Waiting for the working draft from the human genome project. A huge achievement, but not of immediate medical use

The interplay of ethics and genetic technologies in balancing the social valuation of the human genome in UNESCO declarations

This study investigates changes in the social valuation of the human genome over the more than 30 years since the establishment of the Human Genome Project. It offers a descriptive sociological analysis of the three waves of this valuation, mainly by considering three key UNESCO declarations and a relevant report. These waves represent a shifting balance between collectivism and individualism, starting with a broadly constructed valuation of the human genome as common human heritage and moving toward a valuation of dynamic applications within various social and medical contexts (e.g., personalized genomic medicine and genome editing). We seek to broaden the analytical perspective by examining how the declarations' ethical foci are framed within the context of rapidly evolving genetic technologies and their social applications. We conclude by discussing continuity and change in value balancing vis-à-vis changing genomic technologies.

Radiomics for precision medicine: Current challenges, future prospects, and the proposal of a new framework

The advancement of artificial intelligence concurrent with the development of medical imaging techniques provided a unique opportunity to turn medical imaging from mostly qualitative, to further quantitative and mineable data that can be explored for the development of clinical decision support systems (cDSS). Radiomics, a method for the high throughput extraction of hand-crafted features from medical images, and deep learning -the data driven modeling techniques based on the principles of simplified brain neuron interactions, are the most researched quantitative imaging techniques. Many studies reported on the potential of such techniques in the context of cDSS. Such techniques could be highly appealing due to the reuse of existing data, automation of clinical workflows, minimal invasiveness, three-dimensional volumetric characterization, and the promise of high accuracy and reproducibility of results and cost-effectiveness. Nevertheless, there are several challenges that quantitative imaging techniques face, and need to be addressed before the translation to clinical use. These challenges include, but are not limited to, the explainability of the models, the reproducibility of the quantitative imaging features, and their sensitivity to variations in image acquisition and reconstruction parameters. In this narrative review, we report on the status of quantitative medical image analysis using radiomics and deep learning, the challenges the field is facing, propose a framework for robust radiomics analysis, and discuss future prospects.

Molecular profiling and the reclassification of cancer: divide and conquer

Cancer is one of the leading causes of mortality in the world. Choosing the best treatment is dependent on making the right diagnosis. The diagnostic process has been based on light microscopy and the identification of the organ of tumor origin. Yet we now know that cancer is driven by molecular processes, and that these do not necessarily segregate by organ of origin. Fortunately, revolutionary changes in technology have enabled rapid genomic profiling. It is now apparent that neoplasms classified uniformly (e.g., non-small cell lung cancer) are actually comprised of up to 100 different molecular entities. For instance, tumors bearing ALK alterations make up about 4% of non-small cell lung cancers, and tumors bearing epidermal growth factor receptor (EGFR) mutations, approximately 5% to 10%. Importantly, matching patients to therapies targeted against their driver molecular aberrations has resulted in remarkable response rates. There is now a wealth of evidence supporting a divide-and-conquer strategy. Herein, we provide a concise primer on the current state-of-the-art of molecular profiling in the cancer clinic.

Shaking the tree: mapping complex disease genes with linkage disequilibrium

Much effort and expense are being spent internationally to detect genetic polymorphisms contributing to susceptibility to complex human disease. Concomitantly, the technology for detecting and genotyping single nucleotide polymorphisms (SNPs) has undergone rapid development, yielding extensive catalogues of these polymorphisms across the genome. Population-based maps of the correlations amongst SNPs (linkage disequilibrium) are now being developed to accelerate the discovery of genes for complex human diseases. These genomic advances coincide with an increasing recognition of the importance of very large sample sizes for studying genetic effects. Together, these new genetic and epidemiological data hold renewed promise for the identification of susceptibility genes for complex traits. We review the state of knowledge about the structure of the human genome as related to SNPs and linkage disequilibrium, discuss the potential applications of this knowledge to mapping complex disease genes, and consider the issues facing whole genome association scanning using SNPs.

Association study designs for complex diseases

Assessing the association between DNA variants and disease has been used widely to identify regions of the genome and candidate genes that contribute to disease. However, there are numerous examples of associations that cannot be replicated, which has led to skepticism about the utility of the approach for common conditions. With the discovery of massive numbers of genetic markers and the development of better tools for genotyping, association studies will inevitably proliferate. Now is the time to consider critically the design of such studies, to avoid the mistakes of the past and to maximize their potential to identify new components of disease.