Despite potent anticancer activity, the clinical utilization of cisplatin is limited due to nephrotoxicity. As Organic Cation Transporter 2 (OCT2) has been shown to be one of the key transporters involved in the uptake of cisplatin into renal proximal tubules, OCT2 inhibitors such as cimetidine have been explored to suppress cisplatin-induced nephrotoxicity. Nonetheless, the impact of OCT2 inhibition or cimetidine on the anti-cancer effects of cisplatin has not been extensively examined. The main objective of the present study was to quantitatively characterize the anticancer effects of cisplatin and cimetidine and determine their nature of interactions in two cancer cell lines, OCT2-negative hepatocellular carcinoma (HCC) cell line, Huh7, and OCT2-positive breast cancer cell line, MDA-MB-468. First, we determined the static concentration-response curves of cisplatin and cimetidine as single agents. Next, with the help of three-dimensional (3D) response surface analyses and a competitive interaction model, we determined their nature of interactions at static concentrations to be modestly synergistic or additive in Huh7 and antagonistic in MDA-MB-468. These results were consistent with the cell-level pharmacodynamic (PD) modeling analysis which leveraged the time-course effects of drugs as single agents and drug combinations. Our developed PD model can be further used to design future preclinical studies to further investigate the cisplatin and cimetidine combinations in different in vitro and in vivo cancer models. Cisplatin is one of the most potent chemotherapeutic agents and is widely used for the treatment of a variety of cancer types. However, its clinical use is limited due to severe side effects, including nephrotoxicity, ototoxicity, and peripheral neurotoxicity ,2,3. Of Cimetidine is an OCT2 substrate and competitively inhibits the cellular uptake of cisplatin via OCT2 transporters. Therefore, the combination of cimetidine with cisplatin therapy has been investigated to potentially reduce cisplatin-induced nephrotoxicity ,8,10. CiWhile targeting OCT2 seems like an attractive strategy to circumvent cisplatin-induced nephrotoxicity, it is imperative to ensure that such a strategy does not compromise the anticancer efficacy of cisplatin. For instance, OCT2 inhibition or co-treatment with cimetidine can block, at least partially if not fully, the transport of cisplatin into cancer cells , thereby suppressing its activity. While the nephroprotective effects of cimetidine have been extensively characterized in preclinical studies, very few studies have investigated the impact of cimetidine on the anticancer effects of cisplatin ,11. Besi50s). Based on this, CIS and CIM concentrations were selected for static and time-course drug combination studies. Next, the static combinatorial effects of CIS and CIM and the nature of their interactions in the two cancer cell lines were determined with the help of a three-dimensional (3D) response surface analysis using a competitive interaction model. Finally, the time-course effects of the CIS, CIM and their combinations were determined with the help of a mathematical in vitro pharmacodynamic (PD) model, and relevant parameters including interaction parameters were estimated. Thus, with the help of experimental and PD modeling approaches, the impact of OCT2 inhibition or co-treatment with cimetidine on the anticancer effects of cisplatin was evaluated in human cancer cell lines.The main objective of the current study is to quantitatively characterize the anticancer effects of CIS and CIM as single agents and combinations in two cancer cell lines, OCT2-negative liver cancer cell line, Huh7, and OCT2-positive breast cancer cell line, MDA-MB-468, using a combination of experimental data and in vitro pharmacodynamic (PD) modeling approaches. First, the effects of CIS and CIM as single agents in both the cancer cell lines were evaluated with the help of concentration-response curves and by determining their respective in vitro half-maximal inhibitory concentrations (ICThe human hepatocellular carcinoma (HCC) cell line, Huh7, and breast cancer cell line, MDA-MB-468 cells were purchase from American Type Culture Collection (ATCC) . Both cell lines were cultured, maintained, passaged, and used per manufacturer’s instructions and as extensively described previously ,17. Cisplatin and cimetidine were purchased from Millipore Sigma-Aldrich Co. . Cimetidine was dissolved in molecular biology grade water while sodium chloride (NaCl) solution in water was used for cisplatin as per manufacturer’s instructions. While cimetidine stock solutions were stored at −80 °C, but cisplatin stocks were always prepared fresh and stored, if required in dark at 2–8 °C. Fresh serial dilutions from the drug stocks were prepared each time prior to experiments.Cell culture reagents including Dulbecco’s Modified Eagle’s Medium (DMEM), MEM non-essential amino acids, sodium bicarbonate, Penicillin/Streptomycin, Phosphate Buffered Saline (PBS), molecular biology grade water and 0.25% trypsin/2.21 mM EDTA were purchased from Corning . Fetal Bovine Serum (FBS) and Cell Counting kit-8 (CCK-8) were purchased from Millipore Sigma-Aldrich . 3 cells per well (100 µL) of a 96-well plate. After overnight incubation, the cell lines were exposed to varying concentrations of CIS (0.001 to 25 µM), CIM (0.05 to 10 mM), or their combinations for 72 to 96 h for various sets of experiments. The CCK8 cell viability assay was subsequently carried out at different time points as indicated per the manufacturer’s instructions. Briefly, cells are incubated in CCK-8 solution (10 µL/well of a 96-well plate) for an hour with absorbance measured at 450 nm using a microplate spectrophotometer Biotek . Experiments were performed in at least triplicates and compared against controls. Based on growth patterns, Huh7 and MDA-MB-468 cells were seeded at a density of 5 × 10maxI) and concentrations required to achieve 50% of maximal effects (50IC) were estimated for CIS and CIM as single agents in the two human cancer cell lines, Huh7 and MDA-MB-468, at 72 h:R is the response to drug treatments (% cell viability), 0R is the baseline response (% cell viability in absence of drug treatments or control), maxI is the maximal inhibitory effect of drug treatments, 50IC is the drug concentration required to achieve 50% of maxI, C is the drug concentration and γ is the Hill coefficient (Table 1). The concentration-response curves were used to select concentrations for single agents and combinations during time-course and subsequent studies. An inhibitory Hill function was used to characterize the concentration-response curves. The maximal inhibitory effects , max,AI and max,BI are the maximal effects of the two drugs , AC and BC are concentrations of the drugs, ICA50 and ICB50 are the half-maximal inhibitory concentrations of the drugs, Aγ and Bγ are the respective Hill coefficients and ψ is the interaction term. All individual drug-related parameters were fixed from the CIS and CIM concentration-response curve fittings and the interaction term, ψ, was estimated. The apparent interaction between the drugs was antagonistic when ψ > 1, synergistic when ψ < 1 and additive when ψ = 1. Finally, three-dimensional (3D) response surface plots of cell viability versus CIS and CIM concentrations were constructed to visually evaluate the interaction between the drugs for both cell lines. An additive interaction was assumed between both drugs  and 3D response surface plots were generated for both cell lines using Equation (2) with MATLAB version 2017a The MathWorks, Inc. . Finally, observed cell viability data were overlaid on the additive response surface plots to assess location of the data points relative to the additive surface, thus enabling visual assessment of additive, antagonistic or synergistic effects between CIS and CIM in Huh7 and MDA-MB-468. A competitive interaction model ,19 was uThe in vitro cellular level pharmacodynamic model (PD) was developed to evaluate the effects for the single agents, CIS, CIM and their combinations on the cell viability of Huh7 and MDA-MB-468. The corresponding parameters, their definitions and estimated values are listed in Table 2. It was assumed that there was no change in drug concentrations and no degradation of drugs over time in cell culture media. In addition, it was assumed that the two drugs do not interact with each other in the cell culture media for their combination group. R is the % cellular viability (cellular response) at time t, gk is the first-order growth rate constant for cancer cell lines, while R0 is the % cellular viability at time zero.The cellular growth of Huh7 and MDA-MB-468 in the control group (no drug treatment) was best described by an exponential growth function as follows: The PD effect of CIS or CIM on the cell viability of Huh7 and MDA-MB-468 was described by a stimulatory effect (Hill function) on the cell death of cancer cells ,21. Threx represents either CIS or CIM, max,xS is the maximal killing rate constant while SCx50, is the concentration of CIS or CIM required to induce half-maximal cell-killing effect in Huh7 and MDA-MB-468. xK is the cytotoxicity Hill function, 1xK to 3xK are transit compartments with xƮ being the mean transit time between compartments and Cx represents the drug concentration. The differential equations for the effect of CIS or CIM as single agents in Huh7 and MDA-MB-468 are as follows:Both drugs are assumed to exert cytotoxic effects (stimulation of death function) in Huh7 and MDA-MB-468 cancer cell lines. Hence, the PD model for the drug combinations include an interaction parameter, ψ, which was applied to Equation (5a), where ψ = 1, <1, or >1 indicate additive, synergistic or antagonistic interactions, respectively. The following differential equations were used to characterize the combinatorial effects of CIS and CIM in Huh7 and MDA-MB-468:The time-course data of the single agents were characterized first with the single-agent PD models, parameters related to the single agents were estimated, and then fixed while estimating the interaction parameter and characterizing the drug combination data in the two cell lines. All model fittings and simulations were performed using Monolix suites version 2016R1 or higher . 50s for CIS were 1.96 µM and 0.39 µM while that for CIM were 3.19 mM and 3.28 mM in Huh7 and MDA-MB-468, respectively. Thus, MDA-MB-468 was more sensitive towards CIS as compared to Huh7 (~ 5-fold), while both cell lines showed similar sensitivities for CIM as evidenced by their estimated IC50s in the respective cell lines. On the other hand, both CIS and CIM showed similar maximal killing (close to 1) for both the cancer cell lines. A hill coefficient was used to accurately characterize the concentration-response curves for CIM and was estimated at 3.12 and 1.83 for Huh7 and MDA-MB-468, respectively. The inhibitory Hill model captured the concentration-response data well, as demonstrated with observations versus individual prediction plots shown in The two cancer cell lines, Huh7 (OCT2-negative) and MDA-MB-468 (OCT2-positive), were treated with a wide range of either CIS (0.001 to 25 µM) or CIM (0.1 to 10 mM) concentrations for 72 h. The inhibitory Hill model was fitted to the static concentration-response curves of CIS and CIM as single agents in both the cell lines as shown in After evaluating the concentration-response curves of CIS and CIM as single agents, Huh7 and MDA-MB-468 were exposed to a range of CIS and CIM concentrations, both as single agents as well as their combinations for 72 h as indicated in The time course effects of CIS and CIM as single agents as well as their combinations on the cell viability of Huh7 and MDA-MB-468 was characterized with the help of an in vitro cellular level PD response model as shown in The model fittings for the time-course effects of CIS or CIM as single agents in Huh7 and MDA-MB-468 are shown in −1 and 0.018 (±1.4%) h−1, for Huh7 and MDA-MB-468, respectively. The time course effects of CIS or CIM on the cell viability of cancer cell lines over 96 h was adequately captured with the stimulatory effects on cell death characterized with a capacity-limited, Hill function (Equations (4a)–(4e)). The estimated, maximal killing rate constant  for CIS was 0.038 (± 9.63%) h−1 and 0.108 (± 5.75%) h−1 while the CIS concentration inducing 50% of maximal cell killing rate  was estimated to be 4.27 (± 6.32%) µM and 2.48 (± 8.13%) µM for Huh7 and MDA-MB-468, respectively. On the other hand, the estimated maximal killing rate constant  for CIM was 0.106 (± 2.25%) h−1 and 0.096 (± 17.1%) h−1 while the CIM concentration inducing 50% of maximal cell killing rate  was estimated to be ~37 (± 22.7%) mM and ~21 (± 21.3%) mM for Huh7 and MDA-MB-468, respectively. Thus, time-course data analysis also confirmed CIS to be far more potent as compared to CIM  which is consistent with the concentration-response analysis. The delayed effects of CIS and CIM were captured well with the help of three transit compartments (K1 to K3) on the stimulation of death function, with CISƮ and CIMƮ representing the mean transit time. The cell viability of cancer cell lines (in absence of drug treatments) was first characterized with an exponential growth function (Equation (3)) using a first-order growth rate constant, kg which was estimated to be 0.011 (±3.32%) hNext, the time-course combinatorial effects of CIS and CIM on the cell viability or stimulation of death of Huh7 and MDA-MB-468 cell lines was characterized by fixing the parameter estimates of single agents and incorporating an interaction parameter ψ in the model structure as shown in Despite its high antitumor efficacy, the clinical utilization of CIS is limited due to organ toxicities including nephrotoxicity, which can often be dose-limiting ,2,3. Pre50 = 0.39 µM) demonstrated higher sensitivity towards CIS as compared to Huh7 (IC50 = 1.96 µM) by ~5-fold which could be explained, at least partially, due to higher OCT2 expression levels in the former as compared to the latter. The breast cancer cell line, MDA-MB-468 was previously reported to express high levels of OCT2 protein ,23, henc50s for CIM as a single agent were estimated at 3.28 mM and 3.19 mM in MDA-MB-468 and Huh 7, respectively  which are consistent with previous reports [50 estimates, a lower range of CIS concentrations (0.05 µM to 25 µM) and a higher range of CIM concentrations (from 0.05 to 4 mM) were used for subsequent static combination studies. With the help of a competitive interaction model and 3D response-surface analyses of the static single agent and combination profiles, the nature of interaction was determined as slightly synergistic or additive for CIS and CIM in Huh7 but antagonistic in MDA-MB-468 (In addition, we observed that CIM alone also mediated cytotoxicity in the cancer cell lines, although at much higher concentrations . This is reports ,13,14,15 reports . Based oA-MB-468 . FinallyFinally, few studies have previously reported that CIM did not impact the antitumor effects of CIS in vitro or in vivo in OCT2 expressing cancer models; however, those studies evaluated the impact of CIM at only one or two concentrations or dose levels ,11. HencTaken together, the above results indicate that combining CIM can potentially be beneficial in improving anticancer activity of CIS, especially in OCT2-negative cancer cells. Future studies should also aim to investigate the underlying mechanisms contributing to the synergistic interactions of CIS and CIM in OCT2-negative cancer cell lines. While antagonistic interactions were determined in the OCT2-positive cancer cells, it should be noted that the combination can still be beneficial to patients with OCT2-positive tumors in suppressing CIS-induced nephrotoxicity, especially at higher doses or for a longer duration and thereby benefit from the anticancer activity of CIS. Our developed cell-level PD model can be further used to better design future preclinical studies to further investigate the impact of OCT2 inhibition on the effects of cisplatin in different in vitro and in vivo cancer models.


