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Neoplasia
Neoplasia
Neoplasia (New York, N.Y.)
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S1476-5586(24)00088-5
10.1016/j.neo.2024.101046
101046
Original Research
Bromodomain inhibitor treatment leads to overexpression of multiple kinases in cancer cells
Chandrashekar Darshan S. dshimogachandrasheka@uabmc.edu
a1⁎
Afaq Farrukh a1
Karthikeyan Santhosh Kumar a
Athar Mohammad b
Shrestha Sadeep c
Singh Rajesh f
Manne Upender ad2
Varambally Sooryanarayana svarambally@uabmc.edu
ade2⁎⁎
a Department of Pathology, University of Alabama at Birmingham, Birmingham, AL, USA
b Department of Dermatology, University of Alabama at Birmingham, Birmingham, AL, USA
c Epidemiology, University of Alabama at Birmingham School of Public Health, Birmingham, AL, USA
d O'Neal Comprehensive Cancer Center, University of Alabama at Birmingham, Birmingham, AL, USA
e Department of Biomedical Informatics and Data Science, University of Alabama at Birmingham, Birmingham, AL, USA
f Morehouse School of Medicine, Atlanta, GA, USA
⁎ Correspondence author at: Department of Pathology, University of Alabama at Birmingham. dshimogachandrasheka@uabmc.edu
⁎⁎ Correspondence author at: Department of Pathology, University of Alabama at Birmingham, 1825 University Bldg., SHEL 816, Birmingham, AL, 35249-733. svarambally@uabmc.edu
1 Equal contribution.

2 Share Senior Authorship

05 9 2024
11 2024
05 9 2024
57 10104621 5 2024
26 8 2024
26 8 2024
© 2024 The Authors. Published by Elsevier Inc. CCBYLICENSE.
2024

https://creativecommons.org/licenses/by-nc/4.0/ This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
The bromodomain and extraterminal (BET) family of proteins show altered expression across various cancers. The members of the bromodomain (BRD) family contain epigenetic reader domains that bind to acetylated lysine residues in both histone and non-histone proteins. Since BRD proteins are involved in cancer initiation and progression, therapeutic targeting of these proteins has recently been an area of interest. In experimental settings, JQ1, a commonly used BRD inhibitor, is the first known inhibitor to target BRD-containing protein 4 (BRD4), a ubiquitously expressed BRD and extraterminal family protein. BRD4 is necessary for a normal cell cycle, and its aberrant expression activates pro-inflammatory cytokines, leading to tumor initiation and progression. Various BRD4 inhibitors have been developed recently and tested in preclinical settings and are now in clinical trials. However, as with many targeted therapies, BRD inhibitor treatment can lead to resistance to treatment. Here, we investigated the kinases up-regulated on JQ1 treatment that may serve as target for combination therapy along with BRD inhibitors. To identify kinase targets, we performed a comparative analysis of gene expression data using RNA from BRD inhibitor-treated cells or BRD-modulated cells and identified overexpression of several kinases, including FYN, NEK9, and ADCK5. We further validated, by immunoblotting, the overexpression of FYN tyrosine kinase; NEK9 serine/threonine kinase and ADCK5, an atypical kinase, to confirm their overexpression after BRD inhibitor treatment. Importantly, our studies show that targeting FYN or NEK9 along with BRD inhibitor effectively reduces proliferation of cancer cells. Therefore, our research emphasizes a potential approach of utilizing inhibitors targeting some of the overexpressed kinases in conjunction with BRD inhibitors to enhance therapeutic effectiveness.

Keywords

Cancer
Gene Expression
Kinases
Resistance
==== Body
pmcIntroduction

Cancer is a heterogeneous disease and shows multiple molecular alterations during initiation and progression. Various high-throughput technologies have enabled an understanding of the molecular alterations in cancer, including in single cancer cells. These advances have led to identification of therapeutic/diagnostic biomarkers and to the introduction of effective cancer therapy options [1]. Despite the innovations and progress, only a few therapeutic targets are available and viable. Even when there are targets with available drugs, resistance often develops, and cancers recur [2]. It is important to understand molecular factors that contribute drug resistance across cancers. These investigations will aid in developing combination therapies with potentially reduced drug doses that alleviate development of resistance to therapy.

