Robert N. Jorissen

Epidermal growth factor receptor: Mechanisms of activation and signalling

The epidermal growth factor (EGF) receptor (EGFR) is one of four homologous transmembrane proteins that mediate the actions of a family of growth factors including EGF, transforming growth factor-alpha, and the neuregulins. We review the structure and function of the EGFR, from ligand binding to the initiation of intracellular signalling pathways that lead to changes in the biochemical state of the cell. The recent crystal structures of different domains from several members of the EGFR family have challenged our concepts of these processes.

BindingDB: a web-accessible database of experimentally determined protein–ligand binding affinities

BindingDB () is a publicly accessible database currently containing ∼20 000 experimentally determined binding affinities of protein–ligand complexes, for 110 protein targets including isoforms and mutational variants, and ∼11 000 small molecule ligands. The data are extracted from the scientific literature, data collection focusing on proteins that are drug-targets or candidate drug-targets and for which structural data are present in the Protein Data Bank. The BindingDB website supports a range of query types, including searches by chemical structure, substructure and similarity; protein sequence; ligand and protein names; affinity ranges and molecular weight. Data sets generated by BindingDB queries can be downloaded in the form of annotated SDfiles for further analysis, or used as the basis for virtual screening of a compound database uploaded by the user. The data in BindingDB are linked both to structural data in the PDB via PDB IDs and chemical and sequence searches, and to the literature in PubMed via PubMed IDs.

Crystal Structure of a Truncated Epidermal Growth Factor Receptor Extracellular Domain Bound to Transforming Growth Factor α

We report the crystal structure, at 2.5 A resolution, of a truncated human EGFR ectodomain bound to TGFalpha. TGFalpha interacts with both L1 and L2 domains of EGFR, making many main chain contacts with L1 and interacting with L2 via key conserved residues. The results indicate how EGFR family members can bind a family of highly variable ligands. In the 2:2 TGFalpha:sEGFR501 complex, each ligand interacts with only one receptor molecule. There are two types of dimers in the asymmetric unit: a head-to-head dimer involving contacts between the L1 and L2 domains and a back-to-back dimer dominated by interactions between the CR1 domains of each receptor. Based on sequence conservation, buried surface area, and mutagenesis experiments, the back-to-back dimer is favored to be biologically relevant.

The Crystal Structure of a Truncated ErbB2 Ectodomain Reveals an Active Conformation, Poised to Interact with Other ErbB Receptors

ErbB2 does not bind ligand, yet appears to be the major signaling partner for other ErbB receptors by forming heteromeric complexes with ErbB1, ErbB3, or ErbB4. The crystal structure of residues 1-509 of ErbB2 at 2.5 A resolution reveals an activated conformation similar to that of the EGFR when complexed with ligand and very different from that seen in the unactivated forms of ErbB3 or EGFR. The structure explains the inability of ErbB2 to bind known ligands and suggests why ErbB2 fails to form homodimers. Together, the data suggest a model in which ErbB2 is already in the activated conformation and ready to interact with other ligand-activated ErbB receptors.

Metastasis-Associated Gene Expression Changes Predict Poor Outcomes in Patients with Dukes Stage B and C Colorectal Cancer.

Purpose: Colorectal cancer prognosis is currently predicted from pathologic staging, providing limited discrimination for Dukes stage B and C disease. Additional markers for outcome are required to help guide therapy selection for individual patients. Experimental Design: A multisite single-platform microarray study was done on 553 colorectal cancers. Gene expression changes were identified between stage A and D tumors (three training sets) and assessed as a prognosis signature in stage B and C tumors (independent test and external validation sets). Results: One hundred twenty-eight genes showed reproducible expression changes between three sets of stage A and D cancers. Using consistent genes, stage B and C cancers clustered into two groups resembling early-stage and metastatic tumors. A Prediction Analysis of Microarray algorithm was developed to classify individual intermediate-stage cancers into stage A–like/good prognosis or stage D–like/poor prognosis types. For stage B patients, the treatment adjusted hazard ratio for 6-year recurrence in individuals with stage D–like cancers was 10.3 (95% confidence interval, 1.3-80.0; P = 0.011). For stage C patients, the adjusted hazard ratio was 2.9 (95% confidence interval, 1.1-7.6; P = 0.016). Similar results were obtained for an external set of stage B and C patients. The prognosis signature was enriched for downregulated immune response genes and upregulated cell signaling and extracellular matrix genes. Accordingly, sparse tumor infiltration with mononuclear chronic inflammatory cells was associated with poor outcome in independent patients. Conclusions: Metastasis-associated gene expression changes can be used to refine traditional outcome prediction, providing a rational approach for tailoring treatments to subsets of patients. (Clin Cancer Res 2009;15(24):7642–51)

Virtual Screening of Molecular Databases Using a Support Vector Machine

The Support Vector Machine (SVM) is an algorithm that derives a model used for the classification of data into two categories and which has good generalization properties. This study applies the SVM algorithm to the problem of virtual screening for molecules with a desired activity. In contrast to typical applications of the SVM, we emphasize not classification but enrichment of actives by using a modified version of the standard SVM function to rank molecules. The method employs a simple and novel criterion for picking molecular descriptors and uses cross-validation to select SVM parameters. The resulting method is more effective at enriching for active compounds with novel chemistries than binary fingerprint-based methods such as binary kernel discrimination.

