Sparse graphical models for cancer signalling

Protein signalling networks play a key role in cellular function, and their dysregulation is central to many diseases, including cancer. Recent advances in biochemical technology have begun to allow high-throughput, data-driven studies of signalling. In this thesis, we investigate multivariate statistical methods, rooted in sparse graphical models, aimed at probing questions in cancer signalling. First, we propose a Bayesian variable selection method for identifying subsets of proteins that jointly in uence an output of interest, such as drug response. Ancillary biological information is incorporated into inference using informative prior distributions. Prior information is selected and weighted in an automated manner using an empirical Bayes formulation. We present examples of informative pathway and network-based priors, and illustrate the proposed method on both synthetic and drug response data. Second, we use dynamic Bayesian networks to perform structure learning of context-specific signalling network topology from proteomic time-course data. We exploit a connection between variable selection and network structure learning to efficiently carry out exact inference. Existing biology is incorporated using informative network priors, weighted automatically by an empirical Bayes approach. The overall approach is computationally efficient and essentially free of user-set parameters. We show results from an empirical investigation, comparing the approach to several existing methods, and from an application to breast cancer cell line data. Hypotheses are generated regarding novel signalling links, some of which are validated by independent experiments. Third, we describe a network-based clustering approach for the discovery of cancer subtypes that differ in terms of subtype-specific signalling network structure. Model-based clustering is combined with penalised likelihood estimation of undirected graphical models to allow simultaneous learning of cluster assignments and cluster-specific network structure. Results are shown from an empirical investigation comparing several penalisation regimes, and an application to breast cancer proteomic data

Limits of Disclosure

One big focus of attention, criticism, and proposals for reform in the aftermath of the 2008 financial crisis has been securities disclosure. Many commentators have emphasized the complexity of the securities being sold, arguing that no one could understand the disclosure. Some observers have noted that disclosures were sometimes false or incomplete. What follows these issues, to some commentators, is that, whatever other lessons we may learn from the crisis, we need to improve disclosure. How should it be improved? Commentators often lament the frailties of human understanding, notably including those of everyday retail investors—people who do not understand or even read disclosure. This leads, naturally and unsurprisingly, to prescriptions for yet more disclosure, simpler disclosure, and financial literacy education. We believe that improvements in disclosure will not do much to prevent or minimize the effects of future crises. Indeed, the role of disclosure in investment decisions is far more limited, and far less straightforward, than is typically assumed. To caricature a bit for ease of exposition, the straightforward story is as follows: Read carefully, understand what you read, conduct any additional inquiry you deem appropriate, and then decide—if the security seems good, buy it; if not, don’t. Also, consider that whoever is selling you the security knows more than you do about it and has an incentive to present it more favorably than it warrants. But many investors, even sophisticated investors, do not start with cautious or neutral presumptions about a security and do not carefully read the disclosure to appraise the security on its merits before deciding whether to invest. As the literature extensively discusses, investors may be eager to buy “the hot new thing” that their peers are buying. Why do the peers buy it? One part of the story may be the old and often-told explanation: some investors tired of “boring” returns, saw an opportunity to supercharge their yields, and believed the perennial pitch made for new financial instruments—that they offered more return than risk. But our aim is not to explain what motivated investor behavior; our aim is to point out what did not sufficiently motivate investor behavior. Our argument is not just about the present crisis. Indeed, the complex role that disclosure plays in an investor’s decision as to whether to buy a security is just one example of disclosure’s limits. Those limits reflect the complexity of human decisionmaking. Why should disclosure work? The obvious answers are that better information should make for better decisions and that the specter of disclosure should constrain behavior. But these answers are importantly incomplete. Better information should, in principle, lead to better decisions, but other factors may be far more important. This was the case with disclosure regarding the securities at issue in the financial crisis. We discuss another example as well: executive compensation disclosures

