Sequence-based cancer genomics: progress, lessons and opportunities

Targeting C21orf58 is a Novel Treatment Strategy of Hepatocellular Carcinoma by Disrupting the Formation of JAK2/C21orf58/STAT3 Complex

Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related death worldwide. Functionally uncharacterized genes are an attractive repository to explore candidate oncogenes. It is demonstrated that C21orf58 displays an oncogenic role in promoting cell growth, tumorigenesis and sorafenib resistance of HCC cells by abnormal activation of STAT3 signaling. Mechanistically, a novel manner to regulate STAT3 signaling that adaptor C21orf58 forms a ternary complex is reveal with N-terminal domain of STAT3 and SH2 domain of JAK2, by which C21orf58 overactivates wild-type STAT3 by facilitating its phosphorylation mediated by JAK2, and hyper-activates of constitutively mutated STAT3 due to preferred binding with C21orf58 and JAK2. Moreover, it is validated that inhibition of C21orf58 with drug alminoprofen, selected by virtual screening, could effectively repress the viability and tumorigenesis of HCC cells. Therefore, it is identified that C21orf58 functions as an oncogenic adaptor, reveal a novel regulatory mechanism of JAK2/STAT3 signaling, explain the cause of abnormal activity of activated mutants of STAT3, and explore the attractive therapeutic potential by targeting C21orf58 in HCC.

Omics-based molecular techniques in oral pathology centred cancer: prospect and challenges in Africa

BACKGROUND

The completion of the human genome project and the accomplished milestones in the human proteome project; as well as the progress made so far in computational bioinformatics and "big data" processing have contributed immensely to individualized/personalized medicine in the developed world.

MAIN BODY

At the dawn of precision medicine, various omics-based therapies and bioengineering can now be applied accurately for the diagnosis, prognosis, treatment, and risk stratification of cancer in a manner that was hitherto not thought possible. The widespread introduction of genomics and other omics-based approaches into the postgraduate training curriculum of diverse medical and dental specialties, including pathology has improved the proficiency of practitioners in the use of novel molecular signatures in patient management. In addition, intricate details about disease disparity among different human populations are beginning to emerge. This would facilitate the use of tailor-made novel theranostic methods based on emerging molecular evidences.

CONCLUSION

In this review, we examined the challenges and prospects of using currently available omics-based technologies vis-à-vis oral pathology as well as prompt cancer diagnosis and treatment in a resource limited setting.

Understanding cancer complexome using networks, spectral graph theory and multilayer framework

Cancer complexome comprises a heterogeneous and multifactorial milieu that varies in cytology, physiology, signaling mechanisms and response to therapy. The combined framework of network theory and spectral graph theory along with the multilayer analysis provides a comprehensive approach to analyze the proteomic data of seven different cancers, namely, breast, oral, ovarian, cervical, lung, colon and prostate. Our analysis demonstrates that the protein-protein interaction networks of the normal and the cancerous tissues associated with the seven cancers have overall similar structural and spectral properties. However, few of these properties implicate unsystematic changes from the normal to the disease networks depicting difference in the interactions and highlighting changes in the complexity of different cancers. Importantly, analysis of common proteins of all the cancer networks reveals few proteins namely the sensors, which not only occupy significant position in all the layers but also have direct involvement in causing cancer. The prediction and analysis of miRNAs targeting these sensor proteins hint towards the possible role of these proteins in tumorigenesis. This novel approach helps in understanding cancer at the fundamental level and provides a clue to develop promising and nascent concept of single drug therapy for multiple diseases as well as personalized medicine.

