Springer Science and Business Media LLC

Today there exist two medical applications where relatively strong evidence exists to support the broad adoption of genome-informed precision medicine. These are the differential diagnosis of single gene diseases and genotype-based selection of patients for targeted cancer therapies. However, despite the availability of the $1000 genome and $700 exome for research, there is as yet little broad uptake of genomic medicine, even in these applications. Significant impediments to mainstream adoption exist, including unavailability in many institutions, lack of scalability in others, a dearth of physician understanding of interpreted genome or exome results or knowledge of how to translate consequent precision medicine care plans, and a lack of test reimbursement. In short, genomic medicine lacks a breakthrough application. Rapid genome sequencing of acutely ill infants with suspected genetic diseases (STATseq) may become that application when scaled to dozens of trios per day without loss of timeliness or accuracy. Also critical for broad adoption is embedding STATseq in software for timely patient ascertainment, augmented intelligence for interpretation, explanation of results for generalist physicians, and dynamic precision medicine decision support.

Drug addiction is a major public health issue. It is typically a multigenetic brain disorder, implying combined changes of expression of several hundred genes. Psychostimulants (such as cocaine, heroin and amphetamines) induce strong and persistent neuroadaptive changes through a surfeit of gene regulatory mechanisms leading to addiction. Activity-dependent synaptic plasticity of the mesolimbic dopaminergic system, known as the 'reward pathway', plays a crucial role in the development of drug dependence. miRNAs are small non-coding RNAs, particularly abundant in the nervous system, that play key roles as regulatory molecules in processes such as neurogenesis, synapse development and plasticity in the brain. They also act as key spatiotemporal regulators during dendritic morphogenesis, controlling the expression of hundreds of genes involved in neuroplasticity and in the function of synapses. Recent studies have identified changes of several specific miRNA expression profiles and polymorphisms affecting the interactions between miRNAs and their targets in various brain disorders, including addiction: miR-16 causes adaptive changes in production of the serotonin transporter; miR-133b is specifically expressed in midbrain dopaminergic neurons, and regulates the production of tyrosine hydroxylase and the dopamine transporter; miR-212 affects production of striatal brain-derived neurotrophic factor and synaptic plasticity upon cocaine. Clearly, specific miRNAs have emerged as key regulators leading to addiction, and could serve as valuable targets for more efficient therapies. In this review, the aim is to provide an overview of the emerging role of miRNAs in addiction.

A crucial limitation to our understanding of Alzheimer's disease (AD) is the inability to test hypotheses on live, patient-specific neurons. Patient autopsies are limited in supply and only reveal endpoints of disease. Rodent models harboring familial AD mutations lack important pathologies, and animal models have not been useful in modeling the sporadic form of AD because of complex genetics. The recent development of induced pluripotent stem cells (iPSCs) provides a method to create live, patient-specific models of disease and to investigate disease phenotypes in vitro. In this review, we discuss the genetics of AD patients and the potential for iPSCs to capture the genomes of these individuals and generate relevant cell types. Specifically, we examine recent insights into the genetic fidelity of iPSCs, advances in the area of neuronal differentiation, and the ability of iPSCs to model neurodegenerative diseases.

Next-generation sequencing has enabled patient selection for targeted drugs, some of which have shown remarkable efficacy in cancers that have the cognate molecular signatures. Intriguingly, rapidly emerging data indicate that altered genes representing oncogenic drivers can also be found in sporadic non-malignant conditions, some of which have negligible and/or low potential for transformation to cancer. For instance, activating KRAS mutations are discerned in endometriosis and in brain arteriovenous malformations, inactivating TP53 tumor suppressor mutations in rheumatoid arthritis synovium, and AKT, MAPK, and AMPK pathway gene alterations in the brains of Alzheimer’s disease patients. Furthermore, these types of alterations may also characterize hereditary conditions that result in diverse disabilities and that are associated with a range of lifetime susceptibility to the development of cancer, varying from near universal to no elevated risk. Very recently, the repurposing of targeted cancer drugs for non-malignant conditions that are associated with these genomic alterations has yielded therapeutic successes. For instance, the phenotypic manifestations of CLOVES syndrome, which is characterized by tissue overgrowth and complex vascular anomalies that result from the activation of PIK3CA mutations, can be ameliorated by the PIK3CA inhibitor alpelisib, which was developed and approved for breast cancer. In this review, we discuss the profound implications of finding molecular alterations in non-malignant conditions that are indistinguishable from those driving cancers, with respect to our understanding of the genomic basis of medicine, the potential confounding effects in early cancer detection that relies on sensitive blood tests for oncogenic mutations, and the possibility of reverse repurposing drugs that are used in oncology in order to ameliorate non-malignant illnesses and/or to prevent the emergence of cancer.

