Spatial organization and molecular correlation of tumor-infiltrating lymphocytes using deep learning on pathology images

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumor-infiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment

Analysis of an Engineered Salmonella Flagellar Fusion Protein, FliR-FlhB

Salmonella FliR and FlhB are membrane proteins necessary for flagellar export. In Clostridium a fliR-flhB fusion gene exists. We constructed a similar Salmonella fusion gene which is able to complement fliR, flhB, and fliR flhB null strains. Western blotting revealed that the FliR-FlhB fusion protein retains the FlhB protein's cleavage properties. We conclude that the FliR and FlhB proteins are physically associated in the wild-type Salmonella basal body, probably in a 1:1 ratio

18F-FAC PET Selectively Images Liver-Infiltrating CD4 and CD8 T Cells in a Mouse Model of Autoimmune Hepatitis.

Immune cell-mediated attack on the liver is a defining feature of autoimmune hepatitis and hepatic allograft rejection. Despite an assortment of diagnostic tools, invasive biopsies remain the only method for identifying immune cells in the liver. We evaluated whether PET imaging with radiotracers that quantify immune activation (18F-FDG and 18F-1-(2'-deoxy-2'-fluoro-arabinofuranosyl)cytosine [18F-FAC]) and hepatocyte biology (18F-2-deoxy-2-fluoroarabinose [18F-DFA]) can visualize and quantify liver-infiltrating immune cells and hepatocyte inflammation, respectively, in a preclinical model of autoimmune hepatitis. Methods: Mice treated with concanavalin A (ConA) to induce a model of autoimmune hepatitis or vehicle were imaged with 18F-FDG, 18F-FAC, and 18F-DFA PET. Immunohistochemistry, digital autoradiography, and ex vivo accumulation assays were used to localize areas of altered radiotracer accumulation in the liver. For comparison, mice treated with an adenovirus to induce a viral hepatitis were imaged with 18F-FDG, 18F-FAC, and 18F-DFA PET. 18F-FAC PET was performed on mice treated with ConA and vehicle or with ConA and dexamethasone. Biopsy samples of patients with autoimmune hepatitis were immunostained for deoxycytidine kinase. Results: Hepatic accumulation of 18F-FDG and 18F-FAC was 173% and 61% higher, respectively, and hepatic accumulation of 18F-DFA was 41% lower, in a mouse model of autoimmune hepatitis than in control mice. Increased hepatic 18F-FDG accumulation was localized to infiltrating leukocytes and inflamed sinusoidal endothelial cells, increased hepatic 18F-FAC accumulation was concentrated in infiltrating CD4 and CD8 cells, and decreased hepatic 18F-DFA accumulation was apparent in hepatocytes throughout the liver. In contrast, viral hepatitis increased hepatic 18F-FDG accumulation by 109% and decreased hepatic 18F-DFA accumulation by 20% but had no effect on hepatic 18F-FAC accumulation (nonsignificant 2% decrease). 18F-FAC PET provided a noninvasive biomarker of the efficacy of dexamethasone for treating the autoimmune hepatitis model. Infiltrating leukocytes in liver biopsy samples from patients with autoimmune hepatitis express high levels of deoxycytidine kinase, a rate-limiting enzyme in the accumulation of 18F-FAC. Conclusion: Our data suggest that PET can be used to noninvasively visualize activated leukocytes and inflamed hepatocytes in a mouse model of autoimmune hepatitis

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images.

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumor-infiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment

Superficial sarcomas with CIC rearrangement are aggressive neoplasms: A series of eight cases

CIC rearranged sarcomas have significant overlap with Ewing sarcoma, are aggressive, and typically present in deep soft tissue. They most commonly have a t(4;19)(q35;q13) with CIC‐DUX4 fusion. Superficial presentation is rare. We report eight (6F, 2M; median 45‐years‐old, range 14‐65) superficial CIC‐rearranged sarcomas, involving the extremities (n = 4), vulva (n = 2), and trunk (n = 2). The tumors were composed of nodules/sheets of round cells with necrosis and hemorrhage separated by dense hyaline bands. Tumor cells had vesicular chromatin, prominent nucleoli and frequent mitotic figures. One showed pagetoid spread. Targeted next‐generation sequencing was positive for CIC‐DUX4 fusion (6/6); fluorescence in situ hybridization (FISH) was positive for CIC rearrangement (2/3). Eight of eight had evidence of CIC‐DUX4 fusion/rearrangement by molecular techniques. Immunohistochemistry was positive for CD99+ (8/8) and DUX4+ (4/4). FISH for EWSR1 rearrangement was negative (5/5). Of five patients with at least 6 months follow‐up, three of five died of disease, all within 2 years of presentation. One is alive with disease at 48 months. One is disease free at 3 months. Superficial CIC‐rearranged sarcomas should be considered in cases exhibiting features reminiscent of Ewing sarcoma, but with increased pleomorphism and/or geographic necrosis. In contrast to superficial Ewing sarcomas, superficial CIC‐rearranged sarcomas are aggressive.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/155498/1/cup13656.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/155498/2/cup13656_am.pd

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumor-infiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment