A comprehensive catalogue of functional genetic variations in the EGFR pathway: Protein–protein interaction analysis reveals novel genes and polymorphisms important for cancer research

The EGFR pathway is a critical signaling pathway deregulated in many solid tumors. In addition to the initiation and progression of cancer, the EGFR pathway is also implicated in variable treatment responses and prognoses. Genetic variation in the form of Single Nucleotide Polymorphisms (SNPs) can affect the function/expression of the EGFR pathway genes. Here, we applied a systematic and comprehensive approach utilizing diverse public databases and in silico analysis tools to select putative functional genetic variations from 244 genes involved in the EGFR pathway. Our data comprises 649 SNPs. Three hundred sixty SNPs are predicted to have biological consequences (functional SNPs). These SNPs can be directly used in further studies to test their association with risk, treatment response and prognosis in cancer. To systematically cover the EGFR pathway, we also performed a network‐based analysis to further select putative functional SNPs from the genes whose protein products physically interact with the EGFR pathway proteins. We utilized protein–protein interaction information and focused on 14 proteins that have a high degree of connectivity (interacting with ≥10 proteins) with the EGFR pathway genes identified to have functional SNPs (f‐EGFR genes). Two of these proteins (FYN and LCK) had interactions with 17 of the f‐EGFR genes, yet both lacked any putative functional SNP. However, our analysis indicated the presence of potentially functional SNPs in 9 other highly interactive proteins. The genes and their SNPs identified in the network‐based analysis represent potential candidates for gene–gene and SNP–SNP interaction studies in cancer research. © 2009 UICC     

Cancer networks and beyond: Interpreting mutations using the human interactome and protein structure

Over recent years, with the advances in next-generation sequencing, a large number of cancer mutations have been identified and accumulated in public repositories. Coupled to this is our increased ability to generate detailed interactome maps that help to enrich our knowledge of the biological implications of cancer mutations. As a result, network analysis approaches have become an invaluable tool to predict and interpret mutations that are associated with tumour survival and progression. Our understanding of cancer mechanisms is further enhanced by mapping protein structure information to such networks. Here we review the current methodologies for annotating the functional impacts of cancer mutations, which range from analysis of protein structures to protein–protein interaction network studies.     

Quantitative network mapping of the human kinome interactome reveals new clues for rational kinase inhibitor discovery and individualized cancer therapy

The human kinome is gaining importance through its promising cancer therapeutic targets, yet no general model to address the kinase inhibitor resistance has emerged. Here, we constructed a systems biology-based framework to catalogue the human kinome, including 538 kinase genes, in the broader context of the human interactome. Specifically, we constructed three networks: a kinase-substrate interaction network containing 7,346 pairs connecting 379 kinases to 36,576 phosphorylation sites in 1,961 substrates, a protein-protein interaction network (PPIN) containing 92,699 pairs, and an atomic resolution PPIN containing 4,278 pairs. We identified the conserved regulatory phosphorylation motifs (e.g., Ser/Thr-Pro) using a sequence logo analysis. We found the typical anticancer target selection strategy that uses network hubs as drug targets, might lead to a high adverse drug reaction risk. Furthermore, we found the distinct network centrality of kinases creates a high anticancer drug resistance risk by feedback or crosstalk mechanisms within cellular networks. This notion is supported by the systematic network and pathway analyses that anticancer drug resistance genes are significantly enriched as hubs and heavily participate in multiple signaling pathways. Collectively, this comprehensive human kinome interactome map sheds light on anticancer drug resistance mechanisms and provides an innovative resource for rational kinase inhibitor design.     

