The Runt related transcription factors (RUNX) are recognized as key players in suppressing or promoting tumor growth. RUNX3, a member of this family, is known as a tumor suppressor in many types of cancers, although such a paradigm was challenged by some researchers. The TGF-β pathway governs major upstream signals to activate RUNX3. RUNX3 protein consists of several regions and domains. The Runt domain is a conserved DNA binding domain and is considered as the main part of RUNX proteins. Herein, we compared the effects of Runt domains and full-Runx3 in cell viability by designing two constructs of Runx3, including N-terminal region and Runt domain. We investigated the effect of full-Runx3, N-t, and RD on growth inhibition in AGS, MCF-7, A549, and HEK293 cell lines which are different in TGF-β sensitivity, in the absence and presence of TGF-β. The full length RUNX3 did not notably inhibit growth of these cell lines while, the N-t and RD truncates showed different trends in these cell lines. Cell proliferation in the TGF-β impaired context cell lines (AGS and MCF-7) significantly decrease while in the A549 significantly increase. On the other hand, transfection of N-t and RD did not considerably affect the cell proliferation in the HEK293.Our results show that full-lenght RUNX3 did not affect the cell viability. Conversely, the N-t and RD constructs significantly changed cell proliferation. Therefore, therapeutic potentials for these truncated proteins are suggested in tumors with RUNX proteins dysfunction, even in the TGF-β impair context. The Runt related proteins (RUNX) are transcription factors with crucial roles in orchestrating the cell proliferation and differentiation in all stages of cell growth and development. Moreover, these proteins are known as key players in suppressing or promoting tumor growth -4. TheseRUNX3 with 415 amino acids consists of several domains, each of which interacts with various proteins to regulate RUNX activity in a spatio-temporal manner. The Runt domain (RD) which is located in the N-terminal part of RUNX proteins is a conserved DNA binding domain 128aa) with more than 90% identity among three RUNX genes, which binds to a specific motif in DNA . At the aa with mOf further relevance, it is possible to assume that N-terminal region and Runt domain of the full protein, which do not contain inhibition domain or negative regulatory region are capable to inhibit cell growth in the absence of activating pathways. To address this hypothesis, we investigated the role of N-terminal region of the RUNX3 protein in tumor growth suppression by constructing two truncated forms of the Runx3: N-terminal region (1-187aa), and Runt domain (54-187) . Since TOur findings indicate that in the absence and presence of TGF-β exogenous RUNX3 could not provoke notable cell death in different cell lines, whereas the N-t (1-187) and RD (54-187) constructs, did significantly inhibit cell proliferation in AGS and MCF-7 cell lines with impaired TGF-β pathway.Cell culture and transfection2. Cells were seeded for 24h prior to transfection to reach 70-90% confluency. After 24 h. the cell lines were transiently transfected with PolyFect® transfection reagent  according to the manufacturer’s instructions.The human gastric adenocarcinoma AGS cell-line , human embryonic kidney HEK 293 cell-line, and human breast adenocarcinoma MCF-7 cell-line  were cultured and maintained in RPMI-1640 medium .The human lung carcinoma A549 cell line  was cultured and maintained in DMEM  containing 10% FBS   and 1% penicillin-streptomycin  in a humidified atmosphere at 37 °C and 5% COHuman recombinant TGF-β 1 was purchased from AbcamBiotech . Twenty-four h. after transfection, the medium were exchanged in the absence or presence of 10ng/mL of TGF-β.Plasmid ConstructsThe type I isoform of full-length RUNX3 (NM_004350) and RUNX3 truncated mutants, N-t (1-187) and RD (54-187) were amplified by PCR with a C-terminus Histidin-tagged and cloned into the pcDNA3 vector. The final constructs were verified by restriction enzyme digestion and sequencing. The primer sequences have been shown in Cell Proliferation Assay4 cells per well in quadruple. Following 24 h. incubation, cells were transfected using PolyFect®. Cell viability was measured using MTT 3- 2, 5-diphenyltetrazolium bromide (0.5mg/m), 24 and 48 h. after transfection. The formazan crystals were formed after incubation for 4 h. at 37 °C and then dissolved in DMSO. The optical density (OD) was measured using microplate reader  at 570 nm, with 690 nm as a reference wavelength. The change in viability was calculated as percent viability compared to that of the control group (empty vector). To determine the effects of RUNX3 and its constructs on cell proliferation, MTT assay was carried out in three independent experiments. The cells were seeded in 96-well plate at 2×10Statistical analysisAll data were presented as mean ± SEM . The results were analyzed using one-way ANOVA following Tukey post-test. P-values< 0.05 were regarded as statistically significant.Assessment of Transfection efficiencyThe conditions of transfection were optimized by changing the number of cell plated, amount of plasmids and Polyfect reagent. The efficiency of transfection was assessed by transfection of pcDNA3-EGFP to the cells. The transfected cells, were evaluated 24 h. and 48 h. after transfection for the expression of EGFP by fluorescence microscopy. Cell Proliferation Analysis of AGS, HEK, A549, and MCF-7 in the Absence and Presence of TGF-βThe effects of full-RUNX3, N-t and RD constructs on cell proliferation in the absence or presence of TGF-β was carried out in AGS, MCF-7, A549, and HEK 293 cell lines. These cell lines are different in the functionality of TGF-β pathway. AGS cell line is resistant to TGF-β, whereas MCF-7, A549, and HEK 293 cell lines are sensitive to TGF-β -35. In our results, the RUNX3 did not notably induce growth inhibitory effects in all applied cell lines in the presence or absence of TGF-β . These fPrevious studies have pointed to the growth inhibitory effect of RUNX3 in different cell lines, including AGS, MCF-7, A549, and HEK 293-38. HoweGiven the above mentioned results stated thus far, a number of questions are rising:Why RUNX3, which is known as an important tumor suppressor, does not exert any notable growth inhibitory effect in our cell lines?Why N-t and RD constructs have a significant effect on cell proliferation, while the full-RUNX3 does not exert a such effect?Why N-t and RD constructs provoke divergent effects on each cell-line?Why each cell-line shows different behaviour in the presence of TGF-β?-/- mice, which did not show any early-onset gastric hyperplasia and did not develop gastric carcinoma . After publishing these data, many other papers substantiated the onco-suppressory role for RUNX3 in gastric and other tissues. By contrast, Groner team, which are pioneer in RUNX researches ruled out Itoʼresults. They generated RUNX3 arcinoma , 39, 40.arcinoma . Furtherarcinoma . They coithelium . Moreoveithelium . Additioithelium . All theThe Runt domain is a conserved DNA binding domain (128aa) with more than 90% identity among three RUNX genes. This domain is considered as the main part of RUNX proteins since, only this part binds to a specific motif in DNA . FurtherRUNX transcription factor participates in some biological activities of TGF-β signalling . TGF-β sGiven the notion that the AGS cell-line is resistant to TGF-β, as anticipated, the presence or absence of TGF-β did not significantly alter cell proliferation in AGS transfected cells. Moreover, in this cell-line, the N-t and RD could significantly decrease cell proliferation . It is known that RUNX1 is expressed in gastric epithelial cells and functions as a tumor-suppressor in gastric cancer cells . It is pet al. contain the conserved Runt domain with more than 90% identity among RUNX genes. Consequently, they might mimic the activity of all three mammalian RUNX proteins. Since many tumors are resistant to TGF-β, therapeutic potentials for these truncated proteins are suggested in tumors with TGF-β impairment and RUNX proteins dysfunction. These truncated RUNX3 proteins could be utilized in gene therapy or designing small molecule inhibitors . However


In mammals, the Circadian Timing System (CTS) involves a central pacemaker in the brain , which coordinates a network of molecular clocks in each cell from all organs, through the generation of rhythmic physiology and hormonal secretions about 24 hours ,2. The mThe circadian rhythms, with an about 24-hour cycle, could significantly modify the efficacy and toxicity mechanisms of more than 50 anticancer drugs . StrikinEverolimus (EV), a selective inhibitor of mammalian target of rapamycin complex 1 (mTOR1), inhibits the synthesis of cell cycle and glycolysis proteins, resulting in both immunosuppression and anticancer effects . A combiYet, exogenous signals such as serum shock successfully synchronized mTOR activity and cell cycling in our study , as wellIndeed, EV efficacy, as assessed with Go-G1 phase cells accumulation, varied up to four-fold as a consequence of different dosing time over the 24 hours. This striking finding provides not only some useful hints toward EV chronotherapy in human ER+ breast cancers, but also support chronotherapeutics with other anti-cancer drugs, irrespective of canonical genetic circadian clock function in cancer cells. As more and more clock gene-independent rhythms are being discovered, such as the highly conserved peroxiredoxins biochemical circadian clock or the cell autonomous 12-hour clock in mouse liver etc ,7, clockThus, we have shown that, in a breast cancer cell system with disrupted circadian timing system, integrating others cell rhythms in chronotherapy could also increase drug efficacy. This principle may apply to many other cancer systems and treatment types.


There is an error in the first sentence of the “Cell Lines” paragraph of the Materials and Methods section. Dr. Uliana de Simone did not provide cell lines for this study. The correct sentence is: Human astrocytoma D384 cells were provided to the authors as a gift.


