Quantitative spatial evaluation of tumor-immune interactions in the immunotherapy setting of metastatic melanoma lymph nodes

Mapping the single cell spatial immune landscapes of the melanoma microenvironment

Melanoma is a highly immunogenic malignancy with an elevated mutational burden, diffuse lymphocytic infiltration, and one of the highest response rates to immune checkpoint inhibitors (ICIs). However, over half of all late-stage patients treated with ICIs will either not respond or develop progressive disease. Spatial imaging technologies are being increasingly used to study the melanoma tumor microenvironment (TME). The goal of such studies is to understand the complex interplay between the stroma, melanoma cells, and immune cell-types as well as their association with treatment response. Investigators seeking a better understanding of the role of cell location within the TME and the importance of spatial expression of biomarkers are increasingly turning to highly multiplexed imaging approaches to more accurately measure immune infiltration as well as to quantify receptor-ligand interactions (such as PD-1 and PD-L1) and cell-cell contacts. CyTOF-IMC (Cytometry by Time of Flight - Imaging Mass Cytometry) has enabled high-dimensional profiling of melanomas, allowing researchers to identify complex cellular subpopulations and immune cell interactions with unprecedented resolution. Other spatial imaging technologies, such as multiplexed immunofluorescence and spatial transcriptomics, have revealed distinct patterns of immune cell infiltration, highlighting the importance of spatial relationships, and their impact in modulating immunotherapy responses. Overall, spatial imaging technologies are just beginning to transform our understanding of melanoma biology, providing new avenues for biomarker discovery and therapeutic development. These technologies hold great promise for advancing personalized medicine to improve patient outcomes in melanoma and other solid malignancies.

Melanoma-derived mediators can foster the premetastatic niche: crossroad to lymphatic metastasis

The natural history of advanced malignant melanoma demonstrates that, in most cases, widespread tumor dissemination is preceded by regional metastases involving tumor-draining lymph nodes [sentinel lymph nodes (SLNs)]. Under physiological conditions, LNs play a central role in immunosurveillance to non-self-antigens to which they are exposed via afferent lymph. The dysfunctional immunity in SLNs is mediated by tumor secretory factors that allow the survival of metastatic melanoma cells within the LN by creating a premetastatic niche (PMN). Recent studies outline the altered microenvironment of LNs shaped by melanoma mediators. Here, we discuss tumor secretory factors involved in subverting tumor immunity and remodeling LNs and highlight emerging therapeutic strategies to reinvigorate antitumoral immunity in SLNs.

Multiplex Immunofluorescence Image Quality Checking Using DAPI Channel-referenced Evaluation

Multiplex immunofluorescence (MxIF) images provide detailed information of cell composition and spatial context for biomedical research. However, compromised data quality could lead to research biases. Comprehensive image quality checking (QC) is essential for reliable downstream analysis. As a reliable and specific staining of cell nuclei, 4',6-diamidino-2-phenylindole (DAPI) signals were used as references for tissue localization and auto-focusing across MxIF staining-scanning-bleaching iterations and could potentially be reused for QC. To confirm the feasibility of using DAPI as QC reference, pixel-level DAPI values were extracted to calculate signal fluctuations and tissue content similarities in staining-scanning-bleaching iterations for identifying quality issues. Concordance between automatic quantification and human experts' annotations were evaluated on a data set consisting of 348 fields of view (FOVs) with 45 immune and tumor cell markers. Cell distribution differences between subsets of QC-pass vs QC-failed FOVs were compared to investigate the downstream effects. Results showed that 87.3% FOVs with tissue damage and 73.4% of artifacts were identified. QC-failed FOVs showed elevated regional gathering in cellular feature space compared with the QC-pass FOVs. Our results supported that DAPI signals could be used as references for MxIF image QC, and low-quality FOVs identified by our method must be cautiously considered for downstream analyses.