In Vivo Genetic Screens of Patient-Derived Tumors Revealed Unexpected Frailty of the Transformed Phenotype

Author Manuscript Published OnlineFirst on May 13, 2016; DOI: 10.1158/2159-8290.CD-15-1200 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited.

In vivo genetic screens of patient-derived tumors revealed unexpected frailty of the

transformed phenotype

Authors: Daniela Bossi1*, Angelo Cicalese1*, Gaetano I. Dellino1,2, Lucilla Luzi1, Laura

Riva3, Carolina D'Alesio1, Giuseppe R. Diaferia1, Alessandro Carugo1,8, Elena Cavallaro1,

Rossana Piccioni1, Massimo Barberis4, Giovanni Mazzarol4, Alessandro Testori5, Simona

Punzi1, Isabella Pallavicini1, Giulio Tosti5, Luciano Giaco'1, Giorgio Melloni1, Timothy P.

Heffernan6,7, Gioacchino Natoli1, Giulio F. Draetta6,7,8, Saverio Minucci1,9, PierGiuseppe

Pelicci1,2, Luisa Lanfrancone1#

Author Affiliations: 1Department of Experimental Oncology, European Institute of

Oncology, Milan 20141, Italy, 2Department of Oncology and Hemato-oncology,

University of Milan, Milan 20139, Italy, 3Center for Genomic Science of IIT@SEMM,

Fondazione Istituto Italiano di Tecnologia, Milan 20139, Italy, 4Division of Pathology and

5Division of Dermatoncology, European Institute of Oncology, Milan 20141, Italy,

6Department of Genomic Medicine, 7Institute for Applied Cancer Science, 8Department of

Molecular and Cellular Oncology, UT MD Anderson Cancer Center, Houston, TX 77030,

USA, 9Department of Biosciences, University of Milan, Milan 20133, Italy.

* These authors contributed equally to this work

Running title: Identification of novel epigenetic essential genes

Keywords: In vivo screen, patient-derived tumors, metastatic melanoma, epigenetic targets,

essential genes.

Downloaded from cancerdiscovery.aacrjournals.org on October 3, 2021. © 2016 American Association for Cancer Research. Author Manuscript Published OnlineFirst on May 13, 2016; DOI: 10.1158/2159-8290.CD-15-1200 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited.

Financial Support: This work was supported by the European Research Council

Advanced Grant 341131 and the Italian Association for Cancer Research Investigator

Grant 14216.

# Corresponding author: Luisa Lanfrancone, European Institute of Oncology, Department

of Experimental Oncology,Via Adamello, 16 20139 Milano, Italy

Phone: +39 0294375011; Fax: +39 0294375990 E-mail: [email protected]

Disclosure of Potential Conflicts of Interest: The authors whose names are listed

immediately above certify that they have NO affiliations with or involvement in any

organization or entity with any financial interest, or non-financial interest in the subject

matter or materials discussed in this manuscript.

Word count: 6218

Total number of figures: 7 (plus 8 Supplementary Figures and 5 Supplementary Tables in

a single file)

Abstract

Identification of genes maintaining cancer growth is critical to our understanding of

tumorigenesis. We report the first in vivo genetic screen of patient-derived tumors, using

metastatic melanomas and targeting 236 chromatin genes by expression of specific shRNA

libraries. Our screens revealed unprecedented numerosity of genes indispensable for tumor

growth (~50% of tested genes) and unexpected functional heterogeneity among patients

(<15% in common). Notably, these genes were not activated by somatic mutations in the

same patients and are therefore distinguished from mutated cancer driver genes. We

analysed underlying molecular mechanisms of one of the identified genes, the Histone–

lysine N-methyltransferase KMT2D, and showed that it promotes tumorigenesis by

dysregulating a subset of transcriptional enhancers and target genes involved in cell

migration. Assembly of enhancer genomic-patterns by activated KMT2D was highly

patient-specific, regardless of the identity of transcriptional targets, suggesting that KMT2D

might be activated by distinct upstream signaling-pathways.

Significance

Drug-targeting of biologically-relevant cancer-associated mutations is considered a critical

strategy to control cancer growth. Our functional in vivo genetic screens of patient-derived

tumors showed unprecedented numerosity and inter-patient heterogeneity of genes that are

essential for tumor growth, but not mutated, suggesting that multiple, patient-specific

signaling pathways are activated in tumors.

Introduction

Overwhelming evidence suggests that tumor growth is sustained by gene mutations that

confer a selective growth advantage to target cells (cancer driver mutations) (1-3). The

advent of next generation sequencing (NGS) has provided an initial view of the landscape

of cancer-associated mutations (~400,000 non-synonymous mutations with ~18,000 genes

involved), and has uncovered great inter-patient heterogeneity (10-200 mutations per

tumor, with very few recurrent mutations) (1-3).

Not all mutations, however, are drivers. The vast majority (>99.5%) has no effect on

tumorigenesis, and accumulates passively during tumor progression (passenger mutations).

Putative driver mutations are currently identified by statistical methods (based on patterns

and/or frequency of mutations), which allowed prioritization of a few hundreds (1-3). Their

mutational frequency is very low (~60% mutated in <1% of patients), and, as a

consequence, only <5% of patients can be treated with targeted therapies directed against

mutated drivers (4).

Identification of mutated drivers requires formal demonstration of their tumorigenic role

under conditions of in vivo cancer growth. Unfortunately, however, this information is

available for only a fraction of the mutated cancer genes. Loss-of-function experiments

targeting multiple genes were extensively used to investigate tumor vulnerabilities in vivo,

using either cancer cell lines or genetically-engineered tumors which, however, do not

reflect the genetic diversity of human malignancies (5-14).

Here we report the first genetic screen performed on human tumors grown in vivo. We used

metastatic malignant melanomas, rapidly growing tumors with a 10-year survival rate of

<10%. Around 50% of patients carry mutations of BRAF and can be treated with BRAF

inhibitors. Strikingly, these drugs induce objective clinical responses in ~50% of patients.

However, virtually every patient eventually experience disease progression during

treatment, due to the emergence of resistant clones carrying secondary mutations, or

activation of compensatory signaling pathways (15, 16). Thus, there is an urgent need to

identify new cancer drivers and related signaling pathways, to exploit novel approaches of

treatment in melanoma.

Results

Generation of patient-derived xenografts (PDXs) of metastatic melanomas.

For the genetic screens, we used patient-derived xenografts (PDXs) of metastatic melanoma,

obtained by injecting patient-derived bioptic samples (Table S1) in NSG mice (NOD.Cg-

Prkdcscid Il2rgtm1Wjl/SzJ) (Figure 1A). For each sample (PDX1), we generated secondary

PDXs (PDX2). A total of 6 patients were included in this study, three carrying BRAF-

mutations at codon 600 and 3 NRAS-mutations at codon 61 (Table S1). PDX1 and PDX2

samples were phenotypically indistinguishable from the original patients’ tumors, as

evaluated by histopathology (Hematoxylin and Eosin staining; not shown) and melanoma-

marker (S100, HMB-45, Melan-A, CD271 and MITF) analyses (Figure S1).