A cell line representative of human high-grade serous ovarian cancer (HGSOC) should not only resemble its tumour of origin at the molecular level, but also demonstrate functional utility in pre-clinical investigations. Here, we report the integrated proteomic analysis of 26 ovarian cancer cell lines, HGSOC tumours, immortalized ovarian surface epithelial cells and fallopian tube epithelial cells via a single-run mass spectrometric workflow. The in-depth quantification of >10,000 proteins results in three distinct cell line categories: epithelial (group I), clear cell (group II) and mesenchymal (group III). We identify a 67-protein cell line signature, which separates our entire proteomic data set, as well as a confirmatory publicly available CPTAC/TCGA tumour proteome data set, into a predominantly epithelial and mesenchymal HGSOC tumour cluster. This proteomics-based epithelial/mesenchymal stratification of cell lines and human tumours indicates a possible origin of HGSOC either from the fallopian tube or from the ovarian surface epithelium. High-grade serous ovarian cancer is the most common and aggressive ovarian cancer, with uncertain cell of origin. Here, the authors undertake a mass spectrometric analysis of 26 cancer cell lines and identify a protein signature that classifies ovarian cancer tissues into epithelial and mesenchymal groups. Invasive ovarian cancer (OvCa) is a highly heterogeneous disease divided into four major histologic subtypes, namely serous, endometrioid, mucinous and clear cell OvCa. High-grade serous ovarian cancer (HGSOC) is the most common 70%) and aggressive subtype and is primarily responsible for the low survival rate2340% and ag7in vitro and in vivo experiments is apparent. However, these models should be as representative of the tumour as possible, as there is little clinical utility for experimental data obtained in cell lines that do not reflect the disease being studied; these results might be, at best, misleading and, at worst, harmful to patients. The time that has elapsed since many OvCa cell lines were established (some were created more than 30 years ago), coupled with the risk of switching or cross-contamination when propagated for a long time, and the only recent introduction of genomic ‘fingerprinting' techniques, has led to the incorrect assignment of the tissue origin of many OvCa cell lineset al.et al.et al.11Given that it is difficult to perform mechanistic studies with primary tissue, the necessity for cellular models for It is as yet unknown to what extent the molecular characteristics reported by all these studies are represented at the protein level. Genomic and transcriptomic level analyses do not necessarily reflect the phenotype-defining proteomic profile. So far, there has been no direct and in-depth proteomic comparison between OvCa cellular models and tumour tissues. However, given that proteins represent the functional and phenotype-defining units of a cell, a quantitative proteomics approach should be superior to gene expression-driven comparisons alone. We therefore hypothesized that an integrated and streamlined mass-spectrometry (MS)-based proteomics approach1314To the extent that proteomics has reached a reasonable depth of quantification, this has usually involved multi-step workflows with extensive fractionation and correspondingly long measurement times. Since we sought to compare a large number of OvCa proteomes, we instead adapted and applied a recently described method based on a single-run workflow15In total, our analysis resulted in 229,004 unique peptide sequences corresponding to 11,070 distinct protein groups at a peptide and protein false-discovery rate (FDR) of less than 1% refs , whereasWhen we required quantification in at least two of three cell line replicates, we obtained a filtered data set of, in total, 10,926 proteins containing an average of 7,810 protein quantifications per cell line, 8,143 per HGSOC tumour tissue and 7,609 for the primary FTEC replicates . A totalERBB2 in SKOV3IP1 or the proto-oncogene KRAS in KURAMOCHIWe first analysed commonalities and differences in protein expression across 30 cell lines. Average filtered quantification depth and expression levels from the triplicate analyses of each cell line are shown in 79The complete data set covered a large proportion of the KEGG-annotated members of major biological processes and cancer-related signalling pathways, such as all 35 members of the DNA replication pathway and >70% of the p53 pathway . PathwayTP53 mutational status, mutation frequency and DNA copy number alterations924Unsupervised hierarchical clustering based on the expression of 8,487 distinct proteins quantified in at least 10 of 30 cell lines resulted in three main groups . Group IIndependent of the unsupervised hierarchical clustering, we used a principal component analysis (PCA) on the basis of whole-proteome levels, which confirmed the presence of the three main cell line groups . Figure 22With the aim of determining a small group of proteins with the strongest discriminating power between the groups, we used feature selection in combination with Support Vector Machines (SVMs) classification, as previously described28Given the distinct proteome characteristics of the cell line groups, we reasoned that group I cell lines would most closely resemble HGSOC human cancer tissue. To first assess the quantitative robustness of our workflow for tumour tissue analysis, we analysed eight HGSOC tumour proteomes from five patients. This revealed that the proteomes were very similar between bilateral ovarian tumours from the same patient (mean Pearson correlation 0.95), compared with the inter-patient variation 32A cell line representative of human HGSOC should not only resemble its tumour of origin at the molecular level, but more importantly, also demonstrate functional utility in pre-clinical investigations. To exploit our global protein expression data, we first determined all proteins that were significantly different between the cluster 1 and cluster 2 cell lines identified in our study and then bioinformatically determined annotation terms that were statistically enriched in either clustertrans retinoic acid (ATRA)39On the basis of the results of this pathway analysis, we hypothesized that group I cell lines would be more susceptible to the known CRABP2-mediated anti-oncogenic effect of all-It is evident by now that the most commonly used HGSOC cell lines are among those with the least genomic similarity to HGSOC tumours911Here we used MS-based proteomics to offer a more phenotype-associated characterisation of OvCa cell lines than that of genomic and gene expression characterization alone. Our streamlined, single-run workflow allowed us to characterize a large number of cancer proteomes in a relatively short time (4 h). Importantly, the absence of fractionation did not unduly compromise depth of coverage of the proteome as we detected ∼8,000 proteins in cell lines, primary cells and tumours, with a total number of ∼11,000. To put this in perspective, a recent proteomic study of cancer tissues using a different platform consistently detected 2,000 proteins265-integrin (ITGA5), which we previously reported to be an important mediator of early OvCa metastasis5-integrin is regulated by the epithelial differentiation marker E-cadherin , PAX8 and MSLN. It also revealed novel markers for FTEC-derived HGSOC cell lines such as CRABP2 and ASS1, which are highly expressed in serous OvCa compared with the clear cell, endometrioid and mucinous subtypes3074445Group II cell lines were the least HGSOC-like cell lines in this study and, interestingly, the ITGA5/CDH1 axis does not appear to apply to this group. In general, group II lines express high levels of epithelial proteins such as CDH1 and EPCAM, which contributes to their clustering with the group I  cell lines in component 1 of the cell line PCA . However22A 219 microRNA (miRNA)-associated mesenchymal gene signature separated OvCa cases in different data sets into two subtypes: an integrated epithelial subtype and an integrated mesenchymal subtype7Clustering of group I cell lines and HGSOC-1 to -4 tumours with the FTECs provides support for the fallopian tube epithelium as their cell of origin, while the clustering of group III cell lines and HGSOC-5 with the IOSEs, and their lower expression of epithelial proteins, suggests that they may be OSE-derived. This suggests the potential presence of an inherent level of stratification in HGSOC tumours based on their protein expression and cell of origin. Regarding the precursor cell of HGSOC, there are convincing arguments for both FTECs and OSE34535654The underlying differences between the cell lines in the two different clusters were also reflected in the biological pathways associated with their respective proteins. The vitamin A and retinol pathways were highly enriched in group I cell lines, with more than 10-fold expression level differences. Retinoic acid has been used successfully in the treatment of acute promyelocytic leukemia and neuroblastoma and its limited success in other cancers may be due to different cellular responses. On the basis of the proteomic profiling alone, we correctly predicted that group I cell lines would be sensitive to the anti-proliferative, differentiating effects of ATRA, while groups II and III cell lines would be either ATRA-resistant, or responsive to its pro-oncogenic effects. Future investigation into these different mechanisms of action of, and cellular response to, ATRA in group I and group III HGSOC cell lines may inform the investigation of retinoic acid in OvCa.in vivo contexts, where only small numbers of cells are available.In summary, the integrated level of cell line proteomic profiling introduced here provides the OvCa research community with an additional resource to select the most appropriate model for their research. The described 67-protein signature may shed more light on the cell of origin and respective driver proteins of HGSOC and contribute to the further investigation of important clinical problems such as chemotherapy resistance. Apart from opening the door for systematic and routine interrogation of proteome-wide differences in cancer models, our streamlined and high-sensitivity proteomics workflow will be especially attractive in All patient samples were collected at the University of Chicago Medical Center, with approval from the Institutional Review Board. All the patients provided informed consent.n=3 patients). Tumours were immediately snap frozen and stored at −80 °C until sample processing for MS analysis. Serous histology was confirmed by two gynaecologic pathologists .Ovarian tumours from five chemo-naive patients with serous-papillary high-grade, FIGO Stage IIIB/C, OvCa were collected by EL under an IRB-approved protocol at the University of Chicago during the primary debulking surgery or laparoscopy before neo-adjuvant treatment. In some cases, tumours were collected from both ovaries  were isolatedn=3 replicates for each line.Thirty cell lines were included in this study; their sources and respective media are detailed in Cell lysis was performed in lysis