An Epigenomic fingerprint of human cancers by landscape interrogation of super enhancers at the constituent level.

Super enhancers (SE), large genomic elements that activate transcription and drive cell identity, have been found with cancer-specific gene regulation in human cancers. Recent studies reported the importance of understanding the cooperation and function of SE internal components, i.e., the constituent enhancers (CE). However, there are no pan-cancer studies to identify cancer-specific SE signatures at the constituent level. Here, by revisiting pan-cancer SE activities with H3K27Ac ChIP-seq datasets, we report fingerprint SE signatures for 28 cancer types in the NCI-60 cell panel. We implement a mixture model to discriminate active CEs from inactive CEs by taking into consideration ChIP-seq variabilities between cancer samples and across CEs. We demonstrate that the model-based estimation of CE states provides improved functional interpretation of SE-associated regulation. We identify cancer-specific CEs by balancing their active prevalence with their capability of encoding cancer type identities. We further demonstrate that cancer-specific CEs have the strongest per-base enhancer activities in independent enhancer sequencing assays, suggesting their importance in understanding critical SE signatures. We summarize fingerprint SEs based on the cancer-specific statuses of their component CEs and build an easy-to-use R package to facilitate the query, exploration, and visualization of fingerprint SEs across cancers.

Clonal Hematopoiesis as a Molecular Risk Factor for Doxorubicin-Induced Cardiotoxicity: A Proof-of-Concept Study.

PURPOSE: The main dose-limiting toxicity of anthracyclines is cardiotoxicity. Clonal hematopoiesis (CH), somatic mutations in hematopoietic stem or progenitor cells in patients without hematologic malignancy, is also associated with risk for adverse cardiovascular events and worse outcomes overall. We hypothesize that CH increases risk for doxorubicin-induced cardiotoxicity (DIC). METHODS: We conducted a retrospective cohort study in patients treated with doxorubicin for cancer (N = 100). Patients (n = 25) had incident symptomatic heart failure, decline in left ventricular ejection fraction, or arrhythmia. CH was identified using paired peripheral blood and tumor DNA. RESULTS: After adjusting for age at doxorubicin initiation, diabetes, dyslipidemia, and chest radiation, high cumulative dose of doxorubicin (>240 mg/m2; odds ratio [OR], 7.00; 95% CI, 1.77 to 27.74; P = .0056), CH (OR, 8.58; 95% CI, 2.05 to 35.99; P = .0033), and history of smoking (OR, 3.15; 95% CI, 1.00 to 9.93; P = .0495) were associated with DIC. CONCLUSION: This study provides preliminary evidence for CH as a predictive risk factor for DIC, which, with further investigation, could serve as an important precision medicine biomarker for the large number of patients with cancer who have CH.

Pilot Study of Donor-Engrafted Clonal Hematopoiesis Evolution and Clinical Outcomes in Allogeneic Hematopoietic Cell Transplantation Recipients Using a National Registry.

