Personalized Medicine and Stakeholders’ Perceptions

Patients suffer the same form of illness but their responses to therapy may differ greatly. This can be attributed to their genetics or other environmental factors. Personalized Medicine may improve the outcomes of treatment by understanding the individuals, causes of their disease, and the best treatment for their cure

Trust, morality and altruism in the donation of biological material : the case of Portugal

This paper examines a number of social, ethical and cultural issues related to the application of biotechnology. The focus of the paper relies on two different cases of governing biotechnology in Portugal, referring to donation of biological material: the act of donation of eggs and sperm; and volunteers for donation of DNA material for the forensic national DNA database. We analyze the discourses on donation of biological material framing them in rhetorical devices of gift, altruism, informed consent and social responsibility. This comes blended with still unclear and emergent regulation and policies of access, retention, preservation and governing of biological material and of donors’ identification. The risks are mitigated by narratives of science and technology as social progress and providers of public good and health benefits, as well as by underlining the individual responsibility in this domain and by reinforcing the rhetoric of gene quality, based on socio-cultural and bio-genetic criteria

Gene2DisCo : gene to disease using disease commonalities

OBJECTIVE: Finding the human genes co-causing complex diseases, also known as "disease-genes", is one of the emerging and challenging tasks in biomedicine. This process, termed gene prioritization (GP), is characterized by a scarcity of known disease-genes for most diseases, and by a vast amount of heterogeneous data, usually encoded into networks describing different types of functional relationships between genes. In addition, different diseases may share common profiles (e.g. genetic or therapeutic profiles), and exploiting disease commonalities may significantly enhance the performance of GP methods. This work aims to provide a systematic comparison of several disease similarity measures, and to embed disease similarities and heterogeneous data into a flexible framework for gene prioritization which specifically handles the lack of known disease-genes. METHODS: We present a novel network-based method, Gene2DisCo, based on generalized linear models (GLMs) to effectively prioritize genes by exploiting data regarding disease-genes, gene interaction networks and disease similarities. The scarcity of disease-genes is addressed by applying an efficient negative selection procedure, together with imbalance-aware GLMs. Gene2DisCo is a flexible framework, in the sense it is not dependent upon specific types of data, and/or upon specific disease ontologies. RESULTS: On a benchmark dataset composed of nine human networks and 708 medical subject headings (MeSH) diseases, Gene2DisCo largely outperformed the best benchmark algorithm, kernelized score functions, in terms of both area under the ROC curve (0.94 against 0.86) and precision at given recall levels (for recall levels from 0.1 to 1 with steps 0.1). Furthermore, we enriched and extended the benchmark data to the whole human genome and provided the top-ranked unannotated candidate genes even for MeSH disease terms without known annotations

Functional Genomics Complements Quantitative Genetics in Identifying Disease-Gene Associations

An ultimate goal of genetic research is to understand the connection between genotype and phenotype in order to improve the diagnosis and treatment of diseases. The quantitative genetics field has developed a suite of statistical methods to associate genetic loci with diseases and phenotypes, including quantitative trait loci (QTL) linkage mapping and genome-wide association studies (GWAS). However, each of these approaches have technical and biological shortcomings. For example, the amount of heritable variation explained by GWAS is often surprisingly small and the resolution of many QTL linkage mapping studies is poor. The predictive power and interpretation of QTL and GWAS results are consequently limited. In this study, we propose a complementary approach to quantitative genetics by interrogating the vast amount of high-throughput genomic data in model organisms to functionally associate genes with phenotypes and diseases. Our algorithm combines the genome-wide functional relationship network for the laboratory mouse and a state-of-the-art machine learning method. We demonstrate the superior accuracy of this algorithm through predicting genes associated with each of 1157 diverse phenotype ontology terms. Comparison between our prediction results and a meta-analysis of quantitative genetic studies reveals both overlapping candidates and distinct, accurate predictions uniquely identified by our approach. Focusing on bone mineral density (BMD), a phenotype related to osteoporotic fracture, we experimentally validated two of our novel predictions (not observed in any previous GWAS/QTL studies) and found significant bone density defects for both Timp2 and Abcg8 deficient mice. Our results suggest that the integration of functional genomics data into networks, which itself is informative of protein function and interactions, can successfully be utilized as a complementary approach to quantitative genetics to predict disease risks. All supplementary material is available at http://cbfg.jax.org/phenotype

Personalized medicine: new genomics, old lessons

Personalized medicine uses traditional, as well as emerging concepts of the genetic and environmental basis of disease to individualize prevention, diagnosis and treatment. Personalized genomics plays a vital, but not exclusive role in this evolving model of personalized medicine. The distinctions between genetic and genomic medicine are more quantitative than qualitative. Personalized genomics builds on principles established by the integration of genetics into medical practice. Principles shared by genetic and genomic aspects of medicine, include the use of variants as markers for diagnosis, prognosis, prevention, as well as targets for treatment, the use of clinically validated variants that may not be functionally characterized, the segregation of these variants in non-Mendelian as well as Mendelian patterns, the role of gene–environment interactions, the dependence on evidence for clinical utility, the critical translational role of behavioral science, and common ethical considerations. During the current period of transition from investigation to practice, consumers should be protected from harms of premature translation of research findings, while encouraging the innovative and cost-effective application of those genomic discoveries that improve personalized medical care

EPMA position paper in cancer:current overview and future perspectives

At present, a radical shift in cancer treatment is occurring in terms of predictive, preventive, and personalized medicine (PPPM). Individual patients will participate in more aspects of their healthcare. During the development of PPPM, many rapid, specific, and sensitive new methods for earlier detection of cancer will result in more efficient management of the patient and hence a better quality of life. Coordination of the various activities among different healthcare professionals in primary, secondary, and tertiary care requires well-defined competencies, implementation of training and educational programs, sharing of data, and harmonized guidelines. In this position paper, the current knowledge to understand cancer predisposition and risk factors, the cellular biology of cancer, predictive markers and treatment outcome, the improvement in technologies in screening and diagnosis, and provision of better drug development solutions are discussed in the context of a better implementation of personalized medicine. Recognition of the major risk factors for cancer initiation is the key for preventive strategies (EPMA J. 4(1):6, 2013). Of interest, cancer predisposing syndromes in particular the monogenic subtypes that lead to cancer progression are well defined and one should focus on implementation strategies to identify individuals at risk to allow preventive measures and early screening/diagnosis. Implementation of such measures is disturbed by improper use of the data, with breach of data protection as one of the risks to be heavily controlled. Population screening requires in depth cost-benefit analysis to justify healthcare costs, and the parameters screened should provide information that allow an actionable and deliverable solution, for better healthcare provision

In silico discoveries for biomedical sciences

Text-mining is a challenging field of research initially meant for reading large text collections with a computer. Text-mining is useful in summarizing text, searching for the informative documents, and most important to do knowledge discovery. Knowledge discovery is the main subject of this thesis. The hypothesis that knowledge discovery is possible started with the work done by Swanson. He made, as a first finding, links between Raynaud__s disease and fish oil using intermediate medical terms to relate them to each other. This principle was formalized in the AB- C concept. A and C are not directly related to each other but via an intermediate concept B that needs to be discovered. Tex data can be extended by adding other non textual data such as microarray experiments. Then we are in the field of data-mining. The final goal is to do all kinds of discoveries with computer (in silico) using data sources in order to assist biology research to save time and discover more.NBICUBL - phd migration 201