDINGS

Our review focuses on various screening technologies including serum biomarkers, transvaginal ultrasonography as well as multimodality screening that can be used for early detection of ovarian cancer. The efficacy of different screening tools is discussed along with the efforts made to improve sensitivity, specificity and positive predictive value. The initial results of two large population-based screening studies are presented.

SUMMARY

An optimal screening test with high levels of sensitivity and specificity is indispensable for early detection of ovarian cancer. Serological screening with serum biomarkers (serum proteins and autoantibodies) can be used as a first-line screening test. In combination with TVS or color-flow Doppler imaging, this may prove very effective in early detection of ovarian cancer.

Technology Insight: can autoantibody profiling improve clinical practice?

A hallmark of autoimmune diseases is the production of high titers of highly specific autoantibodies, which are routinely measured to guide clinical decision-making. Multiplex antigen microarrays are powerful tools that can provide profiles of the autoantibodies found in blood and other biological fluids. This high-throughput technology allows for rapid identification of antibody and antigen biomarker sets, which is sorely needed in the clinic to improve diagnosis, predictions of prognosis, and selection of targeted therapies. In this article we will describe the antigen microarray technologies that are currently available, and those that are in development. We highlight recent applications for antibody profiling, as well as the challenges that need to be faced before such technologies enter the clinic.

Display technologies: application for the discovery of drug and gene delivery agents

Recognition of molecular diversity of cell surface proteomes in disease is essential for the development of targeted therapies. Progress in targeted therapeutics requires establishing effective approaches for high-throughput identification of agents specific for clinically relevant cell surface markers. Over the past decade, a number of platform strategies have been developed to screen polypeptide libraries for ligands targeting receptors selectively expressed in the context of various cell surface proteomes. Streamlined procedures for identification of ligand-receptor pairs that could serve as targets in disease diagnosis, profiling, imaging and therapy have relied on the display technologies, in which polypeptides with desired binding profiles can be serially selected, in a process called biopanning, based on their physical linkage with the encoding nucleic acid. These technologies include virus/phage display, cell display, ribosomal display, mRNA display and covalent DNA display (CDT), with phage display being by far the most utilized. The scope of this review is the recent advancements in the display technologies with a particular emphasis on molecular mapping of cell surface proteomes with peptide phage display. Prospective applications of targeted compounds derived from display libraries in the discovery of targeted drugs and gene therapy vectors are discussed.

A minimally invasive multiple marker approach allows highly efficient detection of meningioma tumors

Background

The development of effective frameworks that permit an accurate diagnosis of tumors, especially in their early stages, remains a grand challenge in the field of bioinformatics. Our approach uses statistical learning techniques applied to multiple antigen tumor antigen markers utilizing the immune system as a very sensitive marker of molecular pathological processes. For validation purposes we choose the intracranial meningioma tumors as model system since they occur very frequently, are mostly benign, and are genetically stable.

Results

A total of 183 blood samples from 93 meningioma patients (WHO stages I-III) and 90 healthy controls were screened for seroreactivity with a set of 57 meningioma-associated antigens. We tested several established statistical learning methods on the resulting reactivity patterns using 10-fold cross validation. The best performance was achieved by Naïve Bayes Classifiers. With this classification method, our framework, called Minimally Invasive Multiple Marker (MIMM) approach, yielded a specificity of 96.2%, a sensitivity of 84.5%, and an accuracy of 90.3%, the respective area under the ROC curve was 0.957. Detailed analysis revealed that prediction performs particularly well on low-grade (WHO I) tumors, consistent with our goal of early stage tumor detection. For these tumors the best classification result with a specificity of 97.5%, a sensitivity of 91.3%, an accuracy of 95.6%, and an area under the ROC curve of 0.971 was achieved using a set of 12 antigen markers only. This antigen set was detected by a subset selection method based on Mutual Information. Remarkably, our study proves that the inclusion of non-specific antigens, detected not only in tumor but also in normal sera, increases the performance significantly, since non-specific antigens contribute additional diagnostic information.

Conclusion

Our approach offers the possibility to screen members of risk groups as a matter of routine such that tumors hopefully can be diagnosed immediately after their genesis. The early detection will finally result in a higher cure- and lower morbidity-rate.

Epitomics: Global Profiling of Immune Response to Disease Using Protein Microarrays

The immune system retains memory of current and past infections and can sense the presence of cancer by elaborating autoantibodies to tumor proteins. In the presence of an autoimmune disease, the immune system is an efficient, natural biosensor. Therefore we exploit the immune system through a high-throughput process to isolate disease-specific epitopes for diagnostic and therapeutic purposes. These cloned disease-specific antigens are robotically spotted onto protein microarrays and interrogated with serum from the subjects under analyses. These arrays deliver personalized profiles of antigenic exposures and therapeutic targets for personalized immunotherapy. The immune system is the ultimate biosensor, superior to anything a human could create and ready to be exploited for biotechnology and biomedicine.

Proteomic patterns for cancer diagnosis--promise and challenges

Proteomic patterns have been discovered for a variety of cancers and cancer related diseases. The platforms used have been both mass spectrometry and microarrays and the incorporation of computer informatics has resulted in innovative possibilities for novel diagnostics.

Tumor-associated antigen arrays for the serological diagnosis of cancer

The recognition that human tumors stimulate the production of autoantibodies against autologous cellular proteins called tumor-associated antigens (TAAs) has opened the door to the possibility that autoantibodies could be exploited as serological tools for the early diagnosis and management of cancer. Cancer-associated autoantibodies are often driven by intracellular proteins that are mutated, modified, or aberrantly expressed in tumor cells and hence are regarded as immunological reporters that could help uncover molecular events underlying tumorigenesis. Emerging evidence suggests that each type of cancer might trigger unique autoantibody signatures that reflect the nature of the malignant process in the affected organ. The advent of novel genomic, proteomic, and high throughput approaches has accelerated interest in the serum autoantibody repertoire in human cancers for the discovery of candidate TAAs. The use of individual anti-TAA autoantibodies as diagnostic or prognostic tools has been tempered by their low frequency and heterogeneity in most human cancers. However, TAA arrays comprising several antigens significantly increase this frequency and hold great promise for the early detection of cancer, monitoring cancer progression, guiding individualized therapeutic interventions, and identification of novel therapeutic targets. Our recent studies suggest that the implementation of TAA arrays in screening programs for the diagnosis of prostate cancer and other cancers should be preceded by the optimization of their sensitivity and specificity through the careful selection of the most favorable combinations of TAAs.