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2432: A close examination of anti-retroviral drug selection and management in the optima study
- Yuan Huang, Sheldon T. Brown, Shuangge Ma, Tassos Kyriakides
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- Journal:
- Journal of Clinical and Translational Science / Volume 1 / Issue S1 / September 2017
- Published online by Cambridge University Press:
- 10 May 2018, p. 37
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OBJECTIVES/SPECIFIC AIMS: Effective HIV therapeutic options for persons with advanced HIV disease whose regimens have failed multiple times are limited. Current clinical practice utilizes regimens comprised of combinations of anti-retroviral (ARV) drugs. Despite the widespread use of ARV medications, optimization of initial treatment composition and subsequent management remains challenging. The goals of this study are (a) to better understand the ARV treatment structuring using prior clinical and patient information including virtual phenotype data and measures of viral load and CD4 cell count. We evaluated the potential impact of ARV strategies on AIDS-defining events and mortality; (b) to assess and understand differences of treatment composition and management when comparing standard ARV strategy (<5 ARVs) with an intensive ARV strategy (at least 5 ARVs). METHODS/STUDY POPULATION: OPTIMA was a tri-national (United States, Canada, and United Kingdom) randomized open label of alternative ARV treatment strategies for patients with advanced HIV disease (CD4≤300 cells/mm3) and evidence of resistance to 3 classes of ARV medications. OPTIMA used a 2×2 factorial design where the 2 factors were an ARV-free period Versus not; and standard Versus intensive ARV regimen. In this study, we focus on participants enrolled in OPTIMA at US participating sites and utilize demographic and clinical data including baseline virtual phenotype, ARV-related data (initial assignments and changes with drugs and dosages), follow-up lab data, AIDS-defining events, and vital status. RESULTS/ANTICIPATED RESULTS: Among 278 US-OPTIMA participants, 146 were randomly assigned to the standard ARV strategy and the rest were assigned to the intensive ARV strategy. Although not the sole factor, baseline virtual phenotype was used in selecting ARV medications within each assigned strategy. Participants in the standard arm exhibited better agreement between virtual phenotype results and the individual drugs selected for their regimen compared with participants in the intensive arm. This agreement had an almost statistically significant impact on survival time. No significant difference was detected in the frequency of ARV changes between standard and intensive ARV groups. DISCUSSION/SIGNIFICANCE OF IMPACT: Even though per design, OPTIMA assigned participants to an ARV strategy using a binary factor (standard vs. intensive ARV) and assessed its effect on HIV-related disease at a coarse level, the trial’s design and rich database allowed for a closer examination of the ARV drug initial selection and subsequent management. Our findings summarize the patterns and discuss the effects of ARV and their management, on AIDS-defining events and survival. Such findings could provide preliminary, yet important insight, in understanding ARV use practice and could inform the conduct of future HIV treatment trials. Since the trial’s randomization was at the ARV strategy level and not the individual ARV drugs, findings cannot be described in terms of causal pathways for specific ARVs.
2027: Racial differences in leukemia prognosis: New epidemiologic analysis
- Shuangge Ma, Yinjun Zhao, Yu Wang
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- Journal:
- Journal of Clinical and Translational Science / Volume 1 / Issue S1 / September 2017
- Published online by Cambridge University Press:
- 10 May 2018, p. 22
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OBJECTIVES/SPECIFIC AIMS: Research on cancer difference is of significant scientific and practical value. For leukemia, the survival disadvantage of the Blacks has been suggested in multiple studies. However, the existing epidemiologic analysis has multiple technical limitations. The goal of this study is to more accurately quantify so as to better understand different sources of racial differences in leukemia survival. METHODS/STUDY POPULATION: A new statistical method, which is based on robust regression and resampling, is developed. Data are obtained from the SEER (Surveillance, Epidemiology, and End Results) database. Using the “classic” epidemiologic methods as well as the new method, analysis is conducted on the prognosis of 4 leukemia subtypes (ALL, CLL, AML, and CML) for 4 major racial groups (White, non-Hispanic White, Black, and Asian and Pacific Islander). RESULTS/ANTICIPATED RESULTS: After effectively removing differences caused by the observed clinicopathological and demographic factors, the survival disadvantage of the Blacks persists for the following patient groups: ALL and age>14, CLL and age>14, and ALL and age≤14. The quantitative results are significantly different from those from classic epidemiologic analysis. Such observed racial differences are more attributable to the unobserved risk factors and cancer disparity. DISCUSSION/SIGNIFICANCE OF IMPACT: This study provides a more effective and more direct quantification of racial difference in leukemia prognosis. The survival disadvantage of the Blacks which is observed for certain subtypes/age groups deserves further attention but should not be overstated. More data collection and analysis are needed to more accurately decipher racial differences in leukemia and other cancer types.
