Allele age estimators designed for whole genome datasets show only a modest decrease in accuracy when applied to whole exome datasets

Personalized genomics in the healthcare system is becoming increasingly accessible as the costs of sequencing decreases. With the increase in number of genomes, larger numbers of rare variants are being discovered and much work is being done to identify their functional impacts in relation to disease phenotypes. One way to characterize these variants is to estimate the time the mutation entered the population. However, allele age estimators such as Relate, Genealogical Estimator of Variant Age, and time of coalescence, were developed based on the assumption that datasets include the entire genome. We examined the performance of each of these estimators on simulated exome data under a neutral constant population size model and found that each provides usable estimates of allele age from whole-exome datasets. To test the robustness of these methods, analyses were undertaken to simulate data under a population expansion model and background selection. Relate performs the best amongst all three estimators with Pearson coefficients of 0.64 and 0.68 (neutral constant and expansion population model) with a 17 percent and 15 percent drop in accuracy between whole genome and whole exome estimations. Of the three estimators, Relate is best able to parallelize to yield quick results with little resources, however even Relate is only able to scale to thousands of samples making it unable to match the hundreds of thousands of samples being currently released. While more work is needed to expand the capabilities of current methods of estimating allele age, these methods estimate the age of mutations with a modest decrease in performance.

Intact Transition Epitope Mapping-Force Differences between Original and Unusual Residues (ITEM-FOUR)

Antibody-based point-of-care diagnostics have become indispensable for modern medicine. In-depth analysis of antibody recognition mechanisms is the key to tailoring the accuracy and precision of test results, which themselves are crucial for targeted and personalized therapy. A rapid and robust method is desired by which binding strengths between antigens and antibodies of concern can be fine-mapped with amino acid residue resolution to examine the assumedly serious effects of single amino acid polymorphisms on insufficiencies of antibody-based detection capabilities of, e.g., life-threatening conditions such as myocardial infarction. The experimental ITEM-FOUR approach makes use of modern mass spectrometry instrumentation to investigate intact immune complexes in the gas phase. ITEM-FOUR together with molecular dynamics simulations, enables the determination of the influences of individually exchanged amino acid residues within a defined epitope on an immune complex's binding strength. Wild-type and mutated epitope peptides were ranked according to their experimentally determined dissociation enthalpies relative to each other, thereby revealing which single amino acid polymorphism caused weakened, impaired, and even abolished antibody binding. Investigating a diagnostically relevant human cardiac Troponin I epitope for which seven nonsynonymous single nucleotide polymorphisms are known to exist in the human population tackles a medically relevant but hitherto unsolved problem of current antibody-based point-of-care diagnostics.

Preclinical evaluation of potential therapeutic targets in dedifferentiated liposarcoma

Sarcomas are rare cancers with limited treatment options. Patients are generally treated by chemotherapy and/or radiotherapy in combination with surgery, and would benefit from new personalized approaches. In this study we demonstrate the potential of combining personal genomic characterization of patient tumors to identify targetable mutations with in vitro testing of specific drugs in patient-derived cell lines. We have analyzed three metastases from a patient with high-grade metastatic dedifferentiated liposarcoma (DDLPS) by exome and transcriptome sequencing as well as DNA copy number analysis. Genomic aberrations of several potentially targetable genes, including amplification of KITLG and FRS2, in addition to amplification of CDK4 and MDM2, characteristic of this disease, were identified. We evaluated the efficacy of drugs targeting these aberrations or the corresponding signaling pathways in a cell line derived from the patient. Interestingly, the pan-FGFR inhibitor NVP-BGJ398, which targets FGFR upstream of FRS2, strongly inhibited cell proliferation in vitro and induced an accumulation of cells into the G0 phase of the cell cycle. This study indicates that FGFR inhibitors have therapeutic potential in the treatment of DDLPS with amplified FRS2.

