Facilitating precision medicine through analysis of next-generation sequencing projects

Precision medicine constitutes an emerging strategy that aims at the individualization of healthcare by considering the personal molecular features and environmental factors of the patient in question. Genetic biomarkers constitute one dimension of a patient’s molecular phenotype that can allow for treatment stratification. As such, incorporating genetic variability into clinical decision making has raised great interest with drug developers, regulators and in the wider medical community. Importantly however, most studies that evaluated associations of genetic variability with drug reponse or toxicity interrogated only selected, mostly common candidate variants and the prevalence and relevance of rare variants for pharmacogenetics remained largely unexplored. This thesis demonstrates how population-scale Next-Generation Sequencing (NGS) data can be leveraged to map the interindividual and ethnogeographic variability of genes with medical importance. Papers I and II focused on ATP-binding casette (ABC) transporters, as an example of a pharmacogenetically relevant gene family, and show how their variability can have potential predictive value in breast cancer chemotherpy. The human ABC transporter family consists of 48 functionally important membrane proteins which mediate the active transport of a plethora of substrates, including a multitude of endogenous substrates as well as drugs, such as calcium channel blockers and various chemotherapeutics. Because of this physiological and clinical importance, Paper I systematically investigated the interindividual and ethnogeographic variability in the ABC transporter superfamily using NGS data of 138,632 unrelated individuals worldwide, and used an list of sophisticated computational algorithms to estimate their functional relevance. In total, 62,793 exonic variants were discovered, of which 98.5% were rare with minor allele frequencies (MAF) <1.5%. Based on these data, individuals were found to harbor between 9.3 and 13.9 deleterious ABC variants, only 0.3% of which were shared among all populations. As such, this work analyzed the landscape of ABC transporter variability on an unprecedented scale and revealed large interindividual and ethnogeographic variability with potential relevance for the treatment with ABC transporter substrates. Paper II built on these findings by evaluating whether ABC transporter variability was associated with drug response. As drug resistance due to facilitated ABC transporter-mediated efflux of chemotherapeutics constitutes an important cause of morbidity and mortality, ABC transporter variability was evaluated whether it could predict treatment outcomes in breast invasive carcinoma (BRCA), clear cell renal carcinoma (ccRCC) and hepatocellular carcinoma (HCC). In contrast to previous studies, these analyses did not only consider common ABC polymorphisms but considered also rare genetic variants using mutational burden testing. Importantly, variant burden of ABCC1 was found to significantly assoiate with reduced survival in BRCA patients, specifically in those subgroups treated with the MRP1 (the transporter encoded by ABCC1) 2 substrates doxorubicin (p=0.0088) and cyclophosphamide (p=0.0011). In contrast, no association was discovered in tamoxifen-treated patients (p=0.13). Multiple variants enriched in the high mutational burden group affected residues in functionally important transporter domains providing additional mechanistic support. Combined, these results argue for a model in which multiple variants with individually small effect sizes shape drug resistance, thus incentivizing a shift in strategy away from the interrogation of candidate variants and towards the incorporation of germline data for precision cancer medicine. Paper III indicated how publically available sequencing data from individuals can be used to provide accurate estimates of population-specific carrier rates and genetic complexity of 450 human autosomal recessive (AR) diseases. Specifically, population-scale NGS data of individuals free from clinically diagnosed congenital disorders was used to identify disease allele carrier frequencies for 450 AR disorders. Using 85 diseases with known epidemiology, the data showed that our prevalence estimates corresponded well to clinically reported incidences (p<0.001; R=0.68). Furthermore, these data allowed for the first time to evaluate the genetic complexity of the human AR diseasome and estimate population-specific founder effects. As such, these analyses reveal the molecular genetics of AR diseases with unprecedented resolution and provide important insights into epidemiology, complexity and population-specific founder effects, which can provide a powerful resource for clinical geneticists to inform population-adjusted genetic screening programs, particularly in otherwise understudied ethnogeographic groups. In conclusion, by utilizing sophisticated computational methods for the analysis of publically available population-scale sequencing data of >130,000 individuals, this thesis uncovered the landscape of genetic variability in genes with importance for pharmacogenetics and congenital disease. The resulting findings aspire to improve pharmacogenetic interpretations and carrier screening programs and, hopefully, can contribute to the advancement of precision medicine

