Synthetic Lethality Interaction Between Aurora Kinases and CHEK1 Inhibitors in Ovarian Cancer

Identification of druggable vulnerabilities is a main goal in oncogenic diseases where available therapies can only slightly prolong survival (1, 2). Even though new therapies can reduce and delay tumor growth, most cancers become resistant, with the appearance of new clones of cells that are insensitive to the inhibited mechanisms (3).

Advanced ovarian cancer represents an important clinical problem as limited benefit can be obtained from available therapies (4). In this context, it is mandatory to identify druggable mechanisms that are responsible for the oncogenic phenotype and that can augment the effectiveness of existing therapies.

The classical treatment in ovarian cancer includes antimitotic agents like taxanes in combination with DNA-damaging agents like platinum compounds (4). Recently, new agents have been incorporated to the therapeutic armamentarium including antiangiogenic compounds such as the antibody bevacizumab or agents targeting DNA repair mechanisms like PARP inhibitors, among others (4–6).

Several molecular alterations have been described in ovarian cancer including, uncontrolled regulation of mitosis, deficiencies in DNA damage repair mechanisms, or activation of intracellular pathways involved in proliferation and survival (7, 8). In this context, some mitotic protein kinases have been identified as upregulated and linked to worse outcome in ovarian cancer, constituting potential therapeutic targets (9). Drugs against mitotic kinases like Polo-like kinases (PLK), never in mitosis (NIMA), or aurora kinases (AURK) have been recently described (10). Moreover, massive genomic studies have reported overexpression of some of these proteins in ovarian carcinoma (11). Indeed, agents against some of these proteins, such as those targeting AURKs are currently in clinical development (12, 13).

Control of DNA damage is a key function associated with the initiation and maintenance of ovarian cancer (14). Genetic studies have reported frequent mutations in TP53 in high-grade serous ovarian carcinoma (11). Moreover, germline mutations at the homologous recombination repair genes BRCA1 and BRCA2 predispose to ovarian cancer and are currently used to identify tumors susceptible to be treated with PARP inhibitors (5). Both functions, cell-cycle control and DNA repair, are mechanistically linked as cells that have a high proliferation rate acquire more genetic instability, and supervision of DNA lesions and repair becomes more difficult (14). Among the different proteins involved in the regulation and identification of DNA damage, the checkpoint kinase 1 (CHEK1) is a key member (15). It mediates cell-cycle arrest in response to DNA damage by integrating the signals from ATM and ATR (15). In addition, some mitotic kinases like aurora or PLK are involved in the regulation of the DNA damage response through the phosphorylation of cell-cycle regulators (16, 17).

In this context, targeting of DNA repair mechanisms in combination with inhibition of key regulators of the mitotic process could be an exploitable path to treat ovarian cancer.

In this article by using an in silico approach, we identify aurora kinases A and B (AURKA and AURKB) and CHEK1 as an upregulated family of genes in ovarian cancer. Together, AURKA and CHEK1 were amplified in around 12% of ovarian tumors. We show that inhibition of AURKA synergized with the inhibition of the DNA repair regulator CHEK1. The combined inhibition induced cell-cycle arrest, apoptosis, and synergized with chemotherapies, having also an effect on populations of cells with stem cell properties. In addition, the concomitant expression of these genes was linked to poor clinical outcome.

Taking together the data contained in our article paves the way for future preclinical studies and exploratory early-phase clinical trials using this combination.

We used public transcriptomic mRNA data (GEO DataSet, accession number: GDS3592) from nontransformed isolated human epithelial ovarian cells and ovarian carcinoma cells to identify deregulated genes. Affymetrix CEL files were downloaded and analyzed with transcriptome analysis console (TAC) Software, developed by Affimetrix. Normalization was performed using MAS5. Genes that were upregulated with a minimum of 4-fold change were selected. Functional annotation was performed with DAVID Bioinformatics Resources 6.7 and adjusted P value <0.05 to select the enriched gene sets. Data contained at oncomine (https://www.oncomine.org/resource/login.html) were used to confirm the upregulated genes. Copy number alterations including amplifications, deletions, or mutations were evaluated using cBioportal (http://www.cbioportal.org; ref. 18). For the association of gene expression with clinical outcome in early-stage ovarian cancer, we used the Kaplan–Meier (KM) Plotter Online Tool (19).

