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Clouds in the Sky
Vijay Padul

Vijay Padul, PhD

Sr. Scientist - Omics

Inherited components of longevity.

Published on: January 24, 2023

Aging and longevity has a strong genetic component. Every living organism is born with a set of genetic material that constitutes its genome. Humans inherit their genome and associated genetic traits from their parents. In one sense, the genome is the hardware that we are born with and epigenome, which constitutes the state of the chemical modifications to DNA and associated proteins to influence gene expression is the software1. There are many factors that influence longevity which include environment, nutrition, diseases, disorders, genetic factors and epigenetic factors. Of these factors the contribution of genetic factors to heritability of human longevity has been estimated to be 15–40% by various studies1. The contribution of inherited genetic variants may be from longevity promoting variants or the disease predisposing variants, as they increase the incidence of disease and disorders. A hypothesis proposed by Sebastiani et al. (2012) for the prevalence of disease and longevity associated variants with increasing age suggests that the increased frequency of the disease predisposing variants in an individual may negatively affect the longevity of the individual and result in premature death, while higher frequency of the longevity enhancing variants may positively contribute to the longevity potential of the individual and result in exceptional longevity2. The quest to identify genes that contribute to longevity has led to the identification of many genetic variants which have been found to be associated with longevity3. There are few candidate longevity genes that have been consistently replicated in multiple studies which includes TOMM40/APOE/APOC1 gene cluster and FOXO31,4. These genes have been identified in Genome-wide association studies (GWAS) where the association of the genetic variant with certain phenotype is estimated. The GWAS studies my use case-control cohorts or sibling pairs for genome-wide linkage studies. Of the different alleles of APOE gene, ε2ε2 or ε2ε3 genotype has been found to be associated with significantly increased odds to reach extreme longevity, with decreased risk for death2. Identification of the association of certain genetic factors is not enough. Uncovering the molecular mechanism of influence of the variant on the longevity is the next important step. One example is of FOXO3 genetic variants rs2802292 G-allele, which has been shown to have protective effects on several age-related diseases and is associated with lower prevalence of coronary artery disease, lower prevalence of cancer, fewer bone fractures and lower cardiovascular disease incidence. Study of the molecular mechanism reveals that the FOXO3 rs2802292 locus has enhancer functions that positively regulate FOXO3 transcription. The FOXO3 G-allele forms a HSF1 binding site, which induces promoter DNA region and enhancer DNA region interaction through chromatin loop structure. This enables FOXO3 expression and the resultant activity of the aging related genes5. Thus genome level sequencing data of large populations will help to uncover more genes associated with longevity and make personal longevity predictions possible and the study of the molecular mechanisms of the longevity related genetic variants identified in large cohort studies will help in devising longevity intervention strategies such as anti-aging drugs and therapies. Establishing the molecular level contribution of the variant to promote longevity is important to uncover molecular level pathways leading to longevity which will enable the discovery of therapeutics aimed at increasing longevity. References: 1. Morris BJ, Willcox BJ, Donlon TA. Genetic and epigenetic regulation of human aging and longevity. Biochim Biophys Acta BBA - Mol Basis Dis. 2019;1865(7):1718-1744. doi:10.1016/j.bbadis.2018.08.039 2. Sebastiani P, Perls TT. The Genetics of Extreme Longevity: Lessons from the New England Centenarian Study. Front Genet. 2012;3. doi:10.3389/fgene.2012.00277 3. Murabito JM, Yuan R, Lunetta KL. The Search for Longevity and Healthy Aging Genes: Insights From Epidemiological Studies and Samples of Long-Lived Individuals. J Gerontol A Biol Sci Med Sci. 2012;67A(5):470-479. doi:10.1093/gerona/gls089 4. Deelen J, Beekman M, Uh HW, et al. Genome-wide association meta-analysis of human longevity identifies a novel locus conferring survival beyond 90 years of age. Hum Mol Genet. 2014;23(16):4420-4432. doi:10.1093/hmg/ddu139 5. Sanese P, Forte G, Disciglio V, Grossi V, Simone C. FOXO3 on the Road to Longevity: Lessons From SNPs and Chromatin Hubs. Comput Struct Biotechnol J. 2019;17:737-745. doi:10.1016/j.csbj.2019.06.011

Health informatics: Health and genetics data to predict longevity.