Limits of Disclosure

One big focus of attention, criticism, and proposals for reform in the aftermath of the 2008 financial crisis has been securities disclosure. Many commentators have emphasized the complexity of the securities being sold, arguing that no one could understand the disclosure. Some observers have noted that disclosures were sometimes false or incomplete. What follows these issues, to some commentators, is that, whatever other lessons we may learn from the crisis, we need to improve disclosure. How should it be improved? Commentators often lament the frailties of human understanding, notably including those of everyday retail investors—people who do not understand or even read disclosure. This leads, naturally and unsurprisingly, to prescriptions for yet more disclosure, simpler disclosure, and financial literacy education. We believe that improvements in disclosure will not do much to prevent or minimize the effects of future crises. Indeed, the role of disclosure in investment decisions is far more limited, and far less straightforward, than is typically assumed. To caricature a bit for ease of exposition, the straightforward story is as follows: Read carefully, understand what you read, conduct any additional inquiry you deem appropriate, and then decide—if the security seems good, buy it; if not, don’t. Also, consider that whoever is selling you the security knows more than you do about it and has an incentive to present it more favorably than it warrants. But many investors, even sophisticated investors, do not start with cautious or neutral presumptions about a security and do not carefully read the disclosure to appraise the security on its merits before deciding whether to invest. As the literature extensively discusses, investors may be eager to buy “the hot new thing” that their peers are buying. Why do the peers buy it? One part of the story may be the old and often-told explanation: some investors tired of “boring” returns, saw an opportunity to supercharge their yields, and believed the perennial pitch made for new financial instruments—that they offered more return than risk. But our aim is not to explain what motivated investor behavior; our aim is to point out what did not sufficiently motivate investor behavior. Our argument is not just about the present crisis. Indeed, the complex role that disclosure plays in an investor’s decision as to whether to buy a security is just one example of disclosure’s limits. Those limits reflect the complexity of human decisionmaking. Why should disclosure work? The obvious answers are that better information should make for better decisions and that the specter of disclosure should constrain behavior. But these answers are importantly incomplete. Better information should, in principle, lead to better decisions, but other factors may be far more important. This was the case with disclosure regarding the securities at issue in the financial crisis. We discuss another example as well: executive compensation disclosures

Sparse graphical models for cancer signalling

Protein signalling networks play a key role in cellular function, and their dysregulation is central to many diseases, including cancer. Recent advances in biochemical technology have begun to allow high-throughput, data-driven studies of signalling. In this thesis, we investigate multivariate statistical methods, rooted in sparse graphical models, aimed at probing questions in cancer signalling. First, we propose a Bayesian variable selection method for identifying subsets of proteins that jointly in uence an output of interest, such as drug response. Ancillary biological information is incorporated into inference using informative prior distributions. Prior information is selected and weighted in an automated manner using an empirical Bayes formulation. We present examples of informative pathwayand network-based priors, and illustrate the proposed method on both synthetic and drug response data. Second, we use dynamic Bayesian networks to perform structure learning of context-specific signalling network topology from proteomic time-course data. We exploit a connection between variable selection and network structure learning to efficiently carry out exact inference. Existing biology is incorporated using informative network priors, weighted automatically by an empirical Bayes approach. The overall approach is computationally efficient and essentially free of user-set parameters. We show results from an empirical investigation, comparing the approach to several existing methods, and from an application to breast cancer cell line data. Hypotheses are generated regarding novel signalling links, some of which are validated by independent experiments. Third, we describe a network-based clustering approach for the discovery of cancer subtypes that differ in terms of subtype-specific signalling network structure. Model-based clustering is combined with penalised likelihood estimation of undirected graphical models to allow simultaneous learning of cluster assignments and cluster-specific network structure. Results are shown from an empirical investigation comparing several penalisation regimes, and an application to breast cancer proteomic data.EThOS - Electronic Theses Online ServiceEngineering and Physical Sciences Research Council (EPSRC)GBUnited Kingdo

Integrating biological knowledge into variable selection : an empirical Bayes approach with an application in cancer biology

Background: An important question in the analysis of biochemical data is that of identifying subsets of molecular variables that may jointly influence a biological response. Statistical variable selection methods have been widely used for this purpose. In many settings, it may be important to incorporate ancillary biological information concerning the variables of interest. Pathway and network maps are one example of a source of such information. However, although ancillary information is increasingly available, it is not always clear how it should be used nor how it should be weighted in relation to primary data. Results: We put forward an approach in which biological knowledge is incorporated using informative prior distributions over variable subsets, with prior information selected and weighted in an automated, objective manner using an empirical Bayes formulation. We employ continuous, linear models with interaction terms and exploit biochemically-motivated sparsity constraints to permit exact inference. We show an example of priors for pathway- and network-based information and illustrate our proposed method on both synthetic response data and by an application to cancer drug response data. Comparisons are also made to alternative Bayesian and frequentist penalised-likelihood methods for incorporating network-based information. Conclusions: The empirical Bayes method proposed here can aid prior elicitation for Bayesian variable selection studies and help to guard against mis-specification of priors. Empirical Bayes, together with the proposed pathway-based priors, results in an approach with a competitive variable selection performance. In addition, the overall procedure is fast, deterministic, and has very few user-set parameters, yet is capable of capturing interplay between molecular players. The approach presented is general and readily applicable in any setting with multiple sources of biological prior knowledge

