Early and late effects of pharmacological ALK inhibition on the neuroblastoma transcriptome

Background: Neuroblastoma is an aggressive childhood malignancy of the sympathetic nervous system. Despite multi-modal therapy, survival of high-risk patients remains disappointingly low, underscoring the need for novel treatment strategies. The discovery of ALK activating mutations opened the way to precision treatment in a subset of these patients. Previously, we investigated the transcriptional effects of pharmacological ALK inhibition on neuroblastoma cell lines, six hours after TAE684 administration, resulting in the 77-gene ALK signature, which was shown to gradually decrease from 120 minutes after TAE684 treatment, to gain deeper insight into the molecular effects of oncogenic ALK signaling. Aim: Here, we further dissected the transcriptional dynamic profiles of neuroblastoma cells upon TAE684 treatment in a detailed timeframe of ten minutes up to six hours after inhibition, in order to identify additional early targets for combination treatment. Results: We observed an unexpected initial upregulation of positively regulated MYCN target genes following subsequent downregulation of overall MYCN activity. In addition, we identified adrenomedullin (ADM), previously shown to be implicated in sunitinib resistance, as the earliest response gene upon ALK inhibition. Conclusions: We describe the early and late effects of ALK inhibitor TAE684 treatment on the neuroblastoma transcriptome. The observed unexpected upregulation of ADM warrants further investigation in relation to putative ALK resistance in neuroblastoma patients currently undergoing ALK inhibitor treatment

Cross-species analysis of genetically engineered mouse models of MAPK-driven colorectal cancer identifies hallmarks of the human disease

Effective treatment options for advanced colorectal cancer (CRC) are limited, survival rates are poor and this disease continues to be a leading cause of cancer-related deaths worldwide. Despite being a highly heterogeneous disease, a large subset of individuals with sporadic CRC typically harbor relatively few established ‘driver’ lesions. Here, we describe a collection of genetically engineered mouse models (GEMMs) of sporadic CRC that combine lesions frequently altered in human patients, including well-characterized tumor suppressors and activators of MAPK signaling. Primary tumors from these models were profiled, and individual GEMM tumors segregated into groups based on their genotypes. Unique allelic and genotypic expression signatures were generated from these GEMMs and applied to clinically annotated human CRC patient samples. We provide evidence that a Kras signature derived from these GEMMs is capable of distinguishing human tumors harboring KRAS mutation, and tracks with poor prognosis in two independent human patient cohorts. Furthermore, the analysis of a panel of human CRC cell lines suggests that high expression of the GEMM Kras signature correlates with sensitivity to targeted pathway inhibitors. Together, these findings implicate GEMMs as powerful preclinical tools with the capacity to recapitulate relevant human disease biology, and support the use of genetic signatures generated in these models to facilitate future drug discovery and validation efforts

decodeRNA-predicting non-coding RNA functions using guilt-by-association

Although the long non-coding RNA (lncRNA) landscape is expanding rapidly, only a small number of lncRNAs have been functionally annotated. Here, we present decodeRNA (http://www.decoderna.org), a database providing functional contexts for both human lncRNAs and microRNAs in 29 cancer and 12 normal tissue types. With state-of-the-art data mining and visualization options, easy access to results and a straightforward user interface, decodeRNA aims to be a powerful tool for researchers in the ncRNA field

BD-Func: a streamlined algorithm for predicting activation and inhibition of pathways

BD-Func (BiDirectional FUNCtional enrichment) is an algorithm that calculates functional enrichment by comparing lists of pre-defined genes that are known to be activated versus inhibited in a pathway or by a regulatory molecule. This paper shows that BD-Func can correctly predict cell line alternations and patient characteristics with accuracy comparable to popular algorithms, with a significantly faster run-time. BD-Func can compare scores for individual samples across multiple groups as well as provide predictive statistics and receiver operating characteristic (ROC) plots to quantify the accuracy of the signature associated with a binary phenotypic variable. BD-Func facilitates collaboration and reproducibility by encouraging users to share novel molecular signatures in the BD-Func discussion group, which is where the novel progesterone receptor and LBH589 signatures from this paper can be found. The novel LBH589 signature presented in this paper also serves as a case study showing how a custom signature using cell line data can accurately predict activity in vivo. This software is available to download at https://sourceforge.net/projects/bdfunc/

GSAE: an autoencoder with embedded gene-set nodes for genomics functional characterization

Bioinformatics tools have been developed to interpret gene expression data at the gene set level, and these gene set based analyses improve the biologists' capability to discover functional relevance of their experiment design. While elucidating gene set individually, inter gene sets association is rarely taken into consideration. Deep learning, an emerging machine learning technique in computational biology, can be used to generate an unbiased combination of gene set, and to determine the biological relevance and analysis consistency of these combining gene sets by leveraging large genomic data sets. In this study, we proposed a gene superset autoencoder (GSAE), a multi-layer autoencoder model with the incorporation of a priori defined gene sets that retain the crucial biological features in the latent layer. We introduced the concept of the gene superset, an unbiased combination of gene sets with weights trained by the autoencoder, where each node in the latent layer is a superset. Trained with genomic data from TCGA and evaluated with their accompanying clinical parameters, we showed gene supersets' ability of discriminating tumor subtypes and their prognostic capability. We further demonstrated the biological relevance of the top component gene sets in the significant supersets. Using autoencoder model and gene superset at its latent layer, we demonstrated that gene supersets retain sufficient biological information with respect to tumor subtypes and clinical prognostic significance. Superset also provides high reproducibility on survival analysis and accurate prediction for cancer subtypes.Comment: Presented in the International Conference on Intelligent Biology and Medicine (ICIBM 2018) at Los Angeles, CA, USA and published in BMC Systems Biology 2018, 12(Suppl 8):14

