Pralatrexate (PDX) is a novel antifolate approved for the treatment of patients with relapsed/refractory peripheral T-cell lymphoma, but some patients exhibit intrinsic resistance or develop acquired resistance. Here, we evaluated the mechanisms underlying acquired resistance to PDX and explored potential therapeutic strategies to overcome PDX resistance.To investigate PDX resistance, we established two PDX-resistant T-lymphoblastic leukemia cell lines (CEM and MOLT4) through continuous exposure to increasing doses of PDX. The resistance mechanisms were evaluated by measuring PDX uptake, apoptosis induction and folate metabolism-related protein expression. We also applied gene expression analysis and methylation profiling to identify the mechanisms of resistance. We then explored rational drug combinations using a spheroid (3D)-culture assay.14C]-PDX decreased in PDX-resistant CEM cells but not in PDX-resistant MOLT4 cells. There was no significant change in expression of dihydrofolate reductase (DHFR) or folylpolyglutamate synthetase (FPGS). Gene expression array analysis revealed that DNA-methyltransferase 3β (DNMT3B) expression was significantly elevated in both cell lines. Gene set enrichment analysis revealed that adipogenesis and mTORC1 signaling pathways were commonly upregulated in both resistant cell lines. Moreover, CpG island hypermethylation was observed in both PDX resistant cells lines. In the 3D-culture assay, decitabine (DAC) plus PDX showed synergistic effects in PDX-resistant cell lines compared with parental lines.Compared with their parental cells, PDX-resistant cells exhibited a 30-fold increase in half-maximal inhibitory concentration values. Induction of apoptosis by PDX was significantly decreased in both PDX-resistant cell lines. Intracellular uptake of [The resistance mechanisms of PDX were associated with reduced cellular uptake of PDX and/or overexpression of DNMT3B. Epigenetic alterations were also considered to play a role in the resistance mechanism. The combination of DAC and PDX exhibited synergistic activity, and thus, this approach might improve the clinical efficacy of PDX.The online version contains supplementary material available at 10.1186/s12885-021-08607-9. Peripheral T-cell lymphomas (PTCLs) are a heterogeneous group of mature T-cell and natural killer-cell neoplasms accounting for approximately 5–15% of non-Hodgkin’s lymphomas , 3. ConvRecently, pralatrexate  has been approved for the treatment of PTCL in the USA, Japan, and other countries. PDX is a folate analog designed to have a high affinity for reduced folate carrier (RFC1), dihydrofolate reductase (DHFR), and folylpolyglutamate synthetase (FPGS), resulting in increased cytotoxic activity compared with methotrexate (MTX) , 5. PDX MTX is a well-established antifolate that blocks the action of DHFR, and has been used to treat various types of non-Hodgkin’s lymphomas, including primary central nervous system lymphoma and Burkitt’s lymphoma. However, its clinical efficacy in PTCL is limited. PDX has been studied across a variety of non-Hodgkin’s lymphomas –8 and isDespite this promising activity, some patients can acquire resistance to PDX over time. Identifying the underlying mechanisms of drug resistance could lead to rational strategies to prevent or overcome these mechanisms of resistance, thereby improving clinical efficacy. Accordingly, in this study, we newly established two PDX-resistant T-lymphoblastic leukemia cell lines designated CCRF-CEM (CEM) and MOLT-4 (MOLT4) to explore these mechanisms of drug resistance. These cell lines were then used to develop and evaluate complementary drug combinations as a means to overcome acquired resistance to PDX.2 in a humidified atmosphere.The human acute T-lymphoblastic leukemia cell lines CCRF-CEM (CEM) and MOLT4 were purchased from JCRB Cell Bank  in 2015. The cells were cultured in RPMI-1640 medium supplemented with 10% fetal bovine serum at 37 °C under 5% CO6 cells/100 μL were seeded in soft medium into 48-well plates. The cells were placed in a standard CO2 cell culture incubator .Spheroid (3D) cell culture was performed with Col-Tgel  following manufacture protocol. In brief, 2 × 10PDX and forodesine (FDS) were kindly provided by Mundipharma K.K. . FDS is a novel inhibitor of purine nucleoside phosphorylase and was approved for the treatment of relapsed/refractory PTCL in Japan . MTX, cy50). After acquisition of PDX-resistant cells, single-cell cloning was performed by a limiting dilution method according to previous reports -PDX for 0, 5, 10, 20, or 30 min, and cell pellets were dissolved using SOLUENE-350 and Clear-sol l . Radioactivity was measured using a liquid scintillation counter.Cellular uptake of PDX was calculated by a radioisotope assay. The cells .50 values for the PDX-resistant cell lines (CEM/P and MOLT4/P) were 20 nM and 80 nM, respectively. In comparison with the IC50 values of the parental cells , those of the PDX-resistant cell lines were increased by approximately 33-fold , induction of apoptosis was observed in 78.7% of CEM cells and 77.7% of MOLT4 cells, whereas only 4.4% of CEM/P cells and 5.9% of MOLT4/P cells were apoptotic at the same concentrations  expression level was significantly elevated in both CEM/P and MOLT4/P cells versus their parental cells. There were no significant differences in the expression levels of folate metabolism pathway components such as FPGS, DHFR, and thymidylate synthase (TS) in either PDX-resistant cell line  in CEM/P and MOLT4/P cells, respectively. Most of the differentially expressed genes were unique to each cell line, and only 72 genes were commonly up- or downregulated between the PDX-resistance cell lines  is shown in Fig. KDELC2 gene, a glucosyltransferase protein, had increased methylation in CpG islands. The CUL1 and EGLN1 genes had also increased methylation in the shore. On the other hand, for genes with increased expression in both PDX-resistant cells, CBFA2T3, a transcription corepressor, had decreased methylation in CpG island. There were no significant changes in methylation for specific genes involved in folic acid metabolism, such as RFC1, FPGS, DHFR and TYMS, or genes involved in DNA methyltransferase . We also examined PDX sensitivity in DNMT3B knockdown cells, and the IC50 values were 30 and 18 nM in CEM/P-mock and CEM/P-shDNMT3B cells, respectively, suggesting limited recovery of PDX sensitivity  were observed at the ICUsing classical methods to produce drug-resistant cell lines, we generated two cell lines that showed approximately 30-fold greater resistance to PDX than their parental counterparts. The characteristics of the PDX-resistant cells were as follows: (i) decreased internalization of PDX in CEM/P cells owing to reduced RFC expression; (ii) increased expression of DNMT3B; (iii) hypermethylation of CpG islands; and (iv) synergism with the hypomethylating agent DAC.DHFR and/or TS caused by overexpression has been reported, and mutations in these genes can decrease their affinity for antifolates, resulting in antifolate resistance [The potential mechanisms of acquired PDX resistance have gradually been uncovered in recent years , 28, 29,sistance , 30, 31.As shown in Fig. The combination with DAC resulted in high susceptibility to PDX in our resistant cells Fig. , but didWe established PDX-resistant T-cell acute lymphoblastic leukemia cell lines, and the combination of DAC and PDX exhibited a potent synergistic effect in these cells. Reduced cellular uptake of PDX and epigenetic alterations may contribute to the development PDX resistance. These findings will support clinical trials for patients with refractory/relapsed PTCL using PDX in combination with other agents.Additional file 1: Supplementary Data 1. Growth curves and doubling time. The growth curves of parental and PDX-resistant celllines are shown. The cells (1 × 105 cells) were cultured for 96 h and counted every 12 hours. CEM/P, PDX-resistance CEM cell. MOLT4/P, PDX-resistance MOLT4 cell.Additional file 2: Supplementary Data 2. DNA sequence of RFC1.created by author based on COSMIC v86. * Rothem L et al. Biochem J. 2002;367,741–750. Zhao R et al. Mol Pharmacol.1999;56,68–76. Sequencing analysis of exons 2 and 3 of RFC1 (SLC19A1) was performed using genomic PCR and RT-PCR.  Distribution of point mutations in RFC1, as previously reported. (c) Sanger sequencing of RFC1 exon2 and exon3. There were no acquired somatic mutations in PDX-resistant cells. Exon 2 (MOLT4 and PDX-resistant cells), exon 3 (MOLT4 and PDX-resistant cells).Additional file 3: Supplementary Data 3. IC50 values of other agents. Expression of folate metabolism pathway components. Total RNA (10 ng) from parental and PDX-resistant cells was obtained, and mRNA expression was evaluated using Clariom S Arrays. There were no significant differences in the expression levels in either PDX-resistant cell line. SLC19A1; solute carrier family 19 member 1(known as RFC1), SLC46A1; solute carrier family 46 member 1, FPGS; folylpolyglutamate synthase, GGH; gamma-glutamyl hydrolase, DHFR; dihydrofolate reductase, TS; thymidylate synthase. CEM/P, PDX-resistance CEM cell. MOLT4/P, PDX-resistance MOLT4 cell.Additional file 4: Supplementary Data 4. The GESA results of CEM/P and MOLT4/P. Gene set enrichment analysis (GSEA). Significantly enriched gene signatures in GSEA analysis with microarray analysis from parental and PDX-resistant cells using the hallmark gene set. a) GSEA in CEM/P cell, b) GSEA in MOLT4/P cell.Additional file 5: Supplementary Data 5. Comparison of PDX-resistant cells and DNMT3B knockdown cells. Viral supernatants containing DNMT3B-shRNA (TRCN0000035686) and the control non-target shRNA were purchased from Sigma-Aldrich. CEM/P cells were infected in the presence of 25 µg/mL retronectin  for 6 h and selected with puromycin (10 mg/mL).PDX-resistant CEM cells were infected in the presence of 25 mg/mL of Retronectin for 6 hr and selected with puromycin (10 µg/mL). DNMT3B, DNA methyltransferase 3B.


