Sofie Degerman

Biological age predicts health and lifespan better than chronological age, but remains difficult to measure. One leading molecular proxy for biological age is DNA methylation, which underlies age predictors known as “clocks”. These clocks use penalized linear regression to predict chronological age from methylation levels using selected cytosine–guanine pairs (CpGs) along DNA. Although they predict chronological age within a few years and track mortality risk, there are several issues. Different clocks share a vanishingly small number of CpG sites, many of which show weak associations with age. Also, the clocks often do not transfer across methylation array platforms. This paper takes a network approach to better understand these issues. By using 12 public datasets from human blood, we build a co-methylation network of the sites that show the strongest age correlation. After pruning weak links, we find that it has a small number of large modules of covarying CpGs surrounded by many small modules and singleton sites. These modules are biologically interpretable, as they are associated with CpG island contexts and enriched for distinct Gene Ontology functions. We also map five established clocks onto this network (Horvath, Hannum, AltumAge, Skin & Blood, and Han) and find that they select some CpGs from the same module. This suggests that they are more similar than they appear. The network structure also suggests new ways to build clocks. A simple clock that retains one CpG per module matches the performance of established clocks. A second one, built from module-level principal components, outperforms all five established clocks in three validation cohorts and is transferable across array platforms (Illumina Infinium Methylation 450K or EPIC arrays). Overall, the network perspective shifts attention from individual CpG sites to modules of covarying sites. This perspective helps explain why DNA methylation clocks perform so well despite their differences and provides a more systematic approach for developing the next generation of aging biomarkers.

Chromosomal instability (CIN), impaired telomere biology, and aberrant DNA methylation are implicated in colorectal cancer (CRC) development. Tracking these alterations from precancerous lesions through tumors to metastases may reveal biomarkers of CRC initiation and progression. Tissue samples from 44 patients with either high-grade colorectal dysplasia (HGA; n = 13) or advanced metastatic CRC (n = 31) were analyzed. CIN was assessed in all patients using either low-coverage whole-exome sequencing or microarray-based comparative genomic hybridization. In a subset of patients, genome-wide CpG methylation profiling (n = 19) and telomere length measurements (n = 15) were performed. CIN was detected in 85% of HGA patients, spanning focal CNVs in MALAT1 (46%) to recurrent alterations on chromosomes 11, 13, and 20, with PTK6 being the most frequently amplified (61%). CIN was comparable between primary tumors and synchronous metastases but was significantly elevated in metachronous cases. DEK was amplified in all metastases but the aberration was absent in primaries, irrespective of tissue chronicity. Methylation profiling distinguished HGA from adjacent non-dysplastic mucosa (9859 differentially methylated CpGs) and unrelated tumor tissues (17 638 CpGs), whereas primary tumors and metastases differed at only five CpG sites. Both primary tumors and metastases appeared epigenetically younger than colonic mucosa. Metastases exhibited significantly shorter telomeres than both primary tumors (P = .019) and colonic mucosa (P = .001). The amplification of PTK6 may serve as an early biomarker detectable at the HGA stage, while DEK amplification appears crucial for metastatic progression and may represent a therapeutic target. Further validation is needed.

