Suppression of dendritic cell maturation and T cell proliferation by synovial fluid myeloid cells from mice with autoimmune arthritis

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Dissecting Response to Cancer Immunotherapy by Applying Bayesian Network Analysis to Flow Cytometry Data

Cancer immunotherapy, specifically immune checkpoint blockade, has been found to be effective in the treatment of metastatic cancers. However, only a subset of patients achieve clinical responses. Elucidating pretreatment biomarkers predictive of sustained clinical response is a major research priority. Another research priority is evaluating changes in the immune system before and after treatment in responders vs. nonresponders. Our group has been studying immune networks as an accurate reflection of the global immune state. Flow cytometry (FACS, fluorescence-activated cell sorting) data characterizing immune cell panels in peripheral blood mononuclear cells (PBMC) from gastroesophageal adenocarcinoma (GEA) patients were used to analyze changes in immune networks in this setting. Here, we describe a novel computational pipeline to perform secondary analyses of FACS data using systems biology/machine learning techniques and concepts. The pipeline is centered around comparative Bayesian network analyses of immune networks and is capable of detecting strong signals that conventional methods (such as FlowJo manual gating) might miss. Future studies are planned to validate and follow up the immune biomarkers (and combinations/interactions thereof) associated with clinical responses identified with this computational pipeline

Dissecting Response to Cancer Immunotherapy by Applying Bayesian Network Analysis to Flow Cytometry Data

Cancer immunotherapy, specifically immune checkpoint blockade, has been found to be effective in the treatment of metastatic cancers. However, only a subset of patients achieve clinical responses. Elucidating pretreatment biomarkers predictive of sustained clinical response is a major research priority. Another research priority is evaluating changes in the immune system before and after treatment in responders vs. nonresponders. Our group has been studying immune networks as an accurate reflection of the global immune state. Flow cytometry (FACS, fluorescence-activated cell sorting) data characterizing immune cell panels in peripheral blood mononuclear cells (PBMC) from gastroesophageal adenocarcinoma (GEA) patients were used to analyze changes in immune networks in this setting. Here, we describe a novel computational pipeline to perform secondary analyses of FACS data using systems biology/machine learning techniques and concepts. The pipeline is centered around comparative Bayesian network analyses of immune networks and is capable of detecting strong signals that conventional methods (such as FlowJo manual gating) might miss. Future studies are planned to validate and follow up the immune biomarkers (and combinations/interactions thereof) associated with clinical responses identified with this computational pipeline

Synchronous recurrence of concurrent colon adenocarcinoma and dedifferentiated liposarcoma.

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

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

Graphic Summary</p

Supplementary Figure 12 from Peritumoral Immune-suppressive Mechanisms Impede Intratumoral Lymphocyte Infiltration into Colorectal Cancer Liver versus Lung Metastases

Clustered heatmap of relative expression of proteins (GeoMX DSP) per ROI from CRCmetastatic tumor specimens (5 tumors/metastatic site).</p

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

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

Supplementary Figure 11 from Peritumoral Immune-suppressive Mechanisms Impede Intratumoral Lymphocyte Infiltration into Colorectal Cancer Liver versus Lung Metastases

Analysis of immune cells between paired primary and liver metastases.</p