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Kim Blenman, PhD, MS

Assistant Professor of Medicine (Medical Oncology) and Assistant Professor of Computer Science

Research Summary

To date, the major challenge in studies of the tumor microenvironment has been the lack of tools to interrogate the tumor microenvironment in situ. Major technical components of my research involve flow cytometry, histology, and creation of imaging software tools. Flow cytometry is an elegant quantitative approach to identifying the contents of the tumor microenvironment. However, to understand the spatial relationships of the cells and structures in the tumor microenvironment we must assess the space in situ by histology. One of our short-term goals is to change the way that we approach histology by making it fully quantitative and use the approach to generate clinically meaningful results for use in standard of care settings and clinical trials. Therefore, for the past several years we have been working on creating tools as part of our long-term research goal.

We create immune profiles using high dimensional multiplex histology (immunohistochemistry (IHC); immunofluorescence (IF)) assessment of cell characteristics, distributions, spatial relationships/interactions, and target antigen intensities at the single cell level as well as the global level. Our histology method allows for multiplexed staining of up to 8 biomarkers with chromogen-based IHC and at least 20 biomarkers with pure fluorochrome-based IF per tissue section through dye inactivation. Any entities that can be stained through histology can be evaluated using our method (e.g. proteins (secreted; membrane-bound), RNA, DNA, metabolites, etc.).

Quantitative imaging analysis can be performed successfully on any image taken from any standard microscopy system. However, quantitative imaging systems, such as the Vectra Multispectral Imaging System (CRi/Perkin Elmer) and the TissueFAXS RGB Imaging System (TissueGnostics), in which I am an expert and have helped to develop both, provide all-in-one tools for automated imaging and analysis. The quantitative analysis is performed using algorithms and scripts developed using commercial (InForm/Nuance; StrataQuest; MATLAB) software. The algorithms and scripts lead to identification and segmentation (e.g. nucleus, cytoplasm, and membrane) of each cell and/or tissue structure by target antigens tagged with chromogens/fluorochromes. From this data, we can enumerate each cell/structure type, calculate the cell/structure population distributions, determine target antigen intensities, identify spatial relationships between cells and/or structures, and correlate this information with clinical parameters/outcomes and therapeutic response.

Since the approval of the first checkpoint inhibitor in 2011, the interest in and value of the immune system in disease and treatment has skyrocketed. Approaches, such as the methods that we have created, that allows one to fully interrogate the relationships of components in disease microenvironments and use the knowledge to guide and/or create treatment are the future of medicine.

Extensive Research Description

Computational Histology and spatial analysis tools. As the number of visible objects in an image increases, the ability of the human brain to discern patterns in the image decreases. We endeavor to use our software tools to help the human brain identify patterns in high complexity microenvironments and link those patterns to the biology of disease, clinical data, therapeutic response, and health disparity.

Software tools for in silico analysis of images: We currently define the approach that we use to capture the in situ profiles of the tissue microenvironment as Computational Histology. We use standard microscopy to capture multiplexed histology images of up to 20 biomarkers per tissue slide. Since the biomarkers are imaged at different time periods, they can be recombined in silico to study any combination and/or coexpressions of the biomarkers.

To capture the profile of tissue microenvironments, we created algorithms that align the images into a composite image, isolate each cell in the composite image, and identify the positive cells in the composite image. The information collected is used for our recently developed novel image analysis tools for spatial relationships. The tools incorporate methods and metrics used in flow cytometry to create histograms, dot scatterplots, backgates, and cluster/pattern plots for isolation, identification, quantitation, and measurement of spatial relationships of single cells, cell populations, and clusters/patterns of cells. Our method and workflow were recently published in Cytometry A journal and received the honor of being featured on the cover of the journal.

Using some or all aspects of our Computational Histology approach we have found that B cells and neutrophils may have a role in tumor regression in melanoma and breast cancer. In melanoma, we used a mouse tumor regression model that consisted of implanting immunocompetent B6 mice with YUMMER1.7 melanoma cells. We found that B cells including plasmablasts and plasma cells and neutrophils were numerous and increased with the introduction of immunotherapy. Neutrophils were in direct contact with dead or dying melanoma cells and immunotherapy caused neutrophil extracellular traps (NETs)-like formations as well as geographic necrosis. In the clinic, we found that patients that were treated with anti-PD-1 (pembrolizumab; nivolumab) who had high levels of B cells had better progression-free survival.

In breast cancer, we found that sentinel lymph node B cells in patients could predict disease-free survival. Breast cancer patients with higher density of B cells had longer disease-free survival. This benefit was also seen in patients with the triple-negative subtype which is the breast cancer with pathologic complete response rates of 20% to 60% with chemotherapy treatment. Patients that achieve pathologic complete response are less likely to have a distant metastasis. It is also the breast cancer subtype that is more common in premenopausal women of African descent. Overall, our data suggests that B cells may protect against cancer recurrence and potentially distant metastasis.

Earlier Research in Autoimmunity

Generation and phenotypic characterization of a murine model of systemic lupus erythematosus (SLE). SLE is an autoimmune disease characterized by production of autoantibodies against self-nuclear proteins. In the most severe form of SLE, the autoantibody:antigen complexes bind to the basement membrane of kidneys initiating a cascade of events that lead to renal failure. We created congenic mouse strains of SLE-susceptibility genes from the SLE-prone NZM2410 mouse strain on a B6 background. We used a speed-congenic approach, which cut the development time in half. Each of the 4 congenic and multiple sub-congenic strains have unique phenotypes that when recombined together in bi- and tri- congenics was successful in partially or fully reconstituting the original disease. We bred the congenic strains, genotyped, and phenotyped them. The speed congenic approach is used by essentially all labs that are using genetics and mice to address their research questions. Our congenic mice are available to the research community through the Jackson Labs.

Cytokine modulation as a potential key to treatment of SLE. We and others have shown that cytokines are required to keep autoimmune cells alive by inhibition of apoptosis and perpetuation of inflammation. We have shown that there are defects in the TNFα/TNFR1 apoptotic signaling pathway in lupus-prone mice post binding of TNFα to TNFR1. We have also shown that gene-therapy treatment with IL-10 reduces inflammation and delays renal damage in lupus-prone mice.

Coauthors

Research Interests

Breast Neoplasms; Immunotherapy; Leukemia; Lymphoma; Melanoma; Multiple Myeloma; Clinical Trial

Public Health Interests

Health Equity, Disparities, Social Determinants and Justice

Research Images

Selected Publications