Yan, Huihuang

Abstract Studies have reported conflicting findings regarding the contribution of germline variants or somatic genomic drivers to racial disparities in multiple myeloma. To comprehensively investigate somatic drivers in relation to inherited genetics in multiple myeloma, we combined newly sequenced whole-genome sequencing data with publicly available datasets (total n = 1,286). Overall, we did not identify germline or somatic genomic differences that explain the different risk of developing multiple myeloma between patients with genetic similarity to African (AFR) or European (EUR) reference populations. A difference in the detectability and timing of APOBEC-associated and germinal center mutational activity was observed. Integrating epidemiologic data and mutational signature-based temporal estimates, we challenge the assumption that individuals in the AFR group develop multiple myeloma at a younger age. Finally, we demonstrate that, with equal access to efficacious therapies, patients in the AFR and EUR groups have equivalent clinical outcomes. Significance: Multiple myeloma is reported to occur at higher rates in individuals who self-identify as non-Hispanic Black. In this large dataset, genomic drivers occur at the same rate among ancestry groups, except for APOBEC mutagenesis. With equivalent therapy, clinical outcomes did not differ for patients grouped by genetic ancestry similarity.

Abstract Background: CLL incidence differs by ancestry, with higher rates in individuals of European ancestry (EA) than African ancestry (AA). The contribution of epigenetic variation to these disparities remains unclear. We investigated chromatin accessibility, histone modifications, and gene expression in CLL patients of EA and AA to identify ancestry-associated epigenetic signatures. Methods: ATAC-seq was performed on blood clonal B cells from 15 AA and 51 EA patients; CUT&Tag profiling of six histone marks (H3K4me1, H3K4me3, H3K27ac, H3K9me3, H3K27me3, H3K36me3) was done in 8 AA and 12 EA cases; RNA-seq was generated for all samples. Ancestry was genetically confirmed. Differential regions were linked to genes using ENCODE and Hi-C datasets. Results: ATAC-seq revealed 4,955 regions with increased and 3,000 with decreased accessibility in AA vs EA (fold change >2, FDR ≤0.05). Regions with increased accessibility mapped to 2,313 genes; 599 were upregulated and enriched in TNF-α/NF-κB (p=1.40E-53) and apoptosis pathways (p=6.90E-11). Regions with decreased accessibility linked to 1,953 genes; 317 were downregulated, enriched in interferon-γ response (p=1.04E-03) and IL-6/JAK/STAT3 signaling (p=6.41E-03). Histone profiling showed ancestry-specific changes across all marks. For H3K27ac (active enhancer), 68 genes with a decreased signal in AA were downregulated and enriched in interferon-γ (p=1.55E-06) and IL-6/JAK/STAT3 (p=9.62E-05) pathways. Increased H3K27me3 (repressive histone modification) in AA affected 399 genes enriched in IL-6/JAK/STAT3 (p=8.13E-09) and interferon-γ (p=2.48E-05) pathways. These findings suggest coordinated regulation by active and repressive chromatin states. Conclusions: Despite limited sample size, this study suggests differences between AA and EA ancestries may exist in epigenetic signatures in CLL, implicating chromatin accessibility and histone modifications in pathways relevant to tumor biology. These signatures may also contribute to the differences in CLL incidence by ancestry. Future studies with more patients and functional validation experiments are needed to clarify the role of these epigenetic differences in CLL pathogenesis. Citation Format: Zhiquan Wang, Huihuang Yan, Mingma Sherpa, Daniel Bihnam, Cristine Allmer, Chantal McCabe, Daniel O’Brien, Dennis P. Robinson, James R. Cerhan, Neil E. Kay, Sameer A. Parikh, Esteban Braggio, Susan Slager. Ancestry-associated epigenetic differences in chronic lymphocytic leukemia [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 5064.

