Abstract
Primary effusion lymphoma (PEL) is caused by Kaposi’s
sarcoma-associated herpesvirus. Our understanding of PEL is poor and
therefore treatment strategies are lacking. To address this need, we
conducted genome-wide CRISPR/Cas9 knockout screens in eight PEL cell
lines. Integration with data from unrelated cancers identifies 210
genes as PEL-specific oncogenic dependencies. Genetic requirements of
PEL cell lines are largely independent of Epstein-Barr virus
co-infection. Genes of the NF-κB pathway are individually
non-essential. Instead, we demonstrate requirements for IRF4 and MDM2.
PEL cell lines depend on cellular cyclin D2 and c-FLIP despite
expression of viral homologs. Moreover, PEL cell lines are addicted to
high levels of MCL1 expression, which are also evident in PEL tumors.
Strong dependencies on cyclin D2 and MCL1 render PEL cell lines highly
sensitive to palbociclib and [34]S63845. In summary, this work
comprehensively identifies genetic dependencies in PEL cell lines and
identifies novel strategies for therapeutic intervention.
__________________________________________________________________
Our understanding of the genetics and treatment options for primary
effusion lymphoma (PEL) is lacking. Here the authors performed in vitro
CRISPR/Cas9 knock-out screens to identify 210 PEL-specific oncogenic
genes and report strategies for therapeutic intervention.
Introduction
The human oncogenic γ-herpesvirus Kaposi’s sarcoma-associated
herpesvirus (KSHV) causes primary effusion lymphoma (PEL), Kaposi’s
sarcoma, and a subtype of the lymphoproliferative disorder multicentric
Castleman’s disease^[35]1–[36]4. PELs typically occur in the context of
immunosuppression and present as clonal effusions of post-germinal
center B cells into body cavities^[37]5. The current treatment regimen
for PEL is standard chemotherapy and, in HIV/AIDS-associated cases,
combination antiretroviral therapy^[38]6. Despite this, prognosis of
this disease remains poor, with a median survival time of 6
months^[39]7. Thus, better treatment alternatives are urgently needed.
Genetic loci that are translocated or mutated in other B cell
lymphomas, such as the proto-oncogene MYC or tumor suppressor protein
p53 (TP53), are typically unaltered in PEL^[40]8–[41]10. Instead, the
defining feature of this cancer is the presence of KSHV in each tumor
cell. In the vast majority of cells, KSHV undergoes latency, with
expression of only a small number of viral proteins, including latent
nuclear antigen (LANA), a viral interferon regulatory factor
(vIRF3/LANA2), viral homologs of D-type cyclins (vCYC) and FLICE
inhibitory protein/c-FLIP/CFLAR (vFLIP), and a cluster of viral
microRNAs. Most PEL tumors (~80%) are co-infected with the oncogenic
γ-herpesvirus Epstein-Barr virus (EBV), pointing to a role of EBV in
PEL^[42]5. A role for EBV is experimentally supported by the finding
that introduction of EBV into EBV-negative PEL cell lines increases
xenograft formation in severe combined immune deficiency mice^[43]11.
KSHV also enhances EBV-associated B cell lymphomagenesis in a humanized
mouse model^[44]12. Nevertheless, KSHV is clearly the main oncogenic
driver of PEL because EBV-negative cases exist and PEL-derived cell
lines require the constitutive expression of at least LANA, vFLIP, and
vIRF3, regardless of EBV co-infection^[45]13–[46]15. Whether EBV
contributes to the survival and proliferation of dually KSHV- and
EBV-infected PEL cell lines is unknown.
The current model of PEL oncogenesis suggests critical roles for
inhibition of the p53 family of tumor suppressors and the constitutive
activation of nuclear factor kappa B (NF-κB), cytokine, and
PI3K/Akt/mTOR signaling pathways. The viral LANA protein is critical,
as it mediates the episomal maintenance of the KSHV genome during cell
division. LANA also forms a complex with p53 and the p53 ubiquitin
ligase MDM2, and thereby blocks p53 function^[47]16. The function of
p53, and the related p73, can be reactivated in PEL cells with
Nutlin-3a, which disrupts the p53/MDM2 and p53/MDM2/LANA complexes and
triggers apoptosis and cell cycle arrest^[48]9,[49]16–[50]18. In
addition to LANA, vIRF3 also binds and inhibits p53^[51]19. The
essentiality of vFLIP in PEL cell lines is thought to be due to its
direct interaction with the NEMO (encoded by IKBKG) subunit of the
inhibitor of κB kinase (IKK) complex, resulting in the constitutive
activation of pro-survival NF-κB signaling^[52]20–[53]24. PEL cell
lines have furthermore been reported to depend on autocrine and
paracrine signaling by a KSHV homolog of IL6 (vIL-6) and cellular
cytokines, which activate Jak/Stat signaling^[54]25. PEL cell lines are
sensitive to inhibitors of PI3K and mTOR and thus addicted to high
levels of PI3K/Akt/mTOR activity^[55]26,[56]27, although which viral
genes are responsible for this phenotype in PEL cells is unknown. The
role of vCYC expression during latency in PEL remains unclear. vCYC
drives cell cycle progression following ectopic expression, but differs
from cellular D-type cyclins by its preference for cyclin-dependent
kinase 6 (CDK6) as a binding partner^[57]28. vCYC/CDK6 complexes
furthermore exhibit an extended substrate range and are relatively
refractory to inhibition by CDK inhibitors^[58]29.
Gene expression profiling places the transcriptome of PEL cell lines
and tumors closest to that of plasma cell neoplasms, most notably
multiple myeloma^[59]30–[60]32. Accordingly, PELs express high levels
of the transcription factor interferon regulatory factor 4 (IRF4), a
critical oncogene in multiple myeloma^[61]33. More recently, PEL cell
lines were suggested to require an IKZF1-IRF4-MYC transcriptional axis,
which renders them susceptible to the immunomodulatory drugs (IMiDs)
lenalidomide and pomalidomide, due to degradation of IKZF1 and a
consequent loss of IRF4 expression^[62]34.
Our current understanding of which host genes are critical in PEL is
likely incomplete and based largely on candidate approaches. Recently,
CRISPR/Cas9 gene editing has emerged as a powerful platform for
unbiased genome-wide loss-of-function screening^[63]35,[64]36. Here we
utilize genome-wide CRISPR screens for the comprehensive identification
of single-gene dependencies in PEL-derived cell lines. We integrate
resulting data with a newly generated dataset from multiple myeloma and
published screens from 15 other cancer cell types. Our analyses define
210 non-housekeeping genes as PEL-specific oncogenic dependencies
(PSODs). Our data identify novel single-gene addictions. In-depth
validation experiments demonstrate a strong requirement of PEL cells
for IRF4, MDM2, CCND2, and MCL1, all of which are druggable, and for
CFLAR. The newly identified requirements of PEL for cyclin D2 and
c-FLIP are surprising given that KSHV expresses homologs of these
proteins (vCYC and vFLIP). We furthermore show that MCL1 is highly
expressed in PEL tumors and that MCL1 inhibition offers an effective
therapeutic strategy. In sum, our work achieves a detailed
understanding of the genetic requirements of PEL cell lines and
provides important leads for new lines of investigation and novel
therapeutic strategies.
Results
Genome-wide CRISPR/Cas9 knockout screens in PEL cell lines
We performed genome-wide CRISPR/Cas9 knockout screens for essential
host genes in eight PEL cell lines, including four that were
co-infected with EBV (Fig. [65]1a, b). As a control for B cell
malignancies of non-viral etiology, we similarly screened the commonly
used B cell line BJAB and the multiple myeloma cell line KMS-12-BM.
Multiple myeloma shares plasma cell differentiation status with
PEL^[66]31,[67]32. Cas9-expressing cell pools and/or cell clones were
infected with single guide RNA (sgRNA) libraries^[68]37,[69]38 and
cultured for 2–3 weeks to allow sufficient time for the depletion of
cells with inactivated essential and/or fitness genes. sgRNA
composition was assessed by Illumina sequencing and compared to input
libraries using MAGeCK^[70]39. The detailed experimental workflow and
conditions are summarized in Fig. [71]1a, b, Supplementary
Data [72]1–[73]6, and Methods. We observed highly significant depletion
of numerous sgRNAs but only few enrichments, indicating the existence
of many dependencies but few expressed genetic liabilities in cultured
PEL cells (Supplementary Figure. [74]1a, b). Our screens identified on
average 862 genes with false discovery rate (FDR)-adjusted (adj.) p
values of sgRNA depletion (adj. p) < 0.05 (Fig. [75]1c), similar to
results from CRISPR screens reported in non-PEL cancer cell
lines^[76]40–[77]43. This cutoff may include both genes that are
strictly essential and those that are non-essential but confer
increased fitness. We therefore refer to genes that meet this cutoff as
“gene dependencies.” Screens performed in cell clones selected for
optimal editing identified most gene dependencies (Fig. [78]1c and
below). Therefore, variation in the numbers of identified gene
dependencies likely reflects the sensitivity of individual screens due
to variable editing efficiencies. Specifically, screens in BJAB, VG-1,
and BC-5 performed relatively poorly. Gene set enrichment analyses
(GSEAs)^[79]44 of all datasets revealed the expected striking depletion
of sgRNAs for genes with housekeeping functions (Fig. [80]1d, e, and
Supplementary Data [81]7). In conclusion, our CRISPR knockout screens
effectively identified genetic dependencies in PEL cell lines.