In recent years, there has been interest in targeting the epigenetic regulators in cancer. Bromodomain proteins belonging to the bromodomain and extraterminal domain (BET) family are also considered as targets in various cancers. This family consists of four members, BRD2, BRD3, BRD4, and BRDT. These genes are involved in regulating cancer genes that serve as epigenetic readers of histone acetylation. Thus, various small molecule inhibitors of BET are in clinical development. The small molecule inhibitor JQ1, a thienodiazepine compound developed in the early 1990s by Yoshitomi Pharmaceuticals, was one of first BET inhibitors employed in cancer therapy. JQ1 competes with acetylated lysine on histones for acetyl-binding sites of BRD4, prevents its chromatin binding, and disrupts formation of protein complexes [3]. Anti-cancer effects of JQ1 have been reported for various cancer types including prostate cancer, ovarian cancer, osteosarcoma, and triple negative cancer. [[4], [5], [6], [7]].

In addition, there are benefits of using JQ1 in combinatorial treatment for cancer. The synergistic effect of JQ1 was studied in combination with a) an anti-PD-L1 antibody or gemcitabine for treatment of pancreatic ductal adenocarcinoma (PDAC) [8,9], b) a Bcl-2 inhibitor for MYCN-amplified small cell lung cancer [10], c) docosahexaenoic acid (DHA) for colorectal carcinoma [11], d) rapamycin for osteosarcoma [12], and e) arsenic sulfide (As4S4) for colon and gastric cancer [13]. Cancers develop resistance to oncogene-targeted therapies by various mechanisms, including inactivation of drugs, molecular alteration of drug targets, or up-regulation of oncogenes/signaling cascades [2]. Studies have shown that resistance to BET inhibitors in lung adenocarcinoma is mediated by phosphorylation of BRD4 [14]. It has been shown that hyperphosphorylation of BRD4 as a result of arises from decreased PP2A activity in BET bromodomain resistant cells, which leads to increased binding of BRD4 to MED1 and leading to decreased responsiveness to BRD inhibition [15]. BET inhibitor resistance has also been shown in prostate cancer [16]. In the current study, we employed a comparative analysis using publicly available cancer transcriptome profiling data to identify targetable JQ1 therapy resistance genes, focusing on analyzing all the kinases. Other BRD inhibitors include I-BET762, which acts as an immunosuppressant [17]; I-BET151, which reduces glioma cell proliferation [18]; MK-8628 (OTX015), which has reached phase II clinical trials for recurrent glioblastoma multiforme; and BI 894999 and PLX51107, which are in phase1 clinical trials [19]. Although various pan-BET inhibitors have entered clinical trials as monotherapies, these inhibitors show moderate efficacy along with notable side effects [20].

In the current study, utilizing publicly available datasets, we evaluated the expression changes in kinase genes after treatments with BRD inhibitors or knockdown of BRD4 across various cancer cell lines. Our goal was to identify kinases that are overexpressed upon treatment with BRD inhibitors that may reduce the effectiveness of BRD inhibitor treatment. Our analysis identified kinases that are overexpressed in various cancer cell lines upon treatment with a BRD inhibitor. We identified and validated FYN Proto-Oncogene, Src Family Tyrosine Kinase (FYN), never in mitosis gene A (NIMA)-related kinase 9 (NEK9), and AarF Domain Containing Kinase 5 (ADCK5) after treatment with a BRD inhibitor. Our results implicate a role of BRD inhibition in increasing various kinases, and a combination of inhibitors targeting these kinases along with BRD inhibition at lower doses may be a viable strategy for therapy.

Method

Gene expression data selection, collection, and processing

We queried and curated the NCBI Gene Expression Omnibus [GEO] repository [https://www.ncbi.nlm.nih.gov/] for gene expression data from RNA sequencing experiments involving JQ1-treated cancer cell lines. With the query term “JQ1 treatment and cancer”, entry type as “Series”, study type as “Expression profiling by high throughput sequencing” and “Homo sapiens” as organism, we found 158 responses to our search terms. We evaluated each of these hits to identify relevant studies [Supplementary Fig. 1]. Out of the 158 query hits, a) 30 were found to be not related to JQ1 treatment, there were listed due to the presence of terms JQ1 and cancer in their study description, b) 7 were associated with JQ1 treatment of non-cancer cells, c) 20 were related to studies where JQ1 was used in combination with other drugs, d) 18 represented studies using only one control and one JQ1 treatment sample, e) two were related to JQ1 treatment of tumor xenografts, and f) one was for a study with no control samples. After excluding these, we had 56 hits associated with RNA sequencing of JQ1-treated cancer cells, including control samples and technical replicates. For the final comparative analysis, we selected 28 studies with data to cover 16 types of cancer and 46 unique cancer cell lines [[21], [22], [23], [24], [25], [26], [27], [28], [29], [30], [31], [32], [33], [34], [35], [36], [37], [38], [39], [40], [41], [42], [43], [44], [45]]. To investigate the exclusivity of JQ1-induced kinase expression in cancer cells, we gathered RNA-seq data related to a) JQ1 treatment of non-cancer cells such as myofibroblasts (GSE140476), peripheral blood (GSE71595), and normal breast cells (GSE72932) [[46], [47], [48]]; b) iBET (a BRD inhibitor) treatment of cancer cells (GSE167241, GSE100694 and GSE145028) [42,49,50]; and c) siBRD4 transfection of cancer cells (GSE145028, GSE55922, and GSE136151) [42,51,52].