Improved treatment options, such as reduced-intensity conditioning (RIC), enable older patients to receive potentially curative allogeneic hematopoietic cell transplantation (HCT). This progress has led to increased use of older HLA-matched sibling donors. An unintended potential risk associated with older donors is transplantation of donor cells with clonal hematopoiesis (CH) into patients. We aimed to determine the prevalence of CH in older HLA-matched sibling donors pretransplantation and to assess the clinical impact of donor-engrafted CH on HCT outcomes. This was an observational study using donor peripheral blood samples from the Center for International Blood and Marrow Transplant Research repository, linked with corresponding recipient outcomes. To explore engraftment efficiency and evolution of CH mutations following HCT, recipient follow-up samples available through the Bone Marrow Transplant Clinical Trials Network (Protocol 1202) were included. Older donors and patients (both ≥55 years) receiving first RIC HCT for myeloid malignancies were eligible. DNA from archived donor blood samples was used for targeted deep sequencing to identify CH. The associations between donor CH status and recipient outcomes, including acute graft-versus-host disease (aGVHD), chronic GVHD (cGVHD), overall survival, relapse, nonrelapse mortality, disease-free survival, composite GVHD-free and relapse-free survival, and cGVHD-free and relapse-free survival, were analyzed. A total of 299 donors were successfully sequenced to detect CH. At a variant allele frequency (VAF) ≥2%, there were 44 CH mutations in 13.7% (41 of 299) of HLA-matched sibling donors. CH mostly involved DNMT3A (n = 27; 61.4%) and TET2 (n= 9; 20.5%). Post-HCT samples from 13 recipients were also sequenced, of whom 7 had CH+ donors. All of the donor CH mutations (n = 7/7; 100%) were detected in recipients at day 56 or day 90 post-HCT. Overall, mutation VAFs remained relatively constant up to day 90 post-HCT (median change, .005; range, -.008 to .024). Doubling time analysis of recipient day 56 and day 90 data showed that donor-engrafted CH mutations initially expand then decrease to a stable VAF; germline mutations had longer doubling times than CH mutations. The cumulative incidence of grade II-IV aGVHD at day 100 was higher in HCT recipients with CH+ donors (37.5% versus 25.1%); however, the risk for aGVHD by donor CH status did not reach statistical significance (hazard ratio, 1.35; 95% confidence interval, .61 to 3.01; P = .47). There were no statistically significant differences in the cumulative incidence of cGVHD or any secondary outcomes by donor CH status. In subset analysis, the incidence of cGVHD was lower in recipients of grafts from DNMT3A CH+ donors versus donors without DNMT3A CH (34.4% versus 57%; P = .035). Donor cell leukemia was not reported in any donor-recipient pairs. CH in older HLA-matched sibling donors is relatively common and successfully engrafts and persists in recipients. In a homogenous population (myeloid malignancies, older donors and recipients, RICr, non-cyclophosphamide-containing GVHD prophylaxis), we did not detect a difference in cGVHD risk or other secondary outcomes by donor CH status. Subgroup analyses suggest potential differential effects by clinical characteristics and CH mutations. Larger prospective studies are needed to robustly determine which subsets of patients and CH mutations elicit meaningful impacts on clinical outcomes.

Differences in epigenetic age by HIV status among patients with a non-AIDS defining cancer.

OBJECTIVE: People with HIV (PWH) are living longer and experiencing higher numbers of non-AIDS-defining cancers (NADC). Epigenetic aging biomarkers have been linked to cancer risk, and cancer is now a leading cause of death in PWH, but these biomarkers have not been investigated in PWH and cancer. DESIGN: In order to compare epigenetic age by HIV status, HIV-uninfected participants were matched to PWH by reported age, tumor site, tumor sequence number, and cancer treatment status. METHODS: DNA from blood was assayed using Illumina MethylationEPIC BeadChip, and we estimated immune cell composition and aging from three epigenetic clocks: Horvath, GrimAge, and epiTOC2. Age acceleration by clock was computed as the residual from the expected value, calculated using linear regression, for each study participant. Comparisons across HIV status used the Wilcoxon rank sum test. Hazard ratios and 95% confidence intervals for the association between age acceleration and survival in PWH were estimated with Cox regression. RESULTS: Among 65 NADC participants with HIV and 64 without, biological age from epiTOC2 ( P  < 0.0001) and GrimAge ( P  = 0.017) was significantly higher in PWH. Biological age acceleration was significantly higher in PWH using epiTOC2 ( P  < 0.01) and GrimAge ( P  < 0.0001), with the difference in GrimAge remaining statistically significant after adjustment for immune cell composition. Among PWH, GrimAge acceleration was significantly associated with increased risk of death (hazard ratio 1.11; 95% confidence interval (CI) 1.04-1.18). CONCLUSION: We observed a higher epigenetic age in PWH with a NADC diagnosis compared with their HIV-uninfected counterparts, as well as a significant association between this accelerated biological aging and survival for patients diagnosed with a NADC.

PATH-SURVEYOR: pathway level survival enquiry for immuno-oncology and drug repurposing.

