ARID1A-TEAD1 and AXIN1-URI1) in hepatocellular carcinoma, thus corroborating the ability of SLIdR to identify potential drug targets.The development of cancer therapies is limited by the availability of suitable drug targets. Potential candidate drug targets can be identified based on the concept of synthetic lethality (SL), which refers to pairs of genes for which an aberration in either gene alone is non-lethal, but co-occurrence of the aberrations is lethal to the cell. Here, we present SLIdR , a statistical framework for identifying SL pairs from large-scale perturbation screens. SLIdR successfully predicts SL pairs even with small sample sizes while minimizing the number of false positive targets. We apply SLIdR to Project DRIVE data and find both established and potential pan-cancer and cancer type-specific SL pairs consistent with findings from literature and drug response screening data. We experimentally validate two predicted SL interactions ( Synthetic lethality can be used to identify potential drug targets in cancer based on simultaneous inactivation of two genes through genetic aberrations and gene silencing. Here, the authors develop a statistical framework to identify synthetic lethal pairs from large scale perturbation screens across multiple cancer types. PARP inhibitors in BRCA-mutated cancers. The BRCA1/2 genes involved in DNA double-strand break repair are often mutated in breast and ovarian cancers3, and hence such cancer cells rely on alternate DNA repair processes. PARP1 plays a central role in these alternate DNA repair mechanisms5, and therefore inhibiting PARP results in catastrophic double-strand breaks during replication, ultimately leading to cancer cell death7.Synthetic lethality (SL) refers to gene pairs for which an aberration in either gene alone does not affect cell viability, but aberrations in both genes are fatal to the cell. Key to exploiting SL in cancer therapy is the identification of a targetable dependent gene (SL partner) for a given genetically altered gene, such that the two genes form an SL pair Fig. . A class11, or knowledge-driven and based on pathways or interaction networks12. Other computational methods for identifying SL interactions relied on human orthologues of yeast genetic interactions13, inter-species network models14, signaling networks15, and protein-protein interaction networks16.In recent years, high-throughput experiments have enabled the generation of multi-omics observational data and large-scale interventional data in various cancer types. Several computational methods have since been developed to identify and prioritize SL interactions from such diverse molecular data. Early methods involved the identification of mutually exclusive genetic alterations in functionally related genes. These approaches were either de novo and detected patterns in the mutational data27. McDonald et al.27 conducted a large-scale deep RNAi screen, entitled Project DRIVE, that targeted 7,837 genes in 398 Cancer Cell Line Encyclopedia28 (CCLE) models and provided a rich and robust dataset for the identification of SL pairs. However, the authors primarily analyzed gene interactions in a pan-cancer manner.Experimentally, large-scale perturbation screens based on siRNA, shRNA, CRISPR, or small molecules in cell lines have been conventionally used to identify SL interactions29 proposed DAISY, a method that predicts a global network of potential SL interactions in human cells from tumor copy number, expression data, and shRNA screening data from cell lines. Similarly, Sinha et al.30 presented MiSL, an algorithm based on Boolean implications to prioritize mutation-specific SL partners for different cancer types from large pan-cancer multi-omics and perturbation screening data. ISLE is another statistical approach that uses lab-screened SL interactions as inputs and analyzes tumor molecular profiles, patient clinical data, and gene phylogeny relations to identify clinically relevant SL interactions31.Significant efforts have been made in developing integrative computational methods that harness both multi-omics observational data and genetic perturbation screening data. Jerby-Arnon et al.While all these methods mark a significant step forward, general prediction of SL interactions in cancer still remains a challenge. Most methods primarily rely on large sample sizes and multi-omics data, and thus are not suitable for rare cancer types. Furthermore, they use genetic perturbation screening data merely to refine the candidate list of SL pairs derived from analyzing multi-omics data. We hypothesize that such rich large-scale perturbation screens can be exploited further to obtain SL pairs.ARID1A-TEAD1 and AXIN1-URI1, through comprehensive experiments in patient-derived cell lines.Here, we present a statistical framework called SLIdR , a rank-based statistical method for predicting SL pairs from perturbation screens in both pan-cancer and cancer type-specific settings. The predicted SL pairs are validated by large-scale drug-response profiles and literature evidence. Subsequently, by benchmarking SLIdR on simulated data and experimentally identified SL interactions, we demonstrate the improved predictive power and advantage of SLIdR in reducing the number of false positives even when the sample size is small. Finally, we validate two SLIdR predictions in hepatocellular carcinoma, namely 33 for each cancer type and considered these genes to be genetically altered in cell lines if they were subject to non-synonymous mutations or deep deletions (see Methods). We collectively refer to these altered genes as driver genes and their alterations as mutations. SLIdR aims to find SL partners for such drivers from perturbation data. We applied SLIdR to the Project DRIVE dataset27, focusing on cell lines from CCLE28 across various cancer types with available copy number data , we accounted for these co-occurring mutations and predicted the relevant driver genes for each SL partner  has been shown to have a pro-apoptotic effect in breast cancer cells with PIK3CA mutations36. In addition to established pairs, SLIdR also predicted several potential SL pairs, such as KRAS-TRPM7 and TP53-specific SL partners, including TP53BP1, USP28, DDX3, and PNPLA6, which were further supported by evidence in the literature . Of these 13 pairs, BRAF-HTR2C, KEAP1-ATP1A1, NOTCH3-WEE1, and POTEF-PLK1 were significant after multiple testing correction  across 9 different candidate drug compounds , thereby confirming the predictions from SLIdR (see Methods).Next, we systematically validated the predicted pan-cancer SL pairs on the primary PRISM repurposing dataset, a large-scale drug-response profile from the DepMap consortiump-values for these predicted pan-cancer SL pairs in subsets of cell lines grouped by primary sites. We found that a sizable fraction of the pan-cancer signals is indeed cancer type-specific . In particular, four of the eight candidate compounds in pancreatic cancer were MTOR-targeting drugs and were significantly more effective in EIF3C mutated cell lines than WT cell lines  and in the literature , which reached statistical significance after multiple testing correction for stomach cancer . These results demonstrate that SLIdR is capable of finding established and potential cancer type-specific SL pairs.Validating the cancer type-specific predictions on the PRISM drug-response dataset further bolstered our findings and provided druggable targets for 10 cancer types . Subsequently, we compared SLIdR to the Wilcoxon rank sum test and t-test on the simulated data. SLIdR significantly outperformed these tests for predicting SL interactions from simulated data in liver, ovary, and bone cancers  10 combinatorial CRISPR screens26. For these evaluations, we excluded the experimentally identified SL interactions that were not in the set of possible pairwise interactions in the DRIVE dataset, and focused on 978 and 1301 unique experimentally identified SL interactions in the in vitro and CRISPR screens, respectively (see Methods). The retained 1301 unique experimentally identified SL interactions included pairs from 8 of the 10 CRISPR screens. Only four interactions were shared across these 8 combinatorial CRISPR screens. In contrast, SLIdR recovered a significant fraction of established SL interactions  with sensitivities of 12.3% and 11.45% in the in vitro SL screens and CRISPR screens, respectively. With accuracies of ~93% across both sets, these results comprehensively validated SLIdR predictions  17 in vitro SL screens reported by Lee et al.Overall, these results highlight the ability of SLIdR to identify well-established and potential targets in both pan-cancer and cancer type-specific settings. Exploring these SL interactions, we observed that the overlap between pan-cancer and cancer type-specific predictions is limited, re-emphasizing the need to explore the data in both settings, as they provide complementary information. The flexibility of SLIdR to work on both small and large sample sizes makes it an attractive method for this problem.47. To demonstrate the predictive power of SLIdR, we sought to validate two of our top hits in HCC, namely ARID1A-TEAD1 and AXIN1-URI1 , we identified nine SL pairs Fig. . IdentifRI1 Fig. . Both ARlls Fig. . Using dect Fig. .Fig. 4In53. Verteporfin disrupts the YAP-TAZ complex, therefore inhibiting the transcription of the downstream targets54  coactivator, a well-known effector of the Hippo-signaling pathways54 Fig. . Treatmells Fig. , indicatfin Fig. .TEAD1 is specifically synthetic lethal to ARID1A loss-of-function, we employed two HCC-derived cell lines carrying a wild-type ARID1A–Huh-7 and HLE, and modulated ARID1A and TEAD1 expression using siRNAs , a densely vascularized extraembryonic tissue56. We treated ARID1A-silenced and control Huh-7 cells with verteporfin (1 μM) or vehicle (DMSO) for 24 h. We then inoculated the cells into the CAMs and screened the eggs for tumor formation 4 days later . Subsequently, we ran SLIdR in both settings. In the pan-cancer analysis of the CRISPR data, we found 104 SL pairs including KEAP1-NFE2L2, RPL22-RPL22L1, RPL22-WRN, APC-CTNNB1, and MTAP-PRMT5 from the DRIVE pan-cancer analysis. While the overlap between the two screens was significant , we observed that several established and strong candidate pairs of the DRIVE analysis, such as MTAP-MAT2A, MTAP-RIOK1, and TP53-TP53BP1, were missed in the CRISPR analysis by small margins. This could partly be attributed to the reduced power in the CRISPR analysis, as the data had only 266 cell lines in common with the DRIVE data, i.e., 107 cell lines from the DRIVE screen were missing in the CRISPR screen. Another factor is the differences in pre-processing and normalization steps across the two screens. Therefore, using Fisher’s method to combine the corresponding p-values from both screens for each SL pair and retaining only the significant pairs, we identified 162 robust SL pairs across both screens (see Methods). These 162 pairs retained 62% (91 SL pairs) of the original DRIVE candidate SL pairs BRAF-DUSP4, BRAF-MAPK1, and BRAF-PEA15 in skin, GATA3-ESR1 in breast, and CDH1-CTNNB1 in stomach cancers  before multiple testing correction  Occasionally, the results from SLIdR are sensitive to the