The Social Determinants of Health and the Decline in U.S. Life Expectancy: Implications for Appalachia

For the past century, life expectancy in industrialized countries has increased, and the U.S. has shared in that progress. However, beginning in the 1980s, advances in U.S. life expectancy began to lose pace with peer countries. By 1998, U.S. life expectancy had fallen below the average for Organisation for Economic Cooperation and Development nations. U.S. life expectancy peaked in 2014 and has been decreasing for three consecutive years, a trend not been seen since the influenza pandemic a century ago. Put simply, U.S. health is in decline

Mood Disorders and Trauma: What are the Associations?

Objectives: Mood dysregulation in traumatized children may be misdiagnosed as bipolar disorder (BD) and conversely, the diagnosis of BD overlooked. Our aim is to characterize the relationship between trauma and mood dysregulation and pediatric BD. Methods: We are assessing youths ages 8-18 who present with mood symptoms and past trauma divided into two groups: 1. Trauma+Unmodified DSM-IV-TR BD (T+BD) and 2. Trauma+Mood Disorder NOS (T+MD). Differences in clinical variables between groups are analyzed using t-tests for continuous and chi-square tests for categorical variables (α= 0.05). Results: Age at onset of trauma for youth with T+BD (n=10) compared with T+MD (n=10) was similar (2.6±1.8 versus 3.3±1.9 years; p=0.4) as were types of trauma and number of incidents, and age at onset of mood symptoms (T+BD 7±2.5 versus T+MD 7.8±1.8 p=0.4). The T+BD group had higher scores on the sexual abuse subscale of the Childhood Trauma Questionnaire (p=0.04) and BPRS mania subscale (p=0.02), and higher total number of major depressive episodes (p=0.04) and manic episodes (p=0.03) per the KSCID. Youth with T+BD reported a trend toward higher rates of ideation to self-harm compared to youth with T+MD (p=0.08). Both groups had similar PTSD and ADHD symptoms, and similar number of psychotrophic medications (BD 3.6±2.9 MD 2.7±2.1 p=0.4). Finally, family history findings suggest a trend towards higher rates of any Axis I disorder in the T+BD families (p=0.07), and significantly higher rates of anxiety disorders (p=0.05), BD (p=0.04), and schizophrenia (p=0.02). Conclusions: Results suggest differences in clinical presentation and higher rates of BD and schizophrenia in the T+BD families. Taken together, these preliminary results suggest potential biological and genetic vulnerabilities which may predispose children to develop specific mood disorders under certain circumstances; the ability to identify these children early on could change their prognostic trajectory

Physiotherapists have accurate expectations of their patients’ future health-related quality of life after first assessment in a subacute rehabilitation setting

Background. Expectations held by health professionals and their patients are likely to affect treatment choices in subacute inpatient rehabilitation settings for older adults. There is a scarcity of empirical evidence evaluating whether health professionals expectations of the quality of their patients’ future health states are accurate. Methods. A prospective longitudinal cohort investigation was implemented to examine agreement (kappa coefficients, exact agreement, limits-of-agreement, and intraclass-correlation coefficients) between physiotherapists’ (n=23) prediction of patients’ discharge health-related quality of life (reported on the EQ-5D-3L) and the actual health-related quality of life self-reported by patients (n=272) at their discharge assessment (using the EQ-5D-3L). The mini-mental state examination was used as an indicator of patients’ cognitive ability. Results. Overall, 232 (85%) patients had all assessment data completed and were included in analysis. Kappa coefficients (exact agreement) ranged between 0.37–0.57 (58%–83%) across EQ-5D-3L domains in the lower cognition group and 0.53–0.68 (81%–85%) in the better cognition group. Conclusions. Physiotherapists in this subacute rehabilitation setting predicted their patients’ discharge health-related quality of life with substantial accuracy. Physiotherapists are likely able to provide their patients with sound information regarding potential recovery and health-related quality of life on discharge. The prediction accuracy was higher among patients with better cognition than patients with poorer cognition