Fig. 1.
[82]Fig. 1
[83]Open in a new tab
Genome-wide CRISPR knockout screens in PEL cell lines. a Experimental
outline. Cas9-expressing cell pools or clones were infected with the
lentiviral GeCKO v2 or Brunello sgRNA libraries. After complete
puromycin selection, cells were split every 2–3 days and maintained at
500× sgRNA coverage. After 14–18 days, sgRNA composition was analyzed
by Illumina sequencing and MAGeCK. b Cell lines and conditions used in
this study. c Numbers of genes with significantly depleted sgRNAs in
each screen (adj. p value < 0.05). “G” indicates cells were screened
with GeCKO v2 library; all others were screened with Brunello. Cyan:
EBV(+) PEL cells; blue: EBV(−) PEL cells; red: multiple myeloma;
purple: Burkitt’s lymphoma. d Representative gene set enrichment
analysis (GSEA) from BCBL-1 Cas9 clone screened using Brunello library.
Genes were ranked by sgRNA depletion scores, with genes with depleted
sgRNAs at the right end of the x-axis. NES normalized enrichment score,
FDRq FDR-adjusted p value. e Heatmap of GSEA NESs of housekeeping
pathways (Reactome) in all PEL cell lines (see Supplementary
Data [84]7). Screens designated “G” used GeCKO v2 library. f Principal
component analysis of normalized sgRNA reads from EBV(−) (blue shades)
or EBV(+) PEL cells (red shades). sgRNAs from genes that have adj.
p < 0.05 in at least one cell line were examined. Only data from
Brunello screens were considered
Genetic requirements of EBV-negative and -positive PEL cell lines
The high rate of EBV co-infection in PEL tumors and increased tumor
formation of KSHV and EBV co-infected B cells in mouse models point to
a role for EBV in PEL pathobiology. To test whether the presence or
absence of EBV is a major determinant of genetic requirements, we
performed principal component analysis (PCA) on normalized sgRNA counts
(Fig. [85]1f). Using this unbiased approach, PEL cell lines did not
cluster based on their EBV infection status. Analyses of other
principal components (up to PC5) were similarly unable to separate cell
lines based on EBV infection status. These results indicate that EBV(−)
and EBV(+) PEL cell lines in general have highly similar genetic
requirements for their survival.
However, it remains possible that individual genes are selectively
needed in EBV(−) or EBV(+) PEL cell lines, to either compensate for the
absence of EBV or to facilitate latent maintenance of two
herpesviruses. A small number of differentially required genes would be
missed in the global unsupervised clustering analyses above. Indeed, we
identified 35 candidates for genes that preferentially scored as
dependencies in EBV(−) PEL cell lines (Supplementary Figure [86]2). In
contrast, only three gene dependencies were preferentially detected in
the majority of EBV(+) cell lines. Individual candidates for
differential requirements based on EBV co-infection thus exist and
should be investigated in future studies. Overall, however, our CRISPR
screens suggest that EBV(−) and EBV(+) PEL are a single disease driven
by a common set of oncogenic addictions.
The oncogenic landscape of PEL
To pinpoint the most critical single-gene dependencies of PEL cells, we
ranked genes using their median adj. p value of sgRNA depletion across
all eight cell lines (Supplementary Data [87]8). This approach defines
a core set of 712 “PEL gene dependencies” (using a median adj. p < 0.05
cutoff; Fig. [88]2a). Importantly, these genes are required in all or
nearly all PEL cell lines, independently of EBV co-infection, genetic
background, or patient treatment history. Among the top ranked genes on
this list is CCND2, which encodes the G1/S-specific cyclin D2
(Fig. [89]2a). Other top ranked genes are MCL1 and CFLAR, which encode
the anti-apoptotic proteins MCL1 and c-FLIP, respectively. Dependencies
of PEL cell lines on cyclin D2 and c-FLIP have not been reported
previously, while a potential dependency on MCL1 was suggested in
recent studies^[90]45,[91]46. Our screens furthermore revealed a novel
dependency on MDM2, which encodes a negative regulator of p53 and p73.
This requirement for MDM2 by PEL is consistent with previous studies
demonstrating sensitivity of PEL cell lines to re-activation of p53/p73
by the MDM2 inhibitor Nutlin-3a^[92]9,[93]17. Moreover, IRF4 scores as
essential in seven of eight PEL cell lines, confirming a recent
report^[94]34.
Fig. 2.
[95]Fig. 2
[96]Open in a new tab
Genetic dependencies of PEL cell lines. a Significance of dependency of
all genes screened by Brunello library in 8 PEL cell lines. Genes are
ranked using the median adj. p value scores (FDR). A large majority of
genes, including those involved in NF-κB (e.g., RELA, NFKB1, and IKBKG)
and cytokine signaling (e.g., STAT3 and JAK1), score as dispensable in
PEL cells. Genes in yellow are considered housekeeping genes,
non-housekeeping genes are in pink. Ranks among PSODs are in
parentheses. b Workflow and criteria for classifying “housekeeping
genes” and “PEL gene dependencies”, based on CRISPR screens in this
study and 52 publicly available screens. PEL gene dependencies that do
not have housekeeping functions are further considered “PEL-specific
oncogene dependencies” (PSODs). c Pathway enrichment analysis of PSODs
using DAVID for gene sets from GO (orange) or KEGG (blue). Number of
genes included in each enriched pathway is indicated. Full results are
in Supplementary Data [97]11
The 712 PEL gene dependencies above include many genes with
housekeeping functions. To distinguish between housekeeping genes and
those that are specifically required in PEL, we analyzed a total of 60
CRISPR screens representing 16 different cancer cell
types^[98]40–[99]43 (Fig. [100]2b and Supplementary Data [101]1). Genes
that were depleted (median adj. p < 0.25) in the majority of cell lines
per cancer type were classified as a “potential gene dependency” in
this cancer type. Genes that were “potential gene dependencies” in
≥10/16 cancer cell types were further considered “housekeeping genes.”
These cutoffs were chosen to account for false negatives and flag 1050
genes as housekeeping genes (Fig. [102]2b and Supplementary
Data [103]9). Removing these genes from the list of PEL gene
dependencies identifies 210 genes that are specifically required in PEL
but are unlikely to be housekeeping genes. We refer to these 210 genes
as “PEL-specific oncogenic dependencies” (PSODs, Supplementary
Data [104]10). PSODs are expected to include the main PEL-specific
oncogenic drivers. PSODs also likely include genes that are essential
for the episomal maintenance of the KSHV genome and/or to prevent lytic
re-activation. Because viral latency is necessary for PEL, such genes
can be considered non-traditional oncogenes in the broadest sense.
Previous reports suggest that PEL cells are addicted to overexpression
of MYC and high levels of mTOR activity^[105]26. However, these two
genes are not classified as PSODs, because both are flagged as
housekeeping genes (Fig. [106]2a and Supplementary Data [107]9).
CRISPR/Cas9 editing results in complete inactivation and thus these
screens cannot distinguish addictions to overexpression or constitutive
activation of genes from their housekeeping functions.
Pathway analysis of the PSODs showed enrichment in pathways involved in
several metabolic processes (Fig. [108]2c and Supplementary
Data [109]11). These enrichments could point to specific metabolic
demands of PEL cells, an idea that is supported by an emerging
literature on how KSHV reshapes the metabolic status of infected
cells^[110]47.
Strikingly, the list of PSODs included several genes that can be
inhibited by compounds either in pre-clinical development or already in
clinical use for other cancers, such as CCND2, IRF4, MDM2, and MCL1.
PEL is a very rare disease, which complicates clinical trial design.
Thus, repurposing already available drugs is likely the most practical
option for treatment strategies in PEL. These genes were therefore
further investigated below.