WT p-value threshold, resulting in losing important SL pairs. While this threshold is important for filtering out candidates showing increased or decreased viabilities in WT cell lines, the choice of the threshold depends on the data type and processing steps, requiring the user to optimize this threshold to their specific setting. For improved versatility, an extension of SLIdR could include a score to prioritize the pairs instead of filtering based on the WT p-value threshold. (3) As cancer cells can harbor multiple mutations, SLIdR initially identified the same SL partner for different mutations mapping to the same cells, especially in the pan-cancer setting. To overcome this issue and identify the true SL pairs from several potentially confounding mutated genes, we used propensity score matching-based potential outcome models and identified significant SL pairs. Propensity-score matching tries to replicate a randomized experiment by matching samples between mutated and wild-type groups on other confounding mutations, thereby obtaining balanced covariate distributions across the two groups. Consequently, the average causal effect using matching is determined only from a subset of data. Since the mutation matrix is sparse, matching yields under-powered subsets in certain cases, inadequate for robust estimation of the average causal effect. Furthermore, our current approach for controlling confounding drivers is prone to selection bias, because we do not account for the underlying dependency structure between confounding drivers due to small sample sizes. Further research is required to improve the estimation of the causal effect of driver mutations from sparse mutation data. A possible solution would be to use information from other omics data to select the confounders to adjust for, thus improving the overall estimate.While SLIdR was successful in identifying SL pairs, there are several limitations: (1) In its current scope, SLIdR primarily focuses on identifying SL partners from perturbation screening data, copy number, and mutation data. Consequently, it failed to recover the predictions based on expression and pathway reported by McDonald et al.Taken together, SLIdR provides a robust statistical framework for rapid discovery of SL interactions from large-scale perturbation screens in both pan-cancer and cancer type-specific settings. Particularly in precision oncology, SLIdR can help in developing putative mutation-specific and effective personalized therapies.27, as well as corresponding mutation data and copy number data from the Cancer Cell Line Encyclopedia (CCLE) collection28 for 373 cell lines across 23 cancer types.We used viability profiles published in Project DRIVE63 and ATARiS64. The RSA method uses all shRNA reagents targeting a gene and can be used to identify essential, inert, and active genes, while ATARiS only uses a subset of shRNAs with consistent activity across the cell lines and aims to provide a robust gene-level score by discarding shRNA reagents with off-target effects. ATARiS provides a relative score for the gene-level activity by median-centering the data for each reagent and, as a result, cannot distinguish between inert and essential genes.Viability data captures the viability of cell lines for each gene knockdown experiment. In Project DRIVE, 7837 genes were targeted by using an average of 20 pooled shRNAs per gene. The shRNA activities were defined as the quantile normalized log fold change in shRNA read counts 14 days after the start of the knockdown experiment to the shRNA abundance in the input library. The gene-level viability score of each cell line was computed by aggregation of shRNA activities using two computational methods, namely RSA27. Genes with an RSA value ≤ −3 in more than 50% of cancer cell lines were reported as essential genes. In total, 460 and 185 genes were reported essential in cancer type-specific and pan-cancer settings, respectively. The resulting viability matrices consisted of the ATARiS scores for the remaining perturbed genes (rows) for each cell line (columns).To process the viability data, we removed essential genes using the RSA method as was performed in Project DRIVEFor the pan-cancer setting, we focused on genes with mutations or copy number aberrations in more than 30 cell lines. We downloaded mutation data and copy number data from the CCLE website, and binarized them as follows. A gene in a given cell line was assigned a value of 1 if it was subject to non-synonymous mutations, and 0 otherwise. For copy number data, we focused only on deep deletions and binarized a gene in a given cell line by assigning a value of 1 if the gene was homozygously deleted and a value of 0 otherwise. Finally, combining both these data, a driver gene in a given cell line was assigned a value of 1 if it was subject to non-synonymous mutations, deep deletions, or both; and 0 otherwise.33 MAF file from TCGA for each cancer type and focused only on significantly mutated genes . Next, we concentrated on genes with non-synonymous mutations in two or more cell lines, and excluded copy number data as it was very noisy in this setting. Thus, a gene in a given cell line was assigned a value of 1 if it was subject to non-synonymous mutations, and 0 otherwise. The resulting binarized mutation matrices described the mutation profiles for each cell line (column) across all driver genes (rows).In the cancer type-specific setting, to define the set of driver genes, we first used the MutSig 2CV v3.1d and a perturbed gene g. For each driver gene d, we divided the cell lines into two groups according to the mutation status of d, namely wild-type cell lines (WT) and mutated cell lines (Mut). Further, we ranked the perturbed genes by their ATARiS scores, for each mutated and WT cell line, and normalized it between 0 and 1. Due to a large number of perturbed genes, the normalized ranks have many distinct levels and are highly fine-grained. Hence, we assumed the normalized ranks to be continuous.SLIdR is a rank-based statistical framework to identify the presence of synthetic lethal dependency between a driver gene d, g) be a fixed pair of driver and perturbed gene and Cd be the set of cell lines mutated in d of cardinality n. If  is an SL pair, based on the aforementioned definition . The test statistic T for the pair  is then defined as the sum of normalized viability ranks of mutated cell lines Cd perturbed in gene g, H0, the test statistic T is the sum of n independent uniform random variables on the unit interval and hence it follows the Irwin-Hall distribution of order n. The resulting p-value was computed as the lower tail probability P(T  < tobs), where tobs is the observed test statistic. For large n, computation of the Irwin-Hall probability distribution is either computationally expensive or numerically unstable. Therefore, we used the approximation T ∼ N for n > 20.Let  that reached statistical significance (α = 0.1) in the WT cell lines. However, we did not use this filter for the pan-cancer setting due to the diverse nature of the cell types and cancer types. Since knockdown data of several driver genes is unavailable in the Project DRIVE screen, we tested for SL interactions only in one direction and were unable to test the effect on viability of the cell lines when the driver genes alone are knocked down.Conversely, based on the definition of synthetic lethality Fig. , wild-tyM × N), where M is the number of knockdowns and N is the number of driver genes. Therefore, we expect on average one false positive hit among all reported SL hits, for each cancer. Our method is computationally inexpensive as it avoids performing all M × N tests. For each driver, we compute the test statistic for all perturbed genes and sort them in ascending order. The pre-ordering of the test statistics enables us to test for genes until the corresponding p-value is less than the chosen significance level. Further, we note that this approach was in good agreement with controlling the false discovery rate at 10%  in the paired t-test , we stratified the cell lines into WT and mutated based on the mutation status of the driver gene d. Since true SL pairs should exhibit differential viability profiles between mutated and WT cell lines, the viabilities of mutated cell lines from perturbation of the true SL partner gene g were sampled from another distribution s is the minimum of viabilities from the Project DRIVE data for the primary site s. Using these parameters ensured that (i) the perturbation of gene g resulted in reduced viabilities in mutated cell lines than in WT cell lines, and (ii) the simulated viabilities were congruent with the Project DRIVE data. This was repeated for all 30 true SL pairs in each cancer type.We performed a simulation study focusing on both prevalent and rare cancer types, including liver, ovarian, and bone cancers with 13, 14, and 7 cell lines, respectively. For each cancer type, we reused the binarized mutation matrix from the cancer type-specific analysis on the Project DRIVE dataset and simulated the corresponding viabilities. For a given cancer type, the ground truth comprised 30 random pairwise interactions between driver genes and perturbed genes. The viability matrix for each cancer type was simulated in two steps. First, the viabilities of all the cell lines across all perturbations were simulated by sampling from t-test applied to the raw simulated viabilities. For each possible pairwise interaction in a given cancer type, we stratified the cell lines into mutated and WT cell lines based on the mutation status of the driver gene constituting the pair. Then, we tested whether the raw viabilities of the mutated cell lines from the perturbation of the partner gene was less than zero using a one-sided Wilcoxon rank sum test and t-test. The resulting p-values from SLIdR, Wilcoxon rank sum test, and t-test were compared to the true labels in order to plot the ROC and Precision-Recall curves. The true labels comprised a binary vector of all possible pairwise interactions with 1 assigned to true SL pairs and 0 otherwise. This was repeated for all three cancer types.Finally, we applied SLIdR to the simulated data and compared it to Wilcoxon rank sum test and 31, and (ii) 24,651 experimentally identified SL interactions from 10 combinatorial CRISPR screens26  6,033 experimentally identified SL interactions from 17 in vitro focused SL screens reported by Lee et al.37. For both settings, we first filtered out all the pairs exhibiting oncogene-addiction and retained only those pairs with a targetable SL partner gene from the drugs in the screen. As previously described in the SLIdR algorithm, for each predicted SL pair, we stratified the cell lines based on the mutation status of the driver gene into WT cell lines and mutated cell lines. Subsequently, we tested if the drug compound targeting the corresponding SL partner gene resulted in reduced viabilities of mutated cell lines in comparison to WT cell lines using a one-sided t-test. Since multiple drug compounds could target the same SL partner gene, for a given SL pair we tested the differential drug response for each drug compound. We repeated this for all SL pairs and reported the SL pairs with a significant difference in drug