Published on: December 27, 2023

The desire to live a healthy long life has been a constant drive for search of solutions to achieve human longevity. With the advancement in medical science, successful treatment and management of a large number of diseases has become possible. With the capacities of the modern medicine human longevity has also evidently increased. How long can a human live a healthy life? How far can we stretch it? These are the prominent questions for medical research in longevity. Aging biology research has gained prominence in recent times. Moqri et al. (2023) defines aging as “The process of accumulation of consequences of life, such as molecular and cellular damage, that leads to functional decline, chronic diseases, and ultimately mortality” and healthspan as “The period of life prior to onset of chronic disease and disabilities of aging, i.e., in good health”. To achieve longer healthspan and thereby longevity it is important to identify the factors that positively or negatively affect healthy longevity. Study of these factors will be a long term scientific endeavor spanning multiple decades which will involve measurements and recording of all the life processes and health interventions that the human research volunteers have undergone through their lifespan. These records will not only be helpful for the research but also for implementation of the research outcomes in normal practice. If an individual is interested in achieving healthy longevity and wishes to implement research findings to achieve these goals, the individual must have all the record of life processes and medical interventions to predict own longevity and adopt corrective measures. This highlights the importance of health informatics which makes it possible to record and analyze health data that accumulates during a person’s lifetime. The American Medical Informatics Association defines health informatics as “The science of how to use data, information, and knowledge to improve human health and the delivery of health care services.” (Wheeler, 2022). Health informatics will be essential for identification and evaluation of longevity interventions in humans. Identification of valid ‘Biomarkers of aging’ will require analysis of huge number of individual’s lifetime of health data. Health informatics records will be useful for the development and validation of clinical applications. The present challenges in health informatics as in any area of data science includes consensus and standardization of data formats and biomarkers being recorded, precise recording of medical and health data, proper frequency of data recording and reliability of the data. Properly maintained electronic health records (EHR) and personal health records (PHR) will play an essential role in aging and longevity research. For larger utility of scientific community large number of these EHRs should be available in the form of biomedical databases. These databases may serve as major resources for knowledge discovery and predictive analytics. For effective use of big medical and life history data in biomedical research these databases need to adhere to FAIR principles which necessitates Findability, Accessibility, Interoperability and Reusability of the data. To enhance reliability and trustworthiness of shared research data, the data should be compliant to TRUST principles which include Transparency, Responsibility, User focus, Sustainability and Technology. In conclusion, good data is the backbone of good research. Longevity prediction and therapeutic interventions will need huge amount of genetic data and medical records of large number of individuals. FAIR and TRUSTful data will be required for research and diagnostics, and personalized longevity applications. Properly maintained patient medical history and reliable personal life records will be essential for application of aging biomarker based interventions and to achieve this, health informatics will serve as an essential tool in longevity research. References: 1. Moqri M, Herzog C, Poganik JR, et al. Biomarkers of aging for the identification and evaluation of longevity interventions. Cell. 2023;186(18):3758-3775. doi:10.1016/j.cell.2023.08.003 2. Wheeler SE, Block DR, Bunch DR, et al. Clinical Laboratory Informatics and Analytics: Challenges and Opportunities. Clin Chem. 2022;68(11):1361-1367. doi:10.1093/clinchem/hvac157 3. Fang R, Pouyanfar S, Yang Y, Chen SC, Iyengar SS. Computational Health Informatics in the Big Data Age: A Survey. ACM Comput Surv. 2017;49(1):1-36. doi:10.1145/2932707 4. Finkelstein J, Guarino J, Huo X, Borziak K, Parvanova I. Exploring Determinants of Longevity of Biomedical Databases. In: Otero P, Scott P, Martin SZ, Huesing E, eds. Studies in Health Technology and Informatics. IOS Press; 2022. doi:10.3233/SHTI220047 5. Wilkinson MD, Dumontier M, Aalbersberg IjJ, et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci Data. 2016;3(1):160018. doi:10.1038/sdata.2016.18 6. Lin D, Crabtree J, Dillo I, et al. The TRUST Principles for digital repositories. Sci Data. 2020;7(1):144. doi:10.1038/s41597-020-0486-7

Genomics: Can it answer longevity questions?