Clinical oncology in resource-limited settings

Infectious Agents and Cancer is introducing a new section of Clinical Oncology with the main objective of stimulating debate through articles published in the section. Infectious diseases have been the major causes of morbidity and mortality in human populations, and have dominated the medical approach to clinical and public health. Successful efforts to control mortality from acute infections have paved the way for chronic, mostly indolent, infections to become major causes of morbidity. Cancer, hitherto thought to be rare in resource-limited settings, is becoming a major contributor. The changes in mortality patterns are due, in part, to diseases linked to rapid changes in lifestyle, urbanization, and pollution. These diseases include many of the non-infection associated cancers. However, there is a dearth of information about the burden, pathogenesis, and therapeutic approaches about cancer in resource-limited countries. There are also substantial other challenges, including economic, infrastructure, technology, and personnel. The Journal advocates for interactive local–global (lo-bal) efforts to generate relevant knowledge about cancer burden, pathogenesis, and therapeutic approaches using a bottom-up approach to sharpen the focus on local and global relevance of research and clinical and public practice, particularly in resource-limited countries. The section on Clinical Oncology in Infectious Agents and Cancer will harness these “lo-bal” strategies to reduce substantially the time from concept, discovery, and development and implementation of locally and globally applicable diagnostic and therapeutic technologies.     

Proteomic analysis for the early detection and rational treatment of cancer—realistic hope?

Proteomics is an emerging field in medical science focused on the library of proteins specific to a given biosystem, the proteome, and understanding relationships therein. This field incorporates technologies that can be applied to serum and tissue in order to extract important biological information to aid clinicians and scientists in understanding the dynamic biology of their system of interest, such as a patient with cancer. These tools include laser capture microdissection, tissue lysate arrays and mass spectrometry approaches. These new technologies are more potent coupled with advanced bioinformatics analysis. They are used to characterize the content of, and changes in, the proteome induced by physiological changes, benign and pathologic. The application of these tools has assisted in the discovery of new biomarkers and may lead to new diagnostic tests and improvements in therapeutics. These tools additionally can provide a molecular characterization of cancers, which may allow for individualized molecular therapy. Understanding the basic concepts and tools used will illustrate how best to apply these technologies for patient benefit for the early detection of cancer and improved patient care.     

Analysis of Nuclear Encoded Mitochondrial Gene Networks in Cervical Cancer

Background: Cervical cancer (CC) is one of the most common female cancers in many developing and underdeveloped countries. High incidence, late presentation, and mortality suggested the need for molecular markers. Mitochondrial defects due to abnormal expression of nuclear-encoded mitochondrial genes (NEMG) have been reported during cancer progression. Nevertheless, the application of NEMG for the prognosis of CC is still elusive. Herein, we aimed to investigate the associations between NEMG and CC prognosis. Materials and Methods: The differentially expressed genes (DEG) in the TCGA-CESC dataset and NEMGs were retrieved from TACCO and Mitocarta2.0 databases, respectively. The impact of methylation on NEMG expression were predicted using DNMIVD and UALCAN tools. HCMDB tool was used to predict genes having metastatic potential. The prognostic models were constructed using DNMIVD, TACCO, GEPIA2, and SurvExpress. The functional enrichment analysis (FEA) was performed using clusterProfiler. The protein-protein interaction network (PPIN) was constructed to identify the hub genes (HG) using String and CytoHubba tools. Independent validation of the HG was performed using Oncomine and Human Protein Atlas databases. The druggable genes were predicted using DGIdb. Results: Among the 52 differentially expressed NEMG, 15 were regulated by DNA methylation. The expression level of 16, 10, and 7 has the potential for CC staging, prediction of metastasis, and prognosis. Moreover, 1 driver gene and 16 druggable genes were also identified. The FEA identified the enrichment of cancer-related pathways, including AMPK and carbon metabolism in cancer. The combined expression of 10 HG has been shown to affect patient survival. Conclusion: Our findings suggest that the abnormal expression of NEMGs may play a critical role in CC development and progression. The genes identified in our study may serve as a prognostic indicator and therapeutic target in CC.     

Microarray data simulator for improved selection of differentially expressed genes

The development of microarray technology has allowed researchers to measure expression levels of thousands of genes simultaneously. Analysis of these data requires the best normalization and statistical approaches to account for the biological and technical variability inherent in the technique. To approach this problem we have developed a publicly available simulator of microarray hybridization experiments that can be used to help assess the accuracy of bioinformatic tools in discovering significant genes. After analyzing microarray hybridization experiments from over 50 samples, an estimate of various degrees of technical and biological variability was obtained. This information was used to develop a simulator of microarray hybridization data which modeled "normal tissue samples" and "diseased tissue samples" with known, defined, changes in gene expression (a "gold standard"). The data derived from the simulator were then used to evaluate the true positive and false negative rates of several normalization procedures and gene selection techniques. We found that the type of normalization approach used was an important aspect of data analysis. Global normalization was the least accurate approach. Evaluation of gene selection techniques showed that "Significance analysis of microarrays" (SAM) and "Patterns of Gene Expression" (PaGE) were more accurate than simple t-test analysis. We provide access to the microarray hybridization simulator as a public resource for biologists to further test new emerging genomic bioinfomatic tools.     

