preclinical anticancer efﬁcacy of bet bromodomain ... · priority report preclinical anticancer...

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Preclinical Anticancer Efficacy of BETBromodomain Inhibitors Is Determinedby the Apoptotic ResponseAndrew R. Conery1, Richard C. Centore1, Kerry L. Spillane1, Nicole E. Follmer1,Archana Bommi-Reddy1, Charlie Hatton1, Barbara M. Bryant1, Patricia Greninger2,Arnaud Amzallag2, Cyril H. Benes2, Jennifer A. Mertz1, and Robert J. Sims III1

Abstract

Small-molecule inhibitors of the bromodomain and extra-terminal (BET) family of proteins are being tested in clinicaltrials for a variety of cancers, but patient selection strategiesremain limited. This challenge is partly attributed to the het-erogeneous responses elicited by BET inhibition (BETi), includ-ing cellular differentiation, senescence, and death. In this study,we performed phenotypic and gene-expression analyses oftreatment-naive and engineered tolerant cell lines representinghuman melanoma and leukemia to elucidate the dominantfeatures defining response to BETi. We found that de novo and

acquired tolerance to BETi is driven by the robustness of theapoptotic response, and that genetic or pharmacologic manip-ulation of the apoptotic signaling network can modify thephenotypic response to BETi. We further reveal that the expres-sion signatures of the apoptotic genes BCL2, BCL2L1, and BADsignificantly predict response to BETi. Taken together, ourfindings highlight the apoptotic program as a determinantof response to BETi, and provide a molecular basis forpatient stratification and combination therapy development.Cancer Res; 76(6); 1313–9. �2016 AACR.

IntroductionThe development and progression of tumors is driven by

altered transcriptional programs and underlying tumor-specificchromatin states. BET proteins are highly enriched and function-ally essential at gene control elements that drive the expression ofcritical oncogenes such asMYC (1). As such, BET inhibition affectsmany pathways critical for cancer cell growth, leading to broadefficacy in pre-clinical disease models (2–5). However, at themolecular and phenotypic level, response to BETi can be quiteheterogeneous, making it unclear what the critical drivers are fordefining patient stratification strategies. A deeper understandingof the pathways responsible for maximal response to BET inhi-bition will, therefore, refine these strategies and allow for effectivepatient selection.

Materials and MethodsCell line information

Viability analysis was carried out at the MGH Center forMolecular Therapeutics (245 cell line panel), which authenticates

its cell lines by SNP genotyping. CA46, CHL1, CHP212, Colo783,Colo829, Daudi, H929, HCC1143, HCT116, HCT15, HL-60, HT,HT29, K562, Kasumi-1, KOPN8, LP-1, MC116, MDAMB231,Mewo, MOLT4, MV411, Namalwa, OPM-2, Raji, Ramos, REH,RPMI8226, SKMEL28, SKNAS, SKNDZ, ST486, SU-DHL-4, SU-DHL-5, SU-DHL-6, SU-DHL-8, SW48, SW480, SW620, THP-1,U266,WSU-DLCL2, and Z-138 were obtained from ATCC. AMO-1, DOHH2, IGR1, IGR39, JJN-3, KARPAS422, KMS-12PE, Melho,ML2, MOLM-13, MOLP-8, MOLT16, NB4, OCI-AML2, OCI-AML3, OCI-AML5, OCI-LY-19, PL21, RC-K8, RL, Set-2, U-2932,and U2940 were obtained from DSMZ. KMM-1, KMS-26, KMS-20, KMS-27, KMS-34, andKMS-28PEwere obtained fromHSRRB/JCRB. All cell banks authenticate with STR genotyping. Cell lineswere used within 1 to 2 months of thawing from original stocksand were not further authenticated.

Generation of BETi-tolerant cell linesA375 cells (ATCC) were cultured in 1 mmol/L CPI203 for

approximately 90 days. NOMO-1 cells (DSMZ) were culturedwith CPI203 (increasing concentrations) for approximately 9months. Live cells were periodically enriched by centrifugationover Ficoll-Paque (GEHealthcare). Following selection, cells weremaintained in 1 mmol/L CPI203. Parental and BETi-tolerant cellswere authenticated using SNP genotyping with RNA-sequencingdata (Supplementary Tables S3 and S4).

