To enable computational analysis of regulatory networks within the cancer cell in its natural tumor microenvironment, we develop a two-stage histoepigenetic analysis method. The first stage involves iterative computational deconvolution to estimate sample-specific cancer-cell intrinsic expression of a gene of interest. The second stage places the gene within a network module. We validate the method in simulation experiments, show improved performance relative to differential expression analysis from bulk samples, and apply it to illuminate the role of the mesothelin (MSLN) network in pancreatic ductal adenocarcinoma (PDAC). The network analysis and subsequent experimental validation in a panel of PDAC cell lines suggests AKT activation by MSLN through two known activators, retinoic acid receptor gamma (RARG) and tyrosine kinase non receptor 2 (TNK2). Taken together, these results demonstrate the potential of histoepigenetic analysis to reveal cancer-cell specific molecular interactions directly from patient tumor profiles. The lack of correspondence of experimental models with tumor biology in vivo may lead to only partial understanding of oncogenic mechanisms, ultimately resulting in failures of experimental therapies targeting the oncogene. One illustrative example is the glycosylphosphatidylinositol-anchored cell-surface protein mesothelin (MSLN), whose expression is elevated in many pancreatic ductal adenocarcinoma (PDAC) cases1 and is associated with shorter overall survival2. Most of the current knowledge about MSLN’s function in the context of PDAC comes from mouse or cell-line models, which have shown its upregulation increases cancer cell proliferation, cell cycle progression, and cell survival5; while its interaction with MUC16 contributes to cell motility and invasion7. These models have shown limited utility for developing therapies targeting MSLN: while preclinical studies targeting MSLN in pancreatic cancer cell lines and patient-derived xenograft mouse models have resulted in inhibition of cancer growth9, early-stage clinical trials have shown stabilized disease or increase in survival for only a few patients11, with partial and overall responses being mostly low or absent13.The study of oncogene expression in unperturbed cancer cells within their tumor microenvironment provides insights that may not be gained from other more accessible models that may not reflect as closely the tumor biology in vivo15. This is a particularly important concern for PDAC tumors as they can show an abundant and highly variable stromal component, leading to ambiguous interpretation of bulk expression profiles16. For example, a sample with higher bulk expression of MSLN may either contain cancer cells with higher intrinsic expression of MSLN or the expression of MSLN may be constant and the sample may just have higher purity (proportion of cancer cells), resulting in higher bulk expression of the gene. This ambiguity hampers not only cancer-cell intrinsic differential gene expression analysis, but also the analysis of genes of interest in the context of their cancer-cell-specific regulatory networks.One obvious step toward addressing this problem is to study the oncogene and its network within its natural tumor microenvironment. However, transcriptional profiling of tumors is confounded by sample-to-sample differences in tumor purity and cell-type composition14, but cannot explicitly trace differential expression to specific cell types nor place the gene within its network module. Our previously developed Epigenomic Deconvolution (EDec) method17 partially addresses this problem by comparing cell-type-specific profiles starting from two groups of predefined groups of samples. One obvious limitation of EDec is the requirement that the two groups be predefined. This limitation precludes grouping by cell-intrinsic expression of genes of interest such as MSLN. To address this “chicken and egg” problem, we develop a histoepigenetic analysis method that expands on the EDec method in two ways: (1) It integrates within an iterative procedure a two-way clustering step with deconvolution to separate PDAC tumors based on cancer-cell-specific expression of a gene of interest such as MSLN; (2) It estimates a network of genes potentially interacting with the gene of interest. We here validate the method in simulation experiments, compare it to alternative approaches, and apply it to elucidate MSLN’s network within PDAC cancer cells.One approach toward addressing this problem would be to computationally tease apart the associated cancer cell-type-specific differences between groups of tumors. Numerous computational methods have been developed to account for tumor purity differences15. Per sample estimates of cancer cell proportions for these 150 tumors showed high Spearman’s correlation with previous tumor purity estimates calculated using InfiniumPurify18  and ABSOLUTE15    collection. EDec estimated four distinct cell type profiles, provided their per sample proportions, and provided estimates of cell-type-specific DNA methylation and expression profiles. Based on expression of known cell-type-specific marker genes, the profiles could be unambiguously recognized as belonging to stromal, immune, normal epithelial/acinar, and cancer cell types Fig. . Compari16) Fig. . Likewis16) Fig. . These rMSLN expression—addressing complications introduced by tumor purity differences  differences in several cell-type proportions—notably cancer—between groups .We next compared the result of iterative grouping to grouping based on bulk profiles. For this purpose, we split samples into three quartiles for each method and compared the samples in the top (first) and bottom (third) quartile for each method. While the overlap was high Fig. , the excMSLN high expression by both groupings and the sample set showing concordant MSLN low expression also by both groupings , TNK2 (ref.22), CAPG23, and MUC1 (ref.24). Gene set enrichment analyses (GSEA) of these 33 genes identified a highly enriched subset of 11 genes bound by retinoic acid receptor gamma (RARγ/ RARG) in mice25, including MSLN and RARG itself  reduced RARG transcription  stood out for their previously demonstrated ability to phosphorylate and activate AKT. Specifically, ACK1 activates AKT in pancreatic cancer where it also promotes cell survival and correlates with disease progression22. On the other hand, RARγ is known to activate AKT in hepatocellular carcinoma28. These findings were intriguing in light of the previous results implicating MSLN in the activation of AKT in PDAC cell lines via phosphorylation5 by an unknown mechanism.Among the 33 genes in the MSLN in the cell lines with high MSLN expression. As predicted, the knockdown reduced RARγ and ACK1 levels  at both residues (S473 and T308) necessary for AKT activation29. Upon MSLN overexpression in cell lines with low MSLN expression, protein levels of RARγ and ACK1 were increased  overlap was found with genes previously shown to be activated by AKT in vitro, suggesting that the in vitro cell line modeling indeed reflects the behavior of cancer cells in vivo.We next asked if the downstream effects of MSLN-mediated activation of AKT observed in cell line studiesMSLN network module, validate direct interactions of MSLN with TNK2 and RARG within the module, and identify additional genes relevant for understanding MSLN function in cancer cells. Toward this goal, we inferred a Gaussian graphical model (GGM) from the 150 bulk PDAC tumor expression profiles in the TCGA collection . To eliminate confounding due to variations in tumor purity, cell-type proportions (inferred by deconvolution) were added as covariates . The final GGM included the 367 nodes corresponding to the genes differentially expressed between MSLN-high and low tumors within any of the four estimated cell types  stage of histoepigenetic analysis  enrichment of connections between RARG and genes with known motifs for RARγ30 termed retinoic acid response elements (RAREs)  .We then focused on the 88 genes showing differential cancer cell expression between 47) Fig. . Moreove30 and those associated with parvin-beta (PARVB) by GSEA. Significant connectedness was indeed found for both sets , suggesting that the method is sensitive. In contrast, as expected, three random gene sets did not show significant connectedness . Specifically, RARγ regulates the balance of self-renewal and differentiation32. In the developing pancreas, retinoids affect ductal versus acinar differentiation27. Treatment of some pancreatic cancer cell lines with retinoids induces apoptosis, epithelial differentiation, cell cycle arrest, and reduces cell viability35. Some of these effects are mediated by RARγ as observed in the BxPC3 and Capan2 cell lines33, the same cell lines in which we showed reduction of RARγ upon MSLN knockdown. Conversely, cell lines with low RARγ appear resistant to this effect33. Moreover, patient PDAC tumors show reduced levels of retinoids compared to normal counterparts36. Thus, MSLN’s observed effect on pancreatic cancer cell proliferation in vitro4 may partly be mediated by RARγ and its ability to regulate the balance of self-renewal and differentiation depending on the concentrations of retinoids present.Our findings complement current understanding of RARγ and retinoids as important players in PDAC biology and normal development. Because RARγ expression is reduced upon 7, which is associated with worse prognosis in PDAC patients37. Similar to the effects of MSLN in PDAC cell lines5, activated AKT inhibits apoptosis, is involved in cell cycle progression, and mediates cell proliferation38. Our results expand upon the notion that MSLN may activate AKT, suggesting that this activation may be mediated through RARγ or ACK1. Moreover, because MSLN inhibition decreases phospho-AKT levels in other cancers with elevated MSLN levels39, our results may also be relevant for cancers other than PDAC.Another important member of the MSLN network is AKT. There are conflicting reports in the literature regarding the ability of MSLN to affect the phosphorylation and activation of AKT in PDAC cell linesMSLN network have significant implications for current PDAC drug discovery efforts targeting MSLN, RARγ, and their network neighborhood. Specifically, the limited benefit of targeting MSLN may be examined in the context of the tumor-specific state of the downstream mediators suggested by our study. Moreover, the pattern of interactions within the MSLN module has implications for utilizing retinoids and ACK1 inhibition as therapeutic strategies. Pilot clinical trials utilizing retinoids—most not selective for RARγ—have shown limited or no effects, with only some pancreatic cancer patients showing prolonged stable disease41. Preclinical trials showing that inhibition of ACK1 in PDAC cell lines has resulted in AKT suppression and subsequent decrease in cell proliferation, cell cycle arrest, and induction of apoptosis22. Our results suggest that these therapeutic targets should not be viewed in isolation but rather in relation to one another. In addition to the already tested targets, the MSLN network module includes new candidates that may play important roles. Finally, the suggested interactions within the MSLN network can be utilized to identify candidate biomarkers for stratification of patients or as targets for combination therapies.The interactions within the 15, were downloaded from UCSC Xena42. For the methylation dataset, probes with a missing beta value for any individual sample or falling on sex chromosomes were removed. Problematic 450 K probes43 were filtered using the mapping, typeINextBaseSwitch, snp5.common, extBase, and sub30.copy masks from http://zwdzwd.io/InfiniumAnnotation/current/hm450/hm450.hg38.manifest.tsv.gz and multi-mapping, cross-reactive, and probes containing common SNPs from https://github.com/sirselim/illumina450k_filtering. Probes associated (FDR < 0.01) with gender, radiation therapy history, history of diabetes, history of pancreatitis, smoking status, and history of targeted molecular therapy were determined by fitting an analysis of variance model using R’s aov function and removed. As were those associated with age and weight, determined by fitting linear models using R’s lm function.Level 3 DNA methylation, RNA-seq (HiSeqV2), and clinical metadata datasets from the TCGA Pancreatic Cancer cohort (PAAD), not flagged during previous reviewEDec R package was used to infer cell-type-specific methylation and gene expression profiles as previously described17. 