response at a significance level of α = 0.1 along with their multiple testing corrected q-values.We validated the predicted pan-cancer and cancer type-specific SL pairs on the primary PRISM drug-response screen containing the growth inhibitory activity of 4686 drugs tested across 578 human cancer cell linesU) between all the driver genes and druggable perturbed genes and filtered out all the pairs exhibiting oncogene-addiction. Subsequently, we ran permutation tests on random sets of gene pairs. For each run, we sampled K gene pairs from U at random, where K is the number of SL pairs predicted by SLIdR and tested on PRISM screens. For each sampled gene pair, we stratified the cell lines based on the mutation status of the driver gene into WT and mutated cell lines. Then, we tested whether the drug compound targeting the corresponding partner gene reduced the viabilities of mutated cell lines compared to WT cell lines using a one-sided t-test. Since multiple drug compounds could target the same partner gene, we tested the differential drug response for each drug compound for a given gene pair. We repeated this for all random gene pairs and counted the gene pairs with a significant difference in drug response (significance level of α = 0.1). Finally, we computed the empirical p-values by comparing the number of significant drug responses of SLIdR hits and those of random sets.To assess the robustness of the PRISM validation results of SLIdR hits, we compared them to the PRISM results obtained from 1000 sets of random gene pairs. The same process was followed for both pan-cancer and cancer type-specific settings. We first retained only those perturbed genes with at least one matching drug compound in the PRISM screen for each cancer. We then generated a set of all possible pairwise interactions In both pan-cancer and cancer type-specific settings, we reused the mutation data from the Project DRIVE analyses, replacing only the viability scores from DRIVE with the CRISPR viability scores. However, it should be noted that the reduced overlap of cell lines in the CRISPR screen resulted in dropping a few driver genes in a few cancer types. Specifically, a few driver genes were dropped as they lacked non-synonymous mutations in at least two cell lines in the cancer type-specific setting. For the viability matrices, we focused only on the common perturbations between both the screens across common cell lines. Subsequently, the CRISPR viability scores were median centered across cell lines.p-values corresponding to the mutated cell lines from the two screens using Fisher’s method, and filtered those with combined p-value < 1/(M × N), where M is the number of knockdowns and N is the number of driver genes in the Project DRIVE analysis. Finally, we filtered out all the candidate pairs that reached statistical significance (α = 0.1) in the WT cell lines in both screens. However, we did not use this final filter for the pan-cancer setting due to the diverse nature of the cell types and cancer types.We re-ran SLIdR with the same threshold parameters as with Project DRIVE. To identify the robust hits, we took the union of the sets of SL pairs predicted by SLIdR on both the screens. For the SL pairs unique to the DRIVE screen, we determined the significance of the pair in the CRISPR screen, and vice-versa. Consequently, we combined the 66, and tested for mycoplasma infection using a PCR-based test (ATCC). All cell lines were maintained under the conditions recommended by the provider. Briefly, all cell lines were cultured in DMEM supplemented with 5% Fetal Bovine Serum (FBS), non-essential amino-acids (NEAA) and antibiotics (Penicillin/Streptomycin). The cells were incubated at 37 °C in a humidified atmosphere containing 5% CO2. Exponentially growing cells were used for all in vitro studies.Liver cancer-derived cell lines SNU449, Huh-7, and HLE were obtained from the Laboratory of Experimental Carcinogenesis , authenticated by short tandem repeat profiling as previously describedTEAD1, ARID1A, URI1, AXIN1 and TP53, ON-TARGET plus SMARTpool non-targeting control, and DharmaFECT reagent were all purchased from GE Dharmacon  or for 48–72 h . In order to avoid cytotoxicity, the transfection medium was replaced with a complete medium after 24 h.Transient gene knockdown was conducted using ON-TARGET plus siRNA transfection. ON-TARGET plus SMARTpool siRNAs against human ARID1A overexpression, pcDNA6-ARID1A (#39311)67 was obtained from Addgene (https://www.addgene.org), and the empty control vector was pCDNA6-V5/His.b (ThermoFisher Scientific). The expression vectors were transiently transfected using the Lipofectamine 3000 transfection reagent (ThermoFisher Scientific) following the manufacturer’s instructions.For 68. All samples were analyzed in triplicates.Total RNA and proteins were extracted from cells at 75% confluence using TRIZOL . Background impedance of the xCELLigence system was measured for 12 s using 50 μl of room temperature cell culture media in each well of E-plate 16. Cells were grown and expanded in tissue culture flasks as previously described. After reaching 75% confluence, the cells were washed with PBS and detached from the flasks using a short treatment with trypsin/EDTA. 5000 cells were dispensed into each well of an E-plate 16. Growth and proliferation of the cells were monitored every 15 min up to 120 h via the incorporated sensor electrode arrays of the xCELLigence system, using the RTCA-integrated software according to the manufacturer’s parameters. In the case of transient siRNA transfection, cells were detached and plated on xCELLigence 24 h post-transfection.3 exponentially growing cells were plated in a 96-well plate. After 24 h, cells were treated with serial dilution of verteporfin (Supplementary Data 5 × 10https://www.flowjo.com/).Cells were collected 72 h post siRNA transfection, stained with annexinV  supplemented with protease and phosphatase inhibitors. Cell lysates were then treated with a reducing agent, boiled and loaded onto neutral pH, pre-cast, discontinuous SDS-PAGE mini-gel system. After electrophoresis, proteins were transferred to nitrocellulose membranes using the Trans-Blot Turbo Transfer System (Bio-Rad). The transblotted membranes were blocked for 1 h in TBST 3% SureBlock (LubioScience) and then probed with appropriate primary antibodies (1:1000) overnight at 4 °C. List of antibodies and working concentrations are available in . 24 h post-treatment, cells were detached from the culture dish with Trypsin, counted, suspended in 20 μl of medium (DMEM) and mixed with an equal volume of Matrigel. To prevent leaking and spreading of cells, an 8 mm (inner diameter) sterile teflon ring  was placed on the CAMs and the final mixture was grafted onto the chorioallantoic membranes inoculating the cells with a pipette inside the ring70. Embryos were maintained at 37 °C for 4 days after which tumors at the site of inoculation were excised using surgical forceps. Images of each tumor were acquired with a Canon EOS 1100D digital camera. Surface measurements were performed by averaging the volume (height*width*width) of each tumor using ImageJ, as previously described71. Total n ≥ 5 tumors for each condition were analyzed over three independent experiments. Experiments in fertilized eggs were performed before the third period of the embryonation period (before day 14). Experiments in fertilized eggs in this period do not require ethical approval.Fertilized chicken eggs were obtained from Gepro Geflügelzucht AG at day 1 of gestation and were maintained at 37 °C in a humidified (60%) incubator for 9 daysNo statistical method was used to predetermine sample size. For the in vivo experiments, CAMs were allocated randomly to each condition. For the in vivo model, CAMs were screened for tumor formation blindly by two independent scientists.t-test. For comparison involving multiple time points, statistical significance was determined by multiple Student’s t-test corrected for multiple comparisons with the Holm–Sidak method. A p-value < 0.05 was considered statistically significant. For all figures, ‘ns’ indicates not reaching the significance level. For the CAM assay, a two-tailed unpaired Student’s t-test was used. Unless otherwise indicated, all data represent the mean ± standard deviation from at least three independent experiments.For the functional experiment reported in Figs. For details on the reagents used, please refer to Supplementary Data Further information on research design is available in the Supplementary InformationPeer Review FileReporting SummaryDescription of Additional Supplementary FilesDataset 1Dataset 2Dataset 3Dataset 4Dataset 5Dataset 6


Osteosarcoma (OS) is a pediatric malignant bone tumor that predominantly affects adolescent and young adults. It has high risk for relapse and over the last four decades no improvement of prognosis was achieved. It is therefore crucial to identify new drug candidates for OS treatment to combat drug resistance, limit relapse, and stop metastatic spread. Two acquired hallmarks of cancer cells, mitochondria-related regulated cell death (RCD) and metabolism are intimately connected. Both have been shown to be dysregulated in OS, making them attractive targets for novel treatment. Promising OS treatment strategies focus on promoting RCD by targeting key molecular actors in metabolic reprogramming. The exact interplay in OS, however, has not been systematically analyzed. We therefore review these aspects by synthesizing current knowledge in apoptosis, ferroptosis, necroptosis, pyroptosis, and autophagy in OS. Additionally, we outline an overview of mitochondrial function and metabolic profiles in different preclinical OS models. Finally, we discuss the mechanism of action of two novel molecule combinations currently investigated in active clinical trials: metformin and the combination of ADI-PEG20, Docetaxel and Gemcitabine. Osteosarcoma (OS) is considered a rare disease and there has been no improvement in survival rate over the last 40 years. OS is the most frequent primary malignant bone tumor worldwide and predominantly affects children and young adults . For 80%To date, the main pillars of therapeutic management of OS are made of neoadjuvant chemotherapy with five major drugs: cisplatin (CIS), doxorubicin (DOX) and methotrexate (MTX), ifosfamide (IFOS) and etoposide (ETOP) , followe2+ homeostasis and signaling, redox homeostasis, and cell survival or death decisions (via oxidative phosphorylation (OXPHOS) and the source of many biosynthetic intermediates  . Apoptos2+ overload , B-cell lymphoma-w (Bcl-W), and myeloid cell leukemia 1 (Mcl-1) prevent the formation and activation of the apoptosome  family with three subfamilies: anti-apoptotic, pore-forming proteins, and Bcl-2 homology 3 (BH3)-only proteins, whereby the classification is depending on the presence of the Bcl-2 homology domains. Anti-apoptotic members including, e.g., Bcl-2, B-cell lymphoma-extra-large  and/or nuclear factor-kappa B (NF-kB) surviving pathways (complex I) or cytotoxic signaling-mediated apoptosis (complex II) (L) (1) Extrinsic apoptosis: Binding of death ligands to their death receptors, induces the assembly of DISC which consists of adaptor proteins and pro-caspases (pro-caspase-8 and