Published on: November 22, 2023

Quest for human longevity is ongoing since ancient times. Throughout the human history variety of medicinal, lifestyle approaches have been suggested to promote longevity but a conclusive answer for human longevity has not yet been found. In the modern scientific era the search still continues. Here, we will take a look at will the science of genomics will be able to help us in decoding the riddle of longevity and healthy lifespan. Over the last two centuries we have witnessed increased life expectancy worldwide, although it is still highly variable between countries due to unequal rate of development. The reasons for this lifespan extension is mainly due to the improvement of health care facilities, awareness and implementation of hygiene and improvement in nutrition. Even though the lifespan has increased the healthy life expectancy has not increased at the same rate. Age is still the main risk factor for the majority of common diseases contributing to disability. There is an observation in long-lived individuals coming from families expressing exceptional longevity that reaching an old age does not necessarily result in a higher degree of age-related disability. The individuals with exceptional longevity lineage may reach high ages without major disabilities. This suggests that there is a genetic component to longevity. Studies observing mono- and dizygous twins have revealed that the genetic contribution to the variation in human lifespan is about 25–30%. This genetic contribution is mainly apparent after the age of 60 years and seems to increase with age. This leads to the possibility of the genome of long-lived individuals being investigated to identify variants that promote healthy aging and which protect against age-related diseases. Longevity can also be affected by the presence of genetic factors which may provide protection against age-related phenotypes and diseases, these genetic factors are needed to be identified and genomics can help in this goal. The present challenges in the determination of genetic factors responsible for longevity include the contribution and interplay of genetics and lifestyle in longevity. Which among these is the primary factor. Can bad genetics be compensated by good lifestyle choices and diet to achieve longevity? And also vice versa can bad lifestyle and diet reduce longevity? Do genetics and lifestyle complement each other and can these be harnessed to achieve longevity? Genomics using whole genome sequence has revolutionized the study of genetics. With single nucleotide level resolution of genetic data available the possibilities of health effect studies are immense. Previous genetic studies have identified variants in the genes such as Apolipoprotein E (APOE), APOC3, CHRNA3/5, Lp(a), HLA-DQA1/DRB1, SH2B3, CETP, hTERT to be associated with longevity. Despite these advances definite causative association of genetic variants and longevity has not been identified. More comprehensive whole genome level studies will be useful to confirm these findings and identify novel genetic determinants of longevity in population studies. These will also be helpful to identify genetic predisposition to lifestyle diseases. Genomic testing can provide information about presence or absence of genetic disorder or genetic predisposition to diseases or disorders thereby helping in prediction of longevity and taking remedial measures to increase longevity. Genomic studies will also help to identify the effect of genetic variants on physiological factors that contribute to longevity. Increasing use of genome sequencing at population level in the long term will be useful source of data for identifying genomic regions truly contributing to human longevity. Thus the study of human genomics has tremendous potential to help answer the longevity riddle. References: 1. Beekman M, Blanché H, Perola M, et al. Genome-wide linkage analysis for human longevity: Genetics of Healthy Aging Study. Aging Cell. 2013;12(2):184-193. doi:10.1111/acel.12039 2. Deelen J, Beekman M, Capri M, Franceschi C, Slagboom PE. Identifying the genomic determinants of aging and longevity in human population studies: Progress and challenges. BioEssays. 2013;35(4):386-396. doi:10.1002/bies.201200148 3. Deelen J, Beekman M, Uh HW, et al. Genome-wide association meta-analysis of human longevity identifies a novel locus conferring survival beyond 90 years of age. Hum Mol Genet. 2014;23(16):4420-4432. doi:10.1093/hmg/ddu139 4. Passarino G, De Rango F, Montesanto A. Human longevity: Genetics or Lifestyle? It takes two to tango. Immun Ageing. 2016;13(1):12. doi:10.1186/s12979-016-0066-z 5. Serbezov D, Balabanski L, Hadjidekova S, Toncheva D. Genomics of longevity: recent insights from research on centenarians. Biotechnol Biotechnol Equip. 2018;32(6):1359-1366. doi:10.1080/13102818.2018.1532317

A multicenter study uncovers genetic factors that may play a significant role in mice longevity.

