T790M mutation in NCI-H1975 or PTEN loss in NCI-H1650 cells, in lung adenocarcinoma treated with EGFR inhibitors. Interrogating the underpinnings of drug resistance with publicly available CRISPR phenotypic assays assists in prioritizing resistance drivers, offering hypotheses for drug combinations.High-throughput drug screens in cancer cell lines test compounds at low concentrations, thereby enabling the identification of drug-sensitivity biomarkers, while resistance biomarkers remain underexplored. Dissecting meaningful drug responses at high concentrations is challenging due to cytotoxicity, i.e., off-target effects, thus limiting resistance biomarker discovery to frequently mutated cancer genes. To address this, we interrogate subpopulations carrying sensitivity biomarkers and consecutively investigate unexpectedly resistant (UNRES) cell lines for unique genetic alterations that may drive resistance. By analyzing the GDSC and CTRP datasets, we find 53 and 35 UNRES cases, respectively. For 24 and 28 of them, we highlight putative resistance biomarkers. We find clinically relevant cases such as EGFR  •Presenting a strategy for identifying rare drug-resistance markers in cancer cells•Effectively stratifying drug resistance from cytotoxicity in pharmacology screens•Generating hypotheses for resistance mechanisms underlying certain biomarkers•CRISPR screens support resistance hypotheses and shed light on cancer vulnerabilities Cancer drug resistance is the major challenge of modern oncology. Identifying resistance and its biomarkers will empower the next generation of precision medicines. High-throughput pharmacology screens in cancer cell lines have successfully identified drug-sensitivity biomarkers, but drug-resistance biomarkers are underexplored. Intrinsic drug-resistance events are often rare and experimentally indistinguishable from cytotoxicity or artifacts without prior knowledge. To address this, we investigate cell-line populations sensitized to a drug treatment  and characterize those cell lines that do not respond as expected. We highlight unique genetic features harbored by these cell lines and confirm their linkage to drug resistance using CRISPR gene essentiality data. Our analysis and results pave the way for enhanced precision medicine, guide further CRISPR screens, and identify potential drug combinations to tackle resistance. Identifying cancer drug resistance and its biomarkers will empower the next generation of anti-cancer medicines, tailoring treatments to individual patients. Detecting drug resistance in high-throughput pharmacology screens is experimentally challenging. We present a computational framework identifying rare intrinsically resistant cancer cell lines. Our observations provide hypotheses for associated drug-resistance biomarkers, which we validate with independent CRISPR essentiality screens. Our results pave the way for enhancing cancer precision medicine and effective drug combinations to overcome resistance. Precision medicine has raised high hopes to advance the treatment of cancer.,MET amplifications are associated with sensitivity to savolitinib (an MET inhibitor) in high-throughput screens  resistance  that is frequently observed in lung adenocarcinoma patients, but only infrequently found in cancer cell lines, is the EGFRdeletion I, strongmutation D. Notabl,In high-throughput screens, cell lines are commonly treated for a duration of 3–5 days,The identification of infrequent resistance biomarkers in large pharmacology screens is challenging. Screens such as GDSC were optimized for sensitivity biomarker discovery, resulting in drugs being screened at low concentrations to strictly avoid cytotoxicity, i.e., off-target effects. Therefore, sensitive cell lines are those which respond at low concentrations while resistant cell lines are intermingled with the large bulk of non-responding cell lines B–S1D. We50). It is often the case that the range of tested dose concentrations does not include the IC50 of non-responder or resistant cell lines. These values are therefore obtained by curve fitting and extrapolation  cell lines, i.e., cell lines that despite presenting a drug-sensitivity biomarker do not respond to a specific treatment. We integrated pharmacogenomics and CRISPR data from GDSC, CTRP, and the Cancer Dependency Map (DepMap),d less than −1) for GDSC  of the distribution of drug-response metrics in cell lines with a particular sensitivity biomarker, and observing how much this SD decreases when we ignore the most resistant cell line(s). Applying this UNRES detection pipeline, the GDSC datasets yielded 53 UNRES cases with 1–5 top resistant lines for 23 unique sensitivity associations, and 35 in the CTRP dataset for 22 unique sensitivity associations . These correspond to 40.4% and 59.5% of all the sensitivity associations that were analyzed for UNRES presence, respectively. 50 values had been randomly permuted between all cell lines within a tissue  adds a layer of interplay between the effects of false positives in both levels, which are not independent. We applied a hierarchical false discovery rate (HFDR) controlling procedure see  that estThe number of UNRES cell lines is small G and S4HT790M mutation for gefitinib resistance in EGFR mutant lung adenocarcinoma cell lines  inhibitor daporinad in EGFR amplified low-grade glioma cell lines (ERBB2) amplified UACC-812 breast cell line is resistant to the ERBB2 inhibitor lapatinib, with some candidate resistance markers being a nonsense mutation in CHEK2 and amplifications in 2q15 , 20p12.1 , and 1p12 (NOTCH2)  G.BRAF mutants  cell lines. In colorectal adenocarcinoma (COREAD), two cell lines (KM12 and LS-513) do not respond to the BRAF inhibitor PLX-4720, despite being  mutants . The senT790M mutant cell line NCI-H1975 as resistant to several EGFR inhibitors. Similarly, NCI-H1650 cell line shows a pattern of resistance similar to that of EGFR inhibitors, even though it lacks the T790M mutation. Most importantly, NCI-H1650 is uniquely resistant to the newest-generation EGFR inhibitor osimertinib, which also targets EGFRT790M mutants.T790M-targeting drugs WZ8040 and canertinib, both screened in the CTRP dataset  is shown to happen. This information is lost when integrating the gene-level copy-number data into recurrently aberrant copy-number segments using ADMIRE . Genes with a high enrichment score indicate that their knockout provides selective advantage to the PC-9 cells in the presence of gefitinib, thus their loss of function might be associated with drug resistance. Here we highlight mutations uniquely found in NCI-H1650 , NCI-H1975 (EGFRT790M and EGFRL858R mutant), and PC-14 (EGFR exon 20 deleted) were available in the DepMap dataset from the Sanger Institute (CERES). EGFR mutant cell lines. Data for the EGFR, GRB2, and CDC37 have high GPX4 and the interferon signaling components ADAR, USP18, and ISG15. These genes present vulnerabilities specific to PTEN-deficient NCI-H1650 cells. We hypothesize that these vulnerabilities could be exploited in situations where resistance to EGFR inhibitors arises due to the dominance of an alternative signaling pathway such as the PI3K/AKT/mTOR pathway.In the absence of treatment, the UNRES cell line (NCI-H1650) does not depend on EGFR signaling for survival, as EGFR wild-type cell lines to filter out vulnerabilities that would also kill healthy tissue. As we are only interested in genetic vulnerabilities unique to cancer cells, we also performed differential essentiality analysis comparing NCI-H1650 with PIK3CA mutant ovarian serous cystadenocarcinoma cell lines , which is one of the main Ser/Thr phosphatases involved in cell growth and division.PPP2R1A mutations are common across ovarian and endometrial carcinomas,PTEN, KRAS, NF1, PIK3R1, and others . Results from the In OAW-42, the other resistant cell line, we can find T790M, were robustly identified by our analysis across both GDSC and CTRP datasets. Most importantly, our framework was capable of systematically identifying infrequent resistance biomarkers from large pharmacology screens, which previously exclusively relied on prior biological knowledge. Our unbiased approach can detect known biomarkers, along with new putative biomarkers, leading the way for hypothesis generation, which complements pooled CRISPR drug-resistance screens. The analysis can be applied to any large pharmacology screen, helping gain insights into resistance mechanisms from new datasets that will be generated in the coming years.Our analysis pipeline was able to successfully identify infrequent drug-resistance cell lines and putative biomarkers from large pharmacology screens and validate them with CRISPR screens. Established gold standards with strong evidence in clinics, such as EGFR inhibitor resistance mediated by EGFR50s in that tissue, suggesting that any mechanistic difference in drug response is unlikely. Another limitation we observed is that UNRES cell lines with a very low adjusted pvalue “pull” other sensitive cell lines—e.g., the real UNRES case where both NCI-H1975 and NCI-H1650 are resistant to gefitinib was highly significant, but another UNRES case with these two cell lines along with PC-14  was also falsely detected as significant  allows for a more unbiased approach, looking at all unique putative markers (not only cancer-related ones) and hinting at effects that may not be exclusively genetic. This is the case for There is a great opportunity to enhance the results of our analysis pipeline as newer phenotypic datasets become available, in particular CRISPR essentiality screens, both in the presence of a drug of interest  if one of the drugs has a resistance biomarker.michael.menden@helmholtz-muenchen.de.Michael P. Menden is the lead contact of this study and can be reached by e-mail: This study did not generate new unique reagents.http://www.cancerrxgene.org and https://portals.broadinstitute.org/ctrp.v2.1/. The raw deep molecular characterization is available at https://www.ebi.ac.uk/ega/studies/EGAS00001000978 and https://www.ncbi.nlm.nih.gov/geo/. CRISPR essentiality data were downloaded from https://depmap.org/portal/as the gene_effect.csv file from the Sanger CRISPR (CERES) release.All pharmacology data are available at https://github.com/ia327/ayestaran2020_indirect_res, including an interactive Shiny app to explore the generated results.The source code to reproduce all analysis is available at ,50). For the CTRP dataset, IC50 values were estimated with the same curve-fitting method used in the GDSC dataset,Cell-line drug-response data were obtained from the GDSC projectDeep molecular characterization of the screened cell lines was obtained from the GDSC project.50 values, as numerical differences between extrapolated data points might be biologically misleading.Biomarkers of drug sensitivity were identified with ANOVA models for each possible combination of cancer type, drug, and CFE as described in Iorio et al.50 values to CFE status with the covariates microsatellite instability status, cell-culture medium, and cell-line growth properties. The effect size was estimated with a signed Cohen's d statistic,We fit an ANOVA model of ICA CFE was considered to be a biomarker of drug sensitivity under the conservative threshold p value <0.001 . The change in SD when removing the highest i IC50 values will therefore beOur primary interests are cell lines derived from the same cancer type carrying a common CFE, which renders this cell population sensitive to a drug, but with distinct UNRES cell lines. We define as UNRES a cell line or group of cell lines that significantly contributes to the sample SD of ICn IC50 values for that drug and tissue, ignoring CFE status, and calculating the corresponding α = 0.15.Significance of each To quantify the strength of UNRES cases, we additionally calculated a normalized decrease in SD as follows:i IC50 values, defined as the median of the bootstrap distribution of 50 values in the corresponding tissue.where 50 values for each drug within each tissue 100 times and ran our analysis workflows. We summarized the results by counting the number of sensitivity drug-CFE associations, the number of said associations with detected UNRES cases, and the maximum number of UNRES cell lines per association. Significance thresholds were the same as the ones used in the original datasets.To obtain an estimate of detected UNRES due to random chance, we performed a permutation test on both datasets. Thus, we randomly permuted the IC1α = 0.001Test which parent hypotheses in 2α = 0.15 to correct for FDR across all considered For each significant parent hypothesis, test the hypothesis using the Benjamini-Hochberg methodTo account for the dependence in the hierarchical structure of the statistical tests that identify drug-sensitivity biomarkers with UNRES cell lines, we applied the HFDR controlling procedure developed by Yekutieli.The parental hypotheses Furthermore, to calculate a bound for the overall FDR for all families of hypothesis, we used the approximationwhere the number of discoveries is defined by the significant markers in The comparison between identified UNRES cell lines and sensitive populations lacks statistical power because of small sample sizes. Here, we systematically queried the molecular characterization provided by GDSC. For each UNRES cell line, we explored: (1) sensitivity biomarker genes for point mutations that are unique to the UNRES cell line(s); (2) mutually exclusive CFEs: we selected those CFEs that were exclusively mutated in all UNRES cell lines while all sensitive cell lines were wild type, or vice versa; (3) enriched CFEs: in the cases where there were multiple UNRES cell lines, we tested for enriched CFEs with Fisher's exact test between UNRES cell lines and sensitive subpopulation. To narrow down the list of putative markers, we applied a further filter to the identified putative markers by keeping only genes that are included in the GDSC cancer gene list.Data for the comparison of gene essentialities upon gefitinib treatment was obtained from Liao et al.,https://depmap.org), specifically the Sanger dataset (processed with CERES).All other CRISPR essentiality data without any treatment were obtained from the DepMap portal (ess) of the gene in the UNRES cell line (out) versus a sensitive population of k cell lines. For a specific gene g, its change in essentiality (Z score for each gene and each comparison between two cell lines and computes the average Z score across all comparisons. The mentioned Z-transformation is used to account for noisy cell lines before taking the average across all k comparisons.Differentially essential genes were selected based on the differences between the essentiality (Differential essentiality between the UNRES cell line and wild-type resistant cell lines was calculated with the same method.


