A Deep Survival EWAS approach estimating risk profile based on pre-diagnostic DNA methylation: An application to breast cancer time to diagnosis

Abstract

Previous studies for cancer biomarker discovery based on pre-diagnostic blood DNA methylation (DNAm) profiles, either ignore the explicit modeling of the Time To Diagnosis (TTD), or provide inconsistent results. This lack of consistency is likely due to the limitations of standard EWAS approaches, that model the effect of DNAm at CpG sites on TTD independently. In this work, we aim to identify blood DNAm profiles associated with TTD, with the aim to improve the reliability of the results, as well as their biological meaningfulness. We argue that a global approach to estimate CpG sites effect profile should capture the complex (potentially non-linear) relationships interplaying between sites. To prove our concept, we develop a new Deep Learning-based approach assessing the relevance of individual CpG Islands (i.e., assigning a weight to each site) in determining TTD while modeling their combined effect in a survival analysis scenario. The algorithm combines a tailored sampling procedure with DNAm sites agglomeration, deep non-linear survival modeling and SHapley Additive exPlanations (SHAP) values estimation to aid robustness of the derived effects profile. The proposed approach deals with the common complexities arising from epidemiological studies, such as small sample size, noise, and low signal-to-noise ratio of blood-derived DNAm. We apply our approach to a prospective case-control study on breast cancer nested in the EPIC Italy cohort and we perform weighted gene-set enrichment analyses to demonstrate the biological meaningfulness of the obtained results. We compared the results of Deep Survival EWAS with those of a traditional EWAS approach, demonstrating that our method performs better than the standard approach in identifying biologically relevant pathways.

Pre-diagnosis blood DNA methylation profiling of twin pairs discordant for breast cancer points to the importance of environmental risk

“…However, these predictors, as well as earlier work on DNAm and overall BC risk (2,4,6,11,13,14,16) have not differentiated between genetic and environmental risk factors for BC.…”

“…2)), while one study obtained samples at the time of diagnosis, but prior to treatment (16). Two studies focused on case cohorts using time to diagnosis as a variable (4,11), while the other studies included both cases and controls (2,6,13,14,16). In each of the seven studies, the focus was on overall breast cancer risk, not distinguishing between genetic and environmental risk factors.…”

“…BC has been linked to DNAm in blood, as evidenced by specific DNAm sites [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19] and overall average DNAm levels [20] associating with BC. In addition, the studies conducted by Kresovich [2021], Xiong [2022] and Chung [2023] and colleagues [4,16,21] have shown that blood-derived DNAm can be used to predict an individual's risk of developing BC.…”

“…This presents an opportunity for blood-derived DNAm to serve as a complementary measure to current standard BC risk assessment tools [22,23]. However, these predictors, as well as earlier work on DNAm and overall BC risk [2,4,6,14,16,17,19] have not differentiated between genetic and environmental risk factors for BC.…”

“…BC has been linked to DNAm alterations in blood, as evidenced by specific DNAm sites (1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16) and overall average DNAm levels (17) associating with BC. In addition, the studies conducted by Kresovich et al…”