Published on: November 15, 2023

Original author: Maroun Bou Sleiman, et al., 2022 (doi: 10.1126/science.abo3191)

Aging is a yet to be fully understood process where molecular, cellular, and organismal homeostasis is affected by multiple environmental and genetic factors which results in progressive functional decline in physical, mental, and reproductive capacities of the organism. This leads to affliction of multiple morbidities and consequent mortality of the organism. Understanding of the environmental and genetic factors that are central to aging is important to manage the health and wellbeing of the aging population. Many studies have identified the role of nutrient-sensing metabolic pathways, loss of proteostasis, increased genome instability, changes in epigenetic marks, and alterations in telomere length in promoting aging. Despite these advances, the contribution of an individual’s genetic factors, sex, and environment in aging process and life-span determination has not yet been uncovered. Methodology: The authors performed a large multicenter and multiyear study in which they used male and female untreated control mice (UM-HET3, n=3276) from the Interventions Testing Program (ITP) in a four-way intercross to uncover the driving factors of aging. The factors that were studied included genetic (sex, QTLs) and nongenetic factors (litter size, nutrition at early age). Four QTL (Quantitative Trait Loci) mapping analyses were performed which included QTL mapping for each sex separately, and for the combined dataset with and without sex-by-genotype interaction term. Mixed-effects Cox models with site and cohort as random effects and sex as fixed effect was used for analysis as the study was multicentric and conducted over multiple years. The analysis of interplay between early growth, size, and longevity in mice and humans was performed. The relation between growth, body size, and longevity was explored due to observation of overlap between longevity loci and growth QTLs. Age- and genotype-dependent changes in liver gene expression in mice was analyzed. Annotation, prioritization, and validation of candidate longevity genes were performed. The results were integrated with orthogonal datasets to identify prioritized candidate genetic loci and genes. The RNA interference (RNAi)–knockdown of C. elegans orthologs of the top-scoring genes was performed to analyze their effect on longevity. Results: A single QTL at chromosome 12 was identified in a genome scan comprising both sexes. One significant female-specific longevity locus at chromosome 3 was identified. Significant exclusive male loci were not detected. Significant longevity loci was detected in males, only when early deaths were excluded. This indicates that some genetic variations had an effect on the longevity beyond a certain age only. An inverse relationship was observed at the phenotypic level between early body weight and longevity which was found to be more pronounced in males. The liver gene expression analysis identified that female livers had higher interferon-related gene expression, and older mice had overexpressed immune related genes. An interactive resource for conserved longevity gene prioritization was built by the authors by combining the results from the present study with the data from multiple sources in model organisms and humans. The RNAi experiments in C. elegans identified Hipk1, Ddost, Hspg2, Fgd6, and Pdk1 as candidate genes that may affect longevity. Impact of research: This study uncovered genetic and nongenetic factors that determine longevity. The data generated from this study will be useful for designing future studies on longevity. Characterization of genetic and nongenetic determinants of longevity at the population level will be useful for discovering targeted therapies for aging, age related diseases, and longevity.

Nivolumab therapy in addition to chemotherapy improves survival in patients with resectable lung cancer.

Published on: July 05, 2023

Original author: P.M. Forde et al., 2022 (doi: 10.1056/NEJMoa2202170)

Lung cancer is the third most common cancer in the United States. Over 80% of the total lung cancer cases belong to the type non-small cell lung cancer (NSCLC). Among the patients diagnosed with NSCLC, ~20 to 25% have resectable cancer. Patients with resectable NSCLC may be cured by surgery or surgery followed by chemotherapy. Among them 30 to 55% of patients who undergo curative surgery develop recurrence of cancer and eventually die of the disease. For patients with cancer stage that warrant adjuvant chemotherapy, neoadjuvant chemotherapy can be used for the treatment. However, the absolute difference in 5-year recurrence free survival and overall survival with neoadjuvant chemotherapy as compared with surgery alone is modest i.e. only 5 to 6%. Despite recent advances with adjuvant therapies for resectable NSCLC, identification of effective systemic treatments is needed to improve patient survival. Nivolumab is a fully human anti–programmed death 1 (PD-1) antibody which restores the function of existing antitumor T cells, and enhances antitumor immunity through direct or indirect immune-system activation. In early-phase trials, nivolumab based neoadjuvant therapy have shown encouraging clinical activity. Data from phase 3 trials are needed to confirm these findings. In this research article, the results of a phase 3 trial, named as “Check-Mate 816”, which was conducted to evaluate the efficacy and safety of neoadjuvant nivolumab plus chemotherapy as compared with chemotherapy alone in patients with resectable NSCLC has been reported. Methodology: This was an international, open-label, phase 3 trial which enrolled adults with resectable stage IB (≥4 cm) to IIIA NSCLC, and with no previous anticancer therapy. Patients with known ALK translocations or EGFR mutations were excluded. The enrolled patients were randomly assigned in a 1:1 ratio to receive nivolumab plus platinum-doublet chemotherapy or platinum-doublet chemotherapy alone before undergoing definitive surgery. The chemotherapy was given every 3 weeks for three cycles. Surgery was intended to occur within 6 weeks after the completion of neoadjuvant treatment, after which patients in both groups could receive up to four cycles of adjuvant chemotherapy, radiotherapy, or both. Primary end points were event-free survival according to blinded independent central review or complete pathological response according to blinded independent pathological review. Secondary end points included major pathological response, time to death or distant metastases, and overall survival. Results: A total of 773 patients were enrolled, 505 underwent randomization, and 358 were concurrently assigned to receive neoadjuvant nivolumab plus chemotherapy or chemotherapy alone. 176 patients in each group received respective treatment. The median event-free survival was 31.6 months with nivolumab plus chemotherapy and 20.8 months with chemotherapy alone. The percentage of patients with a pathological complete response was 24.0% and 2.2%, respectively. The addition of nivolumab to neoadjuvant chemotherapy did not increase the incidence of adverse events or impede the feasibility of surgery. Impact of research: In this clinical trial neoadjuvant nivolumab plus chemotherapy was found to result in significantly longer event-free survival and a higher percentage of patients with a pathological complete response than chemotherapy alone. This would provide an effective treatment option for the patients with resectable NSCLC.