Background: We have previously reported that the endocannabinoid receptor inverse agonist AM630 is a potent inhibitor of isocitrade dehydrogenase-1 wild-type glioblastoma (GBM) core tumour cell proliferation. To uncover the mechanism behind the anti-tumour effects we have performed a transcriptional analysis of AM630 activity both in the tumour core cells (U87) and the invasive margin cells (GIN-8), the latter representing a better proxy of post-surgical residual disease. Results: The core and invasive margin cells exhibited markedly different gene expression profiles and only the core cells had high expression of a potential AM630 target, the CB1 receptor. Both cell types had moderate expression of the HTR2B serotonin receptor, a reported AM630 target. We found that the AM630 driven transcriptional response was substantially higher in the central cells than in the invasive margin cells, with the former driving the up regulation of immune response and the down regulation of cell cycle and metastatic pathways and correlating with transcriptional responses driven by established anti-neoplastics as well as serotonin receptor antagonists. Conclusion: Our results highlight the different gene sets involved in the core and invasive margin cell lines derived from GBM and an associated marked difference in responsiveness to AM630. Our findings identify AM630 as an anti-neoplastic drug in the context of the core cells, showing a high correlation with the activity of known antiproliferative drugs. However, we reveal a key set of similarities between the two cell lines that may inform therapeutic intervention. Cell fate decisions are key in the homeostatic maintenance of the cellular milieu as well as in the survival of tissues and organisms. Uncontrolled cell proliferation occurs in tumoral cancer cells as a direct consequence of dysregulated cell cycle phases due to a lack of effective cell cycle DNA checkpoints as the cell grows and divides. In the central nervous system (CNS), different systems control cell fate decisions to maintain an effective and functional brain circuitry . PatholoThe high-grade malignant brain tumour, isocitrate dehydrogenase-1 wild-type glioblastoma (GBM), is the most frequent and aggressive primary de novo tumour of the CNS, with a median survival of 14.6 months from diagnosis in patients multimodally treated with surgery, radiotherapy, and chemotherapy . DespiteIn the developed brain the neuromodulatory activity of the endocannabinoid (eCB) system is the control of neural excitability through retrograde signalling to inhibit presynaptic transmitter release . HoweverIn previous studies, we have found that the pharmacological blockade of the CB1 or CB2 receptor signalling pathways impairs ) the in vitro proliferation of human GBM cells obtained from the core region (U87) suggesting that CB2 cannabinoid receptors are somehow involved in the proliferation of these GBM cells . The in With the aim of uncovering the biological mechanisms behind the anti-tumour activity associated with eCB receptor inhibition, we performed a detailed investigation into the gene expression changes driven by receptor inhibition in the context of the core and invasive margin cell populations of GBM. Specifically, we performed a microarray analysis of the gene expression perturbation driven by the CB2 receptor inverse agonist AM630 in GBM primary cell cultures from the central U87 cells and the more clinically relevant invasive margin GIN-8 cells.The selective CB2 inverse-antagonist (AM630) was purchased from Tocris . All other reagents and materials for cell cultures were obtained from standard suppliers.2. Cells grown in 6-well plates were treated with AM630 (5 microM) or vehicle for 24 h at 37 °C. Cells were then harvested for microarray analysis.All cell lines (U87 and GIN-8) used in this project were of human origin, obtained from Dr Ruman Rahmam (University of Nottingham). U87 cells confirmed on short tandem repeat (STR) genotyping, isolated from the core of a GBM tumour and sourced commercially, were used as a biological positive control for GBM cells. The glioma invasive margin (GIN-8) cell line, isolated from the tumour infiltrative edge, were derived in-house from surgeries at the Queen’s Medical Centre, Nottingham (comparable to their respective primary tissue on STR). Monolayer cells cultures were prepared as described previously ,21. Briehttps://www.selectscience.net/products/ovation-rna-seq-system-v2, accessed on 20 December 2021). The resulting expression data were pre-processed using RMA normalisation with the Bioconductor affy package [Following cell treatment, the expression changes relative to control were assayed on a microarray chip with quadruplicate samples. Cells were lysed in Absolutely RNA Miniprep Kit lysis buffer and β-mercaptoethanol . RNA was then extracted, and quality assessed (RNA integrity number ≥ 8) using a Bioanalyser (Agilent Technology). RNA expression levels were measured on Affymetrix Human Genome U133 plus 2.0 (GPL570) chip following library preparation and labelling by Nugen Ovation V2 and Nugen Encore as per the supplier’s recommended procedure . The panel consists of 7932 samples from 17 cancer types. Samples corresponding to a given cancer type were grouped and the cancer type gene expression levels were assigned to the median of the group, resulting in 17 profiles of median expression. The median fragments per kilobase million (FPKM) for the 17 cancer types are given in https://gtexportal.org/, accessed on 20 December 2021) to generate relative rank profiles similar to the microarray procedure discussed above. Because of the non-parametric nature of the profiles, we compared our profiles with the TCGA profiles using the Spearman rank correlation analysis.The GRP corresponding to the untreated cell samples were queried against RNAseq data for a panel of cancer cell lines hosted by The Cancer Genome Atlas (TCGA)   data comTranscription factor co-expression profiles (TFCEP) were generated by collecting pairs of genes with the highest co-expression patterns across the 600,000 expression samples comprising SPIED . Co-exprPathway enrichment analysis was based on a variant of the Kolmogorov–Smirnoff (KS) statistic. This is similar to the approach used in Gene Set Enrichment Analysis (GSEA) . The expWe observe that the distribution of https://cancergenome.nih.gov/, accessed on 20 December 2021) further strengthens our understanding of the basal transcriptomic profile of the core cell population  but not the CB2 receptor (CNR2) see . This leOur next objective was to probe for differences in the the GBM-derived cell lines by looking at the expression changes driven by the CB2 receptor inverse agonist AM630. We found that AM630 drove a substantial transcriptional response in the core cell population. The transcriptional response was symmetrical and substantial, with 1514 and 2168 two-fold up and downregulated genes see . This waThe muted transcriptional response to AM630 in the invasive margin population had only a moderate correlation with the core cell response. Comparing probes with significant expression differences (at the three standard deviation from the null level) between the treatment and control in the two cell populations, we saw a slight correlation in the AM630 response see . This obPathway enrichment of the genes perturbed in the core population showed a clear up regulation of immune response genes and down regulation of cell cycle and metastatic pathways see . In contThe main affected pathways in the boundary of the tumour after AM630 exposure  were epiwww.spied.org.uk, accessed on 20 December 2021) [Another insight into the activity of AM630 can be gained by a direct correlation analysis with the transcriptional activity of other compounds. To this end, we performed a search of the Connectivity Map (CMAP)  databaseer 2021) . As expeer 2021) ,32. Specer 2021)  with thier 2021) . Metergoer 2021) . Anotherer 2021)  with vorer 2021)  and scrier 2021) . The toper 2021) .In contrast, a CMAP analysis of the invasive AM630 margin profile does not return many significantly correlating drugs. Noting the moderate correlation in the core and invasive margin responses we reasoned that a combined profile populated with genes with a significant combined expression change may capture the shared activity in the two cellular contexts. To this end, we generated a combined profile constituting genes with combined Z scores of above five standard deviations from the null using Stouffer’s method  see Tab. Here, tLarge-scale transcriptional data enables the discovery of functional associations between genes , where gDespite multiple clinical trials and research efforts, GBM continues being the most aggressive of all forms of cancer with very limited clinical options to stop progression and dissemination of the tumour. GBM constitutes a complex of interacting cell types with a core population responding to treatments and an invasive margin population that has proved refractory to intervention . UnfortuDifferent signalling pathways associated with the brain endocannabinoid machinery are being investigated as potential therapeutic targets , with reRelative expression analysis of the untreated cells highlights the differences between the core and invasive margin populations. The core cells show a clear correlation with published GBM expression data, whereas the invasive margin population shows no correlation with any of the TCGA cell lines. This is in agreement with recent evidence that reveals an extensive degree of intra-tumour heterogeneity in GBM resulting from a clonal evolution process producing a completely different genetic pattern in the core compared with the invasive margin . This diAs regards the potential activity of AM630, we found a low primary target, the CB2 receptor, expression in either population. This led us to consider potential off-target effects for AM630 activity. Of the off-targets, the CB1 receptor is highly expressed in the core, and the serotonin receptor (HTR2B) is expressed at moderate levels in both populations. This observation is in agreement with the relatively high potency of AM630 in the core population. We hypothesise that AM630 elicits a substantial response in the core cells through antagonism of the CB1 receptor and any activity shared by the drug across the core and invasive margin cells may be driven by serotonin receptor antagonism.Analysing the pathways involved, we found that in the core of the tumour, AM630 is associated with the down regulation of the cell cycle and cancer-associated pathways. In contrast, AM630 up-regulates an immune response together with the TP53 and interferon pathways. The TP53 gene encodes a tumour suppressor protein P53, that plays a critical role in tumour suppression by orchestrating a wide variety of cellular responses inducing tumoral cell death . We can Investigating the drug-driven transcriptional profiles from the CMAP database we found that anti-neoplastic agents have high levels of correlation with the core cell response profile, with some drugs reported to have anti GBM activities. Different studies showed a high degree of expression for different serotonin receptor subtypes in glioblastoma. Of particular interest was the high degree of correlation with serotonin receptor antagonists which may speak to the serotonin receptor HTR2B being the sole off-target of AM630 with significant expression in both the core and invasive margin cell populations. This is of interest in the context of GBM as the serotonin levels have been shown to be elevated in some cancer types . HoweverThe correlation with serotonin receptor antagonists is of interest as the 5-HT serotonin receptors are highly expressed in different types of cancers, including GBM and modulate mitogenic signalling and impact tumour cell viability . In thisGiven the heterogeneity of glioblastoma, further studies are required to elucidate the molecular mechanisms of the observed AM630 anti-tumoral actions and if they can potentially be used in the future as an addition to current therapy.