Background: High risk neuroblastoma (HR-NB) is one the most difficult childhood cancers to cure. These tumours frequently present with DNA damage response (DDR) defects including loss or mutation of key DDR genes, oncogene-induced replication stress (RS) and cell cycle checkpoint dysfunction. Aim: To identify biomarkers of sensitivity to inhibition of Ataxia telangiectasia and Rad3 related (ATR), a DNA damage sensor, and poly (ADP-ribose) polymerase (PARP), which is required for single strand break repair. We also hypothesise that combining ATR and PARP inhibition is synergistic. Methods: Single agent sensitivity to VE-821 (ATR inhibitor) and olaparib (PARP inhibitor), and the combination, was determined using cell proliferation and clonogenic assays, in HR-NB cell lines. Basal expression of DDR proteins, including ataxia telangiectasia mutated (ATM) and ATR, was assessed using Western blotting. CHK1S345 and H2AXS129 phosphorylation was assessed using Western blotting to determine ATR activity and RS, respectively. RS and homologous recombination repair (HRR) activity was also measured by γH2AX and Rad51 foci formation using immunofluorescence. Results:MYCN amplification and/or low ATM protein expression were associated with sensitivity to VE-821 (p < 0.05). VE-821 was synergistic with olaparib  independent of MYCN or ATM status. Olaparib increased H2AXS129 phosphorylation which was further increased by VE-821. Olaparib-induced Rad51 foci formation was reduced by VE-821 suggesting inhibition of HRR. Conclusion: RS associated with MYCN amplification, ATR loss or PARP inhibition increases sensitivity to the ATR inhibitor VE-821. These findings suggest a potential therapeutic strategy for the treatment of HR-NB. Inhibitors of ataxia telangiectasia and Rad3 related (ATR) kinase are currently being tested in early phase clinical trials for patients with adult cancers. ATR is a DNA damage sensor kinase which has a pivotal role in recovery from replication stress (RS). Activated ATR signals to many pathways involved in regulation of origin firing, and stabilisation and restart of stalled replication forks . ATR https://clinicaltrials.gov/). Various determinants of ATR inhibitor sensitivity have been proposed, including RS, loss of G1 checkpoint control and loss of the ataxia telangiectasia mutated (ATM) protein [Two ATR inhibitors, AZD6738 (Astra Zeneca) and M6620 , have entered phase II trials, with a third, BAY1895344 (Bayer) in phase I , deregulation of replication origin firing, limited nucleotide pools or essential replication factors and replication through fragile sites or damaged DNA regions [RS is a common feature of cancer cells and leads to genetic instability. There are many causes of RS including overexpression of proliferation-driving oncogenes  currently remains less than 50% at 5 years despite intensive high-dose multimodal treatment [Neuroblastoma (NB) is a rare embryonal tumour derived from cells of the developing sympathetic nervous system. Around 100 cases are diagnosed a year in the UK, of which 50% are classified as high risk, but accounts for ~10% of paediatric cancer deaths ,16. Longreatment ,18. Survreatment . HR-NB freatment ,21,22,23MYCN oncogene, leading to RS. MYCN-amplified (MNA) tumours also show defective G1 checkpoint arrest [ATM, CHEK1, H2AFX and MRE11 [Fifty percent of HR-NB have amplification of the t arrest . A commot arrest . Many gend MRE11 . TogetheAlthough rare at diagnosis, defects in p53 signalling have been observed in up to 50% relapsed NB tumours ,25, caushttps://clinicaltrials.gov/: NCT04236414, NCT03233204, NCT02392793).Poly ADP-ribose polymerase (PARP) inhibitors also cause RS . PARP isMYCN amplification or ATM deficiency have been shown to have increased sensitivity to single agent olaparib treatment [Preclinical testing of the PARP inhibitor olaparib (Astra Zeneca) in NB shows that PARP inhibition potentiates the cytotoxic effect of a variety of chemotherapy agents and ionising radiation ,29,30,31reatment ,33.We aimed to test if the DDR defects frequently observed in NB would be potential predictive biomarkers of sensitivity to ATR inhibition using VE-821 .MYCN or ATM status, by the accumulation of unrepaired single strand breaks, when PARP is inhibited, and failure to arrest in S-phase when ATR is inhibited.We hypothesise that there will be mutual synergy between ATR and PARP inhibitors by further increasing RS, irrespective of In this study, we identify features of NB cell lines that determine sensitivity to ATR inhibition, for use as potential predictive biomarkers, and examine the effect of ATR inhibition on the cytotoxicity of the PARP inhibitor olaparib.MYCN, 11q and TP53 status to interrogate what features would lead to sensitivity to ATR and PARP inhibitors. The genetic features of these cell lines are listed in To reflect the variety of DDR defects observed in NB tumours, we chose a panel of NB cell line of varying S345 and phospho-CHK2T68 and expression, respectively) by Western blot  in these cell lines as well as baseline activity of ATR, ATM (phospho-CHK1ern blot A. Mean pMNA cell lines show high MYCN protein expression compared to non-MNA cell lines (TP53 mutations show stabilised p53 protein (NMB and Kat100) or no p53 protein expression (SKNAS and IGRN91). The TP53 mutation in the NMB and Kat100 cell lines are point mutations leading to accumulation and stabilisation of the dysfunctional protein . Although there was no significant difference in VE-821 sensitivity between cell lines with or without ATM aberration (ATM mutation and/or 11q deletion), cell lines with low baseline ATM protein expression were significantly more sensitive than cell lines with high baseline ATM expression (p < 0.05). In regression analysis, MYCN and ATM protein expression was negatively and positively correlated with sensitivity to VE-821, respectively . This was not the case when cell lines were analysed based on MYCN amplification and some cell lines, such as SJNB1, which are not MNA but express high levels of MYCN, were relatively sensitive to olaparib.However, when analysed by clonogenic assay , cell liMNA, TP53 wt) cell line, fold sensitisation 1.41. Synergism between VE-821 and olaparib was determined by combination index analysis  where combination index (CI) <1 indicates synergy. PARP inhibition was synergistic with ATR inhibition in all cell lines, irrespective of MYCN or TP53 status , SKNAS , NGP  and N20_R1  cells were treated with 0, 0.1, 1, and 10 µM olaparib with the addition of either 0, 0.1 or 1 μM VE-821 A. VE-8213 status D. This w3 status .S345 and RPA2T21 phosphorylation, we measured phosphorylation of histone 2AX (γH2AX) as a surrogate marker of RS. γH2AX is a marker of DNA double strand breaks (DSB) and RS. In the absence of genotoxic agents, γH2AX primarily marks RS. SHSY5Y, SKNAS, NGP and N20_R1 cells were treated with olaparib 5 μM and/or VE-821 1 μM for 24 h. pCHK1S345 (ATR activation), pCHK2T68 (ATM activation) and γH2AX (RS) protein expression was measured by Western blot (S345) in all cell lines, which was reduced with the addition of 1 μM VE-821 as expected. pCHK2T68 and γH2AX expression increased after treatment with olaparib and was further increased with the addition of VE-821, suggesting increased RS and DNA damage. Another marker of RS is phosphorylation of RPA2S4/S8 by ATM and DNA-PK [S8 was measured after treatment with olaparib 5 μM +/- VE-821 1 μM over the course of 48 h  and NGP  cell lines treated with 1 µM VE-821, 10 µM olaparib or both are shown in We also examined the effect on homologous recombination repair (HRR). PARP inhibition is synthetically lethal with HRR deficiency . ATR inhOverall, the combination of olaparib and VE-821 leads to increased RS due to defective S and G2 cell cycle arrest and suppression of HRR leading to collapsed replication forks.MYCN oncogene in NB cell lines is associated with sensitivity to VE-821, confirming that MYCN driven NB cells are vulnerable to ATR inhibition.It is widely accepted that overexpression of proliferation-inducing oncogenes leads to RS and dependency on ATR signalling . In modeMNA NB by upregulation of the ATM targeting micro-RNA, miR-421 [ATM loss or dysfunction is also associated with sensitivity to VE-821. Our results confirm that reduced ATM function, which can be partially determined by protein level, confers sensitivity to ATR inhibition in NB as it does in other tumour types ,58,59,60 miR-421 . ATM silMYCN and ATM protein expression levels were more powerful determinants of VE-821 sensitivity than the genetic status , and MYCN protein levels positively correlated with VE-821 sensitivity, whereas ATM protein level was negatively correlated with VE-821 sensitivity.