automated-multiplex-immunofluorescence-panel-for-immuno-oncology

Automated Multiplex Immunofluorescence Panel for Immuno-oncology Studies on Formalin-fixed Carcinoma Tissue Specimens

Continued developments in immuno-oncology require an increased understanding of the mechanisms of cancer immunology. The immunoprofiling analysis of ...

MedImmune

Immuno-oncology and cancer immunotherapies are areas of intense research. The numbers and locations of CD8+ tumor-infiltrating lymphocytes (TILs) are important measures of the immune response to cancer with prognostic, pharmacodynamic, and predictive potential. We describe the development, validation, and application of advanced image analysis methods to characterize multiple immunohistochemistry-derived CD8 parameters in clinical and nonclinical tumor tissues.

Measuring multiple parameters of CD8+ tumor-infiltrating lymphocytes in human cancers by image analysis.

Multiplex immunohistochemistry allows the demonstration of multiple protein antigens in individual histological sections of formalin-fixed paraffin-embedded tumors or other types of tissue. Carefully designed and optimized immunohistochemistry (IHC) assays not only maximize the information available from limited tissues, but also enable a higher level interpretation of that information by demonstrating the histo-anatomical relationships among key cell types which express the included biomarkers. Programmable automated IHC instruments support the development and application of complicated multiplex IHC protocols, help save time and effort, and enhance immunostaining quality and reproducibility. Simple data can be extracted from immunostained tissues to include qualitative (descriptive) findings and semiquantitative analysis. The value of multiplex IHC can be increased further by the utilization of image analysis software either to better visualize multiple markers or by applying suitable digital scoring solutions to capture data (automated pathology).Here, we describe a five-marker multiplex based on application of two individual assays to serial sections of non-small cell lung carcinoma (NSCLC). We use this assay to label PD1, PD-L1, CD3, CD68, and cytokeratins in relation to tertiary lymphoid structures (TLS) and other regions of the tumor microenvironment. We illustrate how visualization of the immunostaining results can be used to understand TLS organization and other aspects of the tumor microenvironment, and briefly consider means to further yield additional information.

Multiplex Immunohistochemistry for Image Analysis of Tertiary Lymphoid Structures in Cancer.

The level of PD-L1 expression in immunohistochemistry (IHC) assays is a key biomarker for the identification of Non-Small-Cell-Lung-Cancer (NSCLC) patients that may respond to anti PD-1/PD-L1 treatments. The quantification of PD-L1 expression currently includes the visual estimation by a pathologist of the percentage (tumor proportional scoring or TPS) of tumor cells showing PD-L1 staining. Known challenges like differences in positivity estimation around clinically relevant cut-offs and sub-optimal quality of samples makes visual scoring tedious and subjective, yielding a scoring variability between pathologists. In this work, we propose a novel deep learning solution that enables the first automated and objective scoring of PD-L1 expression in late stage NSCLC needle biopsies. To account for the low amount of tissue available in biopsy images and to restrict the amount of manual annotations necessary for training, we explore the use of semi-supervised approaches against standard fully supervised methods. We consolidate the manual annotations used for training as well the visual TPS scores used for quantitative evaluation with multiple pathologists. Concordance measures computed on a set of slides unseen during training provide evidence that our automatic scoring method matches visual scoring on the considered dataset while ensuring repeatability and objectivity.

Article	Year
Measuring multiple parameters of CD8+ tumor-infiltrating lymphocytes in human cancers by image analysis. Journal for immunotherapy of cancer\| PubMed ID: 29510739	2018
Multiplex Immunohistochemistry for Image Analysis of Tertiary Lymphoid Structures in Cancer. Methods in molecular biology (Clifton, N.J.)\| PubMed ID: 30141009	2018
Deep Semi Supervised Generative Learning for Automated Tumor Proportion Scoring on NSCLC Tissue Needle Biopsies. Scientific reports\| PubMed ID: 30478349	2018

Keith Steele

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