Dynamic Phosphoproteomic Profiling Identifies CK2 as a Critical Survival Kinase in Quiescent Breast Cancer Cells and a Therapeutic Target for Minimal Residual Disease

Dynamic Phosphoproteomic Profiling Identifies CK2 as a Critical Survival Kinase in Quiescent Breast Cancer Cells and a Therapeutic Target for Minimal Residual Disease | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Dynamic Phosphoproteomic Profiling Identifies CK2 as a Critical Survival Kinase in Quiescent Breast Cancer Cells and a Therapeutic Target for Minimal Residual Disease Radoslav Janostiak, Lucia Csergeova This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7287833/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Quiescent cancer cells (QCCs) evade conventional therapies and contribute to minimal residual disease (MRD) and relapse, yet the signaling pathways governing their survival remain poorly understood. Here, we performed integrative proteomic and phosphoproteomic profiling of triple-negative breast cancer cells transitioning between proliferation and serum withdrawal-induced quiescence, followed by reactivation. We identified dynamic remodeling of both proteome and phosphoproteome, with quiescent cells showing downregulation of mitotic drivers and upregulation of extracellular matrix components. Notably, phosphorylation of CK2 substrates was increased during quiescence, and CK2 inhibition using CX-4945 impaired cell survival under nutrient and genotoxic stress, disrupted autophagy, microtubule dynamics, and protein synthesis. Phospho-enrichment and functional assays identified Death-associated protein kinase 3 (DAPK3) as a CK2-regulated effector mediating stress-induced apoptosis. In silico analysis confirmed a link between high CK2 expression and poor chemotherapy response in basal breast cancer. These findings establish CK2 as a critical survival kinase in QCCs and a potential therapeutic target for MRD eradication in breast cancer. Biological sciences/Cell biology/Cell death Biological sciences/Cell biology/Cell division Health sciences/Diseases/Cancer/Breast cancer Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction One of the hallmarks of cancer cells is their high proliferative capacity, which has been the primary target of conventional anticancer therapies such as chemotherapy and radiation 1–3 . These therapies are highly effective against fast proliferative cells however fail to kill slow-cycling or non-cycling cells – so called quiescent cancer cells (QCCs) 4,5 . It has been shown that within the tumor microenvironment there is a stable subpopulation of QCCs which is dynamically regulated and modulated by environmental factors such as hypoxia, nutrient deprivation, loss of cell adhesion, and exposure to anticancer treatments 6–8 . They have been identified across various tumor types, including glioblastoma, lung, breast, colorectal cancers 9–12 . Even though this population represent only a fraction of the total cell number (as low as 0.5%) 11 it has great implication for anticancer therapy efficacy as it can persist the treatment and give rise to minimal residual disease 9,11,13 . Importantly, QCC can surive long periods of time (years) and lead to disease recurrence which is observed in up to 85% of ovarian cancer patients, 30% of breast, 40% of prostate, and nearly 100% of glioblastoma cases 14 . In mammalian cells, the induction and maintenance of quiescence are regulated by a complex interplay of extrinsic and intrinsic signals. Ultimately, these signals converge on the regulation of cyclin-dependent kinases (CDKs), which determine whether a cell re-enters the cell cycle or remains quiescent. Quiescence is promoted by the upregulation of CDK inhibitors like p27 and p21, downregulation of CDKs, and hypo-phosphorylation of the retinoblastoma (Rb) protein. For instance, during serum starvation, increased expression of p27 suppresses CDK4/6 activity, maintaining cells in G0 phase 15 . Quiescent cells are typically identified by high p27 and low Ki67 expression 16,17 , and may also exhibit specific signaling states such as ERK1/2^high/p38^low, or express transcriptional repressors like NR2F1 and kinases such as DYRK1A and DYRK1B 18–20 . Given the capacity of QCC to persist in a cell cycle arrested state and later reignite tumor growth, QCCs pose a significant clinical challenge. Effective strategies to target these cells could involve (i) enhancing initial treatment efficacy through improved fractional killing, (ii) permanently locking them out of the cell cycle (e.g., inducing senescence), or (iii) developing therapies that selectively eliminate them. Importantly, quiescence is distinct from senescence—the latter being a permanent cell cycle exit state—making the potential for reactivation and relapse unique to quiescent cells 21 . Since quiescence is also a survival mechanism, it can be induced by stress response pathways 22 . In this context, Casein Kinase 2 (CK2) has emerged as a key player in regulating cellular stress responses and survival pathways. CK2 is a constitutively active serine/threonine kinase involved in cell cycle control, apoptosis, transcription, and DNA repair 23,24 . It modulates a wide range of signaling proteins, including p53, NF-κB, and components of the unfolded protein response. By stabilizing antioxidant transcription factors like NRF2 and maintaining the function of stress-protective proteins such as heat shock proteins, CK2 enhances cell survival under adverse conditions 25,26 . However, its sustained activation in tumors may support the survival of quiescent or therapy-resistant cells, contributing to disease persistence and recurrence 27,28 . As such, CK2 represents a potential therapeutic target, particularly in strategies aimed at disrupting tumor dormancy and eliminating minimal residual disease. To characterize the signaling events on the transition between proliferation and quiescence of cancer cells, we conducted unbiased phosphoproteomic screen. We analyze a set of conditions including continuously proliferating cells, cells after 48 and 96 hours of serum withdrawal and cells after serum readdition after 20 minutes and 120 minutes and conducted unbiased proteomic and phosphoproteomic screen to identify novel signaling hub regulating the transition. Our screen identified Casein kinase II (CK2) as an important kinase regulating survival of quiescent cells and showed that inhibition of CK2 impairs survival of cells upon nutrient and genotoxic stress. Moreover, we showed that Death associated kinase 3 lies downstream of CK2 and is partially responsible for mediating the effect of CK2 inhibition. In conclusion, through unbiased phosphoproteomic screen we identified CK2 as an important survival kinase in breast cancer and targeting CK2 could be a therapeutic option for minimal residual disease (MRD) eradication. Materials and Methods Cell cultivation MDA-MB-231 and Hs578T cell lines were cultivated in DMEM – high glucose (Sigma-Aldrich), BT549 cell line was cultivated in RPMI (Sigma-Aldrich). Culture media were supplemented with 10% fetal bovine serum (FBS, Biosera), penicillin-streptomycin (100 U/ml, Sigma-Aldrich). For serum starvation, the cells were cultivated in the appropriate media type (DMEM/ RPMI) without the addition of FBS. To synchronize the cell cycle, cells were subjected to serum starvation for two days, followed by cultivation in media supplemented with FBS for one day. To induce quiescence, cells were then maintained in serum-free media for an additional 2 and 4 days. To assess the exit from quiescence, FBS was reintroduced for 20 or 120 minutes. As a control, asynchronously growing cells maintained in FBS-supplemented media were used. Cell viability assay MDA-MB-231, BT549 cells (10,000 cells/well) and Hs578T cells (4,000 cells/well) were seeded into 96-well plates and cultured in appropriate media type (DMEM/ RPMI) containing either 10% fetal bovine serum (FBS) or serum-free media. Cells were treated with respective compounds (CK2i – CX-4645, Doxorubicin) and were incubated at 37°C in a 5% CO₂ atmosphere for indicated timepoints dependent on the assay. At the designated time points, 20 µl of MTT solution (5 mg/ml) was directly added to each well containing 200 µl of media. The cells were then incubated for 1 hour at 37°C. Following incubation, the MTT solution was not removed, and the formazan crystals formed were solubilized by adding 100 µl of dimethyl sulfoxide (DMSO) to each well. The absorbance was measured at 595 nm using a Tecan Infinite ® M200 Pro microplate reader. Colony formation assay For the colony formation assay, 10,000 cells (MDA-MB-231, BT549, Hs578T) per well were seeded into 6-well plates and cultured in media supplemented with 10% fetal bovine serum (FBS). Cells were treated with either DMSO (vehicle control) or increasing concentrations of the CK2 inhibitor (CK2i): 0.2 µM, 0.5 µM, 1 µM, 2 µM, and 5 µM. Afterwards, cells were incubated at 37°C in a humidified atmosphere with 5% CO₂ for 10 days. Following the incubation, the culture media was removed, and the wells were washed twice with phosphate-buffered saline (PBS). Colonies were then fixed and stained with Crystal Violet Solution (5 mg/ml crystal violet in 10% ethanol, 50% methanol, and 40% water). After staining, excess dye was removed by rinsing with water. Images of the stained colonies were captured using a ChemiDoc™ MP Imaging System (Bio-Rad). Quantification was carried out using ImageJ software (https://imagej.net/ij/). Immunoblotting Cell cultures were washed with phosphate-buffered saline (PBS) and lysed in RIPA buffer (150 mM NaCl; 50 mM Tris-HCl, pH 7.5; 0.5% DOC; 0.1% SDS;1 % Triton) supplemented with 1x Halt TM Protease and Phosphatase inhibitor cocktail (Thermo Fisher Scientific, Cat# 78446). Protein concentrations in lysates were determined using the Pierce TM BCA Protein Assay (Thermo Fisher Scientific, Cat# A65453). Protein lysates were diluted in Laemmli sample buffer (4x concentrated: 0.5 M Tris-HCl, pH 6.8; 20% SDS; 40% glycerol; 0.02% Bromophenol blue; 0.2 M DTT). For immunoblotting, samples were separated on SDS-polyacrylamide gels and transferred onto PVDF membranes. Non-specific activity was blocked by incubating membranes for 60 minutes at room temperature in Tris-buffered saline (TBS) with 0.1% Tween® 20 detergent (TBST) containing 5% non-fat dry milk. Membranes were then incubated overnight at 4 °C with primary antibodies, 3 times washed with TBST, and incubated for 1 h at room temperature with horseradish peroxidase (HRP)—conjugated secondary antibodies. After washing 4 times in TBST, the blots were developed using the Immobilon® ECL Ultra Western HRP Substrate (Millipore, Cat# WBULS0100) and ChemiDoc TM MP Imaging system (Bio-Rad, Hercules, California, USA). Quantification of Western blots was carried out using ImageJ software (https://imagej.net/ij/). De novo proteosynthesis assay De novo proteosynthesis was assessed using the Click-iT TM HPG Alexa Fluor TM 488 Protein Synthesis Assay Kit (Invitrogen, Cat# C10428). Cells were pre-treated with 1 µM, 5 µM, or without CK2i for 2 days before the assay followed by incubation with 50 µM Click-iT TM HPG for 2 hours, trypsinized and washed twice with PBS. De novo proteosynthesis was assessed following the manufacturer’s protocol. In brief, cells were fixed using of 3.7% formaldehyde for 15 minutes at room temperature, followed by two washes with 200 µl of 3% BSA in PBS. Permeabilization was performed by incubating cells with 200 µl of 0.5% Triton® X-100 in PBS for 20 minutes at room temperature, followed by two additional washes with 3% BSA in PBS. Next, 200 µl of the Click-iT TM reaction cocktail (containing 5 times less Alexa Fluor TM azide) was added, and cells were incubated accordingly. After incubation, they were washed with 200 µl of Click-iT TM reaction rinse buffer and resuspended in PBS. Protein synthesis was analyzed using CytoFLEX flow cytometer (Beckman Coulter Life Sciences) and data were processed using FloJo software (BD). Immunoprecipitation For immunoprecipitation experiments, cells were grown on 10 cm dish and treated with CX-4945 or vehicle for 24 hours. Cells were lysed in immunoprecipitation buffer (10 mM Tris (pH 7.5), 1% NP-40, and 2 mM EDTA). supplemented with 1x Halt TM Protease and Phosphatase inhibitor cocktail (Thermo Fisher Scientific, Cat# 78446) and protein concentration was estimated using the Pierce TM BCA Protein Assay (Thermo Fisher Scientific, Cat# A65453). Protein A/G magnetic beads (Pierce TM ) were washed in immunoprecipitation buffer and incubated with target antibody or isotype control (1 ug) for 2 hours at 4°C with gentle rotation. Afterwards, beads were washed to remove unbound antibodies and mixed with equal amounts of protein lysates (1000μg) and incubated at overnight at 4°C with gentle rotation. After incubation, beads were washed 4 times in immunoprecipitation buffer and subsequently analyzed using western blot or mass spectrometry. Cell cycle analysis Indicated cell lines were treated with CX-4549 at indicated concentration for 24 hours, harvested by trypsinization, washed twice with ice-cold PBS, and fixed dropwise in 70% ethanol while vortexing gently for 24 hours at −20°C. Prior to staining, cells were washed with PBS and incubated in staining buffer containing 50 µg/mL propidium iodide (PI), 100 µg/mL RNase A, and 0.1% Triton X-100 in PBS for 30 minutes at room temperature in the dark. Stained cells were analyzed using CytoFLEX flow cytometer (Beckman Coulter Life Sciences) and data were processed using FlowJo software (BD). Cell cycle distribution was determined by quantifying the percentage of cells in G0/G1, S, and G2/M phases based on DNA content. Lysotracker staining Cells were treated with indicated concentrations of CX-4549 for 24 hours, trypsinized, counted and 500.000 cells were stained with 50nM Lysotracker TM Green DND-26 (Thermo Fisher Scientific) for 5 min. After incubation, cells were washed two times and resuspended in PBS + 1m EDTA + 1% BSA. Finally, cells were analyzed using using CytoFLEX flow cytometer (Beckman Coulter Life Sciences) and mean fluorescence intensity was extracted using FlowJo software (BD). Microtubule regrowth assay Indicated cell lines were plated onto coverslips and cultivated overnight at 37°C in a humidified incubator. Cells were pretreated with indicated concentration of CK2i for 2 hours followed by treatment with 10 µM nocodazole (Merck) for 3 hours at 37°C to depolymerize microtubules. After treatment, cells were washed three times with warm PBS to remove residual nocodazole and immediately transferred to pre-warmed nocodazole-free complete medium to allow microtubule regrowth. Regrowth was allowed to proceed at 37°C for 15 minutes and cells were fixed with 4% paraformaldehyde in PBS for 10 minutes at room temperature followed by permeabilized with 0.25% Triton X-100 in PBS for 10 minutes. Cells were then blocked with 5% BSA in PBS for 30 minutes, followed by incubation with anti-α-tubulin antibody (DM1A, 1:200, SantaCruz Biotechnology) for 2 hours at room temperature. After washing, coverslips were incubated with Alexa Fluor 488-conjugated anti-mouse secondary antibody (1:1000) for 1 hour at room temperature. Nuclei were counterstained with DAPI, and coverslips were mounted using ProLong TM Mountant (Invitrogen). Images were acquired using Leica Dmi8 inverted fluorescence microscope (Leica). Mass Spectrometry Protein Digestion Samples were mixed with 2% SDC in 100 mM TRIS buffer (pH 8.5), boiled at 95°C for 5 min and further sonicated using micro probe sonicator (Bandelin Sonoplus). Protein concentration was determined using BCA protein assay kit (ThermoFisher Scientific). 