The Stem Cell Discovery Engine: an integrated repository and analysis system for cancer stem cell comparisons

Mounting evidence suggests that malignant tumors are initiated and maintained by a subpopulation of cancerous cells with biological properties similar to those of normal stem cells. However, descriptions of stem-like gene and pathway signatures in cancers are inconsistent across experimental systems. Driven by a need to improve our understanding of molecular processes that are common and unique across cancer stem cells (CSCs), we have developed the Stem Cell Discovery Engine (SCDE)—an online database of curated CSC experiments coupled to the Galaxy analytical framework. The SCDE allows users to consistently describe, share and compare CSC data at the gene and pathway level. Our initial focus has been on carefully curating tissue and cancer stem cell-related experiments from blood, intestine and brain to create a high quality resource containing 53 public studies and 1098 assays. The experimental information is captured and stored in the multi-omics Investigation/Study/Assay (ISA-Tab) format and can be queried in the data repository. A linked Galaxy framework provides a comprehensive, flexible environment populated with novel tools for gene list comparisons against molecular signatures in GeneSigDB and MSigDB, curated experiments in the SCDE and pathways in WikiPathways. The SCDE is available at http://discovery.hsci.harvard.edu

Functional interpretation of single cell similarity maps.

We present Vision, a tool for annotating the sources of variation in single cell RNA-seq data in an automated and scalable manner. Vision operates directly on the manifold of cell-cell similarity and employs a flexible annotation approach that can operate either with or without preconceived stratification of the cells into groups or along a continuum. We demonstrate the utility of Vision in several case studies and show that it can derive important sources of cellular variation and link them to experimental meta-data even with relatively homogeneous sets of cells. Vision produces an interactive, low latency and feature rich web-based report that can be easily shared among researchers, thus facilitating data dissemination and collaboration

GeneSigDB: a manually curated database and resource for analysis of gene expression signatures

GeneSigDB (http://www.genesigdb.org or http://compbio.dfci.harvard.edu/genesigdb/) is a database of gene signatures that have been extracted and manually curated from the published literature. It provides a standardized resource of published prognostic, diagnostic and other gene signatures of cancer and related disease to the community so they can compare the predictive power of gene signatures or use these in gene set enrichment analysis. Since GeneSigDB release 1.0, we have expanded from 575 to 3515 gene signatures, which were collected and transcribed from 1604 published articles largely focused on gene expression in cancer, stem cells, immune cells, development and lung disease. We have made substantial upgrades to the GeneSigDB website to improve accessibility and usability, including adding a tag cloud browse function, facetted navigation and a ‘basket’ feature to store genes or gene signatures of interest. Users can analyze GeneSigDB gene signatures, or upload their own gene list, to identify gene signatures with significant gene overlap and results can be viewed on a dynamic editable heatmap that can be downloaded as a publication quality image. All data in GeneSigDB can be downloaded in numerous formats including .gmt file format for gene set enrichment analysis or as a R/Bioconductor data file. GeneSigDB is available from http://www.genesigdb.org

Pathway analysis and transcriptomics improve protein identification by shotgun proteomics from samples comprising small number of cells - a benchmarking study

BACKGROUND: Proteomics research is enabled with the high-throughput technologies, but our ability to identify expressed proteome is limited in small samples. The coverage and consistency of proteome expression are critical problems in proteomics. Here, we propose pathway analysis and combination of microproteomics and transcriptomics analyses to improve mass-spectrometry protein identification from small size samples. RESULTS: Multiple proteomics runs using MCF-7 cell line detected 4,957 expressed proteins. About 80% of expressed proteins were present in MCF-7 transcripts data; highly expressed transcripts are more likely to have expressed proteins. Approximately 1,000 proteins were detected in each run of the small sample proteomics. These proteins were mapped to gene symbols and compared with gene sets representing canonical pathways, more than 4,000 genes were extracted from the enriched gene sets. The identified canonical pathways were largely overlapping between individual runs. Of identified pathways 182 were shared between three individual small sample runs. CONCLUSIONS: Current technologies enable us to directly detect 10% of expressed proteomes from small sample comprising as few as 50 cells. We used knowledge-based approaches to elucidate the missing proteome that can be verified by targeted proteomics. This knowledge-based approach includes pathway analysis and combination of gene expression and protein expression data for target prioritization. Genes present in both the enriched gene sets (canonical pathways collection) and in small sample proteomics data correspond to approximately 50% of expressed proteomes in larger sample proteomics data. In addition, 90% of targets from canonical pathways were estimated to be expressed. The comparison of proteomics and transcriptomics data, suggests that highly expressed transcripts have high probability of protein expression. However, approximately 10% of expressed proteins could not be matched with the expressed transcripts.The cost of this publication was funded by Vladimir Brusic. (Vladimir Brusic)Published versio