Resistance to taxane-based therapy in breast cancer patients is a major clinical problem that may be addressed through insight of the genomic alterations leading to taxane resistance in breast cancer cells. In the current study we used whole exome sequencing to discover somatic genomic alterations, evolving across evolutionary stages during the acquisition of docetaxel resistance in breast cancer cell lines.Two human breast cancer in vitro models (MCF-7 and MDA-MB-231) of the step-wise acquisition of docetaxel resistance were developed by exposing cells to 18 gradually increasing concentrations of docetaxel. Whole exome sequencing performed at five successive stages during this process was used to identify single point mutational events, insertions/deletions and copy number alterations associated with the acquisition of docetaxel resistance. Acquired coding variation undergoing positive selection and harboring characteristics likely to be functional were further prioritized using network-based approaches.ABCB1 and ABCB4, as well as DMTF1, CLDN12, CROT, and SRI. For MDA-MB-231 numerous copy number losses on chromosome X involving more than 30 genes was observed. Of these genes, CASK, POLA1, PRDX4, MED14 and PIGA were highly prioritized by the applied network-based gene ranking approach. At higher docetaxel concentration MCF-7 subclones exhibited a copy number loss in E2F4, and the gene encoding this important transcription factor was down-regulated in MCF-7 resistant cells.A number of genomic changes were found to be undergoing evolutionary selection, some of which were likely to be functional. Of the five stages of progression toward resistance, most resistance relevant genomic variation appeared to arise midway towards fully resistant cells corresponding to passage 31 (5 nM docetaxel) for MDA-MB-231 and passage 16 (1.2 nM docetaxel) for MCF-7, and where the cells also exhibited a period of reduced growth rate or arrest, respectively. MCF-7 cell acquired several copy number gains on chromosome 7, including ABC transporter genes, including Our study of the evolution of acquired docetaxel resistance identified several genomic changes that might explain development of docetaxel resistance. Interestingly, the most relevant resistance-associated changes appeared to originate midway through the evolution towards fully resistant cell lines. Our data suggest that no single genomic event sufficiently predicts resistance to docetaxel, but require genomic alterations affecting multiple pathways that in concert establish the final resistance stage.The online version of this article (doi:10.1186/s12864-016-2749-4) contains supplementary material, which is available to authorized users. Resistance to taxane therapy in breast cancer patients remains a major clinical problem with approximately 20 % of neo-adjuvant or adjuvant taxane treated patients experiencing disease recurrence with subsequent death from breast cancer. We here hypothesize that characterization of taxane resistance-associated molecular alterations and identification and validation of putative predictive markers will have the potential to result in more effective use of taxanes, with improved survival and reduced toxicities and costs. Currently, only a few predictive biomarkers are used in the clinical management of breast cancer. Among them is the estrogen receptor, used to predict sensitivity to endocrine therapy, and HER2 expression, used to predict sensitivity to HER2-targeting drugs, e.g. Trastuzumab (Herceptin) and Lapatinib. More recently, TOP2A gene aberrations were related to anthracycline sensitivity , 2 and tABCB1 product permeability-glycoprotein (Pgp), which belongs to the superfamily of ATP-binding cassette (ABC) transporters [Taxanes  are mitotic inhibitors and play a key role in the treatment of primary and advanced breast cancer –8. Taxansporters . Pgp is sporters , 11. A msporters , but an Recently, analyses of the whole genome of breast tumors have been presented. Applying next-generation sequencing techniques, new breast cancer-related genes have been suggested , 13, and2 at 37 °C. For maintenance of resistant cells, docetaxel  was added to the complete medium. Cell line identity was verified by the IdentiCell Cell Line Authentication method . The parental cell lines (MCF-7PAR and MDAPAR), four sub-lines (MCF-7SUB and MDASUB) isolated from each of the two cell lines during development of resistance, and the final resistant cell lines (MCF-7RES-65nM and MDARES-150nM) were further characterized (Tables Resistant breast cancer cell lines were developed as previously described . In brieDocetaxel cytotoxicity was assessed using tetrazolium-based semiautomated colorimetric (MTT) assay as previously described . Cells wStability of the resistant phenotype of the final resistant cell lines was investigated by MTT assay following one month of docetaxel withdrawal.0 x ekt assuming proportionality between absorbance and cell number. These analyses were conducted three times and the result from one representative experiment is reported.Cell growth was investigated by crystal violet assay. Briefly, on day 0, cells were seeded in 96 well microtiter plates  and one plate per day was stained on days 2–7; the assay was then performed as previously described . The douTM Two step cell cycle analysis kit . In brief, cells were washed in PBS and stained with 250 μl lysis buffer containing DAPI (10 μg/ml) for 5 min at 37° and thoroughly resuspended. Cells were stabilized by adding 500 μl stabilization buffer and cellular fluorescence quantified. Cell cycle distribution was calculated using the NC-250TM Nucleoview software. These analyses were conducted three times and the result from one representative experiment is reported.Cell cycle was analyzed as follows: In 6 well plates 20E4 cells/well or 15E4 cells/well were seeded of the MCF-7 cell lines and MDA-MB-231 cell lines, respectively. Cells were cultured for 3 days in their standard growth medium supplemented with proper concentration of docetaxel for the resistant cell lines. Cell cycle analysis was performed using the Nucleocounter system NC-250TM spectrophotometer.DNA was purified using Qiagen DNeasy Blood & Tissue Kit  according to the manufacturer’s instructions. The purity of the DNA was analyzed using a NanodropExtracted DNA that passed quality control  was selected for library construction. Agilent SureSelect Human All Exon Kit 50 Mb  was used for exome enrichment and generated Illumina sequencing libraries according to the manufacturer’s protocols . 1 to 2 μg of genomic DNA was sheared into 200 nt fragments on average by the Covaris S2 system , after which the DNA fragments were subsequently end-repaired, extended with an ‘A’ base on the 3′ end, ligated with indexing-specific paired-end adaptors followed by pre-capture PCR. Prepared libraries were hybridized for 24 h with biotinylated oligo RNA baits, and enriched with streptavidin-coated magnetic beads. Cluster generations of the libraries were then performed on Illumina cBot following the manufacturer’s recommended protocols . Finally, the clustered libraries were transferred onto HiSeq 2000 sequencers (Illumina) for 2 × 90 nt paired-end sequencing.Sequencing read quality was inspected using the FastQC software . AdapterWe applied a multi-sample calling strategy using UnifiedGenotyper  to call Copy number alterations (CNAs) were identified using CONTRA , - a toacquired selective-events. In addition, identified acquired selective-events were grouped into three classes according to their likely functional character: Class A variants were regarded as likely-functional events and included splice donor and splice acceptor variants, stop gain and stop loss variants, frameshift variants, initiator codon variants, transcription factor (TF) binding site variants, missense variants (if predicted to be deleterious by either SIFT and/or PolyPhen or if located in a protein domain). Class B variants encompassed events wherein the impact was more difficult to determine without further functional studies, and included inframe insertions and deletions, missense variation of predicted benign character, splice region variants, incomplete terminal codon variants, mature microRNA (miRNA) variants, nonsense-mediated decay (NMD) transcript variants and various types of regulatory variants. Finally, Class C variants contained those variants likely considered benign or ‘silent’, although we cannot completely exclude a potential functional impact of such variants on the protein product, but simply assume a functional character is less likely or would need significant additional evidence to be credible. This class included synonymous variants, stop retained variants, 5 prime UTR variants, 3 prime UTR variants, non-coding exon variants, nc-transcript variants, intron variants, upstream and downstream gene variants and intergenic variants. Figure An identified mutation was classified as acquired in a given resistant subline if it had not been called in the stage preceding it. Also, the preceding stage should have a read depth greater than four at the given position, and there should be no evidence of the alternative allele, i.e., the number of reads supporting the alternative allele should be zero. This allowed us to select mutations with confidence they were acquired in progressive stages and had not been seen before in assessable (sufficient sequence depth) regions. A similar approach was used for the CNAs. Because all CNAs are inferred with preceding stages as reference, they are per definition acquired. As a cell line progresses towards a fully resistant stage, it acquires and accumulates mutations and CNAs, but far from all variants are equally functionally important, and only a fraction of the variants are likely to play a role in the development of resistance to docetaxel. Several strategies were employed to narrow down the list of mutations (and genes) of interest for inclusion in further downstream analysis primarily focusing on the idea that variation under selection would provide a functional advantage to cell survival. A variant was inferred to be under selection if it was acquired in a given stage and then also seen in the following stages as well as in the final resistant stage. Mutation events that were not seen in subsequent stages were not considered in the analysis. Thus only accumulating events were considered, and such variants denoted as PAR/MCF-7RES-65nM and MDAPAR/MDARES-150nM). We correlated available gene expression profiles for these stages with identified copy numbers to search for CNAs that might explain changes observed in the expression of certain genes in the final resistant cells (MCF-7RES-65nM and MDARES-150nM).Gene expression analysis (microarrays) has previously been performed on the cell lines by methods described elsewhere , that inCancer hallmarks  compriseClass A and B variants plus CNA events that correlated with expression and included 916 genes in total, of which 396 had previously been associated with breast neoplasm, and these were then mapped to the hallmark classes.For a subset of the detected variation, we assessed the association of the corresponding genes and the eleven cancer hallmarks. This subset included Class A mutations (see above) and CNAs that associate with a changed expression of the gene were further ranked with GPEC. We ranked genes in each stage as well as across all stages to identify the potentially most important genes for a given stage as well as the most important genes overall.Motivated by the assumption that potential new taxane resistance-associated genes might harbor characteristics similar to those already found to cause resistance to taxanes, we used a network-based gene prioritization strategy implemented in the Cytoscape plugin GPEC . GPEC scClass A mutations or CNAs and annotate these to biological functions. Genes in each stage of the MCF-7 and MDA-MB-231 cell lines were analysed using DAVID Bioinformatics Resources 6.7 [Online enrichment tools were applied in order to identify co-functioning genes in each stage with rces 6.7 , 36 and rces 6.7 , 38. Therces 6.7  and AmiGrces 6.7 . SimilarRES-65nM), and for MDA-MB-231, the final concentration was 150 nM (MDARES-150nM). MCF-7RES-65nM and MDARES-150nM, their