Abstract Introduction: ASXL1-mutant (mt) Chronic Myelomonocytic Leukemia (CMML) is an aggressive myeloid neoplasm characterized by increased proliferation, resistance to epigenetic therapies, and poor survival. Epigenetic remodeling and up-regulation of leukemogenic driver genes are hallmarks of ASXL1mt CMML. Truncating mutations in ASXL1 activate enhancers that drive the expression of key leukemogenic driver genes such as HOXA9 and its co-factor MEIS1. Here, we investigate the chromatin accessibility of these leukemogenic enhancers by ASXL1 genotype at single-cell resolution (GoT-ChA-seq). Methods: After IRB approval, we performed bulk RNA-seq and ChIP-seq (H3K4me1, H3K4me3, H3K27ac, H3K27me3, H2AK119ub, H2BK20ac) on bone marrow (BM) mononuclear cells from 40 patients with CMML (19 ASXL1mt, 21 -wildtype/wt). After quality control, differential analyses of gene expression and histone modification occupancy between ASXL1mt and wt patients were conducted. A consensus peak calling framework was used to define genomic regions with ASXL1mt-specific histone modifications. Canonical enhancers were defined as genomic regions with a co-occupancy of H3K4me1 and H3K27ac. We then added regions marked by H2BK20ac to capture regulatory regions associated with p300 (Narita et al. Nat Genet 2023). We used an in silico prediction strategy to associate regulatory regions marked by strong histone modification occupancy with highly up-regulated leukemogenic driver genes (HOXA6-10, MEIS1). The ReMap database (Hammal et al. Nucleic Acids Res 2022) was queried for transcription factor binding in the genomic regions of interest. We then performed GoT-ChA-seq (Izzo et al. Nature 2024) to investigate chromatin accessibility for ASXL1mt (n=3) and wildtype BM single cells from CMML patients. In vitro colony forming assays were conducted on primary CMML BM cells (11- ASXL1mt and 9 wt CMML), assessing the efficacy of EP31670, a novel, oral, dual inhibitor of p300/BRD4. Results: This study included 40 patients with CMML, 19 ASXL1mt and 21 wt. The variant allele frequencies for ASXL1 were compatible with heterozygosity (median 0.41) and TET2 mutations were balanced between the two groups. We identified 149 candidate regulatory regions correlating with the expression of up-regulated target genes of interest (HOXA6-10, MEIS1) by bulk ChIP- and RNA-seq. Publicly available ChIP-seq data from myeloid cell lines demonstrated enrichment of p300 (in THP-1 cells, enrichment effect size +2.58, p=0.002) and BRD4 (in K-562 cells, enrichment effect size +1.58, p=0.045) in these candidate regulatory regions. Next, we interrogated the chromatin accessibility of ASXL1mt (n=13620) and ASXL1wt (n=24269) single cells from 3 CMML patients using GoT-ChA-seq. Differential accessibility analysis revealed 144 genomic regions, all of which were preferentially accessible in ASXL1mt cells compared to ASXL1wt cells (p<0.05 for all regions). Four of these preferentially accessible genomic regions (p<0.001 for all regions) overlapped with the previously identified candidate regulatory regions for MEIS1. The H3K27ac and H2BK20ac occupancy levels in these regions was strongly associated with the expression of MEIS1 (median Pearson r=0.73, p=1.16x10-7). These candidate regulatory regions were annotated in ENCODE as distal and proximal enhancers. Targeting this biology with the dual p300/BRD4 inhibitor EP31670 in progenitor colony forming assays led to a preferential therapeutic effect in ASXL1mt BM samples (median IC50 = 25nM) compared to in ASXL1wt samples (median IC50 = 74nM, p=0.03). Treatment with EP31670 was also associated with increased differentiation (increased blast forming units-erythroid; BFU-E) and organization of colonies in ASXL1mt vs wt CMML. Conclusions: ASXL1mt CMML is characterized by the up-regulation of several leukemogenic driver genes including HOXA9 and its co-factor MEIS1, secondary to the activation of genotype-specific enhancers. The preferential accessibility of these enhancers can be defined between ASXL1mt and ASXL1wt cells within patients. The enhancers of interest interact with p300 and BRD4 in myeloid cell lines and targeting them with the novel dual p300/BRD4 inhibitor, EP31670, induces therapeutic responses at low nanomolar drug concentrations in vitro, preferentially in ASXL1-mutant cells. These data solidify the biological rationale for targeting p300/BRD4 in ASXL1mt chronic myeloid neoplasms for therapeutic benefit (NCT05488548).

