Table_1_Simulating time-to-event data under the Cox proportional hazards model: assessing the performance of the non-parametric Flexible Hazards Method.docx

Numerous methods and approaches have been developed for generating time-to-event data from the Cox Proportional Hazards (CPH) model; however, they often require specification of a parametric distribution for the baseline hazard even though the CPH model itself makes no assumptions on the distribution of the baseline hazards. In line with the semi-parametric nature of the CPH model, a recently proposed method called the Flexible Hazards Method generates time-to-event data from a CPH model using a non-parametric baseline hazard function. While the initial results of this method are promising, it has not yet been comprehensively assessed with increasing covariates or against data generated under parametric baseline hazards. To fill this gap, we conducted a comprehensive study to benchmark the performance of the Flexible Hazards Method for generating data from a CPH model against parametric methods. Our results showed that with a single covariate and large enough assumed maximum time, the bias in the Flexible Hazards Method is 0.02 (with respect to the log hazard ratio) with a 95% confidence interval having coverage of 84.4%. This bias increases to 0.054 when there are 10 covariates under the same settings and the coverage of the 95% confidence interval decreases to 46.7%. In this paper, we explain the plausible reasons for this observed increase in bias and decrease in coverage as the number of covariates are increased, both empirically and theoretically, and provide readers and potential users of this method with some suggestions on how to best address these issues. In summary, the Flexible Hazards Method performs well when there are few covariates and the user wishes to simulate data from a non-parametric baseline hazard.</p

Additional file 2 of HiTIMED: hierarchical tumor immune microenvironment epigenetic deconvolution for accurate cell type resolution in the tumor microenvironment using tumor-type-specific DNA methylation data

Additional file 2: Figure S1. Correlation between HiTIMED tumor and InfiniumPurify tumor by tumor type across cholangiocarcinoma, kidney papillary cell carcinoma, pancreatic adenocarcinoma, and stomach adenocarcinoma. Figure S2. Methylation state of CpGs in the HiTIMED tumor specific library (L1) and InfiniumPurify default library. Figure S3. HiTIMED tumor purity vs InfiniumPurify tumor purity in thyroid carcinoma. Figure S4. HiTIMED tumor proportion vs other method predicted tumor proportion. Figure S5. HiTIMED immune cell proportions vs true immune cell proportions in artificial mixtures. Figure S6. HiTIMED T cell proportion vs true T cell proportion in artificial mixtures. Figure S7. HiTIMED cell composition in human normal intestinal epithelium and umbilical vein endothelial cells. Figure S8. Performance comparison across HiTIMED, MethylCIBERSORT, and MethylResolver using artificial mixtures. Figure S9. The distribution of the HiTIMED cell composition in TCGA tumors. Figure S10. Cell composition differs substantially and captures sample heterogeneity using HiTIMED-projected proportions. Seventeen cell types were captured for each sample by tumor type. Figure S11. Sensitive analysis comparing outputs from two Cox models with or without cell type proportions adjusted in kidney clear cell carcinoma. Figure S12. Kaplan-Meier survival curves for HiTIMED cells estimates in TCGA tumors. Figure 13. HiTIMED cell comparison and Kaplan-Meier survival curves across immune/angiogenic hot and cold. Figure S14. HiTIMED immune and angiogenic proportions across C1-C6 subtyped TCGA tumor. Figure S15. HiTIMED cell comparisons between drug-sensitive and -resistant metastasized colorectal cancer. Figure S16. HiTIMED cell comparisons in triple−negative breast cancer w/without chemotherapy. Figure S17. Performance comparison across iterations on CpGs selected in HiTIMED for immune and angiogenic cell projection

Additional file 1 of HiTIMED: hierarchical tumor immune microenvironment epigenetic deconvolution for accurate cell type resolution in the tumor microenvironment using tumor-type-specific DNA methylation data

Additional file 1: Table S1. Baseline characteristics of the discovery data sets. Table S2. Baseline characteristics of the validation and application data sets. Table S3. Statistically significant hazard ratios of HiTIMED-projected cell proportions in cancer patients' 5-year survival adjusted for age, gender, tumor stage, HiTIMED-projected tumor proportion, and other cell-type proportions

Additional file 5 of Synergistic anti-proliferative activity of JQ1 and GSK2801 in triple-negative breast cancer

