Non-Coding Variants Connect Enhancer Dysregulation with Nuclear Receptor Signaling in Hematopoietic Malignancies

Home , HIC2, HMGN2, NEUROD1, PER2

Author Manuscript Published OnlineFirst on March 18, 2020; DOI: 10.1158/2159-8290.CD-19-1128 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited.

Kailong Li1,2,5, Yuannyu Zhang1,2,5, Xin Liu1,2,5, Yuxuan Liu1,2,5, Zhimin Gu1,2, Hui Cao1,2, Kathryn E. Dickerson1,2, Mingyi Chen3, Weina Chen3, Zhen Shao4, Min Ni1, Jian Xu1,2,*

1Children’s Medical Center Research Institute, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA

2Department of Pediatrics, Harold C. Simmons Comprehensive Cancer Center, and Hamon Center for Regenerative Science and Medicine, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA

3Department of Pathology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA

4Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China

5These authors contributed equally

*Corresponding Author: [email protected] (J.X.) Mailing address: 6000 Harry Hines Blvd. C. Kern Wildenthal Research Building – NL12.138B UT Southwestern Medical Center Dallas, Texas 75390-8502 Phone: 214-648-6125

Running Title: Non-Coding Variants in Hematopoietic Malignancies

Keywords: Non-Coding Variants, Enhancers, Epigenetics, Leukemia, Nuclear Receptor

Disclosure of Potential Conflicts of Interest: No potential conflicts of interest were disclosed.

Downloaded from cancerdiscovery.aacrjournals.org on September 30, 2021. © 2020 American Association for Cancer Research. Author Manuscript Published OnlineFirst on March 18, 2020; DOI: 10.1158/2159-8290.CD-19-1128 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited.

ABSTRACT

Mutations in protein-coding genes are well established as the basis for human cancer, yet it remains elusive how alterations within non-coding genome, a substantial fraction of which contain cis-regulatory elements (CREs), contribute to cancer pathophysiology. Here, we developed an integrative approach to systematically identify and characterize non-coding regulatory variants with functional consequences in human hematopoietic malignancies. Combining targeted resequencing of hematopoietic lineage-associated CREs and mutation discovery, we uncovered 1,836 recurrently mutated CREs containing leukemia-associated non- coding variants. By enhanced CRISPR/dCas9-based CRE perturbation screening and functional analyses, we identified 218 variant-associated oncogenic or tumor suppressive CREs in human leukemia. Non-coding variants at KRAS and PER2 enhancers reside in proximity to nuclear receptor (NR) binding regions and modulate transcriptional activities in response to NR signaling in leukemia cells. NR binding sites frequently co-localize with non-coding variants across cancer types. Hence, recurrent non-coding variants connect enhancer dysregulation with nuclear receptor signaling in hematopoietic malignancies.

SIGNIFICANCE

We describe an integrative approach to identify non-coding variants in human leukemia, and reveal cohorts of variant-associated oncogenic and tumor suppressive cis-elements including KRAS and PER2 enhancers. Our findings support a model that non-coding regulatory variants connect enhancer dysregulation with nuclear receptor signaling to modulate gene programs in hematopoietic malignancies.

INTRODUCTION

Advances in genome sequencing have provided critical insights into the role of pathogenic DNA alterations as cancer drivers. However, current efforts are focused on protein-coding sequences (or exomes) consisting of only about 1% of human genome. It remains unclear how alterations within non-coding genome contribute to cancer pathophysiology. Similarly, genome-wide genotype-phenotype association studies continue to reveal non-coding sequences that are altered in human diseases, although identification of causal elements remains difficult impeding drug development and therapeutics.

Enhancers are non-coding cis-regulatory elements (CREs) that determine cell identity by coordinating spatiotemporal gene expression. Major progress has been made to identify candidate enhancers by genome-wide mapping of enhancer-associated histone modifications including H3-Lys27 acetylation (H3K27ac) and H3-Lys4 mono-methylation (H3K4me1), transcription factors (TFs), and chromatin features (1-4). The biological significance of enhancers is underscored by gene expression studies showing the deterministic role of enhancers in directing cell-type-specific transcription (2,5-7). Highly marked clusters of enhancers or super-enhancers associated with developmental or cancer-related genes have been identified in various cell types (8-11). These studies suggest a model that a small set of lineage-defining enhancers determine cell identity in development and cancer.

Emerging evidence points to a critical role of non-coding regulatory variants as cancer drivers (12,13). For instance, recurrent mutations in the TERT promoter create new binding motifs for ETS family TFs to enhance gene transcription in familial and sporadic melanoma (14,15). In the context of leukemia, recurrent non-coding mutations upstream of the TAL1 proto-oncogene introduce de novo binding sites for the MYB oncoprotein in T-cell leukemia. MYB associates with these sites with CBP, RUNX1 and TAL1 to create an oncogenic super-enhancer and activates TAL1 expression (16). In another example, a single chromosomal rearrangement repositions a GATA2 enhancer to ectopically activate EVI1 and confer GATA2 haploinsufficiency in inv(3)/t(3;3) acute myeloid leukemia (AML) (17). These studies raise important questions about the extent to which non-coding variants contribute to cancer development, and how they work. Moreover, the functional impact of non-coding somatic mutations found in cancer genome sequencing has not been systematically investigated or validated. The main challenges are to distinguish candidate non-coding cancer drivers from numerous non-functional passenger mutations and to establish causality of non-coding variants in cancer pathophysiology.

Human nuclear receptors (NRs) are a family of signaling-activated TFs that play integral roles in development and cancer (18). The peroxisome proliferator-activated receptors (PPARα, β/δ, and γ) function as obligate heterodimers with the retinoid X receptors (RXRα, β, and γ) in the absence of ligands and bind to hormone response elements together with corepressor proteins. The retinoic acid receptors (RARα, β, and γ) also heterodimerize with RXRs and bind

constitutively to DNA in the absence of ligands. Binding of agonist ligands to PPAR/RXR or RAR/RXR complexes leads to dissociation of corepressors and recruitment of coactivator proteins, resulting in transcriptional activation of downstream target genes. The ability of NRs to rapidly and dynamically respond to various developmental and environmental clues by modulating gene programs makes them versatile cellular ‘sensors’. As such, NRs have historically served as biomarkers for classification of several solid tumors including breast and prostate cancers and targets for hormone therapy (19). In the hematopoietic system, the fusion oncogene PML-RARA provides a diagnostic biomarker for acute promyelocytic leukemia (APL) and the molecular basis for oncoprotein-targeted therapy (20).

In this work, we developed an integrative approach to identify recurrent non-coding variants by targeted resequencing of cis-regulatory elements in human leukemia. A major limiting step towards understanding non-coding cancer variants is the lack of a high-throughput platform to assess their functional effects. To that end, we employed enhanced dCas9-based epigenetic editing and performed CRE perturbation screens. This revealed a cohort of variant-associated oncogenic and tumor suppressive CREs, and established a new molecular link between non- coding regulatory variants and nuclear receptor signaling in modifying gene programs in hematopoietic malignancies.

RESULTS

Identification of non-coding DNA alterations by targeted CRE resequencing

To determine whether human leukemia genomes harbor recurrent non-coding alterations, we generated mutational landscapes of CREs by targeted resequencing (Fig. 1A; Fig. S1). We first annotated hematopoietic lineage-associated CREs based on genome-wide profiling of active enhancer or promoter-associated H3K27ac in various normal and diseased hematopoietic cell types, including CD34+ hematopoietic stem/progenitor cells (HSPCs), lymphocytes, macrophages, monocytes, erythroblasts, lymphoma, acute lymphoblastic leukemia (ALL), and AML (total 72 ChIP-seq datasets for 51 normal and 21 disease samples, respectively; Table S1 and Fig. S1A,B). We then designed a target enrichment panel consisting of 25.2 Mb DNA sequences and 161,328 capture oligos for 22,262 blood cell-associated CREs and 86 leukemia- associated genes including DNMT3A, TET2, IDH2, and EZH2 (Table S2 and Fig. S1). Targeted resequencing was performed on 120 primary samples or leukemia cell lines consisting of 45 AML or myelodysplastic syndromes (MDS) samples, 24 lymphoma samples, 19 ALL samples, and 32 control samples (29 matched lymphocytes from AML or MDS samples, two CD34+ HSPCs from healthy donors and one mixed genomic DNA control; Table S3). The samples were sequenced to an average depth of 80x and 97% on-target enrichment.

A major challenge in non-coding genome sequencing studies has been the lack of a consensus approach for de novo mutation detection with maximized sensitivity and confidence. We sought to identify somatic non-coding variants including single nucleotide variants (SNVs) and insertions/deletions (INDELs) with a higher confidence, rather than to ascertain a larger number

of possible mutations with maximal sensitivity. To this end, we integrated 5 commonly used mutation callers including GATK (21), MuTect (22), Strelka (23), Scalpel (24) and VarScan2 (25), and employed a combination of 4 callers (GATK, MuTect, Strelka and VarScan2) to identify somatic SNVs and another combination (GATK, Strelka, VarScan2 and Scalpel) for somatic INDELs in 29 tumor/normal paired AML samples, respectively (Figs. S1C and S2A). After filtering with RepeatMasker region, high-confidence (Tier I) somatic SNVs and INDELs were identified as those called by at least 2 of 4 mutation callers (Fig. S2A; Table S4). We next identified non-coding variants in all leukemia or lymphoma samples and cell lines by GATK (21) followed by removing common variants found in dbSNP database and/or RepeatMasker regions. The resulting variants were identified as Tier II somatic SNVs and INDELs (Fig. S2B; Table S4). The majority of the identified high-confidence somatic SNVs (76.7%) and INDELs (82.4%) have variant allele frequencies (VAF) less than 50% (Fig. S2C).

We applied the same mutation calling pipeline to the 86 leukemia-associated genes included in the target enrichment panel, and identified recurrently protein-coding gene mutations (Fig. S3A). The mutation rate of coding genes is comparable to or slightly higher than other published studies (Fig. S2D). By combining somatic variants in all leukemia and lymphoma samples, we identified 0 to 446 (median 75) coding mutations and 8 to 530 (median 99) non-coding mutations per megabase DNA in each sample, respectively. The frequencies of both coding and non-coding mutations were significantly higher in AML, lymphoma and ALL samples compared to normal HSPCs or the mixed genomic DNA control (Fig. S3A,B), but not significantly different between samples isolated from bone marrow (BM) and peripheral blood (PB), male and female, AML subtypes (FAB M0 to M4), or different age groups (Fig. S3C-E). By clustering analysis of all non-coding variants across the genome, we identified 4,076 mutational hotspots containing non-coding variants within 100bp of each other (Fig. 1B; see Methods).

To identify recurrently mutated CREs harboring somatic non-coding variants, we mapped the identified SNVs and INDELs to the 22,262 H3K27ac-associated CREs, respectively. CREs containing SNVs in at least six independent leukemia samples or INDELs in at least three leukemia samples were identified as recurrently mutated CREs based on a binominal distribution test (Fig. S2E; see Methods). We reasoned that multiple independent non-coding variants may impact the same CRE by affecting the binding sites (or motifs) of TFs in each CRE. Thus, we required that the same CRE to be recurrently mutated in independent samples, whereas the non-coding variants can be identical or different within the same CRE. By this analysis, we identified 1,836 recurrently mutated CREs containing leukemia-associated non- coding variants (Fig. S2E). The gene targets and functional roles of the vast majority of the identified variant-associated CREs in leukemia pathophysiology remain unknown.

High-throughput functional analysis of recurrently mutated CREs in leukemia

A major limiting step towards understanding non-coding variants in cancer pathophysiology is the lack of a high-throughput and unbiased approach to assess their functional effects. To

overcome this challenge, we recently engineered enhanced CRISPR/dCas9-based epigenetic perturbation systems, enCRISPRi and enCRISPRa, for targeted modulation of CRE activities in native chromatin in situ and in vivo (Fig. 2A) (26). Specifically, we employed the sgRNA design containing two MS2 hairpins recognized by the MCP RNA-binding domains (27). For CRE inhibition (enCRISPRi), we fused dCas9 with LSD1 (or KDM1A), which catalyzes the removal of H3-Lys4 mono- and di-methylation (H3K4me1/2). We next engineered CRE-targeting sgRNAs containing MS2 hairpins to recruit the MCP-KRAB repressor domains. For CRE activation (enCRISPRa), we fused dCas9 with the core domain of p300, which catalyzes H3K27ac, together with sgRNA-MS2 to recruit the MCP-VP64 activator domains. Since H3K27ac and H3K4me1/2 are the signature histone marks for active enhancers and/or promoters (2,28), by inducible expression of dCas9-LSD1 (or dCas9-p300), sgRNA-MS2, and MCP-KRAB (or MCP- VP64), we engineered a CRE-targeting dual repressor or activator system (Fig. 2A) (26).

To investigate the functional role of the recurrently mutated CREs in human leukemia, we performed enCRISPRi and enCRISPRa-mediated perturbation screens in an established human AML cell line MKPL-1 (Fig. 2A). We first designed a pooled sgRNA library containing 4 or 5 individual sgRNAs per CRE (total 9,224 sgRNAs for 1,836 recurrently mutated CREs) and 198 non-targeting sgRNAs as negative controls (Fig. S4A; Tables S5 and S6). We then generated AML cell lines stably expressing Dox-inducible enCRISPRi or enCRISPRa transgenes (Fig. 2B). Upon lentiviral transduction of pooled sgRNAs at MOI < 0.5, the transduced cells were selected, induced for enCRISPRi or enCRISPRa expression, and cultured for 28 days before harvesting the genomic DNA. The sgRNA sequences were PCR amplified and measured by next-generation sequencing to determine the enrichment or dropout in day 28 (T28) relative to day 0 (T0) cells. We performed independent enCRISPRi and enCRISPRa perturbation screens (each with 3 replicate experiments) using the same pooled sgRNAs, respectively. The results from replicate screens were highly consistent (Fig. 2C and Fig. S4B-D). Moreover, the negative control sgRNAs displayed no or minimal dropout or enrichment relative to CRE-targeting sgRNAs in enCRISPRi and enCRISPRa screens (P = 0.69 and 0.472 by Welch’s t-test, respectively; Fig. S4E,F).

We reasoned that CREs showing strong leukemia cell growth inhibition (e.g. sgRNA dropout in T28 relative to T0) upon enCRISPRi-mediated repression and growth enhancement (e.g. sgRNA enrichment in T28 relative to T0) upon enCRISPRa-mediated activation should nominate candidate ‘oncogenic’ CREs. Likewise, CREs showing strong growth enhancement upon enCRISPRi and growth inhibition upon enCRISPRa should nominate candidate ‘tumor suppressive’ CREs. To this end, we combined results from enCRISPRi and enCRISPRa perturbation screens of 1,836 recurrently mutated CREs, and identified 131 candidate oncogenic CREs with significant sgRNA dropout by enCRISPRi (log2 fold change ≤ -1.5 in T28 relative to T0) and sgRNA enrichment by enCRISPRa (Figs. 2D and S4E,F; Table S7). Similarly, we identified 87 candidate tumor suppressive CREs, which displayed significant sgRNA dropout by enCRISPRa (log2 fold change ≤ -1.5 in T28 relative to T0) and sgRNA enrichment by enCRISPRi (Figs. 2D and S4E,F; Table S7). We next categorized all CREs into

annotated promoters and enhancers, and identified the candidate oncogenic or tumor suppressive promoters and enhancers, respectively (Fig. S5A,B; Table S7). The candidate oncogenic or tumor suppressive promoters include several known leukemia-associated genes such as RUNX1, TAL1 and MSI2 (Fig. S5A). Importantly, while several candidate enhancers locate in the proximity of known leukemia-associated genes such as MYB and BCAT1, the vast majority of their gene targets are unknown, and their functional and mechanistic roles in leukemia remain unexplored. In addition, the oncogenic CRE-associated genes displayed strong dependency by CRISPR/Cas9-based screens in multiple AML cell lines (Fig. S6) (29). By comparing the identified 218 functional CREs (131 oncogenic and 87 tumor suppressive) with CREs containing non-coding mutational hotspots, we observed that 82 of 218 (37.6%) functional CREs overlapped with hotspot-containing CREs, indicating that the functional CREs are enriched with non-coding mutational hotspots (P = 4.0e-15 by hypergeometric distribution; Fig. 2E).