The immortal human keratinocyte cell line HACAT was growth in DMEM. Ovarian cancer cell lines were grown in RPMI (OVCAR3, IGROV1) and DMEM (SKOV3, OVCAR8) containing 10% FBS. OVCAR3 and OVCAR8 tumorspheres (TS) were grown in DMEM-F12 plus BSA (0.4%), insulin (5 μg/mL), bFGF (20 ng/mL), and EGF (20 ng/mL). All media were supplemented with 100 U/mL penicillin, 100 μg/mL streptomycin, and 2 mmol/L l-glutamine and cells were maintained at 37°C in a 5% CO₂ atmosphere. All cell lines used were provided by Drs. J. Losada and A. Balmain (from the ATCC) in 2015. In addition, cells were analyzed annually by STR at the molecular biology unit at the Salamanca University Hospital.

Cell culture media and supplements were obtained from Sigma Aldrich. LY2603618, alisertib, AZ3146, and docetaxel were obtained from Selleckchem. Carboplatin was purchased from Pfizer GEP, SL. Cells were mycoplasma free at different evaluations.

Dose–response and synergy studies were assessed by MTT screening assay. Ovarian cancer cell lines were seeded in 48-multiwell plates (1 × 10⁴ cells/well) and treated with the indicated compounds and doses for the indicated times. To determine cell proliferation, MTT was added to the wells for 1 hour at 37°C (0.5 mg/mL). Then, MTT was solubilized with DMSO and absorbance was measured at 562 nm (555–690) in a multiwell plate reader (BMG labtech). Results were plotted as the mean values of three independent experiments. Interaction among drugs was calculated using CalcuSyn Version 2.0 software (Biosoft) by determining combinational index (CI) based on the algorithm reported by Chou and Talalay. Values <1, synergistic effect, values equal to 1, indicate additive effect, and values > 1 represent an antagonistic effect. For three-dimensional (3D) cell culture experiments, OVCAR3 and OVCAR8 cells were seeded in a 48-multiwell plate (1 × 10⁴ cells/well) containing an underlying layer of Matrigel, which was preincubated at 37°C during 30 minutes. The following day, cells were treated with alisertib, LY2603618, or the combination of them. Number and diameter of the 3D colonies were daily monitored under a light microscope for 5 days. For clonogenic experiments, cells were treated with alisertib, LY2603618, and the combination of them for 24 hours (5 × 10⁵ cells/well in a 6-well plate). Then, cells were tripsinized, counted, and resuspended in complete growth medium to perform serial dilutions 1/10. We selected dilutions 3 and 4 and seeded in triplicate in 6-multiwell plates during 10 days, when number of colonies was counted.

For LDH cytotoxicity assay, OVCAR3 and OVCAR8 were seeded in a 48-multiwell plate (1 × 10⁴ cells per well) and, the following day, treated with alisertib, LY2603618, and the combination of both drugs for 72 hours. Then, LDH activity was evaluated following manufactured instructions (Pierce LDH Cytotoxicity Assay Kit, Thermo Fisher Scientific).

For cell-cycle experiments, OVCAR3, OVCAR8, and SKOV3 were seeded and 24 hours later synchronized with double thymidine block (2 mmol/L). Briefly, cells were exposed to thymidine for 18 hours, and then, after recovering in thymidine-free medium for 9 hours, a second exposure was performed for another 18 hours. Then, cells were washed and treated with alisertib, LY2603618, or the combination of them for 24 hours. Nontreated cells were used as a control.

