Guillaume Thibault

Quantitative Multiplex Immunohistochemistry Reveals Myeloid-Inflamed Tumor-Immune Complexity Associated with Poor Prognosis

Here, we describe a multiplexed immunohistochemical platform with computational image processing workflows, including image cytometry, enabling simultaneous evaluation of 12 biomarkers in one formalin-fixed paraffin-embedded tissue section. To validate this platform, we used tissue microarrays containing 38 archival head and neck squamous cell carcinomas and revealed differential immune profiles based on lymphoid and myeloid cell densities, correlating with human papilloma virus status and prognosis. Based on these results, we investigated 24 pancreatic ductal adenocarcinomas from patients who received neoadjuvant GVAX vaccination and revealed that response to therapy correlated with degree of mono-myelocytic cell density and percentages of CD8+ Txa0cells expressing Txa0cell exhaustion markers. These data highlight the utility of inxa0situ immune monitoring for patient stratification and provide digital image processing pipelines to the community for examining immune complexity in precious tissue sections, where phenotype and tissue architecture are preserved to improve biomarker discovery and assessment.

Quantitative analysis of histological tissue image based on cytological profiles and spatial statistics

The cellular heterogeneity and complex tissue architecture of most tumor samples is a major obstacle in image analysis on standard hematoxylin and eosin-stained (H&E) tissue sections. A mixture of cancer and normal cells complicates the interpretation of their cytological profiles. Furthermore, spatial arrangement and architectural organization of cells are generally not reflected in cellular characteristics analysis. To address these challenges, first we describe an automatic nuclei segmentation of H&E tissue sections. In the task of deconvoluting cellular heterogeneity, we adopt Landmark based Spectral Clustering (LSC) to group individual nuclei in such a way that nuclei in the same group are more similar. We next devise spatial statistics for analyzing spatial arrangement and organization, which are not detectable by individual cellular characteristics. Our quantitative, spatial statistics analysis could benefit H&E section analysis by refining and complementing cellular characteristics analysis.

Breast cancer histopathology image analysis pipeline for tumor purity estimation

The translation of genomic sequencing technology to the clinic has greatly advanced personalized medicine. However, the presence of normal cells in tumors is a confounding factor in genome sequence analysis. Tumor purity, or the percentage of cancerous cells in whole tissue section, is a correction factor that can be used to improve the clinical utility of genomic sequencing. Currently, tumor purity is estimated visually by expert pathologists; however, it has been shown that there exist vast inter-observer discrepancies in tumor purity scoring. In this paper, we propose a quantitative image analysis pipeline for tumor purity estimation and provide a systematic comparison between pathologists scores and our image-based tumor purity estimation.

A fully integrated, three-dimensional fluorescence to electron microscopy correlative workflow

While fluorescence microscopy provides tools for highly specific labeling and sensitive detection, its resolution limit and lack of general contrast has hindered studies of cellular structure and protein localization. Recent advances in correlative light and electron microscopy (CLEM), including the fully integrated CLEM workflow instrument, the FEI CorrSightxa0with MAPS, have allowed for a more reliable, reproducible, and quicker approach to correlate three-dimensional time-lapse confocal fluorescence data, with three-dimensional focused ion beam-scanning electron microscopy data. Here we demonstrate the entire integrated CLEM workflow using fluorescently tagged MCF7 breast cancer cells.

Deep learning based Nucleus Classification in pancreas histological images

Tumor specimens contain a variety of healthy cells as well as cancerous cells, and this heterogeneity underlies resistance to various cancer therapies. But this problem has not been thoroughly investigated until recently. Meanwhile, technological breakthroughs in imaging have led to an explosion of molecular and cellular profiling data from large numbers of samples, and modern machine learning approaches including deep learning have been shown to produce encouraging results by finding hidden structures and make accurate predictions. In this paper, we propose a Deep learning based Nucleus Classification (DeepNC) approach using paired histopathology and immunofluorescence images (for label), and demonstrate its classification prediction power. This method can solve current issue on discrepancy between genomic- or transcriptomic-based and pathology-based tumor purity estimates by improving histological evaluation. We also explain challenges in training a deep learning model for huge dataset.

