Transcriptome analysis by RNAseq of leukemia model promoted by MLL-Af4 or MLL-AF9 fusion proteins. We find each fusion protein promotes a specific gene signature correlating to those identified in patients Overall design: Human CD34+ hematopoietic stem and progenitor cells were transduced with retrovirus expressing MLL-Af4 or MLL-AF9. Transduced cells were transplanted into immunodeficient mice to induce lymphoid leukemia or placed in myeloid in vitro culture. CD19+ lymphoid leukemia cells (3 AF9, 6 Af4), control health CD19+CD34+ proB cells (n=3) and 4 pairs of Af4 and AF9 CD33+CD19- myeloid culture cells were collected for RNA-seq
Instructive Role of MLL-Fusion Proteins Revealed by a Model of t(4;11) Pro-B Acute Lymphoblastic Leukemia.
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To identify molecular subtypes of medulloblastoma we have profiled a series of 62 medulloblastoma tumors. Unsupervised hierarchical cluster analysis of these data identified 5 distinct molecular subtypes.
Integrated genomics identifies five medulloblastoma subtypes with distinct genetic profiles, pathway signatures and clinicopathological features.
Purpose: Eliciting effective anti-tumor immune responses in patients who fail checkpoint inhibitor therapy is a critical challenge in cancer immunotherapy, and in such patients, tumor-associated myeloid cells and macrophages (TAMs) are promising therapeutic targets. We demonstrate in an autochthonous, poorly immunogenic mouse model of melanoma that combination therapy with an agonistic anti-CD40 mAb and CSF1R inhibitor potently suppressed tumor growth. Microwell assays to measure multiplex protein secretion by single cells identified that untreated tumors have distinct TAM subpopulations secreting MMP9 or co-secreting CCL17/22, characteristic of an M2-like state. Combination therapy reduced the frequency of these subsets, while simultaneously inducing a separate polyfunctional inflammatory TAM subset co-secreting TNF?, IL-6, and IL-12. Tumor suppression by this combined therapy was partially dependent on T cells, TNF? and IFN?. Together, this study demonstrates the potential for targeting TAMs to convert a “cold” into an “inflamed” tumor microenvironment capable of eliciting protective T cell responses. Methods: Total RNA was purified with the use of QIAzol and RNeasy Mini kit (QIAGEN), in which an on-column DNase treatment was included. Purified RNA was submitted to the Yale Center for Genomic Analysis where it was subjected to mRNA isolation and library preparation. Non-strand specific libraries were generated from 50ng total RNA using the SMARTer Ultra Low Input RNA for Illumina Sequencing kit. Libraries were pooled, six samples per lane, and sequenced on an Illumina HiSeq 2500 (75-bp paired end reads), and aligned using STAR to the GRCm38 (mm10) reference genome. A count-based differential expression protocol was adapted for this analysis(Anders et al., 2013); mappable data were counted using HTSeq, and imported into R for differential expression analysis using the DESeq2.To find differentially regulated sets of genes for signature generation, a 1.5-Log2 fold-change difference between samples and p-adjusted (Holm-Sidak) = 0.01 was used. Results: To begin to understand how these treatments modulated T cells to control tumor growth, and to possibly illuminate additional biomarkers of response, we examined the transcriptomes of CD11b+ Ly6G- cells treated with CD40 or CSF1Ri, alone or in combination, relative to control, using high throughput RNA-sequencing. Principal components analysis (PCA) on the genome-wide dataset demonstrated that treating with CD40 and CSF1Ri individually caused largely non-overlapping changes in transcription, as indicated by their movement along orthogonal principal components (PC) relative to the control. Importantly, combination therapy was visualized as a systems-level combination of each individual treatment in PC space. We then examined the mRNAs most altered by either treatment alone or in combination relative to Controls (Log2FC>1.5, p<.01) by unsupervised hierarchical clustering. Five major gene patterns emerged from the clustering of genes. Cluster #1 comprises genes that are upregulated by CD40 and CSF1Ri+CD40 treatment but are mostly unaffected by CSF1Ri, suggesting that CD40 is the primary driver of this cluster in the combination treatment. Notable genes in this cluster include Tnfa, Ifng??Il12b and Cxcl9; interestingly, for Tnfa and Il12b, CSF1Ri+CD40 appears to have a synergistic effect on expression. In contrast to Cluster #1, Cluster #5 contains genes substantially downregulated by CSF1Ri and CSF1Ri+CD40 treatments, but are largely unaffected by CD40, suggesting that CSF1Ri is the driver of this cluster in the combination treatment. Cluster #5 genes include Cd36 and Fabp4, suggesting alterations in lipid homeostasis in the TAMs after treatment. Cluster #2 includes genes that are modestly upregulated by CD40 and CSF1Ri individually, leading to a stronger upregulation when combined. Finally, Clusters #3 and #4 include, for the most part, genes that are differentially affected by CD40 versus CSF1Ri and for which the combination treatment yields an intermediate response. In summary, these data show that CSF1Ri and CD40 agonism elicit predominantly distinct changes in gene expression in the CD11b+ cells, indicating they target different biological processes in myeloid cells. The net result of the changes in myeloid gene expression from the combination of CSF1Ri+CD40 treatment reveal additive effects by the individual treatments, but also synergy in the expression of several pro-inflammatory genes (e.g., Tnfa, Ifng, Il6 and Il12b). We further examined our dataset with Gene Set Enrichment Analysis (GSEA). Although CSF1Ri and CD40 treatments did not closely match any immunological signatures in the immunological database of MSigDb, combined CSF1Ri+CD40 had a strikingly similar signature to myeloid cells exposed to a variety of inflammatory stimulants, most closely reflected by BMDMs treated with lipopolysaccharide (LPS). This motivated us to look specifically at categories of NF-?B target genes that are significantly affected by LPS treatment, including transcription factors, cytokines and chemokines. Indeed, most of these NF-?B target genes associated with inflammation were strongly upregulated by CSF1Ri+CD40 treatment. Finally, Ingenuity Pathway Analysis identified TNFR1 and TNFR2 signaling and Acute phase response signaling among the top genetic signatures produced by the CSF1Ri+CD40 treatment combination, matching what we observed with GSEA. Thus, gene expression analysis not only revealed several biomarkers of response that may be relevant for assessing therapeutic activity in ongoing clinical trials using these drugs, but illuminated lead biological factors that may cause tumor regression. Conclusions: myeloid-targeted immunotherapies anti-CD40+CSF1R inhibition synergistically induce a pro-inflammatory microenviroment Overall design: mRNA profiles of tumor infiltrating lymphocytes (TILs) in mice were generated by deep sequencing, in triplicate, using Illumina.
