Blood-Based Epigenetic Age Acceleration and Incident Colorectal Cancer Risk: Findings from a Population-Based Case-Control Study.

This study investigates the association between epigenetic age acceleration (EAA) derived from DNA methylation and the risk of incident colorectal cancer (CRC). We utilized data from a random population sample of 9,360 individuals (men and women, aged 45-69) from the HAPIEE Study who had been followed up for 16 years. A nested case-control design yielded 35 incident CRC cases and 354 matched controls. Six baseline epigenetic age (EA) measures (Horvath, Hannum, PhenoAge, Skin and Blood (SB), BLUP, and Elastic Net (EN)) were calculated along with their respective EAAs. After adjustment, the odds ratios (ORs) for CRC risk per decile increase in EAA ranged from 1.20 (95% CI: 1.04-1.39) to 1.44 (95% CI: 1.21-1.76) for the Horvath, Hannum, PhenoAge, and BLUP measures. Conversely, the SB and EN EAA measures showed borderline inverse associations with ORs of 0.86-0.87 (95% CI: 0.76-0.99). Tertile analysis reinforced a positive association between CRC risk and four EAA measures (Horvath, Hannum, PhenoAge, and BLUP) and a modest inverse relationship with EN EAA. Our findings from a prospective population-based-case-control study indicate a direct association between incident CRC and four markers of accelerated baseline epigenetic age. In contrast, two markers showed a negative association or no association. These results warrant further exploration in larger cohorts and may have implications for CRC risk assessment and prevention.

Open access-enabled evaluation of epigenetic age acceleration in colorectal cancer and development of a classifier with diagnostic potential.

Aberrant DNA methylation (DNAm) is known to be associated with the aetiology of cancer, including colorectal cancer (CRC). In the past, the availability of open access data has been the main driver of innovative method development and research training. However, this is increasingly being eroded by the move to controlled access, particularly of medical data, including cancer DNAm data. To rejuvenate this valuable tradition, we leveraged DNAm data from 1,845 samples (535 CRC tumours, 522 normal colon tissues adjacent to tumours, 72 colorectal adenomas, and 716 normal colon tissues from healthy individuals) from 14 open access studies deposited in NCBI GEO and ArrayExpress. We calculated each sample's epigenetic age (EA) using eleven epigenetic clock models and derived the corresponding epigenetic age acceleration (EAA). For EA, we observed that most first- and second-generation epigenetic clocks reflect the chronological age in normal tissues adjacent to tumours and healthy individuals [e.g., Horvath (r = 0.77 and 0.79), Zhang elastic net (EN) (r = 0.70 and 0.73)] unlike the epigenetic mitotic clocks (EpiTOC, HypoClock, MiAge) (r < 0.3). For EAA, we used PhenoAge, Wu, and the above mitotic clocks and found them to have distinct distributions in different tissue types, particularly between normal colon tissues adjacent to tumours and cancerous tumours, as well as between normal colon tissues adjacent to tumours and normal colon tissue from healthy individuals. Finally, we harnessed these associations to develop a classifier using elastic net regression (with lasso and ridge regularisations) that predicts CRC diagnosis based on a patient's sex and EAAs calculated from histologically normal controls (i.e., normal colon tissues adjacent to tumours and normal colon tissue from healthy individuals). The classifier demonstrated good diagnostic potential with ROC-AUC = 0.886, which suggests that an EAA-based classifier trained on relevant data could become a tool to support diagnostic/prognostic decisions in CRC for clinical professionals. Our study also reemphasises the importance of open access clinical data for method development and training of young scientists. Obtaining the required approvals for controlled access data would not have been possible in the timeframe of this study.

Individualized tumor-informed circulating tumor DNA analysis for postoperative monitoring of non-small cell lung cancer.

