Christopher G. Love

Colorectal cancer cell lines are representative models of the main molecular subtypes of primary cancer

Human colorectal cancer cell lines are used widely to investigate tumor biology, experimental therapy, and biomarkers. However, to what extent these established cell lines represent and maintain the genetic diversity of primary cancers is uncertain. In this study, we profiled 70 colorectal cancer cell lines for mutations and DNA copy number by whole-exome sequencing and SNP microarray analyses, respectively. Gene expression was defined using RNA-Seq. Cell line data were compared with those published for primary colorectal cancers in The Cancer Genome Atlas. Notably, we found that exome mutation and DNA copy-number spectra in colorectal cancer cell lines closely resembled those seen in primary colorectal tumors. Similarities included the presence of two hypermutation phenotypes, as defined by signatures for defective DNA mismatch repair and DNA polymerase ε proofreading deficiency, along with concordant mutation profiles in the broadly altered WNT, MAPK, PI3K, TGFβ, and p53 pathways. Furthermore, we documented mutations enriched in genes involved in chromatin remodeling (ARID1A, CHD6, and SRCAP) and histone methylation or acetylation (ASH1L, EP300, EP400, MLL2, MLL3, PRDM2, and TRRAP). Chromosomal instability was prevalent in nonhypermutated cases, with similar patterns of chromosomal gains and losses. Although paired cell lines derived from the same tumor exhibited considerable mutation and DNA copy-number differences, in silico simulations suggest that these differences mainly reflected a preexisting heterogeneity in the tumor cells. In conclusion, our results establish that human colorectal cancer lines are representative of the main subtypes of primary tumors at the genomic level, further validating their utility as tools to investigate colorectal cancer biology and drug responses.

SSRPrimer and SSR Taxonomy Tree: Biome SSR discovery

Simple sequence repeat (SSR) molecular genetic markers have become important tools for a broad range of applications such as genome mapping and genetic diversity studies. SSRs are readily identified within DNA sequence data and PCR primers can be designed for their amplification. These PCR primers frequently cross amplify within related species. We report a web-based tool, SSR Primer, that integrates SPUTNIK, an SSR repeat finder, with Primer3, a primer design program, within one pipeline. On submission of multiple FASTA formatted sequences, the script screens each sequence for SSRs using SPUTNIK. Results are then parsed to Primer3 for locus specific primer design. We have applied this tool for the discovery of SSRs within the complete GenBank database, and have designed PCR amplification primers for over 13 million SSRs. The SSR Taxonomy Tree server provides web-based searching and browsing of species and taxa for the visualisation and download of these SSR amplification primers. These tools are available at .

SNPServer: a real-time SNP discovery tool

SNPServer is a real-time flexible tool for the discovery of SNPs (single nucleotide polymorphisms) within DNA sequence data. The program uses BLAST, to identify related sequences, and CAP3, to cluster and align these sequences. The alignments are parsed to the SNP discovery software autoSNP, a program that detects SNPs and insertion/deletion polymorphisms (indels). Alternatively, lists of related sequences or pre-assembled sequences may be entered for SNP discovery. SNPServer and autoSNP use redundancy to differentiate between candidate SNPs and sequence errors. For each candidate SNP, two measures of confidence are calculated, the redundancy of the polymorphism at a SNP locus and the co-segregation of the candidate SNP with other SNPs in the alignment. SNPServer is available at .

Regulatory Hotspots Are Associated with Plant Gene Expression under Varying Soil Phosphorus Supply in Brassica rapa

Gene expression is a quantitative trait that can be mapped genetically in structured populations to identify expression quantitative trait loci (eQTL). Genes and regulatory networks underlying complex traits can subsequently be inferred. Using a recently released genome sequence, we have defined cis- and trans-eQTL and their environmental response to low phosphorus (P) availability within a complex plant genome and found hotspots of trans-eQTL within the genome. Interval mapping, using P supply as a covariate, revealed 18,876 eQTL. trans-eQTL hotspots occurred on chromosomes A06 and A01 within Brassica rapa; these were enriched with P metabolism-related Gene Ontology terms (A06) as well as chloroplast- and photosynthesis-related terms (A01). We have also attributed heritability components to measures of gene expression across environments, allowing the identification of novel gene expression markers and gene expression changes associated with low P availability. Informative gene expression markers were used to map eQTL and P use efficiency-related QTL. Genes responsive to P supply had large environmental and heritable variance components. Regulatory loci and genes associated with P use efficiency identified through eQTL analysis are potential targets for further characterization and may have potential for crop improvement.