In sum, we identify a set of 210 genes that are specifically required
in PEL cell lines (defined as PSODs). Because these genes do not have
housekeeping functions, they represent attractive therapeutic targets.
Single oncogene dependencies of PEL cells on IRF4 and MDM2
Surprisingly, neither the PEL gene dependencies nor PSODs include key
genes involved in the NF-κB (e.g., RELA, NFKB1, and IKBKG) and cytokine
signaling pathways (e.g., the v-IL6 receptor IL6ST and JAK/STAT family
proteins). These pathways are currently considered critical in PEL, but
the relevant genes scored in only a subset of cell lines (Figs. [111]2a
and [112]3a, b). Even in these cases, the sgRNAs targeting these genes
were only modestly depleted (Fig. [113]3c, d). These results could thus
be false negative or these genes could serve as “fitness genes,” which
provide a subtle advantage to at least a subset of PEL cell lines.
Fig. 3.
[114]Fig. 3
[115]Open in a new tab
PEL cell lines depend on IRF4 and MDM2, but not NF-κB components. a
Current models of NF-κB, vIL-6, IRF4/MYC axis, and p53 regulatory
pathways in PEL. i In the inactive state, the NF-κB subunits p65 and
p50 are sequestered by the IκB complex and prevented from signaling.
Upon activation of the pathway, the IKK complex (NEMO, IKKα, and IKKβ)
is phosphorylated and targets IκB for degradation. This releases the
p65/p50. In PEL, this pathway is thought to be constitutively activated
by interaction of vFLIP with NEMO (IKBKG). ii Autocrine signaling by
vIL-6 is triggered by the intracellular binding to gp130, which
subsequently activates JAK/STAT signaling. iii The IRF4/MYC axis is
proposed as a pro-proliferative transcriptional axis downstream of
IKZF1. iv Activity of the tumor suppressor p53 in PEL is blocked by its
degradation via the LANA-MDM2 complex. Genes in blue were chosen for
validation. b Heatmap of adj. p values of sgRNA of key genes from a
across cell lines screened. On the right are the numbers out of 16
cancer types where the relevant gene scored with a median adj. p < 0.25
in each group (Fig. [116]2b). The Brunello library was used for most of
the screens except where indicated: G, GeCKO v2. c Volcano plot for
genes screened using Brunello library in BC-3 highlighting some high
confidence PEL dependencies (blue), fitness genes (yellow), and
dispensable genes (red). d Degree of depletion of NF-κB genes (pink),
genes that are involved in vIL-6 signaling (blue), and IRF4 (black) in
all PEL cell lines screened by the Brunello library. e Representative
analysis of relative live cell numbers over time after IRF4 knockout in
BC-3 cells, see Supplementary Figure [117]4 for details. f End-point
analysis of several independent growth curves (as in e) for IRF4
knockout in Cas9-expressing BC-3, BCBL-1, or BJAB cell clones. g
Representative western blots of cells in f. h–j Similar to e–g but
following MDM2 knockout. Arrowhead, truncated MDM2 from CRISPR
targeting. AAVS1, control sgRNA targeting the non-coding AAVS1 locus;
PSMD1, sgRNA targeting the housekeeping gene PSMD1. Error bars
represent SEM, n ≥ 3
These unexpected results prompted us to establish a robust workflow for
the validation of individual PSODs. To achieve highest sensitivity,
validation was done in clonal BC-3 and BCBL-1 Cas9 cell lines that
display optimal and consistent gene editing^[118]48 and retain their
ability to undergo lytic re-activation (Supplementary Figure [119]3a).
Parallel genome-wide screens done with a BCBL-1 Cas9 cell pool and a
matched clone had overall similar results, indicating that the clone
remains functionally similar to the parental cell pool (Supplementary
Figure [120]3b). However, superior editing efficiencies in cell clones
resulted in increased sensitivity and the identification of an extended
number of significantly depleted genes, likely fitness genes
(Fig. [121]1c). Using these cell lines, we individually targeted genes
for functional knockout following lentiviral sgRNA transduction
(Supplementary Figure [122]4). sgRNAs against the non-coding AAVS1
locus^[123]49 and the essential proteasome subunit PSMD1^[124]48 served
as negative and positive controls, respectively. Following sgRNA
transduction, absolute live cell counts were monitored over time using
flow cytometry.
We first examined the dependency of PEL cells on IRF4, which scored
highly in seven of eight screens and had previously been shown to be
essential in the PEL cell line BC-3 using RNA interference^[125]34
(Figs. [126]2a and [127]3a, b). IRF4 is furthermore downregulated by
treatment of PEL cell lines with IMiDs, suggesting that a dependency on
IRF4 in PEL cells may be druggable. Targeting IRF4 by three independent
sgRNAs resulted in a rapid and complete loss of viability in BC-3 and
BCBL-1 (Fig. [128]3e–g). These effects were not seen in the
KSHV-negative B cell line BJAB, where IRF4 is not expressed
(Fig. [129]3f, g). These data therefore confirm that IRF4 is indeed
among the most strongly and specifically required cellular genes in PEL
cell lines.
We next targeted MDM2, an oncogenic E3 ubiquitin ligase that triggers
degradation of the tumor suppressor p53 family of proteins
(Fig. [130]3a). Like IRF4, sgRNAs for MDM2 were strongly depleted in
most PEL screens (Fig. [131]3b). A role for MDM2 in the survival of PEL
cells had been strongly suggested by its pharmacological inhibition
using Nutlin-3a, but has not been demonstrated directly^[132]9,[133]17.
sgRNAs for MDM2 triggered rapid cell death in BC-3 and BCBL-1
(Fig. [134]3h–j). Loss of MDM2 resulted in the expected stabilization
of p53 and consequent upregulation of the p53 target p21
(Fig. [135]3j). As in the case of IRF4, targeting MDM2 in BJAB, which
do not express MDM2, did not affect cell viability (Fig. [136]3i, j).
Taken together, this is the first direct demonstration that PEL cell
lines critically require MDM2, despite the p53 inhibitory activities of
LANA and vIRF3.
Having successfully validated requirements for IRF4 and MDM2, we
individually inactivated RELA, NFKB1, and IKBKG, as examples for genes
that unexpectedly did not score as essential in most screens. These
experiments confirmed that these genes are indeed dispensable in BC-3
and BCBL-1 at least in vitro (Supplementary Figure [137]5). Because
these genes score as a group in the BC-3 and BC-1 screens (Fig. [138]3b
and Supplementary Data [139]8), a subtle fitness function in at least
these two cell lines appears likely. We note that our validation setup
may not have the sensitivity to robustly detect fitness effects of less
than two- threefold cumulative reductions in live cell numbers at the
end of ~2- to 3-week growth curves, which are expected to reach
statistical significance in pooled screens. In sum, our results
strongly suggest that the role of genes in the NF-κB and vIL-6
signaling pathways in cultured PEL cell lines may be surprisingly
subtle and should be re-evaluated (see Discussion). Our screens and
validation experiments on the other hand, demonstrate critical
requirements for IRF4 and MDM2 in all PEL cell lines.
PEL cells require cyclin D2 and c-FLIP expression
Our screens identified CCND2, encoding cyclin D2, as the top ranked
PSOD in PEL cell lines (Figs. [140]2a and [141]4a). This dependency was
surprising, given that PEL cell lines express vCYC. Similarly, CFLAR,
which encodes c-FLIP, confidently scored as a PSOD despite expression
of its viral homolog vFLIP (Figs. [142]2a and [143]4a). c-FLIP
functions to block FADD-mediated apoptosis by preventing the activation
of the initiator pro-caspase 8, among several other roles. vCYC and
vFLIP are sufficiently distinct from their cellular counterparts to
exclude cross-inhibition by sgRNAs targeting CCND2 or CFLAR.
Fig. 4.
[144]Fig. 4
[145]Open in a new tab
PEL cell lines are dependent on CCND2 and CFLAR. a Heatmap of adj. p
values of sgRNA depletion of CCND2 and CFLAR in cell lines screened.