Analysis of gene expression in BETi-sensitive and -insensitivecell lines

Cells were treated with CPI203 (3–4 days) and viability wasassessed [CellTiter Glo (Promega) or resazurin (Sigma)]. Expres-sion valueswere obtained from theCancer Cell Line Encyclopedia(6). For qRT-PCR, DCt values for BCL2 and BCL2L1were obtainedby subtracting Ct PPIB fromCt BCL2 or BCL2L1. DDCt values were

1Constellation Pharmaceuticals, Inc., Cambridge, Massachusetts.2Massachusetts General Hospital Cancer Center, Harvard MedicalSchool, Boston, Massachusetts.

Note: Supplementary data for this article are available at Cancer ResearchOnline (http://cancerres.aacrjournals.org/).

A.R. Conery and R.C. Centore contributed equally to this article.

Corresponding Author: Robert J. Sims III, Constellation Pharmaceuticals, 215First Street, Suite 200, Cambridge, MA 02142. Phone: 617-714-0543;Fax: 617-577-0472; E-mail: [email protected]

doi: 10.1158/0008-5472.CAN-15-1458

�2016 American Association for Cancer Research.

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calculated by subtracting the median DCt across all cell lines(Supplementary Data) from the DCt value in each cell line, andlog2 fold change was calculated as log2 (2

�DDCt).

Cell cycle, viability, and apoptosis analysesCells were analyzed for cell-cycle distribution and viable cell

number as described previously (5).

Gene-expression profilingTotal RNA was purified as described previously (5) and RNA

sequencing and alignments were performed at Ocean Ridge Bio-sciences. RNA-seq data have been deposited at GEO as GSE69383.

Comparison of apoptotic and cell-cycle arrest response withGI50

Cell lines were treated with CPI203 (4 days) and viabilityassessed (resazurin; Sigma). Themedian of the values for%subG1

and %G1 increase (relative to DMSO) across all cell lines wascalculated; Z-scores were defined as the number of SDs of thevalues in each cell line from the median.

Lentiviral shRNA transductionLentiviral shRNA constructs targeting BCL2L1 and BCL2 were

obtained from Sigma and transduced according to the manufac-turer's instructions.

BH3 profilingBH3 profiling was performed as described previously (7). BH3

peptides were obtained from Anaspec (BIM: cat#62439, BAD:cat. #64082) or Abgent (HRK: cat. #SP1016a).

Statistical analysisGraphPad Prismwas used for all statistical calculations. Unless

otherwise noted, P valueswere calculated by parametric, unpairedtwo-tailed t tests. P values are indicated as �, 0.01–0.05; ��, 0.001–0.01; ��, 0.0001–0.001; and ��, <0.0001. For linear regressionR2 is the square of the Pearson coefficient r, and the P valueindicates the probability that the slope does not differ from zero.

CompoundsCPI203 has been described previously (8). ABT-737 and ABT-

199 were obtained from Selleck Chemicals.Additional details can be found in Supplemental Information.

Results and DiscussionAlteration of the apoptotic response inmodels of acquired BETitolerance

Long-term culture of A375 (melanoma) and NOMO-1 (acutemyelogenous leukemia) in the BET inhibitor CPI203 generatedcells that could proliferate in either CPI203 or the distinct BETinhibitor JQ1. Two BETi-tolerant A375 clones, A375-t1 and

Figure 1.BETi-tolerant cells display anabrogated apoptotic response to BETiand increased expression of BCL2family genes. A, viability wasmeasured by flow cytometry after 11days (A375, left) or 4 days (NOMO-1,right) and normalized to cell numberin DMSO (A375 and A375-t1) or toparental cell number in DMSO(NOMO-1 and NOMO-1-t). Values arethe mean, n ¼ 3 (A375) or n ¼ 6(NOMO-1), and SEM. B, cells weretreated with 1 mmol/L of CPI203 for 10days (A375) or 4 days (NOMO-1).Values are the mean, n ¼ 3 (A375)or n ¼ 6 (NOMO-1), and SEM. C,expression heatmap of the indicatedgenes ( FC, log2 fold change) intolerant cells relative to parental cells(CPI203 treated).

Conery et al.

Cancer Res; 76(6) March 15, 2016 Cancer Research1314




A375-t2, revealed a modest shift in the GI50 values for BETi(Fig. 1A, left; Supplementary Fig. S1A and Supplementary Fig.S1C, left). BETi-tolerant NOMO-1-t cells proliferated at con-centrations of BETi approximately 30-fold higher than theiroriginal GI50 concentrations (Fig. 1A, right; and SupplementaryFig. S1C, right) and surprisingly grew best in the presence ofCPI203. Both BETi-tolerant cell lines A375-t1 and NOMO-1-tdisplayed a blunted apoptotic response to BETi relative toparental cells (Fig. 1B; Supplementary Fig. S1B and S1D).Notably, A375-t1 and NOMO-1-t were equally or more sensi-tive than parental cells to both cytarabine and doxorubicin,which suggests that they are not resistant to all apoptosis-inducing agents and points toward the use of BETi in combi-nation with cytotoxic chemotherapies (Supplementary Fig.S1E–S1F).

