Manu Setty

CSF-1R inhibition alters macrophage polarization and blocks glioma progression

Glioblastoma multiforme (GBM) comprises several molecular subtypes, including proneural GBM. Most therapeutic approaches targeting glioma cells have failed. An alternative strategy is to target cells in the glioma microenvironment, such as tumor-associated macrophages and microglia (TAMs). Macrophages depend on colony stimulating factor-1 (CSF-1) for differentiation and survival. We used an inhibitor of the CSF-1 receptor (CSF-1R) to target TAMs in a mouse proneural GBM model, which significantly increased survival and regressed established tumors. CSF-1R blockade additionally slowed intracranial growth of patient-derived glioma xenografts. Surprisingly, TAMs were not depleted in treated mice. Instead, glioma-secreted factors, including granulocyte-macrophage CSF (GM-CSF) and interferon-γ (IFN-γ), facilitated TAM survival in the context of CSF-1R inhibition. Expression of alternatively activated M2 markers decreased in surviving TAMs, which is consistent with impaired tumor-promoting functions. These gene signatures were associated with enhanced survival in patients with proneural GBM. Our results identify TAMs as a promising therapeutic target for proneural gliomas and establish the translational potential of CSF-1R inhibition for GBM.

A cooperative microRNA-tumor suppressor gene network in acute T-cell lymphoblastic leukemia (T-ALL)

The importance of individual microRNAs (miRNAs) has been established in specific cancers. However, a comprehensive analysis of the contribution of miRNAs to the pathogenesis of any specific cancer is lacking. Here we show that in T-cell acute lymphoblastic leukemia (T-ALL), a small set of miRNAs is responsible for the cooperative suppression of several tumor suppressor genes. Cross-comparison of miRNA expression profiles in human T-ALL with the results of an unbiased miRNA library screen allowed us to identify five miRNAs (miR-19b, miR-20a, miR-26a, miR-92 and miR-223) that are capable of promoting T-ALL development in a mouse model and which account for the majority of miRNA expression in human T-ALL. Moreover, these miRNAs produce overlapping and cooperative effects on tumor suppressor genes implicated in the pathogenesis of T-ALL, including IKAROS (also known as IKZF1), PTEN, BIM, PHF6, NF1 and FBXW7. Thus, a comprehensive and unbiased analysis of miRNA action in T-ALL reveals a striking pattern of miRNA-tumor suppressor gene interactions in this cancer.

BCL6 positively regulates AID and germinal center gene expression via repression of miR-155

The transcriptional repressor BCL6 reduces miRNA levels in germinal center B cells to increase AID expression.

Inferring transcriptional and microRNA-mediated regulatory programs in glioblastoma

Large‐scale cancer genomics projects are profiling hundreds of tumors at multiple molecular layers, including copy number, mRNA and miRNA expression, but the mechanistic relationships between these layers are often excluded from computational models. We developed a supervised learning framework for integrating molecular profiles with regulatory sequence information to reveal regulatory programs in cancer, including miRNA‐mediated regulation. We applied our approach to 320 glioblastoma profiles and identified key miRNAs and transcription factors as common or subtype‐specific drivers of expression changes. We confirmed that predicted gene expression signatures for proneural subtype regulators were consistent with in vivo expression changes in a PDGF‐driven mouse model. We tested two predicted proneural drivers, miR‐124 and miR‐132, both underexpressed in proneural tumors, by overexpression in neurospheres and observed a partial reversal of corresponding tumor expression changes. Computationally dissecting the role of miRNAs in cancer may ultimately lead to small RNA therapeutics tailored to subtype or individual.

MicroRNA 28 controls cell proliferation and is down-regulated in B-cell lymphomas.

