Prediction of cancer drug sensitivity using high-dimensional omic features

A large number of cancer drugs have been developed to target particular genes/pathways that are crucial for cancer growth. Drugs that share a molecular target may also have some common predictive omic features, e.g., somatic mutations or gene expression. Therefore, it is desirable to analyze these drugs as a group to identify the associated omic features, which may provide biological insights into the underlying drug response. Furthermore, these omic features may be robust predictors for any drug sharing the same target. The high dimensionality and the strong correlations among the omic features are the main challenges of this task. Motivated by this problem, we develop a new method for high-dimensional bilevel feature selection using a group of response variables that may share a common set of predictors in addition to their individual predictors. Simulation results show that our method has a substantially higher sensitivity and specificity than existing methods. We apply our method to two large-scale drug sensitivity studies in cancer cell lines. Both within-study and between-study validation demonstrate the good efficacy of our method

Penalized Estimation Methods and Their Applications in Genomics and Beyond

Various forms of penalty functions have been developed for regularized estimation. The tuning parameter(s) of a penalty function play a key role in penalizing all the noise to be zero and obtaining unbiased estimation of the true signals. For penalty functions with more than one tuning parameters, previous studies have not emphasized on the joint effect of all the tuning parameters. In the first topic, we conduct a theoretical analysis to relate the ranges of tuning parameters of penalty functions with the dimensionality of the problem and the minimum effect size. We exemplify our theoretical results in several well-known penalty functions. The results suggest that a class of penalty functions that bridges $L_0$ and $L_1$ penalties require less restrictive conditions for variable selection consistency. The simulation analysis and real data analysis support these theoretical results. For the second topic, we consider the problem of identifying genomic features to predict cancer drug sensitivity. Several drugs that share a molecular target may also have some common predictive features. Therefore, it is desirable to analyze these drugs as a group to identify the associated genomic features. Motivated by this problem, we develop a new method for high-dimensional feature selection using a group of responses that may share a common set of predictors in addition to their individual predictors. Simulation results show that our method has better performances than existing methods. Between-study validation in real data shows that the genomic features selected for a drug target can form good predictors for other drugs designed for the same target. For the third topic, we address an estimation problem where certain parameter values such as 0 would cause an identifiability issue. In the maximum likelihood estimation framework, due to the issue of the unidentifiable parameter, the maximum likelihood estimator have regular properties only if the likelihood function is specified correctly with respect to the parameter values. We propose a penalized estimation procedure using the adaptive Lasso penalty to address the potential identifiability issue. We study the asymptotic property of the proposed estimator and evaluate our method in extensive simulations and real data analysis.Doctor of Philosoph

Designing penalty functions in high dimensional problems: The role of tuning parameters

Various forms of penalty functions have been developed for regularized estimation and variable selection. Screening approaches are often used to reduce the number of covariate before penalized estimation. However, in certain problems, the number of covariates remains large after screening. For example, in genome-wide association (GWA) studies, the purpose is to identify Single Nucleotide Polymorphisms (SNPs) that are associated with certain traits, and typically there are millions of SNPs and thousands of samples. Because of the strong correlation of nearby SNPs, screening can only reduce the number of SNPs from millions to tens of thousands and the variable selection problem remains very challenging. Several penalty functions have been proposed for such high dimensional data. However, it is unclear which class of penalty functions is the appropriate choice for a particular application. In this paper, we conduct a theoretical analysis to relate the ranges of tuning parameters of various penalty functions with the dimensionality of the problem and the minimum effect size. We exemplify our theoretical results in several penalty functions. The results suggest that a class of penalty functions that bridges L0 and L1 penalties requires less restrictive conditions on dimensionality and minimum effect sizes in order to attain the two fundamental goals of penalized estimation: to penalize all the noise to be zero and to obtain unbiased estimation of the true signals. The penalties such as SICA and Log belong to this class, but they have not been used often in applications. The simulation and real data analysis using GWAS data suggest the promising applicability of such class of penalties

Effects of detraining on functional fitness and lymphocyte subsets in postmenopausal females

Introduction: Aging leads to declines of functional fitness and regular exercise has been recognized to be beneficial for keeping health and preventing degenerative diseases in older adults. Limited evidences connect the relationships among detraining, functional fitness, immunosenescence, and vascular integrity in aged individuals. Thus, the aims of this study was to inspect: How training and detraining influence functional fitness, mobilization of circulating leukocytes and lymphocyte subsets? Method: Twenty-two female volunteers aged 50 to 65 years were recruited as participants in this study. Participants were assigned into training group (TG, n=13) and control group (CG, n=9). The participants in TG were asked to attend exercise program, including aerobic exercise at 70% HRR for 60 min twice per week and resistance training at 60-70% 1RM, 3 sets and 9 exercises for 60 min per week for 16 weeks and subsequently avoid exercise for 6 weeks to investigate detraining effects. CG were asked to stay in their regular lifestyles. A six-items measurement of functional fitness and resting (at fasting status) venous blood samples were collected at before training program (Pre-training), 8th week of training (Mid-training), after training program (Post-training), and 6th week of detraining 6 weeks (Detraining). Blood cell counts (WBC, RBC, HCT, LYM) were measured using an automated cell counter and lymphocyte subsets (CD4, CD8, CD19, CD56) were analyzed by flow cytometry. Data were analyzed by descriptive statistic, mixed two-factors (time × group) measures ANOVA or ACOVA and the significance was set at pResult: Functional fitness of TG was not significantly improved following the training program although it was significantly better than CG in Pre-training. Blood cell counts were not changed and all in normal range. A significant difference in CD19 counts were observed between TG and CG (71.23±32.05 vs. 116.45±67.95 103/mL) in Post-training. CD19 counts in TG were increased at Detraining compared with Mid-training and Post-training (138.08±50.22 vs. 74.92±31.20, 71.23±32.05 103/mL). No alterations in quantity and percentage of CD4, CD8, and CD56 were observed in this study. Conclusion: Findings of this study suggest that both a 16-week moderate exercise program and a 6-week detraining did not significantly change the functional fitness and lymphocyte subsets in postmenopausal females