Wireless Powered Sensor Networks for Internet of Things: Maximum Throughput and Optimal Power Allocation

This paper investigates a wireless powered sensor network (WPSN), where multiple sensor nodes are deployed to monitor a certain external environment. A multi-antenna power station (PS) provides the power to these sensor nodes during wireless energy transfer (WET) phase, and consequently the sensor nodes employ the harvested energy to transmit their own monitoring information to a fusion center (FC) during wireless information transfer (WIT) phase. The goal is to maximize the system sum throughput of the sensor network, where two different scenarios are considered, i.e., PS and the sensor nodes belong to the same or different service operator(s). For the first scenario, we propose a global optimal solution to jointly design the energy beamforming and time allocation. We further develop a closed-form solution for the proposed sum throughput maximization. For the second scenario in which the PS and the sensor nodes belong to different service operators, energy incentives are required for the PS to assist the sensor network. Specifically, the sensor network needs to pay in order to purchase the energy services released from the PS to support WIT. In this case, the paper exploits this hierarchical energy interaction, which is known as energy trading. We propose a quadratic energy trading based Stackelberg game, linear energy trading based Stackelberg game, and social welfare scheme, in which we derive the Stackelberg equilibrium for the formulated games, and the optimal solution for the social welfare scheme. Finally, numerical results are provided to validate the performance of our proposed schemes

Pricing for Reconfigurable Intelligent Surface Aided Wireless Networks: Models and Principles

Owing to the recent advancements of meta-materials and meta-surfaces, the concept of reconfigurable intelligent surface (RIS) has been embraced to meet the spectral- and energy-efficient, and yet cost-effective solutions for the sixth-generation (6G) wireless networks. From an operational standpoint, RISs can be easily deployed on the facades of buildings and indoor walls. Albeit promising, in the actual network operation, the deployment of RISs may face challenges because of the willingness and benefits of RIS holders from the aspect of installing RISs on their properties. Accordingly, RIS-aided wireless networks are faced with a formidable mission: how to balance the wireless service providers (WSPs) and RIS holders in terms of their respective interests. To alleviate this deadlock, we focus on the application of pricing models in RIS-aided wireless networks in pursuit of a win-win solution for both sides. Specifically, we commence with a comprehensive introduction of RIS pricing with its potential applications in RIS networks, meanwhile the fundamentals of pricing models are summarized in order to benefit both RIS holders and WSPs. In addition, a Stackelberg game-based model is exemplified to illustrate the operation of utility-maximization pricing. Finally, we highlight open issues and future research directions of applying pricing models to the RIS-aided wireless networks

Mesh-based variational autoencoders for localized deformation component analysis

Spatially localized deformation components are very useful for shape analysis and synthesis in 3D geometry processing. Several methods have recently been developed, with an aim to extract intuitive and interpretable deformation components. However, these techniques suffer from fundamental limitations especially for meshes with noise or large-scale nonlinear deformations, and may not always be able to identify important deformation components. In this paper we propose a novel mesh-based variational autoencoder architecture that is able to cope with meshes with irregular connectivity and nonlinear deformations. To help localize deformations, we introduce sparse regularization along with spectral graph convolutional operations. Through modifying the regularization formulation and allowing dynamic change of sparsity ranges, we improve the visual quality and reconstruction ability. Our system also provides a nonlinear approach to reconstruction of meshes using the extracted basis, which is more effective than the current linear combination approach. We further develop a neural shape editing method, achieving shape editing and deformation component extraction in a unified framework and ensuring plausibility of the edited shapes. Extensive experiments show that our method outperforms state-of-the-art methods in both qualitative and quantitative evaluations. We also demonstrate the effectiveness of our method for neural shape editing

学会抄録

<p><b>Observation of pulmonary artery sections</b> (200X, HE) The pulmonary artery wall thickness of disease (D) is noticeably increased. In the D sample, 1) the tunica adventicia was more compact and exhibited increased connective tissue; 2) the smooth muscle fiber was thicker; 3) there was excessive fiber production; and 4) the intima was more compact. The arrows indicate the pathological changes.</p

Epigenetic prediction of response to anti-PD-1 treatment in non-small-cell lung cancer: a multicenter, retrospective analysis

