Patients with ovarian cancer often present at advanced stage and, following initial treatment success, develop recurrent drug-resistant disease. PARP inhibitors (PARPi) are yielding unprecedented survival benefits for women with BRCA-deficient disease. However, options remain limited for disease that is platinum-resistant and/or has inherent or acquired PARPi-resistance. PARG, the PAR glycohydrolase that counterbalances PARP activity, is an emerging target with potential to selectively kill tumour cells harbouring oncogene-induced DNA replication and metabolic vulnerabilities. Clinical development of PARG inhibitors (PARGi) will however require predictive biomarkers, in turn requiring an understanding of their mode of action. Furthermore, differential sensitivity to PARPi is key for expanding treatment options available for patients.A panel of 10 ovarian cancer cell lines and a living biobank of patient-derived ovarian cancer models (OCMs) were screened for PARGi-sensitivity using short- and long-term growth assays. PARGi-sensitivity was characterized using established markers for DNA replication stress, namely replication fibre asymmetry, RPA foci, KAP1 and Chk1 phosphorylation, and pan-nuclear γH2AX, indicating DNA replication catastrophe. Finally, gene expression in sensitive and resistant cells was also examined using NanoString or RNAseq.PARGi sensitivity was identified in both ovarian cancer cell lines and patient-derived OCMs, with sensitivity accompanied by markers of persistent replication stress, and a pre-mitotic cell cycle block. Moreover, DNA replication genes are down-regulated in PARGi-sensitive cell lines consistent with an inherent DNA replication vulnerability. However, DNA replication gene expression did not predict PARGi-sensitivity in OCMs. The subset of patient-derived OCMs that are sensitive to single-agent PARG inhibition, includes models that are PARPi- and/or platinum-resistant, indicating that PARG inhibitors may represent an alternative treatment strategy for women with otherwise limited therapeutic options.We discover that a subset of ovarian cancers are intrinsically sensitive to pharmacological PARG blockade, including drug-resistant disease, underpinned by a common mechanism of replication catastrophe. We explore the use of a transcript-based biomarker, and provide insight into the design of future clinical trials of PARGi in patients with ovarian cancer. However, our results highlight the complexity of developing a predictive biomarker for PARGi sensitivity.The online version contains supplementary material available at 10.1186/s13046-021-02124-0. Ovarian cancer is a leading cause of gynaecological cancer-related death, accounting for more than 200,000 deaths globally in 2020 , 2. The TP53 mutations, and extensive copy number variation (CNV) , olapar2 atmosphere. OV56 (Sigma-Aldrich) was grown in DMEM/F12 with FBS reduced to 5% and supplemented with 10 μg/ml insulin, 0.5 μg/ml hydrocortisone, 100 U/ml streptomycin, 100 U/ml penicillin, 2 mM glutamine and maintained at 37 °C in a humidified 5% CO2 atmosphere. All ovarian cancer cell lines were authenticated by the Molecular Biology Core Facility at the CRUK Manchester Institute using Promega Powerplex 21 System, and underwent periodic testing for mycoplasma.The ovarian cancer cell lines OVCAR3 (ATCC), Kuramochi, OVMANA, OVSAHO, OVISE (JCRB Cell Bank) were grown in RPMI; COV362, COV318 (Sigma-Aldrich) and CAOV3 (ATCC) were grown in DMEM; RMG1 (JCRB Cell Bank) was grown in Ham’s F12 (Sigma-Aldrich); all supplemented with 10% foetal bovine serum , 100 U/ml streptomycin, 100 U/ml penicillin (Sigma-Aldrich), 2 mM glutamine (Sigma-Aldrich) and maintained at 37 °C in a humidified 5% COhttps://www.mcrc.manchester.ac.uk/research/mcrc-biobank. Ex vivo ovarian cancer models (OCMs) were expanded from ascites samples from 32 patients, of which seven were published previously  in DMSO-treated cells versus cells treated with 2 Gy X-ray ionising radiation followed by 24 h 1 μM PARPi. Dotted line indicates a 2-fold change, above which cells are considered HRP (below it HRD).Additional file 9 Fig. S9. Low expression of mitotic or apoptotic gene sets does not identify OCMs sensitive to PARGi. (A) XY plot showing correlation between sum of Z scores for mitotic and DNA replication genes for each OCM . (B) Heatmaps showing RNAseq of 29 OCMs ranked by sum of z-scores for 25 mitotic (left) and apoptotic (right) genes where red = high expression; white = average expression; and blue = low expression. (C) XY plots showing colony formation area and the sum of z-scores for the expression of 25 mitotic (left) and apoptotic (right) genes from RNAseq of 29 OCMs. PARGi sensitivity as determined by colony formation assay ; Res, platinum-resistant (tumour progression between 4 weeks and 6 months from completing platinum therapy); Sens, platinum-sensitive (tumour progression ≥6 months from completing platinum therapy). CN, chemonaïve; OS, overall survival; mo, months; WT, wild-type; FR, frameshift; NS, nonsense; IF, immunofluorescence; IHC, immunohistochemistry; For IF and IHC: CK7 and PAX8, coloured box (present), white box (absent), S, strong; P, patchy; F, focal; W, weak. p53 is either mutant-type , wild-type (lighter colour box) or absent nuclear staining (white box). NE, not evaluable (antibody failed); block unavailable (grey box). IB, immunoblotting; For IB: +(sm), band present but at lower than 53 kDa; +(S), strong band present; *At the time of the research biopsy; †Histologically re-classified from HGSOC to intermediate grade (grade 2/moderately differentiated) serous adenocarcinoma following tumour block analysis; ‡cell agar block only, histologically re-classified from HGSOC to suspicion of adenocarcinoma arising from the gynaecological tract. Clinical data for previously characterised OCMs are published [Supplementary Table 2.BRCA1/2 variants detected in OCMs. The table outlines the BRCA1/2 variants detected in the panel of 7 OCMs screened for PARGi and PARPi sensitivity using colony survival and cell proliferation assays. Variants are described using Human Genome Variation Society (HGVS) nomenclature. Key: FR, frameshift; MS, missense; NS, nonsense; VAF, variant allele frequency; VUS, variant of uncertain clinical significance; WT, wild-type. All OCMs underwent both NGS and MLPA testing for BRCA1/2 variants. Supplementary Table 3. EC50 values for OCMs for the inhibitors/compounds tested. The table outlines the EC50 values for the inhibitors/compounds tested in the cell proliferation assay for the panel of 7 OCMs assessed. See Fig. 50 values. Data are mean ± SEM from 3 biological replicates. PRISM could not accurately calculate EC50 for PARGi-resistant cells, therefore for resistant OCMs, the EC50 was approximated as 50 μM  and described in the table as not calculable (NC).ublished , and not


Epithelial Ovarian Cancer is considered to be a ‘silent killer’ and a challenge for gynaecological health across the world due to its asymptotic nature in the early stages, its late-stage diagnosis, high recurrence rate and resistance to currently available treatment methods (chemotherapy). These disheartening figures highlight the need for extensive in vitro studies to better understand this disease. A number of in vitro 3D models are currently available to aid in the study of ovarian cancer and its response to therapeutic methods. In this work, we report, for the first time, a comprehensive comparative study of three widely used 3D in vitro models for ovarian cancer, along with chemotherapy assessment of primary and metastatic cells. Our study highlights the importance of selecting an appropriate 3D in vitro platform, which is based on multiple factors including the origin of cells used, experimental time period and experimental design, even for one specific disease.Epithelial Ovarian Cancer (EOC) is a silent, deadly and aggressive gynaecological disease with a relatively low survival rate. This has been attributed, to some extent, to EOC’s high recurrence rate and resistance to currently available platinum-based chemotherapeutic treatment methods. Multiple groups have studied and reported the effect of chemotherapeutic agents on various EOC 3D in vitro models. However, there are very few studies wherein a direct comparative study has been carried out between the different in vitro 3D models of EOC and the effect of chemotherapy within them. Herein, we report, for the first time, a direct comprehensive systematic comparative study of three different 3D in vitro platforms, namely (i) spheroids, (ii) synthetic PeptiGels/hydrogels of various chemical configurations and (iii) polymeric scaffolds with coatings of various extracellular matrices (ECMs) on the cell growth and response to the chemotherapeutic (Cisplatin) for ovary-derived (A2780) and metastatic (SK-OV-3) EOC cell lines. We report that all three 3D models are able to support the growth of EOC, but for different time periods (varying from 7 days to 4 weeks). We have also reported that chemoresistance to Cisplatin, in vitro, observed especially for metastatic EOC cells, is platform-dependent, in terms of both the structural and biochemical composition of the model/platform. Our study highlights the importance of selecting an appropriate 3D platform for in vitro tumour model development. We have demonstrated that the selection of the best platform for producing in vitro tumour models depends on the cancer/cell type, the experimental time period and the application for which the model is intended. Epithelial Ovarian Cancer (EOC) is the seventh most common cancer amongst women across the world and the third most common gynaecological cancer, ranked after cervical and uterine . Even wiSimilar to other cancers, traditionally, EOC studies including treatment screening are carried out either in (i) 2D in vitro systems such as T-Flasks and petri dishes ,16,17,18Thus, 3D in vitro models are slowly emerging to tide over these issues associated with 2D as well as animal models. To date, 3D models used for ovarian cancer in vitro research include (i) cell spheroids, (ii) hydrogels and (iii) natural or synthetic biomaterial-based polymeric scaffolds. Spheroids are the most commonly used 3D in vitro models for ovarian cancer ,40,41,42+/CD117+ double-positive EOC cancer stem cell (CSC) population between 3D (the hydrogel) and a 2D culture system [Although simple cell-based spheroids are proven to be feasible models for