Deciphering essential cistromes using genome-wideCRISPR screensTeng Feia,b,c,d,e,f,g,1, Wei Lie,g,h,i,1, Jingyu Pengc,d,e,f,g,1, Tengfei Xiaoc,d,e,f,g, Chen-Hao Chene,g, Alexander Wue,g,j,Jialiang Huangg, Chongzhi Zangk, X. Shirley Liue,g,2, and Myles Brownc,d,e,f,2

aCollege of Life and Health Sciences, Northeastern University, 110819 Shenyang, People’s Republic of China; bKey Laboratory of Data Analytics andOptimization for Smart Industry, Northeastern University, Ministry of Education, 110819 Shenyang, People’s Republic of China; cDivision of Molecular andCellular Oncology, Dana-Farber Cancer Institute, Boston, MA 02215; dDepartment of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215;eCenter for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA 02215; fDepartment of Medicine, Brigham and Women’s Hospital andHarvard Medical School, Boston, MA 02215; gDepartment of Data Sciences, Dana-Farber Cancer Institute and Harvard T.H. Chan School of Public Health,Boston, MA 02215; hCenter for Genetic Medicine Research, Children’s National Hospital, Washington, DC 20010; iDepartment of Genomics and PrecisionMedicine, The George Washington School of Medicine and Health Sciences, Washington, DC 20010; jProgram in Computational Biology andQuantitative Genetics, Harvard School of Public Health, Boston, MA 02215; and kCenter for Public Health Genomics, University of Virginia, Charlottesville,VA 22908

Contributed by Myles Brown, October 15, 2019 (sent for review May 16, 2019; reviewed by Mitchell A. Lazar and David J. Mangelsdorf)

Although millions of transcription factor binding sites, or cis-tromes, have been identified across the human genome, definingwhich of these sites is functional in a given condition remainschallenging. Using CRISPR/Cas9 knockout screens and gene essen-tiality or fitness as the readout, we systematically investigated theessentiality of over 10,000 FOXA1 and CTCF binding sites in breastand prostate cancer cells. We found that essential FOXA1 bindingsites act as enhancers to orchestrate the expression of nearbyessential genes through the binding of lineage-specific transcrip-tion factors. In contrast, CRISPR screens of the CTCF cistromerevealed 2 classes of essential binding sites. The first class ofessential CTCF binding sites act like FOXA1 sites as enhancers toregulate the expression of nearby essential genes, while a secondclass of essential CTCF binding sites was identified at topologicallyassociated domain (TAD) boundaries and display distinct charac-teristics. Using regression methods trained on our screening dataand public epigenetic profiles, we developed a model to predictessential cis-elements with high accuracy. The model for FOXA1essentiality correctly predicts noncoding variants associated withcancer risk and progression. Taken together, CRISPR screens of cis-regulatory elements can define the essential cistrome of a givenfactor and can inform the development of predictive models ofcistrome function.

CRISPR screen | cistrome | CTCF | FOXA1 | enhancer

Gene expression in mammalian systems is exquisitely regu-lated by the combinatorial action of a set of transacting

factors and their “cistromes” or genome-wide cis-acting genomictargets. Cistromes include many important regulatory elementsin the genome, such as promoters, enhancers, silencers, or in-sulators. Enhancers are thought to be the most abundant, andenhancer selectivity and activity may determine the action of atranscription factor in a cell type-dependent manner. Enhancerabnormalities contribute to a variety of human diseases, in-cluding cancer (1, 2). Millions of putative enhancers have beenfound in the human genome through high-throughput profilinghistone modification, transcription factor binding sites, andchromatin accessibility (3–5). However, given the surge of cis-trome data generation and enhancer characterization, it remainschallenging to distinguish functional binding sites or enhancersfrom passive binding events or inactive enhancers. In addition,the extent of functional redundancy of enhancers is unknown.Recently, high-throughput CRISPR/Cas9 genetic screening utiliz-ing single-guide RNAs (sgRNAs) or paired-guide RNAs (pgRNAs)have been applied to characterize noncoding genomic regionfunctions (6–8). These studies either interrogate a genomic regionclose to the gene of interest to identify enhancers that regulate thetarget gene (6, 9–11), or perturb hundreds of enhancers and

test their knockout effects on cell growth (12, 13). However, tosystematically evaluate cistrome functions, these current ap-proaches are limited since 1) most of the studies focus on the reg-ulation of only 1 gene; 2) the number of noncoding genomic regionsis limited (only up to a few hundred); and 3) a systematic evaluationof the screening results and associated features is lacking.Here we interrogated the functions of over 10,000 cis-acting

elements that are bound by CTCF or FOXA1 using CRISPRsgRNA screens and delineated binding site essentiality or fitness.FOXA1 (forkhead box protein A1) is a pioneer factor that isthought to open chromatin and promote gene transcriptionthrough binding of other factors (14, 15). CTCF (CCCTC-binding factor) is a highly conserved factor with diverse func-tions in mammalian cells, including transcriptional activation orrepression (16, 17), imprinting (18), insulation (19), and chromatin

Significance

Systematically dissecting the function of a large set of cis-regulatory elements or transcription factor binding sites (cis-tromes) has been technically challenging. Using genome-wideCRISPR screens, we profiled over 10,000 FOXA1 and CTCFbinding sites for their roles in regulating the fitness of breastand prostate cancer cells, and accordingly developed a modelto predict essentiality for cis-elements. These efforts not onlyreveal how the key transcription factors and their cistromesregulate cell essentiality in hormone-dependent cancers butalso highlight an efficient approach to investigate the func-tions of noncoding regions of the genome.

Author contributions: T.F., W.L., J.P., T.X., C.-H.C., C.Z., X.S.L., and M.B. designed research;T.F., W.L., J.P., and T.X. performed research; T.F., W.L., J.P., T.X., C.-H.C., A.W., J.H., X.S.L.,and M.B. analyzed data; and T.F., W.L., J.P., T.X., C.-H.C., X.S.L., and M.B. wrote the paper.

Reviewers: M.A.L., University of Pennsylvania; and D.J.M., The University of Texas South-western Medical Center.

The authors declare a competing interest. T.X. and X.S.L. are cofounders and boardmembers of GV20 Oncotherapy. X.S.L. is on the science advisory board (SAB) of3DMed Care and is a consultant to Genentech. M.B. receives sponsored researchsupport from Novartis. M.B. serves on the SAB of Kronos Bio and as a consultant toH3 Biomedicine. M.B. has served as a consultant to GTx, Inc. and Aleta Biotherapeutics.

This open access article is distributed under Creative Commons Attribution License 4.0 (CC BY).

Data deposition: The CRISPR screening data reported in this paper have been deposited inthe National Center for Biotechnology Information (NCBI) Sequence Read Archive (BioPro-ject accession no. PRJNA434555).

See Commentary on page 24919.1T.F., W.L., and J.P. contributed equally to this work.2To whom correspondence may be addressed. Email: xsliu@ds.dfci.harvard.edu ormyles_brown@dfci.harvard.edu.

This article contains supporting information online at https://www.pnas.org/lookup/suppl/doi:10.1073/pnas.1908155116/-/DCSupplemental.

First published November 14, 2019.

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organization (20). Both factors as well as their binding sites arefrequently mutated in different cancer types (21–23). We validatedtop hits through a focused pgRNA screen and individual knock-out, identified features associated with the essential sites withinthe cistromes, and built machine-learning methods to predict theessentiality of cis-acting elements.