buffer  at 99 °C for 10 min and by 15 min sonication . HGSOC tissues were first homogenized in lysis buffer using an Ultra Turbax blender. Proteins in the lysate were reduced with 10 mM DTT for 30 min and alkylated with 55 mM iodoacetamide for an additional 30 min. Remaining SDS detergent was removed by acetone precipitation. Briefly, acetone (−20 °C) was added to 100 μg of proteins to a final concentration of 80% v/v and the proteins were precipitated overnight at −20 °C. After centrifugation , the detergent-containing supernatant was removed and the protein pellet was washed with 80% acetone (−20 °C). Protein pellets were then resolved in 100 μl 6 M urea/2 M thiourea (in 10 mM Hepes pH 8.0) and digested with 1 μg of LysC for 3 h at room temperature. After adding four volumes of 50 mM ammonium bicarbonate, 1 μg trypsin was added and tryptic digestion carried out overnight. The next day, digestion was stopped by adding 1% TFA. Peptides were finally desalted on C18 StageTips and kept at −20 °C until MS analysis.18 particles . Peptides were separated over a 250 min gradient from 2% to 60%  in buffer B  at 200 nl min−1. The column temperature was constantly set to 50 °C by using an in-house-made column oven. The survey scans  were acquired with a resolution of 70,000 , at m/z 200. A top-five method was used to select up to the five most abundant precursor ions with a charge ≥2. Selected precursor ions were subjected to high-energy collisional dissociation fragmentation at a normalized collision energy of 25 (27 for Q Exactive HF), an isolation window of 2.2 Th (1.4 Th for Q Exactive HF) and a resolution of 17,500 at m/z 200 . For survey scans, ion injections times were set to 20 ms  and 120 ms  for MS/MS scans. Dynamic exclusion of sequenced peptides was set to 30 s. Data were acquired using Xcalibur software (Thermo Scientific).MS analysis was performed using Quadrupole Orbitrap mass spectrometers63MS raw files were analysed with MaxQuant softwarehttps://cptac-data-portal.georgetown.edu/cptacPublic/) and analysed with MaxQuant. Logarithmic reporter intensities were normalized against the control reporter channel  and each sample median normalized before data analysis.Isobaric tag for relative and absolute quantification (ITRAQ)-based TCGA proteome raw files (PNNL study) generated by the Clinical Proteomic Tumor Analysis Consortium (NCI/NIH) were downloaded from the CPTAC data portal . This feature selection method resembles that of a domain expert (biologist) selecting a small subset of proteins that can conveniently be followed up. Furthermore, the required large differences between the groups should allow more robust classification in a clinical setup. The method is conceptually analogous to the standard analysis of variance test and, in fact, performing analysis of variance identifies the selected proteins as significantly differentially expressed. However, using feature selection avoids the need for an FDR cutoff and allows for selecting smaller subsets.The SVMs implementation in Perseus is an adaptation of the well-established and commonly used LIBSVM libraryFor PCA of cell lines, primary cells and HGSOC tissues , we firsFor the clustering of ITRAQ and label-free data , z-scorit-test statistic was used, including a permutation-based FDR of 5% and an s0 valueFor pairwise comparison of proteomes , a two-sThe following cell lines were treated (or untreated) with 7 μM ATRA daily for 7 days in 96-well plates: SNU119, TYKNU, KURAMOCHI, HEYA8, PEO4, 59M, SKOV3IP1 and COV318. On day 8, MTT assays were carried out. Proliferation rates were calculated for ATRA-treated and untreated cells and plotted as percentage relative to untreated cells.The cells were grown in six-well plates and treated with 7 μM ATRA daily for 7 days. The cell lysates were collected in RIPA buffer and 20 μg protein was electrophoresed on 4–20% resolving gels. The following antibodies were used in western blots: anti-rabbit ITGA5 , anti-rabbit PAX8 , anti-rabbit CRABP2  and anti-rabbit GAPDH .Formalin-fixed paraffin-embedded HGSOC and normal tissue sections (5 μm) were deparaffinized in xylene and rehydrated in graded alcohol solutions. One FFPE section of normal (OSE and FTEC) or tumour  tissue was used for immunofluorescence. Antigen retrieval was carried out in 0.01 M sodium citrate buffer with 0.05% Tween 20 (pH 6.0) for 30 min at 95 °C. Briefly, slides were washed 3 × with PBS, blocked with 10% goat serum in phosphate-buffered saline/0.05% Tween (PBST), and incubated overnight with anti-rabbit CRABP2 antibody  or anti-mouse p53 . Following five PBST washes, sections were incubated with Alexa Fluor 488 goat anti-rabbit IgG  or Alexa Fluor 647 goat anti-mouse IgG  and Hoechst  for 1 h, washed 5 × with PBST, 2 × with PBS, and mounted with Prolong Gold Antifade Reagent . The images were obtained with a Zeiss 510 LSM confocal microscope, using the LSM 510 software. As a negative control, the primary antibody was omitted for one section in each set of samples.http://proteomecentral.proteomexchange.org/cgi/GetDataset) via the PRIDEhttp://maxqb.biochem.mpg.de/mxdb/project/list). All other data supporting the findings of this study are available within the article and its The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium .Supplementary Figures 1-6 and Supplementary Table 1Protein Expression ValuesDifferent AnnotationsCell Line Sources


International Journal of Cancer being at the forefront—and funding agencies have recently moved forward to ask for obligatory cell line authentication data. The history of implementing such rules by the International Journal of Cancer exemplifies the difficulties encountered when installing mandatory quality measures in life sciences.Cell lines are used in life science research worldwide as biological surrogates. All cell lines are subject to major limitations when used as research tools, including (i) cross-contamination with other cells cultured in the same laboratory environment and (ii) evolution in vitro that renders a given cell line inappropriate as a surrogate for a specific biological hypothesis. There is ample evidence that cross-contamination or phenotypic drift of cells in culture can generate irreproducible or misleading data. A small number of scientific journals—the  Cell lines are considered to represent cell functions in a given tissue of an organism and, therefore, have become invaluable tools for biomedical research. To maintain the role of biological surrogates, cell lines require constant quality control and assurance of their identity throughout their lifetime in vitro. Particularly if derived from tumor tissues, cells change over time in culture, both phenotypically and genetically. An even more disastrous feature is the cross-contamination that frequently occurs when cells from another culture are introduced by accident, resulting in replacement by a contaminant that has been propagated in the same laboratory or biosafety cabinet and is better adapted and faster growing than the original, authentic culture. The end result of cross-contamination is a cell line that no longer corresponds to the original, authentic culture. In addition to cross-contamination, cell line misidentification can arise from incorrect labeling at the source. Misidentification of cell lines can happen in every laboratory, and without constant and vigorous control of authenticity, it often remains undetected.Postgenomic research highlights continuously more detailed genetic and epigenetic differences between tumor entities originating from the same organ and even within the heterogeneous populations of a single tumor ,2. Thus,We would make far more progress if cancer research labs and centers would follow a similar policy. The German Cancer Research Center has a mandatory requirement that all manuscripts to be submitted must authenticate the cell lines used in the experiments prior to submission. A similar policy is in place at the MD Anderson Cancer Center . Some laProblems with misidentification and cross-contaminations have been regularly brought to light in the cell biology community but have only resulted in very little change. Indeed, worldwide, between 14 and 46% of the most commonly used cell lines are believed to be incorrectly designated –11. ThinA number of genotype-based testing methods are now available, with a recent increase in molecular approaches such as STR profiling and SNP analysis . Cell baInternational Journal of Cancer (IJC) has been publishing articles to increase awareness and understanding of misidentified cell lines and to provide resources for authentication testing [BioTechniques [Clinical Cancer Research [Nature [IJC [Journals are key stakeholders in resolving the problem of misidentified cell lines . For mor testing ,9,19. Sochniques ,24, ClinResearch , Nature  [Nature , and theure [IJC . PromineIJC and other journals [Despite numerous publications by the journals –18, authjournals . All arejournals .IJC. Looking at search results from these five cell lines, it is obvious that overall usage is steadily increasing.IJC and dominated the agenda of the weekly editors’ meetings for some time. As outlined in Nardone’s letter, many factors were weighed in our discussion, including the corruption of biomedical research in general as well as the matters of educating researchers and of compliance. For the IJC editors, how such a measure would affect submissions and the journal’s reputation represented important considerations. Ultimately, we decided that the most effective way of handling the matter would be to establish a consortium of journals requiring proof of the authenticity of cells at manuscript submission. With this goal in mind, the IJC contacted several high-impact general, cell biology, and cancer research journals in October of 2007 and proposed this idea. At a later point in 2009, the journal solicited some granting agencies to adopt a policy that would require mandatory DNA profiling as a prerequisite for receiving funding.Roland Nardone’s 2007 call to action concerning the problem of cell line contamination in biomedical research  promptedIJC decided to “go it alone” [Unfortunately, the publishing community basically did not respond to this call to action. In the few answers the editors received, journals and granting organizations acknowledged the problem but saw the responsibility for control in the hands of reviewers and the scientists themselves. In 2010, since no joint initiative with leading journals and granting organizations could be established, the t alone”  and afteSTEP 1. 