Personalized medicine: Has it started yet? A reconstruction of the early history

Recent Trends and Future Direction of Dental Research in the Digital Era

The digital transformation in dental medicine, based on electronic health data information, is recognized as one of the major game-changers of the 21st century to tackle present and upcoming challenges in dental and oral healthcare. This opinion letter focuses on the estimated top five trends and innovations of this new digital era, with potential to decisively influence the direction of dental research: (1) rapid prototyping (RP), (2) augmented and virtual reality (AR/VR), (3) artificial intelligence (AI) and machine learning (ML), (4) personalized (dental) medicine, and (5) tele-healthcare. Digital dentistry requires managing expectations pragmatically and ensuring transparency for all stakeholders: patients, healthcare providers, university and research institutions, the medtech industry, insurance, public media, and state policy. It should not be claimed or implied that digital smart data technologies will replace humans providing dental expertise and the capacity for patient empathy. The dental team that controls digital applications remains the key and will continue to play the central role in treating patients. In this context, the latest trend word is created: augmented intelligence, e.g., the meaningful combination of digital applications paired with human qualities and abilities in order to achieve improved dental and oral healthcare, ensuring quality of life.

Current applications of precision medicine: a bibliometric analysis

A bibliometric analysis was conducted to describe trends in the publication of precision medicine literature over time. Searches identified 5552 articles with exponential growth from 2012 to 2018. Most were published in medical specialty journals, particularly oncology. Precision medicine definitions focused on tailored/individualized/personalized treatments and genetics/biology. Little attention was given to social and environmental determinants of health and health disparities. To fulfill the promise of precision medicine to positively impact broad populations, work is needed to develop the science of precision medicine for addressing health disparities and social and environmental determinants of health. While some precision medicine definitions include all factors that contribute to individual differences in health (e.g., genes, environments and lifestyles), future empirical work that includes and integrates all three areas is also required.

Developing a Minimally Structured Mathematical Model of Cancer Treatment with Oncolytic Viruses and Dendritic Cell Injections

Mathematical models of biological systems must strike a balance between being sufficiently complex to capture important biological features, while being simple enough that they remain tractable through analysis or simulation. In this work, we rigorously explore how to balance these competing interests when modeling murine melanoma treatment with oncolytic viruses and dendritic cell injections. Previously, we developed a system of six ordinary differential equations containing fourteen parameters that well describes experimental data on the efficacy of these treatments. Here, we explore whether this previously developed model is the minimal model needed to accurately describe the data. Using a variety of techniques, including sensitivity analyses and a parameter sloppiness analysis, we find that our model can be reduced by one variable and three parameters and still give excellent fits to the data. We also argue that our model is not too simple to capture the dynamics of the data, and that the original and minimal models make similar predictions about the efficacy and robustness of protocols not considered in experiments. Reducing the model to its minimal form allows us to increase the tractability of the system in the face of parametric uncertainty.

Zebrafish models for personalized psychiatry: Insights from individual, strain and sex differences, and modeling gene x environment interactions

Currently becoming widely recognized, personalized psychiatry focuses on unique physiological and genetic profiles of patients to best tailor their therapy. However, the role of individual differences, as well as genetic and environmental factors, in human psychiatric disorders remains poorly understood. Animal experimental models are a valuable tool to improve our understanding of disease pathophysiology and its molecular mechanisms. Due to high reproduction capability, fully sequenced genome, easy gene editing, and high genetic and physiological homology with humans, zebrafish (Danio rerio) are emerging as a novel powerful model in biomedicine. Mounting evidence supports zebrafish as a useful model organism in CNS research. Robustly expressed in these fish, individual, strain, and sex differences shape their CNS responses to genetic, environmental, and pharmacological manipulations. Here, we discuss zebrafish as a promising complementary translational tool to further advance patient-centered personalized psychiatry.

Policy Considerations for Mobile Biosensors

Meeting policy requirements is essential for advancing molecular diagnostic devices from the laboratory to real-world applications and commercialization. Considering policy as a starting point in the design of new technology is a winning strategy. Rapid developments have put mobile biosensors at the frontier of molecular diagnostics, at times outpacing policymakers, and therefore offering new opportunities for breakthroughs in global health. In this Perspective we survey influential global health policies and recent developments in mobile biosensing in order to gain a new perspective for the future of the field. We summarize the main requirements for mobile diagnostics outlined by policy makers such as the World Health Organization (WHO), the World Bank, the European Union (EU), and the Food and Drug Administration (FDA). We then classify current mobile diagnostic technologies according to the manner in which the biosensor interfaces with a smartphone. We observe a trend in reducing hardware components and substituting instruments and laborious data processing steps for user-friendly apps. From this perspective we see software application developers as key collaborators for bridging the gap between policy and practice.