Consistent with these phenotypes, through gene-expressionprofiling we observed increased expression of the BCL2 familymember BCL2L1 (encoding BCL2L1, formerly known as BCL-xL)in both A375-t and NOMO-1-t compared with parental cells

treated with CPI203 (Fig. 1C; Supplementary Fig. S2A–S2C). InBETi-treated NOMO-1-t cells, BCL2 was also upregulated com-pared with BETi-treated parental cells (Fig. 1C; SupplementaryFig. S2C).MCL1 and BCL2A1were reduced in both tolerant A375andNOMO-1 cells, highlighting the selective alteration of specificBCL2 family members. In contrast with recent work pointing totheWnt pathway as a determinant of response to BETi (9, 10), wedid not observe increased expression of Wnt target genes in eitherA375 or NOMO-1 BETi resistance models.

BCL2L1 and BCL2 are critical mediators of BETi sensitivity andresistance

Given the expression changes noted above, we focused onthe functional roles of BCL2L1 in A375-t1 and NOMO-1-tcells, and BCL2 in NOMO-1-t cells given that BCL2 wasexpressed at very low levels in A375-t1 (Fig. 1C; Supplemen-tary Fig. S2B). Knockdown of BCL2L1 in A375-t1 cells, and ofBCL2 or BCL2L1 in NOMO-1-t cells, led to dramaticallyreduced proliferation and significantly increased apoptosis in

Figure 2.Increased expression of BCL2 familymembersmediates BETi tolerance. A andB, shRNA-transduced cellswere treatedwith 1mmol/L CPI203 (A, A375-t1 for 11 days; B,NOMO-1-t for 4 days) before assessing viability and the percentage of sub-G1. Values represent the mean and SEM [A, n ¼ 2; B, n ¼ 2 (shLuc and shBCL2)or n ¼ 5 (shBCL2L1)]. P values are relative to shLuc. � , P < 0.05; �� , P < 0.01; �� , P < 0.0001 by parametric, unpaired two-tailed t tests. C, cells were cotreatedwith ABT-737 and 1 mmol/L CPI203 or DMSO (11 days). The percentage of growth in CPI203 is indicated relative to DMSO. Values represent the mean andSEM (n¼ 3, P values are relative to 0 mmol/L ABT-737). � , P < 0.05; �� , P < 0.01, �� , P < 0.001 by parametric, unpaired two-tailed t tests. D, cells were treated with0.25 mmol/L CPI203 and the indicated concentration of ABT-737 (4 days). The percentage of growth in ABT-737 is relative to CPI203-treated cells. Valuesrepresent the mean and SEM (n ¼ 6, P values are relative to 0 mmol/L ABT-737). �� , P < 0.0001 by parametric, unpaired two-tailed t tests.

BH3 Proteins Predict Response to BET Inhibition

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the presence of CPI203 (Fig. 2A and B and Supplementary Fig.S3B–S3C). In addition, overexpression of BCL2L1 in parentalA375 cells resulted in a blunted apoptotic response to BETi(Supplementary Fig. S3A).

Treatment of A375-t1 and NOMO-1-t with ABT-737, an inhib-itor of BCL2, BCL2L1, and BCL2L2 (11), concurrently withCPI203 led to growth suppression and induction of apoptosis,while affecting parental cells to a lesser extent (Fig. 2C and D;Supplementary Fig. S3D–S3E). Consistent with their sustainedexpression of BCL2, NOMO-1-t cells also showed growth inhi-bition with the BCL2-specific inhibitor, ABT-199 (SupplementaryFig. S3F; ref. 12). In both models, the degree of apoptosis induc-tion in tolerant cells cotreated with CPI203 and ABT-737 wascomparablewithparental cells treatedwith BETi alone, suggestingthat pharmacologic inhibition of BCL2 family members canabrogate tolerance to BETi. Notably, combination of the BETinhibitor JQ1 with ABT-199 showed synergistic effects on growthinhibition (13), highlighting the potential of combining BETiwith BH3 peptidomimetics.

Engagement of the apoptotic program is associated with robustphenotypic response to BET inhibition

Next, we tested whether the apoptotic program correlates withde novo sensitivity to BET inhibitors by profiling 51 hematologiccell lines that we termed "sensitive" or "insensitive" based on aGI50 cutoff value of 0.25 mmol/L CPI203. As shown in Fig. 3A, themagnitude of apoptosis, but not the cytostatic response, is highlycorrelated with sensitivity to BET inhibition. This is consistentwith previous work showing that the induction of apoptosis isoften associated with preclinical efficacy (2, 3, 5, 14–16).