​​Multi-omic analysis of low-grade glioma histological subtypes uncovers the significance of tumor purity.​

Published on: March 15, 2023

Original author: Zhao, B., Xia, Y., Yang, F. et al., 2022 (doi: 10.1186/s10020-022-00454-z)

Astrocytoma and Oligodendroglioma are the histological subtypes of glioma brain tumors. Histological classification of gliomas has been the clinical practice before the inclusion of molecular markers by the World Health Organization (WHO) Classification of Tumors of the Central Nervous System recommendations. Molecular characteristics such as IDH mutations, 1p/19q co-deletion stratify the glioma subtypes adequately. Histological features are still relevant for grading of the tumor diagnosis. Several studies have underlined the superiority of molecular features over histological features for classification and prognostic predictions. Tumor purity is the estimated measure of the proportion of tumor cells in the tumor along with normal cells and other cells such as immune cells, fibroblasts, endothelial cells, and normal epithelial cells. Tumor purity can be estimated by pathologists via visual or image analysis. Tumor purity can also be estimated by employing computational methods. The heterogeneity of tumor cells is considered a surrogate feature of diffuse gliomas and can likely be a potential cause of failure of treatment. The authors have attempted to investigate the multi-omics and clinical data of grade 2 IDH-mutant astrocytoma and oligodendroglioma, to uncover the molecular differences between the two types of tumors. The rationale presented is that both these tumors have an indolent natural history and are recognized as distinct entities of neoplasms but there is little knowledge of the molecular differences between them. Methodology: Multi-omics data from The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) such as mRNA, somatic mutations, copy number alternations, DNA methylation, microRNA, epigenetics, immune microenvironment characterization and clinical features of the two types of gliomas was used for in silico analyses. Biological functional analysis, glioma cluster analysis, and quantification of tumor microenvironment were performed. DNA damage repair signature was evaluated and tumor purity was estimated from the available data. Using machine learning algorithms a diagnostic model incorporating tumor purity was established, and the predictive value was evaluated by receiver operative characteristic curves. Results: Chromosomal instability was found to be common in both types of gliomas. Astrocytomas exhibited increased total copy number alterations as compared to that of oligodendrogliomas. A distinct chromosome 4 loss was identified in oligodendrogliomas. Subtyping of oligodendrogliomas according to chr 7 gain/chr 4 loss status presented the worst survival (P = 0.004) and progression-free interval (P < 0.001). Among the two histological subtypes, oligodendroglioma had a higher sub-clonal genome fraction (P < 0.001) and tumor purity (P = 0.001), and astrocytoma had a higher aneuploidy score (P < 0.001). Inflamed immune cell infiltration, activated T cells and potential response to immune checkpoint inhibitors were observed to be present in astrocytomas. Oligodendrogliomas were found to be more homogeneous with increased tumor purity and decreased aggression. The authors found the tumor purity-involved diagnostic model to exhibit great accuracy in identifying astrocytoma and oligodendroglioma. Impact of research: This study facilitates a deeper understanding of the molecular features, immune microenvironment, tumor purity, and prognosis by investigating the similarities and differences between grade 2, IDH-mutant astrocytoma, and oligodendroglioma. This study identified tumor purity as an important genomic factor that closely correlated with DNA damage repair signature and copy number alterations. The authors claim that the diagnostic tool developed by machine learning may offer support for clinical decisions and ultimately improve patient outcomes.