respective parental cell lines and four additional sub-lines per cell line (MCF-7SUB and MDASUB), isolated during development of resistance, were further characterized.Two different human breast cancer cell lines, MCF-7 and MDA-MB-231, which are widely used as models for hormone-dependent and -independent human breast cancer, respectively, were cultured in the presence of increasing concentrations of docetaxel. The development process and investigated subpopulations are outlined in Tables SUB-0.27nM) as well as in the rest of the investigated cells  were resistant compared to the parental MCF-7 cell line . This suggests that the phenotypic alterations in sensitivity to docetaxel are due to genomic alterations.Stability of the final resistant phenotype was confirmed as both MCF-7SUB-15nM.The doubling times for parental MCF-7 and MDA-MB-231 cells plus the derived docetaxel-exposed cell lines are listed in Tables SUB-5nM and MDASUB-15nM considerably fewer cells were in G1 and more cells were in subG1; this pattern, to a lesser degree, was also observed in MDASUB-100nM and MDASUB-150nM   Tables  and 4. S) Tables  and 4. Ttegories  revealedRES-65nM/ MDARES-150nM) compared to the parental cell lines (MCF-7PAR/ MDAPAR). This approach made it possible for us to reduce the amount of false negatives due to insufficient coverage. For instance, if a CNA event was detected in early stages of resistance development, but not found in later stages, despite the fact that the gene in which the CNA event was found was observed to be up-regulated in the final stage, this speaks in favor of the CNA still being present at this stage, but we are just not able to detect it with our exome data alone. The copy number profiles were very different between the two cell lines. Of primary interest for the MCF-7 cells were several copy number gains acquired during exposure to increasing concentrations of docetaxel, most of which resided on chromosome 7, but also on chromosome 4, 5, 8, 11 and 14. The MDA-MB-231 sub-lines predominately acquired copy number losses, primarily on the X chromosome, but also on chromosome 2, 6, 7, 9, 15 and 21,  the acquisition of a number of deleterious mutations as well as a single CNA event in the MDA-MB-231 sub-line was observed. Of particular interest was the acquisition of a number of single point mutations in genes such as RYR2, DDX10, CDH23, ANXA8, CHD3, RHPN2 and HLA-DRB5 as well as a copy number loss in HADHB. These genes are observed to code for proteins having several interactions with proteins already associated with taxane resistance. The genes RYR2, CDH23 and RHPN2 are altered in both cell line model systems. While the RYR2 gene has acquired a deleterious point mutation in MCF-7SUB-0.27nM, it is observed to be down-regulated in MDARES-150nM. The CDH23 gene is altered twice in the MCF-7 cell line in that a deleterious point mutation is acquired in early stage (MCF-7SUB-0.27nM) of resistance development followed by a copy number loss in the gene in the final resistant stage (MCF-7RES-65nM) where we also measured the gene as down-regulated. The gene is down-regulated in the MDARES-150nM as well. The CDH23 protein interacts with the product of ATP2B2, which have been associated with resistance to paclitaxel, where inhibition of ATP2B2 leads to increased sensitivity towards the action of paclitaxel [SUB-0.27nM, while the same gene is observed to be down-regulated in MCF-7RES-65nM, strengthening the hypothesis of a role for this gene in the development of taxane resistance.For the network-based analysis performed across all stages, the top five ranked genes were ABCB4, CDC25B, FES, ACVR1 and RDX for the MCF-7 sub-lines, and JAK2, CARD11, KDM4C, VWF, CA5B for the MDA-MB-231 sub-lines , the acquisition of a number of CNAs was observed in both model systems. The MCF-7SUB-1.2nM had acquired several copy number gains on chromosome 7, which involved the ABC transporters genes ABCB1 and ABCB4. The ABCB1 gene, also known as MDR1, encodes Pgp, and is to date the best characterized and understood chemotherapy efflux transporter [SUB-1.2nM cell line worth mentioning included a copy number gain in IRF9 as well as a copy number loss in CFL1. Overexpression of this IRF9 has been associated with resistance to paclitaxel [Midway through resistance development , but the fact that the genes were also observed as upregulated in MDARES-150nM supports the fact that the copy numbers are still present at this stage. In addition, a copy number gain was observed in the JAK2 gene that encodes a tyrosine-protein kinase. Previously, increased expression of JAK2 has been associated with drug resistance in ovarian cancer [In sub-lines exposed to higher concentrations of docetaxel (7.5nM–15nM) a small number of single point mutations of poptosis . We founn cancer , and difn cancer .SUB-45nM and MDASUB-100nM, a few genomic changes were of special interest. In the MCF-7SUB-45nM, these included copy number losses in HTATIP2, involved in induction of apoptosis, and PDCD2L, involved in the cell cycle. In MDASUB-100nM, a missense mutation (chr7:128587374_G/A) was discovered in IRF5 that encodes the interferon regulatory factor 5, a transcription factor involved in the Toll-like receptor signaling pathway. Another member of the interferon regulatory factor family (IRF9), in which we found a copy number gain in MCF-7SUB-1.2nM, has been associated with paclitaxel response in that upregulation of IRF9 may be associated with resistance to paclitaxel, and suggested as a potential surrogate marker of response [For the cell line stages MCF-7response .In this study, the use of in vitro grown human breast cancer cell lines allowed investigation of genomic alterations during multiple steps in the process towards a docetaxel-resistant phenotype. Variations were prioritized for functional changes as well as those that accumulate over stages of acquiring resistance. A useful extension of this paradigm would be to conduct deeper sequencing in order to examine increasing proportions of resistant sub-clones. In our study, the model system allowed us to identify a number of genomic variations that are consistent with prior knowledge in taxane resistance, and additionally numerous novel genomic variations that were associated with the development of docetaxel resistance. The main conclusion arising from these data is that no single genomic biomarker adequately predicts resistance to docetaxel, but rather requires a panel of markers covering several pathways involved in docetaxel resistance. Specifically, we have identified a number of genetic variations in genes involved in drug transport, microtubule dynamics and cell cycle regulation. A corollary of our observations is that exposure of cells to toxic agents can induce acquisition of stable genomic traits that reduce the effects of these agents, effectively providing a resistance-associated gene pool to tumors. The importance of the disclosed genomic variations - and the fact that docetaxel-resistant cells demonstrated distinct and biologically-relevant DNA aberrations - is that acquisition of docetaxel resistance in clinical breast cancer also is a stable condition that will prevent beneficial effects upon subsequent exposure to the same drug, i.e. repeated docetaxel exposure to patients who relapsed on adjuvant docetaxel treatment. We therefore propose that a set of prioritized findings from our study should be examined at the genomic level in resistant tumors.


The discovery and isolation of different resident cardiac progenitor cells (CPCs) a decade ago, as described by several research groups, stimulated the use of these cells for cardiac regeneration. Human CPCs are moving towards the clinic as one of the most promising cell types for cardiac repair, but the extent to which their molecular profiles vary as a result of donor heterogeneity or different isolation methods remain unclear. Defining a common molecular profile that defines CPC’s is therefore an important goal. Similarly, identifying robust and multilaboratory isolation and culture protocols that generate reproducible cell populations from genetically diverse donors is critical for their translational success.3 parts and cultured as explants to obtain Cardiospheres (CSps) t-test to evaluate significant differentially expressed genes between the individual samples  lines Table . Human Cs Tables  and S4. s Tables , cell cys Tables , stem ces Tables , and gros Tables . Detailes Tables . Since Cs Tables . Ingenuivia direct cell isolation or via explant culture, such as CSps and CDCs. For the first time, however, we showed that upon culture expansion, these cell populations have a very similar gene expression profile, even more pronounced when cultured in comparable culture conditions and even transcended by donor differences. Among the different CPCs analysed, CSps are the most different, probably because of the unselected cell populations and containing more supporting cell population that form CSps and their particular 3D culture structure and thereby different interactions and growing conditions. Surprisingly CDCs, which is a cell population derived from CSps, are more similar with other antigen selected CPCs rather than with CSps, confirming the idea that monolayer and high proliferative culture condition might play an important role in minimizing the differences among the different CPCs analysed. Recently, Dey et al. isolated murine CPCs, based on different surface markers in situ in humans as well but lost upon culture expansion. The expression of these different stem cell markers and their co-expression probably represent different developmental and/or physiological stages of CPCs, rather than intrinsic different CPC populations. For future translation for cardiac cell therapy, our results suggest that we need to take into account the cell donor variability between patients more than the isolation methodology, and further study the correlation between CPC characteristics and e.g. the diseased status of a patient. Our findings are of fundamental importance to create a consensus among different scientists in the field of myocardial regeneration, which should help align future clinical approaches to improve the reported beneficial effects of cell therapy for heart disease by using cardiac derived progenitor cell populations.Taken together, our data suggest that human CPCs can be isolated from patient heart biopsies using different markers, such as c-kit or Sca-1- like, and alternative methodologies,


