Identification of novel regulators of STAT3 activity

Elina Parri; Heikki Kuusanmäki; Arjan J van Adrichem; Meri Kaustio; Krister Wennerberg

doi:10.1371/journal.pone.0230819

. 2020 Mar 31;15(3):e0230819. doi: 10.1371/journal.pone.0230819

Identification of novel regulators of STAT3 activity

Elina Parri ¹, Heikki Kuusanmäki ^1,², Arjan J van Adrichem ¹, Meri Kaustio ¹, Krister Wennerberg ^1,^2,^*

Editor: Alessandro Datti³

PMCID: PMC7108870 PMID: 32231398

Abstract

STAT3 mediates signalling downstream of cytokine and growth factor receptors where it acts as a transcription factor for its target genes, including oncogenes and cell survival regulating genes. STAT3 has been found to be persistently activated in many types of cancers, primarily through its tyrosine phosphorylation (Y705). Here, we show that constitutive STAT3 activation protects cells from cytotoxic drug responses of several drug classes. To find novel and potentially targetable STAT3 regulators we performed a kinase and phosphatase siRNA screen with cells expressing either a hyperactive STAT3 mutant or IL6-induced wild type STAT3. The screen identified cell division cycle 7-related protein kinase (CDC7), casein kinase 2, alpha 1 (CSNK2), discoidin domain-containing receptor 2 (DDR2), cyclin-dependent kinase 8 (CDK8), phosphatidylinositol 4-kinase 2-alpha (PI4KII), C-terminal Src kinase (CSK) and receptor-type tyrosine-protein phosphatase H (PTPRH) as potential STAT3 regulators. Using small molecule inhibitors targeting these proteins, we confirmed dose and time dependent inhibition of STAT3-mediated transcription, suggesting that inhibition of these kinases may provide strategies for dampening STAT3 activity in cancers.

Introduction

STAT3 is constitutively activated in many aggressive cancers where it promotes drug resistance, an inflammatory phenotype and poor clinical outcome [1–3]. Aberrant STAT3 activation in cancer cells mainly occurs through canonical signalling when activated receptor- or non-receptor tyrosine kinases phosphorylate STAT3 at the tyrosine (Y) 705 residue. This results in dimerization of STAT proteins and localization to the nucleus where the STAT dimers bind to specific binding elements in the DNA and induce transcription of their target genes [3, 4]. In cancer, sustained STAT3 activation occurs through several mechanisms, such as cytokine stimulation, mutated cytokine receptors, mutated kinases (such as JAK family kinases), activation of non-receptor kinases (such as Src family kinases) or loss of negative regulators (such as SOCS and PIAS proteins) [5]. However, in rare cases such as large granular lymphocytic leukaemia, aggressive natural killer cell leukaemia and inflammatory hepatocellular adenocarcinoma, activating STAT3 mutations have been found in the Src homology 2 (SH2) domain near the Y705 phosphorylation site that result in a constitutively activated STAT3 [6–10]. From the mutations in the SH2 domain, STAT3(Y640F) causes increased hydrophobicity of the STAT dimerization site (pY+3 pocket) that results in more stable form of STAT3 dimer, constitutive Y705 phosphorylation, enhanced nuclear stability and increased transcriptional activity [6, 7, 9–11].

Since STAT3 is frequently hyperactivated and is thought to mediate malignancy, it is considered as potential therapeutic target for several solid and haematological cancers [12–14]. This has sparked multiple efforts to generate inhibitors, primarily aimed at blocking the SH2-phosphotyrosine dimerization interaction. As a protein-protein interaction, this is a challenging drug target but at least four small-molecule inhibitors have entered early-phase clinical testing [TTI-101 (NCT03195699), WP1066 (NCT01904123), OPB-51602 (NCT02058017, NCT01423903), OPB-31121 (NCT00955812)]. However, most (if not all) reported SH2 binding STAT3 inhibitors are electrophilic resulting in reactivity with cysteine thiols and they therefore have low selectivity towards the SH2 domain in more complex biological settings and often cause off-target toxicities [14–17]. An alternative approach is to inhibit the upstream signals leading to STAT3 activation, including more readily druggable targets such as kinases and cell surface receptors for which there are several approved and investigational agents [5]. Targeting the upstream signalling comes with limitations since it requires a specific aberrant upstream signalling cascade that is known. Furthermore, these approaches do not work well when there are activating mutations in the STAT3 protein itself [9, 18].

Here we set out to test whether inhibition of other, previously unexplored druggable targets can achieve inhibition of STAT3 signalling. First, we showed that constitutively active STAT3 protects cells from cytotoxic drug responses of several drug classes including anti-mitotic inhibitors and inhibitors of apoptosis proteins. Second, using reporter cells expressing either STAT3 wild type (wt) or constitutively active mutant STAT3(Y640F) in an RNAi screen, we identified kinases whose knockdown resulted in altered STAT3-activity. Finally, we confirmed that small molecule inhibition of several of these kinases could block STAT3 activity. Inhibition of these kinases may therefore be alternative strategies to inhibit STAT3 in cancers and other pathological conditions.

Materials and methods

Cell lines, virus production and transduction

HEK293 GloResponse SIE Luc2P Hygro cells (Promega) stably expressing a STAT3-dependent firefly luciferase reporter (here called HEK293sie cells) were grown in DMEM with 10% fetal bovine serum and 200 μg/ml Hygromycin B (Life Technologies). Cells were maintained at 37°C with 5% CO₂ in a humidified incubator. HEK293sieSTAT3(Y640F) and HEK293sieSTAT3(wt) cells were generated as previously described [7]. Generation of lentivirus particles and cell transduction was performed as previously described [18]. All cell lines were grown to larger volume to make assay ready cells and frozen as aliquots in liquid nitrogen. After thawing the assay ready cells, they were maintained in culture conditions without antibiotics. All cell lines were tested negative for mycoplasma using the method described by Choppa et al. (performed by the THL Biobank, Helsinki, Finland) [19, 20].

Drug sensitivity and resistance testing

Drug sensitivity and resistance testing (DSRT) was adapted to screen STAT3(wt) and STAT3(Y640F) mutant overexpressing HEK293sie cells [21]. The screen consisted of 526 approved and investigational oncology compounds (S1 File). All compounds were pre-plated at 5 different concentrations in 10-fold dilutions to black wall clear bottom 384-well plates (Corning, cat. 3712) using an acoustic dispenser (Echo 550, Labcyte Inc.). 0.1% dimethyl sulfoxide (DMSO) and 100 μM benzethonium chloride were used as negative and positive controls, respectively. The list of tested compounds and calculated drug responses can be found in supplementary data 3. The pre-plated drugs were dissolved in 5 μl in cell culture medium with 1:400 CellTox Green (Promega) per well. Subsequently, 20 μl cell suspension (100,000 cells/ml) per well was dispensed into each well. Cells were incubated for 72 hours, at 37°C and 5% CO₂. After 72 hours, cell death and viability were measured in multiplexed manner as described previously [22]. First, CellTox Green (Promega; cytotoxicity readout) was measured using fluorescence (485/520 nm excitation/emission filter) after which 25 μl CellTiter-Glo (Promega; viability readout) was dispensed per well, incubated for 10 minutes at room temperature and measured for luminescence. Both toxicity and viability were measured with a PHERAstar FS plate reader (BMG Labtech). For the time point experiments, cells were plated in 20 μl/well (100,000 cells/ml) and drugs were added in 5 μl after 0, 48, 64 and 68 hours (for 72, 24, 8, 4-hour perturbation) from cell plating. HEK293sieSTAT3(wt) cells were induced with IL6 (50 ng/ml) (eBioscience) 3 hours before readout. 72 hours after cell plating, cell viability and reporter activity were measured with CellTiter-Fluor and ONE-Glo reagents (Promega) according to the manufacturer’s protocol in multiplexed manner and read with a PHERAstar FS plate reader (BMG Labtech).

siRNA screens

Chemically synthesized siRNAs from Ambion Silencer® Select Human siRNA Library V4 (Thermo Fisher Scientific) validated human kinase and phosphatase siRNA library (S2 File) consisting of 710 kinases and 298 phosphatases were dispensed into 384-well white clear bottom plates (Corning, cat. 3707) in 250 nl using an acoustic dispenser (Echo 550, Labcyte Inc.). Pre-dispensed plates were foil sealed and stored at -20°C until use. In the first screen three siRNAs targeting one gene were pooled to one well. In all subsequent screens three individual siRNAs targeting one gene were in separate wells. Validation siRNAs were obtained from Qiagen (S3 File). All Ambion siRNA library and Qiagen siRNA screens were performed with the same protocol. Cells were reverse-transfected with library siRNA (10 nM, for pooled screen 9 nM) and Lipofectamine RNAiMAX Transfection Reagent (Thermo Fisher Scientific, cat. 13778150) using automated transfection as follows: Pre-plated siRNA plates were thawed to room temperature. siRNAs were complexed by addition of 5 μl 1:100 pre- diluted Lipofectamine RNAiMAX in OptiMEM (Invitrogen) using a Multidrop Nano dispenser (Thermo Fisher Scientific) followed by incubation for 20 minutes at room temperature on a plate shaker (400 RPM). Trypsinized and re-suspended cells were plated in 20 μl yielding 2000 cells/well and 10 nM siRNA concentration. Assay plates were incubated at 37°C and 5% CO₂, for 72 hours. Wild type STAT3 cells were induced with IL6 (50 ng/ml) (eBioscience) 3 hours before readout; 2 μl of diluted IL6 was dispensed using a Multidrop Nano. Subsequently, cell viability and reporter activity were measured with CellTiter-Fluor and ONE-Glo (Promega) according to the manufacturer’s protocol in a multiplexed manner and read with a PHERAstar FS plate reader (BMG Labtech).

IFNγ activation site (GAS) response element assays

One million HEK293 cells (Promega) were plated on a T25 dish. After 8 h, cells were transfected with 5 μg of a GAS reporter vector (pGL4[luc2P/GAS-RE/Hygro], Promega) and 15 μl FuGENE HD (Promega, cat no. E2311) according to the manufacturer’s instructions. 16 hours post-transfection, cells were plated on pre-plated siRNAs as described above. Cells were incubated on siRNAs for 48 hours before activation with 100 ng/ml IFNγ (Peprotech) after which incubation was continued for 24 hours. Subsequently, cell viability and reporter activity were measured with CellTiter-Fluor and ONE-Glo reagents (Promega), respectively.

Western blotting

300,000 or 250,000 cells were pre-plated on 6-well plates and treated with drugs for 24, 48 or 72 hours. Compounds and their vendors are listed in supplementary file (S3 File). Cells were lysed in RIPA buffer (Cell Signaling Technology) with 1 mM PMSF. Lysates were briefly sonicated (5 × 2 s) to shear the DNA, the amount of protein was determined using a Qubit^™ Fluorometer (Life Technologies; Thermo Fisher Scientific), after which 25–50 μg of protein was loaded per well on 10% SDS-PAGE gels (Bio-Rad). Proteins were run on SDS-PAGE gel for 60 minutes at room temperature (200V) in 1x Tris/Glycine/SDS (cat. 1610772, BioRad) and transferred to methanol activated (1 minute at room temperature) PVDF membranes (Invitrogen) over night at 4°C (90 mA) in Towbin buffer (25 mM Tris, 192 mM glycine) with 20% methanol. The membranes were blocked in TBS-Tween (150 mM sodium chloride, 50 mM TRIS-HCl, pH 7.6 and 0.05% Tween 20) with 5% bovine serum albumin (BSA) for 1 hour at room temperature. Primary antibodies anti-STAT3 (cat. 2972, Cell Signaling Technology), anti-pSTAT3 Y705 (cat. ab76315, Abcam), anti-pSTAT3 S727 (cat. 9134, Cell Signaling Technology) and anti-α-tubulin (cat. T9026, Sigma Aldrich) were diluted 1:1000 in TBS-Tween + 5% BSA. Anti-phospho epitope antibodies were probed overnight at 4°C and total protein antibodies for 1 hour at room temperature on a rocking plate. Membranes were subsequently washed with TBS-Tween (3 × 5 min) and probed with LI-COR compatible secondary antibody at 1:15,000 dilution in TBS-Tween (Odyssey IRdye 800CW and IRdye680) for 1 hour at room temperature. Membranes were visualized with an Odyssey imaging system (LI-COR Biosciences) and ImageJ version 1.47v was used to quantify band intensities.

Data analysis

DSS, a quantitative drug sensitivity score, which is based on the area under the dose response curve, was used to measure drug efficacies [21, 23]. The DSS values were calculated in the Breeze web-based application [24].

Reporter activity of siRNA in primary screens were normalized to cells (negative control, 100%) and siSTAT3 (positive control, 0%) and in validation assay to non-targeting siRNA (negative control, 100%) and siSTAT3 (positive control, 0%). Viability readouts were normalized to non-targeting siRNA and siDEATH.

Statistical analyses were performed with Prism software version 8.0 (GraphPad Software). Differences between responses were calculated by using the Student’s t-test (unpaired t-test). P-values of <0.05 were considered as statistically significant.