Network Analysis of Cancer-focused Association Network Reveals Distinct Network Association Patterns

Cancer is a complex and heterogeneous disease. Genetic methods have uncovered thousands of complex tissue-specific mutation-induced effects and identified multiple disease gene targets. Important associations between cancer and other biological entities (eg, genes and drugs) in cancer network, however, are usually scattered in biomedical publications. Systematic analyses of these cancer-specific associations can help highlight the hidden associations between different cancer types and related genes/drugs. In this paper, we proposed a novel network-based computational framework to identify statistically over-expressed subnetwork patterns called network motifs (NMs) in an integrated cancer-specific drug-disease-gene network extracted from Semantic MEDLINE, a database containing extracted associations from MEDLINE abstracts. Eight significant NMs were identified and considered as the backbone of the cancer association network. Each NM corresponds to specific biological meanings. We demonstrated that such approaches will facilitate the formulization of novel cancer research hypotheses, which is critical for translational medicine research and personalized medicine in cancer.

A systems biology approach identifies effective tumor-stroma common targets for oral squamous cell carcinoma

The complex interactions between cancer cells and their surrounding stromal microenvironment play important roles in tumor initiation and progression and represent viable targets for therapeutic intervention. Here, we propose a concept of common target perturbation (CTP). CTP acts simultaneously on the same target in both the tumor and its stroma that generates a bilateral disruption for potentially improved cancer therapy. To employ this concept, we designed a systems biology strategy by combining experiment and computation to identify potential common target. Through progressive cycles of identification, TGF-β receptor III (TβRIII) is found as an epithelial-mesenchymal common target in oral squamous cell carcinoma. Simultaneous perturbation of TβRIII in the oral cancerous epithelial cells and their adjacent carcinoma-associated fibroblasts effectively inhibits tumor growth in vivo, and shows superiority to the unilateral perturbation of TβRIII in either cell type alone. This study indicates the strong potential to identify therapeutic targets by considering cancer cells and their adjacent stroma simultaneously. The CTP concept combined with our common target discovery strategy provides a framework for future targeted cancer combinatorial therapies.

Predicting potential cancer genes by integrating network properties, sequence features and functional annotations

The discovery of novel cancer genes is one of the main goals in cancer research. Bioinformatics methods can be used to accelerate cancer gene discovery, which may help in the understanding of cancer and the development of drug targets. In this paper, we describe a classifier to predict potential cancer genes that we have developed by integrating multiple biological evidence, including protein-protein interaction network properties, and sequence and functional features. We detected 55 features that were significantly different between cancer genes and non-cancer genes. Fourteen cancer-associated features were chosen to train the classifier. Four machine learning methods, logistic regression, support vector machines (SVMs), BayesNet and decision tree, were explored in the classifier models to distinguish cancer genes from non-cancer genes. The prediction power of the different models was evaluated by 5-fold cross-validation. The area under the receiver operating characteristic curve for logistic regression, SVM, Baysnet and J48 tree models was 0.834, 0.740, 0.800 and 0.782, respectively. Finally, the logistic regression classifier with multiple biological features was applied to the genes in the Entrez database, and 1976 cancer gene candidates were identified. We found that the integrated prediction model performed much better than the models based on the individual biological evidence, and the network and functional features had stronger powers than the sequence features in predicting cancer genes.

In silico identification of oncogenic potential of fyn-related kinase in hepatocellular carcinoma

MOTIVATION

Cancer development is a complex and heterogeneous process. It is estimated that 5-10% of human genes probably contribute to oncogenesis, whereas current experimentally validated cancer genes only cover 1% of the human genome. Thus hundreds of cancer genes may still remain to be identified. To search for new genes that play roles in carcinogenesis and facilitate cancer research, we developed a systematic workflow to use information saved in a previously established tumor-associated gene (TAG) database.

RESULTS

By exploiting the information of conserved protein domains from the TAG, we identified 183 potential new TAGs. As a proof-of-concept, one predicted oncogene, fyn-related kinase (FRK), which shows an aberrant digital expression pattern in liver cancer cells, was selected for further investigation. Using 68 paired hepatocellular carcinoma samples, we found that FRK was up-regulated in 52% of cases (P < 0.001). Tumorigenic assays performed in Hep3B and HepG2 cell lines revealed a significant correlation between the level of FRK expression and invasiveness, suggesting that FRK is a positive regulator of invasiveness in liver cancer cells.