Pathway-level survival analysis offers the opportunity to examine molecular pathways and immune signatures that influence patient outcomes. However, available survival analysis algorithms are limited in pathway-level function and lack a streamlined analytical process. Here we present a comprehensive pathway-level survival analysis suite, PATH-SURVEYOR, which includes a Shiny user interface with extensive features for systematic exploration of pathways and covariates in a Cox proportional-hazard model. Moreover, our framework offers an integrative strategy for performing Hazard Ratio ranked Gene Set Enrichment Analysis and pathway clustering. As an example, we applied our tool in a combined cohort of melanoma patients treated with checkpoint inhibition (ICI) and identified several immune populations and biomarkers predictive of ICI efficacy. We also analyzed gene expression data of pediatric acute myeloid leukemia (AML) and performed an inverse association of drug targets with the patient's clinical endpoint. Our analysis derived several drug targets in high-risk KMT2A-fusion-positive patients, which were then validated in AML cell lines in the Genomics of Drug Sensitivity database. Altogether, the tool offers a comprehensive suite for pathway-level survival analysis and a user interface for exploring drug targets, molecular features, and immune populations at different resolutions.

A DNA tumor virus globally reprograms host 3D genome architecture to achieve immortal growth.

Epstein-Barr virus (EBV) immortalization of resting B lymphocytes (RBLs) to lymphoblastoid cell lines (LCLs) models human DNA tumor virus oncogenesis. RBL and LCL chromatin interaction maps are compared to identify the spatial and temporal genome architectural changes during EBV B cell transformation. EBV induces global genome reorganization where contact domains frequently merge or subdivide during transformation. Repressed B compartments in RBLs frequently switch to active A compartments in LCLs. LCLs gain 40% new contact domain boundaries. Newly gained LCL boundaries have strong CTCF binding at their borders while in RBLs, the same sites have much less CTCF binding. Some LCL CTCF sites also have EBV nuclear antigen (EBNA) leader protein EBNALP binding. LCLs have more local interactions than RBLs at LCL dependency factors and super-enhancer targets. RNA Pol II HiChIP and FISH of RBL and LCL further validate the Hi-C results. EBNA3A inactivation globally alters LCL genome interactions. EBNA3A inactivation reduces CTCF and RAD21 DNA binding. EBNA3C inactivation rewires the looping at the CDKN2A/B and AICDA loci. Disruption of a CTCF site at AICDA locus increases AICDA expression. These data suggest that EBV controls lymphocyte growth by globally reorganizing host genome architecture to facilitate the expression of key oncogenes.

Statistical Analysis in ChIP-seq-Related Applications.

Chromatin immunoprecipitation sequencing (ChIP-seq) has been widely performed to identify protein binding information along the genome. The sequencing protocol is quite flexible and mature to measure different types of protein binding as long as sequencing parameters are properly tailored to accommodate protein features. Two distinct types of protein binding are point-source-like binding by transcription factors and diffused-distribution binding by histone modifications. Consequently, statistical approaches have been proposed to address ChIP-seq-related questions according to different protein features. In this chapter, we briefly summarize statistical principles, approaches, and tools that are widely implemented in modeling ChIP-seq data, from raw data quality control to final result reporting. We discuss the key solutions in addressing eight routine questions in ChIP-seq applications. We also include discussion on approaches fitting unique data features in different ChIP-seq types. We hope this chapter will serve as a brief guide, especially for ChIP-seq beginners, to provide them with a high-level overview to understand and design processing plans for their ChIP-seq experiments.

DRPPM-PATH-SURVEIOR: Plug-and-Play Survival Analysis of Pathway-level Signatures and Immune Components.

Pathway-level survival analysis offers the opportunity to examine molecular pathways and immune signatures that influence patient outcomes. However, available survival analysis algorithms are limited in pathway-level function and lack a streamlined analytical process. Here we present a comprehensive pathway-level survival analysis suite, DRPPM-PATH-SURVEIOR, which includes a Shiny user interface with extensive features for systematic exploration of pathways and covariates in a Cox proportional-hazard model. Moreover, our framework offers an integrative strategy for performing Hazard Ratio ranked Gene Set Enrichment Analysis (GSEA) and pathway clustering. As an example, we applied our tool in a combined cohort of melanoma patients treated with checkpoint inhibition (ICI) and identified several immune populations and biomarkers predictive of ICI efficacy. We also analyzed gene expression data of pediatric acute myeloid leukemia (AML) and performed an inverse association of drug targets with the patient's clinical endpoint. Our analysis derived several drug targets in high-risk KMT2A-fusion-positive patients, which were then validated in AML cell lines in the Genomics of Drug Sensitivity database. Altogether, the tool offers a comprehensive suite for pathway-level survival analysis and a user interface for exploring drug targets, molecular features, and immune populations at different resolutions.