Abstract Introduction: Protein truncating somatic ASXL1 mutations in CMML are associated with high-risk features and poor outcomes. We have previously demonstrated that ASXL1-mutant (mt) CMML is characterized by permissive chromatin states and overexpression of leukemia stemness genes (Binder et al. Nat Commun; 2022). DLK1 (delta-like homolog 1) is a pseudo-NOTCH like ligand that is maternally imprinted and paternally expressed and is physiologically not expressed beyond embryogenesis. Aberrant DLK1 expression has been associated with poor outcomes in solid tumors and we have shown differential overexpression in ASXL1mt versus wild-type (wt) CMML. Both the promoter and the last exon of DLK1 contain CpG islands that play a role in regulating DLK1. The CpG island within the fifth exon of DLK1, known as the DLK1-differentially methylated region (DMR) [chr14:100,726,892-100,738,224] is completely unmethylated on the maternal allele and partially methylated on the paternal allele. Previous work (Developmental Cell; 2021) has also identified a DLK1 imprinting control region (ICR) known as the IG-DMR [chr14:100,811,001–100,811,037]. DNA methylation state of the IG-DMR has been shown to impact the expression of MEG3 mRNA (lncRNA) which functions as a cofactor to direct PRC2 to the DLK1 promoter, suppressing DLK1 expression. Since the regulation of DLK1 in ASXL1mt CMML is not well understood, we conducted this multiomic analyses to define epigenetic mechanisms of DLK1 overexpression in ASXL1mt CMML. Methods: After IRB approval, we performed bulk RNA-seq, ChIP-seq (H3K4me1/me3, H3K27ac, H3K27me3) and Illumina MethylationEPIC array on bone marrow (BM) mononuclear cells of 40 CMML patients (19 ASXL1mt, 21 wt). scRNA-seq was conducted on 5 ASXL1mt and 3 wt cases. All libraries were sequenced on an Illumina NovaSeq instrument. We assessed these samples for differential gene expression (RNA-seq) and changes in histone modifications (ChIP-seq) between ASXL1mt and wt CMML, specifically at the DLK1-DMR and IG-DMRregulatory loci. We conducted ddPCR (digital droplet PCR) to determine allelic status of DLK1 in ASXL1mtand wt cases. Global and allele/sequence specific DNA methylation changes were determined using Biomodal's duet EvoC methodology (6 base sequencing, including 5mC and 5hmC) in 5 ASXL1mt and 5 wt CMML cases. Results: We validated the overexpression of DLK1 in 19 ASXL1mt versus 21 wt (log2 fold change +4.85, FDR 4.67e-13) CMML BM samples. scRNA-seq on 5 ASXL1mt versus 3 wt samples demonstrated that DLK1 is only expressed in mutant cases, with the expression being limited to HSPCs. ChIP-seq analysis at the DLK1 regulatory loci validated (a) reduced occupancy of H3K27me3, correlating with ASXL1mt associated disruption of the PRC2-complex, and (b) increased co-occupancy of H3K4me1 and H3K27ac in mt cases, validating a permissive chromatin state in ASXL1mt cases at the DLK1 regulatory regions. Through correlation analysis of RNA-seq and ChIP-seq, we defined the promoter for DLK1, characterized by high Pearson correlation coefficients (R > 0.5) and enrichment of H3K27ac and H3K4me3. Differential methylation analysis identified global hypomethylation around the DLK1-DMR and promoter regions. We observed a mean reduction of ~40% in methylation status at the DLK1 promoter in ASXL1mt versus wt cases, suggesting a loss of epigenetic silencing at this locus. This was corroborated by significant enrichment of 5hmC at the fifth exon of DLK1 indicating active DNA demethylation. Conversely, we observed a gain in methylation at the IG-DMR, suggesting loss of imprinting control through MEG3 silencing and DLK1 overexpression. To further determine the impact of imprinting deregulation on overexpression of DLK1 in ASXL1mt CMML, allele-specific methylation was profiled using the Biomodal whole genome methylation data. This demonstrated that the methylation patterns in ASXL1mt CMML cases were significantly deregulated at the DLK1-DMR, with >30% loss of methylation compared to GRCh38 reference alleles. Furthermore, ddPCR was able to confirm bi-allelic expression of DLK1 in ASXL1mt CMML, suggesting that the observed methylation dysregulation leads to loss of imprinting of the maternal allele. Conclusion: In summary, we demonstrate that ASXL1 mutations in CMML disrupt epigenetic pathways, promoting DNA demethylation, resulting in permissive chromatin states and an aberrant and oncogenic re-expression of maternal DLK1, due to loss of imprinting control.