Results

STAT3 activation protects cells from cytotoxic drug responses

To test whether STAT3 hyperactivity causes resistance to certain drug classes we used HEK293 cells expressing a firefly luciferase under a STAT3 responsive sis-inducible element (SIE) promoter (HEK293sie cells) and either STAT3(Y640F) mutant or wild type STAT3. Constitutively active mutant STAT3(Y640F) allowed us to selectively measure the effect of drug responses in a state of high STAT3 activation. We tested 526 oncology compounds in drug sensitivity and resistance profiling (DSRT) and measured cell toxicity by a cell-impermeable DNA-binding dye and viability by a cellular ATP detecting reagent in a multiplexed manner after a 72-hour treatment (S1 File). Subsequently, we calculated a drug sensitivity score (DSS) that measures the area under the dose response curve and represents the efficacy and potency for each compound [21, 23]. When we compared the DSS values of STAT3(wt) and STAT3(Y640F) expressing cells we did not see clear differences in the viability readout (Fig 1A). In the cell toxicity readout, on the other hand, we observed that STAT3(Y640F) is protecting cells from cytotoxic responses against several drug classes (Fig 1B), such as a selective inhibitor of exportin-1 (XPO1), microtubule targeting agents, Polo like kinase 1 (PLK1), heat shock protein 90 (HSP90) and cyclin-dependent kinase (CDK) inhibitors (Fig 1C and 1D). This suggests that the constitutively active STAT3 protected cells from the cytotoxic, but not the cytostatic effects of these drug classes.

Fig 1 — (A) In an ATP-based cell viability readout (CellTiter-Glo, CTG) chemoprotection is not perceptible when constitutively active STAT3(Y640F) was compared to STAT3(wt) expressing cells. (B) In a cytotoxicity readout, the constitutively active STAT3(Y640F) protects cells from cytotoxic effects of several drug classes. (C) Dose-response curves of percent toxicity (CellTox Green, CTX) and percent viability (D) of HEK293sie cells expressing STAT3(Y640F) or STAT3(wt). Data from one representative experiment out of two independent experiments shown.

siRNA screening reveals kinases and a phosphatase that regulate oncogenic hyperactive STAT3

To test how hyperactive STAT3 is regulated we designed an siRNA screen using HEK293sie cells stably co-expressing STAT3(Y640F) or STAT3(wt). The STAT3(Y640F)-expressing cells exhibited a constitutive STAT3 reporter activity while the activity in the STAT3(wt)-expressing cells could be induced by IL6 stimulation. The STAT3(Y640F) expressing HEK293sie cells had a stronger reporter signal than IL6-induced STAT3(wt) expressing cells (3.6-fold) (S1 Fig). Using these cells, we performed an siRNA screen where the impact of knockdown of pooled siRNAs (3 siRNAs per gene) targeting protein/lipid kinase and protein phosphatase genes on STAT3 transcriptional activity was assessed (Fig 2A, S2 File). STAT3 silencing, which almost totally abrogated the STAT3-mediated luciferase signal, was used as a positive control (S1 Fig). In addition, we evaluated the role of JAK1 knockdown in the STAT3(wt) vs. STAT3(Y640F) expressing cells. While JAK1 knockdown nearly completely blocked IL6-stimulated STAT3(wt) transcriptional activity, it had only a partial effect on the STAT3(Y640F) signalling (Fig 2B), as has been reported before [9, 18]. From the primary screen consisting of 1,056 genes, we selected 182 genes, which were retested by assessing the effect of the three individual siRNAs in separate wells (Fig 2A, S2 File).

Fig 2 — (A) General distribution of the first screen with final validated hits is highlighted. From each screen, siRNAs reducing cell viability more than 2 times the standard deviation of the negative controls were excluded. In the validation screen, the siRNAs were obtained from separate source. (B) *JAK1* knockdown had a strong effect on STAT3(wt) transcriptional activity, but not in STAT3(Y640F) expressing cells. Reporter activity was normalized to positive (0%) and negative controls (100%), and cell viability (CellTiter-Fluor).

From these, we identified 25 hit candidate genes where at least 2 of the 3 individual siRNAs confirmed. Seven genes validated in a follow-up screen using three additional siRNAs from a different vendor (Fig 3A and 3B and S2 File). Knockdown of the kinase genes CSNK2, CDC7, DDR2, CDK8, PI4KII and PTPRH resulted in inhibition of both STAT3(Y640F) and STAT3(wt) reporter signals (Fig 3A and 3B). Conversely, knockdown of CSK caused a significant increase of reporter activity in STAT3(Y640F), but not in the IL6-induced STAT3(wt) expressing cells, indicating that CSK could be a selective positive regulator of constitutively activated STAT3 signalling.

Fig 3 — (A-B) Hit genes whose knock-down resulted in changed transcriptional activity of either STAT3(Y640F) (A) or STAT3(WT) (B). Gene hits were normalized to siSTAT3 (positive control, 0% transcriptional activity) and non-targeting siRNA (negative control, 100% transcriptional activity). Red and blue lines represent thresholds (2 times standard deviation of control) for activating and inhibiting hits, respectively. Data comes from one representative experiment out of two independent experiments with three technical replicates. Bars represent mean ± SD. (C) Six out of seven hits were selective for STAT3. Only *PI4KII* knockdown inhibited both STAT3:STAT3 and STAT1:STAT1 transcriptional activity. Data comes from three independent experiments in triplicates. Two or three siRNAs were used against each gene.

To determine whether the gene products of the seven hit genes were selectively regulating STAT3 signalling, we generated HEK293 cells that transiently expressed a luciferase reporter for STAT1-driven IFNγ activation site response element (GAS-RE) (Fig 3C). Only knockdown of PI4KII inhibited both STAT3 and STAT1 transcriptional activity. The rest of the knockdowns did either not have a significant effect on IFNγ activated STAT1 (CDC7, CSNK2A1, DDR2, PTPRH) or were non-conclusive (CDK8, CSK).

Small molecule inhibitors of hit kinases regulate STAT3 activity in a dose and time dependent fashion

To further validate the hit kinases, we tested the effects of STAT3 reporter activity by small molecule inhibitors targeting the kinase activity of the hit proteins. We used ruxolitinib, silmitasertib, senexin B, BMS863233 and regorafenib to target JAK1/2, CSNK2, CDK8, CDC7 and DDR2, respectively. All inhibitors induced reduction of STAT3 reporter activity at earlier time points and lower concentrations than those the cause cytotoxic/cytostatic effects (Fig 4A–4J).

Fig 4 — Kinase inhibitor-mediated inhibition of STAT3 transcriptional activity is slower in cells expressing STAT3(Y640F) (A-E) than in cells expressing STAT3(wt) (F-J). STAT3(wt) cells were induced with 50 ng/ml IL6 3 hours prior to the reporter activity readout. Viability was measured in a multiplexed manner using CellTiter-Fluor. Error bars represent data from at least two independent experiments with two technical replicates.

Using these compounds, we also assessed the temporal effects of kinase inhibition. We observed that the reporter activity inhibition occurred more slowly in STAT3(Y640F) expressing cells than the STAT3(wt) cells, suggesting that the STAT3(Y640F) protein de-phosphorylation is slower. However, this could also be due to the difference in assay settings: in the STAT3(wt) cells we blocked de novo activation of STAT3(wt) while in the STAT3(Y640F) case the inhibitors blocked sustained activation. In agreement with the siRNA data, the JAK1/2 inhibitor ruxolitinib did not inhibit mutant STAT3(Y640F) at any time point, whereas inhibition of IL6-induced STAT3(wt) was seen already after 4 hours (Fig 4A and 4F). The STAT3 reporter inhibition by the CSNK2 inhibitor silmitasertib was notably slower in the STAT3(Y640F) than in the STAT3(wt) cells, where a solid reporter inhibition was seen with the STAT3(wt) already after 4 hours and the corresponding response in the STAT3(Y640F) cells was only seen after 24 hours (Fig 4B and 4G). The CDK8 inhibitor senexin B showed slower reduction of reporter activity in STAT3(Y640F) than in IL6 induced STAT3(wt) expressing cells (Fig 4C and 4H). A similar result was seen with another CDK8 inhibitor; senexin A (S2 Fig). The CDC7 inhibitor BMS863233 led to a slow inhibition of STAT3 activity in both cell lines possibly indicating regulation at the transcriptional level. Due to the lack of a selective DDR2 inhibitor, we used the multikinase inhibitor regorafenib, which is known to be a potent DDR2 inhibitor [25]. Regorafenib has also been previously proposed to inhibit STAT3 through activation of SHP1 [26]. However, SHP1 (PTPN6) is primarily expressed in hematopoietic cells and we did not detect significant expression from RNA sequencing of our cells. Regorafenib was only the compound that clearly showed stronger potency towards STAT3(Y640F) expressing cells at 72 hours. Similar to silmitasertib, senexin B and BMS863233, regorafenib treatment resulted in faster decrease of STAT3 reporter activity in IL6-induced STAT3(wt) expressing cells than in STAT3(Y640F) cells.

We did not have potent or selective inhibitors against PTPRH, PI4KII and CSK and hence we could not validate these hits with small molecules. Based on the results from the primary siRNA screen on STAT3(Y640F) expressing cells, (S3 Fig) we hypothesized that CSK regulates STAT3 through inhibition of Src family kinases (SFKs) that in turn can phosphorylate and thus activate STAT3. SFK inhibition could therefore reduce transcriptional activity of STAT3(wt) and STAT3(Y640F) (S4 Fig). In agreement with this hypothesis, we observed that SFK inhibitors (saracatinib, CCT196969, dasatinib and A419259) resulted in stronger reporter activity reduction after 72-hour treatment in STAT3(Y640F) than in wild type STAT3 expressing cells, but also affected cell viability in both cell models (S4 Fig). The effect of SFK inhibition on cell viability was also observed in the siRNA screens and hence, the SFKs had been excluded as hits at various stages of the siRNA screening and validation path (Fig 2B, S3 Fig). We observed that knockdowns of SRC and LCK caused inhibition of transcriptional activity in STAT3(Y640F) expressing cells, but from these only SRC was highly expressed in HEK293sieSTAT3(Y640F) cells, suggesting that in our model, CSK may regulate STAT3(Y640F) through inhibition of SRC (S3 Fig).

Next, we investigated the mechanism of action of the selected kinase inhibitors and tested the effect of the drugs on STAT3 activity by measuring levels of phosphorylated Y705 STAT3 after compound treatment (Fig 5). We observed that senexin A, senexin B, regorafenib and BMS863233 caused reduction of pY705 in STAT3(Y640F) cells, but none of the compounds were able to completely abolish pY705. As expected, JAK1/2 inhibition with 1 μM ruxolitinib did not markedly reduce pY705 STAT3 level in STAT3(Y640F) expressing cells after 48 or 72 hours (Fig 5), but had a strong inhibitory effect on STAT3(wt) Y705 phosphorylation already after 4 hours (S5 Fig). We noted that even with as high a concentration as 1 μM, 48 to 72-hour treatment with the CSNK2 inhibitor silmitasertib did not cause as strong a reduction in pY705 levels of STAT3(Y640F) cells as previously published [27] (Fig 5). However, silmitasertib reduced S727 phosphorylation in STAT3(Y640F) expressing cells after 48-hour perturbations (Fig 5). The inhibition of CDK8 and CDC7 caused the strongest reduction of both pY705- and pS727-STAT3 in STAT3(Y640F) expressing cells whereas in IL6-induced STAT3(wt) expressing cells, only the JAK1/2 inhibitor reduced pY705-STAT3 and none of the inhibitors had an effect on pS727-STAT3. In addition, CDK8 inhibition affected the levels of total STAT3 protein both in STAT3(wt) and STAT3(Y640F) cells (Fig 5). The hit kinases and the inhibitors used are summarized in Fig 6.

Fig 5 — pY705-, pS727- and total-STAT3 levels in STAT3(Y640F) expressing (A-D) and IL6-induced STAT3(wt) (E-H) cells after 48-72-hour perturbations with the kinase inhibitors. pY705 and pS747-STAT3 are blotted on individual membranes and normalized to total-STAT3 from the same membrane. their All the compounds were used at 1 μM concentration. Mean ± SD. Each dot represents data from an independent experiment. P-values were calculated using Student’s t-test (unpaired).

Discussion

In this study, we sought to identify new druggable approaches to target STAT3 hyperactivity in cancers. First, to study whether hyperactive STAT3 influences therapeutic responses, we performed drug screening in cells expressing a constitutively active mutant STAT3. We discovered that STAT3 hyperactivity protects cells from undergoing cell death and observed STAT3-mediated cytoprotection towards multiple antimitotic as well as pro-apoptotic drugs. This was seen as an effect of reducing apoptotic, but not cytostatic effects. The lack of difference in cytostatic effects in STAT3(wt) and STAT3(Y640F) cells is in agreement with our previous report showing that in a strong viability response (with relatively fast-growing cells), it is often difficult to separate cytotoxic from cytostatic responses [22]. The observed cytostatic effects are in agreement with previous studies showing that STAT3 can protect cancer cells from the apoptotic effects of many individual drugs: STAT3 activity controls the expression of pro-survival genes such as MCL1, BCL2 and BCL2L1 [28, 29]. STAT3 activity can also upregulate members of the IAP protein family proteins such as survivin that prevents caspase activity [30] and extrinsically supress FAS ligand mediated apoptosis [31]. Furthermore, STAT3 can play a role in regulating multidrug resistance gene expression [32].