A CRISPR screen identifies redox vulnerabilities for KEAP1/NRF2 mutant non-small cell lung cancer.

The redox regulator NRF2 is hyperactivated in a large percentage of non-small cell lung cancer (NSCLC) cases, which is associated with chemotherapy and radiation resistance. To identify redox vulnerabilities for KEAP1/NRF2 mutant NSCLC, we conducted a CRISPR-Cas9-based negative selection screen for antioxidant enzyme genes whose loss sensitized cells to sub-lethal concentrations of the superoxide (O2•-) -generating drug ß-Lapachone. While our screen identified expected hits in the pentose phosphate pathway, the thioredoxin-dependent antioxidant system, and glutathione reductase, we also identified the mitochondrial superoxide dismutase 2 (SOD2) as one of the top hits. Surprisingly, ß-Lapachone did not generate mitochondrial O2•- but rather SOD2 loss enhanced the efficacy of ß-Lapachone due to loss of iron-sulfur protein function, loss of mitochondrial ATP maintenance and deficient NADPH production. Importantly, inhibition of mitochondrial electron transport activity sensitized cells to ß-Lapachone, demonstrating that these effects may be translated to increase ROS sensitivity therapeutically.

Racial and ethnic differences in clonal hematopoiesis, tumor markers, and outcomes of patients with multiple myeloma.

Multiple myeloma (MM) incidence, mortality, and survival vary by race and ethnicity, but the causes of differences remain unclear. We investigated demographic, clinical, and molecular features of diverse MM patients to elucidate mechanisms driving clinical disparities. This study included 495 MM patients (self-reported Hispanic, n = 45; non-Hispanic Black, n = 52; non-Hispanic White, n = 398). Hispanic and non-Hispanic Black individuals had an earlier age of onset than non-Hispanic White individuals (53 and 57 vs 63 years, respectively, P < .001). There were no differences in treatment by race and ethnicity groups, but non-Hispanic Black patients had a longer time to hematopoietic cell transplant than non-Hispanic White patients (376 days vs 248 days; P = .01). Overall survival (OS) was improved for non-Hispanic Black compared with non-Hispanic White patients (HR, 0.50; 95% CI, 0.31-0.81; P = .005), although this association was attenuated after adjusting for clinical features (HR, 0.62; 95% CI, 0.37-1.03; P = .06). Tumor mutations in IRF4 were most common in Hispanic patients, and mutations in SP140, AUTS2, and SETD2 were most common in non-Hispanic Black patients. Differences in tumor expression of BCL7A, SPEF2, and ANKRD26 by race and ethnicity were observed. Clonal hematopoiesis was detected in 12% of patients and associated with inferior OS in non-Hispanic Black patients compared with patients without clonal hematopoiesis (HR, 4.36; 95% CI, 1.36-14.00). This study provides insight into differences in molecular features that may drive clinical disparities in MM patients receiving comparable treatment, with the novel inclusion of Hispanic individuals.

Summarizing internal dynamics boosts differential analysis and functional interpretation of super enhancers.

Super enhancers (SEs) are broad enhancer domains usually containing multiple constituent enhancers that hold elevated activities in gene regulation. Disruption in one or more constituent enhancers causes aberrant SE activities that lead to gene dysregulation in diseases. To quantify SE aberrations, differential analysis is performed to compare SE activities between cell conditions. The state-of-art strategy in estimating differential SEs relies on overall activities and neglect the changes in length and structure of SEs. Here, we propose a novel computational method to identify differential SEs by weighting the combinatorial effects of constituent-enhancer activities and locations (i.e. internal dynamics). In addition to overall activity changes, our method identified four novel classes of differential SEs with distinct enhancer structural alterations. We demonstrate that these structure alterations hold distinct regulatory impact, such as regulating different number of genes and modulating gene expression with different strengths, highlighting the differentiated regulatory roles of these unexplored SE features. When compared to the existing method, our method showed improved identification of differential SEs that were linked to better discernment of cell-type-specific SE activity and functional interpretation.