ResultsGenome-Wide CRISPR Screen Targeting the FOXA1 Cistrome. Tosystematically investigate sites in the FOXA1 cistrome whoseloss affects cell viability, we designed genome-wide CRISPR/Cas9knockout screening libraries targeting the binding sites of FOXA1.We first performed genome-wide CRISPR gene screens in estrogenreceptor (ER) positive breast cancer T47D cells with our optimizedsgRNA library targeting ∼18,000 genes in the human genome (SIAppendix, Methods). The quality of the screens is high based onmultiple quality control (QC) measurements (SI Appendix, Fig. S1A–C), and the results confirmed FOXA1 as an essential gene inT47D cells (Fig. 1A and SI Appendix, Table S1). Over 500 genes areidentified as essential with high statistical significance (false dis-covery rate [FDR] < 0.05), including genes in the ER transcriptionfactor network such as ESR1, FOXA1, TRPS1, GATA3, andSPDEF and known essential target genes and downstream signalingmolecules such as MYC, CCND1, and CDK4 (Fig. 1A and SIAppendix, Table S1). In order to design an sgRNA library to in-terrogate the FOXA1 cistrome, we focused on all 1,122 bindingsites within 50 kb of essential genes in T47D and chose 5,000FOXA1 binding sites that have the strongest chromatinimmunoprecipitation-sequencing (ChIP-seq) signals (Fig. 1B, Table1, and SI Appendix, Tables S1 and S2), identified from either ourgene screen or a public CRISPR screen of T47D cells thatemployed the GeCKO v2 library (24) (SI Appendix, Fig. S1B). All ofthe selected binding sites are located within the intronic or inter-genic regions of the genome (SI Appendix, Fig. S1D). For candidateFOXA1 binding sites, we next scanned for all possible sgRNAslocated within the FOXA1 ChIP-seq peak, removed those with lowpredicted CRISPR cutting efficiency and specificity, and chose upto 20 sgRNAs that were closest to the ChIP-seq peak summit. Alsoincluded in the FOXA1 cistrome library are sgRNAs targeting theexons of known essential genes as positive controls and the non-essential AAVS1 “safe harbor” locus as a negative control. Weperformed CRISPR/Cas9 screening in T47D cells under full me-dium condition to evaluate the essentiality of the selected FOXA1binding sites (SI Appendix, Fig. S1E). The sequences encoding thesgRNA were PCR-amplified from the transduced cells at day 0 andafter 4 wk of culture. The abundance of sgRNAs was then quan-tified by high-throughput sequencing, and the data analysis wasperformed using MAGeCK-VISPR, a statistical algorithm that wepreviously developed (25, 26).We compared the degree of essentiality for genes or binding

sites using the “β-score” generated fromMAGeCK-VISPR, which isa measurement of gene selection similar to the term “log foldchange” in differential expression analysis (SI Appendix, Table S3). Apositive (or negative) β-score indicates the corresponding gene/binding site is under positive (or negative) selection in the CRISPRscreen, respectively. Overall, essential genes that serve as positivecontrols were strongly negatively selected as expected (SI Appendix,Fig. S1F). Cell type-specific copy-number variation (CNV) is a well-known confounding factor in CRISPR knockout screens. Weemployed theCNV correction procedure inMAGeCK to reduce theeffects of CNV, by adjusting the β-scores according to the CNVstatus in the corresponding loci (SI Appendix, Methods). This ap-proach reduces the effects of CNV status and allows the accurateevaluation of the functions of binding sites within amplified regions(SI Appendix, Fig. S1G).Overall, 37 FOXA1 binding sites in T47D cells are essential

with statistical significance (FDR < 0.25; Fig. 1C), including 29strong FOXA1 binding sites and 8 binding sites near essential

genes [gene FDR < 0.05 in either our screen or a public T47Dcell screen (24); SI Appendix, Fig. S1B]. Essential genes associ-ated with essential binding sites include estrogen receptor 1(ESR1), the master transcription factor for ER+ breast cancercells, and TRPS1, another transcription factor that is known tobe associated with ER+ breast cancer progression (27). In ad-dition, binding sites near genes that are widely essential for cellgrowth were also found in the screen, including for exampleMRPL9, a mitochondrial large ribosomal subunit protein, andCOX4I1, a cytochrome C oxidase subunit.As one of the top essential FOXA1 binding sites (FOXA1_P146)

in T47D cells is located within the intron of the ESR1 gene itself (SIAppendix, Fig. S2A), we chose it for validation. Using 2 independentsgRNAs targeting this binding site, we confirmed that they induceda high percentage of indels at the binding site (SI Appendix, Fig. S3Aand B), and the CRISPR/Cas9–mediated mutagenesis of theFOXA1_P146 binding site decreased cell proliferation in a com-petitive growth assay (SI Appendix, Fig. S3C). Disruption of thisbinding site by the individual sgRNA-mediated CRISPR/Cas9 tar-geting compromises binding of FOXA1 to the site (SI Appendix,Fig. S3D). We further examined whether FOXA1_P146 couldregulate ESR1 expression, considering the localization of thisbinding site and the functional importance of ESR1 in T47D cells.Individual targeting of FOXA1_P146 reduces ESR1 expression,which is similar to the effect of FOXA1 loss of function by eithersgRNA-mediatedCRISPR/Cas9 knockout orRNA interference (SIAppendix, Fig. S3E andF), suggesting that FOXA1_P146 serves as aFOXA1-dependent enhancer of ESR1 expression.We next studied the link between essential binding sites and

nearby genes (Fig. 1 D and E), as well as epigenetic featuresassociated with essential binding sites (Fig. 1F). In general, es-sential binding sites are more likely to be close to essential genes(compared with all binding sites in the library) and essentialgenes are more likely to be located near essential binding sitescompared with all genes (Fig. 1E). Those genes near essentialFOXA1 binding sites are enriched in breast cancer-relatedfunctions and pathways, including metaplastic/ductal carcinomaof the breast, luminal breast cancer, hypoxia response in MCF7cells, and targets of ESR1 (Fig. 1D). Compared with all bindingsites in the library, essential FOXA1 binding sites tend to have ahigher level of H3K27ac signal (consistent with an active en-hancer) and DNase I hypersensitivity signals (indicating openchromatin; Fig. 1F). However, essential binding sites have asimilar level of FOXA1 binding strength compared with non-essential binding sites, indicating that the intensity of FOXA1binding is not necessarily associated with essentiality.To gain insights into essential binding sites in a different cell

lineage, we performed the same screening experiment in theLNCaP prostate cancer cell line, where FOXA1 is also known tobe essential (SI Appendix, Fig. S4 A–C). The quality of the screenwas confirmed by evaluating the positive control genes that arestrongly negatively selected (SI Appendix, Fig. S4D). Together,both T47D and LNCaP screens identified 72 essential bindingsites in at least 1 cell line (FDR < 0.25; Fig. 2A). Interestingly,many of the essential binding sites are located within the intronsof a protein-coding gene (last row, Fig. 2A). The β-scores ofintronic binding sites and their associated genes are positivelycorrelated (SI Appendix, Fig. S4E). However, it was unclearwhether these intronic binding sites would affect gene functionsvia enhancer regulation or other mechanisms (e.g., RNA stabilityor splicing). To address this question, we examined the H3K27aclevels (an enhancer mark) of intronic binding sites, as well astheir distances to the transcription start site (TSS) of genes (SIAppendix, Fig. S4 F and G). Overall, these “intronic” essentialbinding sites have similarly higher H3K27ac levels as intergenicessential binding sites compared with all of the tested bindingsites, and binding sites closer to the gene TSS are more likely tobe essential (SI Appendix, Fig. S4 F and G). When only focusing

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on intronic sites, the essential intronic sites also have strongersignals for enhancer-associated chromatin features includingH3K4me2, H3K27ac, DNase I sensitivity, and ER binding overthe nonessential intronic sites (SI Appendix, Fig. S4 H and I).

