Identification of cancer genes by mutational profiling of tumor genomes

The transcription factor FOXM1 (Forkhead box M1): proliferation-specific expression, transcription factor function, target genes, mouse models, and normal biological roles

FOXM1 (Forkhead box M1) is a typical proliferation-associated transcription factor, which stimulates cell proliferation and exhibits a proliferation-specific expression pattern. Accordingly, both the expression and the transcriptional activity of FOXM1 are increased by proliferation signals, but decreased by antiproliferation signals, including the positive and negative regulation by protooncoproteins or tumor suppressors, respectively. FOXM1 stimulates cell cycle progression by promoting the entry into S-phase and M-phase. Moreover, FOXM1 is required for proper execution of mitosis. Accordingly, FOXM1 regulates the expression of genes, whose products control G1/S-transition, S-phase progression, G2/M-transition, and M-phase progression. Additionally, FOXM1 target genes encode proteins with functions in the execution of DNA replication and mitosis. FOXM1 is a transcriptional activator with a forkhead domain as DNA binding domain and with a very strong acidic transactivation domain. However, wild-type FOXM1 is (almost) inactive because the transactivation domain is repressed by three inhibitory domains. Inactive FOXM1 can be converted into a very potent transactivator by activating signals, which release the transactivation domain from its inhibition by the inhibitory domains. FOXM1 is essential for embryonic development and the foxm1 knockout is embryonically lethal. In adults, FOXM1 is important for tissue repair after injury. FOXM1 prevents premature senescence and interferes with contact inhibition. FOXM1 plays a role for maintenance of stem cell pluripotency and for self-renewal capacity of stem cells. The functions of FOXM1 in prevention of polyploidy and aneuploidy and in homologous recombination repair of DNA-double-strand breaks suggest an importance of FOXM1 for the maintenance of genomic stability and chromosomal integrity.

Has discovery-based cancer research been a bust?

The completion of the human genome sequence sparked optimism about prospects for new anticancer drug development, but clinical progress over the last decade has proven slower than expected. Here it is proposed that unrealistically high expectations of first-generation discovery-based diagnostics have contributed to this problem. Hypothesis-based single-molecule tests (e.g., mutation screening of KRAS, EGFR, BRAF or KIT genes) continue to change clinical practice incrementally, whereas first-generation multiplex assays--such as gene expression profiling and proteomics--have identified few high-impact therapeutic targets despite numerous correlations with prognosis. To move forward, second-generation multiplex diagnostics should be based not on statistical patterns/associations alone, but on clinically interpretable ('high-signal-to-noise') data such as change-of-function mutations, gene amplifications, recurrent chromosomal anomalies, and abnormal phosphorylation profiles of ERK or mTOR signaling cascades.

Somatic mutations in exocrine pancreatic tumors: association with patient survival

KRAS mutations are major factors involved in initiation and maintenance of pancreatic tumors. The impact of different mutations on patient survival has not been clearly defined. We screened tumors from 171 pancreatic cancer patients for mutations in KRAS and CDKN2A genes. Mutations in KRAS were detected in 134 tumors, with 131 in codon 12 and only 3 in codon 61. The GGT>GAT (G12D) was the most frequent mutation and was present in 60% (80/134). Deletions and mutations in CDKN2A were detected in 43 tumors. Analysis showed that KRAS mutations were associated with reduced patient survival in both malignant exocrine and ductal adenocarcinomas (PDAC). Patients with PDACs that had KRAS mutations showed a median survival of 17 months compared to 30 months for those without mutations (log-rank P = 0.07) with a multivariate hazard ratio (HR) of 2.19 (95%CI 1.09-4.42). The patients with G12D mutation showed a median survival of 16 months (log-rank-test P = 0.03) and an associated multivariate HR 2.42 (95%CI 1.14-2.67). Although, the association of survival in PDAC patients with CDKN2A aberrations in tumors was not statistically significant, the sub-group of patients with concomitant KRAS mutations and CDKN2A alterations in tumors were associated with a median survival of 13.5 months compared to 22 months without mutation (log-rank-test P = 0.02) and a corresponding HR of 3.07 (95%CI 1.33-7.10). Our results are indicative of an association between mutational status and survival in PDAC patients, which if confirmed in subsequent studies can have potential clinical application.

