A Multivariate Model to Measure the Effect of Treatments in Survival to Breast Cancer

An homogeneous Markov process in continuous time with three states (no relapse, relapse, and death) to model the influence of treatments in relapse and survival times to breast cancer is considered. Different treatments such as chemotherapy, radiotherapy, and hormonal therapy, and combinations of these were applied to a cohort of 300 patients after surgery. All patients were seen longitudinally every month. The treatments are introduced as covariates by means of transition intensity, thus providing three covariates. The likelihood function is built from the data and the parameters estimated. Original computational programmes are constructed using the MATHEMATICA and MATLAB programmes, by means of which we estimate the parameters, calculate the transition probability functions, plot the graphs of the survival curves, and fit the survival curves to treatments obtained from the model with the corresponding empirical functions.     

Natural and chemotherapy-induced clonal evolution of tumors

Evolution and natural selection of tumoral clones in the process of transformation and the following carcinogenesis can be called natural clonal evolution. Its main driving factors are internal: genetic instability initiated by driver mutations and microenvironment, which enables selective pressure while forming the environment for cell transformation and their survival. We present our overview of contemporary research dealing with mechanisms of carcinogenesis in different localizations from precancerous pathologies to metastasis and relapse. It shows that natural clonal evolution establishes intratumoral heterogeneity and enables tumor progression. Tumors of monoclonal origin are of low-level intratumoral heterogeneity in the initial stages, and this increases with the size of the tumor. Tumors of polyclonal origin are of extremely high-level intratumoral heterogeneity in the initial stages and become more homogeneous when larger due to clonal expansion. In cases of chemotherapy-induced clonal evolution of a tumor, chemotherapy becomes the leading factor in treatment. The latest research shows that the impact of chemotherapy can radically increase the speed of clonal evolution and lead to new malignant and resistant clones that cause tumor metastasis. Another option of chemotherapy-induced clonal evolution is formation of a new dominant clone from a clone that was minor in the initial tumor and obtained free space due to elimination of sensitive clones by chemotherapy. As a result, in ~20% of cases, chemotherapy can stimulate metastasis and relapse of tumors due to clonal evolution. The conclusion of the overview formulates approaches to tumor treatment based on clonal evolution: in particular, precision therapy, prediction of metastasis stimulation in patients treated with chemotherapy, methods of genetic evaluation of chemotherapy efficiency and clonal-oriented treatment, and approaches to manipulating the clonal evolution of tumors are presented.     

Multivariate frailty models for two types of recurrent events with a dependent terminal event: Application to breast cancer data

Individuals may experience more than one type of recurrent event and a terminal event during the life course of a disease. Follow‐up may be interrupted for several reasons, including the end of a study, or patients lost to follow‐up, which are noninformative censoring events. Death could also stop the follow‐up, hence, it is considered as a dependent terminal event. We propose a multivariate frailty model that jointly analyzes two types of recurrent events with a dependent terminal event. Two estimation methods are proposed: a semiparametrical approach using penalized likelihood estimation where baseline hazard functions are approximated by M‐splines, and another one with piecewise constant baseline hazard functions. Finally, we derived martingale residuals to check the goodness‐of‐fit. We illustrate our proposals with a real dataset on breast cancer. The main objective was to model the dependency between the two types of recurrent events (locoregional and metastatic) and the terminal event (death) after a breast cancer.     

How does marriage affect length of life? Analysis of a French historical dataset from an evolutionary perspective

There are broadly two explanations for why human longevity appears to be extended by marriage. First, there is the social explanation, whereby the companionship, division of labour and the economic support that marriage offers is thought to extend life. Second, there is a selective explanation, whereby those individuals with high potential longevity are more attractive to the opposite sex and therefore more likely to get married. Here we analyse the “TRA” dataset from 19th century France, using an evolutionary approach to address the question of why marriage is linked to longevity, focussing particularly on sex differences. The dataset is based on death and marriage records from all of France between 1798 and 1901 and includes information on age at death, marriage and wealth for individuals whose surnames began with the letters TRA. We find that marriage is positively associated with longevity, particularly for men. In part, this is related to the higher rate of deaths for single males during marriageable age, as compared to a higher rate of deaths for females during marriage. There is a positive association between wealth (at death) and longevity for individuals who were single or married at death, with a stronger effect for singles. Analysis of the effect of spousal age gap on duration of survival after first marriage indicates that men who were married to younger women lived longer, whereas the longevity of women was not associated with the spousal age gap. We put forward an evolutionary perspective on marriage and longevity, hypothesizing that there is an important role for sexual selection in the association between marriage and longevity, with women selecting on characteristics associated with longevity, whilst men select on characteristics associated with reproductive potential.     

