In this study, a novel scorpion venom‐derived peptide named Gonearrestide was identified in an in‐house constructed scorpion venom library through a combination of high‐throughput NGS transcriptome and MS/MS proteome platform. In total, 238 novel peptides were discovered from two scorpion species; and 22 peptides were selected for further study after a battery of functional prediction analysis. Following a series of bioinformatics analysis alongside with in vitro biological functional screenings, Gonearrestide was found to be a highly potent anticancer peptide which acts on a broad spectrum of human cancer cells while causing few if any observed cytotoxic effects on epithelial cells and erythrocytes. We further investigated the precise anticancer mechanism of Gonearrestide by focusing on its effects on the colorectal cancer cell line, HCT116. NGS RNA sequencing was employed to obtain full gene expression profiles in HCT116 cells, cultured in the presence and absence of Gonearrestide, to dissect signalling pathway differences. Taken together the in vitro, in vivo and ex vivo validation studies, it was proven that Gonearrestide could inhibit the growth of primary colon cancer cells and solid tumours by triggering cell cycle arrest in G1 phase through inhibition of cyclin‐dependent kinases 4 (CDK4) and up‐regulate the expression of cell cycle regulators/inhibitors—cyclin D3, p27, and p21. Furthermore, prediction of signalling pathways and potential binding sites used by Gonearrestide are also presented in this study. It has been proven that peptides/proteins are generally the major components of venoms, and many of these have either shown high potential or indeed had such realised, to defeat diseases such as bacterial/fungal infections, cardiovascular disorders, diabetes and cancers., in vivo and ex vivo experiments to lend further substance to the validation of this approach.Based on this platform, we have identified a panel of novel potential anticancer peptides in scorpion venoms. Anticancer peptide research has been performed at a low level for around 50 years now with limited success and understanding of their mechanisms of action is still in the initial stage. To reveal the potential anticancer mechanisms of candidate peptide, a recently invented transcriptome‐centric strategy was employed to predict their putative functions and targeted signalling pathways. Compared to traditional mechanistic study approaches, this was able to monitor the collective responses of all relevant genes without specific mechanistic or targeting hypotheses.In this study, we initially isolated a panel of potential anticancer peptides from venoms using this state‐of‐the‐art high‐throughput platform. Subsequently, a transcriptome‐centric method was applied to address and to reveal the putative anticancer mechanism of the lead peptide candidate, followed by in vitro, in vivo and ex vivo experimental validation. In addition, we hypothesized as to the involvement of specific signalling pathways and potential binding sites through bioinformatic analyses and use of 3D modelling construction software. Finally, one peptide candidate (Gonearrestide) was identified to affect cancer cell proliferation in vitro and reduce tumour growth in vivo, while negligible cytotoxicity was observed on normal human epithelial cells and erythrocytes. These current data suggest that Gonearrestide has a high potential for development as an anticancer drug. In addition, the findings presented here have further validated the ever‐increasing potential of this high‐throughput platform and transcriptome‐centric mechanistic study strategy to reveal additional novel anticancer peptide drug candidates.22.1Androctonus mauritanicus (AMa) and Androctonus australis (Egypt) (AAu), were purchased from Latoxan, Valence, France. The lyophilized venoms were stored at −20°C prior to use.Venoms from the scorpions, 2.2® mRNA DIRECT™ Kit (Ambion by Life Tech), then subjected to a cDNA library construction procedure using a NEBNext® Ultra™ Directional RNA Library Prep Kit for Illumina® (New England BioLabs Inc.). According to the instructions, the cDNA library construction consisted of several main steps. First of all the mRNA fragmentation was achieved by incubating at 94°C with random primers. Then the RNA fragments were subjected to first strand cDNA and second strand cDNA syntheses. After purification with 1.8X Agencourt AMPure XP beads, end repair of the cDNA library was performed by incubating with the end repair reaction buffer and the nucleotide A was added to the 3′ end of the DNA fragments to avoid self‐ ligation. Therefore, the adapters with the nucleotide T at the 3′ end were ligated to the DNA fragments. At last, the DNA fragments with adapters were enriched by PCR reactions and purified by AMPure XP beads. The quality of the cDNA library was verified using an Agilent 2100 Bioanalyzer (Agilent Technologies) with an Agilent DNA 1000 kit (Agilent Technologies). The quantity of the cDNA library was validated by qPCR with a KAPA SYBR® FAST qPCR kit (KAPA Biosystems). The cDNA library was then loaded into a flow cell with oligos complementary to the adapters to generate clusters through bridge amplification. Finally, the transcriptome was obtained by performing RNA Sequencing on the Illumina MiSeq platform. The raw data obtained from the Miseq platform were analysed as follows. Firstly, the index primers used to identify different samples were removed. Secondly, the data were transferred to the program FastQC 0.1.0.1 to filter out the reads with low quality and less than 25 nucleotides. The filtered data were saved as fastq files. At last, the clean reads were de novo assembled using Trinity 2.0.6 software to obtain the transcriptome with default parameters.Five mg sample from each lyophilized scorpion venom was dissolved in 1 mL of cell lysis buffer (Thermo Fisher Scientific). Polyadenylated mRNA was extracted by use of a Dynabeads2.3® 12 10/300 GL column (Sigma Aldrich) to filter out large proteins with molecular masses higher than 10 KD. After this, Tris (2‐chloroethyl) phosphate (TCEP) was used to reduce disulphide bonds, and iodoacetamide (IAA) was used to alkylate free cysteine residues. At last, desalting was performed using PierceTM C18 Spin Tips (Thermo Scientific). Proteomics was performed using the Q Exactive™ Hybrid Quadrupole‐Orbitrap Mass Spectrometer (Thermo Scientific) with a nano‐LC system. Samples were loaded onto the Acclaim pepMap100, 75 μm × 2 cm C18 trap column (Thermo Scientific) using LC‐MS water . Samples were trapped onto the column at 6 μL/min for 5 minutes for pre‐concentration, then the trap column was connected to an EASY‐Spray column, 15 cm × 75 μm ID, 3 μm‐C18 particle sizes (Thermo Scientific) by a 10 port valve automatically. Eluting was performed at a flow rate of 300 nL/min with the gradient mobile phase buffer . Eluting peptide cations were converted to gas‐phase ions by electrospray ionization with a spray voltage of 1.8 kV and capillary temperature of 300°C. Full MS scans of peptide precursors from 300 to 1800 m/z were performed at 70 K resolution, while the AGC target was set to 3 × 106 ion counts with a max injection time of 200 ms. MS2 was performed by an isolation window at 2 m/z with the quadrupole, high energy collision (HCD) fragmentation with normalized collision energy of 27. The resolution was 17.5 K, while ion count target was set to 106 and the max injection time was 110 ms. The top 20 precursor ions were selected in each cycle and only those precursors with a charge state +2 to +5 were isolated for MS2 fragmentation. The fixed first mass of MS2 spectrum range started at 100 m/z. The C‐trap under fill ratio was 2%, which converted to the intensity threshold of 1.7 × 104 for the precursor selection. The dynamic exclusion duration was set to 20 seconds and isotope exclusion was turned on. The LC‐MS/MS raw data were then transferred to PEAKS (version7.5) software for de novo sequencing with default parameters.A second set of 5 mg lyophilized scorpion venom sample was dissolved in phosphate buffer , and subjected to AKTA Avant 150  fractionation using a Superose2.4The software, PEAKS (version8.0) , was used to interrogate the transcriptome database with parameters set as: precursor ion mass tolerance, 3 ppm; fragment ion mass tolerance, 0.01 Da; no enzymes employed; a variety of post‐translational modifications (PTMs) including cysteine carbamidomethylation and oxidation, were used and false discovery rate (FDR) was at ≤1%. The peptides identified high confidence and accuracy, constituted the prospective venom‐derived peptide libraries.2.5® TA Cloning® Kit (Invitrogen) and sequenced using an ABI 3100 automated sequencer. The nucleic acid sequences were isolated by employing the primers below, which were designed to highly‐conserved domains of the 5′‐untranslated regions of previously‐characterized venom peptide cDNAs from scorpions: (i) Primer AMa_Mauri1 (5′‐CATTACTATGTTGATTGTCGATGAAGTC‐3′); (ii) Primer AAm _H2 (5′‐AGTTTGGCACTTCTCTTCGTGAC‐3′); (iii) Primer And_Uni1 (5′‐TAGWCCTGSTRGTYARTCCGA‐3′).A third set of 5 mg lyophilized scorpion venom sample was dissolved in 1 mL of cell lysis/mRNA protection buffer, and polyadenylated mRNA was isolated from each by magnetic oligo‐dT beads as described by the manufacturer (Thermo Fisher Scientific). The isolated mRNA was subjected to 5′‐ and 3′‐rapid amplification of cDNA ends (RACE) procedures using a SMART‐RACE kit . The 3′‐RACE reactions were purified and cloned using a TOPO2.6The prospective novel venom‐derived peptide libraries were subjected to BLAST analyses against the NCBI non‐redundant database and the peptide sequences which produced hits with the sequences on this were then subjected to Pfamscan online searching to identify whether the sequences aligned to any reported toxins. The peptide sequences reported before were removed manually during this step. The novel peptides were manually filtered by removing very short sequences and finally, the venom‐derived peptide library was constructed with all the peptide sequences selected as above. The selected peptide regions were chemically‐synthesized by solid phase peptide synthesis (SPPS) technology.2.72 culture flasks , and cultured in an incubator under 5% CO2/95% air at 37°C. The anti‐proliferative effects of these novel peptides on human cancer cells were measured by employing MTT cell viability assays. Peptides were screened at a concentration of 10 μmol/L with various cell lines: Pancreatic cancer cell lines MIA Paca‐2, PANC‐1, AsPC‐1; Colon cancer cell lines HCT116, DLD‐1, Hke3, Dks8; Breast cancer cell lines MCF‐7, MDA‐MB‐231; Bladder cancer cell line HT‐1376; Human retinal epithelial cancer cell line ARPE‐19; Brain glioma cell line U‐251. Two different normal epithelial human cell lines (colon cell line FHC and breast cell line MCF 10A) were also employed in the screening assays. Peptides with anti‐proliferative activities were then selected and further studied in a dose‐dependent manner in the concentration range from 250 to 1 μmol/L.Cancer cell lines were maintained in their culture medium with 10% (v/v) foetal bovine serum (FBS) and 1% (v/v) penicillin/streptomycin. All cells were seeded into 75 cm2.8Human erythrocytes were washed with PBS until the supernatant was clear, and then samples of a 4% (v/v) suspension were treated with peptide concentrations ranging from 1 to 250 μmol/L at 37°C for 1 hour. Lysis of cells was assessed by haemoglobin release, measured by optical density changes at λ550 nm, and calculated compared to positive controls. Positive control groups and negative control groups were treated with equal volumes of Triton X‐100 and PBS instead of peptide, respectively.2.9Cells were treated with different peptide concentrations from 250 to 10 μmol/L for 24 hours. LDH Cytotoxicity Assay Kit  was then employed to detect the cytotoxicity of peptides according to the manufacturer's instructions. The supernatants were transferred, mixed with the reaction buffer and LDH release was detected using a microplate reader set at 492 nm and calculated compared to positive controls. Positive control groups and negative control groups were treated with equal volumes of Triton X‐100 and PBS instead of peptide, respectively.2.10™ C6. The blank control group was treated with an equal volume of PBS.Cells were treated with peptides at the concentration of 250 μmol/L for 24 hours, after which the Alexa Fluor 488 annexin V/Dead Cell Apoptosis Kit (Invitrogen) was employed to detect apoptosis according to the manufacturer's instructions along with use of a flow cytometer BD Accuri2.11™ Continuous Live‐cell Imaging & Analysis System was employed to record cell growth for three days. The instrument can automatically generate cell growth curves, which are calculated by the cell area attached to the bottom of each well according to the images taken by the instrument every 2 hours. Blank control group was treated with equal volumes of PBS.Cells were seeded into 6‐well plates and treated with peptides at 250 μmol/L for 24 hours, after which the IncuCyte ZOOM2.12http://ftp://ftp.ensembl.org/pub/release-87/fasta/homo_sapiens/dna//Homo_sapiens.GRCh38.dna.primary_assembly.fa.gz) using the splice aligner tophat2 (v2.1.1) with default parameters.q_value cut‐off of <0.05, were considered as