Seeing Through Things:Exploring the Design Space of Privacy-Aware Data-Enabled Objects

Increasing amounts of sensor-augmented research objects have been used in design research. We call these objects Data-Enabled Objects, which can be integrated into daily activities capturing data about people's detailed whereabouts, behaviours, and routines. These objects provide data perspectives on everyday life for contextual design research. However, data-enabled objects are still computational devices with limited privacy awareness and nuanced data sharing. To better design data-enabled objects, we explore privacy design spaces by inviting 18 teams of undergraduate design students to re-design the same type of sensor-enabled home research camera. We developed the Connected Peekaboo Toolkit (CPT) to support the design teams in designing, building, and directly deploying their prototypes in real home studies. We conducted Thematic Analysis to analyze their outcomes which led us to interpret that privacy is not just an obstacle but can be a driver by unfolding an exploration of possible design spaces for data-enabled objects.</p

A combined targeted mutation analysis of IRF6 gene would be useful in the first screening of oral facial clefts

BACKGROUND: Interferon Regulatory Factor 6 (IRF6) is a member of the IRF family of transcription factors. It has been suggested to be an important contributor to orofacial development since mutations of the IRF6 gene has been found in Van der Woude (VWS) and popliteal pterygium syndromes (PPS), two disorders that can present with isolated cleft lip and palate. The association between IRF6 gene and cleft lip and palate has also been independently replicated in many populations. METHODS: We screened a total of 155 Taiwanese patients with cleft lip with or without cleft palate (CL/P); 31 syndromic (including 19 VWS families), 44 non-syndromic families with at least two affected members, and 80 non-syndromic patients through a combined targeted, polymerase chain reaction (PCR)-based mutation analysis for the entire coding regions of IRF6 gene. RESULTS: We found 11 mutations in 57.89% (11/19) of the VWS patients and no IRF6 mutation in 44 of the non-syndromic multiplex families and 80 non-syndromic oral cleft patients. In this IRF6 gene screening, five of these mutations (c.290 A>G, p.Tyr97Cys; c.360-375 16 bp deletion, p.Gln120HisfsX24; c.411_412 insA, p.Glu136fsX3; c.871 A>C, p.Thr291Pro; c.969 G>A, and p.Trp323X) have not been reported in the literature previously. Exon deletion was not detected in this series of IRF6 gene screening. CONCLUSIONS: Our results confirm the crucial role of IRF6 in the VWS patients and further work is needed to explore for its function in the non-syndromic oral cleft with vary clinical features

Association between differential gene expression and body mass index among endometrial cancers from The Cancer Genome Atlas Project

The Cancer Genome Atlas (TCGA) identified four integrated clusters for endometrial cancer (EC): POLE, MSI, CNL and CNH. We evaluated differences in gene expression profiles of obese and non-obese women with EC and examined the association of body mass index (BMI) within the clusters identified in TCGA

Oxidative stress at low levels can induce clustered DNA lesions leading to NHEJ mediated mutations

DNA damage and mutations induced by oxidative stress are associated with various different human pathologies including cancer. The facts that most human tumors are characterized by large genome rearrangements and glutathione depletion in mice results in deletions in DNA suggest that reactive oxygen species (ROS) may cause gene and chromosome mutations through DNA double strand breaks (DSBs). However, the generation of DSBs at low levels of ROS is still controversial. In the present study, we show that H2O2 at biologically-relevant levels causes a marked increase in oxidative clustered DNA lesions (OCDLs) with a significant elevation of replication-independent DSBs. Although it is frequently reported that OCDLs are fingerprint of high-energy IR, our results indicate for the first time that H2O2, even at low levels, can also cause OCDLs leading to DSBs specifically in G1 cells. Furthermore, a reverse genetic approach revealed a significant contribution of the non-homologous end joining (NHEJ) pathway in H2O2-induced DNA repair & mutagenesis. This genomic instability induced by low levels of ROS may be involved in spontaneous mutagenesis and the etiology of a wide variety of human diseases like chronic inflammation-related disorders, carcinogenesis, neuro-degeneration and aging