TERT) expression correlates with telomere content in lung, central nervous system, and leukemia cell lines. Using CRISPR/Cas9 screening data, we show that lower telomeric content is associated with dependency of CST telomere maintenance genes. Increased dependencies of shelterin members are associated with wild-type TP53 status. Investigating the epigenetic regulation of TERT, we find widespread allele-specific expression in promoter-wildtype contexts. TERT promoter-mutant cell lines exhibit hypomethylation at PRC2-repressed regions, suggesting a cooperative global epigenetic state in the reactivation of telomerase. By incorporating telomere content with genomic features across comprehensively characterized cell lines, we provide further insights into the role of telomere regulation in cancer immortality.In cancer, telomere maintenance is critical for the development of replicative immortality. Using genome sequences from the Cancer Cell Line Encyclopedia and Genomics of Drug Sensitivity in Cancer Project, we calculated telomere content across 1299 cancer cell lines. We find that telomerase reverse transcriptase ( Telomeres, repetitive nucleoprotein complexes located at chromosomal ends, are an important component of genomic stability . As protTERT, that lengthens telomeres by catalyzing the addition of TTAGGG nucleotide repeats from an inbuilt RNA template component, TERC  and whole-exome sequencing (WES) data . Recentlr = 0.84, p = 3.7×10−79, n = 286) and moderate agreement between CCLE WGS and CCLE WES estimates , suggesting that our length estimates were not strongly biased by source. Among the three datasets used, CCLE WGS samples each captured at least ten thousand telomeric reads (which we estimated as those with six or more TTAGGG repeats), followed by GDSC WES samples at 100–1000 telomeric reads, and lastly by CCLE WES samples with 10–100 telomeric reads  correlations between telomere content and the age of the original donor   and within , perhaps due to their elevated levels of telomerase expression, which were the highest among all subtypes. The 55 non-cancerous samples profiled as part of the GDSC displayed relatively high telomere contents  , consistd within  cell lind within . Cell liTERT overall and isoform-specific mRNA levels were positively though weakly correlated as determined by RNA sequencing (RNA-seq) both within each subtype , which are key mediators of telomere capping and elongation termination , we found significant (FDR < 0.01) outlier associations between the CST complex genes and genes encoding six additional telomere-associating proteins . These first five additional telomere-associating proteins, together with TERF2IP, comprise the shelterin complex, the protector and regulator of telomere length and topology  outlier when a comprehensive set of hotspot and damaging mutations was compared against sensitivity to ACD and TERF1 dependencies in the Avana panel  were associated with the greatest mean expression of TERT  and generally undetectable in fibroblast-like cell lines. With regard to the telomerase RNA component (TERC), we found strong associations between TERC RNA expression and RNA levels of several small Cajal body-specific RNAs (scaRNAs) and histone subunit RNAs  domain  domain  and is ativation . Althouge length . Despitepression .TERT in cancer is driven by a variety of factors. Aside from copy number amplifications  associated with an increase in TERT expression . Given that cells with biallelic TERT expression presumably express TERT with twice the transcriptional source sites as those with monoallelic TERT expression, this reduced difference may be a consequence of the effects of the TERT promoter mutation in producing particularly robust monoallelic expression  and determined TERT allele-specific expression status for 157 cell lines  of samples, only 9.4% (11 out of 117) of samples expressed CLPTM1L from a single allele and 22% (5 of 22) expressed SLC6A19 from a single allele, and we were unable to assess co-occurrence due to lack of overlap in samples with heterozygous SNPs in both TERT and CLPTM1L or SLC6A19. Furthermore, a search for structural variants in the surrounding 100 kilobase regions yielded no significant associations. However, given that only 106 cell lines had both a known TERT ASE status and the required WGS data for structural variant determination, more genomic annotations may be needed for the discovery of additional mechanisms driving the monoallelic expression of TERT.To further explore allele-specific expression (ASE) patterns of ll lines , an incrll lines . Out of pression . Our expll lines . This hiTERT promoter mutations are characterized by unique patterns of epigenetic marks, namely allele-specific CpG methylation (ASM) and H3K27me3 repressive histone modifications  and TERT promoter mutations. Examination of