To validate the efficacy of enCRISPRi and enCRISPRa-mediated CRE perturbations on target gene expression, we cloned individual sgRNAs for 14 CREs including 5 candidate oncogenic CREs, 4 tumor suppressive CREs, and 5 other CREs (see Methods; Table S5). Upon co- expression of enCRISPRi or enCRISPRa with individual CRE-specific or non-targeting (sgGal4) sgRNAs in MKPL-1 cells, we noted that enCRISPRi resulted in significant downregulation of 13 of 14 genes relative to sgGal4 (≥ 2-fold, P < 0.05; Figs. 2F and S7A-C). Similarly, enCRISPRa resulted in significant upregulation of 12 of 14 genes (≥ 2-fold, P < 0.05). Importantly, we also observed significant gene repression or activation when targeting enCRISPRi or enCRISPRa to 4 of 5 other CREs (Figs. 2F and S7C), whereas no significant sgRNA enrichment or dropout was observed in the enCRISPRi/a screens (Fig. 2D). These results not only provide direct evidence to validate the efficacy of enCRISPRi/a in transcriptional repression/activation of CRE- associated genes, but also demonstrate that the lack of functional effect on AML cell growth (e.g. sgRNA enrichment or dropout) was not due to insufficient CRE repression/activation. To determine the efficacy of enCRISPRi/a across cell lines, we performed similar analyses in MOLM-13 AML and Jurkat ALL cells. We found that 13 of 14 genes were significantly repressed in AML (MKPL-1 and MOLM13) and ALL (Jurkat) cells by enCRISPRi (≥ 2-fold, P < 0.05), and 10 of 14 genes were significantly activated in AML and ALL cells by enCRISPRa (≥ 2-fold, P < 0.05) (Figs. 2F and S7A-C). By comparing the gene expression changes of all 14 CRE perturbations in AML (MKPL-1) and ALL (Jurkat) cells, we observed a significant and positive correlation of the targeted CREs (Pearson correlation coefficient R = 0.844, P < 0.0001; Fig. 2G). These results demonstrate that enCRISPRi and enCRISPRa resulted in consistent repression or activation of CRE-associated genes in both AML and ALL cells.

An enhancer controlling KRAS proto-oncogene harbors non-coding variant in leukemia

By enCRISPRi and enCRISPRa perturbation screens, we identified CRE4399, located ~135kb upstream of KRAS gene, as one of the variant-associated oncogenic enhancers (Figs. 2D and S5B). KRAS is a proto-oncogene that encodes a member of the small GTPase superfamily.

Activating mutations in KRAS are common in solid tumors but relatively rare in hematologic malignancies (30,31); however, KRAS expression is increased and associates with poor survival in AML patients (Fig. S8A,B). CRE4399 displays strong chromatin accessibility by DNase I hypersensitivity (DHS), ATAC-seq and enrichment of H3K27ac signals, and interacts with the KRAS promoter through DNA looping by RNA Pol II (RNAPII)-mediated ChIA-PET or Hi-C analyses in normal CD34+ HSPCs and leukemia cell lines (K562 and THP1) (Figs. 3A and S8C), consistent with a putative enhancer element for KRAS. One of the identified non-coding variants locates at chr12:25538881:C>T within CRE4399 in an AML sample. To establish the functional requirement of variant-associated CRE4399 in KRAS gene regulation, we first performed CRISPR/Cas9-mediated knockout (KO) of CRE4399 in MKPL-1 cells (Fig. S10A,B). We found that the expression of KRAS mRNA was significantly impaired in multiple single-cell- derived CRE4399 KO clones, whereas the neighboring genes within the same topologically associating domain (TAD) were not affected (Figs. 3B and S10A,B). These results demonstrate that CRE4399 functions as a gene-distal enhancer for KRAS and is required for KRAS expression in AML cells.

We next determined the requirement of CRE4399 for AML cell growth by Dox-inducible enCRISPRi-mediated negative selection competition analysis in MKPL-1 cells (Figs. 3C and S8D; see Methods). We found that inducible repression of the KRAS enhancer (CRE4399) by independent sgRNAs resulted in consistent and significant inhibition of AML cell growth in vitro. Finally, we assessed the functional role of the KRAS enhancer in AML growth in vivo using xenotransplantation assays (Fig. 3D). As the positive control, KO of KRAS coding DNA sequences (KRAS KO) significantly impaired MKPL-1 cell growth in xenotransplanted NSG (NOD-scid IL2Rgnull) mice (Fig. 3D,E), consistent with the oncogenic role of KRAS in AML. Importantly, KO of KRAS enhancer (two independent single-cell-derived CRE4399 KO1 and KO2 clones) also impaired AML growth, resulting in less tumor burden with significantly decreased bioluminescence signals of the luciferase-expressing AML cells 4 weeks post- transplantation (Fig. 3D,E). Compared to WT control cells, KRAS enhancer KO cells led to significantly less leukemic burden in bone marrow and peripheral blood of xenotransplanted recipients by flow cytometry and bloodsmear analyses (Fig. 3F,G). These results demonstrate that KRAS and its variant-associated enhancer are required for AML cell growth in vitro and in vivo.

KRAS enhancer variants co-localize with nuclear receptor binding sites

Non-coding variants can interfere with transcriptional regulation by modulating the DNA binding activity of TFs. By surveying candidate TF binding sites (or motifs) within or near KRAS enhancer-associated variant, we identified several motifs for nuclear receptor (NR) family TFs including PPARG (PPARγ) and RXRA (RXRα) that co-localize with the KRAS enhancer variant (Fig. 3H). The canonical sequence of PPAR response element (PPRE) is composed of two AGGTCA-like motifs directionally aligned with a single nucleotide spacer. When heterodimerized with RXRA, the PPARG:RXRA motif may contact two half sites from PPARG

and RXRA, respectively. It is important to note that PPARs may recognize a 12-bp DNA sequence (WAWVTRGGBBAH) instead of the generally accepted 6-bp sequence (AGGTCA) in vitro and in vivo (32). The optimized RXRA hexad binding sequence is RGKTYA, which makes the optimal PPAR/RXRA binding sequence as WAWVTRGGBBAHRGKTYA (32). The predicted PPARG:RXRA motif at the KRAS enhancer appears to match the optimized PPRE with the AML-associated non-coding variant overlapping with one of the half-sites (Fig. S8E). We next determined whether the KRAS enhancer harbors recurrent mutations in other leukemia samples or other types of human cancers. By analyzing the targeting CRE sequencing and whole genome sequencing (WGS) datasets from ICGC (http://icgc.org), COSMIC (33) and cBioPortal (34), we identified 13 additional variants at the KRAS enhancer in 18 individual cancer samples including T-ALL, breast, lung, and pancreatic cancers (Figs. 3I and S8E; Table S8). Importantly, 8 of 14 unique mutation sites at KRAS enhancer locate in the proximity (+/- 10bp) of binding sites for PPARG/RXRA and/or PPARs (Fig. 3I and S8E), suggesting that non-coding variant- mediated alterations of NR binding at KRAS enhancer may be a generalizable mechanism in cancer cells.

PER2, a clock gene, is controlled by a distal enhancer in leukemia cells

By CRE perturbation screens, we also discovered that CRE12661 near the PER2 gene was among the candidate tumor suppressive enhancers in AML cells (Figs. 2D and S5B). PER2 acts as a negative regulator of CLOCK and BMAL1 genes in controlling circadian rhythm (35). Notably, Per2 mutant mice are deficient in circadian clock function and are unusually cancer prone (36). These mice show a neoplastic growth phenotype and an increased sensitivity to γ radiation, indicating a tumor suppressive role. Lowered PER2 expression is common in many tumors including AML (37,38) and is associated with poor survival (Fig. S9A,B), whereas forced expression of PER2 leads to growth inhibition and apoptosis of cancer cells (37). Emerging evidence in several in vivo models also points to important roles of other core circadian genes in tumorigenesis (39,40). Disruption of the circadian machinery leads to changes in cellular function such as cell division and metabolism, both highly relevant to cancer. Despite these findings, the function and regulation of PER2 in human leukemia remain poorly understood.

Located ~11kb downstream of the PER2 gene, CRE12661 displays strong chromatin accessibility and H3K27ac, and interacts with the PER2 promoter by DNA looping in normal CD34+ HSPCs and leukemia cell lines (Figs. 4A and S9C), consistent with a putative gene-distal enhancer for PER2. Using similar approaches, we found that CRISPR/Cas9-mediated CRE12661 KO resulted in significant downregulation of PER2 without affecting neighboring genes within the same TAD in MKPL-1 cells (Figs. 4B and S10C,D). Moreover, inducible enCRISPRa-mediated activation of CRE12661 resulted in consistent and significant inhibition of MKPL-1 cell growth in vitro (Figs. 4C and S9D), consistent with the tumor suppressive function of PER2 enhancer in leukemia. To establish the requirement of PER2 enhancer in leukemia development in vivo, we xenografted CRE12661 KO MKPL-1 cells (two independent single-cell- derived KO1 and KO2 clones) together with PER2 overexpression (OE) cells as controls. We

observed that CRE12661 KO significantly promoted AML growth, tumor burden, and leukemic phenotypes in xenotransplanted NSG mice, whereas PER2 OE significantly inhibited leukemia development in vivo (Fig. 4D-G). Together, these results not only identify a variant-associated gene-distal enhancer for PER2, but also demonstrate that PER2 and its enhancer play a tumor suppressive role in AML cells.

Non-coding variants at a PER2 enhancer co-localize with NR binding sites

By targeted CRE sequencing, we identified two high-confidence non-coding variants within the PER2 enhancer (CRE12661) including chr2:239142998:A>CA in an AML sample. By surveying candidate TF binding sites near the PER2 enhancer variant, we also identified enrichment of PPARG:RXRA and PPARA:RXRA motifs within 10bp of the variant (Figs. 4H and S9E). The predicted PPARG:RXRA motif at the PER2 enhancer contain only one conserved half-site that matches the canonical PPRE, suggesting that it may be a non-canonical PPRE or a weak canonical PPRE (41). Of note, the identified AML-associated variant (chr2:239142998:A>CA) at the PER2 enhancer locates 6bp upstream of the predicted PPARG:RXRA motif (Fig. S11A). The 5’ flanking sequences at PPAR/RXR binding sites have also been shown to be essential for PPAR binding and NR subtype specificity (42-44). Therefore, the AML-associated non-coding variant at PER2 enhancer may also interfere with NR function by affecting the 5’ flanking sequence of the PPARG/RXRA binding site. Moreover, analysis of the targeted CRE sequencing and WGS datasets from ICGC, COSMIC and cBioPortal revealed 10 additional non- coding variants at the PER2 enhancer in 15 individual cancer samples (Fig. 4I; Table S8). Importantly, 9 of 11 unique mutation sites at PER2 enhancer locate in the proximity (+/- 10bp) of binding sites for PPARG/RXRA and/or PPARs (Figs. 4I and S9E), consistent with a broad role for non-coding variants in modulating NR binding at the PER2 enhancer in cancer cells.

Non-coding variants modulate NR-mediated transcriptional activity at KRAS and PER2 enhancers

To directly assess whether the non-coding variants impair NR-dependent transcriptional activities at KRAS and PER2 enhancers, we performed enhancer reporter assays in 3 independent cell types including AML (MKPL-1), erythroleukemia (K562) and 293T (Figs. 5A-C and S11B-E). Compared to the no enhancer control, wild-type (WT) KRAS enhancer sequence significantly activated luciferase reporter expression in all 3 cell types (3.0 to 7.9-fold, P < 0.01; Fig. S11B). The leukemia variant-containing KRAS enhancer sequence (MUT) further increased reporter expression (6.1 to 15.4-fold relative to control, P < 0.01; 1.4 to 2.2-fold relative to WT, P < 0.01). Importantly, activation of RXRs and RARs by 9-cis-RA (45), PPARs by bezafibrate (46) or PPARγ (PPARG) by Rosiglitazone (47), but not PPARα or PPARβ (fenofibrate and GW0742), further enhanced WT KRAS enhancer activity (2.0 to 3.2-fold relative to DMSO, P < 0.001; Figs. 5B and S11D), consistent with the presence of PPRE at the KRAS enhancer. Both PPARG and RXRA are expressed in 3 cell types (Fig. S11F). These results suggest that the KRAS enhancer contains a functional PPRE and PPARG/RXRA can activate KRAS enhancer

upon ligand-induced NR signaling. More importantly, KRAS mutant enhancer displayed increased enhancer activity upon activation of RXR/RAR and PPARG signaling (4.4 to 5.8-fold relative to DMSO, P < 0.001; Figs. 5B and S11D), whereas depletion of PPARG and RXRA significantly impaired NR-mediated activation of KRAS WT and MUT enhancers in AML cells (Fig. S11G-I), suggesting that the non-coding variant at the KRAS enhancer increased both the basal and NR-induced enhancer activities in leukemia cells. Consistent with these findings, we found by ChIP-seq and ChIP-qPCR that RXRA and PPARG strongly associate with the endogenous KRAS enhancer in multiple human cancer cell types including MKPL-1 AML cells (Fig. 5D-G).

To test whether the non-coding variants indeed enhance NR binding at the KRAS enhancer, we performed a series of electrophoretic mobility shift assay (EMSA) experiments (Fig. 5H). First, we confirmed the binding of PPARG and RXRA individually or together using the validated PPARG:RXRA motif-containing DNA probe (Fig. S11J; Table S5). Second, we compared the binding affinity of PPARG/RXRA to the WT or MUT CRE4399 DNA sequence, and observed a modest but significant increase in PPARG/RXRA binding at the MUT sequence (Figs. 5H and S11K). Third, the PPARG/RXRA-binding probe, but not the non-specific competitor, effectively abolished protein binding to both WT and MUT sequences, indicating specific binding of PPARG/RXRA proteins. Finally, PPARG and RXRA antibodies further increased the mobility shift of protein-DNA complexes (supershift; Fig. 5H), suggesting that binding to the KRAS enhancer sequence involves PPARG/RXRA proteins. A significant increase in supershift was also observed at the MUT relative to WT sequence (Figs. 5H and S11K). Together, these results support a model that non-coding variant at KRAS enhancer functions to increase the DNA binding of NR proteins and enhance NR-mediated transcriptional activation of KRAS expression in leukemia cells.

We next investigated the role of AML-associated variant (chr2:239142998:A>CA) at the PER2 enhancer with or without NR agonists. PER2 WT enhancer sequence activated luciferase reporter expression by 2.3 to 35.3-fold, which was significantly impaired by the variant- containing MUT enhancer sequence (Fig. S11C). Activation of RXR/RAR by 9-cis-RA, PPARs by bezafibrate, or PPARG by Rosiglitazone further enhanced WT PER2 enhancer activity by 3.9- to 4.8-fold; however, the variant-containing MUT enhancer displayed decreased activity (Figs. 5C and S11E). Depletion of PPARG and RXRA significantly impaired the PER2 WT and MUT enhancer activity (Fig. S11I), suggesting that the PER2 enhancer variant attenuates both the basal and NR-induced enhancer activities. RXRA and PPARG strongly associated with the PER2 enhancer in cancer cell types including AML cells (Fig. 5E-G). By a series of EMSA experiments, we observed that the PER2 MUT enhancer sequence significantly impaired the binding of PPARG and RXRA proteins (Figs. 5I and S11K). Similar to the KRAS enhancer, PPARG/RXRA binding to the PER2 enhancer was effectively abolished by the PPARG/RXRA- binding probe and supershifted by PPARG/RXRA antibodies. Together, these results support a model that the non-coding variant at the PER2 enhancer functions to impair NR-mediated PER2 activation in leukemia.

Knock-in of non-coding variants modulates KRAS and PER2 enhancer activity in situ

Enhancers function by recruiting sequence-specific TFs, co-regulators and chromatin factors in a native chromatin to control spatiotemporal gene transcription. We next determined whether the leukemia-associated variants impact endogenous enhancer activity by site-specific knock-in (KI) of non-coding variants to the wild-type enhancer sequences in leukemia cells. To this end, we designed sgRNA targeting the endogenous KRAS (CRE4399) or PER2 (CRE12661) enhancer sequence, respectively. We next co-expressed the Cas9 nuclease, KRAS or PER2 enhancer-targeting sgRNA, and the single-strand donor oligo (ssDNA donor) containing the targeted variant and PAM site mutation, which rendered the targeted KI allele resistant to Cas9 cleavage, in human K562 leukemia cells (Fig. 6A). Upon Cas9/sgRNA-mediated cleavage of the targeted WT enhancer DNA, the KI allele containing the non-coding variant was introduced by homology directed repair (HDR).

We generated two independent single-cell-derived KI cell lines (KI-Mut1 and KI-Mut2), each containing non-coding variant at one of the KRAS (CRE4399) or PER2 (CRE12661) enhancer sequence as monoallelic KI, respectively (Fig. S12A,B). We first determined the effects of enhancer variants on the baseline and NR-induced gene expression. Compared to the unmodified WT cells, KI of KRAS enhancer variant (chr12:25538881:C>T) increased the baseline KRAS expression in DMSO-treated KI-Mut cells (2.7 and 2.2-fold, P < 0.001; Fig. 6B). Upon activation of NR signaling by treatment with 1µM 9-cis-RA and 5µM Rosiglitazone, the expression of KRAS was further increased in KI-Mut relative to WT cells (5.5 and 5.1-fold, P < 0.001). These results suggest that the leukemia-associated non-coding variant functions to enhance NR-mediated transcriptional activation of KRAS enhancer in leukemia cells. By contrast, KI of PER2 enhancer variant (chr2:239142998:A>CA) led to modest but significant downregulation of the baseline and NR-induced PER2 expression (Fig. 6C), consistent with the role of PER2 enhancer variant in attenuating its activity in leukemia. We next determined the effects of enhancer variants on the binding of nuclear receptors in native chromatin by ChIP assays using antibodies for PPARG and RXRA in KI-Mut cells. The NR-immunoprecipitated or input control DNA were quantified for allele frequency by amplicon sequencing. Compared to input DNA, PPARG/RXRA ChIP DNA were significantly enriched with KI-Mut relative to WT alleles in two independent KI cell lines (Fig. 6D), suggesting that the KRAS enhancer variant enhanced NR chromatin occupancy in leukemia cells. By contrast, the PER2 KI-Mut allele was significantly depleted relative to WT allele in PPARG/RXRA ChIP DNA (Fig. 6E), suggesting that the PER2 enhancer variant impaired NR chromatin occupancy in leukemia cells. Therefore, by site-specific KI to endogenous enhancer sequences, our findings demonstrate that the leukemia-associated non-coding variants modulate NR chromatin occupancy at KRAS and PER2 enhancers in situ, resulting in altered enhancer function and aberrant expression of KRAS and PER2 in leukemia cells.