therapeutic assessment and very promising tools especially for fast drug screening, they come with certain inherent constraints, including limited culture time; the formation of unrealistically high gradients of nutrients and oxygen, which can lead to the formation of extreme/extended necrotic cores at the spheroid centre; a lack of robust ECM mimicry and a lack of spatial architecture and structural orientation ,32,50,51e system . They ate system .Currently, there are very few reported studies wherein polymeric scaffolds have been used for the development of ovarian cancer 3D models ,64,65,66In addition to these static monocellular models of EOC (containing cancer cells only), efforts have also been made to study the effect of shear stress and fluid flow, as well as the presence of other stromal cells, e.g., mesothelial cells and adipocytes, in EOC 3D models ,71,72,73Despite extensive advancement in the field of EOC in vitro 3D models, there are very few publications available for a direct comparative study between the various types of in vitro 3D models, i.e., simple cell spheroids vs. hydrogels vs. polymeric scaffolds, and most of them compare either different types of hydrogels or spheroid models prepared via different fabrication methods ,74. For In this current study, we have taken a step further to systematically compare the chemotherapeutic (Cisplatin) response  between spheroid, hydrogel and polymeric scaffold-based 3D in vitro models of epithelial ovarian cancer. For such a comparative study, we have used two different cell lines, namely (i) A2780, which is derived from the ovary (primary tumour), and (ii) SK-OV-3, which is derived from the ascites fluid (metastatic site), to identify the impact of cell origin on the growth and drug response in various 3D systems.2. Both cell lines were passaged regularly upon reaching 75–80% confluency with Typle E  till the required cell densities were obtained. The two cell lines were selected to reflect two different stages of ovarian cancer. A 2780 cell line was derived from the ovarian tumour of an untreated patient while SK-OV-3 was derived from the ascites fluid post-metastasis.Human Epithelial Ovarian Cancer (EOC) cell lines A2780  and SK-OV-3  were cultured in RPMI-1640  and McCoy’s 5a  media, respectively, supplemented with 10% Foetal Bovine Serum , 2 mM glutamine  and 1% antibiotic–antimycotic  in a humidified incubator at 37 °C and 5% CO3) at a seeding density of 0.5 × 106 cells/scaffold and cultured for 28 days. Post 28 days (4 weeks) of culture, the chemotherapeutic agent Cisplatin  was added to the culture at a concentration of 50 µM for 1 feeding cycle (48 h) and removed thereafter. The concentration of Cisplatin was selected based on published IC50 data for A2780 and SK-OV-3 in 3D spheroids [Polyurethane (PU) polymeric scaffolds were prepared via the Thermal-Induced Phase Separation (TIPS) method, sterilised and coated (surface modified with physisorption) with the ECM proteins fibronectin and collagen I, as previously described ,76,77. Bpheroids . The sca6 cells/mL. Thereafter, cells were mixed to ensure a homogeneous solution. Aliquots of 200 μL of hydrogels with cells were pipetted into 24-well cell culture inserts with 1 mL of cell culture media added to each well and 200 μL on top of each gel within the inserts. Thereafter, the cell culture plates were incubated at 37 °C and 5%CO2. Cell culture medium was changed every 20 min for the first hour to calibrate the hydrogel to neutral pH and every 2 days thereafter and cultured for a period of 3 weeks. Post 3 weeks of culture, the chemotherapeutic agent Cisplatin  was added to the culture at a concentration of 50µM for 1 feeding cycle (48 h) and removed thereafter. Hydrogels were then assessed 24 h post-treatment via staining, microscopy and image analysis (see following sections).Synthetic PeptiGels  were also used for our study with A2780 and SK-OV-3 cells. An initial fast screening was carried out between 4 different hydrogels of different stiffness and charge: α1 , α2 , α3  and α4  ,80. BaseSpheroids of A2780 and SK-OV-3 cell lines were fabricated using specialised 96-well round-bottom plates, provided by faCellitate . More specifically, to fabricate the spheroids, 200 µL of cell suspension containing 25,000 cells was seeded in each well and cultured for 1 week with media change every 2 days. Post 1 week of culture, chemotherapeutic agent Cisplatin  was added to the culture at a concentration of 50 µM for 1 feeding cycle (48 h) and removed thereafter. Spheroids were then assessed 24 h post-treatment via staining, microscopy and image analysis . To visualise the spatial distribution of live and dead cells pre- and post-treatment, model-specific methods were used. More specifically, PU scaffolds were collected at appropriate time points, snap-frozen in liquid nitrogen for 15 min and then preserved at −80 °C for further analysis, as previously described ,76,77. TPeptiGels and spheroids were stained and imaged live at appropriate time points. Unlike the PU polymeric scaffolds, snap-freezing the spheroids and the hydrogels was not feasible as this process would have destroyed their native structure. Hydrogels and spheroids were stained with 2 μM of calcein-AM (4 mM stock) and 4 μM of ethidium homodimer (2 mM stock) and were incubated at 37 °C for 2 h for proper penetration of the dyes. The solution was then removed, and the samples were washed twice in PBS followed by imaging. PeptiGels were imaged whole using a Nikon Ti-Eclipse inverted confocal microscope . Spheroids were imaged using Cytation 5 Cell Imaging Reader .The caspase 3/7 activity was visualised and quantified in situ to assess the induction of cellular apoptosis after different treatments. As described in Immunofluorescent samples of PU scaffolds and PeptiGels . The particle analyser macro  was used in each individual channel . Multiple samples (n ≥ 3) were imaged and analysed for statistical relevance.Image-based quantification was carried out in a model-specific manner. Within PU scaffolds and PeptiGels, for the quantitative evaluation of (i) live (green) and dead (red) populations, as well as (ii) caspase-positive/apoptotic (green) and non-apoptotic (blue) populations of each image, the percentage of green vs. red (live/dead) or green vs. blue (caspase-positive/caspase-negative) areas of each image was calculated using Image J® software . Multiple samples (n ≥ 3) were imaged and analysed for statistical relevance.In the spheroid model, the average mean grey values for calcein (Live-Dead) and caspase 3/7 (apoptotic) were calculated using the particle measure macro in Image Jn ≥ 3, n ≥ 3). Analysis of variance (one-way ANOVA) followed by the Bonferroni’s multiple comparison test or T-Test (depending on sample) using the GraphPad Prism® software (version 8.00 for Windows) were carried out depending on samples, in order to find statistically significant differences between data (p < 0.05). Untreated samples were considered as controls in all cases. The error bars in the graphs represent the standard error of mean.Statistical analysis was performed for at least 3 independent experiments with at least 3 replicates per time point  cells present even in the untreated spheroids for both the cell lines, with A2780 control spheroids having a significantly higher apoptotic cell number in comparison to the Cisplatin-treated samples A,B,E. FoAs mentioned in Analysis of the apoptotic marker caspase 3/7, for both cell lines, in the hydrogels shows a similar trend . More spAs previously mentioned, fibronectin- and collagen I-coated polymeric (PU) scaffolds were used for the development and maintenance of long-term (4 weeks) 3D in vitro models of EOC (using A2780 and SK-OV-3 cells). Cisplatin was introduced to the culture medium for both A2780 and SK-OV-3 cell lines 4 weeks into culture at a concentration of 50 µM for 48 h . ThereafFurther to cell viability, the induction of cellular apoptosis post Cisplatin treatment in the PU scaffolds was also assessed via caspase 3/7 staining. In this work, we have carried out a systematic comparative study to assess the effect of the type of 3D in vitro model/platform on the response of primary and epithelial ovarian cancer cells to the application of chemotherapy (Cisplatin). The 3D models used for this study were (i) simple cell spheroids, (ii) synthetic hydrogels/PeptiGels and (iii) polymeric scaffolds. Two different cell lines were used to assess the effect of the ‘site of cell line origin’, i.e., A2780, which is derived from the ovary (primary site), and SK-OV-3, which is derived from the ascites fluid (metastatic site). Due to the inherent structural differences between the three 3D models, they can be maintained in culture for different time periods. For example, the average time for which spheroids have been maintained in culture is between 6 and 12 days for EOC ,43,44, wAs described in 50 [50 of 92 ± 3.1 µmol/L in a collagen I gel for carboplatin, while, for the OV-206 patient-derived cell line, it was 154 ± 5.9 for the same drug [PeptiGels, commercially available synthetic peptide-based materials  were used to develop our EOC hydrogel models . Both ce50 ,61. For ame drug . Their oame drug ,94. For ame drug . They atame drug ,86. Our We have previously developed a highly porous and biocompatible PU scaffold via the TIPS method and have shown that a number of different cell types can be cultured long-term within these scaffolds ,76,77,87Overall, in this work, we have conducted a novel, systematic comparative study of the response of EOC to a chemotherapeutic (Cisplatin) in three different in vitro 3D models: (i) spheroids, (ii) synthetic PeptiGels/hydrogels and (iii) polymeric scaffolds with various ECM coatings. Two different cell lines (A2780 and SK-OV-3) were used to understand the impact of the ‘site of origin’ of the cells, as well as to assess platform versatility. We have reported that all three platforms were able to support EOC in vitro 3D models with both cell lines, albeit for different culture time points. Polymeric scaffolds and hydrogels were maintained for 4 weeks and 3 weeks, respectively , highligThe goal of the present study was to carry out, for the first time, a robust comparative study of the growth and chemotherapy response of EOC in three widely different in vitro 3D models: (i) cell spheroids, (ii) synthetic PeptiGels/hydrogels and (iii) polymeric scaffolds of various protein coatings. We have shown the feasibility of using all three models for the culture of EOC cell lines A2780 and SK-OV-3, representing primary and metastatic disease, respectively, and assessed the impact of chemotherapy (Cisplatin) on the cell viability and apoptosis within these models. Our study highlights that the selection of a 3D in vitro platform depends on (i) the planned experimental/assessment time period, (ii) the type of cell to be studied, (iii) the site of cell origin in vivo and (iv) the question that needs to be answered. For example, a rapid screening analysis may benefit from the use of simple cell spheroid models; however, the need to study the effect of ECM proteins on cell growth and chemotherapy response long-term will benefit from structured hydrogel or polymeric scaffolds. Similarly, softer tumours or tumours originating from soft tissues such as the ovary or the omentum may prefer less stiff 3D platforms such as spheroids or hydrogels as compared to stiffer polymeric scaffolds. In conclusion, our study highlights that, as with most tissue engineering applications, there is no ‘one-size-fits-all model’  and the