Given that BETi regulates the expression of antiapoptoticfactors like BCL2 (2), we tested whether apoptosis is associatedwith a change in the expression of key anti- and proapoptoticfactors after CPI203 treatment. A clear trend toward downregula-tion of antiapoptotic factors and upregulation of proapoptoticfactors was observed in cell lines that undergo apoptosis (Fig. 3B,top), and a more robust transcriptional response is significantlycorrelated with the magnitude of the apoptotic response toCPI203 (Fig. 3B, bottom). To functionally test whether these

Figure 3.Robust apoptosis correlates withBETi sensitivity. A, 51 cell lines(Supplementary Data) were treatedwith CPI203 (4 days) and GI50,apoptosis and G0–G1 cell-cycle arrestwere measured. Sensitive, GI50 <0.25mmol/L. Values represent the meanandSEM. B, cell lineswere treatedwithCPI203 (�2 mmol/L) for 24 hours. Top,expression of the indicated genes(qRT-PCR). Bottom, composite genescore (see Supplemental Methods)was plotted against the z-score forsub-G1 increase calculated in A. C,mitochondrial depolarization (JC-1fluorescence) resulting from BIMpeptide in cells treated overnight withDMSO or CPI203 (1 mmol/L). Valuesshown are the mean n ¼ 12 (n ¼ 8 forKasumi-1) and SEM (P values forCPI203 are relative to DMSO at eachBIM peptide concentration).� , P < 0.05; �� , P < 0.001 byparametric, unpaired two-tailedt tests; ns, nonsignificant.

Conery et al.





changes in gene expression were associated with increased apo-ptotic signaling, we used BH3 profiling with the BIMBH3 peptideto assess mitochondrial "priming" (7, 17). In two cell lines thatshow a robust apoptotic response to BET inhibition (MV411 andKasumi-1), preincubation with CPI203 induced measurableincreases in mitochondrial priming, which was not observed in

cell lines with a minimal apoptotic response (Daudi andSUDHL5; Fig. 3C; Supplementary Fig. S4A).

The results reported above suggest that in highly sensitive cells,BETi treatment disproportionatelymodulates apoptotic signalingby altering the expression of anti- and proapoptotic genes.Wheth-er these changes in gene expression are the direct result of release

Figure 4.Basal expression of apoptotic factors predicts BETi response. A, expression (RMA) is shown relative to BETi sensitivity (GI50 <0.25 mmol/L CPI203). B, mitochondrialdepolarization (JC-1 fluorescence) from the indicated cell lines treated with a BAD BH3 peptide (50 mmol/L). Values are the mean of n ¼ 12 (n ¼ 8 forSUDHL4); error is shown in Supplementary Fig. S4B. C, high or low was defined as the expression in the top or bottom third of all cell lines. Dashed line, theoverall response rate (28%). Values represent the mean and SEM (P values were determined by two-tailed Fisher exact t test). �� , P < 0.0001 by parametric,unpaired two-tailed t test. D, expression of BCL2 and BCL2L1 (qRT-PCR) are predictive of phenotypic response to BETi. High or low expressionwas defined as aboveor below themedian (P¼0.002by two-tailed Fisher exact t test). The overall response rate is indicated in the graphby adashed line (66%). �� ,P<0.01 by parametric,unpaired two-tailed t tests.






of BET family proteins from chromatin is not clear. Recent workhas reported that highly BRD4-occupied gene control elementsdefine loci that are highly BET dependent, which includes BCL2family genes (1). However, we have found that high BRD4occupancy and release upon BETi treatment are not universalpredictors of downregulation of any given gene (data not shown).It is likely that identification of predictive features of BET tran-scriptional regulation will facilitate our ability to forecast a robustapoptotic response to BET inhibition.