significantly differentially expressed. Further GO enrichment and pathway visualization of differentially expressed genes was performed by the DAVID online tool (https://david.ncifcrf.gov) and KEGG mapper (http://www.genome.jp/kegg/tool/map_pathway2.html), respectively.Samples were prepared by treating cells with Gonearrestide at 250 μmol/L for 24 hours. Blank control groups and negative control groups were treated with equal volumes of PBS and negative control peptide, respectively. For each group, two replicates were employed. Samples were collected and total RNA was extracted, DNA library preparation was performed as previously described under “Transcriptomics procedures”. Transcriptome sequencing and analysis procedures were performed as follows: TruSeq RNA‐seq libraries were sequenced on the Illumina HiSeq2000 platform. The raw reads were mapped to the human genome  for 5 minutes at room temperature. Density of cells in each cell cycle phase was recorded by flow cytometry using a BD Accuri2.14Cells were treated with peptides at 250 μmol/L for 24 hours. The blank control group was treated with an equal volume of PBS, and regarded as time‐point 0 hour. Cells treated with peptides were then collected at different time‐points  and washed with cold PBS. RIPA buffer  was used to lyse cells for 30 minutes on ice, after which cells were centrifuged at 20 000× g for another 30 minutes in a 4°C pre‐cooled centrifuge. The supernatants were stored at −80°C prior to use. Equal amounts of protein samples at each time‐point, including those from the blank control group, were subjected to SDS‐PAGE and then transferred to a membrane. Membranes were then blocked with 5% non‐fat dry milk in TBST (0.1% Tween) for 2 hour at room temperature, and then incubated with primary antibodies  at 4°C overnight. The next day, TBST (0.1% Tween) was used to wash membranes three times, after which secondary HRP conjugated antimouse/anti‐rabbit antibody was incubated with membranes for 1 hour at room temperature. After washing with TBST (0.1% Tween) another three times, Immobilon Western HRP Substrate was employed for chemiluminescent detection in Western blots.2.15Cells were cultured in an 8‐chamber slide and treated with fluorescently labelled peptides for 1 hour. After this, cells were stained with CellMask Plasma Membrane Stains (Invitrogen) and Hoechst dye (Thermo Fisher) according to the manufacturer's instructions, followed by washing with PBS and fixation with 3.75% formaldehyde. The locations of peptides affecting cells were recorded by confocal laser scanning microscopy.2.16Nude Mice used in this study were obtained from the Animal Facility in the Faculty of Health Sciences at the University of Macau (UM). All the experimental protocols were conducted according to the UM Ethics Committee for Animal Experiments with the approved application UMARE‐035‐2016.6 HCT116 colon cancer cells were xenotransplanted by subcutaneous injection (s. c.) into the flanks of 4 week‐old female nude mice. After 3 days of implantation, mice were then divided into four groups: Group 1 (n = 5) was treated with Gonearrestide at a high concentration (100 μg/tumour) by peritumoral (p. t.) injection for another 2 weeks; Group 2 (n = 5) was treated with Gonearrestide at low concentration (50 μg/tumour) while Groups 3 and 4 (n = 5) were treated with an equal volume of PBS and negative control peptide, respectively. Tumour volume measurement was performed during 2 weeks of treatment. To determine tumour volume by use of an external caliper, the greatest longitudinal diameter (length) and the greatest transverse diameter (width) were determined. Tumour volumes, based on caliper measurements, were calculated by the modified ellipsoidal formula2). At the end of the experiment, tumours were removed under anaesthesia and mice were then euthanized. Tumours were snap‐frozen for further histological analysis. On the one hand, tumours were fixed and cut into slides. Immunohistochemical staining of tumour slides were performed by de‐paraffinizing in xylene and rehydrating in graded ethanol, followed by incubation in sodium citrate buffer (pH 6.0) for high‐pressure antigen retrieval. Afterwards, slides were incubated in 3% hydrogen peroxide to block endogenous peroxidase activity, and incubated with cell cycle checkpoint antibody CDK4 at 4°C overnight. On the second day, the slides were incubated with secondary antibody and subjected to diaminobenzidine staining. After counterstaining with 20% haematoxylin, slides were dehydrated and mounted on cover slips. The IHC‐stained tissue sections were reviewed under a microscope. In addition, mRNA and proteins were isolated from frozen tumours for qPCR and Western blot analysis. The procedures were for both techniques were described before.In vivo anticancer activity of Gonearrestide was evaluated in an HCT116 colon cancer xenograft model. 1 x 102.17P values were calculated by student's t‐test from the mean values of the indicated data. Significant differences were marked with asterisks .Statistical analysis was performed using GraphPad Prism 6 software. 33.13.1.1Androctonus mauritanicus (AMa) and Androctonus australis (Egypt) (AAu), respectively. As these two scorpion species do not have their genomes assembled so far, the transcriptome data were de novo assembled by use of Trinity software. Consequently, there were 112 235 and 46 360 nucleic acid sequences, respectively, obtained for each species . In total, there were 130 peptides matched with UniProt database, which indicated the robustness of our data.3.1.4There were eight peptide sequences isolated through the molecular cloning approach, including three novel peptide sequences. All these eight peptide sequences were found in the overlapping region of the transcriptome and the proteome results, which validated the new high‐throughput approach. The nucleotide and translated open‐reading frames of the three novel sequences were saved Figure .3.1.5The overlapping regions described previously were subjected to a variety of bioinformatic analyses and the filtered results were saved Figure . As a re3.23.2.1In total, 22 peptides were screened with 19 cell lines to identify their potential anti‐proliferative activities, after which four peptides  were found to inhibit the proliferation of human colon cancer cell lines HCT116, DLD‐1, Hke3, Dks8 and the human glioma cell line U‐251. Two types of normal human epithelial cell lines, including the colon cell line FHC and the breast cell line MCF 10A, were also employed here and it was found that the peptides had negligible effects on their proliferation even at highest concentrations. Peptide 13 , indicating that Gonearrestide was a worthy candidate for further study with the view to its clinic application  is released when cell membrane integrity is compromised. In this assay, Gonearrestide did not induce LDH release from HCT116 cells after 24 hours of incubation. This result indicated that Gonearrestide did not disrupt the cell membrane and did not induce cell death. At the same time, the LDH results were consistent with the RNA sequencing data along with the combination of MTT results. Taken together, these data confirmed that Gonearrestide affected cancer cell proliferation through inhibition of growth  with HCT116. Localization of Gonearrestide within the cell should result in green fluorescence signals from the FITC‐labelled peptide. As shown in Figure 3.3.3Annexin V and PI staining along with flow cytometry can differentiate individual cells into different stages. The results of these experiments were shown in Figure 3.3.4As shown in Figure 3.4As Gonearrestide had the best activity on the cancer cell line HCT116 with no obvious cytotoxicity on normal human cell lines and erythrocytes, it was chosen for further experiments to identify its molecular mechanism of action. There were three groups employed in this assay: HCT116 treated with Gonearrestide (HCT116_Gonearrestide), HCT116 treated with a negative control peptide (HCT116_NC) and HCT116 treated with PBS (HCT116_BC). The negative control peptide was one of the previously identified peptides which showed no anticancer cell activity.P value < .05, tool: DAVID) enriched under common genes  have been listed  and phosphatidylethanolamine (PE) can enhance membrane poration by a peptide with anticancer properties.4Naja naja venom exerted a cytotoxic activity on MCF‐7 and MDA‐MB‐231 cells, and thereby induced cancer cell death directly.2, isolated from snake venom from Walterinnesia aegyptia and Vipera ammodytes meriodionalis, respectively, induced apoptosis on a broad spectrum of cancer cell lines.Venom from animals has been proven to be a useful source of drug candidates in natural drug discovery to fight against diseases.Androctonus mauritanicus (AMa) and Androctonus australis (Egypt) (AAu), have been constructed. To confirm the validity of this platform, a traditional cloning approach was also applied in parallel. The traditionally‐derived cloning data provided evidence of the robust nature of the described high‐throughput screening platform. The coupling of transcriptomic and proteomic/peptidomic approaches using bioinformatics produced venom‐derived peptide panels, which could be used to explore the potential therapeutically useful peptides present in respective venoms.To increase the identification of potential venom‐sourced anticancer peptides, a high efficiency and low‐cost screening platform is urgently required. In this study, a high‐throughput screening platform consisting of transcriptome and proteome sequencing is described and has been of proven efficacy. Based on the use of this platform, complete peptide libraries of venoms from the scorpions, Within the scorpion peptide panels, bioinformatics filtration and MTT screening were initially applied to detect novel candidate anticancer peptides. As a result, peptide 13(Gonearrestide) was identified and found to have a potent anticancer activity against human colon cancer cell line HCT116 but with negligible observed cytotoxicity against normal human epithelial cells and erythrocytes. To study the anticancer mechanisms of Gonearrestide, RNA sequencing was used to detect the transcriptome responses in cells following Gonearrestide treatment. Transcriptome sequencing provided a number of advantages over conventional chemical screening strategies. This approach did not require prior identification of specific drug targets. This meant that, based on a multi‐target, pathway‐centric approach, any potential therapeutic targets could be assessed in the transcriptome landscape. Compared with phenotypic screening, transcriptome screening offers more specific gene expression responses providing crucial clues to potential molecular mechanisms.The RNA sequencing results indicated that Gonearrestide could combine with cancer cell membranes followed by inhibition of cell proliferation through a cell cycle arrest effect. Although well‐designed in vitro, in vivo and ex vivo assays, the transcriptome‐centric prediction that Gonearrestide could arrest HCT116 cancer cells in G1 phase and inhibit tumour growth, was proven. The signalling pathway used involved effects on PIP3 and PTEN, resulting in the inhibition of the Akt pathway. PIP3 is a phospholipid that is bound within the layers of the cell membrane and after combining with the cell membrane, Gonearrestide can affect PIP3 by electrostatic attraction to its anionic trisphosphate group. PTEN catalyses the dephosphorylation of PIP3 to produce PIP2, which is a major negative regulator of Akt signalling. It is a tumour suppressor and the phosphatase activity of this protein may also be related to cell cycle regulation preventing cells growing and dividing too rapidly. From the previous anti‐proliferative screening results, it could also be seen that Gonearrestide showed a better anticancer effect on the PTEN wild‐type cell line, HCT116, when compared with the PTEN‐mutated cell lines, PC‐3 and SW872, with cell viabilities of 55%, 84% and 80%, respectively. The inhibition of Akt induced downstream dysregulation of CDK4/6 and CDK2, thereby promoting cell entry from G1 to S phase. In addition, the peptide‐lipid interaction generated by docking also proved the hypothesis that Gonearrestide could bind to PIP3 through electrostatic attraction and resulted in alerting the downstream cell cycle regulation.Current results showed that Gonearrestide could inhibit cell proliferation in vitro through a cell cycle arrest effect and could reduce tumour growth in vivo, while possessing little observable cytotoxicity on human epithelial cells and erythrocytes. Hence, this peptide was deemed to have a great potential to be proceed to clinical trials as an anticancer drug candidate. To enhance the efficiency of anticancer peptide discovery, we emphasize the broad utility of our high‐throughput platform for venom‐derived peptide library construction and selection and also the interfacing of this with a transcriptome‐centric mechanistic study approach. The validity of this approach has been proven by well‐designed experiments described and employed in this study. Moreover, within the scorpion peptide panel, several additional anticancer peptides are awaiting further investigation.The authors confirm that there are no conflict of interest. Click here for additional data file.