Accumulating evidence suggests that hIAPP amyloid deposits that accompany T2DM are not just an insignificant phenomenon derived from the disease progression but that hIAPP aggregation induces processes that impair the functionality and the viability of β-cells. In this review, we particularly focus on hIAPP structure, hIAPP aggregation, and hIAPP-membrane interactions. We will also discuss recent findings on the mechanism of hIAPP-membrane damage and on hIAPP-induced cell death. Finally, the development of successful antiamyloidogenic agents that prevent hIAPP fibril formation will be examined.Human islet amyloid polypeptide (hIAPP) is the major component of the amyloid deposits found in the pancreatic islets of patients with type 2 diabetes mellitus (T2DM). Mature hIAPP, a 37-aa peptide, is natively unfolded in its monomeric state but forms islet amyloid in T2DM. In common with other misfolded and aggregated proteins, amyloid formation involves aggregation of monomers of hIAPP into oligomers, fibrils, and ultimately mature amyloid deposits. hIAPP is coproduced and stored with insulin by the pancreatic islet  In. Inα-helormation , 43. Botchniques . This stAs for all amyloid peptides, hIAPP is produced as a soluble monomer and undergoes oligomerization and amyloid fibril formation via a nucleation-dependent polymerization process . This prβ-sheet breaker. Unlike rodents, dogs, and cow that do not form fibrils, primates, cats, porcine, ferret, and guinea pigs can form amyloid fibrils and are prone to T2DM [In most species, IAPP is expressed as an immature 89-membered amino acid peptide which is ultimately processed into a mature peptide of 37 amino acid residues . Most of to T2DM .β-cell granules of the pancreas where hIAPP is stored at a pH of approximately 5.5 and released into the extracellular compartment, which has a pH of 7.4. Studies in solution have shown that hIAPP aggregation is faster at a pH of 8.8 than at 4.0 and that the fibril morphology is affected by a pH of 2.4 [vide supra). Many substitutions that impact amyloid formation fall within the 20–29 domain confirming the importance of this region. A mutation (Ser → Gly) at position 20, which is found at low levels in certain Asian populations, was found to affect amyloid fibril formation and the development of T2DM in vivo. Indeed, this mutation seems to constitute a risk factor for diabetes, and it has been shown to increase the fibril formation rate in vitro [β-sheet structure. It is therefore suggested that proline substitution has a dominant negative role in fibril formation by either disruption of the nucleation process of hIAPP or by favouring the nonstructured state of the peptide [Several studies have shown that hIAPP sequence can be divided into three parts, (i) the 1–19 region which is responsible for hIAPP/membrane interaction and insertion , 53, , Golgi, or secretory granules of the β-cells . Localisβ-cells then results in ER stress and triggers some malfunction in the folding process of the molecules, as it reaches overcapacity. The accumulation of misfolded protein in the cells along with ER stress cascades into the unfolded protein response (UPR). This regulation process involves simultaneously the production of chaperones to both assist the folding of proteins and limit their aggregation; reducing ER workload by inhibiting the protein synthesis triggering the UPR; enhancing the transportation of misfolded protein to the ubiquitin-proteasome system for degradation; and, as a last resort, triggering of the apoptosis process.The ER serves many different functions in the cell, including assuring the correct native folding and posttranslational modification of peptides and proteins synthesized within the cell but also transportation of those molecules to the Golgi and secretory granules and release into the extracellular matrix. Those properties of the ER are well-balanced and regulated to avoid any misfolding and aggregation of proteins or peptides. However, this equilibrium can be disrupted by any ill-factors such as disturbances in redox regulation or calcium regulation and viral infection, applied on the ER. In particular, and as previously stated, insulin resistance results in a higher biosynthesis of insulin and thus of hIAPP. The consequent overproduction of protein and peptide in the β-cell mass, also linked to apoptosis, enhancing once more the joint synthesis of insulin and hIAPP or the inability of the cell to eliminate cytotoxic oligomers once they are formed in the system.In spite of the various regulation responses to counteract the misfolding of proteins or peptides following ER stress, it is observed that hIAPP still autoassociates and forms toxic oligomers. This behaviour suggests that the prevention mechanism against hIAPP misfolding and therefore aggregation can be saturated and rendered noneffective. Different hypotheses regarding this fact can be evoked among which is the decrease in β-cell apoptosis is observed. Although mechanisms of the apoptotic behaviour of β-cells have yet to be fully elucidated, there have been some hypotheses that have been proposed concerning the different pathways and triggers that induce cell death. The first pathway, called the extrinsic pathway, involves extracellular factors such as membrane disruption, as described previously, or the binding to cell receptors. In particular, it has been described that exogenous or endogenous hIAPP could interact and thus activate the FAS receptor, present on the surface of cells. The activation of this “death receptor” results in apoptosis by in turn activating specific proteins such as caspase-3 [β-cells death, other factors disturbing the main function of ER are likely to enhance hIAPP oligomerization and cell death. Among those, mitochondrial dysfunction, generation of oxygen free radical, defects in autophagy can also be mentioned [To a larger extent, whether the cells are exposed to high concentration of hIAPP and/or if the responses to the peptide's aggregation are revealed to be inefficient, aspase-3 . The secentioned .β-cell toxicity can be induced by an inflammatory response linked to hIAPP. Indeed, it has been found that the insulin resistance and production of hIAPP initiate an increase in the concentration of proinflammatory cytokines such as interleukin 1β (IL-1β), which has been previously described to be cytotoxic to pancreatic islets of Langerhans [Lastly, it has also been suggested that ngerhans , 90–97.β-cell death and T2DM. The development of inhibitors of amyloid formation is therefore of considerable interest in treating patients suffering from T2DM. However, although hIAPP is extremely amyloidogenic, most research has focused on other amyloidogenic proteins like Aβ peptide or α-synuclein, involved in Alzheimer's and Parkinson's disease, respectively. Different classes of inhibitors of hIAPP amyloid formation have been identified and have been tested for their ability to reduce amyloid cytotoxicity, using either cells or in vitro model systems [The amyloid pathway leading to fibrils is supposed to be responsible for  systems –104.β-chain to hIAPP [First of all, insulin is one of the most effective inhibitors of hIAPP amyloid formation , 105–111to hIAPP . A recenπ-π interactions, although the precise mechanism is an issue still under debate [β amyloid formation but is totally ineffective against hIAPP at a one to one ratio. The number and position of hydroxyl groups may also play a role in the mechanism of inhibition. However, it has been demonstrated that Myricetin slows down hIAPP amyloid formation in a 10-fold excess, that is, at very high concentrations. Nevertheless, this molecule is effective in vivo and merits further consideration. Equally of interest is phenol red, a small aromatic polyphenol molecule, which elicits an effect on hIAPP fibril formation at a 4-fold excess of molecule in vitro. Its high efficiency in protecting pancreatic β-cells from the cytotoxic effect of hIAPP makes it a particularly attractive target molecule. In addition, phenol red is a nontoxic and noncarcinogenic compound, in contrast to many polycyclic aromatics [Another valuable class of inhibitors are the polyphenols, which are thought to interact with amyloidogenic proteins via aromatic r debate , 113–116r debate , 118. Inr debate , 115. Inr debate , 118. Efr debate . As withromatics . Unfortu in vitro or in vivo. rIAPP inhibits hIAPP amyloid formation in a dose-dependent manner. Even if the slowdown of the aggregation kinetics exists at 1 : 1 or 1 : 2 hIAPP to rIAPP ratios, the effect on fibril morphology and final quantity is only observed in a 5-fold or 10-fold excess of rIAPP [β-cells and thus cannot be used as an inhibitor [A third class of molecules which are active against hIAPP fibrillation are molecular mimics. This particular strategy is based on molecular recognition thanks to similar molecular structure. For example, rat IAPP (rIAPP) whose sequence differs from hIAPP at only six positions is nonamyloidogenicof rIAPP . As in tof rIAPP , 120. Renhibitor . More renhibitor . This monhibitor .β-cell death induced by hIAPP. Se-PC is thus a promising candidate for antidiabetes drug development due to its activity on the cell media.Finally, an original compound, selenium phycocyanin (Se-PC), has been discovered as an inhibitor, acting in a dose-dependent manner . The com In vitro studies in diluted bulk solution do not adequately reflect the complexity of the cellular surrounding. Thus, the effect of inhibitors can be widely changed according to the medium. This is especially the case for the inhibitor EGCG whose inhibitory activity is lower than that in bulk solution [Unfortunately and despite considerable effort, the mechanism of hIAPP amyloid formation is not understood nor the mode of action of most of the hIAPP amyloid inhibitors. It is even more difficult to understand these mechanisms as most of the experiments described refer to studies in bulk solution . In vitrsolution . AFM imaIt is important to note that some publications referencing hIAPP inhibitors only draw conclusions from ThT assays. The monitoring of amyloid fibril formation via ThT experiments is a very convenient and common technique, but with a large disadvantage concerning the study of inhibitors. Indeed, many potential inhibitors can interfere with the ThT dye, thereby inhibiting the fluorescence of the probe and leading to false positive inhibitors. This is particularly the case for rifampicin or hydroxyflavones that interfere with ThT and might suggest that they inhibit hIAPP amyloid formation, which is contradicted by TEM images , 126. It in vivo? What are the morphology and structure of hIAPP oligomers and hIAPP fibrils in vivo? Why do oligomers and amyloidogenic protein form? What is the exact nature of the toxic species? Much of the research work on hIAPP-membrane structure and hIAPP-membrane interactions is performed on membrane models. Progresses have been made and the results from the biophysical studies have generated some hypotheses. However, an important challenge will be now to connect these biophysical results with the in vivo experiments.Today, there are 382 million people living with diabetes. Diabetes is on the rise all over the world and medical practitioners are struggling to keep pace. Worldwide, one person dies as a consequence of diabetes  every 6 seconds. In this regard, there is currently great interest in the field of islet amyloid. However there are important outstanding issues. Important questions that remain to be answered include the following. What is the mechanism of hIAPP fibril formation


Two-dimensional images of four pairs of lines were processed. Statistical analysis of morphometric characteristics shows that shape characteristics of the metastatic cell line are partly overlapping and partly diverged from the parental line. Significantly, the shape changes fall into two categories, with three paired cell lines displaying a more mesenchymal-like morphology, while the fourth displaying a change towards a more rounded morphology. A neural network algorithm could distinguish between samples of the less metastatic cells from the more metastatic cells with near perfect accuracy. Thus, subtle changes in shape carry information about the genetic changes that lead to invasiveness and metastasis of osteosarcoma cancer cells.Metastatic cancer cells for many cancers are known to have altered cytoskeletal properties, in particular to be more deformable and contractile. Consequently, shape characteristics of more metastatic cancer cells may be expected to have diverged from those of their parental cells. To examine this hypothesis we study shape characteristics of paired osteosarcoma cell lines, each consisting of a less metastatic parental line and a more metastatic line, derived from the former by  Summary: Human and murine invasive osteosarcoma cancer cell lines, developed by selection in vivo from a less invasive parental line, show distinguishable differences in shape from the parental line that fall into two categories: more mesenchymal or more amoeboid. Despite significant advances in treatment of cancer, it remains the leading cause of death in both men and women under 80 years of age in the US , with meCross et al., 2007Guck et al., 2001Direct assays of mechanical changes require fairly complex and expensive instrumentation. However, one can hypothesize that changes in biomechanical properties, including changes in cytoskeletal properties and expression of adhesion proteins, would translate into changes in cell shape. It has been shown previously that changes in gene expression of genes with cytoskeletal function leads to shape changes that can be detected using morphometric characteristics . CytoskeOne of the gold standards of predicting clinical outcome of cancer is tumor grading which includes assessment of cellular morphology from tumor tissue samples. Tumor grading schemes focus on overt changes in cellular morphology such as mitotic index, degree of nuclear pleomorphism and degree of tumor necrosis . What isOur paper is based on the hypothesis that subtle changes in cellular properties should manifest themselves in small but detectable morphological changes because of the importance of cytoskeletal changes for acquisition of invasive capacity. The biomechanical changes that accompany the emergence of aggressive tumor cells should be detectable by assaying the changes in shape of these tumor cells. Moreover, the observation of specific changes in shape may be linked with specific genetic changes in cancers.Anecdotal evidence for the change in cell shape has been well documented. For example, the epithelial to mesenchymal transition (EMT), associated with development of the invasive phenotype in carcinomas , is ofteWe utilized osteosarcoma cell lines due to the high metastatic rate of the cancer. Osteosarcoma (OSA) is the most common primary bone tumor of dogs  and humain vivo development of the highly metastatic line more accurately represents the natural process of formation of metastases.Comparing the morphology of cells that were closely related (except for their degree of invasiveness) was important to minimize variables that would make the sample less homologous. We therefore sought paired osteosarcoma lines, where a more aggressive cell line was developed from a less aggressive ancestor without the use of exogenous transforming agents, as these agents may alter naturally occurring genetic changes leading to metastatic properties of osteosarcoma . WithoutFig. S1). These are glass detergent washed and air dried , glass acid etched and air dried , and siliconized ethanol treated .The morphology-related genetic changes that accompany transformation include both changes in cytoskeletal properties as well as changes in adhesive properties . We deciTable S1). Representative images of the eight different cell lines are shown in We cultured four paired osteosarcoma cell lines with low and high metastatic potential: DUNN and DLM8; K12 and K7M2; MG63 and MG63.2; and Saos2 and SAOS-LM7 on these three surfaces for 48 h, and then fixed, stained and imaged the cells. For simplicity we refer to each pair by the first letter of the parental line, i.e. we refer to the pairs as the D, K, M and S pairs of lines. We stained the cells for actin, the plasma membrane and nucleus. We developed a high-throughput, quantitative image analysis algorithm that chose individual cells not in contact with others, segmented, optimized and thresholded the images to obtain accurate representations of two-dimensional shape and then processed the images to extract 29 morphometric measurements: 21 cellular and 8 nuclear (t-tests between different morphometric measurements of the low metastatic line (low-met) and high metastatic line (high-met) within a paired cell line. In order to adjust for multiple testing, we performed t-tests on all 29 parameters using the Holm–Bonferroni correction . The data showed that metastatic cell lines show distinct differences in shape compared to their non-metastatic counterparts. Significantly, we discovered that three of the four paired cell lines, i.e. the D, K, and S lines displayed a similar pattern of shape changes, while the fourth line, i.e. the M line, displayed a different pattern (Table S2). This suggests that morphological changes due to acquisition of metastatic potential fall into two distinct classes. For simplicity we denote these two patterns as type-1 and type-2. When pooled into two classes, the type-1 cells showed significant differences in 28 out of the 29 parameters we tested, while the type-2 cells showed significant differences in 26 parameters (Table S3).The 29 morphometric parameters were classified into five categories of cell shape: (i) projected cell size, (ii) cell roundness versus elongation, (iii) shape variability, (iv) nuclear size, and (v) nuclear shape. We identified a subset of the 29 parameters that were most often statistically significant across the various cell lines by performing pairwise rrection , and ideTable S2). To determine whether cell volume corresponded with cell area, we measured cell volume using a handheld Scepter® counter . We found that for most pairs the cell volume and the cell area followed the same trend, i.e. the high-met line was smaller in both area and volume for two lines of type I (K and S), while the high-met M line was larger in volume and area than its less metastatic pair. The D line showed an opposite trend with a large volume but smaller area for the high-met line. However the percentage difference in mean volume is much smaller than the percentage change in mean area, suggesting a difference in the spreading behavior of the cells for both type-1 and type-2 cells. This trend of a smaller projected high-met line for type-1 cells and a larger projected high-met line for type-2 cells is consistent across all 12 measures of two-dimensional cell size (Table S2). Within the type 1 cells, the largest difference was shown by the metastatic K12 cells that were over 50% smaller on GDA while the smallest difference was shown by the LM7 cells which were about 23% smaller (Table S2).A striking gross morphological difference between the high-met and low-met line of each pair is a systematic difference in two-dimensional projected area. Less metastatic type-1 cell lines have a significantly larger projected cell area A, and onThe change in size is also anisotropic, as the type-1 high-met lines have a minor axis that is 22% smaller, while the major axis is only 10.5% smaller, thus the minor axis percent difference is about twice that of the major axis B,C, indiThe second category of cell shape assesses how round versus elongated the cell is, and is best represented by the aspect ratio. As suggested by the major and minor axis differences discussed above, the type-1 highly metastatic cell lines had a significantly larger aspect ratio than the low-met lines, indicative of cell elongation D. On aveTable S2; Table S2).Variability of cell shape was characterized by the cell shape parameters of solidity and the coefficient of variation of radii drawn either to the cell perimeter from the center of mass of the convex hull (CV Rad Hull) or from the center of mass of the bounding circle to points on the convex hull (CV Rad Circle). The highly metastatic type-1 cell lines had more variability in radii drawn to both the perimeter . The PCA showed that the geometric characteristics of each cell type were overlapping but clustered distinctly within the space formed by the first three principal components. The overlap between the characteristics of the paired cell lines indicates that the high-met line is still not too dissimilar from the low-met line. However the genetic changes that accompany the acquisition of invasive characteristics have also resulted in the cell shape parameters drifting away from that of the original cell. The maximum overlap of the first three principal components can be seen in the SAOS-LM7 and Saos2 pair  of the multivariate data, comparing each paired line separately ; Fig. S3os2 pair D. The tyos2 pair E, which os2 pair F.Fig. 42 values, where in the NMDS context R2 is a measure of the proportion of the distance variation of the data that is explained by cell line, i.e. from the high-met or low-met comparison within each paired line. 2 values are tabulated in P-values. The maximum proportion of the distance variation that can be attributed to cell line is 0.16 for the D lines on the GAA surface, 0.2 for the K lines and 0.06 for the S lines (both on the GDA surface), and 0.24 for the M lines on the SET surface (0.22 on GDA). All the R2 values are statistically significant and indicate that cell shape parameters of the high-met line, despite significant overlap, have diverged from those of the low-met line. Other surfaces show varying levels of overlap but in general support this conclusion . Interestingly data points corresponding to the high-met line for both type-1 and type-2 cells occupy a greater area in 2-space, suggesting that the high-met lines are characterized by greater heterogeneity of the shape parameters.To test whether we could obtain a better separation using a nonlinear technique, we supplemented PCA by nonmetric multidimensional scaling (NMDS) . NMDS isWe asked whether these subtle but significant differences in cell shape are sufficient to construct a classification algorithm that could correctly classify the low-met and high-met cells. We wrote a neural network machine-learning algorithm to classify a cell into either the low-met or the high-met class, based on its geometric parameters alone, as described in the Materials and Methods section. Following standard practice we divided our data into three mutually exclusive subsets for training, optimizing and validating the neural network respectively. The trained algorithm was then tested blind on the third subset, the validation set, which was not used for any parameter adjustment.The accuracy of classification of the algorithm was found to lie between 60% and 92%,  suggestiNext we asked whether the classification algorithm is capable of accurately classifying random samples of cells from the high-met and the low-met lines. This process can be construed as a simulation of what would happen in a clinical setting: the heterogeneous cancer cell population taken from a tumor biopsy or aspirate would be assayed using morphometric characteristics. The decision algorithm used was that if the majority of cells in the sample are of type A, the sample is of type A, and with this simple rule the algorithm achieves near perfect classification of samples into the correct cell type . For eveNote that the algorithm performs relatively poorly when used to classify samples from all low-met lines against all high-met lines as compared with when it is used on type-1 cells and type-2 cells separately. Thus, shape changes in the three paired lines in type-1 appear similar enough that despite originating from different species and different cell lines, they can be accurately classified into high-met and low-met cells with reasonable accuracy.We have shown that highly metastatic osteosarcoma cell lines derived from less metastatic parental cells show differences in shape that can be broadly classified into two types. In type-1, displayed by 3 out of 4 paired cell lines, the highly metastatic cells are smaller in two-dimensional area, more elongated, and the radius of the cell perimeter from the center of mass is more variable. In type-2 cells, displayed by one cell line pair, the cells become larger, more rounded, with a less variable perimeter. In both cases, the distribution of the geometric characteristics that we measured was more diverse for the high-met cell line. There was a significant overlap between the parameters of the low-met and the high-met line. However use of multivariate data analysis techniques such as PCA and NMDS indicated that despite the overlap, the data points of the two cell lines clustered slightly differently from each other. The differences were sufficient to enable a trained neural network to correctly classify an individual cell as belonging to the high-met or low-met line with over 80% accuracy on at least one surface, and to almost perfectly classify samples of cells from either line.Our data suggest that genomic changes leading to acquisition of invasive properties also give rise to detectable shape changes, and hence shape changes carry information about the state of the cell. While this study was restricted to these four pairs of osteosarcoma cell lines, we suspect that the broad conclusions are more general. Genetic changes that drive the acquisition of invasive properties may affect cell shape in various ways. For example, there could be down-regulation of adhesive proteins, a softening of the cell due to down-regulation of keratin and up-regulation of vimentin and changes in cellular contractility due to Rho-ROCK signaling. Each of these is likely to have a different set of effects on cell shape, and requires further investigation. Identifying and understanding the full typology of shape changes could have a major impact on our knowledge of metastasis and its relation with the cellular cytoskeleton. It may be eventually possible to read out genetic changes