MicroRNAs,stem cells and cancer stem cells

This review discusses the various regulatory charac-teristics of microRNAs that are capable of generating widespread changes in gene expression via post translational repression of many mRNA targets and control self-renewal, differentiation and division of cells. It controls the stem cell functions by controlling a wide range of pathological and physiological processes, including development, differentiation, cellular proliferation, programmed cell death, oncogenesis and metastasis. Through either mRNA cleavage or translational repression, miRNAs alter the expression of their cognate target genes; thereby modulating cellular pathways that affect the normal functions of stem cells, turning them into cancer stem cells, a likely cause of relapse in cancer patients. This present review further emphasizes the recent discoveries on the functional analysis of miRNAs in cancer metastasis and implications on miRNA based therapy using miRNA replacement or anti-miRNA technologies in specific cancer stem cells that are required to establish their efficacy in controlling tumorigenic potential and safe therapeutics.     

Survival analysis of longitudinal microarrays

MOTIVATION: The development of methods for linking gene expressions to various clinical and phenotypic characteristics is an active area of genomic research. Scientists hope that such analysis may, for example, describe relationships between gene function and clinical events such as death or recovery. Methods are available for relating gene expression to measurements that are categorized or continuous, but there is less work in relating expressions to an observed event time such as time to death, response or relapse. When gene expressions are measured over time, there are methods for differentiating temporal patterns. However, methods have not yet been proposed for the survival analysis of longitudinally collected microarrays. RESULTS: We describe an approach for the survival analysis of longitudinal gene expression data. We construct a measure of association between the time to an event and gene expressions collected over time. Statistical significance is addressed using permutations and control of the false discovery rate. Our proposed method is illustrated on a dataset from a multi-center research study of inflammation and response to injury that aims to uncover the biological reasons why patients can have dramatically different outcomes after suffering a traumatic injury (www.gluegrant.org).     

Pseudomonas aeruginosa Eliminates Natural Killer Cells via Phagocytosis-Induced Apoptosis

Pseudomonas aeruginosa (PA) is an opportunistic pathogen that causes the relapse of illness in immunocompromised patients, leading to prolonged hospitalization, increased medical expense, and death. In this report, we show that PA invades natural killer (NK) cells and induces phagocytosis-induced cell death (PICD) of lymphocytes. In vivo tumor metastasis was augmented by PA infection, with a significant reduction in NK cell number. Adoptive transfer of NK cells mitigated PA-induced metastasis. Internalization of PA into NK cells was observed by transmission electron microscopy. In addition, PA invaded NK cells via phosphoinositide 3-kinase (PI3K) activation, and the phagocytic event led to caspase 9-dependent apoptosis of NK cells. PA-mediated NK cell apoptosis was dependent on activation of mitogen-activated protein (MAP) kinase and the generation of reactive oxygen species (ROS). These data suggest that the phagocytosis of PA by NK cells is a critical event that affects the relapse of diseases in immunocompromised patients, such as those with cancer, and provides important insights into the interactions between PA and NK cells.     

Joint regression analysis for survival data in the presence of two sets of semi‐competing risks

In many clinical trials, multiple time‐to‐event endpoints including the primary endpoint (e.g., time to death) and secondary endpoints (e.g., progression‐related endpoints) are commonly used to determine treatment efficacy. These endpoints are often biologically related. This work is motivated by a study of bone marrow transplant (BMT) for leukemia patients, who may experience the acute graft‐versus‐host disease (GVHD), relapse of leukemia, and death after an allogeneic BMT. The acute GVHD is associated with the relapse free survival, and both the acute GVHD and relapse of leukemia are intermediate nonterminal events subject to dependent censoring by the informative terminal event death, but not vice versa, giving rise to survival data that are subject to two sets of semi‐competing risks. It is important to assess the impacts of prognostic factors on these three time‐to‐event endpoints. We propose a novel statistical approach that jointly models such data via a pair of copulas to account for multiple dependence structures, while the marginal distribution of each endpoint is formulated by a Cox proportional hazards model. We develop an estimation procedure based on pseudo‐likelihood and carry out simulation studies to examine the performance of the proposed method in finite samples. The practical utility of the proposed method is further illustrated with data from the motivating example.     

