Robust network-based regularization and variable selection for high-dimensional genomic data in cancer prognosis.

In cancer genomic studies, an important objective is to identify prognostic markers associated with patients' survival. Network-based regularization has achieved success in variable selections for high-dimensional cancer genomic data, because of its ability to incorporate the correlations among genomic features. However, as survival time data usually follow skewed distributions, and are contaminated by outliers, network-constrained regularization that does not take the robustness into account leads to false identifications of network structure and biased estimation of patients' survival. In this study, we develop a novel robust network-based variable selection method under the accelerated failure time model. Extensive simulation studies show the advantage of the proposed method over the alternative methods. Two case studies of lung cancer datasets with high-dimensional gene expression measurements demonstrate that the proposed approach has identified markers with important implications.

Network-based regularization for high dimensional SNP data in the case-control study of Type 2 diabetes.

BACKGROUND: Over the past decades, the prevalence of type 2 diabetes mellitus (T2D) has been steadily increasing around the world. Despite large efforts devoted to better understand the genetic basis of the disease, the identified susceptibility loci can only account for a small portion of the T2D heritability. Some of the existing approaches proposed for the high dimensional genetic data from the T2D case-control study are limited by analyzing a few number of SNPs at a time from a large pool of SNPs, by ignoring the correlations among SNPs and by adopting inefficient selection techniques. METHODS: We propose a network constrained regularization method to select important SNPs by taking the linkage disequilibrium into account. To accomodate the case control study, an iteratively reweighted least square algorithm has been developed within the coordinate descent framework where optimization of the regularized logistic loss function is performed with respect to one parameter at a time and iteratively cycle through all the parameters until convergence. RESULTS: In this article, a novel approach is developed to identify important SNPs more effectively through incorporating the interconnections among them in the regularized selection. A coordinate descent based iteratively reweighed least squares (IRLS) algorithm has been proposed. CONCLUSIONS: Both the simulation study and the analysis of the Nurses's Health Study, a case-control study of type 2 diabetes data with high dimensional SNP measurements, demonstrate the advantage of the network based approach over the competing alternatives.

Real-World Safety and Effectiveness of a Bevacizumab Biosimilar (ABP 215) in Metastatic Colorectal Cancer Patients in Canada.

BACKGROUND: ABP 215 is a biosimilar to the reference product, bevacizumab, and was one of the first biosimilars approved by Health Canada for the first-line treatment of metastatic colorectal cancer (mCRC). This study aimed to address gaps in real-world evidence (RWE) including patient characteristics, treatment safety (primary objective), and effectiveness (secondary objective) for first-line ABP 215 therapy in Canadian patients with mCRC. MATERIALS AND METHODS: Retrospective data were collected in 2 waves, at least 1 year (Wave 1) or 2 years (Wave 2) after commercial availability of ABP 215 at each participating site. RESULTS: A total of 75 patients from Wave 1 and 164 patients from Wave 2 treated with a minimum of 1 cycle of ABP 215 were included. At least one safety event of interest (EOI) was recorded for 34.7% of Wave 1 and 42.7% of Wave 2 patients. The median progression free survival (PFS) for Wave 1 and 2 patients were 9.47 (95% confidence interval [CI]: 6.71, 11.90) and 21.38 (95% CI: 15.82, not estimable) months, respectively. Median overall survival was not estimable for Wave 1 and was 26.45 months for Wave 2. CONCLUSION: The safety and effectiveness of ABP 215 observed in this real-world study were comparable to clinical trial findings and to other RWE with longer PFS in the current study.

Integrating Multi-Omics Data for Gene-Environment Interactions.

Gene-environment (G×E) interaction is critical for understanding the genetic basis of complex disease beyond genetic and environment main effects. In addition to existing tools for interaction studies, penalized variable selection emerges as a promising alternative for dissecting G×E interactions. Despite the success, variable selection is limited in terms of accounting for multidimensional measurements. Published variable selection methods cannot accommodate structured sparsity in the framework of integrating multiomics data for disease outcomes. In this paper, we have developed a novel variable selection method in order to integrate multi-omics measurements in G×E interaction studies. Extensive studies have already revealed that analyzing omics data across multi-platforms is not only sensible biologically, but also resulting in improved identification and prediction performance. Our integrative model can efficiently pinpoint important regulators of gene expressions through sparse dimensionality reduction, and link the disease outcomes to multiple effects in the integrative G×E studies through accommodating a sparse bi-level structure. The simulation studies show the integrative model leads to better identification of G×E interactions and regulators than alternative methods. In two G×E lung cancer studies with high dimensional multi-omics data, the integrative model leads to an improved prediction and findings with important biological implications.

Identification of Prognostic Genes and Pathways in Lung Adenocarcinoma Using a Bayesian Approach.

Lung cancer is the leading cause of cancer-associated mortality in the United States and the world. Adenocarcinoma, the most common subtype of lung cancer, is generally diagnosed at the late stage with poor prognosis. In the past, extensive effort has been devoted to elucidating lung cancer pathogenesis and pinpointing genes associated with survival outcomes. As the progression of lung cancer is a complex process that involves coordinated actions of functionally associated genes from cancer-related pathways, there is a growing interest in simultaneous identification of both prognostic pathways and important genes within those pathways. In this study, we analyse The Cancer Genome Atlas lung adenocarcinoma data using a Bayesian approach incorporating the pathway information as well as the interconnections among genes. The top 11 pathways have been found to play significant roles in lung adenocarcinoma prognosis, including pathways in mitogen-activated protein kinase signalling, cytokine-cytokine receptor interaction, and ubiquitin-mediated proteolysis. We have also located key gene signatures such as RELB, MAP4K1, and UBE2C. These results indicate that the Bayesian approach may facilitate discovery of important genes and pathways that are tightly associated with the survival of patients with lung adenocarcinoma.