250 µg of protein per sample was mixed with 40 mM CAA, 10 mM TCEP and 5 M KOH and heated at 45°C for 5 min. Proteins were digested by 5 µg of trypsin per sample at 37°C overnight. Phosphopeptides were enriched using TiO 2 according to Humphrey et al. 29 . After enrichment peptides were desalted using in-house made stage tips packed with C18 disks (Empore) according to Rappsilber et al. (Rappsilber et al., 2007). nLC-MS 2 Analysis Nano Reversed phase columns (Ion Opticks Ultimate TS 25 cm x 75 µm ID, C18 UHPLC column, 1.7 µm particles, 120 Å pore size) were used for LC/MS analysis. Mobile phase buffer A was composed of water and 0.1% formic acid. Mobile phase B was composed of acetonitrile and 0.1% formic acid. Samples were loaded onto the trap column (C18 PepMap100, 5 μm particle size, 300 μm x 5 mm, Thermo Scientific) for 1 min at 18 μl/min loading buffer was composed of water, 2% acetonitrile and 0.1% trifluoroacetic acid. Peptides were eluted with Mobile phase B gradient from 4% to 25% B in 28 min and from 25% B to 35% in next 2 min followed by 5 min wash with 75 % B. Eluting peptide cations were converted to gas-phase ions by electrospray ionization and analyzed on a Thermo Orbitrap Ascend. Survey scans of peptide precursors from 350 to 1400 m/z were performed in orbitrap at 120K resolution (at 200 m/z) with a 100 % ion count target. Tandem MS was performed by isolation at 1,6 Da with the quadrupole, CID fragmentation with normalized collision energy of 30 % and 10 ms activation time. Fragmentation spectra were acquired in ion trap with scan rate set to Normal. The MS2 ion count target was set to 200 % and the max injection time was 200 ms. Only those precursors with charge state 2–6 were sampled for MS2. The dynamic exclusion duration was set to 30 s with a 10ppm tolerance around the selected precursor and its isotopes. Monoisotopic precursor selection was turned on. Cycle time was se to 2 s. Data analysis All data were analyzed and quantified with the MaxQuant software (version 2.4.13.0) 31 . The false discovery rate (FDR) was set to 1% for both proteins and peptides and we specified a minimum peptide length of seven amino acids. The Andromeda search engine was used for the MS/MS spectra search against the Human database (downloaded from Uniprot in March 2023, containing 20 605 entries). Enzyme specificity was set as C-terminal to Arg and Lys, also allowing cleavage at proline bonds and a maximum of two missed cleavages. Carbamidomethylation of cysteine was selected as fixed modification and N- terminal protein acetylation, methionine oxidation and serine, threonine and tyrosine phosphorylation as variable modifications. The “match between runs” feature of MaxQuant was used to transfer identifications to other LC-MS/MS runs based on their masses and retention time (maximum deviation 0.7 min) and this was also used in quantification experiments. Quantifications were performed with the label-free algorithm in MaxQuant 32 . Data analysis was performed using Perseus 1.6.15.0 software 33 . Only those phosphosites with localization probability higher than 0.75 were used for further data analysis. Data visualizations Heatmaps, volcano plots, and Gene Ontology (GO) pathway analyses were performed using SRplot 34 , an interactive web-based visualization platform for omics data. Kaplan meier curves were constructed using kmplot webtool platform 35 . Kinase motif logos were generated using PhosphoSitePlus webplatform 36 . Results Phosphoproteomic analysis of serum withdrawal-mediated quiescence induction and re-entry into proliferation. To date, there is a vey limited number of large scale proteomics and phosphoproteomics screens comparing proliferating and arrested cells and majority of them is focused on non-transformed cell types or analyzing stable conditions rather than dynamic transition between the two states 37–39 . To address the gap, we conducted performed and unbiased proteomic and phosphoproteomic analysis of cells on the transition from proliferation to serum withdrawal-mediated quiescence followed by serum addition-mediated reentry in cell cycle ( Fig. 1 ). For the analysis we selected triple negative breast cancer cell line MDA MB 231 as a model based on several reasons. It represent the most aggressive breast cancer tumor subtype with 5 year survival at 77% for all the stages, and 12% for stage IV disease which has higher rates of metastasis formation and tumor recurrence peaking at 2-3 years, indicating presence of QCCs 40 Additionaly, QCC population seems to be enriched in TNBC to a bigger extent in comparison to other breast cancer subtypes 41 . To enrich the QCC population we used serum withdrawal as quiescence inductor as it represents more physiological condition compared to cytotoxic compounds or pharmacological induction (e.g. using CDK4/6 inhibitors) 42 . Workflow and sample collection timepoints are outlined in Fig. 1A . 24 hours after plating the cells we collected the sample for continously growing cells and we performed the first synchronization step by serum withdrawal for 48 hours followed by serum readdition for 24 hours. Afterwards, we induced quiescence through serum withdrawal and collected samples 48 hours after witdrawal to capture the initial steps of quiescence induction, and 96 hours after serum withdrawal to capture quiescent cells without triggering excesive apoptosis. At this time, serum was added to the cells to initiate quiescence exit and samples were collected at +20 minutes and +120 minutes to capture initial steps of proteome and phophoproteome remodeling. To validate the approach and selected timepoints, we analyzed the status of the cells using western blot for key proteins indicating proliferative status of the cells – Rb1 phosphorylation, p27Kip and E2F8. Hypophoshorylation of Rb1, absence of E2F8 and increased levels of p27Kip are well established markers of cells in quiescent state 43–45 . Our western blot analysis of selected conditions shows hyperphosphorylated Rb1, high levels of E2F8 and low levels of p27Kip in continuously growing cells ( Supplementary Fig. 1 ). On the other hand, levels of hyper-phosphorylated Rb1 and total E2F8 are low in 96 hours serum-starved cells, while the level of p27Kip is the highest indicating significant enrichment of quiescent cells population ( Supplementary Fig. 1 ). After validation of the approach, the samples for unbiased phosphoproteomic analysis were collected in pentaplicates and processed at the same time followed by label-free mass spectormetry analysis. Overal the proteomic analysis identified 5328 individual proteins across all the conditions, out of which 4419 proteins were identified in all the conditions in every replicate ( Fig. 1B, left ). Furthermore, phosphoproteomic analysis identified 6953 phosphorylation sites present on 2142 proteins across all the conditions. Phosphosites identified across all the conditions were significantly reduced by almost 50% in both phosphorylation sites (3544 sites identified in all conditions) as well as phospohrylated proteins (1327 sites identified in all conditions) which was likely a results of biological change in global phosphorylation level, rather then technical limitation since in condition where higher level of phosphorylation was expected (continuously growing) the number of identified phosphosites as well as phosphoproteins was significantly higher – 5142 phosphosites and 1702 phosphoproteins ( Fig. 1B, right ). Global cellular proteome is significantly remodeled at the exit of the cells from quiescent state To interpret the complex patterns emerging from mass spectrometry (MS)-based proteomics we first constructed volcano plots identifying key changes in cells exiting proliferation and entering quiescence (Fig. 2A ). While we see significant changes in proteome after 48 hours of serum removal, these changes are much more pronounced in cells after 96 hours of serum removal where population of quiescent cells is higly enriched. As expected we can see significant downregulation of key proliferation drivers such as CCNB1, DLGAP5, AURKA, KIF11 or TACC3 and concominant upregulation of tumor microenvironment-affecting proteins such as FN1, THBS1, TNFSF15 or PTX3 ( Fig. 2A ). On the other hand, proteome changes in cells at the exit from quiescence into proliferation mode are less pronounced however uncovers interesting signaling events governing the first steps in the process. At 20 minutes timepoint after serum addition we could see only minor global changes as expected however we identified HMGN2 as a key protein that is being translated, which is in line with its role as as chromatin remodeler enabling expression of key cell cycle genes 46 ( Fig. 2B, left ). At 120 minutes timepoint after serum addtion we can see stimulation of the expression of key transcription factors driving cell cycle – Jun B, Jun D, FOSL1 and ELF1, while also HMGN2 expression still being high. On the other had we can already see degradation of quiescence maintaining proteins such FN1, THBS1 or HSPG2 indicating a key role of proteasome degradation in quiescence exit ( Fig. 2B, right ). To support our approach and analysis, we further constructed heatmap ( Fig. 2C ) as well as conducted pathway enrichment analysis ( Fig. 2D-E ). Heatmap analysis confirmed gradual proteome changes of cells exiting the proliferation into quiescence followed by reactivation by addition of serum, and identified clusters of proteins that are specific for each of the analyzed steps or specific for transition to and from the quiescent state ( Fig. 2C ). Finally, we analyzed enriched GO biological processes and compartments specifically in quiescent condition (Fig. 2D-E ). As expected, major downregulated pathways are associated with DNA replication, cell cycle progression and mitosis ( Fig. 2D ) while pathways such as oxidative phosphorylation or exocytosis are significantly upregulated ( Fig. 2E ). Interestingly, pathways regulating extracellular matrix remodeling are most enriched in quiescent cells further emphasizing the key role of quiescence niche ( Fig. 2E, bottom ). Protein phosphorylation network regulates cellular behavior at the transition points between quiescence and proliferation. To complement the data from proteomic analysis, we analyzed global changes in protein phosphorylation at the transition between proliferation and quiescence in pentaplicates. First we constructed volcano plots ( Fig 3A-B ) and heatmap ( Fig 3C ) to investigate the dynamics of global protein phosphorylation. Both analyses showed continuum of phoshporylations where after 48 hours of quiescence induction we see similar amounts of phosphosites being upregulated as well as downregulated indicating active rewiring of the signaling ( Fig 3A, left ) while in condition enriched for quiescent cells we see majority of the phosphosites being downregulated confirming lower activity of signaling pathways ( Fig 3A, right ). On the other hand, as expected, after serum addition and cell signaling reactivation we see a significant shift in phosphorylation where majority of significantly altered phosphosites are upregulated in early (94%) as well as late (90%) quiescence exit stages ( Fig. 3B ). More detailed analysis of altered phoshporylation revealed patterns specific to individual stages. As expected, activating phosphorylation of proteins regulating cell cycle (Rb1, SRSF1), transcription (CTR9, TCOF1), translation (4EBP1, EIF3D) and mitosis (LMNB2, TOP2A, Ki67, TPX2) was downregulated in both serum starved conditions ( Supplementary Table 1, Fig. 3 ). On the other hand, we identified proteins with increased site specific phosphorylation significantly upregulated in quiescent stage, SCRIB, EDC4, CIC, SAP30, MAP1B, SP4 and STARD3NL being upregulated by more than 10 fold in comparison to continously proliferating cells ( Supplementary Table 1) . Phosphorylation of these proteins thus could serve as active quiescence maintaining signaling that might be targeted to eliminate quiescent cancer cells. While we have identified numerous previously identified sites to be altered according to the established signaling in proliferative and quiescent cells, we have identified 215 previously undescribed phosphorylations present on 141 individual proteins (Fig 3D). Out of these novel sites, there are 36 sites whose phosporylation is significantly altered across different conditions and could represent novel regulatory circuits governing transition between quiescence and proliferation. Identified proteomic and phospohproteomic alterations were validated in vitro as well as in silico patient dataset. Although our proteomic and phosphoproteomic analysis was robuts, we followed to validate some of the findings in two sets of experiments. First we analyzed total levels as well as site-specific phoshporylation of several proteins in 3 triple negative breast cancer cell lines – MDA MB 231, Hs578T and BT549. First we looked at major cell cycle regulator Retinoblastoma protein 1. In MDA MB 231 as well as Hs578T the phoshporylation of CDK-directed sites (S807/S811) was consistently reduced in cells after 96 hours of serum starvation and we did not see significant increase after 20 nor 120 minutes of serum addition ( Fig. 4A ) which is consistent with the fact that Rb1 is not hyperphosphorylated until the beginning of S-phase 47 . Additionally, we analyzed the levels of DLGAP5 as a key mitosis promoting protein and we could see complete absence of the protein in MDA MB 231 and Hs578T cells ( Fig. 4A ). However, in Rb1 negative cell line BT549, DLGAP5 was still present at 96 hours after serum starvation. Additionally, p27 as a major quiescence regulatory protein was consistently increased in cells arrested in G0 phase however it was only partially degraded after serum addition which is consistent with its role as a platform for CDK4/6-Cycline D complex formation 48 . Finally, our in vitro experiments showed major changes in proteosyntetic pathway where phosphorylation of RPS6 S235/236 was the most consistent change across all three cell lines ( Fig. 4A ). Interestingly, total level of 4EPB1 phosphorylation was consistently decreased in serum-starved cells and bounced right back after serum additon as shown by the protein band shifts, however exact position of this phosphorylation seems to be cell line dependend as evidend by distinct pattern of T37/46 phosphorylation indicating a phosphorylation code rather then site-specific events. Additionally, we sought to validate our results using publically available datasets. To this aim we analyzed CPTAC database using cBioPortal webtool ( cbioportal.org ). We analyzed a dataset including 122 patient samples containing genomic as well as proteomic data 49 and split the patient data into high proliferative and low proliferative cohorts based on the amplification status of MYC. MYC amplification was selected because this genetic alteration defines high and low proliferative tumors and this alteration is correlated with higher Ki67 staining across different tumor types, including TNBC 49,50 . To validate our approach we first compared phosporylation of Rb1 and Ki67 in MYC amplified (high proliferation cohort) and MYC not-amplified (low proliferation cohort) and showed that levels of phosphorylation across various sites is significantly decreased for pRb1 (p=0.002) as well as for pKI67 (p=5.57e-15) ( Fig. 4B ). Additionally, we analyzed the total level of cell cycle proteins Cyclin B1 and Ki67 and confirmed higher level of these cell cycle drivers in MYC amplified breast cancer tumors ( Fig. 4C ). After validation of the approach, we compared protein levels of 10 most downregulated proteins from our screen in quiescent conditions with data from patient CPTAC samples ( Fig. 4D-E ). The analysis revealed that all the proteins were upregulated in MYC amplified (high proliferative tumors) to a various extent, UBE2S, CCNB1, TPX2 and NDC80 being significantly changed (p>0.05) thus confirming validity of our in vitro proteomic screen. Next, we compared the phosphorylation status of sites identified in our screen with the ones from CPTAC dataset ( Fig. 4F-G ). There was very limited overlap and we were able to identify 7 sites whose phosphorylation was significantly upregulated in high proliferative cohort – TCOF_pS1191, DDX21_pS71, TPX2_pS738 and Ki67_S357 ( Fig. 4F-G ). CK2 kinase substrates are upregulated in response to nutrient stress. To identify major signaling events in regulation of quiescence entry and maintenance, we analyzed phosphorylation motifs that were upregulated in cells after 96 hours of serum starvation ( Fig. 5A ). One of the motifs whose phosphorylation was upregulated was pS-D-D/E-D/E which is targeted by CK2 ( Fig. 5A ). To test the hypothesis that CK2 substrates phosphorylation is upregulated after serum withdrawal we analyzed the cell lysates using pCK2 substate antibody that recognizes pS/p/T-DXE motif in three TNBC cell lines. We first validated analyzed MDA MB 231 as cell line where we conducted the phosphoproteomic analysis and found out that there was a significant increase in phosphorylation of S/T-DXE motif after serum removal across various molecular weights confirming our previous results ( Fig. 5B, left ). We could see a gradual decrease in signal strength at some molecular weights which could indicate that as the cells go back to cell cycle, CK2 activity is reduced to basal level. Similar results were obtained in Hs578T and BT5489 cells however less pronounced than in MDA MB 231 ( Fig. 5B, center and right ). Because we saw increased CK2 activity in stressed conditions, we asked whether CK2 could be important for therapy response since anticancer therapy is activating stress signaling pathways as well. Thus