Women’s Cancers in Developing Countries: From Research to an Integrated Health Systems Approach

The article focuses on two women’s cancers, breast and cervical cancer, that are much more deadly indeveloping countries than in developed countries. Early detection can make a significant difference for thetreatment outcome of these two cancers and there are now cost-effective tools for prevention and screening. Theauthors propose a new public health approach to these two cancers in developing countries where resources foreffective cancer control are very limited and offer a framework for putting women’s cancers in developingcountries on the global public health agenda. The key areas are: 1. Proposals for a new, integrated public healthapproach to women’s cancers (breast and cervical) in resource poor settings; 2. Reviews of the evidence forcost-effective screening and early detection of breast and cervical cancer, and discussion of some of the lessonslearned from HIV/AIDS on an integrated health systems approach; 3. Outlines of ways to make a priority ofwomen’s cancers in developing countries on the political agenda of international agencies.     

Informatics-enabled behavioral medicine in oncology

For the practicing physician, the behavioral implications of preventing, diagnosing, and treating cancer are many and varied. Fortunately, an enhanced capacity in informatics may help create a redesigned ecosystem in which applying evidence-based principles from behavioral medicine will become a routine part of care. Innovation to support this evolution will be spurred by the "meaningful use" criteria stipulated by the Health Information Technology for Economic and Clinical Health Act of 2009 and by focused research and development efforts within the broader health information ecosystem. The implications for how to better integrate evidence-based principles in behavioral medicine into oncology care through both spheres of development are discussed within the framework of the cancer control continuum. The promise of using the data collected through these tools to accelerate discovery in psycho-oncology is also discussed. If nurtured appropriately, these developments should help accelerate successes against cancer by altering the behavioral milieu.     

Food and cancer: state of the art about the protective effect of fruits and vegetables

Epidemiological studies performed during the last 20 years support an inverse relationship between the individual intake of fruits and vegetables and the risk of cancer. In taking into account some recent conflicting data, a working group of the Nacre network, the French Network for Food and Cancer Research, has conduced a critical analysis of epidemiological and experimental studies, including the preliminary data from the Epic cohort, the European Prospective Investigation into Cancer and Nutrition, to clarify the role of fruits and vegetables to prevent cancer. To date, a high intake of fruits and vegetables (at least, 400 g per day) is appropriate to lower the risk of cancer. Fruits and vegetables provide numerous phytochemicals which, in part, may explain their beneficial effect. Thus, studies in animal models and in cell-culture systems have furnished a lot of information about the potential mechanism by which a diet high in fruits and vegetables may reduce the risk of cancer in humans. However, more investigation in the identification of the biologically active constituents, in the knowledge of their availability and the mechanism by which they contribute to lower the risk of cancer, will increase the scientific support of a public health policy.     

基于TCGA数据库筛选胆管癌肿瘤微环境相关的预后基因

目的:运用ESTIMATE肿瘤微环境评分算法,从来自TCGA数据库的胆管癌数据中筛选与肿瘤微环境相关的基因,并利用生物信息学方法分析所得基因在胆管癌中的预后意义。方法:从TCGA数据库中获得45个胆管癌基因表达数据,利用ESTIMATE肿瘤微环境评分算法对其进行评分后,分为高分/低分组,分别筛选差异表达基因后,取交集获取共表达基因。利用DAVID在线分析工具根据对共表达基因进行 GO 功能富集分析和 KEGG通路富集分析。利用来自TCGA数据库中的胆管癌生存数据对共表达基因进行Kaplan-Meier生存分析,筛选后获得预后基因。结合STRING网站中对差异表达基因构建的蛋白互作网络（protein-protein interaction,PPI）,得到网络枢纽基因后使用其他生物学分析工具验证。结果:在45例胆管癌样本中分析筛选出11个与肿瘤微环境相关的预后基因,这些基因表达的上调对胆管癌的不良预后具有显著意义（P＜0.05）。通过蛋白互作网络分析和其他生物学工具分析获得VAV1基因,该基因在免疫调控、细胞凋亡、RET信号通路调控等方面有重要作用。结论:从本研究中筛选出的VAV1基因可作为胆管癌的分子标志物,为胆管癌的诊断、免疫治疗靶点及预后提供参考。     

