Search bioRxiv⌕ Search

Biology subjects

Semenov, Y. R.

Publications and source records attributed to Semenov, Y. R..

2 recordsLinked to original sources

Multiomic profiling of a unique in-transit melanoma cohort identifies melanoma differentiation as predictor of tumor progression and therapy response

Melanoma patients with in-transit metastasis (ITM), a stage of disease where melanoma has metastasized to sites in between the primary lesion and draining lymph node, vary significantly in their clinical outcomes, but the biology driving differential outcomes in ITM is poorly understood. To elucidate the mechanisms of differential outcomes, we utilized multimodal molecular profiling (WES, RNA-seq, highly multiplexed immunofluorescence, spatial transcriptomics) in 1) evolutionary analysis of longitudinal tumor samples and 2) identifying prognostic tumor intrinsic and microenvironmental features in a unique cohort of patients with unresectable ITM. Among other findings, we observed a persistent dedifferentiated AXL/NGFR clonal lineage pre-existing and following immune checkpoint blockade in in-transit and distant metastases. Concordantly, we found that low pigmentation and high T cell exhaustion signatures were independently associated with distant progression. Our findings highlight tumor cell state and immune dysfunction as key predictors and potential biomarkers of metastatic risk in ITM. STATEMENT OF SIGNIFICANCEWhat drives distant progression in melanoma is unclear. Analyzing tumor and immune features in a rare in-transit melanoma patient cohort, we identify biological signals highlighting how immune and tumor states observable in pre-distant metastasis melanomas shape long-term outcomes, and nominate potential prognostic biomarkers.

cancer biology↗

SpatialCells: Automated Profiling of Tumor Microenvironments with Spatially Resolved Multiplexed Single-Cell Data

BackgroundCancer is a complex cellular ecosystem where malignant cells coexist and interact with immune, stromal, and other cells within the tumor microenvironment. Recent technological advancements in spatially resolved multiplexed imaging at single-cell resolution have led to the generation of large-scale and high-dimensional datasets from biological specimens. This underscores the necessity for automated methodologies that can effectively characterize the molecular, cellular, and spatial properties of tumor microenvironments for various malignancies. ResultsThis study introduces SpatialCells, an open-source software package designed for region-based exploratory analysis and comprehensive characterization of tumor microenvironments using multiplexed single-cell data. ConclusionsSpatialCells efficiently streamlines the automated extraction of features from multiplexed single-cell data and can process samples containing millions of cells. Thus, SpatialCells facilitates subsequent association analyses and machine learning predictions, making it an essential tool in advancing our understanding of tumor growth, invasion, and metastasis. Availability of code and materialshttps://github.com/SemenovLab/SpatialCells.

bioinformatics↗