-/- cell lines [Mutation, expression or dysfunction of the p53 tumour suppressor protein was not associated with sensitivity to ATR inhibition. Middleton et al. also demonstrated that defective p53 signalling did not lead to sensitivity to VE-821 alone, but the addition of VE-821 showed greater potentiation of gemcitabine and ionising radiation induced cytotoxicity in p53ll lines .To summarise, MYCN and ATM protein expression levels, as well as genetic status, could provide useful predictive biomarkers to stratify NB patients who would benefit from an ATR inhibitor.TP53 wt and TP53 mutant NB.Further investigation into combining ATR inhibition with DNA-damaging chemotherapy may provide a novel therapeutic strategy for both ATM nor MYCN status significantly determined sensitivity to PARP inhibition by olaparib. Colicchia et al. previously demonstrated that MYCN-amplification or overexpression renders NB cells especially sensitive to single agent olaparib [In contrast, neither olaparib . SensitiOur results highlight the importance of validating sensitivity found to novel agents in models of disease with other types of cell viability assays. In the case of NB, more evidence is required before the role of MYCN in PARP sensitivity can be established. It may be that in the context of complex genetic alterations, cells may have acquired a spectrum of features leading to resistance, as well as sensitivity to these inhibitors, making it difficult to predict response.MYCN, ATM and TP53 status. ATR inhibition has been shown to enhance PARP inhibitor sensitivity in a variety of adult solid tumour types [In addition to investigating features associated with sensitivity to these inhibitors as single agents, we provide evidence of synergy between ATR and PARP inhibition in NB cell lines. VE-821 enhanced olaparib-induced growth inhibition independently of ur types ,62,63 anur types ,64.S8, S345 expression, Olaparib treatment alone induced markers of RS  and stored at −20 °C in stock solution of 100 mM and 20 mM, respectively, in DMSO .v/v) Fetal Calf Serum  and maintained at 37 °C in a humidified incubator with 5% CO2. Cell lines were routinely tested for Mycoplasma and were confirmed to be negative.Neuroblastoma cell lines used in this study were: SHSY5Y, SKNAS, NGP, N20_R1, NMB, IMR32, Kat100, IGRN91, SJNB1, and GIMEN . All neuCells were seeded in 96-well plates , and allowed to adhere overnight before treatment with ATR or PARP inhibitors alone or in combination for 72 h. Inhibitors were added at 200× dilution to give a final DMSO concentration of 0.05%. Percentage control growth was assessed using the XTT cell proliferation assay  according to the manufacturer’s instructions and using the following formula: (average absorbance test/average absorbance control) × 100. Combination Index (CI) values were determined by the Chou–Talalay method using CalcuSyn v2 .Cell pellets were harvested and protein extracted using PhosphoSafe™ Extraction Buffer  following the manufacturer’s protocol. Protein concentration was quantified using the Pierce BCA protein assay kit (Thermo Fisher Scientific).Proteins were separated using 3–8% Criterion™ XT Tris-Acetate Protein Gel  and transferred onto Hybond-C Extra membrane . Membranes were stained with Ponceau S (Sigma-Aldrich) to control for loading, destained in tris buffered saline, 0.5% tween 20 (TBST) and blocked for 1 h in 5% milk TBST.Primary antibodies were: mouse monoclonal MYCN 1:1000 (Santa Cruz Bio-technology (SCBT), Dallas, TX, USA: A1513), rabbit polyclonal ATM 1:500 , Danvers, MA, USA: 2873S), mouse phospho-ATM (ser1981) 1:1000 (CST: 4526), mouse monoclonal CHK1 1:1000 (SCBT: sc-8408), mouse monoclonal CHK2 1:1000 (SCBT: sc-17747), rabbit polyclonal phosho-CHK1 (ser345) 1:1000 (CST:2341S), rabbit monoclonal phosphor-CHK2 (thr68) 1:1000 (CST:2197S), rabbit polyclonal ATR 1:500 (CST: 2790S), p53 1:1000 , rabbit monoclonal p21 Waf1/Cip1 (12D1) 1:1000 (CST: 2947S), rabbit monoclonal phosopho-H2AX (ser139) 1:2000 (CST: 2577S), rabbit monoclonal phospho-RPA2 (ser8) 1:1000 (CST: 54762S), mouse monoclonal glyceraldehyde 3-phosphate dehydrogenase (GAPDH) 1:5000 (SCBT: sc-47724). Secondary antibodies used were peroxidase-conjugated goat anti-mouse  and anti-rabbit immunoglobulins (Dako) at 1:2500.Protein detection was performed using enhanced chemiluminescence (Bio-Rad) and imaged using the ChemiDoc imaging system . Densitometry was performed using ImageJ image analysis software .v/v) ethanol and stored at −20 °C. Prior to analysis, cells were washed with phosphate buffered saline (PBS), resuspended in 500 μL PBS with 50 μg/mL propidium iodide (Sigma-Aldrich) and 50 μg/mL RNAse A (Sigma-Aldrich), and incubated at 37 °C for 30 min. Samples were analysed on the Attune NxT Flow Cytometer using Invitrogen™ Attune NxT Software (Thermo Fisher Scientific). Data were analysed using FlowJo TM . Experiments were at least n = 3.Cells were harvested post-treatment, fixed in ice-cold 70% (Cells were treated for 24 h with control vehicle (DMSO) and 1 μM VE-821 with or without 10 μM olaparib. Cells were stained with mouse monoclonal anti phospho-Histone H2A.X (Ser139) antibody (SCBT) at 1:500 and rabbit monoclonal anti RAD51 antibody (CST: 8875S) at 1:250. Secondary antibodies used were Alexa 488 conjugated goat anti rabbit and Alexa 546 conjugated goat anti mouse , both at 1:1000. Cells were imaged using a Leica DM6 microscope and Leica Application Suite (LAS) X software . The number of RAD51 foci in each cell and total nuclear fluorescence intensity for γH2AX were quantified using ImageJ software ) and data was plotted using GraphPad Prism v6 .p < 0.05 was taken to be statistically significant.Mann Whitney U and 2-way ANOVA statistical tests were carried out using GraphPad Prism v6 software and MYCN-amplification and protein overexpression and loss of functional ATM protein through deletion or mutation result in vulnerability to ATR inhibition by VE-821. The case for PARP inhibition is less clear and, although there is a trend towards MYCN overexpressing cells being more sensitive to olaparib, the role of MYCN in PARP inhibitor sensitivity needs to be investigated further. We also provide evidence of synergy between ATR and PARP inhibition in NB cell lines independent of MYCN, ATM or TP53 status. This may be due to increased RS, compromised S and G2 checkpoint arrest and/or defective HRR in the presence of both PARP and ATR inhibitors.In the era of precision medicine, cancer specific DDR defects and RS have become attractive targets, with many novel agents entering clinical trials. With the introduction of PARP inhibitors into clinical trials for the treatment of paediatric cancers, including HR-NB, a disease with one of the worst long term prognoses, and ATR inhibitors being tested in adult trials, we sought to investigate which NB specific DDR defects, if any, would lead to sensitivity to these agents alone and in combination. We have shown that Overall, our work gives exciting insights into the use of PARP and ATR inhibitors as a novel treatment strategy for HR-NB.