FOXM1 (Forkhead box M1) in tumorigenesis: overexpression in human cancer, implication in tumorigenesis, oncogenic functions, tumor-suppressive properties, and target of anticancer therapy

FOXM1 (Forkhead box M1) is a typical proliferation-associated transcription factor and is also intimately involved in tumorigenesis. FOXM1 stimulates cell proliferation and cell cycle progression by promoting the entry into S-phase and M-phase. Additionally, FOXM1 is required for proper execution of mitosis. In accordance with its role in stimulation of cell proliferation, FOXM1 exhibits a proliferation-specific expression pattern and its expression is regulated by proliferation and anti-proliferation signals as well as by proto-oncoproteins and tumor suppressors. Since these factors are often mutated, overexpressed, or lost in human cancer, the normal control of the foxm1 expression by them provides the basis for deregulated FOXM1 expression in tumors. Accordingly, FOXM1 is overexpressed in many types of human cancer. FOXM1 is intimately involved in tumorigenesis, because it contributes to oncogenic transformation and participates in tumor initiation, growth, and progression, including positive effects on angiogenesis, migration, invasion, epithelial-mesenchymal transition, metastasis, recruitment of tumor-associated macrophages, tumor-associated lung inflammation, self-renewal capacity of cancer cells, prevention of premature cellular senescence, and chemotherapeutic drug resistance. However, in the context of urethane-induced lung tumorigenesis, FOXM1 has an unexpected tumor suppressor role in endothelial cells because it limits pulmonary inflammation and canonical Wnt signaling in epithelial lung cells, thereby restricting carcinogenesis. Accordingly, FOXM1 plays a role in homologous recombination repair of DNA double-strand breaks and maintenance of genomic stability, that is, prevention of polyploidy and aneuploidy. The implication of FOXM1 in tumorigenesis makes it an attractive target for anticancer therapy, and several antitumor drugs have been reported to decrease FOXM1 expression.

Oncogenomics methods and resources

Today, cancer is viewed as a genetic disease and many genetic mechanisms of oncogenesis are known. The progression from normal tissue to invasive cancer is thought to occur over a timescale of 5-20 years. This transformation is driven by both inherited genetic factors and somatic genetic alterations and mutations, and it results in uncontrolled cell growth and, in many cases, death. In this article, we review the main types of genomic and genetic alterations involved in cancer, namely copy-number changes, genomic rearrangements, somatic mutations, polymorphisms, and epigenomic alterations in cancer. We then discuss the transcriptomic consequences of these alterations in tumor cells. The use of "next-generation" sequencing methods in cancer research is described in the relevant sections. Finally, we discuss different approaches for candidate prioritization and integration and analysis of these complex data.

Ins and outs of systems biology vis-à-vis molecular biology: continuation or clear cut?

The comprehension of living organisms in all their complexity poses a major challenge to the biological sciences. Recently, systems biology has been proposed as a new candidate in the development of such a comprehension. The main objective of this paper is to address what systems biology is and how it is practised. To this end, the basic tools of a systems biological approach are explored and illustrated. In addition, it is questioned whether systems biology 'revolutionizes' molecular biology and 'transcends' its assumed reductionism. The strength of this claim appears to depend on how molecular and systems biology are characterised and on how reductionism is interpreted. Doing credit to molecular biology and to methodological reductionism, it is argued that the distinction between molecular and systems biology is gradual rather than sharp. As such, the classical challenge in biology to manage, interpret and integrate biological data into functional wholes is further intensified by systems biology's use of modelling and bioinformatics, and by its scale enlargement.

Generation of expression clone set for functional proteomics of human gastric and liver cancers

Two thousand sixty-eight multi-purpose expression clones for the 326 candidate genes related to gastric or liver cancers were constructed using the Gateway system. These clones can be expressed as His, Glutathione-S-transferase (GST) or Enhanced version of the green fluorescent protein (EGFP) fusion proteins in E. coli, insect cells or mammalian cells. For the 246 E. coli expression clones, the GST fusion proteins had greater expression efficiency and solubility than the His fusion proteins. Approximately 20% of the expressed proteins had unexpected molecular weights. A detailed sequence analysis of these clones revealed frameshift mutations resulting from insertion, deletion or substitution of nucleotides. The results indicate that these changes in the candidate genes may affect the occurrence of gastric or liver cancers. In addition, when 105 proteins, which were expressed in E. coli at very low or undetectable levels, were expressed in insect cells, 76% of the proteins were expressed very well and most were soluble. We also found that most of the 30 proteins prepared using EGFP mammalian expression clones were localized to cellular compartments expected by Gene ontology (GO) and this localization was unaffected if the EGFP-fusion was at the N-terminal or C-terminal region of the protein. Antibody production and subcellular localization analysis of the candidate genes as well as a screen of genes involved in carcinogenesis pathways are currently in progress using these expression clones. These studies provide a valuable resource for developing a better understanding of the molecular mechanism of carcinogenesis in both gastric and liver cancer and would be very helpful in diagnosis and therapeutic predictions.

Prioritization of candidate cancer genes--an aid to oncogenomic studies

The development of techniques for oncogenomic analyses such as array comparative genomic hybridization, messenger RNA expression arrays and mutational screens have come to the fore in modern cancer research. Studies utilizing these techniques are able to highlight panels of genes that are altered in cancer. However, these candidate cancer genes must then be scrutinized to reveal whether they contribute to oncogenesis or are coincidental and non-causative. We present a computational method for the prioritization of candidate (i) proto-oncogenes and (ii) tumour suppressor genes from oncogenomic experiments. We constructed computational classifiers using different combinations of sequence and functional data including sequence conservation, protein domains and interactions, and regulatory data. We found that these classifiers are able to distinguish between known cancer genes and other human genes. Furthermore, the classifiers also discriminate candidate cancer genes from a recent mutational screen from other human genes. We provide a web-based facility through which cancer biologists may access our results and we propose computational cancer gene classification as a useful method of prioritizing candidate cancer genes identified in oncogenomic studies.