2010—Cell line authentication is recommended.Author instructions amended to include this recommendation.Authors informed on how and where they could have profiling done.STEP 2. Starting from 2011—Cell line authentication, not older than four years, becomes necessary requirement for publication.Author instructions rewritten to reflect new stipulation regarding cell lines.E-mail templates developed to address the new requirements.Manuscript managing system (ScholarOne) updated to include new questions for authors about whether or not they authenticated their cell lines.All manuscripts checked upon submission to verify whether human cell lines were used and whether corresponding files proving authentication were uploaded.Papers flagged until a valid certificate was obtained.Papers are monitored regarding the following: date of submission, title, author, country, resubmission or withdrawal, and final decision.STEP 3. Starting from 2012—Papers not complying with requirement are unsubmitted.The authors use cell lines and do not provide the certificate.The certificate provided does not fulfill the requirements .The authors declare “no cell lines used” but on inspecting the manuscript, it appears that cell lines were used.STEP 4. Statistical analysis of data, including the following:Rates of returning the papers to the authorsRates of resubmission by yearRates of resubmission by continentRates of acceptance by year*From poster submitted to the International Society of Managing and Technical Editors (ISMTE) 2012In addition to the considerable time and cost involved in monitoring the process, these new requirements also had to be included in the author instructions, in the email template sent to the authors to ask for suitable certificates, and in the submission questions in the ScholarOne manuscript system. Once the procedure was in place, we had to describe it in detail for our internal office manual. Especially at the beginning, many researchers were reluctant to check their own cell lines see . In partAuthors claimed that cell lines are a gift from another lab, have already been used in several publications, no previously validated DNA exists for comparison, or they believe that because lines are not in the list of cross-contaminated cell lines they should be ok.Authors only used further-modified cells or claimed that a cell line experiment is not really the main focus of the paper.Authors sent only data sheets from a cell bank and declared this as authentication.IJC form without further STR data for proof, or sent only raw data without a written statement of control and percentage of match.Authors sent data in illegible form, sent just the IJC or cell lines were established more than four years ago but were cultured only for short periods from frozen samples.Authentication of cell lines was older than the four-year limit currently implemented by the Authors wanted to have profiling done while paper was under review or only if they could be assured paper would be accepted.Authentication could not be performed because of a lack of resources or authors were not aware of guideline and were not willing to have lines profiled.Considerable administrative time and cost needed to be invested as a consequence: we estimated approximately 240 hours of additional work over this three-year period to process papers that used cell lines . The same amount of time per paper was needed again to log in the paper and update the Excel file if the paper was resubmitted. On top of that, editorial expertise and time were needed to check the actual documents, which vary for papers with good documentation and manuscripts that include numerous cell lines. In contrast to the administrative times, which in the meantime have evened out with experience, these editorial times still apply and still vary according to quality of documentation and number of cell lines used.IJC enforced stricter rules and a higher percentage of papers (about 17%–18%) were unsubmitted. The majority of manuscripts unsubmitted were from Asia . In 2011, about half of the unsubmitted papers were resubmitted while the other half were withdrawn, and these numbers increased in 2012 and 2013.We analyzed whether there were differences in the resubmission rates between continents and found that a lower percentage of authors from Asia resubmitted their paper  providing exhaustive information to make the reasoning behind new rules transparent, (ii) meeting the arguments that are put forward as excuses for not following new quality measures, and (iii) shifting after a restricted period of discussion or voluntary following of rules to a strict algorithm—will promote this effort. Needless to say, corporate initiatives of journals and scientific societies would greatly foster such quality control measures and accelerate their broad acceptance, as would have happened if the International Journal of Cancer had found more fellow campaigners when the journal introduced mandatory cell line authentication. In addition, providing raw data and implementing rules for feeding data to be published in common databases, as pioneered by the community of genomics researchers, will constitute an important measure in support of these initiatives.Up to now, only a few leading research laboratories and scientific journals are taking measures to change the dreadful situation of the continued use of wrongly denoted cell lines and, thereby, take responsibility for the publication of high-quality controlled data. Common excuses are that this would be the task of reviewers, scientific societies, and funding organizations. However, despite the fantastic work of individual reviewers, in general, the peer review process has mostly failed to detect and monitor the use of falsely designated cell lines. The still rising mountain of incorrect data can only be flattened by a concerted action of leading journal editors, research laboratories, cell banks, international scientific societies, and granting organizations. Beyond cell lines, adequate delineation of biological material poses a particular challenge for other tools and reagents as well, such as proteins or their modifications in tissue sections or fractionated samples using antibodies, patient-derived xenotransplants, or the detailed description of animals used in a study. The need to implement corresponding rules concerning information that is mandatory at submission to a journal has been recently discussed , and itS1 Data(XLSX)Click here for additional data file.S2 Data(XLSX)Click here for additional data file.S3 Data(XLSX)Click here for additional data file.


One of the most challenging problems in the development of new anticancer drugs is the very high attrition rate. The so-called “drug repositioning process” propose to find new therapeutic indications to already approved drugs. For this, new analytic methods are required to optimize the information present in large-scale pharmacogenomics datasets. We analyzed data from the Genomics of Drug Sensitivity in Cancer and Cancer Cell Line Encyclopedia studies. We focused on common cell lines (n = 471), considering the molecular information, and the drug sensitivity for common drugs screened (n = 15). We propose a novel classification based on transcriptomic profiles of cell lines, according to a biological network-driven gene selection process. Our robust molecular classification displays greater homogeneity of drug sensitivity than cancer cell line grouped based on tissue of origin. We then identified significant associations between cell line cluster and drug response robustly found between both datasets. We further demonstrate the relevance of our method using two additional external datasets and distinct sensitivity metrics. Some associations were still found robust, despite cell lines and drug responses’ variations. This study defines a robust molecular classification of cancer cell lines that could be used to find new therapeutic indications to known compounds. Clinical trials are the only real way to assess drug efficacy and toxicity, but this approach is inadequate for testing the hundreds of drugs currently being developed2. Scientists need to test hundreds of drugs on numerous tumor models therefore frequently make use of tumor-derived cell lines5. Such studies aim to identify genomic biomarkers for predicting the responses of individual patients to the drug and, ultimately, for identifying the best drug for each patient.One of the most challenging problems in the development of new anticancer drugs is the very high attrition rate. Less than 5% of the drugs entering phase I trials eventually obtain marketing authorization6 or ERBB2 amplification/overexpression conferring sensitivity to lapatinib7.In 2012, the first large-scale pharmacogenomics studies provided an unprecedented wealth to the scientific community. The Broad Institute-Cancer Cell Line Encyclopedia (CCLE) provided a collection of 1,036 human cancer cell lines from 36 tumor types, tested for 24 anticancer drugs. The Genomics of Drug Sensitivity in Cancer (GDSC) assessed the sensitivity of 727 cell lines, from 29 tissue types, to 138 drugs. Both datasets contain genome-wide gene expression and sequencing data for a subset of genes. These studies have provided unprecedented amounts of information about molecular profiles and drug sensitivity and have validated several known genetic biomarkers, such as the BRAF-V600E mutation sensitizing melanomas to vemurafenib9. Conversely, the recent success of basket studies11 have demonstrated that treatment choices can be based on abnormalities shared by tumors originating from different tissue types.Previous studies assessed drug sensitivity by pooling all the cell lines or by controlling for tissue source. However, with improvements in our knowledge about tumors, it has become clear that genomic, epigenomic, transcriptional, and proteomic analyses of a given cancer can reveal subtypes differing in pathway activity, progression or treatment responseWe present here a comprehensive reanalysis of these two recently published large-scale pharmacogenomics resources. We propose an alternative approach in which cell lines are grouped by transcriptomic profile, based on a biological network-driven gene selection process. This molecular classification of cancer cell lines appeared robust across CCLE and GDSC. We further demonstrated the relevance of this novel classification through the drug response We validate our approach by robustly found in CCLE and GDSC as in two external dataset the significant associations between cell line clusters and drug responses.et al.12. We developed a three-step biological network-driven process based on transcriptomic data for identifying robust clusters of genes. This process was applied in parallel for each dataset. We first selected the most variant genes from the set of 12,153 genes common to GDSC and CCLE, by the inflexion point method. We then performed hierarchical consensus clustering13 to identify robust gene modules. Finally, we used String© database software14 to analyze our gene selection. The goal was to decrease the heterogeneity of each gene cluster. We retained the genes from our initial selection that had (1) high String© database gene connection indices (greater than 0.7), and (2) similar patterns of expression to other genes within the same biological network (correlation coefficient of at least 0.5)  and five in CCLE (n = 210 genes), including a subset of 170 genes common to the two datasets. Distinct functional gene ontologies were associated to each gene modules based on a gene ontology analysis: . We assessed the consistency between the clustering patterns obtained with CCLE and GDSC data, using a heatmap clustering of the similarity matrix as a visualization tool. The heatmap