Significance The majority of non-Hodgkin B-cell lymphomas arise from the malignant transformation of germinal center B cells. The molecular pathogenesis of these malignancies is not fully understood. Although a number of oncogenes and tumor suppressors have been identified among protein-coding genes, the role of microRNAs during lymphomagenesis remains largely unexplored. Our results identify a role for microRNA 28 (miR-28) in normal and malignant germinal center B cells. These data provide new insights on the microRNA-mediated posttranscriptional regulation occurring in normal germinal center B cells as well as during lymphomagenesis. In addition, the identification of a cross talk between miR-28 and v-myc avian myelocytomatosis viral oncogene homolog extends the relevance of our observations to a wide variety of malignancies. Burkitt lymphoma (BL) is a highly aggressive B-cell non-Hodgkin lymphoma (B-NHL), which originates from germinal center (GC) B cells and harbors translocations deregulating v-myc avian myelocytomatosis viral oncogene homolog (MYC). A comparative analysis of microRNAs expressed in normal and malignant GC B cells identified microRNA 28 (miR-28) as significantly down-regulated in BL, as well as in other GC-derived B-NHL. We show that reexpression of miR-28 impairs cell proliferation and clonogenic properties of BL cells by modulating several targets including MAD2 mitotic arrest deficient-like 1, MAD2L1, a component of the spindle checkpoint whose down-regulation is essential in mediating miR-28–induced proliferation arrest, and BCL2-associated athanogene, BAG1, an activator of the ERK pathway. We identify the oncogene MYC as a negative regulator of miR-28 expression, suggesting that its deregulation by chromosomal translocation in BL leads to miR-28 suppression. In addition, we show that miR-28 can inhibit MYC-induced transformation by directly targeting genes up-regulated by MYC. Overall, our data suggest that miR-28 acts as a tumor suppressor in BL and that its repression by MYC contributes to B-cell lymphomagenesis.

SeqGL Identifies Context-Dependent Binding Signals in Genome-Wide Regulatory Element Maps

Genome-wide maps of transcription factor (TF) occupancy and regions of open chromatin implicitly contain DNA sequence signals for multiple factors. We present SeqGL, a novel de novo motif discovery algorithm to identify multiple TF sequence signals from ChIP-, DNase-, and ATAC-seq profiles. SeqGL trains a discriminative model using a k-mer feature representation together with group lasso regularization to extract a collection of sequence signals that distinguish peak sequences from flanking regions. Benchmarked on over 100 ChIP-seq experiments, SeqGL outperformed traditional motif discovery tools in discriminative accuracy. Furthermore, SeqGL can be naturally used with multitask learning to identify genomic and cell-type context determinants of TF binding. SeqGL successfully scales to the large multiplicity of sequence signals in DNase- or ATAC-seq maps. In particular, SeqGL was able to identify a number of ChIP-seq validated sequence signals that were not found by traditional motif discovery algorithms. Thus compared to widely used motif discovery algorithms, SeqGL demonstrates both greater discriminative accuracy and higher sensitivity for detecting the DNA sequence signals underlying regulatory element maps. SeqGL is available at http://cbio.mskcc.org/public/Leslie/SeqGL/.

Early enhancer establishment and regulatory locus complexity shape transcriptional programs in hematopoietic differentiation

We carried out an integrative analysis of enhancer landscape and gene expression dynamics during hematopoietic differentiation using DNase-seq, histone mark ChIP-seq and RNA sequencing to model how the early establishment of enhancers and regulatory locus complexity govern gene expression changes at cell state transitions. We found that high-complexity genes—those with a large total number of DNase-mapped enhancers across the lineage—differ architecturally and functionally from low-complexity genes, achieve larger expression changes and are enriched for both cell type–specific and transition enhancers, which are established in hematopoietic stem and progenitor cells and maintained in one differentiated cell fate but lost in others. We then developed a quantitative model to accurately predict gene expression changes from the DNA sequence content and lineage history of active enhancers. Our method suggests a new mechanistic role for PU.1 at transition peaks during B cell specification and can be used to correct assignments of enhancers to genes.

Identification of Global Alteration of Translational Regulation in Glioma In Vivo