500 informative probes were chosen by running the run_edec_stage_0 command with the “one.vs.rest” option and p value < 0.00001 for six selected reference groups using available methylation profiles from NCBI’s Gene Expression Omnibus44 (GEO)  were downloaded from GEO, QC’d using FastQC, processed using Trim Galore, and aligned using RSEM. The RNA-seq profiles were multiplied by the same proportions as their matched methylation mixtures for all 100 mixtures and reads for each gene per mixture summed to create the mixture’s bulk profile. The iterative group fit method was used to group the RNA-seq mixtures around MSLN expression. Splitting was performed 20 times to check stability and all possible pairwise correlations of the final group members across these runs were compared. Groups were then deconvoluted separately.Methylation datasets for four cell types  were downloaded from GEO . Noisy profiles were generated for each dataset and then the immune, fibroblast, and one of two cancer profiles were mixed in random proportions as previously describedMSLN high (n = 30) and low (n = 35) groups were determined by overlapping samples classified by both the bulk MSLN and the iterative group fit models. The deconvoluted cancer cell profiles for the groups were compared for differential expression using t-tests with the estimated means and standard errors of genes as previously described and with the same caveats17. Bulk differential expression between groups was done with raw  RSEM gene counts downloaded from NCI’s GDC Legacy Archive  using the DESeq2 R package45 with groups (MSLN high or low) with or without EDec-predicted cancer cell proportions as factors. All comparisons used a fold change ≥ 2 and false discovery rate (FDR) q value < 5%.For the TCGA samples, MSLN high group were used for gene set enrichment analysis using the Molecular Signatures Database (MsigDB) web site version 6.3 (ref. 46). The top ten overlaps of these genes were computed against each of the C1–C6 gene set collections using an FDR q value < 0.05. The 689 genes that showed higher expression in the MSLN high group at a p value < 0.05 were compared to the gene list AKT_expression_UP from MsigDB. Enrichment for the 13 gene overlap with the 172 AKT genes was computed using a hypergeometric test.The 33 genes with higher expression in the cancer cell profile of the huge47  and GeneNet48  were used to build networks over log2 transformed RNA-seq values for all 150 TCGA tumor samples on the 367 differentially expressed genes and the proportions of each cell type per sample. Positive controls were a set of 7 RARG related genes and a set of 21 genes related to PARVB expression and negative controls were randomly selected gene sets of size 7, 21, and 88  method (λ = 0.125). GeneNet created two networks, one with all edges and one with the same number of edges present in the huge network. To eliminate confounding due to variation in cell type composition, a node representing cell proportion was added  of a subset of nodes in a network, giving highly connected subsets in the network stronger significance compared to sparsely connected node subsets.The R package MSLN high cancer cells and MSLN and (2) 5 genes with RAREs in their regulatory regions30 and RARG  by at least one standard deviation (σ = 0.0297) were considered as being strong.To assess the directionality of the edge weights, two sets of edge weights were evaluated between: (1) Genes predicted to be differentially expressed in CTD was used to query the cancer, stromal, and immune specific dysregulated genes, as well as the 3-gene subset of MSLN, RARG, and TNK2 for modularity. Enrichment of edges was computed using a hypergeometric test.MSLN overexpression stable cell lines of Panc1, Panc28, and Panc48, as well as MSLN shRNA knockdown cell lines of Capan2, CFPAC1, and PL45 were established and maintained as previously described50. ASPC1, BxPC3, and SU86.86 cells were cultured in RPMI 1640 ; CFPAC1, HPAF-2, MIA Paca2, Panc1, Panc28, Panc48, and PL45 cells were cultured in DMEM ; Capan2 cells were cultured in McCoy’s 5 A medium . All cells were supplemented with 10% fetal bovine serum  and cultured at 37 °C with a humidity of 90–95% and 5% CO2.PDAC cell lines were purchased from ATCC, authenticated by short tandem repeat profiling, and propagated for less than 6 months after authentication. 51. RARG and MSLN measurements were normalized to GAPDH in the PDAC cell panel and relative to controls in the overexpression and knockdown comparisons. Primer sequences (5′–3′):Total RNA for each cell line was extracted using TRIzol reagent  according to the product’s manual. cDNA synthesis was performed with a reverse transcription-PCR kit (Bio-Rad) and real-time PCR was performed by standard procedures as previously describedMSLN forward CTATTCCTCAACCCAGATGCGT and reverse GCACATCAGCCTCGCTCA,Human RARG forward CAAGTGCATCATCAAGATCGTG and reverse GATACGCAGCATCAGGATATCT,Human GAPDH forward TCGACAGTCAGCCGCATCT and reverse CCGTTGACTCCGACCTTCA.Human Cell lysates were prepared using RIPA lysis buffer with protease and phosphatase inhibitor cocktail and were quantitated for protein abundance using the Pierce BCA Protein Assay Kit from Thermo Fisher Scientific. PVDF membranes were blocked in 5% nonfat dry milk/TBST solution and incubated at 1:1000 dilution with: MSLN mAb (D9R5G), RARγ1 mAb (D3A4), AKT Ab #9272, Phospho-AKT (Ser473) mAb (D9E), Phospho-AKT (Thr308) mAb #2965, and Phospho-ACK1 (Tyr284) Ab#3138 from Cell Signaling Technology and ACK1 antibody (A-11) from Santa Cruz Biotechnology. Anti-β-actin Ab (A2228) was at 1:2,000 dilution (Sigma-Aldrich). Immunodetection was performed using SuperSignal West Pico (34577) and Femto (34095) Chemiluminescent Substrate according to the manufacturer’s instructions .https://www.R-project.org/). P values were adjusted for multiple comparisons using the Benjamini & Hochberg method. Survival analyses were done with the R package survival, including multivariate Cox regression using variables (Supplementary Table p values < 0.05 in univariate Cox regression. Real-time PCR results were representative of three independent experiments and compared with two-tailed unpaired Student’s t-test with Welch correction when sample variances were not equal as defined by the Brown–Forsythe test using GraphPad Prism Software Version 6 and 7.Unless otherwise noted, all statistical analyses and bioinformatics were performed using the R programming language version 3.4.0 (Supplementary InformationSupplementary dataSupplementary dataSupplementary dataSupplementary data


Expression of telomerase reverse transcriptase (TERT) and telomerase activity (TA) is a main feature of cancer, contributing to cell immortalization by causing telomeres dysfunction. BIBR1532 is a potent telomerase inhibitor that showed potential anti-tumor activities in several types of cancer, by triggering replicative senescence and apoptosis. In a previous work, we detected, for the first time, TERT expression and TA in preclinical models of feline oral squamous cell carcinoma (FOSCC); therefore, we aimed at extending our investigation by testing the effects of treatment with BIBR1532, in order to explore the role of telomerase in this tumor and foreshadow the possibility of it being considered as a future therapeutic target. In the present study, treatment of FOSCC cell lines SCCF1, SCCF2, and SCCF3 with BIBR1532 resulted in successful inhibition of TA, with subsequent cell growth stoppage and decrease in cell viability. Molecular data showed that up-regulation of cell cycle inhibitor p21, unbalancing of Bax/Bcl-2 ratio, and down-regulation of survival gene Survivin were mostly involved in the observed cellular events. Moreover, BIBR1532 diminished the expression of TERT and its transcriptional activator cMyc, resulting in the down-regulation of epidermal growth factor receptor (EGFR), phospho-ERK/ERK ratio, and matrix metalloproteinases (MMPs)-1/-2 and−9, likely as a consequence of an impairment of TERT extra-telomeric functions. Taken together, our data suggest that BIBR1532 exerts multiple anti-cancer activities in FOSCC by inhibiting telomerase pathway and interfering with signaling routes involved in cell proliferation, cell survival, and invasion, paving the way for future translational studies aimed at evaluating its possible employment in the treatment of this severe tumor of cats. Telomerase is a holoenzyme that extends telomeres, the terminal DNA sequences of chromosomes . This fuIn somatic cells, TERT gene is not transcribed, and telomerase is not active; therefore, telomeric ends shorten at each cell duplication until, after a scheduled number of replications, they reach a critical point of erosion . The celInstead, cancer cells are prone to maintain a highly proliferative rate; therefore, it is not surprising that TERT expression and telomerase activity (TA) represent a common feature of most of human malignancies . A main Due to this multilevel role in carcinogenesis, different strategies are under investigations for telomerase inhibition, such as gene silencing through siRNA or treatment with small molecules . BIBR153Telomerase inhibition represents a promising therapeutic approach for human oral squamous cell carcinoma (hOSCC), where the role of this enzyme is prominent –18. Then5 density in 6-well plates, and after 24 h, BIBR1532 diluted at 25, 50, and 100 μM in culture medium was added to wells. In control plates, drug was replaced with the same amount of DMSO for each dose. After 48 h, wells were read under ZOE Fluorescent Cell Imager (Bio-Rad Laboratories) for scanning and phase contrast photography, and then cells were harvested by trypsinization and analyzed as described below.FOSCC cell lines SCCF1, SCCF2, and SCCF3 developed in the Rosol laboratory are a kind gift from Professor T.J. Rosol (The Ohio State University) and were cultured as previously described , 25, 27.Cell pellets from treatments at 25, 50, and 100 μM for 48 h were resuspended in phosphate buffered saline , and then equal amounts of cell suspension and 0.4% trypan blue (Lonza #17-942E) were mixed, incubated for 1–2 min at room temperature (rt), and counted by the use of Cell Counter (Bio-Rad). Changes in cell viability and growth at each dose of treatment with BIBR1532 were calculated and expressed as percentage compared with the respective control plate treated with DMSO set as 100%.TA in FOSCC cell lines treated at 25, 50, and 100 μM for 48 h was assessed by a telomeric repeat amplification protocol (TRAP) assay by using TRAPeze® Telomerase Detection Kit (Merck #S7700) as previously described . ReactioTotal protein extraction, protein quantification, sodium dodecyl sulfate (SDS)-PAGE, and Western blotting (WB) on cells treated at 50 or 100 μM for 48 h were performed as reported elsewhere . Detecti−ΔΔCt method by using Bio-Rad CFX Manager software  to cDNA, and real-time quantitative PCR (qPCR) for feline EGFR, TERT, and cMyc were performed on cells treated at 50 μM for 48 h, as previously described , 31. Forsoftware . Changest-test was performed using SPSS 17.0 software (SPSS Inc.), and differences were considered to be statistically significant for *P < 0.05 or **P < 0.01.For statistical analysis, Student's BIBR1532 blocks TA by specifically limiting the amount of nucleotide repeats available for substrate elongation; this activity has been detected in numerous preclinical models of cancer; however, it has never been tested in FOSCC cell lines . In ordeThen, to investigate the impact of telomerase inhibition by BIBR1532 on these cell lines, cell growth and viability were evaluated under the same experimental conditions. The treatment induced growth inhibition in SCCF1, SCCF2, and SCCF3 in a dose-dependent manner ; notablyTo further evaluate the possible effects of BIBR1532 on cell morphology, cells were scanned by phase contrast microscope. In agreement with growth arrest data, the number of cells observable in treated wells decreased in a dose-dependent manner, and interestingly, morphologic changes were also visible upon BIBR1532 incubation in all cell lines: unlike untreated cells, growth-arrested SCCFs became flattened, enlarged, with intercellular bridges and increased granularity, often multi-nucleated and