Urine metabolomics as a predictor of patient tolerance and response to adjuvant chemotherapy in colorectal cancer

Colorectal cancer is the third leading cause of cancer-associated mortality in the western world. The ability to predict a patient's response to chemotherapy may be of great value for clinicians and patients when planning cancer treatment. The aim of the current study was to develop a urine metabolomics-based biomarker panel to predict adverse events and response to chemotherapy in patients with colorectal cancer. A retrospective chart review of patients diagnosed with stage III or IV colorectal cancer between 2008 and 2012 was performed. The exclusion criteria included chemotherapy for palliation and patients living outside of Alberta. Data was collected concerning the chemotherapy regimen, adverse events associated with chemotherapy, disease progression and recurrence and 5-year survival. Adverse events were subdivided as follows: Delays in treatment, dose reductions, hospitalizations and chemotherapy regime changes. Patients provided urine samples for analysis prior to any intervention. Nuclear magnetic resonance (NMR) spectra of urine samples were acquired. The 1H NMR spectrum of each urine sample was analyzed using Chenomx NMRSuite v7.0. Using machine learning, predictors were generated and evaluated using 10-fold cross-validation. Urine spectra were obtained for 62 patients. The best predictors resulted in area under the receiver operating characteristic curve values of: 0.542 for chemotherapy dose reduction, 0.612 for 5-year survival, 0.650 for cancer recurrence and 0.750 for treatment delay. Therefore, predictors were developed for response to and adverse events from chemotherapy for patients with colorectal cancer patients. The predictor for treatment delay has the most promise, and further studies will aid its refinement and improvement of its accuracy.

Evaluating optimal therapy robustness by virtual expansion of a sample population, with a case study in cancer immunotherapy

Cancer is a highly heterogeneous disease, exhibiting spatial and temporal variations that pose challenges for designing robust therapies. Here, we propose the VEPART (Virtual Expansion of Populations for Analyzing Robustness of Therapies) technique as a platform that integrates experimental data, mathematical modeling, and statistical analyses for identifying robust optimal treatment protocols. VEPART begins with time course experimental data for a sample population, and a mathematical model fit to aggregate data from that sample population. Using nonparametric statistics, the sample population is amplified and used to create a large number of virtual populations. At the final step of VEPART, robustness is assessed by identifying and analyzing the optimal therapy (perhaps restricted to a set of clinically realizable protocols) across each virtual population. As proof of concept, we have applied the VEPART method to study the robustness of treatment response in a mouse model of melanoma subject to treatment with immunostimulatory oncolytic viruses and dendritic cell vaccines. Our analysis (i) showed that every scheduling variant of the experimentally used treatment protocol is fragile (nonrobust) and (ii) discovered an alternative region of dosing space (lower oncolytic virus dose, higher dendritic cell dose) for which a robust optimal protocol exists.

CorSig: a general framework for estimating statistical significance of correlation and its application to gene co-expression analysis

With the rapid increase of omics data, correlation analysis has become an indispensable tool for inferring meaningful associations from a large number of observations. Pearson correlation coefficient (PCC) and its variants are widely used for such purposes. However, it remains challenging to test whether an observed association is reliable both statistically and biologically. We present here a new method, CorSig, for statistical inference of correlation significance. CorSig is based on a biology-informed null hypothesis, i.e., testing whether the true PCC (ρ) between two variables is statistically larger than a user-specified PCC cutoff (τ), as opposed to the simple null hypothesis of ρ = 0 in existing methods, i.e., testing whether an association can be declared without a threshold. CorSig incorporates Fisher's Z transformation of the observed PCC (r), which facilitates use of standard techniques for p-value computation and multiple testing corrections. We compared CorSig against two methods: one uses a minimum PCC cutoff while the other (Zhu's procedure) controls correlation strength and statistical significance in two discrete steps. CorSig consistently outperformed these methods in various simulation data scenarios by balancing between false positives and false negatives. When tested on real-world Populus microarray data, CorSig effectively identified co-expressed genes in the flavonoid pathway, and discriminated between closely related gene family members for their differential association with flavonoid and lignin pathways. The p-values obtained by CorSig can be used as a stand-alone parameter for stratification of co-expressed genes according to their correlation strength in lieu of an arbitrary cutoff. CorSig requires one single tunable parameter, and can be readily extended to other correlation measures. Thus, CorSig should be useful for a wide range of applications, particularly for network analysis of high-dimensional genomic data.

SOFTWARE AVAILABILITY

A web server for CorSig is provided at http://202.127.200.1:8080/probeWeb. R code for CorSig is freely available for non-commercial use at http://aspendb.uga.edu/downloads.