CONCLUSION

These findings implied that FRK is a multitalented signal transduction molecule that produces diverse biological responses in different cell types in various microenvironments. In addition, our data demonstrated the accuracy of computational prediction and suggested that other predicted TAGs can be potential targets for future cancer research.

AVAILABILITY

The TAG database is available online at the Bioinformatics Center website: http://www.binfo.ncku.edu.tw/TAG/.

Personalized medicine: marking a new epoch in cancer patient management

Personalized medicine (PM) is defined as "a form of medicine that uses information about a person's genes, proteins, and environment to prevent, diagnose, and treat disease." The promise of PM has been on us for years. The suite of clinical applications of PM in cancer is broad, encompassing screening, diagnosis, prognosis, prediction of treatment efficacy, patient follow-up after surgery for early detection of recurrence, and the stratification of patients into cancer subgroup categories, allowing for individualized therapy. PM aims to eliminate the "one size fits all" model of medicine, which has centered on reaction to disease based on average responses to care. By dividing patients into unique cancer subgroups, treatment and follow-up can be tailored for each individual according to disease aggressiveness and the ability to respond to a certain treatment. PM is also shifting the emphasis of patient management from primary patient care to prevention and early intervention for high-risk individuals. In addition to classic single molecular markers, high-throughput approaches can be used for PM including whole genome sequencing, single-nucleotide polymorphism analysis, microarray analysis, and mass spectrometry. A common trend among these tools is their ability to analyze many targets simultaneously, thus increasing the sensitivity, specificity, and accuracy of biomarker discovery. Certain challenges need to be addressed in our transition to PM including assessment of cost, test standardization, and ethical issues. It is clear that PM will gradually continue to be incorporated into cancer patient management and will have a significant impact on our health care in the future.

[Identifying candidate cancer genes based on co-evolving gene modules]

Data of somatic mutation screening of cancer genomes have provided us huge amounts of information for identifying new cancer genes. Current methods for identifying candidate cancer genes based on gene mutation frequencies tend to find cancer genes with high mutation frequencies. However, many genes with low mutation frequencies might also play important roles during tumorigenesis. Based on the assumption that genes with similar phylogenetic profiles and protein-protein interactions might have similar functions and their disruptions might lead to similar disease phenotypes, we proposed a new approach to find candidate cancer genes. First, we searched for protein-protein interaction subnetworks within which proteins have similar phylogenetic profiles, termed as co-evolving gene modules. Then, we identified genes that have at least one non-synonymous mutation in cancer genomes and directly interact with known cancer genes in the same co-evolving gene modules and predicted them as candidate cancer genes. In this way, we found 15 candidate cancer genes, among which only two genes had been identified previously as candidate cancer genes using the methods based on gene mutation frequencies. Thus, the candidate cancer genes with low mutation frequencies can be found by our method.

Epstein-Barr virus-transformation of B-cell lines in ovarian cancer patients: feasibility of genomic storage for unlimited use

OBJECTIVE

The aim of the current study is to test whether immortalized B-lymphocyte cell line via Ebstein-Barr virus (EBV) transformation is feasible and can be an unlimited source of genome wide study.

METHODS

We obtained peripheral whole blood from 5 ovarian cancer patients and immortalized the B-cell lines using EBV transformation. The success rate was analyzed and the bio-identity of the genome was performed using human leukocyte antigen (HLA) identity test.

RESULTS

EBV transformation was successful in all 5 cases (95% confidence interval, 46.3% to 100%). After cryopreservation of EBV-transformed B-cell lines and subsequent thawing, we observed that all cell lines were viable and proliferative. To check bio-identity, HLA-A, B, and DR were tested between the genome of the original samples and the transformed samples. The HLA typing revealed that all observed HLA-A, B, and DR type was identical in 5 cases before and after EBV-transformation.

CONCLUSION

The current results suggest that EBV-transformation of peripheral blood is an efficient tool in genome banking. The EBV-transformed B-cell lines may be a valuable resource of genome in multi-center translational research by the Korean Gynecologic Oncology Group.

