Periodic Reporting for period 1 - MelImmuneOrg (Deciphering the tumor-immune-microenvironment profile and organization within the tumors of melanoma patients undergoing immunotherapy)
Okres sprawozdawczy: 2020-09-01 do 2022-08-31
The main cause of death in melanoma patients is widespread metastases. While it is known that the immune tumor microenvironment plays a vital role in tumor evolution and the metastatic process, there is limited understanding on how distinct tumor, immune and stroma cells interact as a system to collectively define progression and response to treatment, and how distinct anatomical sites contribute to this process. Draining lymph nodes (LNs), through their connection with the primary tumor, apparently play an important role in the anti-tumor response of the immune system. Conversely, they are the first immune organ that could be educated by the tumor, to perform protumorigenic functions. A better understanding of tumor-lymph node interactions may identify those factors critical to metastases development. Tumors are spatially organized ecosystems that are comprised of distinct cell types, each of which can assume a variety of phenotypes defined by coexpression of multiple proteins. To underscore this complexity, and move beyond single cells to multicellular interactions, it is essential to interrogate cellular expression patterns within their native context in the tissue. We have recently pioneered MIBI-TOF (Multiplexed Ion Beam Imaging by Time of Flight) (Keren et al., 2018, 2019), a novel platform that enables simultaneous imaging of forty proteins within intact tissue sections at subcellular resolution. Here, I used MIBI-TOF to chart immune composition, phenotype and organization in the draining LNs of melanoma patients, with and without sentinel lymph node metastases and with and without disease progression within 5 years of follow-up. I stained and acquired by MIBI 2-3 fields of view (FOVs) from each patient. Following image preprocessing I performed cell segmentation using established approaches. Next, I classified cells in sixteen images by conventional methods. We used these images as input to CellSighter, a new deep learning-based pipeline that I participated in developing to perform and expedite cell classification in multiplexed images (Amitay et al, bioRxiv 2022).We are now applying CellSighter on our full dataset. This will enable us to generate maps of the locations of the different cells. We will use different algorithms to identify common organizational patterns of cells and establish the immune networks that are at play across regions and patients. All these will enable us to evaluate how metastases affect the immune organization in the LNs and whether immune signatures in the sentinel LNs are prognostic of metastatic disease.
Aim 1: Developing a panel to study the tumor-immune microenvironment in the draining LNs of melanoma patients
The panel included antibodies to identify tumor cells, fibroblasts, vasculature, different types of immune cells and functional markers implicated in immune evasion and response to immunotherapy. I have performed validation and calibration experiments to examine sensitivity and specificity of the antibodies. Each antibody was first tested by immunohistochemistry. Only antibodies of high sensitivity and specificity were chosen. I conjugated the validated antibodies to metals that allow MIBI-TOF detection, and then tested and titrated these conjugates in MIBI-TOF. Altogether, I devised a final panel of 39 antibodies that were used to stain our cohort.
Aim 2: Selecting samples for the cohort and data acquisition
We have collected a cohort of 80 sentinel LNs of melanoma patients at the time of diagnosis. Half of the patients had metastases in the sentinel LNs and half did not. In addition, they were retrospectively selected such that in each group half of them developed distant metastases and half did not within five years of diagnosis. We have collected the samples and created a tissue microarray (TMA) from the patients to reduce batch effects between patients and the quantity of antibodies. I have stained our data set with the validated antibody panel and acquired MIBI images of all our samples. Altogether 200 0.8x0.8mm2 images were acquired, encompassing ±2,000,000 cells.
Aim 3: Image analysis.
All images require a preprocessing step, intended to remove bleed-through from other channels, noise and antibody aggregates. We have developed Mass based imaging Analysis User Interface (MAUI), a graphical user interface that enables to perform these steps of imaging preprocessing (Baranski, Milo et al. Plos Comput Biol, 2021). Most of the images acquired in this project have passed already these steps and are ready for downstream analysis.