To ensure that our PDXs also retained genomic features of the original tumors, we

compared whole-exome sequencing data of patients’ tissues and corresponding

PDX1s/PDX2s of three patients (two NRAS-mutated: MM13 and MM16; one BRAF-

mutated: MM27). The vast majority of mutations (SNVs/InDels) found in the patients’

melanomas were also present in the corresponding PDXs (>98.8% in PDX1s and >97.3%

in PDX2s; Figure S2A), including relevant melanoma driver mutations (e.g., NRAS, BRAF,

RAC1, CDKN2A, NF1; not shown). Allele frequency of individual SNVs/InDels in the

patients’ samples was highly variable (5%-60%) and, most notably, was maintained in the

PDX1 and PDX2 tumors (Figure S2B), suggesting that the growth pattern of the various

cell subclones composing each tumor is retained after transplantation in NSGs. Thus,

melanomas PDXs fully recapitulate both genomic and biological complexity of the patient

tumors.

Unbiased in vivo pooled shRNA screens in metastatic melanoma patients

One critical feature for the feasibility of in vivo genetic screens is the number of transduced

tumor cells that grow after transplantation in NSG mice (tumor-initiating cells; TICs),

which must be sufficiently large to support the molecular complexity of the library (library

representation). The challenge in achieving this condition is due to the fact that TICs might

represent a fraction of the entire tumor population and that TIC frequencies (and their

growth potential) can vary within the multiple subclones that characterize each tumor. TIC

frequency is relatively high in melanoma samples (17) (>1:86 in our tested samples, not

shown). However, relative number and growth potential of TICs within each subclone

cannot be directly quantified. Thus, we analysed the degree of biological complexity

indirectly, by genetically marking each tumor and testing its capacity to retain the same

molecular complexity after transplantation into NSG mice. Cells from MM13, MM16 and

MM27 PDX1 tumors were infected with a non-targeting lentiviral library containing

~12,500 plasmids with unique barcodes (BC) (13K library) and no-associated shRNAs,

under experimental conditions that allow high representation of individual BCs (~400

transplanted cells/BC) and one retroviral integration per cell (see Methods). Transduced

cells were then injected into NSG mice to obtain PDX2s. Genomic DNA (gDNA) from

PDX1 cells and PDX2 tumors was then analysed by NGS to assess absolute and relative

representations of each BC (Figure 1A). We found the entire molecular repertoire of BCs in

all the analysed samples (not shown). The relative representation of individual BCs was

highly comparable in the two PDX2 tumor replicates (Figure 1B, left panels) and the ratio

between individual BC reads in the two PDX2 tumors and in the PDX1 cells (log2(ratio))

followed a symmetric distribution, with a median that was centered at around zero (Figure

1B, right panels). Thus tumors were capable of supporting the molecular complexity of a

13K library in vivo, despite their heterogeneous growth properties, with less than 5% of

BCs depleted more than 3-fold.

We then screened the three melanomas under the same experimental conditions using a

lentiviral shRNA library targeting 236 epigenetic modulators (10 different shRNAs/gene)

and 4 screening control genes (a total complexity of 2,410 shRNAs; Figure 1A and Table

S2). For all three patients, NGS analyses revealed full representation of the library

complexity (≥99% of the 2,410 BCs; not shown) and high correlation (R=0.81, 0.55 and

0.70; Figure 1C, left panels) in PDX2 tumor replicates. The log2(ratio) distribution of the

BCs was markedly shifted toward a negative value (medians of -2,72, -2.61 and -0.76,

respectively, in patients MM13, MM16 and MM27; Figure 1C, right panels), suggesting

that the library inhibited melanoma growth in vivo. Notably, in each of the three screens,

the positive (PSMA1, KIF11 and RPL30) and the neutral (Renilla luciferase – Luc) controls

behaved as expected (Table S2A).

Genes were scored as candidate hits when >6 different shRNAs were found depleted during

melanoma growth. Depleted shRNAs were identified by applying the median of the

log2(ratio) distribution as cut-off threshold (Figure 1C, lower panels). These analyses led to

the identification of 117 genes that were consistently depleted in at least one of the three

melanomas (Figure 1D; Table S2A). The depleted genes were equally distributed among

the different functional classes of epigenetic modifiers included in the library (Table S2B).

Analyses of the depleted genes among different melanomas, however, showed a high

degree of patient specificity: 66, 69 and 55 genes were counter-selected, respectively, in the

MM13, MM16 and MM27 melanomas, with only 17 (~15%) in common (Figure 1D and

Table S2A).

Validation of the epigenetic shRNA screens

We chose 14 hits with different biological effects in the three samples (Table S2A) and

different levels of shRNA depletion: i) strongly-depleted (under the 1st quartile in the

1Q corresponding log2(ratio) distribution curve of the epigenetic screen, Figure1C; HIT ); ii)

weakly-depleted (between the 1st and the 2nd quartile; HIT2Q), and iii) not-depleted (no-

HIT) (Figure 2A). For their validation, we generated a shRNA library in the pRSI lentiviral

vector (80sh library) containing 28 shRNAs against the 14 hits (2 each), 2 shRNAs against

control genes (Luc and PSMA1), and 50 scrambled (SCR) shRNAs (see Methods and Table

S3).

PDX1 cells from the three patients were transduced with the 80sh library and injected in

NSG mice. For all patients, we found high correlation of BCs frequency in PDX2 tumor

duplicates (R=0.97, 0.99 and 0.99 for MM13, MM16 and MM27, respectively; not shown),

and significant depletion in PDX2 tumors versus PDX1 cells of both HIT1Q and HIT2Q

shRNAs (Figure 2A).

Finally, we validated 4 hits (BAZ1B, SMARCA4, CHD4 and KMT2D genes, Table S2A)

(18-22) by a conventional “single-shRNA” in vivo approach. Three were positive hits in all

three melanomas (BAZ1B, SMARCA4, CHD4), while the fourth (KMT2D) was positive

only in the two NRAS-mutated melanomas.

For each of the four hits and the three tumors, PDX2 cells were independently transduced

with pRSI lentiviruses expressing shRNAs against the hits (two each; Figure 2B), or against

Luc and PSMA1. Silencing efficiency was monitored in transduced cells injected into NSG

mice (Figure S3A-C) and melanoma growth evaluated (Figure 2B). As expected, the

PSMA1-shRNA inhibited tumor growth in all samples, as compared to the Luc-shRNA (90-

99% growth inhibition; Figure 2B). Likewise, the shRNAs silencing BAZ1B, SMARCA4

and CHD4 markedly reduced tumor growth in all three patients (75-99%; Figure 2B). The

shRNAs silencing KMT2D, instead, inhibited growth of the two NRAS-mutated melanomas

(MM13 and MM16), but not of the BRAF-mutated MM27 melanoma (Figure 2B).