These results support that overall the essentiality of intronic sitesis likely the result of their function as enhancers rather thaneffects on the primary transcript, though this possibility for anyindividual site cannot be ruled out without further validation.

1) Binding sites near essential genes

<50kb

2) Other strongest binding sites

…

3 Filtering a. target coding genes b. target multiple loci c. have low efficiency

Remove sgRNAs that:

On candidate binding sites

Choose sgRNAs close to the center of binding site

Binding site Selection

1

sgRNA Scan 2

3

sgRNA Selection 4

A B

D

E

% g

enes

nea

r Es

sent

ial b

indi

ng s

ites

0

4 ***

0.6 0.4 0.2 0.0 0.2 0.4

54

32

10

score (T47D)

FDR=0.25

Adju

sted

p v

alue

(log

sca

le)

1

2

4 5

6-8

1 FOXA1_P146: ESR1 2 FOXA1_P274: MFI2 3 FOXA1_P286: COX4I1 4 FOXA1_P278: MRPL9

Strongest binding sites Binding sites near essential genes

3

5 FOXA1_P171: TRPS1 6 FOXA1_P255: DDX56 7 FOXA1_P208: RPL35A8 FOXA1_P268: EXOC8

-log10 (q value) 0 3 6

Path

way

s

Genes associated with metaplastic and ductal breast cancer

Hypoxia-related genes in MCF7 cells Genes up-regulated in luminal breast cancer

Genes down-regulated in MCF7 under hypoxia condition

Genes in brain relapse of breast cancer

ESR1 target genes

The functional annotations of genes near essential FOXA1 binding sites

q=0.1

C

0 5000 10000 15000 20000

02

46

810

Essential genes in T47D cells

FOXA1, Rank=9, FDR=1.5e-4

Negative selection gene ranking

FDR=0.05

CDK4

SPDEF

ESR1, MYC, CCND1 GATA3

-log1

0 (R

RA

scor

e)

Essential binding sites

Positively selected binding sites

% B

indi

ng s

ites

near

es

sent

ial g

enes

0

30 ***

300

H3K27ac

Distance from the center (bp)

Avg.

H3K

27ac

sig

nal

-300

15.0

5.0

0.0 -100 100

10.0

p=1.12e-10

DNase I

Distance from the center (bp)

Avg.

DN

ase-

Isig

nal

-300 -100 100 300

0.8

0.4

0.0

1.2

p=7.26e-9

Distance from the center (bp)

FOXA1 ChIP-seq

Distance from the center (bp)

Avg.

ChI

P-se

qsi

gnal

-300

6.0

3.0

0.0 -100 100 300

p=0.12

Distance from the center (bp)

ER ChIP-seq

Distance from the center (bp)

Avg.

ChI

P-se

qsi

gnal

-300

0.6

0.3

0.0 -100 100 300

p=5.0e-4

Non-essential FOXA1 binding sites Essential FOXA1 binding sites 95% Confidence Interval

F

Fig. 1. Genome-wide CRISPR screens for FOXA1 binding sites in T47D cells, an ER-positive breast cancer cell line. (A) Top essential genes in T47D cells,identified by genome-wide CRISPR gene screens. A smaller RRA score (identified by the MAGeCK algorithm) indicates a stronger negative selection of thecorresponding gene. (B) The design of the FOXA1 screening library. FOXA1 binding sites are preselected as indicated, followed by an sgRNA scanning toidentify all possible guides within the binding sites. sgRNAs that have low predicted specificity or cleavage efficacy are then filtered. For the remainingsgRNAs, up to 20 guides that are close to the binding site summit are then selected. (C) An overview of the functions of FOXA1 binding sites in T47D cells,including strong intergenic binding sites (blue dots) and essential binding sites near essential genes (red dots). For each binding site, the β-score, a mea-surement of gene selection in the screen, is calculated using the MAGeCK-VISPR algorithm that we previously developed. A positive (or negative) β-scoreindicates the gene/binding site is positively (or negatively) selected, respectively. (D) The functional analysis of genes near essential FOXA1 binding sites usingthe GREAT prediction tool (46). Terms related to breast cancer are highlighted in red. (E) The percentage of all (and essential) FOXA1 binding sites that arewithin 100 kb of essential genes in T47D cells, and the percentage of all (and essential) genes near essential FOXA1 binding sites. Essential genes are geneswith the 10% lowest β-scores from genome-wide CRISPR gene screens in T47D cells. ***P < 0.001. (F) The epigenetic features of essential binding sites vs.nonessential binding sites.

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We set out to systematically evaluate features associated withFOXA1 binding site essentiality. We collected features that arepotentially associated with binding site functions (Fig. 2B), in-cluding the locations of the FOXA1 motif, copy-number status,distance of binding sites to nearby genes, expression of nearby

genes (through RNA-seq), essentiality of nearby genes (throughCRISPR gene screens), as well as high-quality transcriptionfactor and histone mark ChIP-seq data corresponding to bothcell lines from our cistrome database (28, 29). We ranked fea-tures based on their associations with functional or non-functional binding sites identified in both T47D and LNCaP cellscreens, using the Wilcoxon rank-sum test (Fig. 2C). Overall,DNase I signal (representing open chromatin), nearby gene es-sentiality, and nearby gene expression have the strongest asso-ciations with binding site essentiality. Top essential binding sitesare significantly closer to essential genes (Fig. 1E and SI Ap-pendix, Fig. S5A) and highly expressed genes (SI Appendix, Fig.S5B), suggesting that these essential binding sites may functionthrough regulating nearby essential and often highly expressedgenes. As expected, H3K27ac and DNase I signals also separateessential and nonessential binding sites (Fig. 1F and SI Appendix,Fig. S5C), indicating that functional binding sites tend to beactive enhancers.

Table 1. A summary of the CTCF and FOXA1 cistrome-targetinglibraries

FOXA1 library CTCF library Total

Binding sites 6,110 5,564 11,674sgRNAs (12 to 20 sgRNAs per

binding site)96,962 97,002 193,964

Essential genes 146 146Gene-targeting sgRNAs (5 sgRNAs

per gene)730 730

AAVS1-targeting sgRNAs 267 267

sco

re

-0.5

0.

5 0

p=0.007

-0.5

0.

5 0

p=2.5e-11 E

sco

re

-0.4

0.

4 0

p=7.5e-5

-0.4

0.

4 0

p=7.0e-15 F G

T47D LNCaP

5

0

5

5

0

5

C

Distance to nearby genes and gene expression/function

Copy Number Variation

Public ChIP-seq data from cistrome database

Association with binding site functions

Motif

FDR<0.25 FDR>0.25 T47D

specific binding sites

Common binding

sites

sco

re (L

NC

aP)

-0.4

-0.2

0.0

0.2

0.4

-0

.6

score (T47D) -0.4 -0.2 0.0 0.2 0.4 -0.6

D B

A

-log10 (p-val) 0 8

DNase I

NearbyGeneExpH3K27ac

H3K4Me2

ER/AR ChIP-seq CNV

IntronicFOXA1 Motif

FOXA1 ChIP-seq

NearbyGeneEssentiality

FOXA1 ChIP-seq

Distance from the center (bp)

Avg.