Cells were collected and fixed with 70% cold ethanol during 30 minutes. Then, cells were washed twice and propidium iodide/RNAse staining solution (Immunostep S.L.) was added. Results were analyzed on FACSCanto II flow cytometer (BD Biosciences). Percentage of cells in each cell-cycle phase was determined by plotting DNA content against cell number using the FACS Diva software.

For apoptosis and caspase assays, cells were plated and, 24 hours later, pretreated with 50 μmol/L of the pan-caspase inhibitor Z-VAD-FMK, before adding alisertib, LY2603618, or the combination for 72 hours. Treated cells were collected and stained in the dark with Annexin V-DT-634 (Immunostep S.L.) and propidium iodide at room temperature for 1 hour. Apoptotic cells were determined using a FACSCanto II flow cytometer (BD Biosciences). Then, early apoptotic and late apoptotic cells were used in cell death determinations. For detection of CD44 and CD133 proteins, cells were tripsinized, centrifugated, and cell pellets were resuspended and incubated with CD44 (10 μL/sample) and CD133 (10 μL/sample) antibodies at 4°C for 1 hour before being examined using a FACSCanto II flow cytometer (BD Biosciences).

Ovarian cancer cell lines OVCAR3 and OVCAR8 were lysed in apoptosis lysis buffer (20 mmol/L Tris, 140 mmol/L NaCl, 10 mmol/L EDTA, 10% glycerol, 1% NP40, pH 7.0) supplemented with protease inhibitors. Protein concentration was determined by the BCA assay (Pierce) and 50 μg of cell lysates were placed in 96-well plates. Caspase reaction buffer (50 mmol/L HEPES pH 7.4, 300 mmol/L NaCl, 2 mmol/L EDTA, 0.2% CHAPS, 20% sucrose, 20 mmol/L DTT, and 10 μmol/L fluorescently labeled caspase substrate Ac-IETD-AFC or Ac-DEVD-AFC) was added to each well containing cell lysates. The plate was incubated at 37°C for 1 hour and signals were measured at 400/505 nm in a fluorescent reader (BioTek).

Ovarian cancer cells OVCAR3 and OVCAR8 were cultured on glass coverslips, washed with PBS, and fixed in 2% p-formaldehyde (PFA) for 30 minutes at room temperature, followed by a wash in PBS. Monolayers were quenched for 10 minutes with PBS with 50 nmol/L NH₄Cl₂. Then, cells were permeabilized for 10 minutes with 0.1% Triton X-100 in PBS, washed again, and blocked with 0.2% BSA in PBS for 10 minutes. Monolayers were incubated with and subsequently incubated overnight at 4°C with anti-β-tubulin (1:250, Santa Cruz Biotechnology) and anti-Nucleoporin p62 (1:200, BD Transduction Laboratories) primary antibodies. Cells were washed three times (3×) in PBS and incubated with an anti-mouse Alexa Fluor 568 (1:1,000) antibody for 60 minutes. Cells were again washed with PBS (3×) and DAPI (300 nmol/L) was added for 10 minutes and washed twice with PBS before mounting. Fluorescence imaging of cells was performed using an epifluorescence inverted microscope (DMIRE-2, Leica) with a Plan Apo 40× oil immersion objective. Images were obtained in a Zeiss LSM 710 confocal microscope with a Plan Apo 63× oil immersion objective.

OVCAR3 and OVCAR8 cells, grown as TS for 1 week, were dropped on poly-lysine slides for 1 minute and then fixed with 4% of PFA for 10 minutes at room temperature. TS were permeabilized for 5 minutes with 0.1% Triton X-100 in PBS, washed, and blocked with 2% BSA for 30 minutes. Then, cells were incubated for 1 hour with R-phycoerythrin (PE)-coupled CD44 (Inmunostep) or Sox-2 (Millipore). Sox-2 incubated TS were then washed with PBS and incubated with an anti-rabbit Alexa Fluor 568 antibody for 60 minutes. TS were again washed with PBS before mounting with Fluoroshield (Sigma Aldrich). Fluorescence imaging of TS was performed using confocal microscopy (Zeiss LSM 710) with a 63× oil immersion objective.