We next determined the effect of KRAS or PER2 enhancer KI on leukemia development by xenotransplantation of WT or KI cells to NSG mice. We found that KRAS enhancer KI enhanced

leukemogenesis, resulting in significantly increased leukemia burden in the peripheral blood of recipients (Fig. S12C). These results are consistent with the oncogenic role of KRAS enhancer variant in AML by enhancing NR-mediated activation of the proto-oncogene KRAS. Similarly, we noted enhanced leukemia development by PER2 enhancer KI (Fig. S12D), consistent with the function of PER2 enhancer variant in leukemia by impairing NR-mediated activation of the tumor suppressor PER2. Finally, we performed motif enrichment analysis of DNA sequences surrounding the non-coding variants (+/- 25bp) at the functionally validated oncogenic CREs from enCRISPRi and enCRISPRa screens (Fig. 2D). We also identified PPAR, RXRA and RARA as the top enriched NR motifs relative to genomic background (Fig. 6F), suggesting that alterations of NR transcriptional activity by non-coding variants are frequent events in human leukemia. Our analyses of two examples (KRAS and PER2) strongly support the context- specific function of non-coding variants that may confer either loss- or gain-of-function activity (Fig. 6G). Importantly, loss- and gain-of-function activities associated with enhancer variants can be explained by the interference with DNA binding of NRs and the signaling-induced transcriptional activity for at least a subset of affected CREs in vitro and in native chromatin. Taken together, these studies support a model that pathogenic non-coding variants cooperate with nuclear receptors to rewire signaling-dependent gene programs in cancer.

DISCUSSION

Non-coding variants in cancer pathophysiology

Compared to coding mutations that often contribute to cancer development through ‘qualitative’ changes in protein expression, structure or function, non-coding variants display several unique features including: 1) ‘quantitative’ control of gene expression through modulation of cis- regulatory elements and/or 3D genome organization; 2) integration with gene transcription through modulation of DNA binding of TFs, chromatin modifying complexes and/or structure proteins; 3) interaction with cellular signaling pathways and downstream effectors, as illustrated in this study. These features allow a more ‘plastic’ and reversible control of gene expression in response to altered growth and signaling pathways, which may be important for the maladaptation of cancer cells in tumor microenvironments.

Given that non-coding sequences consist of nearly 99% of human genome, and the incidences of non-coding variants significantly outnumbered coding variants, a major challenge is to assign functional relevance and prioritize non-coding variants for in-depth mechanistic studies. The development of the CRE-targeting epigenetic editing system allowed us to systematically interrogate the impact of CRE repression or activation on cell growth phenotypes, and prioritize functionally validated CREs in an orthogonal fashion with mutational analyses. One caveat of using cell growth phenotypes as the functional ‘readout’ is that CRE variants may affect other pathological features such as cancer invasion and/or escape from immune surveillance that are likely missed in growth-based studies. Integrative analyses of both CRE repression (enCRISPRi) and activation (enCRISPRa) screens provide higher confidence to establish the

causality between CRE activity and growth phenotypes. It is reassuring that the top ranked ‘oncogenic’ or ‘tumor suppressive’ CREs consist of promoters or enhancers in the proximity of several known leukemia-associated genes such as RUNX1, MYB, TAL1 and MSI2. More importantly, we also identified variant-associated enhancers for KRAS and PER2 with unexpected but strong effects on cell growth upon activation or repression. Overall, the mutational analyses (Fig. 1), CRE perturbation screens (Fig. 2), functional (Figs. 3,4) and molecular (Figs. 5,6) studies support the model that recurrent non-coding variants impinge on CREs and associated genetic pathways as critical regulatory nodes to control cancer cell phenotypes.

We noted heightened mutation frequency at non-coding mutational hotspots relative to protein- coding sequences or other genomic regions. The increased mutation frequency at the non- coding regulatory genome may be due to mechanisms associated with DNA accessibility, replication timing, chromatin organization and/or impaired nucleotide excision repair (48-51). The higher sequencing coverage in this study may also increase the detection sensitivity in heterogeneous tumor samples. It is also important to note the limitations of using the human reference genome in enhancer annotation and mutation analyses. For instance, genome rearrangements may cause aberrant CRE function by juxtaposition and/or re-organization of chromatin structure, and these aberrations can be missed by short read sequencing. In addition, it is possible that some of the highly recurrent non-coding mutations may be due to mis- annotation and/or non-representative sequences in the reference genome. Hence our studies highlight the importance of follow-up studies, such as the enCRISPRi and enCRISPRa screens described here, to identify ‘functionally’ relevant CREs or genes for detailed molecular studies.

KRAS and PER2 enhancer variants in hematopoietic malignancies

By demonstrating that the KRAS enhancer variant confer gain-of-function activity to promote KRAS expression in leukemia cells, our results provide a mechanism that may explain the lack of KRAS hotspot coding mutations compared to other RAS family proteins such as NRAS. NRAS hotspot coding mutations (e.g. G12D, G13D and Q61R) are prevalent in hematologic malignancies, whereas KRAS mutations are rarely found (30,31). KrasG12D mutation was found to impair normal HSC self-renewal and differentiation of multiple hematopoietic lineages even in the heterozygous state, resulting in rapid and fatal myeloproliferative diseases in mice (52-55), suggesting that KRAS coding mutations may be detrimental to normal HSCs. By contrast, modest overexpression (~2-fold) of the wild-type KRAS protein in mice increased HSC proliferation and repopulating activity, and improved hematopoietic regeneration and survival following high-dose irradiation (56). Non-coding variants at the KRAS enhancer resulted in quantitative changes in KRAS expression without qualitative alterations in KRAS protein, which may be advantageous for the clonal progression of leukemia-initiating cells without impairing normal HSCs. Hence, the dosage regulation of proto-oncogenes through altered non-coding cis- elements may expand the current catalog of genetic alterations as cancer drivers.

Our studies focusing on KRAS and PER2 also support the context-dependent function of non- coding variants that may confer loss- or gain-of-function activity. Interestingly, alterations at both enhancers can be explained by the interference with nuclear receptors to enhance or impair NR binding. Since KRAS and PER2 variant-containing enhancers are located 135kb and 11kb away from the coding sequences, respectively, it remains challenging to assess how enhancer variants affect allele-specific gene transcription at native chromatin in response to NR signaling. Nonetheless, site-specific KI of enhancer variants resulted in alterations of KRAS or PER2 expression in leukemia cells, respectively. Moreover, motifs for NRs are highly enriched in variant-associated enhancers in leukemia and other cancers, suggesting that altered NR binding by non-coding variants may be a generalizable mechanism to deregulate proto- oncogenes or tumor suppressors. These findings contrast with coding mutations that usually function through distinct mechanisms for proto-oncogenes (gain-of-function) and tumor suppressors (loss-of-function). Rather, enhancer variants for proto-oncogenes or tumor suppressors may converge on the same regulatory pathways, such as those reported here, and represent a unique feature of pathogenic non-coding variants.

Non-coding variants link enhancer dysfunction with NR signaling

Non-coding regulatory variants may modulate NR function in human diseases by affecting NR expression or their chromatin binding. For instance, deregulated ER signaling due to non-coding variants at cis-elements controlling the estrogen receptor-α (ESR1) gene underlies the persistent expression of ESR1 in breast cancers (57). Natural genetic variations at PPARG binding sites modulate PPARG binding activity and subsequent expression of quantitative trait loci associated with disease risk and drug response in diabetes (58). Here, we uncover a new molecular link between NR signaling and non-coding variants through dysregulated enhancer function in human leukemia. In hematopoiesis, RXRA controls a set of genes required for HSC self-renewal, and its downregulation allows the differentiation of myeloid lineages (20). Non- coding variants may enhance or attenuate PPAR/RXR binding at a subset of enhancers (e.g. KRAS and PER2) to modulate NR-mediated transcriptional programs. Altered expression of NR target genes may impair HSC self-renewal and/or lineage differentiation to promote the development of hematological disorders. Moreover, physiological or therapeutic activation of NR signaling may further cooperate with variant-containing enhancers, as illustrated in this study, resulting in amplification of the effects associated with dysregulated transcriptional programs. Therefore, our results support a mechanism underlying deregulated NR signaling through interactions with non-coding regulatory variants in human cancer. Comprehensive categorization of non-coding variants may identify new diagnostic biomarkers that are independent of protein-coding genes. Pathogenic non-coding variants may also serve as potential therapeutic targets through genetic (e.g. enCRISPRa, enCRISPRi or base editing) or pharmacological (e.g. NR agonists and antagonists) approaches. Hence, our study provides an integrative framework to identify and functionally dissect non-coding regulatory variants in human diseases including cancer.

AUTHOR CONTRIBUTIONS

Conceptualization, K.L., Y.Z., X.L., Y.L., M.N. and J.X.; Methodology, K.L., Y.Z., X.L., Y.L., Z.G., H.C., K.E.D., M.C., W.C., M.N. and J.X.; Investigation, K.L., Y.Z., X.L., H.C., M.N. and J.X.; Writing – Original Draft, K.L., Y.Z., X.L., Y.L. and J.X.; Writing – Review & Editing, J.X.; Funding Acquisition, J.X.; Supervision, J.X.

ACKNOWLEDGMENTS

We thank Liqiang Wang, Yi Du and David Trudgian at UTSW BioHPC for assistance, Lin Li for technical support, and other Xu laboratory members for helpful discussion and technical support. K.L. and Y.L. were supported by the Cancer Prevention and Research Institute of Texas (CPRIT) training grants (RP160157). X.L. was supported by the American Heart Association postdoctoral fellowship (18POST34060219). J.X. is a Scholar of The Leukemia & Lymphoma Society (LLS) and an American Society of Hematology (ASH) Scholar. This work was supported by the NIH grants R01DK111430 and R01CA230631, by the Leukemia Texas Foundation research award, by the CPRIT grants (RR140025, RP180504, RP180826 and RP190417), and by the Welch Foundation grant I-1942 (to J.X.).

METHODS

Patient Samples Primary human leukemia and lymphoma samples were collected for diagnosis and de-identified for our study. This study was approved by the Institutional Review Board at UT Southwestern (IRB STU 122013-023). Samples were frozen in fetal bovine serum (FBS) with 10% DMSO and stored in liquid nitrogen. Cells and Cell Culture Human MKPL-1, MOLM-13, K562, and Jurkat cells were cultured in RPMI1640 medium containing 10% FBS, 1% penicillin/streptomycin. 293T cells were cultured in DMEM medium containing 10% FBS, 1% penicillin/streptomycin. For agonists treatment, cells were cultured with 9-cis-RA (1µM, Sigma-Aldrich #R4643), ATRA (1µM, Sigma-Aldrich #R2625), Bezafibrate (60µM, Sigma-Aldrich #B9273), Fenofibrate (30µM, Sigma-Aldrich #F6020), GW0742 (0.05µM, Sigma-Aldrich #G3295) or Rosiglitazone (5µM, Sigma-Aldrich #R2408) for 48-72h, respectively. o All cultures were incubated at 37 C in 5% CO2. All cell lines were tested for mycoplasma contamination. No cell lines used in this study were found in the database of commonly misidentified cell lines that are maintained by ICLAC and NCBI BioSample. Mice and Xenograft Experiments NOD-SCID (NSG) mice were purchased from and maintained at the animal core facility of UT Southwestern. Animal studies were approved and conducted under the oversight of the Institutional Animal Care and Use Committee (IACUC) at UT Southwestern. In brief, luciferase cassette was amplified and cloned into pLVX-Puro vector (Clontech, Catalog No. 632164). Lentivirus was produced to infect the target cells. Puromycin selection (1 µg/ml) was performed 3 days after infection. Six to eight weeks old female NSG mice were sub-lethally irradiated (2.5 Gy) half day before the transplantation. Cells (1 x 106/mice) were resuspended in PBS (200 µl/mice) and intravenously transplanted. Transplanted mice underwent in vivo bioluminescence imaging at various time points to evaluate the tumor growth. Briefly, following intraperitoneal injection of 150 mg/kg D-luciferin (Gold Biotechnology), mice were imaged, and bioluminescence intensity was quantitated using Living Image 3.2 acquisition and analysis software (Caliper Life Sciences). Total flux values were determined by drawing regions of interest (ROI) of identical size over each mouse and were presented in photons (p)/second (sec). PER2 and KRAS enhancer KO cells were analyzed side-by-side using the same control mice, thus the same control bioluminescence images were used in Figs. 3D and 4D. Four weeks after transplantation, the peripheral blood and bone marrow cells were assessed for engraftment by flow cytometry. Bloodsmear was performed and stained with May-Grunwald- Giemsa. For enhancer KI xenotransplantation, cells were transduced with pLVX-EF1a-IRES- zsGreen1 lentivirus and intravenously transplanted as described above, followed by intraperitoneal injections of 10 mg/kg rosiglitazone and 1 mg/kg 9-cis-RA twice a week for 6 weeks prior to peripheral blood collection and analysis. Plasmids To generate the inducible dCas9-p300 expression vector, the p300 core domain was amplified from the pcDNA-dCas9-p300-Core vector (Addgene, #61357) and cloned into MluI/BstXI digested pHR-TRE3G-KRAB-dCas9-P2A-mCherry backbone (59). To generate the inducible dCas9-LSD1 expression vector, LSD1 open reading frame (ORF) was amplified and cloned into the pHR-TRE3G-KRAB-dCas9-P2A-mCherry to replace the KRAB domain. To generate the enCRISPRa sgRNA vector, the MCP-VP64-IRES-mCherry cassette was amplified from the

pJZC34 vector (Addgene, #62331) and cloned into BsrGI/EcoRI digested lenti-sgRNA (MS2)- zeo backbone (Addgene, #61427). Then the mCherry cassette was replaced by puromycin or zsGreen1 by In-Fusion® HD Cloning Kit (Clontech). To generate the enCRISPRi sgRNA vector, the KRAB sequence was amplified from the pLV hU6-sgRNA hUbC-dCas9-KRAB-T2A-Puro vector (Addgene, #71236) and cloned into the enCRISPRa sgRNA vector to replace VP64. The lentiviral shRNAs for PPARG (TRCN0000001673) and RXRA (TRCN0000330707) in pLKO.1 vector were obtained from Sigma-Aldrich. Annotation of Blood CRE Repertories and Target Genes We collected 72 independent H3K27ac ChIP-seq datasets from 17 studies (5,7,8,16,60-71) including normal (lymphocyte, monocyte, macrophage, erythroblast, and CD34+ HSPC) and diseased hematopoietic cell types (AML, erythroleukemia, T-ALL, T-lymphoma, and B- lymphoma) (51 normal and 21 disease samples; Table S1). The erythroleukemia K562 cell line was grouped with AML and MDS samples in this study. ChIP-seq raw reads were aligned to the human (hg19) genome assembly using Bowtie2 (72) with –k 1. Only tags that uniquely mapped to the genome were used. Peaks were identified by MACS version 1.4.2 (73) with P value of 10- 5, and ranked by fold enrichment and P value in each dataset. Then 150bp upstream and downstream regions from summits of the top 30% ranked peaks were combined based on subsets in normal and disease groups, respectively. To minimize the number of overlapping regions, the peaks within a distance of 200-400bp were clustered and the overlapping peaks were then merged using Galaxy interval operation tools (https://usegalaxy.org/). In total, we defined 22,262 H3K27ac-associated CREs with an average size of 952bp. For the genome- wide analysis, the nearest neighbor genes within 50kb of a given gene-distal CRE are assigned as the CRE-associated genes. For CREs that no gene was found within 50kb, the associated genes for these CREs were denoted as NA. For the single gene locus (e.g. KRAS and PER2), the CREs were assigned to candidate target gene(s) by the following criteria: 1) the presence of CRE-promoter interactions based on Hi-C and/or ChIA-PET analyses; 2) the expression of the candidate target gene(s) was significantly impaired upon CRISPR/Cas9-mediated KO of the candidate CREs. Human genome assembly (hg19) was used for all gene annotation analyses. Targeted CRE Resequencing Targeted resequencing of CREs was performed using Ovation® Target Enrichment System (NuGEN) following the manufacturer’s protocols. Briefly, probes were designed based on both sense and anti-sense DNA sequences of the targeted genomic region every 200~250bp. Genomic DNA (gDNA) from human primary leukemia samples, control samples or leukemia cell lines were isolated by DNeasy Blood & Tissue Kit (Qiagen) and quantified. After fragmentation to ~500bp using Covaris acoustic shearing following the manufacturer’s protocol, 10~500 ng of gDNA were end-repaired and individually ligated with barcoded adaptors. Following purification using AMPure XP beads (Beckman Coulter), samples are combined for multiplexed target enrichment. During this step, the targeting probes were annealed and extended to create amplifiable DNA molecules. Following a second bead purification, qPCR was performed to determine the optimal number of PCR cycles to use in the library amplification step. After library amplification, the multiplexed, target enriched libraries were purified, quantified, and sequenced on an Illumina NextSeq500 system using the 150bp high output sequencing kit. Initial data processing and alignments were performed using commonly used analytical tools. Specifically, for each sample, the FASTQ files were aggregated into single files for read 1 and 2. The adaptor and low-quality bases (quality < 20) were trimmed using trim_galore version 0.4.0 (https://www.bioinformatics.babraham.ac.uk/projects/trim_galore/) with parameter --stringency 1 -e 0.1 --length 20. Bowtie version 2.1.0 (72) was used to align the read pairs for each sample. PCR duplications were marked using a customized pipeline. Then Picard (version 1.127)

