Developing a predictive model for metastatic potential in pancreatic neuroendocrine tumor.

CONTEXT: Pancreatic neuroendocrine tumors (PNETs) exhibit a wide range of behavior from localized disease to aggressive metastasis. A comprehensive transcriptomic profile capable of differentiating between these phenotypes remains elusive. OBJECTIVE: Use machine learning to develop predictive models of PNET metastatic potential dependent upon transcriptomic signature. METHODS: RNA-sequencing data were analyzed from 95 surgically-resected primary PNETs in an international cohort. Two cohorts were generated with equally balanced metastatic PNET composition. Machine learning was used to create predictive models distinguishing between localized and metastatic tumors. Models were validated on an independent cohort of 29 formalin-fixed, paraffin-embedded samples using NanoString nCounter®, a clinically-available mRNA quantification platform. RESULTS: Gene expression analysis identified concordant differentially expressed genes between the two cohorts. Gene set enrichment analysis identified additional genes that contributed to enriched biologic pathways in metastatic PNETs. Expression values for these genes were combined with an additional 7 genes known to contribute to PNET oncogenesis and prognosis, including ARX and PDX1. Eight specific genes (AURKA, CDCA8, CPB2, MYT1L, NDC80, PAPPA2, SFMBT1, ZPLD1) were identified as sufficient to classify the metastatic status with high sensitivity (87.5% - 93.8%) and specificity (78.1% - 96.9%). These models remained predictive of the metastatic phenotype using NanoString nCounter® on the independent validation cohort, achieving a median AUROC of 0.886. CONCLUSIONS: We identified and validated an eight-gene panel predictive of the metastatic phenotype in PNETs, which can be detected using the clinically-available NanoString nCounter® system. This panel should be studied prospectively to determine its utility in guiding operative versus non-operative management.

Benchmarking multi-ancestry prostate cancer polygenic risk scores in a real-world cohort.

Prostate cancer is a heritable disease with ancestry-biased incidence and mortality. Polygenic risk scores (PRSs) offer promising advancements in predicting disease risk, including prostate cancer. While their accuracy continues to improve, research aimed at enhancing their effectiveness within African and Asian populations remains key for equitable use. Recent algorithmic developments for PRS derivation have resulted in improved pan-ancestral risk prediction for several diseases. In this study, we benchmark the predictive power of six widely used PRS derivation algorithms, including four of which adjust for ancestry, against prostate cancer cases and controls from the UK Biobank and All of Us cohorts. We find modest improvement in discriminatory ability when compared with a simple method that prioritizes variants, clumping, and published polygenic risk scores. Our findings underscore the importance of improving upon risk prediction algorithms and the sampling of diverse cohorts.

Ancestry-specific polygenic risk scores are risk enhancers for clinical cardiovascular disease assessments.

Clinical implementation of new prediction models requires evaluation of their utility in a broad range of intended use populations. Here we develop and validate ancestry-specific Polygenic Risk Scores (PRSs) for Coronary Artery Disease (CAD) using 29,389 individuals from diverse cohorts and genetic ancestry groups. The CAD PRSs outperform published scores with an average Odds Ratio per Standard Deviation of 1.57 (SD = 0.14) and identify between 12% and 24% of individuals with high genetic risk. Using this risk factor to reclassify borderline or intermediate 10 year Atherosclerotic Cardiovascular Disease (ASCVD) risk improves assessments for both CAD (Net Reclassification Improvement (NRI) = 13.14% (95% CI 9.23-17.06%)) and ASCVD (NRI = 10.70 (95% CI 7.35-14.05)) in an independent cohort of 9,691 individuals. Our analyses demonstrate that using PRSs as Risk Enhancers improves ASCVD risk assessments outlining an approach for guiding ASCVD prevention with genetic information.

USING MACHINE LEARNING METHODS TO ASSESS THE RISK OF ALCOHOL MISUSE IN OLDER ADULTS.

The population of older adults, defined in this study as those 50 years of age or older, continues to increase every year. Substance misuse, particularly alcohol misuse, is often neglected in these individuals. To better identify older adults who might not be properly assessed for alcohol misuse, we have derived a risk assessment tool using patients from the United Kingdom Biobank (UKB), which was validated on patients in the Weill Cornell Medicine (WCM) electronic health record (EHR). The model and tooling created stratifies the risk of alcohol misuse in older adults using 10 features that are commonly found in most EHR systems. We found that the area under the receiver operating curve (AUROC) to correctly predict alcohol misuse in older adults for the UKB and WCM models were 0.84 and 0.78, respectively. We further show that of those who self-identified as having ongoing alcohol misuse in the UKB cohort, only 12.5% of these patients had any alcohol-related F.10 ICD-10 code. Extending this to the WCM cohort, we forecast that 7,838 out of 12,360 older adults with no F.10 ICD-10 code (63.4%) may be missed as having alcohol misuse in the EHR. Overall, this study importantly prioritizes the health of older adults by being able to predict alcohol misuse in an understudied population.