Whole-genome cancer analysis as an approach to deeper understanding of tumour biology

Recent advances in DNA sequencing technology are providing unprecedented opportunities for comprehensive analysis of cancer genomes, exomes, transcriptomes, as well as epigenomic components. The integration of these data sets with well-annotated phenotypic and clinical data will expedite improved interventions based on the individual genomics of the patient and the specific disease.

A comparison of two methods for screening CEBPA mutations in patients with acute myeloid leukemia

The goal of the study was to compare the performance of a fluorescence-based multiplex PCR fragment analysis to a direct sequencing method for detecting CEBPA mutations in patients with acute myeloid leukemia. Thirty-three samples were selected from a larger study of 107 cases of acute myeloid leukemia by screening for CEBPA mutations by sequence analysis. Of ten identified mutations, six (insertions and deletions) were detected by both sequencing and fragment methods. The fragment analysis method did not detect the remaining four base substitutions because the method cannot detect changes that result in identically sized products. The multiplex PCR fragment length analysis method therefore failed to detect substitution mutations accounting for 40% of total CEBPA mutations in our patient set. Our results indicate that fragment length analysis should not be used in isolation, and that direct sequencing is required to evaluate CEBPA gene mutational status in acute myeloid leukemia. A combination of the two assays may offer some advantages, chiefly in permitting more sensitive detection by fragment length analysis of insertions and deletions.

Utilizing the molecular gateway: the path to personalized cancer management

BACKGROUND

Personalized medicine is the provision of focused prevention, detection, prognostic, and therapeutic efforts according to an individual's genetic composition. The actualization of personalized medicine will require combining a patient's conventional clinical data with bioinformatics-based molecular-assessment profiles. This synergistic approach offers tangible benefits, such as heightened specificity in the molecular classification of cancer subtypes, improved prognostic accuracy, targeted development of new therapies, novel applications for old therapies, and tailored selection and delivery of chemotherapeutics.

CONTENT

Our ability to personalize cancer management is rapidly expanding through biotechnological advances in the postgenomic era. The platforms of genomics, proteomics, single-nucleotide polymorphism profiling and haplotype mapping, high-throughput genomic sequencing, and pharmacogenomics constitute the mechanisms for the molecular assessment of a patient's tumor. The complementary data derived during these assessments is processed through bioinformatics analysis to offer unique insights for linking expression profiles to disease detection, tumor response to chemotherapy, and patient survival. Together, these approaches permit improved physician capacity to assess risk, target therapies, and tailor a chemotherapeutic treatment course.

SUMMARY

Personalized medicine is poised for rapid growth as the insights provided by new bioinformatics models are integrated with current procedures for assessing and treating cancer patients. Integration of these biological platforms will require refinement of tissue-processing and analysis techniques, particularly in clinical pathology, to overcome obstacles in customizing our ability to treat cancer.

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.

Exploiting ESTs in human health

Expressed Sequence Tags (ESTs) are fragments of cDNA clones. They correspond to the most abundant type of cDNA information available in the public databases. ESTs have been used for expression profiling, gene identification, characterization of differentially expressed genes, and identification of transcript variants among other utilities. In this review I will discuss the major features of the collection of ESTs available in the public domain giving a special emphasis on how this dataset has been used in studies about human diseases.

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.

Aspects of coverage in medical DNA sequencing

Background

DNA sequencing is now emerging as an important component in biomedical studies of diseases like cancer. Short-read, highly parallel sequencing instruments are expected to be used heavily for such projects, but many design specifications have yet to be conclusively established. Perhaps the most fundamental of these is the redundancy required to detect sequence variations, which bears directly upon genomic coverage and the consequent resolving power for discerning somatic mutations.

Results

We address the medical sequencing coverage problem via an extension of the standard mathematical theory of haploid coverage. The expected diploid multi-fold coverage, as well as its generalization for aneuploidy are derived and these expressions can be readily evaluated for any project. The resulting theory is used as a scaling law to calibrate performance to that of standard BAC sequencing at 8× to 10× redundancy, i.e. for expected coverages that exceed 99% of the unique sequence. A differential strategy is formalized for tumor/normal studies wherein tumor samples are sequenced more deeply than normal ones. In particular, both tumor alleles should be detected at least twice, while both normal alleles are detected at least once. Our theory predicts these requirements can be met for tumor and normal redundancies of approximately 26× and 21×, respectively. We explain why these values do not differ by a factor of 2, as might intuitively be expected. Future technology developments should prompt even deeper sequencing of tumors, but the 21× value for normal samples is essentially a constant.