corresponding to specific changes in cell shape . DetermiOur discovery that shape changes fall into two types or classes is also potentially significant. Our hypothesis arising from this work is that these two classes correspond to the two modes of cell migration, i.e. mesenchymal and amoeboid . MesenchOur work suggests that it is possible to develop a consistent reproducible framework for computational morphometrics of cell shape. Further work is required to validate and refine the framework through use of other cell lines, including primary tumor lines, other cancer types and species. A reproducible quantitative framework is important for improving the subjectivity of traditional morphological analysis performed by trained histopathologists. While there is a strong correlation between tumor grade and metastatic outcome, there is not yet an ability to predict metastatic potential, based on tumor grade, in individual cases . One stuDrosophila haemocyte cells into five discrete shapes based upon quantitative shape and morphology metrics, and argued that transitions between these shapes are switch-like. They utilized RNAi to identify genes which play a large role in regulating cell shape, including demonstrating that the loss of PTEN induces elongation of cells. They did not however look for systematic differences between closely related cancer cell lines. While we have not tried to ascertain whether specific types of shapes are present in our data, the message of this paper is that differences in quantitative shape parameters, even within the same type, should carry useful information about the internal state of the cell. The overlap between the multidimensional shape parameters in principal component space or in NMDS space indicates that each pair of the cell lines we study has not diverged significantly in shape characteristics. However both these studies support our contention in this paper that the understanding of cell shape can give significant insight into cell properties and function.A small number of recent publications have highlighted the functional importance of cell shape by using high throughput image analysis to characterize the relation between cellular morphology and cellular properties. Studies of metastasis in cancer cells have focused mainly on changes at the level of gene, protein and microRNA expression, and to a smaller extent, at the level of cellular mechanics. In contrast our work demonstrates that these changes do lead to reproducible changes in shape. More work needs to be done to construct a more comprehensive typology of shape changes in cancer, especially in other cancer types, and to achieve a mechanistic understanding of how changes in gene and protein expression result in changes in cell shape.We utilized four paired cell lines; two of murine origin: DUNN and DLM8, K12 and K7M2, and two of human origin: MG63 and MG63.2, Saos2 and SAOS-LM7. All metastatic lines  have significantly higher rates of pulmonary metastasis reported in the literature with a 200-fold increase in MG63.2, 100% efficacy of DLM8 relative to no pulmonary metastases in DUNN, 100% efficacy of SAOS-LM7, and a 90% efficacy of K7M2 relative to 33% of K12. Additionally, MG63.2, DLM8, SAOS-LM7 and K7M32 cells were reported to show greater migration and invasion than their low-metastatic counterparts: MG63, DUNN, Saos2 and K12 . MG63.2 DUNN, DLM8, K12, and K7M2 cell lines were a gift from Dr D. Thamm , MG63 and MG63.2 cell lines were a gift from Dr D. Duval , and Saos2 and SAOS-LM7 a gift from Dr E. S. Kleinerman . All cell lines were maintained under typical culture conditions at 37°C and 5% carbon dioxide concentration in Dulbecco's Modified Eagle Medium (DMEM) (Sigma). DMEM was supplemented with 10% fetal bovine serum , 20 mM Hepes (Sigma), and 100 Units/ml penicillin with 100 µg/ml streptomycin (Fisher Scientific-Hyclone). Cell lines were not independently authenticated or tested for contamination by us.Cells were cultured on indicated substrate for 48 h. Cells were stained with Wheat Germ Agglutinin, Alexa Fluor 594 Conjugate (Molecular Probes). Cells were fixed in 4% paraformaldehyde then stained with Alexa Fluor 488 Phalloidin and DAPI (Molecular Probes). Cells were imaged under a 20× objective on a Zeiss Axioplan 2 fluorescence microscope  using filter sets: DAPI BP 445/50 blue filter, HQ Texas Red BP 560/40, and Green BP 474/28.Three different surfaces were prepared for this work from either a #1.5 22 mm×22 mm glass coverslip (Richard Allen Scientific) or a #2 22 mm×22 mm siliconized glass coverslip (Hampton Research). The formulated surfaces follow: glass detergent washed and air dried (GDA), glass acid etched and air dried (GAA), and siliconized ethanol treated (SET). GAA and GDA surfaces were initially prepared by sonication for 30 min in a mild detergent solution. Following sonication, the coverslips were sequentially rinsed with Milli-Q (MQ) water, isopropyl alcohol (IPA), and a second rinse with MQ water prior to any further downstream processing. In the case of the GDA surface, no further downstream processing was required and the surfaces were blown dry with sterile 0.2 µm filtered nitrogen with an air gun from an in house boil off nitrogen source. GAA surface was subjected to a downstream 1 M hydrochloric acid etching at 60°C for 12-16 h. After the etching period, the coverslips went through the same rinse process described above (MQ to IPA back to MQ) before being blown dry in the same manner as the GDA surface. The SET surface was subjected to a rinse in 100% ethyl alcohol and then they were blown dry with the nitrogen gun to ensure removal of any residual liquid and debris. Prior to use in cell culture, all surfaces were exposed to UV sterilization to minimize potential contamination risks.XYZ plane using needle. Images were captured and analyzed using Rame Hart DROP Image Advanced software. Contact angle were measured for three different spot on one slide and this was repeated three times on different slides to see variability of slide's contact angle. Representative images are shown in Fig. S1.Contact angles for different substrates were measured using sessile drop method by Rame Hart Goniometer (Model # 100_25_M). 3 µl of Milli-Q water were placed on Volume measurements were made by the Scepter™ Handheld Automated Cell Counter, Millipore, with a 60 µm sensor. First, cells were plated in a culture dish. Once they were ready to be split, they were trypsinized and re-suspended in 1× PBS. After checking that cell density was in the operating range  the Scepter sensor was submerged in the cell suspension. The upper and lower gates were adjusted to remove debris information, and cell volume information recorded.t-tests against the null hypothesis that the volume data for both pairs of each paired line came from a distribution with the same mean. The mean volumes for the two partners in a paired line were statistically different from each other, with P-values much smaller than 0.05 in each case.The distributions of cell volumes were well approximated by a log-normal distribution. Thus we log-transformed and calculated the mean and the standard error of the mean of the resulting normal distribution. The mean cell volume for the cell lines were 1.01, 1.12, 1.60, 1.41, 1.49, 1.43, 1.66, 1.80 pl for DUNN, DLM8, K12, K7M2, Saos2, SAOS-LM7, MG63 and MG63.2, respectively. Thus the percentage differences between the volume of the high-met line from the low-met line are −10.9% (D lines), +11.9% (K lines), +4% (S lines) and −8.4% (M lines). We performed Fig. S2). Once the images had been converted into binary format, they were again loaded into MATLAB for shape analysis. Segmentation was achieved through use of the built-in MATLAB function toolbox so each cell could be individually cropped and reconstructed in a new image in which shape measurements (listed in Table S1) could be made on both the cell and corresponding nuclei and stored for statistical analysis. Minimum Bounding Circle was found using MATLAB function minboundcircle, open source code developed by John D'Errico . Scripts for image processing will be made available upon request.Images of isolated cells that were not in contact with other cells were chosen. To ensure adequate statistical power we picked a sample of 100 such cell images . Images were collected blind in the sense that the students doing the imaging were not previously aware of any differentiating characteristics discovered. The images were collected at one time for each cell line on each surface. The image processing involved three distinct steps; enhancement, conversion into binary format, and automated cropping of each cell for measurement of shape metrics. Three channels were captured as described above. Prior to processing the images were converted into 16 bit TIFF images. The exported TIFF images were loaded into MATLAB where the actin, membrane, and nuclei channels were enhanced separately by contrast stretching. The actin and membrane images were combined into a single TIFF images to get full characterization of the shape. Finally, erosion with a three pixel mask was applied to sharpen edge boundaries. The enhanced TIFF images of the combined membrane-actin image and separate nuclei image were exported into ImageJ analysis for manual conversion into binary formatted images by thresholding  to perform PCA on the data. First, data was standardized so that mean of new data is zero and standard deviation is 1. Then, SVD of the data was computed and the principal components extracted from the right singular vectors of the data. Each data point was then projected into the space formed by the first three principal components, and was plotted for visualization. The variance captured by the first three principal components lie in the range 44%-47% of the total variance for every comparison made.We performed separate analyses for each of the three surfaces  and each paired cell line . First, each of the 29 cell morphology variables was relativized by dividing each value by the maximum value. Statistical software R (version 3.1.2) and package vegan were used to perform all statistical analyses. The Bray–Curtis dissimilarity index was used to perform NMDS. Based on observed stress, convergence behavior, Shepard plots, and parsimony, k was chosen to be 3.The ordination pattern was scaled as follows before plotting. First, centering was done to move the origin to the average of the axes. Second, principal components were used to rotate the configuration so that the variance of points was maximized for the first dimension, with the second dimension explaining the maximum variance of points unexplained by the first. We then displayed the ordination pattern in 2-space. For the factor ‘Metastatic capacity’ (with levels Low and High) and the factor ‘cell line pair’ , we generated two separate color-coded plots with 95% confidence ellipses and labeled locations of the level centroids. For GAA, GDA, and SET, observed stresses were approximately 0.07, 0.07, and 0.08.2 values for ‘metastatic capacity’. Specifically, R2 is a measure of the proportion of the data (distance) variation explained by ‘metastatic capacity’.Permutational multivariate analysis of variance using Bray–Curtis distance between cells (PERMANOVA) was used to obtain Rhttp://www.cs.colostate.edu/∼anderson/cs545/index.html/doku.php), was used to classify data, and is available from the corresponding author upon request. A back-propagation learning algorithm, which uses a scaled conjugate gradient (SCG), was used to design the MLP and tanh(x) was used as the activation function. The SCG was adapted from Nabney's netlab library (A multilayer perceptron (MLP) neural network with one hidden layer, adapted with permission from a version used by Dr Charles Anderson for teaching ( library . Each daP). Thus the percentage of cells predicted to be class 2=1−P. A decision threshold was determined utilizing the false negative rate (FNR) and true positive rate (TPR). When P was bigger than the decision threshold, the sample was classified as class 1, and when it was smaller than the decision threshold as class 2. As detailed in Table S4, the threshold was optimal at 0.6. From the total 100 pairs, the true positive (TP), true negative (TN), false positive (FP) and false negative (FN) were calculated. Using this information, accuracy, false negative rate (FNR) and true negative rate (TPR) were calculated as defined below:To test the accuracy on random samples of cells from each population, after identifying the function structure with the training and test data, we took 100 random paired samples of 10 cells each from the validation data set. The percentage of cells in each sample predicted to be class 1 are recorded (