we analyzed TCGA dataset using KMplot web interface 51 . We first looked at the expression of Csnk2a1 mRNA in subtypes of breast cancer and found out that while in luminal A subtype the expression of Csnk2a1 was not associated with worse patient survival ( Fig 5C ), in other subtypes (luminal B, Her2-enriched and Basal) the expression was significantly negatively correlated with patient survival with the most significant effect in basal subtype with HR = 2.38 ( Fig. 5C ). More importantly, when we analyzed the association of Csnk2a1 expression and response to therapy, we saw that there is no effect of Csnk2a1 expression levels on the response of endocrine therapy ( Fig 5D, top ) standardly used in Luminal A 52 , however there was a strong negative correlation between Csnk2a1 expression and outcome of chemotherapy ( Fig 5D, bottom ) that is standardly used for basal subtype. Inhibition of CK2 has a profound effects on various cellular pathways. To further investigate the role of CK2 in TNBC cells we utilize highly specific inhibitor CX-4945 (silmitasertib) which is already being tested in clinical trials 53 . First we tested the effect of CK2 inhibition on growth of TNBC cell lines using colony formation assay and saw that at low concentrations (1μM) there is a moderate decrease in proliferation in all three cell lines ( Fig. 6A ), while increasing the concentration to 5μM exerted a strong growth inhibition effect ( Fig. 6A ) consistent with previous results 54,55 . Furthermore, we wanted to validate the role of CK2 in the context of previously published results thus we analyzed the effect of CK2 inhibition on authophagy which is one of the known pathways regulated by CK2 56,57 . To this aim we treated the cells with CX-4945 and analyzed the authophagy induction by western blot for LC3 isoforms. Our results show that CK2 inhibition stimulated increased level of the smaller LC3 isoform in all three cell lines at 5 μM, and in MDA-MB-231 and Hs578T even at 1 μM ( Fig. 6B ). We further confirmed our results from western blot using lysotracker (Invitrogen) staining followed by flow cytometry. Lysotracker is a specific dye for acidic compartments and in the context of autophagy it specifically stains autophagosomes 58 . CK2 inhibition increased the size of the autophagosomes leading to increased overall Lysotracker signal detected by flow cytometry in all three cell lines ( Fig. 6C ). Since CK2 has been implicated in regulation of microtubule dynamics 59,60 we wanted to validate that CK2 retains this function also in our conditions. To this aim we performed nocodazole washout experiment to investigate the regrowth dynamics of microtubules after depolymerization. We compared untreated cells with cells pretreated with CK2 inhibitor for 1 hour and qualitatively analyzed the regrowth of microtubules 15 minutes after nocodazole washout. The experiments showed that repolymerization of microbutules in significantly impaired in CK2 pretreated cells in comparison to control and the effect is consistent across all three analyzed cell lines ( Fig. 6D, bottom row, small image inserts ). Additionally, we performed cell cycle analysis using flow cytometry and revealed that CK2 inhibition leads to increased proportion of G2/M phase cells which indicates involvement of microtubules consistent with our previous resutls ( Fig. 6E ). Taken togehter we could draw the conclusion that there is a gradient of sensitivity to CK2 inhibition in the tested cell lines and our results consistently indicate that MDA MB 231 cell line is the least sensitive while Hs578T and BT549 are much more sensitive to CK2 inhibition. Finally, to tie the function of CK2 back to the results from our phosphoproteomic analysis we analyzed two pathways that contained high number of putative CK2 substrates based on the CK2 consensus sequence – proteosynthesis and microtubule dynamics. First we investigated the role of CK2 in sustaining proteosynthesis using Click-iT™ HPG Protein Synthesis Assay (ThermoFisher). We pretreated the cells uising CK2 inhibitor for 24 hours and then incubated the cells with L-homopropargylglycine (HPG) reagent to label nascent proteins for 2 hrs. Subsequent flow cytometry analysis revealed that CK2 inhibition lead to decreased nascent proteosynthesis in all three cell lines, however to a various degree ( Fig. 6F ). In MDA-MB-231 we saw significant impairment of nascent proteosynthesis only in cells treated with 5μM CX-4945, while in other two cell lines (Hs578T, BT549) the effect of CK2 inhibition on proteosynthesis was much more profound, strongest inhibition seen in Hs578T ( Fig. 6F ). CK2 Supports Stress Survival by Suppressing DAPK3-Mediated Apoptosis in TNBC Cells Finally, we wanted to understand what is the role of CK2 in response to stress. To this aim we withdrew FBS to stimulate nutrient stress or treated the cells with doxorubicin to mimick anticancer therapy standardly used for TNBC cancer treatment 61 and simoultanously treated cells with CK2 inhibitor. In serum-starved condition, our experiments revealed that viability of cells is compromised in serum starving cells treated with CK2 inhibitor as assessed by cell survival asssy as well as PARP cleavage ( Fig. 7A-B ). Serum withdrawal or CK2 inhibition individually decreased the rate of proliferation however inhibition of CK2 in combination with serum withdrawal had a profound detrimental effect on cell viability which was consistent with PARP cleavage ( Fig. 7A-B ). When comparing individual cell lines, we see that MDA MB 231 cells were the least sensitive to CK2 inhibition in combination with serum withdrawal ( Fig. 7A-B, top row ), while Hs578T and BT549 were much more sensitive to CK2 inhibition in serum starved conditions and even 1μM CK2 inhibitor resulted in strong PARP cleavage and cell viability decrease ( Fig. 7A-B, middle and bottom row ). Additionally, we performed similar experiments with genotoxic stress mimicking anticancer therapy using doxorubicin. The results are aligned with our experiments using serum withdrawal as stress factor. Treatment of MDA MB 231 and BT549 cell lines with CK2 inhibitor (5μM) potentiates the effect of doxorubicin even at 0.2 μM concentration where we saw significant killing effect after 48 and 72 hours ( Fig. 7C ). In Hs578T cells the co-treatment with CK2 inhibitor (5μM) and doxorubicin (0.2 μM) led to significant decrease in proliferation in comparison to CK2i or doxorubicin alone ( Fig. 7C ). Furthermore we also assessed the apoptosis induction using PARP clevage in these conditions. In MDA MB 231, the inhibition of CK2 potentiates the cytotoxic effect of doxorubicin where 0.5uM doxorubicin lead to minimal PARP cleavage (2.8%) without CK2 inhibition while the same concentration of doxorubicin along with CK2 inhibition lead to substantial PARP cleavage (34.5%) ( Supplementary Fig. 2A ). In more sensitive cell lines Hs578T and BT549, the combinatory treatment had a profound effect even at lower concentrations starting at 0.2uM doxorubicin (Hs578T: 1.7% cleaved PARP in dox-only and 20.2% cleaved PARP in dox+CK2i; BT549: 26.5% cleaved PARP in dox-only and 77.9% cleaved PARP in dox+CK2i) ( Supplementary Fig. 2B-C ). To further characterize CK2 dependencies in TNBC we sought to identify downstream targets of CK2. To this end we treated the MDA MB 231 cells with CK2 inhibitor and then performed pull down using pCK2 substrates antibody to increase the specificity of our approach. Our analysis identified 528 proteins present in all 6 experiments (2 triplicates +/- CK2 inhibitor) out of which 139 with significantly higher presence in not treated pull down in comparison to treated cells (Fig 7C). We have identified known CK2 substrates such as EIF4E, EIF4G2, EIF3D, MDC1, MYH10, CAPZA1 62,63 among the enriched proteins in not treated conditions, while there were multiple previously not described or validated. Pathway analysis of putative substrates showed enrichment of CK2-regulated pathways such as translation regulation, RNA metabolism, stress response and autophagy ( Supplementary Fig. 3A ). One of the most enriched proteins in control samples was Death-associated protein kinase 3 (DAPK3) which has been implicated in regulation of authophagy, apoptosis and cellular contractility 64–66 . Additionally, several of the DAPK3 interacting partners such as MYL9, PPP1R12A, CALM3 or LUZP1 were also enriched in our pull down analysis ( Supplementary Fig. 3B ). Finally, in silico sequence and structure analysis confirmed the presence of putative CK2 phosphorylation consensus motif (T 112 EDE) located on the surface, and interestingly mutation of this site impairs DAPK3’s kinase activity 67 ( Supplementary Fig. 3C) . Therefore we decided to investigate functional association of CK2 and DAPK3. To this end, we stressed MDA MB 231 cells by serum deprivation, treated the cells with CK2 and DAPK3 inhibitors and analyzed apoptotic signaling by PARP cleavage. As expected, inhibition of CK2 in serum-starved cells lead to significant increase in PARP cleavage while co-treatment with DAPK3 inhibitor partially rescued the increase PARP cleavage ( Fig. 7F ) indicating a functional interaction between CK2 and DAPK3. Taken together we proposed a model where in the normal growth conditions, pro apoptotic function of DAPK3 is inhibited by CK2 and likely also other kinases through phosphorylation. When cells encounter adverse environmental conditions, the activity of prosurvival pathways is decreased while the activity of CK2 is sustained which enables inhibition of pro apoptotic function of DAPK3. Finally, inhibition of CK2 would lead to increased activity of DAPK3 and subsequent cell death ( Fig. 7G ). Discussion The ability of cancer cells to enter the quiescent mode poses a major challenge for complete cancer eradication and is crucial for emergence of minimal residual disease (MRD). These cells, which reside in a non-proliferative, dormant state, often evade conventional therapies that target rapidly dividing populations. As a result, they can remain undetected after initial treatment, contributing to MRD and posing a significant risk for relapse. Therefore understanding the molecular mechanisms that maintain quiescence, allow cancer cells to survive long therm in such state and resists the therapy is critical for developing strategies to eradicate MRD and achieve long-term remission. Although the core circuits regulating mammalian cell quiescence such as CDKs, CKIs or Rb1 are well described 47,68 and are in place also in cancer cells to a certain degree, targeting of these mechanisms is not feasible without affecting normal cells. Therefore it is crucial to find upstream regulatory mechanisms specific to cancer cells, however to date there is a limited number of large scale proteomic analyses dynamically capturing the transition from proliferation to quiescence and back to cell cycle. More importantly the quiescence is not passive stage however it it activelly maintained 69–71 . It has been shown that protein posttranslational modifications such as ubiquitination and phosphorylation are key events regulating progression through cell cycle therefore we performed integrative proteomic and phosphoproteomic analysis of transition between proliferation and quiescence of cancer cells to capture the dynamics of protein phosphorylation and overal protein remodelation. As expected, our analysis revealed that proteome is significantly remodeled as cancer cells exit cell cycle with the most pronounced downregulation seen in mitosis promoting proteins such as CCNB1, DLGAP5 or AURKA. On the other hand, there is a significant increase in production of extracellular matrix (ECM) proteins and regulators such as FN1, THBS1 or PTX3. Increased production and stabilization of ECM is and adaptive mechanism in stress response and might be inherent characteristic of quiescent niche 42 . Indeed it has been shown that quiescence breast cancer cells upregulate production of extracellular matrix which is crucial for quiescence maintenance and survival of these cells 42 . ECM provides survival signaling mediated through integrins which supplements the lack of growth-factor stimulated survival signals 72 . Additionally, thicker layer of ECM provide shielding from environmental factors and in the context of cancer biology, it protects quiescent cells from therapy and immune cell attack 73,74 . The key role of ECM in quiescence regulation is further emphasized by strong degradation of FN1 or THBS1 at the exit of cells from quiescence. On the other hand, one of the key proteins for cells reactivation identified in our screen is HMGN2. HMGN2 is a nucleosome remodeling factor that has been implicated in the maintenance of poised chromatin state in different systems 75 . This poised states is characteristic for quiescent cancer stem cells which stimulates the plasticity 76 . Moreover, HMGN2 is important for efficient expression of early cell cycle regulatory genes and its mRNA is relatively stable during the cell cycle 77,78 . In line with our data, we could speculate that HMGN2 is expressed at a basal level in quiescent cells to control reversibility of the cell cycle exit and upon mitogenic stimulation, the translation is increased facilitating expression of early G1 genes such as Jun B, Jun D or FosL1 whose expression is significantly increased after 120 min of mitogenic stimulation in our conditions. Our phosphoproteomic analysis also yielded results consistent with the state of knowledge while also identified potential novel regulatory circuit in breast cancer cell quiescence. Specifically looking at the regulation of quiescent state, we have idenfied casein kinase 2 (CK2) as potentially critical for survival of quiescent cells. CK2 is a constitutively active serine/threonine kinase that was implicated in regulation of various cellular pathways through phosphorylation of its substrates 79 . Kinase motif analysis of our data identified phosphorylation of CK2 consensus acidic sequence (S/T-D/E-D/E-D/E) to be increased in cells entering the quiescence. More detailed analysis of known CK2 substrates showed increased phosphorylation of SCRIB, EDC4, CIC, SAP30, MAP1B, SP4 and STARD3NL at the transition to quiescence indicating that CK2 activity might be either directly regulating entry into the quiescence or important for survival of quiescent cells. We confirmed increased phosphorylation of CK2 substrates in 2 different TNBC cell lines and to a certain degree also in the third one – BT549. Differences in the third cell line might stem out from lack of Rb1 protein which is one of the key core regulator of cell quiescence 80,81 . Interestingly, when we looked at the association of CK2 expression and response to therapy we see that patients with low expression of CSNK2A1 – gene coding alpha subunit of CK2, are responding very well to cytotoxic therapy, while patients with higher CSNK2A1 expression have significantly worse clinical outcome. Since cytotoxic therapy is one of the stimuli driving quiescence as a survival mechanism 21,82 , it further strengthen our hypothesis that CK2 is important for cancer cell quiescence and minimal residual disease. Finally we wanted to test whether inhibition of CK2 would lead to lower survival of quiescent cancer cells. To this aim we stressed the cells by withdrawal of serum to stimulate nutrient depletion-induced quiescence as well as by adding doxorubicin to mimick the anticancer therapy-induced quiescence. Inhibition of CK2 in these stressed conditions led to significant decrease in viability of all three tested cell lines which indicates that CK2 activity is indeed important for survival of cells upon stress. It has been previously shown that CK2 is activated upon various stress in a p38-dependent manner 83 . Increased activitity of CK2 then leads to phosphorylation of its downstream targets that are important for survival upon stress such as NRF2, HSP90 or JNK1 26,84,85 . To further delineate the pathway downstream of CK2 in our model system, we performed pull down experiment using pCK2 substrate antibody and identified the proteins differentially phosphorylated in cell treated with CK2 inhibitor with vehicle treated cells. Besides known CK2 substrates such as EIF4E, EIF4G2, MYH10 or CAPZA1 62,63 , we identified Death Associated Protein Kinase 3 (DAPK3) as a potential CK2 substrate, being one of the proteins with the most significant downregulated phoshporylation after CK2 inhibition. DAPK3 has been initially identified as a pro-apoptotic kinase, integrating signals from various stress pathways and promoting apoptosis and autophagy 64,65,86 . DAPK3 sequence analysis revealed that it contains one potential CK2-phosphorylation site – Threonine 