ChI

P-se

qsi

gnal

-300

1.0

0.5

0.0 -100 100 300

p=0.22 1.5

AR ChIP-seq

Distance from the center (bp)

Avg.

ChI

P-se

qsi

gnal

-300

1.4

0.2

0.0

-100 100 300

p=5.7e-4 0.6

Non-essential FOXA1 binding sites

Essential FOXA1 binding sites

95% Confidence Interval

score TLTLTLTLTL

DNase I

FOXA1

H3K27ac

ER AR

T47D/LNCaP T47D-specific LNCaP-specific

Essential FOXA1 binding sites (FDR<0.25)

FOXA1_P146

-1

score

0 -0.5 0.5 1

Intronic

Others (weak signals)

Fig. 2. Features of FOXA1 binding sites in T47D and LNCaP cells. (A) The chromatin features of selected binding sites with statistical significance coming outof the cistrome screens. (B) Possible features that are tested for the association with binding site functions in the screens. (C) The rankings of all featuresassociated with the functions of FOXA1 binding sites. For each feature, we compare its signal distribution between the top 5% of essential sites vs. other sites.The average of P values (calculated using the Mann–Whitney U test) across 2 cell lines is used to measure the relevance of each feature. (D) The β-scores of allsites in T47D and LNCaP cells. Sites are colored by their appearances in both cell lines: Sites that only appear in T47D or appear in both cells are colored blueand red, respectively. (E and F) The β-score distribution of the strongest FOXA1 or ESR1 sites vs. others in T47D and LNCaP cells. (G) The binding signals of thetop essential FOXA1 binding sites vs. nonessential sites in LNCaP cells.

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Consistent with our observation (Fig. 1F) that FOXA1 bindingstrength is weakly associated with essentiality (Fig. 2C), bindingsites identified as being T47D-specific by virtue of strongFOXA1 binding in T47D cells may also be essential in LNCaPcells (Fig. 2D). This may be because FOXA1 acts as a pioneerfactor to open chromatin and allow other transcription factors tobind the cis-regulatory region to regulate gene expression. Thus,it may not be FOXA1 itself but other key cooperative transcriptionfactors that determine the function of the binding sites. Indeed, ERor androgen receptor (AR) binding strength, rather than FOXA1binding, is more predictive of functional binding sites in T47D (Figs.1F and 2E) and LNCaP cells (Fig. 2 F and G), respectively. Con-versely, essential binding sites are more likely to have stronger ERor AR binding. We confirmed our finding by performing screens inhormone-depleted media and hormone-depleted media supple-mented with 17β-estradiol (E2) in T47D cells (SI Appendix, Fig.S6A). Compared with other binding sites, binding sites with thestrongest ER binding (after E2 induction) appear to be stronglynegatively selected, whereas binding sites with the strongest FOXA1binding (after E2 induction) only show marginal negative selection(SI Appendix, Fig. S6 B and C).

Genome-Wide CRISPR Screen Targeting CTCF Binding Sites. We nextstudied the essentiality of the binding sites of CTCF, a chromatinstructure regulator. Although CTCF was not identified as a topessential gene in T47D or LNCaP cell lines in the gene screensusing a stringent FDR cutoff of <0.05, loss of CTCF is negativelyselected in the screens using a higher FDR (FDR = 0.32 inLNCaP). We have validated this fitness defect (30) upon CTCFknockout in T47D cells (SI Appendix, Fig. S7 A–C). We designedCRISPR/Cas9 knockout screening libraries targeting 2 types ofCTCF binding sites, constitutive CTCF binding sites that areshared across multiple cell types, as well as sites that are specificto T47D and LNCaP cells (Fig. 3A, Table 1, and SI Appendix,Fig. S7D). The sgRNA design and the screening procedure aresimilar to FOXA1 cistrome screens, as previously described (SIAppendix, Table S4). Overall, the screening results in both celllines are of high quality and the MAGeCK-VISPR copy-numbercorrection module reduced the effects of genomic copy-numbervariations (SI Appendix, Fig. S7 E–G and Table S5). There are150 CTCF binding sites that are essential in at least 1 cell linewith statistical significance (FDR < 0.25; Fig. 3B and SI Ap-pendix, Fig. S8A). CTCF binding sites that are specific to T47Dor LNCaP cells tend to have lower β-scores in the correspondingcell types (Fig. 3 B and C), indicating a putative cell type-specificrole for these binding sites. Possible features (SI Appendix, Fig.S8B) associated with the essentiality of CTCF binding sites in-clude open chromatin (DNase I signal), nearby gene expressionand essentiality (SI Appendix, Fig. S8C), as well as chromatinmodification and lineage-specific TF binding (H3K27ac, ER/ARChIP-seq signals) (Fig. 3 D and E). Interestingly, CTCF bindingstrength is strongly associated with binding site essentiality (Fig.3F), in contrast to FOXA1 binding (Figs. 1F and 2G).CTCF may serve as a typical transcription factor to regulate

target gene expression or an insulator that defines boundaries inchromatin structure. Similar to the transcription factor FOXA1,essential CTCF binding sites tend to be closer to essential genes(Fig. 3G) and have higher levels of H3K27ac (Fig. 3H) and ER/AR signals (SI Appendix, Fig. S8D). Genes near essential CTCFbinding sites tend to be more essential (Fig. 4A), and theirfunctions are enriched in DNA damage response pathways (e.g.,UVB irradiation, radiation, and cisplatin and P53/BRCA/PARP1function; Fig. 4B). However, essential CTCF binding sites generallyhave weaker H3K27ac signals compared with FOXA1 binding sites(Figs. 2A and 3D), implying that they do not all function as ca-nonical enhancers. We next examined CTCF binding sites in theboundaries of topologically associated domains (TADs) or in CTCFanchors—the contact regions of chromosome loops [extracted from

Hi-C data (31)] that contain 2 head to head-oriented CTCF motifs(Fig. 4 C and D). Both TADs and anchors are critical to chromo-some loop formation. We found CTCF binding sites in both regionstend to be more essential than others (Fig. 4 C and D), confirmingthe critical roles of both regions. Furthermore, essential bindingsites in CTCF anchors tend to have weaker H3K27ac signalscompared with essential binding sites not in the anchors (Fig. 4E),indicating that these binding sites function in ways distinct fromessential enhancers. To further study the functional consequencesof disrupting this type of CTCF binding site, we chose an essentialCTCF binding site (CTCF_P348), located at the boundary between2 TADs, for further functional validation (SI Appendix, Fig. S9A).Individual sgRNAs induced a high percentage of indels in thecorresponding binding sites (SI Appendix, Fig. S9 C and D), and theCRISPR/Cas9–mediated mutagenesis of the CTCF_P348 bindingsite by 2 independent sgRNAs decreased the cell proliferation inthe competitive growth assay (SI Appendix, Fig. S9E). Disruption ofCTCF_P348 by the individual sgRNA-mediated CRISPR/Cas9targeting compromises the binding of CTCF to these sites (SI Ap-pendix, Fig. S9F).