Moreover, to ensure that the KMT2D-silenced tumor cells, that are still capable of growing

in vivo, are genetically identical to the original PDX1 cells and to shLuc-PDX2 cells, we

performed exome sequencing analysis of residual MM13-shKMT2D and MM27-shKMT2D

PDX2 tumors and their paired PDX1 and shLuc PDX2 tumor. It is worth noting that

residual KMT2D-transduced MM13 and MM27 PDX2 tumors retain the same allelic

distribution of the somatic mutations in the corresponding PDX1 and shLuc-PDX2 tumors

(Figure S2C-D), demonstrating that the in vivo subclones’ composition of each tumor is

retained also after KMT2D silencing and suggesting that KMT2D shRNAs act on the same

population of cells. Together, these results provide full validation of the epigenetic shRNA

screening.

To investigate biological mechanisms of the in vivo tumor growth inhibition, shRNA-

transduced melanoma cells were analysed in vitro for their proliferation and migration

properties, two key features of the in vivo growth potential of melanomas (23). The effect

on proliferation was variable in the different patients, yet all together modest (from 0% to a

max of 50% reduction compared to shLuc; Figure 2C). On the contrary, the effect on

migration was very strong (50-90% reduction for all silenced genes), with the expected

exception of KMT2D-silencing in the MM27 BRAF-mutated melanoma (Figure 2D). The

result was further strengthened by the overexpression of SMARCA4 and CHD4 in short-

term cultures of MM16 PDX2 cells (Figure S4A-F), which significantly induced cell

migration. Together, these results suggest that tumor growth inhibition by silencing of the

four epigenetic targets is closely associated with inhibition of cell migration.

KMT2D activates a migratory transcriptional program in NRAS-mutated melanomas

We then investigated the molecular mechanisms underlying the biological effects of

KMT2D shRNAs. First, we showed that full depletion of KMT2D by CRISPR/Cas9-

mediated targeted deletion in melanoma cells has identical effects on migration of NRAS-

mutated melanoma cells (Figure S5A-I). Then we extended our KMT2D-shRNA analyses

to 3 additional melanomas, one NRAS-mutated (MM23) and two BRAF-mutated (MM2 and

MM25). KMT2D-silencing in PDX2 cells (Figure 3A) markedly reduced tumor growth in

vivo (Figure 3B) and migration in vitro (Figure 3C) in MM23, while exerted no effect in

MM2 and MM25. As expected, no significant effect on cell proliferation was assessed

(Figure 3D). Thus, KMT2D expression is critical for in vivo growth and cell migration of

three NRAS-mutated melanomas (MM13, MM16 and MM23), as compared to the three

BRAF-mutated melanomas (MM2, MM25, MM27). Considering that KMT2D expression

was comparable in the six patients (not shown), these data suggest that vulnerability to

KMT2D silencing is specific of NRAS-mutated melanomas. Notably, expression of a BRAF

mutated allele in melanoma cells harboring a mutated RAS allele reverted the effect of

KMT2D-silencing, suggesting that mutant BRAF signals to migration through KMT2D-

independent pathways (Figure S6A-D).

To characterize the transcriptional program activated by KMT2D in NRAS-mutated

melanomas, we performed RNA sequencing (RNA-seq) analyses of parental versus

KMT2D-silenced PDX2 cells in the two NRAS-mutated (MM13 and MM16) and the BRAF-

mutated (MM27) patients. Comparison of KMT2D-dependent transcription in the three

patients revealed 103 genes specifically de-regulated in the two NRAS-mutated patients

(KMT2D-signature; 64 down-regulated and 39 up-regulated; Figure 4A and Table S4).

RNA-seq data were validated analysing the expression levels of 52 genes (42 de-regulated

in the two NRAS-melanomas; 10 in all three samples). Validation was 100% in the two

NRAS-melanomas, with a very high correlation between RNA-seq and qPCR quantitative

data (R=0.97 and 0.96 in MM13 and MM16, respectively; Figure 4B). As expected, the

BRAF-mutated MM27 melanoma showed a lower validation rate (88%) and accordingly, a

slightly lower Pearson correlation (R=0.82).

Ingenuity Pathway Analyses of the 103 genes (IPA®, QIAGEN Redwood City, (24))

revealed significant enrichment of genes involved in the regulation of ‘cell movement’ and

‘migration’ (n=29, p-value<0.00001) (Table S4). A manually curated literature mining

enlarged the number of genes involved in migration to 43 (~42%). A coherent link between

deregulation imposed by the KMT2D interference in our melanomas and their effect on

migration (i.e. down-regulation of genes promoting migration and up-regulation of genes

counteracting migration) was found in 26 of the 43 genes (7 up- and 19 down-regulations;

not shown). Many of the down-regulated genes are overexpressed in different cancers

(13/19) or have been demonstrated to be critical for tumor growth in vivo (6/19, e.g. SHC4,

AQP1 or RasGRP3 in melanoma) (25-27).

To investigate whether the 103-genes KMT2D-signature is specific to NRAS-mutated

patients, we analysed the expression of 52 (including 29 migratory genes) in NRAS-mutated

and BRAF-mutated melanomas upon KMT2D-silencing (Table S5). The 52 genes followed

the same pattern of regulation in the three NRAS-mutated melanomas, while their regulation

was non-uniform in the three BRAF-mutated samples (Figure 4C). These data demonstrate

that KMT2D supports a transcriptional program in the NRAS melanomas, which mainly

involves genes responsible for cell migration.

KMT2D regulates enhancer activity in the melanoma patients

KMT2D is a member of the Histone–lysine N-methyltransferase 2 (KMT2) family of

proteins that methylate lysine 4 on the histone H3 tail (H3K4) and induce genome

accessibility and transcription. KMT2D predominantly promotes H3K4 mono-methylation

(H3K4me1) at adipocyte-, myocyte- and macrophage-specific enhancers, and transcriptome

changes during adipogenesis and trans-differentiation of pre-adipocytes into myocytes (20,

22, 28).

Thus, we investigated if the effect of KMT2D on transcription is associated with its mono-

methyltransferase activity on enhancers, using the MM16 NRAS-mutated melanoma. To

map KMT2D genomic sites, shKMT2D- or control shLuc-expressing cells were analysed by

ChIP sequencing (ChIP-seq) using anti-KMT2D antibodies. ChIP-seq data revealed 14,258

KMT2D sites in control cells, 6,545 of which were absent (no peak call) in the KMT2D-

silenced cells (Figure 5A). Strikingly, the vast majority of the shKMT2D-sensitive peaks

(89%; n=5,794) mapped to either intergenic regions (~50%) or gene bodies (~50%), while

most of the KMT2D sites present in both control and KMTD2-silenced cells mapped to the

Transcriptional Start Site (TSS) of known genes (82%) (Figure 5A). Thus, our experimental

conditions of KMT2D depletion (~50% reduction; Figure S3C) induced a specific loss of

KMT2D genomic sites at regions outside gene promoters.

To map KMT2D-bound active enhancers, we first selected genomic regions with

H3K4me1-positivity, H3K27Ac-positivity and high H3K4me1 to H3K4me3 ratio (28).

ChIP-seq analyses revealed 20,714 active enhancers in MM16 PDX2 cells (Figure 5B).