methylation patterns at the TERT locus revealed five prominent ASM clusters in the TERT locus, corresponding roughly to the upstream 5 kb region (containing the promoter), part of a CpG island overlapping the first exon, two other parts of this CpG island, and the remaining gene body (n = 485), remaining gene body (n = 493), and exon 1 (n = 478) regions  and the wildtype promoter context  compared to biallelic TERT expressors  with significant differences in methylation levels in TERTp mutant cell lines compared to TERTp wild-type ones. If TERT hypomethylation were a downstream consequence of TERT promoter mutations then we would expect TERT hypomethylation to be an isolated event, and thus there would be few CGIs outside the vicinity of TERT with methylation levels correlated with TERTp mutant status. Surprisingly, we instead found a broad genome-wide distribution of CGIs that were hypomethylated in TERTpmut samples relative to TERTpWT samples  was the top correlate  (TERTpmut-hypomethylated CGIs (FDR < 0.0001) and robust 10-fold enrichment for polycomb repressive complex 2 (PRC2)-repressed regions   to queryted CGIs , we foun regions  previoushylation . Intereshylation . FurtherRT locus  located ll lines , this geTERT promoter status  of PRC2-repressed regions and associated histone modifications as the top enriched region sets  to match the mean of the other.We also attempted to use Telseq to estimate telomeric repeat-containing RNA (TERRA) expression across 1019 RNA-seq samples from the CCLE. However, because the majority of these samples were found to contain little or no reads containing telomeric reads, TERRA capture was determined to be too low for any meaningful analysis.https://portals.broadinstitute.org/ccle/data). Harmonized sample information, telomere content estimates, and other matched annotations are available in Cell lines were annotated with sample descriptors from the CCLE data portal  under the public 19Q4 release . We used the mutation classifications detailed in the Variant_annotation column.We sourced mutations and copy number estimates from the DepMap download portal  measure of less than 0.25 across all cell lines. We excluded exons with missing inclusion values in over 800 cell lines or with a standard deviation of less than 0.1. Pearson correlations were then used to calculate associations between gene and transcript RNA expression levels of TERT and TERC against telomere content estimates as well as other markers. We also constructed linear models regressing merged telomere content as a function of various biomarkers with cell line primary site as a covariate. Moreover, we also calculated linear models of each of CCLE and GDSC telomere content as a function of TERT mRNA levels, TERC mRNA levels, primary site, TERTp mutant status, donor age, and calculated doubling time to examine the contributions of these specific factors. Out of these factors, we found TERT and TERC mRNA levels to be significantly correlated with both CCLE and GDSC telomere contents.We also downloaded processed RNAseq estimates in the form of gene expression, transcript expression, and exon inclusion estimates from the CCLE data portal under the latest release . We also downloaded RPPA estimates (CCLE_RPPA_20181003.csv) and global chromatin profiling results . Before performing subsequent analyses, we transformed transcript and gene expression TPMs by taking a logWe also considered processed methylation estimates available on the CCLE data portal, namely the TSS 1 kb upstream estimates as well as the promoter CpG cluster estimates, which we correlated against telomere content estimates. For these annotations, we filtered out regions with a standard deviation of less than 0.05.TERT and TERC expression associations, as well as telomere content associations, are available in Results of t test. All correlation coefficients and p values were determined using the pearsonr function as part of the scipy.stats Python module.To identify gene dependencies associated with telomere content, we considered knockout/knockdown effects estimated in the Avana CRISPR-Cas9 , Achillex and y under the criteria that the r2 association between the two is among the top five for x vs. all other genes, and among the top five for y vs. all other genes as well. We recursively applied this method four times, which added the five shelterin components ACD, POT1, TERF1, TERF2, and TINF2 to our gene set. To construct the clustered correlation matrix in clustermap function as provided by the Seaborn Python library, with Ward’s method for the determination of the hierarchical clustering.To identify codependencies with the CST complex members, we employed an iterative approach to identify highly ranked correlations. In particular, starting with a seed set of genes (the base case of which was the CST complex), we searched for