Loss of synergistic transcriptional feedback loops drives diverse B-cell cancers

Abstract

Background: The most common B-cell cancers, chronic lymphocytic leukemia/lymphoma (CLL), follicular and diffuse large B-cell (FL, DLBCL) lymphomas, have distinct clinical courses, yet overlapping "cell-of-origin". Dynamic changes to the epigenome are essential regulators of B-cell differentiation. Therefore, we reasoned that these distinct cancers may be driven by shared mechanisms of disruption in transcriptional circuitry.

Methods: We compared purified malignant B-cells from 52 patients with normal B-cell subsets (germinal center centrocytes and centroblasts, naïve and memory B-cells) from 36 donor tonsils using >325 high-resolution molecular profiling assays for histone modifications, open chromatin (ChIP-, FAIRE-seq), transcriptome (RNA-seq), transcription factor (TF) binding, and genome copy number (microarrays).

Findings: From the resulting data, we identified gains in active chromatin in enhancers/super-enhancers that likely promote unchecked B-cell receptor signaling, including one we validated near the immunoglobulin superfamily receptors FCMR and PIGR. More striking and pervasive was the profound loss of key B-cell identity TFs, tumor suppressors and their super-enhancers, including EBF1, OCT2(POU2F2), and RUNX3. Using a novel approach to identify transcriptional feedback, we showed that these core transcriptional circuitries are self-regulating. Their selective gain and loss form a complex, iterative, and interactive process that likely curbs B-cell maturation and spurs proliferation.

Interpretation: Our study is the first to map the transcriptional circuitry of the most common blood cancers. We demonstrate that a critical subset of B-cell TFs and their cognate enhancers form self-regulatory transcriptional feedback loops whose disruption is a shared mechanism underlying these diverse subtypes of B-cell lymphoma.

Funding: National Institute of Health, Siteman Cancer Center, Barnes-Jewish Hospital Foundation, Doris Duke Foundation.

Keywords: B-cell cancer; Epigenetics; Lymphoma; Super-enhancers; Transcriptional regulation and feedback.

Fig. 1

Study design, biospecimens, B-cell purification, molecular profiling, experiments and data analysis workflow. A) Lymph node biopsies and peripheral blood were collected from BCL patients (18 FL, 11 DLBCL, 23 CLL) and tonsils from healthy donors. We purified CD19⁺ malignant B cells from lymph node biopsies (FL, CLL, DLBCL) or peripheral blood (CLL), and isolated CD19⁺ normal B-cells from tonsils . From additional tonsils we sorted germinal center (GC) centrocytes (CD19⁺CD10⁺CD44loCXCR4⁻, 5), GC centroblasts (CD19⁺CD10⁺CD44loCXCR4⁺, 5), naive (CD19⁺CD5⁻CD27⁻, 3), and memory (CD19⁺CD5⁻CD27⁺, 3) control B-cell subsets. From these samples, we performed chromatin immunoprecipitation sequencing (ChIPseq, H3K27ac – 51, H3ac – 47, H3K4me1 – 35)), open chromatin profiling (FAIREseq - 45), RNA sequencing (RNAseq - 28) and microarrays , and whole genome copy number studies (SNP microarray - 42), totaling 328 high resolution molecular profiling studies. Our Integrative Analysis pipeline compared BCL subtypes to healthy control B-cells and identified copy number alterations, differentially bound enhancers, differentially expressed genes, and enhancer-gene associations. These studies also identified >1300 super-enhancers, many of which correlate with significantly altered expression of neighboring genes in BCL compared to control B-cells. We validated one of these, a novel super-enhancer with high levels of epigenetic activity and expression of two nearby genes, FCMR and PIGR, across BCL subtypes, using luciferase reporter assays and demonstrate high levels of FCMR and PIGR protein in primary BCL cells using flow cytometry and immunofluorescence (IF) staining. B) Heatmap shows assays performed for each sample: healthy control (HC) CD19+ B cells; NAIVE B cells; Memory B cells (MEM); Centroblast (CB); Centrocyte (CC); CLL; DLBCL; FL.

