On epigenetic stochasticity, entropy and cancer risk

Cell-type-specific subtyping of epigenomes improves prognostic stratification of cancer

BACKGROUND

Most molecular classifications of cancer are based on bulk-tissue profiles that measure an average over many distinct cell types. As such, cancer subtypes inferred from transcriptomic or epigenetic data are strongly influenced by cell-type composition and do not necessarily reflect subtypes defined by cell-type-specific cancer-associated alterations, which could lead to suboptimal cancer classifications.

METHODS

To address this problem, we here propose the novel concept of cell-type-specific combinatorial clustering (CELTYC), which aims to group cancer samples by the molecular alterations they display in specific cell types. We illustrate this concept in the context of DNA methylation data of liver and kidney cancer, deriving in each case novel cancer subtypes and assessing their prognostic relevance against current state-of-the-art prognostic models.

RESULTS

In both liver and kidney cancer, we reveal improved cell-type-specific prognostic models, not discoverable using standard methods. In the case of kidney cancer, we show how combinatorial indexing of epithelial and immune-cell clusters define improved prognostic models driven by synergy of high mitotic age and altered cytokine signaling. We validate the improved prognostic models in independent datasets and identify underlying cytokine-immune-cell signatures driving poor outcome.

CONCLUSIONS

In summary, cell-type-specific combinatorial clustering is a valuable strategy to help dissect and improve current prognostic classifications of cancer in terms of the underlying cell-type-specific epigenetic and transcriptomic alterations.

Constitutional Epimutations: From Rare Events Toward Major Cancer Risk Factors?

Constitutional epimutations are epigenetic aberrations that arise in normal cells prenatally. Two major forms exist: secondary constitutional epimutations (SCEs), associated with cis-acting genetic aberrations, and primary constitutional epimutations (PCEs), for which no associated genetic aberrations were identified. Some SCEs have been associated with risk of cancer (MLH1 and MSH2 with colon or endometrial cancers, BRCA1 with familial breast and ovarian cancers), although such epimutations are rare, with a total of <100 cases reported. This contrasts recent findings for PCE, where low-level mosaic BRCA1 epimutations are recorded in 5%-10% of healthy females across all age groups, including newborns. BRCA1 PCEs predict an elevated risk of high-grade serous ovarian cancer and triple-negative breast cancer (TNBC) and are estimated to account for about 20% of all TNBCs. A similarly high population frequency is observed for mosaic constitutional epimutations in MGMT, occurring as PCE or SCE, but not in MLH1. Contrasting BRCA1 and MLH1, a potential association with cancer risk for MGMT epimutations is yet unclear. In this review, we provide a summary of findings linking constitutional epimutations to cancer risk with emphasis on PCE. We also highlight challenges in detection of PCE exemplified by low-level mosaic epimutations in BRCA1 and indicate the need for further studies, hypothesizing that improved knowledge about PCE may add significantly to our understanding of cancer risk, carcinogenesis, and potentially development of other diseases as well.

Epigenetic entropy, social disparity, and health and lifespan in the Women's Health Initiative

The pace of aging varies between individuals and is marked by changes in DNA methylation (DNAm) including an increase in randomness or entropy. Here, we computed epigenetic scores of aging and entropy using DNAm datasets from the Women's Health Initiative (WHI). We investigated how different epigenetic aging metrics relate to demographic and health variables, and mortality risk. Income and education, two proxies of socioeconomics (SE), had consistent associations with epigenetic aging and entropy. Notably, stochastic increases in DNAm at sites targeted by the polycomb proteins were significantly related to both aging and SE. While higher income was associated with reduced age-related DNAm changes in White women, the protective effect of income was diminished in Black and Hispanic women, and on average, Black and Hispanic women had relatively more aged epigenomes. Faster pace of aging, as estimated by the DunedinPACE, predicted higher mortality risk, while the maintenance of methylation at enhancer regions was associated with improved survival. Our findings demonstrate close ties between social and economic factors and aspects of epigenetic aging, suggesting potential biological mechanisms through which societal disparities may contribute to differences in health outcomes and lifespan across demographic groups.