Aim: Bendamustine is primarily used for treatment of indolent lymphomas but has shown efficacy in some patients with diffuse large B-cell lymphoma (DLBCL) and multiple myeloma (MM). Molecular-based patient stratification for identification of resistant patients, who will benefit from alternative treatments, is important. The aim of this study was to develop a resistance gene signature (REGS) from bendamustine dose-response assays in cultures of DLBCL and MM cell lines, enabling prediction of bendamustine response in DLBCL and MM patients.Methods: Bendamustine response was determined in 14 DLBCL and 11 MM cell lines. Using baseline gene expression profiles and degree of growth inhibition after bendamustine exposure, a bendamustine REGS was developed and examined for the risk stratification potential in DLBCL (n = 971) and MM  patients divided into prognostic subtypes.Results: Bendamustine resistance significantly correlated with resistance to cyclophosphamide in DLBCL and melphalan in MM cell lines. The bendamustine REGS showed significantly lower bendamustine resistance probabilities in DLBCL patients with GCB subtype tumors and in tumors of the differentiation dependent centrocyte and plasmablast subtypes. In MM patients, pre-BII classified tumors displayed high bendamustine resistance probabilities and the plasma cell subtype had lower bendamustine resistance probability than memory cells. Furthermore, tumors belonging to the 4p14, MAF, and D2 TC subclasses consistently displayed high bendamustine resistance probabilities.Conclusion: Significant differences in predicted response to bendamustine were found in molecular subtypes of DLBCL and MM, encouraging validation in prospective bendamustine-treated cohorts with available gene expression profiles and follow-up data. It contains three structural components: (1) a nitrogen mustard (2-chloroethylamine group) which confers the alkylating function; (2) a benzimidazole ring  giving it the antimetabolite function; and (3) a butyric acid group that confers water solubility. The mechanisms of action of bendamustine include DNA cross-linking, activation of DNA damage response leading to apoptosis, inhibition of mitotic checkpoints, and induction of mitotic catastrophe - a form of non-apoptotic cell death. Bendamustine is known to be well-tolerated and has also been shown to be effective as a single agent in heavily pretreated, high-grade NHL patients.Bendamustine is a bi-functional drug used for treatment of chronic lymphocytic leukemia (CLL) and rituximab-refractory indolent non-Hodgkin’s lymphoma (NHL). DLBCL is an aggressive form of NHL and the standard treatment is immuno-chemotherapy consisting of rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP). Unfortunately, up to one third of patients eventually develop refractory disease or relapse (R/R) in which case they are treated with high dose salvage therapy and autologous stem cell transplantations (ASCT). Not all diagnostic patients are eligible for full-dose R-CHOP due to toxicities which might lead to dose reduction, substitution, or discontinuation of one or more of the drugs. Moreover, a substantial fraction of R/R patients are ineligible for ASCT due to comorbidities and age, and for these no standard salvage therapy exist,8,9. Thus, effective alternative therapies with manageable toxicities are needed. In that context, bendamustine in combination with rituximab therapy has been suggested due to long-term remission for some patients together with low toxicity,10,11. Several clinical studies have tested the effect of combined bendamustine and rituximab treatment in DLBCL patients and reported an acceptable toxicity profile with modest activity, meaning only a subset of the patients respond [Supplementary Table 1],12-18. Thus, strategies for patient stratification based on bendamustine response are needed.The most common type of NHL is diffuse large B-cell lymphoma (DLBCL) and develops from post-germinal, antibody-secreting plasma cells that have undergone somatic hypermutation before they infiltrate the bone marrow. Over the past decades, the survival of MM patients has improved drastically, mainly attributed by advances in novel therapies,22. Melphalan combined with prednisone was the first successful therapy in achieving relatively high response rates in MM and has remained the gold standard for several decades. Of note, a phase III study has later revealed a generally superior outcome when using bendamustine in combination with prednisone in comparison to melphalan combined with prednisone, and it was thus approved as first-line therapy for MM patients not eligible for ASCT. In R/R MM patients, the overall response to bendamustine monotherapy is 30%-55% and higher when combined with other chemotherapy agents [Supplementary Table 1],25-27. As with DLBCL, bendamustine combination treatment has been shown to be safe, effective, and well-tolerated in both diagnostic and R/R MM patients,28,29.Multiple myeloma (MM) accounts for up to 10% of all hematological malignancies,31. MM can be subclassified based on the translocation and cyclin D (TC) classification which distinguishes eight subtypes based on immunoglobin translocations, activation of cyclin D genes, and chromosomal ploidy. A more refined cell-of-origin (COO) based classification system, the “B-cell associated gene signature” (BAGS), also exist for DLBCL and MM where primary cancer cells at the time of diagnosis are associated to normal B-cell subset phenotypes from tonsils or bone marrow, respectively. DLBCL patients are classified into one of the following subtypes: naïve, centrocyte, centroblast, memory B-cell, or plasmablast, and MM patients into: Pre-BI, Pre-BII, immature, naïve, memory, or plasma cell. BAGS provide for both DLBCL and MM independent and significant prognostic information as compared to ABC/GCB and TC, and for DLBCL when compared to the clinical international prognostic index. In addition, the BAGS subtypes have distinct and specific responses to chemotherapeutic agents used in a routine clinical setting for treatment of DLBCL and MM,34. Machine learning models have previously been developed to predict response to components of the R-CHOP regimen in DLBCL and melphalan in MM. These models were successfully generated based on dose-response studies and resistance gene signatures (REGS) in human B-cell cancer cell lines (HBCCL)-37.Various prognostic molecular classifications have been developed based on gene expression profiles (GEPs) of DLBCL and MM cell lines and patient tumor samples. According to the 2016 revision of the World Health Organization classification of lymphoid malignancies, the standard molecular subclassification of DLBCL is activated B-cell-like (ABC) and germinal center B-cell-like (GCB) DLBCL which has proven to have prognostic impact favoring the GCB-DLBCL subtypesIn this study, we hypothesize that bendamustine dose-response assays and GEP of cell lines can be used to find gene expression patterns in which the expression of a group of genes predicts the cellular response to bendamustine. The predicted response can be applied to samples from DLBCL and MM patients to identify the subset of patients that will be sensitive or resistant to treatment. To test this, we developed a REGS from DLBCL and MM cell lines based on GEP and bendamustine dose-response assays, and subsequently investigated their stratification potential of DLBCL and MM patients based on molecular profiles.,38. The cells were cultured at 37 °C in a humidified atmosphere of 95% air and 5% CO2 with the appropriate medium, serum, and supplements HBCCLs consisting of 14 DLBCL cell lines  and 11 MM cell lines  were used. Culturing and DNA barcoding for verification of cell line identities were performed as previously described.Prior to the dose-response experiments, the optimal seeding concentration for each cell line was determined as the highest concentration at which the cells grew at an exponential rate throughout the experiment without drugs added. The effect of bendamustine on viable proliferating cells was measured for the 25 HBCCL. Cells were seeded in 120 µL culture media per well in a 96 well plate 24 h before 16 bendamustine concentrations of two-fold increments were added. The highest bendamustine concentration used was 500 µg/mL in isotonic saltwater. Viability was measured immediately after addition of bendamustine (0 h plate) and after 48 h of drug exposure (48 h plate) by addition of CellTiter reagent  and subsequent absorbance measurement at 492 nm using FLUOstar Optima . Isotonic saltwater was used as controls alongside bendamustine. Border effects were circumvented by only including non-border wells for analysis for each experiment, and experiments were performed in triplicates. Time independent summary statistics were obtained as previously described.CEL files for all cell lines are deposited with GEO accessions GSE53798, GSE99634, and GSE22759. In DLBCL, three individual, diagnostic patient cohorts with available GEP datasets from tumors were used to test the bendamustine REGS, and GEP data were pre-processed as previously described. These encompass the IDRC dataset , the LLMPP dataset , and the MDFCI dataset . Similarly, the following three separate and previously described MM patient cohorts were used to test the bendamustine REGS: HOVON-65 dataset , MRC Myeloma IX dataset , and UAMS dataset .GEPs for each untreated cell line using the Affymetrix Human Genome U133 Plus 2.0 arrays were used. These were generated by purification of RNA from cells, cDNA conversion, biotin-labelling, and lastly hybridization to the GeneChip. The . An Rmarkdown document detailing the analysis is available as Supplementary File 1.For all statistical analyses R version 4.0.2 was used0). In this aspect, previously generated summary statistics for the other drugs, used in the treatment of hematological cancers, were obtained in addition to the bendamustine dose-response assays,40. A generalized pairs plot was created using the R package GGally.Correlations between bendamustine, cyclophosphamide, doxorubicin, vincristine, and melphalan response were determined by assessing the pairwise Pearson’s correlation of the area under the positive parts of the dose-response curves  pre-processed using the Bioconductor package frma. Cell lines were split into tertiles according to their AUC0 values separately for each disease to avoid comparison of diseases and assigned values of sensitive, intermediate, or resistant. Sensitive and resistant cell lines were treated as binary outcomes and GEPs as predictors in a logistic regression model with an elastic net penalty using the R package glmnet. Optimal parameters were obtained using leave-one-out cross-validation, with misclassification error as the loss function.The bendamustine REGS is based on regularized multivariate regression analysis with cross-validated tuning parameters and can assign