Conclusion

Given the assumptions of standard coverage theory, our model gives pragmatic estimates for required redundancy. The differential strategy should be an efficient means of identifying potential somatic mutations for further study.

Suppression subtractive hybridization profiles of radial growth phase and metastatic melanoma cell lines reveal novel potential targets

BACKGROUND

Melanoma progression occurs through three major stages: radial growth phase (RGP), confined to the epidermis; vertical growth phase (VGP), when the tumor has invaded into the dermis; and metastasis. In this work, we used suppression subtractive hybridization (SSH) to investigate the molecular signature of melanoma progression, by comparing a group of metastatic cell lines with an RGP-like cell line showing characteristics of early neoplastic lesions including expression of the metastasis suppressor KISS1, lack of alphavbeta3-integrin and low levels of RHOC.

METHODS

Two subtracted cDNA collections were obtained, one (RGP library) by subtracting the RGP cell line (WM1552C) cDNA from a cDNA pool from four metastatic cell lines (WM9, WM852, 1205Lu and WM1617), and the other (Met library) by the reverse subtraction. Clones were sequenced and annotated, and expression validation was done by Northern blot and RT-PCR. Gene Ontology annotation and searches in large-scale melanoma expression studies were done for the genes identified.

RESULTS

We identified 367 clones from the RGP library and 386 from the Met library, of which 351 and 368, respectively, match human mRNA sequences, representing 288 and 217 annotated genes. We confirmed the differential expression of all genes selected for validation. In the Met library, we found an enrichment of genes in the growth factors/receptor, adhesion and motility categories whereas in the RGP library, enriched categories were nucleotide biosynthesis, DNA packing/repair, and macromolecular/vesicular trafficking. Interestingly, 19% of the genes from the RGP library map to chromosome 1 against 4% of the ones from Met library.

CONCLUSION

This study identifies two populations of genes differentially expressed between melanoma cell lines from two tumor stages and suggests that these sets of genes represent profiles of less aggressive versus metastatic melanomas. A search for expression profiles of melanoma in available expression study databases allowed us to point to a great potential of involvement in tumor progression for several of the genes identified here. A few sequences obtained here may also contribute to extend annotated mRNAs or to the identification of novel transcripts.

Modeling mutations in protein structures

We describe an automated method for the modeling of point mutations in protein structures. The protein is represented by all non-hydrogen atoms. The scoring function consists of several types of physical potential energy terms and homology-derived restraints. The optimization method implements a combination of conjugate gradient minimization and molecular dynamics with simulated annealing. The testing set consists of 717 pairs of known protein structures differing by a single mutation. Twelve variations of the scoring function were tested in three different environments of the mutated residue. The best-performing protocol optimizes all the atoms of the mutated residue, with respect to a scoring function that includes molecular mechanics energy terms for bond distances, angles, dihedral angles, peptide bond planarity, and non-bonded atomic contacts represented by Lennard-Jones potential, dihedral angle restraints derived from the aligned homologous structure, and a statistical potential for non-bonded atomic interactions extracted from a large set of known protein structures. The current method compares favorably with other tested approaches, especially when predicting long and flexible side-chains. In addition to the thoroughness of the conformational search, sampled degrees of freedom, and the scoring function type, the accuracy of the method was also evaluated as a function of the flexibility of the mutated side-chain, the relative volume change of the mutated residue, and its residue type. The results suggest that further improvement is likely to be achieved by concentrating on the improvement of the scoring function, in addition to or instead of increasing the variety of sampled conformations.