shows the number of times that two samples are clustered together across datasets  four clusters of cell lines were derived mostly from tumors from the same tissue of origin. These cell line clusters were named after the organ or cancer subtypes from which most of the cell lines were derived: hematopoietic and lymphoid tissues (HAL), small cell lung cancer (SCLC), skin (SKCM) and breast (BRCA) clusters; (ii) four clusters of cell lines were derived from tissues from the same organ system or had a common embryonic origin: gastrointestinal tract (GI), aerodigestive tract (ADG), glioma and sarcoma (GLSR) and endodermal origin tumors (EDOT) clusters; (iii) three clusters contained cell lines from different tissues of origin. These clusters were named Mixed 1, Mixed 2 and Mixed 3  based on our previously defined gene modules to characterized the transcriptomic profile of cell line clusters . Breast cancer is a heterogeneous disease with a growing number of recognized biological subtypes, including ER+ Her2−, Her2+ and triple-negative breast cancer (TNBC), which is the most aggressive subtype. BRCA cluster contained all the breast cancer cell lines defined as ER+ Her2− (7/7) and Her2+ (7/7). However, only about half the cell lines defined as triple-negative belonged to this cluster . The remaining triple-negative breast cancer cell lines were found in six different clusters of cell lines  Fig. . SCLC cl16.Some clusters could not be defined on the basis of origin from a single tissue type. However, with a more systemic vision, a consistent organization was obtained for four clusters: GI, ADG, GLSR and EDOT. Cell lines derived from tumors of the digestive system belonged to two clusters. The ADG cell line cluster consisted mostly of tumors from the esophagus, upper aerodigestive tract, salivary and also urinary glands, whereas the GI cluster grouped together tumors derived from large intestine, stomach and pancreas cancers. About 70% of the cell lines of the GLSR cluster were derived from tumors of the central nervous system, bone, autonomic ganglia and soft tissue. Finally, the EDOT cell line cluster grouped together cell lines derived from tumors of different tissues  arising from the same germ layer (endoderm). The relevance of the EDOT cluster is supported by studies suggesting that oncogenesis may be initiated by the activation of a common pathway in an endodermal progenitorThe ADG, GI and EDOT clusters all displayed strong expression of the genes of the epithelial phenotype module and weak expression of the ECM gene module. According to GSEA, the migration gene module was less strongly expressed in GI cells. For the EDOT cluster, inconsistencies between the CCLE and GDSC datasets were observed concerning the activation or inhibition of migration gene expression at the transcriptomic level only. The GLSR cluster displayed low levels of expression for the epithelial gene module, and high levels of expression for the ECM and migration modules.Three clusters displayed no particular prevalence of cell lines corresponding to any particular tissue or organ system. They contained cell lines from tumors of 11 to 16 different tissues. We named these clusters Mixed 1, Mixed 2 and Mixed 3. All three of these clusters displayed low levels of epithelial phenotype genes, suggesting that the cell lines they contained were probably mesenchymal. These clusters also displayed an upregulation of ECM genes. Mixed 1 and 2 displayed an upregulation of migration gene expression. These results suggest that some of the cell lines may have been metastatic in origin or subject to drift, from the characteristics of the tissue of origin to a less differentiated state. In this case, transcriptomic profile is more relevant than tissue of origin.17 showed that epithelial/mesenchymal transition (EMT)-associated differences in gene expression were a major determinant of the stratification of cancer cell lines based on transcriptomic profiles. Indeed, we found a significant overlap between our gene selections and a published EMT-derived gene signature consisting of 249 genes18 . We superimposed epithelial/mesenchymal cell line classifications over our gene expression clusters and found a strong association .We investigated a common set of 64 genes for the presence of mutations in CCLE and GDSC datasets. However, many inconsistencies between both datasets led us to focus on a set of eight genes  for which at least 5% of identical cell lines display mutations in both datasets (Supplementary Information). The mutational profile of cell line clusters was then described based on these genes. Mutation profiles clearly distinguished four clusters Fig. . The SCL50 from the dose-response curve. In order to overcome these issues, both study also report the AUC (area under the dose response curve) that can always be calculated.The large-scale drug screening programs of the Broad and Sanger Institutes have provided to the scientific community an unprecedented wealth of publicly available data. Molecular data have been systematically collected for each cell line, but far less information is available for drug screening (Supplementary Information). Moreover, in many cases (25% in CCLE and 45% in GDSC) it was not possible to extract the ICF-statistic separately for IC50 and AUC values for each of the 15 drugs common to CCLE and GDSC. This measurement should capture consistency between the clustering and screening data. It is calculated as the ratio of between-group variance in drug response to the corresponding within-group variance19. High pseudo F values indicate well-separated, compact clusters. We then compared the pseudo F values calculated with our clustering method with those obtained for ‘tissue partitioning’ for a given drug (i.e. each tissue being to correspond to a cluster of cell lines).We evaluated whether our clustering was more discriminant than the tissue of origin of the cell lines, in terms of drug response. We calculated a pseudo t-test comparing the pseudo F values for our clustering with those for ‘tissue partitioning’ . PLX4720 (Raf kinase B inhibitor) and PD0325901 (MEK1 and MEK2 inhibitors) were drugs with the largest pseudo F values in both dataset. Paclitaxel was the only molecule in the panel with a higher pseudo F value for tissue partitioning in CCLE and GDSC. As the drug sensitivity results were not used to determine the clustering of the cell lines, these findings provide independent evidence for a major role of mRNA levels in drug sensitivity.Twelve of the fifteen drugs had a higher ratio in CCLE and GDSC for our clustering than for clustering based on tissue of origin with the IC50 Fig.  and ten 9. We sought to identify associations between clusters of cell lines and “sensitive” or “resistant” drug phenotypes, for the 15 drugs tested in both CCLE and GDSC. For each dataset and each drug separately, we investigated whether the mean IC50 of a given cell line cluster differed significantly from those for the other cell line clusters . We found that lapatinib was significantly inactive to kill cells from clusters SKCM and Mixed 1 in both CCLE and GSK SK Table , we cons50 of a given cell line cluster was significantly different those of the other cell line clusters  and GDSC (129 molecules) studies. For each dataset and each of the 153 drugs separately, we determined whether the mean ICOverall, these results suggest that cancer cell lines can be classified, on the basis of their transcriptomic profile, into 11 clusters that may or may not be specific to the tissue of origin. We demonstrated that transcriptomic clustering was more consistent than clustering on the basis of tissue of origin in terms of drug response whatever the drug sensitivity metric considered. We were also able to find several significant associations between clusters of cell lines and “sensitive” or “resistant” drug phenotypes. Many of these associations were robustly found across four different datasets with three different drug response metrics. As the drug sensitivity results were not used to determine the clustering of the cell lines, these findings provide independent evidence about the relevance of this new classification. Furthermore, we show that when we are trying to associate a group of genes from a consistent biological pathway with a group of cell lines, rather than a single gene with a single drug, robust associations can be established across several pharmacologic datasets.in vivo models, cancer cell lines remain a key tool in cancer research. Patients are usually treated with combination therapy. However, it is important to better understand the mechanisms involved with monotherapies before moving forward to study combination therapies. Here, we introduce a new cell line classification constructed from 471 cell lines derived from tumors from 24 different tissues. A biological network analysis for the most variant genes identified 11 clusters of cell lines. These clusters appeared robust in two large-scale cell line panels. This biologically driven gene selection process, which is probably less sensitive to sample fluctuations than other methods, made it possible to capture strong biological signals that might be concealed by the noise present in microarray data. Several studies have reported that the incorporation of network information improves the stability of gene selection and the biological interpretability of biomarker signatures for a given prediction accuracy25.Despite the progress in the development of In this new classification, a clear distinction was established between non-epithelial cancer cell lines  and epithelial cell lines . This suggests that EMT-associated differences in gene expression are major determinants of the gene expression–based stratification of cancer cell lines. This new molecular clustering system classified more than 65% of the cell lines differently from the currently used tissue-of-origin cell line classification system. Only four clusters consisted mostly of cell lines originating from a single tissue. Furthermore, three clusters include cells with expression profiles stronger than that of the original tissue (Mixed clusters). Thus, 25% of the cells lines displayed no link to any tissue of origin or related organ system.27, with tumors differing in histological features, gene expression profiles, clinical behavior, overall prognosis28 and sensitivity to systemic treatment30. These findings provide strong evidence to suggest that TNBC heterogeneity is reflected at the cell line level. Our results suggest also that particular attention should be paid to the selection of cell lines for studies of particular types or subtypes of cancer.One of the most interesting cases was the triple-negative breast cancer (TNBC). We focused on this subtype, as it is the only subtype of breast cancer without any targeted therapy associated. TNBC were found to be highly heterogeneous, falling into six different clusters. This divergence shows the relevance of studying cell lines from various tumor types. Drug response was dependent on cluster membership, with the EDOT cluster sensitive to chemotherapy, whereas the BRCA cluster was resistant. The widely