Implications of Epigenetic Drift in Colorectal Neoplasia

NIH grants U01CA182940 (G.E. Luebeck, W.D. Hazelton, W.M. Grady, S.K. Madden, K. Curtius), U01CA199336 (G.E. Luebeck, W.D. Hazelton); Barts Charity grant 472-2300, London (K. Curtius) and UK Medical Research Council Rutherford fellowship (K. Curtius); and NIH grants (P30CA15704, U01CA152756, R01CA194663, R01CA220004, U54CA143862, P01CA077852),R.A.C.E. Charities, Cottrell Family Fund, R03CA165153, Listwin Family Foundation, Seattle Translational Tumor Research program, Fred Hutchinson Cancer Research Center (S.K. Madden, M. Yu, K.T. Carter, and W.M. Grady), R01CA189184 (C. Lee, C.M. Ulrich, S.K.Madden, M. Yu, K.T. Carter, and W.M. Grady), R01CA112516, R01CA114467, R01CA120523 (C.M. Ulrich, S.K. Madden, M. Yu, K.T. Carter, and W.M. Grady), Huntsman Cancer Foundation, U01 CA206110, R01CA189184 R01CA 207371 and P30CACA042014 (C.M. Ulrich). U24CA074794 (P.A. Newcomb, S.K. Madden, M. Yu, K.T. Carter, and W.M. Grady). This material is the result of work supported in part by resources from the VA Puget Sound Health Care System and the ColoCare Study

Challenges and opportunities to computationally deconvolve heterogeneous tissue with varying cell sizes using single cell RNA-sequencing datasets

Deconvolution of cell mixtures in "bulk" transcriptomic samples from homogenate human tissue is important for understanding the pathologies of diseases. However, several experimental and computational challenges remain in developing and implementing transcriptomics-based deconvolution approaches, especially those using a single cell/nuclei RNA-seq reference atlas, which are becoming rapidly available across many tissues. Notably, deconvolution algorithms are frequently developed using samples from tissues with similar cell sizes. However, brain tissue or immune cell populations have cell types with substantially different cell sizes, total mRNA expression, and transcriptional activity. When existing deconvolution approaches are applied to these tissues, these systematic differences in cell sizes and transcriptomic activity confound accurate cell proportion estimates and instead may quantify total mRNA content. Furthermore, there is a lack of standard reference atlases and computational approaches to facilitate integrative analyses, including not only bulk and single cell/nuclei RNA-seq data, but also new data modalities from spatial -omic or imaging approaches. New multi-assay datasets need to be collected with orthogonal data types generated from the same tissue block and the same individual, to serve as a "gold standard" for evaluating new and existing deconvolution methods. Below, we discuss these key challenges and how they can be addressed with the acquisition of new datasets and approaches to analysis.Comment: 28 pages; 4 figure

Dysfunctional epigenetic aging of the normal colon and colorectal cancer risk

BACKGROUND: Chronological age is a prominent risk factor for many types of cancers including colorectal cancer (CRC). Yet, the risk of CRC varies substantially between individuals, even within the same age group, which may reflect heterogeneity in biological tissue aging between people. Epigenetic clocks based on DNA methylation are a useful measure of the biological aging process with the potential to serve as a biomarker of an individual's susceptibility to age-related diseases such as CRC. METHODS: We conducted a genome-wide DNA methylation study on samples of normal colon mucosa (N = 334). Subjects were assigned to three cancer risk groups (low, medium, and high) based on their personal adenoma or cancer history. Using previously established epigenetic clocks (Hannum, Horvath, PhenoAge, and EpiTOC), we estimated the biological age of each sample and assessed for epigenetic age acceleration in the samples by regressing the estimated biological age on the individual's chronological age. We compared the epigenetic age acceleration between different risk groups using a multivariate linear regression model with the adjustment for gender and cell-type fractions for each epigenetic clock. An epigenome-wide association study (EWAS) was performed to identify differential methylation changes associated with CRC risk. RESULTS: Each epigenetic clock was significantly correlated with the chronological age of the subjects, and the Horvath clock exhibited the strongest correlation in all risk groups (r > 0.8, p < 1 × 10-30). The PhenoAge clock (p = 0.0012) revealed epigenetic age deceleration in the high-risk group compared to the low-risk group. CONCLUSIONS: Among the four DNA methylation-based measures of biological age, the Horvath clock is the most accurate for estimating the chronological age of individuals. Individuals with a high risk for CRC have epigenetic age deceleration in their normal colons measured by the PhenoAge clock, which may reflect a dysfunctional epigenetic aging process