112 followed by a stretch of acidic aminoacids E-D-E, which corresponds to the CK2 phosphorylation consensus 87 . Interestingly, cancer-associated missense mutation of this site was associated with decreased activity of DAPK3, suggesting its role as tumor suppressor kinase 67 . We hypothesized that CK2-mediated phosphorylation of T112 could lead to downregulation of DAPK3 activity or altered localization which might results in inhibition of apoptosis mediated by lack of extracellular prosurvival signaling. To test the hypothesis, we treated serum starved cells with CK2i as well as DAPK3i and analyzed apoptotic signaling through PAPR cleavage. Our data show that inhibition of DAPK3 leads to partial rescue of PARP cleavage in response to serum starvation and CK2 inhibition. The results are in line with published literature on pro-apoptotic role of DAPK3 88,89 as well as the importance of T112 for DAPK3 functionality 67 . Additionally, our model is supported by another high throughput study that showed that CK2-targeted phosphosites are significantly enriched in gefitinib-resistant PC9 cells compared to parental cells, and one of the sites phosphorylated by CK2 was T112 on DAPK3 88 . While our integrative proteomic and phosphoproteomic approach provides critical insights into the signaling dynamics of quiescent cancer cells (QCCs), we are acknowledgeing several limitations. Our analyses were conducted in vitro in triple-negative breast cancer (TNBC) cell lines. Although we validated several key findings across three independent models and in patient datasets, future studies involving patient-derived organoids or in vivo models will be necessary to confirm the physiological relevance of CK2-dependent signaling in quiescence and minimal residual disease (MRD). Furthermore, while CK2 was implicated as a major survival kinase in QCCs, the exact mechanism by which CK2 regulates downstream effectors such as DAPK3 remains to be fully elucidated. Phosphorylation of DAPK3 at T112 appears functionally relevant, but additional studies including site-directed mutagenesis, phospho-deficient or phospho-mimetic constructs, and rescue experiments are needed to validate its regulatory role. In summary, our study provides the first comprehensive phosphoproteomic characterization of TNBC cells transitioning into and out of quiescence and identifies Casein Kinase 2 (CK2) as a key survival kinase in the quiescent state. We demonstrate that CK2 activity is enhanced during cellular stress, supports survival signaling, and suppresses pro-apoptotic pathways through modulation of targets such as DAPK3. These findings suggest that CK2 enables quiescent cancer cells to withstand both nutrient deprivation and cytotoxic therapy, contributing to minimal residual disease and relapse. Targeting CK2 in combination with conventional therapies may represent a promising strategy to eliminate therapy-resistant quiescent cancer cell populations and improve long-term treatment outcomes in aggressive breast cancer subtypes. Nonetheless, further in vivo and mechanistic studies are required to fully validate CK2’s role in quiescence regulation and therapeutic resistance. Declarations Data Availability Statement The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request. Authors‘ contributions: LC performed majority of the experiments, co-wrote the manuscript and co-prepared the figures. RJ designed and supervised the experiments, co-wrote the manuscript and co-prepared the figures. Both authors approved the final version of the manuscript and figures. Acknowledgements: LC-MS analyses were performed in Laboratory of Mass Spectrometry at Biocev research center, Faculty of Science, Charles University. Funding This work was supported by the Primus Charles University Program “PRIMUS/22/MED/007”, National Institute for Cancer Research (reg. No. LX22NPO5102); European Union - Next Generation EU, Programme EXCELES, Cooperatio Program, research area „207020 Biology" and SVV260763, Charles University. Competing Interests The authors declare no competing financial interests. References Stanton, R. A., Gernert, K. M., Nettles, J. H. & Aneja, R. Drugs that target dynamic microtubules: A new molecular perspective. Med Res Rev 31 , 443–481 (2011). Reuvers, T. G. 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Synergistic activation of stress-activated protein kinase 1/c-Jun N-terminal kinase (SAPK1/JNK) isoforms by mitogen-activated protein kinase kinase 4 (MKK4) and MKK7. Biochem J 352 Pt 1 , 145–54 (2000). Li, G.-M. et al. DAPK3 inhibits gastric cancer progression via activation of ULK1-dependent autophagy. Cell Death Differ 28 , 952–967 (2021). St-Denis, N. et al. Systematic investigation of hierarchical phosphorylation by protein kinase CK2. J Proteomics 118 , 49–62 (2015). Shani, G. et al. Death-Associated Protein Kinase Phosphorylates ZIP Kinase, Forming a Unique Kinase Hierarchy To Activate Its Cell Death Functions. Mol Cell Biol 24 , 8611–8626 (2004). Kawai, T., Matsumoto, M., Takeda, K., Sanjo, H. & Akira, S. ZIP Kinase, a Novel Serine/Threonine Kinase Which Mediates Apoptosis. Mol Cell Biol 18 , 1642–1651 (1998). 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7287833","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":499322192,"identity":"eac20242-8077-465c-938e-3f1fd70966e8","order_by":0,"name":"Radoslav Janostiak","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0002-6602-9389","institution":"First Faculty of Medicine, Charles University","correspondingAuthor":true,"prefix":"","firstName":"Radoslav","middleName":"","lastName":"Janostiak","suffix":""},{"id":499322193,"identity":"0feac787-4425-4c95-9809-94b67669c335","order_by":1,"name":"Lucia Csergeova","email":"","orcid":"https://orcid.org/0009-0007-5021-7661","institution":"Charles University","correspondingAuthor":false,"prefix":"","firstName":"Lucia","middleName":"","lastName":"Csergeova","suffix":""}],"badges":[],"createdAt":"2025-08-04 07:01:57","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7287833/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7287833/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":89480453,"identity":"1a8d6bc0-7a40-4116-8c68-b67cdeef84c2","added_by":"auto","created_at":"2025-08-20 11:48:39","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1920935,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExperimental design for proteomic and phosphoproteomic profiling of quiescence entry and exit. (A) \u003c/strong\u003eSchematic of the experimental workflow showing timepoints for sample collection: continuously growing cells, serum-starved cells (48 hrs and 96 hrs), and serum-reactivated cells (+20 min and +120 min). Cells were processed in pentaplicates for label-free mass spectrometry following tryptic digestion and phosphopeptide enrichment. Proteome coverage included 5328 proteins and 6953 phosphorylation sites across all timepoints. (\u003cstrong\u003eB)\u003c/strong\u003e Ven diagrams indicating numbers of identified peptides (left), phosphosites (middle) and phospho-peptides (right) in each of the condition.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-7287833/v1/f53a0ba040ccc66e2aa3960d.png"},{"id":89481479,"identity":"102c7042-ba6f-4f28-b97c-a0a9cdc0d0b0","added_by":"auto","created_at":"2025-08-20 11:56:40","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2914753,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGlobal proteome remodeling during quiescence and reactivation. (A)\u003c/strong\u003e Volcano plots comparing protein expression at defined timepoints (left: 48 hours; right: 96 hours) relative to continuously growing cells. \u003cstrong\u003e(B)\u003c/strong\u003e Volcano plots comparing protein expression at defined timepoints (left: 48 hours; right: 96 hours) relative to serum-starved states. \u003cstrong\u003e(C)\u003c/strong\u003e Heatmap of differentially expressed proteins across the time course. \u003cstrong\u003e(D)\u003c/strong\u003e GO enrichment analyses (top: GO biological processes; bottom: GO biological compartments) showing downregulated pathways after 4D (96 hours) of serum starvation compared to continuously growing cells. Color indicates false discovery rate (FDR), circle size indicates number of identified hits in the pathway, X-axis shows enrichment score. \u003cstrong\u003e(E)\u003c/strong\u003e GO enrichment analyses (top: GO biological processes; bottom: GO biological compartments) showing upregulated pathways after 4D (96 hours) of serum starvation compared to continuously growing cells. Color indicates false discovery rate (FDR), circle size indicates number of identified hits in the pathway, X-axis shows enrichment score.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-7287833/v1/3c300874d3fc5166fa634b0a.png"},{"id":89480455,"identity":"96991e31-e055-4a2c-81c5-bc865a32e9b1","added_by":"auto","created_at":"2025-08-20 11:48:39","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":3163211,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePhosphoproteomic dynamics during quiescence and exit. (A)\u003c/strong\u003e Volcano plots comparing site-specific phosphorylation levels at defined timepoints (left: 48 hours; right: 96 hours) relative to continuously growing cells. \u003cstrong\u003e(B)\u003c/strong\u003e Volcano plots comparing site-specific phosphorylation levels at defined timepoints (left: 48 hours; right: 96 hours) relative to serum-starved states. \u003cstrong\u003e(C)\u003c/strong\u003eHeatmap of differentially phosphorylated sites across the time course. \u003cstrong\u003e(D)\u003c/strong\u003eTable summarizing novel phosphosites significantly altered during quiescence-proliferation transitions, with selected examples.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-7287833/v1/5ee6d6f8ab56a100f51529c0.png"},{"id":89481480,"identity":"f8748e82-2639-4ac0-8a06-a68513ad8f75","added_by":"auto","created_at":"2025-08-20 11:56:40","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":3915897,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eValidation of protein and phosphorylation changes in TNBC cell lines and patient data. (A)\u003c/strong\u003e Expression and site specific phosphorylation of indicated proteins was analyzed in MDA-MB-231, Hs578T and BT549 in various timepoints corresponding to the transition between quiescence and proliferation. Timepoints were as follows: \u003cem\u003eA\u003c/em\u003e – continuously growing cells; \u003cem\u003eB\u003c/em\u003e – serum starved cells (48hrs); \u003cem\u003eC\u003c/em\u003e – serum starved cells (96hrs); \u003cem\u003eD\u003c/em\u003e - serum reactivated cells (+20min); \u003cem\u003eE\u003c/em\u003e- serum reactivated cells (+120min). α-tubulin was used as loading control. \u003cstrong\u003e(B–C)\u003c/strong\u003e Analysis of site-specific phosphorylation and protein expression of indicated proteins in CPTAC patient samples dataset. \u003cstrong\u003e(D)\u003c/strong\u003e Comparison table of top quiescence-downregulated proteins \u003cem\u003ein vitro\u003c/em\u003e (screening conditions) and in CPTAC patients samples dataset. FC – fold change, ns – not significant, ∗, ∗∗, ∗∗∗, and ∗∗∗∗ represent p values \u0026lt; 0.05, \u0026lt;0.01, \u0026lt;0.001, and \u0026lt;0.0001. \u003cstrong\u003e(E)\u003c/strong\u003e Box plots showing relative protein expression levels of proteins significantly altered in CPTAC dataset in myc-amplified and myc-not amplified breast tumors. \u003cstrong\u003e(F)\u003c/strong\u003eComparison table of top quiescence-downregulated site-specific phosphorylations \u003cem\u003ein vitro\u003c/em\u003e (screening conditions) and in CPTAC patients samples dataset. FC – fold change, ns – not significant, ∗, ∗∗, ∗∗∗, and ∗∗∗∗ represent p values \u0026lt; 0.05, \u0026lt;0.01, \u0026lt;0.001, and \u0026lt;0.0001. \u003cstrong\u003e(G)\u003c/strong\u003e Box plots showing relative site-specific phosphorylation levels of sites significantly altered in CPTAC dataset in myc-amplified and myc-not amplified breast tumors.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-7287833/v1/860389edcd584164317f81be.png"},{"id":89480457,"identity":"de4f2b4d-d98e-4e1c-a198-fb69eacf09fa","added_by":"auto","created_at":"2025-08-20 11:48:40","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":3971397,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCK2 activity and clinical relevance in breast cancer. \u003c/strong\u003e(A) Kinase motif enrichment identifies CK2 consensus motifs upregulated in quiescent cells. (B) CK2-substrate phosphorylation levels in various timepoints corresponding to the transition between quiescence and proliferation was analyzed using western blot. Timepoints were as follows: \u003cem\u003eA\u003c/em\u003e – continuously growing cells; \u003cem\u003eB\u003c/em\u003e – serum starved cells (48 hrs); \u003cem\u003eC\u003c/em\u003e – serum starved cells (96 hrs); \u003cem\u003eD\u003c/em\u003e- serum reactivated cells (+20 min); \u003cem\u003eE\u003c/em\u003e - serum reactivated cells (+120 min). α-tubulin was used as loading control. α-tubulin was used as loading control.\u003cstrong\u003e (C)\u003c/strong\u003e Kaplan-Meier curves showing survival probabilities of patients with high and low expression of \u003cem\u003eCSNK2A1 \u003c/em\u003ebelonging to PAM50 breast cancer subtypes. HR – hazard ratio. \u003cstrong\u003e(D)\u003c/strong\u003e Kaplan-Meier curves showing survival probabilities of patients with high and low expression of \u003cem\u003eCSNK2A1 \u003c/em\u003etreated with endocrine therapy (top) or chemotherapy (bottom). HR – hazard ratio.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-7287833/v1/7c3363927c9034d1b237cb8d.png"},{"id":89480461,"identity":"cccb9e16-a87b-436e-95f4-f362cfeb9f52","added_by":"auto","created_at":"2025-08-20 11:48:40","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":8258420,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCK2 inhibition disrupts autophagy, microtubule dynamics, and protein synthesis. (A)\u003c/strong\u003e Colony formation assays images for indicated TNBC cell lines treated with DMSO or indicated concentrations of CK2 inhibitor CX-4945. \u003cstrong\u003e(B)\u003c/strong\u003e Expression of indicated proteins was analyzed using western blot in indicated cell lines treated with DMSO or CK2 inhibitor. \u003cstrong\u003e(C)\u003c/strong\u003e Bar charts representing mean fluorescence intensity of cells treated with indicated concentrations of CK2 inhibitors stained with Lysotracker\u003csup\u003eTM\u003c/sup\u003e and analyzed by flow cytometry. ns – not significant, ∗, ∗∗, ∗∗∗, and ∗∗∗∗ represent p values \u0026lt; 0.05, \u0026lt;0.01, \u0026lt;0.001, and \u0026lt;0.0001. \u003cstrong\u003e(D)\u003c/strong\u003e Representative images of indicated TNBC cell lines subjected to nocodazole washout experiment, pretreated with CK2 inhibitor or DMSO as control. \u003cstrong\u003e(E)\u003c/strong\u003e Bar charts representing proportion of cells in indicated phases of cell cycle analyzed by flow cytometry. ns – not significant, ∗, ∗∗, ∗∗∗, and ∗∗∗∗ represent p values \u0026lt; 0.05, \u0026lt;0.01, \u0026lt;0.001, and \u0026lt;0.0001. \u003cstrong\u003e(F) \u003c/strong\u003eBar charts representing relative fluorescence intensity of cells treated with indicated concentrations of CK2 inhibitors subjected to Click-IT HPG de novo proteosynthesis assay and analyzed by flow cytometry. ns – not significant, ∗, ∗∗, ∗∗∗, and ∗∗∗∗ represent p values \u0026lt; 0.05, \u0026lt;0.01, \u0026lt;0.001, and \u0026lt;0.0001.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-7287833/v1/7916db45f48a65ba9ebed846.png"},{"id":89480460,"identity":"cc10396f-0038-4c01-9a2d-d4847c6d9d53","added_by":"auto","created_at":"2025-08-20 11:48:40","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":3290048,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCK2 regulates DAPK3 to maintain survival under stress conditions. (A) \u003c/strong\u003eExpression and cleavage of PARP was analyzed in MDA-MB-231, Hs578T and BT549 under indicated conditions (+/- FBS, +/- CK2 inhibition) by western blot. α-Tubulin was used as loading control. \u003cstrong\u003e(B)\u003c/strong\u003e TNBC cell lines (MDA-MB-231, Hs578T, BT549) were treated with indicated concentrations of CX-4945 in serum containing as well as serum-free conditions and analyzed for cell viability using the MTT assay. Relative viability for each cell line relative to DMSO-treated cells at day 1 at respective condition is shown. \u003cstrong\u003e(C)\u003c/strong\u003e TNBC cell lines (MDA-MB-231, Hs578T, BT549) were treated with indicated concentrations of CX-4945 and Doxorubicin and analyzed for cell viability using the MTT assay. Relative viability for each cell line relative to DMSO-treated cells at day 1 at respective condition is shown. \u003cstrong\u003e(D)\u003c/strong\u003e Volcano plot representing upregulated and downregulated proteins identified through phospho-CK2 substrate antibody pull down after treatment of MDA MB 231 with CX-4945. \u003cstrong\u003e(E)\u003c/strong\u003e Expression and cleavage of PARP was analyzed in MDA-MB-231 under indicated conditions (+/- FBS, +/- CK2 inhibition, +/- DAPK3 inhibition) by western blot. α-Tubulin was used as loading control. \u003cstrong\u003e(F)\u003c/strong\u003e Bar chart representing quantification of PARP cleavage shown as ratio of full length (FL) PARP to cleaved PARP from three independent experiments. \u003cem\u003ens\u003c/em\u003e – not significant. \u003cstrong\u003e(G)\u003c/strong\u003e Proposed model representing interaction of CK2 and DAPK3 in TNBC cells.