Using 11 proteomics datasets, mostly available through the PRIDE database, we assembled a reference expression map for 191 cancer cell lines and 246 clinical tumour samples, across 13 lineages. We found unique peptides identified only in tumour samples despite a much higher coverage in cell lines. These were mainly mapped to proteins related to regulation of signalling receptor activity. Correlations between baseline expression in cell lines and tumours were calculated. We found these to be highly similar across all samples with most similarity found within a given sample type. Integration of proteomics and transcriptomics data showed median correlation across cell lines to be 0.58 (range between 0.43 and 0.66). Additionally, in agreement with previous studies, variation in mRNA levels was often a poor predictor of changes in protein abundance. To our knowledge, this work constitutes the first meta-analysis focusing on cancer-related public proteomics datasets. We therefore also highlight shortcomings and limitations of such studies. All data is available through PRIDE dataset identifier PXD013455 and in Expression Atlas. Hence, in order to capture the patient’s tumour biology, it is therefore often more desirable to study primary cells or tissue biopsies.Cancer cell lines are powerful models often used in place of primary cells to study for instance the molecular mechanisms of the disease. Cell lines derived from patients provide an inexpensive source of pure cell populations and are easy to manipulate and characterize. Although they usually retain driver mutations, cell lines often contain ‘additional’ genomic aberrations not present in tumours. They also lack the tumour microenvironment interactions and can undergo divergent evolution during long-term cell culture4, and contains over 20,000 samples in total. The Cancer Cell Line Encyclopedia (CCLE) consortium measured DNA copy-number, mutations and gene expression in 1,072 human cancer cell lines5. Also in this context, the Genomics of Drug Sensitivity in Cancer (GDSC) project provided genomic and gene expression data for over 1,000 cell lines combined with cell line sensitivity measurements related to a wide range of anti-cancer therapeutics6.Thanks to a number of International initiatives and large-scale studies, a variety of omics approaches have been employed to characterise both tumour samples and cell lines at the molecular level. Among them, The Cancer Genome Atlas (TCGA) used next-generation sequencing (NGS) and reverse-phase protein arrays (RPPAs) to generate genome and expression landscapes in approximately 10,000 tumour specimens8. Accordingly, cancer cell lines proteomes have been characterised in a number of MS-based studies17. However, due to technological limitations, these and other currently publicly available proteomics datasets are usually smaller in scope in comparison to analogous genomics and transcriptomics studies. Therefore, although they can routinely cover thousands of gene products, the datasets are usually limited to tens of samples. MS-based protein expression information in tumour specimens has also been obtained, for example through the work of the Clinical Proteomic Tumour Analysis Consortium (CPTAC)19 or by other independent groups22. Additionally, RPPA approaches can be used to characterise a usually much smaller number of proteins23.While the majority of the available data is based on NGS technologies measuring DNA and RNA-level alterations in cancer, proteins are however most often the functional molecules, providing a link between genotype and the phenotype. Proteins are also the targets of many drugs and can be a source of novel biomarkers. Mass spectrometry (MS) is the main proteomics technology, capable of providing system-wide measurements of protein expression, post-translational modifications and/or protein-protein interactions, among other pieces of information24 is the world-leading resource as part of the ProteomeXchange Consortium, storing raw files, processed results and the related metadata coming from thousands of original datasets. Public datasets stored in PRIDE, together with existing ones in other open proteomics repositories  present an opportunity to be systematically reanalysed and integrated, in order to confirm the original results, potentially obtain new insights and be able to answer biologically relevant questions orthogonal to those posed in the original studies. Such integrative meta-analyses have already been successfully employed in different omics data types, especially in genomics and transcriptomics27. For example, Lukk et al.28 integrated thousands of microarray files to compile a map of human gene expression. In metabolomics, Reznik et al.29 used MS data from eleven studies to measure the extent of metabolic variation across tumours. A similar trend is starting to be observed in proteomics, where reuse of public datasets is becoming increasingly popular, with multiple applications31. Some examples where joint reanalysis of large public datasets has been performed involved the creation of comprehensive maps of the human proteome32 and of human protein complexes33, or the characterisation of the functional human phosphoproteome34.Importantly, the MS data underpinning these efforts is now routinely made freely available in the public domain, an opposite situation to the state-of-the-art just a few years ago. Particularly, the PRIDE databaseHowever, to our knowledge no previous studies have attempted to reanalyse and integrate quantitative proteomics datasets in order to provide a global reference map of protein expression in cancer. Here, we provide a reference resource of protein expression across different types of primary tumours and the corresponding cell line models , using public proteomics datasets as the base.35. The resulting aggregated dataset covered 15,443 gene products with at least one unique (unambiguous) peptide evidence, which corresponded to a 67.8% coverage of the entire UniProt reference human proteome . PRIDE dataset identifier PXD013455 contains all the raw data, MQ intermediate files and the combined proteomics analysis results.The overall results of the study have been made publicly available in two EMBL-EBI resources: Expression Atlas (EA)Our first objective was to use the peptides identified in the aggregated dataset to assess for differences in MS-detectable proteome, independent of the tissue of origin and/or the lineage, between tumour and cell line. On average, 6,208 proteins were detected in the majority of tumours of any given type (meaning in ≥50% of samples of that group) and 7,401 proteins in cell line data.tumour-specific peptide set. In contrast, a much larger proportion of peptides was identified only in cell lines (66.0%). This was expected as some of the cell line studies employed extensive fractionation protocols or used multiple digestion enzymes  enrichment analysis of this protein set was performed using GOrilla37 and REVIGO38, revealing that the tumour-specific proteins were most significantly enriched for biological processes associated with regulation of signalling receptor activity (GO:0010469). Other terms, significantly enriched at a FDR  p-value < 0.05 level, included keratinization (GO:0031424), G protein-coupled receptor signalling pathway (GO:0007186), positive regulation of leukocyte chemotaxis (GO:0002690), humoral immune response (GO:0006959) and response to bacterium (GO:0009617) .When we compared the peptides identified across all cancer cell lines versus all the tumours, we observed that only a small fraction was identified exclusively in tumour data. Out of 711,352 peptides, 33,045 (4.6%) were present only in tumours, constituting a Taken together, expression of proteins associated with those pathways is not detectable in the cell lines considered here. However, it is important to consider an inherent limitation in this type of studies. Tumour-unique peptides could have potentially originated from a number of non-tumour sources, for example tumour infiltrating immune cells (a different tissue) due to cross contamination during sample processing.In order to go beyond simple presence/absence qualitative measurements, we generated two matrices containing normalised protein expression measurements across cell lines and tumour samples.We merged the two by cross-referencing the leading razor protein identifiers, which resulted in a union of 8291 protein profiles and an intersection of 4,476 proteins. These quantitative values, i.e. log2-transformed batch normalized ppb Intensities, were used to investigate the similarity between the samples.Compared with the tumour tissues, cell lines showed similar levels of variability in protein expression. This was established using the coefficient of variation (CV) calculated between different cell lines and tumours within a given tumour type (i.e. reflecting cancer lineage sample-to-sample variability). The median CV was below 56% in all cases and in general, the majority of proteins. had CV values below 70% in both cell lines and tumours. Notable exceptions were ovary tumours and prostate cell lines which showed a much larger spread of CVs across individual proteins. Unsurprisingly, the variability between biological replicates (assessed in cell lines only) was lower than between samples from a different biological origin (median CV = 43%).p) values were calculated between all samples based on pairwise complete observations .To investigate the level of agreement in protein expression between endogenous tumour cells and their corresponding cell line models, non-parametric Pearson correlation coefficient  to be higher than between sample types, as it might be expected. The median correlations within cell lines and tumours were similar: 0.81 and 0.83 respectively, whereas it was 0.73 between cell lines and tumours Fig. . Further40 should be