(https://broadinstitute.github.io/picard/) were used to sort and convert SAM to BAM files. RealignerTargetCreator/IndelRealigner from The Genome Analysis Toolkit (GATK version 3.3) (21) was used to realign the reads around insertions and deletions. BaseRecalibrator/PrintReads from GATK was used to perform base quality score recalibration. The resulting BAM files were then used as input for mutation discovery analysis. Mutation Discovery and Validation For tumor-normal pairs of primary leukemia samples, the single-nucleotide variations (SNVs) were identified using GATK (21), MuTect version 1.1.4 (22), Strelka version 1.0.14 (23) and VarScan2 version 2.3.9 (25). The insertions and deletions (INDELs) were identified using GATK, Strelka version 1.0.14, VarScan2 version 2.3.9 and Scalpel version 0.4.1 (24). MuTect version 1.1.4 and Strelka version 1.0.14 were run with default parameters. VarScan2 version 2.3.9 and Scalpel version 0.4.1 were run in somatic mode with the recommended filtering scheme. High-confidence (Tier I) somatic SNVs were defined as SNVs called by at least two out of four mutation callers (GATK, MuTect, Strelka and VarScan2). High-confidence (Tier I) somatic INDELs were defined as INDELs called by at least two out of four callers (GATK, Strelka, Scalpel and VarScan2). Variants located in repeat-masked regions were excluded from our analysis. Repeat-masked regions were downloaded from UCSC (https://genome.ucsc.edu/cgi-bin/hgTables). For all leukemia or lymphoma samples and cell lines, SNVs and INDELs were identified using GATK HaplotypeCaller. SNVs was filtered using - -filterExpression "QD < 2.0 || FS > 60.0 || MQ < 40.0 || MQRankSum < -12.5 || ReadPosRankSum < -8.0". INDELs was filtered using --filterExpression "QD < 2.0 || FS > 200.0 || ReadPosRankSum < -20.0". Common genetic variants included in dbSNP (build 138) were filtered. The resulting variants were identified as the Tier II somatic SNVs and INDELs. The variants in coding regions were annotated by annovar version 2015-06-17 (74). Synonymous SNVs were excluded. To validate the overall targeted sequencing and mutation calling pipelines, we included 3 control samples including two normal CD34+ HSPCs from healthy donors and one mixed genomic DNA control (Ctrl-17) containing equal molar ratio of gDNA from 17 individual non-cancer cell types. To identify recurrently mutated CREs, we determined the number of independent samples using a binomial distribution as previously described (75) with modifications. Briefly, we assumed that the observed number of mutated samples, k, for a CRE followed a binomial distribution, binomial (n, pi), where n was the total number of samples with mutation data and pi was the estimated sample mutation rate for CRE i under the null hypothesis that the region was not recurrently mutated. We could therefore compute the following P value:

P(X k) = 1 P(X < k) = 1 (1 ) ) 푘−1 푛 푗 푛−푗 푗 푖 푖 ≥ − − �푗=0 � � 푝 − 푝 Here we assumed that pi depended on the length Li of the CRE and the estimated nucleotide mutation rate qi for the region under the null hypothesis as follows: ~ 1 (1 ) 퐿푖 The background mutation frequency푝 푖qi was− estimated− 푞푖 by dividing the total number of observed mutations by the length of all CREs. By this analysis, we found that the probability of observing a 952bp CRE (the average size of all CREs) harboring SNVs in ≥ 2 of 29 tumor/normal pairs, ≥ 2 of 28 leukemia samples and ≥ 2 of 29 leukemia cell lines is 0.027. Similarly, the probability of observing a 952bp CRE harboring INDELs in ≥ 1 of 29 tumor/normal pairs, ≥ 1 of 28 leukemia samples and ≥ 1 of 29 leukemia cell lines is 0.098. Identification of Non-Coding Mutational Hotspots

Mutational hotspots were identified as previously described (75) with modifications. Specifically, all high-confidence Tier I mutations within 100bp of each other were merged using BEDTools (76) into hotspot clusters until no cluster was found within 100bp of another cluster. Clusters with only one or two mutations were removed from further consideration. A P value was calculated for each cluster using the negative binomial distribution, taking into account the length of the candidate hotspot cluster, the number of mutations in the cluster, and a background mutation rate for the cluster. The cluster background mutation rate was calculated as the mean of the background mutation probability for each sample that had a mutation represented in the cluster. The background mutation probability of each sample was calculated as the total number of mutations divided by the target region size. P values were adjusted for multiple testing with the multtest R package using the Benjamini-Hochberg method, and hotspot clusters were ranked accordingly. For the illustration of the top 100 recurrently mutated hotspots, Tier II non-coding mutations were included in order to compare mutations across different leukemia and lymphoma samples. Motif Analysis We scanned motifs around high-confidence somatic variants (Tier 1) at KRAS and PER2 enhancers using MotifScan tool (http://bioinfo.sibs.ac.cn/shaolab/motifscan/index.php). Briefly, to search for candidate motif targets in the given DNA sequences, the program scanned the sequences with a window of the same length as the motif, and defined raw motif score of the sequence S in the window as the ratio of the probability to observe target sequence S given the motif's Position Weight Matrix (PWM) M and the probability to observe S given the genome background B. For each annotated motif, we modeled the genome background distribution of motif scores by randomly sampling the genome 106 times, and defined the targets of this motif as the candidates with motif scores higher than the cutoff. The PWM matrix of motifs was downloaded from the Jaspar database (77). The enrichment of each motif was represented by the ratio of motif target densities at target regions compared to random control regions, together with a P value calculated from hypergeometric distribution. ChIP and Data Analysis ChIP-seq or ChIP-qPCR was performed as described (78) using antibodies for H3K27ac (Abcam, ab4729), PPARG (Santa Cruz, sc-271392) or RXRA (Santa Cruz, sc-515928) in HL60, K562, MKPL-1, MV4-11, THP1 and U937 cells, respectively. ChIP-seq libraries were generated using NEBNext Ultra II DNA library prep kit following the manufacturer’s protocol (NEB), and sequenced on an Illumina NextSeq500 system using the 75bp high output sequencing kit. ChIP- seq raw reads were aligned to the human hg19 genome assembly using Bowtie2 (72) with the default parameters. Only tags that uniquely mapped to the genome were used. ATAC-seq and Data Analysis ATAC-seq was performed in K562, MKPL-1, MV4-11, NB4 and U937 cells as previously described (79). ATAC-seq raw reads were trimmed to remove adaptor sequence and aligned to human genome assembly (hg19) using Bowtie2 (72) with default parameters. Only tags that uniquely mapped to the genome were used. ChIA-PET and Hi-C Data Analysis The RNAPII ChIA-PET data in K562 cells was downloaded from GEO with the accession number GSM970213. The in situ Hi-C data for K562 cells and CD34+ HSPCs were downloaded from GEO with accession numbers GSM1551618 and GSM2861708, respectively. The in situ Hi-C data for THP1 cells were downloaded from Juicebox (80). The processed bigwig or hic files

by Juicer (80,81) were visualized using epigenome browser (https://epigenomegateway.wustl.edu/) as ARC or heatmap. RNA Isolation and qRT-PCR Analysis Total RNA was isolated using RNeasy Plus Mini Kit (Qiagen) following manufacturer’s protocol. For quantitative RT-PCR (qRT-PCR) analysis, RNA was reverse-transcribed using iScript cDNA Synthesis Kit (Bio-Rad) following manufacturer’s protocol. qRT-PCR was performed using the iQ SYBR Green Supermix (Bio-Rad) with CFX384 Touch Real-Time PCR Detection System (Bio-Rad). PCR amplification parameters were 95oC (3 min) and 45 cycles of 95oC (15 sec), 60oC (30 sec), and 72oC (30 sec). Primer sequences are listed in Table S5. CRE Perturbation Screen To generate inducible enCRISPRi and enCRISPRa stable cell lines, MKPL-1 cells were transduced with lentiviruses expressing dCas9-LSD1 (or dCas9-p300) and rtTA. Doxycycline (Dox) was added following infection and flow cytometry was used to sort cells expressing mCherry and BFP. These cells were then grown in the absence of doxycycline until mCherry fluorescence returned to uninduced levels. 1,836 recurrently mutated CREs were selected for the perturbation screens. For each CRE, 4 or 5 sgRNAs were designed. Briefly, we divided each CRE into five equal segments, and then designed two or more sgRNAs in the segment closer to the ATAC-seq peak summit. All sgRNAs were designed and selected to minimize off- target cleavage based on publicly available filtering tools (http://crispr.mit.edu/). Negative control sgRNAs were also selected from the previous CRISPRi/a library (59). DNA oligonucleotide library synthesis was completed on a programmable microarray using a B3 Synthesizer (CustomArray). Full-length oligonucleotides (96nt) were amplified for 15 cycles by PCR using Phusion® High-Fidelity DNA Polymerase (referred to as PCR1). PCR2 was performed to remove barcodes and replace with enCRISPRa/i sgRNA vector homology sequences. The amplified sequences were purified for the Gibson assembly reaction. The enCRISPRi/a sgRNA vectors were digested by BsmBI (NEB), dephosphorylated and purified. Gibson assembly was performed to generate the sgRNA library following manufacturer’s protocol. Briefly, 1 μl of the Gibson Assembly reaction was added to 25 µl E.cloni 10G ELITE electro competent cells (Lucigen, 60052-4) and electroporated using Bio-Rad MicroPulser. The transformation was plated onto pre-warmed 24.5 cm2 bioassay plates with ampicillin. The colonies were counted to calculate the library coverage (> 200x). All colonies were collected, and maxiprep was performed to isolate the sgRNA library. Lentiviruses were produced as previously described (5). To perform high-throughput pooled enCRISPRi/a screening, the inducible enCRISPRi/a cell lines were infected with lentiviruses containing the sgRNA library at MOI < 0.5. Two days after infection, cells were selected with 1 µg/ml puromycin for 3 days, and then cultured in fresh medium for 48 hours. Cells were expanded to get enough coverage of sgRNAs (>1,000x) and gDNA were extracted (T0). The remaining cells were cultured in medium with Doxycycline (1 µg/ml) at a density between 500,000 and 1,000,000 cells/ml to maintain the library coverage of at least 1,000 cells per sgRNA. Genomic DNA was harvested from all samples at day 28 (T28), and the sgRNA-containing regions were amplified by PCR and sequenced on an Illumina NextSeq500 sequencer. All the primers are listed in Table S5. Three replicate experiments of each enCRISPRi or enCRISPRa screen were performed. sgRNA sequences were extracted from FASTQ files and matched to the sgRNA sequences of the enCRISPRi/a screen library. Reads of each sgRNA were counted and normalized to the total read counts for each sample. Pearson correlations between replicates were calculated using the log2 transformed sgRNA counts. Normalized sgRNA growth phenotypes were quantified as previously described (59). Specifically, the phenotype of each sgRNA was calculated by log2

transformed sgRNA ratio between end (T28) and starting time points (T0). To calculate phenotype relative to control samples, we further divided them by the median of negative control phenotypes. MAGeCK (82) was used to identify CREs positively or negatively enriched in each screen. We first determined the abundance of sgRNAs using MAGeCK “count” module in the raw FASTQ files. Then we used MAGeCK “test” module to test for robust gene-level enrichment with the following arguments: mageck test -k path/to/count_file -t end_timepoint -c start_timepoint -n sample_name --normcounts-to-file --norm-method total --gene-lfc-method secondbest. This step normalized sgRNA counts by total read counts in each sample and calculated log2 fold change (LFC) of each CRE using sgRNA with the second strongest LFC. To validate enCRISPRi and enCRISPRa screen results, we cloned 14 individual CRE-specific sgRNAs for 5 candidate oncogenic CREs (CRE18528, CRE18532, CRE19253, CRE726, and CRE4399), 4 tumor suppressive CREs (CRE16157, CRE9362, CRE11193, and CRE12661), and 5 other CREs (CRE17030, CRE4896, CRE2305, CRE13899, and CRE9836) into the MCP- KRAB-IRES-zsGreen1 (for enCRISPRi) and MCP-VP64-IRES-zsGreen1 (for enCRISPRa) vectors, respectively. Except two CREs (CRE4399 and CRE12661), the other CREs are annotated promoters. Individual enCRISPRi and enCRISPRa experiments using CRE-specific (sgCRE) or control (sgGal4) sgRNAs were performed in AML (MPKL-1 and MOLM-13) and ALL (Jurkat) cells, respectively, followed by the analysis of gene expression of CRE-associated genes after 72 hours of Dox-induced enCRISPRi or enCRISPRa expression. All sgRNA and primer sequences are listed in Table S5. Negative Selection Competition Assay Negative selection competition assay was performed to investigate the functional role of candidate CREs that may regulate the proliferation of MKPL-1 cells. Briefly, sgRNAs were cloned into the enCRISPRa or enCRISPRi sgRNA vectors containing a zsGreen1 reporter. Lentiviruses were produced to infect the enCRISPRa or enCRISPRi-expressing MKPL-1 cells. Cells were analyzed for zsGreen1 expression 3 days after infection by flow cytometry. Then doxycycline (1 µg/ml) was added to the medium to induce the expression of dCas9-p300 or dCas9-LSD1. Flow cytometry was repeated every 4 days to measure the percentage of sgRNA- expressing cells (zsGreen1+) and the data were normalized to the starting time point. All sgRNA sequences are listed in Table S5. Enhancer KO by CRISPR/Cas9 Enhancer KO in MKPL-1 cells was performed following published protocols (83,84) with modifications. Briefly, sgRNAs for site-specific cleavage of genomic targets were designed following described guidelines, and sequences were selected to minimize off-target cleavage based on publicly available filtering tools (http://crispr.mit.edu/). Oligonucleotides were annealed in the following reaction: 10 μM guide sequence oligo, 10 μM reverse complement oligo, T4 ligation buffer (1x), and 5U of T4 polynucleotide kinase with the cycling parameters of 37°C for 30 min; 95°C for 5 min and then ramp down to 25°C at 5°C/min. The annealed oligos were cloned into the pSpCas9(BB) (pX458) vector (Addgene, #48138) using a Golden Gate Assembly strategy including: 100 ng of circular pX458 plasmid, 0.2 μM annealed oligos, buffer 2 (1x) (NEB), 20 U of BbsI restriction enzyme, 0.2 mM ATP, 0.1 mg/ml BSA, and 750 U of T4 DNA ligase (NEB) with the cycling parameters of 20 cycles of 37°C for 5 min, 20°C for 5 min; followed by 80°C incubation for 20 min. To induce deletions of enhancer DNA regions, CRISPR/Cas9 constructs were transfected into MKPL-1 cells by nucleofection using the ECM 830 Square Wave Electroporation System (Harvard Apparatus). Each construct was directed to flanking the target genomic regions. To enrich for deletion, the top 1-5% of GFP+ cells were sorted 48-72 h post-transfection and plated in 96-well plates. Single-cell-derived clones were isolated and screened for CRISPR-mediated deletion. PCR amplicons were subcloned and 22