We downloaded raw fastq files from the NCBI Sequence Read Archive (SRA) [https://www.ncbi.nlm.nih.gov/sra] using the fastq-dump application in the SRAtoolkit. Firstly, using Trim Galore (v0.4.1) [http://www.bioinformatics.babraham.ac.uk/projects/trim_galore/], adapter sequences and low-quality regions were trimmed from raw sequencing reads. Afterwards, with TopHat v2.1, we mapped reads to the human reference genome (hg38) [53]. Next, aligned reads were sorted using Samtools (Version: 1.3.1) [54]. Finally, we employed HTSeq to enumerate reads related to every human gene (https://htseq.readthedocs.io/en/latest/). Differential expression analysis was performed using DESeq2 in accordance to standard rules [https://bioconductor.org/packages/release/bioc/vignettes/DESeq2/inst/doc/DESeq2.html]. Genes with adjusted P-values <0.05 and absolute fold change of ≥1.5 were considered as differentially expressed. Genes encoding kinases were collected from UniProtKB [https://www.uniprot.org/]. To obtain drugs in preclinical or clinical trials targeting kinases of interest, we utilized OpenTarget (v23.06) platform[https://platform.opentargets.org/].

Cancer cell lines and JQ1 treatment

We evaluated the effect of JQ1 treatment on colorectal (HCT116 and SW480) and breast (MDA-MB-231 and HTB-19) cancer cell lines, which were purchased from American Type Culture Collection (Manassas, VA, USA). HCT116 and SW480 cells were maintained in McCoy's medium, MDA-MB-231 cells in Leibovitz's L-15 Medium, and HTB-19 cells in Eagle's Minimum Essential Medium (EMEM) containing 10% fetal bovine serum (FBS, Invitrogen, Thermo Fisher Scientific, Carlsbad, CA) and penicillin-streptomycin. Mycoplasma screening was performed regularly. These human cancer cells were treated with JQ1 (2 or 4 µM) obtained from MedChemExpress (Catalog # HY-13030) for 24 hr, and then protein lysates were prepared.

Western blotting

Western blot analysis was conducted to assess the protein expression levels of FYN, NEK9, and ADCK5 following treatment of human cancer cells with JQ1. Briefly, samples were loaded on NuPAGE™ 4-12% Bis-Tris Midi Protein Gels, 20-well (Invitrogen, Thermo Fisher Scientific, Carlsbad, CA), to perform sodium dodecyl sulfate-polyacrylamide gel electrophoresis. Separated proteins were electro-transferred from the gels onto Immobilon-P PVDF membranes (EMD Millipore, Billerica, MA). To prevent non-specific binding, the membranes were blocked with blocking buffer (Tris-buffered saline, 0.1% Tween (TBS-T), 5% nonfat dry milk) for 1 hr, followed by incubation overnight with Fyn (Catalog # 4023S; Cell Signaling Technology, MA), NEK9 (Catalog # 11192-1-AP, PTG Labs, IL), or ADCK5 (Catalog # orb215399; Biorbyt Ltd, Cambridge) primary antibodies at 4°C. The membranes were washed and then incubated with horseradish peroxidase-conjugated rabbit secondary antibody (Catalog # SA00001-2; PTG Labs, IL) for 1 hr. The equal loading of protein was confirmed by probing the membranes for β-actin (Catalog # HRP-60008; PTG Labs, IL). Signals were developed with LuminataTM Crescendo chemiluminescence Western blotting substrate to measure antigen-antibody complexes according to the manufacturer's protocol (EMD Millipore, Billerica, MA).

