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

Abstract

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.

Figure 1.

System architecture diagram showing integration of data into the SCDE BioInvestigation Index (BII) and Galaxy instances. CSC-related experiments are submitted by stem cell researchers or selected from public repositories. After curation using the ISA tools and conversion to ISA-Tab format, the associated metadata, raw data files and processed gene lists are stored in the BII. The stem cell-specific gene lists are transformed into standardized gene identifiers to facilitate integration and comparison against similarly formatted reference lists (GeneSigDB, MSigDB, WikiPathways and other SCDE experiments) within Galaxy.

Figure 2.

Screenshots showing elements of the BioInvestigation Index browse view. (A) The results of a free text search using the term ‘intestine’ that retrieves four studies—two human and two murine—that include transcription profiling using DNA microarrays, and ChIP-seq for transcription factor binding and histone modifications; (B) One matching record, SHIVDASANI-S-1, with descriptive text; (C) Annotated experimental factor metadata including time, antibodies used in the chromatin immunoprecipitation (ChIP) experiments, and cell differentiation status; (D) Annotated sample metadata including the tissue and cell types, using the Foundational Model of Anatomy (FMA) and Brenda Tissue (BTO) ontologies, respectively; (E) Download panel for the raw and processed data.

Figure 3.

Composite figure showing the results of a ListMatch query using the set of intestinal differentiation genes that are reduced upon Cdx2 depletion from the SHIVDASANI-S-2 study. (A) SCDE ListMatch input page with options to compare against WikiPathways, GeneSigDB, MSigDB and the SCDE repository. (B) Results of the query against WikiPathways projected onto the canonical pathway representation with matching genes highlighted in red (partial screenshot shown). (C) Querying against GeneSigDB results in a top match to genes related to liver cancer.