基于不同分类模型的基因芯片癌症诊断方法研究

基因芯片技术的发展为生物信息学带来了机遇,使在基因表达水平上进行癌症诊断成为可能。但基因芯片数据高维小样本的特征也使传统机器学习方法面临挑战。本文利用真实的基因表达数据,测试了目前主要的分类方法和降维方法在癌症诊断方面的效果,通过实验对比发现：基于线性核函数的支持向量机可以有效地分类肿瘤与非肿瘤的基因表达,从而为癌症诊断提供借鉴。     

Proteome Heterogeneity in Colorectal Cancer

Tumor heterogeneity is an important feature of colorectal cancer (CRC) manifested by dynamic changes in gene expression, protein expression, and availability of different tumor subtypes. Recent publications in the past 10 years have revealed proteome heterogeneity between different colorectal tumors and within the same tumor site. This paper reviews recent research works on the proteome heterogeneity in CRC, which includes the heterogeneity within a single tumor (intratumor heterogeneity), between different anatomical sites at the same organ, and between primary and metastatic sites (intertumor heterogeneity). The potential use of proteome heterogeneity in precision medicine and its implications in biomarker discovery and therapeutic outcomes will be discussed. Identification of the unique proteome landscape between and within individual tumors is imperative for understanding cancer biology and the management of CRC patients.     

Dissecting cancer heterogeneity with a probabilistic genotype-phenotype model

One of the obstacles hindering a better understanding of cancer is its heterogeneity. However, computational approaches to model cancer heterogeneity have lagged behind. To bridge this gap, we have developed a new probabilistic approach that models individual cancer cases as mixtures of subtypes. Our approach can be seen as a meta-model that summarizes the results of a large number of alternative models. It does not assume predefined subtypes nor does it assume that such subtypes have to be sharply defined. Instead given a measure of phenotypic similarity between patients and a list of potential explanatory features, such as mutations, copy number variation, microRNA levels, etc., it explains phenotypic similarities with the help of these features. We applied our approach to Glioblastoma Multiforme (GBM). The resulting model Prob_GBM, not only correctly inferred known relationships but also identified new properties underlining phenotypic similarities. The proposed probabilistic framework can be applied to model relations between similarity of gene expression and a broad spectrum of potential genetic causes.     

Intra-Tumour Signalling Entropy Determines Clinical Outcome in Breast and Lung Cancer

The cancer stem cell hypothesis, that a small population of tumour cells are responsible for tumorigenesis and cancer progression, is becoming widely accepted and recent evidence has suggested a prognostic and predictive role for such cells. Intra-tumour heterogeneity, the diversity of the cancer cell population within the tumour of an individual patient, is related to cancer stem cells and is also considered a potential prognostic indicator in oncology. The measurement of cancer stem cell abundance and intra-tumour heterogeneity in a clinically relevant manner however, currently presents a challenge. Here we propose signalling entropy, a measure of signalling pathway promiscuity derived from a sample’s genome-wide gene expression profile, as an estimate of the stemness of a tumour sample. By considering over 500 mixtures of diverse cellular expression profiles, we reveal that signalling entropy also associates with intra-tumour heterogeneity. By analysing 3668 breast cancer and 1692 lung adenocarcinoma samples, we further demonstrate that signalling entropy correlates negatively with survival, outperforming leading clinical gene expression based prognostic tools. Signalling entropy is found to be a general prognostic measure, valid in different breast cancer clinical subgroups, as well as within stage I lung adenocarcinoma. We find that its prognostic power is driven by genes involved in cancer stem cells and treatment resistance. In summary, by approximating both stemness and intra-tumour heterogeneity, signalling entropy provides a powerful prognostic measure across different epithelial cancers.     

Recent innovations in tissue-specific gene modifications in the mouse

Annotating the functions of individual genes in in vivo contexts has become the primary task of mouse genetics in the post-genome era. In addition to conventional approaches using transgenic technologies and gene targeting, the recent development of conditional gene modification techniques has opened novel opportunities for elucidating gene function at the level of the whole mouse to individual tissues or cell types. Tissue-specific gene modifications in the mouse have been made possible using site-specific DNA recombinases and conditional alleles. Recent innovations in this basic technology have facilitated new types of experiments, revealing novel insights into mammalian embryology. In this review, we focus on these recent innovations and new technical issues that impact the success of these conditional gene modification approaches.     