probability of resistance to a specific drug utilizing GEP and dose-response data from cell lines,34. Assignment to a BAGS group was determined by the highest predicted probability across the classes, setting the 15% of patients with lowest classification probability in each cohort as unclassified. Kaplan-Meier curves of DLBCL patients were trichotomized based on predicted bendamustine response including p-values from a log-rank test generated using the R package survminer.Cell lines and patient samples from cohorts were assigned to DLBCL or MM BAGS classes using the BAGS classifiers. However, faster growing cells generally appear more sensitive to chemotherapy drugs when using a fixed timepoint, thus to account for this bias under the assumption of exponential growth, the growth rate from treated cells was normalized using untreated cells at both 0h and 48h time points as reference. This allows for a time independent determination of growth inhibition where growth rates are accounted for and hereby the bendamustine response among HBCCL panel can be ranked and compared. The four summary statistics from the drug response experiments are: 50% growth inhibition compared to untreated cells (GI50), total growth inhibition (TGI), the concentration at which the cell population is halved after 48 h (LC48), and AUC0 (the area under the curve above 0) Using the relative growth inhibition for a drug, it is possible to generate cell line specific dose-response curves0 ranging from 219.79  to 351.16  in DLBCL and 250.41  to 378.98  in MM cell lines. The most sensitive cells were SU-DHL-5 and MOLP-2 in DLBCL and MM, respectively, and the most resistant were SU-DHL-10 and RPMI-8226, respectively.Increasing concentrations of bendamustine induced growth inhibition at varying degrees for both DLBCL and MM cell lines 50 and AUC0 values with GI50 ranging from 1.80  to 1.84  and AUC0 ranging from 333.5  to 350.8  Interestingly, the six MM cell lines, U266, KMS-12-PE, NCI-H929, KMS-11, OPM-2, and AMO-1 displayed very similar GI0 values for bendamustine from this study and the cornerstone drugs of standard DLBCL and MM treatment: cyclophosphamide, doxorubicin, vincristine, and melphalan obtained from previous dose-response assays [Supplementary Table 2],40. A significant and positive correlation was regarded as an indication of shared resistance mechanisms between two compared drugs.To investigate co-occurrence of resistance and sensitivity among the HBCCL towards various drugs used in the treatment of hematological cancers, we assessed the pairwise Pearson’s correlation between AUCR = 0.747, P < 0.05) R = 0.44 for both). Melphalan dose-response data was not included for DLBCL cell lines since there were too few treated cell lines for comparison, and it is not widely used in the treatment of DLBCL. In MM, correlation coefficients between the drugs were overall lower R = 0.49) and cyclophosphamide (R = 0.43) - two other alkylating agents. Interestingly, these two drugs displayed the overall strongest and most significant correlation in MM  R = 0.085). In summary, a stronger correlation in response between agents with the similar mechanisms of action was observed, indicating that there are inherent response patterns to similar drugs in the HBCCL.In DLBCL cells, the highest and only significant correlation coefficient was found between bendamustine and cyclophosphamide treatment  [Supplementary Table 3]. Both DLBCL and MM tumors display molecular heterogeneity whereby cells from these diseases can be subclassified into different subtypes showing different prognosis after treatment,54. Thus, we evaluated whether there was a difference in the predicted bendamustine response using the bendamustine REGS on GEPs restricted to DLBCL and MM patients and on different subtypes hereof [Supplementary Table 4].A combination of GEPs from untreated cells in the HBCCL panel and outcome from the bendamustine dose-response assays was used to develop the bendamustine REGS. The minimum misclassification error in the cross-validation was 0.33. The final prediction model trained on the complete dataset, using the optimal parameters obtained from the cross-validation, generated a bendamustine REGS with 13 genes , where ABC patients displayed the highest bendamustine resistance probability in contrast to GCB patients P < 0.01) despite the large variations in resistance probability. The DLBCL patients with reminiscent transcriptional expression patterns matching centrocyte and plasmablasts displayed lower probabilities of bendamustine resistance in comparison to the naïve, centroblast, memory, and unclassified subtypes [Supplementary Figure 1C and D]. Of notice, tumors of the memory subtype in ABC-DLBCL patients displayed high probabilities of bendamustine resistance, whereas they were predicted to be the least bendamustine resistant subtype along with plasmablasts in the GCB-DLBCL patients.In DLBCL, the bendamustine REGS was tested in four independent, diagnostic DLBCL cohorts. As there was no significant difference in bendamustine resistance probabilities between the four cohorts [Supplementary Figure 1B]. Thus, subsequent comparison to the BAGS classification was performed in each individual dataset Supplementary Figure 1E]. Significant differences between the TC subclasses were found in all three datasets, where the highest bendamustine resistance probability was consistently found for the TC classes: 4p14, MAF, and D2 For the MM cohorts there was a significant difference in the distribution of bendamustine resistance probabilities between the three datasets used in vitro, the predictive value of the bendamustine REGS was evaluated in patients treated with cyclophosphamide containing drug regimens. The LLMPP clinical DLBCL dataset, which is split into CHOP and R-CHOP treated patients, was used, and patients were trichotomized into sensitive, intermediate, and resistant groups based on predicted bendamustine response. Bendamustine resistance probabilities using the bendamustine REGS were significantly associated with overall survival in the CHOP treated cohort (P < 0.01), but not in the R-CHOP treated cohort [Supplementary Figure 2A and B]. However, when the bendamustine REGS and a previously generated cyclophosphamide REGS was compared in patients within each of the clinical cohorts, no significant correlation was found between the two REGSs [Supplementary Figure 3].We did not find any bendamustine treated DLBCL cohorts usable for retrospective validation of the prediction model. However, as there was a significant correlation between bendamustine and cyclophosphamide response in the HBCCL n = 971, MM: n = 1,126) with individually assigned bendamustine resistance probabilities, we found that the ABC subtype was the most resistant in DLBCL, and the centrocyte and plasmablast subtypes were predicted to be the least resistant in the DLBCL patient cohorts. Bendamustine resistance probabilities in memory subtype tumors depended on ABC/GCB subclassification. In MM, the Pre-BII subtype had a high bendamustine resistance probability along with TC classes 4p14, MAF, and D2. Thus, molecular and COO based subclassification of patient cohorts can stratify patients based on bendamustine resistance probabilities.In summary, in clinical samples  regarding the drugs before reaching the tumor, and lack of consideration for intra- and inter-patient clonal heterogeneity of tumors. Despite these limitations, malignant cancer cell lines, like the HBCCLs, serve as strong pre-clinical models for disease specific pharmacogenomic studies  as they harbor many of the same mutations and genomic alterations found in malignant B-cells from DLBCL and MM,61. In addition, the HBCCLs were originally developed from patients and display many of the cytogenic characteristics of DLBCL and MM which contribute to oncogenesis and possibly response to treatments. The translational advantage of the HBCCL is the number of DLBCL and MM tumor specific cell lines in contrast to other cell panels such as NCI-60, which only contains two DLBCL and MM like cell lines.The use of malignant HBCCLs . Furthermore, a combined REGS developed from cyclophosphamide, doxorubicin, and vincristine treated cell lines also showed prognostic value in DLBCL.The prognostic potential in patient stratification using the cell line based bendamustine REGS should be validated in large, independent, and prospective randomized clinical cohorts treated with or without bendamustine in combination with standard treatment regimens to assess its clinical potential. At the current time and to our knowledge, no bendamustine treated DLBCL and MM validation cohorts with available GEP data and follow-up exist. We have previously validated a melphalan REGS, where high probabilities of resistance were associated with inferior clinical outcome in the HOVON-65 datasetin vitro, significant differences were found in the overall survival between patients trichotomized based on predicted bendamustine resistance in the CHOP treated cohort. This could suggest that the bendamustine REGS is a pseudo marker for response to treatment with alkylating agents sharing similar mechanisms of action with bendamustine in DLBCL. It is, however, worth noting that the DLBCL patients from the clinical datasets were subjected to a combination treatment containing either four or five drugs with different mechanisms of actions, some of which both interact with each other and the surrounding tumor microenvironment affecting the clinical outcome,62. Additionally, the bendamustine resistance probabilities did not correlate with the cyclophosphamide resistance probabilities in patients. Thus, a bendamustine treated cohort is needed for validation of the bendamustine REGS. Strong and validated predictors of treatment outcome based on clear molecular signatures have a great potential in the clinical setting for the identification of resistant patients who will benefit more from alternative treatments. However, many biological and statistical considerations are needed prior to their development and clinical application.In addition to the correlation between cyclophosphamide and bendamustine response In conclusion, the use of disease-specific cell lines is a strong tool for prognostic classification of patient cohorts as they can be used to create REGSs which can be further applied to clinical samples and used in risk stratification, either alone or in combination with other molecular subclassification models. Here, we evaluated the REGS for bendamustine in COO based subtypes in DLBCL and MM. For DLBCL, we found that the GCB subtype and the differentiation dependent BAGS subtypes of centrocyte, plasmablasts are predicted to have lower bendamustine resistance along with the plasma cell subtype in MM. However, these findings need to be validated in large, independent, prospective patient cohorts with bendamustine-treated patients and with available GEP and follow-up.