Cell viability assay

In order to evaluate the effects of JQ1, saracatinib, dabrafenib alone and their combinations, cell viability assay was performed using breast and colorectal cancer cells. Cell viability was determined using 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay. JQ1, saracatinib (Cat. No# HY-10234), dabrafenib (Cat. No# HY-14660) were obtained from MedChemExpress (NJ, USA). The breast and colorectal cancer cells were plated in 96 well plates and treated with DMSO (vehicle), JQ1 (2 µM), saracatinib (2 µM), dabrafenib (0.1 µM), and combination of JQ1 (2 µM) and saracatinib (2 µM) as well as JQ1 (2 µM) and dabrafenib (0.1 µM), and for 96 hrs. In the combination treatment, the cancer cells were pretreated with JQ1 for 24 hrs and then challenged with saracatinib or dabrafenib. After 96 hrs of treatments, the MTT reagent was added to each well and incubated for 2 hrs. The fromazan crystal formed was dissolved in DMSO and was measured using Synergy™ HTX Multi-Mode Microplate Reader (BioTek, Winooski, VT, USA) at 540 nm. The changes in cell viability after drug treatments were determined by considering vehicle treated cells as 100 % viable.

Results

Comparative transcriptome analysis to identify JQ1-induced kinases

To identify kinases that are overexpressed upon inhibition of BRD4 or due to alteration of its expression by RNA interference, we curated the publicly available RNA-sequencing data (Table 1). We downloaded and processed gene expression data related to 46 cancer cell lines from 28 independent studies. RNA level expression patterns of 637 kinases were analyzed. On JQ1 treatment, differentially expressed kinases (DEKs) varied between studies. Supplementary Fig. 2 shows the distribution of DEKs on JQ1 treatment across studies.Table 1 List of 28 RNA-seq datasets considered in study of the molecular effect of JQ1 treatment on cancer cell lines.