Promoting transcriptome diversity

Although the number of protein-encoding human genes is more limited than many had estimated, the human transcript repertoire is much more diverse than anticipated. In part, transcript diversity is generated through the use of alternative promoters and alternate splicing. In addition, based on discoveries using technologies such as full-length cDNA libraries and whole genome tiling microarrays, it is now likely that non-protein-encoding transcripts comprise a substantial fraction of the human RNA population. Much attention is currently focused on understanding the role of alternative promoters in generating transcript diversity, both for non-protein-encoding (ncRNAs) and protein-encoding RNAs.

PolyScan: an automatic indel and SNP detection approach to the analysis of human resequencing data

Small insertions and deletions (indels) and single nucleotide polymorphisms (SNPs) are common genetic variants that are thought to be associated with a wide variety of human diseases. Owing to the genome's size and complexity, manually characterizing each one of these variations in an individual is not practical. While significant progress has been made in automated single-base mutation discovery from the sequences of diploid PCR products, automated and reliable detection of indels continues to pose difficult challenges. In this paper, we present PolyScan, an algorithm and software implementation designed to provide de novo heterozygous indel detection and improved SNP identification in the context of high-throughput medical resequencing. Tests on a human diploid PCR-based sequence data set, consisting of 90,270 traces from 13 genes, indicate that PolyScan identified approximately 90% of the 151 consensus indel sites and approximately 84% of the 1546 heterozygous indels previously identified by manual inspection. Tests on tumor-derived data show that PolyScan better identifies high-quality, low-level mutations as compared with other mutation detection software. Moreover, SNP identification improves when reprocessing the results of other programs. These results suggest that PolyScan may play a useful role in the post human genome project research era.

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.

Computational prediction of cancer-gene function

Most cancer genes remain functionally uncharacterized in the physiological context of disease development. High-throughput molecular profiling and interaction studies are increasingly being used to identify clusters of functionally linked gene products related to neoplastic cell processes. However, in vivo determination of cancer-gene function is laborious and inefficient, so accurately predicting cancer-gene function is a significant challenge for oncologists and computational biologists alike. How can modern computational and statistical methods be used to reliably deduce the function(s) of poorly characterized cancer genes from the newly available genomic and proteomic datasets? We explore plausible solutions to this important challenge.

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.

Structural and functional properties of genes involved in human cancer

BACKGROUND

One of the main goals of cancer genetics is to identify the causative elements at the molecular level leading to cancer.

RESULTS

We have conducted an analysis of a set of genes known to be involved in cancer in order to unveil their unique features that can assist towards the identification of new candidate cancer genes.

CONCLUSION

We have detected key patterns in this group of genes in terms of the molecular function or the biological process in which they are involved as well as sequence properties. Based on these features we have developed an accurate Bayesian classification model with which human genes have been scored for their likelihood of involvement in cancer.

SNP500Cancer: a public resource for sequence validation, assay development, and frequency analysis for genetic variation in candidate genes

The SNP500Cancer database provides sequence and genotype assay information for candidate SNPs useful in mapping complex diseases, such as cancer. The database is an integral component of the NCI Cancer Genome Anatomy Project (http://cgap.nci.nih.gov). SNP500Cancer reports sequence analysis of anonymized control DNA samples (n = 102 Coriell samples representing four self-described ethnic groups: African/African-American, Caucasian, Hispanic and Pacific Rim). The website is searchable by gene, chromosome, gene ontology pathway, dbSNP ID and SNP500Cancer SNP ID. As of October 2005, the database contains >13 400 SNPs, 9124 of which have been sequenced in the SNP500Cancer population. For each analysed SNP, gene location and >200 bp of surrounding annotated sequence (including nearby SNPs) are provided, with frequency information in total and per subpopulation as well as calculation of Hardy-Weinberg equilibrium for each subpopulation. The website provides the conditions for validated sequencing and genotyping assays, as well as genotype results for the 102 samples, in both viewable and downloadable formats. A subset of sequence validated SNPs with minor allele frequency >5% are entered into a high-throughput pipeline for genotyping analysis to determine concordance for the same 102 samples. In addition, the results of genotype analysis for select validated SNP assays (defined as 100% concordance between sequence analysis and genotype results) are posted for an additional 280 samples drawn from the Human Diversity Panel (HDP). SNP500Cancer provides an invaluable resource for investigators to select SNPs for analysis, design genotyping assays using validated sequence data, choose selected assays already validated on one or more genotyping platforms, and select reference standards for genotyping assays. The SNP500Cancer database is freely accessible via the web page at http://snp500cancer.nci.nih.gov.