Their chemical structures were elucidated by extensive spectroscopic data  and the microhydrolysis method. The isolation of compounds 1–4 represents the first report of polyhydroxylated oleanane triterpenoids from the family Menispermaceae. All isolated compounds were evaluated for their cytotoxicity against five human cancer cell lines, and the inhibitory activity against NO release in LPS-induced RAW 264.7 cells. Compound 3 showed the most potent cytotoxic activities against the A549, SMMC-7721, MCF-7, and SW480 cell lines, with IC50 values of 17.55, 34.74, 19.77, and 30.39 μM, respectively, whereas three remaining ones were found to be inactive. The preliminary structure–activity relationship analysis indicated that the γ-lactone ring at C-22 and C-29, and the olefinic bond at C-12 and C-13 were structurally required for the cytotoxicity of polyhydroxylated oleanane triterpenoids against these four cell lines. Based on lipid-water partition coefficients, compound 3 is less lipophilic than 1 and 4, which agrees with their cytotoxic activities. This confirms the potential of C. pareira var. hirsuta in the tumor treatment.Three new polyhydroxylated oleanane triterpenoids, cissatriterpenoid A−C ( So far, cancer has become the most common disease threatening human health and life worldwide. Natural products, characteristic of low toxicity and high efficiency have played an irreplaceable role in cancer chemotherapy and chemoprevention in the last half-century. Over 60% of clinical anticancer drugs come from natural products, such as paclitaxel, etoposide, camptothecin, and vincristine, etc . OleananCissampelos pareira L. var. hirsuta (Buch. ex DC.) Forman is a perennial climbing shrub of Menispermaceae family, which is widely distributed in the Southwest of China, India. Its whole plant has been commonly used for the treatment of stomachache, cardiodynia, fever, skin disease, and swelling pain, etc , eleven methines , and nine quaternary carbons . The abovementioned data suggested that compound 1 is a polyhydroxylated oleanane triterpenoid derivative with the same 3,16,22,28-tetrahydroxyolean-12-ene skeleton as chichipegenin (4) + ; NMR data (CD3OD), see Cissatriterpenoid A (2): white, amorphous powder; + ; NMR data (CD3OD), see Cissatriterpenoid B (3): white, amorphous powder; + ; NMR data (CD3OD), see Cissatriterpenoid C (1 (10 mg) was dissolved in 1.0 mL of MeOH, then 0.3 mL of 28% sodium methylate solution was added dropwise under an N2 atmosphere. After 4 h stirring at room temperature, the reaction mixture was neutralized by dilute HCl, and extracted with CH2Cl2. The extract was concentrated under reduced pressure, and isolated by preparative HPLC eluted with CH3CN-H2O (60:40) at a flow rate of 6 mL min–1 to obtain chichipegenin (tR 42.2 min) [Compound 2.2 min) .2 in a humidified atmosphere. Cell viability was assessed by conducting colorimetric measurements of the amount of insoluble formazan formed in living cells based on the reduction of 3--5-(3-carboxymethoxyphenyl)-2-(4-sulfophenyl)-2H-tetrazolium (MTS). In brief, 100 µL of adherent cells was seeded into each well in a 96-well cell culture plate and kept for 24 h for adherence. Each tumor cell line was exposed to various concentrations of the test compound in triplicate for 48 h. After incubation, MTS (20 μL) was added to each well, and the incubation continued for 4 h at 37 °C. The absorbance was measured at 492 nm in a 96-well microtiter plate reader. The IC50 value of each compound was calculated by the Reed-Muench’s method. Cisplatin was used as positive control.Using the MTS method previously described , the cyt2. The test compounds were dissolved in DMSO and then diluted to different concentrations by medium. Cells were precultured in 96-well plates (2 × 105 cells/well) for 24 h, and then incubated with serial dilutions of the test compounds and LPS (1.0 μg/mL) for 18 h. Then, 100 μL of Griess reagent and 100 μL of culture supernatant were mixed, and incubated for 5 min. The optical density of the mixture was read at 570 nm by an automated microplate reader. The NO inhibitory rate was determined via a comparison with the control group.NO production in the murine monocytic RAW 264.7 macrophage was evaluated by the previously reported protocol . L-NG-MoC. pareira var. hirsuta resulted in the isolation of three new polyhydroxylated oleanane triterpenoids, cissatriterpenoid A−C (1−3), and one known analogue (4). To date, more than 500 polyhydroxylated oleanane triterpenoids have been found in the families Cyrillaceae, Hippocastanceae, Loganiaceae, Myrsinaceae, Pittosporaceae, Sapindaceae, Symplocaeae, Styracaceae, Theaceae, Lecythidaceae, and Umbelliferae since 1934 [1–4 represent the first report of polyhydroxylated oleanane triterpenoids from the family Menispermaceae. The hydroxyl groups in the structure skeleton provide numerous acylation and glycosylation bonding sites, and facilitate interactions with molecular targets [C. pareira var. hirsuta. The cytotoxic activities of all isolated compounds were tested against HL-60, A549, SMMC-7721, MCF-7, and SW480 cell lines. Compounds 2–4 have the same structural skeleton, so the variation in cytotoxicity between them indicates that the γ-lactone ring at C-22 and C-29, and the olefinic bond at C-12 and C-13 are structurally required for the cytotoxicity of polyhydroxylated oleanane triterpenoids against the A549, SMMC-7721, MCF-7, and SW480 cell lines. Based on the lipid-water partition coefficients, compounds 1–4 were relatively hydrophobic. Furthermore, compound 3 is less lipophilic than 1 and 4, which agrees with their cytotoxic activities. These results reveal that compound 3 may be a potential anticancer lead. Further studies are necessary to explore the anticancer mechanism and cytotoxicity in normal cells. Polyhydroxylated oleanane triterpenoids have been reported to modulate specific signaling pathways and inhibit cancer cell proliferation [Cissampelos. This study not only extends the chemical-structure diversity of oleanane triterpenoids, but also provides a direction for identifying potential anticancer secondary metabolites from traditional Chinese medicine.Phytochemical investigation of nce 1934 . Compoun targets . This alferation , includi


Recent technological developments allow us to measure the status of dozens of proteins in individual cells. This opens the way to understand the heterogeneity of complex multi‐signaling networks across cells and cell types, with important implications to understand and treat diseases such as cancer. These technologies are, however, limited to proteins for which antibodies are available and are fairly costly, making predictions of new markers and of existing markers under new conditions a valuable alternative. To assess our capacity to make such predictions and boost further methodological development, we organized the Single Cell Signaling in Breast Cancer DREAM challenge. We used a mass cytometry dataset, covering 36 markers in over 4,000 conditions totaling 80 million single cells across 67 breast cancer cell lines. Through four increasingly difficult subchallenges, the participants predicted missing markers, new conditions, and the time‐course response of single cells to stimuli in the presence and absence of kinase inhibitors. The challenge results show that despite the stochastic nature of signal transduction in single cells, the signaling events are tightly controlled and machine learning methods can accurately predict new experimental data. This study presents the results and conclusions of the ‘Single Cell Signaling in Breast Cancer DREAM challenge’, where teams were challenged to use state‐of‐the‐art methods for predicting single‐cell signaling from single‐cell and bulk proteomics, transcriptomics and genomics data. This dynamic process of signaling is tightly regulated by the interactions of proteins. Deregulation and major disturbances in this finely tuned machinery can lead to diseases such as cancer  DREAM challenge. The Dialogue for Reverse Engineering Assessments and Methods (DREAM) Challenges provides a framework for participants across the globe to compare their methods for solving biomedical problems  of nodes that are not directly measured from other measurements on the same cells?”, (ii) “to new combinatorial treatments based on how other cell lines respond to that treatment?”, (iii) “to perturbations for which we have no data but know the target?”, and (iv) purely from basal omics data?”