OVCAR3 and OVCAR8 cells were treated with alisertib, LY2603618, or the combination of both drugs at the indicated doses. For PARP and pH2AX apoptotic proteins and pAURKA, AURKA, pAURKB, pAURKC, pCHEK1, CHEK1, Cyclin A, p27, and p21 protein detection, cells were synchronized with double thymidine block before the cells were treated as described in the flow cytometry section. After drug treatment, cells were lysed in cold RIPA lysis buffer supplemented with protease and phosphatase inhibitors (Sigma Aldrich). Then, protein concentration was determined using Pierce BCA (Bicinchoninic acid) Protein Assay Kit (Thermo Fisher Scientific). Fifty micrograms of total protein was loaded in a SDS-PAGE system. Blots were blocked in Tris-buffered saline (TBS)-5% milk and incubated overnight with the following primary human antibodies: the anti-Cyclin B, anti-Wee1, anti-PARP, anti-p21, and anti-GAPDH antibodies were purchased from Santa Cruz Biotechnology. The anti-pH3 antibody was from Millipore Corporation. The anti-pH2AX and anti-pCDK1-Y15, anti-pAurora (A,B,C), anti-Aurora A, anti-pCHEK1, anti-CHEK1, and anti-p27 antibodies were from Cell Signaling Technology. The anti-Cyclin A antibody was purchased from BD Biosciences.

Horseradish peroxidase conjugates of anti-rabbit and anti-mouse IgG were from Bio-Rad Laboratories. Protein bands were visualized by a luminal-based detection system with p-iodophenol enhancement.

OVCAR3 and OVCAR8 TS were mechanically dissociated by pipetting, counted, and cultured in six-well plates (10⁵ cells per well) in the presence of alisertib, LY2603618, or a combination of both drugs. Cells were dissociated again 24 and 48 hours later. At day 3, TS counts were blindly performed on six random fields of view (FOV). TS/FOV was calculated from three independent experiments.

For MTS proliferation assays (MTS Cell Proliferation Assay Kit, Abcam), OVCAR3 and OVCAR8 TS were seeded in a 48-multiwell plate (1 × 10⁴ cells/well) and treated with alisertib, LY2603618, docetaxel, and carboplatin for 72 hours. Then, MTS reagent was added to culture media for 1 hour, following manufacturer's instructions, and the absorbance was measured at 490 nm.

All experiments were performed at least three times. Student t test was used to determine significant statistical differences. Two-way Student t test was used for the statistical analyses (*, P ≤ 0.05; **, P ≤ 0.01; ***, P ≤ 0.001).

To identify molecular functions dysregulated in ovarian tumors, we performed transcriptomic analyses guided by functional genomics. We used a public dataset containing information from nontransformed isolated human epithelial ovarian cells and ovarian carcinoma cells (GEO DataSet accession number: GDS3592). Using a minimum fold change of four, we selected 2,925 genes (Fig. 1A). Functional analyses identified deregulation of relevant functions including cell differentiation, response to stress or cell cycle, among others (Fig. 1A). Taking in consideration the relevant role of cell-cycle mediators in cancer, we searched for druggable kinases contained within this function, identifying only four druggable genes: AURKA, AURKB, CHEK1, and TTK/MPS1. Figure 1B shows these genes with the fold change identified in our analyses (GDS3592) and the confirmation performed using data contained at Oncomine (www.oncomine.org). All genes included within the cell-cycle function are described in Supplementary Table S1.

Before evaluating compounds against these kinases, it was relevant to know whether molecular alterations of these genes exist in ovarian tumors. To do so, we used data from 311 tumors contained at cBioportal (18) observing that AURKA was amplified in 8.7% of tumors and CHEK1 in 3.9%. TTK/MPS1 was amplified in 1.6% and AURKB was not amplified (Fig. 1C).