Brassica ASTRA: an integrated database for Brassica genomic research

Brassica ASTRA is a public database for genomic information on Brassica species. The database incorporates expressed sequences with Swiss-Prot and GenBank comparative sequence annotation as well as secondary Gene Ontology (GO) annotation derived from the comparison with Arabidopsis TAIR GO annotations. Simple sequence repeat molecular markers are identified within resident sequences and mapped onto the closely related Arabidopsis genome sequence. Bacterial artificial chromosome (BAC) end sequences derived from the Multinational Brassica Genome Project are also mapped onto the Arabidopsis genome sequence enabling users to identify candidate Brassica BACs corresponding to syntenic regions of Arabidopsis. This information is maintained in a MySQL database with a web interface providing the primary means of interrogation. The database is accessible at http://hornbill.cspp.latrobe.edu.au.

Widespread FRA1-Dependent Control of Mesenchymal Transdifferentiation Programs in Colorectal Cancer Cells

Tumor invasion and metastasis involves complex remodeling of gene expression programs governing epithelial homeostasis. Mutational activation of the RAS-ERK is a frequent occurrence in many cancers and has been shown to drive overexpression of the AP-1 family transcription factor FRA1, a potent regulator of migration and invasion in a variety of tumor cell types. However, the nature of FRA1 transcriptional targets and the molecular pathways through which they promote tumor progression remain poorly understood. We found that FRA1 was strongly expressed in tumor cells at the invasive front of human colorectal cancers (CRCs), and that its depletion suppressed mesenchymal-like features in CRC cells in vitro. Genome-wide analysis of FRA1 chromatin occupancy and transcriptional regulation identified epithelial-mesenchymal transition (EMT)-related genes as a major class of direct FRA1 targets in CRC cells. Expression of the pro-mesenchymal subset of these genes predicted adverse outcomes in CRC patients, and involved FRA-1-dependent regulation and cooperation with TGFβ signaling pathway. Our findings reveal an unexpectedly widespread and direct role for FRA1 in control of epithelial-mesenchymal plasticity in CRC cells, and suggest that FRA1 plays an important role in mediating cross talk between oncogenic RAS-ERK and TGFβ signaling networks during tumor progression.

A Brassica Exon Array for Whole-Transcript Gene Expression Profiling

Affymetrix GeneChip® arrays are used widely to study transcriptional changes in response to developmental and environmental stimuli. GeneChip® arrays comprise multiple 25-mer oligonucleotide probes per gene and retain certain advantages over direct sequencing. For plants, there are several public GeneChip® arrays whose probes are localised primarily in 3′ exons. Plant whole-transcript (WT) GeneChip® arrays are not yet publicly available, although WT resolution is needed to study complex crop genomes such as Brassica, which are typified by segmental duplications containing paralogous genes and/or allopolyploidy. Available sequence data were sampled from the Brassica A and C genomes, and 142,997 gene models identified. The assembled gene models were then used to establish a comprehensive public WT exon array for transcriptomics studies. The Affymetrix GeneChip® Brassica Exon 1.0 ST Array is a 5 µM feature size array, containing 2.4 million 25-base oligonucleotide probes representing 135,201 gene models, with 15 probes per gene distributed among exons. Discrimination of the gene models was based on an E-value cut-off of 1E−5, with ≤98% sequence identity. The 135 k Brassica Exon Array was validated by quantifying transcriptome differences between leaf and root tissue from a reference Brassica rapa line (R-o-18), and categorisation by Gene Ontologies (GO) based on gene orthology with Arabidopsis thaliana. Technical validation involved comparison of the exon array with a 60-mer array platform using the same starting RNA samples. The 135 k Brassica Exon Array is a robust platform. All data relating to the array design and probe identities are available in the public domain and are curated within the BrassEnsembl genome viewer at http://www.brassica.info/BrassEnsembl/index.html.