Essential Cistrome Modeling and Validation by pgRNA and DiseaseAssociation. To systematically evaluate both FOXA1 and CTCFscreening results and associated features, we built a supportvector machine (SVM)-based regression model to predict theessentiality of enhancers or cistrome binding sites, based on allassociated features extracted from our results. We tested theperformance of this model with the screening data using 5-foldcross-validations. Compared with using single features correlatedwith enhancer function to predict essentiality such as DNase Isensitivity or H3K27ac chromatin modification, our model per-forms significantly better with an area under the curve (AUC) of∼0.8 (Fig. 5 A and B and SI Appendix, Fig. S10 A and B). Wefurther investigated the binding sites for which the SVM modelmade the incorrect prediction, including essential binding sitesthat were predicted as nonessential (false negatives; FNs), ornonessential binding sites that were predicted to be essential(false positives; FPs). We found that FP binding sites tend tohave stronger DNase I sensitivity and H3K27ac marks (similar toessential binding sites), while FN binding sites harbor lowerlevels of such signals (similar to nonessential binding sites; SIAppendix, Fig. S10C). This indicates that some nonessentialbinding sites that have strong epigenetic characteristics of func-tional enhancers may have functions that are not related to cellgrowth or may be redundant with other enhancers in the samegene. On the other hand, some essential binding sites may nothave typical epigenetic signatures, consistent with the existenceof active enhancers that do not bear active epigenetic marks (9).We further evaluated the results of our CTCF/FOXA1 primary

screens using a pgRNA CRISPR screening technology as a vali-dation screen (7). A focused pgRNA library was constructed bytargeting DNase I-accessible regions that are close to the essentialgenes in T47D cells, as well as top binding sites in the primaryCTCF/FOXA1 screens (Table 2 and SI Appendix, Table S7). Up to25 pgRNAs flanking the binding sites or putative enhancers weredesigned (Fig. 5C). As positive controls, pgRNAs targeting theexons or promoters of genes whose loss suppresses (or promotes)cell growth were included. We screened T47D cells grown in fullmedium with 2 biological replicates and analyzed the results usingMAGeCK-VISPR (26) (SI Appendix, Table S8). As expected, thepromoters and exons of essential genes (e.g., ESR1, FOXA1, andRPS9) are strongly negatively selected (SI Appendix, Fig. S10 Dand E), indicating the reliability of the pgRNA screens.There are in total 80 binding sites that are included in both

primary and secondary screens. Among them, we compared bindingsites that are selected with statistical significance (FDR < 0.25) byboth screens (SI Appendix, Fig. S10F). Of the 13 essential bindingsites that are identified as statistically significant in primary screens,

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Fig. 3. Genome-wide CRISPR screens for CTCF binding sites. (A) CTCF binding site selection procedure in screening library design. (B) The β-scores of all CTCF bindingsites in T47D and LNCaP cells. Binding sites are colored by their appearances in both cell lines: Binding sites that only appear in T47D (or LNCaP) are colored in blue (orgreen), while common binding sites are colored in red, respectively. (C) The cumulative distribution of β-scores of T47D cell-specific and LNCaP cell-specific CTCF bindingsites in T47D (red) and LNCaP (blue) cells. The P values are calculated by the Kolmogorov–Smirnov test. (D) The chromatin features of selected binding sites withstatistical significance coming out of the cistrome screens. (E) The rankings of all features associated with the functions of CTCF binding sites. For each feature, wecompare its signal distribution between the top 5% essential binding sites vs. other binding sites. The average of P values (calculated using the Mann–Whitney U test)across 2 cell lines is used to measure the relevance of each feature. (F) The β-score distribution of the strongest CTCF binding sites vs. others, and the binding strengthof the top essential CTCF binding sites vs. other binding sites in T47D cells. (G) The percentage of all (and essential) CTCF binding sites that are within 100 kb of essentialgenes in T47D cells, and the percentage of all (and essential) genes near essential CTCF binding sites. Essential genes are genes with the 10% lowest β-scores fromgenome-wide CRISPR gene screens in T47D cells. **P < 0.01, ***P < 0.001. (H) The epigenetic features of essential binding sites vs. nonessential binding sites.

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70% (9/13) were confirmed to be essential (with FDR < 0.25) in thepgRNA screens, demonstrating the high specificity of the primaryscreening results. There were 25 binding sites in the primary screenwith FDR >0.25 that were statistically significant hits in the vali-dation screen, indicating that screens using the pgRNA may bemore sensitive in identifying essential binding sites. For example,CTCF_69346 (CTCF_P346), a CTCF binding site that is 6 kb awayfrom CTCF_P348, was not negatively selected in the primary screen(FDR = 1.0), but was essential in the pgRNA screen (FDR = 0.002;SI Appendix, Fig. S10E). Overall, pgRNA screening validated thetop hits in primary screens with high accuracy, with an AUC close to0.94 (Fig. 5D).We further evaluated the performance of our predictive

model, using the 125 DNase-seq peaks in the pgRNA library notin the sgRNA libraries used for the primary screens (Fig. 5E).We predicted the essentiality of these 125 DNase-seq peaks us-ing the model trained on FOXA1 primary screening data, andcompared them with the experimental results in the pgRNAscreen. The overall AUC approaches 0.75, demonstrating thehigh performance of our prediction model (Fig. 5F).Finally, we used our predictive model to evaluate the essen-

tiality of enhancers that overlap with disease-associated single-nucleotide polymorphisms (SNPs) that are identified fromgenome-wide association studies (GWAS). We focused only onnoncoding SNPs associated with breast cancer in T47D cells andprostate cancer-associated SNPs in LNCaP cells. Among the 22breast cancer-related traits, 4 are enriched in predicted essentialenhancers with statistical significance (FDR < 20%; Fig. 5 G andH), and “breast cancer (early onset)” is the top trait, followed by“breast cancer (survival).” Enhancers bearing these SNPs havehigher predicted essential scores compared with random DNaseI-hypersensitive sites or ER or FOXA1 binding sites (SI Ap-pendix, Fig. S10G). These enhancers are also proximal to codinggenes that are up-regulated in luminal breast cancer (SI Ap-pendix, Fig. S10H). Similarly, 2 out of 8 prostate cancer-associatedtraits are significantly enriched in essential enhancers, and thestrongest enrichment comes from the “prostate cancer” trait itself(Fig. 5 I and J). Therefore, our predictive model can be used to inferthe functions of GWAS-associated SNPs that affect cell fitness.