\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-7287833/v1/8250ae764adcea0b33ebf655.png"},{"id":91731812,"identity":"b9f713d3-02bc-41ad-85b0-8fb08a1cb910","added_by":"auto","created_at":"2025-09-19 16:17:38","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":27718288,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7287833/v1/4438c73b-983a-4da4-853b-814e3a081e52.pdf"},{"id":89481478,"identity":"fb385a98-d870-4a47-8519-51975db304dc","added_by":"auto","created_at":"2025-08-20 11:56:39","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":533225,"visible":true,"origin":"","legend":"Supplementary Figure 1","description":"","filename":"SupplementaryFigures.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7287833/v1/b2c8b0c99dc612aad7f8a4e9.pdf"},{"id":89480459,"identity":"08469b2f-51f0-40d9-a6c9-746f541a01dc","added_by":"auto","created_at":"2025-08-20 11:48:40","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":212035,"visible":true,"origin":"","legend":"Supplementary Tabe 1","description":"","filename":"SupplementaryTable1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7287833/v1/57282d11f4b725559a91727e.xlsx"}],"financialInterests":"(Not answered)","formattedTitle":"\u003cp\u003eDynamic Phosphoproteomic Profiling Identifies CK2 as a Critical Survival Kinase in Quiescent Breast Cancer Cells and a Therapeutic Target for Minimal Residual Disease\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eOne of the hallmarks of cancer cells is their high proliferative capacity, which has been the primary target of conventional anticancer therapies such as chemotherapy and radiation \u003csup\u003e1\u0026ndash;3\u003c/sup\u003e. These therapies are highly effective against fast proliferative cells however fail to kill slow-cycling or non-cycling cells \u0026ndash; so called quiescent cancer cells (QCCs) \u003csup\u003e\u003cspan lang=\"EN-US\"\u003e4,5\u003c/span\u003e\u003c/sup\u003e. It has been shown that within the tumor microenvironment there is a stable subpopulation of QCCs which is dynamically regulated and modulated by environmental factors such as hypoxia, nutrient deprivation, loss of cell adhesion, and exposure to anticancer treatments \u003csup\u003e\u003cspan lang=\"EN-US\"\u003e6\u0026ndash;8\u003c/span\u003e\u003c/sup\u003e. They have been identified across various tumor types, including glioblastoma, lung, breast, colorectal cancers \u003csup\u003e9\u0026ndash;12\u003c/sup\u003e. Even though this population represent only a fraction of the total cell number (as low as 0.5%) \u003csup\u003e11\u003c/sup\u003e it has great implication for anticancer therapy efficacy as it can persist the treatment and give rise to minimal residual disease \u003csup\u003e9,11,13\u003c/sup\u003e. Importantly, QCC can surive long periods of time (years) and lead to disease recurrence which is observed in up to 85% of ovarian cancer patients, 30% of breast, 40% of prostate, and nearly 100% of glioblastoma cases \u003csup\u003e\u003cspan lang=\"EN-US\"\u003e14\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eIn mammalian cells, the induction and maintenance of quiescence are regulated by a complex interplay of extrinsic and intrinsic signals. Ultimately, these signals converge on the regulation of cyclin-dependent kinases (CDKs), which determine whether a cell re-enters the cell cycle or remains quiescent. Quiescence is promoted by the upregulation of CDK inhibitors like p27 and p21, downregulation of CDKs, and hypo-phosphorylation of the retinoblastoma (Rb) protein. For instance, during serum starvation, increased expression of p27 suppresses CDK4/6 activity, maintaining cells in G0 phase \u003csup\u003e15\u003c/sup\u003e. Quiescent cells are typically identified by high p27 and low Ki67 expression \u003csup\u003e16,17\u003c/sup\u003e, and may also exhibit specific signaling states such as ERK1/2^high/p38^low, or express transcriptional repressors like NR2F1 and kinases such as DYRK1A and DYRK1B \u003csup\u003e18\u0026ndash;20\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGiven the capacity of QCC to persist in a cell cycle arrested state and later reignite tumor growth, QCCs pose a significant clinical challenge. Effective strategies to target these cells could involve (i) enhancing initial treatment efficacy through improved fractional killing, (ii) permanently locking them out of the cell cycle (e.g., inducing senescence), or (iii) developing therapies that selectively eliminate them. Importantly, quiescence is distinct from senescence\u0026mdash;the latter being a permanent cell cycle exit state\u0026mdash;making the potential for reactivation and relapse unique to quiescent cells \u003csup\u003e21\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eSince quiescence is also a survival mechanism, it can be induced by stress response pathways \u003csup\u003e\u003cspan lang=\"EN-US\"\u003e22\u003c/span\u003e\u003c/sup\u003e. In this context, Casein Kinase 2 (CK2) has emerged as a key player in regulating cellular stress responses and survival pathways. CK2 is a constitutively active serine/threonine kinase involved in cell cycle control, apoptosis, transcription, and DNA repair \u003csup\u003e23,24\u003c/sup\u003e. It modulates a wide range of signaling proteins, including p53, NF-\u0026kappa;B, and components of the unfolded protein response. By stabilizing antioxidant transcription factors like NRF2 and maintaining the function of stress-protective proteins such as heat shock proteins, CK2 enhances cell survival under adverse conditions \u003csup\u003e25,26\u003c/sup\u003e. However, its sustained activation in tumors may support the survival of quiescent or therapy-resistant cells, contributing to disease persistence and recurrence \u003csup\u003e27,28\u003c/sup\u003e. As such, CK2 represents a potential therapeutic target, particularly in strategies aimed at disrupting tumor dormancy and eliminating minimal residual disease.\u003c/p\u003e\n\u003cp\u003eTo characterize the signaling events on the transition between proliferation and quiescence of cancer cells, we conducted unbiased phosphoproteomic screen. We analyze a set of conditions including continuously proliferating cells, cells after 48 and 96 hours of serum withdrawal and cells after serum readdition after 20 minutes and 120 minutes and conducted unbiased proteomic and phosphoproteomic screen to identify novel signaling hub regulating the transition. Our screen identified Casein kinase II (CK2) as an important kinase regulating survival of quiescent cells and showed that inhibition of CK2 impairs survival of cells upon nutrient and genotoxic stress. Moreover, we showed that Death associated kinase 3 lies downstream of CK2 and is partially responsible for mediating the effect of CK2 inhibition.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn conclusion, through unbiased phosphoproteomic screen we identified CK2 as an important survival kinase in breast cancer and targeting CK2 could be a therapeutic option for minimal residual disease (MRD) eradication.\u0026nbsp;\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cstrong\u003eCell cultivation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMDA-MB-231 and Hs578T cell lines were cultivated in DMEM \u0026ndash; high glucose (Sigma-Aldrich), BT549 cell line was cultivated in RPMI (Sigma-Aldrich). Culture media were supplemented with 10% fetal bovine serum (FBS, Biosera), penicillin-streptomycin (100 U/ml, Sigma-Aldrich). For serum starvation, the cells were cultivated in the appropriate media type (DMEM/ RPMI) without the addition of FBS. To synchronize the cell cycle, cells were subjected to serum starvation for two days, followed by cultivation in media supplemented with FBS for one day. To induce quiescence, cells were then maintained in serum-free media for an additional 2 and 4 days. To assess the exit from quiescence, FBS was reintroduced for 20 or 120 minutes. As a control, asynchronously growing cells maintained in FBS-supplemented media were used.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCell viability assay\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMDA-MB-231, BT549 cells (10,000 cells/well) and Hs578T cells (4,000 cells/well) were seeded into 96-well plates and cultured in appropriate media type (DMEM/ RPMI) containing either 10% fetal bovine serum (FBS) or serum-free media. Cells were treated with respective compounds (CK2i \u0026ndash; CX-4645, Doxorubicin) and were incubated at 37\u0026deg;C in a 5% CO₂ atmosphere for indicated timepoints dependent on the assay. At the designated time points, 20 \u0026micro;l of MTT solution (5 mg/ml) was directly added to each well containing 200 \u0026micro;l of media. The cells were then incubated for 1 hour at 37\u0026deg;C. Following incubation, the MTT solution was not removed, and the formazan crystals formed were solubilized by adding 100 \u0026micro;l of dimethyl sulfoxide (DMSO) to each well. The absorbance was measured at 595 nm using a Tecan Infinite\u003csup\u003e\u0026reg;\u003c/sup\u003e M200 Pro microplate reader.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eColony formation assay\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor the colony formation assay, 10,000 cells (MDA-MB-231, BT549, Hs578T) per well were seeded into 6-well plates and cultured in media supplemented with 10% fetal bovine serum (FBS). Cells were treated with either DMSO (vehicle control) or increasing concentrations of the CK2 inhibitor (CK2i): 0.2 \u0026micro;M, 0.5 \u0026micro;M, 1 \u0026micro;M, 2 \u0026micro;M, and 5 \u0026micro;M. Afterwards, cells were incubated at 37\u0026deg;C in a humidified atmosphere with 5% CO₂ for 10 days. Following the incubation, the culture media was removed, and the wells were washed twice with phosphate-buffered saline (PBS). Colonies were then fixed and stained with Crystal Violet Solution (5 mg/ml crystal violet in 10% ethanol, 50% methanol, and 40% water). After staining, excess dye was removed by rinsing with water. Images of the stained colonies were captured using a ChemiDoc\u0026trade; MP Imaging System (Bio-Rad). Quantification was carried out using ImageJ software (https://imagej.net/ij/).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImmunoblotting\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCell cultures were washed with phosphate-buffered saline (PBS) and lysed in RIPA buffer (150 mM NaCl; 50 mM Tris-HCl, pH 7.5; 0.5% DOC; 0.1% SDS;1 % Triton) supplemented with 1x Halt\u003csup\u003eTM\u0026nbsp;\u003c/sup\u003eProtease and Phosphatase inhibitor cocktail (Thermo Fisher Scientific, Cat# 78446). Protein concentrations in lysates were determined using the Pierce\u003csup\u003eTM\u003c/sup\u003e BCA Protein Assay (Thermo Fisher Scientific, Cat# A65453). Protein lysates were diluted in Laemmli sample buffer (4x concentrated: 0.5 M Tris-HCl, pH 6.8; 20% SDS; 40% glycerol; 0.02% Bromophenol blue; 0.2 M DTT).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor immunoblotting, samples were separated on SDS-polyacrylamide gels and transferred onto PVDF membranes. Non-specific activity was blocked by incubating membranes for 60 minutes at room temperature in Tris-buffered saline (TBS) with 0.1% Tween\u0026reg; 20 detergent (TBST) containing 5% non-fat dry milk. Membranes were then incubated overnight at 4 \u0026deg;C with primary antibodies, 3 times washed with TBST, and incubated for 1 h at room temperature with horseradish peroxidase (HRP)\u0026mdash;conjugated secondary antibodies. After washing 4 times in TBST, the blots were developed using the Immobilon\u0026reg; ECL Ultra Western HRP Substrate (Millipore, Cat# WBULS0100) and ChemiDoc\u003csup\u003eTM\u0026nbsp;\u003c/sup\u003eMP Imaging system (Bio-Rad, Hercules, California, USA). Quantification of Western blots was carried out using ImageJ software (https://imagej.net/ij/).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDe novo proteosynthesis assay\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDe novo proteosynthesis was assessed using the Click-iT\u003csup\u003eTM\u003c/sup\u003e HPG Alexa Fluor\u003csup\u003eTM\u003c/sup\u003e 488 Protein Synthesis Assay Kit (Invitrogen, Cat#\u0026nbsp;C10428). Cells were pre-treated with 1 \u0026micro;M, 5 \u0026micro;M, or without CK2i for 2 days before the assay followed by incubation with 50 \u0026micro;M Click-iT\u003csup\u003eTM\u003c/sup\u003e HPG for 2 hours, trypsinized and washed twice with PBS. De novo proteosynthesis was assessed following the manufacturer\u0026rsquo;s protocol. In brief, cells were fixed using of 3.7% formaldehyde for 15 minutes at room temperature, followed by two washes with 200 \u0026micro;l of 3% BSA in PBS. Permeabilization was performed by incubating cells with 200 \u0026micro;l of 0.5% Triton\u0026reg; X-100 in PBS for 20 minutes at room temperature, followed by two additional washes with 3% BSA in PBS. Next, 200 \u0026micro;l of the Click-iT\u003csup\u003eTM\u003c/sup\u003e reaction cocktail (containing 5 times less Alexa Fluor\u003csup\u003eTM\u003c/sup\u003e azide) was added, and cells were incubated accordingly. After incubation, they were washed with 200 \u0026micro;l of Click-iT\u003csup\u003eTM\u003c/sup\u003e reaction rinse buffer and resuspended in PBS. Protein synthesis was analyzed using CytoFLEX flow cytometer (Beckman Coulter \u0026nbsp;Life Sciences) and data were processed using FloJo software (BD).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImmunoprecipitation\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor immunoprecipitation experiments, cells were grown on 10 cm dish and treated with CX-4945 or vehicle for 24 hours. Cells were lysed in immunoprecipitation buffer (10 mM Tris (pH 7.5), 1% NP-40, and 2 mM EDTA). supplemented with 1x Halt\u003csup\u003eTM\u0026nbsp;\u003c/sup\u003eProtease and Phosphatase inhibitor cocktail (Thermo Fisher Scientific, Cat# 78446) and protein concentration was estimated using the Pierce\u003csup\u003eTM\u003c/sup\u003e BCA Protein Assay (Thermo Fisher Scientific, Cat# A65453). Protein A/G magnetic beads (Pierce\u003csup\u003eTM\u003c/sup\u003e) were washed in immunoprecipitation buffer and incubated with target antibody or isotype control (1 ug) for 2 hours at 4\u0026deg;C with gentle rotation. Afterwards, beads were washed to remove unbound antibodies and mixed with equal amounts of protein lysates (1000\u0026mu;g) and incubated at overnight at 4\u0026deg;C with gentle rotation. After incubation, beads were washed 4 times in immunoprecipitation buffer and subsequently analyzed using western blot or mass spectrometry.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCell cycle analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIndicated cell lines were treated with CX-4549 at indicated concentration for 24 hours, harvested by trypsinization, washed twice with ice-cold PBS, and fixed dropwise in 70% ethanol while vortexing gently for 24 hours at \u0026minus;20\u0026deg;C. Prior to staining, cells were washed with PBS and incubated in staining buffer containing 50 \u0026micro;g/mL propidium iodide (PI), 100 \u0026micro;g/mL RNase A, and 0.1% Triton X-100 in PBS for 30 minutes at room temperature in the dark. Stained cells were analyzed using CytoFLEX flow cytometer (Beckman Coulter Life Sciences) and data were processed using FlowJo software (BD). Cell cycle distribution was determined by quantifying the percentage of cells in G0/G1, S, and G2/M phases based on DNA content.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLysotracker staining\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCells were treated with indicated concentrations of CX-4549 for 24 hours, trypsinized, counted and 500.000 cells were stained with 50nM Lysotracker\u003csup\u003eTM\u003c/sup\u003e Green DND-26 (Thermo Fisher Scientific) for 5 min. After incubation, cells were washed two times and resuspended in PBS + 1m EDTA + 1% BSA. Finally, cells were analyzed using using CytoFLEX flow cytometer (Beckman Coulter Life Sciences) and mean fluorescence intensity was extracted using FlowJo software (BD).