Alongside these sensitivity measurements high dimensional molecular characterizations of the cell lines are typically available, including gene expression, copy number variation and genomic mutations. We propose a sparse multitask regression model which learns discriminative latent characteristics that predict drug sensitivity and are associated with specific molecular features. We use ideas from Bayesian nonparametrics to automatically infer the appropriate number of these latent characteristics. The resulting analysis couples high predictive performance with interpretability since each latent characteristic involves a typically small set of drugs, cell lines and genomic features. Our model uncovers a number of drug-gene sensitivity associations missed by single gene analyses. We functionally validate one such novel association: that increased expression of the cell-cycle regulator C/EBP Lacrosse, which uses a Bayesian non-parametric prior to model “latent characteristics” of cell-lines that confer sensitivity to specific drugs and are predictable from genomic features. The resulting algorithm improves upon existing work by: a) jointly modeling multiple drugs to share statistical signal b) incorporating prior knowledge in terms of known inhibition targets c) using a sparse latent variable regression approach giving interpretable summaries of detected gene-drug associations. In particular, our analysis uncovers groups of drugs whose efficacy depends on genomic features in a similar way. We find new potential biomarkers of drug sensitivity, one of which we validate experimentally: that panobinostat is less effective when C/EBPδ is over-expressed.A core tenant of precision medicine is that treatment should be tailored to the patient. In the context of cancer, large-scale screens, assaying the sensitivity of many cell-lines to panels of drugs, have the potential to enable discovery of biomarkers of sensitivity to specific therapeutics. However, existing computational approaches have not taken full advantage of these data. We develop a novel multi-task regression model,  Several drug screening studies have assayed the sensitivity of a library of cancer cell lines to an array of anti-cancer compounds. Notable examples are the Cancer Cell Line Encyclopedia , CCLE, GThese data have the potential to both help understand the key differences between cancers and cancer subtypes that drive resistance to specific drugs, and to one day help choose the appropriate drug (or combination of drugs) for an individual patient, the core idea of precision medicine. As a result there is a need for analyses that both identify what differences between cancers cause the observed sensitivity patterns, and which differences can accurately predict what drugs will be efficacious for a tumor based on its genomic profile. The existing analyses of these datasets involve simple per drug regressions, such as elastic net . While tCancer is highly heterogeneous in terms of its genomic features but many cancers share common phenotypic characteristics , the “haLacrosse). The statistical model underlying Lacrosse is a discriminative, Bayesian non-parametric, sparse factor analysis, which falls under the general class of multitask regression models [Lacrosse does not featurize the drugs.To predict cancer drug sensitivity based on latent characteristics derived from genomic-drug screening data, we propose a novel approach, LAtent ChaRacteristics Of Small-molecule SEnsitivity  was recently developed . Sparse A distinct approach is taken by Knijnenburg et al.  who deveLacrosse but which provide exploratory analyses rather than predictive models of drug response. This makes it infeasible to compare to our approach in terms of predictive performance. First, Seashore-Ludlow et al. [Two other recent studies have developed analyses that share ideas with w et al.  developew et al.  applied w et al.  to drug-Lacrosse also has similarities to nuclear norm regression [gression , an extegression  to the mLacrosse is that it allows a graph over the drugs to be specified encoding prior knowledge about which drugs are likely to have similar properties. This graph is used to specify a Markov-random field which explicitly encourages drugs which share edges to have more similar coefficients in the model.An additional unique aspect of Large-scale viability screens such as CCLE have typically summarized dose-response curves in terms of the drug concentration required for 50% inhibition of growth, “IC50”. IC50 however has several weaknesses: a) it is undefined if 50% inhibition is never reached, b) it is noisy due to being overly reliant on viability measurements close to the IC50 value, and c) it ignores differences in effectiveness for doses above the IC50 point. An simple alternative summary is “active area”, the integrated area above the dose-response curve . We haveFor the 24 drugs in CCLE we analyzed the between-drug correlation of sensitivity profiles (active area scores) across 432 cell lines . In someLacrosse is a Bayesian-nonparametric, sparse, multitask regression that jointly predicts viability for multiple drugs using cell line genomic features. Lacrosse posits that subsets of cancer cell lines possess latent characteristics (LCs) which are predictive of their sensitivity profile across different drugs. Whether a cell line possesses a particular LC is identified by the presence of genomic features specific to the LC, that is, the LCs are generated by a sparse regression on the cell line features.F of genomic features (# features × # cell lines), Lacrosse models the matrix Y of viability scores (# drugs × # cell lines) asX are the LCs, and G and B are matrices of (sparse) regression coefficients to be learned  share edges. Connected nodes are encouraged to have the same regression coefficient sparsity pattern using a Markov-random field (MRF) approach. In practice we use this capability to inform the model about which drugs share molecular targets  for each fold. We compared Lacrosse toFA: sparse, nonparametric factor analysis jointly over the molecular characteristics and sensitivity REG: spike and slab Bayesian linear regression glmnet R packageL1: LASSO L1-regularized linear regression  using thglmnetL1 multi: multiresponse regression using group LASSO  resultinKBMTL: kernelized Bayesian multitask learning .Ridge: ridge regression.Elastic Net: with Bayesian multitask multiview linear regression (MVLR) To assess the predictive performance of For each of these approaches we additionally consider an MRF extension see  which enLacrosse outperforms these baselines in terms of its ability to predict sensitivity across drugs for heldout cell lines in CCLE  and genomic features (corresponding to the same gene) improves predictive performance. Performance for FA may have dropped using known relationships since the model capacity is already insufficient to accurately associate genomic features with sensitivity, and added constraints further reduce the effective model capacity. Lacrosse including the MRF-extension achieves a median PVE across folds of 31.5%.CCLE see . The spap = 0.032, paired t-test), and is itself outperformed by Group Elastic Net . In our hands KBMTL underperforms Group LASSO (p = 0.036) and Group Elastic Net (p = 0.005) and has comparable performance to the single task methods. MVLR significantly underperforms the single task methods  although we emphasize that MVLR is only designed for joint modeling of closely related drugs (e.g. those with shared inhibition targets) so it is unsurprisingly it performs poorly at simultaneous modeling of over 500 drugs with many different mechanisms-of-action. Finally Lacrosse outperforms Group Elastic Net by a significant margin . The PVE across folds was not found to be significantly non-normal by a Shapiro-Wilk test for any method, but we none-the-less confirmed all comparisons found significant by t-test were also significant by Wilcoxon signed rank test, apart from KBMTL outperforming Group LASSO. We confirmed the qualitative ordering of the methods held up when assessing performance using the concordance index (Lacrosse obtains a higher mean c-index than Group Elastic Net (0.613 vs 0.608) this difference is not statistically significant.We additionally assessed predictive performance in terms of PVE on the 545 drugs, 783 cell lines CTRPv2 dataset , again uce index . While LLacrosse’s MRF strength parameter and found performance to be robust within a wide range of values , and qualitatively the concordance indices are comparable to in-sample accuracy assessed using pre-validation in CTRPv2, with the exceptions of Nutlin-3 and RAF265. Since CTRPv2 includes all cell lines assayed in CCLE we next assayed generalization in The Genentech Cell Line Screening Initiative (gCSI) [p < 0.05), but MS-275, Bortezomib and Crizotinib are not. On the 37 shared cell lines only Vorinostat shows significant prediction, but this maybe due to limited power with only n = 37 test points. Finally we applied the CTRPv2 model in GDSC1000 [n = 1110 cell lines in GDSC1000, 59 drugs showed significant prediction, compared to 50/69 when using the n = 501 non-overlapping cell lines  [R2 = 0.75, p < 2 × 10−16, Lacrosse gains statistical power by analyzing all the drugs simultaneously.Analysis of the latent characteristics can provide insights into signaling and regulatory pathways predictive of drug response. We tested pairwise relationships between the drugs and genomic features in each LC from CCLE using Spearman correlation, with selected LCs of interest shown in ” (GLDS) . Indeed,Some LCs include known related drugs, e.g. LC 2 involves the DNA-damaging agents irinotecan and topotecan, as well as another chemotherapeutic agent, paclitaxel. It is reassuring to see SLFN11 expression modulating sensitivity to irinotecan and topotecan in this LC, since this is a known and experimentally validated relationship . IncreasLacrosse builds a sparse predictive model so may exclude some meaningful associations. Indeed we find LC4 level is correlated with B-RAF (p = 6 × 10−20) and KIT (p = 0.016) mutations, PDGFRB expression (p = 1 × 10−4) and MAP2K1 copy number (p = 0.03). 