Evolution of seasonal ecological niches in the Passerina buntings (Aves: Cardinalidae)

The evolution of migration has long been considered complex and recent work has demonstrated additional complexity: some species follow the same ecological conditions throughout the year, whereas others 'switch niches' between breeding and wintering ranges. Hypotheses regarding the evolution of migration would generally predict niche-following as primitive, and niche-switching as derived. However, no test has, to our knowledge, yet determined the directionality of evolution of these states within a lineage. We present an analysis of phylogenetic dimensions of seasonal niches in the Passerina buntings that indicates greater evolutionary change in the niches of breeding populations than among those of wintering populations. These results are consistent with hypotheses of (i) niche conservatism (in winter, at least) across a recently speciated lineage; and (ii) the derived state of the breeding (rather than winter) ecological niches of each species.     

Evolution as resistance to entropy. I. Mechanisms of species homeostasis

The idea is discussed that the common output of any evolution is creation of the entities that are increasingly resistant to further evolution. The moving force of evolution is entropy, the tendency to disorder. This general aspiration for chaos is a cause of the mortality of organisms and species, however, being prerequisite for any movement, it creates (by chance) novelties, which may occur (by chance) more resistant to further decay and thus survive. The surviving of those who survive is the most general principle of evolution discovered by Darwin for particular case of biological evolution. The second law of thermodynamics states that our Universe is perishing but its ontology is such that it creates resistance to destruction. The evolution is a history of this resistance. Not only those who die do not survive but also those who evolve. The entities that change (evolve) rapidly disappear rapidly and by this reason they are not observed among both the fossils and now-living organisms. We know only about long-living species. All the existing organisms are endowed with an ability to resist other changing. The following main achievements of the species homeostasis are discussed: high fidelity of DNA replication and effective mechanisms of DNA repair; diploidy; normalizing selection; truncated selection; heterozygote superiority; ability to change phenotype adaptively without changing genotype; parental care and the K-strategy of reproduction; behavior that provides independence of the environment. The global resistance of the living systems to entropy is provided the state that all the essential in biology is determined not by physical-chemical interactions but could semantic rules. A conception of "potential zygotic information" that determines the rules of ontogenesis is proposed. A zygote does not contain this information in explicit form. It is created de novo step by step during ontogenesis and it could not be decoded beforehand. The experimental data on the adaptive mutagenesis and the relevant hypothesis are discussed. It is concluded that the special mechanisms for speeding-up of evolution as created by evolution are impossible conceptually.     

A taxonomy of organ-specific breast cancer metastases based on a protein-protein interaction network

We carried out a systems-level study of the mechanisms underlying organ-specific metastases of breast cancer. We followed a network-based approach using microarray expression data from human breast cancer metastases to select organ-specific proteins that exert a range of functions allowing cell survival and growth in the microenvironment of distant organs. MinerProt, a home-made software application, was used to group organ-specific signatures of brain (1191 genes), bone (1623 genes), liver (977 genes) and lung (254 genes) metastases by function and select the most differentially expressed gene in each function. As a result, we obtained 19 functional representative proteins in brain, 23 in bone, 15 in liver and 9 in lung, with which we constructed four organ-specific protein-protein interaction networks. The network taxonomy included seven proteins that interacted in brain metastasis, which were mainly associated with signal transduction. Proteins related to immune response functions were bone specific, while those involved in proteolysis, signal transduction and hepatic glucose metabolism were found in liver metastasis. No experimental protein-protein interaction was found in lung metastasis; thus, computationally determined interactions were included in this network. Moreover, three of these selected genes (CXCL12, DSC2 and TFDP2) were associated with progression to specific organs when tested in an independent dataset. In conclusion, we present a network-based approach to filter information by selecting key protein functions as metastatic markers or therapeutic targets.     

SVM-T-RFE: a novel gene selection algorithm for identifying metastasis-related genes in colorectal cancer using gene expression profiles

Although metastasis is the principal cause of death cause for colorectal cancer (CRC) patients, the molecular mechanisms underlying CRC metastasis are still not fully understood. In an attempt to identify metastasis-related genes in CRC, we obtained gene expression profiles of 55 early stage primary CRCs, 56 late stage primary CRCs, and 34 metastatic CRCs from the expression project in Oncology (http://www.intgen.org/expo/). We developed a novel gene selection algorithm (SVM-T-RFE), which extends support vector machine recursive feature elimination (SVM-RFE) algorithm by incorporating T-statistic. We achieved highest classification accuracy (100%) with smaller gene subsets (10 and 6, respectively), when classifying between early and late stage primary CRCs, as well as between metastatic CRCs and late stage primary CRCs. We also compared the performance of SVM-T-RFE and SVM-RFE gene selection algorithms on another large-scale CRC dataset and the five public microarray datasets. SVM-T-RFE bestowed SVM-RFE algorithm in identifying more differentially expressed genes, and achieving highest prediction accuracy using equal or smaller number of selected genes. A fraction of selected genes have been reported to be associated with CRC development or metastasis.     