. We defined four corresponding subchallenges Fig .We evaluated 73 models from 27 teams across the four subchallenges and found that machine learning‐based methods worked well for these prediction tasks .The SCSBrC DREAM challenge was organized in three rounds between September and December 2019. A total of 261 individuals registered, and 22, 16, 14, and 20 teams around the world submitted predictions in the final round of each subchallenge, respectively. The teams were ranked in each subchallenge (SC1‐4) based on their predictions on the test dataset  in six cell lines, measured in all the 49 different conditions, totaling 11.9 million cells. The participants submitted their predictions for each measured single cell, and we compared them to the actual measurements of the markers using the root mean square error (RMSE)  was to build a model that can predict the phosphorylation of a node that one may not be able to measure experimentally. To obtain such models, we asked participants to predict the phosphorylation of selected proteins in single cells from specific cell lines and conditions , which strongly outperformed other participants (Bayes factor > 499). For the random prediction, we took the test data and shuffled the unique cell‐ids in each cell line and experimental condition. This way, when we scored this randomized dataset, each cell was predicted by a randomly chosen other cell in the same cell line and experimental condition. Almost all teams performed better than this random prediction . Six teams achieved better predictions than a reference model that we built before the challenge started .First, we ranked the teams and compared their performance with random predictions and with a random forest‐based reference  across all teams  in each condition Fig  top and eams Fig  bottom. icx_bxai = 0.849 to RMSEicx_bxai, corrected = 0.789). This suggests that it is very hard to predict phosphorylation without any data for the phospho‐protein of interest in the cell line where it has to be predicted.The training data did not contain measurements of the initial activation of the test cell lines. To quantify how much the knowledge of this initial phosphorylation influences the accuracy of the predictions, we corrected the predictions by the difference between the predicted and measured mean basal activities of the cell lines. All the predictions improved significantly . Further, while teams 1 and 3 built models for each missing marker separately, the neural network model of team 2 predicted all the 5 missing markers together. Common among the three approaches are that all of them included all the 32 measured markers as model features among others and each team addressed the problem of missing data.P = 0.0731 from a t‐test), using an ensemble of models , and training models on similar cell lines  had the largest individual impact on the score.We compared the building blocks of the methods to see which elements contributed to better predictions Fig . Althougicx_bxai) built a model for each marker independently and included the 32 provided markers, treatment, and time, and the top principal components of the proteomic data as features. Further, when estimating the marker values at time ti, they also considered the median signaling levels of the 32 measured markers at time ti, ti−1, and ti+1. They used subsets of these features to train an ensemble of models: ElasticNet, ExtraTrees, RandomForest, Light Gradient Boosting, and linear model. To make a prediction, they averaged the predictions of all models.While some teams relied on a single method to build their models, the top‐ranked team  and time after the perturbation (P = 0.68) . Cell line HCC2218 has the largest prediction errors , while other cell lines had similarly small prediction error values. The prediction error differences between cell lines and markers suggest that the cell lines show diverse signaling patterns that influence the models’ accuracy  Fig . In part2 fold change −7.66, adj. P‐value = 4.14e‐23; Fig As signaling is a dynamic process, we aggregated the predictions of each team and the test data at each time point and compared the time‐courses. The dynamic response of phosphosites changed strongly across the different perturbations and also between cell lines  in each condition. This way we capture not only the accuracy of individual markers, but also the changing interplay (covariance) between markers in the score Fig . A referreference = 44.67), with up to 48% improvement (median improvement: 39.3%). Of note, a predictor corresponding to the lack of effect of the kinase inhibitor, thus using the measurements to the stimulus alone, leads to a score of RMSEEGF = 29.156. This relatively good score could be due to the fact that each inhibitor showed strong effect only on a few nodes in the measured part of the signaling network. Surprisingly, only the top four teams managed to perform better , contains two biological replicates (replica A and B). Their variation can be considered as an estimate of the upper‐bound of performance as it corresponds to the experimental error. Therefore, we also scored the participants on this subset of conditions and compared their scores to the variation of the measured individual biological replicas. Since SC2 and SC3 used different numbers of cell lines , we computed the prediction error per total number of predicted statistics. This allowed us to summarize the two subchallenges together Fig ; howeverP = 8.8e‐4), less so across treatments (P = 0.034) and not significantly across time (P = 0.54) , followed similar modeling approaches for SC2 (more about the approaches in the Appendix). These teams inferred the median phosphorylation level of the proteins and their covariance matrices for each sample  using linear models . Further, all three predicted the cells in the target conditions using the available conditions of the same cell line, rather than from the same conditions of other cell lines. In the next step, they sampled from a multidimensional Gaussian distribution that followed the inferred statistics in each condition which resulted in the single‐cell predictions. Despite all these similarities, the Z‐score transformation, handling the missing time points and using other statistical features of the single‐cell distributions , led icx_bxai to the first place.Interestingly, five teams, including the top three . The combination of top P‐value: 0.069) than teams relying on other methods , but we were curious how well the predictions captured further single‐cell features. Therefore, we analyzed, for SC2, how the different methods generated heterogeneity and how this matched the heterogeneity found in the data Fig . We coulhods Fig . AnotherThe measured cells show heterogeneity: The single‐cell measurements have the tendency of forming multiple clusters that are driven by the cell cycle states , and where all the cell lines are represented in each main cluster Fig . The gloIn summary, the participants predicted the time response of all markers in response to kinase inhibitors, first (SC2) to inhibitors with known effects and then (SC3) to an inhibitor which was absent from the training data. The results show that predicting distributions of single cells’ populations after perturbations with kinase inhibitors is almost as good as a biological replica and that, at least for some cases, this is possible even if no data are available upon perturbation with those specific inhibitors. The four best teams were better than the reference model, and the top team achieved an accuracy similar to a biological replica. Although overall machine learning‐based methods performed better, sampling methods were better able at capturing single‐cell heterogeneity.et al, Perturbation data contain invaluable information about the directionality, magnitude, and timescale of the cell response. It also reveals causal interactions of nodes; therefore, it is often used for network inference  for all the markers Fig  except fodel Fig .To understand which markers can be modeled more accurately, we compared the marker variance across cell lines to the prediction errors Fig  and we fFurther, we also compared the prediction errors with the longitudinal variance of the markers Fig  that telP‐value = 4.8e‐13; Fig Surprisingly, in contrast to SC1, the prediction errors were similar across cell lines. Although the distribution of the prediction error across the test cell lines showed statistically significant differences  of the RNAseq and proteomic data. These types of models were trained on the cell lines provided and then used to predict the test cell lines. This implicitly assumes that the relationship among the nodes can be transferred between cell lines. This, in turn, could reflect common molecular programs driven by the shared lineage of the cell lines. We would thus expect less transferability when considering different tissues or processes that are less linked to each other as the ones considered here. The predictions strongly correlated with the test data and explained large amounts of variance, suggesting that there are indeed signaling interactions transferable among cell lines.et al, However, we found two major pitfalls in the predictions. First, the models captured much better the trends than the absolute value of nodes’ activities in cases where the basal marker intensity of the test cell lines was much higher than of the training cell lines. If the absolute values are important, this can be an important limitation, but not for downstream analysis where the data are rescaled, e.g.  was fundamentally different from Subchallenge 1. The top‐performing models learnt the signaling relationships among the nodes and how these changed between conditions within each cell line, and transferred this knowledge to the target condition. Further, instead of predicting each individual single cell directly, the top‐performing models derived statistical descriptors of the markers in each condition, predicted the distribution of cells in the target condition, and then sampled that distribution to generate the single cell predictions. We found that the predictions of the top team were almost as accurate as biological replicas, although it should be noted that we only had two replicates. Although models that inferred directly the median and variance of cells performed better than models based on resampling similar cell lines, the analysis of the distribution across cells showed that the latter approach performed better at capturing the cell lines’ heterogeneity.In Subchallenge 4, the task was even more challenging: predict the time‐course response of cell lines for which only basal (before perturbation) data were provided. Many methods performed better than using an average of the provided cell lines as a predictor, showing that participants were able to capture to some degree the differences between the cell lines, particularly for phosphoproteins that did not change much across time, but varied between cell lines.The teams used various prediction algorithms, including linear models , (recurrent‐) neural networks, tree‐based algorithms , deep networks, and custom differential equation‐based models (see Appendix: Prediction methods of the best teams). However, similarly to previous we did