Myeloid-targeted immunotherapies act in synergy to induce inflammation and antitumor immunity.
Specimen part, Cell line, SubjectView Samples
Analysis of mouse primary natural killer (NK) cells and NK cells treated with DKK2 for 24 hours.Sequencing of the mRNAs from DKK2-treated primary NK cells in comparison of those from mock-treated cells suggest an alteration in STAT signaling. Overall design: Mouse primary NK cells were isolated from the spleens and cultured in the presence of 50 ng/ml recombinant murine IL-15 for 24 hours. And then NK cells were treated with mock or 200ng/ml DKK2 for another 24 hours before mRNA was isolated and purified by using RNeasy Plus Mini Kit (Qiagen). A total of two groups of Control NK cells and two groups of DKK2-treated NK cells were individually micromanipulated.
DKK2 imparts tumor immunity evasion through β-catenin-independent suppression of cytotoxic immune-cell activation.
Specimen part, Cell line, Treatment, SubjectView Samples
Expression profiling was performed using uncultured melanocytes and melanoma cell from various mouse models of BrafV600E induced melanocytic proliferation
mTORC1 activation blocks BrafV600E-induced growth arrest but is insufficient for melanoma formation.
Specimen partView Samples
We applied Next-Generation Sequencing (NGS) to miRNAs from blood samples of 48 AD (Alzheimer''s Disease) patients and 22 unaffected controls, yielding a total of 140 unique mature miRNAs with significantly changed expression level. Of these, 82 were higher and 58 lower abundant in samples from AD patients. We selected a panel of 12 miRNAs for a qRT-PCR analysis on a larger cohort of 202 samples including not only AD patients and healthy controls but also patients with other CNS illnesses: Multiple Sclerosis, Parkinson''s Disease, Major Depression, Bipolar Disorder, Schizophrenia, and Mild Cognitive Impairment, which is assumed to represent a transitional period before the development of AD. MiRNA target enrichment analysis of the selected 12 miRNAs indicated an involvement of miRNAs in nervous system development, neuron projection, neuron projection development, and neuron projection morphogenesis, respectively. Using this 12-miRNA signature we were able to differentiate between AD and controls with an accuracy of 93.3%, a specificity of 95.1%, and a sensitivity of 91.5%. The differentiation of AD from other neurological diseases was possible with accuracies between 73.8% and 77.8%. The differentiation of the other CNS disorders from controls yielded even higher accuracies. Overall design: Examination of the miRNA profile in blood samples of 48 AD patients and 22 controls
A blood based 12-miRNA signature of Alzheimer disease patients.
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Red meat consumption is associated with an increased colon cancer risk. Heme, present in red meat, injures the colon surface epithelium by luminal cytotoxicity and reactive oxygen species. This surface injury is overcompensated by hyperproliferation and hyperplasia of crypt cells. Transcriptome analysis of mucosa of heme-fed mice showed, besides stress- and proliferation-related genes, many upregulated lipid metabolism-related PPAR target genes. The aim of this study was to investigate the role of PPAR in heme-induced hyperproliferation and hyperplasia. Male PPAR KO and WT mice received a purified diet with or without heme. As PPAR is proposed to protect against oxidative stress and lipid peroxidation, we hypothesized that the absence of PPAR leads to more surface injury and crypt hyperproliferation in the colon upon heme-feeding. Heme induced luminal cytotoxicity and lipid peroxidation and colonic hyperproliferation and hyperplasia to the same extent in WT and KO mice. Transcriptome analysis of colonic mucosa confirmed similar heme-induced hyperproliferation in WT and KO mice. Stainings for alkaline phosphatase activity and expression levels of Vanin-1 and Nrf2-targets indicated a compromised antioxidant defense in heme-fed KO mice. Our results suggest that the protective role of PPAR in antioxidant defense involves the Nrf2-inhibitor Fosl1, which is upregulated by heme in PPAR KO mice. We conclude that PPAR plays a protective role in colon against oxidative stress, but PPAR does not mediate heme-induced hyperproliferation. This implies that oxidative stress of surface cells is not the main determinant of heme-induced hyperproliferation and hyperplasia.
Dietary heme-mediated PPARα activation does not affect the heme-induced epithelial hyperproliferation and hyperplasia in mouse colon.
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Differential gene expression analysis were performed between Pitx1 silenced SCC cells and controls in two independent SCC lines Overall design: Compared control and Pitx1 deficient cells to define gene sets control by Pitx1 in SCCs.
De Novo PITX1 Expression Controls Bi-Stable Transcriptional Circuits to Govern Self-Renewal and Differentiation in Squamous Cell Carcinoma.
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