Adaptive NetworkProfiler for Identifying Cancer Characteristic-Specific Gene Regulatory Networks

There is currently much discussion about sample (patient)-specific gene regulatory network identification, since the efficiently constructed sample-specific gene networks lead to effective personalized cancer therapy. Although statistical approaches have been proposed for inferring gene regulatory networks, the methods cannot reveal sample-specific characteristics because the existing methods, such as an L₁-type regularization, provide averaged results for all samples. Thus, we cannot reveal sample-specific characteristics in transcriptional regulatory networks. To settle on this issue, the NetworkProfiler was proposed based on the kernel-based L₁-type regularization. The NetworkProfiler imposes a weight on each sample based on the Gaussian kernal function for controlling effect of samples on modeling a target sample, where the amount of weight depends on similarity of cancer characteristics between samples. The method, however, cannot perform gene regulatory network identification well for a target sample in a sparse region (i.e., for a target sample, there are only a few samples having a similar characteristic of the target sample, where the characteristic is considered as a modulator in sample-specific gene network construction), since a constant bandwidth in the Gaussian kernel function cannot effectively group samples for modeling a target sample in sparse region. The cancer characteristics, such as an anti-cancer drug sensitivity, are usually nonuniformly distributed, and thus modeling for samples in a sparse region is also a crucial issue. We propose a novel kernel-based L₁-type regularization method based on a modified k-nearest neighbor (KNN)-Gaussian kernel function, called an adaptive NetworkProfiler. By using the modified KNN-Gaussian kernel function, our method provides robust results against the distribution of modulators, and properly groups samples according to a cancer characteristic for sample-specific analysis. Furthermore, we propose a sample-specific generalized cross-validation for choosing the sample-specific tuning parameters in the kernel-based L₁-type regularization method. Numerical studies demonstrate that the proposed adaptive NetworkProfiler effectively performs sample-specific gene network construction. We apply the proposed statistical strategy to the publicly available Sanger Genomic data analysis, and extract anti-cancer drug sensitivity-specific gene regulatory networks.

Minimum information required for a DMET experiment reporting

AIM

To provide pharmacogenomics reporting guidelines, the information and tools required for reporting to public omic databases.

MATERIAL & METHODS

For effective DMET data interpretation, sharing, interoperability, reproducibility and reporting, we propose the Minimum Information required for a DMET Experiment (MIDE) reporting.

RESULTS

MIDE provides reporting guidelines and describes the information required for reporting, data storage and data sharing in the form of XML.

CONCLUSION

The MIDE guidelines will benefit the scientific community with pharmacogenomics experiments, including reporting pharmacogenomics data from other technology platforms, with the tools that will ease and automate the generation of such reports using the standardized MIDE XML schema, facilitating the sharing, dissemination, reanalysis of datasets through accessible and transparent pharmacogenomics data reporting.

Yesterday's war; tomorrow's technology: peer commentary on 'Ethical, legal, social and policy issues in the use of genomic technologies by the US military'

A recent article by Maxwell J. Mehlman and Tracy Yeheng Li, in the Journal of Law and the Biosciences, sought to examine the ethical, legal, social, and policy issues associated with the use of genetic screening and germ-line therapies ('genomic technologies') by the US Military. In this commentary, we will elaborate several related matters: the relationship between genetic and non-genetic screening methods, the history of selection processes and force strength, and the consequences and ethics of, as Mehlman and Li suggest, engineering enhanced soldiers. We contend, first, that the strengths of genomic testing as a method of determining enrollment in the armed forces has limited appeal, given the state of current selection methods in the US armed forces. Second, that the vagaries of genetic selection, much like other forms of selection that do not bear causally or reliably on soldier performance (such as race, gender, and sexuality), pose a systematic threat to force strength by limiting the (valuable) diversity of combat units. Third, that the idea of enhancing warfighters through germ-line interventions poses serious ethical issues in terms of the control and ownership of 'enhancements' when members separate from service.

The dynamic exome: acquired variants as individuals age

A singular genome used for inference into population-based studies is a standard method in genomics. Recent studies show that spontaneous genomic variants can propagate into new generations and these changes can contribute to individual cell aging with environmental and evolutionary elements contributing to cumulative genomic variation. However, the contribution of aging to genomic changes in tissue samples remains uncharacterized. Here, we report the impact of aging on individual human exomes and their implications. We found the human genome to be dynamic, acquiring a varying number of mutations with age (5,000 to 50,000 in 9 to 16 years). This equates to a variation rate of 9.6x10(-7) to 8.4x10(-6) bp(-1) year(-1) for nonsynonymous single nucleotide variants and 2.0x10(-4) to 1.0x10(-3) locus(-1) year(-1) for microsatellite loci in these individuals. These mutations span across 3,000 to 13,000 genes, which commonly showed association with Wnt signaling and Gonadotropin releasing hormone receptor pathways, and indicated for individuals a specific and significant enrichment for increased risk for diabetes, kidney failure, cancer, Rheumatoid arthritis, and Alzheimer's disease--conditions usually associated with aging. The results suggest that "age" is an important variable while analyzing an individual human genome to extract individual-specific clinically significant information necessary for personalized genomics.