Signalling networks that cause cancer

QuaDMutNetEx: a method for detecting cancer driver genes with low mutation frequency

BACKGROUND

Cancer is caused by genetic mutations, but not all somatic mutations in human DNA drive the emergence or growth of cancers. While many frequently-mutated cancer driver genes have already been identified and are being utilized for diagnostic, prognostic, or therapeutic purposes, identifying driver genes that harbor mutations occurring with low frequency in human cancers is an ongoing endeavor. Typically, mutations that do not confer growth advantage to tumors - passenger mutations - dominate the mutation landscape of tumor cell genome, making identification of low-frequency driver mutations a challenge. The leading approach for discovering new putative driver genes involves analyzing patterns of mutations in large cohorts of patients and using statistical methods to discriminate driver from passenger mutations.

RESULTS

We propose a novel cancer driver gene detection method, QuaDMutNetEx. QuaDMutNetEx discovers cancer drivers with low mutation frequency by giving preference to genes encoding proteins that are connected in human protein-protein interaction networks, and that at the same time show low deviation from the mutual exclusivity pattern that characterizes driver mutations occurring in the same pathway or functional gene group across a cohort of cancer samples.

CONCLUSIONS

Evaluation of QuaDMutNetEx on four different tumor sample datasets show that the proposed method finds biologically-connected sets of low-frequency driver genes, including many genes that are not found if the network connectivity information is not considered. Improved quality and interpretability of the discovered putative driver gene sets compared to existing methods shows that QuaDMutNetEx is a valuable new tool for detecting driver genes. QuaDMutNetEx is available for download from https://github.com/bokhariy/QuaDMutNetExunder the GNU GPLv3 license.

QuaDMutEx: quadratic driver mutation explorer

BACKGROUND

Somatic mutations accumulate in human cells throughout life. Some may have no adverse consequences, but some of them may lead to cancer. A cancer genome is typically unstable, and thus more mutations can accumulate in the DNA of cancer cells. An ongoing problem is to figure out which mutations are drivers - play a role in oncogenesis, and which are passengers - do not play a role. One way of addressing this question is through inspection of somatic mutations in DNA of cancer samples from a cohort of patients and detection of patterns that differentiate driver from passenger mutations.

RESULTS

We propose QuaDMutEx, a method that incorporates three novel elements: a new gene set penalty that includes non-linear penalization of multiple mutations in putative sets of driver genes, an ability to adjust the method to handle slow- and fast-evolving tumors, and a computationally efficient method for finding gene sets that minimize the penalty, through a combination of heuristic Monte Carlo optimization and exact binary quadratic programming. Compared to existing methods, the proposed algorithm finds sets of putative driver genes that show higher coverage and lower excess coverage in eight sets of cancer samples coming from brain, ovarian, lung, and breast tumors.

CONCLUSIONS

Superior ability to improve on both coverage and excess coverage on different types of cancer shows that QuaDMutEx is a tool that should be part of a state-of-the-art toolbox in the driver gene discovery pipeline. It can detect genes harboring rare driver mutations that may be missed by existing methods. QuaDMutEx is available for download from https://github.com/bokhariy/QuaDMutEx under the GNU GPLv3 license.

Computational Methods for Characterizing Cancer Mutational Heterogeneity

Advances in DNA sequencing technologies have allowed the characterization of somatic mutations in a large number of cancer genomes at an unprecedented level of detail, revealing the extreme genetic heterogeneity of cancer at two different levels: inter-tumor, with different patients of the same cancer type presenting different collections of somatic mutations, and intra-tumor, with different clones coexisting within the same tumor. Both inter-tumor and intra-tumor heterogeneity have crucial implications for clinical practices. Here, we review computational methods that use somatic alterations measured through next-generation DNA sequencing technologies for characterizing tumor heterogeneity and its association with clinical variables. We first review computational methods for studying inter-tumor heterogeneity, focusing on methods that attempt to summarize cancer heterogeneity by discovering pathways that are commonly mutated across different patients of the same cancer type. We then review computational methods for characterizing intra-tumor heterogeneity using information from bulk sequencing data or from single cell sequencing data. Finally, we present some of the recent computational methodologies that have been proposed to identify and assess the association between inter- or intra-tumor heterogeneity with clinical variables.

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.

Pathway hunting by random survival forests

MOTIVATION

Pathway or gene set analysis has been widely applied to genomic data. Many current pathway testing methods use univariate test statistics calculated from individual genomic markers, which ignores the correlations and interactions between candidate markers. Random forests-based pathway analysis is a promising approach for incorporating complex correlation and interaction patterns, but one limitation of previous approaches is that pathways have been considered separately, thus pathway cross-talk information was not considered.