considered when selecting the most appropriate cancer model.Overall, the analysis suggests that cell lines have similar baseline protein expression levels to those observed in tumours, as reflected by the high positive correlation found between all the samples. However, the existence of high correlations, even between different lineages, suggest that additional molecular features such as genomics alterations41. In most studies performed in tumour samples so far, the analysis was focused on single cancer types or otherwise, it was limited to a handful of samples for which both mRNA and protein measurements were available. By integrating the aggregated dataset with RNA-seq measurements publicly available already in EA (see Methods section), the correlation between mRNA and protein abundance was calculated for 134 cancer cell lines across 13 lineages, including 6,674 gene products that were overlapping between proteomics and transcriptomics data. The correlations were calculated for a total of 261 proteomics assays as some cell lines were measured in multiple studies/biological replicates.The correspondence between RNA and protein expression has been previously characterised in cell lines, tumour samples and tissuess) was calculated for an average of 6,542 mRNA–protein pairs (some cell lines contained a higher number of mRNA-protein pairs than others), for each of the 261 proteomics assays (from 134 cell lines). Despite that the original omics measurements were performed in independent studies, all the cell lines displayed a statistically significant  positive correlation. The median rs was 0.58 and the values ranged between 0.43 and 0.66 , breast (median rs = 0.56) and ovarian (median rs = 0.58) cell lines had the highest number of replicates and seemed to display on average the highest level of correlation, albeit all other lineages showed very similar values. For example kidney (median rs = 0.52), blood (median rs = 0.54) and brain (median rs = 0.55) cell lines showed the lowest level of correlation . The median gene-wise rs value was 0.31 ranging from −0.40 to 0.82 . A similar result, where multiple processes were enriched at the top of the ranked list, was also reported for colorectal cancer18. The GO terms sets with a largest NES included protein homotetramerization (GO:0051289), alpha-amino acid metabolic process (GO:1901605), and response to xenobiotic stimulus (GO:0009410) , ribosomal small subunit biogenesis (GO:0042274) and ribosomal small subunit assembly (GO:0000028)  Fig. . In cont28) Fig. .Recent large-scale genomics and transcriptomics studies have characterized the molecular diversity of cancer to a great depth. However, in order to understand the relationship between genome and disease phenotypes, information about protein expression is increasingly relevant. Since it is difficult for a single study to cover all proteins of interest and/or to capture diverse biological conditions (such as different tumour types or disease stages), meta-analysis approaches enable the computational integration of multiple studies to provide a combined wide-ranging view.44. Available studies show different levels of agreement over this statement, and in fact only partial concordance has been found for some of these features47. Nevertheless, few studies so far have been performed to explore the level of similarity between cell lines and tumours at a proteome-wide level.This study provides a rich resource including an aggregated view of protein expression in cell lines and tumour samples, provided to the scientific community through two popular resources: the PRIDE database and EA. Cell lines have indeed provided valuable insights into molecular mechanisms involved in cancer and are generally well-accepted as models of tumour biology. This is possible in part due to the high concordance between molecular signatures, such as RNA expression or single-nucleotide polymorphism (SNP) patterns, which are present both in cell lines and in the tumours of the corresponding lineageWe have found that the entire proteomes of breast, colorectal and ovarian cell lines generally mirrored those coming from the tumours. This was indicated by the relatively high level of correlation between baseline protein expression profiles. In fact, only a few cell lines displayed low expression correlations.48. Furthermore, it is known that tumours and cancer cell lines harbour multiple genomic alterations, such as gene fusions or splice variants, which could produce alternative protein sequences. Mutations (e.g. SNPs) can also be acquired as the result of consecutive cell culturing48. Additionally the MS/MS analysis search strategy used in this study focused only on detecting known coding protein sequences, using the UniProt reference proteome, in the same way as performed in all the original studies. Indeed, cell line-specific genome or transcriptome sequences were not available. Therefore, it was not possible to detect any DNA/RNA sequence changes that could manifest at the protein level. However, the effect of this limitation in the analysis should be small. For instance, in a recent comprehensive study comprising different human tissues49, the number of variant peptide sequences detected using matching exome data in the analysis was only 2.4% .It should be pointed out that there are some inherent technical limitations when performing meta-analysis studies like this one. First, although we have used similar quantitative proteomics datasets (only MS1-based quantification approaches performed in Thermo Fischer Scientific Orbitrap instruments), the original data was acquired in different labs in different experimental environments. This inevitably results in the presence of batch effects. We attempted to remove these and to validate the overall methodology. Additionally, it has been shown that different batches from the same cell line type can have a higher degree of heterogeneity than what has been generally assumed, as demonstrated for HeLa cells50, contamination from sample processing and/or contamination from surrounding tissues. Altogether, this highlights some limitations of using in vitro cultured cells. While many aspects of protein expression in cancer can be studied using cell lines alone, others  will likely require better models that are able to model tissue architecture and cell-cell interactions, such as organoids51.When examining peptides detected in tumours that were not present in any of the cell lines, we detected signatures enriched in receptor activity regulators as well as in keratinization. Cell lines are purer than tumour samples, which tend to be contaminated with stromal cells. Although, we made every effort to remove common contaminants from the analysis (keratin and others), the ‘tumour-specific’ proteins detected might not necessarily reflect endogenous tumour biology. These proteins might have been detected due to tumour immune infiltration24. In addition to the concrete limitations mentioned above for this study, there are other more generally applied ones that should be mentioned. First of all, manual curation and annotation of public datasets is an essential step. The current level of annotation for public proteomics datasets is lower when compared with transcriptomics studies. Second, the current most accepted analysis software and methodology require a lot of computation resources in the case of meta-analyses, since all data should ideally be analysed together as a whole. This is not always possible due to the overall size of the aggregated dataset. For practical reasons, here we decided to perform the analysis in two batches (cell-lines and tumours), and combine the results on the peptide level or map protein groups to genes and then combine. In case where it was not feasible we used razor proteins in each group to map between the two datasets.We expect that analogous meta-analysis studies of proteomics datasets will become increasingly popular, due to the unprecedented growth rate of proteomics datasets in the public domainThe availability of these results in widely-used resources such as PRIDE and especially in this case Expression Atlas represent, in our view, the right route for proteomics data to be more accessed and consumed by scientists who are non-expert in proteomics.https://www.ebi.ac.uk/pride/archive/), MassIVE (https://massive.ucsd.edu/), and CPTAC data portal . Raw proteomics data was manually curated to extract processing parameters, experimental design and sample characteristics. The biological metadata was captured in a Sample and Data Relationship Format (SDRF)52 before it was loaded into EA as 11 separate tracks, one for each study.Proteomics data from 11 studies (Online-only Table 53). Initially we identified many studies, but narrowed down our selection to studies where we had both access to raw data and appropriate annotations . We did not consider smaller studies where only one cell line  were analysed. The final set of samples was assembled in September 2018 and is summarised in Online-only Table The proteomics studies were selected since they all employed a similar MS platform (Thermo Fisher Scientific Orbitrap) and the resulting protein quantification could be based on the intensity of the peptide precursor ions (MS1 based quantificationhttps://www.ebi.ac.uk/gxa/home). The following three RNA-seq experiments, designated as ‘baseline’ in EA, were used in this study: E-MTAB-2706, E-MTAB-2770 and E-MTAB-3983. Transcriptomics data have been previously curated in EA, and the biological metadata captured in SDRF format, in a consistent manner with the proteomics data.Transcriptomics data was obtained from EA (mqpar-celllines.xml and mqpar-tumours.xml files included in the PRIDE PXD013455 dataset.Raw LC-MS data was processed using the MaxQuant software. The MS/MS data was searched in two batches (cell line and tumour data separately) against the