Head and neck squamous cell carcinomas (HNSCC) induced by human papillomavirus (HPV) have increased recently in the US. However, the distinct alterations of molecules involved in the death pathways and drug effects targeting inhibitor of apoptosis proteins (IAPs) have not been extensively characterized in HPV(+) HNSCC cells. In this study, we observed the distinct genomic and expression alterations of nine genes involved in cell death in 55% HNSCC tissues, which were associated with HPV status, tumor staging, and anatomic locations. Expression of four genes was statistically correlated with copy number variation. A panel of HPV(+) HNSCC lines showed abundant TRAILR2 and IAP1 protein expression, but were not sensitive to IAP inhibitor birinapant alone, while combinatory treatment with TNFα or especially TRAIL enhanced this drug sensitivity. The death agonistic TRAILR2 antibody alone showed no cell inhibitory effects, whereas its combination with birinapant and/or TRAIL protein demonstrated additive or synergistic effects. We observed predominantly late apoptosis mode of cell death after combinatorial treatments, and pan-caspase (ZVAD) and caspase-8 (ZIETD) inhibitors attenuated treatment-induced cell death. Our genomic and expression data-driven study provides a framework for identifying relevant combinatorial therapies targeting death pathways in HPV(+) HNSCC and other squamous cancer types. In normal human cells, there are two primary pathways that trigger apoptosis: the extrinsic and intrinsic pathways. The former begins with interaction between the death ligands, such as tumor necrosis factor (TNFα) and TNF-related apoptosis inducing ligand , and their corresponding receptors7. The intrinsic pathway begins with stress-induced mitochondrial permeabilization leading to the release of cytochrome complex (cytochrome c) and second mitochondria-derived activator of caspases (SMAC)9. Recently, The Cancer Genome Atlas (TCGA) project of head and neck squamous cell carcinomas (HNSCC) revealed that 44% human papilloma virus negative (HPV−) and 31% HPV(+) patients exhibited genetic or expression alterations of molecules involved in the death signaling pathways10. Furthermore, through the PanCancer Atlas study of five squamous cell carcinomas (SCCs), including head and neck, lung, esophagus, cervix, and bladder, we have identified deregulation of various components of the extrinsic and intrinsic apoptotic pathways, such as alterations in gene expression and genomic amplification of the Fas-associated death domain (FADD) and inhibitor of apoptosis proteins (IAPs)11. HPV(−) HNSCC and other SCC tumors commonly contain co-amplification of chromosome 11q13, harboring the FADD gene, and 11q22, where BIRC2 and BIRC3 genes encode cIAP1 and cIAP2 proteins, respectively. FADD and IAPs are important downstream signaling molecules involved in the extrinsic death pathways, while XIAP is also involved in the intrinsic pathway14. However, it is still not well understood how alterations in the expression of FADD and IAPs affect death pathway signaling and contribute to resistance to therapies targeting the death pathways in cancers, especially in HNSCC differing in HPV status.A critical step in cancer development is to escape the self-regulated program of apoptotic cell death and differentiation, despite retained expression of molecules involved in the death signaling pathways15. In addition, the antibodies could trigger antibody-dependent cell-mediated cytotoxicity (ADCC) and complement-dependent cellular cytotoxicity (CDC), through interactions of the antibody Fc domains with Fc receptors on the cell surface of immune cells, which provide additional death signaling and immune recruitment to eradicate cancer cells17. Despite the promising biological mechanism and relevant preclinical testing, clinical trials with these antibodies have shown lack of clinical efficacy, whether used alone or in combination with chemotherapy18. It has been hypothesized that the failed clinical trials using the antibodies targeting TRAILRs could be due to the resistance of primary tumors to apoptosis, the mechanism for which remains unclear.Recent drug development efforts have been aimed at targeting the extrinsic death pathways, especially through the TRAIL receptors. Humanized monoclonal antibody agonists to TRAILR1 (DR4) and TRAILR2 (DR5) offer longer in vivo half-life and higher specificity against the death receptors compared to TRAIL20. Birinapant is capable of inducing cancer cell death through SMAC mimetic by degrading IAPs through binding, preferentially targeting cIAP1 compared to cIAP2 and XIAP22. Recently, we have investigated the anti-tumor effects of birinapant in HPV(−) HNSCC cells, and observed that cells retaining FADD/BIRC2 amplification and overexpression were sensitive to birinapant, and the effects were enhanced by death agonists TNFα or TRAIL23. Intriguingly, the single HPV(+) HNSCC line included in the panel was sensitized by birinapant to TRAIL to a greater extent than TNFα. Together with TCGA data showing that FADD amplification and overexpression implicated in sensitivity to the combination is not commonly observed in HPV(+) HNSCC, these observations raised the question whether the SMAC mimetic birinapant could be used in combination with TRAIL or TRAIL agonists for treating this type of cancer.In parallel to the development of antibodies targeting the TRAILRs, small molecule inhibitors have been identified to target the molecules involved in the downstream components of the death pathways. Birinapant is a novel bivalent peptidomimetic of the second mitochondria-derived activator of caspases (SMAC), which is an endogenous protein released by mitochondria involved in the intrinsic death pathwayTo address these questions, we characterized the genomic and expression profiles of nine molecules involved in the extrinsic and intrinsic death pathways, including TRAIL (TNFSF10) and its receptors, FADD, BIRC2/3, and XIAP in HNSCC tissues differing in HPV status from TCGA datasets. We observed a distinct pattern of genomic and expression alterations of those molecules with respect to the HPV status. We found that HPV(+) HNSCC cells which harbor less frequent FADD amplification and overexpression, and with more frequent shallow deletion of BIRC2/3 loci, are less sensitive to the SMAC mimetic birinapant. In addition, we observed that HPV(+) HNSCC cells exhibited genomic gain and/or overexpression of TRAIL and TRAILRs, and the stimulation of the death pathway through anti-TRAILR2/DR5 antibody agonist was very effective in inducing cell death in combination with birinapant and/or TRAIL. This study utilized the available genomic and genetic expression profile of HNSCC to develop a mechanism and data-driven hypothesis. This led to experimental support for the incorporation of birinapant, anti-TRAILR antibody, and TRAIL for the treatment of HPV(+) HNSCC cells that were otherwise insensitive to IAP inhibitor birinapant alone.11. However, the differences in the genomic and expression alterations between HPV(+) and (−) tumor types have not been fully elucidated in the latest HNSCC TCGA dataset 11. Prominent genetic changes, such as mutations, gene amplifications, and deep deletions (two-copy loss), as well as altered gene expressions are presented in Oncoprint  were clustered together due to their genomic co-localization at chromosome 8p21.3  and HPV(−) tumors. HPV(−) tumors exhibited higher percentages of overall FADD amplifications, whereas HPV(+) tumors showed a higher percentage of BIRC2/3 single copy loss. The CNV components for XIAP and TNFSF10 exhibited less significant difference or similar distributions between tumors with different HPV status. The TRAIL receptor family members (TNFRSF10A/B/C/D) exhibited significant difference in CNV components between tumors with different HPV status, that HPV(−) tumors had the higher percentage of one-copy loss, and HPV(+) tumors more often displayed neutral or one copy gain  for the death molecules and compared their distributions between HPV(+) and HPV(−) tumors Fig. B. Both FFADD one-copy gain compared to that of OP, and the amplification of FADD with two-copy gain is higher in LR only. OP tumors, enriched for HPV(+) HNSCC, showed the highest percentage of one-copy loss of FADD and BIRC2/3, similar to the distribution of CNV in HPV(+) tumors. One-copy loss of XIAP was also relatively higher for both HPV(+) and LR group compared to other groups. Various degrees of gene amplifications were observed in TNFSF10 (TRAIL) for the majority of tumors from all sites, and tumors from OP and LR exhibited relatively higher percentages of two-copy gene amplification. In contrast, more tumors from LR and OC were characterized by one-copy deletions of TNFRSF10A/B/C/D (TRAILRs), while HPV(+) tumors from OP exhibited higher frequency of one-copy gain. The high percentage loss of FADD and BIRC2/3 and gain in TRAIL and receptors in HPV(+) OP tumors support our hypothesis that these subsets of tumors could differ in sensitivity to birinapant and agents targeting TRAILRs.Furthermore, we investigated CNV changes in distinct primary tumor sites of HNSCC, such as larynx (LR), oral cavity (OC), and oropharynx (OP). The genetic alterations of all the genes differed significantly among the primary tumor sites Fig. C. TumorsTP53 and CASP8, which are important molecules that modulate the cell apoptosis pathway25. The genetic alterations of TP53 and CASP8 from HNSCC TCGA datasets were displayed by Oncoprint, which showed 71% TP53 and 13% CASP8 mutation rates, mainly in HPV(−) HNSCC  HNSCC, whereas those involved in HPV(+) HNSCC are known to include viral inactivation of TP53.We next examined the genetic alterations of https://www.cbioportal.org), and ranked the cancer types based on the frequency of CNV and mutations  tumors, while BIRC3, TNFSF10, and TNFRSF10B/C/D expression levels were higher in HPV(+) tumors  tumor lines, and TNFRSF10A/B/C exhibited decreased expression in HNSCC compared to normal human oral keratinocyte lines (HOK)  HNSCC cell lines compared to the HOK cell line Fig. . FADD prt test, p < 0.01)  cell lines for their surface expression of TRAILR2/DR5/TNFRSF10B by immunofluorescent staining for flow cytometry analysis. From our panel of HPV(+) cell lines tested, UM-SCC-47 and UPCI-SCC-90 were chosen for further investigations based on their drug sensitivity  cell lines. XTT cell density assay was conducted in cells treated with birinapant 1 μM alone or in combination with death agonists TNFα 20 ng/mL or TRAIL 50 ng/mL. Preliminary experiments were conducted to determine the ICays Fig.  and 5 daays Fig. . As obseBased on our observation that TNFRSF10A/B/C/D (TRAILRs) were rarely deleted and their expression levels were relatively higher in HPV(+) tissues Figs. , we hypoIn order to evaluate if the observed decrease in cell density was due to cell cycle and/or cytotoxic effects, DNA cytofluorometric analyses of UPCI-SCC-90 and UM-SCC-47 were conducted, after treatments with these drugs alone, or in double or triple combinations Fig. . For UM-Lastly, Fixed Apoptotic/Necrotic (FAN) flow cytometry analysis was used to discriminate necrotic and apoptotic cells in specific stages. Late stage apoptosis was the primary stage of cell death in both cell lines after 48 h of treatment with single and combination therapies Fig. C,D. UM-S-dependent apoptosis.To further analyze the potential mechanism of the combinatorial therapies leading to cell death, we analyzed the effects of pan-caspase (ZVAD), caspase-8 (ZIETD), and RIPK1 (necrostatin) inhibitors on cell density in both UM-SCC-47 and UPCI-SCC-90 cells after treatment by birinapant alone, or in the double or triple combinations, tested at the different time points  HNSCC cells. Unique genomic and expression signatures in HPV(+) HNSCC tissues were observed, which are distinct from those identified in HPV(−) tumors from TCGA dataset. Most HPV(+) tumors and cell lines rarely exhibited FADD amplification but did have deletions of BIRC2/3 loci on chromosome 11q, while showing TRAIL or TRAILR gene amplification and/or overexpression. These HPV(+) HNSCC lines displayed resistance to birinapant treatment as a single agent in vitro23. The genomic amplification of chromosome 11q13 encodes FADD gene, and 11q22 encodes BIRC2/3 genes for IAP1/2 proteins, which are commonly observed in HPV(−), but not in