Finally, we intersected the KMT2D sites and active enhancers and identified 2,832

KMT2D-bound active enhancers (Figure 5B). Thus, in MM16 melanoma, ~50% of the

shKMT2D-sensitive KMT2D peaks which map outside the gene promoters, were found at

active enhancers.

We then investigated the effect of KMT2D expression on H3K4me1 and H3K27Ac levels

in the KMT2D-bound active enhancers. Upon KMT2D-silencing, the KMT2D-bound active

enhancers showed reduction of H3K4me1 (~50%) or H3K27Ac (~40%, >1.5 fold

reduction) levels (Figures 5C-D), with only half showing both H3K4me1 and H3K27Ac

reduction. This is probably due to the distinct kinetics followed by H3K4me1 and

H3K27Ac during enhancer activation/inactivation (29, 30). Thus, to evaluate the effects of

KMT2D binding on enhancer activity, we considered, as read out, H3K27Ac levels.

Together, these data demonstrate that KMT2D-silencing in MM16 cells leads to

inactivation of a subset of the KMT2D-bound active enhancers, that we named “KMT2D-

dependent enhancers” (H3K27Ac >1.5 fold reduction; n=1,041) (Figure 5D and Figure 6A),

and suggest that this might be due to a KMT2D-dependent reduction in H3K4me1

deposition at the same sites. The lack of any effect of KMT2D on the remaining KMT2D-

bound active enhancers (those showing no reduction of H3K27Ac, named "KMT2D-

independent enhancers", n=897; Figure 6A) might be due to the presence on the same

enhancers of other mono-methyltransferases, such as KMT2C, which is also expressed in

MM16 cells (data not shown).

Notably, the number of the KMT2D-bound and the KMT2D-dependent enhancers in

MM16 PDX cells did not increase significantly when all the KMT2D peaks were

considered and not only the shKMT2D-sensitive ones (3,274 vs 2,832 and 1,133 vs 1,041,

respectively; Figures 5B and 6A). Using the same approach, we mapped 6,510 and 4,117

KMT2D-bound active enhancers in MM13 and MM27 PDX cells, respectively (Figure 6B)

and found that the number of KMT2D-bound active enhancers or KMT2D-dependent

enhancers in common among MM16, MM13 and MM27 was very low, suggesting a

specific KMT2D-enhancer-regulation pattern among different tumors.

KMT2D deregulates specific enhancers and target genes in NRAS-melanomas

We then investigated whether the effect of KMT2D on enhancer activity is mechanistically

linked to its effects on gene-specific transcription. Enhancers influence expression of their

targets over large distances (tens to several hundreds of kilobases), and different

mechanisms - DNA looping, tracking/scanning of intervening sequences - have been

proposed to explain how they associate with target TSSs (31). As an initial assessment of

the relationship of TSSs of KMT2D down-regulated genes with KMT2D-dependent

enhancers, we measured their physical distance in linear genomic sequence, and compared

this value to the distance between TSSs of KMT2D down-regulated genes with KMT2D-

independent enhancers in all three melanomas. We found that the KMT2D-dependent

enhancers are in greater proximity to the closest down-regulated genes (Figures 6A,C),

suggesting that KMT2D-dependent modifications at chromatin of distal enhancers are

mechanistically linked to variations in expression of target genes.

To test this hypothesis, we investigated whether patterns of enhancer activation by KMT2D

correlate with selectivity of its transcriptional effect. To this end, we first investigated

whether the TSSs of 64 NRAS-specific down-regulated genes (Figure 4A) were closer to the

KMT2D-dependent enhancers in the NRAS (MM13 and MM16)- versus BRAF (MM27)-

mutated melanomas. 29 and 45 genes scored as the closest to KMT2D-dependent enhancers

in MM16 and MM13 respectively (median distance of 303.6 and 72.7 Kb; Figure 6D).

Strikingly, the distances between the same genes and the closest KMT2D-dependent

enhancers in MM27 (2.9 and 1.4 Mb; Figure 6D) were 10- and 20-fold greater than in

MM16 and MM13, respectively (Figures 6D), suggesting that these enhancers might not be

involved in the regulation of the NRAS-down modulated genes. We then investigated the

effect of the binding of KMT2D to the identified KMT2D-dependent enhancer sites on the

expression of the closest putative target genes. For this purpose, we selected two KMT2D-

dependent enhancers showing strong H3K27Ac drop (2.2 fold reduction; E1: 18 kb

upstream of the MFGE8 TSS, and E2: 113 kb upstream of the RPL39L TSS; Figure 7A-B)

among the closest to the 29 MM16 down-regulated genes (Figure 6D), and one control

enhancer (E3, Figure 7C) mapping 24 kb upstream of ITPKB TSS. This enhancer, in fact, is

KMT2D-bound but shows no significant H3K27Ac reduction upon shKMT2D (Figure 7D).

To inhibit KMT2D activity at the selected enhancer regions, we used the dCas9-KRAB

fusion protein with two different single guide RNAs: sgRNA #1a or #1b, #2a or #2b, #3a or

#3b, targeting E1, E2, and E3, respectively (Figure 7D). Krüppel associated box (KRAB)

has been reported to efficiently silence transcription by recruiting Kap1 and HP1 proteins

(32) and allows genome-specific targeting when fused to catalytically inactive Cas9

(dCas9). Short-term cultures of MM16 PDX2 cells were independently infected with

sgRNAs and then transduced with dCas9-KRAB construct. Strikingly, four days after

dCas9-KRAB transduction, targeting of the E1 or E2 enhancers reduced significantly the

expression of MFGE8 and RPL39L, respectively, while no ITPKB down-regulation was

observed upon targeting of E3 (Figure 7D). MFGE8 and RPL39L down-regulation was also

observed upon targeting of the KMT2D-dependent enhancers E4 or E5 (mapping farther

away from MFGE8 and RPL39L TSS) with the corresponding sgRNAs (#4a or #4b and #5a

or #5b, respectively; Figure 7D). Together, these data show that specificity of the

transcriptional effect of KMT2D correlates with physical proximity of KMT2D-dependent

enhancers to the target genes.

Discussion

We demonstrated that in vivo genetic screens of patients’ tumors are feasible, at least using

shRNA libraries of relatively high complexity (2,410 shRNAs) and metastatic melanomas.

Melanomas are among the tumors with the highest frequency of TICs, which is a critical

limiting factor of the in vivo screens. Our screening protocol, however, can sustain the high

subclonal complexity of melanomas, suggesting that under the appropriate experimental

conditions, biological complexity may not limit in vivo genetic screens of primary tumors.

In vivo genetic screens allow identification of genes indispensable for cancer maintenance

in patient-derived tumors under in vivo conditions. This is significantly different from the

currently used model systems, mainly cancer cell lines (5-14), and might provide novel

insights into mechanisms of tumor maintenance.