codependencies between two genes Variant_annotation column, and we considered mutations as ‘hotspot’ if they were labeled as such in the corresponding COSMIC (isCOSMIChotspot) or TCGA (isTCGAhotspot) columns. We excluded mutations with a total damaging or hotspot frequency of less than five across all profiled CCLE samples. Mutations were then compared with dependencies using a two-sided Mann-Whitney U test, with the two classes being non-damaging and non-hotspot mutant samples, and damaging and hotspot mutant samples, respectively.To identify significant associations between dependencies and mutations, we compared dependencies against binary categories of damaging/truncating  and hotspot (highly recurrent) mutations. Using the previously downloaded DepMap 19Q4 mutation annotations, we considered mutations as ‘damaging’/‘truncating’ if they were associated with a ‘damaging’ label under the g COSMIC   or dependency-mutation association .To rank and visualize the codependencies shown in Dependency analyses results are available in TERT expression in the context of allele-specificity, we examined cell lines for which DNA (WES or WGS) and RNA (RNAseq) sequencing data were available. To identify heterozygous anchor SNPs, we considered mutations in the TERT gene body called using Mutect 1.1.6 (Allele-specific expression may be detected by looking for discordant counts of reads mapping to single-nucleotide polymorphisms (SNPs) in DNA-sequencing vs. RNA-sequencing reads . In partct 1.1.6  with defct 1.1.6  with argTERT as well as two neighboring genes, SLC6A19 and CLPTM1L. In particular, we examined the odds ratio derived from a binary contingency table with the two sets of categories being the context (DNA vs. RNA) and the allele  of the read counts. To account for edge cases where the denominator of the odds ratio was zero, we added a pseudocount of 0.5 to each category before computing the odds ratio. We then denoted MAE lines as those having an odds ratio computed using the major allele as the denominator of greater than five. In instances where there were multiple informative SNPs, we considered only the SNP with the greatest supporting total RNA-seq read count. In cases where the same SNP was detected across multiple sources , we considered the source with the greatest coverage of the SNP.We then used these RNA and DNA allele frequencies to classify cell lines as monoallelic and biallelic expressors of TERT, SLC6A19, and CLPTM1L are described in Allele-specific calls for TERT genomic region, we utilized RRBS data generated by the CCLE , methylated-unmethylated (mu), unmethylated-unmethylated (uu), and unmethylated-unmethylated (mm). For semi-methylated CpG pairs not subject to ASM, we would expect high and relatively equal frequencies of the mu and um pairs, whereas for ASM CpG pairs, we would expect the allele bias to result in high mm and uu counts and low um and mu counts. To quantify this imbalance, we used the mean square contingency coefficient (Φ) with a pseudocount of 0.5. Namely, for each CpG pair, we computedTo estimate ASM, we employed a strategy similar to the original MethPipe ASM pipeline. For each pair of CpGs, we considered the four combinations of methylation states between the two CpGs: methylated-methylated , CGI_1 (chr5:1294872–1295134), CGI_2 (chr5:1291374–1294439), CGI_3 (chr5: 1289695–1291090), and the remaining gene body (chr5: 1249661–1289359). We computed ASM imbalance values for these regions by taking the mean CpG-pair ASM values.We first examined the ASM levels of the TERT promoter mutations, we searched for correlates with TERT promoter mutation status among average methylation levels of CpG islands (CGIs). CpG island annotations were downloaded from the UCSC genome browser at http://hgdownload.cse.ucsc.edu/goldenpath/hg19/database/cpgIslandExt.txt.gz. To filter out low-coverage CpG islands, we considered only CpG islands with at least eight CpG sites. Methylation levels per island were then estimated by taking the mean across all CpGs profiled within the island. Using the same filtering parameters, we also computed mean ASM estimates across these CGIs.To identify genome-wide methylation events indicative of TERT locus methylation estimates are described in To identify histone modifications associated with TERTp status, we downloaded global chromatin profiling data from the CCLE Data Portal . Correlations between TERTp status and histone modification levels, as well as correlations between H3K9ac1K14ac0 levels and CGI ASM levels, are described in To characterize the regions that were hypomethylated in association with TERT promoter mutations, we utilized Locus Overlap Analysis (LOLA) , which dU test (i.e. regions with the most significant changes were ranked first). The top 1000 CpG islands were then used as the regions of interest, and the set of all CpG islands