UniProt human reference proteome  appended with sequences of common contaminants provided by MaxQuant. Search parameters were chosen to reflect those used in the original publications. In all cases, carbamidomethylation of cysteine was set as fixed modification and oxidation of methionine and N-terminal acetylation were set as variable modifications. For studies that used SILAC labelling, appropriate SILAC settings were selected. Enzyme specificity was set to trypsin, LysC, chymotrypsin or GluC , allowing a maximum of two missed cleavages. MS1 tolerance was set to 10 ppm and MS2 tolerance to 20 ppm for FTMS data and 0.4 Da for ITMS data. PSM (Peptide Spectrum Match), peptide and protein identification FDR was set at 1% at each level. All of the processing parameters are available in the 36. Briefly, the sequencing reads were quality filtered, which involved the removal of adaptor sequences (adaptor trimming), low-quality reads, uncalled bases (e.g. N) and reads arising from bacterial contamination. TopHat254 was used to perform genomics alignment using the reference Ensembl genome (Ensembl release 79). Default TopHat2 parameters were used. The number of reads that mapped to a particular gene (raw counts) were obtained with HTSeq55 and normalised gene abundance was calculated as FPKM (fragments per kilobase of exon model per million reads mapped) values56. EA data matrices can be downloaded in a tab-delimited format from the corresponding dataset entry. Importantly, for each dataset, technical and biological replicates were averaged and quantile normalised within each set of biological replicates using the limma package57. Finally, in cases where a cell line had replicate measurements across datasets, the average FPKM abundance was used.Transcriptomics data stored in EA was previously processed using a standardized pipelineSince quantitative proteomics data originating from different studies is heterogeneous and likely to contain batch effects, we developed and benchmarked a procedure to integrate the quantification results. The procedure is described below.proteinGroups.txt files. In some studies, multiple digestion enzymes were employed to characterize the same sample, for example in the dataset from ref. 10  in the aggregated dataset were acquired in at least three independent studies. It was then assumed that the highest amount of variability in protein expression in those cell lines, and indeed in any other sample type, should arise due to biological differences (i.e. different sample origin), rather than due to technical artefacts, such as the study of origin.First, the presence of batch effects in the reprocessed data was evaluated by plotting density distributions of log2 transformed iBAQ intensities Fig. . Upon viAs seen in Fig. 58). Finally, the main batch effects were removed separately for each dataset (“cell lines” and “tumours”) using the limma R package57 including the study of origin as a covariate (“removeBatchEffect” function). Post-normalisation PCA analysis . This resulted in the cell line dataset containing 6,514 proteins and a 14% of missing values, and the tumour dataset containing 5,363 proteins and a 16% of missing values. Missing values were then imputed using the conventional Singular Value Decomposition method implemented in the pcaMethods R package  were acquired in multiple batches (i.e. studies). However, very few overlapping samples were acquired among the “cell lines” and “tumours” datasets.In order to further benchmark and validate the normalization procedure it should have ideally been applied to a separate set of previously published datasets and/or to a different set of tumour  samples. However, as we have covered most relevant studies, other datasets where we could readily have applied this strategy were not easily available.et al.59. The authors measured absolute abundance of 25 proteins (14 of those present in our data) in 6 cancer cell lines that were present in our aggregated dataset. The cell lines were BT.20, HS578T, MCF10A, MCF7, MDAMB231 and SKBR3. The 25 proteins were from the EGFR-MAPK pathway and covered a wide dynamic range of 102–106 copies per cell. We compared the raw protein abundance measurements  and the  final normalized protein abundances  iBAQ Intensities) to the estimated absolute protein abundances in each cell line, by calculating pairwise Spearman’s rank correlation coefficients (rs) between all relevant assays. An example of such correlation, before and after normalization, for one assay of MCF7 cell line from Lawrence_CellReports_2015 study, is displayed in Fig. ibaq), iBAQ ppb-normalized (ibaq.ppm) and iBAQ ppb-normalized with various missing data imputations and limma-removed batch effect values. Here we benchmarked Limit Of Detection (LOD) imputation (ibaq.ppm.lod.imputed.batch), Local Least Squares (LLS) imputation (ibaq.ppm.lls.imputed.batch), and Singular Value Decomposition imputation (ibaq.ppm.svd.imputed.batch - the method we used in our aggregated dataset). The imputation methods were implemented using the pcaMethods R package5. These results are summarised in Fig. ibaq intensities. The median correlation across all cell lines was 0.815 for ibaq raw values and improved only slightly for the ibaq.ppm.svd.imputed.batch quantification to 0.8286 , but it improved slightly after our normalization procedure (mean rs = 0.6). In addition, the differences between missing data imputation methods were minimal due to the fact that most values were already present in our filtered quantification matrix. In summary, this ‘quality control’ check showed that this procedure was robust and did not bias the final quantification values.We therefore decided to use an orthogonal approach to validate our method. A common strategy in similar cases is to compare the output of missing data imputation and the normalisation procedure to the known absolute concentration coming from spiked-in peptides/proteins in a given sample, or if not available, from a few “marker” proteins with known abundance. In this case there were no spiked-in standards in the studies that were  reanalysed. Therefore, instead we used absolute abundance estimates coming from a separate study by Shi 286 Fig. . ImportaSupplementary Table 1


ALDH1A1 gene using the CRISPR/Cas9 technique. Additionally, we derived an ALDH1A1 positive TOP-resistant cell line with ALDH1A1 expression in all cells via clonal selection. The effect of ALDH1A1 gene knockout or clonal selection on the expression of ALDH1A1, drug transporters (P-gp and BCRP), and ECM (COL3A1) was determined by Q-PCR, Western blot and immunofluorescence. Using MTT assay, we compared drug resistance in two-dimensional (2D) and three-dimensional (3D) cell culture conditions. We did not observe any effect of ALDH1A1 gene knockout on MDR1/P-gp expression and drug resistance in the PAC-resistant cell line. The knockout of ALDH1A1 in the TOP-resistant cell line resulted in a moderate decrease of BCRP and COL3A1 expression and weakened TOP resistance. The clonal selection of ALDH1A1 cells resulted in very strong downregulation of BCPR and COL3A1 expression and overexpression of MDR1/P-gp. This finally resulted in decreased resistance to TOP but increased resistance to PAC. All spheroids were more resistant than cells growing as monolayers, but the resistance mechanism differs. The spheroids’ resistance may result from the presence of cell zones with different proliferation paces, the density of the spheroid, ECM expression, and drug capacity to diffuse into the spheroid.Ovarian cancer is the most common cause of gynecological cancer death. Cancer Stem Cells (CSCs) characterized by drug transporters and extracellular matrix (ECM) molecules expression are responsible for drug resistance development. The goal of our study was to examine the role of aldehyde dehydrogenase 1A1 (ALDH1A1) expression in paclitaxel (PAC) and topotecan (TOP) resistant ovarian cancer cell lines. In both cell lines, we knocked out the  Epithelial ovarian cancer (EOC) has the highest mortality rate of all gynecological malignancies . Most paThe ECM model of drug resistance development postulates that the small population of cells in the tumor is resistant to chemotherapy because of the high expression of ECM molecules and strong CAM-DR ,33,34. UALDH1A1 gene knockout.In our study, the drug-resistant ovarian cancer cell lines revealed a strong expression of drug transporters ,38, the In this study, we used drug-sensitive and resistant ovarian cancer cell lines. Previously, from a drug-sensitive W1 cell line, we derived cell lines resistant to six cytotoxic drugs, among them a W1PR1 cell line resistant to PAC and a W1TR cell line resistant to TOP . In bothALDH1A1 gene in W1PR1 and W1TR cell lines by using the CRISPR-Cas9 system. ALDH1A1 gene expression in investigated cell lines. The transcript level in the W1 cell line was assigned as 1. We observed a statistically significant (p < 0.001) increase in ALDH1A1 transcript level in both PAC resistant cell lines (W1PR1 and W1PR1-C7) in comparison to the W1 drug-sensitive cell line. However, the transcript level in W1PR1-C7 cell lines was about 5-fold lower than in the W1PR1 cell line  compared to the W1 cell line. The CRISPR-Cas9 mediated knockout of ALDH1A1 gene in the W1TR-1p17 cell line resulted in downregulation of ALDH1A1 mRNA level to the level similar to in the W1 cell line   A and abo< 0.001) C.ALDH1A1 gene was confirmed at the protein level by Western blot analysis . Next, we also compared protein expression in all cell lines. The elevated expression of P-gp was confirmed by Western blot analysis. We could observe a considerable increase in P-gp band intensity in both PAC-resistant W1PR1 and W1PR1-C7 cell lines compared to drug-sensitive W1 cell line and moderate band intensity in W1TR-2p17 cell lines  in BCRP mRNA level in W1TR cell line when compared with W1 cell line  in