We observed STAT3-mediated cytoprotection to several microtubule targeting agents. STAT3 activity has previously been associated with paclitaxel resistance in ovarian and colorectal cancer cell lines [33, 34]. Docetaxel resistance have been linked to constitutive STAT3 activation and elevated survivin expression in breast cancer [35]. STAT3 and PLK1 are known to control each other's transcription in a positive feedback loop resulting in cancer cell survival, proliferation and resistance to apoptosis [36]. Together this supports the concept that constitutive STAT3 activity is protecting cancer cells from apoptotic effects of mitotic inhibition. Cells expressing constitutively active STAT3 were also protected from cytotoxic effects of the exportin-1 inhibitor selinexor. exportin-1 inhibition with the selinexor analogue KPT-276 has been shown to repress STAT3 activation and survivin transcription in breast cancer cell lines [37].

Under normal conditions STAT3 signalling can be regulated by several tyrosine kinases and their upstream/extrinsic regulators (i.e. IL6, IL10, LIF, EGF). Widely known STAT3 activators include JAK family proteins, receptor tyrosine kinases such as EGFR and MET and non-receptor tyrosine kinases such as SFKs and Abl [38]. Negative regulators include SOCS that inhibit STAT3 phosphorylation by blocking the enzymatic activity of JAK or by competing with STAT3 of binding to the IL6 receptor complex [39, 40], protein tyrosine phosphatases that dephosphorylate STAT3 (such as PTPRT, PTPRD, SHP2, SHP1 and PTPN9), which dephosphorylate STAT3 [41], and PIAS proteins that block the DNA binding of STAT3 [42].

We found six kinases (CSK, CSNK2, CDC7, DDR2, CDK8, PI4KII) and a phosphatase (PTPRH) that indirectly regulate either oncogenic hyperactive STAT3(Y640F) mutant and/or IL6-induced wild type STAT3 in HEK293sie cells. From these, CSNK2 [27, 43], CDK8 [44] and CSK [45, 46] have been previously linked to STAT3 signalling. CDC7, DDR2 (also CD167b), PI4KII and PTPRH, on other hand, have not previously been associated with STAT activity. The identified kinase/phosphatase regulators were similar between STAT3(Y640F) and IL6-induced STAT3(wt), with the exception of CSK knockdown that resulted in an increased activation of STAT3(Y640F) mutant, but did not have a significant effect on IL6-induced STAT3(wt).

SFKs are known to regulate STAT3 phosphorylation independently of JAKs [47–49] and SFKs are negatively regulated by CSK by phosphorylation of a tyrosine residue at the C-terminal tail site [50]. Our data showed that knockdown of CSK resulted in upregulation of STAT3(Y640F) transcriptional activity, but did not have a clear effect on IL6-induced wild type STAT3 activity. This suggests that CSK may selectively regulate active STAT3(Y640F), but not IL6-induced wild type STAT3. In our cell models, inhibition of SFKs with small-molecule inhibitors reduced STAT3 reporter activity more efficiently in STAT3(Y640F) than in STAT3(wt) expressing cells, but these inhibitors also had a strong effect on cell viability. In agreement with our results, it has been shown that SFK inhibition decreases STAT3(Y640F) activity in Hep3B cells [9].

CSNK2 knockdown resulted in inhibition of both STAT3(Y640F) and IL6-induced wild type STAT3 reporter activity in our experiments. The CSNK2 inhibitor 4,5,6,7-tetrabromobenzotriazole (TBB) has previously been shown to inhibit both IL6 and Hyper-IL6 induced STAT3 phosphorylation in Ba/F3 cells [27]. Furthermore, CSNK2 activity was shown to be required for STAT3(Y640F) activity [27]. However, in our assays CSNK2 inhibition with silmitasertib did not result to strong reduction of pY705 or pS727-STAT3 in either STAT3(Y640F) or IL6-induced STAT3(wt) cells suggesting that this could be cell line dependent.

CDK8 is a nuclear kinase that is directly involved in transcriptional regulation and is part of the Mediator complex [51]. We showed that CDK8 knockdown reduces transcriptional activity of both hyperactive mutant and IL6-induced wild type STAT3. Nuclear CDK8 has been shown to regulate STAT3 and STAT1 S727 phosphorylation. It has been suggested that nuclear CDK8 activity and S727 phosphorylation increases the transcriptional activity of both STAT3 and STAT1 [44, 52–54]. Bancerek and coworkers showed that CDK8 knockdown results in reduced levels of STAT3 S727 phosphorylation when induced with IFNβ, but they did not see changes in STAT3 Y705 phosphorylation [44]. We observed that CDK8 inhibition with 1 μM senexin A or B results in reduced Y705- and S727-STAT3 phosphorylation in STAT3(Y640F) cells, but not in IL6-induced STAT3(wt) cells. Furthermore, senexin A and B reduced total-STAT3 protein in both cell lines.

Our siRNA screen also identified three kinases and one phosphatase (CDC7, PI4KII, DDR2 and PTPRH) that have not previously been linked directly to STAT3. However, another discoidin homology domain-consisting RTK, DDR1, which is closely related to DDR2 has been linked to the regulation of STAT5 [55]. While PTPRH (SAP-1) has not been associated with STAT activity before, several other tyrosine phosphatases such as PTPN11 (SHP2), PTPRD, PTPRT (PTPrho) and PTPN6 (SHP1) have previously been found to act as negative regulators of STAT3 by dephosphorylating active STAT3 [41]. Our results on PTPRH suggest the opposite function, perhaps through removal of STAT3-inhibitory phosphorylations. PTPRH has been shown to be overexpressed in different adenocarcinomas, including colon, pancreas and non-small cell lung cancer [56–58].

In conclusion, our data shows that STAT3 hyperactivity protects cells from cytotoxic effects by several mitotic and pro-apoptotic drugs, arguing that STAT3 inhibition could be used to enhance the effects of standard chemotherapy. In addition, our data suggests that indirect targeting of STAT3 may be a feasible way to reduce constitutively active STAT3 in cancers instead of direct STAT3 inhibition. By using cells expressing the constitutively active STAT3(Y640F) mutant as a tool to study hyperactive STAT3, we show that small molecule inhibitors targeting CSNK2, CDK8, DDR2 and CDC7 may be used to target hyperactive STAT3 in cancer cells and warrant further validation of these targets in cancer models with STAT3 hyperactivity.

Supporting information

S1 Fig. Luciferase activities of controls in primary siRNA screen.

STAT3(Y640F) and IL6 induced STAT3(wt) expressing cells had 2.6-fold change in mean values of non-targeting siRNA (siASN) and 3.6-fold change in mean values of only transfection reagent (cells) treated cells. Bars represent mean and standard deviation of controls on all assay plates in one screen (total 24 technical replicates per each control). Cells and siSTAT3.7 were used to normalize the primary screen.

(TIF)

Click here for additional data file.^{(316.3KB, tif)}

S2 Fig. CDK8 inhibition in STAT3 expressing cells.

CDK8 inhibitor senexin A inhibits both STAT3(Y640F) and STAT3(wt) dose and time dependently. Dots are mean and error bars represent data from two independent experiments with two technical replicates.

(TIF)

Click here for additional data file.^{(386.8KB, tif)}

S3 Fig. Src family kinases in STAT3 expressing cells.

Effect of CSK and Src family kinases (SFKs) knockdowns on STAT3 reporter activity and viability of HEK293sieSTAT3(Y640F) (A) and IL6 induced HEK293sieSTAT3(wt) (B).

(TIF)

Click here for additional data file.^{(590.4KB, tif)}

S4 Fig. Effect of Src inhibitors on reporter activity of IL6 induced STAT3(wt) and STAT3(Y640F) expressing cells.

Src inhibitors (saracatinib, CCT196969, dasatinib and A419259) results in stronger reporter activity reduction after 72h in STAT3(Y640F) that in wild type STAT3 expressing. Dots are mean and error bars represent data from at least two independent experiments with two technical replicates.

(TIF)

Click here for additional data file.^{(936.9KB, tif)}

S5 Fig. Short term perturbation on Y705-phosporylation in IL6 induced STAT3(wt) and STAT3(Y640F) expressing cells.

Four-hour perturbation with JAK1/2 inhibitor ruxolitinib decreases Y705-phosphorylation of STAT3 in IL6 induced STAT3(wt) expressing cells.

(TIF)

Click here for additional data file.^{(700.1KB, tif)}

S1 File. Supporting file for Fig 1.

Drug sensitivity profiling data for viability (CTG) and toxicity (CTX) readouts, including in house ID, drug name, analysis name, DSS, IC50, drug concentration range (min/max conc.), percent inhibition value at each tested concentration (D1-D5), graph etc. of HEK293sieSTAT3(Y640) and HEK293sieSTAT3(wt) cells.

(XLSX)

Click here for additional data file.^{(14.3MB, xlsx)}

S2 File. Supporting file for Figs 2 and 3.

Normalized siRNA screening data for each screen including RefSeq accession number, gene ID, siRNA ID, full gene name gene symbol, percentage reporter activity normalized to positive (0%) and negative (100%) control and percentage cell viability normalized to positive (0%) and negative (100%) control. Each screen is on separate sheet.

(XLSX)

Click here for additional data file.^{(313.7KB, xlsx)}

S3 File. Supporting file for materials and methods.

Vendor information of siRNAs and siRNA screening controls used in Fig 3 and small molecule inhibitors used in Figs 4 and 5 and S1, S3, S4 Figs.

(XLSX)

Click here for additional data file.^{(13.2KB, xlsx)}

S1 Raw images. Raw Western blot scans.

Whole membranes of all shown and analyzed Western blot membranes with lane labelling. Lanes with “X” and/or “italics” are not included in the paper. Blots are supporting Fig 5 and S4 Fig.

(PDF)

Click here for additional data file.^{(9.2MB, pdf)}

Acknowledgments

We gratefully thank Laura Turunen, Jani Saarela, Carina von Schantz-Fant and other members of the FIMM High Throughput Biomedicine unit for technical assistance with regards to screening.

Data Availability

All relevant data are within the paper and its Supporting Information files.

Funding Statement

This project was supported by grants from: Academy of Finland (277293) (KW) https://www.aka.fi/en Sigrid Jusélius Foundation (KW) https://sigridjuselius.fi/en/ Cancer Society of Finland (KW) https://www.cancersociety.fi/ In addition, this project was supported by University of Helsinki/Biocenter Finland Research infrastructure funds. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