We report a personalized tumor-informed technology, Patient-specific pROgnostic and Potential tHErapeutic marker Tracking (PROPHET) using deep sequencing of 50 patient-specific variants to detect molecular residual disease (MRD) with a limit of detection of 0.004%. PROPHET and state-of-the-art fixed-panel assays were applied to 760 plasma samples from 181 prospectively enrolled early stage non-small cell lung cancer patients. PROPHET shows higher sensitivity of 45% at baseline with circulating tumor DNA (ctDNA). It outperforms fixed-panel assays in prognostic analysis and demonstrates a median lead-time of 299 days to radiologically confirmed recurrence. Personalized non-canonical variants account for 98.2% with prognostic effects similar to canonical variants. The proposed tumor-node-metastasis-blood (TNMB) classification surpasses TNM staging for prognostic prediction at the decision point of adjuvant treatment. PROPHET shows potential to evaluate the effect of adjuvant therapy and serve as an arbiter of the equivocal radiological diagnosis. These findings highlight the potential advantages of personalized cancer techniques in MRD detection.

A role for SETD2 loss in tumorigenesis through DNA methylation dysregulation.

SETD2-dependent H3 Lysine-36 trimethylation (H3K36me3) has been recently linked to the deposition of de-novo DNA methylation. SETD2 is frequently mutated in cancer, however, the functional impact of SETD2 loss and depletion on DNA methylation across cancer types and tumorigenesis is currently unknown. Here, we perform a pan-cancer analysis and show that both SETD2 mutation and reduced expression are associated with DNA methylation dysregulation across 21 out of the 24 cancer types tested. In renal cancer, these DNA methylation changes are associated with altered gene expression of oncogenes, tumour suppressors, and genes involved in neoplasm invasiveness, including TP53, FOXO1, and CDK4. This suggests a new role for SETD2 loss in tumorigenesis and cancer aggressiveness through DNA methylation dysregulation. Moreover, using a robust machine learning methodology, we develop and validate a 3-CpG methylation signature which is sufficient to predict SETD2 mutation status with high accuracy and correlates with patient prognosis.

The Relationship between All-Cause Natural Mortality and Copy Number of Mitochondrial DNA in a 15-Year Follow-Up Study.

We explored the relationship between the copy number of mitochondrial DNA (mtDNA-CN) and all-cause natural mortality. We examined a random population sample in 2003/2005 (n = 9360, men/women, 45-69, the HAPIEE project) and followed up for 15 years. Using a nested case-control design, we selected non-external deaths among those free from baseline cardiovascular diseases (CVD) and cancer (n = 371), and a sex- and age-stratified control (n = 785). The odds ratios (ORs) of death were 1.06 (95%CI 1.01-1.11) per one-decile decrease in mtDNA-CN independent of age, sex, metabolic factors, smoking, alcohol intake and education. The age-sex-adjusted ORs of death in the second and first tertiles of mtDNA-CN vs. the top tertile were 2.35 (95% CI 1.70-3.26) and 1.59 (1.16-2.17); an increased risk was confined to the second tertile after controlling for smoking and metabolic factors. The multivariable-adjusted OR of CVD death was 1.92 (95% CI 1.18-3.15) in tertile 2 vs. the top tertile of mtDNA-CN, and for cancer-related death the ORs were 3.66 (95% CI 2.21-6.05) and 2.29 (95% CI 1.43-3.68) in tertiles 2 and 1 vs. the top tertile. In the Siberian population cohort, the mtDNA-CN was an inverse predictor of the 15-year risk of natural mortality, due to the greatest impact of CVD and cancer-related death. The findings merit attention for exploring further the role of mtDNA in human ageing and the diversity of mortality.

Individualized dynamic methylation-based analysis of cell-free DNA in postoperative monitoring of lung cancer.