dispersed TNBC cell lines were mostly mesenchymal, whereas the cell lines of the BRCA cluster were exclusively epithelial. TNBC is increasingly emerging as a heterogeneous disease12. The authors put forward several hypotheses to explain these discrepancies, including differences in experimental protocols, the viability assay and procedures for summarizing dose response and non-observed IC50 . Despite discrepancies between the drug sensitivity data retrieved from different databases, we were able to find some robust combinations. Well-known drug associations were found, such as the sensitivity of SKCM lines to vemurafenib. We also found that cancers with BRAF mutations, such as melanoma31 and cancers with KRAS/BRAF mutations, such as colorectal cancer32, were more sensitive to MEK inhibitors. Furthermore, CDK4/6 inhibition-induced cell death has been noted in cell lines and xenografts derived from patients with T-cell leukemia33. SCLC cell lines have been shown to be resistant to lapatinib, but combination with a cytotoxic agent may yield promising results34.By analyzing several large-scale public data sets, we demonstrated that drug efficacy is significantly associated to transcriptomic profile. A comparative analysis recently showed that the gene-expression profiles of the 471 cell lines shared by CCLE and GDSC were highly concordant whereas the reported cell-line drug sensitivities for the 15 drugs tested in both studies were highly inconsistent35. Several robust associations were found. Targeted drugs were found efficient to treat clusters of cell lines constituted of cell from different tissues. These drugs are known to be active in one or several tissues that constitute theses clusters. It would be of particular interest to test specifically these drugs on the other tissues represented in these cell lines clusters. For example, cluster ADG is mostly constituted of upper-aerodigestive, oesophagus and urinary tract cancer cell lines. ADG cluster was particularly sensitivity to the anti EGFR - erlotinib. If EGFR is a validated target for upper-aerodigestive cancer38 the therapeutic potential of erlotinib has already been highlighted for bladder cancer39 and showed promising results in phase II for oesophagus cancer41.The decline in the number of new treatments approved in recent years is a major challenge for the pharmaceutical industry. One of the reasons for this decline is the lack of systematic evaluation of therapeutic indications for a drug that is either in advanced development phase or has already obtained a marketing authorization. The so-called “drug repositioning process” proposed to find new therapeutic indications to already approved drugs with faster development times and reduced risks. Furthermore, it allows patients to have access to earlier therapeutic advanceset al.42 demonstrated that target’s expression and drug sensitivity were correlated in only 31% of the cases. Grouping cell lines on the basis of their transcriptomic profiles makes it possible to identify subsets of cells with common off-target features. It is then more relevant to compare the drug sensitivity between cell lines of these groups rather than examined the correlation of response of each cell line to a particular drug reported by one dataset with the response of the same cell line to the same drug reported by another dataset. These results suggest that when robust clusters of cell lines based on biologically network-driven approach are considered, consistency between drug responses can be achieved.Different types of drugs have been used in the panels. Around 10% of the 153 drugs screened in CCLE and GDSC, and only 1 out of the 15 drugs in common to both studies, are cytotoxic agents. These drugs are expected to be broadly active among the cell line panel since they are not specific molecules. On the contrary, targeted agents are expected to be active only in a subset of cell lines, at least, those carrying the given target. Furthermore, the recent study published by Rees In conclusion, our cell line classification provides novel insight for pharmacogenomics studies. As cell lines remain the most widely used models for the preclinical evaluation of candidate cancer drugs, further investigation should be made to use this classification in the development of cancer treatments with the aim of reducing the attrition rate.et al.12 . We used this pipeline as described in the original article, but with a different method for the normalization of gene expression. Haibe-Kains et al. normalized gene expression data by frozen robust multiarray analysis, fRMA43. This method was designed to combine several datasets and overcome multiple batch issues. This strategy is relevant when trying to ensure assay reproducibility. Even though this approach would be unlikely to have a major effect on gene expression values, we chose to normalize the gene expression data separately with RMA44, to ensure that the two datasets were perfectly independent. Our analysis focused on 471 cell lines and 15 drugs for which we have transcriptomic and drug sensitivity data available in both the CCLE and GDSC studies. We also collected two large datasets to validate our classification. Data from the GlaxoSmithKline cell line collection were retrieved from Haibe-Kains et al.12. The Genentech Cell Line Screening Initiative data were available from compareDrugScreens R package published by Haverty et al.22.We collected data from the Broad and Sanger Institutes. The CCLE profiled 24 anticancer drugs on 1,036 cell lines. The GDSC screened 138 drugs on 727 cell lines. Both datasets contain genome-wide gene expression and massive parallel sequencing data. All data were recovered, curated and annotated with the pipeline developed by Haibe-Kains 45 was used to select a unique probe set for given genes. The same probe set was used in both datasets for 83% of the genes.Transcriptomic data were restricted to the 12,153 genes common to the two technologies used by GDSC and CCLE . The Jetset methodμM) at which the drug inhibited 50% of maximal cell growth was used to assess drug sensitivity as well as the area under the dose response curve (AUC). We also consider the mean viability statistic when comparing with gCSI. These measurements were converted to a common scale (−log10(M) for IC50,  for AUC and 1 – mean viability for mean viability), such that high values would be correspond to cell lines sensitive to drugs.The micromolar concentration  distribution for gene expression. This method is more data-driven than a fixed threshold for defining the proportion of genes displaying the highest level of variation. The full procedure is described below. For each gene, we: (1) calculated the IQR for all cell lines, (2) sorted the IQR values of the genes in ascending order, to generate an ordered distribution, (3) estimated the major inflection point of the IQR curve as the point on the curve furthest away from a line drawn between the start and end points of the distribution, and (4) retained genes with an IQR higher than the inflection point.We developed a biological network-driven process based on transcriptomic data, to identify robust clusters of genes and cell lines. This process can be broken down into two parts: (A) identification of robust clusters of genes, used for (B) identification of robust clusters of cell lines.http://string-db.org/). We then applied a two-step selection process: (1) we selected strong biological networks by retaining only genes for which connection scores of at least 0.7 were obtained with String© database software, (2) within each biological network, we selected groups of genes for which expression levels were correlated, with a correlation coefficient of at least 0.5. We used the R package clusterProfiler46 for comparing and visualizing gene ontologies profiles among gene modules. (B) We applied a consensus-clustering with hierarchical clustering to the cell line gene expression profiles, using the selected genes to visualize the optimal number of stable cell line clusters.(A) The gene selection process is a three-step procedure. (1) We selected the most variant genes from among the 12,153 genes common to GDSC and CCLE, by the inflexion point method. (2) We performed hierarchical consensus clustering (ConsensusClusterPlus R Package) to identify robust gene modules. The consensus-clustering step, based on Pearson distance and Ward linkage, identified robust clusters of genes. It involved hierarchical clustering by resampling  randomly selected genes. (3) We identified known biological networks, for each gene cluster separately, using String© database software version 9.1 . The analysis was performed separately for each dataset. The results were used to rank genes in order of significance and to search for overrepresented gene modules, by pre-ranked gene set enrichment analysis (GSEA).Gene set enrichment analysis (GSEA) was performed on genes modules built in step (A) of the biological network-driven process described above. We identified up-regulated or down-regulated gene modules, associated with each cell line cluster. An analysis was first performed to identify genes differentially expressed between a particular cluster and all the other cell lines, based on a linear model. For a given cluster Genes with significantly higher frequencies of mutation in a given cluster were identified by one-tailed Fisher’s exact tests. We compared the occurrence of any given mutation in each cell line clusters with that in all the remaining clusters combined.We studied the likeness between the clusterings for CCLE and GDSC, by clustering the cell lines with a similarity matrix . The similarity matrix contains the number of times two cell lines are clustered together in each dataset . This similarity matrix constitutes a natural visualization tool for assessing the consistency between two clustering patterns. In particular, if we associate a color gradient to the 0–2 range of real numbers, such that white corresponds to 0, and dark blue corresponds to 2, and if we assume that the matrix is arranged so that items belonging to the same cluster are adjacent to each other (with the same item order used to index both the rows and the columns of the matrix), a matrix corresponding to a perfect consensus will be displayed as a color-coded heatmap characterized by blue blocks along the diagonal, on a white background. The accuracy was calculated as the number of times two cell lines clustered together divided by the number of possible combinationset al.18. This epithelial-to-mesenchymal transition signature consists of 159 downregulated genes and 90 upregulated genes. We performed a hierarchical clustering of cell lines based on these 249 genes and labeled clusters of cell lines according to the overexpression of known epithelial marker genes, known mesenchymal marker genes or neither.The “epithelial” or “mesenchymal” status of each cell line was defined with the signature identified by Taube n = 31). We subsequently defined breast cancer cell lines overexpressing ESR1 but with a lower level of ERBB2 expression as the ER + Her2- subtype (n = 7), with cell lines overexpressing the ERBB2 gene