Indicated on the right are the numbers of cancer types (out of 16)
where the gene was found to be a potential dependency. The Brunello
library was used for most of the screens except where indicated: G,
GeCKO v2. b, c Knockout of CFLAR. b Representative analysis of relative
live BCBL-1 cell numbers over time following CFLAR knockout. n = 4. c
Representative western blots of c-FLIP[L] and c-FLIP[S] isoforms for b.
d, e Similar to b and c but using CCND2 sgRNAs. Experiments in b–e were
performed together and thus share controls. f Distribution of cell
cycle phase populations in BCBL-1 Cas9 cells upon CCND2 or CFLAR
knockout analyzed by propidium iodide staining of samples on day 4 in
experiments shown in b–e. p Values were calculated by Student’s t test
and compared to sgAAVS1. g Calculated IC[50] values of palbociclib in
the indicated cell lines. Gray bars, non-PEL cells; pink bars, PEL cell
lines; n ≥ 3. h, i Cell cycle analysis of propidium iodide-stained live
BCBL-1 cells treated for 24 h with 220 nM palbociclib (IC[50]). h
Representative histograms of DNA content from propidium iodide
staining. i Distribution of cell cycle phase populations. p Values were
calculated by Student’s t test and compared to PBS-treated cells.
n = 3. All error bars, SEM
Targeting CFLAR for knockout resulted in a rapid decrease in live
BCBL-1 cell numbers (Fig. [146]4b). This is accompanied by a cleavage
of pro-caspase 8 and PARP (Fig. [147]4c), suggesting that c-FLIP is
required to block apoptosis in BCBL-1. Similarly, inactivation of CCND2
in BCBL-1 cells led to a rapid reduction of live cell numbers
(Fig. [148]4d, e). As expected, loss of cyclin D2, but not c-FLIP, led
to cell cycle arrest, as indicated by the accumulation of cells at the
G1 phase of the cell cycle and a corresponding decrease in the
percentage of cells in the S and G2 phases (Fig. [149]4f). However, we
note that loss of cyclin D2 eventually triggered apoptosis in BCBL-1,
as indicated by PARP cleavage (Fig. [150]4e).
This selective and strong dependency of PEL cells on CCND2 could be
exploited as a therapeutic strategy. Palbociclib is a
clinically-approved inhibitor of CDK4 and 6 currently used for treating
ER^+HER2^− breast cancers. Since D-type cyclins function by binding to
CDK4/6, palbociclib also inhibits cyclin D activity. Indeed, 11 tested
PEL cell lines were highly sensitive to palbociclib with IC[50]s
ranging from 73 nM to 1.5 µM (Fig. [151]4g). The inhibitor was
similarly effective against KMS-12-BM, which depends on cyclin D1
overexpression^[152]50. As expected, pablociclib treatment of PEL cell
lines lead to a striking G1 arrest, which is almost complete in BCBL-1
cells (Fig. [153]4h, i, and Supplementary Figure [154]6 for results in
BC-3).
Our data collectively show for the first time that PEL cell lines are
surprisingly dependent on c-FLIP and cyclin D2, which is druggable.
Palbociclib could in principle function by affecting the function of
cyclin D2 and/or vCYC in PEL. Regardless, this drug offers a promising
novel treatment strategy for PEL. Furthermore, our findings demonstrate
that vCYC and vFLIP cannot compensate for the loss of their cellular
counterparts.
MCL1 is critical in PEL cells
MCL1 ranked as the third highest PSOD across all PEL cell lines. MCL1
is a member of the anti-apoptotic BCL2 family of proteins, which
prevent the formation of outer mitochondrial membrane pore channels by
BAX and BAK (Fig. [155]5a)^[156]51. Outer mitochondrial membrane
permeabilization in turn triggers apoptosis via the intrinsic pathway.
Strikingly, of all the BCL2 family members, only MCL1 showed a strong
and consistent requirement in all PEL cell lines (Fig. [157]5b and
Supplementary Figure [158]7). Overexpression of MCL1 by gene
amplification has been observed in a diverse range of cancers^[159]52.
Importantly, MCL1 was recently shown to be susceptible to direct and
specific inhibition by the small molecule [160]S63845 in other
hematological malignancies^[161]53. Previous reports have pointed to a
possible dependency on MCL1 of PEL cells. BH3-profiling of BCBL-1
indicated a hybrid MCL1 signature that was initially attributed to the
expression of the viral BCL2 homolog, a lytic protein^[162]45.
Treatment with a HSP90 inhibitor PU-H71 induces massive apoptosis in
PEL cells by destabilizing HSP90 clients, including MCL1^[163]46.
PU-H71 furthermore synergizes with a pan-BCL2 inhibitor that also
inhibits MCL1. However, neither study directly assessed the
contribution of MCL1 to the survival of PEL cell lines.
Fig. 5.
[164]Fig. 5
[165]Open in a new tab
PEL cell lines are addicted to MCL1. a BCL2 family proteins primarily
function on the outside of the mitochondrial membrane to prevent BAX or
BAK monomers from oligomerizing to form outer membrane pores. Upon
intracellular stress, pro-apoptotic BH3-only proteins are upregulated
and bind to the BCL2 proteins, thereby competing with BAX or BAK. The
free BAX or BAK monomers then oligomerize to form outer mitochondrial
membrane pores, resulting in cytochrome c to the cytosol and initiation
of apoptosis. b Heatmap of adj. p values of sgRNA depletion of the BCL2
family genes across screens. Indicated on the right are the numbers of
cancer types (out of 16) where the gene was found to be potentially
essential. The Brunello library was used for most of the screens except
where indicated: G, GeCKO v2. c Representative growth curve analysis
following MCL1 knockout in BC-3 Cas9 (n = 3, technical replicates). d
End-point analysis of several growth curves for MCL1 knockout in BC-3,
BCBL-1, or BC-2 Cas9 cells (n = 3, biological replicates). e
Representative western blots for MCL1 knockout and PARP cleavage for
experiments in d. All error bars, SEM
A vital role for MCL1 in PEL cells is confirmed by a dramatic loss of
cell viability as early as 3 days following MCL1 sgRNA transduction of
BC-3 (Fig. [166]5c–e). Similarly, striking dependencies on MCL1 were
also seen in the PEL cell lines BCBL-1 and BC-2 (Fig. [167]5d, e). PARP
westerns confirmed that loss of MCL1 is a highly efficient trigger of
apoptosis in PEL cell lines, as expected (Fig. [168]5e). The dependency
on high levels of MCL1 expression is furthermore validated in a panel
of PEL cell lines using short hairpin RNA (shRNA)-mediated knockdown
(Supplementary Figure [169]8). All cell lines, except for BC-2, were
highly sensitive to even modest shRNA-induced reductions of MCL1
expression and underwent apoptosis. The lack of a response in BC-2 is
likely due to the marginal knockdown of MCL1 in this cell line, because
MCL1-specific sgRNAs resulted in rapid cell death in BC-2
(Fig. [170]5d, e). Taken together, our findings demonstrate that PEL
cell lines strongly depend on MCL1 oncogene addiction for their
survival.
MCL1 is a candidate drug target in PEL
The striking and consistent addiction of PEL cells to MCL1 expression
prompted us to test the therapeutic potential of the newly developed
MCL1 inhibitor [171]S63845^[172]53 by assessing its efficacy in a panel
of 11 PEL cell lines. MCL1 inhibition by [173]S63845 proved lethal in
all tested PEL cell lines, with IC[50] values in the nanomolar range
(Fig. [174]6a). Notably, the response to [175]S63845 in PEL cell lines
was comparable to that seen in the MCL1-dependent B cell lines Raji and
KMS-12-BM, which were tested in the earlier report^[176]53. In
contrast, the MCL1-independent cell lines Daudi and MEG-01 were 12- and
77-fold less sensitive to the inhibitor compared to the PEL cell lines,
confirming the expected specificity of treatment^[177]53. In sum, we
show that inhibiting MCL1 activity with [178]S63845 is a highly
effective and promising strategy that should be further developed for
the treatment of PEL.
Fig. 6.
[179]Fig. 6
[180]Open in a new tab
Pharmacological inhibition of MCL1 in PEL and control cell lines and
MCL1 expression in PEL tumors. a Calculated IC[50] values of
[181]S63845 in different cell lines. Based on Kotschy et al.^[182]53,
Daudi and MEG-01 cells are MCL1-independent cell lines while Raji and
KMS-12-BM are MCL1-dependent cell lines. Error bars, SEM; n ≥ 3
biological replicates. b MCL1 staining in tonsilar sections confirms
specificity of our staining protocol. Germinal center cells (GC)
express high levels of MCL1 while mantle zone cells (MZ) express low to
undetectable levels of MCL1. c Example of a histological
characterization of tumor sections from patient D (84-year HIV(−)
female with EBV(+) tumor) with hematoxylin and eosin staining (H&E),
LANA immunohistochemical stain, or MCL1 immunohistochemical stain. d
MCL1 immunohistochemical stains for tumor sections from patients A–C.