examined served as the background universe set. We utilized the LOLAweb application (at http://lolaweb.databio.org/), with the LOLACore and LOLARoadMap sets as the region databases.We ranked hypomethylated CpG island regions by the significance of the change in TERTp wild type vs. TERTp mutant cell lines as assessed by a two-sided Mann-Whitney TERT promoter-mutant hypomethylated CGIs are summarized in Outputs of LOLA on CCLE http://xena.ucsc.edu/, jhu-usc.edu_PANCAN_HumanMethylation450.betaValue_whitelisted.tsv.synapse_download_5096262.xena and EB++AdjustPANCAN_IlluminaHiSeq_RNASeqV2.geneExp.xena). Methylation levels of CGIs was estimated by averaging CpG methylation levels within each CGI, and CGIs with less than four profiled CpGs were excluded. TERT promoter mutation status was obtained from previous estimates .Thank you for submitting your article "Integrated evaluation of telomerase activation and telomere maintenance across cancer cell lines" for consideration by The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.In this manuscript, Hu, Ghandi and Huang describe a number of genomic features that are correlated with telomere content, and focus on the study of cancer cell lines from two different sources  and the Genomics of Drug Sensitivity in Cancer (GDSC). They describe genomic alterations, gene dependencies, telomerase (TERT) expression and promoter mutation, and methylation patterns both in the TERT locus and beyond. The study is impressive in scope, as putting together data from distinct cell lines from different batches and sources is not an easy task.However, the reviewers have a number of suggestions to improve the conclusions and readability of the manuscript.Essential revisions1. About the structure of the manuscript.Two reviewers commented that the manuscript is at times hard to understand due to it covering a large number of observations that not always follow logically from each other. Specifically, it was mentioned that the ATRX/DAXX section does not add much to the paper, and that a substantial part of analyses and supplementary data are not mentioned . Please re-check these issues, and if possible, simplify the paper to convey a clear message.2. About cell lines.Please answer the following questions.a. Does the CCLE provide any information on cell line source? Do the authors have any cell lines with biological replicates from different sources that could determine how this affects telomere length, as in eg. PMID 30089904? Also, are there any technical replicates derived from unique libraries prepared from the same cell line to establish that these show similar measurements?b. Do the authors think their conclusions are affected by the fact that cell lines are immortalised, and this process in many cases includes the activation of TERT? Can their conclusions be extended to living tissues?c. Can the number of cell lines from both sets that were taken forward for downstream analyses be specified in the main text?3. About telomere measurements and correlations.a. The raw telomere content measurements from WGS are not discussed much and quickly discarded in favor of normalized estimates. Looking at the supplementary table these look to be all on a similar and comparable scale for WGS. It strikes me that WGS-based estimates are much more reliable, and some associations may be stronger using these numbers despite being limited to a much smaller sample size. Can the authors show the overlap between WGS/WXS and associated data ? Do the described findings hold when limited to WGS raw estimates or is there insufficient power? Also, an explanation of why correlation is higher between WES and WGS of different datasets than with those of the same dataset would be welcome.b. While the authors look at the patients age at tissue extraction and find a correlation between estimated telomere content and age, one wonders if cell culture specific parameters could not be even stronger determinants. Have the authors looked at time in culture, passage number and population doubling level (PDL) in relation to telomere length?c. Could the authors infer ALT status for a large number of cell lines from other resources? Capital Biosciences was awarded NIH SBIR funding several years ago specifically to determine the ALT status of all cell lines in the ATCC.See for example:1. https://www.eposters.net/pdfs/identification-of-new-alternative-lengthening-of-telomeres-positive-cancer-cell-lines-using-the-c.pdf2. https://www.atcc.org/~/media/PDFs/Presentations/HOC%20poster%20ALT.ashxAt a minimum, perhaps the authors could annotate lines known to exhibit ALT as ALT+ and others as ALT unknown?d. It is slightly surprising there is a positive correlation between telomere content and TERT mRNA in CNS since CNS tumors are often ALT+ (and telomerase-). Possibly none of the CNS lines assayed were ALT+? Perhaps the correlation between TERT mRNA and telomere content needs to be adjusted for ALT status because of the presumed interaction. Could the authors incorporate multiple correlates of telomere content in a model and determine their independent contributions and interactions? Perhaps this could be limited to WGS and using the raw measurements.e. Some correlations with TC  are likely to be biased by the important TC differences between tissue types. Can the authors perform this analysis on each tissue separately, or normalize TC to use relative TC within a tissue type?4. About figures.a. Figure 1.