Genetic Risk Factors for End-Stage Hip Osteoarthritis Treated With Total Hip Arthroplasty: A Genome-wide Association Study.

BACKGROUND: Although a genetic component to hip osteoarthritis (OA) has been described, focused evaluation of the genetic components of end-stage disease is limited. We present a genomewide association study for patients undergoing total hip arthroplasty (THA) to characterize the genetic risk factors associated with end-stage hip osteoarthritis (ESHO), defined as utilization of the procedure. METHODS: Patients who underwent primary THA for hip OA were identified in a national patient data repository using administrative codes. Fifteen thousand three hundred and fifty-five patients with ESHO and 374,193 control patients were identified. Whole genome regression of genotypic data for patients who underwent primary THA for hip OA corrected for age, sex, and body mass index (BMI) was performed. Multivariate logistic regression models were used to evaluate the composite genetic risk from the identified genetic variants. RESULTS: There were 13 significant genes identified. Composite genetic factors resulted in an odds ratio 1.04 for ESHO (P < .001). The effect of genetics was lower than that of age (Odds Ratio (OR): 2.38; P < .001) and BMI (1.81; P < .001). CONCLUSION: Multiple genetic variants, including 5 novel loci, were associated with end-stage hip OA treated with primary THA. Age and BMI were associated with greater odds of developing end-stage disease when compared to genetic factors.

Identification of Novel Genetic Markers for the Risk of Spinal Pathologies: A Genome-Wide Association Study of 2 Biobanks.

BACKGROUND: Identifying genetic risk factors for spinal disorders may lead to knowledge regarding underlying molecular mechanisms and the development of new treatments. METHODS: Cases of lumbar spondylolisthesis, spinal stenosis, degenerative disc disease, and pseudarthrosis after spinal fusion were identified from the UK Biobank. Controls were patients without the diagnosis. Whole-genome regressions were used to test for genetic variants potentially implicated in the occurrence of each phenotype. External validation was performed in FinnGen. RESULTS: A total of 389,413 participants were identified from the UK Biobank. A locus on chromosome 2 spanning GFPT1, NFU1, AAK1, and LOC124906020 was implicated in lumbar spondylolisthesis. Two loci on chromosomes 2 and 12 spanning genes GFPT1, NFU1, and PDE3A were implicated in spinal stenosis. Three loci on chromosomes 6, 10, and 15 spanning genes CHST3, LOC102723493, and SMAD3 were implicated in degenerative disc disease. Finally, 2 novel loci on chromosomes 5 and 9, with the latter corresponding to the LOC105376270 gene, were implicated in pseudarthrosis. Some of these variants associated with spinal stenosis and degenerative disc disease were also replicated in FinnGen. CONCLUSIONS: This study revealed nucleotide variations in select genetic loci that were potentially implicated in 4 different spinal pathologies, providing potential insights into the pathological mechanisms. LEVEL OF EVIDENCE: Prognostic Level III. See Instructions for Authors for a complete description of levels of evidence.

Population-Level Prevalence of Rare Variants Associated With Atrial Fibrillation and its Impact on Patient Outcomes.