A comparative map viewer integrating genetic maps for Brassica and Arabidopsis

BackgroundMolecular genetic maps provide a means to link heritable traits with underlying genome sequence variation. Several genetic maps have been constructed for Brassica species, yet to date, there has been no simple means to compare this information or to associate mapped traits with the genome sequence of the related model plant, Arabidopsis.DescriptionWe have developed a comparative genetic map database for the viewing, comparison and analysis of Brassica and Arabidopsis genetic, physical and trait map information. This web-based tool allows users to view and compare genetic and physical maps, search for traits and markers, and compare genetic linkage groups within and between the amphidiploid and diploid Brassica genomes. The inclusion of Arabidopsis data enables comparison between Brassica maps that share no common markers. Analysis of conserved syntenic blocks between Arabidopsis and collated Brassica genetic maps validates the application of this system. This tool is freely available over the internet on http://bioinformatics.pbcbasc.latrobe.edu.au/cmap.ConclusionThis database enables users to interrogate the relationship between Brassica genetic maps and the sequenced genome of A. thaliana, permitting the comparison of genetic linkage groups and mapped traits and the rapid identification of candidate genes.

New computational tools for Brassica genome research

With the increasing quantities of Brassica genomic data being entered into the public domain and in preparation for the complete Brassica genome sequencing effort, there is a growing requirement for the structuring and detailed bioinformatic analysis of Brassica genomic information within a user-friendly database. At the Plant Biotechnology Centre, Melbourne, Australia, we have developed a series of tools and computational pipelines to assist in the processing and structuring of genomic data, to aid its application to agricultural biotechnology research. These tools include a sequence database, ASTRA, a sequence processing pipeline incorporating annotation against GenBank, SwissProt and Arabidopsis Gene Ontology (GO) data and tools for molecular marker discovery and comparative genome analysis. All sequences are mined for simple sequence repeat (SSR) molecular markers using ‘SSR primer’ and mapped onto the complete Arabidopsis thaliana genome by sequence comparison. The database may be queried using a text-based search of sequence annotation or GO terms, BLAST comparison against resident sequences, or by the position of candidate orthologues within the Arabidopsis genome. Tools have also been developed and applied to the discovery of single nucleotide polymorphism (SNP) molecular markers and the in silico mapping of Brassica BAC end sequences onto the Arabidopsis genome. Planned extensions to this resource include the integration of gene expression data and the development of an EnsEMBL-based genome viewer.

BRAF V600E Mutant Colorectal Cancer Subtypes Based on Gene Expression

Purpose: Mutation of BRAF at the valine 600 residue occurs in approximately 10% of colorectal cancers, a group with particularly poor prognosis. The response of BRAF mutant colorectal cancer to recent targeted strategies such as anti-BRAF or combinations with MEK and EGFR inhibitors remains limited and highly heterogeneous within BRAF V600E cohorts. There is clearly an unmet need in understanding the biology of BRAF V600E colorectal cancers and potential subgroups within this population. Experimental Design: In the biggest yet reported cohort of 218 BRAF V600E with gene expression data, we performed unsupervised clustering using non-negative matrix factorization to identify gene expression–based subgroups and characterized pathway activation. Results: We found strong support for a split into two distinct groups, called BM1 and BM2. These subtypes are independent of MSI status, PI3K mutation, gender, and sidedness. Pathway analyses revealed that BM1 is characterized by KRAS/AKT pathway activation, mTOR/4EBP deregulation, and EMT whereas BM2 displays important deregulation of the cell cycle. Proteomics data validated these observations as BM1 is characterized by high phosphorylation levels of AKT and 4EBP1, and BM2 patients display high CDK1 and low cyclin D1 levels. We provide a global assessment of gene expression motifs that differentiate BRAF V600E subtypes from other colorectal cancers. Conclusions: We suggest that BRAF mutant patients should not be considered as having a unique biology and provide an in depth characterization of heterogeneous motifs that may be exploited for drug targeting. Clin Cancer Res; 23(1); 104–15. ©2016 AACR.