– This is an informative figure. Can a clarification of the total number of cell lines be added?– It says it is 683 but from the main text it seems that many more were taken forward for analysis ? Why are the others not depicted here?– If the outlier U2-OS cell line is removed from the 44 bone cell lines, is the bone cell line average still the highest?– Can the ATRX/DAXX expression be included?b. Figure 2.– Figure 2a-b: the authors claim that dependency toward TRF1 and the CST complex is higher in cells with short telomeres. However, they show a positive correlation between TC and AVANA dependency. This may be interpreted that cells with longer telomeres are more sensitive to loss of TRF1 or the CST complex. That could be explained by their role during telomere replication, and the likely higher replication stress at longer telomeres. If this is not the correct explanation, can the explanation be reworded, as it is confusing?– Figure 2d-e: Does each dot represent a cell line? If so, are the conclusions (model in 2f) drawn out of only 1 cell with a mutation in a hotspot?c. Figure 3a.– Here, it seems that cells with lower PML expression are more sensitive to DAXX suppression. Could the analysis be done with telomerase positive cells only? Similarly, cells with higher levels of ZMYM3 are more sensitive to DAXX suppression. What does that mean biologically? Can you please specify what are the dots?, do they refer to cell lines? So these all represent the top score of their corresponding cell line?5. Other pointsa. The introduction and the narrative tell us that telomerase is activated, that telomeres are usually longer in cancer, etc. Therefore, the phrase "The 55 non-cancerous samples profiled as part of the GDSC also displayed relatively high telomere contents , consistent with previous reports of widespread telomere shortening in cancer ." seems to contradict what has been said. So, is it expected that the normal samples would have long telomeres? Would this be expected physiologically or is it an effect from these being cell lines? Also – is the information for these normal cell lines displayed in Figure 1? Reviewers recommend to add the normals to this figure so it is easier to compare between normal and cancer tissues.b. Could the definition of what a dependency means, exactly, be included? Does it assess correlated gene expression? Does it assess activation of one gene upon knockout of the other? It would be helpful to spell this out in the main text. This may help understand the observed dependencies between the members of the shelterin complex and those of the CST complex.c. Page 6 says "increased sensitivities to knockout of the CST complex components, which are key mediators of telomere capping and elongation termination , were correlated with lower telomere content." However, it seems that supplementary figure 3b indicates that the CST genes are positively correlated with telomere content? If this is an index of sensitivity, could this be indicated somewhere? . Could TERF1 be highlighted in Figure S3b please, DRIVE dependencies?d. About methylation studies. In page 11 it says "TERTp mutants exhibited strong and significant (P < 0.01) increases in ASM  in the promoter (n = 485), remaining gene body (n = 493), and exon 1 (n = 478) regions", but then in page 12, line 292 it says "The observation that TERT promoter mutants display a hypomethylated TERT locus…" – does this not directly contradict the statement above? Or is it referring to methylation on a specific region? Could this be clarified please?Reviewer #1:In this manuscript, Hu, Ghandi and Huang describe a number of genomic features that are correlated with telomere content, and focus on the study of cancer cell lines from two different sources  and the Genomics of Drug Sensitivity in Cancer (GDSC). They describe genomic alterations, gene dependencies, telomerase (TERT) expression and promoter mutation, and methylation patterns both in the TERT locus and beyond. The analyses are carefully done and largely convincing, and impressive in scope as putting together data from distinct cell lines from different batches and sources is not an easy task.Their major claims are that:– TERT expression correlates with telomere content in lung, central nervous system, and leukemia cell lines. This claim is supported by their analyses of telomere tract quantification and TERT mRNA levels in the same cell lines . However, some negative correlations in other cell lines were also found, which are currently unexplained.