An effective integrated grading system for meningioma tumor grading that incorporates molecular features.

Published on: January 11, 2023

Original author: Joseph Driver et al., 2022 (doi: 10.1093/neuonc/noab213)

Meningioma is the most common primary intracranial tumor in the United States. There are approximately 35,000 new cases diagnosed every year. meningioma has an estimated population prevalence of approximately 1 in every 100 adult persons over the age of 45. A meningioma is a tumor that has its origin in the meninges. Meninges are the protective membranes that surround the brain and spinal cord. Though meningioma is called a brain tumor, it is not technically a brain tumor, as it does not arise from the brain tissue. Meningiomas are slow-growing tumors that often continue to grow without any symptoms. The excessive growth of meningioma tumors inside the cranium may lead to the compression or squeezing of the adjacent brain, nerves, and vessels. This may lead to severe effects on normal brain function. The clinical care of meningioma is guided by the World Health Organization (WHO) grading system for meningioma. This is a 3-tiered grading system (grade I, II, and III) which is based on histopathological features, and the extent of surgical resection. The limitation of the WHO grading system is that the behavior of several meningiomas does not conform to their assigned WHO grade. This limitation of WHO grading is due to the interobserver variability in the histological assessment of the tumor. The second factor is the potential for under-sampling of a tumor type with known histologic and molecular heterogeneity. The third factor is that the assessment of histologic features may not predict the malignant potential of the tumor. As there are a large number of cases of meningioma, it is necessary to identify a reliable, prognostically relevant, and accessible predictor of clinical outcome. This research article describes the formulation of a simple-to-apply, transparent, scalable, and accurate molecularly integrated grading system that can overcome the limitations of the WHO grading system. To develop this grading system, the authors evaluated 699 meningiomas with detailed clinical, imaging, histologic, and molecular annotation. Methodology: The study divided the patients into a discovery cohort of 527 unique meningioma patients and a validation cohort of 172 meningioma patients. Whole-genome microarray for DNA copy-number profiling targeted mutational profiling, and methylation-based copy-number profiling data from the patients were used for the study. Analysis was performed to assess whether the inclusion of chromosomal copy-number data from the tumor samples improved the prediction of time-to-recurrence for patients with meningioma who were treated with surgery, in comparison with the prediction using the WHO grading schema. Models were developed to devise a grading score reflective only of intrinsic tumor factors and to exclude the potential confounding influence of varying treatment on subsequent tumor recurrence. Covariates that define the WHO grading scheme i.e., mitotic index, the presence of atypical features, and brain invasion, and covariates such as MIB-1 proliferative index and chromosome arm-level and CDKN2A copy number variations were investigated. Each of these covariates was individually evaluated for association with time-to-recurrence using the log-rank test. Covariates were further investigated using Cox LASSO regularization, random survival forests, and gradient boosting. An Integrated Grade was constructed using the chosen shared high-risk features with the best predictive performance for tumor recurrence. The Integrated Grade was internally validated in the discovery cohort and externally validated in 2 independent validation cohorts. Results: A 3-tiered grading scheme (Integrated Grades 1-3) that is prognostically relevant and that reflects tumor-intrinsic factors, was developed, This Integrated Grade system incorporated mitotic count, focal hemizygous or homozygous loss of CDKN2A, and loss of 1p, 3p, 4p/q, 6p/q, 10p/q, 14q, 18p/q, and 19p/q. WHO grade was found to be significantly associated with the developed Integrated Grade, but still 32% of cases were reclassified using the Integrated Grade to either a lower-risk or higher-risk Integrated Grade compared to their assigned WHO grade. The Integrated Grading scheme significantly improved the ability to predict recurrence risk compared to the WHO grading scheme. The Integrated Grade more accurately identified meningioma patients at risk for recurrence, relative to the WHO grade. For this analysis time-dependent area under the curve, average precision, and the Brier score were used. The Integrated Grade was found to be superior to the WHO grade in assessing overall survival on long-term follow-up Impact of research: The study shows that the incorporation of copy-number alterations status, into a grading scheme for meningioma, is useful to identify high-risk tumors. The study puts forth a molecularly integrated grading scheme for meningioma that significantly improves upon the currently used WHO grading system in the prediction of progression-free survival. This framework implements available genomic technologies and if broadly adopted by clinicians, could present an advance in the care of meningioma patients.