HPV(+) HNSCC  HNSCC and SCCs from other anatomical sites, including the lung, esophagus, cervix and bladder11. Overexpression of IAPs in tumor cells inhibit the intrinsic and extrinsic caspases and cell death signaling pathways and induce resistance to death ligands effects mediated through TNF-α and TRAIL receptors31. To target IAP proteins, the SMAC mimetics such as birinapant were developed. However, results from early-phase clinical trial testing in human solid tumors showed limited clinical efficacy as a single agent, which indicated that sensitivity to birinapant was not just associated with its target IAP protein expression13. Rather, we found that the HPV(−) HNSCC cell lines harboring both FADD and BIRC2 amplification and overexpression were more sensitive to birinapant in combination with TNF-α or TRAIL in vitro, as well as in animal models in vivo when combined with radiation, which induces death ligand expression23. In contrast to these HPV(−) HNSCC tissues and cell lines, in this study we observed that most HPV(+) HNSCC tissues and cell lines did not exhibit FADD and BIRC2 amplification and mRNA overexpression  HNSCCon Figs. .TP53 and CASP8, which are known to play important roles in cell apoptosis in the updated HNSCC TCGA cohort25. We observed the differential genetic alterations of TP53 and CASP8 between HPV(−) versus HPV(+) HNSCC  HNSCC24. TP53 is also mutated mainly in HPV(−) HNSCC, but with a much lower rate in HPV(+) cancers, where the TP53 protein is inactivated instead by HPV E6 oncoprotein through constant binding and degradation of TP53 protein32. In addition, the HPV(+) cell lines used in this study are wildtype CASP8 and wild type TP5326, which are consistent with the literature and HNSCC TCGA data. The caspase pathway seems intact in HPV(+) HNSCC cell lines tested in this study, indicating that the drug induced cell death was mainly through the caspase mediated mechanism  cells supports this hypothesis -mediated pro-survival pathway35. A third mechanism by which HPV(+) tumorigenesis with profound TRAIL expression and intact TRAILR signaling could evade cell death may be explained with the relatively abundant protein expression of IAPs  HNSCC tissue and cell lines exhibited additionally altered genomic and expression signatures involved in the death pathway, specifically the amplifications and overexpression of TRAIL and TRAIL receptors. Almost 24% of HNSCC samples exhibited genetic amplifications and overexpression of TRAIL genes on 3q26.31, regardless of HPV status, and TRAIL expression was even higher in HPV(+) tumor tissues and cell lines  level by c-FLIP, or more downstream, at the mitochondrial level by overexpression of IAP or Bcl-2 family members, leading to cell death resistance as shown by our and other laboratories39. To block downstream inhibitory molecules against death signaling, the combined actions of drugs such as birinapant and TRAILR agonists could be a logical step to enhancing the apoptotic signaling mediated by TRAIL and TRAILRs15.We observed that there are less samples with genetic deletion and more samples with copy number gain of TRAILRs in HPV(+) HNSCC tissues Figs. , 2, and tin Fig. . AllenswTRAIL amplification and expression are even higher in cases of lung SCC (46%), as well as 18% of FADD amplification and overexpression. These cancer types along with HNSCC could be suitable for future investigation using combination of birinapant with TRAILR agonist. In our current study, due to the limitation of therapeutic anti-DR5 antibody availability, we were not able to test this combinatorial effect with birinapant in the HPV(+) HNSCC xenograft model in vivo. To test the drug efficacy targeting the death pathways in HNSCC and other SCC tumor models in future studies, and the examination of multiple death molecules indicated in this study using genomic and expression dataset could be informative.In a broader scope that includes other cancer types, a dominant gene amplification pattern of the nine genes surveyed was observed in all five types of SCCs, including those from lung, esophagus, cervical, and bladder, plus ovarian cancers ; unconventional and bispecific antibodies targeting DR4/5 and other molecules; chimeric antigen receptors (CARs) targeting TRAILRs; or antibody drug conjugate-like (ADC-like) TRAIL derivatives, to overcome cell resistance to death signaling11. Tumor tissue, adjacent normal tissue, and normal whole blood samples were obtained from patients at contributing centers with informed consent according to their local Institutional Review Boards . TCGA Project Management has collected necessary human subjects documentation to ensure the project complies with 45-CFR-46 (the “Common Rule”). The program has obtained documentation from every contributing clinical site to verify that IRB approval has been obtained to participate in TCGA. Such documented approval may include one or more of the following: (1) An IRB-approved protocol with Informed Consent specific to TCGA or a substantially similar program. In the latter case, if the protocol was not TCGA-specific, the clinical site PI provided a further finding from the IRB that the already-approved protocol is sufficient to participate in TCGA; (2) A TCGA-specific IRB waiver has been granted; (3) A TCGA-specific letter that the IRB considers one of the exemptions in 45-CFR-46 applicable. The two most common exemptions cited were that the research falls under 46.102(f)(2) or 46.101(b)(4). Both exempt requirements for informed consent, because the received data and material do not contain directly identifiable private information; (4) A TCGA-specific letter that the IRB does not consider the use of these data and materials to be human subjects research. This was most common for collections in which the donors were deceased. The relevant guidelines and regulations were followed during the study.TCGA specimens were obtained with informed consent under an IRB approved protocolhttp://www.cbioportal.org/) and presented in Oncoprint, bar graph, and dot plot formats.The complete HNSCC TCGA data were extracted from the latest PanCancer Atlas or Firehose Legacy datasets. Significant focal copy number alterations were extracted from the GISTIC2.0 processing pipeline, and data aggregated on significantly altered lesions are plotted by false discovery rate (FDR) less than 5% and p-values less than 0.05. Differential gene expression analysis of tumor versus normal samples was performed using DeSeq, and all data was log2 transformed (Bioconductor Version 2.12). Genetic altered genes and expression analysis were performed using the online Cancer Genomics cBioportal database 41. Authentication of UM-SCC lines was done at the University of Michigan by DNA genotyping of alleles for 9 loci, D3S1358, D5S818, D7S820, D8S1179, D13S317, D18S51, D21S11, FGA, vWA, and the amelogenin locus as described previously40. These cell lines were maintained in MEM or DMEM with 10% Fetal Calf Serum  at 37 °C with 5% CO2 for a maximum of 8 weeks. Primary human oral keratinocytes (HOK) were cultured for less than 5 passages, in accordance with the supplier's protocol .A panel of eight HPV-positive HNSCC lines, including UM-SCC-47, -104, -105, UD-SCC-2, and 93Vu147T, were provided by Drs. Thomas E Carey, Mark E. Prince, and Carol R Bradford, or described in previous publicationsHPV(+) HNSCC cell lines were titrated with birinapant alone , at varying concentrations, or in combination. The dosing of TNF-α (20 ng/mL), TRAIL (50 ng/mL), and anti-human TRAILR2 polyclonal antibody (400 ng/mL) were used alone and in combination. These reagents were purchased from R&D Systems .50) was determined after 72 or 120 h using the nonlinear four-parameter regression function in GraphPad Prism . To differentiate between apoptotic and necroptotic cell death, cells were treated with ZVAD, ZIETD, or necrostatin .Cells were plated in 96-well plates at predetermined densities for each cell line based on respective doubling times and log-phase growth patterns. Treatment with birinapant 1 µM, TNF-α 20 ng/mL, TRAIL 50 ng/mL, or their combinations occurred 24 h following plating. Cell density was measured using XTT Cell Proliferation Assay Kit from Trevigen . Analysis was performed on a microplate reader 72 or 120 h following treatment with drug and/or death agonist. Each cell line and dilution were assayed in six replicates. Half-maximal Inhibitory concentration , polyclonal anti-cIAP-2 , polyclonal anti-XIAP , and polyclonal anti-FADD . Whole-cell lysates were collected and lysed in NP-40 buffer  supplemented with 1% Halt protease inhibitor and 1% Halt phosphate inhibitor cocktails from ThermoFisher Scientific . Lysates were vortexed and frozen at − 80 °C prior to use. Protein concentrations were determined using the Pierce BCA Protein Assay Kit (ThermoFisher Scientific). Equal amounts of lysates (15 µg) for each cell line were loaded into 4–12% gradient Bis–Tris gels (Invitrogen) and transferred to PVDF membranes for 6 min using the iBlot system (Invitrogen), according to the manufacturer’s standard protocol. Membranes were blocked for one hour in Odyssey blocking buffer  and incubated with the primary antibody overnight at 4 °C. Membranes were washed before incubation with species-appropriate Odyssey secondary antibodies for 1 h at room temperature. Quantification of the protein expression detected by Western blotting was done by protein densitometry normalized to actin with ImageJ .UPCI-SCC-90 and UM-SCC-47 cells were harvested at about 60% confluency using TrypLE Express  for 10 min at 37 °C. After collecting and washing, the detached cells were incubated for 30 min at 37 °C to allow for recuperation of surface receptors while minimizing reattachment and cell death. The rested cells were subsequently blocked with Human TruStain FcX  and stained with PE-conjugated anti-CD262 (TRAILR2/DR5) monoclonal antibody . Lastly, the cells were stained with Zombie Violet Fixable Viability Dye  and were fixed with 2% paraformaldehyde prior to analysis using BD LSRFortessa Cell Analyzer (BD Biosciences). Jurkat cells (ATCC) were stained using the same method as described above and used as a positive control for surface TRAILR2/DR5 expression. The gating used to determine TRAILR2/DR5 surface receptor positivity was based on the population of cells not stained with TRAILR2/DR5 antibody, and the quantification of TRAILR2/DR5 positivity was based only on the live-cell population.UPCI-SCC-90 and UM-SCC-47 cells were plated on 10-cm plates at 1 million cells per plate. Cells were treated 24 h after plating and harvested 48 h after treatment. Cells were counted and stained with propidium iodide (PI) using BD Cycletest Plus DNA Kit prior to analysis on a FACS Canto flow cytometer (BD Biosciences). Data from 10,000 cells per treatment group and time point were analyzed using BD FACSDiva (BD Biosciences) software.42. Cells were fixed with 2% paraformaldehyde prior to analysis using BD LSRFortessa Cell Analyzer (BD Biosciences). Cells treated with 0.1% Triton X prior to staining were used as the positive control for the viability dye as well as the threshold for viability gating.A two-color fluorescent staining followed by flow cytometry was used to analyze the proportions of early/late apoptotic and necrotic cells. Fixed Apoptotic/Necrotic (FAN) cell test was used with Zombie Violet Fixable Viability Dye  to identify cells with damaged plasma membrane, and anti-phosphatidylserine (PS) antibody conjugated to Alexa Fluor 488  to identify apoptotic cells exposing PS on their surfacet test.The statistical analysis of % copy number variation (CNV) in HNSCC patients from TCGA datasets was conducted by Fisher exact test. The correlation between CNV and mRNA expression in HNSCC with different HPV status was calculated by Spearman correlation coefficient significance test with p-values presented. The relative gene expression among different normal and tumor groups, DR5 protein expression among the different cell lines detected by the flow cytometry, and data from XTT assays and colorimetric assays were presented as mean ± standard deviation (SD) or standard error (SE), and the statistical significances were calculated by the Student’s TCGA specimens were obtained with informed consent under an IRB approved protocol.All authors give consent for the publication of the manuscript in Molecular Cancer.Supplementary Information.