– Lower telomeric content is associated with dependency of CST telomere maintenance components. This is supported by an analysis of the Avana dependency dataset, which has been previously published. This makes sense biologically and is clear in Figure S3.– Increased dependencies of shelterin member genes are associated with wild-type TP53 status. An extended analysis as the one before in the Avana and DRIVE dependency datasets support this claim. This would mean that cells are less sensitive to shelterin depletion when they are mutated in TP53, which makes sense biologically.– Monoallelic expression in TERT promoter-mutant contexts. The analysis behind this claim, based on alignment of RNA reads to heterozygous regions and the subsequent counting of reads mapping to each allele, seems robust, although it would have been nice to see a few more details .– TERT promoter-mutant cell lines show hypomethylation at PRC2-repressed regions. This claim is based on an analysis of genome-wide reduced representation bisulfite sequencing (RRBS) data in nearly 1000 cell lines, and the finding of associations between TERT promoter mutation and CpG methylation of different regions of the TERT gene and other loci. An overlap analysis showed that these methylated regions were enriched for PRC2-repressed regions.Reviewer #2:All in all, I believe the claims in this study are well supported. This work constitutes a valuable resource for the scientific community, and has made new observations that contribute to the elucidation of telomere maintenance mechanisms.Kevin Hu, Mahmoud Ghandi and Franklin W. Huang present their important work on determining telomere content across > 1000 cancer cell lines. In addition to this highly valuable dataset, the authors present several scientific vignettes wherein various questions are addressed using their curated resource. Using CRISPR/Cas9 screening data from the same set of cell lines, they find that sensitivity to CST  knockout was associated with telomere content and that cell lines sensitive to CST knockout demonstrated lower telomere content. Shelterin complex members  were further identified as co-dependencies to CST knockout.They find that cell lines with monoallelic expression, as in a TERT promoter mutant setting, also demonstrate allele-specific methylation. Interestingly, TERT promoter mutant demonstrated gene body hypomethylation not observed in other monoallelic TERT expressors. Moreover, this hypomethylation was found to be genome-wide and not restricted to the TERT locus. Finally, nearly all of this hypomethylation was localized in PRC2-repressed regions.The hypothesis-driven vignettes are clearly of interest in providing correlative insights that could fuel future mechanistic studies. More importantly, they exemplify the strengths of the resource. Nevertheless, the primary value of the manuscript in my opinion is the enormous resource of telomere content estimates for cell lines widely used in biomedical research. The authors can do a more thorough job showing the reader that their measurements are reliable and whether or not they would be generally applicable to fresh cell lines purchased from a vendor.1. WGS-based telomere content estimates are likely much more reliable than WXS-based estimates. The authors do not comment on this.2. Cell lines from different sources can vary substantially. For example, HeLa strains from different laboratories can show vastly different karyotypes (PMID 30778230). These caveats are not currently discussed.3. Telomere length attrition is replication dependent, however the authors have not looked at whether their measurements are associated with population doublings in individual samples or across multiple samples taken at different PDLs.4. ALT status is an important confounder of telomere content. The authors hint at ALT status in their manuscript at various points, but do not incorporate ALT status into their analyses.Reviewer #3:5. The combination of a number of parameters is going to determine telomere content in cell lines. It would be helpful to understand the combined contribution of multiple parameters.In this manuscript, Huang and colleagues used public whole genome or whole exome sequencing datasets to establish telomere content in over a thousand cancer cells lines. Once telomere content was established, they linked it to other available sets of analysis, including RNAseq libraries and genome-wide CRISPR or RNAi screens.First, they found that telomere content is highly variable among tissue types, with highest telomere content (TC) in