W1TR-1p17 and W1TR-2p17 cell lines when compared to W1TR cell line (BCRP transcript compared to W1TR cell line (BCRP transcript was observed in W1TR-2p17 cell line (about 1500-fold) and was similar to the level observed in the drug-sensitive W1 cell line  and in W1TR-1p17 cell line . However, in W1TR-2p17 cell line with overexpression of ALDH1A1, COL3A1 transcript level decreased approximately 150-fold when compared to W1TR cell line (p < 0.001)  B. The ex< 0.001) C. Howeve< 0.001) D.ALDH1A1 affects these parameters. In 2D conditions, W1, W1PR1, W1PR1-C7, W1TR, W1TR-1p17, W1 TR-2p17 cells grow as a monolayer in tissue culture flasks   .Because in W1TR-2p17 cell line we detected expression of P-gp, we also compared resistance to PAC in TOP-resistant cell lines B. We obsNext, we compared TOP resistance level in W1, W1TR, W1TR-1p17 and W1TR-2p17 cell lines. All cell lines presented a dose-dependent reduction of cell viability in response to increased drug concentration . The comNext, we investigated the response to PAC and TOP of the same cell lines growing as spheroids. After spheroids were formed, cells were incubated with increased PAC concentration for 72 h. The results for PAC- and TOP-resistant cell lines are presented in separated charts. A. In theA three-step response curve was also observed in W1TR cell line B. InitiaWe observed a concentration-dependent response to TOP in all examined cell lines . A compaDrug resistance is one of the main problems in the treatment of cancers. While some cancers are intrinsically resistant to chemotherapy, others develop resistance in the course of drug treatment . Below 2Although the presence of a cytotoxic drug in the environment induces chemoresistance in TOP- and PAC- resistant variants of W1 cell line, not all cells express ALDH1A1. This suggests that ALDH1A1 expression is not a leading component of the TOP- and PAC-resistance phenomenon. Evidence suggests that ALDH1A1 expression is directly involved in resistance to cyclophosphamide ,54 but aALDH1A1 gene in W1PR1 (80% of ALDH+ cells) and W1TR (4% of ALDH+ cells) cells resulted in a total loss of ALDH1A1 expression and lack of ALDH1A1+ cells. However, knocking out the ALDH1A1 gene did not influence MDR1/P-gp expression, suggesting that ALDH1A1 expression is not required for MDR1 gene expression in this cell line. Different ALDH1A1 dependent and independent regulation of drug transporters’ expression has been described in the literature [MDR1/P-gp, BCRP and MRP1 was regulated by ALDH1A1-NEK-2 axis, and silencing of ALDH1A1 expression resulted in downregulation of P-gp, MRP1, and BCRP expression and increased sensitivity to CIS [According to the CSCs model of drug resistance development, CSCs are intrinsically resistant to chemotherapy because of the expression of drug transporters such as P-gp or BCRP and the presence of another resistance mechanism ,45,55. Aterature ,56,57. Iy to CIS . Very siALDH1A1 in this cell line resulted in complete loss of ALDH1A1 protein expression and lack of ALDH1A1+ cells. However, in contrast to the W1PR1 cell line, we observed uniform decreased expression of BCRP protein, suggesting the role of ALDH1A1 in regulating BCRP expression. The role of ALDH1A1 pathways in the regulation of BCRP expression has been described [ALDH1A1 gene knockout also resulted in reduced expression of COL3A1, but this was, rather, related to a decrease in the number of ALDH1A1/COL3A1+ cells. The regulation of COL3A1 expression by ALDH1A1 has never been described so far.Resistance to TOP in the W1TR cell line was related to the uniform expression of BCRP protein in all cells. Additionally, in this cell line, we previously observed cells with very high expression of COL3A1, which was secreted to the cell culture medium forming a spider web structure, therefore also suggesting the role of COL3A1 in TOP resistance ,22. Furtescribed ,57. HoweMDR1/P-gp in this cell line.Surprisingly, in the W1TR-2p17 cell line, characterized by ALDH1A1 expression in all cells, we observed loss of BCRP and COL3A1 expression related to TOP-resistance in the parental W1TR cell line ,38. ThisWe observed differences in 2D morphology as well as density and shape of 3D spheroids between W1 and drug-resistant cell lines. However, changes in ALDH1A1 expression did not change cell morphology and spheroids’ shape. It suggests that the determinant of cells morphology and the features of spheroids is a drug-sensitive/resistant phenotype but not the presence of ALDH1A1+ cells.ALDH1A1 gene knockout influences the chemosensitivity of cells. In 2D cell culture conditions, all cells are equally exposed to cytotoxic agents, and the level of resistance should be related to the expression of drug resistance proteins. Contrary to our expectations, W1PR1 and W1PR1-C7 cells exhibited similar chemoresistance levels, suggesting that ALDH1A1 expression is not a key player in PAC-resistance. The PAC-resistance of those cells seems instead to be driven by abundant P-gp expression, as PAC is a well-known substrate of P-gp [MDR1/P-gp expression. This further confirms that the primary role in PAC-resistance at the cellular level is an expression of P-gp. Moreover, BCRP was described as a prominent protein in TOP-resistance [ALDH1A1 gene knockout on BCRP expression and TOP-resistance.After knockout experiments we performed a cytotoxicity assay to determine how  of P-gp ,6,7. Fursistance , and oursistance ,58,59. IALDH1A1 gene in TOP-resistant cell line resulted in decreased TOP-resistance and correlated with a decrease in BCRP protein level. The above observation confirms the role of BCRP protein in TOP-resistance, and provides more insight into the mechanism of cells’ resensitization regulated by ALDH1A1 inhibition that we described previously [Knockout of eviously .ALDH1A1 gene showed lower expression of the COL3A1 gene and protein when compared with parental TOP-resistant cell line. To our best knowledge, the decrement of TOP-resistance in W1TR-1p17 cell line might be linked with decreased COL3A1 expression. However, further studies are required on the precise mechanism of this dependence. Although the expression of BCRP and COL3A1 in W1TR-2p17 cell line was low, we observed a medium level of TOP-resistance, which suggests other forms of mechanism-driven resistance. Based on literature data and our knowledge, we know that TOP-resistance can also be related to P-gp expression [In a recent study, we have proposed the role of COL3A1 expression in TOP-resistance . In thispression ,59,60,61pression . Thus, tThe traditional 2D cell culture method is a well-known and relatively simple cytotoxicity assessment method. However, it does not reflect tumor tissue conditions where interaction between tumor cells and tumor microenvironment can influence drug response . TherefoSurprisingly, in 3D cell culture conditions W1 cell line was more resistant to PAC than the drug resistant W1PR1 cell line and W1PR1-C7 variant. Additionally, W1TR cells were far less resistant to TOP than W1 cells when growing as spheroids, and not as a monolayer (2.5-fold in 3D vs. 50-fold in 2D). In W1 spheroids treated with either PAC or TOP, we did not observe a typical dose-dependent response as in the 2D condition, but rather a three-step response curve. Initially, low PAC concentration decreases cell viability significantly, but further increasing the dose of PAC did not enhance the response up to extremely high PAC concentration, resulting in a cytotoxic effect. In contrast to W1 cell line, W1PR1 and W1PR1-C7 cell lines were resistant to lower PAC-concentration, and only high drug concentration was able to reduce spheroids’ viability. A similar observation was previously made by our team for spheroids derived from A2780 PAC-resistant cell lines . The posIn W1 spheroids treated with TOP, low drug dose reduced spheroids’ viability to about 60% via action on highly proliferating cells from the peripheral zone. This can be explained by TOP’s ability to inhibit topoisomerase I which is active in highly proliferating cells ,67. A fuIt has also been shown that some ECM proteins can inhibit TOP-induced apoptosis in a dose-dependent manner . The othALDH1A1 gene knockout on drug resistance in a model of drug sensitive and drug resistant cell lines. Further studies are needed to evaluate the contribution of MDR1/P-gp and BCRP in this system.Summarizing this part of the investigation, we can conclude that drug response in a 2D cell culture condition is related to the expression level of drug resistance molecules and, in our study, mainly P-gp and BCRP. Reaction to drug in 3D spheroids is related to their properties, such as spheroid density and expression of ECM molecules, expression of drug transporters and ability of a drug to migrate into the spheroid. In general, in all cell lines, we observed an increase in drug resistance in 3D condition compared to 2D conditions. An extreme increase in resistance to both PAC and TOP was observed in drug-sensitive W1 cell lines that correspond to high cell density in this spheroid. Our results are in line with others, showing higher resistance of 3D spheroids than 2D cell culture, and thus 3D cell culture methods appear adequate in simulating important tumor characteristics ,72,73. Hv/v) fetal bovine serum (FBS), 2 pML-glutamine, penicillin (100 units/mL), streptomycin (100 units/mL) and amphotericin B (25 μg/mL) at 37 °C in a 5% CO2 atmosphere. Culture medium (RPMI-1640), fetal bovine serum, antibiotic-antimycotic solution, L-glutamine, BSA, DAPI mounting medium, TOP and PAC were purchased from Sigma . To create 3D spheroids, cells were trypsinized, resuspended in the growth medium and transferred to a nonadherent surface 96-wells plate  in 10,000 cells per well. Phase images of 2D and 3D cell culture were acquired using an inverted microscope .The human primary ovarian cancer cell line W1 was established from the tumor