MelImmuneOrg · Deciphering the tumor-immune-microenvironment profile and organization within the tumors of melanoma patients undergoing immunotherapy

Deciphering the tumor-immune-microenvironment profile and organization within the tumors of melanoma patients undergoing immunotherapy

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.

Data: CORDIS, © European Union