Fig. 2

Super-enhancers are selectively gained and lost at key transcriptional regulators in B-cell cancers. A) Venn diagram shows the overlap of differentially accessible (FAIREseq) or differentially bound chromatin (H3K27ac-, H3ac-, H3K4me1- ChIPseq) (absolute log2 fold change > 2, FDR < 0.01) in WUSM BCL compared to healthy control CD19⁺ B-cells sorted from tonsils. B) Heatmaps show the log2 ratio of signal for H3K27ac-, H3ac-, H3K4me1-, and FAIRE-seq at differentially bound (as in A) H3K27ac peaks between all BCL subtypes and hc B-cells. C) Venn diagram shows the shared and unique super-enhancer (SE) calls in BCL subtypes and healthy control (HC) B-cells. D) Bar graphs show RNA-seq log2 fold changes of genes near enhancers and SE with increased H3K27ac signal (log2 fold change > 1, FDR < 0.01) between BCL subtypes and healthy control B-cells compared to genes near enhancers and SE that are not increased, grouped by BCL subtype. E) The same as (D), but for genes near enhancers and SE with decreased H3K27ac signal (log2 fold change < 2, FDR < 0.01). Mann Whitney test, ns not significant, * P < 0.05, ** P < 0.01, *** P < 0.001, **** P < 0.0001. Mean with 95% confidence intervals shown. F-K) SE-associated genes with significantly increased (F-H) or decreased (I-K) expression and differentially bound SEs in BCL subtypes. Normalized RNA-seq TCF4 (F) or DNMT1 (I) gene counts grouped by BCL subtype and HC B-cells (DESeq2, ** adj p < 0.01, **** adj p < 0.0001, ***** adj p < 1e-5). Normalized RNA microarray signal for TCF4 (G) or DNMT1 (J) grouped by BCL subtype and healthy control germinal center (GC) centroblasts (CB), centrocytes (CC), naive and memory B-cells (limma * adj p < 0.05, ** adj p < 0.01, *** adj p < 0.001). Scatter plot shows significant correlation of TCF4 (H) or DNMT1 (K) expression (RNAseq) with H3K27ac levels at the associated SE (Spearman correlation).

Fig. 3

A novel super-enhancer associated with dysregulated high expression of PIGR and FCMR mRNA and protein. A) Genome browser screenshot shows signal for histone modifications (ChIPseq) and chromatin accessibility (FAIREseq - WUSM samples; DNase-seq - small intestine sample from Roadmap Epigenomic Project) for the FCMR/PIGR locus. Regulatory elements that were tested in luciferase assays are highlighted in gray. GeneHancer regulatory elements (red - promoter; gray - enhancer; darker color indicates higher confidence) and the identified super-enhancer are also shown. All tracks are RPKM normalized. Regions of copy number alteration (CNA) are shown by a red bar (amplifications identified in FL samples). B) Luciferase reporter assay results for all regulatory elements shown in (A). Assays were read in triplicate and performed at least twice. Mean with standard deviation shown. C-F) Expression of FCMR (C-D) and PIGR (E-F) determined by RNAseq (C&E) and RNA microarray (D&F) for BCL subtypes and healthy control B cells and B cell subsets (germinal center (GC) centroblasts (CB), centrocytes (CC), naive and memory B cells). RNAseq analyzed by DESeq2; microarrays analyzed by limma. * adj p > 0.05, ** adj p < 0.01, *** adj p < 0.001, **** adj p < 0.0001, ns not significant. G) Immunofluorescence microscopy for PIGR in purified primary CLL B-cells, MEC1 (CLL cell line), LS180 (colorectal cancer cell line), and HH (T-cell lymphoma cell line).

Fig. 4

A subset of genome copy number alterations in BCL have corresponding epigenetic and expression changes. A) Pie chart shows the percentage of enhancers containing genome copy number alterations (CNA) in at least 10% of BCL samples. Amp - amplification, Del - deletion, Amp&Del - amplification or deletion detected in the same region in different samples. B) Mean ⁺/− standard deviation (SD) of log2 fold change of BCL versus healthy control (HC) B cells for open chromatin (FAIREseq) and epigenetic marks (H3K27ac, H3ac, H3K4me1 ChIPseq) in enhancers containing CNA or not. Letters above SD lines indicate statistical comparator group (A - amplification, D - deletion, N - No CNA); superscript indicates adjusted p value (* p < 1e−04, ** p < 1e−5, *** p < 2e−16, ns not significant; one-way ANOVA with Tukey HSD). C) Plot shows the K27ac log2 fold change of BCL versus HC B cells of differentially bound (DB) enhancers with overlapping Amp, Del, or no CNA. Statistics as in (B). D) Volcano plot shows log2 fold change and -log10 adjusted p value for expression (RNAseq) in DLBCL versus HC B cells. Differentially expressed genes (absolute log2 fold change >1 and adjusted p < 0.01) are pink. Genes with differential expression and CNA are highlighted by text color and triangle shape and color: red/point up = amplification, blue/point down = deletion. Not differentially expressed and no CNA = grey. E) Volcano plot as in (D) for FL versus healthy control B-cells. F) Whole genome plot of number of BCL samples with copy number amplification (red) or deletion (blue) in 100kb bins. Differentially expressed genes (from D or E) with CNA in > 10% of BCL samples are labeled on the upper panel if they overlap an amplified region and on the lower panel for deleted regions. Gene label color indicates if expression is increased (red) or decreased (blue) in BCL relative to HC B cells.