RESULTS

In this article, we develop a new pathway hunting algorithm for survival outcomes using random survival forests, which prioritize important pathways by accounting for gene correlation and genomic interactions. We show that the proposed method performs favourably compared with five popular pathway testing methods using both synthetic and real data. We find that the proposed methodology provides an efficient and powerful pathway modelling framework for high-dimensional genomic data.

AVAILABILITY

The R code for the analysis used in this article is available upon request.

Exposure to depleted uranium does not alter the co-expression of HER-2/neu and p53 in breast cancer patients

Background

Amongst the extensive literature on immunohistochemical profile of breast cancer, very little is found on populations exposed to a potential risk factor such as depleted uranium. This study looked at the immunohistochemical expression of HER-2/neu (c-erbB2) and p53 in different histological types of breast cancer found in the middle Euphrates region of Iraq, where the population has been exposed to high levels of depleted uranium.

Findings

The present investigation was performed over a period starting from September 2008 to April 2009. Formalin-fixed, paraffin-embedded blocks from 70 patients with breast cancer (62 ductal and 8 lobular carcinoma) were included in this study. A group of 25 patients with fibroadenoma was included as a comparative group, and 20 samples of normal breast tissue sections were used as controls. Labeled streptavidin-biotin (LSAB+) complex method was employed for immunohistochemical detection of HER-2/neu and p53.

The detection rate of HER-2/neu and p53 immunohistochemical expression were 47.14% and 35.71% respectively in malignant tumors; expression was negative in the comparative and control groups (p < 0.05).

HER-2/neu immunostaining was significantly associated with histological type, tumor size, nodal involvement, and recurrence of breast carcinoma (p < 0.05), p53 immunostaining was significantly associated with tumor size, nodal involvement and recurrence of breast cancer (p < 0.05). There was greater immunoexpression of HER-2/neu in breast cancer in this population, compared with findings in other populations.

Both biomarkers were positively correlated with each other. Furthermore, all the cases that co-expressed both HER-2/neu and p53 showed the most unfavorable biopathological profile.

Conclusion

P53 and HER-2/neu over-expression play an important role in pathogenesis of breast carcinoma. The findings indicate that in regions exposed to high levels of depleted uranium, although p53 and HER-2/neu overexpression are both high, correlation of their expression with age, grade, tumor size, recurrence and lymph node involvement is similar to studies that have been conducted on populations not exposed to depleted uranium. HER-2/neu expression in breast cancer was higher in this population, compared with results on non-exposed populations.

Cellular Apoptosis and Cytotoxicity of Phenolic Compounds: A Quantitative Structure−Activity Relationship Study

In this comprehensive study on the caspase-mediated apoptosis-inducing effect of 51 substituted phenols in a murine leukemia cell line (L1210), we determined the concentrations needed to induce caspase activity by 50% (I50) and utilized these data to develop the following quantitative structure-activity relationship (QSAR) model: log 1/I50 = 1.06 B5(2) + 0.33 B5(3) - 0.18pi(2,4) - 0.92. B5(3) and B5(2) represent steric terms, while pi(2,4) represents the hydrophobic character of the substituents on the ring. The strong dependence of caspase-mediated apoptosis on mostly steric parameters suggests that the process is a receptor-mediated interaction with caspases or mitochondrial proteins being the likely targets. Conversely, cytotoxicity studies of 65 electron-releasing phenols in the L1210 cell line led to the development of the following equation: log 1/ID50 = -1.39sigma+ - 0.28 B5(2,6) + 0.16 log P - 0.58I(2) - 1.04I(1) + 3.90. The low coefficient with log P may pertain to cellular transport that may be enhanced by a modest increase in overall hydrophobicity, while the presence of sigma+ is consistent with the suggestion that radical stabilization is of prime importance in the case of electron-releasing substituents. On the other hand, the QSAR for the interactions of 27 electron-attracting phenols in L1210 cells, log 1/ID50 = 0.56 log P - 0.30 B5(2) + 2.79, suggests that hydrophobicity, as represented by log P is of critical importance. Similar cytotoxicity patterns are observed in other mammalian cell lines such as HL-60, MCF-7, CCRF-CEM, and CEM/VLB. The significant differences between the cytotoxicity and apoptosis QSAR for electron-releasing phenols suggest that cytotoxicity involves minimal apoptosis in most of these substituted monophenols.