8 - Penalized Integrative Analysis of High-Dimensional Omics Data
- from Part B - Vertical Integrative Analysis (General Methods)
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- By Jin Liu, Duke-NUS Graduate Medical School, Xingjie Shi, Shanghai University of Finance and Economics, China, Jian Huang, University of Iowa, Shuangge Ma, Capital University of Economics and Business, China
- George Tseng, University of Pittsburgh, Debashis Ghosh, Pennsylvania State University, Xianghong Jasmine Zhou, University of Southern California
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- Book:
- Integrating Omics Data
- Published online:
- 05 September 2015
- Print publication:
- 23 September 2015, pp 174-204
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Summary
Abstract
With omics data, results generated from single-dataset analysis are often unsatisfactory. Integrative analysis methods conduct the joint analysis of data from multiple independent studies or on multiple correlated responses, can effectively increase power, and outperform single-dataset analysis and meta-analysis. In this chapter, we review the penalized integrative analysis methods under both the homogeneity and heterogeneity models. Computation using the coordinate descent approach is described. We also discuss several important extensions. The analysis of a genome-wide association study demonstrates the applicability of reviewed methods.
Introduction
In the study of complex diseases such as cancer, cardiovascular diseases, and autoimmune diseases, profiling studies are nowroutinely conducted, generating “large d, small n” data, where the number of omics features profiled (genes, SNPs, methylation loci, etc.) d is much larger than the sample size n. Many different types of analyses can be conducted. For example, Chapters 3 and 4 were focused on identifying meaningful networks. In this chapter, our analysis goal is to identify a small subset of omics measurements that are associated with disease outcomes or phenotypes. Such measurements are also referred to as “markers” in the literature and in this chapter. Statistically, this is a variable selection problem. The development of integrative analysis methods has been partly motivated by the following examples.
8.1.1 Example 1
Consider the analysis of data generated in multiple independent studies with comparable designs. For example, in Ma et al. (2011), four pancreatic cancer data sets are collected and analyzed. The four data sets were generated in four independent studies, all having a case-control design, collecting mRNA gene expression measurements and searching for genes associated with the risk of pancreatic cancer. In high-dimensional omics studies, it has been recognized that the results generated in single-data-set analysis often have unsatisfactory properties such as low reproducibility. Among many possible contributing factors, the most important one is perhaps the small n. Multi-data-set analysis can effectively increase sample size and outperform single-data-set analysis (Guerra and Goldstein, 2009). This perspective has been explained in multiple chapters of this book. When the designs of multiple studies are “close enough”, it can be reasonable to expect that they identify the same set of markers.
Sparse group penalized integrative analysis of multiple cancer prognosis datasets
- JIN LIU, JIAN HUANG, YANG XIE, SHUANGGE MA
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- Journal:
- Genetics Research / Volume 95 / Issue 2-3 / June 2013
- Published online by Cambridge University Press:
- 12 August 2013, pp. 68-77
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In cancer research, high-throughput profiling studies have been extensively conducted, searching for markers associated with prognosis. Owing to the ‘large d, small n’ characteristic, results generated from the analysis of a single dataset can be unsatisfactory. Recent studies have shown that integrative analysis, which simultaneously analyses multiple datasets, can be more effective than single-dataset analysis and classic meta-analysis. In most of existing integrative analysis, the homogeneity model has been assumed, which postulates that different datasets share the same set of markers. Several approaches have been designed to reinforce this assumption. In practice, different datasets may differ in terms of patient selection criteria, profiling techniques, and many other aspects. Such differences may make the homogeneity model too restricted. In this study, we assume the heterogeneity model, under which different datasets are allowed to have different sets of markers. With multiple cancer prognosis datasets, we adopt the accelerated failure time model to describe survival. This model may have the lowest computational cost among popular semiparametric survival models. For marker selection, we adopt a sparse group minimax concave penalty approach. This approach has an intuitive formulation and can be computed using an effective group coordinate descent algorithm. Simulation study shows that it outperforms the existing approaches under both the homogeneity and heterogeneity models. Data analysis further demonstrates the merit of heterogeneity model and proposed approach.
Gene network-based cancer prognosis analysis with sparse boosting
- SHUANGGE MA, YUAN HUANG, JIAN HUANG, KUANGNAN FANG
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- Journal:
- Genetics Research / Volume 94 / Issue 4 / August 2012
- Published online by Cambridge University Press:
- 06 September 2012, pp. 205-221
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High-throughput gene profiling studies have been extensively conducted, searching for markers associated with cancer development and progression. In this study, we analyse cancer prognosis studies with right censored survival responses. With gene expression data, we adopt the weighted gene co-expression network analysis (WGCNA) to describe the interplay among genes. In network analysis, nodes represent genes. There are subsets of nodes, called modules, which are tightly connected to each other. Genes within the same modules tend to have co-regulated biological functions. For cancer prognosis data with gene expression measurements, our goal is to identify cancer markers, while properly accounting for the network module structure. A two-step sparse boosting approach, called Network Sparse Boosting (NSBoost), is proposed for marker selection. In the first step, for each module separately, we use a sparse boosting approach for within-module marker selection and construct module-level ‘super markers’. In the second step, we use the super markers to represent the effects of all genes within the same modules and conduct module-level selection using a sparse boosting approach. Simulation study shows that NSBoost can more accurately identify cancer-associated genes and modules than alternatives. In the analysis of breast cancer and lymphoma prognosis studies, NSBoost identifies genes with important biological implications. It outperforms alternatives including the boosting and penalization approaches by identifying a smaller number of genes/modules and/or having better prediction performance.