Abstract Motivation Efforts to address health disparities are often limited by the lack of robust computational tools for inferring genetic ancestry by calculating an individual’s genetic similarity to continental groups. We have already shown that a preferred alternative to self-described race is using ancestry-informative markers (AIMs) that can be classified into ancestral components and used to estimate their similarity to those of known populations to identify continental groups. However, real-world genomic data can present challenges, including limited availability of germline DNA, a small number of AIMs for each sample, and the use of different variant calling software, limiting the application of existing solutions. Results Here, we describe a novel supervised machine-learning tool AncestryGeni, which infers genetic ancestry for samples with even a hundred markers and is applicable to any genomic data, including whole exome sequencing (WES) and RNA sequencing (RNA-Seq) data. Applying AncestryGeni to a real-world genomic dataset obtained from the Multiple Myeloma Research Foundation (MMRF) CoMMpass study, we show that it is more accurate than the commonly used FastNGSadmix when using nonstandard genomic material. We also demonstrate that when using AncestryGeni, the tumor-derived sequence obtained from WES and RNA-Seq can be a robust data source to accurately estimate an individual’s genetic similarity to a continental group. Availability and implementation AncestryGeni pipeline is available at https://github.com/eelhaik/AncestryGeni/tree/main.

Abstract Clonal hematopoiesis (CH) of indeterminate potential (CHIP), driven by somatic mutations in leukemia-associated genes, confers increased risk of hematologic malignancies, cardiovascular disease, and all-cause mortality. In blood of healthy individuals, small CH clones can expand over time to reach 2% variant allele frequency (VAF), the current threshold for CHIP. Nevertheless, reliable detection of low-VAF CHIP mutations is challenging, often relying on deep targeted sequencing. Here, we present UNISOM, a streamlined workflow for enhancing CHIP detection from whole-genome and whole-exome sequencing data that are underpowered, especially for low VAFs. UNISOM utilizes a meta-caller for variant detection, in couple with machine learning models which classify variants into CHIP, germline, and artifact. In whole-exome sequencing data, UNISOM recovered nearly 80% of the CHIP mutations identified via deep targeted sequencing in the same cohort. Applied to whole-genome sequencing data from Mayo Clinic Biobank, it recapitulated the patterns previously established in much larger cohorts, including the most frequently mutated CHIP genes and predominant mutation types and signatures, as well as strong associations of CHIP with age and smoking status. Notably, 30% of the identified CHIP mutations had < 5% VAFs, demonstrating its high sensitivity toward small mutant clones. This workflow is applicable to CHIP screening in population genomic studies. The UNISOM pipeline is freely available at https://github.com/shulanmayo/UNISOM and https://ngdc.cncb.ac.cn/biocode/tool/7816.