not find evidence for the idea that a specific type of algorithm performed significantly better than other types of models for these prediction challenges. Across subchallenges, we found that the ensemble of predictions of multiple teams is better than an average team, which means that the risk for a bad prediction can be mitigated by combining multiple approaches. However, the combined prediction is not significantly better than the best team, which might be because the best team already used an ensemble of models.et al, Only some teams used the prior knowledge we provided, and none added any further external prior knowledge. Teams that did use this knowledge did not perform better, in contrast to the previous network inference challenge  and open source code that reproduces the results. Further, we collected information on the methods via a questionnaire.A requirement for the challenge participation is a summary of method, which is publicly available , six cell lines , and all measured timepoints :We found that the RMSE score changes in each condition, most significantly based on the cell line that was predicted. Therefore, for a robust ranking of participants, we resampled 1000 times the conditions used to compute the final score with replacement and computed the bootstrap score on each sample. Finally, the team are compared by Bayes factors based on the bootstrap scores:scoreTx is the score of team Tx, 1 is the indicator function.where Nmarker=35 markers) for each validation cell line and condition (treatment and time). For each condition i, we compared the mean marker value from the gold standard data μ with the mean of the predicted sample μ^.N):In subchallenges 2 and 3, the participants submitted 10`000 representative single‐cell predictions  and 500 random cells from each condition.In SC1, our model predicted the missing marker based on the measured markers in each individual cell. We built a random forest model for each marker independently, across all conditions using the y (mean of a marker or entry in the covariance matrix), for each cell line and time point:The idea for the reference model in SC2 was to predict the means and covariance matrix of the markers in the missing conditions based on the available conditions and then use a simulator that generates samples from a multivariate normal distribution following the estimated mean and covariance values. Thus, first we computed the mean and covariance matrix of the markers in each cell line, treatment, and time point. Then, we built an independent random forest model for each statistical variable Ki‐67, in cell line BT474, time 0, treatment iMEK was predicted based on the available values of Ki‐67 levels in the same cell line, in time 0, in {EGF, iEGFR, iPI3K, iPKC} conditions. For the training of the models, we selected the same 7 training cell lines as in SC1.For example, the mean value of marker We have not built a reference model for subchallenge 3.ycl,tr,time) can be described as the superposition of the average cell line response  and cell line‐specific response difference , i.e.,In subchallenge 4, we hypothesized that each marker response  was calculated by taking the mean of all the training cell lines for each marker, in each condition (treatment and time point). Note that all the three terms in Equation  based on the data that is available for the test conditions, i.e., based on the proteomic data and based on the basal activation of markers (“full” condition). We also used the treatment, time, and stimulation status  as model features. We trained a model for each reporter independently based on all the training cell lines.The average cell line response (Equation  can be en) were scored, from the minimum of one team up to the maximum of all teams. In subchallenges 1 and 4, the predicted values of different teams were combined by taking the median of the predictions. In subchallenges 2 and 3, representative cells were predicted and scored based on the statistics. Here, we used two approaches: (i) Predictions were combined by sampling an equal number of predicted values per condition from all selected submissions and (ii) computing the statistics for each submission and then averaging across the selected teams. In all subchallenges, the resulting ensembles were scored the same way as individual submissions. For the random combination of teams, we repeated the procedure 100 times to see the distribution of score.Predictions of randomly selected teams and the top‐performing teams were aggregated. Different sizes of ensembles (We used Random Forest (RF) models in SC1 and SC2 to combine the predictions for the test cell lines. We used a cross‐validation scheme for the model training and for the predictions to avoid overfitting these RF models and to generate predictions for all the test cell lines. We divided the data into sixfold: In SC1, each fold of data contained a single test cell line, and in SC2, each fold contained two cell lines. This way the training and predictions of the RF models also mimic the setup of the subchallenges; i.e., they are trained on part of the cell lines and predict other cell lines. Then in six iterations, we trained our RF models on fivefold and predicted the sixth fold until we had predictions for all the test data and could compare the performance of RF predictions with the participants’ predictions.In SC1, we trained a random forest model per marker and used the single‐cell predictions from the teams and the time point as continuous variables and the treatment and predicted marker as one‐hot encoded features. Due to the chosen cross‐validation scheme, we could not use “cell line” as a feature. Therefore, we also included the 32 non‐predicted marker values as model features.In SC2, our RF predicted the mean and covariances per condition using the mean and covariance matrix from the participants’ predictions. The model also included as features the median expression per marker at treatment EGF. The predicted statistics were used to create a multivariate normal distribution, from which 10,000 cells per condition were sampled and subsequently scored.Histone H3 (p.H3), iododeoxyuridine (IdU), retinoblastoma protein (p.RB), and cyclinB , G0‐phase and G1‐phase are both characterized by low expression of IdU, p.H3, and cyclinB, but we could not differentiate between G0 and G1 based on the expression of retinoblastoma protein (p.RB). High expression of IdU and low expression of p.H3 characterize S‐phase; high expression of p.H3 with low IdU identifies M‐phase; finally, low expression of IdU and p.H3 with high expression of cyclinB identifies G2 phase. Apoptotic cells were detected by high cleaved Caspase 3 (cleavedCas) levels.Cell cycle phases were identified from the single‐cell data based on the markers AG, MT, JS‐R, and BB conceived and designed the Single Cell Signaling in Breast Cancer DREAM challenge with the help of TY and VC. AG and AD analyzed the challenge results with the help of MT and JT, under the supervision of JS‐R. The top‐performing approaches were designed by BG, WC, and HS. The DREAM Consortium provided predictions, method implementations, and descriptions. AG and JS‐R drafted the manuscript with inputs from MT, AD, and JT. All authors read, commented, and approved the final manuscript.JSR has received funding from GSK and Sanofi and expects consultant fees from Travere Therapeutics. MT is an employee of Biognosys AG .AppendixClick here for additional data file.


Endometriosis is a common gynecological disease characterized by the presence of endometrial tissue outside the uterus causing chronic inflammation, severe pain, and infertility. However, the innate immunity of gamma-delta (γδ) T lymphocytes in endometriosis has not been characterized. Women with endometriosis present numerous endocrine and immune dysfunctions and elevated risk for endometrial, ovarian, and breast cancers. The tyrosine kinase EphA2 is often overexpressed in cancer including endometrial carcinoma.n = 19) and compared the counts with that of age- and sex-matched healthy donors (n = 33) using flow cytometry. Vδ1 and Vδ2 T cells isolated from healthy donors were used against KLE, RL-95, and Ishikawa endometrial tumor cells in 4 h flow cytometric cytotoxicity assays. The EphA2 blocking studies were performed using antibody, small-molecule inhibitor ALW-II-41-27, and the CRISPR/Cas9.We analyzed Vδ1 and Vδ2 γδ T cells in peripheral blood and paired peritoneal fluid samples in endometriosis patients (p < 0.01) and peritoneal fluid (p < 0.001). No differences were found for circulating Vδ2 T cells compared with peritoneal fluid samples. We observed inherent cytotoxic reactivity of Vδ1 and Vδ2 γδ T lymphocytes against endometrial tumor cells. Importantly, we found reduced specific lysis of EphA2-positive cell lines KLE and RL-95 by Vδ1 T cells in the EphA2 antibody blocking studies and by the EphA2 inhibitor. Furthermore, Vδ1 T-cell-mediated killing was significantly decreased in RL-95 cell EPHA2 knockout. Finally, potent cytolytic activity exerted by Vδ1 T cells was significantly reduced in EPHA2 knockouts in renal A-498 and colon HT-29 carcinoma cell lines.We determined Vδ1 T cells substantially reduced in patients’ peripheral blood (We determined variable levels of Vδ1 and Vδ2 γδ T cells in endometriosis patients. We observed inherent cytotoxic reactivity of γδ T-cell subsets against endometrial cell lines. Specifically, we found that blocking of EphA2 expression resulted in significant inhibition of endometrial tumor killing mediated by Vδ1 γδ T cells. These results suggest that EphA2 is involved in tumor cell lysis and contributes to susceptibility to Vδ1 γδ T cells cytotoxic reactivity. Endometriosis is a hormone-dependent gynecological disease characterized by the presence of endometrial tissue outside the uterine cavity. The disease affects around 10% of reproductive-aged women . HormonaEndometrial cancer (EC) is the most common malignancy of the female reproductive system . It tendHuman gamma-delta (γδ) T lymphocytes play critical roles in immune surveillance mediating potent inflammatory response and contributing to prominent tumor killing , 19. γδ The Eph receptors represent the largest family of receptor tyrosine kinases. Together with their respective ligands, they have been extensively studied for the roles they play during embryonic development, particularly within the central nervous system . As a unWe conducted the