BACKGROUND: Whole exome sequencing may identify rare pathogenic/likely pathogenic variants (LPVs) that are linked to atrial fibrillation (AF). The impact of LPVs associated with AF on a population level on outcomes is unclear. OBJECTIVES: This study sought to examine the association of LPVs with AF and their impact on clinical outcomes using the UK Biobank, a national repository of participants with available whole exome sequencing data. METHODS: A total of 200,631 individuals in the UK Biobank were studied. Incident and prevalent AF, comorbidities, and outcomes were identified using self-reported assessments and hospital stay operative, and death registry records. LPVs were determined using arrhythmia and cardiomyopathy gene panels with LOFTEE and ClinVar to predict variants of functional significance. RESULTS: Compared with control subjects, there was a modestly increased prevalence of LPVs among 9,585 patients with AF (2.0% vs 1.7%, respectively; P = 0.01). Among those with prevalent AF at <45 years of age, 4.2% were LPV carriers. LPVs in TTN and PKP2 were associated with AF with adjusted odds ratios of 2.69 (95% CI: 1.57-4.61) and 2.69 (95% CI: 1.54-4.68), respectively. There was no significant difference in combined ischemic stroke, heart failure hospitalization, and mortality among patients who have AF with and without LPVs (25.1% vs 23.8%; P = 0.49). Among participants with AF and available cardiac magnetic resonance imaging data, LPV carriers had lower left ventricular ejection fractions than non-LPV carriers (42% vs 52%; P = 0.027). CONCLUSIONS: Patients with AF had a modestly increased prevalence of LPVs. Among reference arrhythmia and cardiomyopathy genes, the contribution of rare variants to AF risk at a population level is modest and its impact on outcomes appears to be limited, despite an association of LPVs with reduced left ventricular ejection fraction among patients with AF.

Genome-Wide Association Study of Adhesive Capsulitis Suggests Significant Genetic Risk Factors.

BACKGROUND: Adhesive capsulitis of the shoulder involves loss of passive range of motion with associated pain and can develop spontaneously, with no obvious injury or inciting event. The pathomechanism of this disorder remains to be elucidated, but known risk factors for adhesive capsulitis include diabetes, female sex, and thyroid dysfunction. Additionally, transcriptional profiling and pedigree analyses have suggested a role for genetics. Identification of elements of genetic risk for adhesive capsulitis using population-based techniques can provide the basis for guiding both the personalized treatment of patients based on their genetic profiles and the development of new treatments by identification of the pathomechanism. METHODS: A genome-wide association study (GWAS) was conducted using the U.K. Biobank (a collection of approximately 500,000 patients with genetic data and associated ICD-10 [International Classification of Diseases, 10th Revision] codes), comparing 2,142 patients with the ICD-10 code for adhesive capsulitis (M750) to those without. Separate GWASs were conducted controlling for 2 of the known risk factors of adhesive capsulitis-hypothyroidism and diabetes. Logistic regression analysis was conducted controlling for factors including sex, thyroid dysfunction, diabetes, shoulder dislocation, smoking, and genetics. RESULTS: Three loci of significance were identified: rs34315830 (in WNT7B; odds ratio [OR] = 1.28; 95% confidence interval [CI], 1.22 to 1.39), rs2965196 (in MAU2; OR = 1.67; 95% CI, 1.39 to 2.00), and rs1912256 (in POU1F1; OR = 1.22; 95% CI, 1.14 to 1.31). These loci retained significance when controlling for thyroid dysfunction and diabetes. The OR for total genetic risk was 5.81 (95% CI, 4.08 to 8.31), compared with 1.70 (95% CI, 1.18 to 2.36) for hypothyroidism and 4.23 (95% CI, 2.32 to 7.05) for diabetes. CONCLUSIONS: The total genetic risk associated with adhesive capsulitis was significant and similar to the risks associated with hypothyroidism and diabetes. Identification of WNT7B, POU1F1, and MAU2 implicates the Wnt pathway and cell proliferation response in the pathomechanism of adhesive capsulitis. LEVEL OF EVIDENCE: Prognostic Level III . See Instructions for Authors for a complete description of levels of evidence.

Characterization of Genetic Risk of End-Stage Knee Osteoarthritis Treated with Total Knee Arthroplasty: A Genome-Wide Association Study.

BACKGROUND: End-stage knee osteoarthritis (OA) is a highly debilitating disease for which total knee arthroplasty (TKA) serves as an effective treatment option. Although a genetic component to OA in general has been described, evaluation of the genetic contribution to end-stage OA of the knee is limited. To this end, we present a genome-wide association study involving patients undergoing TKA for primary knee OA to characterize the genetic features of severe disease on a population level. METHODS: Individuals with the diagnosis of knee OA who underwent primary TKA were identified in the U.K. Biobank using administrative codes. The U.K. Biobank is a data repository containing prospectively collected clinical and genomic data for >500,000 patients. A genome-wide association analysis was performed using the REGENIE software package. Logistic regression was also used to compare the total genetic risk between subgroups stratified by age and body mass index (BMI). RESULTS: A total of 16,032 patients with end-stage knee OA who underwent primary TKA were identified. Seven genetic loci were found to be significantly associated with end-stage knee OA. The odds ratio (OR) for developing end-stage knee OA attributable to genetics was 1.12 (95% confidence interval [CI], 1.10 to 1.14), which was lower than the OR associated with BMI (OR = 1.81; 95% CI, 1.78 to 1.83) and age (OR = 2.38; 95% CI, 2.32 to 2.45). The magnitude of the OR for developing end-stage knee OA attributable to genetics was greater in patients <60 years old than in patients ≥60 years old (p = 0.002). CONCLUSIONS: This population-level genome-wide association study of end-stage knee OA treated with primary TKA was notable for identifying multiple significant genetic variants. These loci involve genes responsible for cartilage development, cartilage homeostasis, cell signaling, and metabolism. Age and BMI appear to have a greater impact on the risk of developing end-stage disease compared with genetic factors. The genetic contribution to the development of severe disease is greater in younger patients. LEVEL OF EVIDENCE: Prognostic Level III . See Instructions for Authors for a complete description of levels of evidence.