Table 1:SI	GEO ID	Cancer	Cell line	Samples (SRA ID)	
1	GSE158552	Hepatocellular carcinoma	HepG2	Control HepG2 [SRR12708918, SRR12708919, SRR12708920]
JQ1-treated HepG2 [SRR12708921, SRR12708922, SRR12708923]	
2	GSE192903	Pancreatic cancer	PANC1	JQ1-treated PANC1 24 hr [SRR17406463, SRR17406464, SRR17406465]
Control PANC1 [SRR17406466, SRR17406467, SRR17406468]	
3	GSE110634	T-cell acute lymphoblastic leukemia	ALL-SIL	JQ1-treated ALL-SIL 24 hr [SRR6727685, SRR6727686, SRR6727687]
DMSO-treated ALL-SIL 24 hr [SRR6727688, SRR6727689, SRR6727690]	
4	GSE107405	Medulloblastoma	MB002	JQ1-treated MB002 6 hr [SRR6325945, SRR6325946, SRR6325947]
DMSO-treated MB002 6 hr [SRR6325942, SRR6325943, SRR6325944]	
5	GSE94488	BRAF metastatic melanoma	501MEL	JQ1-treated 501MEL 6 hr [SRR5228528, SRR5228529]
Control 501MEL [SRR5228526, SRR5228527]	
NRAS metastatic melanoma	SKmel147	JQ1-treated SKmel147 6 hr [SRR5228520, SRR5228521]
Control SKmel147 [SRR5228518, SRR5228519]	
6	GSE103449	Prostate cancer	LNAR	JQ1-treated LNAR [SRR6003972, SRR6003973, SRR6003974, SRR6003975]
Vehicle treated LNAR [SRR6003968, SRR6003969, SRR6003970, SRR6003971]	
LREX	JQ1-treated LREX [SRR6003980, SRR6003981, SRR6003982, SRR6003983]
Vehicle treated LREX [SRR6003976, SRR6003977, SRR6003978¸ SRR6003979]	
7	GSE113604	Ewing sarcoma	CadoES	JQ1-treated CadoES 48 hr [SRR7059713, SRR7059714]
DMSO-treated CadoES 48 hr [SRR7059711, SRR7059712]	
CHLA10	JQ1-treated CHLA10 48 hr [SRR7059717, SRR7059718]
DMSO-treated CHLA10 48 hr [SRR7059715, SRR7059716]	
RDES	JQ1-treated RDES 48 hr [SRR7059725, SRR7059726]
DMSO-treated RDES 48 hr [SRR7059723, SRR7059724]	
SKNMC	JQ1-treated SKNMC 48 hr [SRR7059729, SRR7059730]
DMSO-treated SKNMC 48 hr [SRR7059727, SRR7059728]	
8	GSE115550	Breast cancer	MDA-MB-231	JQ1-treated MDA-MB-231 18hr (500nM) [SRR7287127, SRR7287132, SRR7287137]
DMSO-treated MDA-MB-231 18hr [SRR7287125, SRR7287130, SRR7287135]	
9	GSE118548	Colon cancer	T84	JQ1-treated T84 24 hr [SRR7693483, SRR7693484, SRR7693485]
DMSO-treated T84 24 hr [SRR7693477, SRR7693478, SRR7693479]	
10	GSE73318	Colon cancer	COLO205	JQ1-treated COLO205 24 hr [SRR2481166, SRR2481167, SRR2481168]
DMSO-treated COLO205 24 hr [SRR2481163, SRR2481164, SRR2481165]	
COLO320	JQ1-treated COLO320 24 hr [SRR2481178, SRR2481179, SRR2481180]
DMSO-treated COLO320 24 hr [SRR2481175, SRR2481176, SRR2481177]	
HCT15	JQ1-treated HCT15 24 hr [SRR2481172, SRR2481173, SRR2481174]
DMSO-treated HCT15 24 hr [SRR2481169, SRR2481170, SRR2481171]	
HCT116	JQ1-treated HCT116 24 hr [SRR2481148, SRR2481149, SRR2481150]
DMSO-treated HCT116 24 hr [SRR2481145, SRR2481146, SRR2481147]	
HT29	JQ1-treated HT29 24 hr [SRR2481160, SRR2481161, SRR2481162]
DMSO-treated HT29 24 hr [SRR2481157, SRR2481158, SRR2481159]	
SW480	JQ1-treated SW480 24 hr [SRR2481154, SRR2481155, SRR2481156]
DMSO-treated SW480 24 hr [SRR2481151, SRR2481152, SRR2481153]	
11	GSE77295	Leukemia	K562	JQ1-treated K562 48 hr [SRR3129074, SRR3129075]
DMSO-treated K562 48 hr [SRR3129072, SRR3129073]	
12	GSE79253	Acute lymphoblastic leukemia	MOLT4	JQ1-treated MOLT4 6 hr [SRR3231793, SRR3231794, SRR3231795]
DMSO-treated MOLT4 6 hr [SRR3231787, SRR3231788, SRR3231789]	
13	GSE80153	Neuroblastoma	BE(2)-C	JQ1-treated BE2C 24 hr [SRR5441938, SRR5441939, SRR5441940]
DMSO-treated BE2C 24 hr [SRR5441935, SRR5441936, SRR5441937]	
14	GSE82032	Breast cancer	HCC1143	JQ1-treated HCC1143 [SRR5573778, SRR5573779]
DMSO treated HCC1143 [SRR3605775, SRR3605776, SRR3605777]	
15	GSE82329	Ovarian cancer	A1847	JQ1-treated A1847 48 hr [SRR3642609, SRR3642610, SRR3642611, SRR3642612]
DMSO treated A1847 48 hr [SRR3642605, SRR3642606, SRR3642607, SRR3642608]	
A2780	JQ1-treated A2780 48 hr [SRR3642601, SRR3642602, SRR3642603, SRR3642604]
DMSO treated A2780 48 hr [SRR3642599, SRR3642600]	
OVCAR5	JQ1-treated OVCAR5 48 hr [SRR3642617, SRR3642618, SRR3642619, SRR3642620]
DMSO treated OVCAR5 48 hr [SRR3642613, SRR3642614, SRR3642615, SRR3642616]	
16	GSE83724	Alveolar rhabdomyosarcoma	RH41	JQ1-treated RH41 6 hr [SRR3720809, SRR3720810, SRR3720811]
DMSO treated RH41 6 hr [SRR3720812, SRR3720813, SRR3720814]	
17	GSE87153	Prostate cancer	MR49F	JQ1-treated MR49F 24 hr [SRR2063523, SRR2063529, SRR2063535]
Vehicle treated MR49F 24 hr [SRR2063511, SRR2063512, SRR2063513]	
18	GSE87419	Breast cancer	SUM159	JQ1-treated SUM159 24 hr [SRR4299766, SRR4299770, SRR4299774]
DMSO treated SUM159 24 hr [SRR4299764, SRR4299768, SRR4299772]	
19	GSE91062	Leukemia	SET2	JQ1-treated SET2 [SRR6466400, SRR6466401, SRR6466402]
DMSO treated SET2 [SRR6466394, SRR6466395, SRR6466396]	
20	GSE92456	Burkitt lymphoma	RAJI	JQ1-treated RAJI [SRR5114326, SRR5114327, SRR5114328, SRR5114329, SRR5114330]
DMSO treated RAJI [SRR5114321, SRR5114322, SRR5114323, SRR5114324, SRR5114325]	
21	GSE94057	Breast cancer	ALF1	JQ1-treated ALF1 2 hr [SRR5201051, SRR5201055]
DMSO treated ALF1 2 hr [SRR5201050, SRR5201054]	
22	GSE95153	Melanoma	M93	JQ1-treated M93 48 hr [SRR5277334, SRR5277335, SRR5277336]
DMSO treated M93 48 hr [SRR5277331, SRR5277332, SRR5277333]	
23	GSE99175	Glioblastoma	U87	JQ1-treated U87 24 hr [SRR5583419, SRR5583420]
DMSO treated U87 24 hr [SRR5583421, SRR5583422]	
24	GSE126779	Prostate cancer	DU145	JQ1-treated DU145 [SRR8592517, SRR8592518, SRR8592519]
DMSO treated DU145 [SRR8592514, SRR8592515, SRR8592516]	
Prostate cancer	PC3	JQ1-treated PC3 [SRR8592535, SRR8592536, SRR8592537]
DMSO treated PC3 [SRR8592532, SRR8592533, SRR8592534]	
25	GSE174266	Multiple myeloma	MM1S	JQ1-treated MM1S 2 hr [SRR14496819, SRR14496820, SRR14496821]
DMSO treated MM1S 2 hr [SRR14496822, SRR14496823, SRR14496824]	
26	GSE162564	Prostate cancer	22RV1	JQ1-treated 22Rv1 24 hr [SRR13187182, SRR13187183, SRR13187184]
Control 22Rv1 [SRR13187176, SRR13187177, SRR13187178]	
27	GSE119744	Chronic lymphocytic leukemia	MEC1	JQ1-treated MEC1 [SRR7815331, SRR7815332, SRR7815333]
DMSO treated MEC1 [SRR7815327, SRR7815329, SRR7815330]	
MEC2	JQ1-treated MEC2 [SRR7815337, SRR7815338, SRR7815339]
DMSO treated MEC2 [SRR7815334, SRR7815335, SRR7815336]	
OSUCLL	JQ1-treated OSUCLL [SRR7815343, SRR7815344, SRR7815345]
DMSO treated OSUCLL [SRR7815340, SRR7815341, SRR7815342]	
CII	JQ1-treated CII [SRR7815349, SRR7815350, SRR7815352]
DMSO treated CII [SRR7815346, SRR7815347, SRR7815348]	
28	GSE145028	Small cell lung cancer	NCI-H1963	JQ1-treated NCI-H1963 [SRR11051317, SRR11051318]
DMSO treated NCI-H1963 [SRR11051313, SRR11051314]	