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMicrotubule regrowth assay\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIndicated cell lines were plated onto coverslips and cultivated overnight at 37\u0026deg;C in a humidified incubator. Cells were pretreated with indicated concentration of CK2i for 2 hours followed by treatment with 10 \u0026micro;M nocodazole (Merck) for 3 hours at 37\u0026deg;C to depolymerize microtubules. After treatment, cells were washed three times with warm PBS to remove residual nocodazole and immediately transferred to pre-warmed nocodazole-free complete medium to allow microtubule regrowth. Regrowth was allowed to proceed at 37\u0026deg;C for 15 minutes and cells were fixed with 4% paraformaldehyde in PBS for 10 minutes at room temperature followed by permeabilized with 0.25% Triton X-100 in PBS for 10 minutes. Cells were then blocked with 5% BSA in PBS for 30 minutes, followed by incubation with anti-\u0026alpha;-tubulin antibody (DM1A, 1:200, SantaCruz Biotechnology) for 2 hours at room temperature. \u0026nbsp;After washing, coverslips were incubated with Alexa Fluor 488-conjugated anti-mouse secondary antibody (1:1000) for 1 hour at room temperature. Nuclei were counterstained with DAPI, and coverslips were mounted using ProLong\u003csup\u003eTM\u003c/sup\u003e Mountant (Invitrogen). Images were acquired using Leica Dmi8 inverted fluorescence microscope (Leica).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMass Spectrometry\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eProtein Digestion\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eSamples were mixed with 2% SDC in 100 mM TRIS buffer (pH 8.5), boiled at 95\u0026deg;C for 5 min and further sonicated using micro probe sonicator (Bandelin Sonoplus). Protein concentration was determined using BCA protein assay kit (ThermoFisher Scientific). 250 \u0026micro;g of protein per sample was mixed with 40 mM CAA, 10 mM TCEP and 5 M KOH and heated at 45\u0026deg;C for 5 min. Proteins were digested by 5 \u0026micro;g of trypsin per sample at 37\u0026deg;C overnight. Phosphopeptides were enriched using TiO\u003csub\u003e2\u0026nbsp;\u003c/sub\u003eaccording to Humphrey et al. \u003csup\u003e29\u003c/sup\u003e. After enrichment peptides were desalted using in-house made stage tips packed with C18 disks (Empore) according to Rappsilber et al. (Rappsilber et al., 2007).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003enLC-MS 2\u0026nbsp;Analysis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNano Reversed phase columns (Ion Opticks Ultimate TS 25 cm x 75 \u0026micro;m ID, C18 UHPLC column, 1.7 \u0026micro;m particles, 120 \u0026Aring; pore size) were used for LC/MS analysis. Mobile phase buffer A was composed of water and 0.1% formic acid. Mobile phase B was composed of acetonitrile and 0.1% formic acid. Samples were loaded onto the trap column (C18 PepMap100, 5 \u0026mu;m particle size, 300 \u0026mu;m x 5 mm, Thermo Scientific) for 1 min at 18 \u0026mu;l/min loading buffer was composed of water, 2% acetonitrile and 0.1% trifluoroacetic acid. Peptides were eluted with Mobile phase B gradient from 4% to 25% B in 28 min and from 25% B to 35% in next 2 min followed by 5 min wash with 75 % B. Eluting peptide cations were converted to gas-phase ions by electrospray ionization and analyzed on a Thermo Orbitrap Ascend. Survey scans of peptide precursors from 350 to 1400\u0026nbsp;m/z were performed in orbitrap at 120K resolution (at 200\u0026nbsp;m/z) with a 100 % ion count target. Tandem MS was performed by isolation at 1,6 Da with the quadrupole, CID fragmentation with normalized collision energy of 30 % and 10 ms activation time. Fragmentation spectra were acquired in ion trap with scan rate set to Normal. The MS2 ion count target was set to 200 % and the max injection time was 200 ms. Only those precursors with charge state 2\u0026ndash;6 were sampled for MS2. The dynamic exclusion duration was set to 30 s with a 10ppm tolerance around the selected precursor and its isotopes. Monoisotopic precursor selection was turned on. Cycle time was se to 2 s.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eData analysis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAll data were analyzed and quantified with the MaxQuant software (version 2.4.13.0)\u003csup\u003e\u003cspan lang=\"EN-US\"\u003e31\u003c/span\u003e\u003c/sup\u003e. The false discovery rate (FDR) was set to 1% for both proteins and peptides and we specified a minimum peptide length of seven amino acids. The Andromeda search engine was used for the MS/MS spectra search against the\u0026nbsp;\u003cem\u003eHuman\u003c/em\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003edatabase\u003cem\u003e\u0026nbsp;\u003c/em\u003e(downloaded from Uniprot in March 2023, containing 20 605 entries).\u0026nbsp;Enzyme specificity was set as C-terminal to Arg and Lys, also allowing cleavage at proline bonds and a maximum of two missed cleavages. Carbamidomethylation of cysteine was selected as fixed modification and N- terminal protein acetylation, methionine oxidation and serine, threonine and tyrosine phosphorylation as variable modifications. The \u0026ldquo;match between runs\u0026rdquo; feature of MaxQuant was used to transfer identifications to other LC-MS/MS runs based on their masses and retention time (maximum deviation 0.7 min) and this was also used in quantification experiments. Quantifications were performed with the label-free algorithm in MaxQuant \u003csup\u003e32\u003c/sup\u003e. Data analysis was performed using Perseus 1.6.15.0 software\u003csup\u003e33\u003c/sup\u003e. Only those phosphosites with localization probability higher than 0.75 were used for further data analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData visualizations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHeatmaps, volcano plots, and Gene Ontology (GO) pathway analyses were performed using SRplot \u003csup\u003e34\u003c/sup\u003e, an interactive web-based visualization platform for omics data. Kaplan meier curves were constructed using kmplot webtool platform \u003csup\u003e35\u003c/sup\u003e. Kinase motif logos were generated using PhosphoSitePlus webplatform \u003csup\u003e36\u003c/sup\u003e. \u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003ePhosphoproteomic analysis of serum withdrawal-mediated quiescence induction and re-entry into proliferation.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo date, there is a vey limited number of large scale proteomics and phosphoproteomics screens comparing proliferating and arrested cells and majority of them is focused on non-transformed cell types or analyzing stable conditions rather than dynamic transition between the two states \u003csup\u003e37\u0026ndash;39\u003c/sup\u003e. To address the gap, we conducted performed and unbiased proteomic and phosphoproteomic analysis of cells on the transition from proliferation to serum withdrawal-mediated quiescence followed by serum addition-mediated reentry in cell cycle (\u003cstrong\u003eFig. 1\u003c/strong\u003e). For the analysis we selected triple negative breast cancer cell line MDA MB 231 as a model based on several reasons. It represent the most aggressive breast cancer tumor subtype with 5 year survival at 77% for all the stages, and 12% for stage IV disease which has higher rates of metastasis formation and tumor recurrence peaking at 2-3 years, indicating presence of QCCs \u003csup\u003e\u003cspan lang=\"EN-US\"\u003e40\u003c/span\u003e\u003c/sup\u003e Additionaly, QCC population seems to be enriched in TNBC to a bigger extent in comparison to other breast cancer subtypes \u003csup\u003e41\u003c/sup\u003e. To enrich the QCC population we used serum withdrawal as quiescence inductor as it represents more physiological condition compared to cytotoxic compounds or pharmacological induction (e.g. using CDK4/6 inhibitors) \u003csup\u003e42\u003c/sup\u003e. \u0026nbsp; Workflow and sample collection timepoints are outlined in \u003cstrong\u003eFig. 1A\u003c/strong\u003e. 24 hours after plating the cells we collected the sample for continously growing cells and we performed the first synchronization step by serum withdrawal for 48 hours followed by serum readdition for 24 hours. Afterwards, we induced quiescence through serum withdrawal and collected samples 48 hours after witdrawal to capture the initial steps of quiescence induction, and 96 hours after serum withdrawal to capture quiescent cells without triggering excesive apoptosis. At this time, serum was added to the cells to initiate quiescence exit and samples were collected at +20 minutes and +120 minutes to capture initial steps of proteome and phophoproteome remodeling. To validate the approach and selected timepoints, we analyzed the status of the cells using western blot for key proteins indicating proliferative status of the cells \u0026ndash; Rb1 phosphorylation, p27Kip and E2F8. Hypophoshorylation of Rb1, absence of E2F8 and increased levels of p27Kip are well established markers of cells in quiescent state \u003csup\u003e43\u0026ndash;45\u003c/sup\u003e. Our western blot analysis of selected conditions shows hyperphosphorylated Rb1, high levels of E2F8 and low levels of p27Kip in continuously growing cells (\u003cstrong\u003eSupplementary Fig. 1\u003c/strong\u003e). On the other hand, levels of hyper-phosphorylated Rb1 and total E2F8 are low in 96 hours serum-starved cells, while the level of p27Kip is the highest indicating significant enrichment of quiescent cells population (\u003cstrong\u003eSupplementary Fig. 1\u003c/strong\u003e). After validation of the approach, the samples for unbiased phosphoproteomic analysis were collected in pentaplicates and processed at the same time followed by label-free mass spectormetry analysis. Overal the proteomic analysis identified 5328 individual proteins across all the conditions, out of which 4419 proteins were identified in all the conditions in every replicate (\u003cstrong\u003eFig. 1B, left\u003c/strong\u003e). Furthermore, phosphoproteomic analysis identified 6953 phosphorylation sites present on 2142 proteins across all the conditions. Phosphosites identified across all the conditions were significantly reduced by almost 50% in both phosphorylation sites (3544 sites identified in all conditions) as well as phospohrylated proteins (1327 sites identified in all conditions) which was likely a\u0026nbsp;results of biological change in global phosphorylation level, rather then technical limitation since in condition where higher level of phosphorylation was expected (continuously growing) the number of identified phosphosites as well as phosphoproteins was significantly higher \u0026ndash; 5142 phosphosites and 1702 phosphoproteins (\u003cstrong\u003eFig. 1B, right\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGlobal cellular proteome is significantly remodeled at the exit of the cells from quiescent state\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo interpret the complex patterns emerging from mass spectrometry (MS)-based proteomics we first constructed volcano plots identifying key changes in cells exiting proliferation and entering quiescence \u003cstrong\u003e(Fig. 2A\u003c/strong\u003e). While we see significant changes in proteome after 48 hours of serum removal, these changes are much more pronounced in cells after 96 hours of serum removal where population of quiescent cells is higly enriched. As expected we can see significant downregulation of key proliferation drivers such as CCNB1, DLGAP5, AURKA, KIF11 or TACC3 and concominant upregulation of tumor microenvironment-affecting proteins such as FN1, THBS1, TNFSF15 or PTX3 (\u003cstrong\u003eFig. 2A\u003c/strong\u003e). On the other hand, proteome changes in cells at the exit from quiescence into proliferation mode are less pronounced however uncovers interesting signaling events governing the first steps in the process. At 20 minutes timepoint after serum addition we could see only minor global changes as expected however we identified HMGN2 as a key protein that is being translated, which is in line with its role as as chromatin remodeler enabling expression of key cell cycle genes \u003csup\u003e46\u003c/sup\u003e (\u003cstrong\u003eFig. 2B, left\u003c/strong\u003e). At 120 minutes timepoint after serum addtion we can see stimulation of the expression of key transcription factors driving cell cycle \u0026ndash; Jun B, Jun D, FOSL1 and ELF1, while also HMGN2 expression still being high. On the other had we can already see degradation of quiescence maintaining proteins such FN1, THBS1 or HSPG2 indicating a\u0026nbsp;key role of proteasome degradation in quiescence exit (\u003cstrong\u003eFig. 2B, right\u003c/strong\u003e). To support our approach and analysis, we further constructed heatmap (\u003cstrong\u003eFig. 2C\u003c/strong\u003e) as well as conducted pathway enrichment analysis (\u003cstrong\u003eFig. 2D-E\u003c/strong\u003e). Heatmap analysis confirmed gradual proteome changes of cells exiting the proliferation into quiescence followed by reactivation by addition of serum, and identified clusters of proteins that are specific for each of the analyzed steps or specific for transition to and from the quiescent state (\u003cstrong\u003eFig. 2C\u003c/strong\u003e). Finally, we analyzed enriched GO biological processes and compartments specifically in quiescent condition \u003cstrong\u003e(Fig. 2D-E\u003c/strong\u003e). As expected, major downregulated pathways are associated with DNA replication, cell cycle progression and mitosis (\u003cstrong\u003eFig. 2D\u003c/strong\u003e) while pathways such as oxidative phosphorylation or exocytosis are significantly upregulated (\u003cstrong\u003eFig. 2E\u003c/strong\u003e). Interestingly, pathways regulating extracellular matrix remodeling are most enriched in quiescent cells further emphasizing the key role of quiescence niche (\u003cstrong\u003eFig. 2E, bottom\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eProtein phosphorylation network regulates cellular behavior at the transition points between quiescence and proliferation.\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo complement the data from proteomic analysis, we analyzed global changes in protein phosphorylation at the transition between proliferation and quiescence in pentaplicates. First we constructed volcano plots (\u003cstrong\u003eFig 3A-B\u003c/strong\u003e) and heatmap (\u003cstrong\u003eFig 3C\u003c/strong\u003e) to investigate the dynamics of global protein phosphorylation. Both analyses showed continuum of phoshporylations where after 48 hours of quiescence induction we see similar amounts of phosphosites being upregulated as well as downregulated indicating active rewiring of the signaling (\u003cstrong\u003eFig 3A, left\u003c/strong\u003e) while in condition enriched for quiescent cells we see majority of the phosphosites being downregulated confirming lower activity of signaling pathways (\u003cstrong\u003eFig 3A, right\u003c/strong\u003e). On the other hand, as expected, after serum addition and cell signaling reactivation we see a\u0026nbsp;significant shift in phosphorylation where majority of significantly altered phosphosites are upregulated in early (94%) as well as late (90%) quiescence exit stages (\u003cstrong\u003eFig. 3B\u003c/strong\u003e). \u0026nbsp;More detailed analysis of altered phoshporylation revealed patterns specific to individual stages. As expected, activating phosphorylation of proteins regulating cell cycle (Rb1, SRSF1), transcription (CTR9, TCOF1), translation (4EBP1, EIF3D) and mitosis (LMNB2, TOP2A, Ki67, TPX2) was downregulated in both serum starved conditions (\u003cstrong\u003eSupplementary Table 1, Fig. 3\u003c/strong\u003e). On the other hand, we identified proteins with increased site specific phosphorylation significantly upregulated in quiescent stage, SCRIB, EDC4, CIC, SAP30, MAP1B, SP4 and STARD3NL being upregulated by more than 10 fold in comparison to continously proliferating cells (\u003cstrong\u003eSupplementary Table 1)\u003c/strong\u003e. Phosphorylation of these proteins thus could serve as active quiescence maintaining signaling that might be targeted to eliminate quiescent cancer cells. While we have identified numerous previously identified sites to be altered according to the established signaling in proliferative and quiescent cells, we have identified 215 previously undescribed phosphorylations present on 141 individual proteins (Fig 3D). Out of these novel sites, there are 36 sites whose phosporylation is significantly altered across different conditions and could represent novel regulatory circuits governing transition between quiescence and proliferation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIdentified proteomic and phospohproteomic alterations were validated in vitro as well as in silico patient dataset.