pro-caspase-10). Assembly of the DISC complex results in the activation of caspase-8 and caspase-10 and thus, caspase-3 and caspase-7. The crosstalk of extrinsic apoptosis and mitochondrial intrinsic apoptosis is mediated by the activation of caspase-8 and the cleavage of BID to tBID. tBID activates the MOMP-mediated caspases to induce cell death.c, AIF and SMAC/Diablo, followed by activation of the apoptosome including caspase-9, caspase-3, caspase-7 into the cytosol.2) Intrinsic apoptosis can be activated by various intracellular stimuli that lead to the activation of pro-apoptotic proteins (including pore-forming proteins and BH3-only proteins). It induces the release of apoptogenic factors such as cytochrome Considered as an important apoptosis signal transduction pathway in cancer cells, extrinsic apoptosis pathway can be triggered by diverse DNA-damaging agents such as DOX, CIS, and ETOP  Friesen. This typlex II)  (Carneirplex II) . When NF II) (L) . Complex II) (L) . Active  II) (L)  (Li et a II) (L)   (L) .1) ExtriL in high metastatic osteosarcoma cell line (M8), in contrary to low metastatic osteosarcoma cell lines . Furthermore, high-expressing Bcl-XL mRNA tissues from 72 OS patients without prior chemotherapy are correlated with lower prognosis. Interestingly, not only Bcl-XL but other anti-apoptotic members (Bcl-2 and Mcl-1) also showed significantly higher protein expression in OS patient tissue samples, compared to non-tumor tissues samples. By knocking-down Bcl-XL, OS cell lines showed a significant reduction of cell proliferation and higher apoptotic rate, confirmed by the increase of caspase-3. In vitro, targeting apoptosis in OS has been shown to enhance chemosensitivity. Results supporting this observation include that genetically (siRNA) or pharmacologically  inhibiting anti-apoptotic members such as Bcl-2 and Bcl-XL, in various OS cell line  induced sensitivity to CIS and DOX treatment. In contrast, overexpression of pore-forming proteins such as Bax, induced chemosensitivity of the Saos2 OS cell line (L-inhibitor (A-1331852), Mcl-1 inhibitor (S63845) and/or Bcl-2 inhibitor (ABT-199/Venetoclax) were tested on a panel of pediatric cancer cell lines, including U2O2, MG-63 cell lines. Surprisingly, only dual inhibition of Bcl-XL and Mcl-1 displayed a significant higher anti-tumoral effects in all tested pediatric cell lines and in vivo embryonic chicken model, rather than the combination with Bcl-2 inhibitor , since these two proteins control the hepatic integrity in a gene-dose dependent fashion , which are induced by excessive reactive oxygen species (ROS), inducing oxidative stress (via the antiporter system (SLC7A11/SLC3A2), which is then converted into cysteine to produce GSH  and 4-hydroxynonenal (4HNE)] are commonly used to evaluate ferroptosis ] in S models    Jouan-L. Based oγ (IFγ) or lipopolysaccharide (LPS) and its receptors , depending on the composition; complex IIa contains TRADD, RIPK1, FADD, and caspase-8 while complex IIb only contains RIPK1, FADD, and caspase-8 (Necroptosis is initiated by the interaction between a wide range of stimuli such as tumor necrosis factor α (TNFα), interferon eceptors . The moseceptors . The bineceptors   using TRADD as a platform. The ubiquitination of RIPK1 results in the recruitment of the IKK complex and/or TAK complex, promoting the NF-κB survival pathway. The complex II activation requires the deubiquitylation of RIPK1, thus forming the complex IIa  and pro-caspase-8 or the complex IIb . Both complex IIa and IIb can induce apoptosis. Therefore, activation of necroptosis is obtained only when caspase-8 is inhibited or depleted by recruiting RIPK1, RIPK3, and MLKL to form the necrosome. MLKL-mediated necroptosome causes the disruption of plasma membrane and cell lysis.in vitro and in vivo studies  necrosome complex induces mitochondrial dysfunction and thereby necroptosis  Marshal Tian et. The RIP studies   were identified to interact with caspase activation and recruitment domain (PYD/CARD)  Wu et a. Inflammne pores   and bacteria and is independent of the inflammasome pathway. It is associated with the activation of caspase-4/5 nd lysis  , 3) ATP production by OXPHOS and glycolysis, 4) synthesis of certain biosynthetic intermediates  pro-apoptotic MOMP, 2) ROS- or Camediates , openingmediates .in vivo they remain important contributors across a wide variety of cancer types  levels  on the MOM, adenine nucleotide translocator (ANT) on the MIM, Hexokinase II (HKII), glycogen synthase kinase 3 β (GSK3β), cyclophilin D (CypD) and also members of apoptotic Bcl-2 family  is a pathophysiological state of the mitochondrial inner membrane (MIM). It is related to specific permeability conditions, including Canditions  (Baumgarnditions  (Seidlmanditions  (Cromptonditions . The mPT) levels  (Bernard) levels   levels . The funll death . Of note2 family    . Targeti Bcl-XL)  (Li et a Bcl-XL) , p. 2 (S Bcl-XL) , p. 2 (D Bcl-XL) .Immunohistochemical analysis of 30 OS patient derived specimens showed the overexpression of HKII in 83.3% OS specimen, compared with normal bone tissues. This overexpression has been also found in a panel of OS cell lines . Therefore, knock-down of siRNA in MG63 and U2OS led to decrease Warburg effect and remarkably increases apoptotic rates. However, 2-Deoxy-D-Glucose (2-DG)—a glycolysis inhibitor through HKII did not display any significant changes in cell viability. The authors suggested that the current inhibitors such as 2-DG or 3-bromopyruvate are not efficient due to their low specificity. Subsequently, targeting the interaction of HKII and VDAC could be beneficial to reduce Warburg effect observed in OS . Beside Cancer cell lines have been instrumental models to discover tumor proliferation mechanisms, characterize functional profiles, and as models for drug discovery over sever decades . UnfortuRecently, the metabolic comparison between the noncancerous osteoblast cell line (hFOB), parental OS cell lines (Saos2 and HOS), and its metastatic subtypes (LM7 and 143B), has helped deepen our understanding . By examIn contrast, metastatic LM7 and 143B cell line models contain many enlarged rounded mitochondria, showing mitochondrial swelling characteristics. These results were also confirmed in mouse 143B xenografts and diagnosed human OS biopsy specimens without any previous treatments. Therefore, metastatic LM7 and 143B cell lines showed significantly lower OCR and higher ECAR, compared to their non-cancerous and parental counterparts, indicating the presence of Warburg effect in metastatic OS cell lines. Furthermore, the ΔΨm and the mitochondrial membrane integrity were totally reduced, indicating mitochondrial depolarization, compared with these counterparts . As a coMoreover, it has recently been shown that the cell energetic profile measured as the OXPHOS/glycolysis ratio might reflect the stemness of OS cells. de novo serine biosynthesis. Upregulation of PHGDH has been observed in many cancers, including melanoma, breast cancer, leukemia and colorectal cancer , confirmed by the accumulation of unsaturated fatty acid by lipidomic analysis. Most strikingly, this was not sufficiently incorporated in the TCA cycle. The lipidomic analysis demonstrated that the increase of unsaturated fatty acids inside PHGDH-inhibited OS cell line was likely from dysfunctional mitochondria, rather than fatty acid biosynthesis. The exact mechanism, however, needs to be shown. In addition, inhibition of PHGDH also resulted in an increase of branched-chain amino acid (BCAA) metabolite levels  that has recently been identified as a potential target for OS treatment . PHGDH il cancer   treated-HOS (MNNG/HOS) line and the metastatic HOS-derived 143B line . Malignaterparts . High leoduction . Under goduction .in vitro as well as in an orthotopic xenograft model, by combining the GLS inhibitor with metformin, a mitochondrial Complex I (CI) inhibitor  inhibitor, only a partial reduction of cell growth was observed, suggesting the capacity of these cells to switch the primary nutrient source. Increased, growth inhibition was observed nhibitor . The mecIn addition to the analysis of tumor tissue, IDH1 genetic alterations, including OS (NCT04195555)   2 and Ena2813135)  (Gatz et2813135) . Despitein vivo condition. In conclusion, further investigation of lipidomic profiling into in vivo primary and metastatic OS models could confirm their observations and enable targeting lipid synthesis in OS treatment  and OCT-1  and increased expression of mesenchymal markers (N-cadherin and vimentin)  Stoker . Beyond ll lines   which is consistent with many different studies in other cancers , p. 2 C M. Liu , p. 2. DD1 genes  . The study is divided into two groups, the first group of poor responders for post neoadjuvant chemotherapy and the second group of patients (including OS patients and Ewing sarcoma patients) in complete remission after the first relapse to clinically evaluate the event-free survival and toxicity of metformin .via the increase of PTEN, resulting in inhibiting cell survival PI3K/Akt pathway. Metformin targets the complex I of mtETC and alters mitochondrial activity. Thus, an over-production of ROS was found to promote both apoptosis and protective autophagy via the JNK/c-Jun pathway. Cell cycle arrest, characterized by the cell accumulation in the G2/M phase, was found in metformin-treated OS cell lines by elevating Cyclin D1 protein expression.Metformin resensitizes CIS-resistant OS stem cells to CIS and promotes apoptosis by increasing levels of cleaved caspase-3 and caspase-9; it induces reduced glycolytic flux as indicated by decreased glucose uptake, lactate, and ATP production; it prevents the EMT-mediated metastasis by the loss of mesenchymal marker vimentin and the gain of epithelial marker E-cadherin via two reactions, catalyzed by argininosuccinate (ASS) for the conversion from citrulline + aspartate to argininosuccinate and argininosuccinase (ASL) for the conversion from argininosuccinate to arginine + fumarate , citrate and lactate were significantly decreased, indicating the inhibition of the Warburg effect and cellular glucose dependence in ASS1-deficient cell lines. Moreover, arginine starvation also caused the increase of extracellular glutamine uptake with higher expression of glutaminolysis-related actors such as GLS and glutamate dehydrogenase. This process determines the metabolic switch from aerobic glycolysis to glutamine metabolism to fuel OXPHOS activity through the TCA cycle in ADI-PEG20 treated cell lines. This metabolic vulnerability was then explored using the combination of ADI-PEG20 and GLS inhibitor bis-2-ethyl sulfide (BPTES), resulting in better reduction in a broad range of ASS1-deficient cell lines, including OS cell line.In ASS1- deficient breast cancer, arginine starvation by ADI-PEG 20 displayed the dysregulated mitochondrial bioenergetics by decreasing 50% of total ATP levels and inhibiting oxygen consumption rate in MDA-MB-231 cells. Transcriptomic analysis comparison between treated and un-treated MDA-MB-231 cell lines showed several mitochondrial candidates such as members of complex I, complex II, complex III, complex IV and complex V of mtETC, thus confirming the effect of ADI-PEG20 on mitochondrial respiratory