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Felt, K.

Publications and source records attributed to Felt, K..

3 recordsLinked to original sources

Pan-cancer spatial characterization of key immune biomarkers in the tumor microenvironment

Deciphering the composition and spatial organization of the tumor immune microenvironment (TiME) is key to uncovering the mechanisms driving cancer progression and treatment response. Spatial biology techniques like multiplex immunofluorescence (mIF) offer detailed insights into the TiME but are often limited to retrospective research studies of individual cancer types. Conversely, bulk omics techniques have been studied in pan-cancer settings but fail to capture single-cell spatial information. Here, we provide a pan-cancer spatial characterization of key biomarkers (CD8, FOXP3, PD-1, PD-L1) of the TiME using data from a mIF assay performed prospectively in a clinical setting on 2,019 tumors across 14 major cancer types. By integrating interpretable compositional and spatial metrics, we identified patterns of TiME variation that are conserved across cancer types and stages. We assess associations between these TiME spatial factors and tumor, genomic, and clinical features, where the results both extend prior findings and uncover new links. Altogether, our findings offer pan-cancer insights of the TiME to further the fields of spatial biology and cancer immunology.

cancer biology↗

Systematic benchmarking of imaging spatial transcriptomics platforms in FFPE tissues

Emerging imaging spatial transcriptomics (iST) platforms and coupled analytical methods can recover cell-to-cell interactions, groups of spatially covarying genes, and gene signatures associated with pathological features, and are thus particularly well-suited for applications in formalin fixed paraffin embedded (FFPE) tissues. Here, we benchmarked the performance of three commercial iST platforms on serial sections from tissue microarrays (TMAs) containing 23 tumor and normal tissue types for both relative technical and biological performance. On matched genes, we found that 10x Xenium shows higher transcript counts per gene without sacrificing specificity, but that all three platforms concord to orthogonal RNA-seq datasets and can perform spatially resolved cell typing, albeit with different false discovery rates, cell segmentation error frequencies, and with varying degrees of sub-clustering for downstream biological analyses. Taken together, our analyses provide a comprehensive benchmark to guide the choice of iST method as researchers design studies with precious samples in this rapidly evolving field.

cancer biology↗

Spatially aware deep learning reveals tumor heterogeneity patterns that encode distinct kidney cancer states

Background Abstract Background Results Discussion Methods Data Availability Code Availability Author Contributions Competing Interests Extended Data References Renal cell carcinoma (RCC) is among the 10 most common cancers worldwide and is comprised of several histological subtypes1. The clear cell histological subtype (ccRCC) is the most common form of RCC and accounts for the vast majority (75-80%) of metastatic cases1. In addition to highly recurrent mutations in hypoxia (VHL) and chromatin regulator genes (e.g. PBRM1, BAP1, SETD2), ccRCC exhibits extensive genomic intratumoral heterogeneity (ITH)2, which wa ...

cancer biology↗