Research on new cancer drugs is performed either through gene knockout studies or phenotypic screening of drugs in cancer cell-lines. Both of these approaches are costly and time-consuming. Computational framework, e.g., genome-scale metabolic models (GSMMs), could be a good alternative to find potential drug targets. The present study aims to investigate the applicability of gene knockout strategies to be used as the finding of drug targets using GSMMs. We performed single-gene knockout studies on existing GSMMs of the NCI-60 cell-lines obtained from 9 tissue types. The metabolic genes responsible for the growth of cancerous cells were identified and then ranked based on their cellular growth reduction. The possible growth reduction mechanisms, which matches with the gene knockout results, were described. Gene ranking was used to identify potential drug targets, which reduce the growth rate of cancer cells but not of the normal cells. The gene ranking results were also compared with existing shRNA screening data. The rank-correlation results for most of the cell-lines were not satisfactory for a single-gene knockout, but it played a significant role in deciding the activity of drug against cell proliferation, whereas multiple gene knockout analysis gave better correlation results. We validated our theoretical results experimentally and showed that the drugs mitotane and myxothiazol can inhibit the growth of at least four cell-lines of NCI-60 database. Various tissues and organs like breast, central nervous system (CNS), colon, lung, ovary, prostate, renal are affected by cancer. Millions of people are suffering from cancer and the number is increasing4. Available drugs that are used for treating cancer, unfortunately, have many side effects5. Therefore, there is an increasing demand for new therapies with better therapeutic windows, implying that the drug will target a particular cell type (such as tumour cells) with no or minimum negative effects on healthy cells. The search for such suitable therapeutic windows is an important and challenging problem in the case of cancer. Another problem with the existing cancer drugs is that a particular drug shows different responses when applied to different individuals. This is because the effects of a drug on a patient depend not only on the interaction with its targets but also on the activities of many other enzymes which form a complex network of metabolic reactions in which the products of a reaction become the substrates of other reactions6. This leads to the emerging field of personalized medicine and personalized drug-choice7. Thus there is a need for developing new anti-cancer drugs taking care of the above problems and demands for an in-depth mechanistic understanding of cancer9.Chronic progressive diseases such as cancer do not derive from abnormal functioning of a single gene or a single pathway but reflect the disorder of complex intracellular and intercellular networks that link tissues and organs14. Another approach is the phenotypic screening of drugs in cancer cell-lines to find its effect on the cell growth18. Both of these processes are very costly and time consuming21. Therefore, a computational method, like metabolic networks, could be a good alternative to find drugs having better selective ability in killing cancerous cells24. One tool which is particularly suitable to deal with problems like personalized medicine and finding a larger therapeutic window is genome-scale metabolic models (GSMMs)6. GSMM is the reconstructions of metabolic networks that connect gene–protein–metabolic reactions26. Different reactions containing stoichiometric coefficients are used to build a stoichiometric matrix to obtain quantitative information on how different metabolites are linked to each reaction in the network. Through flux balance analysis (FBA) on these GSMMs, one can evaluate the metabolic capabilities of the cell, e.g., the capability of synthesizing biomass building blocks. GSMMs have successfully been used in cancer drug development31. For example, Folger et al.27 made a generic metabolic model of cancer and predicted some growth-supporting genes for cancer, which were validated with experimental shRNA data. GSMMs were also used to predict the putative effects of drugs of DrugBank database6, but the results were not compared with the experimental data.Gene knockout study in cancer cell-lines is used to see the effect of an existing cancer drug or to develop new cancer drug29 using Personalized Reconstruction of Metabolic models (PRIME) approach. They established their cell-specific model based on molecular and phenotypic data. It is shown that the models can find drug targets that inhibit the proliferation of specific cell-lines. Their models can also infer on the prognosis of breast and lung cancer. In this study, we applied their model for a more comprehensive study on the single and multiple gene knockout effects on the growth rate of the cancer cell-line and compared the results with the online experimental database. We also attempted to capture the underlying mechanisms associated with the observed growth reduction rate due to gene knockout. We further analysed our top-ranked genes to get potential targets which were then validated experimentally. It is also observed that multiple knockout tests give a better correlation with experimental observation than single-gene knockout results.The present study is an attempt to explore gene knockout strategies that apply GSMMs in finding possible targets and mechanisms related to cancer disease. For the analysis, we considered the GSMMs of the NCI-60 cell-lines built by Yizhak et al.29 to obtain the GSMMs of cancer from different tissue types. The networks were made using the molecular (gene expression) and phenotypic data (proliferation rate) of cancer cell-lines by applying PRIME method. These data were used to constrain the bounds on the flux values of the corresponding reactions in metabolic networks. The data for NCI-60 collection were taken from the study by Lee et al.32, in which RPMI-1640 was used to grow the cell-lines experimentally. We used 60 cancer metabolic networks across 9 tissue types of NCI-60 panel to find cancer drug targets which inhibit cell growth across all cell-lines. We aimed to rank metabolic genes according to their growth inhibitory effect in cancer cell-lines.We used the study of Yizhak et al.34 and predicted the growth rate of cancer cell-lines after knocking out each gene one by one. Taking the average of fractional cell growth (FCG) of 60 cell-lines, we obtained the mean FCG of each 1905 genes , metabolically less efficient (MLE), zero flux genes and blocked genes. There are 71 essential genes, 470–530 pFBA optima, 230–280 ELE, 545–577 MLE, 82 zero flux genes and 427 blocked genes across 60 cancer cell-lines models. We looked for the classification of the 143 growth reducing genes and found that these genes contain all the 71 essential genes and the rests are pFBA optima The production fluxes of the metabolites involved in the biomass reaction is changed due to gene knockout. The biomass reaction in the GSMMs uses 43 metabolites as substrate, termed as biomass metabolites , which contains 7440 reactions and 2194 transcripts, using protein expression data from the Human Protein Atlas37. These models consist of 2426 ± 467 reactions (± s.d.) and 1262 ± 204 transcripts. They applied a published algorithm39 to predict the flux activity states of the genes by applying an optimization method. The method maximizes the consistency between gene expression and the corresponding enzyme activity. A comparison of the result of normal cells with that of the cancer cell-lines will help us to find targets that can reduce the growth of cancer cells but has minimal effect on the normal cells. Our gene knockout simulation result gave us 143 genes whose knockout can reduce the growth rate in cancer cell-line metabolic models. Some of these genes have multiple isoforms. After removing those isoforms, the gene list reduced to 121 unique genes and all of them could be potential drug targets. However, to be a potential drug target, a gene should show a minimal activity in normal cells. So, we looked for the activity of these 121 genes on normal cell models and found from the literature26 that only 13 genes , which is the third complex in the electron transport chain in mitochondria and the subunit proteins are encoded by these ten genes. It plays a critical role in ATP generation process by catalysing electron transfer from ubiquinol to cytochrome c, coupled to proton transport from the matrix space to the intermembrane space of mitochondria41.Our top-ranked genes can be a potential drug target if their knockout does not significantly affect the growth of non-cancerous cells. So, we need to see the effect of our growth reducing genes on normal cell model. The 60 cell-line panel covers nine different tissues. So, we considered nine models42 and myxothiazol (CYTB inhibitor)43 for in-vitro studies. Mitotane is reported to show anticancer activity in some cell-lines such as NCI-H295, Hela, HepG2, IMR-32 and HEK29342. So we considered four cell-lines  from NCI-60 cell-line panel, which are different from the cell-lines reported in Ref.42. The effect of these two inhibitors on the cell viability was studied by adding the inhibitor at different concentrations and measuring the growth rate of cell-lines. Fold change in the growth rate  was calculated for each cell-line at different drug concentrations for both the drugs and was plotted in Fig. To validate our simulation results for identifying potential drug targets, we searched for the inhibitors of these 13 genes and used two commercially available inhibitors, i.e., mitotane (SOAT1 inhibitor)44 on the knockdown effect of 1444 genes in 30 cell-lines  by reconstructing and analysing personalized GSMMs for six HCC patients. Raškevičius et al.6 used GSMMs to predict the putative effects against cancer of those compounds which are structurally similar to human metabolite and also gave a concept of finding therapeutic windows through GSMMs. Turanil et al.48 introduced a drug repositioning based method via GSMMs to predict therapeutic agents for cancer treatment. They reconstructed prostate cancer-specific GSMM by combining personalized GSMMs (n > 450) and proteomics data. They used drug-perturbed gene expression data of three cell-lines  from the ConnectivityMap2 (CMap2)49 to reveal drug off-targets by predicting novel gene–drug interactions and evaluated in-silico cell viability. The present study aimed to develop a knockout strategies for identifying potential drug targets and the associated mechanisms using GSMM and gene expression data. This will help us to get novel drug targets as well as targets that might be used for drug repurposing. We used existing GSMMs of NCI-60 cell-lines29 to predict the anti-proliferative activity of single metabolic genes as targets against cancer and ranked them accordingly. We got 143 genes whose knockout reduced the cell growth across all the metabolic models of the NCI-60 panel. We also obtained a list of 1488 genes whose knockout does not show any effect on the growth rate of any cancer model. We searched for the underlying mechanism for such reduction in the growth rate of cancer cells by 143 genes and found that the biomass reduction score (BRS) of those genes were much higher than those of 1488 genes. The synergic effect in biomass reaction is much more for 143 gene list than 1488 gene list. It is observed that there are 12 biomass metabolites which are influenced by almost all the 143 genes but not by any of the 1488 genes for all the 60 cell-lines. Glycogen is the top-ranked among those 12 biomass metabolites. It is already reported that glycogen pathway is up-regulated in various cancers51 and funneled into glycolysis to promote cell growth, invasion and metastasis52. Likewise, glycogen is used by cancer cells to survive under nutrient starvation condition53. Considering its importance, inhibition of glycogen metabolism has become a new potential strategy for cancer treatment56.Recently, GSMM gained a lot of attention in drug discovery. It has been used to study drugs related to cancer. Ghaffari et al.42 and additionally we have showed its effect on 4 more cell-lines. These cell-lines are taken from different tissues like blood peripheral, colon, adrenocortical gland, cervix, liver, kidney and brain. We have used mitotane to inhibit SOAT1, which is already known for the treatment of adrenocortical carcinoma and Cushing’s syndrome59. However, the inhibitor of CYTB, myxotyiazol, is not known as an anticancer