present study to determine the numbers of γδ T-cell subsets in endometriosis patients. We demonstrate for the first time the prominent cytotoxicity of γδ T cells against endometrial tumor cell lines. Next, we show that the EphA2 receptor is highly important in tumor recognition and killing by Vδ1 γδ T cells.n = 19) have been enrolled from the Department of Gynecology and Obstetrics, Faculty Hospital Brno. The study was approved by the local institutional ethics committee of the Faculty of Medicine, Masaryk University. The study was performed in accordance with the Declaration of Helsinki. Written informed consents were obtained from all patients. Endometriosis was assessed according to the revised American Fertility Society (r-AFS) classification during laparoscopy. Patients received no hormonal therapy for a minimum of 3 months prior to laparoscopic surgery.Patients (The patient characteristics are shown in n = 33) were collected at the Transfusion and Tissue Bank, Faculty Hospital Brno. Peripheral blood mononuclear cells (PBMCs) were isolated from blood by density gradient centrifugation using Lymphoprep (Stem Cell Technologies) following the manufacturer’s recommendations. Plasma samples were collected and stored at −80°C.Peripheral blood (PB) and peritoneal fluid (PF) samples were obtained from endometriosis patients and were processed within 2 h of collection. PF samples were taken during the planned surgery from lower pelvis cavity by fine needle suction from cavum Douglasi at the opening phase of diagnostic laparoscopy prior the surgical procedure as less invasive technique for more patients than tissue biopsy. At the same time, it allows to obtain sufficient volumes of biological material for subsequent analysis. Buffy coats from age- and sex-matched healthy volunteers (® CRL1622™) and RL95-2 (ATCC® CRL1671™) were purchased from ATCC . Ishikawa cell line was purchased from Sigma-Aldrich . KLE and RL95-2 cell lines were maintained in Dulbecco’s modified Eagle’s medium (DMEM/F12) supplemented with 10% fetal bovine serum (FBS) and 2% penicillin/streptomycin ; RL95-2 cells with the addition of 5ug/ml of insulin . The Ishikawa cell line was maintained in MEM supplemented with 5% FBS and 2% penicillin/streptomycin.Endometrial carcinoma cell lines KLE . Cell lines  were cultured in DMEM/F12 with 10% FBS. All cells were grown at 37°C in 5% CO2 atmosphere up to 70%–80% confluence; adherent cells were harvested by using gentle dissociation solution TrypLE  and counted by using Trypan blue exclusion.In addition, human tumor cell lines including myeloma  and chronic myeloid leukemia  were purchased from DSMZ . Renal (A-498), prostate (DU-145), breast adenocarcinoma (MCF-7) and histocytic lymphoma (U937), chronic myeloid leukemia (K562), acute monocytic leukemia (THP-1) and glioblastoma (U87 MG) cells were purchased from ATCC. Glioblastoma cells (U-373 MG and U251 MG) were purchased from The European Collection of Authenticated Cell Culture (ECACC). Myeloma cell lines  were a kind gift from Dr. Krejci . Cells lines  were cultured in RPMI-1640 containing 10% FBS, 2mM L-glutamine, and 2% penicillin/streptomycin. Cell lines  were cultured in modified Eagle’s medium (MEM) (Sigma Aldrich) with 10% FBS, 2 mM The EphA2 inhibitor ALW-II-41-27 was purchased from MedChem Express . It was dissolved in sterile DMSO at 10 mM stock concentration and solution stored in aliquots at −20°C.® (BD Biosciences) and data analyzed using FlowJo® software . Forward and side scatter gating were used to discriminate live cells from dead cells and γδ T cells were derived from SSC vs. FSC-gated bulk PBMCs with doublet exclusion (FSC-A vs. FCS-H). To determine the placement of the gates, appropriate fluorescence minus one (FMO) and unstained controls were used.The cell phenotype was assessed by fluorescence-activated cell sorting (FACS) by using staining with the monoclonal antibodies MICA, MICB, CD112, CD155, B7-H6 , and EphA2 . The tumor cell lines were harvested, washed with cold phosphate-buffered saline  containing 2% FBS, and incubated for 30 min on ice with fluorescently labelled monoclonal antibodies. Gamma-delta T cells were identified in freshly isolated PBMCs labelled with CD3 , Vδ1 TCR , Vδ2 TCR  or Vδ2 TCR . CD27 , and CD45RA  were used for immunophenotyping. Samples were washed and acquired using FACSCantoFresh γδ T-cell populations were sorted by positive selection using anti-TCR Vδ1  or anti-TCR Vδ2  monoclonal antibodies and magnetic microbeads  according to manufacturer’s instruction. The cell purity was routinely greater at 97%.Freshly sorted Vδ1 or Vδ2 γδ T lymphocytes were incubated with tumor target cells at indicated effector:target (E:T) 5:1 and 10:1 ratios in duplicates for 4 h co-culture at 37°C as described previously . Briefly® Gene Expression Assay . Samples were analyzed on StepOne™ Real-Time PCR Systems (Applied Biosystems).Total RNA has been extracted from tumor cell lines using RNeasy Mini kit (Qiagen) according to manufacturer’s instruction. RNA was eluted in RNAse-free water and stored in −80°C. Complementary DNA (cDNA) has been synthesized using 20 ng/µl total RNA that has been reverse transcribed using High-Capacity cDNA Reverse Transcription Kit (Applied Biosystems). The glyceraldehyde 3-phosphate dehydrogenase (GAPDH) housekeeping gene has been used as an internal control by quantitative real-time polymerase chain reaction . cDNAs were amplified using TaqManThe EPHA2 gene knockout was performed with the EPHA2 CRISPR gRNA + Cas9 in Lenti-particles (supplied from antibodies-online GmbH) and used closely following manufacturer’s instructions. Briefly, vector pLenti-U6-sgRNA-SFFV-Cas9-2A-Puro (product number ABIN5252263) was used to generate EPHA2 knockouts in human endometrial cancer line RL95-2, renal carcinoma cell line A-498, and colon carcinoma cell line HT-29. After infection, positive clones were selected by 3.5 μg/ml puromycin, and the single clones were transferred separately into 48-well plates and further passaged. The EPHA2 knockouts were confirmed by flow cytometry after antibody staining .t-test was used to determine significant differences between groups. Differences between sample groups were evaluated with the nonparametric Mann-Whitney U test. p < 0.05 values were considered to be significant. Data are expressed as mean ± standard deviation (SD).Data analyses were performed using GraphPad Prism5 software . The Student’s n = 19) and compared the frequencies with age- and sex-matched healthy donors . Flow cytometric analysis of peripheral blood mononuclear cells (PBMCs) where proportion of Vδ1 and Vδ2 γδ T cells among leukocyte gate followed by the percentage of CD3 lymphocytes is shown in p = 0.008)  in endometriosis patients compared with HD , as shown in p = 0.0002)  and HD controls . No differences in Vδ2 T-cell counts were observed between the patients PB  and HD controls .First, we aimed to determine the two major populations of γδ T cells  in peripheral blood (PB) samples from patients with endometriosis (p = 0.001) in Second, we analyzed γδ T-cell infiltration in patient’ peritoneal fluid (PF) samples and compared the counts with paired PB. We found most patients with dramatically reduced Vδ1 T cells in PF  compared with PB samples   and TEMRA (CD27-CD45RA+) phenotype in patients PB shown in n = 4) at 5:1 E:T against KLE , RL95-2  and Ishikawa  in We analyzed the cytotoxic function of Vδ1 and Vδ2 γδ T cells freshly sorted from healthy donors against endometrial tumor cell lines including Ishikawa, KLE, and RL95-2. We determined the 4-h killing reactivity shown as percentages of specific lysis of Vδ1 and Vδ2 γδ T cells at 5:1 and 10:1 E:T ratio. All of the tested γδ T lymphocytes efficiently killed the tumor targets. First, the summary of Vδ1 T cell-mediated killing , RL95-2 , and Ishikawa . Prominent ability of Vδ2 γδ T cells to recognize and kill endometrial tumor targets was observed at 10:1 E:T against KLE , RL95-2  and Ishikawa . Altogether, the endometrial tumor killing was comparable for γδ T-cell subsets isolated from different donors and was reproducible between the assays for all cell lines.Second, summary of Vδ2 T-cell-mediated killing against KLE, RL95-2, and Ishikawa Figure 4 To elucidate possible mechanisms involved in γδ T cell cytotoxicity, we evaluated several molecules typically involved in γδ T-cell killing including the MICA and MICB as ligands for the NKG2D receptor; CD112 and CD155 as ligands for the DNAM-1 receptor; and ligand B7-H6 for the NKp30 receptor. We analyzed the surface expression of MICA, MICB, CD112, CD155, and B7-H6 on target endometrial tumor cell lines including Ishikawa, KLE and RL95-2 by flow cytometry. Variable expression of these markers is shown in Next, we aimed to determine whether the EphA2 is involved in γδ T-cell killing. In the blocking experiments, we first preincubated the target cells with the EphA2 antibody prior to 4-h cytotoxicity assays and then analyzed the specific lysis of KLE   in endometrial cell line RL95-2 by the CRISPR/Cas9 method. The loss of EPHA2 significantly reduced specific lysis by 35%–90% (median 45%) by Vδ1 T cells in knockout versus wild type (WT) at 5:1 E:T ratio is shown in versus KO cells and also in the addition of ALW-II-41-27 inhibitor (1 µM) at 10:1 E:T in Finally, to confirm the inhibition of the EPHA2 resulting in significantly decreased To further verify these results, we generated EPHA2 knockouts in A-498  and HT-29 (colon) tumor cell lines which had previously showed the highest EphA2 expression in In summary, we evaluated Vδ1 γδ T-cell cytotoxicity against tumor cells and found consistently that EphA2 expressed on cancer cells show susceptibility to cell lysis by tumor-reactive Vδ1 T cells.It is well accepted that women with endometriosis exhibit numerous immune dysfunctions and that the immune system plays a central role in its etiology, infertility, increased risk of ovarian carcinoma, or poor pregnancy outcomes . PathogeIn this study, we determined for the first time the numbers of Vδ1 and Vδ2 γδ T-cell subsets in peripheral blood and peritoneal fluids in patients with endometriosis. We observed dramatically reduced numbers of circulating Vδ1 T cells in endometriosis women compared with healthy donors; however, no differences were found for Vδ2 T cells between endometriosis patients and healthy controls. Interestingly, we described the presence of both Vδ1 and Vδ2 γδ T cell subsets in the peritoneal fluid.Next, we demonstrated for the first time the cytotoxicity of γδ T-cell subsets against endometrial tumor cell lines including Ishikawa, KLE, and RL95-2. Both Vδ1 and Vδ2 γδ T cells were able to lyse tumor cell lines at low 5:1 E:T ratios with specific lysis ranging between 20% and 68% in the 4-h killing assays. We have shown earlier similar cytotoxicity of γδ T cells against solid tumor cell lines including DU145 (prostate), MCF7 (breast), and A498  carcinomas . Togetheversus wild type at 5:1 and 10:1 E:T ratios. Fourth, to further validate the inhibition of cell lysis by tumor-reactive Vδ1 T cells, we used EPHA2 knockouts in renal and colon carcinoma cell lines. Both A-498 KO and HT-29 KO cells showed significant protection from specific lysis mediated by Vδ1 T cells compared with WT cells at 10:1 ratio.In order to elucidate possible mechanisms involved in γδ T-cell cytotoxicity, we chose the EphA2 as it is often overexpressed in many cancers including endometrial carcinomas , 29 and in vivo in lung cancer models  and the Ministry of Health, Czech Republic-Conceptual Development of Research Organization . 