Simple Linear Cancer Risk Prediction Models With Novel Features Outperform Complex Approaches.

PURPOSE: The ability to accurately predict an individual's risk for cancer is critical to the implementation of precision prevention measures. Current cancer risk predictions are frequently made with simple models that use a few proven risk factors, such as the Gail model for breast cancer, which are easy to interpret, but may theoretically be less accurate than advanced machine learning (ML) models. METHODS: With the UK Biobank, a large prospective study, we developed models that predicted 13 cancer diagnoses within a 10-year time span. ML and linear models fit with all features, linear models fit with 10 features, and externally developed QCancer models, which are available to more than 4,000 general practices, were assessed. RESULTS: The average area under the receiver operator curve (AUC) of the linear models (0.722, SE = 0.015) was greater than the average AUC of the ML models (0.720, SE = 0.016) when all 931 features were used. Linear models with only 10 features generated an average AUC of 0.706 (SE 0.015), which was comparable to the complex models using all features and greater than the average AUC of the QCancer models (0.684, SE 0.021). The high performance of the 10-feature linear model may be caused by the consideration of often omitted feature types, including census records and genetic information. CONCLUSION: The high performance of the 10-feature linear models indicate that unbiased selection of diverse features, not ML models, may lead to impressively accurate predictions, possibly enabling personalized screening schedules that increase cancer survival.

Prediction of primary venous thromboembolism based on clinical and genetic factors within the U.K. Biobank.

Both clinical and genetic factors drive the risk of venous thromboembolism. However, whether clinically recorded risk factors and genetic variants can be combined into a clinically applicable predictive score remains unknown. Using Cox proportional-hazard models, we analyzed the association of risk factors with the likelihood of venous thromboembolism in U.K. Biobank, a large prospective cohort. We then created a polygenic risk score of 36 single nucleotide polymorphisms and a clinical score determined by age, sex, body mass index, previous cancer diagnosis, smoking status, and fracture in the last 5 years. Participants were at significantly increased risk of venous thromboembolism if they were at high clinical risk (subhazard ratio, 4.37 [95% CI, 3.85-4.97]) or high genetic risk (subhazard ratio, 3.02 [95% CI, 2.63-3.47]) relative to participants at low clinical or genetic risk, respectively. The combined model, consisting of clinical and genetic components, was significantly better than either the clinical or the genetic model alone (P < 0.001). Participants at high risk in the combined score had nearly an eightfold increased risk of venous thromboembolism relative to participants at low risk (subhazard ratio, 7.51 [95% CI, 6.28-8.98]). This risk score can be used to guide decisions regarding venous thromboembolism prophylaxis, although external validation is needed.

A polygenic-score-based approach for identification of gene-drug interactions stratifying breast cancer risk.

An individual's genetics can dramatically influence breast cancer (BC) risk. Although clinical measures for prevention do exist, non-invasive personalized measures for reducing BC risk are limited. Commonly used medications are a promising set of modifiable factors, but no previous study has explored whether a range of widely taken approved drugs modulate BC genetics. In this study, we describe a quantitative framework for exploring the interaction between the genetic susceptibility of BC and medication usage among UK Biobank women. We computed BC polygenic scores (PGSs) that summarize BC genetic risk and find that the PGS explains nearly three-times greater variation in disease risk within corticosteroid users compared to non-users. We map 35 genes significantly interacting with corticosteroid use (FDR < 0.1), highlighting the transcription factor NRF2 as a common regulator of gene-corticosteroid interactions in BC. Finally, we discover a regulatory variant strongly stratifying BC risk according to corticosteroid use. Within risk allele carriers, 18.2% of women taking corticosteroids developed BC, compared to 5.1% of the non-users (with an HR = 3.41 per-allele within corticosteroid users). In comparison, there are no differences in BC risk within the reference allele homozygotes. Overall, this work highlights the clinical relevance of gene-drug interactions in disease risk and provides a roadmap for repurposing biobanks in drug repositioning and precision medicine.