Exosomal ncRNAs in reproductive cancers†

Extracellular vesicles, particularly exosomes, play a pivotal role in the cellular mechanisms underlying cancer. This review explores the various functions of exosomes in the progression, growth, and metastasis of cancers affecting the male and female reproductive systems. Exosomes are identified as key mediators in intercellular communication, capable of transferring bioactive molecules such as microRNAs, proteins, and other nucleic acids that influence cancer cell behavior and tumor microenvironment interactions. It has been shown that non-coding RNAs transported by exosomes play an important role in tumor growth processes. Significant molecules that may serve as biomarkers in the development and progression of male reproductive cancers include miR-125a-5p, miR-21, miR-375, the miR-371 ~ 373 cluster, and miR-145-5p. For female reproductive cancers, significant microRNAs include miR-26a-5p, miR-148b, miR-205, and miRNA-423-3p. This review highlights the potential of these noncoding RNAs as biomarkers and prognostics in tumor diagnostics. Understanding the diverse roles of exosomes may hold promise for developing new therapeutic strategies and improving treatment outcomes for cancer patients.

Interpretable deep learning of single-cell and epigenetic data reveals novel molecular insights in aging

Deep learning (DL) and explainable artificial intelligence (XAI) have emerged as powerful machine-learning tools to identify complex predictive data patterns in a spatial or temporal domain. Here, we consider the application of DL and XAI to large omic datasets, in order to study biological aging at the molecular level. We develop an advanced multi-view graph-level representation learning (MGRL) framework that integrates prior biological network information, to build molecular aging clocks at cell-type resolution, which we subsequently interpret using XAI. We apply this framework to one of the largest single-cell transcriptomic datasets encompassing over a million immune cells from 981 donors, revealing a ribosomal gene subnetwork, whose expression correlates with age independently of cell-type. Application of the same DL-XAI framework to DNA methylation data of sorted monocytes reveals an epigenetically deregulated inflammatory response pathway whose activity increases with age. We show that the ribosomal module and inflammatory pathways would not have been discovered had we used more standard machine-learning methods. In summary, the computational deep learning framework presented here illustrates how deep learning when combined with explainable AI tools, can reveal novel biological insights into the complex process of aging.

Epigenetic ageing clocks: statistical methods and emerging computational challenges

Over the past decade, epigenetic clocks have emerged as powerful machine learning tools, not only to estimate chronological and biological age but also to assess the efficacy of anti-ageing, cellular rejuvenation and disease-preventive interventions. However, many computational and statistical challenges remain that limit our understanding, interpretation and application of epigenetic clocks. Here, we review these computational challenges, focusing on interpretation, cell-type heterogeneity and emerging single-cell methods, aiming to provide guidelines for the rigorous construction of interpretable epigenetic clocks at cell-type and single-cell resolution.

Mapping the Temporal Landscape of Breast Cancer Using Epigenetic Entropy

Although generally unknown, the age of a newly diagnosed tumor encodes valuable etiologic and prognostic information. Here, we estimate the age of breast cancers, defined as the time from the start of growth to detection, using a measure of epigenetic entropy derived from genome-wide methylation arrays. Based on an ensemble of neutrally fluctuating CpG (fCpG) sites, this stochastic epigenetic clock differs from conventional clocks that measure age-related increases in methylation. We show that younger tumors exhibit hallmarks of aggressiveness, such as increased proliferation and genomic instability, whereas older tumors are characterized by elevated immune infiltration, indicative of enhanced immune surveillance. These findings suggest that the clock captures a tumor's effective growth rate resulting from the evolutionary-ecological competition between intrinsic growth potential and external systemic pressures. Because of the clock's ability to delineate old and stable from young and aggressive tumors, it has potential applications in risk stratification of early-stage breast cancers and guiding early detection efforts.