Sustained activation of mitogen-activated protein kinases and activator protein 1 by the hepatitis B virus X protein in mouse hepatocytes in vivo

Transcriptional activation of diverse cellular genes by the X protein (HBx) of hepatitis B virus (HBV) has been suggested as one of the mechanisms for HBV-associated hepatocellular carcinoma. However, such functions of HBx have been studied using transformed cells in culture and have not been examined in the normal adult hepatocytes, a natural host of HBV. Using an efficient hepatocyte-specific virus-based gene delivery system developed in our laboratory earlier, we studied the HBx action in vivo. We demonstrate that following virosome-mediated delivery of HBx DNA, a large population (>50%) of hepatocytes express the HBx protein in a dose-dependent manner, which induces a significant increase in the activity of extracellular signal-regulated kinases (ERKs) in the livers of HBx-transfected mice. Inhibition of HBx-induced ERK activation following intravenous administration of PD98059, a mitogen-activated protein kinase kinase kinase (MEK) inhibitor, confirmed the requirement for MEK in the activation of ERKs by HBx. Induction of ERK activity by HBx was sustained for up to 30 days. Interestingly, sustained activation of c-Jun N-terminal kinases (JNKs) for up to 30 days was also noted. Such constitutive ERK and JNK activation as a consequence of continued HBx expression also led to sustained stimulation of further downstream events, such as increased levels of c-Jun and c-Fos proteins along with the persistent induction of activator protein 1 binding activity. Taken together, our data suggest a critical role of these molecules in HBx-mediated cell transformation.

Ras-dependent cell cycle commitment during G2 phase

Synchronization used to study cell cycle progression may change the characteristics of rapidly proliferating cells. By combining time-lapse, quantitative fluorescent microscopy and microinjection, we have established a method to analyze the cell cycle progression of individual cells without synchronization. This new approach revealed that rapidly growing NIH3T3 cells make a Ras-dependent commitment for completion of the next cell cycle while they are in G2 phase of the preceding cell cycle. Thus, Ras activity during G2 phase induces cyclin D1 expression. This expression continues through the next G1 phase even in the absence of Ras activity, and drives cells into S phase.

Non-thiol farnesyltransferase inhibitors: the concept of benzophenone-based bisubstrate analogue farnesyltransferase inhibitors

Replacement of the thiol in a benzophenone-based CAAX-peptidomimetic farnesyltransferase inhibitor by a carboxylic acid moiety resulted in a marked drop in inhibitory potency. Transformation of these carboxylic acid derivatives into bisubstrate analogues by addition of a lipophilic alkyl chain, which should be able to occupy considerable portions of the farnesyl binding region in the farnesyltransferase's active site, resulted in a regain of the inhibitory activity. These bisubstrate analogues represent new lead structures for non-thiol farnesyltransferase inhibitors.

Proteomic analysis of colonic crypts from normal, multiple intestinal neoplasia and p53-null mice: a comparison with colonic polyps

In order to observe cellular changes caused by mutation of the tumor suppressors, APC and p53, we have generated protein expression profiles of mouse colon epithelial cells using two-dimensional electrophoresis (2-DE). Crypts, polyps and stroma were isolated from normal, multiple intestinal neoplasia (MIN) and p53-null mice, each with a C57Black/6J background, and subjected to 2-DE in two separate pH ranges (pH 3-10 and pH 6-11). No significant differences in protein expression patterns were observed between the normal, MIN and p53-null colon epithelial crypts. However, 64 proteins from the MIN polyps showed a 2-fold or greater difference in intensity that was statistically significant as assessed by the Wilcoxon rank-sum test (p < or = 0.05). Of these, calreticulin, carbonic anhydrase I and a new member of the glutathione-S-transferase theta family of proteins have so far been identified using an in-gel digestion protocol coupled with reversed-phase high performance liquid chromatography (RP-HPLC) ion-trap mass spectrometry. In addition, 38 marker proteins have been identified in a continuing effort to generate a comprehensive 2-DE database of proteins expressed by mouse colon epithelial cells (these databases are available at http://www.ludwig.edu.au/jpsl/jpslhome. html).

Genetic instability and darwinian selection in tumours

Genetic instability has long been hypothesized to be a cardinal feature of cancer. Recent work has strengthened the proposal that mutational alterations conferring instability occur early during tumour formation. The ensuing genetic instability drives tumour progression by generating mutations in oncogenes and tumour-suppressor genes. These mutant genes provide cancer cells with a selective growth advantage, thereby leading to the clonal outgrowth of a tumour. Here, we discuss the role of genetic instability in tumour formation and outline future work necessary to substantiate the genetic instability hypothesis.