Abstract Most disease-associated single-nucleotide polymorphisms (SNPs) identified by genome-wide association studies (GWASs) reside in noncoding regions. There is increasing evidence that they play regulatory roles through epigenetic mechanisms by altering local chromatin state, 3D chromatin structure, and looping interactions. We and others have identified 42 CLL risk loci from GWASs. At 12 risk loci located outside of gene promoters, we previously demonstrated that functional SNPs are highly enriched in enhancers and super-enhancers that mostly target BCL2 family genes and transcriptional regulators. However, the study was limited by using only public epigenomic data generated in lymphoblastoid cell lines (LCL) and normal immune related cell types. To gain a better understanding of how the functional SNPs alter local chromatin state and modulate target gene expression in CLL, we generated RNA-seq data (n=588) and transposase-accessible chromatin with sequencing (ATAC-seq) data (n=69) in CLL tumor B cells. We performed molecular quantitative trait loci (QTL) analysis with index and correlated SNPs (R2 0.5) at 41 risk loci (excluding the HLA locus). The analyses identified cis expression QTLs (eQTLs), within 1Mb of genes’ transcription start site, at 25 risk loci and chromatin accessibility QTLs (caQTLs, within peak) at 11 risk loci, with both eQTLs and caQTLs at 8 risk loci. We identified a total of 54 eGenes, for which the expression is affected by nearby SNPs within 1Mb, including 40 novel eGenes at 16 CLL risk loci not previously reported. Notably, based on publicly available chromosome interaction data from CLL, LCL, and normal B cells, for over 70% of the novel eGenes, their promoters showed looping interactions with the regions carrying the eQTLs. Further, 13 SNPs were identified as both caQTL and eQTL, with the associated novel eGenes including CD81 at 11p15.5, IPCEF1 at 6q25.2, as well as CXXC1 and MBD1 at 18q21.1. The result suggests that these SNPs play a role in altering chromatin accessibility contributed to gene expression variation. At 11p15.5 risk locus, SNP rs2651823 (A/G) represents a novel caQTL and eQTL. The alternative G allele is associated with increased chromatin accessibility and CD81 expression. Hi-C data from GM12878 and CD20+ B cells supported interaction between the open chromatin region carrying rs2651823 and CD81 promoter. CD81 facilitates the trafficking of CD19 to the surface of activated B cells, and CD81-null mice showed impaired immune response. In a mouse model, CD81 contributes to tumor progression and metastasis in Lewis lung carcinoma. In summary, our molecular QTL analysis revealed that functional SNPs, located in CLL risk loci, modulate chromatin accessibility and expression of downstream target genes that could impact CLL risk. Citation Format: Huihuang Yan, Zhiquan Wang, Mingma Hoel, Cristine Allmer, Chantal McCabe, Daniel O’Brien, Dennis Robinson, James Cerhan, Sameer Parikh, Esteban Braggio, Susan Slager. Molecular QTL analyses identify functional variants at inherited risk loci in chronic lymphocytic leukemia [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 2763.

Background: Immune dysfunction is a hallmark of chronic lymphocytic leukemia (CLL), and several studies suggest that hypogammaglobulinemia at the time of CLL diagnosis may predict shorter time to first therapy (TTFT; Parikh et al, 2015). However, it is unclear if hypogammaglobulinemia can predict TTFT independent of the CLL-International Prognostic Index (CLL-IPI) and the tumor mutational load (TML). We sought to determine if serum immunoglobulin (Ig) levels at diagnosis can independently predict outcomes in patients with CLL and its precursor, high-count monoclonal B-cell lymphocytosis (MBL). Methods: We used the Mayo Clinic CLL Database to identify newly diagnosed CLL/MBL consented within 2 years of diagnosis. Next generation sequencing (NGS) was performed using a 59 gene panel (SureSelect) to detect mutated CLL genes. Serum Ig levels were quantitated by radial immunodiffusion (Immunoplates). Serum Ig levels below the lower limit of normal were defined as hypogammaglobulinemia. We used Chi-squared or Fisher's exact tests to compare discrete variables and the Kruskal Wallis test for continuous variables. TTFT was calculated from date of diagnosis to date of first treatment or date last known to be untreated. We used Cox regression models to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for TTFT and overall survival (OS) associations. Results: We identified 895 newly diagnosed patients (588 CLL and 307 MBL) who had undergone NGS for recurrent somatic mutations and had serum Ig levels available at the time of diagnosis. The median absolute B-cell count was 6.7 x109/L; 613 (69%) were male, 375 (42%) had unmutated IGHV genes, and 81 (9%) had TP53 disruption. Based on CLL-IPI, 412 (46%) patients had low risk, 285 (32%) had intermediate risk, and 198 (22%) had high/very high risk. The most commonly mutated genes were NOTCH1 (14%), SF3B1 (11%), TP53 (10%), NFKBIE (8%), ATM (6%), and BIRC3 (6%). Based on the number of genes with high impact or hotspot mutations, we classified 40%, 29%, and 31% patients with TML scores of 0, 1, or 2+, respectively. At CLL/MBL diagnosis, the median serum IgG, IgA, and IgM was 891 mg/dL (111-2620), 121 mg/dL (1-1150), and 44 mg/dL (5-1320), respectively; 284 (32%) patients had low serum IgG, 128 (17%) had low serum IgA, and 322 (42%) had low serum IgM. Low serum IgA was associated with mutations in NOTCH1, SF3B1, ATM, BIRC3 and NFKBIE, low IgM was associated with SF3B1 and BIRC3 mutations (all comparisons, P < .05), and low IgG was associated with BIRC3 mutation (P = .05). The median follow-up for the cohort was 8.4 years. A total of 359 patients progressed requiring therapy (299 CLL and 60 MBL), and 265 patients died (177 CLL and 88 MBL). The TTFT was significantly shorter for patients with low serum IgG compared to normal serum IgG at diagnosis (median 4.4 vs 10.8 years) and similar findings were observed in individuals with low serum IgA (1.7 vs 10.8 years) and those with low serum IgM (4.6 vs 11.1 years). On univariable analysis, the following were associated with a shorter TTFT: low serum IgG (HR: 1.8, 95% CI 1.4-2.2), low serum IgA (HR: 3.5, 95% CI 2.7-4.4), low serum IgM (HR: 1.9, 95% CI 1.5-2.3), TML score of 1 (HR: 1.7, 95% CI 1.3-2.2), TML of 2+ (HR: 4.4, 95% CI 3.4-5.7), CLL-IPI intermediate risk (HR: 3.7, 95% CI 2.8-4.8), and CLL-IPI high/very high risk (HR: 7.6, 95% CI 5.8-10.1) (all comparisons, P < .0001). In multivariable analyses after adjusting for CLL-IPI and TML, low serum levels of IgG (HR: 1.8, 95% CI: 1.5-2.3), IgA (HR: 2.4, 95% CI: 1.8-3.0), and IgM (HR: 1.8, 95% CI: 1.5-2.3) were independently associated with a shorter TTFT (all comparisons, P < .0001). No significant differences in OS were detected between patients with low and normal serum IgG, IgM, and IgA on univariate analysis. Similar results for TTFT and OS were observed after adding clonal B-cell count to the Cox model. Conclusions: Our study shows that low serum Ig levels at the time of initial diagnosis is predictive of TTFT independently of the CLL-IPI and TML, which can further risk stratify individuals with newly diagnosed CLL/MBL. Additionally, low serum IgA, IgM, and IgG levels at diagnosis are significantly associated with specific somatic mutations in individuals with newly diagnosed CLL/MBL.

Introduction: Cyclin D1, encoded by the CCND1 gene, is primarily known as a major regulator of cell cycle progression. Emerging studies have demonstrated a role for cyclin D1 in regulating gene transcription. In mantle cell lymphoma with t(11;14), cyclin D1 strongly binds to the promoters of highly transcribed genes and its overexpression led to genome-wide transcriptional downregulation. However, the transcriptional role for cyclin D1 in t(11;14) MM remains undefined. This study aims to elucidate the global chromatin and transcriptional changes induced by cyclin D1 in t(11;14) MM. Methods: Knockout (KO) of CCND1 in U266B1, a MM cell line carrying t(11;14), was performed by the Synthego (Redwood City, CA) using CRISPR/Cas9, which generated a frameshift insertion of base pair (bp) “A” after 69,641,478 bp within exon 1. Two independent KO clones were used for downstream analysis. ChIP-seq and ATAC-seq were performed for KO (n=2) and wild-type clones (WT, n=2). ChIP-seq used antibodies against H3K4me3 (promoter) and H3K4me1 and H3K27ac (enhancer). Libraries were sequenced to 2x51 bp on the Illumina NextSeq 2000 platform. Total RNA libraries (in triplicate) were sequenced to 2x150 bp. ChIP-seq and ATAC-seq reads were mapped to hg38 with BWA, and peaks were identified by MACS2 at FDR 0.01. RNA-seq reads were mapped to hg38 with STAR and gene raw counts were estimated using featureCounts. Differential peaks and differentially expressed genes between KO and WT were identified using edgeR at FDR 0.05 and fold-change 2. Only peaks present in at least 2 samples and genes having reads-per-million