In silico whole-genome scanning of cancer-associated nonsynonymous SNPs and molecular characterization of a dynein light chain tumour variant

Dynein light chain 1 functions in somatic cyst cells regulate spermatogonial divisions in Drosophila

Stem cell progeny often undergo transit amplifying divisions before differentiation. In Drosophila, a spermatogonial precursor divides four times within an enclosure formed by two somatic-origin cyst cells, before differentiating into spermatocytes. Although germline and cyst cell-intrinsic factors are known to regulate these divisions, the mechanistic details are unclear. Here, we show that loss of dynein-light-chain-1 (DDLC1/LC8) in the cyst cells eliminates bag-of-marbles (bam) expression in spermatogonia, causing gonial cell hyperplasia in Drosophila testis. The phenotype is dominantly enhanced by Dhc64C (cytoplasmic Dynein) and didum (Myosin V) loss-of-function alleles. Loss of DDLC1 or Myosin V in the cyst cells also affects their differentiation. Furthermore, cyst cell-specific loss of ddlc1 disrupts Armadillo, DE-cadherin and Integrin-βPS localizations in the cyst. Together, these results suggest that Dynein and Myosin V activities, and independent DDLC1 functions in the cyst cells organize the somatic microenvironment that regulates spermatogonial proliferation and differentiation.

Surface-enhanced Raman scattering based ligase detection reaction

Genomics provides a comprehensive view of the complete genetic makeup of an organism. Individual sequence variations, as manifested by single nucleotide polymorphisms (SNPs), can provide insight into the basis for a large number of phenotypes and diseases including cancer. The ability rapidly screen for SNPs will have a profound impact on a number of applications, most notably personalized medicine. Here we demonstrate a new approach to SNP detection through the application of surface-enhanced Raman scattering (SERS) to the ligase detection reaction (LDR). The reaction uses two LDR primers, one of which contains a Raman enhancer and the other a reporter dye. In LDR, one of the primers is designed to interrogate the SNP. When the SNP being interrogated matches the discriminating primer sequence, the primers are ligated and the enhancer and dye are brought into close proximity enabling the dye's Raman signature to be detected. By detecting the Raman signature of the dye rather than its fluorescence emission, our technique avoids the problem of spectral overlap which limits number of reactions which can be carried out in parallel by existing systems. We demonstrate the LDR-SERS reaction for the detection of point mutations in the human K-ras oncogene. The reaction is implemented in an electrokinetically active microfluidic device that enables physical concentration of the reaction products for enhanced detection sensitivity and quantization. We report a limit of detection of 20 pM of target DNA with the anticipated specificity engendered by the LDR platform.

Delocalization of the microtubule motor Dynein from mitotic spindles by the human papillomavirus E7 oncoprotein is not sufficient for induction of multipolar mitoses

Dynein is a minus end-directed microtubule motor that transports numerous cargoes throughout the cell. During mitosis, dynein motor activity is necessary for the positioning of spindle microtubules and has also been implicated in inactivating the spindle assembly checkpoint. Mutations in dynein motor and/or accessory proteins are associated with human disease, including cancer, and the delocalization of dynein from mitotic spindles has been correlated with an increased incidence of multipolar spindle formation in some cancer cells that contain supernumerary centrosomes. The high-risk human papillomavirus type 16 (HPV16) E7 oncoprotein induces centrosome overduplication and has been shown to cause multipolar mitotic spindle formation, a diagnostic hallmark of HPV-associated neoplasias. Here, we show that HPV16 E7 expression leads to an increased population of mitotic cells with dynein delocalized from the mitotic spindle. This function maps to sequences of HPV16 E7 that are distinct from the region necessary for centrosome overduplication. However, contrary to previous reports, we provide evidence that dynein delocalization by HPV16 E7 is neither necessary nor sufficient to cause the formation of multipolar mitoses.

Predicting cancer involvement of genes from heterogeneous data

Background

Systematic approaches for identifying proteins involved in different types of cancer are needed. Experimental techniques such as microarrays are being used to characterize cancer, but validating their results can be a laborious task. Computational approaches are used to prioritize between genes putatively involved in cancer, usually based on further analyzing experimental data.

Results

We implemented a systematic method using the PIANA software that predicts cancer involvement of genes by integrating heterogeneous datasets. Specifically, we produced lists of genes likely to be involved in cancer by relying on: (i) protein-protein interactions; (ii) differential expression data; and (iii) structural and functional properties of cancer genes. The integrative approach that combines multiple sources of data obtained positive predictive values ranging from 23% (on a list of 811 genes) to 73% (on a list of 22 genes), outperforming the use of any of the data sources alone. We analyze a list of 20 cancer gene predictions, finding that most of them have been recently linked to cancer in literature.

Conclusion

Our approach to identifying and prioritizing candidate cancer genes can be used to produce lists of genes likely to be involved in cancer. Our results suggest that differential expression studies yielding high numbers of candidate cancer genes can be filtered using protein interaction networks.