TRB) sequencing of sorted CD4+CD25+CD127low regulatory T cells (Treg cells) and CD4+ conventional T cells (Tcon cells) in order to track longitudinal changes in the TCR repertoire. GVHD following DLI was associated with less diverse but clonally expanded CD4+CD25+CD127low Treg and CD4+ Tcon TCR repertoires, while patients without GVHD exhibited healthy-like repertoire properties. Moreover, the diversification of the repertoires upon GVHD treatment was linked to steroid-sensitive GVHD, whereas decreased diversity was observed in steroid-refractory GVHD. Finally, the unbiased sample analysis revealed that the healthy-like attributes of the CD4+CD25+CD127low Treg TCR repertoire were associated with reduced GVHD incidence. In conclusion, CD4+CD25+CD127low Treg and CD4+ Tcon TRB repertoire dynamics may provide a helpful real-time tool to improve the diagnosis and monitoring of treatment in GVHD following DLI.Donor lymphocyte infusion (DLI) can (re-)induce durable remission in relapsing patients after allogeneic hematopoietic stem-cell transplantation . However, DLI harbors the risk of increased non-relapse mortality due to the co-occurrence of graft-versus-host disease (GVHD). GVHD onset may be caused or accompanied by changes in the clonal T-cell receptor (TCR) repertoire. To investigate this, we analyzed T cells in a cohort of 21 patients receiving DLI after alloHSCT. We performed deep T-cell receptor β ( Donor lymphocyte infusions (DLIs) are used as a curative treatment option in relapsing patients (therapeutic DLIs). They are also applied for the conversion of mixed chimerism or persisting minimal residual disease (pre-emptive DLIs) as well as to prevent relapse in high-risk disease (prophylactic DLIs) . The donrom GVHD . Howeverrom GVHD , the onsrom GVHD . Thus, a+ B cells, as well as naïve CD8+ cells, CD27+ NK cells and mononuclear cells, is associated with higher rates of GVHD [reg) cells was not protective for GVHD in that study. Treg cells have been described as the main T-cell subset maintaining self-tolerance in the immune system. Consequently, they contribute to the prevention of autoimmune and inflammatory diseases such as GVHD [reg cells was shown to prevent GVHD while retaining GVL activity after alloHSCT [reg-depleted DLI was shown to improve GVL [A recent report analyzing the cell subset composition of DLI products suggested that a higher proportion of CD27 as GVHD . The adoalloHSCT ,11, and rove GVL .reg TCR repertoire in the peripheral blood early after alloHSCT was associated with protection from GVHD within the first 100 days post alloHSCT [+ TCR repertoire analysis after alloHSCT, reduced diversity and clonal expansion were observed in acute GVHD (aGVHD) [+ TCR repertoire in the blood [The use of the T-cell receptor (TCR) repertoire analysis has improved our knowledge about overall T-cell diversity kinetics, including the outgrowth of individual specific T-cell clones. Easy-to-handle tools to decipher TCR-based repertoire dynamics in the context of DLI are just evolving and mostly involve spectratyping ,14,15. HalloHSCT . In an u (aGVHD) ,22,23,24he blood .+CD25+CD127low Treg cells and sorted CD4+ Tcon cells while observing the emergence and clinical course of GVHD during a study period of 36 months post DLI. In this study, we show that patients without GVHD post DLI exhibited broad CD4+CD25+CD127low Treg and CD4+ Tcon TRB repertoires similar to healthy individuals. Moreover, we observed a diversification of the CD4+CD25+CD127low Treg and CD4+ Tcon repertoires upon steroid treatment, which was indicative of GVHD treatment response. Finally, we examined whether the features of healthy-like repertoires early after DLI could predict the occurrence of GVHD. Overall, these findings could guide the diagnosis and monitoring of treatment in GVHD following DLI.In the setting of DLI, where immune-cell and TCR reconstitution has fully taken place, the role of immune-cell repertoire dynamics with regard to the risk of developing GVHD remains to be elucidated. Here, in a cohort of patients receiving DLI after alloHSCT, we longitudinally assessed the TCR repertoire via the deep sequencing of the TCR beta chains of sorted CD4p = 0.057; p = 0.024) and a trend towards higher NRM (p = 0.067) for GVHD compared with noGVHD during the 36 months of follow-up , we performed correlational analyses between absolute cell counts and 1/D. Higher values of 1/D indicated a more diverse repertoire. There were no correlations between 1/D and absolute cell counts neither for patient nor for healthy control (HC) samples in both CD4+CD25+CD127low Treg and CD4+ Tcon populations . Furthermore, a lower CD4+CD25+CD127low Treg diversity was observed in GVHD samples compared with noGVHD (p = 0.006), and patients without GVHD exhibited a CD4+CD25+CD127low Treg repertoire similar to that of healthy individuals . The noGVHD patient samples were matched for timing with regard to the sampling time points post DLI. For the control group, we used the average diversity of each healthy individual’s repertoires. The intergroup comparison of these subgroups revealed aGVHD to be the main driver of the observed differences in the diversity of both CD4 and HCs B. Again,+CD25+CD127low Treg 1/D  and CD4+ Tcon 1/D  but not for CD4+ Tcon 1/D  after GVHD was diagnosed) compared with the last time point prior to the onset of aGVHD (median of 66 d (5–70 d) before GVHD was diagnosed). Upon the occurrence of aGVHD, we observed a decrease in CD4Tcon 1/D D.+CD25+CD127low Treg and CD4+ Tcon repertoires compared with patients without GVHD or HCs. Conversely, patients not developing GVHD after DLI showed healthy-like repertoires.Taken together, GVHD after DLI was associated with less diverse CD4+CD25+CD127low Treg and CD4+ Tcon repertoire diversity in GVHD, which suggests the expansion of GVHD specific clonotypes, we visualized the repertoire proportion of individual clones comparing four representative samples for aGVHD, c/oGVHD, noGVHD and HCs , whereas there was a drop in diversity of −58.58% in steroid-refractory GVHD  linked to steroid sensitivity. Clonality analyses complemented the diversity findings by showing an increased clonality in the top 25% of the repertoires as well as expanded top 20 clones in steroid-refractory samples, while the opposite was the case in the steroid-sensitive samples for both CD4+CD25+CD127low Treg and CD4+ Tcon repertoires  of all patient samples taken at the first time point after DLI and categorized them into expanded (<25th percentile of HC 1/D) and not expanded, i.e., healthy-like samples (1/D ≥ 25th percentile of HC 1/D). The comparison of the clinical characteristics of patients with expanded vs. healthy-like diversity revealed no significant differences between the groups induce long-term remission via donor T-cell clones directed against recurring or persisting malignant cells, GVL often co-occurs with GVHD as result of unselective allo-reactivity ,7. To ru+CD25+CD127low Treg and CD4+ Tcon TRB repertoires in patients treated with DLI. Although the absolute count of immune cells did not influence the repertoire diversity  and functional assays to assess the role of cell functionality in the protection from GVHD.To date, the monitoring of GVHD following DLI depends on clinical assessment and histological confirmation. There is emerging evidence that GVHD after alloHSCT is associated with an expanded TCR repertoire, and the role as a potential biomarker has been discussed ,22,23,24iversity  and a biratyping ,22, as eeg cells , were noeg cells , we sortlonality . Immune-ed aGVHD . This isalloHSCT . HoweveralloHSCT ,21. In aficities ,30; howe+CD25+CD127low Treg and CD4+ Tcon repertoires, approaching healthy-like properties in steroid-sensitive patients, while steroid refractoriness was associated with opposite effects upon steroid treatment .The treatment of higher-grade GVHD relies on systemic steroids as first-line therapy. Therapy might be escalated with additional immunosuppression when criteria for steroid-refractory GVHD are met . Our detreatment . Evidenc T cells ; however T cells . However+CD25+CD127low Treg repertoire  prior to the application of DLI. However, this remains a challenging goal considering the highly individual and thus very complex immune-cell repertoire interactions in allo-reactivity.As +CD25+CD127low Treg and CD4+ Tcon TRB repertoires. Upon steroid therapy, we observed the diversification of the repertoires in steroid-sensitive GVHD patients. Moreover, a healthy-like CD4+CD25+CD127low Treg repertoire early after DLI was linked to a reduced incidence of GVHD during the 36 months of follow-up. While these findings warrant further testing, they hold promise to improve the diagnosis and monitoring of treatment in GVHD following DLI, thereby improving the safety of DLI.In conclusion, GVHD following DLI was associated with less diverse and clonally expanded CD4n = 5), second alloHSCT before DLI (n = 1), ongoing cGVHD at DLI (n = 1) and withdrawal from treatment (n = 1) were excluded from the trial. Details of study recruitment and sampling are given in A total of 29 patients treated with unmanipulated DLI for clinical or molecular relapse or increased host chimerism after alloHSCT were recruited between 2015 and 2017 in the department of Hematology, Hemostasis, Oncology and Stem-Cell Transplantation at Hannover Medical School. The institutional review board approved the study #2604-2015), and written informed consent was obtained from all study participants. Patients with an HLA-mismatched donor  and (overlap) chronic GVHD (c/oGVHD) were categorized according to the EBMT-NIH-CIBMTR task force panel recommendation , using tn = 3), individuals taking immunosuppressive medication (n = 1) and those with cold symptoms requiring symptomatic medication on more than three concurrent study visits (n = 1) were excluded from the trial. Moreover, samples of healthy individuals with self-reported wounds, cold symptoms or dental procedures prior to the blood draw were excluded  provided their written informed consent. Smokers (excluded . In tota+CD25+CD127low Treg and CD4+ Tcon was performed on a BD FACSAria Fusion (BD Biosciences) cell sorter  were extracted from patient whole-blood samples using the Ficoll gradient as described elsewhere  and storl sorter . Flow cy2O) were run together with the patient samples during all PCR steps.RNA was extracted from the sorted cells; this was followed by reverse transcription using SMARTer RACE 5′-3′ PCR Kit (Clontech) using a customized protocol . Next, thttps://github.com/MHHIMMUNOLOGY/MHHTCR_ANALYSIS, accessed on 21 August 2022). For TCR repertoire analysis we used VDJtools [http://cran.R-project.org, accessed on 21 August 2022) treemap package.Sequencing data analysis included the preparation of FastQ files, followed by extraction using MIXCR (version 2.1.11) with the alignment of nucleotide sequences based on the IMGT database (22.05.2018), including the correction of PCR errors. MIXCR default options were used for alignment and assembling. To avoid biased sample analysis due to diverging sequencing depths, we randomly normalized all samples to 20,000 reads using a custom bash script resulting in a standardized downsampling function. This was part of our streamline data analysis pipeline, ensuring a standardized and reproducible analysis of sequencing samples, including decompression, conversion into the FASTQ file format and random normalization  or the two-tailed Wilcoxon matched-pairs signed rank test  was used. Hazard ratios for the comparison of cumulative incidence curves were calculated with the Mantel–Haenszel test. The competing risk analysis of the cumulative incidence of relapse (CIR) and non-relapse mortality (NRM), analyzing the means of cumulative incidence curves, was performed with Gray’s test [Statistical analyses were performed with Prism 8 (GraphPad). Normality distribution was tested with the Shapiro–Wilk test. The group comparisons of normally distributed numerical variables was conducted with unpaired two-tailed Student’s y’s test .