tissue of an untreated 54-year-old Caucasian female patient diagnosed for serous ovarian adenocarcinoma (G3. FIGO IIIc) . Cells gALDH1A1 gene editing was performed in W1PR1 and W1TR cells using CRISPR-Cas9 gene-editing technology. The HDR (Homology Directed Repair) mediated CRISPR/Cas9 knockout kit was used in this study . The CRISPR/Cas9 knockout system used in this study consists of 3 plasmids: ALDH1A1 gRNA vector 1 Target Sequence: GAATCTTCAAATCGGTGAGT (KN200723G1), ALDH1A1 gRNA vector 2 Target Sequence: AGGTAAGTCTGGCGTGCCTG (KN200723G2), donor DNA containing left and right homologous arms and GFP-puro functional cassette (KN200723-D). The plasmids were transfected into the W1PR1 and W1TR cells by electroporation  according to manufacturer’s protocol. After the indicated selection period with puromycine, single-cell colonies were picked and analyzed for ALDH1A1 expression at the mRNA and protein levels. As a result, we obtained W1PR1 cell line with ALDH1A1 gene knockout. This cell line was named W1PR1-C7. We also obtained W1TR cell line with ALDH1A1 gene knockout and this cell line was named W1TR-1p17. In parallel with the W1TR cell line we also developed by clonal selection a subline with expression of ALDH1A1 in all cells to create a similar condition to ALDH1A1 knockout cell lines. This cell line was also transfected with donor DNA containing left and right homologous arms and GFP-puro functional cassette (KN200723-D) and was cultured in the presence of puromycin.The The drug resistance of the investigated cell lines was determined by the MTT cell survival assay . For 2D culture 4000 cells for 3D culture 10,000 cells of each cell line were seeded in each well of the 96-well plates. After 48 h of culturing (2D and 3D) cells were treated with fresh medium supplemented with or without increasing concentrations of PAC and TOP. After 72 h of incubation at 37 °C, the MTT experiments were performed according to the manufacturer’s protocol. The absorbance was measured using a microplate reader (BioTek (Agilent), Santa Clara, CA, USA) at 570 nm with a reference wavelength of 720 nm, according to the manufacturer’s protocol. As a negative control, a cell-free culture medium containing the MTT reagents was used. Each experiment was repeated three times and each concentration in a given experiment was tested in duplicate. The IC50 value of both drugs was determined in each cell line.ALDH1A1, MDR1, BCRP, and COL3A1 gene expression in examined cell lines. According to the protocol, RNA isolation was performed with the Gene Matrix Universal RNA Purification Kit , and Q-PCR experiments were performed using 2 μg of RNA for cDNA synthesis and the M-MLV reverse transcriptase kit  and thermal cycler . GAPDH), β-actin, hypoxanthine-guanine phosphoribosyl-transferase 1 (HRPT1) and beta-2-microglobulin (β2M). The graphs were generated using SigmaPlot 11.0 . The amplification was performed with the use of 12.5 μL of ROX SYBR qPCR Master Mix , 1 μL of each primer  supernatant was collected and the Bradford protein assay system  was applied to determine the protein concentrations. For the Western blot analysis, proteins  and protein molecular weight marker  were separated via 4–20% mini-PROTEAN®-TGX precast gel using the SDS-PAGE technique and afterward transferred to a nitrocellulose membrane . In the next step, the membrane was incubated in blocking buffer (5% milk in TBS/Tween20 (0.1 M Tris-HCl. 0.15 M NaCl 0.1% Tween 20)) for 1 h. Next, the membrane was incubated overnight with an appropriate primary antibody (Ab) followed by incubation with the corresponding HRP-conjugated secondary Ab. The following primary antibodies and dilutions were used: rabbit monoclonal anti-ALDH1A1 at dilution of 1:200 , mouse monoclonal anti-P-gp ab used at dilution 1:50 , rabbit monoclonal anti-BCRP Ab at dilution 1:1000 , mouse polyclonal ant- COL3A1 ab at dilution 1:1000 , rabbit polyclonal anti-GAPDH Ab at dilution 1:1000 . The following secondary antibodies and dilutions were used: anti-rabbit HRP-conjugated Ab, 1:1000  and anti-mouse HRP-conjugated Ab. 1:1000 . The bands’ detection was analyzed using a chemiluminescence detection kit  and Hyperfilm ECL .Protein levels were assessed by Western blotting. Cell lysates were prepared using a RIPA buffer  with a protease inhibitor cocktail . After centrifugation  for 10 min for fixation and permeabilization. Then, cells were washed with PBS and blocked in 3% BSA (Bovine Serum Albumin) for 30 min at room temperature. After that, cells were incubated with relevant primary antibodies. The following primary antibodies and dilutions were used: rabbit monoclonal anti-ALDH1A1 at dilution 1:100 , mouse monoclonal anti-P-gp at dilution 1:50 , rabbit monoclonal anti-BCRP at dilution 1:300 , mouse polyclonal ant- COL3A1 at dilution 1:100 . After incubation with primary antibodies , cells were washed with PBS and incubated with corresponding secondary antibodies  for 1 h at room temperature. Finally, cells were washed with PBS and mounted in DAPI mounting medium. The expression analysis and pictures were taken with a fluorescence microscope .p < 0.05, p < 0.01 and p < 0.001.The statistical analysis was performed using Excel 2016 software . The statistical significance of the differences was determined by applying Student’s t-test at MDR1/P-gp in PAC resistant cell lines seems to be ALDH1A1 independent. In contrast, changes of ALDH1A1 expression in TOP-resistant cell line resulted in BCRP, MDR1/P-gp and COL3A1 expression changes. In all cases, the same cell line was more resistant in 3D cell culture condition than in the 2D cell culture condition. The level of resistance in the 2D cell culture condition was related to the expression level of drug transporters. The main determinate of resistance in 3D spheroids seems to be the presence of different cells zones, the density of spheroids, and the capacity of the drug to diffuse into the cellular/ECM structure.This study investigated the relationship between ALDH1A1 expression and expression of drug transporters and ECM molecules in PAC- and TOP-resistant ovarian cancer cell lines. Regulation of


Here we summarize and discuss recent preclinical data on the CNS as a target of acute GVHD and the known mechanisms contributing to neurotoxicity with a focus on microglia and T cells. We also discuss open questions in the field and place the findings made in mouse models in a clinical context. While in mice the neurological deficits can be assessed in a controlled fashion, in patients the etiology of the CNS damage is difficult to attribute to acute GVHD versus infections, vascular events, and drug-induced toxicity. Ultimately, we discuss novel therapies for GVHD of the CNS. Our understanding of the biological mechanisms that lead to neurotoxicity after allo-HCT increased over the last decade. This review provides insights into CNS manifestations of GVHD versus other etiologies of CNS damage in mice and patients.Allogeneic hematopoietic cell transplantation  is a curative therapy for patients with hematological malignancies. Acute Graft  About 50% of the patients with severe acute GVHD fail to respond to corticosteroids, and steroid-refractory severe GVHD has a dismal prognosis with a 1-year survival rate of less than 20%  derived cell infiltrates  often consist of fluradabine and busulphan and minimum dose conditioning regimens use low dose TBI and busulphan , 72. CycIn addition to neurotoxicity caused by the conditioning regimen, the GVHD prophylaxis or treatment, anti-viral drugs, antibiotics and anti-fungal agents can cause toxicity to the CNS. The calcineurin inhibitors cyclosporine A (CSA) and tacrolimus are widely used for GVHD prophylaxis as they block T cell activity . HoweverAntimicrobials or anti infectious drugs employed in the treatment of opportunistic infections during GVHD also pose significant threat to the CNS. Neutropenia together with encephalitis induced stroke, and vertigo are the major side effects of medications including acyclovir, gancyclovir . In addiIn aggregate, a plethora of infections, vascular events, and drug-induced toxicities can cause neurogical symptoms that need to be ruled out before diagnosing CNS-GVHD.The NIH Consensus Conference on criteria for clinical trials in GVHD recommends the following measures in patients with suspected CNS-GVHD : CSF celDespite recent advances in the clinical management of acute GVHD, CNS-GVHD is still a life threatening complication that is often difficult to diagnose. Preclinical studies have shown that allogeneic T cells infiltrate the CNS during GVHD and activate different cell types including microglia and other myeloid cells. CNS-GVHD causes damage to neurons and endothelial cells. While CNS-GVHD accounts for some of the neurological symptoms observed after allo-HCT it is important to also consider infections, vascular events, and drug-induced toxicity. Treatment of these complications e.g. reducing CSA when CSA induced neurotoxicity is suspected could exacerbate CNS-GVHD. In case of drug toxicities the responsible drugs should be changed and avoided if CNS symptoms are severe. Therefore, to improve patient outcome it is desirable to identify biomarkers that help early identification and diagnosis of CNS-GVHD in particular when other organs are not affected by GVHD.JV and RZ developed the overall concept of this article and wrote the manuscript. All authors contributed to the article and approved the submitted version.The article processing charge was funded by the Baden-Wuerttemberg Ministry of Science, Research and Art and the University of Freiburg in the funding programme Open Access Publishing. This article was supported by the Deutsche Forschungsgemeinschaft  – SFB-1479 – Project ID: 441891347, SFB TRR167, SFB850 (to RZ), by the Germany’s Excellence Strategy (CIBSS – EXC-2189 – Project ID 390939984 to RZ) and by ERC Consolidator grant (681012 GvHDCure to RZ).RZ received honoraria from Novartis, Incyte and Mallinckrodt.The remaining 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.