Aim 4: Cell classification
To perform downstream analysis in which we determine the organization of the tumor-immune microenvironment we need to identify individual cells in the images in a process named segmentation. To perform segmentation on our images we have used Mesmer, an algorithm that has automated this task (Greenwald et al., 2022). Following cell segmentation, the expression of each protein is quantified in each cell and is summarized in a cell table. This table serves as input for cell classification, where the type and phenotype of each cell in the tissue is inferred using the combination of co-expressed proteins, in combination with prior biological knowledge. For example, a cell that expresses CD3 and CD8 is classified as a CD8+ T cell. Cell classification methods typically use manual gating or clustering of the cell table.Inferring cell classifications from multiplexed images has unique challenges. Imaging artifacts are main challenges, and they include background noise and antibody aggregates. Biological factors also contribute to the difficulty of cell classification. In tissues and specifically in LNs cells are densely packed and cells are situated next to each other. In addition, cells extend cellular projection. These factors lead to signal spillover, whereby protein signals from one cell overlap with neighboring cells. All these issues make the task of cell classification challenging and time consuming and it requires sequential rounds of clustering, gating, and manual annotation by an expert. We have performed cell classification on 16 images from our dataset and identified twenty-four cell types including different types of immune cells, stromal cells, endothelial cells and tumor cells. The cells expressed their corresponding proteins. We have overlayed the different cell types on the images creating cell maps. Examination of these images revealed large diversity between patients .To improve the cell classification process, we have developed CellSighter (Amitay et al, bioRxiv 2022), a deep learning-based pipeline to perform cell classification in multiplexed images. CellSighter reaches 80-100% accuracy for most cell types. CellSighter can be applied across datasets thus leading to data integration and standardization. In summary CellSighter reduces significantly the time needed for cell classification and improves accuracy and consistency across datasets.
The panel included antibodies to identify tumor cells, fibroblasts, vasculature, different types of immune cells and functional markers implicated in immune evasion and response to immunotherapy. I have performed validation and calibration experiments to examine sensitivity and specificity of the antibodies. Each antibody was first tested by immunohistochemistry. Only antibodies of high sensitivity and specificity were chosen. I conjugated the validated antibodies to metals that allow MIBI-TOF detection, and then tested and titrated these conjugates in MIBI-TOF. Altogether, I devised a final panel of 39 antibodies that were used to stain our cohort.
Aim 2: Selecting samples for the cohort and data acquisition
We have collected a cohort of 80 sentinel LNs of melanoma patients at the time of diagnosis. Half of the patients had metastases in the sentinel LNs and half did not. In addition, they were retrospectively selected such that in each group half of them developed distant metastases and half did not within five years of diagnosis. We have collected the samples and created a tissue microarray (TMA) from the patients to reduce batch effects between patients and the quantity of antibodies. I have stained our data set with the validated antibody panel and acquired MIBI images of all our samples. Altogether 200 0.8x0.8mm2 images were acquired, encompassing ±2,000,000 cells.
Aim 3: Image analysis.
All images require a preprocessing step, intended to remove bleed-through from other channels, noise and antibody aggregates. We have developed Mass based imaging Analysis User Interface (MAUI), a graphical user interface that enables to perform these steps of imaging preprocessing (Baranski, Milo et al. Plos Comput Biol, 2021). Most of the images acquired in this project have passed already these steps and are ready for downstream analysis.
Aim 4: Cell classification
To perform downstream analysis in which we determine the organization of the tumor-immune microenvironment we need to identify individual cells in the images in a process named segmentation. To perform segmentation on our images we have used Mesmer, an algorithm that has automated this task (Greenwald et al., 2022). Following cell segmentation, the expression of each protein is quantified in each cell and is summarized in a cell table. This table serves as input for cell classification, where the type and phenotype of each cell in the tissue is inferred using the combination of co-expressed proteins, in combination with prior biological knowledge. For example, a cell that expresses CD3 and CD8 is classified as a CD8+ T cell. Cell classification methods typically use manual gating or clustering of the cell table.Inferring cell classifications from multiplexed images has unique challenges. Imaging artifacts are main challenges, and they include background noise and antibody aggregates. Biological factors also contribute to the difficulty of cell classification. In tissues and specifically in LNs cells are densely packed and cells are situated next to each other. In addition, cells extend cellular projection. These factors lead to signal spillover, whereby protein signals from one cell overlap with neighboring cells. All these issues make the task of cell classification challenging and time consuming and it requires sequential rounds of clustering, gating, and manual annotation by an expert. We have performed cell classification on 16 images from our dataset and identified twenty-four cell types including different types of immune cells, stromal cells, endothelial cells and tumor cells. The cells expressed their corresponding proteins. We have overlayed the different cell types on the images creating cell maps. Examination of these images revealed large diversity between patients .To improve the cell classification process, we have developed CellSighter (Amitay et al, bioRxiv 2022), a deep learning-based pipeline to perform cell classification in multiplexed images. CellSighter reaches 80-100% accuracy for most cell types. CellSighter can be applied across datasets thus leading to data integration and standardization. In summary CellSighter reduces significantly the time needed for cell classification and improves accuracy and consistency across datasets.
My work during my MSCA fellowship will have profound impact on understanding tumor-immune interactions in melanoma, which may lead in the future to better care for patients. More immediately, the tools that I have developed, including an antibody panel to profile the immune microenvironment in melanoma, as well as computational tools for pre-processing of multiplexed images and cell classification in multiplexed images will greatly enhance the rapidly-growing field of multiplexed imaging.