BACKGROUND: The feasibility of DNA methylation-based assays in detecting minimal residual disease (MRD) and postoperative monitoring remains unestablished. We aim to investigate the dynamic characteristics of cancer-related methylation signals and the feasibility of methylation-based MRD detection in surgical lung cancer patients. METHODS: Matched tumor, tumor-adjacent tissues, and longitudinal blood samples from a cohort (MEDAL) were analyzed by ultra-deep targeted sequencing and bisulfite sequencing. A tumor-informed methylation-based MRD (timMRD) was employed to evaluate the methylation status of each blood sample. Survival analysis was performed in the MEDAL cohort (n = 195) and validated in an independent cohort (DYNAMIC, n = 36). RESULTS: Tumor-informed methylation status enabled an accurate recurrence risk assessment better than the tumor-naïve methylation approach. Baseline timMRD-scores were positively correlated with tumor burden, invasiveness, and the existence and abundance of somatic mutations. Patients with higher timMRD-scores at postoperative time-points demonstrated significantly shorter disease-free survival in the MEDAL cohort (HR: 3.08, 95% CI: 1.48-6.42; P = 0.002) and the independent DYNAMIC cohort (HR: 2.80, 95% CI: 0.96-8.20; P = 0.041). Multivariable regression analysis identified postoperative timMRD-score as an independent prognostic factor for lung cancer. Compared to tumor-informed somatic mutation status, timMRD-scores yielded better performance in identifying the relapsed patients during postoperative follow-up, including subgroups with lower tumor burden like stage I, and was more accurate among relapsed patients with baseline ctDNA-negative status. Comparing to the average lead time of ctDNA mutation, timMRD-score yielded a negative predictive value of 97.2% at 120 days prior to relapse. CONCLUSIONS: The dynamic methylation-based analysis of peripheral blood provides a promising strategy for postoperative cancer surveillance. TRIAL REGISTRATION: This study (MEDAL, MEthylation based Dynamic Analysis for Lung cancer) was registered on ClinicalTrials.gov on 08/05/2018 (NCT03634826). https://clinicaltrials.gov/ct2/show/NCT03634826 .

Subclassification of epithelioid sarcoma with potential therapeutic impact.

Epithelioid sarcoma is a rare and aggressive mesenchymal tumour, the genetic hallmark of which is the loss of expression of SMARCB1, a key member of the SWItch/Sucrose Non-Fermentable (SWI/SNF) chromatin remodelling complex. Hampered by its rarity, epithelioid sarcoma has received little research attention and therapeutic options for this disease remain limited. SMARCB1-deficient tumours also include malignant rhabdoid tumour, atypical teratoid and rhabdoid tumour, epithelioid malignant peripheral nerve sheath tumour, and poorly differentiated chordoma. Histologically, it can be challenging to distinguish epithelioid sarcoma from malignant rhabdoid tumour and other SMARCB1-deficient tumours, whereas methylation profiling shows that they represent distinct entities and facilitates their classification. Methylation studies on SMARCB1-deficient tumours, although not including epithelioid sarcomas, reported methylation subgroups which resulted in new clinical stratification and therapeutic approaches. In addition, emerging evidence indicates that immunotherapy, including immune checkpoint inhibitors, represents a promising therapeutic strategy for SMARCB1-deficient tumours. Here, we show that some epithelioid sarcomas share methylation patterns of malignant rhabdoid tumours indicating that this could help to distinguish these entities and guide treatment. Using gene expression data, we also showed that the immune environment of epithelioid sarcoma is characterised by a predominance of CD8+ lymphocytes and M2 macrophages. These findings have potential implications for the management of patients with epithelioid sarcoma. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Multi-omics integrated circulating cell-free DNA genomic signatures enhanced the diagnostic performance of early-stage lung cancer and postoperative minimal residual disease.

BACKGROUND: Liquid biopsy is a promising non-invasive alternative for cancer screening and minimal residual disease (MRD) detection, although there are some concerns regarding its clinical applications. We aimed to develop an accurate detection platform based on liquid biopsy for both cancer screening and MRD detection in patients with lung cancer (LC), which is also applicable to clinical use. METHODS: We applied a modified whole-genome sequencing (WGS) -based High-performance Infrastructure For MultIomics (HIFI) method for LC screening and postoperative MRD detection by combining the hyper-co-methylated read approach and the circulating single-molecule amplification and resequencing technology (cSMART2.0). FINDINGS: For early screening of LC, the LC score model was constructed using the support vector machine, which showed sensitivity (51.8%) at high specificity (96.3%) and achieved an AUC of 0.912 in the validation set prospectively enrolled from multiple centers. The screening model achieved detection efficiency with an AUC of 0.906 in patients with lung adenocarcinoma and outperformed other clinical models in solid nodule cohort. When applied the HIFI model to real social population, a negative predictive value (NPV) of 99.92% was achieved in Chinese population. Additionally, the MRD detection rate improved significantly by combining results from WGS and cSMART2.0, with sensitivity of 73.7% at specificity of 97.3%. INTERPRETATION: In conclusion, the HIFI method is promising for diagnosis and postoperative monitoring of LC. FUNDING: This study was supported by CAMS Innovation Fund for Medical Sciences, Chinese Academy of Medical Sciences, National Natural Science Foundation of China, Beijing Natural Science Foundation and Peking University People's Hospital.