Our screens revealed unprecedented numerosity and unexpected degree of functional

heterogeneity among individual tumors. We screened 236 genes in three melanoma patients

and identified around 60 critical genes per tumor, with only 17 (~15%) in common and a

total of 117 involved (e.g. ~50% of all tested genes). We also compared the results obtained

in our in vivo shRNA screens with in vitro genome-wide RNAi screens performed on

A2058 (BRAFV600E), COLO783 (BRAFV600E), SK-MEL-5 (BRAFV600E) and HS944T

(NRASQ61K) metastatic melanoma cell lines (33) (see details in Supplementary Methods)

(33). We re-analysed the published in vitro dataset designing as hits those genes whose

shRNAs depletion has an ATARiS gene score <-1 corresponding to a gene depletion of 2-

fold (see details in Supplementary Methods). We focused our analysis on the epigenetic

genes present in the in vivo and in vitro screens (a total of 109 genes) and we found: i) 39 of

109 (~36%) positive hits in the genome-wide in vitro screens (Figure S7A), and 58 of 109

(~53%) in our in vivo screen of PDX melanomas (Figure S7B); ii) no common hits among

the four cell lines, and a low frequency of common hits among the three PDX melanomas

(8 of 58); iii) a total of 22 (20%) common hits among cell lines and PDX tumors, with few

common hits when comparing single PDX tumors with the melanoma cell lines (Figure

S7C-E). The comparison of our in vivo genetic screen in melanoma PDXs with the

available in vitro screen done in melanoma cell lines showed the same high heterogeneity

of epigenetic genes critical for tumor growth or cell viability. We think that this is unlikely

due to a peculiar role of epigenetic genes in the regulation of the transformed phenotype.

Indeed, the same melanomas were screened with shRNA libraries of metabolic genes and

deubiquitinating enzymes/helicases (335 and 287 genes, respectively), which gave similar

results (data not shown). Alternatively, the observed functional-complexity and patient-

heterogeneity might reflect multiple layers of adaptation of individual tumors to the

continuously changing tumor environment or genomic context, with consequent activation

of multiple and non-redundant signaling pathways and high frailty of the transformed

phenotype. Notably, the identified melanoma hits do not appear to be activated by somatic

mutations in our patients, despite epigenetic genes are frequently mutated in cancer,

including melanomas (34, 35). The frequency of SNVs in the identified hits, in fact, was

slightly lower than in the non-hits (Figures S8A, B).

Genes carrying biological relevant mutations (cancer driver genes) (2) are considered the

best candidates for the development of targeting drugs. Our data, however, suggest that

somatically mutated genes are not necessarily the most critical genes for tumor

maintenance, since virtually all the identified hits in our melanomas were not somatically

mutated. This might have important clinical implications, since it would expand

significantly the pool of druggable genes, and, for each patient, the number of critical genes

for which a drug is available (actionable genes). For example, 10 of the melanoma essential

genes that we have identified are indeed actionable (Figure S8C). This is particularly

relevant for the NRAS-mutated melanomas, which currently have few treatment options

(36).

We investigated the biological and molecular mechanisms in tumorigenesis of KMT2D, one

of the NRAS-melanoma essential genes that we have identified. KMT2D is frequently

mutated in a variety of cancers (20). Most cancer-associated KMT2D mutations are frame

shift and nonsense alterations, suggesting that it functions as a tumor suppressor. Several

lines of evidence, however, suggest that KMT2D might also exhibit oncogenic properties

for various tumors. It is overexpressed in breast and colon carcinomas, where it is

associated with poor prognosis, while its silencing significantly reduces migration in

colorectal and breast cancer cell lines and growth in a mouse xenograft of bladder cancer

(20). We showed here that KMT2D-depletion reduces cell migration and inhibits in vivo

growth of NRAS-mutated melanomas. Notably, KMT2D was found in its germ line

configuration in the same patients, suggesting alternative mechanisms of KMT2D activation

in these tumors.

We have initially investigated the consequences of KMT2D activation in NRAS-mutated

melanomas. KMT2D is the major candidate enhancer H3K4me1 methyltransferase in

mammals (22). We showed that KMT2D-silencing leads to inactivation of a subset of

KMT2D-bound enhancers (reduced H3K4me1 and H3K27Ac) and down-regulation of a

subset of genes that are critical for cell migration, including MFGE8 and RPL39L. Down-

regulation of either gene in several cancer types, and in melanoma and breast carcinomas

specifically, reduces cell migration, and their expression correlates with a more aggressive

phenotype and unfavorable outcome in the patients (37-39). Notably, KMT2D target genes

were the most proximal to the KMT2D-dependent enhancers, suggesting that KMT2D

promotes tumorigenesis by deregulating enhancer activity.

Patterns of gene expression and enhancer deregulations, however, showed great

heterogeneity in the three analysed patients (two NRAS-mutated and one BRAF-mutated),

even in the two RAS-mutated where KMT2D functions as melanoma essential gene. We

identified ~3,000 KMT2D-regulated genes, with very few genes common to the three

patients (~5%). The overlap in the two NRAS-mutated patients was ~11%, even lower than

that observed between each of the two NRAS-melanomas and the BRAF-melanoma (~21%)

(Figure 4A). Likewise for the KMTD2-dependent enhancers: 2,803 in total, 0.07% common

to the three patients (2/2803), 1.5% common to the two RAS-mutated patients (35/2418),

4.7% (71/1526, MM16/MM27) and 0.6% (10/1774, MM13/MM27) in common between

the NRAS-mutated and BRAF-mutated melanomas (Figure 6B). Even when analyses of

enhancer-gene patterns was restricted to the 69 genes up-regulated by KMT2D in both

NRAS patients, the closest KMT2D enhancers differ in the two patients (not shown),

suggesting that assembly of enhancer genomic patterns by activated KMT2D is highly

tumor-specific, regardless of the identity of the selected transcriptional targets. A central

feature of enhancers is their ability to function as platforms for the recruitment of multiple

transcription factors, thus ensuring integration of multiple intrinsic and environmental

signaling-pathways. We speculate that activation of KMT2D in the two NRAS-melanomas

follows completely distinct upstream signaling-pathways, which might reflect cellular

responses to tumor-specific environmental or genetic context.

Methods

Animals. NOD.Cg-Prkdcscid Il2rgtm1Wjl/SzJ (termed NSG) male mice (6-12 weeks age;

15-25 gr weight) were purchased from Charles River. In vivo studies were performed after

approval from our fully authorized animal facility, notification of the experiments to the

Ministry of Health (as required by the Italian Law)(IACUCs Nº 02/2012 and Nº 758/2015-

PR) and in accordance to EU directive 2010/63.

Patient-derived xenograft (PDX) generation. The in vivo mouse model generated in the

present study was similar to that previously described (17), with few modifications. Briefly,

tissue biopsies of metastatic melanomas were collected from patients whose informed

consent was obtained in writing according to the policies of the Ethics Committee of the

European Institute of Oncology and regulations of Italian Ministry of Health. The studies

were conducted in full compliance with the Declaration of Helsinki. Tumors were

mechanically dissociated and subsequently digested with enzymatic combination of

Collagenase Type III (1 mg/ml, Worthington Biochem) and Dispase (0.5 U/ml,

STEMCELL-Technologies) for 45 min at 37°C. After incubation, cells were treated

with RBC lysis buffer (155 mM NH4Cl, 12 mM NaHCO3, 0.1 mM EDTA) to remove

erythrocytes, and then filtered (40 µm cell strainer) to obtain a single cell suspension.