86/253 (34%) genes in the KEGG MAPK pathway are significantly correlated with LC4 level across cell lines, a 1.5× enrichment compared to background (Fisher’s exact p = 0.002). Other interesting associations in LC 4 are those with TMC6 and SPRY2. SPRY2 is downstream of the MAPK pathway, but SPRY2 inhibition also activates MAPK and can lead to tumorigenesis [LC 4 includes mutation of B-RAF as a genomic feature and drives sensitivity to the B-RAF inhibitor RAF265 and MEK inhibitors (PD0325901 and selumetinib/AZD6244). As a result, we conclude LC 4 corresponds to the MAPK pathway, a signaling cascade involving B-RAF, MEK and ERK see . Since Ligenesis , suggestigenesis . PotentiThe influence of JunB on the VEGF inhibitor sorafenib and heat shock protein 90 (HSP90) inhibitor tanespimycin in LC 12 is likely due to regulation of the VEGF pathway by JunB , and theδ, a transcription factor that regulates cell cycle progression and apoptotis, and is therefore a putative tumor suppressor [δ has not previously been associated with drug sensitivity, high expression is known be associated with poor prognosis across glioblastomas [β is recognized as a synergistic master regulator with STAT3 of the mesenchymal phenotype in aggressive glioma [δ mRNA expression and sensitivity to panobinostat is replicated in LC 2 from CTRPv2, with the pairwise relationship also being highly significant .In LC 5 we observe that sensitivity to the HDAC-inhibitor panobinostat is associated with C/EBPppressor . While Clastomas  and the e glioma . Since tδ expression and sensitivity to panobinostat is strongest in breast cancer cell lines (δ and has low sensitivity to panobinostat (δ will increase panobinostat sensitivity. We treated BT-549 cells overnight with either a short interfering (si) RNA targeting C/EBPδ or a control non-targeting siRNA. We confirmed cells remained viable after C/EBPδ knock-down (δ knock-down (KD) increases sensitivity to panobinostat: a reduction in viability to 66% is detectable for the KD condition at a drug concentration of 1μM, whereas for the control cells viability remains at 97% (μM viability is reduced to 46% and 63% for the KD and control respectively. Using 4-parameter log-logistic growth curves estimated using the drc R package [μM (KD) and 17.0μM (control). Based on a ANOVA comparing one vs. two cubic spline fits, the difference in these response curves is statistically significant (p = 0.03).The relationship between C/EBPll lines . Of thesbinostat , allowinock-down  and thatock-down . Cells ws at 97% . At 10μM package  IC50 valLacrosse, a model capable of learning latent characteristics which explain observed sensitivity of cancer cell lines to groups of related drugs, and which are predictable from genomic features. Using a Bayesian nonparametric approach Lacrosse is able to adaptively learn an appropriate number of latent characteristics from data. Compared to kernel methods such as KBMTL, Lacrosse’s underlying sparse regression allows straightforward interpretation via visual inspection of the small number of LCs. Lacrosse allows straightforward incorporation of “soft” prior knowledge in the form of a graph over drugs representing known drug-drug relationships . Including such prior information in a non-Bayesian method such as nuclear norm regression would be challenging. Lacrosse uncovers both known and novel associations, one of which we were able to validate experimentally. Our finding that reducing C/EBPδ expression increases sensitivity to panobinostat suggests a therapeutic avenue if C/EBPδ expression could be reduced in vivo using a similar siRNA approach or alternative targeted methods such as anti-sense oligonucleotides [We introduced leotides .Lacrosse is relatively computational intensive, taking around 10h to complete 10,000 Gibbs sampling iterations on a modern workstation, compared to only around 20min for group Lasso  on the same data. However, any future clinical application would not require retraining the model, only performing prediction, which is extremely fast once the model is trained. We experimented with running Lacrosse on subsets of cell-lines corresponding to specific cancer types, but found the reduced sample size resulted in substantially reduced predictive performance and a smaller number of LCs. One interesting future direction would be to extend Lacrosse to be multitask over not only drugs but also cancer types, allowing sharing of statistical strength across all cell lines but allowing tissue-of-origin specific associations when supported by the data.Our positive functional validation result is encouraging, but there are limitations of our analysis. Genomic features, especially gene expression and CNV, have high levels of correlation so it is difficult to say which out of set of correlated features is most likely to be causally related to sensitivity. However, correlation is less problematic if we are primarily interested in the model’s ability to predict sensitivity, a valuable task in its own right due to its potential application in precision medicine. We focused here on the CCLE and CTRPv2 dataset due to their dense coverage of drug-cell line pairs. In principle it would be beneficial to integrate additional large scale drug viability assays such as GDSC . HoweverAn intriguing direction is to utilize more features of the drugs than just known inhibition targets. For example, Menden et al.  showed pz-transformed . Mutations in 1651 genes determined by hybrid capture sequencing were summarized to a binary variable for each assayed gene denoting 1 for any nonsynonymous change and 0 otherwise . We used the CCLE recommended data where variants that are a) common polymorphisms, b) have allelic fraction <10%, c) are putative neutral variants or d) are located outside of the CDS for all transcripts are removed. Per gene DNA copy number as determined using Affymetrix SNP6.0 arrays were obtained . Pharmacologic profiles were taken from CCLE_NP24.2009_Drug_data_2015.02.24.csv, specifically columns “IC50” and “ActArea”.Cell line molecular profiles and drug sensitivity scores were obtained from the CCLE data portal. For mRNA expression gene-centric RMA-normalized values were log and then For CTRPv2 data was obtained using PharmacoGx . The mRNK model,K → ∞, and rearranging columns of Z carefully, we obtain a stochastic process most easily described by a culinary metaphor. Consider a buffet with a seemingly infinite number of dishes (latent factors/LCs) arranged in a line. The first customer (drugs) starts at the left and samples Poisson(α) dishes. The dth customer (drug) moves from left to right sampling dishes with probability mk is the number of customers to have previously sampled dish k. Having reached the end of the previously sampled dishes, he tries Zdk of the D × K binary feature allocation matrix Z is 1 if and only if customer d tried dish k. In Lacrosse element Zdk corresponds to whether drug d is influenced by LC k.The Indian buffet process , IBP defLacrosse generative process the Indian buffet process matrix, Z, determines which elements of a factor loadings matrix, G are non-zero. G is then used in the model,ϵ, w represent noise, Y are the sensitivity measurements, X are the latent factors, B are regression coefficients, and F are the genomic features. For our application D is the number of drugs, N is the number of cell lines and P is the number of molecular features, including gene expression, CNV and mutations. Here K is the number of latent factors. The model is closely related to reduced rank regression, with the addition of the noise w on X.In the G:Zdk ∈ {0, 1} indicates whether Gdk is non-zero, δ0 is a delta point mass at 0. By using the Indian buffet process as a prior on Z we allow the model an unbounded number of latent characteristics K.We use a spike and slab prior on elements of B,We also use a spike and slab prior on Y, specifically the hierarchical priorG is the Gamma distribution. We use an analogous prior on the noise for X, denoted w, since some latent characteristics may be better predicted by the molecular characteristics than others.We know that the sensitivity profile for some drugs is more easily predicted than others, so we use diagonal rather than spherical (isotropic) noise on Eigen (eigen.tuxfamily.org) and interfaced to R using RCpp/RCppEigen [Inference is performed using 10,000 iterations of standard Gibbs sampling  implemenCppEigen .same gene will have an edge between them. Following [Zk: to bewd′d is the edge weight between drugs d′ and d in the MRF. We use an analogous prior on Vkp to couple the probability of having non-zero coefficients in B for different features associated with the same gene.The relationships between drugs and between features are easily represented by a Markov random field (MRF). For example, two drugs sharing a common target molecule are linked in the MRF, and two any features such CNV and gene expression for the ollowing  we modif2 at 37°C.Human breast carcinoma cell line BT-549 was obtained from American Type Culture Collection  and cultured in RPMI-1640 medium, 2mM L-Glutamine , supplemented with 10% FBS , and 0.023 U/ml (BT-549) in a humidified atmosphere with 5% COδ) silencing, cells were seeded (150 000 into 6-well plate or 10 000 cells into 96-well plate) and grown overnight prior to transfection. Cells were transfected using a non-targeting Silencer Select Negative Control siRNA  or siRNA targeting C/EBPδ  using Lipofectamine 2000  according to the manufacturer protocol. Reagents were diluted in Opti-MEM reduced serum medium  and transfection complexes were added to the cells at a final concentration of 20 nM. Transfection media was replaced with 10% FBS antibiotic-free RPMI with panobinostat (range of concentration from 0.01μM to 10μM) after overnight incubation with siRNAs and incubated for 24 h. Cells were harvested for assessment of knock-down efficiency using Western blot analysis or viability using RealTime-Glo MT Cell Viability Assay  according to the manufacturer protocol.For CCAAT/enhancer binding protein delta (C/EBP3VO4) supplemented with the protein inhibitor cocktail Complete Mini, EDTA-free . Lysates were briefly sonicated, vortexed, incubated 5 min at 4°C and vortexed again. Cellular debris were cleared by centrifugation at 12 000 rpm during 10 min. Supernatants were aliquoted and stored at -80°C for further use. Protein quantification assay was performed using a BCA Protein Assay kit . The protein extracts (15 μg) were applied on a 12% polyacrylamide-SDS gel electrophoresed at 200V during 45 min and transferred to a Immobilon transfer membrane  using the Mini Trans-Blot Cell  settled at 160V for 1 h. The membrane was blocked with 5% reconstituted skim milk powder in TBST solution (10 mM Tris–HCl pH 7.4 containing 150 mM NaCl and 0.05% Tween 20). The blots were incubated with CEBPD antibody  in TBST overnight at 4°C. After washing with TBST, horseradish peroxidase-conjugated secondary antibodies  were applied and the blots developed by the Enhanced Chemiluminescence Detection System . Levels of beta-tubulin were used as an internal standard for equal loading.Cell protein extracts were prepared with lysis buffer containing 50 mM Tris pH 8, 2% sodium dodecyl sulphate (SDS), 5 mM Ethylenediaminetetraacetic acid (EDTA), 5 mM Ethylene glycol-bis (2-aminoethylether)-N,N,N’,N’-tetraacetic acid (EGTA), 25 mM sodium fluoride (NaF) and 1 mM sodium orthovanadate Click here for additional data file.S2 Fig(EPS)Click here for additional data file.S3 Fig(EPS)Click here for additional data file.S4 Figde novo we did not explore this approach further.We tested LOBICO using only mutation data from CTRPv2 and found it was consistently and severely outperformed by LASSO in terms of predictive performance. LASSO here refers to linear regression on the AUCs, whereas GLM is L1-regularized logistic regression on the same binarized response as for LOBICO. 10-fold cross-validation was used for all methods: to choose λ for the regression approaches and the model complexity for LOBICO (using the same 8 complexity settings as used in the LOBICO paper). It is possible that LOBICO would be more competitive if we used a smaller set of known important mutations. Since we are interested in discovering such relationships (EPS)Click here for additional data file.S5 Fig(EPS)Click here for additional data file.S6 Fig(EPS)Click here for additional data file.S7 Fig(EPS)Click here for additional data file.S8 Figp-values are from a Spearman correlation test. LCs noted in the text are shown in (EPS)Click here for additional data file.S9 Fig(EPS)Click here for additional data file.S10 Figa. By bright-field microscopy cells appear healthy/viable after knock-down. b. Western blot analysis confirms that C/EBPδ protein levels are substantially reduced following overnight (O/N) treatment with the targeting siRNA, and that this knock-down remains substantial after 24 hours.(TIFF)Click here for additional data file.