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The global burden of colorectal cancer is high. Chemotherapy has been the backbone of colorectal cancer therapy for decades. Toxic side effects and frequently occurring drug resistances remain challenging problems. Therefore, exploring natural compounds with low or even no toxicity holds great potential. However, natural curcumin is poorly absorbed, limiting its clinical use. Therefore, our focus was to screen different molecular types of colorectal cancer to find the ones with the highest sensitivity to curcumin. We observed very individual responses to curcumin for various colorectal cancer cell lines. Most curcumin-sensitive cell lines were of the microsatellite-stable molecular type, and expressed high baseline levels of the IκBα protein. Contrarily, curcumin-resistant lines were mainly microsatellite instable, with low baseline IκBα levels. Considering all of the data obtained, we conclude that patients with microsatellite-stable tumors and high baseline IκBα protein expression would benefit from treatment with novel curcumin formulations and derivatives.Clinical utilization of curcumin in colorectal cancer (CRC) was revived as a result of the development of novel curcumin formulations with improved bioavailability. Additionally, identification of biomarkers for curcumin sensitivity would also promote successful clinical applications. Here, we wanted to identify such biomarkers in order to establish a predictive model for curcumin sensitivity. Thirty-two low-passage CRC cell lines with specified tumor characteristics were included. Curcumin suppressed cell proliferation, yet sensitivity levels were distinct. Most curcumin-sensitive CRC cell lines were microsatellite stable and expressed high levels of IκBα. The predictive capacity of this biomarker combination possessed a statistical significance of 72% probability to distinguish correctly between curcumin-sensitive and -resistant CRC cell lines. Detailed functional analyses were performed with three sensitive and three resistant CRC cell lines. As curcumin’s mode of action, inhibition of NF-κB p65 activation via IκBα was identified. In consequence, we hypothesize that novel curcumin formulations—either alone or, more likely, in combination with standard therapeutics—can be expected to prove clinically beneficial for CRC patients with high IκBα expression levels. In the database “Global Cancer Observatory”, the recent statistics for 2020 revealed that 1,931,590 individuals for both genders and all ages combined were diagnosed with colorectal cancer (CRC), thus accounting for approximately 10.0% of newly diagnosed cancer cases. More than 935,000 (9.4%) deaths of CRC patients per year make it the second most common cancer-related cause of death [However, chemotherapy is still the backbone of clinical CRC management. Toxic side effects and drug resistances remain very challenging problems, thus limiting the efficacy of therapy and largely affecting patients’ quality of life ,5,6; theCurcuma longa (turmeric), which belongs to the ginger family [Curcumin, a natural yellow coloring agent, is a phytochemical derived from r family ,8; it har family , and hasr family ,10.cyclin D1, Bcl-2, Bcl-xL, COX-2, and MMP-9.In fact, most of the anti-inflammatory and antitumor properties of curcumin are considered to be mediated via blocking of the NF-κB pathway’s activation. This pathway has been shown to be upregulated in CRC, and is a major cause of drug resistance ,12. ZhouHowever, Wong et al.  reported® and liposomal complexes, which are currently under investigation—promise the possibility of a “revival” of this very old natural compound for oncological purposes [However, novel drug formulations—such as Merivapurposes ,16,17. IAs various CRC cell lines differ in their response to treatments due to genomic and phenotypic alterations and molecular mechanisms ,20,21,22The goal of the present study was to (1) assess the sensitivity of CRC cell lines with defined molecular alterations to curcumin, (2) identify the mode of action by mechanistically analyzing sensitive CRC cell lines, (3) identify biomarkers associated with curcumin sensitivity, and (4) predict the sensitivity of additional CRC cell lines to curcumin by establishing a predictive model using the previously identified biomarkers.A total of 32 low-passage patient-derived cell lines, established from 30 colon cancer (CC) and 2 rectal cancer (RC) patients, were used in the present study . CRC pat50 of curcumin ranged from 6.69 μM to 18.49 μM. Values of 0.17 μM to 80.95 μM for 5-FU, 0.04 μM to 12.80 μM for irinotecan, and <0.01 μM to 8.72 μM for oxaliplatin were observed [50 values of irinotecan and oxaliplatin were significantly lower than that of curcumin in CRC cells, with all p-values < 0.001. Using Spearman’s correlation analysis, no significant correlation between curcumin and any of the standard drugs was identified; the correlation coefficients were −0.12 for 5-FU (p = 0.545) (p = 0.446) (p = 0.089)  A, −0.15 = 0.446) B, and −0= 0.089) C; the laAs shown in 50 concentrations. The correlation coefficient was −0.72 and p was < 0.001 , and even an obvious dose-dependent response to curcumin was observed (p < 0.05). Cell death increased to 80% dead cells in the HROC357 and HROC69 cell lines when treated with 20 μM curcumin (p < 0.05) , 10 μM, 15 μM, and 20 μM curcumin for 72 h. As expected, cell death in HROC24, HROC113, and HROC285 was not significantly affected, even at the highest curcumin concentration of 20 μM. Conversely, the cell death rate was significantly higher in all three curcumin-sensitive cell lines compared to the control group (observed A. In HRO < 0.05) A. p < 0.001 (Confluent curcumin-sensitive (HROC18 and HROC357) and -resistant (HROC24 and HROC87) cells were treated with 0 µM (control) and 10 μM curcumin overnight, scratched, and the wounds’ diameters were subsequently measured daily for up to 5 days. Migration was not affected by curcumin in HROC24 and HROC87; however, in HROC18 and HROC357 cells, the cells’ ability to migrate was consistently weaker after 10 μM curcumin treatment compared to controls,  < 0.001 B. After incubation with 25 μM curcumin for 24 h, the immunoblot showed that the expression of the NF-κB p65 target protein survivin significantly decreased in HROC18, HROC24, and HROC357 cells, but not in HROC285 cells. The expression of Bcl-xL—another NF-κB p65 target protein—was unchanged in all cell lines except in curcumin-sensitive HROC18 cells, where it showed a lower expression. In addition, we also observed that curcumin induced accumulation of the DNA damage marker ɣH2AX in all four CRC cell lines—especially in curcumin-sensitive HROC357 cells .p < 0.05, p < 0.05). The increase in IκBα expression after exposure to 20 μM curcumin was as high as 70% , 10 μM, 15 μM, and 20 μM curcumin treatment for 24 h. In general, IκBα levels increased in all six CRC cell lines with rising curcumin concentrations A. Treatmr = −0.943, p = 0.017). Similar inverse correlations were obtained for 15 μM (r = −0.986 (p < 0.001)) and 20 μM curcumin (r = −0.928 (p = 0.006)), confirming that the cell viability is negatively associated with the IκBα expression level in CRC cell lines after curcumin treatment.Next, correlations of cell viability and IκBα expression levels after curcumin treatment  were investigated. For curcumin treatment at 10 μM concentration, cell viability and IκBα levels in the CRC cell lines were strongly correlated B, and haWe further measured the expression of NF-κB p65, as well as cytokines and chemokines induced by NF-κB p65 . After iMoreover, no significant decrease in IL-6 secretion B was obsBased on the above results, we hypothesized that curcumin exhibits its anticancer effects via IκBα, which inhibits the NF-κB p65 nuclear translocation. In order to verify this, CRC cells were incubated with 50 µM caffeic acid phenethyl ester (CAPE)—an inhibitor of NF-κB—for 24 h. After that, an increase in IκBα levels was consistently seen in HROC18 and HROC24 cells A, and ceFurthermore, we evaluated the IL-8 and CXCL-1 levels in CRC cells after incubation with curcumin and CAPE for 24 h. A significant decrease in IL-8 and CXCL-1 levels was consistently found in HROC18 and HROC24 cells compared to controls C,D. In HAPC, K-Ras, B-Raf, PIK3CA, TP53, ATM, SMAD4, RAD50, POLE, and PTEN—were investigated as possible biomarkers associated with the IC50 values of curcumin. CRC cell lines with mutated SMAD4 were significantly more sensitive to curcumin . No further correlations were found  than in microsatellite-instable (MSI) CRC cell lines  (p = 0.008) (Stratified by microsatellite (MS) status, the IC= 0.011) A. This s= 0.008) B.p = 0.035), with a 72% probability to correctly distinguish curcumin-sensitive from curcumin-resistant CRC cell lines  curve presented a statistically significant predictive value (area under the ROC curve (AUC) = 0.72, 95%CI: 0.54–0.90, ll lines C. APC, K-Ras, B-Raf, PIK3CA, TP53, ATM, SMAD4, RAD50, POLE, and PTEN are usually considered to be relevant CRC biomarkers [SMAD4 to be associated with curcumin sensitivity. Agarwal et al. [SMAD4 and TP53; however, SMAD4 was not considered as a biomarker with the ability to predict the sensitivity of CRC cells to curcumin. Even though 30 patient-derived low-passaged CRC cell