DiscussionDissecting the functions of putative cis-regulatory elements inmammalian cells has been challenging. We have established aCRISPR/sgRNA–based screening approach to investigate theessentiality of the binding sites of 2 important transcriptionfactors, FOXA1 and CTCF. We validated the essential roles oftop hits and the regulation of their target genes using experi-mental approaches including CRISPR/pgRNA screening. Basedon the screening data, we further evaluated genomic and epi-genomic features associated with essential enhancers, and builtmachine-learning models that predict the functions of sites thatare not included in the screen. The model can also be used toexplain disease-associated SNPs that affect cell growth.Our study demonstrated genome-wide cistrome screens as a

promising technology to characterize the functions of transcrip-tion factor binding in detail. By comparing the essential FOXA1and CTCF cistromes, we found that essential binding sites andessential genes tend to be close to each other for both the in-terrogated FOXA1 and CTCF cistromes. Although a minorsubset of interrogated FOXA1 binding sites (18.3%) was chosenbased on their proximity (<50 kb) to essential genes, theremaining FOXA1 sites and all of the CTCF binding sites werechosen independent of this proximity criterion. These resultsindicate a general link between essential binding sites and theessential coding regions nearby. We further identified distinctfeatures associated with the essentiality of FOXA1 and CTCFbinding sites. FOXA1 binding sites bear the hallmarks of ca-nonical enhancers, evidenced by strong DNase I and H3K27acsignals, and their functions can be predicted from nearby geneexpression and essentiality. In contrast, CTCF essential bindingsites fall into 2 classes: They may be either transcriptional en-hancers or critical elements in chromosome organization. Bind-ing sites of the first type have characteristics similar to FOXA1binding sites, while the second class of sites are distinct. In-terestingly, CTCF binding strength predicts the essentiality ofa binding site, while no association is found between thestrength of FOXA1 binding to a site and its essentiality. Thismay be because FOXA1 works together with other transcrip-tion factors such as ER and AR to regulate gene expression,

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Fig. 4. Essential CTCF binding sites display distinct types of CTCF binding. (A) The β-score distribution of genes near essential CTCF binding sites, comparedwith all genes in the genome. The P value is calculated using the Mann–Whitney U test. (B) The functional analysis of genes near essential CTCF binding sitesusing the GREAT prediction tool (46). Enriched terms related to DNA damage and stress response are highlighted in red. (C) The β-score distribution of bindingsites in the boundaries of TADs. (D) The β-score distribution of binding sites in CTCF anchors, or regions that contact chromosome loops and with CTCF motifsthat are head-to-head oriented. The anchor annotation is extracted from Hi-C experiments (31). (E) The H3K27ac signal strength distribution of essentialbinding sites in CTCF anchors, other essential binding sites, and all CTCF binding sites.

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and thus the binding strength of 1 single transcription factormay not capture the important features of combinatorialtranscription factor binding. These results also highlight thatmodeling the functionality of the binding sites of differentclasses of transcription factors will require additional functionalscreens.Several computational methods are available to predict active

enhancers from genetic and epigenetic features (32–36) or fromthe screening experiments measuring the expression of a gene (9,11). It is still tremendously challenging to systematically assignmany enhancers to their bona fide target genes or particularphenotypes experimentally. A recent study identified hundreds

of enhancer–gene pairs using a high-throughput CRISPR in-activation (dCas9-KRAB) approach to perturb enhancers fol-lowed by single-cell RNA sequencing (37). Here our approachinvestigated over 10,000 putative enhancers downstream of 2essential transacting factors and linked them to a specific cellularfunction or phenotype (essentiality or fitness). We generatedgenome-scale experimental data of cis-acting element perturba-tions, and constructed a machine-learning model based on theseexperimental data to predict functional binding sites. We appliedthis strategy to identify the functional cis-elements that arecritical to breast and prostate cancer cell growth. In contrast,previous computational methods cannot predict the possible

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Fig. 5. Predicting and validating essential enhancers. (A) The receiver operator characteristic (ROC) curves of different approaches for predicting FOXA1binding site essentialities using different combinations of features. The AUC values using the SVM and individual features are also shown. (B) The precision-recall characteristic (PR) curves of A. The area under the PR curve (AUPR) of different approaches is shown. (C) An overview of the design of the pgRNA libraryand the screening strategy. Up to 25 pgRNAs are designed to knock out each binding site. (D) The ROC curve of the primary screening results in pgRNAscreens. (E) The validation procedure of the prediction model using 125 DNase I binding sites that are not included in the sgRNA screening library. (F) The ROCcurve of the prediction model (and 2 single features) in predicting the functions of 125 DNase I binding sites. (G) The enrichment of breast cancer-relatedvariants over essential enhancers in T47D cells. The adjusted P values (using the χ2 test) are shown. Circle sizes indicate the fold enrichment over essentialenhancers (>2 or <2). (H) The predicted score distribution of all FOXA1-bounded enhancers, and enhancers that carry variants of “breast cancer (early onset).”The P value is calculated using the Wilcoxon rank-sum test. (I and J) The same analysis of G and H in LNCaP cells.

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phenotypes that each enhancer is involved with, or are based onthe data from a limited number of enhancers proximal to the targetgene. Our model may also be helpful to prioritize the functional orimportant binding sites from other ChIP-seq data. In addition, ourexperimental and computational framework here can be extendedto study the function of cis-regulatory elements in other contexts.CRISPR/Cas9 screens for noncoding elements can be based

on either sgRNA (6, 10–13) or pgRNA (7, 9, 38, 39) strategies.In our study, we performed primary sgRNA-based screening andvalidated the results using a pgRNA approach. We found thatpgRNA screening not only confirmed top hits found in sgRNA-based screening but also identified additional hits that were notfound by sgRNA screens. The combinatorial use of both sgRNAand pgRNA approaches could be very helpful to reduce the falsepositives as introduced by the potentially off-target effects oflow-specificity guides in sgRNA strategies (40).There are potential limitations to our current studies. First,

our screening approach only selects binding sites that affect cellgrowth, and does not identify enhancers that play other func-tional roles (e.g., differentiation). Large-scale enhancer screensfor phenotypes other than cell growth (e.g., protein expressionthat can be selected through FACS sorting) will enable func-tional studies of enhancers of a variety of functions. Second,genomic deletion of putative cis-elements may also alter otherfunctional elements such as long noncoding RNAs, which maycontribute to the screening outcome as well. A refined sgRNAdesign based on these results may be possible to reduce overlapwith other annotated genomic features. The use of orthogonalvalidation approaches may also reduce the number of falsepositives. Third, systematic library bias (mainly due to differen-tial sgRNA efficiency for each targeting element or gene for eachlibrary) is inevitable for all current CRISPR screening studies,which may also necessitate the use of independent approaches orrepeated studies employing a variety of libraries to fully capturethe essential cistrome. In addition, some studies have reportedthe preferential binding of Cas9 to open chromatin regions (41).To reduce the biases of Cas9 binding, we used sgRNAs targetingstrong FOXA1 binding sites as negative controls in the FOXA1cistrome screening study. However, in the future it will be de-sirable to further model and correct for the binding preferencesof Cas9, by designing negative controls in nonfunctional, openchromatin regions, and by considering the effect of open chro-matin in the analysis.In summary, we have demonstrated the feasibility of screening

for the function of large numbers of cis-regulatory elements in apooled format using a CRISPR/Cas9 sgRNA and pgRNA ap-proach. For lineage-selective enhancer-binding transcriptionfactors such as FOXA1, we have developed a model based onepigenomic features that is predictive of the essential function ofa subset of the transcription factor binding sites for cell growth inculture. Importantly, the sites predicted by this model signifi-cantly overlap with germline variants associated with cancer riskand progression identified by GWAS, demonstrating that thefeatures selected for the model by the CRISPR/Cas9 sgRNAscreening results are clinically relevant.

Materials and MethodsDetailed description of materials and methods can be found in SIAppendix, Methods.

Cell Culture and Reagents. Breast cancer T47D cells were maintained inDulbecco’s modified Eagle’s medium (DMEM) supplemented with 10% fetalbovine serum (FBS) as the full medium condition. When stimulated with theestrogen 17β-estradiol, T47D cells were cultured in phenol red-free DMEMwith 10% charcoal/dextran–treated FBS for at least 3 d after switching fromthe full media. Prostate cancer LNCaP cells were cultured in RPMI 1640 mediasupplemented with 10% FBS as the full medium condition. HEK293FT cells weregrown in DMEM with 10% FBS. The antibodies were purchased from the fol-lowing companies: GAPDH (FL-335; Santa Cruz; sc-25778), ERα (HC-20; SantaCruz; sc-543), FOXA1 (Abcam; ab23738), and CTCF (EMD Millipore; 07-729).