Chronic kidney disease (CKD) is an inherently systemic disease that refers to a long-term loss of kidney function. The progression of CKD has repercussions for other organs, leading to many kinds of extrarenal complications. Intensive studies are now being undertaken to reveal the risk factors and pathophysiological mechanism of this disease. During the past 20 years, increasing evidence from clinical and basic studies has indicated that klotho, which was initially known as an anti-aging gene and is mainly expressed in the kidney, is significantly correlated with the development and progression of CKD and its complications. Here, we discuss in detail the role and pathophysiological implications of klotho in ion disorders, the inflammation response, vascular calcification, mineral bone disorders, and renal fibrosis in CKD. Based on the pathogenic mechanism of klotho deficiency and klotho decline in urine early in CKD stage 2 and even earlier in CKD stage 1, it is not difficult to understand that soluble klotho can serve as an early and sensitive marker of CKD. Moreover, the prevention of klotho decline by several mechanisms can attenuate renal injuries, retard CKD progression, ameliorate extrarenal complications, and improve renal function. In this review, we focus on the functions and pathophysiological implications of klotho in CKD and its extrarenal complications as well as its potential applications as a diagnostic and/or prognostic biomarker for CKD and as a novel treatment strategy to improve and decrease the burden of comorbidity in CKD. Chronic kidney disease (CKD) is a progressive systemic disease that irreversibly alters the function and structure of the kidney, over months or years. CKD progression has repercussions for other organs, exerting multiple negative systemic effects on numerous organs, including those of the cardiovascular system, leading to cardiovascular diseases, which increase the risk of mortality . In the The klotho gene is mainly expressed in the cell surface membrane of proximal and distal renal tubules –12. UderKlotho family members include α-, β-, and γ-klotho genes based on their predicted primary sequences , 19. β- α-Klotho is composed of five exons that correspond 1,012 amino acids in the human protein and 1,014 amino acids in the mouse protein Fig.  23]. Th. Th23]. 2D) ; th; th14]; As the FGF23 co-receptor, klotho mediates FGF23 to regulate mineral ion  homeostasis via klotho-FGFR complexes. Moreover, a recent study acknowledged that klotho is an on-demand non-enzymatic molecular scaffold protein that promotes FGF23 signalling. The identification of lipid rafts and sialogangliosides as the membrane receptors of soluble klotho helps us to understand more about how klotho functions as a circulating hormone or local autocrine/paracrine factor. However, klotho functions exert pleiotropic actions in the circulation. Thus, the klotho crystal structure, secretion, and regulation mechanism should be clarified in detail. A further understanding of the relation between klotho levels and CKD as well as its potential applications in vivo is very important for future therapeutic application.


Head and neck squamous cell carcinoma (HNSCC) is the sixth most common cause of cancer mortality in the world. To improve the quality of diagnostics and patients’ treatment, new and effective biomarkers are needed. Recent studies have shown that the expression level of different types of long non-coding RNAs (lncRNAs) is dysregulated in HNSCC and correlates with many biological processes. In this study, the response of lncRNAs in HNSCC cell lines after exposure to irradiation and cytotoxic drugs was examined. The SCC-040, SCC-25, FaDu, and Cal27 cell lines were treated with different radiation doses as well as exposed to cisplatin and doxorubicin. The expression changes of lncRNAs after exposure to these agents were checked by quantitative reverse transcription-polymerase chain reaction (qRT-PCR). Target prediction was performed using available online tools and classified into specific biological processes and cellular pathways. The results indicated that the irradiation, as well as chemoexposure, causes changes in lncRNA expression and the effect depends on the cell line, type of agents as well as their dose. After irradiation using the dose of 5 Gy significant dysregulation of 4 lncRNAs, 10 Gy-5 lncRNAs, and 20 Gy-3 lncRNAs, respectively, were observed in all cell lines. Only lncRNAs Zfhx2as was down-regulated in all cell lines independently of the dose used. After cisplatin exposure, 14 lncRNAs showed lower and only two higher expressions. Doxorubicin resulted in lower expressions of eight and increased four of lncRNAs. Common effects of cytotoxic drugs were observed in the case of antiPEG11, BACE1AS, PCGEM1, and ST7OT. Analysis of the predicted targets for dysregulated lncRNAs indicated that they are involved in important biological processes, regulating cellular pathways connected with direct response to irradiation or chemoexposure, cellular phenotype, cancer initiating cells, and angiogenesis. Both irradiation and chemoexposure caused specific changes in lncRNAs expression. However, the common effect is potentially important for cellular response to the stress and survival. Further study will show if lncRNAs are useful tools in patients’ treatment monitoring. Head and neck squamous cell carcinoma (HNSCC) occurs in epithelial tissue of aerodigestive tract. Next to surgical resection, the main standard treatment of HNSCC is radio(chemo)therapy. Furthermore, in patients with recurrent and/or metastatic HNSCC chemotherapy with or without cetuximab can be used. However, this type of cancer is characterized by a high resistance to radio(chemo)therapy or systemic treatment alone. It results in poor prognosis and high mortality rates among these patients ,2,3,4. THead and neck squamous cell carcinomas (HNSCCs) are difficult to treat because of their defense adaptations to monotherapy strategies, forcing it into using combined treatment modalities of surgical resection, radiotherapy, and multidrug systemic treatment. Radiotherapy is used in the cases of organ preservation, inoperable tumors, or tumors poorly developed. It is usually effective in the treatment of primary tumor and local metastasis to lymph nodes. However, many cancer cells are developing radio resistance that makes radiotherapy inefficient, leading to recurrence of the disease and patient death ,7,8. CytApproximately 93% of human genome may be transcribed as RNA, however, only 2% of it may be translated into protein. Some part of the rest of transcripts, earlier suspected as “junk” is involved in many biological processes .Long non-coding RNA can be divided as tumor suppressors and oncogenes and play crucial roles in cancer development and survival by regulating processes such as cell proliferation, apoptosis, cell metabolism, response to different types of cellular stresses, angiogenesis, and cell survival and metastatic processes . An impoIn this study, the response of 96 lncRNAs in HNSCC cell lines after exposure to irradiation and chemotherapeutics was examined and its influence on different processes important in cancer development and response to cellular stress was considered.The four different HNSCC cell lines SCC-040 , SCC-25 (tongue cancer model), FaDu , and Cal27 (tongue cancer model) were used for the study. The SCC-040 and SCC-25 cell lines were maintained according to the instructions from the DSMZ . The FaDu cell line was cultured, as described previously . Cal27 cThe cell lines were irradiated using GammaCell 1000 Elite  with a dose of 5 Gy, 10 Gy, and 20 Gy. Before irradiation cells were seeded to a culture bottle  and incubated for about 12 h. After the radiation cell lines were incubated for the next 24 h, attached cells were collected.50) value of cisplatin and doxorubicin for each cell line was defined using the 3--2,5-diphenyltetrazolium bromide (MTT) assay, as described previously [50 concentration, in The half maximal inhibitory concentration , according to the isolation protocol for total RNA (including the lncRNA fraction). Next, the quality and quantity of isolated RNA samples were examined using NanoDrop 2000 spectrophotometer , followed by 28S and 18S rRNA band estimation (1% agarose gel electrophoresis in TAE (Tris-acetate-EDTA (Ethylenediaminetetraacetic acid)) buffer).2, 1.5 µL ATP, and 0.5 µL polyA polymerase and incubated for 30 min in 37 °C. Next, a 0.5 µL Oligo(dT) adapter was added and heated for 5 min to 60 °C and then cooled to room temperature. In a final step, 4 µL of reverse transcription (RT) Buffer, 2 µL deoxynucleotide (dNTP) mix, 1.5 µL 0.1 M dithiothreitol (DTT), and 1.5 µL Random Primer Mix and 1 µL reverse transcriptase were added and incubated for 60 min in 42 °C followed by 10 min in 95 °C.Reverse transcription was performed using LncProfiler quantitative polymerase chain reaction (qPCR) Array Kit . It consists of three steps: Poly-A tailing, annealing anchor dT adaptor, and complementary DNA (cDNA) synthesis. In the first step 5 µL (1 µg) of RNA was mixed with 2 µL 5 × PolyA Buffer, 1 µL MnClComplementary DNA was mixed with 1.750 mL 2× LightCycler 480 SYBR Green I Master buffer (Roche) and 1.480 mL nuclease free water, and 26 µL of the mixture was dropped into wells on a 96-well qRT-PCR plate. Next, 4 µL lncRNA primers from Primer Plate (component of the LncProfiler qPCR Array Kit) was loaded onto the plate and the qPCR reaction was performed using the following protocol: Preincubation (50 °C for 2 min and 95 °C for 10 min), 60 cycles of 2-step amplification (95 °C for 15 s and 60 °C for 1 min), and a melting step. Reactions were performed using a LightCycler 96 (Roche). The results were compared to normal, non-treated cell lines. Expression of lncRNAs was examined among four HNSCC cell lines. Each cell line was compared to its non-treated, relevant control cell line. The results show only these lncRNAs which expression was found to be dysregulated among all examined cell lines.http://www.funcpred.com) [http://rtools.cbrc.jp/cgi-bin/RNARNA/index.pl [http://www.pantherdb.org) to classify the genes into specific biological processes and cellular pathways [Prediction of molecular interactions of selected lncRNAs and other genes in silico was performed using FuncPred (red.com)  and httpindex.pl  online tpathways .t-test. All data was shown as 2−ΔCt values or 2−ΔΔCt. Error bars represent the standard error of the mean (SEM) and a p-value < 0.05 was considered to be significant.To compare Ct values, the statistical analysis was performed using GraphPad Prism 5  software with a paired https://software.broadinstitute.org/morpheus/).The heat map and clustering were made using MORPHEUS–Versatile matrix visualization and analysis software online tool  is shown as heat maps in Next, the influence of irradiation on lncRNA expression was analyzed in all cell lines. The exposure to 5 Gy dose resulted in dysregulated expression of four lncRNAs. The HOTAIR, as well as HOXA3as, were significantly up-regulated compared to control, non-irradiated cell lines. On the other hand, expression of SNHG5 and Zfhx2as was down-regulated after irradiation, shown in In HNSCC cell lines after 10 Gy irradiation, expression of 5 lncRNAs was significantly down-regulated compared to controls: CAR Intergenic 10, Dio3os, HAR1A, Zfhx2as, and HAR1B, In the case of 20 Gy, only three lncRNAs were observed to be significantly dysregulated. Expression of HOXA6as and Zfhx2as was significantly down-regulated and PTENP1 was up-regulated compared to non-irradiated control cells, After exposure to cisplatin and doxorubicin, the differences in lncRNA expression pattern among HNSCC cell lines were observed. In the case of FaDu cell line treated with cisplatin, the expression of most lncRNAs was upregulated, however, the same cell line treated with doxorubicin showed lower expression of tested lncRNAs. SCC-040 and SCC-25 cell lines showed similar expression panel of lncRNAs. Doxorubicin caused lower expression of a vast number of lncRNAs and the same lncRNAs were upregulated after cisplatin exposure. Cal27 cell line showed similar