function. Moreover, ADI-PEG20 treatment results in ROS over-production, decrease of mitochondrial membrane potential, mitochondrial fragmentation in ASS1-deficient MDA-MB-231 cells. Furthermore, in this breast cancer model, ASS1 gene due to metabolic adaptation, resulting in the resistance mechanism of ASS1-deficient cancer cells   Figure . Of noteMetabolic reprogramming is considered a central hallmark in cancer. It allows not only utilization of various energy sources depending on their availability in the tumor microenvironment, but most importantly fuel biomass synthesis. In line with the increased attention to this field across disciplines, we reflect in this summary on the current knowledge of cell death and metabolic reprogramming at different OS stages.in vitro and in vivo models. We summarize the evidence that supports the interconnectivity of different mitochondria-related RCD pathways, with a focus on fundamental notions of apoptosis, ferroptosis, necroptosis, and autophagy and their involvement in cell fate, progression, and metastasis. The development of RCD inducers has recently emphasized the importance of a balance between apoptosis and autophagy, which is crucial for cell fate and drug efficacy. Autophagy also plays an essential role in the induction of ferroptosis and necroptosis by regulating metabolic pathways and ROS generation. Furthermore, necroptosis and extrinsic apoptosis share many common actors within the signal transduction pathways. Therefore, the crosstalk between apoptosis, ferroptosis, necroptosis, pyroptosis and autophagy creates an orchestra of RCD, emphasizing many new targets that could become useful suggestions for the development of OS therapeutic strategies in the future.In this review, we discuss how mitochondria-related RCD and metabolism directly impact the proliferation of benign, malignant, and metastatic OS Many RCD and metabolism inducers or inhibitors are actively under investigation and have recently opened new areas for potential mono- or combinatorial OS therapies. However, at this stage undoubtedly further studies are needed to build fundamental knowledge of mitochondria-related RCDs and metabolic adaptation for novel OS treatments. Because of the high incidence of OS metastasis, relapse, and drug resistance, novel pharmacological approaches are urgently needed in this patient category. Currently, metformin and the combination of ADI-PEG20, Docetaxel, and Gemcitabine are in active clinical studies for OS treatment. This will thus provide a first benchmark of translational relevance of these findings and hopefully open the door to further novel treatment strategies for OS.The question of how OS cells can precisely modulate cell death machineries and metabolism remains to be fully elucidated. Notably, comparing OS mitochondrial metabolism with that of other cancer types remains a future challenge and could be instrumental to develop novel OS therapeutic approaches in the near future.


C. trifoliatreated A2780 cell lines for further consideration as a chemotherapeutic agents.It is of interest to report the chemotherapeutic (drug target based) potential of n-hexane Cayratia trifolia L. (C.trifolia) extract on A2780 cell lines. mRNA and protein expression analysis of the human chemokine receptor (CXCR4) and human epidermal growthfactor receptors-2 (HER2) were studied using RT-PCR analysis and western blot analysis. The results show significant cell growth inhibition with minimal IC50 values of 46.25 ± 0.42 micro g/mL against A2780 cell lines. mRNA and protein expression wereconsiderably reduced in  The frequent use of synthetic drugs cause numerous side effects with drug resistance . NaturalWe have shown the antioxidant, antimicrobial and cytotoxicity potential of n-hexane extract of Cayratia trifolia L . TherefoThe whole plant of C. trifolia was collected from in the campus of Annai College of Arts and Science, Kovilacheri, Kumbakonam, Thanjavur District, Tamil Nadu, India and the plant was authenticated by Dr. P. Sathyanarayanan, Botanical survey of India, TNAUCampus, Coimbatore (voucher number is BSI/SRC/5/23/2010-2011/Tech.1527). The plant material was shade dried, powdered and stored in air tight container at 4°C for future analysis [The dried plant material was subjected to n-hexane extraction using exhaustive extraction procedure . BrieflyThe cytotoxicity assay was determined by MTT assay . BrieflyTotal RNA isolation, cDNA conversion and real-time PCR:mRNA expression levels of CXCR 4; HER2 were examined using real-time PCR. The total RNA was isolated by using using a TRIR kit and estimated spectrometrically by the method of Laneve et al. (2014) .The RNAAfter the 24 h treatment period the cells were lysed in RIPA buffer containing 1X protease inhibitor cocktail, and protein concentrations were determined by Lowry's method . Cell lyThe obtained results from the assays were showed as mean ± SD. The Statistical evaluations were measured through statistical package program .The in vitro cytotoxicity activity of n-hexane extract of C.trifolia was investigated using different concentrations ranging from 3.12 to 200 µg/mL against A2780 ovarian cancer cell lines. n-hexane extract showed 86% of cell growth inhibition at thehighest concentration of 200 µg/mL, where as cyclophosphamide showed 88% ie, significant cell growth inhibitory activity  by n-hexane extract  was obseA part of the chemokine superfamily, chemokine receptor 4 (CXCR4) is a particular stromal cell factor-1  receptor, which is a strongly conserved G protein-coupled 7-transmembrane receptor. The only chemokine receptor expressed in about 80% ofovarian cancer tissues, although not in the usual ovarian epithelium, is CXCR4, rendering it a potential candidate for targeted ovarian cancer therapy . HER2 waThe n-hexane extract of C.trifolia significantly reduced CXCR4 and HER2 mRNA and protein expression A2780 cell lines. However, role of C.trifolia n-hexane extract on further downstream signalling molecules need to be studied to validate the data.


Cancer stem cells play a crucial role in tumor initiation, metastasis, and resistance to treatment. Cellular heterogeneity and plasticity complicate the isolation of cancer stem cells. The impact of intra-tumor cellular heterogeneity using a label-free approach remains understudied in the context of treatment resistance. Here, we use the sedimentation field–flow fractionation technique to separate, without labeling, cell subpopulations of colorectal cancer cell lines and primary cultures according to their biophysical properties. One of the three sorted cell subpopulations exhibits characteristics of cancer stem cells, including high tumorigenicity in vivo and a higher frequency of tumor-initiating cells compared to the other subpopulations. Due to its chemoresistance, two- and three-dimensional in vitro chemosensitivity assays highlight the therapeutic relevance of this cancer stem cell subpopulation. Thus, our results reveal the major implication of intra-tumor cellular heterogeneity, including cancer stem cells in treatment resistance, thanks to our label-free cell sorting approach. This approach enables—by breaking down the tumor—the study the individualized response of each sorted tumor cell subpopulation and to identify chemoresistance, thus offering new perspectives for personalized therapy. Despite therapeutic advances, colorectal cancer (CRC) remains a major cause of mortality worldwide . Early dIn previous work, our laboratory used SdFFF as a tool to isolate cell subpopulations independently of surface marker expression from CRC cell lines . Results2 in MEM medium  for WiDr and RPMI GlutaMAX medium  for SW480, SW620, and T84 cell lines, both supplemented with 10% fetal bovine serum (FBS), 1 mM sodium pyruvate , 100 IU/mL penicillin and 100 mg/mL streptomycin , and only for WiDr 1% non-essential amino acids  . T,9. T2,9]Using SdFFF, we were able to study the individualized response of each tumor cell subpopulation to therapies. Our results showed that F3 was the most resistant of the cell subpopulations sorted to 5-FU for all cell lines . SimilarOur chemosensitivity experiments were performed in a two-dimensional (2D) culture model. We are aware that tumor organization is more complex and may influence the response to treatments. Thus, to overcome the limitations of this model, we further investigated the response to therapies using a three-dimensional (3D) culture model.We then examined the impact of 5-FU-based chemotherapies on the colonospheres from the SdFFF-sorted cell subpopulations. To this end, we performed the sphere formation assay, a 3D cell culture model mimicking tumor organization in vitro. This test relies on the ability of cancer cells to form colonospheres—i.e., microtumor-like spheroids—from a single progenitor cell and is used to assess the tumorigenic potential of solid tumors in vitro . Thus, wOverall, these results confirmed those obtained in 2D. The F3 subpopulation was predominantly the most resistant to monochemotherapy and chemotherapy combinations among the sorted cell subpopulations. As observed in 2D, the results in 3D indicate that F3 cells resist by inducing proliferation after treatment, revealed by an increased ability to form colonospheres and a larger size. Thus, in our study, we identified two therapeutically relevant cell subpopulations: the F1 subpopulation appears to be chemosensitive while the F3 is chemoresistant, from both early and advanced CRC cell lines. Our findings demonstrated that our SdFFF-based cell sorting approach is capable of revealing chemoresistance in a cell subpopulation. Nevertheless, we continued our analysis to test our approach on primary cultures that are more representative of tumor heterogeneity and complexity. To confirm the promising results obtained from the cell lines, we performed the same experiments using CRC primary cultures (CPP). CPP14 was established from an early-stage CRC while CPP35 was derived from a tumor that had invaded the peritoneum—i.e., at a more advanced stage—with clinical data summarized in Next, we evaluated the response of these sorted cell subpopulations to chemotherapies in 2D and 3D cell culture models for each primary culture. In 2D, we analyzed cell viability after 5-FU treatment, as well as induced proliferation and apoptosis A–C. No sFinally, we investigated the response of SdFFF-sorted cell subpopulations in a 3D culture model to 5-FU alone or in combination with other chemotherapies. In the absence of treatment, both primary cultures had the ability to form colonospheres D. QuantiTherefore, our study highlighted the relevance of our label-free approach to study the individualized response of each tumor cell subpopulation to chemotherapies from cell lines and primary cultures in order to identify chemoresistances.Our study addressed the impact of intratumoral cellular heterogeneity in the context of treatment resistance, without relying on surface marker expression to sort cell subpopulations. Regardless of cell line and stage, the SdFFF technique allowed isolation of cell subpopulations with distinct phenotypical and functional characteristics. Compared to previous results , we show+ CSCs limits tumor growth but does not prevent tumor recurrence due to re-emergence of