Breast cancer in the personal genomics era

Breast cancer is a heterogeneous disease with a complex etiology that develops from different cellular lineages, progresses along multiple molecular pathways, and demonstrates wide variability in response to treatment. The "standard of care" approach to breast cancer treatment in which all patients receive similar interventions is rapidly being replaced by personalized medicine, based on molecular characteristics of individual patients. Both inherited and somatic genomic variation is providing useful information for customizing treatment regimens for breast cancer to maximize efficacy and minimize adverse side effects. In this article, we review (1) hereditary breast cancer and current use of inherited susceptibility genes in patient management; (2) the potential of newly-identified breast cancer-susceptibility variants for improving risk assessment; (3) advantages and disadvantages of direct-to-consumer testing; (4) molecular characterization of sporadic breast cancer through immunohistochemistry and gene expression profiling and opportunities for personalized prognostics; and (5) pharmacogenomic influences on the effectiveness of current breast cancer treatments. Molecular genomics has the potential to revolutionize clinical practice and improve the lives of women with breast cancer.     

Exome Analysis Reveals Differentially Mutated Gene Signatures of Stage,Grade and Subtype in Breast Cancers

Breast cancers exhibit highly heterogeneous molecular profiles. Although gene expression profiles have been used to predict the risks and prognostic outcomes of breast cancers, the high variability of gene expression limits its clinical application. In contrast, genetic mutation profiles would be more advantageous than gene expression profiles because genetic mutations can be stably detected and the mutational heterogeneity widely exists in breast cancer genomes. We analyzed 98 breast cancer whole exome samples that were sorted into three subtypes, two grades and two stages. The sum deleterious effect of all mutations in each gene was scored to identify differentially mutated genes (DMGs) for this case-control study. DMGs were corroborated using extensive published knowledge. Functional consequences of deleterious SNVs on protein structure and function were also investigated. Genes such as ERBB2, ESP8, PPP2R4, KIAA0922, SP4, CENPJ, PRCP and SELP that have been experimentally or clinically verified to be tightly associated with breast cancer prognosis are among the DMGs identified in this study. We also identified some genes such as ARL6IP5, RAET1E, and ANO7 that could be crucial for breast cancer development and prognosis. Further, SNVs such as rs1058808, rs2480452, rs61751507, rs79167802, rs11540666, and rs2229437 that potentially influence protein functions are observed at significantly different frequencies in different comparison groups. Protein structure modeling revealed that many non-synonymous SNVs have a deleterious effect on protein stability, structure and function. Mutational profiling at gene- and SNV-level revealed differential patterns within each breast cancer comparison group, and the gene signatures correlate with expected prognostic characteristics of breast cancer classes. Some of the genes and SNVs identified in this study show high promise and are worthy of further investigation by experimental studies.     

Heterogeneity mapping of protein expression in tumors using quantitative immunofluorescence

Morphologic heterogeneity within an individual tumor is well-recognized by histopathologists in surgical practice. While this often takes the form of areas of distinct differentiation into recognized histological subtypes, or different pathological grade, often there are more subtle differences in phenotype which defy accurate classification (Figure 1). Ultimately, since morphology is dictated by the underlying molecular phenotype, areas with visible differences are likely to be accompanied by differences in the expression of proteins which orchestrate cellular function and behavior, and therefore, appearance. The significance of visible and invisible (molecular) heterogeneity for prognosis is unknown, but recent evidence suggests that, at least at the genetic level, heterogeneity exists in the primary tumor(1,2), and some of these sub-clones give rise to metastatic (and therefore lethal) disease. Moreover, some proteins are measured as biomarkers because they are the targets of therapy (for instance ER and HER2 for tamoxifen and trastuzumab (Herceptin), respectively). If these proteins show variable expression within a tumor then therapeutic responses may also be variable. The widely used histopathologic scoring schemes for immunohistochemistry either ignore, or numerically homogenize the quantification of protein expression. Similarly, in destructive techniques, where the tumor samples are homogenized (such as gene expression profiling), quantitative information can be elucidated, but spatial information is lost. Genetic heterogeneity mapping approaches in pancreatic cancer have relied either on generation of a single cell suspension(3), or on macrodissection(4). A recent study has used quantum dots in order to map morphologic and molecular heterogeneity in prostate cancer tissue(5), providing proof of principle that morphology and molecular mapping is feasible, but falling short of quantifying the heterogeneity. Since immunohistochemistry is, at best, only semi-quantitative and subject to intra- and inter-observer bias, more sensitive and quantitative methodologies are required in order to accurately map and quantify tissue heterogeneity in situ. We have developed and applied an experimental and statistical methodology in order to systematically quantify the heterogeneity of protein expression in whole tissue sections of tumors, based on the Automated QUantitative Analysis (AQUA) system(6). Tissue sections are labeled with specific antibodies directed against cytokeratins and targets of interest, coupled to fluorophore-labeled secondary antibodies. Slides are imaged using a whole-slide fluorescence scanner. Images are subdivided into hundreds to thousands of tiles, and each tile is then assigned an AQUA score which is a measure of protein concentration within the epithelial (tumor) component of the tissue. Heatmaps are generated to represent tissue expression of the proteins and a heterogeneity score assigned, using a statistical measure of heterogeneity originally used in ecology, based on the Simpson's biodiversity index(7). To date there have been no attempts to systematically map and quantify this variability in tandem with protein expression, in histological preparations. Here, we illustrate the first use of the method applied to ER and HER2 biomarker expression in ovarian cancer. Using this method paves the way for analyzing heterogeneity as an independent variable in studies of biomarker expression in translational studies, in order to establish the significance of heterogeneity in prognosis and prediction of responses to therapy.     