had vacuolated cytoplasm .Treatment with BIBR1532 of different cancer cell lines causes cell cycle arrest and apoptosis through activation of transcriptional pathways triggered by telomeres dysfunction, such as the increase of cell cycle inhibitor p21 and the unbalancing of Bax/Bcl-2 ratio –38. To fIn SCCF1, the highest increase of p21 gene expression was yielded at 25 μM, and this was followed by a gradual decrease at 50 and 100 μM, whereas a dose-dependent effect was observed in treated SCCF2 and SCCF3 cells ; howeverWhen analyzing Bax and Bcl-2 expression and their molecular ratio, the following results were observed: SCCF1 showed a dose-related reduction of Bcl-2 at 25 and 50 μM but not at 100 μM ; howeverBIBR1532 affects cell survival in leukemia cell lines by reducing the expression of the Survivin gene; therefore, its mRNA levels were also investigated in treated SCCF cells , 38. In In addition to the canonical inhibitory effects on TA, treatment with BIBR1532 has been shown to down-regulate the expression of cMyc in a series of cancer cell lines, resulting in decreased TERT transcription –39. ConsBeyond its role as catalytic subunit of telomerase, TERT exerts several extra-telomeric functions; among these, transcriptional regulation of EGFR is of particular relevance, especially in cancer , 5, 6. HTreatment with BIBR1532 decreases the protein expression of several MMPs in different types of cancer cells, through down-regulation of TERT , 9. To aWith the present study, we aimed at expanding our ongoing investigation on the role of TERT and TA in FOSCC by treating tumor cell lines with the specific inhibitor BIBR1532 and evaluating its potential anti-cancer activities . In SCCFFelis catus Papillomavirus type-2 E6, thus providing a likely explanation to the different degrees of activation of downstream genes between the two cell lines (Replicative senescence is triggered by telomeres attrition through up-regulation of the cell cycle inhibitor p21, and optionally, it may shift to apoptosis through unbalancing of Bax/Bcl-2 ratio . Insteadll lines . Howeverll lines . Neverthll lines , 38; ourll lines .Taken together, these data suggest that inhibition of TA by BIBR1532 in SCCF cells switches off cell proliferation and, despite with variable degrees and molecular responses, promotes cell death mostly by engaging p53-dependent pathways triggered by telomeres shortening, in agreement with literature data , 49–51. TERT gene is under transcriptional regulation by cMyc, a well-known oncogene over-expressed in FOSCC and previously detected in SCCF cells , 26, 52.in vitro. Considering the main role of EGFR in the proliferation of FOSCC-derived cells (Regulation of EGFR is among the non-canonical functions of TERT , and inted cells , it is lFOSCC is characterized by highly invasive phenotype and metastatic potential, possibly due to the expression of MMP-1/-2 and−9 as in the human counterpart . It has in vitro models of cancer (In conclusion, this work demonstrates that BIBR1532 exerts a series of anti-cancer activities linked to the inhibition of canonical telomerase pathway and TERT extra-telomeric functions in FOSCC cell lines see , paving f cancer , 49, 53.The original contributions presented in the study are included in the article/GA, LL, KP, and GDL performed the experiments. GA, GB, and PM drafted the manuscript. GA, GDL, PM, GG, BDU, and GB conceived the study. All authors approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Klotho has been recognized as a gene involved in the aging process in mammals for over 30 years, where it regulates phosphate homeostasis and the activity of members of the fibroblast growth factor (FGF) family. The α-Klotho protein is the receptor for Fibroblast Growth Factor-23 (FGF23), regulating phosphate homeostasis and vitamin D metabolism. Phosphate toxicity is a hallmark of mammalian aging and correlates with diminution of Klotho levels with increasing age. As such, modulation of Klotho activity is an attractive target for therapeutic intervention in the diseasome of aging; in particular for chronic kidney disease (CKD), where Klotho has been implicated directly in the pathophysiology. A range of senotherapeutic strategies have been developed to directly or indirectly influence Klotho expression, with varying degrees of success. These include administration of exogenous Klotho, synthetic and natural Klotho agonists and indirect approaches, via modulation of the foodome and the gut microbiota. All these approaches have significant potential to mitigate loss of physiological function and resilience accompanying old age and to improve outcomes within the diseasome of aging. Klotho  (FGF23) ; it is t (FGF23) .The Klotho protein that was identified as the FGF23 co-receptor was the α-Klotho isoform, which hereafter is termed Klotho. Klotho is expressed mainly on the cell surface membrane of proximal and distal renal tubules –20, thouKlotho regulates many pathways involved in aging processes, such as the regulation of phosphate homeostasis, insulin signaling and Wnt signaling , 22. AddKlotho expression levels and its circulating level decline during aging. In humans, Klotho deficiency features medial calcification, intima hyperplasia, endothelial dysfunction, arterial stiffening, hypertension, impaired angiogenesis, and vasculogenesis  .As Klotho-deficient phenotypes have been attenuated and rescued by Klotho gene expression, or supplementation, it is suggestive that Klotho has a protective effect with regard to the vasculature .Mice deficient in Klotho exhibit a phosphate imbalance and hyperphosphatemia due to impaired urinary phosphate excretion, but significantly develop a complex progeric phenotype including poor growth, atrophy of multiple organs, vascular calcification (VC), sarcopenia, cardiac hypertrophy and fibrosis, cognitive impairment, and shortened lifespan , 2, 5. CAs Klotho has been regarded as an anti-aging gene, in order to more fully understand how Klotho might impact age-related disease, this review focuses on what we actually mean by the aging process, its relevance to chronic kidney disease (CKD) as a disease of accelerated aging, and how this is impacted by Klotho. Furthermore, we discuss how modulation of Klotho might be exploited to mitigate the effects of the diseasome of aging.Rather than simply being a collection of morbidities arising during the final decades of life, aging is an active process across the entire life-course. It has been described as an accumulation of physiological and molecular deficits accruing at different rates, both within different tissues and organs in the same individual and between different individuals . Aging lIn mammals, aging has a number of distinct additional features to its common hallmarks, comprising phosphate toxicity, diminished global Nrf2 expression, and microbial dysbiosis . These fSerum phosphate (Pi) levels shows an exceptionally strong negative correlation with lifespan in mammals , 36, PhoPertinent to these observations are observations of Klotho signaling independent of FGFR4 in the heart, liver, and lung , 42. In Cyp24a1, which encodes 24-hydroxylase, responsible for the conversion to inactive vitamin D by a Klotho dependent mechanism , which are colloids comprised of calcium-Pi bound to fetuin A, a circulating inhibitor of vascular calcification , 37, 44.echanism . Cy27b1 n of PTH . Serum ln of PTH , 51. Notn of PTH . This haCKD is part of the diseasome of aging and displays an overlapping phenotype with Klotho-deficient mice. It has been suggested that Klotho function is tightly correlated with the pathogenic mechanisms underpinning CKD , 53–55.CKD is a progressive systemic disease that affects 10–12% of the population . The preChronic low-grade inflammation also contributes to the premature aging phenotype in CKD  and othein vivo  treatment. Accordingly, the stable delivery of soluble Klotho has been reported to reduce chronic hyperphosphatemia and VC both in vitro and in vivo  and renal fibrosis have both been associated with diminished Klotho expression. Studies in a mouse model of CKD-MBD have indicated that loss of Klotho expression is a key event in renal and bone injuries and that restoration of Klotho expression attenuated CKD related bone defects . These oin vivo  in keepiThe weight of basic science, pre-clinical, and clinical evidence suggests that modulation of Klotho function is an exciting and viable target for therapeutic interventions. As such, it is a prime therapeutic target for intervention in a range of diseases within the diseasome of aging, including CKD , 123. GiAs such, Klotho-mediated therapies may be classed as geroprotective , and senomorphic (i.e. suppression of senescence effects without cytotoxicity). It is important to note, however, that the context of their application and time in the life course applied are key elements for any success. While most basic and pre-clinical studies have focussed on mid- to end of life application, data from early life interventions are sparse and require a more robust evaluation. This is pertinent to the concept of antagonistic pleiotropy, as aging is a continuous process occurring across the life course, thus suggesting that the effects of any interventions will be dependent on the context of the stage of the life course at which they are applied. Cellular senescence is likely to be subject to antagonistic pleiotropy, as senescent cells are not desirable in young individuals, but are essentially anti-oncogenic in the old. The potential for any intervention to generate adverse effects, especially subtle epigenetic or cryptic effects, thus needs evaluated from this perspective. Initial evidence, however, suggests that most interventions appear to be well-tolerated and have some beneficial effective. Indeed, over-expression of α-Klotho has been reported to reverse the phenotype and extended life-span in Klotho-deficient mice , 124.Targeting Klotho in CKD is particularly pertinent on a number of fronts, Firstly, accurate detection of CKD at an early stage has proven problematic and currently no biomarkers are available which are suitably accurate, easily measured and sensitive enough . CurrentSeveral studies have suggested that pharmaceutical replacement or supplementation of Klotho can improve complications associated with CKD . Pre-cliActivation of Klotho has been reported to reduce levels of oxidative stress , improvein vitro by modulation of Klotho promoter expression , a DNA methyltransferase inhibitor, to increase Klotho expression ~1.5- to 3-fold pression . While tDeacylation: Inhibition of histone deacetylases using HDAC inhibitors, such as trichostatin A or valproic acid have been shown to reduce inhibition of Klotho expression by TNF/TWEAK signaling in the kidney .in vitro. This induction was blocked by PPAR-c antagonists or by small-interfering RNA-mediated gene silencing of PPAR-c, suggesting this is a PPAR-c-dependent mechanism  receptor is also important in the aging process. Activation of the AT1 receptor by AngII is implicated in the age-related developments of hypertension, diabetes, and kidney disease. Disruption of the AT1 receptor gene in mice extends life-span and lowers levels of oxidative stress . AngioteVitamin D derivatives e.g. paricalcitol: which has been shown to induce Klotho expression in rodent CKD models , 146. AnExogenous recombinant peptide has also been used to raise soluble α-Klotho concentrations , 148 andBlockade of FGF23 using Burosumab, has been demonstrated to be effective in control and improvement of rickets in patients with X-linked hypophosphataemia , 151. UsNumerous studies have demonstrated the effectiveness of exercise to prevent, reverse, or attenuate premature aging , 154. ExNumerous compounds have been approved for use in CKD but with only some positive outcomes seen suggesting the extent of reactivating and or replacing Klotho and its effects are yet to be clarified .A number of studies have intimated that Klotho expression is amenable to nutritional intervention. Age-related changes in Klotho gene expression, particularly renal Klotho expression, and vitamin D metabolism can be affected by dietary Pi intake. As such, nutritional acquisition of Pi can radically affect age-related health, which opens up an easy avenue for therapy. There is good rationale for such an approach, as Pi has been linked to accelerated biological aging  and