Melanoma cells obtained from dissociation of lymph node metastases were separated from

leukocytes by Magnetic Activated Cell Sorting, using CD45-microbeads (Miltenyi-

Biothec130-045-801) and LD columns (Miltenyi- Biothec130-042-901), reaching an

enrichment of more than 90% (as assessed by FACS). To generate primary patient-derived

xenografts (PDX1s), 100,000 - 500,000 dissociated cells were resuspended in a 3:1 mix

of L15 medium and Matrigel Matrix (Corning 354248) and subcutaneously injected into

the flank of NSG mice. Tumor formation was monitored weekly, and tumor diameters

measured with calipers. Mice were sacrificed when tumors reached the volume of ~0.5

cm3. PDX1 tumors were dissociated as patient biopsies, and either serially transplanted to

obtain secondary and tertiary PDXs (PDX2s and PDX3s) or snap-frozen. Tissue biopsies

and PDXs were characterized by immunohistochemistry analysis (see below). Purity of

PDX-dissociated human melanoma cells (≥95%) was evaluated, analysing HLA

(BD555555) expression by FACS Aria.

Libraries and Plasmids. All libraries and plasmids were purchased by Cellecta (Cellecta

Inc.) and assembled into the pRSI vector backbone, containing the puromycin-resistance

marker and the GFP fluorescent reporter. shRNAs were under the control of a constitutive

U6 promoter and univocally associated to a barcode cassette (BC) of 18 degenerated, non-

overlapping nucleotides. The non-targeting library (13K library) consists of 12415 vectors,

each carrying a unique BC and no- associated shRNA. The epigenetic shRNA library (see

Table S2) contains 2410 vectors, targeting 236 genes (10 different shRNAs per targeted

gene), 3 positive controls (PSMA1, KIF11 and RPL30) and 1 neutral control (Luciferase).

80 pRSI vectors, each one expressing an shRNA associated to a unique BC (see Table S3),

were purchased by Cellecta and either pooled together to obtain a small-scale shRNA

library (80sh library) or used individually. 50 Scrambled (SCR) shRNAs were synthesized

starting from sequences of the following genes: E.coli lactose operon (GenBankJ01636.1),

trp operon (GenBankV00372.1) and kanamicin resistance (GenBankAJ002684.1); S.aureus

chloramphenicol resistance (GenBankAB481130.1); vector pHV1249 ampicillin resistance

(GenBankAF307748.1); Anabaena nitrogen fixation (GenBankJ05111.1); P.abies rubisco

large subunit (GenBankX75478.1); C.arabica rubisco large subunit (GenBankAJ419827.1);

S.cerevisiae genes ACH1 (Gene ID 852266), TPS1 (Gene ID 852423), PNC1 (Gene ID

852846), ILV1 (Gene ID 856819), YMC2 (Gene ID 852401). Each gene was divided in a

pool of 21-bp oligonucleotides using the sliding window scan through the sequence. All

sequences were aligned against human (hg18) and mouse (mm9) genomes using two

aligner programs, Bowtie (40) and blastn. SCR shRNAs were selected among those

sequences that do not align against human and mouse genomes using both aligner programs.

Cell culture and infection. The A375 and SK-MEL-30 melanoma cell lines were obtained

from IZSBS in 2001 and from DSMZ in 2002, respectively. Cells are routinely tested

(every two months) and authenticated by Gene Print 10 System Promega in 2015 (A375)

and 2011 (SK-MEL-30). A375 were cultured in DMEM 10% fetal bovine serum (FBS) and

SK-MEL-30 in RPMI 10% FBS. Media were supplemented with 200 mM glutamine, 100

U/mL penicillin, and 100 µg/mL streptomycin. PDX cells were maintained in IMDM

medium supplemented with 200mM L-Glutamine and 10% fetal bovine serum (FBS).

Concentrated lentiviral particles (TU, transducing units) from libraries or single plasmids

were either purchased by Cellecta or produced by transfecting 293T cells, as described in

the Cellecta User Manual. Lentiviral particles, together with 4µg/ml polybrene, were added

to the PDX cell standard medium for 16 hours. 48 hours post infection, the medium was

replaced and puromycin (2µg/ml) added for 3 days. For library infection, PDX1 cells were

infected using a Multiplicity of Infection (MOI) = ~0.2 TU/cell. Conversely, in the in vivo

validation, in vitro studies, RNA-seq and ChIP-seq experiments, PDX2 cell were infected

at a MOI = ~3, with single or pooled shRNAs silencing specific target genes (see main text).

In vivo shRNA screening. Five millions 13K library or 1 million epigenetic library

transduced cells were injected subcutaneously in duplicate or triplicate, respectively in

NSG mice. 400,000 80sh library transduced cells were transplanted in duplicate. Tumors

were harvested when reached ~0.5 cm3 in volume. BC representation was determined by

next- generation sequencing (NGS) using the Illumina/Solexa platform (Hiseq 2000). NGS

libraries were obtained according to Illumina manual. A BC specific, sequencing primer

was utilized. BCs were identified by aligning each sequencing read to the 13K library and

to the barcoded-libraries using the Bowtie aligner (40), and by considering only those BCs

having, at most, three mismatches for each alignment. BC frequency was calculated in

PDX1 cells (fc) and PDX2 tumors (ft) dividing each BC count by the total number of

aligned reads. For each BC, log2(ratio) was calculated as the base 2 log of the ft to fc

ratio. Details for gDNA extraction, nested PCR condition and NGS as reported in the

Cellecta User Manual.

In vivo validation. For the individual in vivo validation 100,000 PDX2 infected cells were

transplanted in quadruplicate in NSG mice. Each gene was silenced by two different

shRNAs. Tumor volume calculated using the modified ellipsoid formula: 1/2 (Length ×

Width2).

In vitro studies. 5,000 PDX2 infected cells were incubated for 72 hr, and proliferation was

measured by CellTiter-Glo

assays (Promega). The migration assay was performed using 8.0µm pore size, fibronectin

pre-coated, inserts in 24-well plates. Duplicates of 100,000 cells in serum-free medium

were plated in the upper chamber and complete medium was added to the lower chamber.

After 36 hours of incubation, cells, which had migrated to the lower surface of the inserts

were stained with 0.5% crystal violet. Five images of each insert were acquired and

analysed with ImageJ software as described (41).

Exome-sequencing. gDNA of patients’ samples was extracted from frozen tissues,

containing at least 85% melanoma cells (MM13 and MM27), or formalin-fixed, paraffin-

embedded (FFPE) tissues, containing around 60% of neoplastic cells (MM16). gDNA was

also obtained from matched patients’ non-tumor tissues (normal counterpart) and paired

xenograft (PDX1 and PDX2) tissues. gDNA was prepared and whole-exome sequencing

performed according to standard protocols (See Supplementary Methods). Sequencing

alignment to human genome and subsequent bioinformatic analysis is fully described in

Supplementary Methods.