Individual heterogeneity in reproductive rates and cost of reproduction in a long‐lived vertebrate

Individual variation in reproductive success is a key feature of evolution, but also has important implications for predicting population responses to variable environments. Although such individual variation in reproductive outcomes has been reported in numerous studies, most analyses to date have not considered whether these realized differences were due to latent individual heterogeneity in reproduction or merely random chance causing different outcomes among like individuals. Furthermore, latent heterogeneity in fitness components might be expressed differently in contrasted environmental conditions, an issue that has only rarely been investigated. Here, we assessed (i) the potential existence of latent individual heterogeneity and (ii) the nature of its expression (fixed vs. variable) in a population of female Weddell seals (Leptonychotes weddellii), using a hierarchical modeling approach on a 30‐year mark–recapture data set consisting of 954 individual encounter histories. We found strong support for the existence of latent individual heterogeneity in the population, with “robust” individuals expected to produce twice as many pups as “frail” individuals. Moreover, the expression of individual heterogeneity appeared consistent, with only mild evidence that it might be amplified when environmental conditions are severe. Finally, the explicit modeling of individual heterogeneity allowed us to detect a substantial cost of reproduction that was not evidenced when the heterogeneity was ignored.     

Heterogeneity of stress gene expression and stress resistance among individual cells of Saccharomyces cerevisiae

Knowledge of gene expression and cellular responses in microorganisms is derived from analyses of populations consisting of millions of cells. Analytical techniques that provide data as population averages fail to inform of culture heterogeneity. Flow cytometry and fluorescence techniques were used to provide information on the heterogeneity of stress-responsive gene expression and stress tolerance in individual cells within populations. A sequence of DNA encoding the heat shock and stress response elements of the Saccharomyces cerevisiae HSP104 gene was used to express enhanced green fluorescent protein (EGFP). When integrated into the genome of yeast strain W303-1A, intrinsic expression of EGFP increased about twofold as cells progressed from growth on glucose to ethanol utilization in aerobic batch cultures. Staining of cells with orange/red fluorescent propidium iodide (PI), which only enters cells that have compromised membrane integrity, revealed that the population became more tolerant to 52 degrees C heat stress as it progressed from growth on glucose and through the ethanol utilization phase of aerobic batch culture. Exposure of cultures growing on glucose to a mild heat shock (shift from 25 degrees C to 37 degrees C) resulted in significantly increased expression of EGFP in the population. However, there was heterogeneity in the intensity of fluorescence of individual cells from heat-shocked cultures, indicating variability in the strength of stress response in the clonal population. Detailed analysis of the heterogeneity showed a clear positive trend between intensity of stress response and individual cell resistance, measured in terms of PI exclusion, to heat stress at 52 degrees C. Further experiments indicated that, although the mean gene expression by a population is influenced by the genetic background, the heterogeneity among individual cells in clonal populations is largely physiologically based.     

Utilization of Genomic Signatures to Identify Phenotype-Specific Drugs

Genetic and genomic studies highlight the substantial complexity and heterogeneity of human cancers and emphasize the general lack of therapeutics that can match this complexity. With the goal of expanding opportunities for drug discovery, we describe an approach that makes use of a phenotype-based screen combined with the use of multiple cancer cell lines. In particular, we have used the NCI-60 cancer cell line panel that includes drug sensitivity measures for over 40,000 compounds assayed on 59 independent cells lines. Targets are cancer-relevant phenotypes represented as gene expression signatures that are used to identify cells within the NCI-60 panel reflecting the signature phenotype and then connect to compounds that are selectively active against those cells. As a proof-of-concept, we show that this strategy effectively identifies compounds with selectivity to the RAS or PI3K pathways. We have then extended this strategy to identify compounds that have activity towards cells exhibiting the basal phenotype of breast cancer, a clinically-important breast cancer characterized as ER-, PR-, and Her2- that lacks viable therapeutic options. One of these compounds, Simvastatin, has previously been shown to inhibit breast cancer cell growth in vitro and importantly, has been associated with a reduction in ER-, PR- breast cancer in a clinical study. We suggest that this approach provides a novel strategy towards identification of therapeutic agents based on clinically relevant phenotypes that can augment the conventional strategies of target-based screens.     