defined as the Her2 + subtype (n = 7).Breast cancer subtypes were defined with a bimodal mixture of two Gaussian distributions for ESR1, PGR and ERBB2 gene expression. Triple-negative (TN) breast cancer cell lines were defined by an absence of estrogen and progesterone receptor expression and a lack of ERBB2 overexpression/amplification  for each drug. The pseudo F statistic is the ratio of between-cluster variance to within-cluster variance19. It is defined as [Between-cluster variance/(N-K)]/[Within-cluster variance/(K-1)], where N is the number of observations (N = 471) and K is the number of clusters (K = 11 or K = 24). Large values of pseudo F indicate well-separated, tight clusters.We investigated the relevance of our clustering for drug sensitivity, by comparing the results obtained for this method with those for ‘tissue partitioning’ (i.e. each tissue of origin being considered to correspond to a cluster of cell lines). We calculated the pseudo 50 for clarity. For a given cluster k, cell lines were partitioned into two groups j = {Cluster-k, non-Cluster-k}. We then compared the mean IC50 values of the two groups in a t test. The sign of the statistical test was used to define the phenotype as sensitive (t > 0) or resistant (t < 0). We accounted for multiple testing, by calculating the FDR-adjusted p-value for each drug. An FDR-adjusted p-value < 0.05 was considered significant.The sensitivity and resistant phenotypes of each cell line for a given drug were defined by comparing the drug sensitivity measure between cell lines from any given cluster and the cell lines in all remaining clusters combined. We focus on ICA summary of each cell line cluster, with information regarding tissue composition, molecular profile and drug profile, and other supplementary data for this article can be accessed from the publisher’s website.All authors read and approved the final manuscript.Supplementary Information


Clonogenic assays are the gold standard for determining radiosensitivity, which governs tumor response to radiation therapy. Although multiple studies of clonogenic assays on cancer cell lines have been published, the robustness of this technique has not been examined by comparative analysis of data from different studies. To address this issue, we investigated the inter-assay precision of clonogenic assays by analyzing in-house and published data on A549, a cell line frequently studied in this context. The coefficients of variation for SF2, the surviving fraction after 2 Gy irradiation, and D10, the radiation dose that reduces survival to 10%, were below 30% for both in-house data obtained from 20 independent experiments performed under consistent experimental settings  and data collected from 192 publications using diverse experimental settings. Multivariate analyses of the published data revealed that timing of cell seeding significantly affected SF2. These data indicate that SF2 and D10 of clonogenic assay have acceptable inter-assay precision, and that timing of cell seeding influences the inter-assay precision of SF2. These results provide a rationale for combined analysis of published clonogenic assay data, which may help to discover robust biological properties associated with tumor radiosensitivity. Clonogenic assays are the gold standard for determining reproductive cell death induced by ionizing radiation (IR) . Multiplin vitro experiments and data acquired from 192 publications identified by a comprehensive literature search, we analyzed the coefficient of variation (CV) of clonogenic survival and the influence of experimental settings on assay results.To address this issue, we evaluated the inter-assay precision of clonogenic assay by analyzing in-house and published data on A549, a cell line frequently studied using this method. Using in-house data acquired from 20 independent Literature search in MATERIALS AND METHODS for details). We found that the lung cancer cell line A549 is most frequently used in studies of sensitivity to X-rays or γ-rays using clonogenic assays, and has been described in 192 papers. From these papers, we extracted the values of SF2, SF4, SF6, SF8, D10, and D50 as endpoints for clonogenic survival . In addition, we extracted information regarding experimental settings that can affect clonogenic survival, including radiation type, dose rate, and timing of cell seeding [To analyze inter-assay precision of clonogenic assays, we first conducted a literature search to compile data obtained using these methods. We sought to acquire the greatest quantity of data on clonogenic assays from a specific human cancer cell line treated with X-rays or γ-rays. To this end, we performed a comprehensive literature search for 1039 human cancer cell lines registered in CCLE , and acquired the values of SF0 Figure .2, D10, and D50  that a CV of lower than 30% is acceptable for assessment of potential biomarkers , 7. For 2 and D10 remained below 30%, comparable to those of the in-house experiments . Consistent with this, multivariate analysis revealed that the timing of cell seeding had a significant impact on SF2 (P = 0.04), but we observed no significant impact of these experimental settings on any other clonogenic survival endpoints tested , dose rate, and timing of cell seeding (before or after irradiation) did not have a significant impact on SF2 and D10 were below 30% for both datasets regardless of experimental settings, indicating that clonogenic assay has acceptable inter-assay precision as a biomarker for tumor radiosensitivity [In this study, we investigated inter-assay precision of clonogenic assays as a measure for radiosensitivity, using in-house and published data on A549, a cell line frequently studied using this method. The CV values of SFsitivity , 7. To t2 and D10 in clonogenic assay, providing a rationale for combined analysis of multiple published datasets, leading to greater statistical power. Comprehensive correlation analysis using published clonogenic assay data with available omics data, such as gene mutation status and mRNA expression profiles, will contribute to elucidation of robust biological properties associated with tumor radiosensitivity, which will in turn promote the development of precision cancer medicine for radiation therapy.To elucidate the biological properties associated with tumor responses to radiation therapy, several groups analyzed the association of radiosensitivity, which they assessed by performing clonogenic assays, with omics data. However, due to the time- and labor-intensiveness of the assay and the cost of purchasing cell lines, the number of cell lines analyzed in a given study has been limited, below a hundred in most cases , 9. The 2 and D10 which exhibit acceptable inter-assay precision  under consistent experimental settings. Although these results do not exclude the possibility that specific treatment settings affect clonogenic survival, we can at least conclude that the inter-assay precision of SF2 and D10 obtained using diverse radiation types and dose rates is comparable to that obtained using consistent treatment settings when the data are subjected to meta-analysis, as in this study. The results of this study also revealed that the timing of cell seeding significantly influences SF2. Therefore, theoretically, the data should be stratified by the timing of cell seeding when SF2 values are subjected to meta-analysis. On the other hand, the CV values for “before irradiation” and “after irradiation” dataset is 19% and 24%, respectively, which is not evidently smaller than that for the entire dataset  of the papers that provided high outliers employed “before irradiation” as the timing of cell seeding, while 100% (3/3) of the papers that provided low outliers employed “after irradiation” Figure ; any oth60Co, 137Cs, etc.) on clonogenic survival in greater detail. These factors can affect clonogenic survival, and should therefore be investigated in the future. Finally, we analyzed only one cell line; because the results may differ in other cell lines with genetic profiles distinct from those of A549 cells, an analysis using different cell lines should be performed in the future.This study has the following limitations. First, no firm consensus has been established regarding the criteria for inter-assay precision for assessment of potential biomarkers. Although we followed the recommendation of the FDA , 7, inte2 and D10 have acceptable inter-assay precision as endpoints for clonogenic survival. These results provide a rationale for the combined analysis of published clonogenic assay data, which may contribute to improvement of precision cancer medicine in radiation therapy by facilitating the discovery of robust biological profiles associated with tumor radiosensitivity.In summary, we conducted a comprehensive meta-analysis of in-house and published data on clonogenic assay for IR-treated A549 cells. The results showed that SFcell line name AND (X-rays OR gamma rays OR radiation)”; both Primary name and Aliases described in name. Publications in languages other than English were excluded. Among the cell lines examined, the three with the most related papers were MCF7, A549, and HepG2 . Two radiation oncologists (EN and TBMP) examined all the manuscripts related to these three cell lines in their entirety, and identified publications containing data obtained from clonogenic assays after treatment with X-rays or γ-rays alone. Papers in which cells were treated with empty vector, siRNA, or reagent vehicles in the setting described as “radiation alone” were excluded because such treatments could potentially influence clonogenic survival. However, papers in which cells were treated with dimethyl sulfoxide (DMSO) in the setting described as “radiation alone” were not excluded, based on the observation that DMSO treatment does not influence clonogenic survival after irradiation , where SF is the surviving fraction and D is the dose. Then, the D10 and D50 values were calculated by solving the resulting equations for survivals of 50% and 10%, respectively.From 192 papers listed in l places . For theescribed . BrieflyA549 cells were purchased from ATCC and cultured using DMEM (Gibco) supplemented with 10% fetal bovine serum (Sigma).2, SF4, SF6, SF8, respectively. The values for D10 and D50 were acquired as described in Acquisition of clonogenic survival data from the literature, above. The average values for each clonogenic survival endpoint were used for subsequent analyses.Clonogenic assay was performed as described previously , 13. Bri2, SF4, SF6, SF8, D50, and D10 were analyzed as continuous variables. The differences in SF2, SF4, SF6, SF8, D10, or D50 between different radiation types and between the timings of cell seeding were examined by Mann–Whitney U test. The correlation of dose rate with SF2, SF4, SF6, SF8, D10, or D50 was examined by Spearman’s rank correlation test. The influence of radiation type, dose rate, and the timing of cell seeding on SF2, SF4, SF6, SF8, D10, or D50 was examined by multivariate analysis using multiple linear regression. All statistical analyses were performed using EZR , a graphical user interface for R  [P values < 0.05.Radiation type  and the timing of cell seeding  were analyzed as categorical variables. Dose rate, SF. 3.3.2) . Differe