value 0.1 in at least 1 sample were included, excluding those from chrY. Pathway analysis was performed with the Enrichr package. Results: Differential expression analysis identified 2-fold more genes that were down-regulated than up-regulated in KO compared to WT (467 vs. 233). Pathway analysis revealed an enrichment of TNF-alpha signaling via NF-kappaB, inflammatory response, and apoptosis in association with down-regulated genes, while up-regulated genes are enriched in myogenesis, KRAS signaling, and epithelial mesenchymal transition pathway. In parallel, differential chromatin analysis identified 2.5% (1,161) of the peaks from H3K27ac and 0.5-1% (359-610) of the peaks for the other 3 marks that showed significant changes of signal levels between KO and WT. For the promoter mark H3K4me3, there are largely a comparable number of peaks showing increased (191 peaks) and decreased (168 peaks) signals, respectively, in KO compared to WT. However, for ATAC-seq and H3K4me1, about 70% of the differential peaks showed an increased signal in KO, in contrast with H3K27ac for which 60% of differential peaks showed decreased signal in KO. The preferential loss of H3K27ac occupancy is consistent with the observation of a higher proportion of genes being down-regulated in KO. These results indicated that knockout of CCND1 led to a global increase of chromatin accessibility, but a reduction of H3K27ac and gene expression. Further annotation revealed that, for H3K27ac and H3K4me1/ATAC-seq, 92% and 97% of the differential peaks were located >2kb away from the transcription start sites (TSSs) of expressed genes. Even for H3K4me3, 88% of the differential peaks are >2kb away from TSSs. The results suggest that the changes of chromatin upon CCND1 knockout predominantly occurred at distal regulatory regions. For up- and down-regulated genes, we estimated ChIP-seq and ATAC-seq signals in the TSS 2kb regions, revealing that up-regulated genes were overall associated with increased signals. Further analysis found that, for ATAC-seq and H3K4me3, 44% and 59% of the differential peaks located in the promoter regions were associated with differentially expressed genes, which dropped to ~20% for the two enhancer marks. These data indicate that changes in chromatin state impact the expression of the associated genes. Conclusion: Knockout of CCND1 impacts local chromatin state, particularly at enhancer regions, and the transcriptional program. In promoter-proximal regulatory regions, the changes of chromatin accessibility and H3K4me3 are strongly correlated with the expression changes of nearby genes. Further studies will identify the genes targeted by the differential enhancers and their possible roles in MM pathogenesis mediated by cyclin D1 following the t(11;14) event.

Chronic lymphocytic leukemia (CLL) is characterized by multiple copy number alterations (CNAs) and somatic mutations that are central to disease prognosis, risk stratification, and mechanisms of therapy resistance. Fluorescence in situ hybridization (FISH) panels are widely used in clinical applications as the gold standard for screening prognostic chromosomal abnormalities in CLL. DNA sequencing is an alternative approach to identifying CNAs but is not an established method for clinical CNA screening. We sequenced DNA from 509 individuals with CLL or monoclonal B-cell lymphocytosis (MBL), the precursor to CLL, using a targeted sequencing panel of 59 recurrently mutated genes in CLL and additional amplicons across regions affected by clinically relevant CNAs [i.e., del(17p), del(11q), del(13q), and trisomy 12]. We used the PatternCNV algorithm to call CNA and compared the concordance of calling clinically relevant CNAs by targeted sequencing to that of FISH. We found a high accuracy of calling CNAs via sequencing compared to FISH. With FISH as the gold standard, the specificity of targeted sequencing was >95%, sensitivity was >86%, positive predictive value was >90%, and negative predictive value was >84% across the clinically relevant CNAs. Using targeted sequencing, we were also able to identify other common CLL-associated CNAs, including del(6q), del(14q), and gain 8q, as well as complex karyotype, defined as the presence of 3 or more chromosomal abnormalities, in 26 patients. In a single and cost-effective assay that can be performed on stored DNA samples, targeted sequencing can simultaneously detect CNAs, somatic mutations, and complex karyotypes, which are all important prognostic features in CLL.