expression panel in both cases—cisplatin and doxorubicin, Next, the influence of cisplatin and doxorubicin on lncRNA expression was analyzed in all cell lines. For HNSCC cell lines after exposure to cisplatin dysregulation of 16 lncRNAs was observed. Among them, 14 lncRNAs were significantly down-regulated compared to non-treated controls: AIR, antiPEG11, BACE1AS, CAR Intergenic 10, DISC2, MEG3, ncR-uPAR, PCGEM1, PRINS, PSF Inhibiting RNA, PTENP1, SNHG6, SRA, and ST7OT. Only two lncRNAs showed higher expressions: IPW and lincRNA-ROR, Exposure to doxorubicin caused significantly dysregulation of 12 lncRNAs in HNSCC cell lines. The down-regulation of 8 lncRNAs: antiPEG11, BACE1AS, EgoA, lincRNA-p21, Malat1, PCGEM1, UM9-5, and ST7OT was observed. The up-regulation was indicated for: Evf1 and EVF2, lincRNA-SFMBT2, Nespas, and Zfas1 compared to non-treated cell lines, The possible molecular interactions between dysregulated lncRNAs and specific genes after irradiation or chemoexposure were checked and analyzed in silico using PANTHER Classification System. Analysis of the available results indicated that lncRNAs may be involved in important biological processes and cellular pathways connected with direct response to irradiation and chemo exposure such as cell cycle, apoptosis, RAS pathway, and p53 pathway, with cell phenotype and cancer initiating cells such as cadherin, Wnt, TGF-beta, EGFR, and Notch signaling pathways as well as angiogenesis, Head and neck squamous cell carcinoma is the sixth most common cancer worldwide, with a high rate of mortality due to high chemo and radioresistance of cancer cells. Moreover, HNSCC characterizes a vast number of cancer initiating cells, which are highly resistant to commonly used treatment strategies, making therapy ineffective ,21. It iThis study focused on influence of irradiation and cytotoxic agents on lncRNAs expression in SCC-040, SCC-25, FaDu, and Cal27 cell lines as in vitro models of HNSCC. To our knowledge, it is one of the first studies describing the response of lncRNAs after irradiation as well as after cisplatin and doxorubicin exposure in HNSCC cell lines panel. The lack of similar studies causes difficulties in results comparison. However, our observations are consistent with some data based on different models.First of all, we indicated that expression of lncRNA depends on the type of cell line as well as the dose of irradiation. Nie et al. also noticed that lncRNAs response to X-ray irradiation is a dose-dependent in human bronchial epithelial cell lines . MoreoveFaDu is known as high radioresistant cell line ,24. The Interestingly, only the dose of 20 Gy caused an increase in expression of suppressor PTENP1. Lie et al. indicated that decreased expression of PTENP1 promotes malignant progression and is associated with the poor survival of HNSCC patients ,30. MoreZfhx2as was the only one of the examined lncRNAs that was down-regulated in all cell lines, regardless of the irradiation dose. Zfhx2as (Zinc finger homeobox 2 antisense) is an antisense strand of lncRNA negatively regulating expression of Zfhx2, which takes part in the regulation of transcription processes by binding to DNA. It was indicated, that mutation in Zfhx2 gene is correlated with congenital hypoalgesia . HoweverWhat is more, some reports notify, that there are many evidences confirming influence of lncRNAs on chemoresistance of different types of cancer . Over-exMany reports show huge impact of chemotherapeutics on long non-coding RNAs’ expression. The lncRNAs’ expression depends on the type of chemotherapeutic as well as on type of a cell line. What is more, dysregulated expression of lncRNAs is correlated with chemoresistance of many tumour types ,34,35,36Our analysis of predicted targets for lncRNAs changed after irradiation and chemoexposure indicated that these non-coding RNAs influence cellular processes and pathways participating in direct response to these agents such as cell cycle, apoptosis, RAS pathway, p53 pathway, as well as on factors connected with cellular phenotype and cancer initiating cells such as cadherin, Wnt, TGF-beta, EGFR, and Notch signaling pathways and also on angiogenesis. It underlines the potential role of examined lncRNAs in response to irradiation and drugs response.It is worth mentioning that expression profiles of lncRNA significantly differ from each other in the case of radiation as well as exposure to chemotherapeutics. Long non-coding RNAs are responsible for many processes that occur in healthy cells as well as in cancer cells. They probably have a huge impact on cell response to stress factors such as radiotherapy and chemotherapy. It is crucial to describe exact role of lncRNAs in these processes. Discovering the mechanisms of chemo and radioresistance will allow enhancing the effectiveness of common therapies in HNSCC treatment and allow the use of lncRNAs as potential prognostic biomarkers, predictive biomarkers, or even as targeted therapy.Ionizing radiation as well as examined chemotherapeutics caused dysregulation of lncRNAs’ expression among HNSCC model cell lines. What is more, ionizing radiation and chemotherapeutics showed different panels of changed lncRNAs. Many of those dysregulated lncRNAs are correlated with cell proliferation, apoptosis inhibition, or even radio and chemoresistance. This knowledge will help in better understanding the processes that occur in cancer cells after being exposed to standard treatment and finally in developing new therapies based on regulation of those dysregulated lncRNAs resulting in the improvement of commonly used therapies.


In vitro pharmacogenomics seeks to correlate in vitro drug sensitivity testing across panels of genetically distinct cell models with genomic, gene expression or proteomic data to identify predictive biomarkers of drug response. However, the vast majority of in vitro pharmacogenomic studies performed to date are limited to dose-response screening upon a single viability assay endpoint. In this article we describe the application of multiparametric high content phenotypic screening and the theta comparative cell scoring method to quantify and rank compound hits, screened at a single concentration, which induce a broad variety of divergent phenotypic responses between distinct breast cancer cell lines. High content screening followed by transcriptomic pathway analysis identified serotonin receptor modulators which display selective activity upon breast cancer cell cycle and cytokine signaling pathways correlating with inhibition of cell growth and survival. These methods describe a new evidence-led approach to rapidly identify compounds which display distinct response between different cell types. The results presented also warrant further investigation of the selective activity of serotonin receptor modulators upon breast cancer cell growth and survival as a potential drug repurposing opportunity.Heterogeneity in disease mechanisms between genetically distinct patients contributes to high attrition rates in late stage clinical drug development. New personalized medicine strategies aim to identify predictive biomarkers which stratify patients most likely to respond to a particular therapy. However, for complex multifactorial diseases not characterized by a single genetic driver, empirical approaches to identifying predictive biomarkers and the most promising therapies for personalized medicine are required.  Advances in next generation sequencing (NGS) and classification of patients into molecularly defined subgroups support personalized medicine strategies, which utilize predictive biomarkers to identify patient subgroups, which are most likely to respond to a specific therapy.in vitro cell based assay screening technologies that enable rapid screening of large numbers of approved drugs, experimental drugs and diverse chemical libraries across panels of genetically distinct cell lines combined with genetic and proteomic profiling are well placed to support more unbiased evidence-led preclinical approaches to personalized medicine discovery.ex vivo material  are more readily available for drug testing than in solid tumors, patient-derived samples have recently been utilized for potential drug repositioningRecent advances of In vitro pharmacogenomics describes the application of compound screening across genetically distinct cell types and correlation of drug sensitivity with genomic and gene expression datasets to elucidate drug mechanism of action and identify biomarkers of response.in vitro pharmacogenomics across genetically distinct panels of cancer cell lines and large databases linking gene expression data and drug sensitivity have been developed.in vitro pharmacogenomic studies performed to date have used single cell viability endpoints, which include application to complex models and/or patient biopsies.in vitro pharmacogenomics and drug repurposing studies.The integration of automated high-throughput microscope platforms with the latest advances in multiparametric image analysis, multivariate statistics, machine learning and new computational biology resources enable more sophisticated classification of cell phenotypes across cell based assay systems at scale. These advances support the new disciplines of high content analysis and phenotypic profiling which compare similarities and dissimilarities between drug MOA across cell based assays.in vitro pharmacogenomic and drug repurposing studies beyond simple univariate assay endpoints towards more complex assays, phenotypic endpoints and novel therapeutic classes. Here, we describe the application of a high content Cell Painting assay and the TCCS method to a 1280 compound FDA-approved small molecule library screen across a panel of 8 genetically distinct human breast cancer cell lines. We used the TCCS method to rank compounds which display the most divergent phenotypic response across the breast cancer cell panel. We identified a series of 4 known serotonin receptor modulators  which rank among the top 12 compounds promoting the most distinct phenotypic response between breast cancer cell line pairs. Transcriptomic pathway analysis performed on the most potent serotonin modulator (triflupromazine) demonstrates selective down regulation of multiple cell cycle pathways and upregulation of tumor necrosis factor (TNF) signaling pathways in the most sensitive breast cancer cell type. Thus, combined high content phenotypic screening, and pathway network analysis identifies an existing serotonin receptor modulator with selective activity upon breast cancer cell cycle and TNF signaling pathways representing a potential drug repurposing opportunity for specific breast cancer types.We recently described the application of a method, Theta Comparative Cell Scoring (TCCS), which uses multiparametric high content imaging data to quantify how similar or dissimilar a compound induced phenotype is between genetically distinct cell types.2The Prestwick Chemical Library of 1280 FDA-approved compounds at a single concentration of 1 μM was profiled in a phenotypic high-content image based assay across eight breast cancer cell-lines  which are classified into three clinical subtypes; estrogen receptor (ER) positive, HER2 amplified or triple negative (TN) for ER, progesterone receptor and HER2 A.Fig. 1SWe sought to identify compounds that induced distinct phenotypic responses between cell lines using a Cell Painting assay as an unbiased phenotypic profiling method.in vitro pharmacogenomic studies over traditional cell proliferation and viability assays include: Speed ; Single-point concentration testing relative to dose-response testing substantially influencing throughput; Suitability for discriminating phenotypic response in heterogeneous cell subpopulations or co-culture assays. Applicable to any assay or endpoint that can be discerned by image-based phenotypic analysis including disease models and therapeutic classes not defined by cell growth or viability.Using GFP-NucLight expressing breast cancer cells, selected hits were tested as an 8 point semi-log range of concentrations (0.3 nM to 1 µM) and cell proliferation was monitored over 72 h in the