Integrative analysis on histopathological image for identifying cellular heterogeneity

This study has brought together image processing, clustering and spatial pattern analysis to quantitatively analyze hematoxylin and eosin-stained (HE) tissue sections. A mixture of tumor and normal cells (intratumoral heterogeneity) as well as complex tissue architectures of most samples complicate the interpretation of their cytological profiles. To address these challenges, we develop a simple but effective methodology for quantitative analysis for HE section. We adopt comparative analyses of spatial point patterns to characterize spatial distribution of different nuclei types and complement cellular characteristics analysis. We demonstrate that tumor and normal cell regions exhibit significant differences of lymphocytes spatial distribution or lymphocyte infiltration pattern.

Abstract A19: Lymphatic vessels as a biomarker in human melanoma

The aim of our work is to assess the local lymphatic vasculature within the tumor microenvironment as a possible biomarker for response to immunotherapy. Targeting the immune checkpoint molecules CTLA-4 and PD-1 is showing unprecedented clinical impact in human melanoma patients. However, a significant subset of patients does not respond to therapy. Therefore efforts have been made to identify biomarkers predictive of survival and response to immunotherapy, and evaluation of the immune microenvironment was shown to have high prognostic power. Characterization of tumor-infiltrating T cells with respect to density, phenotype and location has been demonstrated to predict survival and metastasis in retrospective studies and might be superior to classical TNM staging. Despite these promising results current strategies are not 100% predictive and additional cell populations within the tumor microenvironment are likely to affect the overall anti-tumor immune response. Interestingly, the density of tumor-associated lymphatic vessels was found to be decreased in patients with metastatic colorectal cancer compared to non-metastatic patients. In addition, our recent work using metastatic melanoma samples from the Broad Institute9s TCGA database demonstrated that expression of lymphatic genes (LYVE-1 and podoplanin) correlates with expression levels of immune cell markers. In mice lacking dermal lymphatic vessels immune infiltration into experimental melanoma was significantly decreased. At the same time these tumors displayed decreased immune suppression and improved tumor control by transferred cytotoxic T cells. These results suggest that lymphatic vessels are essential for establishment of an efficient anti-tumor immune response, but have a role in negatively regulating anti-tumor immunity during tumor progression. We therefore hypothesize that local lymphatic vessel density within the tumor microenvironment predicts immune infiltrate and response to immunotherapy. To simultaneously evaluate immune and vascular components in human formalin-fixed samples of melanoma we are using a multiplex-immunohistochemistry-based approach, which allows for examination of up to twelve markers by sequential staining of a single marker at a time. Tissue regions that include tumor/stroma borders and show high CD8 + T cell infiltrates are selected for analysis. This is followed by tissue segmentation based on the presence of a tumor marker and automated detection of cell populations within intra-tumoral regions and the invasive margin at the tumor border. Analysis using image cytometry allows us to quantify and correlate presence and location of multiple cell types within the tumor and the surrounding stroma. Our work will contribute to improved stratification of cancer patients with respect to possible response to immunotherapy. Citation Format: Julia Femel, Takahiro Tsujikawa, Guillaume Thibault, Jamie Booth, Amanda W. Lund. Lymphatic vessels as a biomarker in human melanoma. [abstract]. In: Proceedings of the AACR Special Conference on Tumor Immunology and Immunotherapy; 2016 Oct 20-23; Boston, MA. Philadelphia (PA): AACR; Cancer Immunol Res 2017;5(3 Suppl):Abstract nr A19.

Adaptive H-Extrema for Automatic Immunogold Particle Detection

Quantifying concentrations of target molecules near cellular structures, within cells or tissues, requires identifying the gold particles in immunogold labelled images. In this paper, we address the problem of automatically detect them accurately and reliably across multiple scales and in noisy conditions. For this purpose, we introduce a new contrast filter, based on an adaptive version of the H-extrema algorithm. The filtered images are simplified with a geodesic reconstruction to precisely segment the candidates. Once the images are segmented, we extract classical features and then classify using the majority vote of multiple classifiers. We characterize our algorithm on a pilot data and present results that demonstrate its effectiveness.