In vitro expression studies carried out using human cell lines and primary mouse tumor cultures showed that expression levels of both MURC/cavin-4 and Cav-3, while being low or undetectable during cell proliferation, became robustly increased during myogenic differentiation, as detected via semi-quantitative RT-PCR and immunoblotting analysis. Furthermore, confocal microscopy analysis performed on human RD and RH30 cell lines confirmed that MURC/cavin-4 mostly marks differentiated cell elements, colocalizing at the cell surface with Cav-3 and labeling myosin heavy chain (MHC) expressing cells. Finally, MURC/cavin-4 silencing prevented the differentiation in the RD cell line, leading to morphological cell impairment characterized by depletion of myogenin, Cav-3 and MHC protein levels. Overall, our data suggest that MURC/cavin-4, especially in combination with Cav-3, may play a consistent role in the differentiation process of RMS.The purpose of this study was to investigate whether MURC/cavin-4, a plasma membrane and Z-line associated protein exhibiting an overlapping distribution with Caveolin-3 (Cav-3) in heart and muscle tissues, may be expressed and play a role in rhabdomyosarcoma (RMS), an aggressive myogenic tumor affecting childhood. We found MURC/cavin-4 to be expressed, often concurrently with Cav-3, in mouse and human RMS, as demonstrated through  CTNNB1, FBXW7 and BCOR) . In . In MURC


Tumor classification based on their predicted responses to kinase inhibitors is a major goal for advancing targeted personalized therapies. Here, we used a phosphoproteomic approach to investigate biological heterogeneity across hematological cancer cell lines including acute myeloid leukemia, lymphoma, and multiple myeloma.Mass spectrometry was used to quantify 2,000 phosphorylation sites across three acute myeloid leukemia, three lymphoma, and three multiple myeloma cell lines in six biological replicates. The intensities of the phosphorylation sites grouped these cancer cell lines according to their tumor type. In addition, a phosphoproteomic analysis of seven acute myeloid leukemia cell lines revealed a battery of phosphorylation sites whose combined intensities correlated with the growth-inhibitory responses to three kinase inhibitors with remarkable correlation coefficients and fold changes (> 100 between the most resistant and sensitive cells). Modeling based on regression analysis indicated that a subset of phosphorylation sites could be used to predict response to the tested drugs. Quantitative analysis of phosphorylation motifs indicated that resistant and sensitive cells differed in their patterns of kinase activities, but, interestingly, phosphorylations correlating with responses were not on members of the pathway being targeted; instead, these mainly were on parallel kinase pathways.This study reveals that the information on kinase activation encoded in phosphoproteomics data correlates remarkably well with the phenotypic responses of cancer cells to compounds that target kinase signaling and could be useful for the identification of novel markers of resistance or sensitivity to drugs that target the signaling network. Hematologic malignancies are a group of neoplastic diseases that originate from the transformation of bone marrow-derived cells. This group, which includes leukemias, lymphomas, and myelomas, is extraordinarily heterogeneous, which reflects the complexity of normal hematopoiesis and the immune system . AlthougEssentially all cancers are driven by deregulation of protein kinase cascades downstream of growth factor, antigen, and G protein-coupled receptors . ConsequPhosphorylation is a posttranslational modification regulated by the activity of kinases and phosphatases. By definition, each phosphorylation site is the result of a kinase/phosphatase reaction pair. Changes in phosphorylation status can alter many aspects of protein biology, including their localization, protein-protein interactions, stability, and enzymatic activity . AlthougThe application of new proteomic techniques for phosphopeptide quantification is contributing to an improved understanding of cancer cell biology -23. SeveIn the current study, label-free mass spectrometry (MS) was first used to analyze the phosphoproteomes of nine different hematological cancer cell lines. Unsupervised analysis of the data based on principal component analysis (PCA) and hierarchical clustering classified these cell lines according to their pathological origin, namely acute myeloid leukemia (AML), lymphoma, or multiple myeloma. Through a lasso linear regression analysis we assessed the potential of this data in predicting the level of sensitivity of seven AML to three kinase inhibitors: PI-103 (a PI3K/mTOR inhibitor), MEK-i, and JAK-i. Finally we identified phosphopeptides whose intensities across the cell lines correlated with the sensitivity to three inhibitors. Our results revealed a battery of phosphorylation sites whose intensities strongly correlated with responses of our AML panel to the compounds (with R > 0.9 and > 100-fold difference between most sensitive and most resistant cell line). These data therefore indicate that MS-based quantitative phosphoproteomics has the potential to classify cell lines into distinct subgroups according to their pathological origin and to their sensitivity to drugs that target kinase signaling. Phosphorylation sites that correlated with resistance were enriched in basic and proline-directed motifs, whereas those that correlated with sensitivity were mainly acidic or hydrophobic containing, thus suggesting that the relative activities of basophilic, proline directed, and acidophilic kinases may determine responses to the inhibitors. Therefore, the results of our study indicate that unbiased profiling of phosphorylation has the potential to stratify AML cells based on their responses to signaling inhibitors because these responses may be dependent on the combination of pathways  active in cells, rather than on the activity of the target kinase/pathway only.A group of nine different hematological cell lines, consisting of three acute myeloid leukemia (AML), three lymphoma, and three multiple myeloma cell lines  of the phosphoproteins with those proteins present in the whole human proteome  for each phosphopeptide identified and quantified in our study. These data were then subjected to principal component analysis (PCA). When analyzing the nine cell lines together, inspection of the PCA outputs Figure  revealedThe ability of phosphoproteomics to classify hematological cell lines was also assessed by unsupervised hierarchical clustering. This analysis also classified the samples into three main groups corresponding to AML, lymphoma, and multiple myeloma, and clustered replicates for each cell line within those groups. Interestingly lymphoma and multiple myeloma cells were clustered together, consistent with AML cells being the most differentiated group Figure . This isStringent statistical analysis  identified 609 phosphopeptides differentially regulated in the AML cell lines, of which 544 showed increased phosphorylation and 65 decreased phosphorylation, 46 were different in lymphoma cells (30 increased and 16 decreased) and 53 in multiple myeloma (27 increased and 26 decreased) Figure . RepreseIn order to assess whether phosphoproteomicscould also be used to classify cells according to their responses to kinase inhibitor treatment, we compared phosphorylation patterns with the sensitivity of a panel of seven AML cell lines  approach based on lasso regression , detaileEach training phase was composed by a three-fold cross validation estimation of the lasso shrinkage parameter (involving 18 of the corresponding training set only) followed by an optimization phase of the regressor coefficients (on the training set) and was repeated 20 times. Finally an average trained model was assembled across these 20 iterations and used to predict drug-responses for the corresponding test set.versus the observed ones is depicted in Figure P value = 1.15 × 10-10) and JAK-i . When considered together, the observed/predicted viability scores for these two drugs was still high and significant  while the overall correlation considering all the three drugs was weaker  and no statistically significant R was observed on the MEK-i scores alone. Scatter plots for individual drugs, with cell line identifiers and all the R scores and P values are provided in Additional file A scatter plot of the predicted viability scores for each of the test set Finally, for each of the three tested drugs, we determined a final descriptive model by pooling together (as detailed in the method section) all the models generated in the corresponding LOCLO iterations. These final models, provided in Additional file In Figure Taken together these results suggest that phosphoproteomics data have the potential to predict responses to the analyzed compounds in cell line models and that a relatively small core-set of phosphopeptides is strongly associated to viability scores.To complement the analysis based on LASSO, we also performed a correlation study to identify further phosphopeptides associated with responses to the three inhibitors. Figure We also investigated whether a combination of several phosphopeptides could be used to find associations between our panel of AML cells and their response to PI-103. As Figure Phosphopeptide intensities were also correlated with the responses to MEK-i and JAK-i. Phosphopeptides showing the stronger association with resistance or sensitivity to MEK-i are shown in Figure To investigate whether phosphopeptides associated with resistance/sensitivity in AML cells Figures  to 6 wouP < 0.001) in the dataset of phosphorylation sites that correlated with the resistance to PI-103  or with resistance  or Ribosomal S6 (gene name RPS6), which are known to be downstream of PI3K/mTOR(Ref ), did noWe also found that while the intensities of phosphorylated STAT5A, a marker of JAK activities, did not correlate with the responses of our AML cell panel to JAK-i, the intensities of phosphorylated Ribosomal S6 (a marker of PI3K/mTOR pathway activation ) did corPhosphoprotein concentrations in cells can be a consequence of the expression level of the phosphoprotein bearing the site of modification as well as of the activity of the kinases and phosphatases acting on these sites. To investigate whether the phosphorylation sites that correlated with resistance or sensitivity in this study were the result of differential kinase activities across the AML cell line panel or these were just a reflection of phosphoprotein gene expression, we performed a further bioinformatics analysis of the data aimed at assessing common phosphorylation motifs in phosphopeptides correlating with the responses to the inhibitors. In this, phosphorylation motifs were obtained from the data using motif-X  and fromFigure Tumor classification informs therapeutic strategies and provides information on prognosis . A classIn order to investigate whether phosphoproteomics may be used to classify hematological cancers based on their phenotype, and to provide proof-of-principle of the approach, we analyzed the most abundant phosphoproteomes of nine hematological cancer cell lines. Qualitative analysis of the data based on Gene Ontologies revealed that our analysis was broad and included phosphopeptides from all types of proteins, including those in cell membranes, cytosol, and subcellular compartments. While membrane proteins were slightly less represented than expected, proteins involved in cell cycle regulation, proliferation, and translation, and protein kinases were particularly well represented in our phosphoprotein dataset.As for the quantitative analyses, our data showed that cancer cells of different phenotypes