RNA-sequencing. Total RNA extracted from PDX2 infected cells was purified and

libraries sequenced on an Illumina HiSeq2000. Bioinformatic analysis is fully described in

Supplementary Methods

ChIP-sequencing and bioinformatics analysis. PDX2 infected cells were subjected to

Chromatin ImmunoPrecipitation (ChIP). Briefly, after cross-linking, cells were lysed,

sonicated and incubated with specific antibodies (See Supplementary Methods for details).

Samples were sequenced, aligned to human genome. Bioinformatic analysis is fully

described in Supplementary Methods.

Immunohistochemistry. Tissue fragments from patient biopsies or PDXs were formalin-

fixed and paraffin-embedded. After deparaffinization, sections were treated according to

standard protocols, as detailed in Supplementary Methods.

Enhancer repression by CRISPRi technology in PDX cells. The experiments were

performed using the CRISPRi technology (31). Detailed experimental protocol is indicated

in Supplementary Methods.

Quantitative-RT-PCR. Total RNA was extracted with the Qiagen RNeasy Mini Kit, and

reverse transcribed using random hexamers (Improm-II, Promega). RNA expression of

KMT2C, KMT2D and BAZ1B was determined by q-RT-PCR using the Fast SYBR Green

Master Mix (Applied- Biosystem) and the iCycler iQ real-time detection system

software (Bio-Rad). RPLP0 was used as housekeeping gene. RNA expression of KMT2D-

regulated genes was determined by TaqMan Gene Expression assays (Applied-Biosystem),

using single tube assays or 384-well microfluidic cards. Up to 8 different housekeeping

genes were utilized as normalizers. Three biological replicates were included for each

experiment. Primer and TaqMan assay details are available upon request.

Data access

Sequencing data have been submitted to the NCBI Gene Expression Omnibus (GEO) under

accession Nº GSE71854.

Authors’ Contributions

Conception and design: A. Cicalese, D. Bossi, S. Minucci, PG. Pelicci and L.

Lanfrancone, with inputs from TP. Heffernan, G.F. Draetta and other authors.

Development of methodology: D. Bossi, A. Cicalese, S. Punzi, C. D’Alesio, I.

Pallavicini and E. Cavallaro performed in vitro experiments. L. Lanfrancone, S. Minucci,

A. Cicalese, D. Bossi, T.P. Heffernan designed viral vectors with input from G.F. Draetta

and PG. Pelicci. A. Cicalese, D. Bossi, L. Giaco’, L. Luzi and L. Lanfrancone designed

and developed small-scale libraries. A. Cicalese, D. Bossi, C. D’Alesio and A. Carugo

performed in vivo studies. D. Bossi, G.I. Dellino and G.R. Diaferia designed and

performed CRISPR/Cas9/KRAB experiments.

Acquisition of data (provided animals, acquired and managed patients, provided

facilities, etc.): A. Cicalese, D. Bossi, R. Piccioni, G.I. Dellino, M. Barberis, G. Mazzarol,

A. Testori and G. Tosti.

Analysis and interpretation of data (e.g., statistical analysis, biostatistics,

computational analysis): L. Luzi, G. Melloni and L. Riva performed bioinformatics

analyses with input from G.I. Dellino, A. Cicalese, D. Bossi and L. Lanfrancone.

Writing, review, and/or revision of the manuscript: D. Bossi, A. Cicalese, G.I. Dellino,

L. Luzi, S. Minucci, G. Natoli, PG. Pelicci and L. Lanfrancone.

Administrative, technical, or material support (i.e., reporting or organizing data,

constructing databases): A. Cicalese, D. Bossi, L. Luzi, G. Melloni and L. Riva.

Study supervision: D. Bossi, A. Cicalese, PG. Pelicci and L. Lanfrancone.

Acknowledgments

We thank M. Varasi for insightful discussion. We thank A. Gobbi and M. Capillo for

excellent support in animal work and L. Rotta for excellent sequencing support. We thank

G. Giardina, C. Spinelli, A. Papait, D. Di Gesto, F. Cataldo, I. Davidson and E. Pasqualucci

for providing reagents and protocols.

We wish to thank all members of the Department of Experimental Oncology for discussion

and reagents. We thank the Genomic Unit (IEO), the Mouse Facility (Cogentech), the DNA

service (Cogentech) and the Cell Biology Unit (IEO).

PierGiuseppe Pelicci and Luisa Lanfrancone are members of the EurOPDX Consortium.

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Figure Legend

Figure 1. In vivo shRNA screening in PDXs from three melanoma patients. A) ShRNA

screening strategy: surgical specimens of metastatic melanomas were dissociated and

injected subcutaneously into NSG mice to obtain primary PDX1 tumors. PDX1 cells were

infected with either the control (13K) or the epigenetic libraries and re-transplanted (PDX2

tumors). Genomic DNAs extracted from PDX2 tumors and transduced PDX1 cells were

subjected to PCR amplification and NGS to quantify the BCs. B-C) Left panels in B and C

represent the scatter plots of BC frequency of the two replicate tumors (T1 and T2) arising

from the transplantation of MM13, MM16 and MM27 transduced PDX1 cells expressing

either the 13K (B) or the epigenetic library (C). Gray dotted lines represent the axis

bisectors. Pearson correlations (R) are reported. Right panels in B and C show the log2(ratio)

distribution of the BC reads in the PDX2 tumors (mean of replicates) and in the transduced

PDX1 cells. The gray dotted line in C represents the cut off threshold (reported in bold gray

in the x-axis) set to calculate the significantly depleted shRNAs (gray portion of the

distribution curve). Medians are reported. D) Venn diagram reporting the number of genes

scoring as depleted hits in the three PDXs.

Figure 2. Validation of shRNA screenings. A) Log2(ratio) analysis (mean of the

replicates) of indicated PDX2 tumors expressing the 80sh-Library. Box plots show the

distribution of SCRs, HIT1Q, HIT2Q and no HITs shRNAs (see text). N: sample size. Black

and blue dots: shLuc and shPSMA1. B) PDX2 cells expressing shRNAs (two shRNAs/gene,

1 and 2) whose silencing target the indicated genes were injected in recipient mice. Tumor

volumes (mean±SD) of PDX3 melanomas were measured after 5, 7 and 4 weeks in MM13,

MM16 and MM27, respectively. C-D) MM13, MM16 and MM27 PDX2 cells were

infected with a pool of two shRNAs targeting each indicated gene and plated for

luminescent-based proliferation (C) or transwell migration (D) assays. C) Growth curve of

shRNAs-infected cells. Proliferation values (mean±SD) are expressed as ratio of the mean

luminescent values in the shRNA expressing cells compared to their controls at time of

plating (Day 0). D) Relative migration (mean±SD) expressed as a ratio of silenced versus

control (Luc shRNA) cell migration values, calculated by ImageJ analysis. *, ** and ***: p-

values (calculated by the Student T-test ) < 10-2, 10-3 and 10-6 respectively.

