In most colorectal cancer (CRC) patients, outcome cannot be predicted because tumors with similar clinicopathological features can have differences in disease progression and treatment response. Therefore, a better understanding of the CRC biology is required to identify those patients who will benefit from chemotherapy and to find a more tailored therapy plan for other patients. Based on unsupervised classification of whole genome data from 188 stages I–IV CRC patients, a molecular classification was developed that consist of at least three major intrinsic subtypes (A‐, B‐ and C‐type). The subtypes were validated in 543 stages II and III patients and were associated with prognosis and benefit from chemotherapy. The heterogeneity of the intrinsic subtypes is largely based on three biological hallmarks of the tumor: epithelial‐to‐mesenchymal transition, deficiency in mismatch repair genes that result in high mutation frequency associated with microsatellite instability and cellular proliferation. A‐type tumors, observed in 22% of the patients, have the best prognosis, have frequent BRAF mutations and a deficient DNA mismatch repair system. C‐type patients (16%) have the worst outcome, a mesenchymal gene expression phenotype and show no benefit from adjuvant chemotherapy treatment. Both A‐type and B‐type tumors have a more proliferative and epithelial phenotype and B‐types benefit from adjuvant chemotherapy. B‐type tumors (62%) show a low overall mutation frequency consistent with the absence of DNA mismatch repair deficiency. Classification based on molecular subtypes made it possible to expand and improve CRC classification beyond standard molecular and immunohistochemical assessment and might help in the future to guide treatment in CRC patients.

Objective: To develop gene expression profiles that characterise KRAS -, BRAF - or PIK3CA -activated- tumours, and to explore whether these profiles might be helpful in predicting the response to the epidermal growth factor receptor (EGFR) pathway inhibitors better than mutation status alone. Design: Fresh frozen tumour samples from 381 colorectal cancer (CRC) patients were collected and mutations in KRAS , BRAF and PIK3CA were assessed. Using microarray data, three individual oncogenic and a combined model were developed and validated in an independent set of 80 CRC patients, and in a dataset from metastatic CRC patients treated with cetuximab. Results: 175 tumours (45.9%) harboured oncogenic mutations in KRAS (30.2%), BRAF (11.0%) and PIK3CA (11.5%). Activating mutation signatures for KRAS (75 genes), for BRAF (58 genes,) and for PIK3CA (49 genes) were developed. The development of a combined oncogenic pathway signature-classified tumours as ‘activated oncogenic’, or as ‘wildtype-like’ with a sensitivity of 90.3% and a specificity of 61.7%. The identified signature revealed other mechanisms that can activate ERK/MAPK pathway in KRAS , BRAF and PIK3CA wildtype patients. The combined signature is associated with response to cetuximab treatment in patients with metastatic CRC (HR 2.51, p<0.0009). Conclusion: A combined oncogenic pathway signature allows the identification of patients with an active EGFR-signalling pathway that could benefit from downstream pathway inhibition.

Background: MammaPrint was developed as a diagnostic tool to predict risk of breast cancer metastasis using the expression of 70 genes. To better understand the tumor biology assessed by MammaPrint, we interpreted the biological functions of the 70-genes and showed how the genes reflect the six hallmarks of cancer as defined by Hanahan and Weinberg. Results: We used a bottom-up system biology approach to elucidate how the cellular processes reflected by the 70-genes work together to regulate tumor activities and progression. The biological functions of the genes were analyzed using literature research and several bioinformatics tools. Protein-protein interaction network analyses indicated that the 70-genes form highly interconnected networks and that their expression levels are regulated by key tumorigenesis related genes such as TP53, RB1, MYC, JUN and CDKN2A. The biological functions of the genes could be associated with the essential steps necessary for tumor progression and metastasis, and cover the six well-defined hallmarks of cancer, reflecting the acquired malignant characteristics of a cancer cell along with tumor progression and metastasis-related biological activities. Conclusion: Genes in the MammaPrint gene signature comprehensively measure the six hallmarks of cancer-related biology. This finding establishes a link between a molecular signature and the underlying molecular mechanisms of tumor cell progression and metastasis.

FurinDB (freely available online at http://www.nuolan.net/substrates.html) is a database of furin substrates. This database includes experimentally verified furin cleavage sites, substrates, species, experimental methods, original publications of experiments and associated drugs targeting furin substrates. The current database release contains 126 furin cleavage sites from three species: mammals, bacteria and viruses. A main feature of this database is that all furin cleavage sites are recorded as a 20-residue motif, including one core region (eight amino acids, P6–P2’) and two flanking solvent accessible regions (eight amino acids, P7–P14, and four amino acids, P3’–P6’), that represent our current understanding of the molecular biology of furin cleavage. This database is important for understanding the molecular evolution and relationships between sequence motifs, 3D structures, cellular functions and physical properties required by furin for cleavage, and for elucidating the molecular mechanisms and the progression of furin cleavage associated human diseases, including pathogenic infections, neurological disorders, tumorigenesis, tumor invasion, angiogenesis, and metastasis. FurinDB database will be a solid addition to the publicly available infrastructure for scientists in the field of molecular biology.

Furin cleaves diverse types of protein precursors in the secretory pathway. The substrates for furin cleavage possess a specific 20-residue recognition sequence motif. In this report, based on the functional characterisation of the 20-residue sequence motif, we developed a furin cleavage site prediction tool, PiTou, using a hybrid method composed of a hidden Markov model and biological knowledge-based cumulative probability score functions. PiTou can accurately predict the presence and location of furin cleavage sites in protein sequences with high sensitivity (96.9%) and high specificity (97.3%). PiTou's prediction scores are biological meaningful and reflect binding strength and solvent accessibility of furin substrates. A prediction result is interpreted within cellular contexts: subcellular localisation, cellular function and interference by other dynamic protein modifications. Combining next-generation sequencing, PiTou can help with elucidating the molecular mechanism of furin cleavage-associated human diseases. PiTou has been made freely available at the associated website.