Corresponding scale bars are depicted in lower right corner of each
image
MCL1 is highly expressed in PEL tumors
The exquisite dependence of PEL cell lines on MCL1 and the feasibility
of its therapeutic targeting by [183]S63845 led us to examine the
relevance of this oncogene in PEL tumors. Immunohistochemistry
specifically detected high levels of MCL1 in germinal centers but not
in marginal zones of tonsillar sections, as expected^[184]54
(Fig. [185]6b). Importantly, tumor samples from four independent PEL
cases at the Northwestern Memorial Hospital since 2010 showed very high
expression of MCL1 specifically in the KSHV-infected tumor cells
(Fig. [186]6c, d). High levels of MCL1 expression in PEL tumor cells
further support its oncogenic role and viability as a drug target in
PEL.
Discussion
The oncogenic mechanisms underlying PEL are poorly understood and this
disease consequently remains largely incurable. Here we utilized
genome-wide loss-of-function CRISPR screens to reveal genetic
dependencies of eight PEL cell lines. Comparative analysis of similar
datasets from 16 different types of cancer cell lines, including a
newly generated dataset from multiple myeloma, allowed us to discover
210 non-housekeeping single-gene dependencies of PEL (PSODs). We
validate several of these novel dependencies and reveal cyclin D2 and
MCL1 as attractive candidates for drug targets in PEL. This work thus
serves as an unbiased and comprehensive resource for human genes that
are critical in PEL cell lines and points to novel strategies for
therapeutic intervention in this aggressive lymphoma. A revised working
model of PEL biology is presented in Fig. [187]7.
Fig. 7.
[188]Fig. 7
[189]Open in a new tab
Revised working model of the main host oncogenic gene dependencies in
PEL cell lines. a Genes involved in NF-κB and Jak/Stat signaling are
dispensable in most PEL cell lines and may serve fitness functions. b
Addiction to constitutively active mTOR signaling was not captured in
this study, due to confounding housekeeping function of mTOR. c
Critical PEL-specific oncogene dependencies (PSODs) on IRF4, MDM2,
CCND2, CFLAR, and MCL1 (in blue), most of which are druggable using
agents shown in red
PEL cell lines co-infected with EBV exhibited a similar set of
essential gene dependencies for survival as EBV(−) PEL. Consistent with
this observation, only one of four gene expression profiling studies
reported a separation of PEL cell lines based on EBV
co-infection^[190]12,[191]30–[192]32. This same report identified only
40 differentially expressed genes between the two groups^[193]30, none
of which scored differentially in our screens. Thus, EBV(+) and EBV(−)
PEL cell lines overall exhibit largely similar transcriptomes and
similar genetic requirements for survival in culture. This finding may
reflect the dominant role of KSHV in PEL and/or convergent oncogenic
mechanisms in EBV(+) and EBV(−) PEL cell lines. Our findings do not
rule out a role for EBV in PEL pathogenesis and in EBV(+) PEL cell
lines in vitro. Indeed, our study identified a small number of
candidates for genes that may exhibit differential requirements based
on EBV status. Overall, the role of EBV in PEL lymphomagenesis and
established PEL cell lines will require additional investigation.
Future studies should also specifically address a requirement for EBV
in EBV(+) PEL cell lines, by antagonizing EBV genome maintenance.
Our screens and validation experiments surprisingly suggest that key
genes of the NF-κB signaling pathway are not individually essential in
PEL, but perhaps act as fitness genes in a subset of cell lines (BC-1
and BC-3; Fig. [194]7). The current assumption that all PEL cell lines
are addicted to constitutive NF-κB signaling stems from inhibitor
studies using Bay 11-7082^[195]20,[196]55. Multiple studies since then
have demonstrated NF-κB-independent toxicity of Bay 11-7082 in
different cancer cell types^[197]56–[198]58. The efficacy of Bay
11-7082 against PEL cell lines may thus be due to pleiotropic effects
and not solely due to inhibition of NF-κB signaling. Given that genes
in the NF-κB pathway are individually non-essential in PEL cell lines,
it is unlikely that this pathway controls the expression of the highly
essential oncogenes IRF4 and MCL1 in PEL cells, although this has been
observed in other cancers^[199]59,[200]60. A potential role for NF-κB
in PEL was also postulated based on abundant evidence that ectopic
expression of vFLIP can activate this pathway via an interaction with
NEMO and the finding that vFLIP is essential in PEL cell
lines^[201]13,[202]14,[203]20–[204]24. RNAi of vFLIP in the PEL cell
line BC-3 has been shown to dampen NF-κB reporter activity^[205]14.
However, these reports do not directly show that the activation of
NF-κB indeed underlies vFLIP essentiality in PEL cell lines and
relevant human genes have not previously been targeted for functional
knockdown in PEL cells. Our screens and validation experiments show
that IKBKG is dispensable in a large majority of PEL cell lines. While
the interaction between vFLIP and NEMO is well documented, it remains
possible that essential functions of vFLIP in PEL cell lines are
independent of this interaction and NF-κB activation. Importantly,
vFLIP is also known to inhibit autophagy and death receptor
signaling^[206]22,[207]61 and is important for c-FLIP expression, at
least in BC-3^[208]14. Moreover, the strong requirement of PEL cell
lines for c-FLIP and cyclin D2 reveals that expression of vFLIP and
vCYC in PEL cell lines cannot compensate for the loss of these cellular
proteins. In principle, vFLIP and vCYC expression could serve to
overexpress c-FLIP and cyclin D2-like functions. On the other hand, it
is possible that vFLIP and vCYC have independent and non-redundant
functions. One important caveat of our approach is that we employ
single-gene knockouts to interrogate gene dependencies. We therefore
cannot rule out compensating mechanisms, such as the non-canonical arm
of the NF-κB signaling pathway or redundancies between the NF-κB and
cytokine signaling pathways. Lastly, these pathways could have more
relevant roles in vivo and become individually non-essential during
adaptation to cell culture. Indeed, a primary PEL tumor was reported to
have dramatically higher levels of active nuclear NF-κB than the BC-5
cell line, which was derived from the same tumor^[209]20. Because the
manipulation of NF-κB signaling by KSHV gene products is well
established, the relevance of this pathway to lymphomagenesis should be
carefully addressed using non-heterologous systems, such as PEL tumor
samples and patient derived xenografts, which is beyond the scope of
this study.
A practical utility of our study is the discovery of drug targets that
can be further developed for therapeutic intervention in PEL
(Fig. [210]7). We present the first direct evidence for a key role of
MDM2 in PEL cells, providing a genetic basis for the use of its
inhibitor Nutlin-3a^[211]9,[212]17,[213]18,[214]62 as a candidate
therapeutic strategy. Moreover, our study strongly supports a critical
role of IRF4 addiction in PEL cells. Our data thus strengthen the
rationale for the development of IMiDs, which have been shown to
trigger loss of IRF4 expression in PEL^[215]34, as a treatment
strategy. In line with this, lenalidomide is currently in clinical
trials for classical and extra-cavitary PEL^[216]63. We note, however,
that our screens and validation experiments failed to support a
recently reported role of IKZF1 upstream of IRF4 in PEL^[217]64.
Because IKZF1 has recently been reported as the relevant IMiD effector
in PEL, the mechanisms of action of IMiDs in PEL require further
investigation.
Importantly, we identified novel druggable dependencies of PEL cell
lines on cyclin D2 and MCL1. While palbociclib is highly effective in
controlling proliferation in culture, cell cycle arrest in PEL cells
eventually triggers cell death, making palbociclib a promising
treatment strategy. [218]S63845, on the other hand, exerts a direct
cytotoxic effect—by directly neutralizing MCL1 and activating apoptosis
in MCL1-dependent tumors in culture and in vivo in other
cancers^[219]53. At least BC-3 is weakly dependent on the BCL2 family
member BCL2L1 (encoding Bcl-xL; Supplementary Figure [220]7). However,
BC-3 cells were just as sensitive to [221]S63845 and MCL1 knockdown as
MCL1-only-dependent PEL cell lines. Altogether, the highly selective
addiction to MCL1 and the high sensitivity of PEL cell lines to even a
partial decrease in its expression makes MCL1 an appealing and viable
strategy for therapeutic intervention.
In conclusion, we utilized genome-wide CRISPR screens to identify PEL
core dependencies that serve as effective druggable genetic
vulnerabilities. Importantly, repurposing of existing inhibitors for
PSODs could significantly alleviate the difficulty in developing drugs
and designing clinical trials for this exceedingly rare cancer. We
anticipate that our study will establish a framework for future studies
on PEL transformation, KSHV biology, and inspire the development of
much needed therapeutic interventions.
Methods
Cells
HEK-293T cells were cultured in Dulbecco’s modified Eagle’s medium
(Lonza, Walkersville, MD), supplemented with 10% fetal bovine serum
(FBS; Corning, Manassas, VA) and 10 µg/mL gentamycin (VWR, Radnor, PA).