Next, we analyzed the antiproliferative capacity of agents against the proteins coded for those genes that were amplified (AURKA, CHEK1, and TTK/MPS1) in a panel of four ovarian cell lines including OVCAR3, OVCAR8, IGROV1, and SKOV3. To assess the effect on proliferation of new drugs targeting these kinases, we used three compounds: LY2603618 against the CHEK1 (20, 21), alisertib (MLN8237) against AURKA (22, 23), and AZ3146 against TTK/MPS1 (24, 25). The most antiproliferative effect was observed for LY2603618 and alisertib, targeting AURKA and CHEK1 (Fig. 1D and E). We observed less activity for AZ3146 (Fig. 1F). To evaluate the therapeutic index of LY2603618 and alisertib, we used the nontransformed cell line model HACAT, observing that the doses required to produce antiproliferative effect were higher than in tumoral ones (Supplementary Fig. S1).

Next, we decided to combine inhibitors of CHEK1 and AURKA together as were the most active compounds in our cellular screening. Combination of LY2603618 with alisertib was synergistic at most of the doses tested (Fig. 2A). Increasing doses of LY2603618 augmented the effect of a fixed dose of alisertib in OVCAR8 and OVCAR3 (Fig. 2B). Studies using semi-solid media with Matrigel showed a similar effect with the combination, being the effect more evident in OVCAR3 (Fig. 2C). We also explored the effect of the combination using clonogenic assays showing similar results (Supplementary Fig. S2A). Finally, we confirmed the cytotoxic effect by performing a LDH assay of the combination compared with each agent alone in OVCAR3 and OVCAR8 (Supplementary Fig. S2B).

Given the fact that the combination of both compounds inhibited cell proliferation when evaluated in different models, we decided to explore whether this effect was due to a cell-cycle arrest or an induction of apoptosis. To do this, we used the two most sensitive cell lines, OVCAR3 and OVCAR8. Treatment with alisertib showed a profound arrest at the G₂–M phase, whereas treatment with LY2603618 showed a slight increase at G₁ that was more evident in OVCAR8 (Fig. 3A). Treatment with the combination produced an arrest at G₂–M in OVCAR8; effect that was less evident in OVCAR3 and SKOV3 (Fig. 3A; Supplementary Fig. S3). The biochemical evaluation of G₂–M components showed an increase in pH3 in OVCAR8 at 12 hours, indicative of arrest in M phase (Fig. 3B). This effect was less seen in OVCAR3. No clear modifications of p21 and p27 were observed with the combination (Supplementary Fig. S3).

To confirm the effect of alisertib and LY2603618 on AURK and CHEK1, we performed Western blot studies with these compounds given alone or in combination at 12 and 24 hours. LY2603618 was able to inhibit the phosphorylation of CHEK1 and alisertib did not affect other AURK isoforms like AURKB or AURKC. Treatment with nocodazole arrested cells at G₂ phase (Fig. 3C).

To analyze the effect of the combination on the mitotic process, we evaluated the percentage of aberrant mitotic spindles. Combination of LY2603618 and alisertib showed an increase in the formation of aberrant spindles that was more profound in OVCAR8, in line with the arrest observed at G₂–M and the increase in pH 3 (Fig. 3D). Supplementary Figure S4A shows random images in OVCAR3 and OVCAR8 of aberrant mitotic spindles for each treatment.

Next, we evaluated the effect of this combination on cell death. As can be seen in Fig. 4A, administration of the combination induced apoptosis in both OVCAR3 and OVCAR8 cells. Treatment with the pan-caspase inhibitor Z-VAD-FMK partially reduced the induction of apoptosis, demonstrating that some of the apoptosis induction was mediated by caspases (Fig. 4B). This observation was further confirmed by the evaluation of caspase-3 activity in OVCAR3 and OVCAR8 (Supplementary Fig. S4B) The biochemical analysis demonstrated that the combination increased pH2AX in both cell lines, a marker of DNA damage and did not induce PARP degradation (Fig. 4C).