analyzed by Sanger DNA sequencing to confirm non-homologous end-joining (NHEJ)-mediated repair upon double-strand break formation. The positive single-cell-derived clones containing deletion of the targeted sequences were expanded and processed for analysis. Enhancer Reporter Assays The genomic sequences containing wild-type or mutant allele of the enhancers were amplified and cloned into the firefly luciferase reporter constructs pGL4.24[luc2P/minP]. The reporter constructs (1 µg) were co-transfected with pRL-SV40-Renilla luciferase constructs (50 ng) into 293T, K562 or MKPL-1 cells maintained in DMEM containing 10% charcoal/dextran-treated FBS (HyClone) using FuGENE® 6 (Promega) or ECM 830 Square Wave Electroporation System (Harvard Apparatus) according to the manufacturer’s protocols. The nuclear receptor ligands were added to the medium 8h after transfection. Cells were maintained in DMEM containing 10% charcoal/dextran-treated FBS and harvested after 48h, and luciferase activity was measured by Dual-Luciferase Assay system (Promega). Briefly, cells were washed twice with PBS. Cell lysates were prepared by adding 100 µl of PLB to each well, followed by incubation for 15 min at room temperature. Cell lysates were transferred to white opaque 96-well microplates (3 replicates for each sample), and firefly luminescence was measured after adding 30 µl LAR II. After adding an equal volume of Stop&Glo Reagent to the wells, the Renilla luminescence was measured. To determine whether the sensitivity to the mutated enhancers is dependent on PPARG and RXRA expression, shRNA lentiviruses were produced to transduce MKPL-1 cells. Puromycin selection (1 µg/ml) was performed 3 days after infection. Selected cells and control cells were maintained in DMEM containing 10% charcoal/dextran-treated FBS for 48h prior to electroporation, and the luciferase activity was measured by Dual-Luciferase Assay system as described above. Electrophoretic Mobility Shift Assay EMSA was performed using a Thermo Fisher Scientific LightShift Chemiluminescent EMSA kit following the manufacturer’s instructions. Briefly, human PPARG and RXRA open read frames (ORFs) were amplified and cloned to the lentiviral vectors pLVX-EF1a-IRES-zsGreen1 (Clontech, #631982) and pLVX-EF1a-IRES-mCherry (Clontech, #631987), respectively. Lentiviruses were produced as previously described (5). MKPL-1 cells were then infected with both lentiviruses and zsGreen1+mCherry+ cells were sorted and expanded. Cell nuclear extracts were prepared using NE-PER Nuclear and Cytoplasmic Extraction Reagents (Thermo Fisher Scientific) according to the manufacturer’s protocols. Unlabeled and biotin-labelled probes were synthesized and annealed on a PCR thermal cycler (Bio-Rad) by heating up the mixture at 95 °C for 5 min and lowering the temperature to room temperature at a ramp of 0.1°C/s. EMSA reactions included 1x binding buffer, 50 ng/µl poly(dI-dC), 2.5% glycerol, 0.05% Nonidet P-40, 5 mM MgCl2, 2 μl nuclear extracts, and 20 fM biotin-labelled probes. Specificity of mobility shifts was analyzed by including 8 pM unlabeled WT or mutant PPAR/RXR competitor oligonucleotides. Supershift assays were performed by including 2 µg antibodies against PPARG (Santa Cruz, sc-7273X) and/or RXRA (Santa Cruz, sc-515929X). Reactions were incubated at room temperature for 20 min, size-separated on a 6% DNA retardation gel, and transferred to a charged nylon membrane (Hybond-XL, GE/Amersham Biosciences). Membranes were crosslinked at 120 mJ/cm2 using Spectrolinker™ XL-1500. Free or protein- bound biotin-labelled probes were detected using streptavidin-horseradish peroxidase conjugates and chemiluminescent substrates according to the manufacturer’s protocols. Intensity of the resulting bands was measured by Image J. All probe sequences are listed in Table S5. Site-Direct Knock-In of Enhancer Variants

The sgRNAs for site-specific cleavage of genomic targets were cloned into the pSpCas9(BB) (pX458) vector (Addgene, #48138) using a Golden Gate Assembly strategy. To generate independent single-cell-derived knock-in clones containing the non-coding variants, 5 µg sgRNA vector, 3 µM ssDNA donor and cell suspension were combined and electroporated using the ECM 830 Square Wave Electroporation System (Harvard Apparatus) according to the manufacturer’s protocol. To enrich for knock-in cells, the top 1-5% of GFP+ cells were sorted 48- 72h post-transfection and plated in 96-well plates. Single-cell-derived clones were isolated and screened for CRISPR-mediated knock-in. Briefly, genomic DNA was extracted and amplified by specific genotyping primers. The PCR products were then subjected to NcoI (CRE4399) or BssSI (CRE12661) digestion, respectively. Positive clones were further validated by Sanger DNA sequencing. The validated single-cell-derived knock-in clones were expanded and processed for subsequent analyses. Sequences of sgRNAs, ssDNA donors, and genotyping primers are listed in Table S5. To determine the effects of enhancer variants on the baseline and NR-induced target gene expression, the single-cell-derived knock-in clones were maintained in DMEM containing 10% charcoal/dextran-treated FBS for 48h before treatment with DMSO or NR agonists (1µM of 9- cis-RA and 5µM Rosiglitazone). Total RNA was isolated after 48h and qRT-PCR analysis was performed to measure KRAS and PER2 expression. To determine the effects of enhancer variants on the chromatin occupancy of nuclear receptors, the single-cell-derived knock-in clones were maintained in DMEM containing 10% charcoal/dextran-treated FBS for 48h before the treatment with NR agonists (1µM 9-cis-RA and 5µM Rosiglitazone). Cells were fixed after 48h and ChIP experiments were performed using antibodies for PPARG (Santa Cruz, sc- 271392) and RXRA (Santa Cruz, sc-515928). The NR ChIP or input control DNA were PCR amplified and quantified for allele frequency by amplicon sequencing. QUANTIFICATION AND STATISTICAL ANALYSIS Statistical details including N, mean and statistical significance values are indicated in the text, figure legends, or Method Details. Error bars in the experiments represent standard error of the mean (SEM) or standard deviation (SD) from either independent experiments or independent samples. All statistical analyses were performed using GraphPad Prism, and the detailed information about statistical methods is specified in figure legends or Methods Details. DATA AND SOFTWARE AVAILABILITY All raw and processed targeted CRE resequencing, ATAC-seq and ChIP-seq data are available in the Gene Expression Omnibus (GEO): GSE137656.

REFERENCES 1. Andersson R, Gebhard C, Miguel-Escalada I, Hoof I, Bornholdt J, Boyd M, et al. An atlas of active enhancers across human cell types and tissues. Nature 2014;507(7493):455-61 doi 10.1038/nature12787. 2. Heintzman ND, Hon GC, Hawkins RD, Kheradpour P, Stark A, Harp LF, et al. Histone modifications at human enhancers reflect global cell-type-specific gene expression. Nature 2009;459(7243):108-12 doi 10.1038/nature07829. 3. Heintzman ND, Stuart RK, Hon G, Fu Y, Ching CW, Hawkins RD, et al. Distinct and predictive chromatin signatures of transcriptional promoters and enhancers in the human genome. Nature genetics 2007;39(3):311-8 doi 10.1038/ng1966. 4. Visel A, Blow MJ, Li Z, Zhang T, Akiyama JA, Holt A, et al. ChIP-seq accurately predicts tissue- specific activity of enhancers. Nature 2009;457(7231):854-8 doi 10.1038/nature07730. 5. Huang J, Liu X, Li D, Shao Z, Cao H, Zhang Y, et al. Dynamic Control of Enhancer Repertoires Drives Lineage and Stage-Specific Transcription during Hematopoiesis. Developmental Cell 2016;36(1):9-23 doi 10.1016/j.devcel.2015.12.014. 6. Rada-Iglesias A, Bajpai R, Swigut T, Brugmann SA, Flynn RA, Wysocka J. A unique chromatin signature uncovers early developmental enhancers in humans. Nature 2011;470(7333):279-83 doi 10.1038/nature09692. 7. Xu J, Shao Z, Glass K, Bauer DE, Pinello L, Van Handel B, et al. Combinatorial assembly of developmental stage-specific enhancers controls gene expression programs during human erythropoiesis. Developmental Cell 2012;23(4):796-811 doi 10.1016/j.devcel.2012.09.003. 8. Hnisz D, Abraham BJ, Lee TI, Lau A, Saint-Andre V, Sigova AA, et al. Super-enhancers in the control of cell identity and disease. Cell 2013;155(4):934-47 doi 10.1016/j.cell.2013.09.053. 9. Parker SC, Stitzel ML, Taylor DL, Orozco JM, Erdos MR, Akiyama JA, et al. Chromatin stretch enhancer states drive cell-specific gene regulation and harbor human disease risk variants. Proceedings of the National Academy of Sciences of the United States of America 2013;110(44):17921-6 doi 10.1073/pnas.1317023110. 10. Whyte WA, Orlando DA, Hnisz D, Abraham BJ, Lin CY, Kagey MH, et al. Master transcription factors and mediator establish super-enhancers at key cell identity genes. Cell 2013;153(2):307-19 doi 10.1016/j.cell.2013.03.035. 11. Loven J, Hoke HA, Lin CY, Lau A, Orlando DA, Vakoc CR, et al. Selective inhibition of tumor oncogenes by disruption of super-enhancers. Cell 2013;153(2):320-34 doi 10.1016/j.cell.2013.03.036. 12. Khurana E, Fu Y, Chakravarty D, Demichelis F, Rubin MA, Gerstein M. Role of non-coding sequence variants in cancer. Nature reviews Genetics 2016;17(2):93-108 doi 10.1038/nrg.2015.17. 13. Zhou S, Treloar AE, Lupien M. Emergence of the Noncoding Cancer Genome: A Target of Genetic and Epigenetic Alterations. Cancer discovery 2016;6(11):1215-29 doi 10.1158/2159-8290.cd-16- 0745. 14. Horn S, Figl A, Rachakonda PS, Fischer C, Sucker A, Gast A, et al. TERT promoter mutations in familial and sporadic melanoma. Science (New York, NY) 2013;339(6122):959-61 doi 10.1126/science.1230062. 15. Huang FW, Hodis E, Xu MJ, Kryukov GV, Chin L, Garraway LA. Highly recurrent TERT promoter mutations in human melanoma. Science (New York, NY) 2013;339(6122):957-9 doi 10.1126/science.1229259. 16. Mansour MR, Abraham BJ, Anders L, Berezovskaya A, Gutierrez A, Durbin AD, et al. Oncogene regulation. An oncogenic super-enhancer formed through somatic mutation of a noncoding intergenic element. Science (New York, NY) 2014;346(6215):1373-7. 17. Groschel S, Sanders MA, Hoogenboezem R, de Wit E, Bouwman BA, Erpelinck C, et al. A single oncogenic enhancer rearrangement causes concomitant EVI1 and GATA2 deregulation in leukemia. Cell 2014;157(2):369-81 doi 10.1016/j.cell.2014.02.019. 18. Evans RM, Mangelsdorf DJ. Nuclear Receptors, RXR, and the Big Bang. Cell 2014;157(1):255-66 doi 10.1016/j.cell.2014.03.012. 19. Dhiman VK, Bolt MJ, White KP. Nuclear receptors in cancer - uncovering new and evolving roles through genomic analysis. Nature reviews Genetics 2018;19(3):160-74 doi 10.1038/nrg.2017.102. 20. de The H, Chen Z. Acute promyelocytic leukaemia: novel insights into the mechanisms of cure. Nature reviews Cancer 2010;10(11):775-83 doi 10.1038/nrc2943.

21. McKenna A, Hanna M, Banks E, Sivachenko A, Cibulskis K, Kernytsky A, et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome research 2010;20(9):1297-303 doi 10.1101/gr.107524.110. 22. Cibulskis K, Lawrence MS, Carter SL, Sivachenko A, Jaffe D, Sougnez C, et al. Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples. Nature biotechnology 2013;31(3):213-9 doi 10.1038/nbt.2514. 23. Saunders CT, Wong WS, Swamy S, Becq J, Murray LJ, Cheetham RK. Strelka: accurate somatic small-variant calling from sequenced tumor-normal sample pairs. Bioinformatics (Oxford, England) 2012;28(14):1811-7 doi 10.1093/bioinformatics/bts271. 24. Fang H, Bergmann EA, Arora K, Vacic V, Zody MC, Iossifov I, et al. Indel variant analysis of short- read sequencing data with Scalpel. 2016;11(12):2529-48 doi 10.1038/nprot.2016.150. 25. Koboldt DC, Zhang Q, Larson DE, Shen D, McLellan MD, Lin L, et al. VarScan 2: somatic mutation and copy number alteration discovery in cancer by exome sequencing. Genome research 2012;22(3):568-76 doi 10.1101/gr.129684.111. 26. Li K, Liu Y, Cao H, Zhang Y, Gu Z, Liu X, et al. Interrogation of enhancer function by enhancer- targeting CRISPR epigenetic editing. Nature communications 2020;11(1):485 doi 10.1038/s41467- 020-14362-5. 27. Konermann S, Brigham MD, Trevino AE, Joung J, Abudayyeh OO, Barcena C, et al. Genome-scale transcriptional activation by an engineered CRISPR-Cas9 complex. Nature 2015;517(7536):583-8 doi 10.1038/nature14136. 28. Creyghton MP, Cheng AW, Welstead GG, Kooistra T, Carey BW, Steine EJ, et al. Histone H3K27ac separates active from poised enhancers and predicts developmental state. Proceedings of the National Academy of Sciences of the United States of America 2010;107(50):21931-6 doi 10.1073/pnas.1016071107. 29. Tsherniak A, Vazquez F, Montgomery PG, Weir BA, Kryukov G, Cowley GS, et al. Defining a Cancer Dependency Map. Cell 2017;170(3):564-76.e16 doi 10.1016/j.cell.2017.06.010. 30. Li S, Balmain A. A model for RAS mutation patterns in cancers: finding the sweet spot. 2018;18(12):767-77 doi 10.1038/s41568-018-0076-6. 31. Wandler A, Shannon K. Mechanistic and Preclinical Insights from Mouse Models of Hematologic Cancer Characterized by Hyperactive Ras. Cold Spring Harbor perspectives in medicine 2018;8(4) doi 10.1101/cshperspect.a031526. 32. Tzeng J, Byun J, Park JY, Yamamoto T, Schesing K, Tian B, et al. An Ideal PPAR Response Element Bound to and Activated by PPARalpha. PloS one 2015;10(8):e0134996 doi 10.1371/journal.pone.0134996. 33. Forbes SA, Beare D, Boutselakis H, Bamford S, Bindal N, Tate J, et al. COSMIC: somatic cancer genetics at high-resolution. Nucleic acids research 2017;45(D1):D777-d83 doi 10.1093/nar/gkw1121. 34. Cerami E, Gao J, Dogrusoz U, Gross BE, Sumer SO, Aksoy BA, et al. The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer discovery 2012;2(5):401-4 doi 10.1158/2159-8290.cd-12-0095. 35. Albrecht U, Sun ZS, Eichele G, Lee CC. A differential response of two putative mammalian circadian regulators, mper1 and mper2, to light. Cell 1997;91(7):1055-64. 36. Fu L, Pelicano H, Liu J, Huang P, Lee C. The circadian gene Period2 plays an important role in tumor suppression and DNA damage response in vivo. Cell 2002;111(1):41-50. 37. Gery S, Gombart AF, Yi WS, Koeffler C, Hofmann WK, Koeffler HP. Transcription profiling of C/EBP targets identifies Per2 as a gene implicated in myeloid leukemia. Blood 2005;106(8):2827-36 doi 10.1182/blood-2005-01-0358. 38. Ye Y, Xiang Y, Ozguc FM, Kim Y, Liu CJ, Park PK, et al. The Genomic Landscape and Pharmacogenomic Interactions of Clock Genes in Cancer Chronotherapy. Cell systems 2018;6(3):314-28.e2 doi 10.1016/j.cels.2018.01.013. 39. Papagiannakopoulos T, Bauer MR, Davidson SM, Heimann M, Subbaraj L, Bhutkar A, et al. Circadian Rhythm Disruption Promotes Lung Tumorigenesis. Cell metabolism 2016;24(2):324-31 doi 10.1016/j.cmet.2016.07.001. 40. Puram RV, Kowalczyk MS, de Boer CG, Schneider RK, Miller PG, McConkey M, et al. Core Circadian Clock Genes Regulate Leukemia Stem Cells in AML. Cell 2016;165(2):303-16 doi 10.1016/j.cell.2016.03.015.