Unique ER PR expression pattern in breast cancers with CHEK2 mutation: a hormone receptor and HER2 analysis based on germline cancer predisposition genes.

PURPOSE: Estrogen-receptor (ER) and progesterone-receptor (PR) expression levels in breast cancer, which have been principally compared via binomial descriptors, can vary widely across tumors. We sought to characterize ER and PR expression levels using semi-quantitative analyses of receptor staining in germline pathogenic variant (PV) carriers of cancer predisposition genes. METHODS: We conducted a retrospective chart review of patients who underwent germline genetic testing for cancer predisposition genes at a tertiary cancer center genetics clinic. We performed comparisons of semi-quantitative ER and PR percentage staining levels across carriers and non-carriers of cancer predisposition genes. RESULTS: Breast cancers from BRCA1 PV carriers expressed significantly lower ER (15.2% vs 78.2%, p < 0.001) and lower PR (6.8% vs 41.1%, p < 0.001) staining compared to non-PV carriers. Similarly, breast cancers of BRCA2 (66.7% vs 78.2%, p = 0.005) and TP53 (50.6% vs 78.2%, p = 0.015) PV tumors also displayed moderate decreases in ER staining. Conversely, CHEK2 tumors displayed higher ER (93.1% vs 78.2%, p = 0.005) and PR (72% vs 48.8%, p = 0.001) staining when compared to non-PV carriers. We observed a wide range of dispersion across the ER and PR staining levels of the carriers and noncarriers. ER and PR ranges of dispersion of CHEK2 tumors were uniquely narrower than all other groups. CONCLUSION: The findings of our study suggest that precise expression levels of ER and PR in breast cancers can vary widely. These differences are further augmented when comparing expression staining across PV and non-PV carriers, suggesting potentially unique tumorigenesis and progression pathways influenced by germline cancer predisposition genes.

DRPPM-EASY: A Web-Based Framework for Integrative Analysis of Multi-Omics Cancer Datasets.

High-throughput transcriptomic and proteomic analyses are now routinely applied to study cancer biology. However, complex omics integration remains challenging and often time-consuming. Here, we developed DRPPM-EASY, an R Shiny framework for integrative multi-omics analysis. We applied our application to analyze RNA-seq data generated from a USP7 knockdown in T-cell acute lymphoblastic leukemia (T-ALL) cell line, which identified upregulated expression of a TAL1-associated proliferative signature in T-cell acute lymphoblastic leukemia cell lines. Next, we performed proteomic profiling of the USP7 knockdown samples. Through DRPPM-EASY-Integration, we performed a concurrent analysis of the transcriptome and proteome and identified consistent disruption of the protein degradation machinery and spliceosome in samples with USP7 silencing. To further illustrate the utility of the R Shiny framework, we developed DRPPM-EASY-CCLE, a Shiny extension preloaded with the Cancer Cell Line Encyclopedia (CCLE) data. The DRPPM-EASY-CCLE app facilitates the sample querying and phenotype assignment by incorporating meta information, such as genetic mutation, metastasis status, sex, and collection site. As proof of concept, we verified the expression of TP53 associated DNA damage signature in TP53 mutated ovary cancer cells. Altogether, our open-source application provides an easy-to-use framework for omics exploration and discovery.

Cancer Detection and Classification by CpG Island Hypermethylation Signatures in Plasma Cell-Free DNA.