As stem cells play a major role in ovarian relapse, we decided to explore the effect of the combination on stem cell properties. We first explored expression of the stem cell biomarkers CD44 and CD133 in OVCAR3 and OVCAR8 after treatment with alisertib, LY2603618, and the combination in the overall population of adherent cells. We observed that combination slightly reduced surface expression of CD44 and CD133 when compared with each agent given alone (Fig. 5A; Supplementary Fig. S5). As cells grown as TS better recapitulate stem cell properties (26), we evaluated the effect of each drug alone or the combination on secondary TS formation. Figure 5B shows enrichment in the stem cell markers CD44 and SOX2 on OVCAR3- and OVCAR8-derived TS, confirming that derived TS are an appropriate model to evaluate the effect of drugs. The combination was able to reduce the formation of TS in a greater manner than when each agent was given alone (Fig. 5C). Finally, we evaluated the effect on TS of both agents given alone or in combination with chemotherapy. As can be seen in Supplementary Fig. S6, the combination increased the effect of individual treatments.

To translate our findings to the clinical setting, we evaluated the synergistic interaction of LY2603618 and alisertib alone or when given with docetaxel and carboplatin. We decided to use these chemotherapies as platinum-based agents and taxanes are the main treatment in ovarian cancer. The combination augmented the effect of each agent given alone (Fig. 6A), and showed a synergistic interaction for most of the doses used in three cell lines, SKOV3, OVCAR3, and OVCAR8 (Supplementary Fig. S1). These data demonstrate that the administration of LY2603618 and alisertib could have a potential translation to patients if given with standard chemotherapy.

Finally, we decided to evaluate the association of these kinases with clinical outcome using public transcriptomic data (19). The concomitant expression of AURKA and CHEK1 was linked to detrimental progression-free survival (PFS) and overall survival (OS) in early-stage ovarian cancer patients (Fig. 6B). These data confirmed the implication of these kinases in the oncogenic phenotype of ovarian tumors.

In this article, we describe a synergistic antitumoral effect between AURKA and CHEK1 inhibitors in ovarian cancer. We used an in silico analysis to identify relevant functions that can be pharmaceutically inhibited. These studies permitted the identification of some kinases that regulate cell-cycle progression. Of note, the upregulated genes identified in this small dataset were confirmed with data contained at Oncomine, a database that includes a large number of patients. Moreover, some of these genes participate in pathways that are involved in the pathophysiology of this tumor (11). By using public data from TCGA, we identified that AURKA and CHEK1 were amplified in 8.7% and 3.9% of ovarian tumors, respectively; providing the rational for exploring agents against these kinases. Although our in silico approach is valid, confirmation of our results using data from a prospective cohort of patients would reinforced the findings.

Next, we aimed to explore agents against the identified proteins. Interestingly alisertib, a kinase inhibitor against AURKA, and LY2603618 a CHEK1 inhibitor, were the most active compounds compared with the TTK/MPS1 inhibitor AZ3146. It is relevant to mention that some AURKs, as well as other FOXM1-controlled cell-cycle proteins such as CDC25, cyclin B, and PLK1, have been reported to be overexpressed in ovarian cancer (11). In addition, among the combination of agents used, AURKA inhibition synergized with CHEK1 inhibitors, and this was not observed when combining other agents. Alisertib is an AURKA inhibitor that is currently at its late stage in clinical development in non–small cell lung cancer (NSCLC) and has shown activity in several solid tumors (12, 13). CHEK1 inhibitors including LY2603618 are in early-stage clinical development in different tumor types, mainly NSCLC (21).

When evaluating the mechanism of action, alisertib induced arrest at the G₂–M phase as it is an agent that affects kinases involved in the formation of the mitotic spindle. Inhibition of CHEK1 with LY2603618 induced arrest at G₁. Combination of both agents increased G₂–M arrest at different levels, and produced a profound induction of apoptosis. This finding is of remarkable interest as each agent alone showed a cytostatic effect, but the combination was cytotoxic, a desirable effect when treating cancer patients.