LGR5+ CSCs from proliferating LGR5- cells [- cells in CRC, with subsequent re-emergence of LGR5+ CSCs at the metastatic site [- non-CSCs are able to transform into LGR5-expressing CSCs, highlighting the phenotypic plasticity of these cells that appears to be crucial for primary tumor growth and metastasis. These results highlight the complexity of identifying as well as isolating these CSCs due to their shared expression with non-CSCs and their cellular plasticity. All of these data converge on evidence that stemness is not as hierarchical and fixed as originally thought, but rather dynamic and endowed with considerable plasticity. It is this polydispersity of CSCs that justified the SdFFF label-free cell sorting approach, offering new perspectives to isolate CSC-enriched subpopulations.Initially identified in acute myeloid leukemia, CSCs were later discovered in many solid cancers such as CRC based on the expression of surface markers . In CRC,5- cells ,34. Intrtic site . Thus, t3, approximately 46 days for F1 versus 37 days for F3. Interestingly, for TP, the threshold volume is reached very slightly before the F3 subpopulation, which demonstrates the importance of cellular heterogeneity in tumor development with the requirement of CSCs for tumor initiation, as well as the presence of non-CSCs to enhance tumor growth.A consensus has emerged suggesting that their functional capabilities, particularly their tumorigenic potential and chemoresistance, are more reliable for identifying CSCs than surface markers . Our resCSCs are of particularly therapeutic interest. Although chemotherapies kill most tumor cells, CSCs are able to escape the lethal effect of these drugs, which can lead to tumor recurrence. One of the main reasons for treatment failure is that anticancer drugs often only target actively cycling tumor cells and therefore do not affect CSCs, which are frequently in a quiescent or minimally proliferative state. The results of our study support this hypothesis: the cell subpopulation enriched in CSC features was quiescent/low proliferative and chemoresistant, while the subpopulation of actively cycling cells was more sensitive. Similar results have been reported in Kreso et al. . RemarkaTherapies targeting CSCs are a promising therapeutic approach. However, the development of anticancer agents capable of specifically targeting CSCs has proven very difficult or has shown limited efficacy . These dIn summary, we have demonstrated the relevance of using SdFFF to obtain a chemoresistance signature. Thus, the CSC-enriched subpopulation will allow to specifically explore the signaling pathways associated with therapeutic resistance. Since this technique provides a model reflecting intratumoral cellular heterogeneity, it will be valued in the study of the response to therapies of each cell subpopulation of a biological sample. Our study thus opens new perspectives for the understanding of CSC-related resistance and offer solutions specifically adapted to a more personalized and targeted therapy of colorectal cancer.


Human and animal cell lines serve as model systems in a wide range of life sciences such as cancer and infection research or drug screening. Reproducible data are highly dependent on authenticated, contaminant-free cell lines, no better delivered than by the official and certified biorepositories. Offering a web portal to high-throughput information on these model systems will facilitate working with and comparing to these references by data otherwise dispersed at different sources.celldive.dsmz.de. A wide variety of data sources are generated such as RNA-seq transcriptome data and STR (short tandem repeats) profiles. Several starting points ease entering the database via browsing, searching or visualising. This web tool is designed for further expansion on meta and high-throughput data to be generated in future. Explicated examples for the power of this novel tool include analysis of B-cell differentiation markers, homeo-oncogene expression, and measurement of genomic loss of heterozygosities by an enlarged STR panel of 17 loci. We here provide DSMZCellDive to access a comprehensive data source on human and animal cell lines, freely available at Sharing the data on cell lines by the biorepository itself will be of benefit to the scientific community  since it (1) supports the selection of appropriate model cell lines, (2) ensures reliability, (3) avoids misleading data, (4) saves on additional experimentals, and (5) serves as reference for genomic and gene expression data. For more than 70 years, cell lines have become indispensable for life sciences, especially for biomedical research. Human and animal cell lines represent cost-effective model systems of almost unlimited availability. Furthermore, they are relatively easy to manipulate. The vast majority of cell lines are derived from spontaneously immortalised tumour cells carrying specific characteristics. They have become essential models not only for cancer research such as investigating mechanisms of tumorigenesis, targets for therapy, or drug efficacies but also for other areas of biological sciences. Cell lines are made available to the scientific community by cell lines banks, who also control and guarantee for cell line authenticity. In general both, diagnosis and clinical parameters of the donor as well as molecular characteristics of the cell line itself, are required for the selection of an appropriate model cell line for a specific research question.. However, so far the mentioned sources contain only limited information about genetic or transcriptional aberrations characterising a cell line. Today, molecular characteristics obtained from high-throughput sequencing data from cell lines become more and more relevant for the selection of appropriate models. In this context, the cell lines project database ofCOSMIC  provides mutation profiles and copy number variations of over 1,000 cancer cell lines. Further publicly accessible datasets for download are multiple high-throughput sequencing data from the CCLE (Cancer Cell Lines Encyclopedia) panelvia theDepMap Portal which also offers interactive data visualisation. In order to support the search for appropriate cell line models Jeonget al. developed the online databaseGEMiCCL which enables the comparison of genomic, transcriptomic and copy number data generated in different projects, including CCLE, COSMIC and the NCI-60 cell lines panel. Importantly, the comparison of the data from different sources points out thate.g. mutation data can strongly vary for a cell line depending on the data source. This observation is not surprising, as selective culture conditions can foster evolution of cell lines, impacting the genetic and transcriptional diversity of the same cell line between two laboratories. Thus, the major drawbacks during the selection process for an appropriate model cell line are either that the molecular data cannot easily be traced back to the source of the cell line and culture conditions used for the generation of the data, and that bioinformatics skills are required to re-analyse publicly available omics data after downloading.Usually, the required information about cell lines has to be gathered from different sources, often including the study of literature. In addition to the information provided in the cell line data sheets of the biorepositories themselves, several online databases independent of the biorepositories can be consulted. The largest available online platform listing key information and references from continuous cell lines is CellosaurusDSMZCellDive, a novel web portal that offers access to evaluated RNA-seq data and STR profiles generated from material of human and animal cell lines that are provided to the scientific community via theDSMZ catalogue.So far, most molecular data including high-throughput sequencing data are available from different sources but not from the cell line repository itself. This fact ultimately confronts the selection of cell lines with the question from which resource the cell line with the appropriate molecular characteristics is actually available. To overcome this limitation we developedAll data were provided by internal sources at the DSMZ, extracted from various formats using tailored scripts, and finally transferred into an SQL database. The data were integrated by using cell culture identifiers, such as the name of the cell line and DSMZ ACC-No. The SQL database is based on MariaDB Version 10.3. PHP 7 is used to generate the web pages and to query an internal SQL database. The JavaScript libraries jQuery and Plotly.js are used to do asynchronous server requests and draw charts, respectively. Heat maps are created using the R library heatmaply. The PHP library Parsedown is used to display the COI barcoding report.schema.org) to do basic queries. The Bioschemas initiative (bioschemas.org) aims to extend these vocabularies for life sciences. We have integrated Bioschemas profiles and added a machine-readable markup for all cell lines. The Bioschemas representations enhance interoperability and standardisation of DSMZCellDive.The importance of machine-readability is increasing steadily. So far, web search engines use a standardised vocabulary  information, STR (short tandem repeat) profiles, and COI (cytochrome c oxidase I) DNA barcodes are bundled at start as listed in. In contrast to the cited paper, the non-malignant cell line NC-NC, a B lymphoblastoid cell line, is included and RNA-seq data were quantifiedvia Salmon and normalisedvia DESeq2 in order to keep pace with state-of-the-art data analysis.The vast majority of data entries in DSMZCellDive is composed of RNA-seq data. We started with our published data on 100 human leukemia and lymphoma cell lines (LL-100 panel). Although the HLA gene cluster on chromosome 6 is highly polymorphic, it is not suitable for cell line authentication due to a low exclusion rate and instability of gene expression. Furthermore, HLA typing is important for cancer research since determination of tissue compatibility by tumour neoantigen binding to HLA surface proteins and rejection of specific HLA alleles play a role. Here, HLA typing was determined on LL-100 RNA-seq datavia arcasHLA, an alignment-based tool.HLA genes encode proteins in the major histocompatibility complex (MHC) which play a central role in discriminating self and non-selfvia Amelogenin. STR typing is serving as a reference technique for identity control of human cell lines at biological resource centers and available as global standard (ANSI/ATCC ASN-0002-2021 (2021). Authentication Of Human Cell Lines: Standardization Of Short Tandem Repeat (STR) Profiling. ANSI eStandards Store). Data sources were kindly provided by ATCC, JCRB, and amended by DSMZ.The applied genotyping system (Promega Powerplex 18D) uses STR microsatellite repeats which are located at 17 specific genomic loci that are highly polymorphic in human populations including gender determinationDNA barcoding for animal samples is frequently based on the Cytochrome c oxidase subunit 1 (COI or COX1) DNA sequence known to exert specific differences between species - prerequisite for species identification. DSMZCellDive harbours COI barcodes for all animal cell lines available at