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Several studies linked DNA cytosine deaminase APOBEC3 to mutational process driving carcinogenesis . HoweverTriple negative breast cancer cell line HCC1806 was transduced with lentiviruses containing Vif-1 and Vif-2 genes in fusion with ZsGreen reporter gene producing two different cell lines named as VIF1 and VIF2 cells. Before cells treatment with doxorubicin, the expression of the fluorescent marker and Vif was confirmed by Fluorescent Activated Cell Sorting (FACS-AriaIII) and PCR, respectively. Characterisation of doxorubicin dose-and time-responsive cell viability was performed using MTT assay.Discussion and conclusions: The increased viability of doxorubicin treated VIF2 cells correspond to the development of cells resistance to doxorubicin. This resistance of VIF2 cells is probably related to the APOBEC3 inhibition by Vif 2 protein. Our results raise concerns about general use of doxorubicin as breast cancer treatment, especially when APOBEC3 expression is low.High-titers of Vif-delivering lentiviruses were produced and used to transduce efficiently the HCC1806 cells. More than 99% of sorting population expressed ZsGreen indicating that Vif-1 and Vif-2 genes were integrated in genomic DNA and expressed in VIF1 and VIF2 cell lines. After treatment with doxorubicin for 24 h, all cell lines showed significant decrease of viability when compared with untreated cells, proportional to the concentration of doxorubicin . Compari


The wet chemical etching method was used to synthesize MXene followed by a simple chemical mixing method for surface modification of Ti3C2 MXene with PPG and PEG molecules. SEM and XRD analyses were performed to examine surface morphology and elemental composition, respectively. FTIR and UV-vis spectroscopy were used to confirm surface modification and light absorption, respectively. The cell lines used to study the cytotoxicity of MXene and surface-modified MXenes in this study were normal (HaCaT and MCF-10A) and cancerous (MCF-7 and A375) cells. These cell lines were also used as controls  to measure their baseline cell viability under the same lab environment. The surface-modified MXenes exhibited a sharp reduction in cell viability towards both normal (HaCaT and MCF-10A) and cancerous (MCF-7 and A375) cells but cytotoxicity was more pronounced towards cancerous cell lines. This may be due to the difference in cell metabolism and the occurrence of high pre-existing levels of reactive oxygen species (ROS) within cancerous cells. The highest toxicity towards both normal and cancerous cell lines was observed with PEGylated MXenes followed by PPGylated and bare MXenes. The normal cell’s viability was barely above 70% threshold with 250 mg/L PEGylated MXene concentration whereas PPGylated and bare MXene were less toxic towards normal cells, even at 500 mg/L concentration. Moreover, the toxicity was found to be directly related to the type of cell lines. In general, the HaCaT cell line exhibited the lowest toxicity while toxicity was highest in the case of the A375 cell line. The photothermal studies revealed high photo response for PEGylated MXene followed by PPGylated and bare MXenes. However, the PPGylated MXene’s lower cytotoxicity towards normal cells while comparable toxicity towards malignant cells as compared to PEGylated MXenes makes the former a relatively safe and effective anticancer agent.The MXenes are a novel family of 2-D materials with promising biomedical activity, however, their anticancer potential is still largely unexplored. In this study, a comparative cytotoxicity investigation of Ti Cancer is the major cause of death among non-communicable diseases and acts as a barrier to increasing life expectancy in the 21st century. The World Health Organization (WHO) report 2015, revealed that cancer is the first or second leading cause of mortality before 70 years of age in 91 of 172 countries worldwide [For the treatment of cancers, various approaches such as surgery, chemotherapy, radiotherapy, immunotherapy, and hormone therapy are in use. Among these, chemotherapy and radiotherapy therapy have been widely used to treat most cancer types. These treatment modalities have deleterious side effects due to high energy irradiation exposure and cytotoxic drugs, which are toxic to normal tissues as well. Furthermore, these techniques do not guarantee a satisfying cure.Recently, low energy photothermal therapy (PTT) to fight against cancer has gained immense scientific interest due to its utilization of low energy radiations (visible to the infrared range) and effective hyperthermic effect. The principle of PTT is the production of heat energy near tumor cells. The heating effect would successfully kill the cancer cells without harming the normal cells as cancerous cells are more sensitive to high temperatures as compared to healthy cells . The ide2) [With the advancement in nanotechnology, various nanoparticles, i.e., gold (Au) nanoparticles and surface-modified Au particles , silver 2) , copper 2) , self-as2) , and dye2) .n+1XnTx”), X is for nitrogen and/or carbon, M is any ‘transition metal”, and T can be any surface functional group. The 2-D sheet-like structures are obtained from selective etching of the A-group element from its most common precursor in MAX phase ceramic material by using strong acid-like hydrofluoric acid [3C2) were investigated against normal and cancerous cells. The results revealed high cytotoxicity towards cancerous cells and a lower effect on normal cells under room temperature without external stimulation [2 was surface modified with doxorubicin (DOX) for enhanced effectiveness and photoactive drug release on the target area [3C2 modified with soybean phospholipids [Recently, a new class of materials, two-dimensional (2-D) transition metal carbides/nitrides (MXenes), has been widely investigated in the biomedical field due to their biocompatibility, wide surface area, and high photoresponse. The MXenes  surface-modified Ti3C2 MXene, Ti3C2-PEGylated MXene, and Ti3C2-PPGylated MXene on human cancer cells and (2) there is no significant difference in the photothermal therapy potential of bare Ti3C2 MXene, Ti3C2-PEGylated MXene, and Ti3C2-PPGylated MXene against human cancer cells.The null hypotheses of the present study were: (1) there is no significant difference in the cytotoxic potential of bare Ti3C2 MXene has been synthesized through selective etching of MAX phase (Ti3AlC2) as described in our previous work [4HF) was prepared in DI water by stirring for 1 h at room temperature. Afterward, the etching process was followed by the addition of 1g of Ti3AlC2 material to the homogenous solution. The addition of Ti3AlC2 to the solution was conducted slowly to avoid overheating (exothermic reaction), followed by stirring at 40 °C for 48 h. After completion of the etching process, a dilute solution of NaOH was added slowly until the pH of the solution reached 6, followed by filtering and rinsing the solid product using DI water several times. The product was washed further 4 times at 3500 rpm using an ultrahigh-speed centrifuge. Then the achieved multi-layered Ti3C2Tx was dispersed in isopropyl alcohol and sonication was done for one hour for delamination. To get delaminated MXene, the power of the sonicator was set to 70%, and the switching time was set to 7/3 s. Then the obtained MXene flakes were dried for 12 h using a vacuum oven. A stock of 15 g MXene in powder form was prepared and stored in the dark condition below 4 °C for future use.2-D Tious work . In brie3C2 MXene, 5 g MXene was mixed with 100 mL of PPG and PEG respectively, sonicated for 1 h in two intervals, and stirred overnight using a magnetic stirrer. The stock solution was then treated with DI water and functionalized particulates were separated from the liquid phase using a centrifuge (6000 rpm). The functionalized particulates were rinsed with DI water five times and stored in glass vials under dark conditions. A defined amount of functionalized and bare particulates was dispersed in DI water before use.For the surface functionalization of TiScanning electron microscopy (SEM) images were obtained using a Mira3–SAMX TESCAN  in secondary electron mode to examine the surface morphology of as-synthesized MXene. X-ray diffraction (XRD) patterns were recorded using a Bruker D8-Discover machine  to confirm the composition of MXene and the proper etching of Al from the MAXphase. The IRAffinity-1S Fourier Transformed Infrared (FTIR) spectrometer  was utilized to confirm surface modification of MXene with PEG and PPG.3C2 MXene, Ti3C2-PEG MXene, and Ti3C2-PPG MXene on human cell lines was studied in a lab environment by gradually increasing the concentrations of the concerned material from 0 mg/L to 500 mg/L. The cell lines included in the study were MCF-7 , MCF-10A , A375 , and HaCaT . DMEM  culture medium was used for MCF-7, A375, and HaCaT cell lines, whereby this culture medium was supplemented with fetal bovine serum (FBS) with a strength of ten percent volume/volume. The necessary antibiotics were also added, which were one percent of streptomycin and penicillin (v/v) respectively. An essential amino acid, one percent of L-glutamine (v/v), was also added. While for the culture of MCF-10A cells, DMEM medium was supplemented with 5% (v/v) horse serum, human insulin (10 µg/mL), the epithelial growth factor (10 ng/mL), and hydrocortisone in the strength of 5 μg/mL. Other culture requirements were 5% CO2, 95% humidity, and 37 °C temperature.The effect of 2-D Ti4 cells. Then, incubation was carried out to ensure surface adherence of the cells. After removal of the supernatant, the bare MXene, PEGylated MXene, and PPGylated MXenes in different concentrations