poorer renal function  Higher aOther pre-clinical studies have indicated that a high fat diet (HFD) as a surrogate for an energy rich and calorie dense western diet, results in age-related alterations in renal α-Klotho expression that could affect the responsiveness of dietary phosphate to vitamin D metabolism .Alternative nutritional interventions have been designed to modulate Klotho-FGF23 inter-activity in CKD and thus address significant features associated with its pathophysiology including hyperphosphatemia, iron deficiency, and anemia [reviewed by ]. Interv3 and calcium levels were unaffected. However, the high magnesium reduced bone mineral density and presented treated animals with possible osteomalacia security and FG23 levels. In young adults studied in the context of coronary artery risk development, this has been tested under the hypothesis that food insecurity would be associated with intake of food and drinks with higher levels of phosphate additives, with potential impact on increased levels of FGF23 as a compensatory response to higher dietary phosphate intake . Pool etp = 0.001; I2 of 36%) in vitamin D deficient individuals.Secondary analysis of the placebo-controlled Styrian Vitamin D Hypertension trial  trial inn = 74) enrolled in the FIT-AGEING study, a negative association has been demonstrated between total alcoholic intake and S-Klotho plasma levels , even when adjusted for body composition parameters (in vitro (NRK-52E cells) and in vivo (mouse kidney) with involvement of the ATF3/c-Jun pathway  has not featured as a mediator of such effects, though this is intuitive with respect to β-Klotho, as its FGF19 substrate is secreted by bile acids produced by gut microbiota (The gut microbiota has emerged as a significant contributory factor to age related health [reviewed in ], where crobiota .There is also an emerging relationship between the gut microbiota and the vitamin D/FGF23 axis. In murine models, Bora et al.  have demThe emerging field of Geroscience seeks to address the cellular and molecular mechanisms underpinning the aging process and to understand how, when and why these become dysregulated, as the diseasome of aging manifests. Modulation of Klotho has emerged as a prime target for interventions within this space, having the potential to complement and synergise with other emerging therapies, including senolytics and live biotherapeutics, as well as more conventional pharmaceuticals. The ability to couple any emerging therapies with nutritional and exercise based approaches, offers a more holistic means of translating these to the general population and thus improving healthspan. Applications to age related diseases, such as CKD are exciting, and have already demonstrated clinical benefit with more promised to come.All authors contributed to formulation, writing, discussion, and editing of the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest


LA-HSA) resulted in substantial particle uptake with low cytotoxicity. In contrast, uptake of lauric acid-coated nanoparticles (SPIONLA) was substantially increased but accompanied by higher toxicity. The presence of an external magnetic field significantly increased cellular uptake of both particles, although cytotoxicity was not significantly increased in any of the cell lines. SPIONs coated with lauric acid and/or human serum albumin show different patterns of uptake and toxicity in response to an external magnetic field. Consequently, the results indicate the potential use of SPIONs as vehicles for MDT in head and neck cancer.Superparamagnetic iron oxide nanoparticles (SPIONs) feature distinct magnetic properties that make them useful and effective tools for various diagnostic, therapeutic and theranostic applications. In particular, their use in magnetic drug targeting (MDT) promises to be an effective approach for the treatment of various diseases such as cancer. At the cellular level, SPION uptake, along with SPION-mediated toxicity, represents the most important prerequisite for successful application. Thus, the present study determines SPION uptake, toxicity and biocompatibility in human head and neck tumor cell lines of the tongue, pharynx and salivary gland. Using magnetic susceptibility measurements, microscopy, atomic emission spectroscopy, flow cytometry, and plasma coagulation, we analyzed the magnetic properties, cellular uptake and biocompatibility of two different SPION types in the presence and absence of external magnetic fields. Incubation of cells with lauric acid and human serum albumin-coated nanoparticles (SPION LA) and SPIONs coated with lauric acid and human serum albumin (SPIONLA-HSA) on various head and neck cancer cell lines. Accordingly, the primary objective of the present work was to investigate the effects of both SPION systems on viability, cell proliferation and uptake in head and neck tumor cell lines of squamous cell carcinoma of the pharynx, tongue and parotid gland. In addition, the influence of magnetically enhanced SPION accumulation, as a prerequisite for their suitability as potential vehicles for MDT, was investigated by culturing the cells either without or on magnetic plates. Furthermore, biocompatibility with human blood representing the requirement for future in vivo applications served as secondary objective and was analyzed using coagulation assays with human blood plasma.Squamous cell carcinoma of the head and neck is the sixth most common malignant tumor in the world, with 500,000 new cases every year . In part2⋅4H2O), iron (III) chloride hexahydrate (FeCl3⋅6H2O) and Sartorius ultrafiltration tubes Vivaspin 20, PES with a molecular weight cut-off (MWCO) of 100 kDa were purchased from VWR, Darmstadt, Germany. Iron reference standards were bought from Bernd Kraft GmbH, Duisburg, Germany. Glass coverslips, sterile Rotilabo® syringe filters with cellulose mixed ester membranes, dialysis bags , nitric acid, DMSO, trypsin, PBS, ethanol, disodium hydrogen phosphate, gelatin and formalin were supplied by Roth, Karlsruhe, Germany. Propidium iodide (PI), Triton X-100, lauric acid and Ribonuclease A were purchased from Sigma-Aldrich, St. Louis, MO, USA. Anexin V-FITC (AxV-FITC), Hoechst 33342, Alexa Fluor 488 Phalloidin and 1,1′,3,3,3′,3′-hexamethylindodicarbocyanine iodide (DiIC1(5)) were purchased from Life Technologies, Darmstadt, Germany. Ringer’s solution were purchased from Fresenius Kabi AG, Bad Homburg, Germany. Fetal calf serum (FCS) superior, were purchased from Thermo Fisher Scientific, Waltham, MA, USA. EMEM, DMEM: F-12 and McCoy’s 5A Medium were purchased from ATCC®, Manassas, VA, USA. Hydrocortisone was purchased from Pfizer, New York City, NY, USA. Ferucarbotran (Resovist®) were purchased from I’rom Group, Tokyo, Japan and Ferumoxytol (Rienso®) was purchased from Takeda Pharmaceutical Company Limited, Osaka, Japan. Water used in all experiments was of ultrapure quality produced by the Milli-Q® system from Merck, Darmstadt, Germany. Other materials and equipment used in this study includes: Human serum albumin (HSA) solution ; Agilent 4200 microwave plasma-atomic emission spectrometer (MP-AES) ; Malvern Zetasizer ; MS2G Susceptometer, Bartington® Instruments, Oxon, UK); aPTT Kit and 25 mM CaCl2 solution ; MC4 plus plasma coagulometer ; Magnetic plates ; S-15-08-N magnets ; FaDu (ATCC® HTB-43™), SCC-9 (ATCC® CRL-1629™), A-253 (ATCC® HTB-41™) and Detroit 562 (ATCC® CCL-138™) cell lines ; Gallios cytofluorometer and Kaluza software version 2.0 ; Incucyte® ; xCELLigence ; Mounting Medium ; Zeiss Axio Observer. Z1 fluorescent microscope ; Nanolive Fluo-3D Cell Explorer® and Steve software v.1.6.3496® , Thermal mixer , KrosFlo® Tangential flow ultrafiltration equipment .Iron (II) chloride tetrahydrate  was performed according to a protocol of Zaloga et al., 2016 . Fu. Fu25]. y images . This beLA-HSA or SPIONLA, either in presence (+) or in absence (−) of a magnetic force. We then analyzed the cellular iron content by microwave plasma-atomic emission spectrometry (MP-AES) as well as compared the data with the results of holotomography and flow cytometry  and atomic emission spectroscopy (AES) . We therytometry . WithoutLA and SPIONLA-HSA on viability, necrosis, apoptosis, mitochondrial membrane integrity, DNA fragmentation, and cell cycle  measurements using flow cytometry after DiIC1(5) and Hoechst staining  staining . At 50 and 100 µgFe/mL, SPIONLA-HSA had no negative effect on cell proliferation. At the lowest concentration, cell growth was even enhanced, indicating a known effect of SPION-induced enhanced cell growth at lower concentrations that is reduced or abolished at increasing SPION concentrations due to cytotoxic effects [The lower cytotoxicity of SPIONstaining . All celstaining . The mea imaging . The eff effects .LA and SPIONLA-HSA were added in ascending concentrations to fresh human citrate-stabilized plasma. After addition of activated partial thromboplastin time (aPPT) solution, coagulation was started with CaCl2 solution and clotting time was automatically monitored  increase in cellular particles. Interestingly, after incubation with SPIONLA at the same concentration, the increase for iron detected by MP-AES was very similar (5.9-fold to 12.9-fold), indicating the presence of a similar magnetic susceptibility for SPIONLA and SPIONLA-HSA. Thus, the presence of a magnetic field allows a 5.9-fold to 13.8-fold increase in particle uptake, which is similar for all cell lines and particles studied. However, there are significant cell type-dependent differences in absolute SPION uptake. While cells derived from tongue carcinoma (SCC-9) incorporated the most particles, closely followed by salivary gland carcinoma cells (A-253), the efficiency of cells derived from metastases of the hypopharynx (FaDu) and pharynx (Detroit 562) to take up particles was significantly lower. These cell-based differences were enhanced after application of a magnetic field. Thus, the observed variations in SPION uptake on a cellular level are not only due to the physicochemical properties of the coating but also to the different characteristics of head and neck cancer cells. Interestingly, despite an enhanced uptake, application of an external magnetic field revealed only a slight effect on cell viability in FaDu cells, whereas there was no obvious impairment of cell viability in Detroit 562, A-253, or even SCC-9 cells, which exhibited the highest SPION loadings. This is a surprising result, as increased SPION content can be expected to increase cytotoxicity. However, the magnetic field could induce the formation of larger aggregates that are only deposited on the cell membrane and do not enter the cell by active uptake mechanisms. Previously, a study addressing the magnetic settling of SPIONs demonstrated that magnetic fields lead to particle aggregation and a faster settling of magnetic nanoparticles [LA -incubated cells). Flow cytometric quantification via SSc also underrepresents the particle amount because detached particle aggregates were gated out and apoptotic and necrotic cells, which may contain high particles quantities, were excluded from SSc determination to avoid including the increased SSc of dying cells , showed a comparatively higher particle uptake of 3.0 pg/cell. However, comparing the results is limited as breast cancer cells were not exposed to a magnetic field and incubated with the highest SPION concentration of 75 μgFe/mL. The results presented strongly suggest that despite the heterogeneity of cellular origin, the feasibility of MDT may be expanded to various entities beyond breast and head and neck cancer cell lines. However, SPIONLA exhibited substantial toxicity and decreased biocompatibility compared with SPIONLA-HSA (LA-HSA is to be considered as the more appropriate particle to be used for MDT in head and neck cancer.Our study is consistent with Poller et al., who demonstrated that breast cancer cells incubated with SPIONtoxicity . Notablyr values . The detONLA-HSA . Based oThe present work provides new results on the interaction of SPIONs with head and neck cancer cell lines. The examined cell lines demonstrated a distinct ability to take up superparamagnetic iron oxide nanoparticles. Applying an external magnetic field does not impair cell growth and viability. Accordingly, the results indicate that head and neck cancers may serve as potential candidates for magnetic drug targeting using SPIONs.