References

1.Corcoran RB, Contino G, Deshpande V, Tzatsos A, Conrad C, Benes CH, et al. STAT3 plays a critical role in KRAS-induced pancreatic tumorigenesis. Cancer research. 2011;71(14):5020–9. 10.1158/0008-5472.CAN-11-0908 [DOI] [PMC free article] [PubMed] [Google Scholar]
2.Lee HJ, Zhuang G, Cao Y, Du P, Kim HJ, Settleman J. Drug resistance via feedback activation of Stat3 in oncogene-addicted cancer cells. Cancer cell. 2014;26(2):207–21. 10.1016/j.ccr.2014.05.019 . [DOI] [PubMed] [Google Scholar]
3.Yu H, Lee H, Herrmann A, Buettner R, Jove R. Revisiting STAT3 signalling in cancer: new and unexpected biological functions. Nature reviews Cancer. 2014;14(11):736–46. 10.1038/nrc3818 [DOI] [PubMed] [Google Scholar]
4.Yu H, Pardoll D, Jove R. STATs in cancer inflammation and immunity: a leading role for STAT3. Nature reviews Cancer. 2009;9(11):798–809. 10.1038/nrc2734 . [DOI] [PMC free article] [PubMed] [Google Scholar]
5.Johnson DE, O'Keefe RA, Grandis JR. Targeting the IL-6/JAK/STAT3 signalling axis in cancer. Nat Rev Clin Oncol. 2018;15(4):234–48. Epub 2018/02/07. 10.1038/nrclinonc.2018.8 [DOI] [PMC free article] [PubMed] [Google Scholar]
6.Jerez A, Clemente MJ, Makishima H, Koskela H, Leblanc F, Peng Ng K, et al. STAT3 mutations unify the pathogenesis of chronic lymphoproliferative disorders of NK cells and T-cell large granular lymphocyte leukemia. Blood. 2012;120(15):3048–57. 10.1182/blood-2012-06-435297 [DOI] [PMC free article] [PubMed] [Google Scholar]
7.Koskela HL, Eldfors S, Ellonen P, van Adrichem AJ, Kuusanmaki H, Andersson EI, et al. Somatic STAT3 mutations in large granular lymphocytic leukemia. The New England journal of medicine. 2012;366(20):1905–13. 10.1056/NEJMoa1114885 [DOI] [PMC free article] [PubMed] [Google Scholar]
8.Ohgami RS, Ma L, Merker JD, Martinez B, Zehnder JL, Arber DA. STAT3 mutations are frequent in CD30+ T-cell lymphomas and T-cell large granular lymphocytic leukemia. Leukemia. 2013;27(11):2244–7. 10.1038/leu.2013.104 . [DOI] [PubMed] [Google Scholar]
9.Pilati C, Amessou M, Bihl MP, Balabaud C, Nhieu JT, Paradis V, et al. Somatic mutations activating STAT3 in human inflammatory hepatocellular adenomas. The Journal of experimental medicine. 2011;208(7):1359–66. 10.1084/jem.20110283 [DOI] [PMC free article] [PubMed] [Google Scholar]
10.Dufva O, Kankainen M, Kelkka T, Sekiguchi N, Awad SA, Eldfors S, et al. Aggressive natural killer-cell leukemia mutational landscape and drug profiling highlight JAK-STAT signaling as therapeutic target. Nat Commun. 2018;9(1):1567 Epub 2018/04/21. 10.1038/s41467-018-03987-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
11.de Araujo ED, Orlova A, Neubauer HA, Bajusz D, Seo HS, Dhe-Paganon S, et al. Structural Implications of STAT3 and STAT5 SH2 Domain Mutations. Cancers (Basel). 2019;11(11). Epub 2019/11/14. 10.3390/cancers11111757 [DOI] [PMC free article] [PubMed] [Google Scholar]
12.Kanna R, Choudhary G, Ramachandra N, Steidl U, Verma A, Shastri A. STAT3 inhibition as a therapeutic strategy for leukemia. Leuk Lymphoma. 2018;59(9):2068–74. Epub 2017/11/23. 10.1080/10428194.2017.1397668 . [DOI] [PubMed] [Google Scholar]
13.Laudisi F, Cherubini F, Monteleone G, Stolfi C. STAT3 Interactors as Potential Therapeutic Targets for Cancer Treatment. Int J Mol Sci. 2018;19(6). Epub 2018/06/20. 10.3390/ijms19061787 [DOI] [PMC free article] [PubMed] [Google Scholar]
14.Beebe JD, Liu JY, Zhang JT. Two decades of research in discovery of anticancer drugs targeting STAT3, how close are we? Pharmacol Ther. 2018;191:74–91. Epub 2018/06/23. 10.1016/j.pharmthera.2018.06.006 . [DOI] [PubMed] [Google Scholar]
15.Kolosenko I, Yu Y, Busker S, Dyczynski M, Liu JP, Haraldsson M, et al. Identification of novel small molecules that inhibit STAT3-dependent transcription and function. Plos One. 2017;12(6). ARTN e0178844 10.1371/journal.pone.0178844 WOS:000404118300018. [DOI] [PMC free article] [PubMed] [Google Scholar]
16.Ali AM, Gomez-Biagi RF, Rosa DA, Lai PS, Heaton WL, Park JS, et al. Disarming an Electrophilic Warhead: Retaining Potency in Tyrosine Kinase Inhibitor (TKI)-Resistant CML Lines While Circumventing Pharmacokinetic Liabilities. Chemmedchem. 2016;11(8):850–61. 10.1002/cmdc.201600021 WOS:000374693500014. [DOI] [PMC free article] [PubMed] [Google Scholar]
17.McMurray JS. A new small-molecule Stat3 inhibitor. Chem Biol. 2006;13(11):1123–4. Epub 2006/11/23. 10.1016/j.chembiol.2006.11.001 . [DOI] [PubMed] [Google Scholar]
18.Kuusanmaki H, Dufva O, Parri E, van Adrichem AJ, Rajala H, Majumder MM, et al. Drug sensitivity profiling identifies potential therapies for lymphoproliferative disorders with overactive JAK/STAT3 signaling. Oncotarget. 2017;8(57):97516–27. Epub 2017/12/13. 10.18632/oncotarget.22178 [DOI] [PMC free article] [PubMed] [Google Scholar]
19.Choppa PC, Vojdani A, Tagle C, Andrin R, Magtoto L. Multiplex PCR for the detection of Mycoplasma fermentans, M. hominis and M. penetrans in cell cultures and blood samples of patients with chronic fatigue syndrome. Mol Cell Probes. 1998;12(5):301–8. Epub 1998/10/21. 10.1006/mcpr.1998.0186 . [DOI] [PubMed] [Google Scholar]
20.Vojdani A, Choppa PC, Tagle C, Andrin R, Samimi B, Lapp CW. Detection of Mycoplasma genus and Mycoplasma fermentans by PCR in patients with Chronic Fatigue Syndrome. FEMS Immunol Med Microbiol. 1998;22(4):355–65. Epub 1999/01/08. 10.1111/j.1574-695X.1998.tb01226.x . [DOI] [PubMed] [Google Scholar]
21.Pemovska T, Kontro M, Yadav B, Edgren H, Eldfors S, Szwajda A, et al. Individualized systems medicine strategy to tailor treatments for patients with chemorefractory acute myeloid leukemia. Cancer discovery. 2013;3(12):1416–29. 10.1158/2159-8290.CD-13-0350 . [DOI] [PubMed] [Google Scholar]
22.Gautam P, Karhinen L, Szwajda A, Jha SK, Yadav B, Aittokallio T, et al. Identification of selective cytotoxic and synthetic lethal drug responses in triple negative breast cancer cells. Molecular cancer. 2016;15(1):34 Epub 2016/05/12. 10.1186/s12943-016-0517-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
23.Yadav B, Pemovska T, Szwajda A, Kulesskiy E, Kontro M, Karjalainen R, et al. Quantitative scoring of differential drug sensitivity for individually optimized anticancer therapies. Scientific reports. 2014;4:5193 10.1038/srep05193 [DOI] [PMC free article] [PubMed] [Google Scholar]
24.Potdar S, Ianevski A, Mpindi J-P, Bychkov D, Fiere C, Ianevski P, et al. Breeze: an integrated quality control and data analysis application for high-throughput drug screening. Bioinformatics. 2020. 10.1093/bioinformatics/btaa138 [DOI] [PMC free article] [PubMed] [Google Scholar]
25.Wilhelm SM, Dumas J, Adnane L, Lynch M, Carter CA, Schutz G, et al. Regorafenib (BAY 73–4506): a new oral multikinase inhibitor of angiogenic, stromal and oncogenic receptor tyrosine kinases with potent preclinical antitumor activity. Int J Cancer. 2011;129(1):245–55. Epub 2010/12/21. 10.1002/ijc.25864 . [DOI] [PubMed] [Google Scholar]
26.Fan LC, Teng HW, Shiau CW, Lin H, Hung MH, Chen YL, et al. SHP-1 is a target of regorafenib in colorectal cancer. Oncotarget. 2014;5(15):6243–51. 10.18632/oncotarget.2191 WOS:000347919500031. [DOI] [PMC free article] [PubMed] [Google Scholar]
27.Aparicio-Siegmund S, Sommer J, Monhasery N, Schwanbeck R, Keil E, Finkenstadt D, et al. Inhibition of protein kinase II (CK2) prevents induced signal transducer and activator of transcription (STAT) 1/3 and constitutive STAT3 activation. Oncotarget. 2014;5(8):2131–48. Epub 2014/04/20. 10.18632/oncotarget.1852 [DOI] [PMC free article] [PubMed] [Google Scholar]
28.Bhattacharya S, Ray RM, Johnson LR. STAT3-mediated transcription of Bcl-2, Mcl-1 and c-IAP2 prevents apoptosis in polyamine-depleted cells. Biochem J. 2005;392(Pt 2):335–44. Epub 2005/07/29. 10.1042/BJ20050465 [DOI] [PMC free article] [PubMed] [Google Scholar]
29.Epling-Burnette PK, Liu JH, Catlett-Falcone R, Turkson J, Oshiro M, Kothapalli R, et al. Inhibition of STAT3 signaling leads to apoptosis of leukemic large granular lymphocytes and decreased Mcl-1 expression. The Journal of clinical investigation. 2001;107(3):351–62. 10.1172/JCI9940 [DOI] [PMC free article] [PubMed] [Google Scholar]
30.O'Connor DS, Grossman D, Plescia J, Li F, Zhang H, Villa A, et al. Regulation of apoptosis at cell division by p34cdc2 phosphorylation of survivin. Proc Natl Acad Sci U S A. 2000;97(24):13103–7. Epub 2000/11/09. 10.1073/pnas.240390697 [DOI] [PMC free article] [PubMed] [Google Scholar]
31.Ivanov VN, Bhoumik A, Krasilnikov M, Raz R, Owen-Schaub LB, Levy D, et al. Cooperation between STAT3 and c-jun suppresses Fas transcription. Mol Cell. 2001;7(3):517–28. Epub 2001/07/21. 10.1016/s1097-2765(01)00199-x . [DOI] [PubMed] [Google Scholar]
32.Zhang X, Xiao W, Wang L, Tian Z, Zhang J. Deactivation of signal transducer and activator of transcription 3 reverses chemotherapeutics resistance of leukemia cells via down-regulating P-gp. Plos One. 2011;6(6):e20965 Epub 2011/06/17. 10.1371/journal.pone.0020965 [DOI] [PMC free article] [PubMed] [Google Scholar]
33.Duan Z, Foster R, Bell DA, Mahoney J, Wolak K, Vaidya A, et al. Signal transducers and activators of transcription 3 pathway activation in drug-resistant ovarian cancer. Clin Cancer Res. 2006;12(17):5055–63. Epub 2006/09/05. 10.1158/1078-0432.CCR-06-0861 . [DOI] [PubMed] [Google Scholar]
34.Zhang Z, Wang A, Li H, Zhi H, Lu F. STAT3-dependent TXNDC17 expression mediates Taxol resistance through inducing autophagy in human colorectal cancer cells. Gene. 2016;584(1):75–82. Epub 2016/03/16. 10.1016/j.gene.2016.03.012 . [DOI] [PubMed] [Google Scholar]
35.Gritsko T, Williams A, Turkson J, Kaneko S, Bowman T, Huang M, et al. Persistent activation of stat3 signaling induces survivin gene expression and confers resistance to apoptosis in human breast cancer cells. Clin Cancer Res. 2006;12(1):11–9. Epub 2006/01/07. 10.1158/1078-0432.CCR-04-1752 . [DOI] [PubMed] [Google Scholar]
36.Zhang Y, Du XL, Wang CJ, Lin DC, Ruan X, Feng YB, et al. Reciprocal activation between PLK1 and Stat3 contributes to survival and proliferation of esophageal cancer cells. Gastroenterology. 2012;142(3):521–30 e3. Epub 2011/11/24. 10.1053/j.gastro.2011.11.023 . [DOI] [PubMed] [Google Scholar]
37.Cheng Y, Holloway MP, Nguyen K, McCauley D, Landesman Y, Kauffman MG, et al. XPO1 (CRM1) Inhibition Represses STAT3 Activation to Drive a Survivin-Dependent Oncogenic Switch in Triple-Negative Breast Cancer. Mol Cancer Ther. 2014;13(3):675–86. 10.1158/1535-7163.MCT-13-0416 WOS:000334318300012. [DOI] [PMC free article] [PubMed] [Google Scholar]
38.Song L, Turkson J, Karras JG, Jove R, Haura EB. Activation of Stat3 by receptor tyrosine kinases and cytokines regulates survival in human non-small cell carcinoma cells. Oncogene. 2003;22(27):4150–65. Epub 2003/07/02. 10.1038/sj.onc.1206479 . [DOI] [PubMed] [Google Scholar]
39.Heinrich PC, Behrmann I, Haan S, Hermanns HM, Muller-Newen G, Schaper F. Principles of interleukin (IL)-6-type cytokine signalling and its regulation. Biochem J. 2003;374(Pt 1):1–20. Epub 2003/05/30. 10.1042/BJ20030407 [DOI] [PMC free article] [PubMed] [Google Scholar]
40.Croker BA, Kiu H, Nicholson SE. SOCS regulation of the JAK/STAT signalling pathway. Semin Cell Dev Biol. 2008;19(4):414–22. Epub 2008/08/19. 10.1016/j.semcdb.2008.07.010 [DOI] [PMC free article] [PubMed] [Google Scholar]
41.Wu ML, Song DY, Li H, Yang Y, Ma XD, Deng S, et al. Negative regulators of STAT3 signaling pathway in cancers. Cancer Manag Res. 2019;11:4957–69. 10.2147/CMAR.S206175 WOS:000469770700001. [DOI] [PMC free article] [PubMed] [Google Scholar]
42.Yuan J, Zhang F, Niu R. Multiple regulation pathways and pivotal biological functions of STAT3 in cancer. Scientific reports. 2015;5:17663 Epub 2015/12/04. 10.1038/srep17663 [DOI] [PMC free article] [PubMed] [Google Scholar]
43.Rozovski U, Harris DM, Li P, Liu Z, Jain P, Veletic I, et al. Constitutive Phosphorylation of STAT3 by the CK2-BLNK-CD5 Complex. Mol Cancer Res. 2017;15(5):610–8. Epub 2017/01/29. 10.1158/1541-7786.MCR-16-0291 [DOI] [PMC free article] [PubMed] [Google Scholar]
44.Bancerek J, Poss ZC, Steinparzer I, Sedlyarov V, Pfaffenwimmer T, Mikulic I, et al. CDK8 kinase phosphorylates transcription factor STAT1 to selectively regulate the interferon response. Immunity. 2013;38(2):250–62. Epub 2013/01/29. 10.1016/j.immuni.2012.10.017 [DOI] [PMC free article] [PubMed] [Google Scholar]
45.Aki D, Mashima R, Saeki K, Minoda Y, Yamauchi M, Yoshimura A. Modulation of TLR signalling by the C-terminal Src kinase (Csk) in macrophages. Genes Cells. 2005;10(4):357–68. Epub 2005/03/19. 10.1111/j.1365-2443.2005.00839.x . [DOI] [PubMed] [Google Scholar]
46.Read RD, Bach EA, Cagan RL. Drosophila C-terminal Src kinase negatively regulates organ growth and cell proliferation through inhibition of the Src, Jun N-terminal kinase, and STAT pathways. Molecular and Cellular Biology. 2004;24(15):6676–89. 10.1128/MCB.24.15.6676-6689.2004 WOS:000222764800014. [DOI] [PMC free article] [PubMed] [Google Scholar]
47.Schreiner SJ, Schiavone AP, Smithgall TE. Activation of STAT3 by the Src family kinase Hck requires a functional SH3 domain. The Journal of biological chemistry. 2002;277(47):45680–7. Epub 2002/09/24. 10.1074/jbc.M204255200 . [DOI] [PubMed] [Google Scholar]
48.Bowman T, Garcia R, Turkson J, Jove R. STATs in oncogenesis. Oncogene. 2000;19(21):2474–88. Epub 2000/06/13. 10.1038/sj.onc.1203527 . [DOI] [PubMed] [Google Scholar]
49.Bromberg JF, Wrzeszczynska MH, Devgan G, Zhao YX, Pestell RG, Albanese C, et al. Stat3 as an oncogene. Cell. 1999;98(3):295–303. 10.1016/s0092-8674(00)81959-5 WOS:000081950300005. [DOI] [PubMed] [Google Scholar]
50.Chong YP, Chan AS, Chan KC, Williamson NA, Lerner EC, Smithgall TE, et al. C-terminal Src kinase-homologous kinase (CHK), a unique inhibitor inactivating multiple active conformations of Src family tyrosine kinases. The Journal of biological chemistry. 2006;281(44):32988–99. Epub 2006/09/09. 10.1074/jbc.M602951200 . [DOI] [PubMed] [Google Scholar]
51.Knuesel MT, Meyer KD, Donner AJ, Espinosa JM, Taatjes DJ. The human CDK8 subcomplex is a histone kinase that requires Med12 for activity and can function independently of mediator. Mol Cell Biol. 2009;29(3):650–61. Epub 2008/12/03. 10.1128/MCB.00993-08 [DOI] [PMC free article] [PubMed] [Google Scholar]
52.Staab J, Herrmann-Lingen C, Meyer T. CDK8 as the STAT1 serine 727 kinase? JAKSTAT. 2013;2(3):e24275 Epub 2013/09/27. 10.4161/jkst.24275 [DOI] [PMC free article] [PubMed] [Google Scholar]
53.Lodige I, Marg A, Wiesner B, Malecova B, Oelgeschlager T, Vinkemeier U. Nuclear export determines the cytokine sensitivity of STAT transcription factors. The Journal of biological chemistry. 2005;280(52):43087–99. Epub 2005/10/01. 10.1074/jbc.M509180200 . [DOI] [PubMed] [Google Scholar]
54.Wen Z, Zhong Z, Darnell JE Jr. Maximal activation of transcription by Stat1 and Stat3 requires both tyrosine and serine phosphorylation. Cell. 1995;82(2):241–50. Epub 1995/07/28. 10.1016/0092-8674(95)90311-9 . [DOI] [PubMed] [Google Scholar]
55.Faraci-Orf E, McFadden C, Vogel WF. DDR1 signaling is essential to sustain Stat5 function during lactogenesis. J Cell Biochem. 2006;97(1):109–21. Epub 2005/09/17. 10.1002/jcb.20618 . [DOI] [PubMed] [Google Scholar]
56.Matozaki T, Suzuki T, Uchida T, Inazawa J, Ariyama T, Matsuda K, et al. Molecular cloning of a human transmembrane-type protein tyrosine phosphatase and its expression in gastrointestinal cancers. The Journal of biological chemistry. 1994;269(3):2075–81. Epub 1994/01/21. . [PubMed] [Google Scholar]
57.Sato T, Soejima K, Arai E, Hamamoto J, Yasuda H, Arai D, et al. Prognostic implication of PTPRH hypomethylation in non-small cell lung cancer. Oncol Rep. 2015;34(3):1137–45. Epub 2015/07/03. 10.3892/or.2015.4082 [DOI] [PMC free article] [PubMed] [Google Scholar]
58.Seo Y, Matozaki T, Tsuda M, Hayashi Y, Itoh H, Kasuga M. Overexpression of SAP-1, a transmembrane-type protein tyrosine phosphatase, in human colorectal cancers. Biochem Biophys Res Commun. 1997;231(3):705–11. Epub 1997/02/24. 10.1006/bbrc.1997.6139 . [DOI] [PubMed] [Google Scholar]