Social and Clinical Determinants of COVID-19 Outcomes: Modeling Real-World Data from a Pandemic Epicenter.

IMPORTANCE: As the United States continues to accumulate COVID-19 cases and deaths, and disparities persist, defining the impact of risk factors for poor outcomes across patient groups is imperative. OBJECTIVE: Our objective is to use real-world healthcare data to quantify the impact of demographic, clinical, and social determinants associated with adverse COVID-19 outcomes, to identify high-risk scenarios and dynamics of risk among racial and ethnic groups. DESIGN: A retrospective cohort of COVID-19 patients diagnosed between March 1 and August 20, 2020. Fully adjusted logistical regression models for hospitalization, severe disease and mortality outcomes across 1-the entire cohort and 2- within self-reported race/ethnicity groups. SETTING: Three sites of the NewYork-Presbyterian health care system serving all boroughs of New York City. Data was obtained through automated data abstraction from electronic medical records. PARTICIPANTS: During the study timeframe, 110,498 individuals were tested for SARS-CoV-2 in the NewYork-Presbyterian health care system; 11,930 patients were confirmed for COVID-19 by RT-PCR or covid-19 clinical diagnosis. MAIN OUTCOMES AND MEASURES: The predictors of interest were patient race/ethnicity, and covariates included demographics, comorbidities, and census tract neighborhood socio-economic status. The outcomes of interest were COVID-19 hospitalization, severe disease, and death. RESULTS: Of confirmed COVID-19 patients, 4,895 were hospitalized, 1,070 developed severe disease and 1,654 suffered COVID-19 related death. Clinical factors had stronger impacts than social determinants and several showed race-group specificities, which varied among outcomes. The most significant factors in our all-patients models included: age over 80 (OR=5.78, p= 2.29x10-24) and hypertension (OR=1.89, p=1.26x10-10) having the highest impact on hospitalization, while Type 2 Diabetes was associated with all three outcomes (hospitalization: OR=1.48, p=1.39x10-04; severe disease: OR=1.46, p=4.47x10-09; mortality: OR=1.27, p=0.001). In race-specific models, COPD increased risk of hospitalization only in Non-Hispanics (NH)-Whites (OR=2.70, p=0.009). Obesity (BMI 30+) showed race-specific risk with severe disease NH-Whites (OR=1.48, p=0.038) and NH-Blacks (OR=1.77, p=0.025). For mortality, Cancer was the only risk factor in Hispanics (OR=1.97, p=0.043), and heart failure was only a risk in NH-Asians (OR=2.62, p=0.001). CONCLUSIONS AND RELEVANCE: Comorbidities were more influential on COVID-19 outcomes than social determinants, suggesting clinical factors are more predictive of adverse trajectory than social factors.

Leveraging phenotypic variability to identify genetic interactions in human phenotypes.

Although thousands of loci have been associated with human phenotypes, the role of gene-environment (GxE) interactions in determining individual risk of human diseases remains unclear. This is partly because of the severe erosion of statistical power resulting from the massive number of statistical tests required to detect such interactions. Here, we focus on improving the power of GxE tests by developing a statistical framework for assessing quantitative trait loci (QTLs) associated with the trait means and/or trait variances. When applying this framework to body mass index (BMI), we find that GxE discovery and replication rates are significantly higher when prioritizing genetic variants associated with the variance of the phenotype (vQTLs) compared to when assessing all genetic variants. Moreover, we find that vQTLs are enriched for associations with other non-BMI phenotypes having strong environmental influences, such as diabetes or ulcerative colitis. We show that GxE effects first identified in quantitative traits such as BMI can be used for GxE discovery in disease phenotypes such as diabetes. A clear conclusion is that strong GxE interactions mediate the genetic contribution to body weight and diabetes risk.

Clinical, regional, and genetic characteristics of Covid-19 patients from UK Biobank.