41. Zhang Y, Dallner OS, Nakadai T, Fayzikhodjaeva G, Lu YH, Lazar MA, et al. A noncanonical PPARgamma/RXRalpha-binding sequence regulates leptin expression in response to changes in adipose tissue mass. Proceedings of the National Academy of Sciences of the United States of America 2018;115(26):E6039-e47 doi 10.1073/pnas.1806366115. 42. Palmer CN, Hsu MH, Griffin HJ, Johnson EF. Novel sequence determinants in peroxisome proliferator signaling. The Journal of biological chemistry 1995;270(27):16114-21. 43. Juge-Aubry C, Pernin A, Favez T, Burger AG, Wahli W, Meier CA, et al. DNA binding properties of peroxisome proliferator-activated receptor subtypes on various natural peroxisome proliferator response elements. Importance of the 5'-flanking region. The Journal of biological chemistry 1997;272(40):25252-9. 44. Chandra V, Huang P, Hamuro Y, Raghuram S, Wang Y, Burris TP, et al. Structure of the intact PPAR-gamma-RXR- nuclear receptor complex on DNA. Nature 2008;456(7220):350-6 doi 10.1038/nature07413. 45. Heyman RA, Mangelsdorf DJ, Dyck JA, Stein RB, Eichele G, Evans RM, et al. 9-cis retinoic acid is a high affinity ligand for the retinoid X receptor. Cell 1992;68(2):397-406. 46. Tenenbaum A, Motro M, Fisman EZ. Dual and pan-peroxisome proliferator-activated receptors (PPAR) co-agonism: the bezafibrate lessons. Cardiovascular diabetology 2005;4:14 doi 10.1186/1475-2840-4-14. 47. Forman LM, Simmons DA, Diamond RH. Hepatic failure in a patient taking rosiglitazone. Annals of internal medicine 2000;132(2):118-21. 48. Lawrence MS, Stojanov P, Polak P, Kryukov GV, Cibulskis K, Sivachenko A, et al. Mutational heterogeneity in cancer and the search for new cancer-associated genes. Nature 2013;499(7457):214-8 doi 10.1038/nature12213. 49. Schuster-Bockler B, Lehner B. Chromatin organization is a major influence on regional mutation rates in human cancer cells. Nature 2012;488(7412):504-7 doi 10.1038/nature11273. 50. Polak P, Lawrence MS, Haugen E, Stoletzki N, Stojanov P, Thurman RE, et al. Reduced local mutation density in regulatory DNA of cancer genomes is linked to DNA repair. Nature biotechnology 2014;32(1):71-5 doi 10.1038/nbt.2778. 51. Sabarinathan R, Mularoni L, Deu-Pons J, Gonzalez-Perez A, Lopez-Bigas N. Nucleotide excision repair is impaired by binding of transcription factors to DNA. Nature 2016;532(7598):264-7 doi 10.1038/nature17661. 52. Braun BS, Archard JA, Van Ziffle JA, Tuveson DA, Jacks TE, Shannon K. Somatic activation of a conditional KrasG12D allele causes ineffective erythropoiesis in vivo. Blood 2006;108(6):2041-4 doi 10.1182/blood-2006-01-013490. 53. Counter CM, Schubbert S, Zenker M, Rowe SL, Boll S, Klein C, et al. Germline KRAS mutations cause Noonan syndrome. Nature reviews Cancer 2006;38(3):331-6. 54. Van Meter ME, Diaz-Flores E, Archard JA, Passegue E, Irish JM, Kotecha N, et al. K-RasG12D expression induces hyperproliferation and aberrant signaling in primary hematopoietic stem/progenitor cells. Blood 2007;109(9):3945-52 doi 10.1182/blood-2006-09-047530. 55. Braun BS, Tuveson DA, Kong N, Le DT, Kogan SC, Rozmus J, et al. Somatic activation of oncogenic Kras in hematopoietic cells initiates a rapidly fatal myeloproliferative disorder. Proceedings of the National Academy of Sciences of the United States of America 2004;101(2):597-602. 56. Sasine JP, Himburg HA, Termini CM, Roos M, Tran E, Zhao L, et al. Wild-type Kras expands and exhausts hematopoietic stem cells. JCI insight 2018;3(11) doi 10.1172/jci.insight.98197. 57. Bailey SD, Desai K, Kron KJ, Mazrooei P, Sinnott-Armstrong NA, Treloar AE, et al. Noncoding somatic and inherited single-nucleotide variants converge to promote ESR1 expression in breast cancer. Nature genetics 2016;48(10):1260-6 doi 10.1038/ng.3650. 58. Soccio RE, Chen ER, Rajapurkar SR, Safabakhsh P, Marinis JM, Dispirito JR, et al. Genetic Variation Determines PPARgamma Function and Anti-diabetic Drug Response In Vivo. Cell 2015;162(1):33-44 doi 10.1016/j.cell.2015.06.025. 59. Gilbert LA, Horlbeck MA, Adamson B, Villalta JE, Chen Y, Whitehead EH, et al. Genome-Scale CRISPR-Mediated Control of Gene Repression and Activation. Cell 2014;159(3):647-61 doi 10.1016/j.cell.2014.09.029. 60. Bernstein BE, Stamatoyannopoulos JA, Costello JF, Ren B, Milosavljevic A, Meissner A, et al. The NIH Roadmap Epigenomics Mapping Consortium. Nature biotechnology 2010;28(10):1045-8 doi 10.1038/nbt1010-1045.

61. Ernst J, Kheradpour P, Mikkelsen TS, Shoresh N, Ward LD, Epstein CB, et al. Mapping and analysis of chromatin state dynamics in nine human cell types. Nature 2011;473(7345):43-9 doi 10.1038/nature09906. 62. Kasowski M, Kyriazopoulou-Panagiotopoulou S, Grubert F, Zaugg JB, Kundaje A, Liu Y, et al. Extensive variation in chromatin states across humans. Science (New York, NY) 2013;342(6159):750-2 doi 10.1126/science.1242510. 63. Knoechel B, Roderick JE, Williamson KE, Zhu J, Lohr JG, Cotton MJ, et al. An epigenetic mechanism of resistance to targeted therapy in T cell acute lymphoblastic leukemia. 2014;46(4):364-70 doi 10.1038/ng.2913. 64. Lin CY, Loven J, Rahl PB, Paranal RM, Burge CB, Bradner JE, et al. Transcriptional amplification in tumor cells with elevated c-Myc. Cell 2012;151(1):56-67 doi 10.1016/j.cell.2012.08.026. 65. Maurano MT, Humbert R, Rynes E, Thurman RE, Haugen E, Wang H, et al. Systematic localization of common disease-associated variation in regulatory DNA. Science (New York, NY) 2012;337(6099):1190-5 doi 10.1126/science.1222794. 66. Pham TH, Benner C, Lichtinger M, Schwarzfischer L, Hu Y, Andreesen R, et al. Dynamic epigenetic enhancer signatures reveal key transcription factors associated with monocytic differentiation states. Blood 2012;119(24):e161-71 doi 10.1182/blood-2012-01-402453. 67. Schmidl C, Hansmann L, Lassmann T, Balwierz PJ, Kawaji H, Itoh M, et al. The enhancer and promoter landscape of human regulatory and conventional T-cell subpopulations. Blood 2014;123(17):e68-78 doi 10.1182/blood-2013-02-486944. 68. Saeed S, Quintin J, Kerstens HH, Rao NA, Aghajanirefah A, Matarese F, et al. Epigenetic programming of monocyte-to-macrophage differentiation and trained innate immunity. Nature genetics 2014;345(6204):1251086. 69. Wang H, Zang C, Taing L, Arnett KL, Wong YJ, Pear WS, et al. NOTCH1-RBPJ complexes drive target gene expression through dynamic interactions with superenhancers. Proceedings of the National Academy of Sciences of the United States of America 2014;111(2):705-10 doi 10.1073/pnas.1315023111. 70. Chapuy B, McKeown MR, Lin CY, Monti S, Roemer MG, Qi J, et al. Discovery and characterization of super-enhancer-associated dependencies in diffuse large B cell lymphoma. Cancer cell 2013;24(6):777-90 doi 10.1016/j.ccr.2013.11.003. 71. Weinstein JS, Lezon-Geyda K, Maksimova Y, Craft S, Zhang Y, Su M, et al. Global transcriptome analysis and enhancer landscape of human primary T follicular helper and T effector lymphocytes. Blood 2014;124(25):3719-29 doi 10.1182/blood-2014-06-582700. 72. Langmead B, Trapnell C, Pop M, Salzberg SL. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome biology 2009;10(3):R25. 73. Zhang Y, Liu T, Meyer CA, Eeckhoute J, Johnson DS, Bernstein BE, et al. Model-based analysis of ChIP-Seq (MACS). Genome biology 2008;9(9):R137. 74. Wigler M, Schatz MC, Narzisi G, Wang K, Li M, Hakonarson H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nature protocols 2010;38(16):e164. 75. Weinhold N, Jacobsen A, Schultz N, Sander C, Lee W. Genome-wide analysis of noncoding regulatory mutations in cancer. Nature genetics 2014;46(11):1160-5 doi 10.1038/ng.3101. 76. Quinlan AR, Hall IM. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics (Oxford, England) 2010;26(6):841-2 doi 10.1093/bioinformatics/btq033. 77. Khan A, Fornes O, Stigliani A, Gheorghe M, Castro-Mondragon JA, van der Lee R, et al. JASPAR 2018: update of the open-access database of transcription factor binding profiles and its web framework. Nucleic acids research 2018;46(D1):D260-d6 doi 10.1093/nar/gkx1126. 78. Liu X, Zhang Y, Chen Y, Li M, Zhou F, Li K, et al. In Situ Capture of Chromatin Interactions by Biotinylated dCas9. Cell 2017;170(5):1028-43.e19 doi 10.1016/j.cell.2017.08.003. 79. Gu Z, Liu Y, Cai F, Patrick M, Zmajkovic J, Cao H, et al. Loss of EZH2 Reprograms BCAA Metabolism to Drive Leukemic Transformation. Cancer discovery 2019;9(9):1228-47 doi 10.1158/2159-8290.cd-19-0152. 80. Durand NC, Robinson JT, Shamim MS, Machol I, Mesirov JP, Lander ES, et al. Juicebox Provides a Visualization System for Hi-C Contact Maps with Unlimited Zoom. Cell systems 2016;3(1):99-101 doi 10.1016/j.cels.2015.07.012.

81. Rao SS, Huntley MH, Durand NC, Stamenova EK, Bochkov ID, Robinson JT, et al. A 3D map of the human genome at kilobase resolution reveals principles of chromatin looping. Cell 2014;159(7):1665- 80 doi 10.1016/j.cell.2014.11.021. 82. Li W, Xu H, Xiao T, Cong L, Love MI, Zhang F, et al. MAGeCK enables robust identification of essential genes from genome-scale CRISPR/Cas9 knockout screens. Genome biology 2014;15(12):554 doi 10.1186/s13059-014-0554-4. 83. Cong L, Ran FA, Cox D, Lin S, Barretto R, Habib N, et al. Multiplex genome engineering using CRISPR/Cas systems. Science (New York, NY) 2013;339(6121):819-23 doi 10.1126/science.1231143. 84. Mali P, Yang L, Esvelt KM, Aach J, Guell M, DiCarlo JE, et al. RNA-guided human genome engineering via Cas9. Science (New York, NY) 2013;339(6121):823-6 doi 10.1126/science.1232033.

FIGURE LEGENDS

Figure 1. An integrative approach to identify functional non-coding variants in human leukemia (A) Schematic of an integrative approach to identify non-coding variants with functional consequences in human leukemia. (B) Heatmap shows the top 100 non-coding mutational hotspots identified by targeted resequencing in human leukemia. The chromosome coordinate, nearest neighbor genes, and annotation (E: enhancer; P: promoter) for each mutational hotspot are shown on the left. The cumulative numbers of mutated hotspots and samples are shown on the top and right, respectively. Samples are categorized by disease types (AML or MDS, lymphoma, ALL and normal) and sources (patient and cell line) with each sample ID shown on the bottom.

Figure 2. Identification of tumor suppressive and oncogenic CREs by perturbation screens (A) Schematic of CRE perturbation screens by enCRISPRi and enCRISPRa epigenetic editing. (B) Validation of Dox-inducible expression of dCas9-p300 and dCas9-LSD1 proteins in 293T and MKPL-1 stable cell lines by Western blot. β-tubulin was analyzed as a loading control. (C) Validation of CRE perturbation screens by replicate experiments. CRE-specific sgRNAs (black) and non-targeting control sgRNAs (red) are shown. The x- and y-axis denote the normalized log2 fold changes of sgRNA enrichment or dropout in T28 relative to T0 samples. The Pearson correlation coefficient (R) values are shown. (D) CRE perturbation screens by enCRISPRa and enCRISPRi identified candidate tumor suppressive and oncogenic CREs in human leukemia. Scatter plot is shown for each CRE or control sgRNA ranked by the log2 fold changes of sgRNA enrichment or dropout in T28 relative to T0 samples by enCRISPRa (x-axis) and enCRISPRi (y-axis) screens. Dots represent sgRNA- targeted top candidate tumor suppressive CREs (green), top candidate oncogenic CREs (blue), all other CREs (grey) or non-targeting controls (red). The CRE ID, chromosome coordinates, enhancer nearest neighbor genes (up to two genes within 50kb) or promoter-associated genes, and annotations (E: enhancer; P: promoter) for the top 50 candidate tumor suppressive (left) or oncogenic CREs (right) are shown. The complete lists are shown in Table S7. (E) Non-coding mutational hotspots are enriched with functional CREs. Venn diagram shows the overlap between all CREs (22,262), the mutational hotspot-containing CREs (3,523 CREs for 4,076 mutational spots) and the functional CREs identified from enCRISPRi and enCRISPRa screens (218). P value was calculated using hypergeometric distribution. (F) Validation of enCRISPRi and enCRISPRa-mediated CRE perturbations in AML (MKPL-1 and MOLM13) and ALL (Jurkat) cells. Dot plots are shown for the log2 fold changes of mRNA expression of CRE-associated genes in enCRISPRi or enCRISPRa expressing cells with CRE- specific (sgCRE) or non-targeting (sgGal4) sgRNAs, respectively. Results are mean ± SEM (N = 4 or 5 CRE-associated genes) and analyzed by a two-sided student’s t-test. *P < 0.05, **P < 0.01, ***P < 0.001, n.s. not significant. The individual data points for each CRE are shown in Fig. S7. (G) enCRISPRi and enCRISPRa-mediated CRE perturbations modulated target gene expression in both AML and ALL cells. Scatter plots are shown for mRNA expression of CRE- associated genes in MKPL-1 AML (y-axis) and Jurkat ALL (x-axis) cells upon enCRISPRi (circle dots) or enCRISPRa (triangle dots) perturbations with sgRNAs for tumor suppressive (green),

oncogenic (blue) or other CREs (grey). The x- and y-axis denote the log2 fold changes of mRNA expression of CRE-associated genes in cells with CRE-specific sgRNAs relative to the non-targeting (sgGal4) control, respectively. The Pearson correlation coefficient (R) value is shown. Two-tailed P value with confidence internal 95% is shown.

Figure 3. Regulation of KRAS by a non-coding variant-associated enhancer (A) Chromatin signatures at the KRAS locus in human primary CD34+ HSPCs and leukemia cell lines are shown. The variant-associated CRE4399 and KRAS promoter are depicted by red and green lines, respectively. The long-range DNA interactions identified by RNAPII ChIA-PET are shown at the bottom. (B) KO of CRE4399 in AML (MKPL-1) cells impaired the mRNA expression of KRAS but not other neighboring genes within the same topologically associating domain (TAD). (C) Inducible repression of CRE4399 by enCRISPRi impaired MKPL-1 cell growth by a negative competition assay. (D) KO of CRE4399 or KRAS gene impaired MKPL-1 cell growth in NSG mice. Bioluminescence intensity is shown for each mouse at 4 hours, 2 and 4 weeks post- transplantation. (E) Quantification of bioluminescence intensity is shown for xenografted NSG mice. Results are mean ± SEM (N = 5 or 6 recipients per genotype) and analyzed by a two-way ANOVA. (F) Representative bloodsmear images of NSG mice after 4 weeks of xenotransplantation of control, CRE4399 KO (KO1 and KO2), or KRAS KO MKPL-1 cells. The inset images indicate the zoom-in view. The representative leukemia cells are indicated by arrowheads. Scale bars, 200 µm and 20 µm for full and insert images, respectively. (G) Frequencies of leukemia cells in bone marrow and peripheral blood of xenografted NSG mice. Results are mean ± SEM (N = 4 or 5 recipients per genotype) and analyzed by a one-way ANOVA followed by Dunnett’s test for multiple comparisons. (H) Motif analysis is shown by the distance between TF motifs and the KRAS enhancer variant. The x-axis shows the distance to each motif. The y-axis shows a function (1.05-distance) of the distance to each motif to illustrate the closest motifs, as previously described (57). This function measures motif enrichment in the range 0 to 1 within 20bp of the variant. Data points indicate the location of a TF motif. (I) Identification of non-coding variants at CRE4399 in leukemia (red) and other tumor samples (black). The location and number of each variant are shown. Variants overlapping with motifs for PPARG/RXRA or PPARs are shown as colored lines at the bottom. Results are mean ± SEM and analyzed by a two-sided student’s t-test, unless stated otherwise. *P < 0.05, **P < 0.01, ***P < 0.001, n.s. not significant.

Figure 4. Regulation of PER2 by a non-coding variant-associated enhancer (A) Chromatin signatures at the PER2 locus in human primary CD34+ HSPCs and leukemia cell lines are shown. The variant-associated CRE12661 and PER2 promoter are depicted by red and green lines, respectively. The long-range DNA interactions identified by RNAPII ChIA-PET are shown at the bottom. (B) KO of CRE12661 in MKPL-1 cells impaired PER2 expression of but not other neighboring genes. (C) Inducible activation of CRE12661 by enCRISPRa impaired MKPL-1 cell growth by a negative competition assay.

(D) KO of CRE12661 promoted MKPL-1 cell growth, whereas overexpression of PER2 impaired cell growth in NSG mice. Bioluminescence intensity is shown for each mouse at 4 hours, 2 and 4 weeks post-transplantation. The same images for control mice are used in Fig. 3D. (E) Quantification of bioluminescence intensity is shown for xenografted NSG mice. Results are mean ± SEM (N = 5 or 6 recipients per genotype) and analyzed by a two-way ANOVA. (F) Representative bloodsmear images of NSG mice after 4 weeks of xenotransplantation of control, CRE12661 KO (KO1 and KO2) or PER2 OE MKPL-1 cells. (G) Frequencies of leukemia cells in bone marrow and blood of xenografted NSG mice. Results are mean ± SEM (N = 4 or 5 recipients per genotype) and analyzed by a one-way ANOVA. (H) Motif analysis is shown by the distance between TF motifs and the PER2 enhancer variant. The x-axis shows the distance to each motif. The y-axis shows a function (1.05-distance) of the distance to each motif as described in Fig. 3H. (I) Identification of non-coding variants at CRE12661 in leukemia (red) and other tumor samples (black). The location and number for each variant are shown. Variants overlapping with motifs for PPARG/RXRA or PPARs are shown as colored lines at the bottom. Results are mean ± SEM and analyzed by a two-sided student’s t-test, unless stated otherwise. *P < 0.05, **P < 0.01, ***P < 0.001, n.s. not significant.