Clinical Relevance of Serum Aquaporin-4 Antibody Levels in Neuromyelitis Optica

Neuromyelitis optica (NMO) is an inflammatory disease that selectively affects the optic nerves and spinal cord. The discovery of NMO-IgG targeting aquaporin-4 (AQP4) in NMO patients suggested that NMO is a distinct entity, with a fundamentally different etiology from that of multiple sclerosis (MS). Although NMO usually leads to grave disability because of the more severe tissue destruction compared with classical MS, there have been several reports describing a benign form of NMO over a long disease term. NMO-IgG/AQP4 antibodies show high specificity but medium sensitivity for NMO, while the clinical relevance of AQP4 antibody titers remains to be determined. We aimed to clarify the clinical relevance of AQP4 antibody levels determined by a bridging enzyme-linked immunosorbent assay in 38 patients with NMO or NMO spectrum disorder. The AQP4 antibody levels were higher in patients with optic neuritis (ON) than in those without ON (p = 0.0164). Among the 12 patients examined in a longitudinal study, four showed an increase in the ELISA values during some relapses, and eight showed no clear correlation between the ELISA values and relapse. Of the four patients who demonstrated a steady rise in the antibody levels over time, two patients had no concomitant relapses, despite elevation of the AQP4 antibody levels. We conclude that high AQP4 antibody levels are associated with the occurrence of ON, but that the antibody levels themselves are not closely correlated with the onset of relapse.     

The generation of new protein functions by the combination of domains

During evolution, many new proteins have been formed by the process of gene duplication and combination. The genes involved in this process usually code for whole domains. Small proteins contain one domain; medium and large proteins contain two or more domains. We have compared homologous domains that occur in both one-domain proteins and multidomain proteins. We have determined (1) how the functions of the individual domains in the multidomain proteins combine to produce their overall functions and (2) the extent to which these functions are similar to those in the one-domain homologs. We describe how domain combinations increase the specificity of enzymes; act as links between domains that have functional roles; regulate activity; combine within one chain functions that can act either independently, in concert or in new contexts; and provide the structural framework for the evolution of entirely new functions.     

单羧酸转运蛋白基因SNP 与肝细胞肝癌预后的关联研究

目的:探讨单羧酸转运蛋白基因(monocarboxylate transporter,MCT)单核苷酸多态性(single nucleotide polymorphism,SNPs)与肝细胞肝癌(hepatocellular carcinoma,HCC)根治术患者预后的关系。方法:运用Sequenom i PLEX分型技术对830例原发性HCC患者MCT家族(MCT1、MCT2和MCT4)基因上的8个功能性SNP位点进行基因分型,并分析这些SNP与HCC患者预后的相关性。结果:MCT1基因rs1049434位点和MCT2基因rs995343位点基因型与HCC患者总体生存期及无复发生存期均显著相关(P0.05)。携带MCT1 AT基因型或TT基因型的患者死亡及复发风险均显著低于携带AA基因型的患者(HR=0.72;P=0.042或HR=0.64;P=0.002);携带MCT2 CT基因型或TT基因型的患者死亡及复发风险均显著高于携带CC基因型的患者(HR=1.64;P=0.018或HR=1.52;P=0.026)。而且,MCT1基因rs1049434位点和MCT2基因rs995343位点对HCC预后存在显著的累积效应,携带2个危险基因型的患者死亡及复发风险分别是没有危险基因型患者的2.16倍和2.54倍。此外,携带2个危险基因型的HCC患者在术后行TACE辅助治疗后死亡及复发风险均显著降低(P0.05)。结论:MCT1和MCT2基因上的功能性SNP位点有可能作为HCC根治术后预后评估和TACE辅助治疗反应预测的独立标志物。     

Binding, stimulating and blocking TSH receptor antibodies to the thyrotropin receptor as predictors of relapse of Graves' disease after withdrawal of antithyroid treatment.