Additionalfile 5: S5 file. Gene enrichment analysis. Metabolic pathways enriched with upregulated anddownregulated genes in three different TNBC cell lines and three differenttreatment conditions. Available at https://doi.org/10.7910/DVN/KWVOJV

Supplementary Table 3 from DNA Methylation-Derived Neutrophil-to-Lymphocyte Ratio: An Epigenetic Tool to Explore Cancer Inflammation and Outcomes

Results from a Cox-regression model fit to the HNSCC data set.</p

Additional file 1 of Synergistic anti-proliferative activity of JQ1 and GSK2801 in triple-negative breast cancer

Additional file 1: S1 file. This file contains thefigures and tables generated during the data analysis and is available at https://doi.org/10.7910/DVN/BEW3OR . Fig.S1. Analysis of gene counts. Boxplot (A) before and (B) after normalization explaining the distribution of genecounts; (C) Heat map of samples (D) Heat map of gene counts among the samples. Fig. S2. Multidimensional analysis of samples. Multidimensional scalingplots before (left) and after (right) normalization explaining the distributionof control and treated samples among three different breast cancer cell lines. Fig.S3. Number of upregulated anddownregulated metabolic pathways. The number of upregulated anddownregulated pathways in the threedifferent treatment conditions (JQ1, GSK2801 and JQ1 +GSK2801) across threedifferent TNBC cell lines. The unique and shared number of pathways amongdifferent treatment conditions are represented. Fig. S4. Optimized conformation of JQ1 and GSK2801. The structures are represented in stickmodel with the hydrogen bond donor (blue) and acceptor (red) surface areas. Theelectron density clouds are represented in green dots. Table S1. Table. RNASeq data sets. RNASeq expression data retrieved from GEOdatabase. Three different TNBC cell lines were treated with JQ1 and GSK2801alone and in combination for 72 hours. DMSO was used as a vehicle and served asan internal control. Table S2.The genes and primers. Primers usedfor the evaluation of gene expression in the breast cancer cell lines underdifferent treatment conditions. Table S3-S5. Smear plots and volcano plots. DEGs observed inMDA-MB-231, HCC-1806 and SUM-159 cell lines among different treatmentconditions. Green dots represent downregulated and red dots upregulated genes. Table S6. Ramachandran plots. The stereochemicalvalidations were done by generating the Ramachandran plots for the homologymodels of five downregulated proteins

Supplementary Table 4 from DNA Methylation-Derived Neutrophil-to-Lymphocyte Ratio: An Epigenetic Tool to Explore Cancer Inflammation and Outcomes

Results from a Cox-regression model fit to the Bladder cancer data set.</p

SF1 from Mitochondrial Haplotype Alters Mammary Cancer Tumorigenicity and Metastasis in an Oncogenic Driver–Dependent Manner

Supplementary Figure S1: Genotyping of MNX strains. Representative gels showing genotyping of MNX and matching wild-type strains. Total DNA was isolated from tail clips of weanling mice. DNA was amplified using primers that span A: 9461 Câ†'T polymorphism in C57BL/6J mitochondria which does not allow the incorporation of a Bcl1 restriction site, which is present in FVB/NJ or B: 9348 Aâ†'G polymorphism present in BALB/cJ but not FVB/NJ which does not allow the restriction site for Pflf1 to incorporate, this DNA was then exposed to restriction enzymes, A: Bcl1 and B: Pflf1 and resolved on an agarose gel.</p

Supplementary Table S1: Primer sequences for RFLP sites and Her2 transgene from Mitochondrial Haplotype Alters Mammary Cancer Tumorigenicity and Metastasis in an Oncogenic Driver–Dependent Manner

Supplementary Table S1: Primer sequences for RFLP sites and Her2 transgene</p

Supplemental Figure 6 from Mitochondrial Genomic Backgrounds Affect Nuclear DNA Methylation and Gene Expression

Supplemental Figure 6. Cell Cycle Analysis. Cell cycle analysis using propidium iodide (PI) staining was performed on mouse embryo fibroblasts (MEFs) harvested from the following strains: FF, FC, BB, FB, CC, CH, HH, HC (as described in Table 1). Cell cycle was performed by flow cytometry and analyzed using FACS Diva.</p