drug, but we have seen its growth inhibiting effect on four cancer cell-lines. This could be a potential novel repurposed drug and need further evaluation. Moreover, literature survey showed that inhibition of SOAT1 and CYTB do not have any significant influence on the growth of the normal cells62. Literature also supports the validity of our other identified targets -induced angiogenesis in endothelial cells and is proposed to be applied as a drug for human cancer68. Cytochrome c-1 (CYC1) is found to play an important role in breast cancer patients. Knocking down of CYC1 inhibits proliferation in human breast cancer cell-lines69. In another study, it was observed that silencing CYC1 by shRNA transfection also inhibits proliferation in human osteosarcoma (OS) cells70. Another identified gene target UQCRFS1 appears to be involved in the progression of gastric cancers and in the development of more aggressive phenotype of breast cancer72. Lentivirus-mediated knockdown of UQCRC2 suppresses cell growth and colony formation in RKO and HCT116 cells, result in cell cycle arrest and induce cell apoptosis in vitro in colorectal cancer (CRC)73. The knockdown of ACACA expression inhibits cell proliferation in prostate74 and breast75 cancer cell-lines.We looked for potential targets from these 143 genes which showed a significant reduction in the growth rate following knockout. To be a potential target, these genes need to show the minimal knockout effect on the normal cells. We obtained 13 such targets from 143 genes whose knockout reduced the proliferation rate of cancer cells but were inactive across all the 9 normal cell models. One of the main features of the identified targets was that they were showing their effect in all the NCI-60 cell-lines. To experimentally validate the effect of these targets on multiple cell-lines, we chose SOAT1 and CYTB and showed that it’s inhibition reduces the growth rate in multiple cell-lines. Inhibition of SOAT1 is known to reduce growth rate in 5 cell-lines78. On the other hand, when applied multiple gene knockout strategies, we obtained a higher and significant rank-correlations with the experimental results. It leads to the conclusion that multiple genes knockout show a better result than single-gene knockout, confirming the similar observations by other studies81. This might be because genes or proteins interact in a complex network, where alternative pathways always exist to carry the function82. Though the single knockout results did not give the desired correlations, the gene ranking obtained using this strategy seemed to be significant. It was noticed that a drug could only be active if it has at least one target belonging to the top rank. In case, none of the targets is from the top rank then that drug is observed to be ineffective.We used Spearman rank-correlation method to compare our single-gene knockout ranking obtained from the GSMM with the experimental data. Most of the observed rank-correlations were very low and/or not significant. This was in agreement with other studies where single-gene knockout does not show the desired resultThe developed strategy to use gene knockout in GSMMs to identify drug targets has general applicability with any novel set of gene expression data associated with a tumour. The tumour-specific GSMM can be built from the gene expression data to go for the single or multiple gene knockout strategies. We proposed to use drug information for multiple gene knockout strategies that can also serve as drug repurposing technique. This method could be used to identify novel drug targets as well as targets that might be used by existing drugs on novel kinds of tumour. It is, therefore, possible to identify genes that are more relevant to specific cancer. The developed methodology could also be used to screen for common therapy. This strategy was used in the present study where we obtained 143 genes whose knockout were showing significant growth reduction across all cell-lines 86 would help to construct a more accurate model of cancer cell-lines and will certainly enhance the possibility of finding new targets.Finally, we want to mention that the GSMM models use the metabolic flux values of reactions for prediction. It is known that these fluxes depend on protein’s post-translational state as well as corresponding metabolite levels33 for observing gene knockout effect on the growth rates of 60 cancer cell-line models. To mimic the knockout condition of a gene, we changed the lower and upper bounds of flux values of the reactions associated with the gene to zero. In the case of simultaneous knockout of multiple genes, the bounds of all reactions of these participating genes were set to zero. To measure the growth rate, we used two models as input for MOMA: one representing the knockout condition and the other one representing the wild-type condition of a particular cell-line. The fractional cell growth (FCG) was then calculated by taking the ratio of growth rates in knockout condition to wild-type condition. COBRA toolbox was used from the MATLAB package34 for MOMA. Though there exists several genes associated with the same reaction with non-trivial AND and OR combinations, in order to keep our analysis simple, we followed the literatures88 and assumed zero flux for the reaction associated with the knockout gene.We used minimization of metabolic adjustment (MOMA) techniqueIn GSMMs, the identity of genes is given in gene ID format and therefore the gene symbols need to be converted into gene IDs. For this purpose, we used the Uniprot database, which provides the mapping between gene IDs and gene symbols of the metabolic genes. Using this method, one can find out the metabolic genes from any given gene set having gene ID information. One can also extract genes from any given gene set, which are present in the cancer cell-line metabolic models, by taking intersection with the 1905 gene IDs present in the cancer models.Flux balance analysis (FBA) was applied to get the flux values of each of the metabolic reactions present in the metabolic networks under the wild-type and gene knockout condition. Then, the production flux rate of a metabolite was obtained by summing up the flux values of the reactions in which it is produced. Finally, the ratio between the production flux rates under the two different conditions was calculated.Total 43 metabolites are used as the substrate in the biomass reaction, and we termed them as biomass metabolites  have information about its targets and the corresponding mechanism of action. These targets are given in the form of gene names. There are 1684 drugs which have gene ID information of at least one metabolic target (see method “Gene symbol to Gene ID conversion”). We then searched for the drugs that can decrease the activity of at least one metabolic target. For this purpose, we selected the following three mechanisms of actions: (i) inhibition, (ii) antagonist, (iii) inverse agonist. We found 410 drugs out of 1684 which decreased the activity of at least one metabolic gene. Out of these 410 drugs, only 380 drugs have at least one inhibitory type target on cancer cell-lines GSMMs.In DrugBank database46 has NSC ID. So, we first converted their IDs into a single ID for further analysis. In the DrugBank database, the conversion of drug ID to PubChem CID or CAS ID is provided and the conversion of CAS ID to NSC ID is given in the chemical data section of the NCI-60 growth inhibition database. We therefore used CAS ID information of the drugs to link these two datasets. There are 373 drugs, out of previously described 380 drugs, which have CAS ID information and out of them only 200 drugs have NSC IDs. However, all NSC IDs do not have information regarding GI50 value. So, we finally obtained 64 drugs with NSC IDs that have a GI50 score in negative log value.DrugBank database has DrugBank ID, whereas NCI-60 growth inhibition database90.GI50 score is an important measure of drug activity. It quantifies the dosage of the drug required to inhibit the cell growth by Supplementary Information 1Supplementary Information 2Supplementary Information 3Supplementary Information 4


Panax ginseng cell lines leads to a decreasing synthesis of the biologically active substances used in traditional medicine. To gain insight into the cellular mechanisms which may influence this process, we analyzed variations within the rDNA cluster of the Oriental ginseng cell lines. The cell lines were cultivated for 6 and 24 years; the number of nucleoli and chromosomes was analyzed. The complete 18S rDNA sequences were cloned and sequenced. The nucleotide polymorphism and phylogenetic relations of the sequences were analyzed, and the secondary structures for separate 18S rRNA regions were modeled. The 18S rDNA accumulated mutations during cell cultivation that correlate well with an increase in the number of chromosomes and nucleoli. The patterns of nucleotide diversity are culture-specific and the increasing polymorphism associates with cytosine methylation sites. The secondary structures of some 18S rRNA regions and their interaction can alter during cultivation. The phylogenetic tree topologies are particular for each cell line.The observed alterations in rDNA clusters are associated with a somaclonal variation, leading to changes in the pattern of intracellular synthesis during cell cultivation. The identified divergent rRNAs could provide additional gene expression regulation in P. ginseng cells by forming heterogeneous ribosomes.Long-term cultivation of  Panax ginseng Meyer is one of the most valuable herbs used in Chinese traditional medicine. However, the uncontrolled harvesting of wild ginseng roots has led to an endangered status of this unique plant, so one of the important parts of its conservation strategy is the cultivation of P. ginseng cells in vitro. Plant cell cultures are a promising source for the large-scale and cost-effective production of high-purity valuable secondary metabolites. Plant cell lines have attracted scientists’ attention due to their ability to produce biologically active substances with strong therapeutic effects against various diseases and aging [nd aging ,2,3,4,5,nd aging ,2,3,4. TP. ginseng cell cultures is the decrease in genomic stability, including polyploidy, aneuploidy, and multi-nuclearization [actin), genes participating in ginsenoside biosynthesis (PAL and DDS), and a development-regulating gene (SERK) [It has been demonstrated that the in vitro culture of plant tissue induces various mutations and changes in epigenetic marks, and genetic variation has been observed both in cultured cells and in plants regenerated from the cultured cells. These mutations include cytological abnormalities such as ploidy changes and chromosome rearrangements, single-base substitutions, changes in the copy number of repeated sequences, and alterations in DNA methylation patterns ,7,8. Sucrization . Anothere (SERK) .Panax ginseng plant, which in theory may affect the production of the biologically active compounds [The rRNA gene for small ribosomal subunit (SSU) is a part of the rDNA cistron, which consists of three coding  and four spacer regions , external transcribed spacer (ETS), and intergenic spacer (IGS)). To satisfy the transcriptional requirements, a high copy number of the rDNA cistrons is needed for each organism, and mechanisms of the concerted evolution are directed to their homogenization within genomes and within species . However, intraspecific and intragenomic sequence variability appear to be common characteristics for the rDNA sequences ,17,18. Pompounds .Panax ginseng cell lines, which are different at the time of cultivation and in the tissue source. We focused our attention on the nucleotide polymorphism of the cell cultures in order to determine whether the existence of differences among 18S rDNA sequences could decrease the ginsenoside biosynthesis. We assessed the sequence polymorphism by cloning and sequencing multiple copies of the 18S rDNA, reconstructing phylogenetic relationships as well as modeling putative secondary structures for some regions of the 18S rRNA molecule. We also analyzed cytogenetic characteristics of genomic instability, including the number of chromosomes per cell and the number of nucleoli per nucleus.The purpose of this study was to determine the patterns of intragenomic nucleotide polymorphism in the complete 18S rDNA sequences of 1s and 1o were established in 1988 at the Laboratory of Biotechnology (IBSS FEB RAS). Line 1s was obtained from the stem of a 2-month-old plant of Panax ginseng, and line 1o was obtained from the ovary of a wild-grown plant. The cell line 9s