TSH-receptor autoantibodies (TRAbs) are a valuable diagnostic tool for confirming a diagnosis of Graves' disease (GD). While there is evidence that high TRAb levels are associated with relapse of GD, whether a discrimination of TRAb into stimulating (TSAb) and blocking (TBAb) autoantibodies would benefit the clinician in terms of outcome prediction remains unclear. To address this issue, we have determined TRAb, TSAb and TBAb levels in serum samples of ninety-six euthyroid patients with GD taken four weeks after antithyroid drug withdrawal (ATDT). Forty-seven patients (49 %) underwent relapse of GD within two years. Amongst those, forty-one (87 %) had been positive for TRAb and thirty-five (74 %) for TSAb after treatment. All patients except one were negative for TBAb. The correlation between TRAb and TSAb in those treated GD patients was relatively weak (r = 0.268, p < 0.001). Based on a cut-off limit of 1.5 IU/l, the positive and negative predictive values with respect to prediction of relapse were too low for any clinical relevance (TRAb: 49 % and 54 %; TSAb: 51 % and 55 %). However, when a cut-off level above 10 IU/l was used, the positive and negative predictive values increased to 83 % and 62 %. The additional measurement of TSAb or TBAb in those samples after therapy did not add additional information, even at higher decision thresholds. In conclusion, differentiation of TRAb into TSAb and TBAb is of no help in the prediction of relapse of GD in euthyroid patients at the end of ATDT, and only high TRAb levels are associated with relapse.     

Analyzing infant mortality with geoadditive categorical regression models: a case study for Nigeria

In this paper, we analyze infant mortality in Nigeria based on the data set from the 1999 Nigeria Demographic and Health Survey (NDHS). We investigate spatial patterns at a highly disaggregated level of Nigerian states and consider non-linear effects of mother's age at birth. Time to the occurrence of a child's death can intuitively be considered to be categorical in nature and the determinants of a child's death may differ in different age groups. Thus, it may be desirable to investigate separately the death of a child in the first month and in the remaining 11 months of the first year of life. To avoid selection bias, the data set used for this case study is based on information on children who were born 12 months preceding the survey. Inference is Bayesian and is based on Markov chain Monte Carlo (MCMC) techniques. We find that spatial variation and the determinants of death indeed differ considerably for the two age groups considered.     

Evolution in fine-grained environments I. Environmental runs and the evolution of homeostasis

In this paper we discuss the problem of evolution when individual organisms are subjected to heterogeneous environments within their own lifetimes. We first develop a model of environmental heterogeneity in which there are two discrete environmental states. Transitions between states are governed by a stochastic matrix. Next, we examine how an organism responds to this heterogeneity. We assume that L consecutive time units of the environmental process are sampled during the normal life span of the organism, and that the individual's fitness is determined in part by a component unrelated to this heterogeneity and by other components that describe the fitness response to the heterogeneity. The fitness responses are functions of the environmental state and of how long the organism has been previously exposed to that state; i.e., fitness response is dependent upon the environmental context. We then discuss how this individually experienced heterogeneity is translated to the populational level. Finally, genetic constraints are overlaid so that the tools of population genetics may be used to make evolutionary predictions.     

Estimation and prediction in a multi-state model for breast cancer

An important aim in clinical studies in oncology is to study how treatment and prognostic factors influence the course of disease of a patient. Typically in these trials, besides overall survival, also other endpoints such as locoregional recurrence or distant metastasis are of interest. Most commonly in these situations, Cox regression models are applied for each of these endpoints separately or to composite endpoints such as disease-free survival. These approaches however fail to give insight into what happens to a patient after a first event. We re-analyzed data of 2795 patients from a breast cancer trial (EORTC 10854) by applying a multi-state model, with local recurrence, distant metastasis, and both local recurrence and distant metastasis as transient states and death as absorbing state. We used an approach where the clock is reset on entry of a new state. The influence of prognostic factors on each of the transition rates is studied, as well as the influence of the time at which intermediate events occur. The estimated transition rates between the states in the model are used to obtain predictions for patients with a given history. Formulas are developed and illustrated for these prediction probabilities for the clock reset approach.     

The tumor macroenvironment and systemic regulation of breast cancer progression

Breast cancer is the most common malignancy among women worldwide and is the most common cause of death for women between 35 and 50 years of age. Women with breast cancer are at risk of developing metastases for their entire lifetime and, despite local and systemic therapies, approximately 30% of breast cancer patients will relapse (Jemal et al., 2010). Nearly all breast cancer related deaths are due to metastatic disease, even though metastasis is considered to be an inefficient process. In some cases, tumor cells disseminate from primary sites at an early stage, but remain indolent for protracted periods of time before becoming overt, life-threatening tumors. Little is known about the mechanisms that cause these indolent tumors to grow into malignant disease. Because of this gap in our understanding, we are unable to predict which breast cancer patients are likely to experience disease relapse or develop metastases years after treatment of their primary tumor. A better understanding of the mechanisms and signals involved in the exit of tumor cells from dormancy would not only allow for more accurate selection of patients that would benefit from systemic therapy, but could also lead to the development of more targeted therapies to inhibit the signals that promote disease progression. In this review, we address the systemic, or "macroenvironmental", contribution to tumor initiation and progression and what is known about how a pro-tumorigenic systemic environment is established.