the DSMZ.celllines tablevia one-to-many connections to its primary keycell_id. Since one cell line can have multiple STR profiles, a meta table is used to connect profiles to cell lines. This should not be confused with the fact that one cell line may carry multiple STR alleles per STR loci, a phenomenon called microsatellite instability (MSI). The whole COI barcoding DNA sequence as well as the report is saved as markdown in a single text field as currently no search or comparison operations are planned on these data, since species-specificity only not genetic individuality is resolved by this technique.All data were extracted from various data formats and integrated into a relational SQL database. The database structure contains seven tables in total and can be extended with more data types easily . All datThe DSMZCellDive website was designed to be easily extensible by more data types. For this reason, the web layout contains a sidebar instead of a classical navbar, as it allows more content. The header contains breadcrumbs to meet the requirements of the hierarchical structure of the page. The starting page gives an overview on all available data types and provides links to all tables and tools. Each data source has its own overview page, describing the data, available literature and providing further links.Each RNA-seq data set has its own entry page and supports visualisation as bar chart or heat map. An interactive web interface enables the user to enter genes (up to five for bar charts and up to 20 for heat maps), select tumour entities or cell lines directly, and choose whether normalised data, raw counts, or TPM (transcripts per kilobase million) values should be used. Currently, only RNA-seq data from the LL-100 panel are available; however, it is planned to add more data sets in the near future. For this reason, an integrated view on all RNA-seq data from the different projects was added early on. Since data are normalised within each project, normalised values between projects should not be compared, only TPM data and bar charts are available here.browser and asearch tool. While the browser allows access to unique comprehensive STR data sets of DSMZ and other major cell lines banks, the search tool allows users to compare own STR profiles for similarity matching with regard to authenticity. As a result of the search closest matches according to the following equation are displayed and the ratio to the reference is given in percent distance as described previously.The STR data sets come with aThe result page offers further information on the cell lines and is cross-linked to the respective cell line detail page, which also enables access to the cell line data sheet of the DSMZ catalogue.i.e. the species, cell type, morphology, and culture media. Depending on whether it is a human or animal cell line, STR profiles or COI DNA barcode reports are displayed, respectively. If the cell line belongs to any RNA-seq project, a histogram of all genes is shown, as well as HLA typing data.All data are integrated on cell line level. Each of the currently almost 900 cell lines has its own detail page, including meta data on the cell line,.CD1A, CD19, CD24, CD27, CD34, CD38, CD40, CD79A, CD79B, CD80, CR2, CXCR4, IL2RA, MME, MS4A1, SDC1, SLAMF7, andTNFRSF8) across the B-cell derived cell lines included in the LL-100 panel. Intriguingly, specific B-cell markers get lost in the tumour cells. For example, although CD19 is usually expressed in all B-cells from pre-B-cells until the terminal differentiation to plasma cells, its expression is typically absent in Hodgkin lymphoma (HL), plasma cell leukemia (PCL), primary effusion lymphoma (PEL) and in a fraction of Diffuse large B-cell lymphoma (DLBCL). Accordingly,CD19 loss is also seen in cell lines representing HL, PCL, and PEL  belongs to the ANTP class (according to theDrosophila antennapedia gene) and consists of 48 members in humans. Their physiological expression pattern in the hematopoietic compartment has been termed “NKL-code“ comprising eleven genes. This code is a useful tool to evaluate deregulated NKL homeobox genes in myeloid and lymphoid leukemia/lymphoma patients. To date, 24 aberrantly activated NKL homeobox genes are described in T-cell acute lymphoblastic leukemia (T-ALL), representing the strongest group of oncogenes in this malignancy. TLX1 (formerly HOX11) and TLX3 (HOX11L2) are the most frequently deregulated NKL homeobox genes in T-ALL while NKX2-5 is only rarely expressed.According to their conserved homeobox sequences, these genes are arranged in eleven classes and several subclasses.TLX3 andNKX2-5 as barplots. Both genes are expressed in particular T-ALL cell lines while silenced in cell lines derived from other leukemia/lymphoma entities. NKL homeobox gene VENTX is physiologically expressed in lymphoid T-cell progenitors and myeloid conventional dendritic cells (cDC). MUTZ-3 is an AML (acute myeloid leukemia) cell line derived from a cDC progenitor. This cell line expresses VENTX and represents a useful model to investigate differentiation of dendritic cells and their derived malignancies.VENTX, TLX3 andNKX2-5 in selected cell lines as heat map, demonstrating strikingly highVENTX expression in MUTZ-3. Thus, DSMZcellDive is a useful tool to identify and illustrate normal and aberrant (homeobox) gene expression in leukemia/lymphoma cell lines.Cell lines are useful models to investigate regulation and function of oncogenes including homeobox genes. To identify a leukemia/lymphoma cell line expressing a particular homeo-oncogene, our published LL-100 dataset and the here presented online tool DSMZCellDive may assist to find a suitable cell line modelin vitro evolution of tumour cell lines is well known, the underlying genomic alterations often remain obscure. Despite elaborate quality control via STR genotyping of lot charges, crucial genetic changes of a tumour model may remain hidden to the applying scientist.Althoughet al. describes a large genetic divergence between THP-1 cells from a European and a US biorepository. Although globally standardised STR genotyping in biorepositories is primarily used to prevent the spread of misidentified cell lines, STR typing can distinguish subclones from parental cell lines when minor changes in the STR profile have occurred due to MSI or LOH  can be demonstrated in one of the most commonly used models for AML research, the cytokine-dependent cell line THP-1. A recent publication by Noronha or LOH . Specifictively , indicatin vitro data towards trusted scientific conclusions.Thus, DSMZCellDive enables scientists to verify the authenticity of cell lines by providing an extended STR-17 panel, which measures the degree of correspondence between the original cell line and already slightly modified cell lines. Since all commonly measured STR loci for authentication are combined in the STR-17 panel, this is independent of the STR typing kits used. By presenting the diploid STR datasets in two columns, LOH and UPD events can be deduced, which in turn immediately shows users how often and at what point their own STR data has deviated from the STR reference profile. The described tools will increase reliability ofe.g. to support the selection of appropriate model cell lines, while cell lines are acquired. Furthermore, its concept allows implementation of future data readily and, more importantly, provides reliable data of cell lines available at DSMZ, which can serve as reference data for industry and science.With the interactive web portal DSMZCellDive at hand, access to cell lines omics and meta data is bundled at one site. DSMZCellDive presents its own RNA-seq data exclusively from cell lines of the DSMZ. The novel web portal allows researchers to browse and visualize authentic NGS data generated according to high quality standardsAll data underlying the results are available as part of DSMZCellDive and no additional source data are required.https://celldive.dsmz.deWebsite:Source code at github to download and process: https://github.com/JKoblitz/DSMZCellDiveWebsite:https://github.com/claupomm/RNA-seq_ll100 RNA-seq:https://github.com/claupomm/HLA-analysisHLA analysis:Zenodo for archived source code at time of publication:https://doi.org/10.5281/zenodo.6404422Website:https://doi.org/10.5281/zenodo.6401600RNA-seq pipeline:https://doi.org/10.5281/zenodo.6401594HLA analysis:MIT LicenseLicense: I confirmed that I had read this submission and found that the author had properly modified the manuscript.Are the conclusions about the tool and its performance adequately supported by the findings presented in the article?YesIs the rationale for developing the new software tool clearly explained?YesIs the description of the software tool technically sound?YesAre sufficient details of the code, methods and analysis (if applicable) provided to allow replication of the software development and its use by others?YesIs sufficient information provided to allow interpretation of the expected output datasets and any results generated using the tool?YesReviewer Expertise:cell biology, genetic mutationI confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. In this manuscript, the authors are concerned with the creation of a new web portal that creates a database of cell metadata and characteristic analysis information in cell banks and biorepositories, and will be able to provide useful information for researchers using cell lines. This manuscript can be highly evaluated as an initiative. We also hope that information such as RNA-seq will be expanded in the future, and that fusion gene search based on sequence information and data sharing with other banks will also be expected. However, I think it will be a better manuscript if you reconsider the following points, so please consider modifying it.MajorIn the RNA-seq heat map analysis, the clustering results performed based on the similarity of the expression profile of each gene are displayed, but the clustering result based on the similarity of the expression profile of each cell line should also be displayed.MinorThe legend in Fig. 4B is not appropriate. There are no cell lines highlighted in yellow, while there is no indication of the color of cells highlighted in red.Although the data in Fig. 4B shows only 9 loci data as an example of LOH, it is considered that 17 loci data is collected in this database, so please describe the verification using all the data.Also, why not explain the example of MSI-H using MOLT-4 etc.?Are the conclusions about the tool and its performance adequately supported by the findings presented in the article?YesIs the rationale for developing the new software tool clearly explained?YesIs the description of the software tool technically sound?YesAre sufficient details of the code, methods and analysis (if applicable) provided to allow replication of the software development and its use by others?YesIs sufficient information provided to allow interpretation of the expected output datasets and any results generated using the tool?YesReviewer Expertise:cell biology, genetic mutationI confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. We thank the reviewer for their valuable comments. Please find our responses here:Major "In the RNA-seq heat map analysis, the clustering results performed based on the similarity of the expression profile of each gene are displayed, but the clustering result based on the similarity of the expression profile of each cell line should also be displayed."Thank you for your note on the heat map display. Clustering by cell line has been offered as option in the past, albeit by cell line solely without gene clustering. We now enabled clustering by cell line line and gene simultaneously (option “both”).  