Although microarray technology allows the investigation of the transcriptomic make-up of a tumor in one experiment, the transcriptome does not completely reflect the underlying biology due to alternative splicing, post-translational modifications, as well as the influence of pathological conditions (for example, cancer) on transcription and translation. This increases the importance of fusing more than one source of genome-wide data, such as the genome, transcriptome, proteome, and epigenome. The current increase in the amount of available omics data emphasizes the need for a methodological integration framework. We propose a kernel-based approach for clinical decision support in which many genome-wide data sources are combined. Integration occurs within the patient domain at the level of kernel matrices before building the classifier. As supervised classification algorithm, a weighted least squares support vector machine is used. We apply this framework to two cancer cases, namely, a rectal cancer data set containing microarray and proteomics data and a prostate cancer data set containing microarray and genomics data. For both cases, multiple outcomes are predicted. For the rectal cancer outcomes, the highest leave-one-out (LOO) areas under the receiver operating characteristic curves (AUC) were obtained when combining microarray and proteomics data gathered during therapy and ranged from 0.927 to 0.987. For prostate cancer, all four outcomes had a better LOO AUC when combining microarray and genomics data, ranging from 0.786 for recurrence to 0.987 for metastasis. For both cancer sites the prediction of all outcomes improved when more than one genome-wide data set was considered. This suggests that integrating multiple genome-wide data sources increases the predictive performance of clinical decision support models. This emphasizes the need for comprehensive multi-modal data. We acknowledge that, in a first phase, this will substantially increase costs; however, this is a necessary investment to ultimately obtain cost-efficient models usable in patient tailored therapy.

Technological advances in molecular and in silico research have enabled significant progress towards personalized transplantation medicine. It is now possible to conduct comprehensive biomarker development studies of transplant organ pathologies, correlating genomic, transcriptomic and proteomic information from donor and recipient with clinical and histological phenotypes. Translation of these advances to the clinical setting will allow assessment of an individual patient's risk of allograft damage or accommodation. Transplantation biomarkers are needed for active monitoring of immunosuppression, to reduce patient morbidity, and to improve long-term allograft function and life expectancy. Here, we highlight recent pre- and post-transplantation biomarkers of acute and chronic allograft damage or adaptation, focusing on peripheral blood-based methodologies for non-invasive application. We then critically discuss current findings with respect to their future application in routine clinical transplantation medicine. Complement-system-associated SNPs present potential biomarkers that may be used to indicate the baseline risk for allograft damage prior to transplantation. The detection of antibodies against novel, non-HLA, MICA antigens, and the expression of cytokine genes and proteins and cytotoxicity-related genes have been correlated with allograft damage and are potential post-transplantation biomarkers indicating allograft damage at the molecular level, although these do not have clinical relevance yet. Several multi-gene expression-based biomarker panels have been identified that accurately predicted graft accommodation in liver transplant recipients and may be developed into a predictive biomarker assay.

Large-scale cancer genomic studies have revealed that the genetic heterogeneity of the same type of cancer is greater than previously thought. A key question in cancer genomics is the identification of driver genes. Although existing methods have identified many common drivers, it remains challenging to predict personalized drivers to assess rare and even patient-specific mutations. We developed a new algorithm called DawnRank to directly prioritize altered genes on a single patient level. Applications to TCGA datasets demonstrated the effectiveness of our method. We believe DawnRank complements existing driver identification methods and will help us discover personalized causal mutations that would otherwise be obscured by tumor heterogeneity. Source code can be accessed at http://bioen-compbio.bioen.illinois.edu/DawnRank/ .