Network modeling of the transcriptional effects of copy number aberrations in glioblastoma

DNA copy number aberrations (CNAs) are a characteristic feature of cancer genomes. In this work, Rebecka Jörnsten, Sven Nelander and colleagues combine network modeling and experimental methods to analyze the systems-level effects of CNAs in glioblastoma

Fused elastic net EPoC

Use mRNA and copy number aberration (CNA) to build a network using an extension of our EPoC method (Jörnsten et al., 2011; Abenius et al., 2012). Extension with generalized fused LASSO and elastic net generalized fused LASSO which is for this project. Two different cancers, breast cancer (N=766) and ovarian cancer (N=560). Small subset (p = 250) of all 10321 genes that exist in the intersect gene list of the two cancers. EPoC summary The EPoC method first standardize mRNA and CNA, then CNA direct effects on their corresponding mRNA are calculated (univariate regression) and subtracted from mRNA to form the residual mRNA. This residuals will act as response and CNA will act as predictors. Fused Objective The objective of the standard fused lasso penalizes differences between consecutive coefficients (Tibshirani et al., 2005): βfused = arg min ||y − Xβ|| β 2 p∑ 2 + λ1||β||1 + λ2 |βi−1 − βi| i=2 standard fused LASSO partFused Objective The objective of the standard fused lasso penalizes differences between consecutive coefficients (Tibshirani et al., 2005): βfused = arg min ||y − Xβ|| β 2 p∑ 2 + λ1||β||1 + λ2 |βi−1 − βi| i=2 standard fused LASSO part This has been extended to a more general case where all pairwise differences are penalized (Petry et al., 2011): βpwfused = arg min ||y − Xβ|| β 2 p ∑ p∑ 2 + λ1||β||1 + λ2 |βi − βj| i=1 j=i+1 pairwise fused LASSO partFused Objective cont. This can be even more generalized to (Ye and Xie, 2010) β = arg min ||y − Xβ|| β 2 2 + λ1||β||1 + λ2||Lβ||1, where matrix L ∈ {−1, 0, 1} m×p tell which absolute differences between β coefficients to penalize. For standard fused lasso L becomes a matrix with ones on the diagonal and −1 on the superdiagonal

License LGPL-3

Description Estimates sparse matrices A or G using fast lasso regression from mRNA transcript levels Y and CNA profiles U. Two models are provided, EPoC A where AY + U + R = 0 and EPoC G where Y = GU + E,the matrices R and E are so far treated as noise. For details see the reference and the manual page of ‘lassoshooting’

System-scale network modeling of cancer using EPoC

One of the central problems of cancer systems biology is to understand the complex molecular changes of cancerous cells and tissues, and use this understanding to support the development of new targeted therapies. EPoC (Endogenous Perturbation analysis of Cancer) is a network modeling technique for tumor molecular profiles. EPoC models are constructed from combined copy number aberration (CNA) and mRNA data and aim to (1) identify genes whose copy number aberrations significantly affect target mRNA expression and (2) generate markers for long- and short-term survival of cancer patients. Models are constructed by a combination of regression and bootstrapping methods. Prognostic scores are obtained from a singular value decomposition of the networks. We have previously analyzed the performance of EPoC using glioblastoma data from The Cancer Genome Atlas (TCGA) consortium, and have shown that resulting network models contain both known and candidate disease-relevant genes as network hubs, as well as uncover predictors of patient survival. Here, we give a practical guide how to perform EPoC modeling in practice using R, and present a set of alternative modeling frameworks

System-scale network modeling of cancer using EPoC

One of the central problems of cancer systems biology is to understand the complex molecular changes of cancerous cells and tissues, and use this understanding to support the development of new targeted therapies. EPoC (Endogenous Perturbation analysis of Cancer) is a network modeling technique for tumor molecular profiles. EPoC models are constructed from combined copy number aberration (CNA) and mRNA data and aim to (1) identify genes whose copy number aberrations significantly affect target mRNA expression and (2) generate markers for long- and short-term survival of cancer patients. Models are constructed by a combination of regression and bootstrapping methods. Prognostic scores are obtained from a singular value decomposition of the networks. We have previously analyzed the performance of EPoC using glioblastoma data from The Cancer Genome Atlas (TCGA) consortium, and have shown that resulting network models contain both known and candidate disease-relevant genes as network hubs, as well as uncover predictors of patient survival. Here, we give a practical guide how to perform EPoC modeling in practice using R, and present a set of alternative modeling frameworks