Comparative analysis of DEKs across studies led to identification of 36 kinase-coding genes (ADCK5, DGKQ, MKNK1, TESK2, NEK9, NAGK, PEAK1, JAK1, MKNK2, STK17A, ULK1, FGFR1, MAP3K3, CLK1, PDPK1, ERN1, PINK1, PIP5K1C, FGFR3, MAP3K9, GSK3B, PIM2, PSKH1, PNKP, PIP5KL1, STK3, TTBK2, ACVR2A, PLK3, STK38L, MAK, SIK2, DAPK3, RIPK1, FYN, and GRK5) that were up-regulated (absolute FC > 1.5 and adjusted P-value <0.05) on JQ1 treatment in at least 23 cancer cell lines [Fig. 1].Fig. 1 Comparative transcriptome analysis identifies JQ1-induced kinases. Heatmap showing expression status of 36 kinase-coding genes in 46 cancer cell lines from 28 independent studies. Genes up-regulated by absolute fold change ≥1.5 and ≥2 are indicated in light red and red color, respectively; genes down-regulated by absolute fold change ≤ -1.5 and ≤ -2 are indicated in light blue and blue color, respectively.

Fig 1:

Comparative transcriptome analysis revealed the exclusivity of JQ1-induced kinase expression in cancer cells

By comparative analyses, the influence of JQ1 treatment on non-cancer cells was analyzed for 36 kinase-coding genes [Fig. 2A]. After JQ1 exposure, other than CLK1 and STK17A, all kinases showed up-regulation in at least one type of non-cancer cells. Similarly, we assessed the effect of iBET on expression of 36 kinases in cancer cells using 3 independent RNA-seq datasets. FYN, GAK3B, and PSKH1were the only kinases unaffected by iBET.Fig. 2 Expression pattern of JQ1-induced kinases in other RNA-seq datasets. Heatmap showing expression status of 36 JQ1-induced kinases in JQ1-treated non-cancer cells (myofibroblasts, peripheral blood, and normal breast cells); iBET-treated cancer cells; and siBRD4-transfected cancer cells. Genes up-regulated by absolute fold change ≥1.5 and ≥2 are indicated in light red and red color, respectively; genes down-regulated by absolute fold change ≤ -1.5 and ≤ -2 are indicated in light blue and blue color, respectively.