Uncovering the link between chromatin activity, gene expression in myeloid cells and Alzheimer’s disease risk.

Published on: October 07, 2022

Original author: Novikova, G., Kapoor, et al., 2021 (doi: 10.1038/s41467-021-21823-y)

Alzheimer’s disease (AD) is a progressive neurological disorder commonly affecting people older than 65 years of age. The neurodegenerative progression of the disorder causes the death of the brain cells which results in brain atrophy (shrinkage). This leads to the onset of dementia which causes a decline in memory and thinking. As a result, the patient’s ability to behave normally and function independently in society is severely affected. Presently there is no cure available for AD. The treatments available currently can only improve the symptoms. Understanding of the pathogenesis and the predisposing genetic factors are needed to devise effective therapeutic measures. One of the theories of AD pathogenesis suggests the buildup of abnormal amyloid protein plaques around brain cells as a possible cause of the disease. Other theories suggest the role of cholinergic deficits, a role of abnormal neurofibrillary tangles of tau proteins, and a role of synaptic damage in the neocortex and limbic system. Also, several risk factors such as old age, head injury, infections, genetic and environmental risk factors may play a role in the disease. Several genetic risk factors that may play a role in the development of AD have been discovered over the years. These include the inheritance of variants in genes such as APP, PSEN-1, PSEN-2, and ApoE. Even after multiple years of efforts the underlying cause and mechanism of pathological changes in AD have not yet been deciphered. Over the years, multiple Genome-wide association studies (GWAS) have identified more than 40 loci associated with Alzheimer’s disease (AD). Despite these genetic and molecular studies the causal variants, regulatory elements, genes, and pathways leading to AD pathogenesis have not been uncovered. This hinders the mechanistic understanding of AD pathogenesis and thereby the possibility of devising curative strategies. In this article, the authors have utilized integrative analysis of GWAS, chromatin interactions (promoter-capture Hi–C), and eQTL datasets myeloid cells (monocytes and macrophages) and performed further analysis using Summary data-based Mendelian Randomization (SMR) method. The authors had previously shown that AD risk alleles are enriched in myeloid-specific epigenomic annotations. In this article, they have continued their work and showed that the AD risk alleles are specifically enriched in active enhancers of monocytes, macrophages, and microglia. They also identify transcription factor binding motifs that are overrepresented in these regulatory elements. Methodology: The research study employs integrative analysis of GWAS, ChIP-Seq, ATAC-Seq, promoter-capture Hi–C, and eQTL datasets from monocytes and macrophages to identify candidate causal genes. In the first step of the study, myeloid active enhancers that contain AD risk alleles (AD risk enhancers) were mapped to their target genes by integrating promoter-capture Hi–C and eQTL datasets from monocytes and macrophages. This led to the identification of candidate causal genes in eleven genome wide significant and five suggestive AD risk loci, which includes TP53INP1, APBB3, RABEP1, and SPPL2A. In the next stage of analysis, to investigate the causal relationship between chromatin activity, target gene expression, and AD risk modification they conducted SMR analysis. Specific active chromatin regions, that likely modify AD risk by regulating the expression of one or more of their target genes, were identified in 12 loci. They further fine-mapped AD risk enhancers which lead to the identification of candidate functional variants that likely affect transcription factor binding and regulate gene expression in seven loci. In the final step, they validated one of these variants in the MS4A locus in human induced pluripotent stem cell (hiPSC)-derived microglia and brain. Results: The study found that the AD risk alleles are specifically enriched in active enhancers of monocytes, macrophages, and microglia. Integration of AD GWAS signals with myeloid epigenomic annotations, chromatin interactions (promoter-capture Hi–C), and eQTL datasets identified candidate causal genes in sixteen AD risk loci. Further integration of AD GWAS signals with myeloid epigenomic annotations, chromatin activity (hQTL) and eQTL datasets identified candidate causal genes in twelve AD risk loci. Fine-mapping using myeloid epigenomic annotations identified candidate causal variants in seven AD risk loci. The study further found that a candidate causal variant in the MS4A locus disrupts an anchor CTCF binding site and that this variant is associated with reduced chromatin accessibility and increased MS4A6A gene expression in myeloid cells. This variant was validated in human induced pluripotent stem cell (hiPSC)-derived microglia and brain. Conclusion: This study uncovers a link between chromatin activity, gene expression and AD risk in myeloid cells. This suggests a molecular mechanism of action of candidate functional variants in several AD risk loci. The study also identifies specific AD risk enhancers that harbor these variants and regulate target gene expression. These identified AD risk enhancers most likely modulate disease susceptibility by altering the biology of myeloid cells. Impact of research: This study integrated AD GWAS with multiple myeloid genomic datasets to explore the mechanisms of AD risk alleles and suggested candidate functional variants, regulatory elements and genes that likely modulate AD disease susceptibility. This will help in understanding the molecular mechanism behind the AD pathogenesis.