IncuCyte ZOOM microscope. GFP cell-count normalized to the DMSO negative control was used as a measure of cell proliferation over 72 h  and despOf the top 15 compound induced-phenotypic differences between cell lines determined by Cell Painting and TCCS analysis, half involved HCC1954 cells. Among the top 12 FDA-approved drugs which promote distinct phenotypic response between cell lines are 4 structurally distinct small molecule drugs with previously reported serotonin modulator activity; fluvoxamine, cisapride, protriptyline and triflupromazine. Fluvoxamine (DrugBank #DB00176) is an approved antidepressant which functions pharmacologically as a selective serotonin reuptake inhibitor. Fluvoxamine blocks the reuptake of serotonin at the serotonin reuptake pump of the neuronal membrane, enhancing the actions of serotonin on 5HT1A autoreceptors. Studies have also demonstrated that fluvoxamine has virtually no affinity for α1- or α2-adrenergic, β-adrenergic, muscarinic, dopamine D2, histamine H1, GABA-benzodiazepine, opiate, 5-HT1, or 5-HT2 receptors. Cisapride (DrugBank #DB00604) was approved as a treatment for heartburn due to gastroesophageal reflux disease. Cisapride acts through the stimulation of the serotonin 5-hydroxytryptamine  receptors which increases acetylcholine release in the enteric nervous system. Cisapride does not induce muscarinic or nicotinic receptor stimulation, nor does it inhibit acetylcholinesterase activity. Triflupromazine (DrugBank #DB00508) is a member of a class of drugs called phenothiazine’s and is used to treat schizophrenia and other psychotic disorders. Triflupromazine binds to the dopamine D1 and dopamine D2 receptors and inhibits their activity. Triflupromazine also binds the muscarinic acetylcholine receptors (M1 and M2) and the serotonin receptor 5-HT2. Protriptyline (DrugBank #DB00344) is a dibenzocycloheptene-derivative tricyclic antidepressant (TCA). TCAs are structurally similar to phenothiazines and are potent inhibitors of serotonin and norepinephrine reuptake and also block histamine H1 receptors, α1-adrenergic receptors and muscarinic receptors.SLC6A2 and SLC6A4) and triflupromazine  as well as their wider family members using RNAseq data from the Cancer Cell Line Encyclopedia , CCNE2 (cyclin E2) and CCNA2 (cyclin A2). Enrichment analysis of the upregulated gene set revealed activation of the TNFR1 signaling pathway suggesting induction of apoptosis via TNF signaling , while completing breast cancer therapy. Epidemiology studies on breast cancer recurrence risk related to concurrent use of SSRI antidepressants and tamoxifen indicated breast cancer patients taking SSRIs were at no increased risk of developing breast cancer3We have applied a high content Cell Painting assay and the Theta Comparative Cell Scoring (TCCS) method to quantify a similarity score of phenotypic response between genetically distinct breast cancer cell types. TCCS was used to identify four serotonin modulator small molecules  which rank among the top 12 FDA approved compounds promoting the most distinct phenotypic response between breast cancer cell lines. NanoString transcriptomic profiling and pathway network analysis was applied to demonstrate that triflupromazine exerts selective activity upon the inhibition of multiple cell cycle pathways and upregulation of TNF receptor signaling in sensitive relative to non-sensitive breast cancer lines. While the current study has made use of a diverse panel of established breast cancer cell lines which may have limited utility in guiding personalized medicine strategies, the methods and results presented in this article demonstrate the value of multiparametric high content phenotypic profiling and pathway network analysis. These methods can also be applied to isogenic and primary patient-derived cell assays composed of genetically distinct cell types to advance phenotypic screening, drug repurposing and future pharmacogenomic analysis across diverse phenotypes.44.1l-glutamine, incubated at 37 °C, humidified and 5% CO2. Optimized cell number per 384 well plate: HCC1569 (1500 cells/well); HCC1954 (1500 cells/well); KPL4 (750 cells/well); MCF7 (1500 cell/well); MDA-MB-231 (750 cell/well); MDA-MB-157 (2000 cells/well); SKBR3 (2000 cells/well); T47D (1500 cell/well). Optimized cell number per 96 well plate: HCC1569 (3000 cells/well); HCC1954 (3000 cells/well); KPL4 (2000 cells/well); MCF7 (3000 cell/well); MDA-MB-231 (2000 cell/well); MDA-MB-157 (3500 cells/well); SKBR3 (3500 cells/well); T47D (3000 cell/well). Cells were seeded in 96- or 384-well optical bottomed imaging plates (#655090 and #781091 Greiner) for image based assays, respectively or standard 96 well plates for all other assays (#655180 Greiner). Plates were incubated for 24 h in a tissue culture incubator before the addition of compounds.Cell-lines were all grown in DMEM (#21969-035 Gibco) and supplemented with 10% fetal bovine serum and 2 mM 4.2The lentiviral based IncuCyte® NucLight Reagent (Sartorius) was used to label all breast cancer cell lines with a nuclear-restricted green fluorescent protein (GFP) to enable accurate quantification of cell growth over time. The IncuCyte® platform acquired images and calculated the number of cell nuclei to provide a cell count assay endpoint at sequential time points following cell plating and compound treatment. All analyses presented was performed at 72 h following compound treatment. Note: the morphology and growth rate of the GFP-NucLight labelled cells were confirmed to behave similarly to parental cell lines. NucLight cells were used for the IncuCyte® cell proliferation experiment only, parental cell lines were used for Cell Painting, cell viability and NanoString experiments.4.3For cell viability assays, seeding density for 96-well plate assay format was optimized for each individual cell line to ensure a linear assay range. Cells were seeded in 96 well plates and cultured for 24 h before treatment. Cells were incubated with compounds for 72 h, untreated cells were incubated with 0.1% DMSO (v/v). Alamar Blue  was added (10% v/v) to each well and plates incubated for 3 h. Fluorescence emission was read on an EnVision 2101 multilabel plate reader . All conditions were normalized to plate DMSO control wells.4.4Handling of the Prestwick FDA-approved compound library was performed using a Biomek FX. 1.5 μL from master plates containing 1 mM compound in DMSO was transferred to an intermediate plate containing 74.5 μL of cell culture media for a 1:50 dilution. From the intermediate plate 2.5 μL was transferred to the cell assay plate containing 50 μL volume for a second dilution of 1:20 resulting in a final 1;1000 dilution of compound stock at 0.1%DMSO (v/v) in each assay well. Assay plates were barcoded with cell-line and a sequential number corresponding to the compound source plate.4.5Cell PaintingWe selected staurosporine as our phenotypic positive control as it induces a distinctive change in cell morphology across all the breast cancer cell lines at the same concentration (0.3 μM), most likely as a consequence of its broad substrate specificity. This makes staurosporine a very useful control compound for Cell Painting-like morphology analysis across cell line panels, even though there is no clinical application for this compound. Negative (0.1% DMSO) and positive (0.3 μM staurosporine) controls were used for normalization of plate effects and evaluation of the performance of basal cell line normalization of distinct cell morphologies .4.6Imaging was carried out on an ImageXpress micro XL  a multi-wavelength wide-field fluorescent microscope equipped with a robotic plate loader . Images were captured in 5 fluorescent channels at 20× magnification, exposure times were kept constant between plates and batches as to not influence intensity values.4.7Images were analyzed using CellProfiler v2.1.1 to extract morphological features, quantifying 340 morphological features per cell. Briefly, cell nuclei were segmented in the Hoechst stained image based on intensity, clumped nuclei were separated based on shape. Nuclei objects were used as seeds to detect and segment cell-bodies in the cytoplasmic stains of the additional channels. Subcellular structures such as nucleoli and Golgi apparatus were segmented and assigned to parent objects (cells). Using these masks marking the boundary of cellular objects, morphological features are measured for multiple image channels returning per object measurements.4.8Pre-processing: Out of focus and low-quality images were detected through saturation and focus measurements and removed from the dataset. Image averages of single object (cell) measurements were aggregated by taking the median of each measured feature per image. Feature selection was performed by calculating pair-wise correlations of features and removing one of a pair of features that have correlation greater than 0.9, and removing features with very low (<1e−5) or zero variance. To normalize the inherent morphological variation between cell lines, features were standardized on a plate-by-plate basis by dividing each feature by the median DMSO response for that feature and scaled by a z-score (z) to a zero mean and unit variance. Tabular data from CellProfiler measuring morphological features for each cell was aggregated to an image median. Principal components were calculated using the prcomp function in R v3.2, with no centering or scaling as this was performed manually beforehand. The number of principal components to use in the analysis can be determined by specifying beforehand the proportion of variance in the data that should be kept, and then finding the minimum number of principal components that account for that proportion of variance in the dataset.In order to center the principal component data so that the medoid of the negative control was positioned on the origin, the median value for each feature column for the negative control data was calculated. Then by calculating how much this differs from the origin for each feature, all principal component values were adjusted by this difference. Inactive compounds were identified by determining a minimum cut-off distance to the negative control centroid in principal component space. This was calculated by first finding the l1 norm from each compound to the negative control centroid. The standard deviation of all these distances was calculated and any compound which was within 2 standard deviations of the negative control centroid was deemed inactive, if a compound was found to be inactive in any one of the eight cell lines it was removed from the analysis.Phenotypic characterization: Following data pre-processing, distinct phenotypic responses between active compounds were calculated using the TCCS method.4.9T47D and HCC1954 cells were seeded in 6 well plates for 24 h before drug treatment for a further 24 h. Media was removed and RNA was extracted using an RNeasy Mini Kit with on column DNAse digestion (#74104 and #79254 Qiagen) following the manufacturer’s protocol. RNA quality was assessed using A260/A280 ratio and concentrations normalized. 100 ng of RNA was analyzed by the using an nCounter® PanCancer Pathways Panel . Gene expression data was analyzed using nSolver software (NanoString) and data was normalized following the NanoString protocol and analyzed using the advanced analysis module to calculate differential ratios between different drug treatments. Data was exported into Cytoscape4.10RNAseq datasets (19Q3 release) from 54 breast cancer cell lines from the Cancer Cell Line Expression (broadinstitute.org/ccle) was analyzed for genes of interest. Data is RNAseq TPM (transcripts per million) gene expression data for protein coding genes using RSEM. Data is Log2 transformed, using a pseudo-count of 1. Database was subset for cell lines and genes of interest and overlaid on a boxplot of the expression data for all breast cancer cell lines in the database. Upper and lower “hinges” correspond to the first and third quartiles. Whiskers extend 1.5 × (interquartile range) of the hinge. Data points beyond the end of the whiskers are outliers.The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.