lines with known SMAD mutational status—consisting of 4 mutated and 26 wild-type cell lines—were analyzed in the present study, this point needs to be further clarified in future studies using an even larger number of suited CRC cell models.The present study is the first report on the identification of a potential biomarker for curcumin sensitivity in CRC cells. Mutations in omarkers ,30,31,32omarkers . Here, wl et al.  reportedMMR gene expression levels are regarded as important biomarkers in cancer progression, for prognosis of outcome, and even for assessment of the efficacy of adjuvant chemotherapy [More, the MS status and otherapy ,36,37. IShakibaei et al.  reportedMMR genes, have a high risk of cellular carcinogenesis caused by elevated oxidative stress [Lynch syndrome (LS) patients, who are characterized by germline mutations in four of the e stress ,44,45. He stress . Since de stress , it is rHowever, several studies have come to differing conclusions. Majumdar et al. reported a better effect of curcumin together with resveratrol on MSI-positive HCT116 cells, but not on HT29 cells, which are MSS . Wei et 50 values were found in MSI compared to MSS CRC cell lines. In contrast to the contradictory studies mentioned above, all cell lines used in the present study were below passage number 50, and can thus still be considered as closely mirroring the biology of the original patients’ tumors [In our analysis, curcumin could decrease cell viability and induce cell death in all 32 CRC cell lines tested. However, higher curcumin IC’ tumors .Moreover, the key inhibitory protein of NF-κB p65 in the NF-κB signaling pathway—IκBα—was for the first time identified as an important biomarker with the potential to precisely predict curcumin sensitivity. We found that curcumin-sensitive cell lines expressed significantly higher levels of IκBα even without treatment, while curcumin-resistant cell lines frequently expressed low baseline IκBα levels. Mechanistically, the NF-κB signaling pathway represents the classical pathway responsible for the anticancer action of curcumin. Resistance to radiation therapy in most patients has been linked to an activation of the NF-κB pathway, and Sandur et al. suggested that inhibiting this pathway could resensitize tumors to radiotherapy . Indeed,Deregulation of NF-κB and its downstream targets has been proposed as an important targetable mechanism in CRC therapy resistance . IκBα isDifferent gene expression patterns have been identified in oxaliplatin-resistant and -sensitive CRC cell lines after treatment with curcumin plus oxaliplatin. Compared to oxaliplatin-sensitive CRC cells, curcumin inhibited the NF-κB p65 level in oxaliplatin-resistant cells more effectively . SimilarTaken together, experimental evidence has proven that curcumin treatment is associated with inhibition of NF-κB p65, activation of IκBα, suppression of IκBα phosphorylation, and downregulation of NF-κB-p65-related antiapoptotic and cell-proliferation-activating proteins ,59. It i50 of curcumin and the baseline IκBα level in CRC cell lines was observed, along with a strong association of cell viability with the IκBα expression levels after curcumin treatment. Surprisingly, IκBα expression levels were higher in MSS compared to MSI CRC cell lines. When combining IκBα levels with MS status as a biomarker for predicting curcumin sensitivity, the combination could correctly distinguish the curcumin-sensitive CRC cell lines from curcumin-resistant CRC cell lines with an approximately 72% predictive power. The major finding of the present study—that MSS CRC cells have higher IκBα levels, and are more sensitive to curcumin treatment—is novel and surprising. Nevertheless, since these data were obtained with a relatively large number of low-passaged cell lines, even allowing for statistical comparison, they can be considered to be relevant. Moreover, a significant correlation between the ICMMR gene expression was negatively correlated with inflammation-associated transcription factor (including NF-κB) expression [Our findings clearly raise the question of whether there is a link between the NF-κB pathway and MSI status. MSI cancers are prototypically hypermutated, and are well known to possess a variety of immune escape mechanisms to evade high immune pressure . Among tpression . Anotherpression . More impression . In conclusion, data from different studies indicate that cancer responses to curcumin—especially CRC—are mediated by inhibition of NF-κB p65 via IκBα. This additionally seems to be linked to the MMR system. However, the exact mode of action still needs to be determined. n = 32) were established and characterized as previously described [2 and 37 °C). Cells below passage 50 were used in order to ensure comparability of molecular characteristics between parental tumor and tumor-derived cell lines. Directly patient-derived and patient-derived xenograft (PDX)-derived CRC cell lines , 1 nM–5000 µM 5-FU, 0.1 nM–1000 µM irinotecan, and 1.3 nM–20 µM oxaliplatin  [A total of 1.0–1.5 × 10Subsequently, cells were washed with PBS, then stained using 0.2% crystal violet solution . After that, cells were again washed twice and allowed to dry completely. Photos were taken, and stained biomaterial present in the wells was quantified by dissolving in 1% SDS (AppliChem) and measuring the absorption at 570 nm (reference: 620 nm) ,66. Cell50 values of standard drugs from our previous work. These values were obtained using the method described above. In detail, the cited IC50 values of 5-FU and irinotecan in HROC87 and HROC113 were from [50 of irinotecan in HROC18 was from [50 of oxaliplatin in HROC57 was from [It is worth mentioning that we cited the results of ICere from , in HROCere from , and in ere from ; the IC5was from  and in Hwas from ; while twas from , and in was from . 5 CRC cells per well were seeded in a 6-well plate and left for incubation overnight. Cultures were exposed to curcumin and CAPE  as single or combination treatments for 72 h. Cell death was evaluated by apoptosis assay using Annexin V-FITC  and propidium iodide  labeling and flow cytometric analysis on a FACSCalibur device  with CellQuest™ Pro software (Becton Dickinson), and 20,000 events were recorded for each measurement. The data were analyzed using the following formula: 100% − (Annexin V- PI-) % [A total of 2 × 10- PI-) % .5 CRC cells were fixed using 400 μL of 2% Formafix  followed by an incubation in 400 μL of 1 × buffer P  for 10 min. For staining, cells were incubated in 100 μL of 1 × buffer P with or without 5 μL of PE-anti- IκBα antibody , and protected from light for 30 min at 4 °C. IκBα expression was analyzed by flow cytometry as described above, and 10,000 events were recorded for each measurement.In order to evaluate the baseline IκBα level, 5 × 10To assess the IκBα levels in cells after treatment, cells were pretreated with 5% FCS medium containing curcumin and CAPE as single or combination treatments for 24 h, prior to PE-anti- IκBα staining.Fully confluent CRC cells were incubated in medium without FCS, with and without the addition of 10 μM curcumin, for 24 h. Afterwards, the monolayers were scratched using a 20 μL pipette tip to generate a wound in the cell layer. The wounds’ diameters were measured daily for up to 5 days using a light microscope . 5 CRC cells were allowed to adhere in a 6-well plate overnight. Medium containing 5% FCS with or without 25 μM curcumin was changed, and cells were incubated for 7 days. Supernatants were collected and measured by ELISA according to the manufacturer’s recommendations for IL-6 (cat: 31670069) and IL-8 (cat: 31670089) , as well as CXCL-1 . In order to test the effects of CAPE on IL-8 and CXCL-1 levels, cells were incubated with curcumin, CAPE, or a combination of curcumin and CAPE for 24 h prior to measurement.A total of 5 × 105 CRC cells per well were seeded in a 6-well plate. After incubation overnight, the medium was changed to 5% FCS medium plus/minus 25 μM curcumin. After 24 h, cells were pelleted and collected, and immunoblots were performed using anti-survivin , anti-ɣH2AX , anti-Bcl-xL , and anti-NF-κB p65 . A total of 5 × 10r). Student’s t-test and ANOVA were used to compare the mean differences between groups, and linear regression was used for comparison of trends by the slopes of regression lines. All results were taken from at least three independent measurements. The predictive ability of biomarkers for curcumin sensitivity was analyzed using the ROC, and is presented as the AUC and the 95% confidence interval. p-Values lower than 0.05 were considered significant. All statistical analyses were performed using GraphPad Prism 6.0 .Cell viability at gradient concentrations of curcumin was plotted by a heatmap visualization technique using the TBtools software . PearsonIn summary, this study demonstrated that curcumin could inhibit CRC cell viability, increase cell apoptosis, and suppress cell migration. The 32 CRC cell lines analyzed varied greatly in their response to curcumin. The primary focus was to identify the subset of CRC cell lines that are highly sensitive to curcumin. By unraveling common features of the responsive cell lines, possible biomarkers were identified; these were subsequently suggested as predictors of curcumin response. Most curcumin-sensitive CRC cell lines had the following characteristics: MSS and/or high baseline IκBα expression levels. Meanwhile, curcumin-resistant CRC cell lines were mostly MSI, and showed low baseline IκBα levels. The predictive ability of this biomarker combination was statistically significant, with a predictive power of around 72% in a classical ROC/AUC analysis.These data further fuel the ongoing efforts to improve the bioavailability of curcumin in order to pave the way for successful clinical application. Based on our large panel of patient-derived low-passaged CRC cell models with known sensitivity and biomarker expression, novel curcumin-derived candidate drugs could now easily be screened. Since the HROC model collection also includes a large number of matching in vivo PDX models, even the complete preclinical drug testing could be managed.Based on the data obtained so far, we postulate that MSS patients with high baseline IκBα expression levels can benefit from clinical treatment with novel curcumin formulations and derivatives. Further studies will also have to analyze the combination of curcumin with standard chemotherapeutics.