A phase Ib-IIa trial of 9 repurposed drugs combined with temozolomide for the treatment of recurrent glioblastoma- CUSP9v3-2021

Published on: June 29, 2022

Original author: Marc-Eric Halatsch, et al., 2021 (doi: 10.1093/noajnl/vdab075)

Glioblastoma is the most common and most aggressive primary malignant brain tumor in adults. Despite therapeutic interventions, the glioblastoma prognosis remains very poor as most patients die within 12 months after diagnosis. Current standard treatment for glioblastoma (GBM) is neurologically safe maximal surgical resection of the tumor, irradiation and chemotherapy with temozolomide. This treatment results in progression-free survival of 6.7 months, overall survival of 16.0 months, and 2-year overall survival of 30.7%. This poor survival is due to the diffuse nature of the tumor where cancer cells migrate and start growing in the surrounding normal part of the brain. This leads to recurrence of the tumor which usually takes place within a year after initial treatment. The recurrent cancer cells evolve and develop resistance to the drug therapy and form a heterogeneous tumor which no longer responds to drug therapy. Due to this glioblastoma is one of the most challenging malignancies to treat in all of oncology. Currently, there is no commonly accepted standard of care for recurrent glioblastoma, and no therapeutic regimen has proven to be safe and effective for this fatal tumor. As the dismal prognosis of glioblastoma may be related to the ability of glioblastoma cells to develop mechanisms of treatment resistance, one way to tackle this treatment resistance may be to use multiple drugs simultaneously, which may target multiple pathways in glioblastoma cells, which will leave minimum scope for the malignant cells to develop drug resistance. In 2013, Halatsch et al., proposed a new concept to treat patients with recurrent glioblastoma called Coordinated Undermining of Survival Paths (CUSP). The CUSP therapeutic approach aims to block growth-driving signaling pathways active in glioblastoma by using multiple drugs. The group adopted drug repurposing strategy. They identified nine already-marketed non-oncological drugs with evidenced efficacy to inhibit one or more of the identified growth and cell survival pathways in glioblastoma cells. These 9 drugs were proposed to be used with low-dose, continuous temozolomide. They considered pharmacology, drug interaction, and safety considerations for this combination. This 9 drug treatment regimen was called as CUSP9v3 which means ‘Coordinated Undermining of Survival Paths combining 9 repurposed non-oncological drugs with metronomic temozolomide—version 3’. The research article describes the phase Ib/IIa trial of the CUSP9v3 protocol to assess the safety of the treatment regimen in recurrent glioblastoma patients. This phase Ib/IIa trial included ten adults with histologically confirmed glioblastoma with recurrent or progressive tumor. The CUSP9v3 treatment consisted of nine drugs which are aprepitant, auranofin, celecoxib, captopril, disulfiram, itraconazole, minocycline, ritonavir, and sertraline. These drugs were added to metronomic low-dose temozolomide. Treatment was continued until toxicity or progression. Primary endpoint was dose-limiting toxicity defined as either any unmanageable grade 3–4 toxicity or inability to receive at least 7 of the 10 drugs at ≥ 50% of the per-protocol doses at the end of the second treatment cycle. Out of ten patients, 9 evaluable patients met the primary endpoint while one patient was not evaluable for the primary endpoint (safety). The treatment regimen was concluded to be well-tolerated. The most frequent dose modification or pausing was required for the drugs Ritonavir, temozolomide, captopril, and itraconazole. The most common adverse events to occur were nausea, headache, fatigue, diarrhea, and ataxia. Progression-free survival at 12 months was 50%. This phase Ib/IIa trial concluded that CUSP9v3 can be safely administered in patients with recurrent GBM under careful monitoring. The group is planning a randomized phase II trial to assess the efficacy of the CUSP9v3 regimen in glioblastoma. This research article presents the first step in establishing that an extensive multi-drug regimen is tolerable in glioblastoma patients.

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