and origin had markedly different patterns of phosphorylation, thus indicating that phosphorylation could be used to classify these cells according to phenotype. PCA and unsupervised hierarchical clustering analysis classified cell lines according to their pathology , thus indicating that the phosphoproteomes of cells of the same pathology are more similar than those derived from different diseases. Furthermore, PC1 in the PCA and unsupervised hierarchical clustering clearly separated AML cells that have a myeloid lineage from the other two diseases that have a lymphoid lineage.We evaluated the potential ability of phosphoproteomics data in predicting drug response through a robust cross-validation framework based on Lasso regression analysis, showing that the phosphorylation levels of a relatively small subset of phosphopeptides was informative with regard to treatment outcomes Figure . In addiet al. found a weak correlation between responses to PI3K inhibitors and phosphorylation of Akt , which  [et al.  and Alco [et al. , the levOur data therefore suggest that the activity of pathways parallel to those being targeted by the specific inhibitors might contribute to intrinsic resistance to kinase inhibitors, as observed in cells that acquired resistance as a result to chronic exposure to compounds that target signaling nodes ,28,51,52Our analysis identified phosphorylation motifs that on the whole correlated with resistance or sensitivity to kinase inhibitors Figure  indicati2. For experiments, 10 x106 cells at 0.5 × 106 cells/mL were seeded 24 h before harvesting for each replicate.Cell lines were obtained as indicated in Additional file ® AQueousOne Solution Cell Proliferation assay, Promega Corporation, Madison, WI, USA).Cell lines were seeded in 96-well plates and treated 24 h later with 1 μM PI-103 , 10 μM MEK inhibitor I , or 1 μM JAK inhibitor  for a further 72 h. Cell viability was assessed by MTS assay (CellTiter 963VO4 and 1 mMNaF. Cell pellets were lyzed with 1 mL denaturing buffer (8 M urea in 20 mM HEPES pH 8.0) supplemented with phosphatase inhibitors  and further homogenized by sonication (three pulses of 15 s). Cell debris was removed by centrifugation , protein levels in the supernatant were quantified by Bradford analysis and an aliquot containing 500 μg of protein was diluted to a final volume of 1 mL in denaturing buffer. Cysteines were reduced and alkylated by sequential incubation with 4.1 mMdithiothreitol and 8.3 mMiodoacetamide for 15 min at room temperature in the dark. Samples were diluted to a final concentration of 2 M urea using 20 mM HEPES (pH 8.0) and proteins were digested with TLCK-Trypsin (20 TAME units/mg) for 16 h at 37°C. Trypsin was removed by centrifugation and the resultant peptide solutions desalted by solid-phase extraction with Sep-Pak C18 columns  following the manufacturer's instructions.Cells were harvested by centrifugation at 300 × g for 5 min and washed twice with ice cold Dulbecco's Phosphate Buffered Saline (DPBS) supplemented with 1 mM Na3 (same volume as beads) and washed six times with 50% ACN/0.1% TFA. Unbound peptides were discarded and beads were sequentially washed with 300 μL of 50% acetonitrile/0.1% TFA and 300 μL of 50% acetonitrile/1% TFA. Phosphopeptides were recovered by incubating the beads twice with 300 μL50% acetonitrile/1.5% ammonia water pH 11.0 for 1 min, after which the recovered solutions were pooled, acidified (by addition of 10% formic acid), dried in a speedvac, and stored at -80°C until analysis.Phosphopeptides were enriched by IMAC using a modified protocol previously described . In briePhosphopeptides were resuspended in 0.1% TFA and analyzed in a LC-MS/MS system in random order to remove potential batch effects. Phosphoprotein separation was performed in a nanoflow ultra-high pressure liquid chromatography system  using a BEH 100 μm × 100 mm column (Waters) and a binary mobile phase gradient with 0.1% formic acid in LC-MS grade water (A) 0.1% formic acid in LC-MS grade acetonitrile (solution B). Gradients used were as follows: 1% B for 5 min, 1% B to 35% B over 100 min, a 5-min wash at 85% B, and a 7-min equilibration step at 1% B. The instrument delivered a flow rate of 5 μL/min (loading) and 400 nL/min (gradient elution) with an operating back pressure of about 3,000 psi. Phosphopeptides were directly eluted into an LTQ-Orbitrap XL mass spectrometer . This instrument acquired full scan survey spectra  with a resolution of 60,000 at m/z 400. A maximum of the five most abundant multiply charged ions present in each survey spectrum were automatically mass-selected in a data dependent manner, fragmented by collision-induced dissociation (CID) , and analyzed in the LTQ. Thus, full-MS scans were followed by a maximum of 5 MS/MS scans  resulting in a maximum duty cycle of 2.5 s. For CID fragmentation multi-stage activation was enabled. Because chromatographic peaks were about 30 s at the base, these settings ensured that there were at least 10 data points per extracted ion chromatogram (XIC). In the data dependent acquisition, a dynamic exclusion was enabled with the exclusion list restricted to 500 entries, exclusion duration of 40 s and mass window of 10 ppm.For phosphopeptide identification, mascot Distiller 2.3.2 was used to smoothen and centroid the MS/MS data. The processed files were searched against the human sequence library in the SwissProt database  using tPhosphopeptide quantification was performed as described before ,28. BrieTo avoid interference from low intensity phosphopeptides that have the lowest quantification quality , a peak Pvalues were < 0.01 and the fold difference was > 2. To correlate peptide phosphorylation with drug sensitivity, phosphopeptide intensities derived from each cell line replicate were correlated with their viability after drug treatment at either 1 μM or 10 μM relative to DMSO-treated control. The correlation between relative phosphopeptide intensity and viability was calculated using Pearson's coefficient (R) of either linear or logarithm transformed phosphorylation data.For stringent identification of differences across samples, phosphopeptides were considered differentially phosphorylated between cell types when the Bonferroni-corrected ANOVA and Tukey λ in the cost function.Least absolute shrinkage and selection operator (Lasso) regression is a multivariate variable selection technique with a penalization approach that controls the number of regressors to be included in the optimized model . This isn x pmatrix X  and p = number of quantified phosphopeptides ) containing the column-wise normalized  intensities of all the phosphopeptides measured across the seven cell lines (three measurements for each cell line).For each of the three drugs we considered a We assessed the potential of these data in predicting the level of resistance/sensitivity of the seven cell lines to three kinase inhibitors through a 'leave-one-cell-line-out' approach based on lasso linear regression. With this method, seven different models were trained for each drug, leaving out the three samples corresponding to each of the seven cell lines, in turn. Then the trained models were used to make predictions on the samples that were left out in the corresponding training phase. The predictive ability was evaluated in terms of correlation between predicted and observed percentage of surviving cells when compared to the negative control, after the drug treatment.D, and a cell line C left out from the training phase, let Y be the vector containing the 18 viability scores (respectively for the remaining 18 samples) and λ a non-negative number. We solved through lasso regression the problem:For a given drug n is the number of observations ;i yis the viability score of sample i following treatment with D; xi is the row vector containing the normalized intensities of the p phosphopeptides when measured in the i-th sample; 0 βand B are a scalar and a p-vector, respectively. B contains the coefficients of the regressors  to be optimized. As λ increases, the number of non-zero components (hence phosphopeptides with non-null coefficient in the model) decreases.where λ parameter with a three-fold cross-validation on the remaining 18 samples and solved equation (1) for vector B without considering the samples of the left out cell line. In order to reduce the instability of the final models across the three-fold cross-validation used to determine λ, these two final steps were repeated 20 times (for each left-out cell line) and the entries of the resulting B vector averaged across these 20 iterations, ending up in the final average model D, C M. The frequency of observing a non-null coefficient for each regressor across the 20 iterations  was also computed and reported in the final results. The viability of each left-out cell line C was finally predicted through the corresponding D, CM.We determined the optimal value for the D, C Mon the left-out samples across the seven different cell lines C and the three drugs D comparable to each other, these values were normalized  together with the predictions of D, C Mon the corresponding training set. For the same reason, to produce the scatter plot in Figure μ = 0, δ = 1) drug-wisely.In order to make the values predicted through by D* Mof response to drug D, the coefficients of all the phosphopeptides (and their non-null coefficient frequencies) were averaged across the seven corresponding D, CM.To produce a final descriptive model Phosphopeptides whose average non-null coefficient frequency is > 50% in these final descriptive models are those reported in the insets of Figure Proteins containing phosphopeptides that significantly correlated with phenotypes were used for gene ontology (GO) and pathway enrichment analysis using either an in-house script that matched ontologies listed in SwissProt to each gene product or by David analysis tools . As for 5 cells/mL. Cells were harvested by centrifugation at 300 × g for 5 min, washed twice with ice cold Dulbecco's Phosphate Buffered Saline (DPBS), supplemented with 1 mM Na3VO4 and 1 mMNaF. Cell pellets were lyzed with lysis buffer  supplemented with 5% skim milk. Then membranes were incubated o/n with primary antibody and secondary antibody for 1 h. Protein bands were detected using ECL. Primary antibodies pMARKS S153/156  and Vinculin  were used at 1:1,000 and 1:10,000 dilution, respectively. Secondary antibodies were used at 1:5,000 dilution.AML cell lines were seeded at 5 × 10Mass spectrometry data have been deposited to the ProteomeXchange with identifier PXD000217.AML: acute myeloid leukemia; GO: gene ontology; IMAC: immobilized metal affinity chromatography; PCA:principal component analysis; XIC: extracted ion chromatogram.PC performed research, analyzed data, and wrote the paper; MPA performed research; FI analyzed data and wrote the paper; JCRP analyzed data; BV contributed reagents and edited the paper; JSR analyzed data; SJ contributed reagents and edited paper; PRC designed research, analyzed data, and wrote the paper.BV is an Advisor to GSK  and Activiomics . PRC is an Advisor to Activiomics .All authors read and approved the final manuscript.Table S1 - Hematological cell lines used to compare phosphoproteomes of different hematological cancers. Table S2 - AML cell lines used to correlate sensitivity to kinase inhibitors with phosphoproteomics data.Click here for fileFigure S1 - Workflow and distribution of the identified phosphorylation sites.Click here for fileDataset 1 - Identities and quantitative values of all phosphopeptides identified in AML, lymphoma, and multiple myeloma cell lines.Click here for fileFigure S2 - Protein classes represented in the phosphoproteomes of hematological cancer cell lines.Click here for fileFigure S3 - Representative examples of phosphopeptides differentially regulated in AML, lymphoma, and multiple myeloma cell lines.Click here for fileDataset 2 - Correlation of phosphoprotein data with responses to kinase inhibitors in AML.Click here for fileP valuesFigure S4 - Scatter plots between predicted/observed viability scores for individual drugs with cell lines identifiers, correlations scores, and .Click here for fileDataset 3 - Final descriptive models of drug responses as resulting from the lasso regression analysis. Listed are predictive phosphopeptides together with their average coefficients and inclusion frequency.Click here for fileFigure S5 - Association between the markers of sensitivity to kinase inhibitors found for AML cells with the sensitivity to the same inhibitors in lymphoma and multiple myeloma cells.Click here for fileFigure S6 - Pathway analysis of phosphopeptides that correlate with the responses to PI-103.Click here for fileFigure S7 - An inhibitor of PKC reduced the viability of AML cells resistant to PI-103 inhibition and had an additive effect with PI-103.Click here for file