tissues that have higher frequencies of ALT activation. They also found weak correlation with TERT or TERC expression. They then analyzed the correlation between telomere content and gene dependencies and found that dependencies to TRF1 and the CST correlates with TC. Independently of TC, they analyzed correlations between gene dependency of telomeric proteins or ATRX and DAXX with mutations or transcriptomic and proteomic profiles. Finally, they analyzed the link between TERT allelic expression and epigenetic marks on TERT promoter.Although such broad analysis of telomere length or telomere proteins / TERT dependencies is interesting, it appears hard to really see the biological significance of certain correlations or the novelty of the findings. Essential revisions1. About the structure of the manuscript.Two reviewers commented that the manuscript is at times hard to understand due to it covering a large number of observations that not always follow logically from each other. Specifically, it was mentioned that the ATRX/DAXX section does not add much to the paper, and that a substantial part of analyses and supplementary data are not mentioned . Please re-check these issues, and if possible, simplify the paper to convey a clear message.Thank you for this comment and we agree. To improve the flow and clarity of the paper we have now removed the ATRX/DAXX section from the paper. We have also revised the axis labels of Figure S6c and d such that CERES and DEMETER2 dependency are replaced by Avana and DRIVE . We appreciate the opportunity to simplify the paper.2. About cell lines.Please answer the following questions.a. Does the CCLE provide any information on cell line source?Yes, the CCLE does provide additional cell line source attributes. We have added these sources, as well as additional information on growth medium, freezing medium, doubling time, pathologist’s annotation, and TCGA code to Supplementary Table S1.Do the authors have any cell lines with biological replicates from different sources that could determine how this affects telomere length, as in eg. PMID 30089904?The CCLE WES, CCLE WGS, and GDSC WES datasets are of different biological replicates, and we show that there is a moderate correlation between CCLE WES and CCLE WGS telomere content as well a strong correlation between CCLE WGS and GDSC WES telomere content.Also, are there any technical replicates derived from unique libraries prepared from the same cell line to establish that these show similar measurements?We thank the reviewers for raising this question. Within the CCLE WGS dataset, we have since determined that the HEL 92.1.7 cell line was twice-sequenced. We have now run Telseq on this second sequencing run and found both estimates to be about 4 kilobases, showing a similar measurement. We have updated the manuscript accordingly.b. Do the authors think their conclusions are affected by the fact that cell lines are immortalised, and this process in many cases includes the activation of TERT?As cell lines are immortalised frequently through the activation of TERT, we think this is a useful setting in which to study telomerase expression and telomere length. So while we cannot exclude the fact that this common process is affected differently in cancer tissues, we think the immortalization process in cell lines offers a reasonable comparison to cancers and think our conclusions can also apply to cancer tissues.Can their conclusions be extended to living tissues?This is a very interesting question. We think that while there certainly are limitations of cancer cell lines in terms of their similarity to tumor tissues, the extent to which they can be studied and perturbed affords significant advantages, and common next-gen sequencing methods can be used alongside to assess telomere content, since many advanced cancers are now undergoing next-gen sequencing as part of routine clinical care. We do think our conclusions about the sensitivities of cancer cell lines based on telomere content to depletion of telomere maintenance mechanisms opens potential new strategies for targeting cancers by taking into account telomere content/length that will need further validation.c. Can the number of cell lines from both sets that were taken forward for downstream analyses be specified in the main text?Yes, we have now specified this number in the main text.3. About telomere measurements and correlations.a. The raw telomere content measurements from WGS are not discussed much and quickly discarded in favor of normalized estimates. Looking at the supplementary table these look to be all on a similar and comparable scale for WGS.Although raw telomere content measurements from WGS are reliable based on the results of the original Telseq paper, we ultimately used a z-scored transformation to make these estimates more compatible with the