Finding a Husband: Using Explainable AI to Define Male Mosquito Flight Differences.

Mosquito-borne diseases account for around one million deaths annually. There is a constant need for novel intervention mechanisms to mitigate transmission, especially as current insecticidal methods become less effective with the rise of insecticide resistance among mosquito populations. Previously, we used a near infra-red tracking system to describe the behaviour of mosquitoes at a human-occupied bed net, work that eventually led to an entirely novel bed net design. Advancing that approach, here we report on the use of trajectory analysis of a mosquito flight, using machine learning methods. This largely unexplored application has significant potential for providing useful insights into the behaviour of mosquitoes and other insects. In this work, a novel methodology applies anomaly detection to distinguish male mosquito tracks from females and couples. The proposed pipeline uses new feature engineering techniques and splits each track into segments such that detailed flight behaviour differences influence the classifier rather than the experimental constraints such as the field of view of the tracking system. Each segment is individually classified and the outcomes are combined to classify whole tracks. By interpreting the model using SHAP values, the features of flight that contribute to the differences between sexes are found and are explained by expert opinion. This methodology was tested using 3D tracks generated from mosquito mating swarms in the field and obtained a balanced accuracy of 64.5% and an ROC AUC score of 68.4%. Such a system can be used in a wide variety of trajectory domains to detect and analyse the behaviours of different classes, e.g., sex, strain, and species. The results of this study can support genetic mosquito control interventions for which mating represents a key event for their success.

The Relationship between Epigenetic Age and Myocardial Infarction/Acute Coronary Syndrome in a Population-Based Nested Case-Control Study.

We investigated the relationship between 'epigenetic age' (EA) derived from DNA methylation (DNAm) and myocardial infarction (MI)/acute coronary syndrome (ACS). A random population sample was examined in 2003/2005 (n = 9360, 45-69, the HAPIEE project) and followed up for 15 years. From this cohort, incident MI/ACS (cases, n = 129) and age- and sex-stratified controls (n = 177) were selected for a nested case-control study. Baseline EA (Horvath's, Hannum's, PhenoAge, Skin and Blood) and the differences between EA and chronological age (CA) were calculated (ΔAHr, ΔAHn, ΔAPh, ΔASB). EAs by Horvath's, Hannum's and Skin and Blood were close to CA (median absolute difference, MAD, of 1.08, -1.91 and -2.03 years); PhenoAge had MAD of -9.29 years vs. CA. The adjusted odds ratios (ORs) of MI/ACS per 1-year increments of ΔAHr, ΔAHn, ΔASB and ΔAPh were 1.01 (95% CI 0.95-1.07), 1.01 (95% CI 0.95-1.08), 1.02 (95% CI 0.97-1.06) and 1.01 (0.93-1.09), respectively. When classified into tertiles, only the highest tertile of ΔAPh showed a suggestion of increased risk of MI/ACS with OR 2.09 (1.11-3.94) independent of age and 1.84 (0.99-3.52) in the age- and sex-adjusted model. Metabolic modulation may be the likely mechanism of this association. In conclusion, this case-control study nested in a prospective population-based cohort did not find strong associations between accelerated epigenetic age markers and risk of MI/ACS. Larger cohort studies are needed to re-examine this important research question.

Evaluation of Epigenetic Age Acceleration Scores and Their Associations with CVD-Related Phenotypes in a Population Cohort.