MicroRNAs (miRs) regulate gene expression through translation inhibition of target mRNAs. One of the most promising approaches for cancer therapy is through mimicking or antagonizing the action of miRs. In this report, we analyzed the miRnome profile of several human breast cancer cell lines to determine the influence of estrogen receptor (ER) silencing previously shown to result in epithelial to mesenchymal transition (EMT) and enhanced tumor invasion.de novo ER-) and ER-silenced (acquired ER-) pII and IM-26 or ER-expressing (YS1.2) siRNA transfected derivatives of MCF7 cells was deep sequenced on Illumina NextSeq500. Respective miRnomes were compared with edgeR package in R and Venny2.1 and target prediction performed with miRTarBase. Mimics and inhibitors of selected differentially expressed miRs associated with EMT mediators  were transfected into pII cells and mRNA targets, as well as E-cadherin and keratin 19  were measured using taqman PCR.MicroRNA extracted from MDA-MB-231  and targets (using miR antagonists) for general cancer therapies aimed at regulating either individual or multiple mRNAs, but also to re-sensitize endocrine resistant breast cancers by turning them back into a type that will be susceptible to endocrine agents. •Around 50–60% of microRNAs were significantly differentially expressed between ER- and ER + breast cancer cell lines.•Transfection of miR-200c-3p mimic into ER -ve cells induced MET and reduced cell motility.•Transfecting of miR-449a inhibitor into ER -ve cells reduced cell invasion but did not induce EMT. All cell lines were maintained as monolayers at 37 °C in an incubator gassed with an atmosphere of 5% CO2 at 95% humidity and cultivated in Advanced Dulbecco's Minimum Essential Medium (DMEM) supplemented with 5% fetal bovine serum (FBS), 600 μg/ml l-glutamine, 100 U/ml penicillin, 100 μg/ml streptomycin and 6 ml/500 ml 100 x non-essential amino acids .YS1.2 was derived from a transfection with ER shRNA plasmid that 2.2Cultures of all tested cell lines were grown to approximately 80% confluency in 75 cm flasks, detached by trypsinisation, centrifuged for 5 min at 600 g, washed with 1 ml PBS and centrifuged again for 5 min at 600 g. Cell pellets . miRNA expression data were normalized based on the total read count per sample. Read count for each miRNA was divided by the total read count in that sample and multiplied by the median total read count across all samples. After normalization, data were log2-transformed. Sample-sample clustering  was performed on pairwise Pearson correlation coefficients based on the miRNA level read counts or the normalized miRNA expression data. Differential gene expression analysis was performed separately by pairwise comparisons. For each analysis, only miRNAs with a read count >4 in at least three samples were retained for further analysis. Differential gene expression analysis was performed on the raw read count data with the edgeR package in R which is a software for examining differential expression of replicated count data from Bioconductor . Volcanode novo resistant cell line MDA-MB-231 and acquired resistance cell lines pII and IM26 were compared and predicted using the miRTarBase and analyzed as mentioned before.Venn diagrams were constructed using the Venny2.1 software (Juan Carlos Oliveros n.d. “Venny2.1) in order to compare between the ER-cell lines MDA-MB-231, pII and IM26, and with the ER + cell line YS1.2. The average of three triplicates was taken except for when two out of three had no expression and the third value was less than 3 then this miRNA was not considered, otherwise even if the mean was less than 4 the miRNA was considered for the Venn diagram. Members that were exclusively expressed in YS1.2, MDA-MB-231, all ER-cell lines or all ER-acquired resistant cell lines were considered only if significantly differentially expressed in all comparisons. Targets of miRNAs and members in the miRNA families that were differentially expressed between ER- and ER + cells were predicted using miRTarBase , and onl2.3For measurements of gene expression, total RNA was extracted using the RNeasy kit from Qiagen, (USA). Purification was carried out according to the manufacturers' instructions. Quantity and quality were measured using NanoDrop 1000 spectrophotometer (Thermo Scientific) and by 2100 Bioanalyzer microfluidic gel electrophoresis system . The purified RNA samples were stored in RNAse-free distilled water at −80 °C. cDNA was synthesized from 1 μg of total RNA using High-Capacity cDNA Reverse Transcription Kit in the presence of RNase inhibitor 2000U  following the manufacturers’ instructions. The PCR amplification was carried out on a thermal cycler  with the following parameters: 25 °C for 10 min, 37 °C for 120 min, 85 °C for 5 min.2.4t values were used to calculate ratios of target to control gene using the excel spreadsheet developed by Pfaffl [Levels of Dicer, CTNND2 and ZEB1 mRNAs were measured using corresponding TaqMan Gene Expression Assays  according to the manufacturer's instructions. The PCR cycling was carried out in a 7600 Fast real time instrument (ABI) under the following conditions: 50 °C (2 min) hold, 95 °C (10 min) hold, then 40 cycles of 95 °C for 15 s and 60 °C for 1 min. Target gene expression was normalized to endogenous controls (actin and GAPDH). Cy Pfaffl .2.5pII cells were seeded at density of 1.5 × 10 [2.62. On the following day (48 h transfection), cells were harvested, counted, and diluted to 1 × 106 cells per ml in serum-free medium. After that, 100 μl of cells were added to the top chamber of the Cultrex dish. The lower chamber was loaded with 500 μl of DMEM supplemented with 30% FBS (used as a chemoattractant). Cells were incubated at 37 °C, 5% CO2 and allowed to invade to the bottom chamber. After 24 h, the top and the bottom chambers were aspirated and washed with 1 x cell wash buffer. Calcein-AM/cell dissociation solution complex was added to the bottom chamber and left for 1 h at 37 °C, 5% CO2. Cells internalize Calcein-AM and intracellular esterases cleave the acetomethylester (AM) moiety generating fluorescent free calcein. Invading cells were determined by recording the fluorescence emission using a microplate reader with a filter set of excitation/emission 485/535 nm.Cell invasion was assessed by Cultrex® 24 Well BME cell invasion assay purchased from Trevigen (Cat no. 3455-024-K) according to the manufacturer's instructions. For this, the invasion chamber was coated with 100 μl of 1 x basement membrane extract (BME) solution and incubated overnight at 37 °C. After 24 h transfection, cells were serum starved overnight at 37 °C and 5% CO2.7In addition to the statistical procedures already mentioned in above sections, Student's two tailed unpaired t-test or one-way ANOVA test followed by Bonferroni post hoc test were used to compare means of individual groups: p < 0.05 was considered statistically significant.33.1acquired (pII and IM26) and the de novo resistant ER-cell line (MDA-MB-231) showed that they differed in only 12–14% of expressed miRNAs.Approximately 20% of the 2588 miRNAs listed in the human miRBase sequences repository available at the time of this study were expressed in individual breast cancer cell lines  A. Fig. . the YS1.2 cell line all volcano plots show similar trends and indicate a high number of significantly differentially expressed miRNAs with wide fold changes. Interestingly, miRNAs do not show the very large differences that are often seen when comparing mRNA levels [de novo resistant cell line with the acquired resistance cell lines show differences in expression of much less magnitude. Volcano plots comparing the two acquired resistance ER-cell lines show differences of even less magnitude.In A levels . Volcanotop 50 most significantly differentially expressed miRNAs for each comparison. There is substantial similarity in the miRNAs differentially expressed in different comparisons; 38 miRNAs are shared between the three heat maps comparing YS1.2 with IM-26, and pII and MDA-MB-231 respectively. Acquired resistance ER-cell lines pII and IM-26 were also compared to the de novo resistant MDA-MB-231 cell line. In this case, only 17 differentially expressed miRNAs are shared between the two heat maps. When comparing pII and IM-26 to each other only 4 miRNAs are shared with the two other heat maps that compare de novo with acquired resistance lines  which indicate that the differentially expressed miRNAs reflect individual differences between the two cell lines.Heat maps with hierarchical clustering were constructed to show the 3.2acquired resistance ER-cell lines pII and IM-26, and the de novo resistant ER-cell line MDA-MB-231 .3.4For functional analysis targets were either predicted from miRDB database or were searched for as experimentally validated targets in miRTarBase database as mentioned in methods. This is an integrated web server for identifying miRNA-target interactions.Gene-sets associated with EMT, metastasis, motility, invasion, estrogen receptor pathway and cell adhesion were selected from the Molecular Signature Database and predicted targets for each miRNA were retrieved from miRDB 5.0. Fisher's Exact test was performed to assess the over-representation of each miRNA's target genes in each of the pre-selected gene-sets explained in methods. Only 7 results were found for all tests which are summarized in the network maps per biological process . For bio4de novo resistant MDA-MB-231 and two MCF7 derived lines in which ER had been silenced with shRNA , and also a transfected MCF7 line that had failed to down-regulate ER . Some miRNAs were subsequently selected for further studies on pII cells. We found that approximately 20% of the 2588 known miRNA sequences in the human miRnome was found to be expressed in our breast cancer cell lines. This is a common finding that miRNAs are generally down-regulated in cancer compared to normal tissues [acquired with the de novo resistant ER-cell lines  is an adhesion protein; a member of the p-120ctn superfamily . EpithelIn regard to miR-449a, pII cells transfected with miR-449a inhibitor showed no significant change in epithelial or mesenchymal markers. miR-449a was found to target Fos and Met in hepatocellular carcinoma and Flot2 in gastric cancer which was found to reduce features of EMT in these cells . The expThus, in summary, in this study we have shown that miR-449a could be considered as a tumor suppressive miRNA in ER + tumors, and that miR-200c, which targets ZEB1 and ZEB2 (major regulators of EMT) can reverse EMT in our cell line pII an ER-acquired resistance cell line. miR-29a-3p needs to be further investigated. Taken together, the net action of miRNAs determines the cell phenotype. Apparently, most of the miRNAs work as tumour suppressers rather than oncogenes since in both ER- and ER + cell lines they inhibit cell proliferation, invasion, and metastasis. This would be consistent with their lower expression in cancer cells. It seems that the reduction of the total miRNA in breast cancer is a mechanism to increase cancer ability to metastasize and invade other tissues. It would be useful to compare miRnome profiling with proteomics to determine the optimal combination of miRNAs that could be introduced or inhibited for targeted therapy. From our analysis we can conclude that EMT can be regulated by miRNAs by targeting mRNAs that are important in EMT regulation, and to re-sensitize endocrine resistant breast cancers by turning them back into a type that will be susceptible to endocrine agents . The miRConceived and designed the experiments: MAK, YAL. Performed the experiments: AA, MAK. Analyzed the data: YL, MAK, AA. Contributed reagents/materials/analysis tools: YL, MAK. Wrote the paper: MAK, YAL, AA.The authors confirm that there is no conflict of interest.