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

Last decade has led to the accumulation of large amounts of data on cancer genetics, opening an unprecedented access to the mapping of cancer genes in the human genome. Single-nucleotide polymorphisms (SNPs), the most common form of DNA variation in humans, emerge as an invaluable tool for cancer association studies. These genotypic markers can be used to assay how alleles of candidate genes correlate with the malignant phenotype, and may provide new clues into the genetic modifications that characterize cancer onset. In this cancer-oriented study, we detail an SNP mining strategy based on the analysis of expressed sequence tags among publicly available databases. Our whole-genome approach provides a comprehensive and unbiased description of nonsynonymous SNPs (nsSNPs) in tumoral versus normal tissues. To gain further insights into the possible relationships between genetic variation and altered phenotype, locations of a subset of nsSNPs were mapped onto protein domains known to be critical for protein function. Computational methods were also used to predict the potential impact of these cancer-associated nsSNPs on protein structure and function. We illustrate our approach through the detailed biochemical and structural characterization of a previously unknown cancer-associated mutation (G79C) affecting the 8 kDa dynein light chain (DNCL1).

Large-scale Transcriptome Analyses Reveal New Genetic Marker Candidates of Head, Neck, and Thyroid Cancer

A detailed genome mapping analysis of 213,636 expressed sequence tags (EST) derived from nontumor and tumor tissues of the oral cavity, larynx, pharynx, and thyroid was done. Transcripts matching known human genes were identified; potential new splice variants were flagged and subjected to manual curation, pointing to 788 putatively new alternative splicing isoforms, the majority (75%) being insertion events. A subset of 34 new splicing isoforms (5% of 788 events) was selected and 23 (68%) were confirmed by reverse transcription-PCR and DNA sequencing. Putative new genes were revealed, including six transcripts mapped to well-studied chromosomes such as 22, as well as transcripts that mapped to 253 intergenic regions. In addition, 2,251 noncoding intronic RNAs, eventually involved in transcriptional regulation, were found. A set of 250 candidate markers for loss of heterozygosis or gene amplification was selected by identifying transcripts that mapped to genomic regions previously known to be frequently amplified or deleted in head, neck, and thyroid tumors. Three of these markers were evaluated by quantitative reverse transcription-PCR in an independent set of individual samples. Along with detailed clinical data about tumor origin, the information reported here is now publicly available on a dedicated Web site as a resource for further biological investigation. This first in silico reconstruction of the head, neck, and thyroid transcriptomes points to a wealth of new candidate markers that can be used for future studies on the molecular basis of these tumors. Similar analysis is warranted for a number of other tumors for which large EST data sets are available.

Cancer genes and the pathways they control

The revolution in cancer research can be summed up in a single sentence: cancer is, in essence, a genetic disease. In the last decade, many important genes responsible for the genesis of various cancers have been discovered, their mutations precisely identified, and the pathways through which they act characterized. The purposes of this review are to highlight examples of progress in these areas, indicate where knowledge is scarce and point out fertile grounds for future investigation.

Using germ-line genetic variation to investigate and treat cancer

For many years, there has been spirited debate as to the relative importance of environmental and genetic factors in the pathogenesis of cancer. Current efforts to annotate the human genome for germ-line genetic variants should establish the foundation for dissecting the contribution of genetics to the risk for cancer susceptibility. Population-based studies should be conducted to determine the influence of germline genetic variation on cancer outcomes, including the efficacy of anti-cancer drugs and the risk for life-threatening toxicities. Although we are early in the investigation of the influence of germline genetics on cancer outcomes, it is likely that, in the future, it will be possible to individualize therapeutic interventions. In turn, knowledge of genetic risk factors could afford opportunities for prevention, early intervention and minimization of deleterious toxicities associated with cancer therapy.

