Editors: Donna E. Hansel, MD, PhD, division head of pathology and laboratory medicine, MD Anderson Cancer Center, Houston; James Solomon, MD, PhD, assistant professor, Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York; Erica Reinig, MD, assistant professor and medical director of molecular diagnostics, University of Wisconsin-Madison; Marcela Riveros Angel, MD, molecular genetic pathology fellow, Department of Pathology, OHSU; Andrés G. Madrigal, MD, PhD, assistant professor, clinical, Ohio State University Wexner Medical Center, Columbus; Maedeh Mohebnasab, MD, assistant professor of pathology, University of Pittsburgh; and Alicia Dillard, MD, clinical pathology chief resident, New York-Presbyterian/Weill Cornell Medical Center.
A blood-based epigenetic age predictor for adolescents and young adults
May 2023—One in four children worldwide have unregistered births according to 2019 data from UNICEF. While efforts are underway to mitigate this staggering statistic by prioritizing documentation of birth, millions of people still cannot prove their date of birth. Age-assessment methods, most commonly used in forensics, have relied on bone radiography. However, more recently, chronologic age-prediction models have been developed based on knowledge of how epigenetics change with age. Epigenetics is the modification of gene expression without changing the underlying genetic code. One mechanism by which cells can modulate gene expression involves methylation of cytosines, often in the promoter region of genes. The human genome contains approximately 28 million DNA methylation sites, many of which change with age. Epigenetic clocks predict chronological age using age-associated methylation at CpG sites. Although epigenetic clocks are not new, one designed specifically for adolescents and young adults has not been explored, according to the authors. Adolescents may serve as a prime demographic for age predictors because our society affords them special rights of protection. The authors designed a novel age predictor for adolescents and young adults (PAYA) between 12 and 25 years old based on machine-learning methods using 450,000 methylation array data from the Cardiovascular Risk in Young Finns study and Environmental Risk (E-Risk) Longitudinal Twin study. The data sets were divided into a training cohort and validation/testing cohort. The training data set consisted of 2,316 blood samples from people ranging in age from 10 to 60 years old. The testing data set contained 920 samples from people who were approximately 18 years old. The authors compared the narrow age-span training data set (12 to 25 years old; n=973 samples) with the broad age-span data set (10 to 60 years old; n=2,316 samples) and noted that the narrow data set improved performance. Using a regression model, the authors found that 267 methylation sites were the most useful for predicting age. These sites had little overlap with methylation sites used in other pediatric age-assessment clocks. The majority of the age predictions were found to be within one year of the target average age of 18.5 years old. Fewer than five percent of age predictions were found to be more than two years from the target average age, and even fewer still deviated by more than three years. The authors also examined the effects of traumatic and physiological stress by reviewing and comparing DNA methylation sites associated with childhood trauma. Only one methylation site from the review list was included in the age predictor, with modest effect on prediction. While the results are promising, further analysis involving different ethnicities and environmental conditions is crucial to assessing the robustness of the predictor.
Aanes H, Bleka O, Dahlberg PS, et al. A new blood based epigenetic age predictor for adolescents and young adults. Scientific Reports. 2023;13(1). https://doi.org/10.1038/s41598-023-29381-7
Correspondence: Dr. Veslemoy Rolseth at vesrol@ous-hf.no
Ability of whole genome analysis to identify driver and double-hit events in relapsed/refractory myeloma
Multiple myeloma is a challenging and devastating disease caused by a clonal proliferation of plasma cells. Despite the availability of several lines of therapy, patients tend to relapse at some point during treatment. Due to a high rate of relapse and mortality, the molecular profile of relapsed/refractory multiple myeloma (rrMM) is of interest in furthering understanding of the disease’s progression. The authors used whole genome sequencing data to compare the molecular aberrations in tumor samples from rrMM patients with those in samples from unrelated, newly diagnosed multiple myeloma (ndMM) patients. The case-control study involved 418 rrMM whole genome analysis samples from 386 patients, derived from six immunomodulatory agent clinical therapy trials, and ndMM whole genome analysis samples from 198 unrelated patients used as the control. The rrMM group was classified, based on refractory status, into two forms of immunomodulatory therapy—lenalidomide resistant and pomalidomide resistant. However, the majority of patients had been previously treated with other therapies, including stem cell transplantation. The rrMM and ndMM samples underwent matched tumor-normal whole genome sequencing to identify single nucleotide variants, insertions/deletions, and copy number alterations. After examining coding and noncoding variation in the genome, the authors proposed 10 previously unreported candidate driver genes. These genes were observed in the rrMM and ndMM samples. However, the most marked changes in cancer cell fraction (an adjusted calculation of the variant allele frequency) were noted in the therapy-resistant samples. The drivers DUOX2, EZH2, and PIGO, in particular, were determined to be unique to the rrMM samples. These three drivers demonstrated mutual exclusivity, which suggests different and likely segregated pathways of therapeutic resistance. In 14.2 percent of treated tumors, a mutational driver was not identified, but these cases tended to have an associated copy number alteration or chromosomal abnormality. Comparison of the two immunomodulatory therapies showed distinct MAML3 driver gene variation and IGL and MYC translocations. Biallelic events have been associated with relapse in multiple myeloma, and biallelic CDKN2A and CREBBP were found to be isolated to the rrMM group in this study. In terms of copy number alterations, disease progression was identified in the rrMM samples as overexpression of 1q gain, 6q loss of heterozygosity, and 17p loss of heterozygosity. These findings provide additional insight into the complex genomic landscape of new onset and relapsing multiple myeloma. Functional analysis of these candidate drivers is necessary to corroborate the authors’ findings and further understanding of the role of these novel drivers in therapeutic resistance.
Ansari-Pour N, Samur M, Flynt E, et al. Whole-genome analysis identifies novel drivers and high-risk double-hit events in relapsed/refractory myeloma. Blood. 2023;141(6):620–633.
Correspondence: Dr. Anjan Thakurta at anjan.thakurta@ndorms.ox.ac.uk