Genetic and epigenetic heterogeneity in cancer: A genome‐centric perspective

Genetic and epigenetic heterogeneity (the main form of non‐genetic heterogeneity) are key elements in cancer progression and drug resistance, as they provide needed population diversity, complexity, and robustness. Despite drastically increased evidence of multiple levels of heterogeneity in cancer, the general approach has been to eliminate the “noise” of heterogeneity to establish genetic and epigenetic patterns. In particular, the appreciation of new types of epigenetic regulation like non‐coding RNA, have led to the hope of solving the mystery of cancer that the current genetic theories seem to be unable to achieve. In this mini‐review, we have briefly analyzed a number of mis‐conceptions regarding cancer heterogeneity, followed by the re‐evaluation of cancer heterogeneity within a framework of the genome‐centric concept of evolution. The analysis of the relationship between gene, epigenetic and genome level heterogeneity, and the challenges of measuring heterogeneity among multiple levels have been discussed. Further, we propose that measuring genome level heterogeneity represents an effective strategy in the study of cancer and other types of complex diseases, as emphasis on the pattern of system evolution rather than specific pathways provides a global and synthetic approach. Compared to the degree of heterogeneity, individual molecular pathways will have limited predictability during stochastic cancer evolution where genome dynamics (reflected by karyotypic heterogeneity) will dominate. J. Cell. Physiol. 220: 538–547, 2009. © 2009 Wiley‐Liss, Inc.     

Early and delayed aspects of nuclear reprogramming during cloning

The successful production of viable progeny following adult somatic cell nuclear transfer (cloning) provides exciting new opportunities for basic research for investigating early embryogenesis, for the propagation of valuable or endangered animals, for the production of genetically engineered animals, and possibly for developing therapeutically valuable stem cells. Successful cloning requires efficient reprogramming of gene expression to silence donor cell gene expression and activate an embryonic pattern of gene expression. Recent observations indicate that reprogramming may be initiated by early events that occur soon after nuclear transfer, but then continues as development progresses through cleavage and probably to gastrulation. Because reprogramming is slow and progressive, cloned embryos have dramatically altered characteristics in comparison with fertilized embryos. Events that occur early following nuclear transfer may be essential prerequisites for the later events. Additionally, the later reprogramming events may be inhibited by sub-optimum culture environments that exist because of the altered characteristics of cloned embryos. By addressing the unique requirements of cloned embryos, the entire process of reprogramming may be accelerated, thus increasing cloning efficiency.     

Cytotoxic effect of co-expression of human hepatitis A virus 3C protease and bifunctional suicide protein FCU1 genes in a bicistronic vector

Recent reports on various cancer models demonstrate a great potential of cytosine deaminase/5-fluorocytosine suicide system in cancer therapy. However, this approach has limited success and its application to patients has not reached the desirable clinical significance. Accordingly, the improvement of this suicide system is an actively developing trend in gene therapy. The purpose of this study was to explore the cytotoxic effect observed after co-expression of hepatitis A virus 3C protease (3C) and yeast cytosine deaminase/uracil phosphoribosyltransferase fusion protein (FCU1) in a bicistronic vector. A set of mono- and bicistronic plasmid constructs was generated to provide individual or combined expression of 3C and FCU1. The constructs were introduced into HEK293 and HeLa cells, and target protein synthesis as well as the effect of 5-fluorocytosine on cell death and the time course of the cytotoxic effect was studied. The obtained vectors provide for the synthesis of target proteins in human cells. The expression of the genes in a bicistronic construct provide for the cytotoxic effect comparable to that observed after the expression of genes in monocistronic constructs. At the same time, co-expression of FCU1 and 3C recapitulated their cytotoxic effects. The combined effect of the killer and suicide genes was studied for the first time on human cells in vitro. The integration of different gene therapy systems inducing cell death (FCU1 and 3C genes) in a bicistronic construct allowed us to demonstrate that it does not interfere with the cytotoxic effect of each of them. A combination of cytotoxic genes in multicistronic vectors can be used to develop pluripotent gene therapy agents.     

Genomic analysis of epithelial ovarian cancer

Ovarian cancer is a major health problem for women in the United States. Despite evidence of considerable heterogeneity, most cases of ovarian cancer are treated in a similar fashion. The molecular basis for the clinicopathologic characteristics of these tumors remains poorly defined. Whole genome expression profiling is a genomic tool, which can identify dysregulated genes and uncover unique sub-classes of tumors. The application of this technology to ovarian cancer has provided a solid molecular basis for differences in histology and grade of ovarian tumors. Differentially expressed genes identified pathways implicated in cell proliferation, invasion, motility, chromosomal instability, and gene silencing and provided new insights into the origin and potential treatment of these cancers. The added knowledge provided by global gene expression profiling should allow for a more rational treatment of ovarian cancers. These techniques are leading to a paradigm shift from empirical treatment to an individually tailored approach. This review summarizes the new genomic data on epithelial ovarian cancers of different histology and grade and the impact it will have on our understanding and treatment of this disease.