For transporter substrates, differences between the two cell lines were less pronounced but the drugs displayed different inhibition potencies when evaluated by the two methods. Our study illustrates the inherent limitations when comparing transporter inhibition data from different laboratories and stresses the importance of including appropriate control experiments with reference compounds when investigating new drugs of interest.High-affinity monoamine transporters are targets for prescribed medications and stimulant drugs of abuse. Therefore, assessing monoamine transporter activity for candidate medications and newly-emerging drugs of abuse provides essential information for industry, academia, and public health. Radiotracer binding and uptake inhibition are the gold standard assays for determining drug–transporter interaction profiles. The combined results from such assays yield a unique biochemical fingerprint for each compound. Over time, different assay methods have been developed to assess transporter activity, and the comparability of data across various assay platforms remains largely unclear. Here, we compare the effects of six well-established stimulants in two different cell-based uptake inhibition assays, one method using adherent cells and the other using suspended cells. Furthermore, we compare the data from transfected cell lines derived from different laboratories and data reported from rat synaptosomes. For transporter inhibitors, IC These transporters contain 12 transmembrane helical domains with intracellular amino and carboxy termini (High-affinity monoamine transporters (MATs) for serotonin (5-hydroxytryptamine [5-HT]), dopamine, and norepinephrine  are transmembrane proteins of the solute carrier 6 family that transport substrate molecules across the plasma membrane bilayer, using ion gradients , and the transporter substrates d-amphetamine, 3,4-methylenedioxymethamphetamine (MDMA), fenfluramine, and 4-methylmethcathinone (mephedrone) were chosen as drugs of interest. With this study, we aim to better understand how different assay variables influence the results obtained, and examine whether the identification of certain trends in the data might facilitate a better comparison of results.Currently, the two main assay systems used to assess uptake inhibition characteristics of new compounds are transporter-transfected human embryonic kidney (HEK) 293 cells and synaptosomes derived from rat brain. Transfected cells bear the advantage of expressing pure populations of a single human transporter of interest, whereas synaptosomes consist of sealed nerve endings which possess all of the protein machinery for transmitter synthesis, release, metabolism, and uptake. The specific experimental methods employed by different research laboratories can vary considerably, leading to high variability in transporter inhibition potency values, as reported in the scientific literature for transfected cells  and for Transporter inhibition was assessed in two different laboratories, one performing experiments on adherent cells (referred to as “method 1”) and the other performing experiments on cells in suspension (referred to as “method 2”). Each of the laboratories independently established stable cell lines expressing human isoforms of SERT, DAT or NET using their stock of HEK 293 cells. For reasons of simplicity, human MAT expressing cells established in Vienna and Basel, will henceforth be referred to as “cell line 1” and “cell line 2,” respectively. Both cell lines have been previously used to assess the pharmacological profiles of stimulants in multiple studies Table 1.d-amphetamine sulfate (Sigma Aldrich) and d-amphetamine hydrochloride (Lipomed), fenfluramine hydrochloride (Lipomed), MDMA hydrochloride (Lipomed), and mephedrone hydrochloride (Lipomed).Due to the limited availability of certain drugs in different countries, the contributing laboratories obtained some of the test drugs from different sources. The following test drugs were included: cocaine hydrochloride , MDPV hydrochloride (Lipomed), 2 ∙ 2H2O, 2 mM MgCl2 ∙ 6H2O, pH 7.3) and Krebs-Ringer Bicarbonate buffer . KHB used for binding experiments at DAT additionally contained 10 µM ZnCl2. The lysis buffer used in method 2 was composed of 0.05 M Tris–HCl, 50 mM NaCl, 5 mM EDTA, and 1% NP-40 in purified water. Radiotracers used in method 1 and uptake saturation experiments were purchased from American Radiolabeled Chemicals, Saint Louis, MO, USA ([3H]-1-methyl-4-phenylpyridinium ([3H]-MPP+), 80 Ci/mmol) or from Perkin Elmer, Boston, MA, USA . Radiotracers used in method 2 were purchased from Perkin Elmer,  or Anawa, Zürich, Switzerland . [3H]-CFT , 82.9 Ci/mmol, used in binding experiments was obtained from Perkin Elmer Boston, MA, USA.Buffers used for the uptake inhibition, uptake saturation, and radioligand binding experiments were as follows: Krebs HEPES buffer  and subsequently grown in 10-cm tissue culture dishes. Upon reaching approximately 80% confluence, the cells were washed once with 6 ml of phosphate-buffered saline  and detached with 1 ml of trypsin-EDTA (Sigma-Aldrich). The cell trypsinization was stopped after 3 min by adding 9 ml of pre-warmed DMEM. The cells were seeded onto poly-D-lysine-coated  96-well culture plates at the density of 0.2 × 106 cells/ml 24 h prior to the experiment and grown as monolayers.HEK 293 cells used to create cell lines stably expressing MAT proteins in Vienna, Austria were purchased from LGC Standards GmbH, Wesel, Germany. Human isoforms of DAT and SERT were transfected using jetPRIME transfection reagent  according to manufacturer's instructions. The human isoform of NET and rat isoforms of DAT, NET, and SERT were transfected using the CaPOlsewhere . Cells w4 method as previously described  stably transfected with the human NET, DAT, or SERT using the CaPOescribed  were crei). To initiate uptake, the preincubation buffer was exchanged for a buffer containing various concentrations of the test drugs, vehicle control, or monoamine-specific inhibitors with the addition of 20 nM [3H]-MPP+, 200 nM [3H]-dopamine, or 100 nM [3H]-5-HT. The uptake was carried out at room temperature for 1 min (for DAT and SERT) or 3 min (for NET) and the reaction was subsequently stopped by rapid removal of the uptake buffer and washout with ice-cold KHB. Thereafter, 300 µl of 1% SDS was added to the wells and cell lysates were transferred into 2 ml of scintillation cocktail. The amount of accumulated [3H]-substrate was determined by liquid scintillation counting on a Packard Tri-Carb 2300 TR liquid scintillation analyzer.Assays were performed as previously described by 6 cells/ml. For [3H]-dopamine uptake experiments, the buffer was additionally supplemented with 0.2 mg/ml ascorbic acid. The cell suspension (100 μl per well) was transferred into a round bottom 96-well polypropylene storage plate  and incubated in triplicate with 25 μl of KRB containing the test drugs in the range of 1 nM to 900 μM final assay concentration , vehicle control, or monoamine-specific inhibitors  for 10 min at 450 rpm and ambient temperature on a rotary shaker. To initiate uptake transport, 50 μl of [3H]-norepinephrine, [3H]-dopamine or [3H]-5-HT dissolved in KRB were added at a final concentration of 5 nM for additional 10 min. Thereafter, 100 μl of the cell suspension was transferred into 0.5-ml microtubes  that contained 50 μl of 3 M KOH covered with 200 μl silicon oil . The tubes were centrifuged with a Mikro 220 centrifuge  for 3 min at 16,550 × g to transport the cells through the silicone oil phase into the KOH. The tubes were frozen in liquid nitrogen and the cell pellets were cut from the bottom of the tubes and transferred into 6 ml scintillation vials (Perkin-Elmer) that contained 0.5 ml lysis buffer. The samples were shaken for 1 h at 700 rpm before 3 ml scintillation fluid  was added. After 1-h equilibration time, monoamine uptake was quantified by liquid scintillation counting on a Packard Tri-Carb 1900 TR liquid scintillation analyzer. Nonspecific uptake in the presence of selective inhibitors was subtracted from the total counts.Upon 70%–90% confluence, HEK 293 cells cell lines stably expressing NET, DAT, or SERT were washed once with 10 ml of PBS (Gibco) and detached with 2 ml (SERT) or 4 ml (NET and DAT) of 0.05% Trypsin-EDTA (Gibco). After 2 min, the trypsinization was stopped with 10 ml (SERT) or 8 ml (NET and DAT) of pre-warmed culture medium, the cell suspension was transferred into a 15-ml polypropylene tube (Techno Plastic Products), and the cells were then centrifuged at 1100 revolutions per minute (rpm) for 2 min at ambient temperature with a Heraeus Multifuge X1R . Thereafter, the medium was removed, and the cells were washed once with KRB, centrifuged again, and then resuspended in KRB at a density of 3 × 10Cell Culture section, washed once with 100 µl of room temperature KHB and incubated for 1 min (for DAT and SERT) and 3 min (for NET) at room temperature in 50 µl of the KHB containing various concentrations of the [3H]-substrate. The dilution row of [3H]-substrate was created by mixing various concentrations of non-tritiated substrates with a constant amount of [3H]-substrates . 100% of uptake was obtained in the presence of [3H]-substrate only and non-specific uptake was determined in the presence of monoamine-specific inhibitors.The cells were prepared as described for adherent cells in the 3H]-CFT. Saturation binding was performed in KHB containing various concentrations of non-tritiated β-CFT and 10 nM [3H]-CFT. After the incubation, cells were washed twice with 100 µl of ice-cold KHB and lysed with 1% SDS. The amount of released tritiated substrate was quantified by liquid scintillation counting on a Packard Tri-Carb 2300 TR liquid scintillation analyzer.For the measurement of inhibitor binding, cells were washed once with 100 µl of room temperature KHB and incubated for 30 min at room temperature in 50 µl of KHB containing various concentrations of inhibitors and 10 nM -CFT binding by cocaine at SERT (8-fold) followed by inhibition by d-amphetamine at NET and MDPV at SERT (3-fold difference). Approximately half of the IC50 values did not differ among two cell lines and the rest differed by a factor of 2 or less  lower than Km and Vmax values of human SERT expressing cell line 1  lower when compared to the two human NET-expressing cell lines. Values for rat DAT were 2-fold higher than in both cell lines expressing human DAT. The binding site density (Bmax) for β-CFT binding was similar for DAT and NET among all three cell lines . Notably, the IC50 values for the binding inhibition by cocaine at all rat transporter-transfected cell lines showed typically less than a 2-fold difference when compared to the IC50 values of the uptake inhibition in rat synaptosomes and 2.5- and 4-fold difference for SERT and NET cell lines, respectively, when MDPV was used  and should not affect the outcome  dopaminergic activity over serotonergic activity  of these catecholamine