[Towards an integrated cellular and molecular: definition of breast cancers]

Breast cancer is a major health problem as well as scientifically poorly understood. Our knowledge of breast cancer is however rapidly progressing in several directions. First, genomic studies are establishing a new molecular classification of breast cancers. Molecular subtypes have been identified and are being associated with the histoclinical forms of breast cancers. Second, genetic alterations are discovered and classified, generating new potential therapeutical targets. Third, mammary stem cells have been identified in the normal mammary epithelium. Their altered counterparts have been identified in tumors and are being characterized. These combined studies allow a new integrated cellular and molecular definition of breast cancers and a conceptual basis that will help the management of the disease.

Distinguishing cancer-associated missense mutations from common polymorphisms

Missense variants are commonly identified in genomic sequence but only a small fraction directly contribute to oncogenesis. The ability to distinguish those missense changes that contribute to cancer progression from those that do not is a difficult problem usually only accomplished through functional in vivo analyses. Using two computational algorithms, Sorting Intolerant from Tolerant (SIFT) and the Pfam-based LogR.E-value method, we have identified features that distinguish cancer-associated missense mutations from other classes of missense change. Our data reveal that cancer mutants behave similarly to Mendelian disease mutations, but are clearly distinct from either complex disease mutations or common single-nucleotide polymorphisms. We show that both activating and inactivating oncogenic mutations are predicted to be deleterious, although activating changes are likely to increase protein activity. Using the Gene Ontology and data from the SIFT and LogR.E-value metrics, a classifier was built that predicts cancer-associated missense mutations with a very low false-positive rate. The classifier does remarkably well in a number of different experiments designed to distinguish polymorphisms from true cancer-associated mutations. We also show that recurrently observed mutations are much more likely to be predicted to be cancer-associated than rare mutations, suggesting that our classifier will be useful in distinguishing causal from passenger mutations. In addition, from an expressed sequence tag-based screen, we identified a previously unknown germ line change (P1104A) in tumor tissues that is predicted to disrupt the function of the TYK2 protein. The data presented here show that this novel bioinformatics approach to classifying cancer-associated variants is robust and can be used for large-scale analyses.

Isolation, expression, and characterization of the human ZCRB1 gene mapped to 12q12

While isolating morphine-dependence-related genes with differential display, we cloned a novel human gene, zinc finger CCHC-type and RNA-binding motif 1 (ZCRB1, alias MADP-1) encoding a nuclear protein (217 residues). The ZCRB1 gene consists of eight exons and seven introns. It is mapped to 12q12, which is within a locus reported for Parkinson disease (M. Funayama et al., Ann. Neurol. 51 (2002) 296-301). The 5'-flanking region contains an enhancer core motif and binding sites for AP-1, AP-2, and LF-A1. ZCRB1 is characterized by an RNA-binding motif and a CCHC zinc finger motif. The latter overlaps the C..C...GH....C core nucleocapsid motif. ZCRB1 is conserved from zebrafish to human and shares homology with cold-inducible RNA-binding protein. Transfection assay showed that ZCRB1 is located in the nucleoplasm, but outside the nucleolus. ZCRB1 gene expression was stimulated by morphine, inhibited by 30-36 degrees C, and up-regulated by 39 degrees C incubation in SH-SY5Y neural cells. Zcrb1 gene expression is highest in the heart and testes, lower in the cerebellum, and lowest in the liver in mice. ZCRB1 mRNA expression is specifically elevated in hepatocarcinoma HepG2 cells. These data provide new clues for further understanding of morphine dependence, heat shock, and hepatocarcinoma.

Oncology exploration: charting cancer medicinal chemistry space

Approaches for the experimental determination of protein-ligand molecular interactions are reliant on the quality of the compounds being tested. The application of large, randomly designed combinatorial libraries has given way to the creation of more-focused 'drug-like' libraries. Prior to synthesis, we wish to screen the potential compounds to remove undesired chemical moieties and to be within a required range of physiochemical properties. We have used a principal-component analysis (PCA) computational approach to analyze the 3D descriptor space of active and non-active (hit-like) cancer medicinal chemistry compounds. We define hit-like those molecules passing the unmodified OpenEye FILTER program. Our analysis indicates that these compounds occupy quite different regions in space. Cancer-active compounds exist in a much greater volume of space than generic hit-like space and most of them fail the commonly applied filters for orally bioavailable drugs. This is of great significance when designing orally bioavailable cancer target drugs.