Additionally, we programmed a series of analyses, including genomic profiling, enrichment analysis, immune infiltration analysis, and therapy-related analysis to identify the mechanism of the SLC24A3 in the process of cancer in CESC. Meanwhile, qRT-PCR was used to validate that the expression of SLC24A3 mRNA in Hela and SiHa cell lines was significantly lower than in PANC-1 and HUCEC cell lines. Our finding elucidated that the SLC24A3, a sodium-calcium regulator of cells, is an indispensable factor which can significantly influence the prognosis of patients with CESC and could provide novel clinical evidence to serve as a potential biological indicator for future diagnosis and pain therapy.The aim of this study was to explore cervical carcinoma and screen a suitable gene as the biomarker used for prognosis evaluation as well as pain therapy. Low expression levels of solute carrier family 24 member 3 (SLC24A3) was involved in the appearance and development of numerous malignancies. Nevertheless, the prognostic value of SLC24A3 expression with cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) patients remains uncertain. During the present study, SLC24A3 expression in CESC was retrieved from TCGA, GEO, and MSigDB databases. Based on TCGA and GEO profiles, we performed survival and difference analyses about SLC24A3 both in two GEO (GSE44001 and GSE63514) and TCGA-CESC cohorts (all  Cervical carcinoma is the principal cause of mortality in female, specifically in some developing countries, and they are the second most universal cancer type in gynecology –5. AccorCancer pain, simultaneously, could be the most common and unbearable symptoms in cancer patients, which has been shown to be associated with depression, anxiety, and reduced quality of life in cancer survivors of varied diagnoses . Currenthttp://cancergenome.nih.gov/abouttcga/policies/publicationguidelines) was applied to download gene expression quantification data and clinical information of female with CESC. Nevertheless, because the TCGA database lacks normal tissue data matched with cervical cancer, the total number of samples is only three. Hence, the Genotype-Tissue Expression (GTEx) Portal (http://xena.ucsc.edu/) was applied to obtain the expression values of the normal cervix tissue. The Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo) was applied to download gene expression and clinical data of patients in GSE44001 and GSE63514 cohorts. The Molecular Signatures Database (MSigDB) (http://software.broadinstitute.org/gsea/msigdb/index.jsp) was used to extract pain-related genes. Since the data were downloaded from public databases, there is no need to get approval from ethics committee.The Cancer Genome Atlas (TCGA) database  > 1 as the cut-off criteria. We calculated the Spearman coefficients of the DEGs and key pain candidate genes, while defining DEGs with p value < 0.05 as key pain candidate genes.RNA-seq data information of 306 tumor cases and 13 normal cases was obtained from the TCGA and GTEx official website. Differential expression genes (DEGs) were screened via “edgeR” R package by comparing SLC24A3 expression of the tumor and normal cases. We chose the p values were adjusted via the false discovery rate (FDR) approach. If the mean methylation difference was >0.2 with an FDR of 5%, DMCs were reported.306 tumor and 13 normal cases were chosen to perform differential methylation analysis. Probes containing single-nucleotide polymorphism (SNPs), probes with over 10% missing values, and probes in chromosome X were excluded from the analysis. And the “imputeTS” R package was applied to impute the missing values of the selected central pattern generators (CPGs). Differentially methylated CpGs (DMCs) were determined via the Wilcoxon rank-sum test, and the N = 64) with 1000 permutations.The xCell algorithm, an R package (version 1.1.0), was applied to quantify the abundance of tumor stromal cells with CESC patients, which evaluates the integrated levels of 64 stromal cell types. xCell, a gene signature-based approach, combines the advantages of gene set enrichment with deconvolution methods, which well fits with RNA-seq and microarray data. And the gene expression data were prepared according to the xCell instructions, run R package, and undertaken using the xCell signature (https://www.cancerrxgene.org/) was used to download transcription data for almost 1000 cancer cell lines, drug response measurements as area under the curve (AUC) for antitumor drugs in cancer cell lines, and targets/pathways of drugs. The Spearman correlation analysis was performed to calculate the association between drug sensitivity and risk score. Then, we regarded |Rs| > 0.2 and applied Benjamini and Hochberg to adjust FDR and regarded an FDR < 0.05 as significant correlation. “pRRophetic” R package [50) for comparing the drug sensitivity.The Genomics of Drug Sensitivity in Cancer (GDSC)  database package  was usedThe “mafools” R package  was applHela and SiHa are human cervical cancer cell lines which were used as the test groups. Among them, Hela was a cell line of cervical adenocarcinoma and SiHa was a cell line of cervical squamous cell carcinoma. We utilized PANC-1, a cell line of pancreatic cancer, as a positive control for SLC24A3 expression. Meanwhile, we utilized HUCEC, a cell line of normal cervical, as a negative control for SLC24A3 expression. All of them were obtained from the American Type Culture Collection . We used DMEM contained with 10% FBS  to incubate Hela and SiHa cells. Then, we used RPMI 1640 supplementing 10% FBS  to incubate PANC-1 and HUCEC cells. The cells are cultured in an incubator at 37°C with 5% CO2.We used TRIzol reagent  to collect and lyse cells. Then, M-MLV reverse transcriptase  was utilized to obtain cDNA. Utilizing a LightCycler 480 II RT-PCR System , qRT-PCR was carried out as follows: 95°C for half a minute and then 40 cycles of 95°C for 5 seconds and 60°C for half a minute. In our study, glyceraldehyde-3-phosphate dehydrogenase (GAPDH) gene was utilized as an internal control; sequences of primers amplified in this study were contained in Table t-test via SPSS 26.0.In this study, R programming language was the major tool for processing data. The DEGs between CESC samples and healthy controls were acquired via the “EdgeR” R package, whereas |logFC| > 2 was screened as differentially expressed pain-related genes. The box plot and the forest plot were conducted by the “ggpubr” and “forestplot” R packages, respectively. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were done via the “enrichplot” R package. Survival curves and survival analysis were conducted by the “survminer” as well as “survival” R packages. “Clusterprofiler” R package was applied for quantification of gene set activity on the gene set enrichment analysis (GSEA). The version of R bioconductor utilized in this study was R 4.1.2. The qualitative data were analyzed by p < 0.05) were screened to perform the further GSEA analysis.The flow chart of this study is presented in MSigDB , methylation status, and SLC24A3-expressed levels as inputs; our analysis proved that methylation status and the expression of SLC24A3 were significantly associated with the survival outcomes , activated dendritic cell (p = 1.72e − 02), CD56 bright natural killer cell (p = 5.62e − 03), central memory CD4 T cell (p = 4.5e − 05), central memory CD8 T cell (p = 1.12e − 06), effector memory CD4 T cell (p = 2.16e − 05), effector memory CD8 T cell (p = 1.33e − 02), gamma delta T cell (p = 4.73e − 02), immature B cell (p = 4.66e − 03), immature dendritic cell (p = 5.23e − 05), macrophage (p = 1.03e − 03), mast cell (p = 2.92e − 02), MDSC (p = 2.05e − 03), memory B cell (p = 3.37e − 02), natural killer cell (p = 2.88e − 04), natural killer T cell (p = 2.41e − 03), neutrophil (p = 1.33e − 02), plasmacytoid dendritic cell (p = 5.08e − 04), regulatory T cell (p = 1.27e − 03), T follicular helper cell (p = 4.25e − 06), type 1 T helper cell (p = 6.77e − 05), type 2 T helper cell (p = 4.58e − 02), a total of 22 immune cells, was enriched in SL24A3 high-expressed group . That consisted with our bioinformatics analysis and further validated that the SLC24A3 was lower expressed among patients with CESC.qRT-PCR revealed that the levels of SLC24A3 mRNA were obviously downregulated in Hela and SiHa cell lines compared with PANC-1 and HUCEC cell lines . Then, we retrieved pain-related gene from MSigDB database. The GSEA analysis revealed that only one gene was enriched in the pain pathway. Thus, we successfully screened out the gene SLC24A3. This study manifested that SLC24A3 had a considerable difference of expression in tumor and normal tissues of CESC, and we also proved that low-expressed SLC24A3 has well survival rates in patients with CESC. Intriguingly, we found that SLC24A3 has significantly positive correlation with a classical pain gene—SLC6A4. Previous studies have indicated that the human 5-HT transporter (5-HTT) gene (SLC6A4) features some polymorphisms in its promoter region (5-HTTLPR) that impact the 5-HTT expression , 29. IncTo verify the genetic alterations in SLC24A3 in CESC, we drew a panorama gene mutation showing the location and number of somatic mutations in SLC24A3. Copy number variations (CNV) are a usual form of genetic instability, which could directly alter the expression level of particular genes, which plays essential roles in susceptibility to disease , 32. In To further understand the functional role of the SLC24A3, GO, KEGG, and GSEA were performed, and the results indicated that SLC24A3 was greatly enriched in biological processes such as DNA repair, mitochondrion organization, NCRNA metabolic process, and reactome cell cycle mitotic.Immune infiltration of tumors is intimately related to clinical consequences in CESC. Tumor-infiltrating immune cells (TIIC) form an ecosystem in the tumor microenvironment to modulate cancer development and have exhibited underlying prognostic value . Tumor cDrug sensitivity analysis was performed to validate whether risk score could predict patients' sensitivity to chemotherapy. The results of drug analysis indicated that the therapeutic effect of mitomycin C, bleomycin, and gemcitabine could be improved when the SLC24A3 was low expressed. TMB and IPS were applied to evaluate the potential response of immune checkpoint blockade (ICB) therapies. The immunotherapy results revealed that the higher the TMB and IPS, the better the immunotherapy effect. In summary, these findings may be beneficial to clinical decision-making.The results of qRT-PCR demonstrated that the SLC24A3 mRNA was lower expressed in Hela and SiHa cell lines compared with PANC-1 (a positive control) and HUCEC (a negative control) cell lines. It was noteworthy that SLC24A3 mRNA was significantly higher expressed in PANC-1 and serves as human pancreatic cancer cell lines, compared with HUCEC cell lines. This suggested that SLC24A3 was differentially expressed in different cancer type compared with normal control tissues. As far as we know, however, no previous research has investigated whether SLC24A3 is involved in occurrence and development of pancreatic cancer. Hence, we were unable to understand how SLC24A3 exerted its function in pancreatic cancer during occurrence and development. Nonetheless, that did not influence our conclusion that the experimental results of qRT-PCR consisted with the above bioinformatics analysis obviously.κB pathway [Further, in the existing studies, scientists have revealed that Nckx3 (gene SLC24A3), one of the six isoforms of Na+/K+/Ca2+ exchanger family, is able to operate either in forward or in reverse mode relying on both ion gradients and membrane potential –42. Besi pathway . At the MSigDB databases. Then, SLC24A3 was obtained, enriched in the pain signaling pathway by GSEA. Subsequently, genomic profiling, enrichment analysis, immune infiltration, and chemotherapy and immunotherapy analysis were performed. All those gene bioinformatics suggested that SLC24A3 could be a possible molecular targeting mechanism for the prevention and pain treatment of CESC, which needs to be tested in further clinical trials.In conclusion, firstly, we retrieved five pain-related genes from TCGA, GEO, and Admittedly, some limitations indeed exist in this study. First, gene expression profile utilized in present study was from different platforms; this discrepancy may produce bias in the analysis process. Moreover, experiments to illustrate the underlying function and mechanism of SLC24A3 in the therapy of cervical cancer pain were not performed yet. Last but not least, we need to conduct in vitro model and experimental studies to validate the assumption we proposed according to the functions of the SLC24A3, along with the predicted drugs. Therefore, the function of SLC24A3 of CESC is encouraging enough to warrant advanced exploration.


