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.
The DENV and the Zika (ZIKV) FVs frequently co-circulate and generally cause mild self-liming febrile illnesses. However, a secondary infection with a heterologous DENV serotype may lead to life threatening dengue hemorrhagic fever (DHF) and dengue shock syndrome (DSS). DHF/DSS have been linked to antibody dependent enhancement of infection (ADE), a phenomenon that occurs when antibodies (Abs) formed against an initial infection with one serotype of DENV cross-reacts but does not neutralize a heterologous DENV serotype in a secondary infection. Furthermore, Abs raised against the ZIKV have been observed to cross-react with the DENV and vice versa, which can potentially cause ADE and lead to severe DENV disease. The ZIKV can be transmitted vertically and has been linked to devastating congenital defects such as microcephaly in newborns. FDA approved treatments do not exist for DENV and ZIKV illnesses. Thus, there is a need for safe and effective treatments for these co-circulating viruses. Here, a tetravalent bispecific antibody (bsAb) targeting the ZIKV and all four serotypes of the DENV was expressed in the Nicotiana benthamiana (N. benthamiana) plant. Functional assays of the DENV/ZIKV bsAb demonstrated binding, neutralization, and a significant reduction in ADE activity against both the DENV and the ZIKV.
A single chain variable fragment (scFv) and a diabody based on an antibody directed against the immune checkpoint inhibitor PD-L1, were also expressed in N. benthamiana leaves. The smaller sizes of the scFv and diabody confers them with the ability to penetrate deeper tissues making them beneficial in diagnostics, imaging, and possibly cancer therapy. The past few decades has seen long strives in recombinant protein production in plants with significant improvements in production, safety, and efficacy. These characteristics make plants an attractive platform for the production of recombinant proteins, biologics, and therapeutics.
Cancer autoantibody biomarker discovery and validation using nucleic acid programmable protein array
Despite wide applications of high-throughput biotechnologies in cancer research, many biomarkers discovered by exploring large-scale omics data do not provide satisfactory performance when used to predict cancer treatment outcomes. This problem is partly due to the overlooking of functional implications of molecular markers. Here, we present a novel computational method that uses evolutionary conservation as prior knowledge to discover bona fide biomarkers. Evolutionary selection at the molecular level is nature's test on functional consequences of genetic elements. By prioritizing genes that show significant statistical association and high functional impact, our new method reduces the chances of including spurious markers in the predictive model. When applied to predicting therapeutic responses for patients with acute myeloid leukemia and to predicting metastasis for patients with prostate cancers, the new method gave rise to evolution-informed models that enjoyed low complexity and high accuracy. The identified genetic markers also have significant implications in tumor progression and embrace potential drug targets. Because evolutionary conservation can be estimated as a gene-specific, position-specific, or allele-specific parameter on the nucleotide level and on the protein level, this new method can be extended to apply to miscellaneous “omics” data to accelerate biomarker discoveries.