Figure 3. Validation of KMT2D function in MM23, MM2 and MM25 PDXs. A-D)

MM23 (NRAS-mutated) or MM2 and MM25 (BRAF-mutated) PDX2 cells expressing

control (Luc) or KMT2D (pool of two) shRNAs were injected in recipient mice (B), or

plated for a luminescent-based proliferation (C) or transwell migration (D) assays. A)

QPCR analysis of KMT2D mRNA levels in PDX2 silenced cells at time of transplantation

or plating of in vitro assays. RPLP0 mRNA level was used as housekeeper. B) In vivo

melanoma growth in NSG mice. Tumor volumes (mean±SD) of PDX melanomas were

measured after 6, 5 and 4 weeks in MM23, MM3 and MM25, respectively. C) Relative

migration (mean±SD) of PDX silenced cells calculated as described in Figure 2. D)

Relative growth of PDX2 silenced cells after 3 days of culture. Values (mean±SD) are

expressed as ratio of the mean proliferation value in the silenced cells compared to control

(Luc shRNA) cells. *: p-value are calculated by the Student T-test (p<0.01).

Figure 4. Transcriptomic analysis of KMT2D-silenced PDX cells. A) MM13, MM16 and

MM27 PDX2 cells expressing control (shLuc) or KMT2D (shKMT2D, pool of two hairpins)

shRNAs were subjected to RNA-seq analysis. The Venn diagrams show genes significantly

down- or up-regulated upon KMT2D-silencing (p-adjusted <0.05) in the three PDXs. B)

Validation of the RNA-seq was performed by qPCR. RNA expression of 52 genes (n)

chosen among those regulated by KMT2D silencing was calculated using specific Taqman

Assays. Scatter plots show the correlation between the log2 expression fold change (FC)

calculated by qPCR (x-axis) and RNA-seq (y-axis) in MM13, MM16 and MM27 PDX2

cells. The percentages of genes showing similar FC (Validation) were reported. Regression

lines (dotted lines) calculated by minimal squares methods and relative Pearson correlations

(R) as reported. Red dots: genes specifically regulated in the three PDXs (Common), Open

circles: genes commonly regulated in MM13 and MM16 (NRAS). C) Heat map matrix

representing the Pearson correlation (R, color scale as reported) among the expression FC

of the 52 genes (calculated by qPCR) in 3 NRAS- and 3 BRAF-mutated PDXs (as reported).

The panel highlights the clustering of the three NRAS-mutated PDXs.

Figure 5. Effect of KMT2D-silencing on H3K4 monomethylation and H3K27

acetylation of active enhancers. A-D) Anti-KMT2D, -H3K4me1, -H3K4me3 and -

H3K27ac ChIP-seq analyses of MM16 PDX2 cells expressing either control (shLuc) or

KMT2D (shKMT2D, pool of two hairpins) shRNAs. A) Histogram of KMT2D peaks

common to shLuc and shKMT2D cells or specific for shLuc cells. The bars show KMT2D-

peaks mapping, or not, to proximal promoters (TSS and noTSS, respectively). B) Venn

diagram showing the KMT2D-bound active enhancers as the overlap between the 5,794

“noTSS-associated KMT2D peaks” identified in (A) and the active enhancers. C-D) Heat

map of genomic co-localization of KMT2D and H3K4me1 (C) or H3K27ac (D), in shLuc-

and shKMT2D-cells. KMT2D-bound active enhancers showing reduction of H3K4me1 (C)

or H3K27ac (D) peak amplitude upon KMT2D interference are represented. Regions are

sorted from highest to lowest KMT2D coverage in shLuc cells. Average quantitation of

each histone mark is shown at the bottom. FC: Fold Change. N: sample size.

Figure 6. Analysis of enhancer-promoter distances. A) As described in the main text and

in Figure 5, in MM16 cells the KMT2D-bound active enhancers were so defined: 1) non-

TSS-associated genomic regions, 2) KMT2D-binding presents in shLuc (+) and absent (no

peak call) in shKMT2D cells (-), 3) H3K4me1- and H3K27Ac-positivity (+) and 4) high

H3K4me1 to H3K4me3 ratio (H3K4me3 low, see Methods). Among the KMT2D-bound

active enhancers, the KMT2D-dependent enhancers were those with H3K27Ac-reduction

greater than 1.5-fold (H3K27Ac FC≤-1.5), while the KMT2D-independent enhancers

were those showing no H3K27ac-reduction upon KMT2D-silencing (H3K27Ac FC>0). In

MM13 and MM27 samples, the KMT2D-bound active enhancers are defined as in MM16,

with the exception that the KMT2D binding upon KMT2D-silencing was not done. To have

comparable datasets, MM16 was also analysed as MM13 and MM27 (MM16*). The table

summarizes the analysis of the KMT2D-bound enhancer to gene distance analysis (see main

text). For MM16, MM16*, MM13 and MM27 KMT2D enhancers, the table reports the

numbers of genes down-modulated upon KMT2D-silencing (as shown in Fig 4A) and the

numbers and the median distances from the closest down-modulated-gene of KMT2D

dependent and –independent enhancers. P-values (Wilcoxon test) between the median

distances of KMT2D-dependent and –independent enhancer are reported. Notably the

analysis shows very similar results between MM16 and MM16* KMT2D-enhancers. B)

Venn diagrams showing the overlaps between MM16*, MM13 and MM27 KMT2D-bound

active or KMT2D-dependent enhancers. C) Boxplots showing the distribution of the

distances between KMT2D-dependent or KMT2D-independent enhancers and the TSS of

the nearest down-regulated genes in each melanoma (MM13, MM16 and MM27). D)

Boxplots showing the distribution of the distances between the 29 (MM16, upper panel) or

45 (MM13, lower panel) TSS of the down-regulated genes (see text) and the KMT2D-

dependent enhancers of MM13, MM16 and MM27, as indicated.

Figure 7. dCas9-KRAB down-regulates genes near to KMT2D-dependent enhancers

(A-C) Visualization in the UCSC Genome Browser of anti-KMT2D, -H3K4me1, -

H3K4me3 and -H3K27ac ChIPseq signals at enhancers of shLuc or shKMT2D long term

culture of MM16 PDX2 cells. KMT2D-dependent enhancers (black boxes E1, E2, E4 and

E5) flanking the MFGE8 and RPL39L genes (A-B) and KMT2D-bound control enhancer

(black box E3) flanking the ITPKB gene (C) are shown. D) Table showing the genomic

coordinates of sgRNAs targeting the E1-E5 enhancers (columns 1-3), number of sgRNAs

(column 4), enhancers (column 5), target genes (column 6), their distance (column 7), and

reduction of H3K27ac levels after KMT2D-silencing (column 8). The relative expression of

the target genes (assayed by qPCR, normalized to control cells, column 9) is reported as

mean ± SE of duplicates (two sgRNAs per enhancer).

In vivo genetic screens of patient-derived tumors revealed unexpected frailty of the transformed phenotype

Daniela Bossi, Angelo Cicalese, Gaetano I. Dellino, et al.

Cancer Discov Published OnlineFirst May 13, 2016.

Updated version Access the most recent version of this article at: doi:10.1158/2159-8290.CD-15-1200

Supplementary Access the most recent supplemental material at: Material http://cancerdiscovery.aacrjournals.org/content/suppl/2016/05/13/2159-8290.CD-15-1200.DC1

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