Figure 5. Non-coding variants modify nuclear receptor signaling in leukemia (A) Schematic of enhancer reporter assays with or without agonists for nuclear receptors. (B) KRAS (CRE4399) enhancer variant significantly increased enhancer activity upon activation of RXR and PPARG signaling in MKPL-1 cells. Results are mean ± SEM of 3 independent experiments. The differences between control (Ctrl) and WT or MUT enhancer were analyzed by a two-way ANOVA with Turkey correction for multiple comparisons. *P < 0.05, **P < 0.01, ***P < 0.001. The differences between DMSO and NR agonist-treated groups were analyzed by a two-way ANOVA with Turkey correction for multiple comparisons. ###P < 0.001. (C) PER2 (CRE12661) enhancer variant significantly decreased enhancer activity upon activation of RXR and PPARG signaling in MKPL-1 cells. Results are mean ± SEM of 3 independent experiments and analyzed by a two-way ANOVA. (D) RXRA and PPARG strongly associate with the KRAS enhancer in various cell types including MKPL-1 AML cells by ChIP-seq analysis. The annotated KRAS enhancer (CRE4399) is shown as shaded lines. The zoom-in view is also shown in which the AML-associated enhancer variant is indicated by the red line. (E) RXRA and PPARG strongly associate with the PER2 enhancer in various cell types including MKPL-1 cells. The annotated PER2 enhancer (CRE12661) is shown as shaded lines and the AML-associated enhancer variant is indicated by the red line. (F) Validation of PPARG binding to KRAS and PER2 enhancers by ChIP-qPCR analysis. The ChIP signal (% of input) is shown for the negative control (NC; chr2:211,337,339-211,337,429; hg19) genomic region, the positive control (PC; chr17:41,400,528-41,400,677; hg19), and KRAS (CRE4399) and PER2 (CRE12661) enhancers. Results are mean ± SEM (N = 4 replicate experiments) and analyzed by a one-way ANOVA. ***P < 0.001. (G) Validation of RXRA binding to KRAS and PER2 enhancers by ChIP-qPCR analysis. Results are mean ± SEM (N = 4 replicate experiments) and analyzed by a one-way ANOVA. ***P < 0.001. (H) Non-coding variant at the KRAS enhancer increased PPARG and RXRA binding. EMSA was performed using MKPL-1 nuclear extracts co-transduced with PPARG and RXRA

expressing lentiviruses. Excess unlabeled PPARG/RXRA-binding probe, but not non-specific competitor, abolished the gel shift with both WT and MUT CRE4399 probes. PPARG and RXRA antibodies supershifted protein-DNA complexes. A representative image from 3 independent experiments is shown. The quantification of binding intensity is shown in Fig. S11K. (I) Non-coding variant at the PER2 enhancer impaired PPARG and RXRA binding. The quantification is shown in Fig. S11K.

Figure 6. Non-coding variants connect enhancer dysfunction with nuclear receptor signaling in leukemia (A) Schematic of site-specific KI of KRAS or PER2 non-coding variant to the endogenous enhancer sequences. Unmodified (WT) and validated single-cell-derived KI-Mut cells were treated with DMSO or NR agonists, followed by analysis of gene expression and NR binding. (B) KI of KRAS (CRE4399) enhancer variant increased KRAS expression upon activation of RXR and PPARG signaling in K562 cells. Results are mean ± SEM of 4 independent experiments. The differences between control (DMSO) and two single-cell-derived KI cell lines (KI-Mut1 and KI-Mut2) were analyzed by a two-way ANOVA with Turkey correction for multiple comparisons. **P < 0.01, ***P < 0.001, n.s. not significant. The differences between DMSO and NR agonist-treated groups were analyzed by a two-way ANOVA. ###P < 0.001. (C) KI of PER2 (CRE12661) enhancer variant decreased PER2 expression upon activation of RXR and PPARG signaling in K562 cells. Results are mean ± SEM of 4 independent experiments, and analyzed by a two-way ANOVA. *P < 0.05, **P < 0.01, n.s. not significant. (D) KI of KRAS (CRE4399) enhancer variant increased PPARG/RXRA binding to the KRAS KI- Mut enhancer allele in K562 cells. The y-axis shows the log2 fold change of ChIP signals (normalized read counts) at the KI-Mut vs WT alleles in input (control) and ChIP samples. Results are mean ± SEM of 3 independent experiments. The differences between control (input) and ChIP samples in two single-cell-derived KI cell lines (KI-Mut1 and KI-Mut2 indicated as green and orange colors) were analyzed by a student’s t-test. ***P < 0.001. (E) KI of PER2 (CRE12661) enhancer variant decreased PPARG/RXRA binding to the PER2 KI-Mut enhancer allele in K562 cells. The y-axis shows the log2 fold change of ChIP signals at the KI-Mut vs WT alleles in input and ChIP samples. Results are mean ± SEM of 3 independent experiments. (F) PPAR and RXR are the top enriched NR motifs at the functionally validated oncogenic CREs from enCRISPRi and enCRISPRa screens. The y-axis shows the negative log10 of P values calculated by hypergeometric distribution. The x-axis shows the log2 fold changes of motif enrichment scores relative to random control regions. The significantly enriched or depleted NR motifs (P ≤ 1x10-4) are indicated by red and green data points, respectively. (G) Models for the cooperation between non-coding variants, enhancer dysregulation and NR signaling. The schematic of ligand-dependent transcriptional regulation by NRs is also shown. PPAR and RAR proteins function as obligate heterodimers with RXRs in the absence of ligands and bind to hormone response elements together with corepressor proteins. Binding of agonist ligands leads to dissociation of corepressors and recruitment of coactivator proteins, resulting in transcriptional activation of downstream target genes.

A Annotation of Blood Targeted CRE Mutation Discovery Functional and Molecular CRE Repertoires Sequencing and Validation Studies

SNV enCRISPRi CRE MCP-KRAB INDEL sgRNA

dCas9-LSD1 enCRISPRa Structural Variants CRE MCP-VP64 sgRNA

dCas9-p300

B AML or MDS Lymphoma ALL

Patient Cell Line Patient Cell Line Patient Cell Line Normal

Mutation Number 40 30 0 1 2 ≥3 20 Nearest neighbor Chromosome

10 Hotspots genes coordinates 0 Number of ZNF486 chr19_20262455_20262504 E LZTFL1 chr3_45883921_45884028 P ZNF93 chr19_20067323_20067395 E WDR82, PPM1M, TWF2, MIRLET7G chr3_52287452_52287531 E LOC100996324 chr18_12750158_12750480 P PHOX2A, INPPL1 chr11_71962314_71962389 E STPG2 chr4_98838431_98838438 E NA chr8_126518499_126518504 E SCN4A, CD79B chr17_62023618_62023834 E ZNF563 chr19_12444358_12444370 P CCDC66 chr3_56628322_56628416 E TTC7B chr14_91225675_91225702 E NA chr2_430305_430366 E AKNA, DFNB31 chr9_117150826_117150838 E PNLDC1, MRPL18 chr6_160240357_160240490 E EIF3D chr22_36924402_36924608 P NA chr2_240547966_240547993 E RP9 chr7_33149183_33149292 P NA chr1_172863533_172864002 E INPP5D chr2_234074654_234074878 E PKHD1 chr6_51929009_51929244 E CORO2A chr9_100931439_100931492 E C9ORF92 chr9_16290558_16290578 E TMEM253, ZNF219 chr14_21567494_21567524 P LINC00877 chr3_72136565_72136619 E BAK1, GGNBP 1, LINC00336 chr6_33539553_33539620 E NA chr11_134524533_134524810 E TTC41P chr12_104322545_104322574 P TPM1 chr15_63340415_63340706 P CHD2 chr15_93443231_93443297 P GTPBP1 chr22_39101661_39101691 P LINC01397, UBL4B chr1_110645566_110645642 E TMEM39A chr3_119182419_119182650 P ATP1B3 chr3_141596250_141596271 P NDFIP1 chr5_141474774_141475001 E RALGDS chr9_135996831_135996866 P MAP1A chr15_43810421_43810475 P CD1B chr1_158299277_158299455 E RAP1GAP chr1_21953013_21953227 E GNAI2, SEMA3B chr3_50297251_50297262 E TLR1 chr4_38805839_38806208 P NA chr4_181315692_181315729 E GSTA5 chr6_52704419_52704476 E CYTH3 chr7_6311591_6311650 P SSBP1, WEE2-AS1 chr7_141438075_141438186 P PARP12 chr7_139762201_139762283 E ST7 chr7_116750665_116750789 E AGAP11 chr10_88731057_88731322 P ZP1, PTGDR2, PRPF19 chr11_60639444_60639458 E GTF2A2 chr15_59949405_59949690 P TMEM107, VAMP2, MIR4521, PER1 chr17_8076910_8076956 E SYCE2 chr19_13030129_13030268 P GUSBP11, RGL4 chr22_24039490_24039510 E TNRC6B chr22_40672413_40672602 E HMGN5 chrX_80377358_80377376 E ACAP2 chr3_195179240_195179302 E TRAPPC11, RWDD4 chr4_184580652_184580688 P TMEM128 chr4_4250717_4250729 P DUSP1 chr5_172198273_172198288 P CDC123 chr10_12312915_12313220 E CWC25, MIR4727 chr17_36981573_36981764 P THRA, MED24 chr17_38224764_38224876 E ZNF180 chr19_45004551_45004824 P CCDC130 chr19_13842282_13842584 E LYPD3, PHLDB3 chr19_43968118_43968313 E CEACAM4 chr19_42132890_42132894 P RMDN2 chr2_38152123_38152287 P SMCO1 chr3_196256007_196256027 E LINC01094 chr4_79579083_79579108 E NDRG1 chr8_134315293_134315304 E OAT chr10_126106936_126107014 P Non-Coding Mutational Hotspots (Top 100) Non-Coding Mutational Hotspots (Top NA chr10_11726734_11726881 E SORL1 chr11_121459863_121459878 E CEP295 chr11_93395163_93395197 P NA chr12_117073109_117073269 E SYNM, TTC23 chr15_99663986_99664128 E NA chr15_101390034_101390095 E RAP1GAP2 chr17_2717694_2717938 E LINC00662, LOC101927151 chr19_28271434_28271457 E NA chr19_21752022_21752228 E GRAP2 chr22_40347002_40347028 E EPS8L3 chr1_110306210_110306576 P MIR4437 chr2_182170873_182170986 P NCK2 chr2_106413956_106414065 E CRYBG3 chr3_97630101_97630216 E BCLAF1, MTFR2 chr6_136581166_136581187 E LOC101927354, C1GALT1 chr7_7297653_7297669 E NA chr8_41200685_41200700 E HSD17B12 chr11_43810235_43810242 E KMT2A, TTC36, TMEM25 chr11_118393676_118393762 E BRSK2 chr11_1404848_1404876 E CPB2 chr13_46653930_46653999 E EZH1, CNTNAP1, MIR6780A, CCR10 chr17_40852899_40852962 E ELL chr19_18632319_18632452 P FLVCR1, FLVCR1-AS1 chr1_213032232_213032263 P BMP1 chr8_22022104_22022144 P ERCC6 chr10_50650541_50650558 E NA chr10_88340237_88340290 E NSDHL, CETN2 chrX_151999593_151999633 P ZNF826P chr19_20606947_20606955 P 101 100 Raji l−17 NB4 Ri−1 REH 3004 3139 3175 3365 2966 3095 3710 4009 4358 3753 3749 4409 3624 4267 3573 4459 3279 3324 3106 3101 3173 2949 3208 3171 K562 HL60 U937 BC−2 Loucy Jurkat 3450T 3373T 3230T 4861T 4864T 4715T 3348T 4212T 3644T 4274T 3678T 4233T 3938T 3966T 4363T 4229T 4143T 4293T 4404T 4467T 3953T 4245T 4799T 3491T 2990T 3255T GDM1 Toledo U2932 0 20 40 60 80 HuT78 Farage Pfeiffer THP−1 Karpas Ctr MKPL-1 U266B1 MV4−11 OCI− LY7 OCI− LY3 Kasumi−2 RCH− ACV SU−DHL−4 SU−DHL−6 4696T−MDS 3236T−MDS 3496T−MDS SU−DHL−10 Number of 4528T−CD34 4528T−CD14 4535T−CD14 4535T−CD34 WSU−DLCL2 Mutated Samples CD34−HSPC−2008 CD34−HSPC−2015

Samples

Figure 2

A B enCRISPRa enCRISPRi enCRISPRi enCRISPRa sgRNA pool 293T MKPL-1 293T MKPL-1 CRE kD - + - + kD - + - + Dox

KRAB VP64 235 257 Cas9

2 x MS2 2 x MS2 H3K27ac 55 55 β-tubulin

ATAC-seq CRE LSD1 CRE p300 C 6 CRE sgRNAs 6 CRE sgRNAs sgRNA 4 Non-targeting 4 Non-targeting sgRNAs sgRNAs 2 2

sgRNA pool T0 + Dox T28 0 0

9,422 sgRNAs Selection 28 days -2 -2 MOI < 0.5 -4 -4 Cells expressing -6 -6 Replicate 2, enCRISPRi enCRISPRa or Genomic DNA Replicate 2, enCRISPRa -8 R = 0.942 -8 R = 0.882 enCRISPRi -10 -10 -8 -6 -4 -2 0 2 4 6 -6 -4 -2 0 2 4 + TRE3G sgRNA enrichment or dropout Replicate 1, enCRISPRa Replicate 1, enCRISPRi