transporters, whereas the sequence homology with SERT binding site is much lower   .The comparison of inhibition potencies of substances at cell lines expressing human or rat MATs 5 result50 values for the inhibition of [3H]-CFT binding were more consistent compared to the IC50 values for the uptake inhibition. Similar to uptake inhibition, the most notable difference in the IC50 values for binding inhibition was observed for SERT cell lines . The next highest variation between two cell lines was observed in the case of norepinephrine uptake inhibition induced by d-amphetamine, MDMA, and mephedrone, even though there was no difference in Kd and Bmax values for β-CFT binding. There was no difference in β-CFT binding inhibition among all substances when two different DAT cell lines were assayed. This is in agreement with no differences in Kd and Bmax values for the two DAT cell lines.The ICll lines Table 3.When the two human MATs expressing cell lines were compared to rat MATs expressing cell lines, transporter density varied for SERT and DAT Table 4.A puzzling finding of this study was the fact that when using the method with cells in suspension, only one of the two stably DAT-transfected cell lines could be used, whereas with the other cell line no sigmoidal uptake curve was observed for any of the substances. The same phenomenon has recently been observed for catechol metabolites of MDMA and 3,4-methylenedioxymethcathinone (methylone) . A possiin situ versus rat SERT expressed in HEK cells. Similarly, all stimulants exhibited higher potencies at synaptosomal rat DAT and rat NET compared to the respective recombinant transporters expressed in HEK cells. An exception to this was mephedrone, which exhibited higher potency at recombinant rat NET compared to synaptosomal rat NET. The observed discrepancies might be related to fundamental differences between synaptosomes and intact cells. Synaptosomes are tissue homogenates containing an enriched preparation of sealed nerve endings, which may facilitate a higher probability for drug–transporter interactions. Furthermore, transporter proteins expressed in synaptosomes are surrounded by neuron-specific protein partners that are not present in HEK cells. Finally, procedural differences between the synaptosome experiments vs. cell-based experiments could be a factor. For example, the incubation times for MAT uptake assays in adherent cells are 1–3 min whereas incubation times for synaptosomes are 5–30 min , the National Institute on Drug Abuse, National Institutes of Health (grant No. DA 00523 to MB), the Austrian Science Fund , the Vienna Science and Technology Fund , and the Swiss National Science Foundation .The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.The reviewer SB declared a past co-authorship with several of the authors ML, MB, HS, and DL to the handling editor.


Immunoblot analysis showed elevated levels of γ-H2AX and cleaved PARP proteins upon drug combination treatment, indicating increased levels of DNA damage (double-strand break events: DSBs) and apoptosis induction, respectively. We propose that the mechanism of synergy involves SAHA relaxing condensed chromatin, resulting in higher levels of G4 formation. In turn, CM03 can stabilise a greater number of G4s, leading to the downregulation of more G4-containing genes as well as a higher incidence of DSBs due to torsional strain on DNA and chromatin structure.The stabilisation of G-quadruplexes (G4s) by small-molecule compounds is an effective approach for causing cell growth arrest, followed by cell death. Some of these compounds are currently being developed for the treatment of human cancers. We have previously developed a substituted naphthalene diimide G4-binding molecule (CM03) with selective potency for pancreatic cancer cells, including gemcitabine-resistant cells. We report here that CM03 and the histone deacetylase (HDAC) inhibitor SAHA (suberanilohydroxamic acid) have synergistic effects at concentrations close to and below their individual GI Pancreatic cancer is the fourth leading cause of cancer-related deaths in the USA and is expected to be the second or the third in developed and high-income countries in the next ten years . The mosWe have recently developed a class of potential drugs for PDAC that are based on the naphthalene diimide (ND) chemotype ,4. The lG-quadruplex (G4) sequences comprising short G-rich repetitive elements can be found in promoter and 5′ and 3′ untranslated regions of many genes (being over-represented in cancer-related genes) and in telomeres ,14,15,16A number of drugs that cause epigenetic remodeling such as methylation inhibitors and histone deacetylase (HDAC) inhibitors are in clinical trials as anticancer drugs ,30,31, aInhibition of chromatin condensation by the HDAC inhibitor SAHA, thus exposing potential G4 sites within a gene, for example in regulatory or non-coding regions.G4-binding compounds can maintain the unwinding of duplex regions of G4-containing genes within active chromatin by stabilizing G4 structures within these genes.This study has taken as a testable hypothesis the concept that the two effects can act in conjunction and thus synergize each other:We have used the clinically approved HDAC drug SAHA to investigate potential synergistic effects with a potent G4 binding compound, the naphthalene diimide compound CM03, which was developed in these laboratories .50 values of at least one of them, cell growth inhibition of 50% or more is produced. 50 values for the various compounds and drugs used in this study. Synergy between two compounds means that when administered together at lower concentrations than the GI50 value for each individual drug. Less pronounced effects are apparent in the GR3-PANC-1 cell line.Effects in the two gemcitabine-resistant PDAC cell lines are shown in The drug combination between SAHA and CM03 also shows a synergistic induction of apoptosis and DNA damage in the wild-type MIA PaCa-2 and PANC-1 cell lines, based on immunoblotting analysis of markers for apoptosis and DNA damage . Higher Previous studies on CM03 and gemcitabine using RNA-seq methods to map the transcriptome following drug treatment in PDAC cells have identified those genes downregulated by these drugs ,9,10. FiThe cell-based study reported here has demonstrated that a G-quadruplex ligand (CM03), in combination with the HDAC inhibitor SAHA, can produce >50% synergistic cell growth inhibition in the pancreatic cancer cell lines MIA PaCa-2 and PANC-1, as well as in these derived gemcitabine resistant lines. The study has identified effective two-drug combinations that show these levels of growth inhibition at concentrations below their individual GI50 values. Two other HDAC inhibitors, panobinostat and romidepsin, also show a synergistic effect in combination with CM03 . HoweverWe propose the following model for the synergistic effect between SAHA and CM03. SAHA, by inhibiting HDACs, induces chromatin relaxation and the formation of euchromatin regions , resultiWe have previously reported that CM03 in cells produces a modest DNA damage response , althougIt is also possible that the CM03-mediated downregulation of epigenetic modifiers such as demethylases and methyltransferases can contribute indirectly to the overall synergistic effect by preventing a re-condensation of relaxed chromatin. For instance, PRDM16 is a methyltransferase that maintains the integrity of heterochromatin, and its impairment along with PRDM3 leads to the disintegration of heterochromatic structures  Therefor50 values of 10–15 nM in PDAC cell lines  patients. The current clinical choice of FOLFIRINOX  combination therapy is a demanding therapy and is restricted to those patients with good performance status . Gemcita2O. SAHA  was obtained from Cayman Chemical Co. . It was used without further purification and was dissolved in dimethyl sulfoxide (DMSO). CM03 and gemcitabine were dissolved in phosphate-buffered saline (PBS). Stocks of 1 mM and 10 mM were prepared for each compound and kept frozen and away from light. Prior to addition to cell culture media, compounds were filtered using a 0.22 µm pore-size filter unit to maintain sterility.CM03 had been previously synthesized and purified as described  and was l-glutamine and 4500 mg/L glucose from Sigma-Aldrich (Cat. No. D6429). Media were supplemented with 10% FBS from ThermoFisher  (Cat. No. 10270106) and antibiotics 100 U/mL penicillin and 0.1 mg/mL streptomycin from Sigma-Aldrich (Cat. No. P4333). With MIA PaCa-2 and its germ-resistant form, 2.5% horse serum from ThermoFisher (Cat. No. 16050130) was also added. All cell lines were cultured in humidified incubators at 37 °C and 5% CO2 and passaged for 2–3 days. The cell lines were routinely tested to ensure that they were mycoplasma-free by a RT-qPCR-based method.MIA PaCa-2 (Cat. No. CRL-1420) and PANC-1 (Cat. No. CRL-1469) cell lines were obtained from ATCC. Parental and related gemcitabine-resistant cell lines, developed in-house, were all maintained in Dulbecco’s Modified Eagle’s Medium (DMEM) and already contained 2 mM Gemcitabine-resistant cell lines were generated from parental MIA PaCa-2 and PANC-1 pancreatic cancer lines by incrementally increasing the gemcitabine concentration in the culture medium over extended periods of time, as previously described . The res50 of each compound. After treatment, cells were fixed with 10% trichloroacetic acid (TCA) and cell viability was measured using a version of the sulforhodamine B (SRB) assay [First, 2000 cells/well of two pancreatic cancer cell lines (MIA PaCa-2 and PANC-1) or their generated gemcitabine-resistant form (GR3-MIA and GR3-PANC-1) were seeded in 96-well plates and incubated overnight. The drug combination SAHA + CM03 was evaluated in the synergy experiments. Cells were treated with two-drug combinations or single drugs for 96 h. A matrix format of 8 × 5 was used to assess all two-drug combinations using a range of concentrations several-fold above below the GIB) assay . https://www.proteinsimple.com/wes.html) to run the cell lysates and detect proteins of interest according to the manufacturer’s instructions. Antibodies used were for cleaved PARP , γ-H2AX , and GAPDH .MIA PaCa-2 and PANC-1 cells were seeded and treated with 0.4 µM CM03, SAHA (1 µM for MIA PaCa-2 and 4 µM for PANC-1) or in combination of both compounds, at these concentrations. The treatment continued for 24 h in MIA PaCa-2 cells and for 48 h in PANC-1 cells because 24 h treatment was insufficient for observing its effect on PANC-1 cells. Untreated or vehicle treated (DMSO) cells were used as a control. Cells were collected and lysed with RIPA lysis buffer  supplemented with protease and phosphatase inhibitors . The lysate concentration was quantified using the Pierce™ BCA Protein Assay Kit . Capillary-based automated Western blotting was performed on a Wes machine  based on the Chou-Talalay non-constant ratio method [For the SRB assays, the fraction affected was determined by taking the mean absorbance at 540 nm for each treated cell group expressed as a fraction of the absorbance of untreated or vehicle control-treated (DMSO) control cells. All experiments were performed in triplicate, and the mean ± SEM values were determined from at least three independent experiments. The combination index (CI) values were calculated using the CalcuSyn (version 2.0) software package , and the resulting intensity values were normalized to the loading control GAPDH. The one-way ANOVA test was used to determine the statistical significance of any changes in protein levels. Data represents mean ± SEM of 3 independent experiments. *