We evaluated associations between nine epigenetic age acceleration (EAA) scores and 18 cardiometabolic phenotypes using an Eastern European ageing population cohort richly annotated for a diverse set of phenotypes (subsample, n = 306; aged 45-69 years). This was implemented by splitting the data into groups with positive and negative EAAs. We observed strong association between all EAA scores and sex, suggesting that any analysis of EAAs should be adjusted by sex. We found that some sex-adjusted EAA scores were significantly associated with several phenotypes such as blood levels of gamma-glutamyl transferase and low-density lipoprotein, smoking status, annual alcohol consumption, multiple carotid plaques, and incident coronary heart disease status (not necessarily the same phenotypes for different EAAs). We demonstrated that even after adjusting EAAs for sex, EAA-phenotype associations remain sex-specific, which should be taken into account in any downstream analysis involving EAAs. The obtained results suggest that in some EAA-phenotype associations, negative EAA scores (i.e., epigenetic age below chronological age) indicated more harmful phenotype values, which is counterintuitive. Among all considered epigenetic clocks, GrimAge was significantly associated with more phenotypes than any other EA scores in this Russian sample.

GenomeChronicler: The Personal Genome Project UK Genomic Report Generator Pipeline.

In recent years, there has been a significant increase in whole genome sequencing data of individual genomes produced by research projects as well as direct to consumer service providers. While many of these sources provide their users with an interpretation of the data, there is a lack of free, open tools for generating reports exploring the data in an easy to understand manner. GenomeChronicler was developed as part of the Personal Genome Project UK (PGP-UK) to address this need. PGP-UK provides genomic, transcriptomic, epigenomic and self-reported phenotypic data under an open-access model with full ethical approval. As a result, the reports generated by GenomeChronicler are intended for research purposes only and include information relating to potentially beneficial and potentially harmful variants, but without clinical curation. GenomeChronicler can be used with data from whole genome or whole exome sequencing, producing a genome report containing information on variant statistics, ancestry and known associated phenotypic traits. Example reports are available from the PGP-UK data page (personalgenomes.org.uk/data). The objective of this method is to leverage existing resources to find known phenotypes associated with the genotypes detected in each sample. The provided trait data is based primarily upon information available in SNPedia, but also collates data from ClinVar, GETevidence, and gnomAD to provide additional details on potential health implications, presence of genotype in other PGP participants and population frequency of each genotype. The analysis can be run in a self-contained environment without requiring internet access, making it a good choice for cases where privacy is essential or desired: any third party project can embed GenomeChronicler within their off-line safe-haven environments. GenomeChronicler can be run for one sample at a time, or in parallel making use of the Nextflow workflow manager. The source code is available from GitHub (https://github.com/PGP-UK/GenomeChronicler), container recipes are available for Docker and Singularity, as well as a pre-built container from SingularityHub (https://singularity-hub.org/collections/3664) enabling easy deployment in a variety of settings. Users without access to computational resources to run GenomeChronicler can access the software from the Lifebit CloudOS platform (https://lifebit.ai/cloudos) enabling the production of reports and variant calls from raw sequencing data in a scalable fashion.

The Personal Genome Project-UK, an open access resource of human multi-omics data.

Integrative analysis of multi-omics data is a powerful approach for gaining functional insights into biological and medical processes. Conducting these multifaceted analyses on human samples is often complicated by the fact that the raw sequencing output is rarely available under open access. The Personal Genome Project UK (PGP-UK) is one of few resources that recruits its participants under open consent and makes the resulting multi-omics data freely and openly available. As part of this resource, we describe the PGP-UK multi-omics reference panel consisting of ten genomic, methylomic and transcriptomic data. Specifically, we outline the data processing, quality control and validation procedures which were implemented to ensure data integrity and exclude sample mix-ups. In addition, we provide a REST API to facilitate the download of the entire PGP-UK dataset. The data are also available from two cloud-based environments, providing platforms for free integrated analysis. In conclusion, the genotype-validated PGP-UK multi-omics human reference panel described here provides a valuable new open access resource for integrated analyses in support of personal and medical genomics.