Fig 2:

Finally, to differentiate the effect of BRD4 knockdown from BRD4 inhibition by JQ1 treatment, we analyzed the expression status of 36 kinases from three independent RNA-seq datasets related to siBRD4-transfected cancer cells. In two independent studies, MAP3K3, NEK9, and PSKH1 were up-regulated by siBRD4 transfection; FYN, GRK5, RIPK1, ACVR2A, PLK3, STK38L, MAK, SIK2, GSK3B, PIM2, ERN1, CLK1, ULK1, MKNK2, and TESK2 showed no up-regulation in either of three studies. FYN and GSK3B were only kinases that showed no elevated expression after iBET treatment or siBRD4 transfection of cancer cells.

In-silico analysis to estimate drug targetability of JQ1-induced kinases

We compiled a list of investigational or approved drugs targeting each of the 36 kinases. Analysis using OpenTarget and ChEMBL revealed that no drugs were available to target 25 of 36 kinases. FGFR1, FGFR3, JAK1, and FYN were the most targetable kinases with 26, 24, 20, and 6 investigational/approved drugs, respectively [Supplementary Table 1].

Immunoblot analysis to confirm JQ1-induced kinase overexpression

Among JQ1 induced kinases, few (FYN, NEK9, and ADCK5) were selected for validation [Fig. 2B]. We treated colorectal (HCT116 and SW480), and breast (MDA-MB-231 and HTB-19) cancer cell lines with JQ1 and evaluated the expression of these kinases in treated cells compared with untreated control cancer cells. For JQ1-treated breast and colorectal cancer cells, immunoblot analysis showed elevated expressions of FYN, NEK9, and ADCK5 at the protein level [Fig. 3A & B; Supplementary Fig. 3], confirming the observation from the RNA-seq analysis.Fig. 3 JQ1 treatment of colorectal and breast cancer cells induces the expression of FYN, NEK9, and ADCK5 proteins, while targeting these kinases with their inhibitors along with JQ1 effectively suppresses cell proliferation. A, Breast cancer cells (MDA-MB-231 and HTB-19) B, Colorectal cancer cells (HCT116 and SW480) were treated with JQ1 (2 or 4 µM) for 24 hr; after that, protein lysates were prepared. Expressions of FYN, NEK9, and ADCK5 were determined by western blot analysis. Equal loading of protein was confirmed by stripping the membrane and probing it for β-actin. C, Breast cancer cells (MDA-MB-231 and HTB-19) D, Colorectal cancer cells (HCT116 and SW480) were treated with JQ1 (2 μM), saracatinib (2 μM), dabrafenib (0.1 μM) and their combination for 96 hrs. After treatments, the cell viability was determined by MTT assay. Error bars indicate means ± SD, *P < 0.05; **P < 0.01; ***P < 0.001 versus vehicle control. #P < 0.001 versus JQ1.

Fig 3:

Combination of FYN or NEK9 inhibitor along with JQ1 reduces cell proliferation

To assess whether combination of bromodomain inhibitor (JQ1) and inhibitors of FYN and NEK9 kinases further suppress cell proliferation, we performed MTT assay using breast cancer (MDA-MB-231 and HTB-19) and colorectal cancer (HCT-116 and SW480) cell lines which showed robust induction of FYN and NEK9 after JQ1 treatment (Fig. 3A, B). Here, we used saracatinib, a FYN kinase inhibitor, and dabrafenib, a potent inhibitor of NEK9 to inhibit their activity [[55], [56], [57]]. We found that individually JQ1, saracatinib and dabrafenib had modest effect on cell proliferation. However, a combination of JQ1 with either FYN or NEK9 kinase inhibitor showed significant inhibition of cell proliferation compared to individual inhibitors [Fig. 3C & D]. These data indicate a potential approach of utilizing inhibitors targeting these kinases in conjunction with BRD inhibitors to enhance therapeutic efficacy and prolong the time for onset of resistance.

Discussion

Recent advances in next-generation sequencing technologies enabled better understanding of molecular networks altered in cancers and development of drugs for identified targets. However, in most cases, therapy resistance develops after treatment with targeted therapies [58]. Understanding and uncovering the mechanisms underlying therapy resistance will help mitigate the impact of drug resistance development during treatment, and developing of optimal drug combinations to lower individual drug doses, thereby diminishing the likelihood of therapy ineffectiveness or extending therapeutic efficacy.

