Modeling Between-Study Heterogeneity for Improved Replicability in Gene Signature Selection and Clinical Prediction

In the genomic era, the identification of gene signatures associated with disease is of significant interest. Such signatures are often used to predict clinical outcomes in new patients and aid clinical decision-making. However, recent studies have shown that gene signatures are often not replicable. This occurrence has practical implications regarding the generalizability and clinical applicability of such signatures. To improve replicability, we introduce a novel approach to select gene signatures from multiple datasets whose effects are consistently nonzero and account for between-study heterogeneity. We build our model upon some rank-based quantities, facilitating integration over different genomic datasets. A high-dimensional penalized generalized linear mixed model is used to select gene signatures and address data heterogeneity. We compare our method to some commonly used strategies that select gene signatures ignoring between-study heterogeneity. We provide asymptotic results justifying the performance of our method and demonstrate its advantage in the presence of heterogeneity through thorough simulation studies. Lastly, we motivate our method through a case study subtyping pancreatic cancer patients from four gene expression studies. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.</p

Supplementary Figure 2 from GSK-3α Promotes Oncogenic KRAS Function in Pancreatic Cancer via TAK1–TAB Stabilization and Regulation of Noncanonical NF-κB

PDF file - 181K, Left: Kras regulates TAK1 levels. HKRAS cells were transiently transfected with non-targeting or Kras siRNA for 72 hours. TAK1 was immunoprecitated from whole cell extracts and immunoblotted for specified antibodies. Right: TAK1 inhibition induces apoptosis. MiaPaCa-2 cells were treated with the TAK1 inhibitor, 5Z-7-oxozaenol at indicated concentrations for 24 hours. Whole cells extracts were immunoblotted with specified antibodies.</p

Supplementary Figure 6 from GSK-3α Promotes Oncogenic KRAS Function in Pancreatic Cancer via TAK1–TAB Stabilization and Regulation of Noncanonical NF-κB

PDF file - 118K, GSK-3 inhibition leads to a decrease in TAK1 protein Panc-1 and MiaPaCa-2 cells were treated with GSK-3 inhibitor, AR-A014418 or vehicle control, DMSO in the presence or absence of MG132 for 24 hours. Whole cell extracts were immunoblotted for indicated antibodies.</p

Supplementary Figure 3 from GSK-3α Promotes Oncogenic KRAS Function in Pancreatic Cancer via TAK1–TAB Stabilization and Regulation of Noncanonical NF-κB

PDF file - 113K, TAK1 inhibition decreases cell proliferation in Kras+ cells. Indicated cell lines were seeded in E-plate-16 (Roche) at 3000 cells/well/100μl. 24hours after plating, cells were treated with TAK1 inhibitor 5Z-7-oxozaenol (0.625μM), or vehicle control, DMSO. Cell impedance was measured every 2 hours for the entire course of experiment using RTCA.</p

Supplementary Figure 5 from GSK-3α Promotes Oncogenic KRAS Function in Pancreatic Cancer via TAK1–TAB Stabilization and Regulation of Noncanonical NF-κB

PDF file - 63K, GSK-3 inhibition inhibits TAK1 mediated signaling. Panc-1 cells were treated with GSK-3 inhibitor, AR-A014418 (30M) for 24 hours and whole cell lysates immunoblotted for indicated antibodies.</p

Supplementary Figure 7 from GSK-3α Promotes Oncogenic KRAS Function in Pancreatic Cancer via TAK1–TAB Stabilization and Regulation of Noncanonical NF-κB

PDF file - 85K, NF-κB2 depletion leads to increase in PARP cleavage. Indicated cell lines were transiently transfected with specified siRNAs, and 72 hours later whole cell extracts immunoblotted for cleaved PARP.</p

Supplementary Methods from GSK-3α Promotes Oncogenic KRAS Function in Pancreatic Cancer via TAK1–TAB Stabilization and Regulation of Noncanonical NF-κB

PDF file - 72K</p

Supplementary Figure 4 from GSK-3α Promotes Oncogenic KRAS Function in Pancreatic Cancer via TAK1–TAB Stabilization and Regulation of Noncanonical NF-κB

PDF file - 3743K, TAK1 inhibition alters cell cycle regulation. Panc-1 cells were treated with TAK1 inhibitor, 5Z-7-oxozaenol or vehicle control, DMSO for 24 hours. Cells were fixed and stained with Propidium Iodide and analyzed by Flow cytometry.</p

Supplementary Figure 9 from GSK-3α Promotes Oncogenic KRAS Function in Pancreatic Cancer via TAK1–TAB Stabilization and Regulation of Noncanonical NF-κB

PDF file - 263K, Kras regulates GSK-3α mRNA levels. HKRAS cells were transiently transfected with non-targeting control or KRAS siRNA. RNA was isolated, reverse transcribed to cDNA and real time RT-PCR performed for Kras and GSK-3α. Gus B was used as endogenous control.</p

Supplementary Figure 8 from GSK-3α Promotes Oncogenic KRAS Function in Pancreatic Cancer via TAK1–TAB Stabilization and Regulation of Noncanonical NF-κB

PDF file - 1842K, GSK-3 inhibition leads to a decrease in TAK1 levels and p100-p52 processing in a statistically significant manner. All data obtained with independent experiments with Panc-1 and MiaPaCa-2, treated with GSK-3 inhibitor for 24 hours (right panel) or transiently transfected with indicated siRNA's for 72 hours (left panel) were quantified by densitometric analyses and is expressed as mean�SD. Statistical evaluation of the differences were calculated using students two-tailed t test and P value < 0.05 was considered significant.</p