Showing papers by "Pablo Tamayo published in 2018"

Systematic Evaluation of Molecular Networks for Discovery of Disease Genes

Abstract: Gene networks are rapidly growing in size and number, raising the question of which networks are most appropriate for particular applications. Here, we evaluate 21 human genome-wide interaction networks for their ability to recover 446 disease gene sets identified through literature curation, gene expression profiling, or genome-wide association studies. While all networks have some ability to recover disease genes, we observe a wide range of performance with STRING, ConsensusPathDB, and GIANT networks having the best performance overall. A general tendency is that performance scales with network size, suggesting that new interaction discovery currently outweighs the detrimental effects of false positives. Correcting for size, we find that the DIP network provides the highest efficiency (value per interaction). Based on these results, we create a parsimonious composite network with both high efficiency and performance. This work provides a benchmark for selection of molecular networks in human disease research.

Tumor innate immunity primed by specific interferon-stimulated endogenous retroviruses

Abstract: Mesenchymal tumor subpopulations secrete pro-tumorigenic cytokines and promote treatment resistance1-4 This phenomenon has been implicated in chemorefractory small cell lung cancer and resistance to targeted therapies5-8, but remains incompletely defined Here, we identify a subclass of endogenous retroviruses (ERVs) that engages innate immune signaling in these cells Stimulated 3 prime antisense retroviral coding sequences (SPARCS) are oriented inversely in 3' untranslated regions of specific genes enriched for regulation by STAT1 and EZH2 Derepression of these loci results in double-stranded RNA generation following IFN-γ exposure due to bi-directional transcription from the STAT1-activated gene promoter and the 5' long terminal repeat of the antisense ERV Engagement of MAVS and STING activates downstream TBK1, IRF3, and STAT1 signaling, sustaining a positive feedback loop SPARCS induction in human tumors is tightly associated with major histocompatibility complex class 1 expression, mesenchymal markers, and downregulation of chromatin modifying enzymes, including EZH2 Analysis of cell lines with high inducible SPARCS expression reveals strong association with an AXL/MET-positive mesenchymal cell state While SPARCS-high tumors are immune infiltrated, they also exhibit multiple features of an immune-suppressed microenviroment Together, these data unveil a subclass of ERVs whose derepression triggers pathologic innate immune signaling in cancer, with important implications for cancer immunotherapy

Assembly and activation of the Hippo signalome by FAT1 tumor suppressor

Abstract: Dysregulation of the Hippo signaling pathway and the consequent YAP1 activation is a frequent event in human malignancies, yet the underlying molecular mechanisms are still poorly understood. A pancancer analysis of core Hippo kinases and their candidate regulating molecules revealed few alterations in the canonical Hippo pathway, but very frequent genetic alterations in the FAT family of atypical cadherins. By focusing on head and neck squamous cell carcinoma (HNSCC), which displays frequent FAT1 alterations (29.8%), we provide evidence that FAT1 functional loss results in YAP1 activation. Mechanistically, we found that FAT1 assembles a multimeric Hippo signaling complex (signalome), resulting in activation of core Hippo kinases by TAOKs and consequent YAP1 inactivation. We also show that unrestrained YAP1 acts as an oncogenic driver in HNSCC, and that targeting YAP1 may represent an attractive precision therapeutic option for cancers harboring genomic alterations in the FAT1 tumor suppressor genes.

Visualizing and Interpreting Single-Cell Gene Expression Datasets with Similarity Weighted Nonnegative Embedding

Abstract: High-throughput single-cell gene expression profiling has enabled the definition of new cell types and developmental trajectories. Visualizing these datasets is crucial to biological interpretation, and a popular method is t-stochastic neighbor embedding (t-SNE), which visualizes local patterns well but distorts global structure, such as distances between clusters. We developed similarity weighted nonnegative embedding (SWNE), which enhances interpretation of datasets by embedding the genes and factors that separate cell states on the visualization alongside the cells and maintains fidelity when visualizing local and global structure for both developmental trajectories and discrete cell types. SWNE uses nonnegative matrix factorization to decompose the gene expression matrix into biologically relevant factors; embeds the cells, genes, and factors in a 2D visualization; and uses a similarity matrix to smooth the embeddings. We demonstrate SWNE on single-cell RNA-seq data from hematopoietic progenitors and human brain cells. SWNE is available as an R package at github.com/yanwu2014/swne.