Expressions of genes related to cell cycle control  were evaluated by qRT-PCR by the LightCycler 480 System (Roche). GAPDH and ACTB housekeeping genes were used for the normalization of gene expressions. IC50 value of CAPE in MCF-7 cells was calculated as 75µM. It was shown that IC50 dose of CAPE induced significant upregulation in expressions of cell cycle control genes, compared to control cells. CAPE increases the expression of genes that are important in cell cycle control, suggesting that this component can be used as an effective chemopreventive agent in breast cancer cells. We aimed to find the effect of caffeic acid phenethyl ester (CAPE) on the expression profiles of cell cycle control genes in breast cancer cell line (MCF-7). The cytotoxic effect of CAPE on MCF-7 cell line was found with an XTT analysis. Total RNA was isolated from the cells exposed to IC RB1) is known as a tumor suppressor. RB1 mutations include all familial and sporadic forms of retinoblastoma. The RB gene is a common mutation in various human cancers . In our study, the increase in the expression of ATM, CDC34 was found to be significant. ATM and CDC34 genes can contribute to cancer treatment as cell cycle regulators. A decrease in CDK5RAP1 was found to induce cell cycle arrest and apoptosis by ROS/JNK signaling pathways in MCF-7 cell lines. CDK5RAP1 is very important in the development of clinical treatments for cancer [Silencing of CDC34 could lead to the inhibition of cell proliferation and suppression of colony formation . Increasr cancer . In addir cancer . The inc


Predicting the response of cancer cell lines to specific drugs is an essential problem in personalized medicine. Since drug response is closely associated with genomic information in cancer cells, some large panels of several hundred human cancer cell lines are organized with genomic and pharmacogenomic data. Although several methods have been developed to predict the drug response, there are many challenges in achieving accurate predictions. This study proposes a novel feature selection-based method, named Auto-HMM-LMF, to predict cell line-drug associations accurately. Because of the vast dimensions of the feature space for predicting the drug response, Auto-HMM-LMF focuses on the feature selection issue for exploiting a subset of inputs with a significant contribution.This research introduces a novel method for feature selection of mutation data based on signature assignments and hidden Markov models. Also, we use the autoencoder models for feature selection of gene expression and copy number variation data. After selecting features, the logistic matrix factorization model is applied to predict drug response values. Besides, by comparing to one of the most powerful feature selection methods, the ensemble feature selection method (EFS), we showed that the performance of the predictive model based on selected features introduced in this paper is much better for drug response prediction. Two datasets, the Genomics of Drug Sensitivity in Cancer (GDSC) and Cancer Cell Line Encyclopedia (CCLE) are used to indicate the efficiency of the proposed method across unseen patient cell-line. Evaluation of the proposed model showed that Auto-HMM-LMF could improve the accuracy of the results of the state-of-the-art algorithms, and it can find useful features for the logistic matrix factorization method.https://github.com/emdadi/Auto-HMM-LMF.We depicted an application of Auto-HMM-LMF in exploring the new candidate drugs for head and neck cancer that showed the proposed method is useful in drug repositioning and personalized medicine. The source code of Auto-HMM-LMF method is available in  Computational models for personalized medicine make it possible to understand cancer cell lines on the basis of genomic information. This knowledge makes it possible to recommend individualized therapies to patients with different types of cancer by measuring drug responses. Many effective anticancer drugs have already been developed for each cancer type, such as breast cancer, lung cancer, ovary cancer and Brain cancer. For example, docetaxel, paclitaxel, carboplatin, cisplatin, vinorelbine and eribulin are just a few examples of drugs used to treat breast cancer. Since drug response to cancer treatment depends on multiple factors such as the patient's genomic profile, this process is a complicated problem in cancer treatment. These challenges have generated large-scale experiments on human cancer cell lines and various anticancer drugs. For instance, two datasets Genomics of Drug Sensitivity in Cancer  (GDSC) aSeveral recommender system-based models were proposed for predicting drug response. Wang et al. adopted a similarity-regularized matrix factorization (SRMF) method to predict anticancer drug responses of cell lines using the gene expression profile in cell lines and drugs’ chemical structures. They indicated that rapamycin (an mTOR inhibitor) could be a new therapeutic agent for non-small cell lung cancer . SuphaviIdentifying the optimal subset of features from many genetic candidate features is a crucial issue for classification models for predicting drug response. Thus, a large number of algorithms have proposed using different approaches for feature selection. Xu et al. proposed AutoBorutaRF method based on feature selection for predicting drug response. This method first built an autoencoder network, and it used Boruta algorithm  to selecThis study presents a feature selection-based method for drug response prediction, named Auto-HMM-LMF, to efficiently predict cell line-drug associations. Gene expression profile, copy number alteration, single-nucleotide mutation, tissue type information of the cell line, and drugs’ chemical structure information were incorporated. Two strategies based on autoencoder and hidden Markov model-multinomial mixture model are used for selecting the essential features of input information. The autoencoder networks are applied on gene expression profile, copy number alteration data. Also, hidden Markov model and multinomial mixture model are applied on mutation data. A proper evaluation of the Auto-HMM-LMF method using tenfold cross-validation was carried out to compare it with the state-of-the-art methods. Results show its performance is superior for the tested data sets. Also, by comparing to the ensemble feature selection method (EFS), we showed that two considered strategies for feature selection in the Auto-HMM-LMF method could select proper features that significantly improved the prediction result.EXP) is constructed using selected features. Similarly, the similarity matrix (SimCNV) is created using the selected feature by applying the autoencoder feature selection method on copy number alteration information. In the next step, the similarity matrix (SimMUT) is generated using a novel feature selection approach based on the hidden Markov model and multinomial mixture model on single-nucleotide mutation data. Two similarity matrices (SimIC50) and (SimTISSUE) are achieved using IC50 values of cell lines across the drugs and tissue type information of each cell line, respectively. Finally, for constructing the latent vectors for each cell line and the drug and predicting whether the cell line is sensitive to the drug or not, the logistic matrix factorization method is applied. For assigning the IC50 values to two labels, sensitivity and resistance, we used the strategy introduced in previous researches i,j=1,….,t which ai,j is the transition probability from qi to qj.A = i=1,…,t, j=1,…,s where ei,j is the probability that state qi emits cj.E =  is the total number of times the ith mutation category were observed as isolated mutations in samples. By applying vector T to the initialized vector g and emission matrix E, their estimated values were achieved.1, …, OT, which is decomposed into close and isolated subsequences,{C1, I1, C2, I2, …, Ck1, Ik2}, is formulated as follows:After training two above models, the probability of sequence occurrence Ot), the state with maximum probability value (Qt) is obtained from the following formula:The Viterbi algorithm  was applFinally, we append these two most probable paths of states to construct the final path corresponding to the patient. Then, the numbers of observed states (signature) are calculated as signature frequency per sample or signature activities for each path. For example, a patient with BRCA cancer has a vector with size 12 corresponding to the number of signatures, and the elements of this vector are calculated based on the number of times each state is observed in the final path.For considering the relationship between signature activities and gene expression profiles for each patient, we downloaded the gene expression files for patients from the International Cancer Genome Consortium Data Portal . The numSince similar cell lines and similar drugs may have similar drug responses, the similarities between cell lines and drugs can improve drug response prediction , 18.The similarity matrices are required for the identification of the nearest neighbors in the logistic matrix factorization model. Gene expression profile, copy number alteration, single-nucleotide mutation, and tissue type information are used for cell line similarity, and chemical structures of drugs are used for drug similarity. So, five similarities between each pair of cell lines and the similarity between each pair of drugs are defined as follows:EXP) is the similarity based on the selected features of the gene expression profile, in which the numbers of identified essential genes for gene expression profile by autoencoder are 798 and 1189 for two datasets GDSC and CCLE, respectively. SimEXP is defined as the Pearson correlation between the gene expression vector of each pair of n cell lines, arranged in an n × n matrix.(SimCNV) is the similarity based on the selected features of copy number alteration data, which 67 and 127 useful features selected by autoencoder in GDSC and CCLE, respectively. SimCNV matrix is defined as an n × n matrix by Pearson correlation between the copy number alteration vector of each pair of cell lines.(SimMUT) is the similarity based on the selected features of single nucleotide mutation information by the hidden Markov model and multinomial mixture model. 