PLoS One. doi: 10.1371/journal.pone.0230819.r001

Decision Letter 0

Alessandro Datti

2 Jan 2020

PONE-D-19-33342

Identification of novel regulators of STAT3 activity

PLOS ONE

Dear Dr. Wennerberg,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Remarks and critiques advanced by three Reviewers are appended to this letter.

In essence, there is a need for a better understanding of the quality and significance of the results shown. This can be achieved by simply amending the text with requested clarifications, additional comments, inclusion of new references and, should it be the case, a convincing rebuttal. Response to point 1 (Reviewer 1) can be omitted as the Discussion paragraph (line 348 to line 357) can be considered satisfactory. With regard to point 2 (Reviewer 1), please consider combining Fig. S5 with Fig.5 (and properly rearrange the text) should that be a more effective option to present available results.

We would appreciate receiving your revised manuscript by Feb 16 2020 11:59PM. When you are ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter.

To enhance the reproducibility of your results, we recommend that if applicable you deposit your laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. For instructions see: http://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). This letter should be uploaded as separate file and labeled 'Response to Reviewers'.
A marked-up copy of your manuscript that highlights changes made to the original version. This file should be uploaded as separate file and labeled 'Revised Manuscript with Track Changes'.
An unmarked version of your revised paper without tracked changes. This file should be uploaded as separate file and labeled 'Manuscript'.

Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out.

We look forward to receiving your revised manuscript.

Kind regards,

Alessandro Datti, Ph.D.

Academic Editor

PLOS ONE

Journal Requirements:

1. When submitting your revision, we need you to address these additional requirements.

Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

http://www.journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and http://www.journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

2. PLOS ONE now requires that authors provide the original uncropped and unadjusted images underlying all blot or gel results reported in a submission’s figures or Supporting Information files. This policy and the journal’s other requirements for blot/gel reporting and figure preparation are described in detail at https://journals.plos.org/plosone/s/figures#loc-blot-and-gel-reporting-requirements and https://journals.plos.org/plosone/s/figures#loc-preparing-figures-from-image-files. When you submit your revised manuscript, please ensure that your figures adhere fully to these guidelines and provide the original underlying images for all blot or gel data reported in your submission. See the following link for instructions on providing the original image data: https://journals.plos.org/plosone/s/figures#loc-original-images-for-blots-and-gels.

In your cover letter, please note whether your blot/gel image data are in Supporting Information or posted at a public data repository, provide the repository URL if relevant, and provide specific details as to which raw blot/gel images, if any, are not available. Email us at plosone@plos.org if you have any questions.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: I Don't Know

Reviewer #3: Yes

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The study authored by Parri et al reports on the identification of partially novel STAT3-regulators. The manuscript is very well written, the figures are clearly represented and overall the study is easy to read and to follow. Especially the description of the materials and methods section is very detailed. The manuscript shows a highly interesting and comprehensive data-set using a sophisticated screening assay (measuring viability, cytotoxicity and STAT3 transcriptional activity) targeting more than 1000 genes and narrowing them down to 7 targets regulating STAT3 activity.

There are still a few open questions:

1. Celltiter-Glo and CellTox Green measure viability and cytotoxicity, respectively. It is thus anticipated and was recently nicely shown that these two assays should complement each other and basically show opposite result (please see https://www.ncbi.nlm.nih.gov/books/NBK540958/). It would be very interesting to know, what the authors think that the reasons might be for the observed differences in cytotoxicity, while the viability stays unchanged when treating the cells with different chemicals and inhibitors (Figure 1).

2. As CDK8, CDC7 and CSNK2A1 represent serine/threonine kinases, it is puzzling why the authors chose to analyse the effect of the respective inhibitors on STAT3-Y705 phosphorylation, rather than STAT3-S727 phosphorylation. It would be advisable to add the effect of kinase inhibition on STAT3-S727 phosphorylation (according to Figure 5). And to make the story complete, please add the inhibitor treatment and STAT3-pY/pS-analysis on IL-6-treated STAT3-WT cells.

In line 335, the authors mention that CDK8-i causes the strongest reduction of pY705 posphorylation, but Figure 5 actually shows that CDC7-i is equally potent.

It is intriguing that serine kinase inhibition impacts on tyrosine-phosphorylation. It would be highly interesting to know the authors‘ thoughts about the reason for the reduced Y705-STAT3 phosphorylation upon serine-kinase inhibition.

3. With regards to the observation, that some of the inhibitors showed a clear kinetic in the STAT3 reporter activity assays in STAT3-mutant cells, the authors conclude that this suggests a difference in protein phosphorylation turnover. Could this observation also be a result of constitutive STAT3 phosphorylation in the mutant cell line compared to de-novo-STAT3 activation upon IL-6 in WT cells? Or could this point towards an indirect effect of kinase-inhibition on STAT3 activity? It will be interesting to hear the author’s view on this.

4. The reporter assays shown in Figure 4 suggest that in most cases, kinase inhibition has a more potent effect in STAT3-Y740F cells compared to IL-6-treated STAT3-WT cells. Please discuss these findings in the manuscript. These observations are particularly interesting, as Figure 1 shows that constitutively activated STAT3 protects cells from cell toxicity.

5. The authors mention that the data in Figure 3 shows that only PI4KII inhibited STAT3 and STAT1 activity. To this reviewer, the effect of CDK8 KD looks very similar in this assay, and also CSK KD seems to have some (counterintuitive) effect on STAT1-activity. Please explain the rational why the authors do not mention these, and please add and/or discuss. Additionally, the authors claim that the KD generally caused stronger inhibition of WT STAT3 activity than mutant STAT3. It is not clear how the authors came to this conclusion. Please add the respective data to support this conclusion.

6. In line, Figure 4 shows that the inhibition of CSNK2 and DDR2 have greater effects on STAT3-reporter activity in STAT3-Y640F mutant cells compared to STAT3-WT cells. Please discuss the discrepancy to the previous point.

7. In lines 286-289 the authors mention that they see „stronger reduction“ or reporter activity in STAT3-Y740F cells than in STAT3-WT cells, but the major difference is not the reporter activity, but the kinetics of it. The authors may consider changing their wording.

8. In the discussion in line 387 the authors mention that CDK8 has not been linked to STAT3 before, although afterwards they clearly discuss the previous findings by Bancerek et al. Likewise, also CSK has been previously (indirectly) linked to STAT3 phosphorylation.

Minor points:

1. Please explain the molecular details and functional consequences of the STAT3(Y640F) mutation in the manuscript (especially with regards to Y705, S727 phosphorylation and target gene transcription).

2. Please indicate whether the mycoplasma-tests were positive/negative.

3. Please show the data mentioned in line 215 and 219 (reporter assay signal in STAT3-Y740F versus IL-6-stimulated STAT3-WT cells; STAT3 siRNA silencing).

4. Is there a reason, why the authors do not mention PI4KII and PTPRH in the abstract?

5. The sentence in lines 273-275 is difficult to understand.

6. For the purpose of consistency, please add the name of the CDC7 inhibitor in line 289.

7. Please indicate in Figure 5, which samples have been analysed after 48 or 72 hours, respectively.

8. In line 384 the authors mention that the six kinases and one phosphatase regulate EITHER STAT3-mutant OR STAT3-WT in HEK cells, but it should read AND/OR.

9. In the legend of Figure S2: Please add „STAT3“- reporter activity... (line 618).

10. In Figure 1C and 1C the axis of the GSK-461364 inhibior are not set to 100.

11. The data in Figure 2D is displayed in a rather complicated manner. Maybe the authors find an easier way to plot their data.

Reviewer #2: The supplemental files contain processed data, but should be labeled as to their content, and how data was normalized or processed. The supplemental files need legends and supplemental text. I did not see it?

Reviewer #3: STAT3 mediates signalling downstream of cytokine and growth factor receptors where it acts as a transcription factor for its target genes, including oncogenes and cell survival regulating genes. STAT3 has been found to be persistently activated in many types of cancers, primarily through its tyrosine phosphorylation (Y705), and it is often taken as an target gene for the cancer therapeutic agents. In this paper, the authors find that constitutive STAT3 activation protects cells from cytotoxic drug responses of several drug classes. After performing a kinase and phosphatase siRNA screen with cells expressing either a hyperactive STAT3 mutant or IL6-induced wild type STAT3, the authors suggest that inhibiting the kinases (CDC7, CSNK2, DDR2 and CDK8 may provide strategies for dampening STAT3 activity in cancers. The manuscript is well organized, but I would like to encourage the authors to do some improvements.