Neuroblastoma is the most common extracranial solid tumor during infancy and childhood.Outcome of high-risk and late-stage disease remains poor despite intensive treatment regimens.Suppressing inhibitor of apoptosis proteins (IAPs) using Smac mimetics (SM) significantly sensitizes neuroblastoma (NB) cells for chemotherapy, however strongly dependent on the cytotoxic drug combined with SM.de novo NB cell lines (n=8).Therefore, a systematic analysis of the impact of SM in combination with different classes of chemotherapeutics was of crucial importance. Treatment of NB cell lines with SM LCL161 and vinca alkaloids revealed a strong synergistic inhibition of proliferation and significant induction of apoptosis in virtually all established and In contrast, combination of anthracyclines or topoisomerase inhibitors with LCL161 showed a synergism for single drugs and/or cell lines only.Furthermore, we could show that insensibility to LCL161-mediated sensitization for chemotherapeutics is associated with aberrant activation of anaplastic lymphoma kinase (ALK) by common mutation F1174L. Inhibition of ALK using TAE684 is able to overcome this resistance in a synergistic fashion, a finding that could be highly relevant for improvement of neuroblastoma therapy. Neuroblastoma is a malignant disease of the sympathetic nervous system and the most prevalent solid extracranial tumor during infancy and childhood. Prognosis for non-high risk disease is good with 5-year survival > 90%, at the same time more than 50% of patients suffer from metastatic disease at time of diagnosis , 2. OutcApoptosis is a form of programmed cell death that is necessary to eradicate impaired cells and to enable realization of developmental programs. Deregulated apoptotic machinery is one of the hallmarks of cancer contributing to development and expansion of diverse malignancies .The family of inhibitor of apoptosis proteins (IAP) are essential regulators of apoptosis. X-linked IAP (XIAP), the most prominent and best-analyzed IAP, is increased in different cancers including neuroblastoma contributing to chemotherapy resistance and unfavorable outcome -10. Caspin vitro and in a syngeneic mouse model using SM LBW242 and LCL161 [Smac mimetics (SM) were able to induce apoptosis and to significantly increase efficacy of classic treatment in several experimental cancer models , 13. In d LCL161 . LBW242 d LCL161 . Severald LCL161 .Sensitization of neuroblastoma cells for chemotherapy using SM was highly synergistic for vincristine, in part synergistic for doxorubicin and antagonistic for etoposide .If the synergistic interaction of SM is drug-dependent or related to the molecular background of the neuroblastoma cells is still unanswered.Therefore, a systematic effect analysis of different classes of antineoplastic drugs in combination with SM was the logical next step to shed light on this relevant issue. In the current study, we could show that in all except one of the tested neuroblastoma cell lines vinca alkaloids  with SM LCL161 displayed a strong synergistic effect on proliferation and a significant apoptosis induction in line with the results obtained before. Using anthracyclines  or topoisomerase inhibitors  in contrast a synergism with LCL161 was detectable for single drugs and/or cell lines only.in vivo as well [www.clinicaltrials.gov).Smac mimetic LBW242 displayed a synergistic gain of chemotherapy on neuroblastoma cell lines in a drug-dependent manner . Differe as well . Furthern = 6) using Western blot analysis  in the presence of SM LCL161 monotherapy. Significant inhibition of proliferation was detected using high micromolar concentrations with IC50 of 49.4-77.9 μM  and vincristine (IC50 24.5-117 nM). Combined treatment of LCL161 with each of the vinca alkaloids enhanced the antiproliferative potential of the drugs in a synergistic manner in all cell lines except SK-N-BE(2)-M17  showed a concentration-dependent inhibition of proliferation Figure . Vinblas7 Figure +1B. In t50 of 42-223 nM. The IC50 of the other two anthracyclines were 0.35-1.58 μM for daunorubicin and 0.12-2.58 μM for doxorubicin. Addition of LCL161 enhanced the negative effect on proliferation of daunorubicin and doxorubicin in all cell lines, except Kelly, synergistically  were unresponsive against etoposide with IC50 of 4.6-29.1 μM  and topotecan (IC50 0.08-1.17 μM) was inhibited to a similar degree -M17 Figures . In fourNeuroblastoma cell lines were treated with LCL161 in combination with anthracyclines to evaluate the effects on apoptosis induction Figures . In all 50) 0.63) inhibition of proliferation in the combination treatment. Add-on of LCL161 to idarubicin had no significant effect in the cell lines Kelly, SK-N-BE(2)-M17 and SK-N-SH and only a low increase in apoptosis was seen in the cell line SH-EP TET21N -M17 that displayed a synergistic  or mutations in p53 or anaplastic lymphoma kinase (ALK) are commonly found in advanced stage or relapsed tumors and are associated with an adverse outcome -25. TherFor this reason, genetic features of NB cell lines were correlated to combination index (CI) scores reflecting synergistic (CI < 1) or additive/antagonistic (CI ≥ 1) interaction of chemotherapeutic drugs with LCL161 Tables  and 2.U test it became evident that status of MYCN, deletion of chromosome arm 1p or mutated p53 could not account for significant differences between groups (data not shown). In contrast, in cell lines with activating mutation of ALK (ALKF1174L) a significantly higher CI than in ALKwt cells was observed for LCL161 and its combination with idarubicin (CI(IC50) 1.1 vs. 0.76, p = 0.05) or SN-38 (CI(IC50) 0.94 vs. 0.8, p = 0.05)  was detectable for doxorubicin and LCL161 (CI(IC50) 0.8 vs. 0.46, p = 0.127).Using the Mann-Whitney ) Figure . The larn = 6) was efficiently inhibited using a low micromolar range of TAE684  to the synergistic range  were observed  and late apoptotic (Annexin V and propidium iodide positive) cells  in neuroblastoma not only is effective to increase the effect of chemo drugs but also is able to synergistically interact with Smac mimetic LCL161. The latter is highly relevant for cells that are resistant for combination of either TAE684 or LCL161 with chemotherapy.These findings show that TAE684-induced inhibition of activated ALK  and 3 drugs of each class of chemotherapeutics it became apparent that vinca alkaloids generally showed a synergism and significant increase in apoptosis induction with SM LCL161  of almost all (7 of 8) tested NB cell lines were much higher as the achievable plasma steady-state concentration of ~12 μM (7 μg/ml) as only one cell line was established before therapy [With this in mind it is not surprising that the IC therapy .50 13 nM-1.56 μM vs. Cmax of 56 nM (22 ng/ml)) [50 80 nM-1.17 μM vs. Cmax of 209 nM (88 ng/ml)) [A comparable pattern of resistance was seen for SN-38 (IC ng/ml))  and topo ng/ml)) . Cross-r ng/ml)) .50 26.7 μM), SN-38 (IC50 0.53 μM) and topotecan (IC50 0.78 μM) but all drugs showed a synergism with LCL161. This synergism lowered the IC50 to 20.4 μM for etoposide, to 0.38 μM for SN-38 and to 0.22 μM for topotecan. As these concentrations are only clinically achievable for topotecan, it would however have relevant clinical implications to find molecular markers that are causal for these findings.The use of Smac mimetics in contrast could nevertheless be a promising strategy to overcome topoisomerase inhibitor resistance in some cases. The cell line SK-N-AS disclosed triple resistance against etoposide (ICF1174L). Interestingly, other molecular aberrations like MYCN status did not influence impact of LCL161. Point mutations activating the ALK tyrosine kinase domain were found in familial and sporadic neuroblastomas with high frequency [Systematical analysis of susceptibility of different drug classes for LCL161 revealed a so far unknown association with mutation of ALK  is necessary for cellular proliferation and survival [F1174L targeted expression has been demonstrated to be tumorigenic in mice inducing neuroblastomas that resemble the human disease [F1174L mice were dependent on ALKF1174L activity as treatment with ALK inhibitor TAE684 induced a complete tumor regression.In neuroblastoma the most common and aggressive modified ALK variant  [2-atmosphere and a relative humidity of 95%.Neuroblastoma cell lines Kelly, NB1691luc, SH-EP TET21N, SK-N-AS, SK-N-BE(2)-M17 and SK-N-SH as well as recently established ifswald)  were useifswald) . NB1691lifswald) . Neurobl2O to designated concentrations.Novartis Pharma generously provided Smac mimetic LCL161. Cytostatic drugs daunorubicin, doxorubicin, etoposide, idarubicin, topotecan, SN-38 (active metabolite of irinotecan), vinblastine, vincristine and vindesine were obtained from Sigma-Aldrich . NVP-TAE684 was purchased from Selleckchem . Working solutions were prepared by dilution of drugs with medium or ddHβ-actin . Specific protein bands were visualized using IRDye 680RD or 800CW secondary antibodies (LI-COR) and LI-COR Odyssey infrared imaging system.Cell lysates were prepared with radioimmunoprecipitation assay (RIPA) buffer (Sigma-Aldrich) supplemented with Complete protease inhibitor cocktail and PhosSTOP - Phosphatase Inhibitor Cocktail (Roche). Standard procedures for Western blotting were followed using the following primary antibodies: cIAP-1 , XIAP  and ® Total ALK and Phospho-ALK (Tyr1604) Chemiluminescent Sandwich ELISA Kits  according to the manufacturer's protocols. Protein concentrations of lysates were determined using Bio-Rad Protein Assay and bovine serum abumin as protein standard.Expression of total ALK and phospho-ALK was quantified using PathScan50 values were determined using in-house software (Microsoft Excel).Proliferation assays were performed with Cell Proliferation Reagent WST-1  according to the manufacturer's protocol. Cells were seeded in culture medium in 96-well plates to adhere overnight. Cytostatic drugs and LCL161 were added to the cells for an incubation period of 48 hours. Following incubation with WST-1 for 2 hours, absorbance was measured with an ELISA reader. IC2). Apoptosis was detected by Annexin V-FITC  and propidium-iodide (PI)  staining and flow cytometry.Cells were seeded in cell culture medium in 24-well plates to adhere overnight. Cells were treated with the indicated reagents for 48 h. Cells were harvested, washed twice with PBS and resuspended in Annexin V binding buffer  + (d2/D2), where D1 and D2 represent the required doses of drug 1 and 2 to produce x% effect and d1 and d2 the required doses of drug 1 and 2 to produce the same effect if used in combination. Statistical significance of differences between experimental groups was determined using the Student t-test or the Mann-Whitney U test. A two tailed p-value ≤ 0.05 was regarded as significant.Combination indices (CI) for cellular proliferation assays were calculated using the Chou-Talay method to determine synergism (CI ≤ 0.9), additivity (CI > 0.9-1.1), or antagonism (CI > 1.1), if combining LCL161 with cytostatic drugs . The CI