Oncogenomics and the development of new cancer therapies

Scientists have sequenced the human genome and identified most of its genes. Now it is time to use these genomic data, and the high-throughput technology developed to generate them, to tackle major health problems such as cancer. To accelerate our understanding of this disease and to produce targeted therapies, further basic mutational and functional genomic information is required. A systematic and coordinated approach, with the results freely available, should speed up progress. This will best be accomplished through an international academic and pharmaceutical oncogenomics initiative.

RASL11A, member of a novel small monomeric GTPase gene family, is down-regulated in prostate tumors

We performed a genome-wide search for novel loci encoding for Ras-related proteins based on the genome mapping coordinates of the cancer-derived EST dataset at GenBank. Partial sequences from two novel human genes were identified and subsequently used for full length transcript cloning. RASL11A and ARL9 belong to two novel subfamilies coding for small GTPases that we found to be highly conserved among eukaryotes. The Arl9/Arl10 subfamily displays a conserved interswitch toggle that places it evolutionarily closer to the Arf family. Rasl11 proteins are more closely related to the Ras branch of GTPases. All orthologues newly identified here exhibit an Asn residue in place of the highly conserved Thr35 of the G domain, suggesting that the universal switch mechanism of small GTPases may be structurally different in this subfamily. We determined by Northern blot that RASL11A is transcribed in several human tissues and that it is down-regulated in prostate tumors as measured by quantitative real-time PCR. These results highlight a previously uncharacterized subfamily of Ras-related genes that may have a tumor suppressor role in prostate cancer.

SNP500Cancer: a public resource for sequence validation and assay development for genetic variation in candidate genes

The SNP500Cancer Database provides sequence and genotype assay information for candidate single nucleotide polymorphisms (SNPs) useful in mapping complex diseases, such as cancer. The database is an integral component of the NCI's Cancer Genome Anatomy Project. SNP500Cancer provides bi-directional sequencing information on a set of control DNA samples derived from anonymized subjects (102 Coriell samples representing four self-described ethnic groups: African/African-American, Caucasian, Hispanic and Pacific Rim). All SNPs are chosen from public databases and reports, and the choice of genes includes a bias towards non-synonymous and promoter SNPs in genes that have been implicated in one or more cancers. The web site is searchable by gene, chromosome, gene ontology pathway and by known dbSNP ID. As of July 2003, the database contains over 3400 SNPs, 2490 of which have been sequenced in the SNP500Cancer population. For each analyzed SNP, gene location and over 200 bp of surrounding annotated sequence (including nearby SNPs) are provided, with frequency information in total and per subpopulation, and calculation of Hardy-Weinberg Equilibrium (HWE) for each subpopulation. Sequence validated SNPs with minor allele frequency > 5% are entered into a high-throughput pipeline for genotyping analysis to determine concordance for the same 102 samples. The website provides the conditions for validated genotyping assays. SNP500Cancer provides an invaluable resource for investigators to select SNPs for analysis, design genotyping assays using validated sequence data, choose selected assays already validated on one or more genotyping platforms, and select reference standards for genotyping assays. The SNP500Cancer Database is freely accessible via the web page at http://snp500cancer.nci.nih.gov/.

A pilot study of high-throughput, sequence-based mutational profiling of primary human acute myeloid leukemia cell genomes

In this pilot study, we used primary human acute myeloid leukemia (AML) cell genomes as templates for exonic PCR amplification, followed by high-throughput resequencing, analyzing approximately 7 million base pairs of DNA from 140 AML samples and 48 controls. We identified six previously described, and seven previously undescribed sequence changes that may be relevant for AML pathogenesis. Because the sequencing templates were generated from primary AML cells, the technique favors the detection of mutations from the most dominant clones within the tumor cell mixture. This strategy represents a viable approach for the detection of potentially relevant, nonrandom mutations in primary human cancer cell genomes.