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.

In silico whole-genome screening for cancer-related single-nucleotide polymorphisms located in human mRNA untranslated regions

BACKGROUND

A promising application of the huge amounts of genetic data currently available lies in developing a better understanding of complex diseases, such as cancer. Analysis of publicly available databases can help identify potential candidates for genes or mutations specifically related to the cancer phenotype. In spite of their huge potential to affect gene function, no systematic attention has been paid so far to the changes that occur in untranslated regions of mRNA.

RESULTS

In this study, we used Expressed Sequence Tag (EST) databases as a source for cancer-related sequence polymorphism discovery at the whole-genome level. Using a novel computational procedure, we focused on the identification of untranslated region (UTR)-localized non-coding Single Nucleotide Polymorphisms (UTR-SNPs) significantly associated with the tumoral state. To explore possible relationships between genetic mutation and phenotypic variation, bioinformatic tools were used to predict the potential impact of cancer-associated UTR-SNPs on mRNA secondary structure and UTR regulatory elements. We provide a comprehensive and unbiased description of cancer-associated UTR-SNPs that may be useful to define genotypic markers or to propose polymorphisms that can act to alter gene expression levels. Our results suggest that a fraction of cancer-associated UTR-SNPs may have functional consequences on mRNA stability and/or expression.

CONCLUSION

We have undertaken a comprehensive effort to identify cancer-associated polymorphisms in untranslated regions of mRNA and to characterize putative functional UTR-SNPs. Alteration of translational control can change the expression of genes in tumor cells, causing an increase or decrease in the concentration of specific proteins. Through the description of testable candidates and the experimental validation of a number of UTR-SNPs discovered on the secreted protein acidic and rich in cysteine (SPARC) gene, this report illustrates the utility of a cross-talk between in silico transcriptomics and cancer genetics.

Mining expressed sequence tags identifies cancer markers of clinical interest

BACKGROUND

Gene expression data are a rich source of information about the transcriptional dis-regulation of genes in cancer. Genes that display differential regulation in cancer are a subtype of cancer biomarkers.

RESULTS

We present an approach to mine expressed sequence tags to discover cancer biomarkers. A false discovery rate analysis suggests that the approach generates less than 22% false discoveries when applied to combined human and mouse whole genome screens. With this approach, we identify the 200 genes most consistently differentially expressed in cancer (called HM200) and proceed to characterize these genes. When used for prediction in a variety of cancer classification tasks (in 24 independent cancer microarray datasets, 59 classifications total), we show that HM200 and the shorter gene list HM100 are very competitive cancer biomarker sets. Indeed, when compared to 13 published cancer marker gene lists, HM200 achieves the best or second best classification performance in 79% of the classifications considered.

CONCLUSION

These results indicate the existence of at least one general cancer marker set whose predictive value spans several tumor types and classification types. Our comparison with other marker gene lists shows that HM200 markers are mostly novel cancer markers. We also identify the previously published Pomeroy-400 list as another general cancer marker set. Strikingly, Pomeroy-400 has 27 genes in common with HM200. Our data suggest that a core set of genes are responsive to the deregulation of pathways involved in tumorigenesis in a variety of tumor types and that these genes could serve as transcriptional cancer markers in applications of clinical interest. Finally, our study suggests new strategies to select and evaluate cancer biomarkers in microarray studies.

Bioinformatic screening of human ESTs for differentially expressed genes in normal and tumor tissues

Background

Owing to the explosion of information generated by human genomics, analysis of publicly available databases can help identify potential candidate genes relevant to the cancerous phenotype. The aim of this study was to scan for such genes by whole-genome in silico subtraction using Expressed Sequence Tag (EST) data.

Methods

Genes differentially expressed in normal versus tumor tissues were identified using a computer-based differential display strategy. Bcl-xL, an anti-apoptotic member of the Bcl-2 family, was selected for confirmation by western blot analysis.

Results

Our genome-wide expression analysis identified a set of genes whose differential expression may be attributed to the genetic alterations associated with tumor formation and malignant growth. We propose complete lists of genes that may serve as targets for projects seeking novel candidates for cancer diagnosis and therapy. Our validation result showed increased protein levels of Bcl-xL in two different liver cancer specimens compared to normal liver. Notably, our EST-based data mining procedure indicated that most of the changes in gene expression observed in cancer cells corresponded to gene inactivation patterns. Chromosomes and chromosomal regions most frequently associated with aberrant expression changes in cancer libraries were also determined.

Conclusion

Through the description of several candidates (including genes encoding extracellular matrix and ribosomal components, cytoskeletal proteins, apoptotic regulators, and novel tissue-specific biomarkers), our study illustrates the utility of in silico transcriptomics to identify tumor cell signatures, tumor-related genes and chromosomal regions frequently associated with aberrant expression in cancer.