Genome-scale analysis identifies paralog lethality as a vulnerability of chromosome 1p loss in cancer

Abstract: Functional redundancy shared by paralog genes may afford protection against genetic perturbations, but it can also result in genetic vulnerabilities due to mutual interdependency1-5. Here, we surveyed genome-scale short hairpin RNA and CRISPR screening data on hundreds of cancer cell lines and identified MAGOH and MAGOHB, core members of the splicing-dependent exon junction complex, as top-ranked paralog dependencies6-8. MAGOHB is the top gene dependency in cells with hemizygous MAGOH deletion, a pervasive genetic event that frequently occurs due to chromosome 1p loss. Inhibition of MAGOHB in a MAGOH-deleted context compromises viability by globally perturbing alternative splicing and RNA surveillance. Dependency on IPO13, an importin-β receptor that mediates nuclear import of the MAGOH/B-Y14 heterodimer9, is highly correlated with dependency on both MAGOH and MAGOHB. Both MAGOHB and IPO13 represent dependencies in murine xenografts with hemizygous MAGOH deletion. Our results identify MAGOH and MAGOHB as reciprocal paralog dependencies across cancer types and suggest a rationale for targeting the MAGOHB-IPO13 axis in cancers with chromosome 1p deletion.

Comprehensive Genomic Profiling Reveals Diverse but Actionable Molecular Portfolios across Hematologic Malignancies: Implications for Next Generation Clinical Trials.

Abstract: Background: The translation of genomic discoveries to the clinic is the cornerstone of precision medicine. However, incorporating next generation sequencing (NGS) of hematologic malignancies into clinical management remains limited. Methods: We describe 235 patients who underwent integrated NGS profiling (406 genes) and analyze the alterations and their potential actionability. Results: Overall, 227 patients (96.5%) had adequate tissue. Most common diagnoses included myelodysplastic syndrome (22.9%), chronic lymphocytic leukemia (17.2%), non-Hodgkin lymphoma (13.2%), acute myeloid leukemia (11%), myeloproliferative neoplasm (9.2%), acute lymphoblastic leukemia (8.8%), and multiple myeloma (7.5%). Most patients (N = 197/227 (87%)) harbored ≥1 genomic alteration(s); 170/227 (75%), ≥1 potentially actionable alteration(s) targetable by an FDA-approved (mostly off-label) or an investigational agent. Altogether, 546 distinct alterations were seen, most commonly involving TP53 (10.8%), TET2 (4.6%), and DNMT3A (4.2%). The median tumor mutational burden (TMB) was low (1.7 alterations/megabase); 12% of patients had intermediate or high TMB (higher TMB correlates with favorable response to anti-PD1/PDL1 inhibition in solid tumors). In conclusion, 96.5% of patients with hematologic malignancies have adequate tissue for comprehensive genomic profiling. Most patients had unique molecular signatures, and 75% had alterations that may be pharmacologically tractable with gene- or immune-targeted agents.

Genomic and proteomic alterations in desmoplastic small round blue- cell tumors

Abstract: PurposeDesmoplastic small round blue-cell tumors (DSRCTs) are sarcomas that contain the t(11;22) (p13;q12) translocation EWS-WT1 fusion protein. Because this is a rare tumor type, prospective clinical trials in DSRCT are challenging. Patients are treated in a manner similar to those with Ewing sarcoma; however, differences in prognosis and clinical presentation suggest fundamental differences in biology and potentially different therapeutic implications. This study aimed to characterize the molecular characteristics of DSRCT tumors to explore unique therapeutic options for this extremely rare and aggressive cancer type.MethodsThirty-five DSRCT tumors were assessed using next-generation sequencing, protein expression (immunohistochemistry), and gene amplification (chromogenic in situ hybridization or fluorescence in situ hybridization). Three patients had tumor mutational load, which was calculated as somatic nonsynonymous missense mutations sequenced with a 592-gene panel. Gene expression data were obtain...