G-DOC: A Systems Medicine Platform for Personalized Oncology

Currently, cancer therapy remains limited by a “one-size-fits-all” approach, whereby treatment decisions are based mainly on the clinical stage of disease, yet fail to reference the individual''s underlying biology and its role driving malignancy. Identifying better personalized therapies for cancer treatment is hindered by the lack of high-quality “omics” data of sufficient size to produce meaningful results and the ability to integrate biomedical data from disparate technologies. Resolving these issues will help translation of therapies from research to clinic by helping clinicians develop patient-specific treatments based on the unique signatures of patient''s tumor. Here we describe the Georgetown Database of Cancer (G-DOC), a Web platform that enables basic and clinical research by integrating patient characteristics and clinical outcome data with a variety of high-throughput research data in a unified environment. While several rich data repositories for high-dimensional research data exist in the public domain, most focus on a single-data type and do not support integration across multiple technologies. Currently, G-DOC contains data from more than 2500 breast cancer patients and 800 gastrointestinal cancer patients, G-DOC includes a broad collection of bioinformatics and systems biology tools for analysis and visualization of four major “omics” types: DNA, mRNA, microRNA, and metabolites. We believe that G-DOC will help facilitate systems medicine by providing identification of trends and patterns in integrated data sets and hence facilitate the use of better targeted therapies for cancer. A set of representative usage scenarios is provided to highlight the technical capabilities of this resource.     

CT影像组学在指导肺癌精准放疗中的应用进展

肺癌的精确诊断、放疗敏感性和正常组织放射性损伤的准确预测是实现肺癌精准放疗的必要前提。影像组学（radiomics）作为肺癌精准治疗发展史上一个具有里程碑意义的辅助工具,可以通过应用自动和半自动算法对肺癌影像资料的感兴趣区域提取大量影像特征,寻找这些特征与临床诊疗数据之间的深层关系,揭示肺癌的发生、发展及临床转归规律。影像组学可以无创获取肺部肿瘤整体异质性信息,在良恶性肺结节的判定、肿瘤基因表型和放疗反应的预测等方面具有巨大临床应用潜能。本文就CT影像组学在辅助肺癌放疗方面的最新研究进行综述。      

How well Informed are Cancer Patients Prior to a Life Cycle of Injectable Chemotherapy Drug Treatment?

Cancer patient dissatisfaction, due to a long waiting time for chemotherapy treatment, is a common complaint. To improve patient satisfaction, our pharmaceutical team was prompted to design a series of information tools for injectable chemotherapy drug treatment (ICDT) patients. This study was based on French Health Authorities recommendations. All three stages were monitored: the preparation stage using a 204 patient survey, the design stage, and the assessment stage with a 12 point questionnaire patient evaluation. An information brochure and a 10-min film were designed which chronologically described key stages in the life cycle of ICDT. Both tools were assessed by 29 and 84 patients respectively. The questionnaire confirmed that this approach met the needs of more than 90 % of patients. The brochure and the film also accurately met the objectives and improved the understanding of the chemotherapy long waiting time which resulted in higher patient satisfaction. The designed tools will continue to evolve with changes in oncology practices based on various indicators defined in the study. Our study proposes an original method to assist health professionals to better inform cancer patients regarding the preparation of ICDT. It is also a part of a continuous quality program to assure quality outpatient healthcare.     

A Competency-Based Approach to Expanding the Cancer Care Workforce Part III—Improving Cancer Pain and Palliative Care Competency

As part of an effort to address shortages in the cancer workforce, C-Change developed competency standards and logic model-driven implementation tools for strengthening the cancer knowledge and skills of non-oncology health professionals. These standards and tools were applied by four diverse grant programs to yield gains in the management of pain and palliative care, thereby improving the quality of care for individuals experiencing or recovering from cancer treatment. The results from the four grant sites and tools used to achieve them are described in this article.     