1. In Figure 3, what is the meaning of the three bar columns in each group? And why are there only two bars in the CDK8 group of Fig 3C?

2. It is very interesting that 1μM ruxolitinib could not markedly reduce pY705 STAT3 level in STAT3(Y640F) expressing cells after 48 or 72 hours, but had a strong inhibitory effect on STAT3(wt) Y705 phosphorylation already after 4 hours. Therefore, I suggest that the authors use figure 4S as the formal figure and discuss this phenomenon further.

3. If the authors could draw a graph of these small molecular inhibitors on the targets of STAT3 signalling, it would be more helpful for the readers to understand the paper.

**********

6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

Reviewer #3: No

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files to be viewed.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email us at figures@plos.org. Please note that Supporting Information files do not need this step.

Attachment

Submitted filename: PONE-D-19-33342 JD.docx

Click here for additional data file.^{(14.4KB, docx)}

PLoS One. 2020 Mar 31;15(3):e0230819. doi: 10.1371/journal.pone.0230819.r002

Author response to Decision Letter 0

4 Mar 2020

Reviewer #2? (comments sent as separate MS Word document without reference to Reviewer number)

In these experiments, the authors have searched for previously unexplored druggable targets that inhibit STAT3 signalling, in cells with both WT and mutant STAT3. They show that constitutively active STAT3 protects cells from cytotoxic responses to several

drug classes. They use transcription reporters in cells expressing STAT3 WT and STAT3(Y640F) in an RNAi screen, and identified kinases that altered activity. They use small molecule inhibition of several of kinase hits, which also blocked STAT3 activity, and conclude that targeting these kinases may therefore be an alternative strategy to inhibit STAT3 in cancers and other pathological conditions.

Comments:

The Introduction is informative with a nice rational, and its ends with a brief description of type of experiments. Previous work on drug sensitivity of STAT3 is referenced well. The experimental results are described in detail and look reasonable, but need a better take-away and what is the further work needed. Its is not clear what is novel in this study. In the discussion, they note that “in the siRNA screen three kinases and one phosphatase were identified (CDC7,415 PI4KII, DDR2 and PTPRH), which have not previously been linked directly to STAT3”. A number of other hits were discussed as previously identified regulators of STAT3, which adds confidence to their iRNA screen.

We feel that the novelty of our study lies in the comprehensive profiling of kinases and phosphatases that may regulate STAT3 activity. In this process (as mentioned by the reviewer), we identify both previously identified STAT3-regulalting proteins and ones that have not been linked to STAT3 activity. Importantly, we show that small molecule inhibitors of several of these targets can also induce STAT3 inhibition, opening up for follow-up studies in more complex models. To further emphasize these conclusions, we have adjusted the closing statement in the discussion (lines 427-435).

However, drug screening does not appear to yield specificity for inhibition of activated STAT3(Y640F) compared to IL6 stimulated WT, nor for viability of STAT3(Y640F) cancer vs normal cells. The manuscript should be edited for clarity in this regard.

We agree with the reviewer that siRNA and small molecule inhibition in general did not look significantly different between STAT3(wt) and STAT3(Y640F) expressing cells for the tested hit genes. This means that other than CSK, we did not identify clear STAT3 mutant-selective regulators. On the other hand, it means that the identified targets can be explored both in cancers with STAT3 mutations and in cancers with hyperactive wild-type STAT3. We have clarified this in the text at lines 226-232 and 375-379.

Other comments:

1. The Discussion is long and the 1st paragraph repeats previous ideas from the intro.

The discussion has been shortened and we have tried to avoid repetition of ideas from the introduction.

2. There too much description of the result in the Figure legends, which are in the Results section. Figure Legends should be concise, noting the method, and any specifics; the number biological and technical repeats and statistical significance and method.

We have edited the figure legends accordingly.

3. How many independent experiments and what do error bars represent?

We have included this information to the figure legends.

4. The Figure legends should all be together after the references in manuscripts.

Figure legends have been moved to after the references.

5. Fig 2B “at least” is 2 words.

This has been corrected.

6. Fig 2A and B could be simply described in the methods, as they are repetitive and contain a lot of text.

We have removed Fig 2A and 2B from the main figures.

There are still a few open questions:

We have now addressed this point in the discussion (lines 330-333). We have previously described that in a strong viability response (with relatively fast-growing cells), it is often difficult to separate cytotoxic from cytostatic responses [1]. However, the viability inhibition curves in general in Fig 1 go a bit deeper in the case where greater cytotoxicity is induced.

This is an important point and we have therefore added data also on pS727 levels with all inhibitor treatments (Figure 5).

In line 335, the authors mention that CDK8-i causes the strongest reduction of pY705 phosphorylation, but Figure 5 actually shows that CDC7-i is equally potent.

We agree that CDC7 inhibition is similar in its potency to reduce pY705 levels to CDK8 inhibition and we have therefore have corrected this on lines 316-317.

Our phospho-STAT3 western blotting data suggest that all hit genes likely are indirect regulators of STAT3 (not directly phosphorylating or dephosphorylating STAT3) and therefore assume that the signals that are affected by CDC7 or CDK8 inhibition ultimately lead to reduced phosphotyrosine levels through altered regulation of tyrosine kinases/phosphatases. We have addressed this in in the results and discussion (lines 316-322 and lines 372-380).

We appreciate this point and agree that it is also possible that the difference between STAT3(wt) and STAT3(Y640F) expressing cells is due to the difference in assays. We have therefore addressed this in the manuscript on lines 254-264.

After reviewing the data, we find that only the DDR2 inhibitor regorafenib has a more potent effect in STAT3(Y640F) cells at later time points. Other treatments show highly similar effects in STAT3 mutant and wt cells (in the attached figure blue and green lines at 72h). At shorter time points (gray and red lines at 4h) the compounds are more effective in IL6-induced STAT3(wt)-cells. The figure below has data from manuscript Fig 4 and Fig S2 and clarifies this. We have added this to the manuscript to lines 254-263 and 279-280.

In figure 4 we observed an error in the silmitasertib viability curve that has been corrected. Furthermore, we added data from one more biological replicate to the figure and edited the figure legend accordingly.

We agree that the CDK8 KD is very similar in in STAT1 assay, but we could not conclude an effect by CDK8 KD on STAT1 transcriptional activity because of having tested only two siRNAs. We have clarified this in the text on lines 241-243.

The counterintuitive CSK KD on effect to STAT3 vs STAT1 could be due to that SRC is activated by IFNγ and is involved in the activation of STAT3 but not STAT1 [2]. However, the effects we see on STAT1 transcriptional activity by CSK knockdown is not consistent enough across the tested siRNAs to draw a conclusion that it causes an inhibition rather than an activation. We have clarified this in the manuscript on lines 241-242 and in discussion lines 376-380.

Additionally, the authors claim that the KD generally caused stronger inhibition of WT STAT3 activity than mutant STAT3. It is not clear how the authors came to this conclusion. Please add the respective data to support this conclusion.

We observed stronger general inhibition of STAT3(wt) in the primary screen (Figure 2A and figure below), but this observation didn’t carry through in the validation screen and the same difference was not observed in the final hits. Therefore, we have removed the claim from the manuscript (from figure legend 3).

The previous comment was about the siRNA screen as whole as mentioned earlier, we realized that this effect was not consistent through the follow-up screens and we therefore removed that statement. As discussed in point 4, in figure 4, although it may first look like several of the small molecule inhibitors were effective at inhibiting STAT3 reporter activity in STAT3 mutant cell than in STAT3 wt cells, only the DDR2 inhibitor has a clear selective effect.

It is true that the major difference is the kinetics of the two cell lines. We have changed the wording in the manuscript (lines 269-271).

We agree that these were previous links that we had failed to report and the text has now been corrected (lines 374-375).

Minor points:

The mutations in the SH2 domain, STAT3(Y640F) result in increased hydrophobicity of the STAT3 dimerization site (pY+3 pocket) that results in a more stable form of STAT3 dimer, constitutive Y705 phosphorylation, enhanced nuclear stability and increased transcriptional activity. The STAT3(Y640F) mutation does not have an effect on S727 phosphorylation [3]. We have clarified this in the manuscript (lines 45-49).

2. Please indicate whether the mycoplasma-tests were positive/negative.

Mycoplasma tests were negative and we have clarified this in the manuscript (lines 87-88)

3. Please show the data mentioned in line 215 and 219 (reporter assay signal in STAT3-Y740F versus IL-6-stimulated STAT3-WT cells; STAT3 siRNA silencing).

We have made supplementary figure using raw luciferase data addressing the data mentioned in lines 215 – 219 (Fig S1) and the text is edited accordingly. Furthermore, we have labelled separately siSTAT3 and siDEATH in figure 2A where the positive control is siSTAT3 and assay control is siDEATH.

4. Is there a reason, why the authors do not mention PI4KII and PTPRH in the abstract?

The reason why we initially did not mention PI4KII and PTPRH in the abstract was that we did not have selective small molecule inhibitors against these proteins. We have included them now in the abstract.

5. The sentence in lines 273-275 is difficult to understand.

The sentence: “Generally, the reporter activity inhibition occurred more slowly in STAT3(Y640F) expressing cells than the STAT3(wt) cells, suggesting that the STAT3(Y640F) protein phosphorylation turnover is slower.” Has been edited to: “We observed that the reporter activity inhibition occurred more slowly in STAT3(Y640F) expressing cells than the STAT3(wt) cells, suggesting that the STAT3(Y640F) protein de-phosphorylation is slower.“ Lines 254-260.

6. For the purpose of consistency, please add the name of the CDC7 inhibitor in line 289.

We have corrected this to the text.

7. Please indicate in Figure 5, which samples have been analysed after 48 or 72 hours, respectively.

We have labelled the samples accordingly in figure 5.

8. In line 384 the authors mention that the six kinases and one phosphatase regulate EITHER STAT3-mutant OR STAT3-WT in HEK cells, but it should read AND/OR.

The text has been edited according to the suggestion.

9. In the legend of Figure S2: Please add „STAT3“- reporter activity... (line 618).

We have edited the Fig S2 legend according to the suggestion.

10. In Figure 1C and 1C the axis of the GSK-461364 inhibior are not set to 100.

The GSK-461364 data had points outside the range of the ranges of the other data sets and we had therefore set a different scale on the y-axis for this inhibitor. As the reviewer suggests, it makes sense to have the graphs coherent and we have therefore now set the y-axis maximum to 125 for all the inhibitors in figure 1C and 1D.

11. The data in Figure 2D is displayed in a rather complicated manner. Maybe the authors find an easier way to plot their data.

Figure 2D (now Figure 2B) has been simplified.

We have added supplemental legends and text explaining the data in supplemental files and corrected the labelling of the files.

1. In Figure 3, what is the meaning of the three bar columns in each group? And why are there only two bars in the CDK8 group of Fig 3C?

Each bar represents different siRNAs targeting the labeled gene (i.e Hs_CDC7_1, Hs_CDC7_2, Hs_CDC7_5). The used siRNAs are listed in supporting file S3. The bars are matched so that in the figures 3 A, B, C individual siRNAs are in same order, with exception with siCDK8 in Fig 3C where only the first two siRNAs are matched. The figure has been edited and the different siRNAs are labelled to clarify this.

There are only two bars for CDK8 because we only had 2 siRNAs for this gene.

The difference between the effects on the wild type vs. mutant STAT3 likely comes from that we are comparing de novo vs. persistent phosphorylation. In the STAT3(wt) expressing cells STAT3 is activated by the addition of IL6 after the ruxolitinib addition whereas in the STAT3(Y640F) the Y640F is already phosphorylated before adding ruxolitinib. Therefore, the effects on the wt phosphorylation are assessing the impact of directly blocking JAK-dependent phosphorylation of STAT3 (caused by IL6 stimulation). In the mutant case, we assess the impact on turnover of the phosphorylation (dephosphorylation followed by re-phosphorylation) of STAT3. Further enhancing the differential effect, The STAT3(Y640F) mutant has been described to cause a more stable dimer than the wild type protein, causing a slower rate of dephosphorylation. Since the pY705 activation by IL6 in STAT3(wt) cells is fairly fast and short-lived we used a 20-minute IL6 stimulation prior to lysing the cells for western blots and a 3-hour stimulation for the luciferase reporter readout. Ruxolitinib serves in this study as control for de novo vs. constitutive STAT3 activation.

To clarify these differences between the wt and mutant-expressing cells, we have addressed this in the text on lines 45-49 and 254-263.

3. If the authors could draw a graph of these small molecular inhibitors on the targets of STAT3 signalling, it would be more helpful for the readers to understand the paper.

We have added a summary figure 6 of the siRNA screen hits and inhibitors targeting them.

References

1. Gautam P, Karhinen L, Szwajda A, Jha SK, Yadav B, Aittokallio T, et al. Identification of selective cytotoxic and synthetic lethal drug responses in triple negative breast cancer cells. Molecular cancer. 2016;15(1):34. Epub 2016/05/12. doi: 10.1186/s12943-016-0517-3. PubMed PMID: 27165605; PubMed Central PMCID: PMCPMC4862054.

2. Qing Y, Stark GR. Alternative activation of STAT1 and STAT3 in response to interferon-gamma. The Journal of biological chemistry. 2004;279(40):41679-85. Epub 2004/07/31. doi: 10.1074/jbc.M406413200. PubMed PMID: 15284232.