Modelling bistable tumour population dynamics to design effective treatment strategies

Abstract: Despite recent advances in targeted drugs and immunotherapy, cancer remains "the emperor of all maladies" due to inevitable emergence of resistance. Drug resistance is thought to be driven by mutations and/or dynamic plasticity that deregulate pathway activities and regulatory programs of a highly heterogeneous tumour. In this study, we propose a modelling framework to simulate population dynamics of heterogeneous tumour cells with reversible drug resistance. Drug sensitivity of a tumour cell is determined by its internal states, which are demarcated by coordinated activities of multiple interconnected oncogenic pathways. Transitions between cellular states depend on the effects of targeted drugs and regulatory relations between the pathways. Under this framework, we build a simple model to capture drug resistance characteristics of BRAF-mutant melanoma, where two cell states are described by two mutually inhibitory - main and alternative - pathways. We assume that cells with an activated main pathway are proliferative yet sensitive to the BRAF inhibitor, and cells with an activated alternative pathway are quiescent but resistant to the drug. We describe a dynamical process of tumour growth under various drug regimens using the explicit solution of mean-field equations. Based on these solutions, we compare efficacy of three treatment strategies: static treatments with continuous and constant dosages, periodic treatments with regular intermittent phases and drug holidays, and treatments derived from optimal control theory (OCT). Based on these analysis, periodic treatments outperform static treatments with a considerable margin, while treatments based on OCT outperform the best periodic treatment. Our results provide insights regarding optimal cancer treatment modalities for heterogeneous tumours, and may guide the development of optimal therapeutic strategies to circumvent drug resistance and due to tumour plasticity.

Visualizing and interpreting single-cell gene expression datasets with Similarity Weighted Nonnegative Embedding

Abstract: High throughput single-cell gene expression profiling has enabled the characterization of novel cell types and developmental trajectories. Visualizing these datasets is crucial to biological interpretation, and the most popular method is t-Stochastic Neighbor embedding (t-SNE), which visualizes local patterns better than other methods, but often distorts global structure, such as distances between clusters. We developed Similarity Weighted Nonnegative Embedding (SWNE), which enhances interpretation of datasets by embedding the genes and factors that separate cell states alongside the cells on the visualization, captures local structure better than t-SNE and existing methods, and maintains fidelity when visualizing global structure. SWNE uses nonnegative matrix factorization to decompose the gene expression matrix into biologically relevant factors, embeds the cells, genes and factors in a 2D visualization, and uses a similarity matrix to smooth the embeddings. We demonstrate SWNE on single cell RNA-seq data from hematopoietic progenitors and human brain cells. The SWNE R package and the scripts used for this paper can be found at: https://github.com/yanwu2014/swne.

Visualizing single-cell RNA-seq datasets with Similarity Weighted Nonnegative Embedding (SWNE)

Abstract: High throughput single-cell RNA-seq (scRNA-seq) has enabled the discovery of novel cell types, the identification of trajectories during development, and the characterization of responses to genetic perturbations. The most popular visualization method for scRNA-seq is t-Stochastic Neighbor embedding (t-SNE), which accurately captures the local structure of datasets, but often distorts global structure, such as distances between clusters. We developed a method for visualization and interpretation of scRNA-seq datasets, Similarity Weighted Nonnegative Embedding (SWNE), which captures both the global and local structure of the data, and embeds biological information directly onto the visualization. SWNE uses nonnegative matrix factorization (NMF) to decompose the gene expression matrix into biologically relevant factors, embeds both the cells and the factors in a two dimensional visualization, and uses a similarity matrix to ensure that cells which are close in the original gene expression space are also close in the visualization. SWNE can also embed genes onto the visualization directly, while the embedded biological factors can be interpreted via their gene loadings, enhancing biological interpretatability. We demonstrate SWNE9s ability to visualize and facilitate interpretation of hematopoietic progenitors and cells from the human visual cortex and cerebellum. The SWNE R package and the scripts used for this paper can be found at: https://github.com/yanwu2014/swne.

Abstract 1310: Systematic evaluation of gene networks for discovery of disease genes

Abstract: Gene networks are rapidly growing in size and number, raising the question of which networks are most appropriate for a particular application. Here, we evaluate 21 human genome-wide interaction networks for their ability to recover gene sets associated with 446 different diseases and 9 cancer hallmarks. While all networks have some ability in these recovery tasks, we observe a wide range of performance with STRING, GeneMANIA and GIANT networks having the best performance overall. A general tendency is that performance scales with network size, suggesting that new interaction discovery currently outweighs the detrimental effects of false positives. Correcting for size, we find that the DIP network provides the highest efficiency (value per interaction). Based on these results we create a parsimonious composite network with both high efficiency and absolute performance, which outperforms any single resource. This work provides a benchmark for selection of molecular networks in human disease research. Citation Format: Justin K. Huang, Daniel E. Carlin, Michael K. Yu, Wei Zhang, Jason F. Kreisberg, Pablo Tamayo, Trey Ideker. Systematic evaluation of gene networks for discovery of disease genes [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 1310.