22 and 67 essential genes identified by this strategy from GDSC and CCLE datasets, respectively. Then, the Jaccard similarity is applied on each pair of single nucleotide mutation vectors corresponding to n cell lines, and SimMUT is constructed as an n × n matrix.(SimIC50) is the similarity between cell lines based on their IC50 values. This definition of similarity between cell lines proposed by Liu is based on the correlation between response IC50 values of the cell lines n×n that represents cell line neighborhood information as follow:Since the correlation coefficient between each pair of the above similarity matrices is very low, there is no collinearity between matrices, and they can be linearly combined. We constructed an integrated matrix similarity, SimDRUG). For each drug, a zero–one vector of size 881 is considered where 881 is the number of known chemical substructures of a drug. In this vector, 1 indicates the presence of a substructure of the drug and 0 otherwise. SimDRUG = [SDij]m×m is constructed as an m × m matrix by Jaccard similarity between each of the chemical substructures vector corresponding to the m drugs. For a drug di, the set Nk(di) denotes the k-most similar drugs to di (except di) using SimDRUG matrix. The adjacency matrix, B = [bij]m×m, describes the drug neighborhood information as follows:The similarity between drugs is constructed based on chemical substructures , Ec and d = (Ed + T) as diagonal elements (n is the numbers of cell lines). Ed and IC50) matrix are unknown, the (SimCL) matrix cannot be used for finding the k-nearest neighbors for the new cell line. We used the strategy introduced in the DSPLMF method for estimation (SimIC50) matrix. DSPLMF method is designed a Decision Tree Classifier model for estimation (SimIC50) matrix using the gene expression profile, copy number alteration, and single-nucleotide mutation information of the new cell line [i is sensitive to the drug dj can be modeled as a logistic function as follows:For drug response prediction of cancer cell lines from GDSC and CCLE datasets using selected features, the DSPLMF method introduced based on the logistic matrix factorization method  is appliectively . Two parmentclasspt{minimaFinally, a threshold is applied on probabilities to assign a sensitive or resistance class to each new cell line-drug pair.Using the feature selection approaches is one of the common methods to reduce the dimensions of the features in drug response prediction problems. In some of the previous predictive methods, such as AutoBorutaRF, the autoencoder approaches are used for selecting significant features of genomic information. One of the most powerful methods of selecting features is the EFS method proposed by Neumann et al. The EFS method integrated eight different feature selection methods and normalized all individual outputs to a common scale, an interval from 0 to 1 , 22. FirTP (true positive): The number of cell lines labeled with sensitivity and predicted as sensitivity.TN (true negative): The number of cell lines labeled with resistance and predicted as resistance.FP : The number of cell lines labeled with resistance and predicted as sensitivity.FN : The number of cell lines labeled with sensitivity and predicted as resistance.We compared the Auto-HMM-LMF method to six classification models, DSPLMF, EFS-LMF, CaDRReS, SRMF, AutoBorutaRF, and SVM-RFE for different metrics. DSPLMF and AutoBorutaRF are designed as the classification models, but the CaDRReS and SRMF methods predicted IC50 values as output. So, for comparison of these models with the Auto-HMM-LMF and EFS-LMF methods, we applied the median of predicted IC50 values for each drug as a classification threshold. If the predicted IC50 value corresponding to a cell line-drug pair is smaller than this threshold, the sensitive class was assigned to it; otherwise, it was labeled with resistance class. Seven metrics Accuracy, Recall, Precision, Specificity, F1Score, Matthews correlation coefficient (MCC) and area under the receiver operating characteristic curve (AUC) are used that; these criteria are formulated as follows:Tables To demonstrate the Auto-HMM-LMF method’s performance in different tissue types, we examine whether our proposed method can achieve good performance when considering specific cell line tissue types. In this way, 73 Haematopoietic and lymphoid cell lines in the GDSC dataset are considered, and seven criteria evaluate the Auto-HMM-LMF method. We trained the Auto-HMM-LMF method by these cell lines, and we applied a tenfold cross-validation approach for drug response prediction of considered cell lines. As is shown in Table We plotted the Bar chart of Pearson correlation coefficients of observed drug responses and predicted values for 24 drugs in the CCLE dataset. As is shown in Fig. Drug repositioning is the process of selecting a known drug for an alternative pharmacological purpose. For this issue, we considered 37 US Food and Drug Administration (FDA) approved drugs that were not tested in the GDSC dataset from the study of Choi et al. . The AutAzacitidine: Azacitidine is a type of drug called a hypomethylating agent, and a study reported that Azacitidine and Cisplatin are effective for the treatment of head and neck cancer .Regorafenib: Regorafenib is an oral multi-kinase inhibitor that targets receptor tyrosine kinase (RTK). Klinghammer et al. established a panel of 65 head and neck squamous cell carcinoma, and they demonstrated that combinational treatment of regorafenib and Everolimus is useful in these patients .Everolimus: Everolimus is used as an immunosuppressant to prevent rejection of organ transplants in the treatment of cancer. Recently, a study showed that patients with TP53 mutations benefited significantly from Everolimus in head and neck cancer .Fulvestrant: Fulvestrant is a drug used to treat hormone receptor (HR)-positive metastatic. Grünow et al.  showed tInterferonAlf a–2B: InterferonAlf a–2B is an antiviral or antineoplastic drug that is an effective treatment in head and neck cancer .Porfimer: Porfimer is a photosensitizer, and it is used in radiation therapy in cancer treatment. An in vivo study suggested that this drug can be used in treatment for HNSC patients .lmiquimod: Imiquimod (INN) is a prescription drug that acts as an immune response modifier used to treat basal cell carcinoma. The study showed that topical Imiquimod might offer a reasonable and well-tolerated palliative treatment option for patients .Decitabine or 5–aza–2′–deoxycytidine is a nucleic acid synthesis inhibitor for cancer treatment. Cisplatin resistance in head and neck squamous cell carcinoma reduces survival. Viet et al.  showed tHydroxyurea: Hydroxyurea is an anti-cancer agent used to treat melanoma, resistant, recurrent, and metastatic cancer types. A study displayed Hydroxyurea is a single active agent in head and neck cancer. It has been used clinically as a radiation-enhancing drug with radiotherapy .V andetanib: V andetanib acts as a kinase inhibitor of several cell receptors, and it is an anti-cancer drug for the treatment of cancer cell lines. Sano et al.  approvedCarf ilzomib: Carf ilzomib is an anti-cancer drug acting as a selective proteasome inhibitor. By upregulation of pro-apoptotic Bik, Carf ilzomib and ONX0912 potently induced apoptosis in HNSC cell lines .As it can be seen in Fig. These results indicate that the Auto-HMM-LMF model can be useful in drug repositioning. Also, five drugs  may be novel therapeutic drugs for HNSC.Since the numbers of cell lines and drugs in the GDSC dataset are higher than the CCLE dataset, we tuned the hyperparameters on the GDSC dataset, and we used the obtained values of the hyperparameters in both datasets. In this way, the tenfold cross-validation procedure is applied to GDSC, and hyperparameters are determined by maximizing the AUC criterion.The learned hyperparameters using the GDSC dataset are shown in Table This paper proposed the Auto-HMM-LMF method based on feature selection approaches and logistic matrix factorization strategy to predict drug response. The proposed prediction model showed higher predictive efficiency than the existing computational models. Also, we demonstrated that the Auto-HMM-LMF model could be useful in drug repositioning. So, we identified five drugs  for HNSC treatment. To illustrate the biological significance of the features selected by the hidden Markov model (HMM) and multinomial mixture model (MMM) on mutation data in the Auto-HMMLMF method, we further consider cancer cell lines related to breast cancer (BRCA) and two important processes, namely MMR and HRD. This study selected 30 significant genes by considering the Spearman correlation coefficient between their gene expression file and signature activity for 12 signatures of BRCA cancer cell lines. Among these genes, the gene expression of four genes namely PMS2, MLH1, MSH2, and MSH6 has high Spearman correlation coefficient with signatures 6, 20, and 26 activities. The results of the Spearman correlation between the expression of these genes and three mutation signature activities in BRCA are shown in Fig. Also, the expression of ten other selected genes by proposed model, namely BRCA1, BRCA2, ATM, CHEK2, MRE11A, NBN, FANCA, PALB2, RAD51C, RAD50 has high Spearman correlation coefficient with activity of mutation signature 3. The Spearman correlation coefficients between the expressions of these genes with a signature 3 activity are shown in Fig. In this study, we developed a feature selection-based method, Auto-HMM-LMF, to predict cancer cell lines’ response to drugs in the GDSC and CCLE datasets. For feature selection of gene expression and copy number variation data, two autoencoder networks are designed. For feature selection of single nucleotide mutation information, the novel approach based on the hidden Markov model (HMM) and multinomial mixture model (MMM) is applied. Auto-HMM-LMF shows better overall prediction performance than the state-of-the-art prediction methods. Also, by comparing to one of the most powerful feature selection methods, the EFS method, we showed that the performance of the predictive model based on selected features introduced in this paper is much better for drug response prediction. Also, we suggest that the proposed model can be useful in numerous therapeutic research areas, such as drug repositioning and personalized medicine. Finally, we found substantial evidence that the selected features and predicted responses by Auto-HMM-LMF have significant consistency with many previous studies.Additional file 1. The results of the correlation between some genes and signature activity in 14 cancer types with high Spearman correlation coefficients.Additional file 2. The similarity matrices based on tissue type information corresponding to GDSC dataset.Additional file 3. The similarity matrices based on tissue type information corresponding to CCLE dataset.Additional file 4. The scatter plots of observed and predicted drug responses by the Auto-HMM-LMF model of 20 drugs in the CCLE dataset.