Basal expression levels of BCL2, BCL2L1, and BAD serve aspredictive biomarkers for BETi treatment

Given our functional data, we tested whether the baselinemRNAexpression level of apoptotic factors (Supplementary TableS1) could predict phenotypic response to BETi in a panel of 245cell lines (Supplementary Table S2; Supplementary Information).We found that five genes, BCL2, BCL2L2, BCL2L1, BAD, andBCLAF1, were differentially expressed with high significance, withBCL2 being the most differentially expressed gene (Supplemen-tary Table S1, Fig. 4A). We reasoned that BETi may selectivelytarget cells that are dependent on BCL2, and tested for BCL2dependence in a subset of cell lines usingmitochondrial exposureto the BAD BH3 peptide (18). We observed that mitochondrialdepolarization in response to BAD peptide is correlated with theexpression of BCL2 in a set of 6 cell lines (Fig. 4B, top), consistentwith published data (18), and that the degree of mitochondrialdepolarization was inversely correlated with the GI50 of BETi (Fig.4B, bottom). There was no significant mitochondrial depolariza-tion in response to the HRK BH3 peptide, arguing against anycontribution of BCL2L1 to the BAD depolarization response(Supplementary Fig. S4B). As both inhibitors seem to preferen-tially target cell lines that are dependent on BCL2 for survival, wenoted that theGI50 values ofCPI203 andABT-199 are significantlycorrelated (Supplementary Fig. S4C; ref. 12).

We next tested whether the expression levels of these apoptoticmediators could predict a robust response to BET inhibition.Selection based on the expression of a one or two genes signif-icantly increased the response rate, with selection of cells withhigh BCL2 expression and low expression of either BCL2L1 orBAD improving the response rate to 65% or 75%, respectively(Fig. 4C and Supplementary Fig. S5A). Importantly, selectionbased on these factors was not merely enriching for cell lines ofhematologic origin, as these selection criteria increased theresponse rate of isolated hematologic or solid tumor cell lines,and of acute lymphoblastic leukemia cell lines (SupplementaryFig. S5B–S5D). We also noted that selection based on these

biomarkers selected for the most sensitive tumor subtypes, whichmay point to indications beyond hematologic malignancies thatwill benefit from BETi (Supplementary Fig. S4D). Finally, theenrichment of phenotypically sensitive cell lines was observedwhen gene expression was measured by qRT-PCR or Westernblotting (Fig. 4D; Supplementary Fig. S6), indicating that thesecriteria may be used with clinically relevant methods.

Although they have tremendous promise, BET inhibitors alsobring new challenges for selecting patient populations and fordetermining optimal drug combinations. This work identifiesthe apoptotic signaling network and the expression of factorssuch as BCL2, BCL2L1, and BAD as patient selection biomarkersthat will help BET inhibitors to reach their full therapeuticpotential.

Disclosure of Potential Conflicts of InterestN.E. Follmer is a senior scientist in Merck & Co., Inc. B.M. Bryant has

ownership interest (including patents) in Constellation Pharmaceuticals.P.Greninger is a headof research andhas ownership interest (including patents)in Constellation Pharmaceuticals. J.A. Mertz has ownership interest (includingpatents) in Constellation Pharmaceuticals. R.J. Sims III is a vice president ofresearch, reports receiving other commercial research support, and has owner-ship interest (including patents) inConstellation Pharmaceuticals. No potentialconflicts of interest were disclosed by other authors.

Authors' ContributionsConception and design: A.R. Conery, R.C. Centore, N.E. Follmer, P. Greninger,C.H. Benes, J.A. Mertz, R.J. Sims IIIDevelopment of methodology: A.R. Conery, R.C. Centore, N.E. Follmer,R.J. Sims IIIAcquisition of data (provided animals, acquired and managed patients,provided facilities, etc.): A.R. Conery, R.C. Centore, K.L. Spillane, N.E. Follmer,A. Bommi-Reddy, C.H. BenesAnalysis and interpretation of data (e.g., statistical analysis, biostatistics,computational analysis): A.R. Conery, R.C. Centore, K.L. Spillane,N.E. Follmer, C. Hatton, B.M. Bryant, P. Greninger, A. Amzallag, C.H. Benes,J.A. Mertz, R.J. Sims IIIWriting, review, and/or revision of themanuscript: A.R. Conery, R.C. Centore,P. Greninger, C.H. Benes, J.A. Mertz, R.J. Sims IIIAdministrative, technical, or material support (i.e., reporting or organizingdata, constructingdatabases):C.Hatton, B.M. Bryant, P.Greninger, R.J. Sims IIIStudy supervision: P. Greninger, J.A. Mertz, R.J. Sims III

AcknowledgmentsThe authors thank the many Constellation employees in their support of

these studies.

Received June 2, 2015; revised December 4, 2015; accepted December 28,2015; published OnlineFirst January 12, 2016.

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2016;76:1313-1319. Published OnlineFirst January 12, 2016.Cancer Res Andrew R. Conery, Richard C. Centore, Kerry L. Spillane, et al. Determined by the Apoptotic ResponsePreclinical Anticancer Efficacy of BET Bromodomain Inhibitors Is

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