were added separately to the well plates, followed by incubation for 24 h. The control cell lines were cultured and incubated with fresh growth medium without MXenes. After the completion of incubation time with studied materials, phosphate-buffered saline  was used to wash the cells. Then, for each well, 100 μL of MTT (Sigma-Aldrich) solution was added in a concentration of 0.5 mg/mL in PBS. The cell lines were kept away from light. Incubation with MTT solution was performed for 4 h. After careful removal of the supernatant, 100 μL/well of Dimethyl Sulfoxide  solution was added to dissolve the “violet formazan crystals” and spectrophotometric absorbance was determined at 570 nm. By using Equation (1), the cell viability was calculated in percentage in comparison to control cell lines.The methyl tetrazolium (MTT) assay was used to measure in vitro cytotoxicity of the surface-modified MXenes. Each of the four cell lines was inoculated in a 96 well plate with each well containing 1 × 10i = the average of spectrophotometric absorbance for the studied material group,ac = the average of spectrophotometric absorbance for the control group.aCytotoxicity tests for each cell line exposed to MXene, PEGylated MXene, and PPGylated MXene samples, with and without irradiation, were conducted using a set of five replicates for each concentration and expressed as mean ± standard error.2. The bare and surface-modified MXenes (0.2 mg) were each suspended in DI water (10 mL) in a specimen container, and irradiated for 5 min prior to cytotoxicity analysis. A test apparatus was developed to irradiate the specimens with infrared radiation (IR) using a Niahode IR Laser Diode (China). p < 0.05.Statistical analysis was performed using GraphPad Prism 9.2.0 . Data resulting from cytotoxicity experiments were subjected to one-way analysis of variance (ANOVA) followed by Tukey’s post hoc test for comparison between test groups of every tested concentration. Significance level was set at The XRD analysis was performed to verify the formation of MXene and proper etching of Al from the MAX phase. −1 and 3310 cm−1, which confirms the presence of the hydroxyl group. In all of the specimens, these were assigned to the absorbed moisture from the environment and highly hydrogen-bonded OH or extremely strong coordinated H2O. Furthermore, a peak at 620 cm−1 was possibly due to the Ti-O bond deformation vibration [−1), similar to alcohol and ethers, C-H bending (1400–1500 cm−1), and C-H stretching (2850–3000 cm−1) peaks can be observed in the case of PEGylated and PPGylated MXenes. The C-O stretching is seen at 1120 cm−1 for PEG and 1097 cm−1 for PPG. The aliphatic C-H stretching peak is observed at 2901 cm−1 for PEG and at 2860 cm−1 for PPG. The C-H bending vibration is seen at 1399 cm−1 and 1120 cm−1 for PEG and 1458 cm−1 and 1390 cm−1 for PPG. These findings confirm that the surface modification of MXene with PEG and PPG were successfully achieved [ibration . As evidachieved ,24.To identify the light absorption properties, UV-vis spectroscopy of bare MXene and functionalized MXenes were performed. p < 0.05) except for normal cell lines (HaCaT and MCF-10A) at 375 mg/L and 500 mg/L where the cytoxicity caused by bare MXene and PPGylated MXene were comparable. However, all of the test samples were relatively more toxic towards malignant cells as compared to normal cells. This can be explained on the basis of the differences in cellular metabolism and permeability. MXene exerts its biological activity on living cells by the generation of reactive oxygen species (ROS). This property makes MXenes exhibit anticancer potential due to the already increased baseline amount of reactive oxygen species within cancer cells, owing to increased anabolic and catabolic reactions [eactions . ROS plaeactions .p = 0.7046) and 500 mg/L (p = 0.05). Interestingly, at 500 mg/L sample concentration, both bare and surface-modified MXenes showed no significant difference in the cytotoxicity towards A375 cells (p > 0.05). On the other hand, for normal cell lines, the cell viability was well above 70% when exposed to bare MXene and PPGylated MXene even at 500 mg/L concentration, while with PEGylated MXene, the MCF-10A viability started to drop from 250 mg/L onwards. The HaCaT cell line was the least affected of all the studied samples, which might be because of its immortalized nature.It was observed that the MXene cytotoxicity was also directly related to the type of cell lines, the minimum for HaCaT cell lines and the maximum towards A375 cell lines. A sharp increase in toxicity towards both the normal and cancerous cell lines was observed for PEGylated and PPGylated MXenes. At higher concentrations, the normal cell’s viability dropped to below 70% for PEGylated MXenes, thus, according to ISO standard (ISO 10993-1:2018) , the gro2 intensity. A steady increase in temperature of samples was observed by irradiation with near-infrared wavelength. The temperature response was not largely affected by surface modification.The relationship between temperature and irradiation exposure time is presented in The highest increase in the rate of temperature was observed in the case of PEGylated MXene followed by PPGylated MXene and bare MXene. This increase can be explained based on the material’s suspension stability under irradiation conditions, which resulted in improved efficiency of the conversion of light to thermal energy . Moreove2 irradiation intensity.The percentage change in cell viability after incubation with increasing concentration of irradiated bare MXene, PEGylated MXene, and PPGylated MXene was also observed and is presented in The rise in sample concentration led to a reduction in the cell viability of all tested cell lines. The cytotoxicity effect of surface-modified MXenes were significantly higher than the bare MXene at all concentrations regardless of the cell type. Furthermore, the cell viability of cancerous cells  dropped to 70% at just 70 mg/L concentration of PEGylated MXene. In the case of PPGylated MXene, the cell viability of only MCF-7 cancerous cells dropped to below 70% at 70 mg/L while the A375 cell line required 150 mg/L of PPGylated MXene to achieve below 70% cell viability. As for bare MXene, the concentration required to induce a similar effect in the A375 cell line was 250 mg/L. At higher sample concentrations and the same irradiation level, the normal cells were also affected. At 150 mg/L concentration of PEGylated MXene, the cell viability of normal cells was barely above 70% whereas, in the case of PPGylated MXene, a similar effect was only observed at 250 mg/L. Generally, the irradiated surface-modified MXenes showed a marked increase in the toxicity against cancer cell lines as compared to similar MXene samples without irradiation.The half-maximal inhibitory concentration (IC50) was also calculated using the linear extrapolation method (using ED50 plus v 1.0 software) and presented in 2. IC50 values after irradiation for bare MXene were 1597.96 mg/L for HaCaT, 968.67 mg/L for MCF-10A, 374.49 for MCF-7, and 427.54 mg/L for A375. IC50 values after irradiation for PEGylated MXene were 474.66 mg/L for HaCaT, 429.83 mg/L for MCF-10A, 241.16 mg/L for MCF-7, and 268.83 mg/L for A375. The IC50 values after irradiation for PPGylated MXene were 576.03 mg/L for HaCaT, 513.24 mg/L for MCF-10A, 272.04 mg/L for MCF-7, and 305.87 mg/L for A375. The cytotoxicity effect on malignant cells was more pronounced. It can be observed that the PEGylated MXene were the most toxic towards malignant cell lines followed by the PPGylated MXenes and bare MXenes. On the other hand, bare MXene is the least toxic towards normal cell lines followed by PPGylated MXene and PEGylated MXene.On the one hand, in the case of PPGylated MXene, the IC50 values for HaCaT and MCF-10A were calculated to be 1602.80 mg/L and 985.95 mg/L, respectively, which are much closer to bare MXene. The IC50 values of PEGylated and PPGylated MXenes for malignant cell lines were comparable, where the values for MCF-7 were 315.81 mg/L and 313.51 mg/L respectively while for A375, the IC50 values were 335.93 mg/L and 340.12 mg/L respectively, without irradiation. A further reduction of IC50 values towards both normal and malignant cells was observed when the samples were irradiated using an 808 nm laser at a low power of 500 mW/cmThe shelf life observation of bare and surface-modified MXenes was also observed. In this study, a comparative cytotoxicity investigation has been conducted for PEGylated and PPGylated MXene nanoflakes/sheets against normal cell lines and cancerous cell lines. The toxicity is strongly related to the type of cell lines and a more pronounced toxicity effect has been observed for cancerous cell lines compared to normal cell lines (with and without irradiation). Further, it has been observed that PEGylated MXenes are more toxic towards cancerous cell lines followed by PPGylated and bare MXenes. The effect becomes more pronounced at higher concentrations and with irradiation. Furthermore, it was found that surface modification of MXene with PPG proves relatively safer to normal cell lines as compared to PEG based MXene, even at high concentrations and also with irradiation. Without irradiation, PPGylated MXene caused similar toxicity to MCF-7 cell line (cancerous cell line) as PEG-modified MXene but lower toxicity for A375 cell lines. However, after irradiation, PPG and PEG-modified MXenes had comparable IC50 values for both MCF-7 and A375 malignant cell lines. These results suggest that PPG modified MXene may prove to be a safer photothermal and anticancer agent as compared to PEG-modified MXene and thus, can be further explored in cancer therapies. Moreover, systematic in vivo studies using animal models should be performed to further investigate the anticancer potential and possible side effects of the PPG surface modified MXene.