Most suspension cells (APK-1, BC-2, BC-3, BC-5, VG-1, CRO/AP5, BCLM,
HBL-6, KMS-12-BM, and MEG-01) were maintained in RPMI-1640 (Lonza)
containing 20% FBS or Serum Plus-II (Sigma), 10 µg/mL gentamycin, and
0.05 mM β-mercaptoethanol (Bio-Rad, Hercules, CA). The following cell
lines were grown in similar medium but were supplemented with 10% FBS
or Serum Plus Medium: BJAB, BC-1, JSC-1, BCBL-1, Raji, and Daudi. All
suspension cell lines were maintained at concentrations between
2 × 10^5 and 12 × 10^5 cells/mL during routine culture and experiments,
and were routinely tested for potential mycoplasma contamination. BC-1,
BC-2, BC-3, BCBL-1, KMS-12-BM, and BJAB were validated by short tandem
repeat (STR) profiling through the Northwestern University NUSeq core
facility. For other PEL cell lines reference STR profiles are not
available and these cell lines were therefore validated by PCR for KSHV
or EBV infection status.
CRISPR library preparation in Escherichia coli
GeCKO v2 libraries A and B^[222]38,[223]65 and Brunello library^[224]37
were obtained from Addgene. The GeCKO v2 libraries A and B contain
122 411 unique sgRNAs that target 19 050 human genes and 1864 miRNAs.
The inclusion of 6 sgRNAs for each coding gene and of 4 sgRNAs for each
miRNA accounts for potential off-target effects of individual sgRNAs.
miRNA data are not reported here due to several confounding caveats,
such as overlapping essential genes and the less straight forward
targeting of non-coding RNAs using CRISPR/Cas9. The Brunello library
was designed to target 19 114 human genes with 76 411 sgRNAs (4
sgRNAs/gene). Libraries were transformed separately into Endura
ElectroCompetent E. coli cells (Lucigen, Middleton, WI) in four
electroporation reactions, each containing 25 µL bacteria and 100 ng
plasmid library. Electroporations were performed in 1 mm cuvettes (VWR)
using an exponential decay pulse (10 µF, 600 Ω, 1800 V) with a Gene
Pulser Xcell (Bio-Rad). Each reaction was immediately cultured in a
17 × 100 mm tube containing 2 mL Recovery Medium (Lucigen) for 1 h at
37 °C on a bacterial shaker at 250 rpm. Broth cultures from each
library were pooled, plated onto 20 LB-ampicillin plates, and grown for
14–15 h at 32 °C. This yielded >1500-fold sgRNA representation based on
colony numbers obtained from serial dilutions. Bacterial cells were
harvested directly from plates and plasmid DNA was extracted over four
columns of PerfectPrep Endofree Maxi Kit (5 Prime, Hilden, Germany).
Cloning of individual sgRNA constructs
All sgRNAs were cloned into plentiGuide-Puro^[225]38 using the BsmBI
cloning site (New England Biolabs, Ipswich, MA). sgRNA sequences
targeting specific genes were designed using publicly available
tools^[226]37,[227]66 or were derived from the Brunello library. sgRNA
sequences and sequences of primers used for cloning are listed in
Supplementary Data [228]12.
Lentivirus preparation
For library production, 10^7 HEK-293T cells were seeded per 15 cm dish
the day before transfection. Plasmids expressing Cas9 or sgRNAs (11 µg)
were co-transfected with 5.48 µg pMD2.G and 8.24 µg psPAX2 using 158 µL
of a 15.6 mM solution of Polyethylenimine HCl MAX (Linear, MW 40,000,
Polysciences, Warrington, PA). For the co-transfection of the GeCKO
libraries, 5.5 µg each of Libraries A and B were added per transfection
reaction. For the Brunello library, a total of 11 µg was used per 15 cm
dish. For individual sgRNA lentivirus production, reactions were scaled
down based on relative surface area where needed. After 6–8 h, media
were replaced with RPMI-1640 containing 20% FBS. Lentiviruses were
harvested after 3 days, by centrifugation of the supernatant at
1200 × g for 8 min and filtration through a 0.45 µm membrane. Viral
titers were determined in naive cells by infecting these cell lines
with increasing amounts of lentiviruses in the presence of 4 µg/mL
polybrene. After 1 day, 1 µg/mL puromycin (for sgRNA and library
lentiviruses) or 10 µg/mL blasticidin (for Cas9 viruses) was added.
Live cell numbers were determined by trypan blue exclusion after 2
(puromycin selection) or 3 days (blasticidin selection) and used to
calculate viral titers.
Cas9-expressing cell lines
B cells (2–5 × 10^5 cells/mL) were transduced with lentiCas9-Blast
virus^[229]38 at MOIs between 0.7 and 1.5 in the presence of 4 µg/mL
polybrene. The next day, the medium was replaced and supplemented with
10 µg/mL blasticidin. Selection was continued for 3–5 days until no
viable cells remained in an untransduced control plate treated with
blasticidin. Pooled Cas9 cells were expanded under blasticidin
selection up to their transduction with the CRISPR libraries.
Clonal Cas9-expressing B cells were derived from pooled Cas9 cells
using limiting dilution cloning into round bottom 96-well plates for
2–3 weeks under blasticidin selection. Resulting clones were screened
for optimal Cas9-Flag expression by anti-Flag M2 immunoblotting
(Sigma-Aldrich, St. Louis, MO). Clones expressing the highest Cas9
levels were functionally screened for editing efficiency following
transduction with a negative control sgRNA AAVS1 or a positive control
sgRNA against the housekeeping gene PSMD1 as previously
reported^[230]48. Cell viability was monitored for 7–10 days. Clones
that resulted in robust cell death upon transduction with PSMD1 sgRNA
were considered optimal for Cas9-mediated editing and used for
subsequent validation experiments.
CRISPR screens
Dropout screens were performed similarly to Sanjana et al.^[231]38.
B cell pools expressing Cas9 were produced as described above. Cell
pools were seeded in eight 15 cm dishes per replicate at a density of
7.5 × 10^5 cells/mL in 50 mL complete medium per dish and infected with
GeCKO or Brunello library viruses at an multiplicity of infection (MOI)
of 0.3 in the presence of polybrene (4 µg/mL). Three replicate
infections were set up per cell line. Cell numbers were chosen to allow
for a theoretical ~500× representation per sgRNA and replicate.
After 24 h, cells were recovered by centrifugation and plated in fresh
media containing puromycin (1 µg/mL) to select for library transduced
cells. A matched plate with untransduced cells was similarly put under
selection. After 2–3 days, or when no viable cells remained in the
control plate, cells were washed with phosphate-buffered saline (PBS)
and seeded in fresh media without antibiotics in five 15 cm plates per
replicate, at 3 × 10^5 cells/mL in 50 mL/plate. Leftover cells were
pelleted and snap-frozen. These samples were used for “input” DNA
library preparation for the GeCKO libraries.
Cell numbers were monitored every 2 days and re-adjusted to 3 × 10^5
cells/mL in 50 mL in each of five 15 cm plates per replicate. This
approach maintains a theoretical ~500× coverage for each sgRNA. At days
14–18, surviving cells were washed with PBS, collected, and cell
pellets were snap-frozen.
Purification of genomic DNA
Genomic DNA was purified from frozen cell pellets following a published
protocol^[232]67. Working on ice, 0.15–0.2 g of cell pellet was thawed,
and lysed in 6 mL NK lysis buffer (50 mM Tris, 50 mM EDTA, and 1% SDS,
pH 8) containing 30 µL of 20 mg/mL proteinase K (Roche, Penzberg,
Germany). Cell lysates were incubated for 14–16 h in a 55 °C water
bath. The following day, 30 µL of 10 mg/mL RNase A (Zymo Research,
Irvine, CA) was added and incubated at 37 °C for 1 h. Lysates were
chilled on ice for 15 min. Proteins were precipitated by adding 2 mL
pre-chilled 7.5 M ammonium acetate. The mixture was distributed over
several 1.5 mL microfuge tubes and spun at 15 000 × g for 10 min at
4 °C. Clarified lysates were transferred to new 15 mL tubes and 0.7
volume of isopropanol was added to precipitate genomic DNA. DNA was
pelleted at 2700 × g for 30 min at 4 °C. Genomic DNA was washed briefly
with ice-cold 70% ethanol. Residual ethanol was removed and pellets
were air-dried for 5 min. Purified DNA was dissolved in 1 mL Buffer EB
(Qiagen, Hilden, Germany) for 1 h at 65 °C 1400 rpm. When undissolved
DNA remained, solutions were placed on a rocker at 4 °C overnight. DNA
preparations were stored at 4 °C.