Alterations in the BET family of proteins, commonly observed for human cancers, plays role in cancer initiation and development [20]. Hence, targeting BRD proteins and development of bromodomain inhibitors have emerged as a central focus for cancer researchers and led to the generation of many inhibitors. Although there are no FDA-approved BET inhibitors, many are being tested in vitro and in preclinical models, and some BRD inhibitors [NCT03936465, NCT01713582 (OTX015)] are in clinical trials [20,59]. JQ1 is a pan-BET inhibitor displaying equivalent inhibitory potency towards first (BD1) and second (BD2) bromodomain of the BET proteins [60]. Like most anticancer drugs, BET inhibitor treatment led to adverse effects in patients. Even with high efficacy, BET inhibitors pose a challenge in clinical treatment, as treated tumor/cancer cells can develop resistance [61]. BET inhibitor resistance has been tackled by pairing a BET inhibitor with other medications such as chemotherapy agents, PARP inhibitors, PI3K inhibitors, or immune checkpoint inhibitors [59]. In the current study, we performed a kinase-focused comparative analysis using publicly available gene expression data related to a) JQ1- and other BET inhibitor-treated cancer cells and b) cancer cells with BRD knockdown by RNA interference.

Our in-silico analyses focused on identifying targetable kinases for combination therapy with JQ1 across cancer types. Collection, processing, and comparative analysis of profiles for kinase gene expression across 46 cancer cell lines led to identification of 36 kinase-coding genes (including FYN, NEK9, and ADCK5) with elevated expression on JQ1 treatment in at least 50% of cancer cell lines. For cancer cells, FYN and GSK3B remained unaffected by iBET treatment or siBRD4 transfection. Drug targetability analyses showed FGFR1, FGFR3, JAK1, and FYN as the most targetable kinases.

We validated our in-silico findings by treating cancer cells with BRD inhibitors and performing immunoblot assays. For these analyses of colorectal and breast cancer cells, we used JQ1-treated cells. For JQ1 treatment, we also observed elevated expression of FYN, NEK9, and ADCK5 at the protein level, indicating activation of tyrosine kinase signaling. The increase in expression of several kinases may potentially reduce the effectiveness of BRD inhibitors. FYN is a member of the Src family kinases (SFKs) that is altered in various cancers, involved in cell proliferation, the epithelial-mesenchymal transition, invasion, metastasis, tumorigenesis, and development of drug resistance [62]. NEK9, a serine/threonine kinase, is a regulator of cell motility, cytoskeleton reorganization, and mitotic progression of the cell cycle High expression of NEK9 protein in cancers contributes to development of resistance [63,64]. In various cancers, ADCK5, a member of an atypical kinase family, is upregulated [65,66]. ADCK5, by phosphorylating SOX9, regulates pituitary tumor transforming gene-1 (PTTG1), leading to elevated lung cancer cell migration, invasion, and metastasis [65]. The present study highlights FYN, NEK9, and ADCK5 as potential targets, showing the potential of combination therapy for patients who have developed drug resistance for JQ1. Our studies also showed that inhibiting cancer cells using a combination of BRD inhibitor along with either FYN or NEK9 inhibitor provide strong effect in blocking cancer cell proliferation. Future studies will elucidate the mechanism of inhibition of cancer cell proliferation with the combination therapy and the effect of combination therapy using in vivo models.

Funding

S.V. and U.M were supported by U54CA118948. S.V. was also supported by 10.13039/100014038 DOD funding, W81XWH-19-1-0588 .

CRediT authorship contribution statement

Darshan S. Chandrashekar: Writing – review & editing, Writing – original draft, Visualization, Validation, Methodology, Formal analysis, Data curation, Conceptualization. Farrukh Afaq: Writing – review & editing, Writing – original draft, Validation, Methodology, Formal analysis, Data curation. Santhosh Kumar Karthikeyan: Writing – review & editing, Visualization, Validation, Formal analysis. Mohammad Athar: Writing – review & editing. Sadeep Shrestha: Writing – review & editing. Rajesh Singh: Writing – review & editing. Upender Manne: Supervision, Writing – review & editing. Sooryanarayana Varambally: Writing – review & editing, Writing – original draft, Supervision, Investigation, Funding acquisition, Conceptualization.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Appendix Supplementary materials

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Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.neo.2024.101046.
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