Although cancer heterogeneity, even within individual tumors with different treatment responses of subcloncal cells populations, suggests the need for personalized medicine, most funding and efforts go to conventional single gene-based research and comparative-effectiveness research. Cancer arises from changes in the DNA sequence in the genomes of cancer cells. These accelerating somatic mutations dysregulate signaling pathways, including EGFR, Wnt/Notch, Hedgehog and others, with a central role in cell growth, proliferation, survival, angiogenesis and metastasis. All of these genetic alterations can now be discovered using next-generation DNA sequencing technology. This high-throughput technology can achieve two major goals: first, to complete the catalogue of driver mutations, including point mutations, rearrangements and copy-number changes, by full and targeted sequencing; and second, to explore the functional role of cancer genes and their interactions by genome-wide RNA, serial analysis of gene expression, microRNAs, protein-DNA interactions, and comprehensive analyses of transcriptomes and interactomes. This review article discusses the challenges, including costs, in completing the catalogue of driver mutations for each cancer type and understanding how cancer genomes operate as whole biological systems. Now high-quality clinical treatment and outcomes (death or survival) data from biobanks, and extensive genetics and genomics data for some common tumors, including breast, colorectal and pancreatic cancer, are available. In this article, we will describe how all these clinical and genetics data could be integrated into reverse engineering-based network modeling to approach the extremely complex genotype-phenotype map. This clinico-genome systems model, published for the first time, opens the way for the discovery of new molecular innovations, both predictive markers and therapies, towards personalized treatment of cancer. Instead of the comparative-effectiveness research or personalized medicine debate, harmonization of both can revolutionize cancer management.

Novel clinico-genome network modeling for revolutionizing genotype-phenotype-based personalized cancer care

Although cancer heterogeneity, even within individual tumors with different treatment responses of subcloncal cells populations, suggests the need for personalized medicine, most funding and efforts go to conventional single gene-based research and comparative-effectiveness research. Cancer arises from changes in the DNA sequence in the genomes of cancer cells. These accelerating somatic mutations dysregulate signaling pathways, including EGFR, Wnt/Notch, Hedgehog and others, with a central role in cell growth, proliferation, survival, angiogenesis and metastasis. All of these genetic alterations can now be discovered using next-generation DNA sequencing technology. This high-throughput technology can achieve two major goals: first, to complete the catalogue of driver mutations, including point mutations, rearrangements and copy-number changes, by full and targeted sequencing; and second, to explore the functional role of cancer genes and their interactions by genome-wide RNA, serial analysis of gene expression, microRNAs, protein–DNA interactions, and comprehensive analyses of transcriptomes and interactomes. This review article discusses the challenges, including costs, in completing the catalogue of driver mutations for each cancer type and understanding how cancer genomes operate as whole biological systems. Now high-quality clinical treatment and outcomes (death or survival) data from biobanks, and extensive genetics and genomics data for some common tumors, including breast, colorectal and pancreatic cancer, are available. In this article, we will describe how all these clinical and genetics data could be integrated into reverse engineering-based network modeling to approach the extremely complex genotype– phenotype map. This clinico–genome systems model, published for the first time, opens the way for the discovery of new molecular innovations, both predictive markers and therapies, towards personalized treatment of cancer. Instead of the comparative-effectiveness research or personalized medicine debate, harmonization of both can revolutionize cancer management.

Novel clinico-genome network modeling for revolutionizing

Prostate cancer is now the commonest cancer in men in the Western world, with over 34,000 new cases per annum and a lifetime risk of 1 in 11 in the UK (Cancer Research UK Factsheets 2008). However, its aetiology remains very poorly understood. There is substantial worldwide variation in disease incidence, with the disease being much commoner in the Western countries than in the developing world and the Far East (Marugame and Katanoda 2006). These observations, together with studies of incidence rates in migrant populations (Lee et al. 2007), suggest strongly that lifestyle risk factors are important determinants of prostate cancer risk. To date, however, no definite lifestyle risk factors have been identified.

The Identification of Rare and Common Variants Which Predispose to Prostate Cancer

association studies (GWASs) is amplifying the numbers of genetic variants known to be associated with the risk of developing specific types of cancer. Three recently published studies have extended the list of common genetic variants associated with prostate cancer. In their two-stage GWAS, Gilles Thomas and colleagues identified four new loci — one on chromosome 7, two on chromosome 10 and one on chromosome 11 — that are associated with an increased risk of developing prostate cancer. The loci on chromosome 10 include microseminoprotein β (MSMB), a proposed biomarker for prostate cancer, and C-terminal binding protein 2 (CTBP2), a transcriptional corepressor that is highly expressed in prostate tissue. The locus on chromosome 7 encodes JAZF1, another transcriptional repressor that is translocated in endometrial cancer. Rosalind Eeles and colleagues carried out a GWAS of blood samples from a UK population of 1,854 men diagnosed with prostate cancer who were less than 60 years of age or had a familial history of the disease and 1,894 controls with low serum prostate-specific antigen (PSA). In addition, by screening a further 3,268 cases of prostate cancer and 3,366 controls from the UK and Australia, seven novel susceptibility loci were identified on chromosomes 3, 6, 7, 10, 11, 19 and X, with three of the loci containing candidate susceptibility genes encoding MSMB on chromosome 10 (in agreement with Thomas and colleagues), Lemur tyrosine kinase 2 (LMTK2) on chromosome 7 and kallikrein-related peptidase 3 (KLK3) on chromosome 19. Interestingly, they also found a single nucleotide polymorphism (SNP), rs5945619, that lies between NUDT10 and NUDT11 on the X chromosome. Julius Gudmundsson and colleagues also discovered a SNP in this region, rs5945572. They used data from their previously published GWAS on prostate cancer to follow up possible X-linked SNPs and possible variants associated with aggressive disease. The X-linked SNPs might explain why some families show greater risk between brothers rather than between fathers and sons. They also identified a SNP at 2p15 that is strongly associated with more aggressive forms of prostate cancer. All of these studies indicate that prostate cancer is a genetically complex disease with many weak to moderate genetic variants that might lead to cancer development. Further studies are needed to assess the biological significance of these new susceptibility loci and their potential candidate genes. Nicola McCarthy

Genetics: A numbers game

Genome-wide association studies (GWAS) provide a powerful approach to identify common, low-penetrance disease loci without prior knowledge of location or function. GWAS have been conducted in five of the commonest cancer types: breast, prostate, colorectal and lung, and melanoma, and have identified more than 20 novel disease loci, confirming that susceptibility to these diseases is polygenic. Many of these loci were detected at low power, indicating that many further loci will probably be detected with larger studies. For the most part, the loci were not previously suspected to be related to carcinogenesis, and point to new disease mechanisms. The risks conferred by the susceptibility alleles are low, generally 1.3-fold or less. The combined effects may, however, be sufficiently large to be useful for risk prediction, and targeted screening and prevention, particularly as more loci are identified.

/pdf/genome-wide-association-studies-in-cancer-5lnuepy9ax.pdf

Genome-wide association studies in cancer

5810 Prostate cancer (PrCa) is the commonest cancer in developed countries. It exhibits consistent evidence of familial aggregation, but the causes of this aggregation are mostly unknown. To identify common PrCa susceptibilility alleles, we conducted a genome-wide association study using 1,854 PrCa cases with clinically detected prostate cancer diagnosed at

Identification of multiple novel prostate cancer susceptibility loci by a genome-wide association study

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

David Dearnaley

Author Tools

Chat about Author

Papers

Identification of multiple novel prostate cancer susceptibility loci by a genome-wide association study