D Tumor Suppressive CREs Oncogenic CREs

CRE Chromosome Nearest CRE2191 CRE Chromosome Nearest CRE19872 CRE18011 CRE210 CRE12661 ID coordinates genes E/P (PRKCQ-AS1; CRE18564 ID (TOX) (SIM1) (SPEN; (PER2) coordinates genes E/P PRKCQ) (ICA1) FLJ37453) CRE2191 chr10:6622165-6623239 PRKCQ-AS1;PRKCQ P 2 CRE9782 CRE1161 chr1:146696609-146697700 FMO5 P CRE18564 chr7:8215498-8216965 ICA1 E CRE923 (BSG) CRE6590 CRE2208 chr10:8287120-8287832 LINC00708 E CRE923 chr1:94312038-94313684 BCAR3;MIR760 P (BCAR3; (FMN1;TMCO5B) CRE13414 chr21:36261257-36262671 RUNX1 P CRE350 chr1:26827000-26828194 HMGN2;RPS6KA1 E MIR760) CRE350 CRE20804 chr9:96928325-96929213 MIRLET7A1;MIRLET7F1 E CRE16157 chr4:183837553-183839385 DCTD P (HMGN2; CRE17502 chr6:27470012-27470721 ZNF184 E CRE9782 chr19:571003-572971 BSG P RPS6KA1) CRE18973 chr7:75367805-75369511 HIP1 P CRE13645 chr22:20747916-20748772 ZNF74 P CRE16157 CRE676 chr1:47696820-47697746 TAL1 P CRE19872 chr8:60031112-60032935 TOX P (DCTD) CRE18528 chr7:6098255-6099259 EIF2AK1 P CRE6590 chr15:33493747-33494735 FMN1;TMCO5B E CRE18532 chr7:6387722-6389229 FAM220A P CRE18372 chr6:159289942-159291725 C6orf99 P 0 CRE11749 chr2:85132395-85133594 TMSB10 P CRE10392 chr19:19976653-19977762 ZNF253 P CRE6955 chr15:70820443-70821348 NA E CRE15272 chr3:187871255-187872821 LPP;LPP-AS2 P CRE10923 chr19:50860281-50861408 NAPSA;NAPSB E CRE18011 chr6:100795540-100797229 SIM1 E CRE18187 chr6:135504517-135505261 MYB E CRE9362 chr17:80022771-80024344 DUS1L P CRE21452 chrX:24260990-24261833 ZFX E CRE18493 chr7:2933701-2934454 CARD11;GNA12 E CRE3461 chr11:58345090-58346561 LPXN;ZFP91 P CRE15386 chr4:925395-926355 GAK;TMEM175 P CRE2336 chr10:27541273-27542255 LRRC37A6P P CRE14603 chr3:57741420-57742382 SLMAP P CRE19440 chr7:149157199-149158530 ZNF777 P CRE11193 chr2:9695628-9696614 ADAM17 P -2 CRE11619 chr2:65454438-65455887 ACTR2 P CRE11563 chr2:61990869-61991991 NA E CRE21034 chr9:127882943-127884195 SCAI;PPP6C E CRE8469 chr17:18163661-18164706 MIEF2;FLII P CRE3575 chr11:64084943-64085746 TRMT112;PRDX5 P CRE1592 chr1:179334231-179335393 AXDND1 P CRE10913 chr19:50379407-50381413 TBC1D17;AKT1S1 P CRE6013 chr14:52487681-52488440 NID2;C14orf166 E CRE4346 chr12:14409708-14410606 NA E CRE6194 chr14:73393076-73394163 DCAF4 P CRE4399 CRE5134 chr12:120729447-120730585 SIRT4;PXN E CRE19307 chr7:130080230-130081447 CEP41 P (KRAS) CRE302 chr1:24717819-24718917 STPG1;NIPAL3 E CRE4519 chr12:48099412-48100456 RPAP3 P CRE4398 CRE1243 chr1:151118617-151119720 SEMA6C P CRE20131 chr8:116660008-116661198 TRPS1 E CRE21622 chrX:51636112-51637065 MAGED1 P (KRAS) CRE8682 chr17:35447019-35447926 ACACA;AATF E -4 CRE14041 chr22:46453388-46454310 PRR34-AS1;PRR34 E CRE8291 chr17:4699228-4700114 PSMB6 P CRE676 CRE18532 CRE6288 chr14:81687251-81687975 GTF2A1 P CRE850 chr1:85039161-85040173 CTBS P (TAL1) (FAM220A) CRE10197 chr19:13278523-13279241 IER2;STX10 E CRE15902 chr4:107629151-107629921 NA E CRE20879 chr9:104159977-104161656 ZNF189;MRPL50 P P CRE18187 CRE21853 chrX:102941837-102942975 MORF4L2;MORF4L2-AS1 CRE18973 CRE2030 chr1:236030212-236031340 LYST P CRE20513 chr9:34611607-34612582 RPP25L P (MYB) CRE12525 chr2:220042083-220043315 FAM134A;CNPPD1 P (HIP1) CRE1857 chr1:220219140-220220593 EPRS P CRE9914 chr19:3434613-3435744 NFIC;SMIM24 E CRE1225 chr1:150265803-150266742 MRPS21 P CRE20942 chr9:115625021-115625725 SNX30;SLC46A2 E CRE3624 chr11:65292339-65293467 SCYL1 P -6 CRE13414 CRE2637 chr10:75625477-75626252 CAMK2G;C10orf55 E enCRISPRi, log2 Enrichment (T28/T0) CRE16100 chr4:159593272-159594085 ETFDH;C4orf46 P (RUNX1) CRE16028 chr4:141677432-141678382 TBC1D9 P CRE16007 chr4:139409505-139410233 NA E sgRNAs targeting: CRE4181 chr12:1905658-1906492 CACNA2D4;ADIPOR2 E CRE829 chr1:78244796-78245875 FAM73A P CRE2208 CRE9489 chr18:12750264-12751100 LOC100996324 P CRE210 chr1:16174350-16175665 SPEN;FLJ37453 P All other CREs (LINC00708) CRE20197 chr8:126517993-126518698 NA E CRE15492 chr4:9692867-9693706 NA E CRE208 chr1:16160379-16162282 FLJ37453;SPEN E CRE7300 chr16:685892-687101 C16orf13 P Non-targeting controls CRE1245 chr1:151137838-151139325 LYSMD1;SCNM1 P CRE9953 chr19:4638110-4640206 TNFAIP8L1 P CRE12 502 chr2:218990178-218990892 CXCR2 P CRE3360 chr11:39392559-39393437 NA E -8 CRE19253 chr7:117823550-117824621 LSM8 P CRE18581 chr7:16460306-16461220 ISPD P Tumor suppressive CREs CRE14380 chr3:42705321-42706477 ZBTB47;LOC101928323 E CRE296 chr1:24126114-24128109 GALE P CRE726 chr1:54355079-54355938 YIPF1 P CRE4612 chr12:53614787-53615630 RARG P Oncogenic CREs CRE1161 CRE2044 chr1:236958591-236959468 MTR P CRE16355 chr5:40678875-40679834 PTGER4 P (FMO5) CRE12149 chr2:153574105-153575086 ARL6IP6;PRPF40A P CRE15384 chr4:774868-776706 LOC100129917 P CRE4969 chr12:104609311-104610168 TXNRD1 P CRE8086 chr16:83972527-83973363 OSGIN1;MLYCD E CRE3651 chr11:65728616-65729612 SART1 P CRE153 chr1:10269924-10271441 KIF1B P CRE17909 chr6:74170579-74172329 MTO1 P -8 -6 -4 -2 0 2 enCRISPRa, log2 Enrichment (T28/T0)

E F Oncogenic CREs Tumor suppressive CREs All other CREs G MKPL-1 (AML) All CREs 5 R = 0.844 4 *** 4 4 *** * ** P < 0.0001 ** *** 2 *** *** 2 2 n.s. 2.5 0 0 0 3,441 82 136 -2 -2 -2 Jurkat (sgCRE / sgGal4) * -4 -4 -4 -5 -2.5 52.5 (ALL) P = 4.0e-15 *** ** *

mRNA log2 Fold Change *** * * **

-6 *** -6 -6

31-MLOM

1-LPKM

1-LPKM 1-LPKM

1-LPKM -2.5

takruJ takruJ

takruJ takruJ takruJ All CREs (22,262) takruJ sgRNAs targeting: enCRISPRienCRISPRa Hotspot-associated CREs (3,523) Tumor suppressive CREs Functional CREs (218) Oncogenic CREs enCRISPRi enCRISPRa enCRISPRi enCRISPRa enCRISPRi enCRISPRa -5 All other CREs

A B WT chr12:25,313,000-25,660,000 CRE4399 10 kb CRE4399 KO

CD34 HSPC 0 - 60 0.03 *** 0.024 DHS 0 - 40 MKPL-1 0.018 0.012

0 - 60 KRAS K562 0.006

0 - 60 mRNA expression MV4-11 0 0.0012 n.s. 0 - 30 U937 0.001

ATAC-seq 0.0008 0 - 60 NB4 0.0006 0.0004

0 - 13 CASC1 CD34 HSPC 0.0002 mRNA expression 0 - 370 0 MKPL-1 0.25 0 - 70 K562 0.2 n.s. 0 - 240 0.15 MV4-11 0.1 H3K27ac ChIP-seq 0 - 170 U937 ETFRF1 0.05 mRNA expression 0 0 - 10 0.0008 ChIA-PET n.s. RNAPII 0.0006

K562 0.0004

0.0002 LMNTD1

mRNA expression 0

0.0004 CASC1 ETFRF1 KRAS LMNTD1 0.0003 n.s. 0.0002

0.0001 D0 +Dox D4~16 RASSF8

C D Control mRNA expression 0 sgRNA-zsGreen1 4 ~ 16 days Disease CRE4399 KO Phenotypes Negative Selection or KRAS KO 4 Weeks enCRISPRi AML Cells NSG + TRE3G Measure zsGreen1 (%) Control CRE4399 KO1 CRE4399 KO2 KRAS KO 150 n.s. n.s. *** n.s. *** *** *** n.s. *** *** n.s.*** *** *** D0 * n.s. 100 D4 D8 4 hours

D12 3.0e8 1.5e5 8.5e5 50 D16 (Normalized to D0) % zsGreen1+ Cells 2 weeks 0 sgGal4 sg1 sg2 sg3 KRAS 4 weeks

E 3.0e7 20 Control 9

ens ity 16 CRE4399 KO1 F Control CRE4399 KO1 CRE4399 KO2 KRAS KO ****

12 CRE4399 KO2 **** **** d, x10 ) x10 on d, KRAS KO 8

4 (Photons/Sec e Int ioluminescenc e B 0 0 2 4 Weeks

G Bone Marrow Blood H chr12:25538881:C>T I KRAS CRE4399 60 * 60 NEUROD1 * CTCFL ZNF740 * ZIC1 * NR2C2 chr12:25,538,591-25,539,583 * TFDP1 PPARG:RXRA PPARG:RXRA * GLIS2 3 E2F1 40 40 TFAP2A THAP1 HIC2 Leukemia RELA ZBTB7A GLI2 INSM1 2 Other Cancers KLF9 GSC SP3 a Cel ls Leukemi a PITX3 KLF16 −distance 20 20 KLF5 KLF4 1 SP1

% of of % HNF4A

1.05 (bp) TFAP2A EGR4 Mutation Number Mutation EGR3 RHOXF1 0 0 0 99 190 290 393 559 639 730 924 0.4 0.5 0.6 0.7 0.8 0.9 1.0 PPARG:RXRA Control Control −20 −10 0 10 20 KRAS KO KRAS KO CRE4399CRE4399 KO1 KO2 CRE4399CRE4399 KO1 KO2 Distance from mutation (bp) PPAR

Figure 4

WT A B CRE12661 KO CRE12661 Chr2:239,060,000-239,215,000 5 kb 0.16 **** 0 - 15 CD34 HSPC 0.12 DHS MKPL-1 0 - 45 0.08 PER2 0 - 40 0.04 K562 mRNA expression 0 0 - 60 MV4-11 0.1 n.s. 0 - 30 0.08 U937 0.06

ATAC-seq 0 - 30 0.04 NB4 HES6 0 - 100 0.02 CD34 HSPC mRNA expression 0 0 - 100 MKPL-1 1 n.s. 0 - 100 0.8 K562 0.6 0 - 250 0.3 MV4-11 H3K27ac ChIP-seq 0 - 240 0.2 LOC643387 U937 mRNA expression 0 3- 16 0.3 n.s. ChIA-PET 0.24 RNAPII 0.18 K562 0.12 ILKAP 0.06 mRNA expression 0 0.004 n.s. ERFE ILKAP LOC643387 HES6 PER2 0.003

0.002

EFRE 0.001 D0 +Dox D4~16 C D mRNA expression 0 sgRNA-zsGreen1 4 ~ 16 days Control Disease CRE12661 KO Negative Selection 4 Weeks Phenotypes or PER2 OE enCRISPRa AML Cells NSG + TRE3G Measure zsGreen1(%) 150 n.s. Control CRE12661 KO1 CRE12661 KO2 PER2 OE n.s. *** *** *** *** n.s. *** *** *** n.s.*** n.s. n.s.** ** ** D0 * * * 100 D4 D8 4 hours D12 50 D16 3.0e8 1.5e5 8.5e5 (Normalized to D0) % zsGreen1+ Cells

0 2 weeks sgGal4 sg1 sg2 sg3 sg4 PER2 E 50 4 weeks

Control 3.0e7

40 9 CRE12661 KO1 ens ity CRE12661 KO2 30 F Control CRE12661 KO1 CRE12661 KO2 PER2 OE

PER2 OE * d, x10 ) x10 econ d,

20 n.s. **** (Photons/S nce Int ce nce iolumin es 10 B

0 0 2 4 Weeks

G Bone Marrow Blood H chr2:239142998:A>CA I CRE12661 PER2 ** RUNX1 *** CDX2 n.s. ** IRF1 PPARA:RXRA 100 100 * STAT1::STAT2 LEF1 * MEF2C PPARG:RXRA chr2:239,142,836-239,143,619 PPARA:RXRA HESX1 80 80 CEBPA MNX1 3 PDX1 Leukemia EN1 PAX4 Other Cancers PRRX1 2 60 60 CTCFL BSX FOXP3 SIX1 −distance GLI2 1 40 40 SP2 MYB RORA a Cel ls Leukemi a Mutation Number Mutation 1.05 (bp) ZBTB7A NR2F1 0 VDR 20 20 E2F6 17 144 178 225 324 420 % of of % 0.4 0.5 0.6 0.7 0.8 0.9 1.0 PPARG:RXRA 0 0

0.3 PPAR Control Control −20 −10 0 10 20 PER2 OE PER2 OE Distance from mutation (bp) CRE12661CRE12661 KO1 KO2 CRE12661CRE12661 KO1 KO2

A Control B CRE4399 (KRAS) C CRE12661 (PER2) **** 40 300 ****

Ctrl **** Ctrl **** ### 9-cis-RA **** ### **** **** ****

(RXRs and RARs) **** ### WT **** ### WT

**** **** ****

30 ### **** **** ### MUT MUT

200

ATRA **** ### ###

(RARs) ###

**** ### **** ###

**** **** **** **** luc **** **** ### n.s. **** **** **** n.s. *** **** 100 **** Bezafibrate *** n.s. **** **** 10 **** (PPARs) **** **** **** **** *** pA site Enhancer Enhancer Activity (RLU) Enhancer Activity (RLU) WT vs Mut Others 0 0 (Other NRs)

ATRA(RARs) ATRA(RARs) DMSO (PPARs) (PPARα) (PPARβ) (PPARγ) DMSO (PPARs) (PPARα) (PPARβ) (PPARγ) 9-cis-RA GW0742 9-cis-RA GW0742 (RXRs,RARs) BezafibrateFenofibrate (RXRs,RARs) BezafibrateFenofibrate Rosiglitazone Rosiglitazone

D KRAS (chr12:25,499,820-25,577,946) E PER2 (chr2:239,103,933-239,182,058) CRE4399 5 kb CRE12661 5 kb

0-129 0-20 CD34 HSPC CD34 HSPC DHS DHS HepG2 0-25 HepG2 0-32

HT29 0-37 HT29 0-17 PPARG PPARG 0-10 0-20 MKPL-1 MKPL-1 ChIP-seq ChIP-seq HepG2 0-18 HepG2 0-31

0-6.6 0-14 RXRA MKPL-1 RXRA MKPL-1

chr12:25,534,975-25,542,788 0.5 kb chr2:239,139,088-239,146,903 0.5 kb CD34 HSPC 0-132 CD34 HSPC 0-20 DHS DHS HepG2 0-27 HepG2 0-22 HT29 0-42 HT29 0-8 0-22 PPARG MKPL-1 0-11 PPARG MKPL-1 0-13 0-20 ChIP-seq HepG2 ChIP-seq HepG2 0-6.6 0-15

RXRA MKPL-1 RXRA MKPL-1

chr12:25538881:C>T chr2:239142998:A>CA F PPARG 2.0 *** CRE4399 (KRAS) CRE12661 (PER2) 1.6 H I WT MUT WT MUT 1.2 *** Specific competitor Specific competitor - - + - - - - + - - 0.8 *** - - + - - - - + - -

0.4 Non-specific competitor - - - + - - - - + - Non-specific competitor - - - + - - - - + -

ChIP Signal (% of Input) 0 PPARG/RXRA antibody - - - - + - - - - + PPARG/RXRA antibody - - - - + - - - - + NC PC Nuclear extract - + + + + - + + + + Nuclear extract - + + + + - + + + + CRE4399 CRE12661 Supershift Supershift G RXRA 1.0 PPARG/RXR complex *** PPARG/RXR complex 0.8 ***

0.6 ***

0.4

0.2

ChIP Signal (% of Input) 0 NC PC Free probe Free probe

CRE4399 CRE12661

Figure 6

A B CRE4399 (KRAS) C CRE12661 (PER2)

** 8 1.6 ** n.s. + *** *** n.s. n.s. ### ** 6 1.2 * n.s. Cas9 sgRNA ### WT WT KI-Mut1 KI-Mut1 WT 4 ** 0.8 *** n.s. KI-Mut2 KI-Mut2

2 0.4 ssDNA donor Relative mRNA expression Relative mRNA expression 0 0.0 HDR DMSO NR DMSO NR agonists agonists KI-Mut D E CRE12661 (PER2) PAM site mutation CRE4399 (KRAS) 1.5 0.2 Targeted variant *** *** 0.0 WT KI-Mut 1.0 -0.2 0.5 -0.4 (KI-Mut / WT) (KI-Mut / WT) log2 fold change

log2 fold change 0.0 DMSO or -0.6 *** NR agonists -0.5 -0.8 ***

Gene expression Input ChIP Input ChIP Input ChIP Input ChIP NR binding KI-Mut1 KI-Mut2 KI-Mut1 KI-Mut2

Agonist F Depleted Enriched G Ligand Co-repressors Co-activators

3.0 PPAR PPAR RXR RXR or RAR or RAR VDR PPARA:RXRA 2.5 Response Element Gene Response Element Gene

RORA NR1H4 2.0 NR2F1 PPARG RXRA Other PPARG RXRA Other value) NR1A4:RARA RXRA RARA NRs RXRA RARA NRs 1.5 mRNA mRNA

KRAS CRE4399 −log10 (P- 1.0 KRAS CRE4399 WT MUT 0.5 mRNA

0.0 CRE12661 PER2 CRE12661 PER2 −2 −1 0 1 2 WT MUT Log2 Fold Change Normal Cancer

Non-Coding Variants Connect Enhancer Dysregulation with Nuclear Receptor Signaling in Hematopoietic Malignancies

Kailong Li, Yuannyu Zhang, Xin Liu, et al.

Cancer Discov Published OnlineFirst March 18, 2020.

Updated version Access the most recent version of this article at: doi:10.1158/2159-8290.CD-19-1128

Supplementary Access the most recent supplemental material at: Material http://cancerdiscovery.aacrjournals.org/content/suppl/2020/03/13/2159-8290.CD-19-1128.DC1

Author Author manuscripts have been peer reviewed and accepted for publication but have not yet been Manuscript edited.

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