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAlthough our proteomic and phosphoproteomic analysis was robuts, we followed to validate some of the findings in two sets of experiments. First we analyzed total levels as well as site-specific phoshporylation of several proteins in 3 triple negative breast cancer cell lines \u0026ndash; MDA MB 231, Hs578T and BT549. First we looked at major cell cycle regulator Retinoblastoma protein 1. In MDA MB 231 as well as Hs578T the phoshporylation of CDK-directed sites (S807/S811) was consistently reduced in cells after 96 hours of serum starvation and we did not see significant increase after 20 nor 120 minutes of serum addition (\u003cstrong\u003eFig. 4A\u003c/strong\u003e) which is consistent with the fact that Rb1 is not hyperphosphorylated until the beginning of S-phase \u003csup\u003e47\u003c/sup\u003e. Additionally, we analyzed the levels of DLGAP5 as a\u0026nbsp;key mitosis promoting protein and we could see complete absence of the protein in MDA MB 231 and Hs578T cells (\u003cstrong\u003eFig. 4A\u003c/strong\u003e). However, in Rb1 negative cell line BT549, DLGAP5 was still present at 96 hours after serum starvation. Additionally, p27 as a major quiescence regulatory protein was consistently increased in cells arrested in G0 phase however it was only partially degraded after serum addition which is consistent with its role as a platform for CDK4/6-Cycline D complex formation \u003csup\u003e48\u003c/sup\u003e. Finally, our \u003cem\u003ein vitro\u003c/em\u003e experiments showed major changes in proteosyntetic pathway where phosphorylation of RPS6 S235/236 was the most consistent change across all three cell lines (\u003cstrong\u003eFig. 4A\u003c/strong\u003e). Interestingly, total level of 4EPB1 phosphorylation was consistently decreased in serum-starved cells and bounced right back after serum additon as shown by the protein band shifts, however exact position of this phosphorylation seems to be cell line dependend as evidend by distinct pattern of T37/46 phosphorylation indicating a phosphorylation code rather then site-specific events. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAdditionally, we sought to validate our results using publically available datasets. To this aim we analyzed CPTAC database using cBioPortal webtool (\u003ca href=\"https://www.cbioportal.org/\"\u003ecbioportal.org\u003c/a\u003e). We analyzed a dataset including 122 patient samples containing genomic as well as proteomic data \u003csup\u003e49\u003c/sup\u003e and split the patient data into high proliferative and low proliferative cohorts based on the amplification status of MYC. MYC amplification was selected because this genetic alteration defines high and low proliferative tumors and this alteration is correlated with higher Ki67 staining across different tumor types, including TNBC \u003csup\u003e49,50\u003c/sup\u003e. To validate our approach we first compared phosporylation of Rb1 and Ki67 in MYC amplified (high proliferation cohort) and MYC not-amplified (low proliferation cohort) and showed that levels of phosphorylation across various sites is significantly decreased for pRb1 (p=0.002) as well as for pKI67 (p=5.57e-15) (\u003cstrong\u003eFig. 4B\u003c/strong\u003e). Additionally, we analyzed the total level of cell cycle proteins Cyclin B1 and Ki67 and confirmed higher level of these cell cycle drivers in MYC amplified breast cancer tumors (\u003cstrong\u003eFig. 4C\u003c/strong\u003e). After validation of the approach, we compared protein levels of 10 most downregulated proteins from our screen in quiescent conditions with data from patient CPTAC samples (\u003cstrong\u003eFig. 4D-E\u003c/strong\u003e). The analysis revealed that all the proteins were upregulated in MYC amplified (high proliferative tumors) to a various extent, UBE2S, CCNB1, TPX2 and NDC80 being significantly changed (p\u0026gt;0.05) thus confirming validity of our \u003cem\u003ein vitro\u003c/em\u003e proteomic screen. Next, we compared the phosphorylation status of sites identified in our screen with the ones from CPTAC dataset (\u003cstrong\u003eFig. 4F-G\u003c/strong\u003e). There was very limited overlap and we were able to identify 7 sites whose phosphorylation was significantly upregulated in high proliferative cohort \u0026ndash; TCOF_pS1191, DDX21_pS71, TPX2_pS738 and Ki67_S357 (\u003cstrong\u003eFig. 4F-G\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCK2 kinase substrates are upregulated in response to nutrient stress.\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo identify major signaling events in regulation of quiescence entry and maintenance, we analyzed phosphorylation motifs that were upregulated in cells after 96 hours of serum starvation (\u003cstrong\u003eFig. 5A\u003c/strong\u003e). One of the motifs whose phosphorylation was upregulated was pS-D-D/E-D/E which is targeted by CK2 (\u003cstrong\u003eFig. 5A\u003c/strong\u003e). To test the hypothesis that CK2 substrates phosphorylation is upregulated after serum withdrawal we analyzed the cell lysates using pCK2 substate antibody that recognizes pS/p/T-DXE motif in three TNBC cell lines. We first validated analyzed MDA MB 231 as cell line where we conducted the phosphoproteomic analysis and found out that there was a\u0026nbsp;significant increase in phosphorylation of S/T-DXE motif after serum removal across various molecular weights confirming our previous results (\u003cstrong\u003eFig. 5B, left\u003c/strong\u003e). We could see a\u0026nbsp;gradual decrease in signal strength at some molecular weights which could indicate that as the cells go back to cell cycle, CK2 activity is reduced to basal level. Similar results were obtained in Hs578T and BT5489 cells however less pronounced than in MDA MB 231 (\u003cstrong\u003eFig. 5B, center and right\u003c/strong\u003e). \u0026nbsp;Because we saw increased CK2 activity in stressed conditions, we asked whether CK2 could be important for therapy response since anticancer therapy is activating stress signaling pathways as well. Thus we analyzed TCGA dataset using KMplot web interface \u003csup\u003e51\u003c/sup\u003e. We first looked at the expression of \u003cem\u003eCsnk2a1\u0026nbsp;\u003c/em\u003emRNA in subtypes of breast cancer and found out that while in luminal A subtype the expression of \u003cem\u003eCsnk2a1\u0026nbsp;\u003c/em\u003ewas not associated with worse patient survival (\u003cstrong\u003eFig 5C\u003c/strong\u003e), in other subtypes (luminal B, Her2-enriched and Basal) the expression was significantly negatively correlated with patient survival with the most significant effect in basal subtype with HR = 2.38 (\u003cstrong\u003eFig. 5C\u003c/strong\u003e). More importantly, when we analyzed the association of \u003cem\u003eCsnk2a1\u0026nbsp;\u003c/em\u003eexpression and response to therapy, we saw that there is no effect of \u003cem\u003eCsnk2a1\u0026nbsp;\u003c/em\u003eexpression levels on the response of endocrine therapy (\u003cstrong\u003eFig 5D, top\u003c/strong\u003e) standardly used in Luminal A \u003csup\u003e\u003cspan lang=\"EN-US\"\u003e52\u003c/span\u003e\u003c/sup\u003e , however there was a strong negative correlation between \u003cem\u003eCsnk2a1\u003c/em\u003e expression and outcome of chemotherapy (\u003cstrong\u003eFig 5D, bottom\u003c/strong\u003e) that is standardly used for basal subtype.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInhibition of CK2 has a\u0026nbsp;profound effects on various cellular pathways.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo further investigate the role of CK2 in TNBC cells we utilize highly specific inhibitor CX-4945 (silmitasertib) which is already being tested in clinical trials \u003csup\u003e53\u003c/sup\u003e. First we tested the effect of CK2 inhibition on growth of TNBC cell lines using colony formation assay and saw that at low concentrations (1\u0026mu;M) there is a\u0026nbsp;moderate decrease in proliferation in all three cell lines (\u003cstrong\u003eFig. 6A\u003c/strong\u003e), while increasing the concentration to 5\u0026mu;M exerted a\u0026nbsp;strong growth inhibition effect (\u003cstrong\u003eFig. 6A\u003c/strong\u003e) consistent with previous results \u003csup\u003e54,55\u003c/sup\u003e. Furthermore, we wanted to validate the role of CK2 in the context of previously published results thus we analyzed the effect of CK2 inhibition on authophagy which is one of the known pathways regulated by CK2 \u003csup\u003e56,57\u003c/sup\u003e. To this aim we treated the cells with CX-4945 and analyzed the authophagy induction by western blot for LC3 isoforms. Our results show that CK2 inhibition stimulated increased level of the smaller LC3 isoform in all three cell lines at 5 \u0026mu;M, and in MDA-MB-231 and Hs578T even at 1 \u0026mu;M (\u003cstrong\u003eFig. 6B\u003c/strong\u003e). We further confirmed our results from western blot using lysotracker (Invitrogen) staining followed by flow cytometry. Lysotracker is a specific dye for acidic compartments and in the context of autophagy it specifically stains autophagosomes \u003csup\u003e58\u003c/sup\u003e. CK2 inhibition increased the size of the autophagosomes leading to increased overall Lysotracker signal detected by flow cytometry in all three cell lines (\u003cstrong\u003eFig. 6C\u003c/strong\u003e). Since CK2 has been implicated in regulation of microtubule dynamics \u003csup\u003e59,60\u003c/sup\u003e we wanted to validate that CK2 retains this function also in our conditions. To this aim we performed nocodazole washout experiment to investigate the regrowth dynamics of microtubules after depolymerization. We compared untreated cells with cells pretreated with CK2 inhibitor for 1 hour and qualitatively analyzed the regrowth of microtubules 15 minutes after nocodazole washout. The experiments showed that repolymerization of microbutules in significantly impaired in CK2 pretreated cells in comparison to control and the effect is consistent across all three analyzed cell lines (\u003cstrong\u003eFig. 6D, bottom row, small image inserts\u003c/strong\u003e). Additionally, we performed cell cycle analysis using flow cytometry and revealed that CK2 inhibition leads to increased proportion of G2/M phase cells which indicates involvement of microtubules consistent with our previous resutls (\u003cstrong\u003eFig. 6E\u003c/strong\u003e). Taken togehter we could draw the conclusion that there is a\u0026nbsp;gradient of sensitivity to CK2 inhibition in the tested cell lines and our results consistently indicate that MDA MB 231 cell line is the least sensitive while Hs578T and BT549 are much more sensitive to CK2 inhibition. Finally, to tie the function of CK2 back to the results from our phosphoproteomic analysis we analyzed two pathways that contained high number of putative CK2 substrates based on the CK2 consensus sequence \u0026ndash; proteosynthesis and microtubule dynamics. First we investigated the role of CK2 in sustaining proteosynthesis using Click-iT\u0026trade; HPG Protein Synthesis Assay (ThermoFisher). We pretreated the cells uising CK2 inhibitor for 24 hours and then incubated the cells with L-homopropargylglycine (HPG) reagent to label nascent proteins for 2 hrs. Subsequent flow cytometry analysis revealed that CK2 inhibition lead to decreased nascent proteosynthesis in all three cell lines, however to a\u0026nbsp;various degree (\u003cstrong\u003eFig. 6F\u003c/strong\u003e). In MDA-MB-231 we saw significant impairment of nascent proteosynthesis only in cells treated with 5\u0026mu;M CX-4945, while in other two cell lines (Hs578T, BT549) the effect of CK2 inhibition on proteosynthesis was much more profound, strongest inhibition seen in Hs578T (\u003cstrong\u003eFig. 6F\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCK2 Supports Stress Survival by Suppressing DAPK3-Mediated Apoptosis in TNBC Cells\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFinally, we wanted to understand what is the role of CK2 in response to stress. To this aim we withdrew FBS to stimulate nutrient stress or treated the cells with doxorubicin to mimick anticancer therapy standardly used for TNBC cancer treatment \u003csup\u003e61\u003c/sup\u003e and simoultanously treated cells with CK2 inhibitor. In serum-starved condition, our experiments revealed that viability of cells is compromised in serum starving cells treated with CK2 inhibitor as assessed by cell survival asssy as well as PARP cleavage (\u003cstrong\u003eFig. 7A-B\u003c/strong\u003e). Serum withdrawal or CK2 inhibition individually decreased the rate of proliferation however inhibition of CK2 in combination with serum withdrawal had a\u0026nbsp;profound detrimental effect on cell viability which was consistent with PARP cleavage (\u003cstrong\u003eFig. 7A-B\u003c/strong\u003e). When comparing individual cell lines, we see that MDA MB 231 cells were the least sensitive to CK2 inhibition in combination with serum withdrawal (\u003cstrong\u003eFig. 7A-B, top row\u003c/strong\u003e), while Hs578T and BT549 were much more sensitive to CK2 inhibition in serum starved conditions and even 1\u0026mu;M CK2 inhibitor resulted in strong PARP cleavage and cell viability decrease (\u003cstrong\u003eFig. 7A-B, middle and bottom row\u003c/strong\u003e). Additionally, we performed similar experiments with genotoxic stress mimicking anticancer therapy using doxorubicin. The results are aligned with our experiments using serum withdrawal as stress factor. Treatment of MDA MB 231 and BT549 cell lines with CK2 inhibitor (5\u0026mu;M) potentiates the effect of doxorubicin even at 0.2 \u0026mu;M concentration where we saw significant killing effect after 48 and 72 hours (\u003cstrong\u003eFig. 7C\u003c/strong\u003e). In Hs578T cells the co-treatment with CK2 inhibitor (5\u0026mu;M) and doxorubicin (0.2 \u0026mu;M) led to significant decrease in proliferation in comparison to CK2i or doxorubicin alone (\u003cstrong\u003eFig. 7C\u003c/strong\u003e). Furthermore we also assessed the apoptosis induction using PARP clevage in these conditions. In MDA MB 231, the inhibition of CK2 potentiates the cytotoxic effect of doxorubicin where 0.5uM doxorubicin lead to minimal PARP cleavage (2.8%) without CK2 inhibition while the same concentration of doxorubicin along with CK2 inhibition lead to substantial PARP cleavage (34.5%) (\u003cstrong\u003eSupplementary Fig. 2A\u003c/strong\u003e). In more sensitive cell lines Hs578T and BT549, the combinatory treatment had a\u0026nbsp;profound effect even at lower concentrations starting at 0.2uM doxorubicin (Hs578T: 1.7% cleaved PARP in dox-only and 20.2% cleaved PARP in dox+CK2i; BT549: 26.5% cleaved PARP in dox-only and 77.9% cleaved PARP in dox+CK2i) (\u003cstrong\u003eSupplementary Fig. 2B-C\u003c/strong\u003e). To further characterize CK2 dependencies in TNBC we sought to identify downstream targets of CK2. To this end we treated the MDA MB 231 cells with CK2 inhibitor and then performed pull down using pCK2 substrates antibody to increase the specificity of our approach. Our analysis identified 528 proteins present in all 6 experiments (2 triplicates +/- CK2 inhibitor) out of which 139 with significantly higher presence in not treated pull down in comparison to treated cells (Fig 7C). We have identified known CK2 substrates such as EIF4E, EIF4G2, EIF3D, MDC1, MYH10, CAPZA1 \u003csup\u003e\u003cspan lang=\"EN-US\"\u003e62,63\u003c/span\u003e\u003c/sup\u003e among the enriched proteins in not treated conditions, while there were multiple previously not described or validated. Pathway analysis of putative substrates showed enrichment of CK2-regulated pathways such as translation regulation, RNA metabolism, stress response and autophagy (\u003cstrong\u003eSupplementary Fig. 3A\u003c/strong\u003e). One of the most enriched proteins in control samples was Death-associated protein kinase 3 (DAPK3) which has been implicated in regulation of authophagy, apoptosis and cellular contractility \u003csup\u003e64\u0026ndash;66\u003c/sup\u003e. Additionally, several of the DAPK3 interacting partners such as MYL9, PPP1R12A, CALM3 or LUZP1 were also enriched in our pull down analysis (\u003cstrong\u003eSupplementary Fig. 3B\u003c/strong\u003e). Finally, \u003cem\u003ein silico\u003c/em\u003e sequence and structure analysis confirmed the presence of putative CK2 phosphorylation consensus motif (T\u003csup\u003e112\u003c/sup\u003eEDE) located on the surface, and interestingly mutation of this site impairs DAPK3\u0026rsquo;s kinase activity \u003csup\u003e67\u003c/sup\u003e (\u003cstrong\u003eSupplementary Fig. 3C)\u003c/strong\u003e. Therefore we decided to investigate functional association of CK2 and DAPK3. To this end, we stressed MDA MB 231 cells by serum deprivation, treated the cells with CK2 and DAPK3 inhibitors and analyzed apoptotic signaling by PARP cleavage. As expected, inhibition of CK2 in serum-starved cells lead to significant increase in PARP cleavage while co-treatment with DAPK3 inhibitor partially rescued the increase PARP cleavage (\u003cstrong\u003eFig. 7F\u003c/strong\u003e) indicating a\u0026nbsp;functional interaction between CK2 and DAPK3. Taken together we proposed a\u0026nbsp;model where in the normal growth conditions, pro apoptotic function of DAPK3 is inhibited by CK2 and likely also other kinases through phosphorylation. When cells encounter adverse environmental conditions, the activity of prosurvival pathways is decreased while the activity of CK2 is sustained which enables inhibition of pro apoptotic function of DAPK3. Finally, inhibition of CK2 would lead to increased activity of DAPK3 and subsequent cell death (\u003cstrong\u003eFig. 7G\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe ability of cancer cells to enter the quiescent mode poses a\u0026nbsp;major challenge for complete cancer eradication and is crucial for emergence of minimal residual disease (MRD). These cells, which reside in a non-proliferative, dormant state, often evade conventional therapies that target rapidly dividing populations. As a result, they can remain undetected after initial treatment, contributing to MRD and posing a significant risk for relapse. Therefore understanding the molecular mechanisms that maintain quiescence, allow cancer cells to survive long therm in such state and resists the therapy is critical for developing strategies to eradicate MRD and achieve long-term remission.