was established in 2006 from the stem of a juvenile ginseng plant grown in an open experimental nursery. The analyzed P. ginseng cell lines have been passaged into fresh nutrient medium monthly for 6 (line 9s) and 24 (lines 1s and 1o) years, totaling more than 70 and 280 passages, respectively. The cell line was cultivated at 24–25 °C in the dark on solid W medium [p-chlorophenoxyacetic acid (W4CPA) with a 30–35-day subculture interval.The long-term cultivated cell lines The cytological materials were prepared and analyzed as previously described . Cell cu2, 0.01% gelatin (w/v), 0.1 mM Triton X-100, 0.25 mM of each deoxynucleotide triphosphate, and 10 pM of each primer. We also used a mix (1:8) of Pfu and Taq DNA polymerases  to minimize amplification errors. The polymerase chain reaction (PCR) primer sequences and temperature conditions were the same as those in [The isolation of total DNA was performed as described previously . Amplifithose in .The PCR products of two independent PCR reactions were separately cloned into the pTZ57R/T plasmid using the InsTAclone PCR product kit . The PCR products were used for cloning procedures without any modification. Clones were generated according to the protocol recommended by the manufacturer and supplied with the cloning kit. The clones were amplified using M13 (‒20)  primers for colony screening and sequencing. The sequence of each clone was determined by an ABI 3130 Genetic Analyzer  using additional nested primers . The nucTriticum aestivum based on cryoelectron microscopy data [Panax ginseng plant [http://mfold.rna.albany.edu), recommended for most RNA folding, using free-energy folding algorithms [A published model for the SSU rRNA secondary structure of wheat opy data  was usedng plant . Primarygorithms . The expπ) along the gene sequence  was analyzed using the DnaSP version 5.10 [The Basic Local Alignment Search Tool program used for the alignments was performed using the MEGA 5 software package . The dision 5.10 . The intion 5.10  and miniion 5.10 .P. ginseng cell lines the number of nucleoli in the interphase nuclei and chromosomes in cells varied significantly. The existence of mixoploidy in cell populations with different levels of variability as well as the presence of hyper-diploid and hypo-diploid, euploid, and aneuploid cytotypes was observed in all cell cultures. The highest level of chromosomal instability was characteristic of the long-term-cultivated (old) cell lines 1s and 1o, in which the number of chromosomes varied from 6 to 150, and hyper-diploid cells with 48–80 chromosome number constituting the modal class (over 50%)  constituted the cells chromosome number from 24 to 60  A. In sho24 to 60 A.9s, the structure of the interphase nuclei did not differ from that of differentiated leaf tissue [1s and 1o  between the mean number of nucleoli in the nucleus and the average number of chromosomes in the cell.The structure of the interphase nucleus also varied after the establishment of plants into in vitro culture . In the f tissue , while ss and 1o . In the s and 1o B. The raectively . Compari9s, 90% were found to have 1–2 (55%) and 3–4 (35%) nucleotide differences (P. ginseng plant from GenBank (accession number D83275). The clones of the long-term-cultivated cell lines had no identical sequences to the 18S rDNA of the P. ginseng plant from GenBank. In the 1o cell line, the majority of clones (83%) differed in 1–2 and 3–4 nucleotides. In the 1s cell line, the number of such clones was significantly lower (52%), however, only line 1s presented clones with differences in 7–10 and more than 10 nucleotides. The 18S rDNA clones with 5–6 nucleotide substitutions were identified in both long-term-cultivated cultures  and A→G and T→C in 1o (> 47%) (9s and more than 30% of nucleotide substitutions both in 1s and 1o were associated with sites of cytosine methylation (9s cell lines represented substitutions of T (NH2−) with other nucleotides ; we named them “DNA amination” substitutions. In the long-term-cultivated cell lines 1s and 1o, these mutations were less numerous (about 19–26%), as can be seen in Almost half of the transitions (> 46%) in the short-term-cultivated cell line  (> 47%) . About 2hylation . More th1s. However, the number of amination substitutions was almost the same in the stem cell lines and slightly lower in the ovary cell line  (The mean numbers of nucleotide substitutions per the 18S rRNA gene sequence (2.05–5.5), as well as those associated with sites of methylation (0.45–1.7), were significantly higher in the long-term-cultivated cell lines, particularly in ctively) . In spitctively) .9s). The value of nucleotide diversity (π) for the cell lines 9s, 1s, and 1o were estimated to be 0.00226, 0.00550, and 0.00360, respectively. The distribution patterns of nucleotide diversity, as well as hotspots of variation in the 18S rDNA sequences, were different for each cell line of P. ginseng  and h7 possessed canonical structures; the majority of h8 and all the h17 alleles had a common alternative specific hairpin structure. h6 and h8 formed one and two cell-line-specific alternative structures, respectively. The 1s alternative variant of h6 is a singular configuration , possessing only one interior loop and an increased apical helix. The first alternative configuration of h8 had a shortened apical helix with increased hairpin and interior loops, and the second one also had an additional one-nucleotide bulge loop  and the average number of nucleotide substitutions (K = 4.095–9.937) for the 18S rRNA gene sequences differed among the cell lines, and were the highest in 1s cell line. According to these indices, cell lines were closer to plant P. ginseng (D = 0.001–0.003) than to each other (D = 0.003–0.005) and the least variable line 9s revealed the highest similarity with the cultivated plant.The total dataset of the 18S rRNA gene contained 113 sequences of length 1809 bp that resulted in 111 ribotypes, and no common ribotypes for cell cultures were identified which could be evidence of high specificity in a ginseng cell line. The values of both pairwise genetic 9s contained 19 alleles with 40 variable sites and 39 singleton mutations. The MJ network appeared as a star-like topology, suggesting a recent expansion from an ancestral sequence. The dataset for the old cell cultures contained 165 and 135 variable sites, of which 22 and 11 were parsimony-informative for 1s and 1o, respectively. The number of ribotypes for both old cell lines was 46. The MJ network for the 1s cell line was relatively complex, comprising one major and a few secondary order star-like structures. The MJ network for the 1o cell culture was more similar to that of 9s, but rays in this reconstruction were much longer  . Appareng plants .For a long time, it was believed that through the mechanisms of concerted evolution the 18S rRNA genes are invariant within the genome and only a small variability may occur among individuals. It has since been found that in some cases this rule is not followed, and nucleotide variability can be substantial within the same genome ,17,18,42P. ginseng cell cultures, the processes maintaining the 18S rRNA gene sequence homogeneity known as concerted evolution can fail to operate properly for at least two reasons: the high number of gene copies (resulting from polyploidization) and increased levels of nucleotide substitutions. The predisposition to nucleotide variability is inherited by the culture cells from their ancestors (tissue source).Additionally, it has previously been shown that the 18S rRNA gene from ginseng plants already contains multiple nucleotide substitutions . Thus, i1s) appeared to be higher than that of the ginseng plant leaves [P. ginseng actin, PAL, DDS, and SERK genes [The intragenomic mechanisms which may influence mutation fixation rates in gene families (particularly rDNA) comprise such processes as mutation, natural selection, genetic drift, and molecular drive  . The levt leaves  and the RK genes . HoweverRK genes ,18, suggArabidopsis and maize) revealed single-nucleotide substitutions in protein-coding genes as the predominant type of mutations  [ts/tv ratio was considerably lower in the long-cultivated cell lines, which could be the evidence of the very high level (comparable with interspecific) of genetic differentiation predicted for the long-cultivated cell lines [The whole-genome sequencing analysis performed for polyploid plants (aclones) . Multiplaclones) . Herein,ll lines . Analogoll lines .A large number of nucleotide substitutions could suggest an occurrence of pseudogenes among the cloned 18S rDNA gene sequences. Due to their high evolutionary rate, pseudogenes contribute to the extreme diversity of nuclear rDNA . PseudogP. ginseng cell lines. The secondary structures for entire 18S rRNA were estimated to be quite conserved across the species studied, although those for variable regions differed significantly among themselves; however, no intraspecific variations have been reported until the present [In intraspecific comparison, the existence of alternative rRNA conformations for particular variable regions  could be evidence of both their high divergence and functional damage including pseudogenization. Consequently, data on the rRNA secondary structures could predict the possibility of disrupting the ribogenesis and protein synthesis in  present ,49,50. A present . A commo present . At the  present .Escherichia coli, Saccharomyces cerevisiae, and humans), h33 , and h27. The h27 lied in the location of potentially large conformational dynamics and packed against the decoding center in h44 [E. coli 16S rRNA, h27 was shown to have an innate tendency to exist in dynamic equilibrium between two conformations; a conformational switching model (between “accurate” and “error-prone” conformations) has non-been confirmed [P. ginseng cell lines.In our study, no alternative conformations were obtained for highly conserve h18 , corresponding quite well with the consensus sequence (GUUGGUUUU) inferred for different groups of organisms [The most significant of the nucleotide differences among cell cultures which resulted in alterations of the secondary structures was localized in h8—that is, near the interaction site between two hypervariable regions, ES3 and ES6. Perhaps variations in h8 (both for interspecific and cell-line comparisons) are at least partially due to its adjacency with these regions. All the known 18S rRNA reconstructions support an interaction between ES3 and ES6, although its precise significance remains to be clarified. In rganisms ,55. Simirganisms ,57,58 anrRNA sequence variations may alter the structure and function of the ribosome and its interplay with components of a cellular milieu, since the functions of many RNA types are defined by the formation of intramolecular base pairs ,59,60. TOur results reveal significant alterations in the methylation profile for cell cultures different in age and tissue source ginseng. In plants, cytosine methylation is a common and flexible epigenetic regulator of gene expression that is important in normal development, in creating phenotypic diversity, and in modulating agronomical traits ,56,59,66P. ginseng cell lines, which could affect the synthesis of highly specific biologically active ginsenosides. The divergent rRNA alleles identified in this study could provide additional gene expression regulation in P. ginseng cells in response to cultivation by means of the constitutions of heterogeneous ribosomes. The findings provide a framework for future exploration of how encoded rRNA variants can give rise to functionally heterogeneous ribosomes altering the pattern of intracellular synthesis. An understanding of the underlying mechanisms could help to prevent the loss of ability to produce biologically active substances in cell lines making cultivation more successful. The negative effects of long-term cultivation can be reduced by appropriate selection of the cell culture tissues, which is important for applied biotechnology. Advanced cell technologies based on the ginseng cell cultures will be able to provide humans with the necessary and effective heart drugs, high-quality cosmetics, and anti-aging remedies.The data available in the literature indicate a high potential for the use of cell cultures; however, their implementation requires additional knowledge of physiology, metabolism, and genome of the plant cell, as well as new and non-standard approaches to solving the existing problems of cell engineering. Herein, during cell cultivation, we observed extensive age- and tissue-specific alterations, cytological and molecular, in the rDNA clusters of