Background: Combination chemotherapy provides significant survival advantage in patients with advanced gastro-oesophageal adenocarcinoma compared with best supportive care. Peri-operative chemotherapy is standard of care for patients with operable disease. We hypothesised that biomarkers of genomic instability and inflammation may have clinical utility in these patients. Methods: We initiated open-label, non-randomised biomarker studies in patients with advanced disease due to receive Epirubicin, Cisplatin and Capecitabine (ECX)/Epirubicin, Cisplatin and Fluorouracil (ECF) or Epirubicin, Oxaliplatin and Capecitabine (EOX)/Epirubicin, Oxaliplatin and Fluorouracil (EOF) regimens (advanced study, n = 375), and in patients planned to receive perioperative chemotherapy with the same regimen (peri-operative study, n = 306). Relative telomere length (RTL) in peripheral blood mononuclear cells (PBMCs) and plasma levels of 10 inflammatory cytokines were analysed to determine association with progression-free and overall survival, and response. Blood samples were collected prior to treatment and on each treatment cycle. Both studies comprised biomarker discovery and validation cohorts. Here we report analysis of the discovery cohorts. Results: In advanced disease, high pre-treatment levels of IL8 and IL10 associated with poor Progression Free Survival (PFS) and Overall Survival (OS) in univariate analysis, and IL6 with poor OS. In multivariate analysis, IL6 and IL8 remained associated with OS, and IL8 with PFS. In the perioperative study, cytokine levels were significantly lower and no relationships were observed. There was no association between RTL and any endpoint in either study. Conclusions: Pre-treatment RTL was not prognostic, although IL6/IL8 were negative prognostic factors in advanced disease. Levels of these were lower in patients with localised disease, suggesting an association with disease progression. Further analysis of systemic inflammatory status in gastro-oesophageal adenocarcinoma may be promising for development of future predictive biomarker signatures.

Epigenetic modifications are dynamic and reversible, making them attractive targets for therapeutic intervention in cancer. Although several epigenetic drugs (epidrugs) have been clinically approved, their application in T-cell acute lymphoblastic leukemia (T-ALL) remains limited, and predictive biomarkers of response are lacking. Here, we present a mass spectrometry (MS)-based pharmacoepigenetic approach to profile histone post-translational modifications (hPTMs) to identify signatures associated with epidrug sensitivity in T-ALL. Baseline hPTM landscapes were previously established by our group for 21 T-ALL cell lines using liquid chromatography–tandem mass spectrometry (LC–MS/MS). Here, we treated these cell lines with a panel of nine epidrugs including anthracyclines, histone deacetylase inhibitors, and DNA methyltransferase inhibitors. Correlation of cell viability data with hPTM levels revealed distinct hPTM signatures linked to sensitivity for each drug class. These signatures were subsequently evaluated in T-ALL patient-derived xenograft (PDX) models. However, we our analysis revealed substantial discepancies in hPTM sensitivity signatures compared to those observed in vitro. Co-variation network analysis highlighted divergence in hPTM-hPTM correlation between the two models, underscoring limitations of cell lines for modeling dynamic epigenetic regulation in vivo. Our findings establish a framework for MS-based hPTM profiling in T-ALL and emphasize the importance of model selection in developing predictive epigenetic biomarkers. Graphical Global hPTM profiling of 21 T-ALL cell lines was performed using LC-MS/MS, as previously published by Provez et al. In parallel, the 21 cell lines were treated with a dilution series of nine epidrugs, categorized into three distinct classes, to determine their IC50 values. Finally, Spearman correlation analysis was performed to assess the relationship between hPTM levels and drug sensitivity. Figure created with Biorender.com .

Relapsed and refractory disease in children with T-cell acute lymphoblastic leukemia (R/R T-ALL) remains a major clinical challenge. Outcomes for children who relapse or exhibit resistance to initial treatments are dismal, with survival rates frequently below 25% despite aggressive therapy. To minimize toxicities and improve outcomes, individualized precision medicine approaches targeting the underlying biology of R/R T-ALL are especially important, considering that T-ALL is characterized by genetic, epigenetic and posttranscriptional heterogeneity, and organ and niche specificities (e.g. the central nervous system), all of which underlie disease progression and therapy resistance. Here, we summarize the current understanding of the complexity of pediatric T-ALL biology and how such knowledge may be clinically leveraged, emphasizing the need for innovative therapeutic routes to improve outcomes for children with R/R T-ALL. Emerging approaches that hold promise or show palpable results include proteasome inhibitors, BCL-2 antagonists, and JAK (for JAK- and IL-7R-driven cases), ABL and SRC family tyrosine kinase (for LCK-activated cases), MEK or PI3K-mTOR inhibitors. MYC-targeting agents, DNA demethylating agents, histone deacetylase inhibitors, splicing modulators, or drugs exploring T-ALL metabolic vulnerabilities, are other examples for potential pharmacological intervention. Immunotherapies, particularly CAR T-cell products targeting CD7 and other markers, but also biologics (e.g. targeting CD38), are under development and increasing interest. These agents should be rationally integrated into precision medicine combination therapies informed by genetic, epigenetic, and posttranscriptional insights that will be essential to refine risk stratification and minimize the risk of resistance. Novel strategies leveraging artificial intelligence and machine learning could accelerate discovery and optimize treatment frameworks.