BACKGROUND: Coronavirus disease 2019 (Covid-19) has rapidly infected millions of people worldwide. Recent studies suggest that racial minorities and patients with comorbidities are at higher risk of Covid-19. In this study, we analyzed the effects of clinical, regional, and genetic factors on Covid-19 positive status. METHODS: The UK Biobank is a longitudinal cohort study that recruited participants from 2006 to 2010 from throughout the United Kingdom. Covid-19 test results were provided to UK Biobank starting on March 16, 2020. The main outcome measure in this study was Covid-19 positive status, determined by the presence of any positive test for a single individual. Clinical risk factors were derived from UK Biobank at baseline, and regional risk factors were imputed using census features local to each participant's home zone. We used robust adjusted Poisson regression with clustering by testing laboratory to estimate relative risk. Blood types were derived using genetic variants rs8176719 and rs8176746, and genomewide tests of association were conducted using logistic-Firth hybrid regression. RESULTS: This prospective cohort study included 397,064 UK Biobank participants, of whom 968 tested positive for Covid-19. The unadjusted relative risk of Covid-19 for Black participants was 3.66 (95% CI 2.83-4.74), compared to White participants. Adjusting for Townsend deprivation index alone reduced the relative risk to 2.44 (95% CI 1.86-3.20). Comorbidities that significantly increased Covid-19 risk included chronic obstructive pulmonary disease (adjusted relative risk [ARR] 1.64, 95% CI 1.18-2.27), ischemic heart disease (ARR 1.48, 95% CI 1.16-1.89), and depression (ARR 1.32, 95% CI 1.03-1.70). There was some evidence that angiotensin converting enzyme inhibitors (ARR 1.48, 95% CI 1.13-1.93) were associated with increased risk of Covid-19. Each standard deviation increase in the number of total individuals living in a participant's locality was associated with increased risk of Covid-19 (ARR 1.14, 95% CI 1.08-1.20). Analyses of genetically inferred blood types confirmed that participants with type A blood had increased odds of Covid-19 compared to participants with type O blood (odds ratio [OR] 1.16, 95% CI 1.01-1.33). A meta-analysis of genomewide association studies across ancestry groups did not reveal any significant loci. Study limitations include confounding by indication, bias due to limited information on early Covid-19 test results, and inability to accurately gauge disease severity. CONCLUSIONS: When assessing the association of Black race with Covid-19, adjusting for deprivation reduced the relative risk of Covid-19 by 33%. In the context of sociological research, these findings suggest that discrimination in the labor market may play a role in the high relative risk of Covid-19 for Black individuals. In this study, we also confirmed the association of blood type A with Covid-19, among other clinical and regional factors.

Clinical and Genetic Characteristics of Covid-19 Patients from UK Biobank.

We conducted an analysis of 669 Covid-19 positive patients within the UK Biobank cohort, a prospective cohort including over 500,000 participants. Our analyses led to several findings. We found that black participants in the cohort were over four times more likely to be diagnosed with Covid-19 than white participants. In order to assess for confounding, we produced - to our knowledge - the first multivariable adjusted estimate of the association of racial characteristics with Covid-19. Our adjusted estimates indicated that black participants remained at over threefold increased risk of Covid-19 relative to white participants. Exploratory analyses identified that 22.9% of Covid-19 positive black patients were using either angiotensin converting enzyme inhibitors or angiotensin II receptor blockers, relative to just 6.7% of all black participants. Our genetic analyses confirmed the finding of a previous report noting an association of blood type A with Covid-19, and we discovered a novel genetic association with HLA DQA1_509 that remained significant even after Bonferroni correction.

Corrupted coordination of epigenetic modifications leads to diverging chromatin states and transcriptional heterogeneity in CLL.

Cancer evolution is fueled by epigenetic as well as genetic diversity. In chronic lymphocytic leukemia (CLL), intra-tumoral DNA methylation (DNAme) heterogeneity empowers evolution. Here, to comprehensively study the epigenetic dimension of cancer evolution, we integrate DNAme analysis with histone modification mapping and single cell analyses of RNA expression and DNAme in 22 primary CLL and 13 healthy donor B lymphocyte samples. Our data reveal corrupted coherence across different layers of the CLL epigenome. This manifests in decreased mutual information across epigenetic modifications and gene expression attributed to cell-to-cell heterogeneity. Disrupted epigenetic-transcriptional coordination in CLL is also reflected in the dysregulation of the transcriptional output as a function of the combinatorial chromatin states, including incomplete Polycomb-mediated gene silencing. Notably, we observe unexpected co-mapping of typically mutually exclusive activating and repressing histone modifications, suggestive of intra-tumoral epigenetic diversity. Thus, CLL epigenetic diversification leads to decreased coordination across layers of epigenetic information, likely reflecting an admixture of cells with diverging cellular identities.