The unavailability of sufficient numbers of human primary cells is a major roadblock for in vitro repair of bone and/or cartilage, and for performing disease modelling experiments. Immortalized mesenchymal stromal cells (iMSCs) may be employed as a research tool for avoiding these problems. The purpose of this review was to revise the available literature on the characteristics of the iMSC lines, paying special attention to the maintenance of the phenotype of the primary cells from which they were derived, and whether they are effectively useful for in vitro disease modeling and cell therapy purposes. This review was performed by searching on Web of Science, Scopus, and PubMed databases from 1 January 2015 to 30 September 2019. The keywords used were ALL =  AND (cartilage OR chondrogenesis OR bone OR osteogenesis) AND human). Only original research studies in which a human iMSC line was employed for osteogenesis or chondrogenesis experiments were included. After describing the success of the immortalization protocol, we focused on the iMSCs maintenance of the parental phenotype and multipotency. According to the literature revised, it seems that the maintenance of these characteristics is not guaranteed by immortalization, and that careful selection and validation of clones with particular characteristics is necessary for taking advantage of the full potential of iMSC to be employed in bone and cartilage-related research. Therapeutic options capable of restoring the physiological properties of bone and cartilage are still lacking ,2. Due tTissue engineering techniques, combining scaffolds and cells, must undergo in vitro testing before translation into clinic or preclinical models. Human mesenchymal stromal cells (MSCs) are often employed in bone and cartilage tissue engineering approaches because of their proliferation and multidifferentiation abilities ,8,9. HowNowadays, a high number of MSC lines that display specific characteristics and differentiation capabilities have been generated and are valuable tools as part of models of disease and tissue repairing strategies. Different MSC lines have been employed for testing ,23,24,25The aim of this review was to analyze the characteristics of the MSC lines that are being used currently. Also, we aimed to investigate whether the MSC lines keep the phenotype of the primary cells from which they were derived, and if they could indeed be good models of tissue regeneration and disease.This review was carried out by employing Web of Science, Scopus, and PubMed databases from 1st January 2015 to 30th September 2019. In order to identify the human immortal MSC (iMSC) lines that are being used currently in the fields of bone and cartilage research, the keywords used were ALL =  AND (cartilage OR chondrogenesis OR bone OR osteogenesis) AND human). Only original research studies were included in the analysis. References of the selected articles were included when relevant, and duplicates were excluded. After screening the title/abstract or full text, articles in which no human iMSC line was employed for osteogenesis or chondrogenesis experiments were excluded. The PRISMA flow diagram  is shownFor each iMSC line identified, we collected the following data: immortalization genes and method employed; tissue of origin and donor characteristics; if the iMSC line was clonal; whether it was tumorigenic and how its tumorigenicity had been assayed; and if it had been validated by short tandem repeat (STR) genotyping, as seen in Immortalization is the process by which cells acquire an unlimited proliferation potential by bypassing senescence . There aThe initial in vitro growth arrest of human primary MSCs is presumed to be due to nonreplicative senescence, which is regulated by p53 and Rb-related pathways. In response to stress, the tumor suppressor p53 is phosphorylated and liberated from its binding to E3 ubiquitin ligase Mdm2, hence activating the senescence pathways. During quiescence, unphosphorylated Rb proteins control cell proliferation by binding and inhibiting E2F transcription factors, thus blocking cell cycle progression. During cell growth, signaling pathways that phosphorylate Rb proteins are activated, promoting its disassociation from E2F and allowing for the expression of E2F-dependent genes necessary for cell division ,108. InhBoth simian virus 40 large T antigen (SV40LT) and human papillomavirus (HPV) E6/E7 gene transduction promote cell cycle progression by interfering with p53 and Rb-mediated pathways. SV40LT binds to these two proteins, thus releasing the activity of E2F transcription factors and avoiding growth arrest . HPV E6/HPV E6/E7 genes have also been used for immortalizing MSCs derived from bone marrow ,58, and,It has been stated that hTERT transduction allows senescence evasion while maintaining in vitro and in vivo osteogenic ability of MSCs ,91. TranHowever, in addition to giving rise to MSC lines derived from bone marrow from young and/or healthy donors ,63,71, wTransduction of hTERT has also been employed in combination with E6/E7 ,101,104 Out of the 35 iMSC lines included in this review whose osteogenic potential had been tested, only one line showed no mineralization ability. This hASCs-T cell line was derived from adipose tissue and immortalized with a combination of hTERT and SV40LT . All theIn four out of the thirty iMSC lines which displayed osteogenic potential, this differentiation ability was increased in comparison with primary MSCs, in terms of mineralization ,85 and oOn the other hand, there were four iMSC lines that showed reduced osteogenic potential in comparison with primary MSCs, all of them transduced with hTERT alone: BMA13H, hASCs-T, GB/hTERT MSCs, and Pelt cells. Two of these cell lines, BMA13H and hASCs-T, were incompletely immortalized ,85, and Of note, all the reviewed MSC cell lines immortalized with hTERT and E6/E7 were described to maintain or enhance their osteogenic potential. Interestingly, the hTERT and E6/E7-transduced iMSC line 3A6 showed a higher osteogenic potential than their E6/E7-only-transduced counterpart, the KP cells , suggestA reduction of osteogenic potential was also observed in some MSC cell lines immortalized with hTERT and SV40LT, such as hASCs-TS . Song etOnly 23 iMSC lines out of the 38 included in this review have been submitted for analysis of their chondrogenic potential. Two iMSC lines did not show any chondrogenic potential when assessed in two-dimensional culture: UE6E7T-2, derived from bone marrow and transduced with E6/E7 and hTERT ; and iSuIn many cases, the chondrogenic potential of iMSCs has been scarcely analyzed by one single histochemical staining, either alcian blue staining (ABS) or toluidine blue staining (TBS) ,91,101. Despite this, it has been shown that low chondrogenic iMSCs can stimulate chondrocyte differentiation when cocultured , possiblThe adipogenic potential of 30 out of 38 iMSC lines was assessed, mainly by oil red O staining (OROS), but also by gene expression analysis of adipogenesis-related genes, such as transcription factor PPARγ, with different results, as seen in In 2006, the International Society for Cell Therapy proposed a panel of cell surface markers to identify human MSC, including CD73, CD90, and CD105 . HoweverFor example, Abarrategi et al. (2018) noticed a lowering of CD73 and CD105 expression in iMSCs in comparison with primary MSCs , and AleIn addition, it is not clear whether the expression of a certain surface marker of this traditional panel confers an advantage to differentiate towards a specific lineage; for example, it has been shown that there are no differences in chondrogenic potential of MSCs caused by CD105 expression . TherefoIt is known that polyclonal expansion favors selection of faster growing cells, while clone characterization and selection may enable the maintenance of a subpopulation of cells with more desirable characteristics . VariatiHalf of the iMSC lines that are reviewed here were clonal, while the other half were nonclonal. However, some clones were not selected by their differentiation abilities or surface markers expression profile, but were randomly picked  or choseOf note, only eight of the iMSC lines reviewed here—four of them derived from the same donor—have been submitted to short tandem repeat (STR) analysis to confirm whether they originated from one particular donor. This validation is important, especially if these iMSC lines are employed for basic research about MSCs biology; for instance, comparing cell lines originating from young and elderly donors or investigating the characteristics or behavior of these cells in skeletal diseases. Using this approach, Jayasuriya et al. (2018) generated and analyzed clonal iMSC lines from knee articular cartilage of osteoarthritic patients, identifying the existence of two MSC populations in human osteoarthritic cartilage; one preferentially undergoing chondrogenesis and the other exhibiting higher osteogenic potential. In this case, the generated cell lines were properly submitted to STR genotyping .MSCs have been described as resistant to malignant transformation, requiring the combination of several events to achieve an oncogenic phenotype. Primary MSCs have been widely used in clinical trials, but immortalized MSCs transduced with proto-oncogenes can eventually become tumorigenic, making them useless for clinical approaches, but not for research purposes. The tumorigenicity of 18 out of 38 iMSC lines included in this review was investigated by either soft agar colony formation assay or in vivo tumorigenicity test in immunodeficient mice (IDM). There were only two cases in which these cells showed signs of tumorigenicity; c-Fos-transduced 3 Hits hMPCs  and highHowever, oncogenic mutations may arise during passaging of iMSCs . In thisIt is important to note that hTERT expression also has a role in the achievement of cellular capacities related to tumorigenesis, such as angiogenesis and immune system evasion. In the same way, the inactivation of p53 and Rb by SV40LT and E6/E7 proteins is related to the acquisition of cancer-related features. Moreover, if hTERT is re-expressed after SV40LT or E6/E7 transduction, its recovery could favor the fixation of aberrant karyotypes that lead to malignant phenotypes . UnsurprOne of the fundamental characteristics of MSCs is their ability to form ectopic “ossicles” which mimic the architecture of bone marrow . The ectA number of iMSC lines have been generated in an attempt to overcome the limitations associated with primary MSCs. These cell lines have had many in vitro applications, including testing of engineered scaffolds for bone and cartilage repair, decellularized extracellular matrix production, investigation of the MSCs differentiation process at the molecular level, optimization of the current differentiation protocols, and analysis of their behavior in the pathological joints. However, the application of these cells has been for research purposes only as they present a risk of tumorigenicity.Several approaches have been employed to confer an unlimited proliferation potential to MSCs, mainly involving viral genes and hTERT transduction, with different degrees of success. It is still unclear which set of genetic alterations are necessary and sufficient for MSC immortalization, but it probably involves abrogation of replicative and nonreplicative senescence.Alterations of the multidifferentiation potential of MSCs after immortalization have been described, with the osteogenic potential being the best conserved in fully immortalized MSC lines. However, there are also iMSC lines capable of differentiating towards the chondrogenic lineage when cultured in 3D environment. In addition, some studies suggest that other characteristics of iMSC lines, such as secretion of trophic factors, are maintained in MSCs despite immortalization.The ideal iMSC line should retain the phenotypic and functional characteristics of MSCs, as well as a normal karyotype. The usefulness of these cell lines in bone and cartilage regenerative medicine research would be increased if clones were carefully selected and validated. Before employing iMSCs for basic or applied bone and cartilage research, their characteristics should be fully understood. Moreover, the maintenance of these characteristics should be assessed periodically throughout passaging, as immortalization does not guarantee it, and both polyclonal expansion and low-density seeding should be avoided to prevent malignant transformation. If these requirements are fulfilled, iMSC lines could be useful and convenient tools for basic research, testing of tissue engineering approaches, and production of biotechnological products, among other applications.