Advances in dynamic modeling of colorectal cancer signaling-network regions,a path toward targeted therapies

The interconnected network of pathways downstream of the TGFβ, WNT and EGF-families of receptor ligands play an important role in colorectal cancer pathogenesis.We studied and implemented dynamic simulations of multiple downstream pathways and described the section of the signaling network considered as a Molecular Interaction Map (MIM). Our simulations used Ordinary Differential Equations (ODEs), which involved 447 reactants and their interactions.Starting from an initial “physiologic condition”, the model can be adapted to simulate individual pathologic cancer conditions implementing alterations/mutations in relevant onco-proteins. We verified some salient model predictions using the mutated colorectal cancer lines HCT116 and HT29. We measured the amount of MYC and CCND1 mRNAs and AKT and ERK phosphorylated proteins, in response to individual or combination onco-protein inhibitor treatments. Experimental and simulation results were well correlated. Recent independently published results were also predicted by our model.Even in the presence of an approximate and incomplete signaling network information, a predictive dynamic modeling seems already possible. An important long term road seems to be open and can be pursued further, by incremental steps, toward even larger and better parameterized MIMs. Personalized treatment strategies with rational associations of signaling-proteins inhibitors, could become a realistic goal.     

The Future of Cancer Surveillance

Cancer surveillance is entering an exciting era where the scope of its activities will be expanded and the amount, quality, and depth of information on cancer will be richer and more readily available to practitioners, decision makers and the public. This future is being built on a solid history of accomplishment that has placed cancer foremost among all chronic diseases in the organization and implementation of a systematic and integrated monitoring enterprise that is of essential value in both clinical medicine and public health. The future of cancer surveillance will be driven not only by innovations in methods of cancer surveillance itself, but also by developments in information technology and communication and by revolutionary new tools used in the delivery of medical care. At the same time, it will be a challenge to ensure levels of privacy and confidentiality needed to maintain the public trust. In the Cancer Surveillance and Information Summit, a 2004 conference sponsored by C-Change, experts from the field and from allied and related disciplines in both the public and private sectors met to consider the future of the cancer surveillance enterprise. Seven recommendations, detailed in this article, emerged from the conference to guide future growth and development. Steps that can and should be taken by all individuals and groups involved in cancer surveillance were included. The shared view is that cancer surveillance and information is essential to fulfill a vision for a future where cancer is prevented, detected early, and either cured or managed successfully as a chronic illness.     

Patients' perceptions of transrectal prostate biopsy: a qualitative study

This study explored men's experiences of transrectal prostate biopsy. Fifty men who had had a prostate biopsy talked about the experience as part of an in-depth interview; 36 were interviewed in 2000 about all aspects of prostate cancer, and 14 in 2005 about their experience of prostate-specific antigen testing, subsequent investigations and treatment. Men were recruited via urologists, general practitioners and support groups. In both studies, we aimed to include men of various ages, from different backgrounds, who lived, and had been investigated and treated, in different parts of the UK. A qualitative interpretive approach was taken, combining thematic analysis with constant comparison. Most men described the procedure as merely 'uncomfortable', but some found it stressful, exhausting and extremely painful. Worries included the fear that cancer cells might pass from a man to his wife during ejaculation, and that a biopsy might spread cancer cells to other parts of the body. Men should be given detailed information before a biopsy, so that they are well aware of what might happen. They should also be given the opportunity to voice their fears, so that they can be reassured, and offered some form of pain relief.     

The properties of high-dimensional data spaces: implications for exploring gene and protein expression data

High-throughput genomic and proteomic technologies are widely used in cancer research to build better predictive models of diagnosis, prognosis and therapy, to identify and characterize key signalling networks and to find new targets for drug development. These technologies present investigators with the task of extracting meaningful statistical and biological information from high-dimensional data spaces, wherein each sample is defined by hundreds or thousands of measurements, usually concurrently obtained. The properties of high dimensionality are often poorly understood or overlooked in data modelling and analysis. From the perspective of translational science, this Review discusses the properties of high-dimensional data spaces that arise in genomic and proteomic studies and the challenges they can pose for data analysis and interpretation.