Optimization results.

Performance in terms of Kendall-Tau Stability (robustness) and Harrell C-Index (survival prediction performance) for all the Deep Survival Network architecture trained in optimization. Performance is averaged across K splits; 95% confidence intervals are reported in parentheses. In the first column (Input) the input shape (J), referring to the granularity of the CpG Island agglomeration. In the second column (Latent) the dimensionality of the Survival NN layer before output. In the second column (Architecture), the list of layers with respective number of nodes, up until before output layer (one single output node for all architectures). (PDF)</p

Analysis of CpG sites and their correlation.

(a) Pie chart: frequencies of the Intergenic (red) and Intragenic (green and blue) CpG Islands. Most (48% of the total) intragenic islands intersect one gene element (gene body or gene promoter), whereas 14% of CpG Islands are intragenic and overlap gene body and promoter of the same gene. (b) Histogram: distribution of the pairwise Pearson correlations of CpGs pairs in the same island for the ‘blue’ category. Only 2% of pairwise comparisons have a negative Pearson correlation coefficient. (JPG)</p

Kaplan-Meyer plots of <i>F</i>₁₂₀ CpG Islands.

Entire set of Kaplan-Meyer curves for all CpG Islands grouped in feature F120. (PDF)</p

Standard EWAS weights profile.

Weights profile for Standard EWAS approach, where each CpG Island is associated with the p-value of the test statistic in an independent CoxPH model. Panel A reports the p-values without Bonferroni adjustment, panel B reports the same p-values after Bonferroni adjustment. The red line denotes the p-value threshold of 0.05. (PNG)</p

Performance comparison with and without pretraining.

Performance comparison of Deep Survival EWAS algorithm with and without pretraining each of the K survival models. Each row reports results (in terms of KT stability and Concordance Index) for one of the tested model architectures. (PDF)</p

Deep Survival EWAS Gene Set enrichment analyses results.

Deep Survival EWAS GSEA results for all four reference groups. Each sheet in the file contains the results for one reference group (respectively: (a) BC controls, (b) Matching Controls, (c) All controls, (d) All controls with cases). The first column reports the KEGG pathway, followed by pathway code and empirical p-value (last column). (XLSX)</p

Standard EWAS association results.

Results of independent modeling of CpG Islands w.r.t. TTD. The table reports p-values and test statistics obtained by modeling one univariate CoxPH for each island. (XLSX)</p

Detailed data description.

Supporting information on cohort description and DNA methylation data extraction and preprocessing. (PDF)</p

Latent space PCA plots.

PCA plots in 2D of the embedded points (i.e. each data point is a patient) in the 16-dimensional latent spaces defined by the best model exploited for Deep Survival EWAS (i.e. input 128 nodes, bottleneck 16 nodes). Each row of plots represents one of the K = 10 splits, i.e. one of the trained Deep Survival models. The plots on the left represent the latent space after pre-training, the plots on the right represent the latent space after supervised fine-tuning of the model to predict the survival outcome. Patients are grouped and colored according to 4 time-to-event classes. (PDF)</p

Post-hoc analysis results.

(A) Distribution of aggregated DNAm beta-values in the feature (F120) associated with the highest impact in the estimated effect profile. Subjects are binned according to their TTD into four classes. (B) Kaplan-Meier curves of low DNAm (below 25th percentile of β-values distribution) and High DNAm (above 75th percentile) populations’ in CpG Island 9:34518796–34619343. This island belongs to the cluster that is aggregated into feature F120. The plot reports the Log-Rank test p-value for the difference between the two groups; the lower part of the plot reports the count of subjects in High DNAm and Low DNAm populations for CpG Island 9:34518796–34619343, according to TTD.</p