29 research outputs found

    Synchronous recurrence of concurrent colon adenocarcinoma and dedifferentiated liposarcoma.

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    A 62-year-old man presented with concurrent sigmoid colon adenocarcinoma and small bowel mesenteric dedifferentiated liposarcoma. Following surgical resection of the colon cancer, complete excision of the mesenteric sarcoma and adjuvant folinic acid, fluorouracil and oxaliplatin (FOLFOX) chemotherapy, the patient demonstrated no radiological evidence of disease for more than 2 years. The patient then developed synchronous recurrence of both cancers: the colon cancer metastasised to the liver and a pelvic lymph node, and the liposarcoma recurred in the original location. The patient underwent additional chemotherapy with complete response of the metastatic colon cancer and stable disease for the liposarcoma. The recurrent mesenteric tumour was subsequently resected. Although concurrent cancers have been reported, this unique case of synchronous recurrence raises interesting hypotheses regarding host-tumour interaction and immune surveillance

    FIGURE 4 from Peritumoral Immune-suppressive Mechanisms Impede Intratumoral Lymphocyte Infiltration into Colorectal Cancer Liver versus Lung Metastases

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    Higher antigen presentation potential in lung metastases. A, Correlations of the immune cell density between histologic regions. PCC were calculated from the density of each immune cell type in different histologic regions. B, Representative images of primary and metastatic colorectal cancer tumors stained with mIF panel 4. The cancer cells, TCF1+ CD4 T cells, stem-like CD8 T cells and APCs were color coded and identified as Fig. 1C. The yellow dash lines indicate the boundaries between inner and outer invasive margin. C, Mean number of TCF1+ CD4 T cells and stem-like CD8 T cells around 10 µm radius of APCs and mean number of APCs around 10 µm radius of TCF1+ CD4 T cells and stem-like CD8 T cells. D, Paired comparison of cell density between each two organs in the different histologic regions. Statistical significance was determined by Wilcoxon signed-rank test. E, GSEA of liver and lung metastases samples from GES48468. Kyoto Encyclopedia of Genes and Genomes (KEGG) databases were used to determine significantly modified pathways. Bars in red and blue represent, respectively, a positive and negative enrichment in the associated pathway. The x axis shows the normalized enrichment score (NES) of the analysis, and the y axis the enriched pathways. F, Heat map of top genes differentially expressed in GES48468 primary and metastatic colorectal cancer tumor samples from KEGG_ANITGENE_PROCESSING_AND_PRESENTATION pathway.</p

    FIGURE 6 from Peritumoral Immune-suppressive Mechanisms Impede Intratumoral Lymphocyte Infiltration into Colorectal Cancer Liver versus Lung Metastases

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    Analysis of immune cells between paired primary and liver metastases. A, Representative images of the tumor core from two individual patient's paired primary colorectal cancer and metastatic liver tumors. B, Correlations of CD4 and CD8 T cells in the paired primary and liver metastases. R2 were calculated with the density of immune cells in different histologic regions.</p

    FIGURE 1 from Peritumoral Immune-suppressive Mechanisms Impede Intratumoral Lymphocyte Infiltration into Colorectal Cancer Liver versus Lung Metastases

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    Metastatic colorectal cancer tumor specimens and cell population evaluated by mIF. A, Kaplan–Meier curve of overall survival in patients with colorectal cancer with metastases in liver (n = 17), lung (n = 12), and peritoneum (n = 8). Numbers below each x axis indicate the number of patients at risk and those in parentheses are the number of events. Statistical significance was determined by log-rank test (B). Representative images showing four panels of mIF staining from consecutive sections of a colorectal cancer primary tumor. C, Cell lineages identified by hierarchical gating of lineage and functional biomarkers during image analysis of mIF staining. D, Representative images of a colorectal cancer lung metastasis showing manual segmentation of five compartments (tumor core, inner invasive margin, outer invasive margin, juxta tumor, and distal tissue) and tissue segmentation by machine learning algorithm (cancer island, stroma, and necrosis).</p
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