CRISPR Library Synthesis and Construction. The pooled synthesized oligoswere PCR-amplified and then cloned into the lentiCRISPRv2-puro vector viathe BsmBI site by Gibson assembly. The ligated Gibson assembly mix wastransformed into self-prepared electrocompetent DH5α Escherichia coli byelectrotransformation to reach efficiency with at least 20× coverage repre-sentation of each clone in the designed library. The transformed bacteriawere cultured directly in liquid LB medium for 16 to 20 h at low temperature(16 °C) to minimize the recombination events in E. coli. The library plasmidswere then extracted with the GenElute HP Endotoxin-Free Plasmid MaxiprepKit (Sigma; NA0410-1KT). To confirm the designed guide RNA sequenceswere successfully cloned into the plasmid library, we PCR-amplified theguide RNA sequences, prepared sequencing libraries, and employed theNextSeq 500 sequencing platform to validate the inserted gRNA sequencesas a stringent QC for the plasmid library. After alignment to our designedsequences, more than 99.92% of designed gRNA sequences were present inour plasmid libraries, indicating the high quality of the libraries.

Pooled Genome-Wide CRISPR Screen. FOXA1 and CTCF cistrome-targetingplasmid libraries under the lentiviral lentiCRISRPv2-puro backbone werefirst transfected along with pCMV8.74 and pMD2.G packaging plasmids intoHEK293FT cells using X-tremeGENE HP DNA Transfection Reagent (Roche;6366236001) to generate a CRISPR component-expressing lentivirus. Virus-containing media were harvested at 48 and 72 h posttransfection, and themediawere spun down at 1,000 rpm for 5min to remove floating cells and celldebris. The virus supernatant was carefully collected, aliquotted, and storedat −80 °C for further use. The virus titer and MOI (multiplicity of infection)were tested before proceeding to the genome-wide screen.

For the full medium screen, 1 × 108 to 2 × 108 T47D or LNCaP cells wereinfected with CTCF or FOXA1 cistrome-targeting lentiviral libraries with MOI∼0.3. Two days later, the infected cells were selected with puromycin (3.5 μg/mLfor T47D cells and 1.5 μg/mL for LNCaP cells) for 3 d to get rid of any non-infected cells before changing back to normal media. After 2 d of recovery postpuromycin selection, around a half portion of cells (at least 3 × 107 cells, ∼300×coverage for each library) was collected as the day 0 sample and storedat −80 °C for later genomic DNA isolation. The remaining half of cells werecontinually cultured until 4 wk later before harvesting as the end-pointsample. For screens in T47D cells under vehicle and E2 condition, cells werecultured in either vehicle (ethanol) or 10 nM E2-containing white mediumfor an additional 5 wk after harvesting the day 0 sample. Genomic DNAfrom day 0 and end-time point samples was extracted.

Gene screens in T47D and LNCaP cells cultured under full medium wereperformed similar to the cistrome screens. The sgRNA library for gene screenstargeting ∼18,000 genes in the human genome was designed by our labo-ratory with an up-to-date algorithm to improve the specificity and efficacyof gRNAs and is described in our recent studies (42). Samples of day 0 and-day 21 were used to quantify the gRNA abundance with a similar library

Table 2. A summary of the secondary paired-guide RNA screening library

Binding sites Paired-guide RNAs

Enhancers near negatively selected genes (ESR1, FOXA1, GATA3, MYC) 92 2,291 (25 pairs per binding site)Enhancers near positively selected genes (PTEN, TSC1, RB1, CSK) 46 1,150 (25 pairs per binding site)Selected hits in CTCF/FOXA1 screens 58 1,450 (25 pairs per binding site)Promoters of the selected genes N.A. 259 (∼25 pairs per promoter)Positive control (pairs targeting AAVS1 loci and the exons of essential genes) 146 genes 730 (5 pairs per gene)Negative control (pairs targeting AAVS1 loci) N.A. 400

N.A., not applicable.

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preparation protocol as the cistrome screens. The data were analyzed byMAGeCK-VISPR (26).

Genetic and Epigenetic Features Associated with Screening Outcomes. Wecollected a set of genetic and epigenetic features in T47D and LNCaP. TheH3K27ac and RNA-seq data were extracted from our previous studies (43).Other ChIP-seq data were extracted from our cistrome database (cistrome.org),and only datasets that passed the quality control measurements in the data-base were used for downstream analysis. For each putative enhancer identifiedby DNase I, the normalized ChIP-seq signals of the 150-bp window (centeredon the DNase I peak summit) were collected as features. For histone modifi-cation ChIP-seq data, the window size was extended to 300 bp.

Predicting Enhancer Functions. For building machine-learning models topredict enhancer functions, we use both essential and nonessential sites inthe screening as training samples. Since the number of significant sites is few,we increased the threshold (negative rank < 300) to select more (but lessstatistically significant) sites as essential sites. Nonessential sites were chosensuch that they were neither negatively nor positively selected (P > 0.5), andtheir absolute log fold change was less than 0.1. In all of the datasets, es-sential and nonessential sites were balanced (essential-to-nonessential rateis between 0.85 and 1.1).

The SVM toolkit in the scikit-learn package (https://scikit-learn.org) wasused for training and prediction. We used a genetic algorithm combinedwith SVM (GA-SVM) to select best feature combinations (44, 45). Briefly, GA-SVM is an iterative process, where a set of feature combinations are subjected

to randomly adding/removing/changing 1 feature at each iteration. Featuresthat reach better prediction performance have a higher chance of going to thenext iteration. This process is repeated several times to select the best com-bination of features. The entire dataset was split into the training and vali-dation sets, where the training dataset was used to train the SVM, and thearea under the receiver operator characteristic value calculated on the vali-dation set was used to evaluate feature combinations.

GWAS-associated SNPs and their traits were downloaded from the GWASCatalog (https://www.ebi.ac.uk/gwas/). If the location of the SNP overlapswith a known DNase I peak in T47D or LNCaP, the corresponding DNase Ibinding site serves as the SNP-bearing enhancer. If no DNase I peak overlapswith the SNP location, we then search for possible FOXA1, ER, or GATA3binding sites. If none of these peaks overlaps with a SNP, we then consider a150-bp window centered on that SNP as an “enhancer” for downstreamanalysis. The prediction algorithm was applied to evaluate whether theseSNP-associated enhancers are essential.

ACKNOWLEDGMENTS. This project was supported by the National HumanGenome Research Institute, NIH (R01HG008728 to M.B. and X.S.L.), NationalNatural Science Foundation of China (31871344), Fundamental ResearchFunds for the Central Universities (N172008008, N182005005), 111 Project(B16009), Program for Innovative Talents of Higher Education Institutions inLiaoning Province (LR2017018) (to T.F.), Startup Fund from the Center forGenetic Medicine Research and Gilbert Family Neurofibromatosis Institute atChildren’s National Medical Center, and a Research Starter Grant in Infor-matics from the Pharmaceutical Research and Manufacturers of AmericaFoundation (to W.L.).

1. M. Lupien et al., Growth factor stimulation induces a distinct ER(alpha) cistromeunderlying breast cancer endocrine resistance. Genes Dev. 24, 2219–2227 (2010).