\u003c/p\u003e\n\u003cp\u003eAlthough the core circuits regulating mammalian cell quiescence such as CDKs, CKIs or Rb1 are well described \u003csup\u003e47,68\u003c/sup\u003e and are in place also in cancer cells to a certain degree, targeting of these mechanisms is not feasible without affecting normal cells. Therefore it is crucial to find upstream regulatory mechanisms specific to cancer cells, however to date there is a limited number of large scale proteomic analyses dynamically capturing the transition from proliferation to quiescence and back to cell cycle. More importantly the quiescence is not passive stage however it it activelly maintained \u003csup\u003e69\u0026ndash;71\u003c/sup\u003e. It has been shown that protein posttranslational modifications such as ubiquitination and phosphorylation are key events regulating progression through cell cycle therefore we performed integrative proteomic and phosphoproteomic analysis of transition between proliferation and quiescence of cancer cells to capture the dynamics of protein phosphorylation and overal protein remodelation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAs expected, our analysis revealed that proteome is significantly remodeled as cancer cells exit cell cycle with the most pronounced downregulation seen in mitosis promoting proteins such as CCNB1, DLGAP5 or AURKA. On the other hand, there is a significant increase in production of extracellular matrix (ECM) proteins and regulators such as FN1, THBS1 or PTX3. Increased production and stabilization of ECM is and adaptive mechanism in stress response and might be inherent characteristic of quiescent niche \u003csup\u003e42\u003c/sup\u003e. Indeed it has been shown that quiescence breast cancer cells upregulate production of extracellular matrix which is crucial for quiescence maintenance and survival of these cells \u003csup\u003e42\u003c/sup\u003e. ECM provides survival signaling mediated through integrins which supplements the lack of growth-factor stimulated survival signals \u003csup\u003e\u003cspan lang=\"EN-US\"\u003e72\u003c/span\u003e\u003c/sup\u003e. Additionally, thicker layer of ECM provide shielding from environmental factors and in the context of cancer biology, it protects quiescent cells from therapy and immune cell attack \u003csup\u003e73,74\u003c/sup\u003e. The key role of ECM in quiescence regulation is further emphasized by strong degradation of FN1 or THBS1 at the exit of cells from quiescence. On the other hand, one of the key proteins for cells reactivation identified in our screen is HMGN2. HMGN2 is a nucleosome remodeling factor that has been implicated in the maintenance of poised chromatin state in different systems \u003csup\u003e75\u003c/sup\u003e. This poised states is characteristic for quiescent cancer stem cells which stimulates the plasticity \u003csup\u003e76\u003c/sup\u003e. Moreover, HMGN2 is important for efficient expression of early cell cycle regulatory genes and its mRNA is relatively stable during the cell cycle \u003csup\u003e77,78\u003c/sup\u003e. In line with our data, we could speculate that HMGN2 is expressed at a\u0026nbsp;basal level in quiescent cells to control reversibility of the cell cycle exit and upon mitogenic stimulation, the translation is increased facilitating expression of early G1 genes such as Jun B, Jun D or FosL1 whose expression is significantly increased after 120 min of mitogenic stimulation in our conditions.\u003c/p\u003e\n\u003cp\u003eOur phosphoproteomic analysis also yielded results consistent with the state of knowledge while also identified potential novel regulatory circuit in breast cancer cell quiescence. Specifically looking at the regulation of quiescent state, we have idenfied casein kinase 2 (CK2) as potentially critical for survival of quiescent cells. CK2 is a constitutively active serine/threonine kinase that was implicated in regulation of various cellular pathways through phosphorylation of its substrates \u003csup\u003e79\u003c/sup\u003e. Kinase motif analysis of our data identified phosphorylation of CK2 consensus acidic sequence (S/T-D/E-D/E-D/E) to be increased in cells entering the quiescence. More detailed analysis of known CK2 substrates showed increased phosphorylation of SCRIB, EDC4, CIC, SAP30, MAP1B, SP4 and STARD3NL at the transition to quiescence indicating that CK2 activity might be either directly regulating entry into the quiescence or important for survival of quiescent cells. We confirmed increased phosphorylation of CK2 substrates in 2 different TNBC cell lines and to a certain degree also in the third one \u0026ndash; BT549. Differences in the third cell line might stem out from lack of Rb1 protein which is one of the key core regulator of cell quiescence \u003csup\u003e80,81\u003c/sup\u003e. Interestingly, when we looked at the association of CK2 expression and response to therapy we see that patients with low expression of \u003cem\u003eCSNK2A1\u003c/em\u003e \u0026ndash; gene coding alpha subunit of CK2, are responding very well to cytotoxic therapy, while patients with higher CSNK2A1 expression have significantly worse clinical outcome. Since cytotoxic therapy is one of the stimuli driving quiescence as a survival mechanism \u003csup\u003e21,82\u003c/sup\u003e, it further strengthen our hypothesis that CK2 is important for cancer cell quiescence and minimal residual disease.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFinally we wanted to test whether inhibition of CK2 would lead to lower survival of quiescent cancer cells. To this aim we stressed the cells by withdrawal of serum to stimulate nutrient depletion-induced quiescence as well as by adding doxorubicin to mimick the anticancer therapy-induced quiescence. Inhibition of CK2 in these stressed conditions led to significant decrease in viability of all three tested cell lines which indicates that CK2 activity is indeed important for survival of cells upon stress. It has been previously shown that CK2 is activated upon various stress in a p38-dependent manner \u003csup\u003e83\u003c/sup\u003e. Increased activitity of CK2 then leads to phosphorylation of its downstream targets that are important for survival upon stress such as NRF2, HSP90 or JNK1 \u003csup\u003e26,84,85\u003c/sup\u003e. To further delineate the pathway downstream of CK2 in our model system, we performed pull down experiment using pCK2 substrate antibody and identified the proteins differentially phosphorylated in cell treated with CK2 inhibitor with vehicle treated cells. Besides known CK2 substrates such as EIF4E, EIF4G2, MYH10 or CAPZA1 \u003csup\u003e\u003cspan lang=\"EN-US\"\u003e62,63\u003c/span\u003e\u003c/sup\u003e, we identified Death Associated Protein Kinase 3 (DAPK3) as a potential CK2 substrate, being one of the proteins with the most significant downregulated phoshporylation after CK2 inhibition. DAPK3 has been initially identified as a pro-apoptotic kinase, integrating signals from various stress pathways and promoting apoptosis and autophagy \u003csup\u003e64,65,86\u003c/sup\u003e. DAPK3 sequence analysis revealed that it contains one potential CK2-phosphorylation site \u0026ndash; Threonine 112 followed by a stretch of acidic aminoacids E-D-E, which corresponds to the CK2 phosphorylation consensus \u003csup\u003e87\u003c/sup\u003e. Interestingly, cancer-associated missense mutation of this site was associated with decreased activity of DAPK3, suggesting its role as tumor suppressor kinase \u003csup\u003e67\u003c/sup\u003e. We hypothesized that CK2-mediated phosphorylation of T112 could lead to downregulation of DAPK3 activity or altered localization which might results in inhibition of apoptosis mediated by lack of extracellular prosurvival signaling. To test the hypothesis, we treated serum starved cells with CK2i as well as DAPK3i and analyzed apoptotic signaling through PAPR cleavage. Our data show that inhibition of DAPK3 leads to partial rescue of PARP cleavage in response to serum starvation and CK2 inhibition. The results are in line with published literature on pro-apoptotic role of DAPK3 \u003csup\u003e88,89\u003c/sup\u003e as well as the importance of T112 for DAPK3 functionality \u003csup\u003e67\u003c/sup\u003e. Additionally, our model is supported by another high throughput study that showed that CK2-targeted phosphosites are significantly enriched in gefitinib-resistant PC9 cells compared to parental cells, and one of the sites phosphorylated by CK2 was T112 on DAPK3 \u003csup\u003e88\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eWhile our integrative proteomic and phosphoproteomic approach provides critical insights into the signaling dynamics of quiescent cancer cells (QCCs), we are acknowledgeing several limitations. Our analyses were conducted in vitro in triple-negative breast cancer (TNBC) cell lines. Although we validated several key findings across three independent models and in patient datasets, future studies involving patient-derived organoids or in vivo models will be necessary to confirm the physiological relevance of CK2-dependent signaling in quiescence and minimal residual disease (MRD).\u0026nbsp;Furthermore, while CK2 was implicated as a major survival kinase in QCCs, the exact mechanism by which CK2 regulates downstream effectors such as DAPK3 remains to be fully elucidated. Phosphorylation of DAPK3 at T112 appears functionally relevant, but additional studies including site-directed mutagenesis, phospho-deficient or phospho-mimetic constructs, and rescue experiments are needed to validate its regulatory role.\u003c/p\u003e\n\u003cp\u003eIn summary, our study provides the first comprehensive phosphoproteomic characterization of TNBC cells transitioning into and out of quiescence and identifies Casein Kinase 2 (CK2) as a key survival kinase in the quiescent state. We demonstrate that CK2 activity is enhanced during cellular stress, supports survival signaling, and suppresses pro-apoptotic pathways through modulation of targets such as DAPK3. These findings suggest that CK2 enables quiescent cancer cells to withstand both nutrient deprivation and cytotoxic therapy, contributing to minimal residual disease and relapse. Targeting CK2 in combination with conventional therapies may represent a promising strategy to eliminate therapy-resistant quiescent cancer cell populations and improve long-term treatment outcomes in aggressive breast cancer subtypes. Nonetheless, further in vivo and mechanistic studies are required to fully validate CK2\u0026rsquo;s role in quiescence regulation and therapeutic resistance.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eAuthors\u0026lsquo; contributions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLC performed majority of the experiments, co-wrote the manuscript and co-prepared the figures.\u003c/p\u003e\n\u003cp\u003eRJ designed and supervised the experiments, co-wrote the manuscript and co-prepared the figures.\u003c/p\u003e\n\u003cp\u003eBoth authors approved the final version of the manuscript and figures.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLC-MS analyses were performed in Laboratory of Mass Spectrometry at Biocev research center, Faculty of Science, Charles University.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Primus Charles University Program \u0026ldquo;PRIMUS/22/MED/007\u0026rdquo;, \u0026nbsp;National Institute for Cancer Research (reg. No. LX22NPO5102); European Union - Next Generation EU, Programme EXCELES, Cooperatio Program, research area \u0026bdquo;207020 Biology\u0026quot; and SVV260763, Charles University.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing financial interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eStanton, R. A., Gernert, K. M., Nettles, J. H. \u0026amp; Aneja, R. Drugs that target dynamic microtubules: A new molecular perspective. \u003cem\u003eMed Res Rev\u003c/em\u003e \u003cstrong\u003e31\u003c/strong\u003e, 443\u0026ndash;481 (2011).\u003c/li\u003e\n\u003cli\u003eReuvers, T. G. A., Kanaar, R. \u0026amp; Nonnekens, J. 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ZIP Kinase, a Novel Serine/Threonine Kinase Which Mediates Apoptosis. \u003cem\u003eMol Cell Biol\u003c/em\u003e \u003cstrong\u003e18\u003c/strong\u003e, 1642\u0026ndash;1651 (1998).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7287833/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7287833/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Quiescent cancer cells (QCCs) evade conventional therapies and contribute to minimal residual disease (MRD) and relapse, yet the signaling pathways governing their survival remain poorly understood. Here, we performed integrative proteomic and phosphoproteomic profiling of triple-negative breast cancer cells transitioning between proliferation and serum withdrawal-induced quiescence, followed by reactivation. We identified dynamic remodeling of both proteome and phosphoproteome, with quiescent cells showing downregulation of mitotic drivers and upregulation of extracellular matrix components. Notably, phosphorylation of CK2 substrates was increased during quiescence, and CK2 inhibition using CX-4945 impaired cell survival under nutrient and genotoxic stress, disrupted autophagy, microtubule dynamics, and protein synthesis. Phospho-enrichment and functional assays identified Death-associated protein kinase 3 (DAPK3) as a CK2-regulated effector mediating stress-induced apoptosis. In silico analysis confirmed a link between high CK2 expression and poor chemotherapy response in basal breast cancer. These findings establish CK2 as a critical survival kinase in QCCs and a potential therapeutic target for MRD eradication in breast cancer.","manuscriptTitle":"Dynamic Phosphoproteomic Profiling Identifies CK2 as a Critical Survival Kinase in Quiescent Breast Cancer Cells and a Therapeutic Target for Minimal Residual Disease","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-20 11:48:35","doi":"10.21203/rs.3.rs-7287833/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e0c60a87-df9d-462d-a8ba-b09d40a3b840","owner":[],"postedDate":"August 20th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":53444786,"name":"Biological sciences/Cell biology/Cell death"},{"id":53444787,"name":"Biological sciences/Cell biology/Cell division"},{"id":53444788,"name":"Health sciences/Diseases/Cancer/Breast cancer"}],"tags":[],"updatedAt":"2025-09-19T16:09:16+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-20 11:48:35","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7287833","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7287833","identity":"rs-7287833","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}