As most of cancer-related deaths are associated with tumor relapses and tumor cells with stem cell properties are involved in this process, we decided to explore the effects of the combination on the stem cell population. The combination of both compounds reduced the expression of stem cell biomarkers in a greater magnitude than each agent alone. Moreover, self-renewal capability was also altered, as observed when evaluating secondary TS formation after treatment. Some studies have described the effect of the individual inhibition of these kinases on the stem cell properties (27, 28). However, this is the first time that this effect has been described with the combination.

Translating preclinical drug combinations to potential uses in the clinical setting is a main goal. To do so, we explored the activity of this combination with standard chemotherapy used in ovarian cancer, including docetaxel and carboplatin. OVCAR3 and OVCAR8 showed different sensitivity to docetaxel and carboplatin, but administration of LY2603618 and alisertib increased the activity of each agent given alone. Globally, these results open the possibility to further explore these combinations in the clinical setting.

Finally, we identified that expression of AURKA and CHEK1 was associated with detrimental outcome in early-stage ovarian cancer. These findings, together with the genomic studies that reported deregulation of cell-cycle and DNA repair pathways in ovarian cancer, have two important implications. First, the available data demonstrate the oncogenic activity of these kinases in ovarian cancer. Second, molecular analyses of the pathways in which these genes participate may be used to select patients sensitive to these agents. Of note, administration of targeted agents against amplified genes has shown clinical utility as is the case for HER2 in breast cancer (29).

In conclusion, we describe a novel combination of agents for the treatment of ovarian cancer with potential implications in the clinical setting. Amplification of AURKA and CHEK1 is observed in more than 12% of the cases, opening the possibility to develop this combination in patients with amplifications of these genes.

No potential conflicts of interest were disclosed.

Conception and design: A. Ocaña

Development of methodology: A. Alcaraz-Sanabria, C. Nieto-Jiménez, V. Corrales-Sánchez, F. Andrés-Pretel, E.M. Galán-Moya, A. Ocaña

Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): A. Alcaraz-Sanabria, L. Serrano-Oviedo, M. Burgos, J. Llopis, E.M. Galán-Moya

Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): A. Alcaraz-Sanabria, C. Nieto-Jiménez, F. Andrés-Pretel, M. Burgos, E.M. Galán-Moya, A. Pandiella, J.C. Montero

Writing, review, and/or revision of the manuscript: F. Andrés-Pretel, M. Burgos, E.M. Galán-Moya, A. Pandiella, A. Ocaña

Administrative, technical, or material support (i.e., reporting or organizing data, constructing databases): V. Corrales-Sánchez

Study supervision: A. Ocaña

We would like to thank G. Serrano for her technical support. A. Ocana would like to thank his parents Pilar Fernandez and Santiago Ocana for their support during his professional life.

This work has been supported by Instituto de Salud Carlos III (PI16/01121), CIBERONC, ACEPAIN; Diputación de Albacete and CRIS Cancer Foundation (to A. Ocaña). Ministry of Economy and Competitiveness of Spain (BFU2015-71371-R), the Instituto de Salud Carlos III through the Spanish Cancer Centers Network Program (RD12/0036/0003) and CIBERONC, the scientific foundation of the AECC and the CRIS Foundation (to A. Pandiella). The work carried out in our laboratories received support from the European Community through the regional development funding program (FEDER). J.C. Montero is a recipient of a Miguel Servet fellowship program (CP12/03073) and received research support from the ISCIII (grants PI15/00684). E.M. Galan-Moya is funded by the Implementation Research Program of the UCLM (UCLM resolution date: 31/07/2014), with a contract for accessing the Spanish System of Science, Technology and Innovation-Secti (cofunded by the European Commission/FSE funds).

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