Post-transcriptional regulation of gene expression contributes to the protein output of a cell, however, methods for measuring translational regulation in complex in vivo systems are lacking. Here, we describe a sensitive method for measuring translational regulation in defined cell populations from heterogeneous tissue in vivo. We adapted the translating ribosome affinity purification (TRAP) methodology to measure the relative occupancy of individual mRNA transcripts in translating ribosomes in the Olig2-positive tumor cell population in a genetically engineered mouse model (GEM) of glioma. Global measurement of paired ribosome-bound and total cellular mRNA populations from tumor cells in vivo identified a broad distribution of relative ribosome occupancies amongst mRNA species that was highly reproducible across biological samples. Comparison of the translation state of glioma cells to non-transformed oligodendrocyte progenitor cells in normal brain identified global alteration of translation in tumor, and specifically of genes involved in cell division and synthetic metabolism. Furthermore, investigation of alteration in steady state translational efficiencies upon loss of PTEN, one of the most frequently mutated and deleted tumor suppressors in glioma, identified differential translation of proteins involved in cellular respiration, canonically regulated by PI3K/Akt signaling, and cellular glycosylation profiles, deregulation of which is known to be associated with tumor progression. Application of the translation efficiency profiling method described here to other biological contexts and conditions would extend our knowledge of the scope and impact of this important mode of gene regulation in complex in vivo systems.

Integrated genomic profiling identifies microRNA-92a regulation of IQGAP2 in locally advanced rectal cancer.

Locally advanced rectal cancer (LARC) is treated with chemoradiation prior to surgical excision, leaving residual tumors altered or completely absent. Integrating layers of genomic profiling might identify regulatory pathways relevant to rectal tumorigenesis and inform therapeutic decisions and further research. We utilized formalin‐fixed, paraffin‐embedded pre‐treatment LARC biopsies (n=138) and compared copy number, mRNA, and miRNA expression with matched normal rectal mucosa. An integrative model was used to predict regulatory interactions to explain gene expression changes. These predictions were evaluated in vitro using multiple colorectal cancer cell lines. The Cancer Genome Atlas (TCGA) was also used as an external cohort to validate our genomic profiling and predictions. We found differentially expressed mRNAs and miRNAs that characterize LARC. Our integrative model predicted the upregulation of miR‐92a, miR‐182, and miR‐221 expression to be associated with downregulation of their target genes after adjusting for the effect of copy number alterations. Cell line studies using miR‐92a mimics and inhibitors demonstrate that miR‐92a expression regulates IQGAP2 expression. We show that endogenous miR‐92a expression is inversely associated with endogenous KLF4 expression in multiple cell lines, and that this relationship is also present in rectal cancers of TCGA. Our integrative model predicted regulators of gene expression change in LARC using pre‐treatment FFPE tissues. Our methodology implicated multiple regulatory interactions, some of which are corroborated by independent lines of study, while others indicate new opportunities for investigation.

Single-cell Map of Diverse Immune Phenotypes Driven by the Tumor Microenvironment

Knowledge of the phenotypic states of immune cells in the tumor microenvironment is essential for understanding immunological mechanisms of cancer progression and immunotherapy responses, as well as the development of novel treatments. By combining single-cell RNA-seq data from over 45,000 immune cells collected from eight primary breast carcinomas, as well as matched normal breast tissue, peripheral blood, and lymph node, we created an immune map of breast cancer. We developed a preprocessing pipeline, SEQC, and a Bayesian clustering and normalization method, Biscuit, to address the computational challenges inherent to single-cell RNA-seq data, enabling integration of data across patients. This atlas revealed significant similarity between normal and tumor tissue resident immune cells. However, we observed continuous tumor-specific phenotypic expansions driven by environmental cues. Our results argue against discrete activation states in T cells and the polarization model of macrophage activation in cancer, with important implications for characterizing tumor-infiltrating immune cells.Knowledge of immune cell phenotypes in the tumor microenvironment is essential for understanding mechanisms of cancer progression and immunotherapy response. We created an immune map of breast cancer using single-cell RNA-seq data from 45,000 immune cells from eight breast carcinomas, as well as matched normal breast tissue, blood, and lymph node. We developed a preprocessing pipeline, SEQC, and a Bayesian clustering and normalization method, Biscuit, to address computational challenges inherent to single-cell data. Despite significant similarity between normal and tumor tissue-resident immune cells, we observed continuous tumor-specific phenotypic expansions driven by environmental cues. Analysis of paired single-cell RNA and T cell receptor (TCR) sequencing data from 27,000 additional T cells revealed the combinatorial impact of TCR utilization on phenotypic diversity. Our results support a model of continuous activation in T cells and do not comport with the macrophage polarization model in cancer, with important implications for characterizing tumor-infiltrating immune cells.