Library amplification and sequencing
Primer sequences for sequencing library construction were based on
Sanjana et al.^[233]38. Primers used for library preparation were
polyacrylamide gel electrophoresis-purified and synthesized by IDT
(Coralville, IA).
Conditions for amplification of integrated lentivirus insert from
genomic DNA were established by pilot PCR reactions to optimize amounts
of genomic DNA, DNA polymerase, and number of PCR cycles for each
replicate, cell line, and time point. Primer sequences are listed in
Supplementary Data [234]13.
The final protocol used for PCR 1 for the GeCKO libraries is described
below. For each 50 µL PCR reaction, 0.15 mM dNTP, 0.15 µM forward
primer v2Adaptor_F^[235]38, 0.15 µM reverse primer R1^[236]65, 3 µg
purified genomic DNA, 1× Q5 Reaction Buffer, and 0.5 µL Q5 High
Fidelity DNA polymerase (NEB). For each replicate, a total of 133
parallel PCR reactions were performed on a total of 400 µg genomic DNA
to maintain 500× coverage for sgRNA amplification. PCR conditions were
as follows: initial denaturation at 98 °C for 90 s, followed by
specific number of PCR cycles of 98 °C for 10 s, 60 °C for 10 s, and
72 °C for 10 s. The number of cycles, chosen based on pilot PCRs, for
BC-3, BCBL-1, and BJAB were 27, 25, and 29, respectively, to result in
non-saturating amplification.
The final protocol used for PCR for the Brunello libraries is described
below. For each 50 µL PCR reaction, 0.15 mM dNTP, 0.13 µM forward
primer v2Adaptor_F, 0.13 µM reverse primer R1_new, 6 µg purified
genomic DNA, 1× Standard Taq buffer (NEB), and 0.5 µL Taq polymerase.
For each replicate, a total of 42 parallel PCR reactions were performed
on a total of 252 µg genomic DNA to maintain 500× coverage for sgRNA
amplification. PCR conditions were as follows: initial denaturation at
95 °C for 2 min, followed by specific number of PCR cycles of 95 °C for
25 s, 55 °C for 25 s, and 68 °C for 30 s. The number of cycles, chosen
based on pilot PCRs, was between 21 and 24 to result in non-saturating
amplification.
The PCR 1 reactions for each replicate and time point were combined and
used directly to optimize conditions for PCR 2. PCR mixes were
essentially similar to PCR 1, but contained 1 µL of pooled PCR 1
products as template, and 0.15 µM each of forward and reverse primers
(Supplementary Data [237]13). Cycling conditions were optimized for
each PCR 1 product using Q5 polymerase. For the final PCR 2
amplification, a total of 6 (Brunello) or 12 (GeCKO) reactions per
sample were prepared with two reactions each for a different staggered
forward Illumina primer. Each replicate and time point used one
barcoded reverse Illumina primer.
The PCR reactions for each PCR 2 were pooled, concentrated through a
column, and resolved in 2% agarose gels. Gel slabs were ground using
disposable plastic pestles and dissolved in three volumes of Buffer DF
(IBI Scientific, Peosta, IA) for 5 min at room temperature on a
thermomixer. PCR products were purified over two columns of a Gel
Extraction kit (IBI Scientific) and eluted in a total of 40 µL water.
Purified libraries were quantified using a Qubit 2.0 Fluorometer and
the Qubit dsDNA HS Assay Kit (Thermo Fisher Scientific, Waltham, MA),
and quality control was performed using an Agilent 2100 Bioanalyzer and
High Sensitivity DNA Kit (Agilent Technologies, Santa Clara, CA).
Individual libraries were further quantified by quantitative PCR, using
the KAPA Library Quantification Kit (Kapa Biosystems, Wilmington, MA).
Multiplexed samples were sequenced on the Illumina HiSeq 2500 platform
using 100 bp single-end reads (GeCKO) or on the HiSeq 4000 using 50 bp
single-end reads.
Processing of sequencing reads
The 5′ and 3′ adapter sequences were removed from demultiplexed
sequencing reads. Remaining reads were aligned to the CRISPR library
files using Bowtie. The number of reads per sgRNA were counted and
summarized in a read count table. Supplementary Data [238]14 summarizes
library statistics.
Sliding window analysis
A sliding window method^[239]43,[240]68 was used to account for the
potential effects of genomic copy number on CRISPR screens identifying
essential genes in cell lines. For this, CRISPR gene scores computed
from normalized sgRNA counts like those seen in Aguirre et al.^[241]40
were used to quantify the fitness effect of gene knockout in a cell
line. Briefly, under a scenario in which there are m total genes being
screened in cell line j, the CRISPR gene score for gene i targeted by n
sgRNAs is given by the following equation:
[MATH: CRISPRgenescoreij
=∑k=1nsgRNAscoreijkn :MATH]
where sgRNA score[ijk] is given as the z-score transformation of the
log-fold change between early and late samples in cell line j for a
sgRNA k targeting gene i:
[MATH: sgRNAscoreijk=log2latecountijkearlycountijk :MATH]
[MATH: sgRNAscoreijk=sgRNAscoreijk-AVEsgRNAscore<
mi>jSTDEVsgRNAscorej :MATH]
Next, we computed a neighborhood score for each gene by counting the
number of low CRISPR scores (<5th percentile for a cell line) in the 40
gene genomic window surrounding the gene (20 “upstream genes” and 20
“downstream” genes). Genes were flagged as suspect in a cell line if
their genomic neighborhood score was >12.
Depletion and enrichment analyses
To test for significant depletion or enrichment of sgRNAs for each
gene, Sliding Window Analysis-corrected read count tables from this
study or published screens^[242]40–[243]43 were analyzed using the
Robust Rank Approach module of MAGeCK v0.5.7^[244]39. This program was
designed to calculate both depletion and enrichment scores for genes
based on median normalized counts of each sgRNA.
Statistical analyses
GSEA^[245]44 was performed using the ranked depletion scores calculated
by MAGeCK. Pathway enrichment analysis for the PSODs was performed
using DAVID v6.7. Additional statistical tests were performed using
Python, R, and GraphPad Prism using appropriate biological replicates
for each test.
Defining housekeeping genes and PSODs
To define housekeeping genes, we compared CRISPR screens from publicly
available datasets^[246]40–[247]43 and this study. We limited our
analyses to 17 583 genes that were screened in all libraries used. We
then grouped the 60 cell lines according to cancer type to create 16
groups. We considered a gene to be a potential dependency in each group
if targeting sgRNAs were significantly depleted (median FDR-adj.
p < 0.25). Finally, we classified a gene to be a “housekeeping” gene if
it scored in at least 10 of 16 cancer cell types. Cutoffs were chosen
to allow for false negatives, which result from variable editing
efficiencies in CRISPR screens (Fig. [248]1c).
To define PSODs, we first calculated the median adjusted p value of
depletion of each gene in the eight PEL screens performed using the
Brunello library. For BCBL-1, data from the clone were chosen. Genes
that had a median adjusted p value < 0.05 were classified as PEL gene
dependencies. Removing housekeeping genes from these PEL Gene
Dependencies identified 210 PSODs.
Principal component analysis
We performed PCA to investigate whether EBV(+) and EBV(−) PEL cell
lines are distinguished by their genetic requirements. For these
analyses, only screens that were done using the Brunello library were
considered to avoid clustering based on the CRISPR library used. As
input files, we used normalized read counts for aligned sgRNA reads
(rlog) using the DESeq2 package^[249]69.
PCA were performed using the FactoMineR package^[250]70. Using data
from all eight PEL screens, we tested sgRNAs from: (1) genes that were
significantly depleted in at least one PEL cell line (FDR-adj.
p < 0.05); or (2) PSODs. Other principal components up to PC5 were
considered. None of these allowed for a separation of EBV(+) and EBV(−)
cell lines. Results for PC1 and PC2 from setting (1) are shown in
Fig. [251]1f.
Growth curves
Clonal Cas9-expressing B cells were seeded at 2 × 10^5 cells/mL in
500 µL in 24-well plates or 1 mL in a 12-well plates and transduced
with sgRNA lentiviruses at an MOI of ~3. After 24 h, puromycin
(1.2 µg/mL) was added to eliminate untransduced cells. At 3 days post
transduction, absolute live cell counts were determined by
fluorescence-activated cell sorting (FACS) analysis relative to a known
number of spiked-in SPHERO AccuCount 5.0–5.9 µm Particles (Spherotech,
Lake Forest, IL). Cells were counted every 2–3 days and cell numbers
were re-adjusted to 2 × 10^5 cells/mL at each passage, unless live cell
numbers dropped below 2 × 10^5 cells/mL, when cultures were left
undisturbed. Puromycin treatment was maintained until antibiotic
selection in the untransduced control cells was complete. Live cell
counts for each sgRNA sample were normalized to live cell counts
obtained for sgAAVS1 control transduced samples, which were set to 1.