2. J.-S. Roe et al., Enhancer reprogramming promotes pancreatic cancer metastasis. Cell170, 875–888.e20 (2017).

3. R. E. Thurman et al., The accessible chromatin landscape of the human genome.Nature 489, 75–82 (2012).

4. S. Djebali et al., Landscape of transcription in human cells. Nature 489, 101–108 (2012).5. B. E. Bernstein et al., The NIH Roadmap Epigenomics Mapping Consortium. Nat. Bio-

technol. 28, 1045–1048 (2010).6. M. C. Canver et al., BCL11A enhancer dissection by Cas9-mediated in situ saturating

mutagenesis. Nature 527, 192–197 (2015).7. S. Zhu et al., Genome-scale deletion screening of human long non-coding RNAs using

a paired-guide RNA CRISPR-Cas9 library. Nat. Biotechnol. 34, 1279–1286 (2016).8. M. C. Canver et al., Variant-aware saturating mutagenesis using multiple Cas9 nu-

cleases identifies regulatory elements at trait-associated loci. Nat. Genet. 49, 625–634 (2017).9. Y. Diao et al., A tiling-deletion-based genetic screen for cis-regulatory element

identification in mammalian cells. Nat. Methods 14, 629–635 (2017).10. N. Rajagopal et al., High-throughput mapping of regulatory DNA. Nat. Biotechnol.

34, 167–174 (2016).11. C. P. Fulco et al., Systematic mapping of functional enhancer-promoter connections

with CRISPR interference. Science 354, 769–773 (2016).12. G. Korkmaz et al., Functional genetic screens for enhancer elements in the human

genome using CRISPR-Cas9. Nat. Biotechnol. 34, 192–198 (2016).13. N. E. Sanjana et al., High-resolution interrogation of functional elements in the

noncoding genome. Science 353, 1545–1549 (2016).14. J. S. Carroll et al., Chromosome-wide mapping of estrogen receptor binding reveals

long-range regulation requiring the forkhead protein FoxA1. Cell 122, 33–43 (2005).15. M. Lupien et al., FoxA1 translates epigenetic signatures into enhancer-driven lineage-

specific transcription. Cell 132, 958–970 (2008).16. J. E. Phillips, V. G. Corces, CTCF: Master weaver of the genome. Cell 137, 1194–1211

(2009).17. C.-T. Ong, V. G. Corces, CTCF: An architectural protein bridging genome topology and

function. Nat. Rev. Genet. 15, 234–246 (2014).18. A. C. Bell, G. Felsenfeld, Methylation of a CTCF-dependent boundary controls im-

printed expression of the Igf2 gene. Nature 405, 482–485 (2000).19. A. C. Bell, A. G. West, G. Felsenfeld, The protein CTCF is required for the enhancer

blocking activity of vertebrate insulators. Cell 98, 387–396 (1999).20. Z. Tang et al., CTCF-mediated human 3D genome architecture reveals chromatin to-

pology for transcription. Cell 163, 1611–1627 (2015).21. W. A. Flavahan et al., Insulator dysfunction and oncogene activation in IDH mutant

gliomas. Nature 529, 110–114 (2016).22. R. Katainen et al., CTCF/cohesin-binding sites are frequently mutated in cancer. Nat.

Genet. 47, 818–821 (2015).23. C. E. Barbieri et al., Exome sequencing identifies recurrent SPOP, FOXA1 and MED12

mutations in prostate cancer. Nat. Genet. 44, 685–689 (2012).24. A. J. Aguirre et al., Genomic copy number dictates a gene-independent cell response

to CRISPR-Cas9 targeting. Cancer Discov. 6, 914–929 (2016).

25. W. Li et al., MAGeCK enables robust identification of essential genes from genome-scale CRISPR/Cas9 knockout screens. Genome Biol. 15, 554 (2014).

26. W. Li et al., Quality control, modeling, and visualization of CRISPR screens withMAGeCK-VISPR. Genome Biol. 16, 281 (2015).

27. J. Q. Chen et al., Expression and relevance of TRPS-1: A new GATA transcription factorin breast cancer. Horm. Cancer 2, 132–143 (2011).

28. T. Liu et al., Cistrome: An integrative platform for transcriptional regulation studies.Genome Biol. 12, R83 (2011).

29. Q. Wang et al., CR cistrome: A ChIP-seq database for chromatin regulators and histonemodification linkages in human and mouse. Nucleic Acids Res. 42, D450–D458 (2014).

30. T. Hart et al., High-resolution CRISPR screens reveal fitness genes and genotype-specific cancer liabilities. Cell 163, 1515–1526 (2015).

31. S. S. P. Rao et al., A 3D map of the human genome at kilobase resolution revealsprinciples of chromatin looping. Cell 159, 1665–1680 (2014).

32. J. Ernst, M. Kellis, ChromHMM: Automating chromatin-state discovery and charac-terization. Nat. Methods 9, 215–216 (2012).

33. Y. He et al., Improved regulatory element prediction based on tissue-specific localepigenomic signatures. Proc. Natl. Acad. Sci. U.S.A. 114, E1633–E1640 (2017).

34. N. Rajagopal et al., RFECS: A random-forest based algorithm for enhancer identifi-cation from chromatin state. 9, e1002968 (2013).

35. Y. Lu, W. Qu, G. Shan, C. Zhang, DELTA: A distal enhancer locating tool based onAdaBoost algorithm and shape features of chromatin modifications. PLoS One 10,e0130622 (2015).

36. F. Liu, H. Li, C. Ren, X. Bo, W. Shu, PEDLA: Predicting enhancers with a deep learning-based algorithmic framework. Sci. Rep. 6, 28517 (2016).

37. M. Gasperini et al., A genome-wide framework for mapping gene regulation viacellular genetic screens. Cell 176, 377–390.e19 (2019).

38. J. P. Shen et al., Combinatorial CRISPR-Cas9 screens for de novo mapping of geneticinteractions. Nat. Methods 14, 573–576 (2017).

39. K. Han et al., Synergistic drug combinations for cancer identified in a CRISPR screenfor pairwise genetic interactions. Nat. Biotechnol. 35, 463–474 (2017).

40. J. Tycko et al., Mitigation of off-target toxicity in CRISPR-Cas9 screens for essentialnon-coding elements. Nat. Commun. 10, 4063 (2019).

41. X. Wu et al., Genome-wide binding of the CRISPR endonuclease Cas9 in mammaliancells. Nat. Biotechnol. 32, 670–676 (2014).

42. C.-H. Chen et al., Improved design and analysis of CRISPR knockout screens. Bio-informatics 34, 4095–4101 (2018).

43. T. Xiao et al., Estrogen-regulated feedback loop limits the efficacy of estrogen re-ceptor-targeted breast cancer therapy. Proc. Natl. Acad. Sci. U.S.A. 115, 7869–7878(2018).

44. J. J. Liu et al., Multiclass cancer classification and biomarker discovery using GA-basedalgorithms. Bioinformatics 21, 2691–2697 (2005).

45. E. Alba, J. Garcia-Nieto, L. Jourdan, E.-G. Talbi, “Gene selection in cancer classificationusing PSO/SVM and GA/SVM hybrid algorithms” in IEEE Congress on EvolutionaryComputation (IEEE, 2007), pp. 284–290.

46. C. Y. McLean et al., GREAT improves functional interpretation of cis-regulatory re-gions. Nat. Biotechnol. 28, 495–501 (2010).

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