Cell-free DNA (cfDNA) methylation has emerged as a promising biomarker for early cancer detection, tumor type classification, and treatment response monitoring. Enrichment-based cfDNA methylation profiling methods such as cfMeDIP-seq have shown high accuracy in the classification of multiple cancer types. We have previously optimized another enrichment-based approach for ultra-low input cfDNA methylome profiling, termed cfMBD-seq. We reported that cfMBD-seq outperforms cfMeDIP-seq in the enrichment of high-CpG-density regions, such as CpG islands. However, the clinical feasibility of cfMBD-seq is unknown. In this study, we applied cfMBD-seq to profiling the cfDNA methylome using plasma samples from cancer patients and non-cancer controls. We identified 1759, 1783, and 1548 differentially hypermethylated CpG islands (DMCGIs) in lung, colorectal, and pancreatic cancer patients, respectively. Interestingly, the vast majority of DMCGIs were overlapped with aberrant methylation changes in corresponding tumor tissues, indicating that DMCGIs detected by cfMBD-seq were mainly driven by tumor-specific DNA methylation patterns. From the overlapping DMCGIs, we carried out machine learning analyses and identified a set of discriminating methylation signatures that had robust performance in cancer detection and classification. Overall, our study demonstrates that cfMBD-seq is a powerful tool for sensitive detection of tumor-derived epigenomic signals in cfDNA.

Characterizing batch effects and binding site-specific variability in ChIP-seq data.

Multiple sources of variability can bias ChIP-seq data toward inferring transcription factor (TF) binding profiles. As ChIP-seq datasets increase in public repositories, it is now possible and necessary to account for complex sources of variability in ChIP-seq data analysis. We find that two types of variability, the batch effects by sequencing laboratories and differences between biological replicates, not associated with changes in condition or state, vary across genomic sites. This implies that observed differences between samples from different conditions or states, such as cell-type, must be assessed statistically, with an understanding of the distribution of obscuring noise. We present a statistical approach that characterizes both differences of interests and these source of variability through the parameters of a mixed effects model. We demonstrate the utility of our approach on a CTCF binding dataset composed of 211 samples representing 90 different cell-types measured across three different laboratories. The results revealed that sites exhibiting large variability were associated with sequence characteristics such as GC-content and low complexity. Finally, we identified TFs associated with high-variance CTCF sites using TF motifs documented in public databases, pointing the possibility of these being false positives if the sources of variability are not properly accounted for.

CYB561A3 is the key lysosomal iron reductase required for Burkitt B-cell growth and survival.

Epstein-Barr virus (EBV) causes endemic Burkitt lymphoma, the leading childhood cancer in sub-Saharan Africa. Burkitt cells retain aspects of germinal center B-cell physiology with MYC-driven B-cell hyperproliferation; however, little is presently known about their iron metabolism. CRISPR/Cas9 analysis highlighted the little-studied ferrireductase CYB561A3 as critical for Burkitt proliferation but not for that of the closely related EBV-transformed lymphoblastoid cells or nearly all other Cancer Dependency Map cell lines. Burkitt CYB561A3 knockout induced profound iron starvation, despite ferritinophagy ad plasma membrane transferrin upregulation. Elevated concentrations of ascorbic acid, a key CYB561 family electron donor, or the labile iron source ferrous citrate rescued Burkitt CYB561A3 deficiency. CYB561A3 knockout caused catastrophic lysosomal and mitochondrial damage and impaired mitochondrial respiration. Conversely, lymphoblastoid B cells with the transforming EBV latency III program were instead dependent on the STEAP3 ferrireductase. These results highlight CYB561A3 as an attractive therapeutic Burkitt lymphoma target.

TIMEx: tumor-immune microenvironment deconvolution web-portal for bulk transcriptomics using pan-cancer scRNA-seq signatures.

SUMMARY: The heterogeneous cell types of the tumor-immune microenvironment (TIME) play key roles in determining cancer progression, metastasis and response to treatment. We report the development of TIMEx, a novel TIME deconvolution method emphasizing on estimating infiltrating immune cells for bulk transcriptomics using pan-cancer single-cell RNA-seq signatures. We also implemented a comprehensive, user-friendly web-portal for users to evaluate TIMEx and other deconvolution methods with bulk transcriptomic profiles. AVAILABILITY AND IMPLEMENTATION: TIMEx web-portal is freely accessible at http://timex.moffitt.org. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.