The Saenger Lab
Research
Figure 4. Comparison of overall survival (OS) of CRT treated patients using CD3+CD4+FOXP3+ (Treg cell) to CD3+ (T cell) ratio. A) Comparison of the ratios of CD3+CD4+FOXP3+ Treg cells to total CD3+ T cells within the tumor compartment between CRT patients who lived <2 years (Short Term Survivor, n = 6) and >2 years (Long Term Survivor, n = 8) after diagnosis (p = .0007). B) Kaplan Meier curve demonstrating the difference in survival between patients with lower (Low Treg/CD3, n = 7) and higher (High Treg/CD3, n = 7) than median CD3+CD4+FOXP3+ cell to CD3+ cell ratios (p = .0121 by Mantel-Cox). Representative quantitative mIF images of C) a CRT patient surviving >2 years and D) a CRT patient surviving <2 years stained for DAPI (blue), CD3 (yellow), CD4 (magenta) and FOXP3 (green). White bars represent 100 μm. (*≤0.05, **≤0.01, ***≤0.001, ****≤0.0001).
Analysis Info
Immunotherapy has transformed the treatment landscape for melanomas and others such as in liver and pancreatic cancer. In order to improve patient care and disease-specific survival (DSS) as a result of selected immunotherapies, advancements in clinically applicable histopathologic methods for examining the TME are essential. Our lab research focuses on analyzing immune cell data obtained from FFPE patient tissue samples with the goal of accurately correlating markers to clinical outcomes for patients' response to immunotherapy.
Immune infiltrates in collected tissue samples are examined with quantitative multiplex immunofluorescence (qmIF). This method addresses visualization of antigens simultaneously and allows us to evaluate the TME using tissue segmentation, multiparameter phenotyping, and quantitative spatial analysis (qSA). The produced multispectral images are analyzed using machine learning algorithms in order to determine immune cell densities, proximity to tumor, and spatial assessment of immune cell phenotypes within the TME.
Image Derived Melanoma Biomarkers Project Description
There is an urgent need for characterization of prognostic biomarkers in order to estimate patient risk and gain insight to determine how individual patients will respond to therapy. Imaging analysis tools that utilize immunofluorescence and H&E stained slides are digitally and computationally processed and analyzed to produce features. These features are correlated with clinical outcomes to produce a biomarker for melanoma outcome prognosis. Through quantitative evaluation of various biomarkers such as tumor-infiltrating lymphocytes (TIL), neutrophils, T cells, and macrophages, the immune activity within the tumor can be accessed and enhance our understanding of the immunosuppressive tumor environment. Our machine learning algorithm allows for precise and reproducible quantification of the TME from the qmIF images and will allow us to better predict clinical outcomes of immunotherapy.
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