SS18::SSX1 fusion gene, and recapitulates the molecular features of human synovial sarcoma tumours as shown by proteomic profiling. Comparative analysis of drug response profiles with two other established synovial sarcoma cell lines (SYO-1 and HS-SY-II) finds that ICR-SS-1 harbours intrinsic resistance to doxorubicin and is sensitive to targeted inhibition of several oncogenic pathways including the PI3K-mTOR pathway. Collectively, our studies show that the ICR-SS-1 cell line model may be a valuable preclinical tool for studying the biology of anthracycline-resistant synovial sarcoma and identifying new salvage therapies following failure of doxorubicin.Synovial sarcoma is a rare translocation-driven cancer with poor survival outcomes, particularly in the advanced setting. Previous synovial sarcoma preclinical studies have relied on a small panel of cell lines which suffer from the limitation of genomic and phenotypic drift as a result of being grown in culture for decades. Patient-derived xenografts (PDX) are a valuable tool for preclinical research as they retain many histopathological features of their originating human tumour; however, this approach is expensive, slow, and resource intensive, which hinders their utility in large-scale functional genomic and drug screens. To address some of these limitations, in this study, we have established and characterised a novel synovial sarcoma cell line, ICR-SS-1, which is derived from a PDX model and is amenable to high-throughput drug screens. We show that ICR-SS-1 grows readily in culture, retains the pathognomonic  Synovial sarcoma is a rare mesenchymal tumour type that accounts for 8–10% of all soft tissue sarcomas . It typiIn order to identify new therapeutic strategies and gain a better understanding of the biology of this disease, it is necessary to develop preclinical models of synovial sarcoma. Given its rarity, there are very few synovial sarcoma cell line models available in public repositories, with only five identified in the most recent census of sarcoma cell line models . EstabliCIC::DUX4 sarcoma [To bridge this gap, patient-derived xenografts (PDX), where tumours obtained from patients are serially passaged in mice, have emerged as a valuable tool for preclinical research as they retain much of the molecular and histopathological features of the original human tumour ,19. Seve sarcoma ,28,29,30In this study, we established a novel synovial sarcoma cell line, ICR-SS-1, which was derived from a PDX model from The Jackson Laboratory biorepository. We have further undertaken a comparative analysis of the drug response profiles of ICR-SS-1 versus two established commercially available synovial sarcoma cell lines (SYO-1 and HS-SY-II). To our knowledge, this is the first study to report a matched PDX-derived cell line for synovial sarcoma.http://tumor.informatics.jax.org/mtbwi/pdxDetails.do?modelID=J000104314, accessed on 17 June 2022). This PDX was derived from a tumour obtained from a 21-year-old male diagnosed with a grade 3 metastatic synovial sarcoma. This PDX model has been shown by The Jackson Laboratory to harbour the SS18::SSX1 fusion gene. PDX tumours were serially passaged in NOD scid gamma (NSG) mice and tumour volume calculated by ½ × length × width2 (ICR-SS-1 was established from a publicly available PDX model (J000104314) deposited in The Jackson Laboratory biorepository , 1× penicillin/streptomycin, 10 ng/mL EGF , 10 µg/mL hydrocortisone , 0.5 mg/mL collagenase (Sigma Aldrich), 0.1 mg/mL hyaluronidase (Sigma Aldrich), 100 units/mL DNase I (Sigma Aldrich), 10 µM Y-27632 , and 5% FBS (Gibco). Red blood cells were lysed using RBC lysis buffer  and remaining cells were incubated with 0.05% trypsin-EDTA (Gibco) at 37 °C. After trypsinisation, cells were treated with 1 mg/mL DNase I (Sigma Aldrich) at 37 °C, before passing through a 70 µm strainer. Mouse cell depletion beads  were used to remove contaminating murine cells.2. Medium was replenished twice weekly.Dissociated and mouse cell depleted PDX tumour cells were cultured in DMEM/Ham’s F12 1:1 with 15 mM HEPES, 1× penicillin/streptomycin, 2.4 mM L-glutamine, 5 µM Y-27632 (LC Laboratories), 5 µg/mL insulin (Sigma Aldrich), 400 ng/mL hydrocortisone (Sigma Aldrich), 10 ng/mL EGF (Peprotech), 250 ng/mL amphotericin B (Thermo Fisher Scientific), 9.62 ng/mL cholera toxin (Sigma Aldrich), and 10% FBS (Gibco). Following successful continuous growth for >10 passages in culture, the cell line was designated ICR-SS-1. HS-SY-II , SYO-1 , and NIH-3T3  cells were cultured in DMEM supplemented with 1× penicillin/streptomycin and 10% FBS (Gibco). SK-UT-1 cells  were cultured in MEM supplemented with 1× penicillin/streptomycin and 10% FBS (Gibco). Cells were grown at 37 °C with 5% COg and grown at 37 °C with 5% CO2 for 3 days before imaging.For spheroid formation, ICR-SS-1 cells (1000/well) were seeded into 96-well, round-bottom, ultra-low attachment plates (Corning). Plates were spun at 1000× ICR-SS-1 cells (1000/well) were seeded into 96-well, flat-bottom, black-walled plates . Plates were fixed in 10% neutral-buffered formalin solution (Sigma Aldrich) at respective timepoints and stained with 5 µg/mL Hoechst 33342 . Plates were scanned and cells were counted using a Celigo imaging cytometer. Cell counts were normalised to day 1 and an exponential growth equation was fitted to the data using GraphPad Prism  in order to determine doubling time.SS18 forward primer and one of SSX1, SSX2, or SSX4 reverse primers. ACTB was used as a loading control. Primer sequences are provided in RNA was extracted from J000104314 tissue and ICR-SS-1 cells using RNeasy mini and QIAshredder kits , following the manufacturer’s instructions, and used to generate cDNA via a Superscript III kit (Invitrogen). The HS-SY-II cell line was used as a positive control. PCR mixtures were made containing the common SS18::SSX or ACTB amplification are presented in Thermocycler conditions used for PTGER2 forward primer and a common reverse PTGER2 primer. Primer sequences are provided in DNA was extracted from ICR-SS-1, NIH-3T3, and SK-UT-1 cells using a DNeasy blood and tissue kit (Qiagen), following the manufacturer’s instructions. NIH-3T3 was used as a mouse positive control and SK-UT-1 was used as a human positive control. PCR mixtures were made containing either a human- or mouse-specific PTGER2 amplification are presented in Thermocycler conditions used for human or mouse Genomic DNA was harvested from J000104314 PDX tissue and ICR-SS-1 cells using a DNeasy blood and tissue kit (Qiagen), following the manufacturer’s instructions. DNA was quantified using a Qubit dsDNA HS assay kit, and STR profiles were then analysed via Eurofins cell line authentication service.ICR-SS-1, HS-SY-II, and SYO-1 cells were seeded in T25 culture flasks and incubated for 72 h in order to reach 80% confluence. Cells were then lysed in 8 M urea and 0.1 M ammonium bicarbonate (ABC), and protein concentration was measured by bicinchoninic acid (BCA) assay. For each cell line, 40 ug of total protein was reduced with 10 mM dithiothreitol at 56 °C for 40 min and alkylated with 55 mM iodoacetamide at 25 °C for 30 min in the dark. After dilution to final concentration of 2 M urea and 0.1 M ABC, each sample was digested with 0.4 g of trypsin (Thermo Scientific) at 37 °C overnight. The resulting digest was acidified to pH < 4 by trifluoroacetic acid (TFA), desalted on Pierce C18 Spin Columns (Thermo Scientific) according to the manufacturer’s protocol, and dried in a SpeedVac.Dried samples were resuspended in mobile phase A , spiked with iRT peptides , and 2 ug of total peptides were loaded onto a 2 cm × 0.1 mm trap column self-packed with ReproSil Pur C18AQ  beads. Sequential window acquisition of all theoretical mass spectra (SWATH)-mass spectrometry data were acquired using the same instrument parameters as previously described in Milighetti et al. . The acqhttps://biit.cs.ut.ee/gprofiler/gost, accessed on 17 June 2022) and the following setup: full list of identified proteins used as a background; Benjamini–Hochberg FDR method with 0.1 FDR threshold; GOBP and Hallmark gene set databases downloaded from MSigDB  [Proteomic profiles of the three synovial sarcoma cell lines and FFPE tissue samples were analysed by significance analysis of microarrays (SAM) using the samR package  in R. Fone 2022) ,39.t-test in Perseus. For this analysis, the log2 transformed, quantile normalised dataset was used. Technical replicates for individual cell lines were kept as separate samples and proteins with no valid values across the three cell lines were removed. A permutation-based FDR threshold of 0.05 and artificial within-group variance of 0.1 were applied in Perseus to identify significant differentially regulated proteins [Proteomic profiles of ICR-SS-1 and the other two synovial sarcoma cell lines were compared by a two-tailed proteins . Lists oICR-SS-1 (2000/well), HS-SY-II (3000/well), and SYO-1 (2000/well) cells were seeded in clear 96-well, flat-bottom plates . Plates were incubated for 24 h before replacing media with a panel of small molecule inhibitors at a concentration of 500 nM for all drugs except NVP-AUY922, which was at a concentration of 50 nM  analysis at 16 loci . There wSS18::SSX fusion gene. To evaluate if this fusion was present and retained in the ICR-SS-1 cell line, we undertook PCR analysis of SS18 and the three different possible fusion partners . Using the HS-SY-II cell line, which has the SS18::SSX1 fusion gene as a positive control [SS18::SSX1 fusion gene  gene as previously described [PTGER2  comprising four histological subtypes including synovial sarcoma [We have previously undertaken proteomic profiling by SWATH-mass spectrometry of tumour specimens from a cohort of soft tissue sarcoma patients , while the commercial SS cell lines showed elevated levels of proteins which are E2F targets   compared to the other cell lines  ( < 0.01) A. Pazopa < 0.01) B. Furthe < 0.01) C,D. The  < 0.01) C. An eva < 0.01) D, suggesSS18::SSX1 fusion gene and faithfully recapitulates the molecular features of human synovial sarcoma tumours as shown by mass spectrometry analysis.Synovial sarcoma is a rare cancer type with poor outcomes in the advanced setting despite multidisciplinary clinical management. There is a lack of effective systemic therapies for these patients and therefore an urgent need to develop new approaches to tackle this disease. Key to the identification of novel agents is the availability of well-characterised preclinical cell line models. To date, only five synovial sarcoma cell lines are available in public biorepositories , all of SS18::SSX fusion gene), it is well-established that in synovial sarcoma patients there is a wide heterogeneity in observed clinical responses to systemic therapies. For instance, only a subset of advanced synovial sarcoma patients benefits from treatment with chemotherapeutic agents such as doxorubicin and trabectedin [Despite being driven by a single translocation (the bectedin ,45,46. Tbectedin . This suBy subjecting ICR-SS-1 to a targeted small molecule inhibitor panel and comparing the drug responses to the two other synovial sarcoma cell lines, we identified agents from three different drug target classes which are effective in all three cell lines. Of these, both SYO-1 and HS-SY-II have previously been shown to be sensitive to PLK-1 inhibition  while BEIn summary, we have developed and characterised a matched PDX-derived cell line for synovial sarcoma which will add to the arsenal of preclinical tools available for the study of this rare cancer. In particular, the ICR-SS-1 cell line model may be valuable for studies focused on the biology of anthracycline-resistant sarcoma and identifying novel ways to tackle this clinically challenging problem.