Minor"The legend in Fig. 4B is not appropriate. There are no cell lines highlighted in yellow, while there is no indication of the color of cells highlighted in red."Many thanks to the reviewer for the hint. We have now displayed the colors correctly in Fig. 4B and described the cell lines highlighted in red as genetically unbiased.  "Although the data in Fig. 4B shows only 9 loci data as an example of LOH, it is considered that 17 loci data is collected in this database, so please describe the verification using all the data."Figure 4B has been redone so that all STR locations are now captured and the matches across STR 17 are completely shown.  "Also, why not explain the example of MSI-H using MOLT-4 etc.?"An explanation of the MSI phenomenon based on MOLT-4 would unfortunately go beyond the scope of this paper. For a satisfactory explanation, the failure of DNA MMR and the origin of DNA replication errors would have to be presented, so that the scope in this manuscript would be exceeded. In addition, it would need to be discussed much more deeply that the rule established by Amanda Capes-Davies for relatedness of cell lines when there is at least 80% similarity of the STR profile does not apply to MSI cell lines : 2510-2519). Finally, MSI lines need additional tests as described in the STR standard of 2021 (ANSI/ATCC ASN-0002-2021 (2021) Authentication Of human cell lines: standardization of Short Tandem Repeat (STR) profiling. ANSI eStandards Store).   In the submitted article of Koblitz et al., DSMZCellDive, a comprehensive data source for human and animal cells, will be presented, one aim is to provide high-throughput information through a web portal that aggregates data and references that are otherwise spread across multiple sources.  A variety of data sources are generated, such as RNA-seq transcriptome data and STR (Short Tandem Repeats) profiles, which are essential for working with cell lines.  The new tool is explained and described in detail, e.g. how to access the database via browsing, searching or visualization. The database can be extended with meta- and high-throughput data that will be generated in the future to finally allows a comprehensive summary and detailed characterizations of a cell line. To illustrate the power of the tool, analysis of B-cell differentiation markers, homeo-oncogenes, homeo-oncogene expression, and measurement of genomic loss of heterozygotes through an expanded STR panel of 17 loci were described.  DSMZCellDive offers enormous added value to the scientific community by supporting the selection of suitable model cell lines and thus ensuring the reliability and reproducibility of experiments.  Optimal in vitro conditions also save resources in the long term. The comprehensive data acquisition and provision of the tool leads to a massive upgrading of in vitro models, which enables the reduction of animal experiments and thus sustainably implements the 3R principles in research.Are the conclusions about the tool and its performance adequately supported by the findings presented in the article?YesIs the rationale for developing the new software tool clearly explained?YesIs the description of the software tool technically sound?YesAre sufficient details of the code, methods and analysis (if applicable) provided to allow replication of the software development and its use by others?YesIs sufficient information provided to allow interpretation of the expected output datasets and any results generated using the tool?YesReviewer Expertise:Cell line establishment, Cell line characterization, cell line quality checkI confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. We thank the reviewer for the evaluation of our work and appreciate the positive comments.


Daphne genkwa plant, is known to have pharmacological properties; however, its modulatory effect on multidrug resistance, which is (MDR) mediated by ATP-binding cassette (ABC) drug transporters, has not been investigated. In this study, we examine the interaction between hydroxygenkwanin, ABCB1, and ABCG2, which are two of the most well-characterized ABC transporters known to contribute to clinical MDR in cancer patients. Hydroxygenkwanin is not an efflux substrate of either ABCB1 or ABCG2. We discovered that, in a concentration-dependent manner, hydroxygenkwanin significantly reverses ABCG2-mediated resistance to multiple cytotoxic anticancer drugs in ABCG2-overexpressing multidrug-resistant cancer cells. Although it inhibited the drug transport function of ABCG2, it had no significant effect on the protein expression of this transporter in cancer cells. Experimental data showing that hydroxygenkwanin stimulates the ATPase activity of ABCG2, and in silico docking analysis of hydroxygenkwanin binding to the inward-open conformation of human ABCG2, further indicate that hydroxygenkwanin sensitizes ABCG2-overexpressing cancer cells by binding to the substrate-binding pocket of ABCG2 and attenuating the transport function of ABCG2. This study demonstrates the potential use of hydroxygenkwanin as an effective inhibitor of ABCG2 in drug combination therapy trials for patients with tumors expressing higher levels of ABCG2.Hydroxygenkwanin, a flavonoid isolated from the leaves of the  ATP-binding cassette (ABC) transporters are a superfamily of proteins that utilize the chemical energy derived from ATP hydrolysis to translocate compounds, including many of the most commonly prescribed anticancer drugs, across biological membranes ,2. Two oDaphne genkwa Sieb.et Zucc plant, which is commonly known as blue daphne or lilac daphne, and it is native to East Asia [The potential application of inherently well-tolerated, plant-derived, bioactive compounds for the sensitization of multidrug-resistant cancer cells has been explored as an alternative approach ,25. Numeast Asia . It has ast Asia ,35, antiast Asia , and it ast Asia . In addiast Asia ,43,44,45In this study, we investigated the potential reversal effect of hydroxygenkwanin on MDR, mediated by ABCB1 and ABCG2. We discovered that at low, sub-toxic concentrations, hydroxygenkwanin could selectively re-sensitize ABCG2-overexpressing multidrug-resistant cancer cells to cytotoxic anticancer drugs. Moreover, we demonstrated that hydroxygenkwanin reverses ABCG2-mediated MDR by modulating the drug efflux function of ABCG2, without altering its expression at the protein level in these cancer cells. These findings were further supported by data from ATPase assays and molecular docking analysis. Overall, our study demonstrates the potential of using hydroxygenkwanin as a chemotherapy adjuvant to improve the efficacy of anticancer drugs.50 values of hydroxygenkwanin  and a half-maximal effective concentration value (EC50) of approximately 19 nM. Our data indicate that hydroxygenkwanin interacts with, and has a high affinity with, ABCG2, which is in accordance with previous reports that have demonstrated other chemosensitizing agents binding to the drug-binding pocket of ABCG2 and stimulating ABCG2-mediated ATP hydrolysis [Next, the effect of hydroxygenkwanin on the Vdrolysis ,63,64,65drolysis ,54,66.We found that hydroxygenkwanin attenuates ABCG2-mediated drug efflux, as shown after an in silico molecular docking analysis of hydroxygenkwanin in the inward-open structure of human ABCG2 (PDB:6VXH)  was perfDaphne genkwa Sieb.et Zucc, a well-known traditional Chinese medicinal plant [The ability of ABCG2 to transport a wide range of conventional anticancer drugs and molecularly targeted agents contributes to poor clinical outcomes in cancer patients . Regrettal plant . Previoual plant , oral sqal plant , and glial plant . Moreoveal plant . Recentlal plant  and by ial plant . In thisFirst, the intrinsic cytotoxicity of hydroxygenkwanin was determined in drug-sensitive parental cell lines and multidrug-resistant sublines overexpressing ABCB1 or ABCG2. We found that hydroxygenkwanin is equally cytotoxic to both parental and multidrug-resistant cell lines, thus suggesting that it is not rapidly transported out of cancer cells by either ABCB1 or ABCG2 . More imIn summary, this study demonstrates that hydroxygenkwanin is a potent and selective modulator for ABCG2. Collectively, our findings indicate that further study is needed to evaluate its potential use as a chemotherapy adjuvant to improve the efficacy of anticancer drugs in patients with tumors expressing relatively high levels of ABCG2.Hydroxygenkwanin was purchased from Selleckchem . The TOOLS Cell Counting (CCK-8) kit was acquired from Biotools Co., Ltd. . Paclitaxel, vincristine, colchicine, mitoxantrone, SN-38, topotecan, and all other chemicals were obtained from Sigma , unless otherwise stated.2 humidified air, and grown in media supplemented with 10% fetal calf serum (FCS), 2 mM L-glutamine, and 100 units/mL of a penicillin–streptomycin solution mixture; then, the cell lines were screened periodically for mycoplasma contamination using a TOOLS Mycoplasma Detection Kit. The cell lines were kindly provided by Drs. Michael Gottesman and Susan Bates .The ABCB1-overexpressing KB-V1 cell line, derived from human epidermal cancer, was maintained in DMEM supplemented with 1 μg/mL of vinblastine . The ABC2 humidified air. Cytotoxic drugs were subsequently added at various concentrations, in the absence or presence of hydroxygenkwanin or a reference inhibitor (0.5% (v/v) final concentration of DMSO in all wells), for an additional 72 h before being processed, as previously noted [50) value of each drug regimen was calculated using a fitted concentration–response curve obtained from at least three independent experiments.The cytotoxicity of cytotoxic drugs was determined by standard MTT and CCK-8 assays, as previously noted ,88. Briely noted . The 50%Flow cytometry assays, with the ABCG2 substrate, pheophorbide A (PhA) (395 nm excitation and 670 nm emission), were performed, as previously noted ,89. BrieAn immunoblot assay for the detection of human ABCG2, with the positive loading control, tubulin, was performed using BXP-21 (1:1000 dilution) and anti-alpha tubulin   antibodies, respectively, as previously noted .i assay, using total membrane vesicles prepared from ABCG2 baculovirus-infected High-Five insect cells, as previously noted [The vanadate (Vi)-sensitive ATPase activity of ABCG2 was determined based on the endpoint Ply noted ,90.The ABCG2 protein (PDB:6VXH)  and ligaExperimental values are presented as an average ± standard deviation (SD) or as an average ± standard error of the mean (SEM); they were calculated from dose–response data, from at least three independent experiments, using GraphPad Prism software  and KaleidaGraph software . Unpaired two-tailed Student’s t-tests were performed to analyze the difference between experimental data and control, or improvement in fit data.