treatments, and neo-antigens are the targets of immune system in cancer patients who
respond to the treatments. The cancer vaccine field is focused on using neo-antigens from
unique point mutations of genomic sequence in the cancer patient for making
personalized cancer vaccines. However, we choose a different path to find frameshift
neo-antigens at the mRNA level and develop broadly effective cancer vaccines based on
frameshift antigens.
In this dissertation, I have summarized and characterized all the potential frameshift
antigens from microsatellite regions in human, dog and mouse. A list of frameshift
antigens was validated by PCR in tumor samples and the mutation rate was calculated for
one candidate – SEC62. I develop a method to screen the antibody response against
frameshift antigens in human and dog cancer patients by using frameshift peptide arrays.
Frameshift antigens selected by positive antibody response in cancer patients or by MHC
predictions show protection in different mouse tumor models. A dog version of the
cancer vaccine based on frameshift antigens was developed and tested in a small safety
trial. The results demonstrate that the vaccine is safe and it can induce strong B and T cell
immune responses. Further, I built the human exon junction frameshift database which
includes all possible frameshift antigens from mis-splicing events in exon junctions, and I
develop a method to find potential frameshift antigens from large cancer
immunosignature dataset with these databases. In addition, I test the idea of ‘early cancer
diagnosis, early treatment’ in a transgenic mouse cancer model. The results show that
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early treatment gives significantly better protection than late treatment and the correct
time point for treatment is crucial to give the best clinical benefit. A model for early
treatment is developed with these results.
Frameshift neo-antigens from microsatellite regions and mis-splicing events are
abundant at mRNA level and they are better antigens than neo-antigens from point
mutations in the genomic sequences of cancer patients in terms of high immunogenicity,
low probability to cause autoimmune diseases and low cost to develop a broadly effective
vaccine. This dissertation demonstrates the feasibility of using frameshift antigens for
cancer vaccine development.
In this dissertation, I propose a scenario for using immunosignature technology to detect breast cancer early and to implement an early treatment strategy by using the PD-L1 immune checkpoint inhibitor. I develop a methodology to describe the early diagnosis and treatment of breast cancer in a FVB/N neuN breast cancer mouse model. By comparing FVB/N neuN transgenic mice and age-matched wild type controls, I have found and validated specific immunosignatures at multiple time points before tumors are palpable. Immunosignatures change along with tumor development. Using a late-stage immunosignature to predict early samples, or vice versa, cannot achieve high prediction performance. By using the immunosignature of early breast cancer, I show that at the time of diagnosis, early treatment with the checkpoint blockade, anti-PD-L1, inhibits tumor growth in FVB/N neuN transgenic mouse model. The mRNA analysis of the PD-L1 level in mice mammary glands suggests that it is more effective to have treatment early.
Novel discoveries are changing understanding of breast cancer and improving strategies in clinical treatment. Researchers and healthcare professionals are actively working in the early diagnosis and early treatment fields. This dissertation provides a step along the road for better diagnosis and treatment of breast cancer.
Background: High-throughput technologies such as DNA, RNA, protein, antibody and peptide microarrays are often used to examine differences across drug treatments, diseases, transgenic animals, and others. Typically one trains a classification system by gathering large amounts of probe-level data, selecting informative features, and classifies test samples using a small number of features. As new microarrays are invented, classification systems that worked well for other array types may not be ideal. Expression microarrays, arguably one of the most prevalent array types, have been used for years to help develop classification algorithms. Many biological assumptions are built into classifiers that were designed for these types of data. One of the more problematic is the assumption of independence, both at the probe level and again at the biological level. Probes for RNA transcripts are designed to bind single transcripts. At the biological level, many genes have dependencies across transcriptional pathways where co-regulation of transcriptional units may make many genes appear as being completely dependent. Thus, algorithms that perform well for gene expression data may not be suitable when other technologies with different binding characteristics exist. The immunosignaturing microarray is based on complex mixtures of antibodies binding to arrays of random sequence peptides. It relies on many-to-many binding of antibodies to the random sequence peptides. Each peptide can bind multiple antibodies and each antibody can bind multiple peptides. This technology has been shown to be highly reproducible and appears promising for diagnosing a variety of disease states. However, it is not clear what is the optimal classification algorithm for analyzing this new type of data.
Results: We characterized several classification algorithms to analyze immunosignaturing data. We selected several datasets that range from easy to difficult to classify, from simple monoclonal binding to complex binding patterns in asthma patients. We then classified the biological samples using 17 different classification algorithms. Using a wide variety of assessment criteria, we found ‘Naïve Bayes’ far more useful than other widely used methods due to its simplicity, robustness, speed and accuracy.
Conclusions: ‘Naïve Bayes’ algorithm appears to accommodate the complex patterns hidden within multilayered immunosignaturing microarray data due to its fundamental mathematical properties.