KRAS Mutation Is Associated with Lung Metastasis in Patients with Curatively Resected Colorectal Cancer

Purpose: Oncogene mutations contribute to colorectal cancer development. We searched for differences in oncogene mutation profiles between colorectal cancer metastases from different sites and evaluated these as markers for site of relapse. Experimental Design: One hundred colorectal cancer metastases were screened for mutations in 19 oncogenes, and further 61 metastases and 87 matched primary cancers were analyzed for genes with identified mutations. Mutation prevalence was compared between (a) metastases from liver (n = 65), lung (n = 50), and brain (n = 46), (b) metastases and matched primary cancers, and (c) metastases and an independent cohort of primary cancers (n = 604). Mutations differing between metastasis sites were evaluated as markers for site of relapse in 859 patients from the VICTOR trial. Results: In colorectal cancer metastases, mutations were detected in 4 of 19 oncogenes: BRAF (3.1%), KRAS (48.4%), NRAS (6.2%), and PIK3CA (16.1%). KRAS mutation prevalence was significantly higher in lung (62.0%) and brain (56.5%) than in liver metastases (32.3%; P = 0.003). Mutation status was highly concordant between primary cancer and metastasis from the same individual. Compared with independent primary cancers, KRAS mutations were more common in lung and brain metastases (P < 0.005), but similar in liver metastases. Correspondingly, KRAS mutation was associated with lung relapse (HR = 2.1; 95% CI, 1.2 to 3.5, P = 0.007) but not liver relapse in patients from the VICTOR trial. Conclusions: KRAS mutation seems to be associated with metastasis in specific sites, lung and brain, in colorectal cancer patients. Our data highlight the potential of somatic mutations for informing surveillance strategies. Clin Cancer Res; 17(5); 1122–30. ©2011 AACR.

SMAD2, SMAD3 and SMAD4 mutations in colorectal cancer.

Activation of the canonical TGF-β signaling pathway provides growth inhibitory signals in the normal intestinal epithelium. Colorectal cancers (CRCs) frequently harbor somatic mutations in the pathway members TGFBR2 and SMAD4, but to what extent mutations in SMAD2 or SMAD3 contribute to tumorigenesis is unclear. A cohort of 744 primary CRCs and 36 CRC cell lines were sequenced for SMAD4, SMAD2, and SMAD3 and analyzed for allelic loss by single-nucleotide polymorphism (SNP) microarray analysis. Mutation spectra were compared between the genes, the pathogenicity of mutations was assessed, and relationships with clinicopathologic features were examined. The prevalence of SMAD4, SMAD2, and SMAD3 mutations in sporadic CRCs was 8.6% (64 of 744), 3.4% (25 of 744), and 4.3% (32 of 744), respectively. A significant overrepresentation of two genetic hits was detected for SMAD4 and SMAD3, consistent with these genes acting as tumor suppressors. SMAD4 mutations were associated with mucinous histology. The mutation spectra of SMAD2 and SMAD3 were highly similar to that of SMAD4, both in mutation type and location within the encoded proteins. In silico analyses suggested the majority of the mutations were pathogenic, with most missense changes predicted to reduce protein stability or hinder SMAD complex formation. The latter altered interface residues or disrupted the phosphorylation-regulated Ser-Ser-X-Ser motifs within SMAD2 and SMAD3. Functional analyses of selected mutations showed reductions in SMAD3 transcriptional activity and SMAD2-SMAD4 complex formation. Joint biallelic hits in SMAD2 and SMAD3 were overrepresented and mutually exclusive to SMAD4 mutation, underlining the critical roles of these three proteins within the TGF-β signaling pathway.

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.

A statistical approach for detecting genomic aberrations in heterogeneous tumor samples from single nucleotide polymorphism genotyping data

We describe a statistical method for the characterization of genomic aberrations in single nucleotide polymorphism microarray data acquired from cancer genomes. Our approach allows us to model the joint effect of polyploidy, normal DNA contamination and intra-tumour heterogeneity within a single unified Bayesian framework. We demonstrate the efficacy of our method on numerous datasets including laboratory generated mixtures of normal-cancer cell lines and real primary tumours.