Background: Anti-programmed death-1 (PD-1) treatment for advanced non-small-cell lung cancer (NSCLC) has improved the survival of patients. However, a substantial percentage of patients do not respond to this treatment. We examined the use of DNA methylation profiles to determine the efficacy of anti-PD-1 treatment in patients recruited with current stage IV NSCLC. Methods: In this multicentre study, we recruited adult patients from 15 hospitals in France, Spain, and Italy who had histologically proven stage IV NSCLC and had been exposed to PD-1 blockade during the course of the disease. The study structure comprised a discovery cohort to assess the correlation between epigenetic features and clinical benefit with PD-1 blockade and two validation cohorts to assess the validity of our assumptions. We first established an epigenomic profile based on a microarray DNA methylation signature (EPIMMUNE) in a discovery set of tumour samples from patients treated with nivolumab or pembrolizumab. The EPIMMUNE signature was validated in an independent set of patients. A derived DNA methylation marker was validated by a single-methylation assay in a validation cohort of patients. The main study outcomes were progression-free survival and overall survival. We used the Kaplan-Meier method to estimate progression-free and overall survival, and calculated the differences between the groups with the log-rank test. We constructed a multivariate Cox model to identify the variables independently associated with progression-free and overall survival. Findings: Between June 23, 2014, and May 18, 2017, we obtained samples from 142 patients: 34 in the discovery cohort, 47 in the EPIMMUNE validation cohort, and 61 in the derived methylation marker cohort (the T-cell differentiation factor forkhead box P1 [FOXP1]). The EPIMMUNE signature in patients with stage IV NSCLC treated with anti-PD-1 agents was associated with improved progression-free survival (hazard ratio [HR] 0·010, 95% CI 3·29 × 10 −4–0·0282; p=0·0067) and overall survival (0·080, 0·017–0·373; p=0·0012). The EPIMMUNE-positive signature was not associated with PD-L1 expression, the presence of CD8+ cells, or mutational load. EPIMMUNE-negative tumours were enriched in tumour-associated macrophages and neutrophils, cancer-associated fibroblasts, and senescent endothelial cells. The EPIMMUNE-positive signature was associated with improved progression-free survival in the EPIMMUNE validation cohort (0·330, 0·149–0·727; p=0·0064). The unmethylated status of FOXP1 was associated with improved progression-free survival (0·415, 0·209–0·802; p=0·0063) and overall survival (0·409, 0·220–0·780; p=0·0094) in the FOXP1 validation cohort. The EPIMMUNE signature and unmethylated FOXP1 were not associated with clinical benefit in lung tumours that did not receive immunotherapy. Interpretation: Our study shows that the epigenetic milieu of NSCLC tumours indicates which patients are most likely to benefit from nivolumab or pembrolizumab treatments. The methylation status of FOXP1 could be associated with validated predictive biomarkers such as PD-L1 staining and mutational load to better select patients who will experience clinical benefit with PD-1 blockade, and its predictive value should be evaluated in prospective studies

MiR-133a in Human Circulating Monocytes: A Potential Biomarker Associated with Postmenopausal Osteoporosis

Background: Osteoporosis mainly occurs in postmenopausal women, which is characterized by low bone mineral density (BMD) due to unbalanced bone resorption by osteoclasts and formation by osteoblasts. Circulating monocytes play important roles in osteoclastogenesis by acting as osteoclast precursors and secreting osteoclastogenic factors, such as IL-1, IL-6 and TNF-a. MicroRNAs (miRNAs) have been implicated as important biomarkers in various diseases. The present study aimed to find significant miRNA biomarkers in human circulating monocytes underlying postmenopausal osteoporosis. Methodology/Principal Findings: We used ABI TaqManH miRNA array followed by qRT-PCR validation in circulating monocytes to identify miRNA biomarkers in 10 high and 10 low BMD postmenopausal Caucasian women. MiR-133a was upregulated (P = 0.007) in the low compared with the high BMD groups in the array analyses, which was also validated by qRT-PCR (P = 0.044). We performed bioinformatic target gene analysis and found three potential osteoclast-related target genes, CXCL11, CXCR3 and SLC39A1. In addition, we performed Pearson correlation analyses between the expression levels of miR-133a and the three potential target genes in the 20 postmenopausal women. We did find negative correlations between miR-133a and all the three genes though not significant. Conclusions/Significance: This is the first in vivo miRNA expression analysis in human circulating monocytes to identif