3. Pilati C, Amessou M, Bihl MP, Balabaud C, Nhieu JT, Paradis V, et al. Somatic mutations activating STAT3 in human inflammatory hepatocellular adenomas. The Journal of experimental medicine. 2011;208(7):1359-66. doi: 10.1084/jem.20110283. PubMed PMID: 21690253; PubMed Central PMCID: PMC3135371.

Attachment

Submitted filename: Response to reviewers.pdf

Click here for additional data file.^{(490KB, pdf)}

PLoS One. doi: 10.1371/journal.pone.0230819.r003

Decision Letter 1

Alessandro Datti

10 Mar 2020

Identification of novel regulators of STAT3 activity

PONE-D-19-33342R1

Dear Dr. Wennerberg,

We are pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it complies with all outstanding technical requirements.

Within one week, you will receive an e-mail containing information on the amendments required prior to publication. When all required modifications have been addressed, you will receive a formal acceptance letter and your manuscript will proceed to our production department and be scheduled for publication.

Shortly after the formal acceptance letter is sent, an invoice for payment will follow. To ensure an efficient production and billing process, please log into Editorial Manager at https://www.editorialmanager.com/pone/, click the "Update My Information" link at the top of the page, and update your user information. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.

If your institution or institutions have a press office, please notify them about your upcoming paper to enable them to help maximize its impact. If they will be preparing press materials for this manuscript, you must inform our press team as soon as possible and no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

With kind regards,

Alessandro Datti, Ph.D.

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #1: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #1: Yes

**********

6. Review Comments to the Author

Reviewer #1: (No Response)

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

PLoS One. doi: 10.1371/journal.pone.0230819.r004

Acceptance letter

Alessandro Datti

12 Mar 2020

PONE-D-19-33342R1

Identification of novel regulators of STAT3 activity

Dear Dr. Wennerberg:

I am pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please notify them about your upcoming paper at this point, to enable them to help maximize its impact. If they will be preparing press materials for this manuscript, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

For any other questions or concerns, please email plosone@plos.org.

Thank you for submitting your work to PLOS ONE.

With kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Alessandro Datti

Academic Editor

PLOS ONE

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Fig. Luciferase activities of controls in primary siRNA screen.

(TIF)

Click here for additional data file.^{(316.3KB, tif)}

S2 Fig. CDK8 inhibition in STAT3 expressing cells.

(TIF)

Click here for additional data file.^{(386.8KB, tif)}

S3 Fig. Src family kinases in STAT3 expressing cells.

Effect of CSK and Src family kinases (SFKs) knockdowns on STAT3 reporter activity and viability of HEK293sieSTAT3(Y640F) (A) and IL6 induced HEK293sieSTAT3(wt) (B).

(TIF)

Click here for additional data file.^{(590.4KB, tif)}

S4 Fig. Effect of Src inhibitors on reporter activity of IL6 induced STAT3(wt) and STAT3(Y640F) expressing cells.

(TIF)

Click here for additional data file.^{(936.9KB, tif)}

S5 Fig. Short term perturbation on Y705-phosporylation in IL6 induced STAT3(wt) and STAT3(Y640F) expressing cells.

Four-hour perturbation with JAK1/2 inhibitor ruxolitinib decreases Y705-phosphorylation of STAT3 in IL6 induced STAT3(wt) expressing cells.

(TIF)

Click here for additional data file.^{(700.1KB, tif)}

S1 File. Supporting file for Fig 1.

(XLSX)

Click here for additional data file.^{(14.3MB, xlsx)}

S2 File. Supporting file for Figs 2 and 3.

(XLSX)

Click here for additional data file.^{(313.7KB, xlsx)}

S3 File. Supporting file for materials and methods.

Vendor information of siRNAs and siRNA screening controls used in Fig 3 and small molecule inhibitors used in Figs 4 and 5 and S1, S3, S4 Figs.

(XLSX)

Click here for additional data file.^{(13.2KB, xlsx)}

S1 Raw images. Raw Western blot scans.

Whole membranes of all shown and analyzed Western blot membranes with lane labelling. Lanes with “X” and/or “italics” are not included in the paper. Blots are supporting Fig 5 and S4 Fig.

(PDF)

Click here for additional data file.^{(9.2MB, pdf)}

Attachment

Submitted filename: PONE-D-19-33342 JD.docx

Click here for additional data file.^{(14.4KB, docx)}

Attachment

Submitted filename: Response to reviewers.pdf

Click here for additional data file.^{(490KB, pdf)}

Data Availability Statement

All relevant data are within the paper and its Supporting Information files.

[pone.0230819.ref001] 1.Corcoran RB, Contino G, Deshpande V, Tzatsos A, Conrad C, Benes CH, et al. STAT3 plays a critical role in KRAS-induced pancreatic tumorigenesis. Cancer research. 2011;71(14):5020–9. 10.1158/0008-5472.CAN-11-0908 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0230819.ref002] 2.Lee HJ, Zhuang G, Cao Y, Du P, Kim HJ, Settleman J. Drug resistance via feedback activation of Stat3 in oncogene-addicted cancer cells. Cancer cell. 2014;26(2):207–21. 10.1016/j.ccr.2014.05.019 . [DOI] [PubMed] [Google Scholar]

[pone.0230819.ref003] 3.Yu H, Lee H, Herrmann A, Buettner R, Jove R. Revisiting STAT3 signalling in cancer: new and unexpected biological functions. Nature reviews Cancer. 2014;14(11):736–46. 10.1038/nrc3818 [DOI] [PubMed] [Google Scholar]

[pone.0230819.ref004] 4.Yu H, Pardoll D, Jove R. STATs in cancer inflammation and immunity: a leading role for STAT3. Nature reviews Cancer. 2009;9(11):798–809. 10.1038/nrc2734 . [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0230819.ref005] 5.Johnson DE, O'Keefe RA, Grandis JR. Targeting the IL-6/JAK/STAT3 signalling axis in cancer. Nat Rev Clin Oncol. 2018;15(4):234–48. Epub 2018/02/07. 10.1038/nrclinonc.2018.8 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0230819.ref006] 6.Jerez A, Clemente MJ, Makishima H, Koskela H, Leblanc F, Peng Ng K, et al. STAT3 mutations unify the pathogenesis of chronic lymphoproliferative disorders of NK cells and T-cell large granular lymphocyte leukemia. Blood. 2012;120(15):3048–57. 10.1182/blood-2012-06-435297 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0230819.ref007] 7.Koskela HL, Eldfors S, Ellonen P, van Adrichem AJ, Kuusanmaki H, Andersson EI, et al. Somatic STAT3 mutations in large granular lymphocytic leukemia. The New England journal of medicine. 2012;366(20):1905–13. 10.1056/NEJMoa1114885 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0230819.ref008] 8.Ohgami RS, Ma L, Merker JD, Martinez B, Zehnder JL, Arber DA. STAT3 mutations are frequent in CD30+ T-cell lymphomas and T-cell large granular lymphocytic leukemia. Leukemia. 2013;27(11):2244–7. 10.1038/leu.2013.104 . [DOI] [PubMed] [Google Scholar]

[pone.0230819.ref009] 9.Pilati C, Amessou M, Bihl MP, Balabaud C, Nhieu JT, Paradis V, et al. Somatic mutations activating STAT3 in human inflammatory hepatocellular adenomas. The Journal of experimental medicine. 2011;208(7):1359–66. 10.1084/jem.20110283 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0230819.ref010] 10.Dufva O, Kankainen M, Kelkka T, Sekiguchi N, Awad SA, Eldfors S, et al. Aggressive natural killer-cell leukemia mutational landscape and drug profiling highlight JAK-STAT signaling as therapeutic target. Nat Commun. 2018;9(1):1567 Epub 2018/04/21. 10.1038/s41467-018-03987-2 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0230819.ref011] 11.de Araujo ED, Orlova A, Neubauer HA, Bajusz D, Seo HS, Dhe-Paganon S, et al. Structural Implications of STAT3 and STAT5 SH2 Domain Mutations. Cancers (Basel). 2019;11(11). Epub 2019/11/14. 10.3390/cancers11111757 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0230819.ref012] 12.Kanna R, Choudhary G, Ramachandra N, Steidl U, Verma A, Shastri A. STAT3 inhibition as a therapeutic strategy for leukemia. Leuk Lymphoma. 2018;59(9):2068–74. Epub 2017/11/23. 10.1080/10428194.2017.1397668 . [DOI] [PubMed] [Google Scholar]

[pone.0230819.ref013] 13.Laudisi F, Cherubini F, Monteleone G, Stolfi C. STAT3 Interactors as Potential Therapeutic Targets for Cancer Treatment. Int J Mol Sci. 2018;19(6). Epub 2018/06/20. 10.3390/ijms19061787 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0230819.ref014] 14.Beebe JD, Liu JY, Zhang JT. Two decades of research in discovery of anticancer drugs targeting STAT3, how close are we? Pharmacol Ther. 2018;191:74–91. Epub 2018/06/23. 10.1016/j.pharmthera.2018.06.006 . [DOI] [PubMed] [Google Scholar]

[pone.0230819.ref015] 15.Kolosenko I, Yu Y, Busker S, Dyczynski M, Liu JP, Haraldsson M, et al. Identification of novel small molecules that inhibit STAT3-dependent transcription and function. Plos One. 2017;12(6). ARTN e0178844 10.1371/journal.pone.0178844 WOS:000404118300018. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0230819.ref016] 16.Ali AM, Gomez-Biagi RF, Rosa DA, Lai PS, Heaton WL, Park JS, et al. Disarming an Electrophilic Warhead: Retaining Potency in Tyrosine Kinase Inhibitor (TKI)-Resistant CML Lines While Circumventing Pharmacokinetic Liabilities. Chemmedchem. 2016;11(8):850–61. 10.1002/cmdc.201600021 WOS:000374693500014. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0230819.ref017] 17.McMurray JS. A new small-molecule Stat3 inhibitor. Chem Biol. 2006;13(11):1123–4. Epub 2006/11/23. 10.1016/j.chembiol.2006.11.001 . [DOI] [PubMed] [Google Scholar]

[pone.0230819.ref018] 18.Kuusanmaki H, Dufva O, Parri E, van Adrichem AJ, Rajala H, Majumder MM, et al. Drug sensitivity profiling identifies potential therapies for lymphoproliferative disorders with overactive JAK/STAT3 signaling. Oncotarget. 2017;8(57):97516–27. Epub 2017/12/13. 10.18632/oncotarget.22178 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0230819.ref019] 19.Choppa PC, Vojdani A, Tagle C, Andrin R, Magtoto L. Multiplex PCR for the detection of Mycoplasma fermentans, M. hominis and M. penetrans in cell cultures and blood samples of patients with chronic fatigue syndrome. Mol Cell Probes. 1998;12(5):301–8. Epub 1998/10/21. 10.1006/mcpr.1998.0186 . [DOI] [PubMed] [Google Scholar]

[pone.0230819.ref020] 20.Vojdani A, Choppa PC, Tagle C, Andrin R, Samimi B, Lapp CW. Detection of Mycoplasma genus and Mycoplasma fermentans by PCR in patients with Chronic Fatigue Syndrome. FEMS Immunol Med Microbiol. 1998;22(4):355–65. Epub 1999/01/08. 10.1111/j.1574-695X.1998.tb01226.x . [DOI] [PubMed] [Google Scholar]

[pone.0230819.ref021] 21.Pemovska T, Kontro M, Yadav B, Edgren H, Eldfors S, Szwajda A, et al. Individualized systems medicine strategy to tailor treatments for patients with chemorefractory acute myeloid leukemia. Cancer discovery. 2013;3(12):1416–29. 10.1158/2159-8290.CD-13-0350 . [DOI] [PubMed] [Google Scholar]

[pone.0230819.ref022] 22.Gautam P, Karhinen L, Szwajda A, Jha SK, Yadav B, Aittokallio T, et al. Identification of selective cytotoxic and synthetic lethal drug responses in triple negative breast cancer cells. Molecular cancer. 2016;15(1):34 Epub 2016/05/12. 10.1186/s12943-016-0517-3 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0230819.ref023] 23.Yadav B, Pemovska T, Szwajda A, Kulesskiy E, Kontro M, Karjalainen R, et al. Quantitative scoring of differential drug sensitivity for individually optimized anticancer therapies. Scientific reports. 2014;4:5193 10.1038/srep05193 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0230819.ref024] 24.Potdar S, Ianevski A, Mpindi J-P, Bychkov D, Fiere C, Ianevski P, et al. Breeze: an integrated quality control and data analysis application for high-throughput drug screening. Bioinformatics. 2020. 10.1093/bioinformatics/btaa138 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0230819.ref025] 25.Wilhelm SM, Dumas J, Adnane L, Lynch M, Carter CA, Schutz G, et al. Regorafenib (BAY 73–4506): a new oral multikinase inhibitor of angiogenic, stromal and oncogenic receptor tyrosine kinases with potent preclinical antitumor activity. Int J Cancer. 2011;129(1):245–55. Epub 2010/12/21. 10.1002/ijc.25864 . [DOI] [PubMed] [Google Scholar]