Fig. 5

BCL-altered transcription factor expression profiles associated with genome copy number alterations and differentially bound enhancers. A) Heatmap shows normalized gene count z-scores (RNA-seq) for transcription factors (TF) differentially expressed (adjusted p-value < 0.01, absolute log2 fold change > 1, n = 283) in at least one BCL subtype compared to healthy control (HC) B-cells. Six clusters of distinct expression profiles were detected; the number of genes is shown beneath each cluster label. B) Bar chart show the number of TFs with copy number alterations and differentially bound (DB) enhancers in each cluster. (Left) Copy number (CN) amplified, DB increased H3K27ac in BCL (DB up); (Right) CN deletion, DB decreased H3K27ac in BCL (DB down). C) Volcano plot shows log2 fold change and -log10 adjusted p value for expression of TFs (as in A) in BCL versus HC B cells. TF genes with overlapping DB enhancers are highlighted by color (red = increased, blue = decreased, grey = unchanged level of histone acetylation) and size (corresponding to the number of DB enhancers). Color of the gene label corresponds to the status of the overlapping DB enhancers (red = all increased, blue = all decreased, purple = both increased and decreased DB enhancers overlap). D) XY plot shows the number of total enhancers overlapping each TF (Y axis) by TF gene size in kilobases (kb) (X axis). Size and colors as in (C). E) Whole genome frequency plot of number of BCL samples with copy number amplification (red) or deletion (blue) (100kb bins). TFs (from A) with at least 3 samples with overlapping CN alterations (CNA) are labeled on the upper panel if they overlap an amplified region and on the lower panel for deleted regions. Gene label color indicates if expression is increased (red) or decreased (blue) in BCL relative to HC B cells.

Fig. 6

BCL-altered transcription factors have super-enhancer powered transcriptional feedback loops. A) Half box/dot-plot shows the log2 fold change of K27ac in BCL versus healthy control (HC) B cells for super-enhancers (SE) within 250kb (left) or overlapping (right) the differentially expressed TF genes (red: clusters 1-3, upregulated in BCL, blue: clusters 4-6, downregulated in BCL, from Fig. 5). Wilcoxon test *** p < 0.001; **** p < 0.0001. B) Cartoon depicts distal and overlapping SEs with high (purple) or low (grey) levels of H3K27ac regulating the expression of a TF gene from one of clusters 1-3 (red) or 4-6 (blue). TFs from the same (matching) cluster groups (1-3/4-6) bind within the SEs and control TF expression. C) Volcano plot shows log2 fold change and -log10 adjusted p value for expression of TFs (from Fig. 5) in BCL versus HC B cells. TF genes with SE within 250kb are highlighted by diamonds, with size indicating number of SEs and color corresponding to the percentage of matching TF cluster group peaks within the SE(s), red = TF gene in Clusters 1-3, blue = TF gene in Clusters 4-6 (ENCODE 3 TF ChIPseq). Gene labels include the number of SEs if greater than one. D) Alluvial plot segregates TFs with overlapping SEs and ENCODE 3 ChIPseq data. “Self” TF peaks: ChIPseq binding peaks for a TF were found in the SE overlapping the gene encoding that TF, an indication of TF self-regulation. E) For 15 TFs with “self” TF peaks in overlapping super-enhancers from (D), bar graphs show the number of constituent enhancers with (red/blue) or without (grey) binding peaks of the “self” TF. Red = TF in Clusters 1-3, blue = TF in Clusters 4-6.