Current intensive treatment of pediatric T-cell acute lymphoblastic leukemia (T-ALL) has substantial side effects, highlighting a need for novel biomarkers to improve risk stratification. Canonical biomarkers, such as genetics and immunophenotype, are largely not used in pediatric T-ALL stratification. This study aimed to validate the prognostic relevance of DNA methylation CpG island methylator phenotype (CIMP) risk stratification in 2 pediatric T-ALL patient cohorts: the Nordic Society of Paediatric Haematology (NOPHO) ALL2008 T-ALL study cohort (n = 192) and the Dutch Childhood Oncology Group (DCOG) ALL-10/ALL-11 validation cohorts (n = 156). Both cohorts revealed that combining CIMP classification at diagnosis with measurable residual disease (MRD) at treatment day 29 (D29) or 33 (D33) significantly improved outcome prediction. The poor prognosis subgroup, characterized by CIMP low/D29 or D33 MRD ≥ 0.1%, had a cumulative incidence of relapse (pCIR5yr) of 29% and 23% and overall survival (pOS5yr) of 59.7% and 65.4%, in NOPHO and DCOG, respectively. Conversely, a good prognosis subgroup was also identified representing CIMP high/D29 or D33 MRD < 0.1% with pCIR5yr of 0% and 3.4% and pOS5yr of 98.2% and 94.8%, in NOPHO and DCOG, respectively. For NOPHO, MRD was also evaluated on D15, and the relapse prediction accuracy of CIMP/D29 MRD (0.79) and CIMP/D15 MRD (0.75) classification was comparable, indicating potential for earlier stratification. The evaluation of the biology behind the CIMP subgroups revealed associations with transcriptome profiles, genomic aberrations, and mitotic history, suggesting distinct routes for leukemia development. In conclusion, integrating MRD assessment with the novel CIMP biomarker has the potential to improve risk stratification in pediatric T-ALL and guide future therapeutic decisions.

Air pollution is increasingly discussed as a risk factor for dementia, but the biological mechanisms are not yet fully understood. Cellular integrity markers like telomere length are relevant to investigate in relation to air pollution exposure in this context, as they are associated with aging and dementia. Previous studies on air pollution and telomere length have somewhat mixed results, which may partly be due to differences in air pollution modelling, composition, and concentrations. The study aimed to investigate the relationship between source-specific air pollution exposure and telomere length in a low-level air pollution area. Data were obtained from the Betula project, a longitudinal study in Northern Sweden dedicated to studying aging, memory and dementia. A total of 519 participants who were recruited between 1988 and 1995 were included, whose relative telomere length was measured, and who were followed-up with respect to dementia for more than 20 years. To estimate annual mean air pollution concentrations, a dispersion model linked to each participant’s residential address was employed. We conducted linear regression to explore the associations between annual mean air pollution concentrations at participants’ residences and relative leukocyte telomere length. In the whole sample, there was no support for air pollution to affect telomere length, with regression slope estimates close to zero and p-values >0.10. There were tendencies for a positive association of longer telomere length and higher exposure to air pollution among individuals who were later diagnosed with dementia (N = 74), but these findings were not conclusive (p-values >0.10). The findings raise questions about susceptibility to air pollution and the state of the inflammatory response in individuals who later will develop dementia. Given the imprecise nature of these estimates, caution is advised in their interpretation however, and validation in other studies is essential.