To plot viability curves, dilution factors were factored in at each
passage to report cumulative live cell numbers relative to sgAAVS1. To
facilitate statistical comparisons between replicate experiments,
cumulative live cell numbers relative to the sgAAVS1 control at the end
point of the growth curve analyses were plotted and compared. End
points were set when no live cells remained, when cultures reached
their minimum live cell numbers, or at 2–3 weeks into the experiment if
no or only modest reductions in proliferation or viability were
observed. Growth curves for different sgRNAs were done in parallel over
several experiments. For this reason sgAAVS1 and sgPSMD1 data in these
panels are not entirely independent. However, each experiment included
these controls and data come from independent replicates in each case.
shRNA knockdown of MCL1
Empty pGIPZ vectors, or pGIPZ expressing a scrambled shRNA control
(Catalog # RHS4346) or 3′ untranslated region-directed MCL1-specific
shRNAs (clone IDs: V2LHS_72724, “sh24”; and V2LHS_72721, “sh21”) were
packaged using pMD2.G and psPAX2 as above. The medium was replaced
6–24 h after transfection and, 72 h after transfection, filtered virus
supernatant was concentrated ~20× by ultracentrifugation (Beckman SW28
rotor, 25 000 rpm, 1 h, 4 °C). Resulting virus preparations were
titrated on BJAB cells, using flow cytometry of green fluorescent
protein (GFP)-positive cells, on day 2 or 3 after infection. BJAB do
not respond to partial MCL1 knockdown (not shown) and are therefore
suitable for accurate titration of these vectors. PEL cell lines were
infected at equal MOIs, at a final concentration of 200 000 cells/mL.
The actual MOIs used depend on each cell line, but were estimated to
range from 1 to 5, based on the percentages of GFP-positive cells
observed following infection (~60–100%). Twenty-four hours after
infection, cells were collected by centrifugation and resuspended in
fresh medium containing 1.5 µg/mL puromycin, a concentration that
typically killed control cells within 24 h. On day 3 after
transduction, a portion of the cells was harvested for western. The
remaining cells were diluted by a factor of 2 and cultured for one
additional day. On day 4 after transduction, FACS was used to determine
absolute live cell counts as outlined above for growth curve analyses.
Live cell counts were normalized to live cell counts obtained for GIPZ
control transduced samples, which were set to 1.
Western blotting
At designated time points following sgRNA lentivirus transduction,
cells were collected and washed with PBS. Cells were lysed for 20 min
with ice-cold RIPA buffer containing 1× protease inhibitor cocktail III
(Calbiochem, EMD Millipore, Darmstadt, Germany) and 1× PhosSTOP
phosphatase inhibitor cocktail (Roche, Mannheim, Germany) in 0.5 mL
tubes and subjected to seven cycles of sonication (30 s on and 30 s
off) in a 4 °C water bath using the Bioruptor Sonication System
(Diagenode, Denville, NJ) at the high-intensity setting. Lysates were
cleared by centrifugation at 14 000× g for 10 min at 4 °C. Protein
concentrations were determined using the BCA Protein Assay Kit (Thermo
Fisher Scientific). Equivalent amounts of protein (10–30 µg) were
resolved in Bolt 4–12% Bis-Tris gradient gels (Thermo Fisher
Scientific) and transferred to 0.22 µm nitrocellulose membranes.
Specific proteins were detected following overnight incubation at 4 °C,
using primary antibodies listed in Supplementary Data [252]15. Primary
antibodies were detected with IRDye 800 CW-conjugated goat anti-rabbit
or anti-mouse IgG (LI-COR Biosciences, Lincoln, NE) and imaged with the
Odyssey Fc Dual-Mode Imaging System (LI-COR). MDM2 western blots were
visualized with SuperSignal West Femto Maximum Sensitivity Substrate
(Thermo Fisher Scientific) using horseradish peroxidase-conjugated
anti-rabbit or anti-mouse IgG antibodies (Cell Signaling Technology,
Danvers, MA). Uncropped western blots are found in Supplementary
Figs. [253]9, [254]10.
Determination of IC[50] values for palbociclib and [255]S63845
B cell lines and MEG-01 cells were seeded in eight wells of a 96-well
plate at 5.6 × 10^4 cells/mL in 90 µL volumes of complete media with
10–20% FBS (5000 cells total, Day 0). Serially diluted palbociclib
(Sigma-Aldrich) or [256]S63845 (ApexBio) were added in 10 µL volumes to
seven of the wells (threefold serial dilutions from 20 to 0.027 µM
final concentrations). For the negative control wells, 0.1% dimethyl
sulfoxide (for [257]S63845) or water (for palbociclib) in complete
media was added. After 3 days, 20 µL of cells were harvested into a
white half-area 96-well plate and lysed with 20 µL CellTiter-Glo 2.0
Reagent (Promega) for 2 min on a plate vortexer. Luminescence was read
using the CellTiter-Glo program of the GloMax-Multi Detection System
(Promega). IC[50] values were calculated using the three-parameter dose
response inhibitor fit in GraphPad Prism.
Cell cycle analysis
In all, 2–5×10^5 PEL cells were harvested and washed with ice-cold PBS.
Cells were fixed and permeabilized with 500 µL ice-cold 70% ethanol in
PBS for at least 1 h at −20 °C. After fixation, cells were washed with
PBS and stained with 300 µL propidium iodide/RNase staining buffer (BD
Pharmigen) at 4 °C in the dark for at least 15 min. Stained cells were
immediately analyzed for propidium iodide fluorescence using BD
FACSCanto II. Cell cycle analysis was performed using the Cell Cycle
platform in FlowJo v10. Model fittings were done with either the Watson
Pragmatic algorithm or Dean-Jett-Fox algorithm with unconstrained or
constrained settings (G1 × 2), minimizing the root mean square error.
Immunohistochemical staining on PEL tumor sections
After approval by the Institutional Review Board of Northwestern
University, pathology records were searched for cases with the
diagnosis of PEL between January 2010 and April 2016. A total of nine
cases from four patients were identified (Supplementary Data [258]16).
All of the samples were cytology cell blocks from pleural effusion or
peritoneal fluid. Medical records of the patients, as well their
available pathology slides were reviewed by a pathologist (A.B.). LANA
immunohistochemical (IHC) stain (mouse monoclonal antibody; Cell
Marque; 265M-18) and EBER in situ hybridization (EBER1 DNP probe;
Ventana; 760–1209) were previously performed as part of patient’s
routine diagnostic work up at the time of first diagnosis. MCL1 IHC
stain was performed as part of this project at Northwestern Pathology
Core facility. Briefly, 5 µm sections of the formalin-fixed
paraffin-embedded cytology cell blocks were used for these stains. IHC
was performed using a monoclonal antibody against MCL-1 (Cell Signaling
39224) at a dilution of 1:200. Tonsillar specimens were utilized as
controls.
Data availability
The deep-sequencing data for the CRISPR screens are available in SRA
[259]SRP081136.
Electronic supplementary material
[260]Supplementary Information^ (1.3MB, pdf)
[261]41467_2018_5506_MOESM2_ESM.pdf^ (71.3KB, pdf)
Description of Additional Supplementary Files
[262]Supplementary Data 1^ (12KB, xlsx)
[263]Supplementary Data 2^ (35.3MB, xlsx)
[264]Supplementary Data 3^ (4.4MB, xlsx)
[265]Supplementary Data 4^ (23.5MB, xlsx)
[266]Supplementary Data 5^ (86MB, xlsx)
[267]Supplementary Data 6^ (50MB, xlsx)
[268]Supplementary Data 7^ (271.7KB, xlsx)
[269]Supplementary Data 8^ (1.4MB, xlsx)
[270]Supplementary Data 9^ (21.5KB, xlsx)
[271]Supplementary Data10^ (12.2KB, xlsx)
[272]Supplementary Data 11^ (38.4KB, xlsx)
[273]Supplementary Data 12^ (11KB, xlsx)
[274]Supplementary Data 13^ (10.2KB, xlsx)
[275]Supplementary Data 14^ (12.4KB, xlsx)
[276]Supplementary Data 15^ (9.8KB, xlsx)
[277]Supplementary Data 16^ (8.9KB, xlsx)
Acknowledgements