[pone.0230819.ref026] 26.Fan LC, Teng HW, Shiau CW, Lin H, Hung MH, Chen YL, et al. SHP-1 is a target of regorafenib in colorectal cancer. Oncotarget. 2014;5(15):6243–51. 10.18632/oncotarget.2191 WOS:000347919500031. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0230819.ref027] 27.Aparicio-Siegmund S, Sommer J, Monhasery N, Schwanbeck R, Keil E, Finkenstadt D, et al. Inhibition of protein kinase II (CK2) prevents induced signal transducer and activator of transcription (STAT) 1/3 and constitutive STAT3 activation. Oncotarget. 2014;5(8):2131–48. Epub 2014/04/20. 10.18632/oncotarget.1852 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0230819.ref028] 28.Bhattacharya S, Ray RM, Johnson LR. STAT3-mediated transcription of Bcl-2, Mcl-1 and c-IAP2 prevents apoptosis in polyamine-depleted cells. Biochem J. 2005;392(Pt 2):335–44. Epub 2005/07/29. 10.1042/BJ20050465 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0230819.ref029] 29.Epling-Burnette PK, Liu JH, Catlett-Falcone R, Turkson J, Oshiro M, Kothapalli R, et al. Inhibition of STAT3 signaling leads to apoptosis of leukemic large granular lymphocytes and decreased Mcl-1 expression. The Journal of clinical investigation. 2001;107(3):351–62. 10.1172/JCI9940 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0230819.ref030] 30.O'Connor DS, Grossman D, Plescia J, Li F, Zhang H, Villa A, et al. Regulation of apoptosis at cell division by p34cdc2 phosphorylation of survivin. Proc Natl Acad Sci U S A. 2000;97(24):13103–7. Epub 2000/11/09. 10.1073/pnas.240390697 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0230819.ref031] 31.Ivanov VN, Bhoumik A, Krasilnikov M, Raz R, Owen-Schaub LB, Levy D, et al. Cooperation between STAT3 and c-jun suppresses Fas transcription. Mol Cell. 2001;7(3):517–28. Epub 2001/07/21. 10.1016/s1097-2765(01)00199-x . [DOI] [PubMed] [Google Scholar]

[pone.0230819.ref032] 32.Zhang X, Xiao W, Wang L, Tian Z, Zhang J. Deactivation of signal transducer and activator of transcription 3 reverses chemotherapeutics resistance of leukemia cells via down-regulating P-gp. Plos One. 2011;6(6):e20965 Epub 2011/06/17. 10.1371/journal.pone.0020965 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0230819.ref033] 33.Duan Z, Foster R, Bell DA, Mahoney J, Wolak K, Vaidya A, et al. Signal transducers and activators of transcription 3 pathway activation in drug-resistant ovarian cancer. Clin Cancer Res. 2006;12(17):5055–63. Epub 2006/09/05. 10.1158/1078-0432.CCR-06-0861 . [DOI] [PubMed] [Google Scholar]

[pone.0230819.ref034] 34.Zhang Z, Wang A, Li H, Zhi H, Lu F. STAT3-dependent TXNDC17 expression mediates Taxol resistance through inducing autophagy in human colorectal cancer cells. Gene. 2016;584(1):75–82. Epub 2016/03/16. 10.1016/j.gene.2016.03.012 . [DOI] [PubMed] [Google Scholar]

[pone.0230819.ref035] 35.Gritsko T, Williams A, Turkson J, Kaneko S, Bowman T, Huang M, et al. Persistent activation of stat3 signaling induces survivin gene expression and confers resistance to apoptosis in human breast cancer cells. Clin Cancer Res. 2006;12(1):11–9. Epub 2006/01/07. 10.1158/1078-0432.CCR-04-1752 . [DOI] [PubMed] [Google Scholar]

[pone.0230819.ref036] 36.Zhang Y, Du XL, Wang CJ, Lin DC, Ruan X, Feng YB, et al. Reciprocal activation between PLK1 and Stat3 contributes to survival and proliferation of esophageal cancer cells. Gastroenterology. 2012;142(3):521–30 e3. Epub 2011/11/24. 10.1053/j.gastro.2011.11.023 . [DOI] [PubMed] [Google Scholar]

[pone.0230819.ref037] 37.Cheng Y, Holloway MP, Nguyen K, McCauley D, Landesman Y, Kauffman MG, et al. XPO1 (CRM1) Inhibition Represses STAT3 Activation to Drive a Survivin-Dependent Oncogenic Switch in Triple-Negative Breast Cancer. Mol Cancer Ther. 2014;13(3):675–86. 10.1158/1535-7163.MCT-13-0416 WOS:000334318300012. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0230819.ref038] 38.Song L, Turkson J, Karras JG, Jove R, Haura EB. Activation of Stat3 by receptor tyrosine kinases and cytokines regulates survival in human non-small cell carcinoma cells. Oncogene. 2003;22(27):4150–65. Epub 2003/07/02. 10.1038/sj.onc.1206479 . [DOI] [PubMed] [Google Scholar]

[pone.0230819.ref039] 39.Heinrich PC, Behrmann I, Haan S, Hermanns HM, Muller-Newen G, Schaper F. Principles of interleukin (IL)-6-type cytokine signalling and its regulation. Biochem J. 2003;374(Pt 1):1–20. Epub 2003/05/30. 10.1042/BJ20030407 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0230819.ref040] 40.Croker BA, Kiu H, Nicholson SE. SOCS regulation of the JAK/STAT signalling pathway. Semin Cell Dev Biol. 2008;19(4):414–22. Epub 2008/08/19. 10.1016/j.semcdb.2008.07.010 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0230819.ref041] 41.Wu ML, Song DY, Li H, Yang Y, Ma XD, Deng S, et al. Negative regulators of STAT3 signaling pathway in cancers. Cancer Manag Res. 2019;11:4957–69. 10.2147/CMAR.S206175 WOS:000469770700001. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0230819.ref042] 42.Yuan J, Zhang F, Niu R. Multiple regulation pathways and pivotal biological functions of STAT3 in cancer. Scientific reports. 2015;5:17663 Epub 2015/12/04. 10.1038/srep17663 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0230819.ref043] 43.Rozovski U, Harris DM, Li P, Liu Z, Jain P, Veletic I, et al. Constitutive Phosphorylation of STAT3 by the CK2-BLNK-CD5 Complex. Mol Cancer Res. 2017;15(5):610–8. Epub 2017/01/29. 10.1158/1541-7786.MCR-16-0291 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0230819.ref044] 44.Bancerek J, Poss ZC, Steinparzer I, Sedlyarov V, Pfaffenwimmer T, Mikulic I, et al. CDK8 kinase phosphorylates transcription factor STAT1 to selectively regulate the interferon response. Immunity. 2013;38(2):250–62. Epub 2013/01/29. 10.1016/j.immuni.2012.10.017 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0230819.ref045] 45.Aki D, Mashima R, Saeki K, Minoda Y, Yamauchi M, Yoshimura A. Modulation of TLR signalling by the C-terminal Src kinase (Csk) in macrophages. Genes Cells. 2005;10(4):357–68. Epub 2005/03/19. 10.1111/j.1365-2443.2005.00839.x . [DOI] [PubMed] [Google Scholar]

[pone.0230819.ref046] 46.Read RD, Bach EA, Cagan RL. Drosophila C-terminal Src kinase negatively regulates organ growth and cell proliferation through inhibition of the Src, Jun N-terminal kinase, and STAT pathways. Molecular and Cellular Biology. 2004;24(15):6676–89. 10.1128/MCB.24.15.6676-6689.2004 WOS:000222764800014. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0230819.ref047] 47.Schreiner SJ, Schiavone AP, Smithgall TE. Activation of STAT3 by the Src family kinase Hck requires a functional SH3 domain. The Journal of biological chemistry. 2002;277(47):45680–7. Epub 2002/09/24. 10.1074/jbc.M204255200 . [DOI] [PubMed] [Google Scholar]

[pone.0230819.ref048] 48.Bowman T, Garcia R, Turkson J, Jove R. STATs in oncogenesis. Oncogene. 2000;19(21):2474–88. Epub 2000/06/13. 10.1038/sj.onc.1203527 . [DOI] [PubMed] [Google Scholar]

[pone.0230819.ref049] 49.Bromberg JF, Wrzeszczynska MH, Devgan G, Zhao YX, Pestell RG, Albanese C, et al. Stat3 as an oncogene. Cell. 1999;98(3):295–303. 10.1016/s0092-8674(00)81959-5 WOS:000081950300005. [DOI] [PubMed] [Google Scholar]

[pone.0230819.ref050] 50.Chong YP, Chan AS, Chan KC, Williamson NA, Lerner EC, Smithgall TE, et al. C-terminal Src kinase-homologous kinase (CHK), a unique inhibitor inactivating multiple active conformations of Src family tyrosine kinases. The Journal of biological chemistry. 2006;281(44):32988–99. Epub 2006/09/09. 10.1074/jbc.M602951200 . [DOI] [PubMed] [Google Scholar]

[pone.0230819.ref051] 51.Knuesel MT, Meyer KD, Donner AJ, Espinosa JM, Taatjes DJ. The human CDK8 subcomplex is a histone kinase that requires Med12 for activity and can function independently of mediator. Mol Cell Biol. 2009;29(3):650–61. Epub 2008/12/03. 10.1128/MCB.00993-08 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0230819.ref052] 52.Staab J, Herrmann-Lingen C, Meyer T. CDK8 as the STAT1 serine 727 kinase? JAKSTAT. 2013;2(3):e24275 Epub 2013/09/27. 10.4161/jkst.24275 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0230819.ref053] 53.Lodige I, Marg A, Wiesner B, Malecova B, Oelgeschlager T, Vinkemeier U. Nuclear export determines the cytokine sensitivity of STAT transcription factors. The Journal of biological chemistry. 2005;280(52):43087–99. Epub 2005/10/01. 10.1074/jbc.M509180200 . [DOI] [PubMed] [Google Scholar]

[pone.0230819.ref054] 54.Wen Z, Zhong Z, Darnell JE Jr. Maximal activation of transcription by Stat1 and Stat3 requires both tyrosine and serine phosphorylation. Cell. 1995;82(2):241–50. Epub 1995/07/28. 10.1016/0092-8674(95)90311-9 . [DOI] [PubMed] [Google Scholar]

[pone.0230819.ref055] 55.Faraci-Orf E, McFadden C, Vogel WF. DDR1 signaling is essential to sustain Stat5 function during lactogenesis. J Cell Biochem. 2006;97(1):109–21. Epub 2005/09/17. 10.1002/jcb.20618 . [DOI] [PubMed] [Google Scholar]

[pone.0230819.ref056] 56.Matozaki T, Suzuki T, Uchida T, Inazawa J, Ariyama T, Matsuda K, et al. Molecular cloning of a human transmembrane-type protein tyrosine phosphatase and its expression in gastrointestinal cancers. The Journal of biological chemistry. 1994;269(3):2075–81. Epub 1994/01/21. . [PubMed] [Google Scholar]

[pone.0230819.ref057] 57.Sato T, Soejima K, Arai E, Hamamoto J, Yasuda H, Arai D, et al. Prognostic implication of PTPRH hypomethylation in non-small cell lung cancer. Oncol Rep. 2015;34(3):1137–45. Epub 2015/07/03. 10.3892/or.2015.4082 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0230819.ref058] 58.Seo Y, Matozaki T, Tsuda M, Hayashi Y, Itoh H, Kasuga M. Overexpression of SAP-1, a transmembrane-type protein tyrosine phosphatase, in human colorectal cancers. Biochem Biophys Res Commun. 1997;231(3):705–11. Epub 1997/02/24. 10.1006/bbrc.1997.6139 . [DOI] [PubMed] [Google Scholar]

PERMALINK

Identification of novel regulators of STAT3 activity

Elina Parri

Heikki Kuusanmäki

Arjan J van Adrichem

Meri Kaustio

Krister Wennerberg

Roles

Abstract

Introduction

Materials and methods

Cell lines, virus production and transduction

Drug sensitivity and resistance testing

siRNA screens

IFNγ activation site (GAS) response element assays

Western blotting

Data analysis

Results

STAT3 activation protects cells from cytotoxic drug responses

Fig 1. Constitutive activation of STAT3 protects cells from several drug classes.

siRNA screening reveals kinases and a phosphatase that regulate oncogenic hyperactive STAT3

Fig 2. Small interfering RNA (siRNA) screen to identify regulators of hyperactive STAT3.

Fig 3. Genes regulating STAT3(wt) or STAT3(Y640F) reporter activity.

Small molecule inhibitors of hit kinases regulate STAT3 activity in a dose and time dependent fashion

Fig 4. Dose and time dependency of small molecule inhibitors to STAT3 expression.

Fig 5. The effect of kinase inhibitors on STAT3 levels in HEK293sieSTAT3(Y640F) or HEK293sieSTAT3(wt) cells.

Fig 6. Schematic illustration of hit kinases/phosphatase and inhibitors targeting the kinases.

Discussion

Supporting information

Acknowledgments

Data Availability

Funding Statement

References

Decision Letter 0

Alessandro Datti

Roles

Author response to Decision Letter 0

Decision Letter 1

Alessandro Datti

Roles

Acceptance letter

Alessandro Datti

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

Similar articles

Cited by other articles

Links to NCBI Databases