Claudin 11 (CLDN11) in malignant melanoma (MM) of the skin, including six melanoma cell lines, 39 primary melanoma, 41 metastases of MM and 52 nevus cell nevi (NCN). CLDN11 promoter hypermethylation was found in 19 out of 39 (49%) of the primary MM and in 21 out of 41 (51%) of the MM metastases, but only in eight out of 52 (15%) of NCN . Moreover, a significant increase in the methylation level of CLDN11 from primary melanomas to MM metastases was revealed (p = 0.003). Methylation of CLDN11 was significantly more frequent in skin metastases (79%) compared to brain metastases . CLDN11 methylation was also found in five out of six MM cell lines (83%) and its promoter hypermethylation correlated with a reduced expression. Treatment of MM cell lines with a DNA methylation inhibitor reactivated CLDN11 transcription by its promoter demethylation. In summary, CLDN11 proved to be an epigenetically inactivated tumor related gene in melanomagenesis, and analysis of CLDN11 methylation level represents a potential tool for assisting in the discrimination between malignant melanoma and nevus cell nevi.Epigenetic inactivation of tumor-related genes is an important characteristic in the pathology of human cancers, including melanomagenesis. We analyzed the epigenetic inactivation of  INK4a/RB pathway and the Hippo/Ras Association Domain Family (RASSF) pathway [RASSF10) and activation of proto-oncogenes  [Claudin 11 (CLDN11) could be a useful epigenetic biomarker for identifying malignant melanoma [CLDN11 is a member of the claudin family that encodes integral membrane proteins and is involved in the formation of the paracellular tight junction seal in tissues [CLDN11 harbors a Claudin_2/PMP22 domain (CLDN family (CLDN1 to 27) have been identified in the human genome [CLDN11 it has been reported that it is silenced in gastric cancer by promoter hypermethylation and its inactivation is associated with invasiveness of this cancer [CLDN11 in primary cutaneous melanoma [Malignant melanoma is a malignant skin cancer showing a rising incidence worldwide . Several pathway ,3,4. Abe., BRAF) ,6. Apart., BRAF) ,8. This melanoma . CLDN11 2 domain a that isn genome . Expresss cancer . A genommelanoma . HoweverCLDN11 in malignant melanomas (MM) in more detail. Here, we report a significant increase in the methylation level of CLDN11 in MM metastases compared to primary MM and nevus cell nevi.The aim of our study was to illuminate the epigenetic inactivation of Claudin 11 (CLDN11) has been reported in primary melanomas [CLDN11 and according CpGs are shown in CLDN11 in malignant melanoma (MM) cell lines, we have analyzed its aberrant methylation in buf1280, C8161, IGR1, MeWo, SKMEL13, SKMEL28, lung cancer (A549), cervix cancer (HeLa) and human fibroblast (HF) by COBRA  hypermethylation of CLDN11 was detected  and melanoma cell line SKMEL13. Methylation of CLDN11 was also observed in A549 and HeLa cancer cells  by RT-PCR  skin metastases of MM, but only in five out of 16 (31%) brain metastases of MM  NCN showed weak CLDN11 methylation. In primary melanoma a significantly higher frequency of CLDN11 methylation  was revealed (p = 0.001). Interestingly, primary melanoma exhibited lower methylation levels compared to metastases .Subsequently, we analyzed the methylation of quencing . In 21 odetected . Methylatastases b,c. We oClaudin gene family consists of 27 members, which encode membrane proteins of the paracellular tight junction. Claudin 11 (CLDN11) was recently identified as a member that is hypermethylated in human cancers including malignant melanoma (MM) [CLDN11 is frequently hypermethylated in cutaneous MM, including primaries and metastases . Additionally to previous studies, we have performed quantitative methylation analysis and revealed significantly increased methylation levels of MM metastases especially in skin metastases  ,14. Heretastases . Moreovecription . Gao et  [CLDN11 . Especiatastases . Since mll lines b. There ll lines b. To datCLDN11 in different skin samples. Here we report significantly elevated methylation levels in metastatic MM compared to primary cancers  in dysplastic NCN  in cutaneous melanoma.Aberrant epigenetic regulation of other t cancer ,20. For arcinoma ,22. Methectively ,24. Moreentities ,26,27. T2 and 1xPenicillin/Streptomycin in according medium. Primary tissues and cancer cell lines were previously published . All patTaqI  for 1 h at 65 °C and resolved on 2% TBE gel. Methylation status was quantified utilizing the primer CLDN11Seq1 (ATTTTGTTTTTTTTTAYGTTTTTTTT) and PyroMark Q24 . Eleven CpGs are included in the analyzed region of CLDN11 and mean methylation was calculated .DNA was isolated by phenol-chloroform extraction and then bisulfite treated prior to COBRA analysis and pyrosequencing . Methylalculated a. For inRNA was isolated using the Isol-RNA lysis procedure . RNA was DNase  digested and then reversely transcribed . RT-PCR p-values are two-sided and considered significant for p < 0.05.Categorical variables were plotted into contingency tables and evaluated using Fisher’s exact probability test. All reported CLDN11 promoter occurs frequently in MM, but was rarely found in NCN. This data suggests that CLDN11 may encode a novel melanoma-specific tumor suppressor gene. Further studies are necessary to elaborate the exact tumor suppressor function of CLDN11. Furthermore, quantitative CLDN11 methylation analysis may serve as candidate biomarker tool in melanomagenesis in combination with other markers , since low CLDN11 methylation levels were also observed in NCN.In summary, our results show that hypermethylation of


The management of patients with advanced stages of head and neck cancer requires a multidisciplinary and multimodality treatment approach which includes a combination of surgery, radiation, and chemotherapy. These toxic treatment protocols have significantly improved survival outcomes in a distinct population of human papillomavirus (HPV) associated oropharyngeal cancer. HPV negative head and neck squamous cell carcinoma (HNSCC) remains a challenge to treat because there is only a modest improvement in survival with the present treatment regimens, requiring innovative and new treatment approaches. Oncolytic viruses used as low toxicity adjunct cancer therapies are novel, potentially effective treatments for HNSCC. One such oncolytic virus is Respiratory Orphan Enteric virus or reovirus. Susceptibility of HNSCC cells towards reovirus infection and reovirus-induced cell death has been previously demonstrated but has not been compared in HPV positive and negative HNSCC cell lines.To compare the infectivity and oncolytic activity of reovirus in HPV positive and negative HNSCC cell lines.50). EC50 values were compared.Seven HNSCC cell lines were infected with serial dilutions of reovirus. Two cell lines (UM-SCC-47 and UM-SCC-104) were positive for type 16 HPV. Infectivity was measured using a cell-based ELISA assay 18 h after infection. Oncolytic activity was determined using an alamar blue viability assay 96 h after infection. Non-linear regression models were used to calculate the amounts of virus required to infect and to cause cell death in 50% of a given cell line (EC50 for infectivity at 18 h ranged from multiplicity of infection (MOI) values (PFU/cell) of 18.6 (SCC-9) to 3133 (UM-SCC 104). EC50 for cell death at 96 h ranged from a MOI (PFU/cell) of 1.02×102 (UM-SCC-14A) to 3.19×108 (UM-SCC-47). There was a 3×106 fold difference between the least susceptible cell line (UM-SCC-47) and the most susceptible line (UM-SCC 14A) EC50 for cell death at 96 h.HPV negative cells were more susceptible to viral infection and oncolysis compared to HPV positive cell lines. ECHPV negative HNSCC cell lines appear to demonstrate greater reovirus infectivity and virus-mediated oncolysis compared to HPV positive HNSCC. Reovirus shows promise as a novel therapy in HNSCC, and may be of particular benefit in HPV negative patients. Head and neck squamous cell carcinoma (HNSCC) is a devastating disease that affects all aspects of the patient’s life, even in survivorship . The manReoviridae, is a non-enveloped, double stranded RNA virus that infects the upper respiratory and gastrointestinal tracts of humans with minimal symptoms ). This means that 102 reovirus particles per cell were sufficient to cause 50% cell death in this cell line. The remaining cell lines from most to least susceptible to reovirus-mediated oncolysis and their corresponding EC50 MOI (PFU/cell) values were FaDU , SCC-9 , SCC-25 , UM-SCC-38 , UM-SCC-104 , and UM-SCC-47   and the most resistant cell line UM-SCC-47 (HPV positive). For both infectivity at 18 h and oncolysis at 96 h, the HPV negative cells were more susceptible than the HPV positive cells by highly significant values. Our study is the first to compare the oncolytic activity of reovirus in HPV positive and negative head and neck cancer cell lines. Also, it is the first to compare reovirus infectivity among head and neck cancer cell lines.Reovirus has variable infectivity and oncolytic activity in head and neck cancer cell lines and the mechanism behind this variable susceptibility has yet to be elucidated but is likely multifactorial. Our findings suggest an important difference in the susceptibility of head and neck cancer cells to reovirus based on HPV status. The HPV negative cell lines used were much more susceptible than the HPV positive cells to both infection by reovirus and virus-mediated oncolysis. There was a >150 fold difference in the amount of virus required to infect 50% of cells in the most susceptible cell line (SCC-9) and the least susceptible cell line (UM-SCC-104). Similarly, there was a dramatic difference between oncolysis based on HPV status. There was a 3x10HPV positive (vs negative) oropharyngeal squamous cell carcinoma (OPSCC) has been shown to have a more favourable response to treatment with surgical and non-surgical treatments ,7. HowevNovel therapies are needed in head and neck cancer, especially in patients with HPV negative malignancies. Conventional therapy is associated with substantial morbidity and long-term complications , and proFurther research is required to identify additional molecular markers for susceptibility to reovirus to identify patients most likely to benefit from adjunctive reovirus therapy. HPV negative patients, a group with a poor prognosis relative to those with HPV-related head and neck cancer, are identified as a group to target in future reovirus trials. Ongoing and future trials investigating reovirus in head and neck cancer may need to perform subgroup analysis based on HPV status.Commonly described features of HNSCC cell lines include tumor subsite, staging, and treatment modalities utilized. Although clinically relevant, the smoking history of the patients from which these cell lines have been derived is not well described in the literature. The smoking status of the patients from which FaDU and SCC-9 were derived is not documented. The source of SCC-25 had an extensive history of smoking . Of the in vitro experiments is variable. Confounding factors between the cell lines used beyond HPV status may have an impact on results. Head and neck cancer is a molecularly and genetically heterogeneous entity [There are several limitations to this study. The behavior of cell lines in s entity ,49. TherHPV negative cell lines appear to be more susceptible to reovirus infection and oncolysis than their HPV positive counterparts. Reovirus shows promise as a potential novel therapy in HPV negative head and neck cancer.Prior to commencement, health research ethics board approval was obtained from the University of Alberta Health Research Ethics Board.