WES-based content estimates. In addition, we applied a log-transformation on all telomere content estimates because of a strong skew towards cell lines with markedly high telomere contents.To illustrate that our transformations do not markedly affect the results, we have added the raw CCLE WGS telomere content estimates to Figure S5a (correlation plots of CST complex dependencies versus telomere content). In these examples, we show that raw estimates are still correlated with the dependencies, although the linear relationship is weaker due to the skew in the raw telomere content.It strikes me that WGS-based estimates are much more reliable, and some associations may be stronger using these numbers despite being limited to a much smaller sample size.We agree with this point, and we would like to note that we indeed ran analyses such as the associations between telomere content and dependency , between telomere content and age as well as tissue type , and between telomere content and TERT and TERC transcriptomics. For Figure S3, we decided against using WGS estimates along because the overlap between WGS and datasets such as mass-spectrometry protein expression and DRIVE dependencies would have been relatively small, and because we believed that any strong associations would have been present anyway, as with the Avana associations with TERF1 and the CST complex members.Can the authors show the overlap between WGS/WXS and associated data ?Yes, we have now added a summary figure to Figure S1 showing the totals within each dataset as well as the number of individual cell lines containing each annotation, in the format of a binary heatmap.Do the described findings hold when limited to WGS raw estimates or is there insufficient power?The described findings hold when limited to WGS raw estimates. The transformation used to produce the WGS transformed estimates was just a log-transformation followed by a linear z-score scaling, so our findings are not so sensitive to whether the WGS raw or transformed estimates were used. To illustrate, we have added plots of raw CCLE WGS telomere content versus CST complex dependencies to Figure S5.Also, an explanation of why correlation is higher between WES and WGS of different datasets than with those of the same dataset would be welcome.We have added histograms showing the distribution of the total numbers of reads containing at least 6 telomeric repeats among cell lines for each sequencing dataset to Figure S1. CCLE WGS lines had around 100,000 such reads, whereas GDSC WES lines had about 100-1,000 and most CCLE WES lines had less than 100. These read numbers suggest that CCLE WGS offers the most accurate estimates, followed by GDSC WES and then CCLE WES. Moreover, the difference in total telomeric reads detected would also explain why the correlation between CCLE WGS and GDSC WES (r=0.84) is much higher than that between CCLE WGS and CCLE WES (r=0.71), which itself exceeds that of CCLE WES versus GDSC WES (r=0.60).b. While the authors look at the patients age at tissue extraction and find a correlation between estimated telomere content and age, one wonders if cell culture specific parameters could not be even stronger determinants. Have the authors looked at time in culture, passage number and population doubling level (PDL) in relation to telomere length?We thank the Reviewer for this suggestion. We were able to find annotations for calculated doubling time in the CCLE, which appear to be weakly negatively correlated with telomere content. We have appended these plots to Figure S2.c. Could the authors infer ALT status for a large number of cell lines from other resources? Capital Biosciences was awarded NIH SBIR funding several years ago specifically to determine the ALT status of all cell lines in the ATCC.See for example:1. https://www.eposters.net/pdfs/identification-of-new-alternative-lengthening-of-telomeres-positive-cancer-cell-lines-using-the-c.pdf2. https://www.atcc.org/~/media/PDFs/Presentations/HOC%20poster%20ALT.ashxAt a minimum, perhaps the authors could annotate lines known to exhibit ALT as ALT+ and others as ALT unknown?We have compiled a table of ALT status across the linked studies as well as additional ones from the literature and appended these to Table S1, with 76 annotated cell lines in total.d. It is slightly surprising there is a positive correlation between telomere content and TERT mRNA in CNS since CNS tumors are often ALT+ (and telomerase-). Possibly none of the CNS lines assayed were ALT+? Perhaps the correlation between TERT mRNA and telomere content needs to be adjusted for ALT status because of the presumed interaction. Could the authors incorporate multiple correlates of telomere content in a model and determine their independent contributions and interactions? Perhaps this could be limited to WGS and using the raw measurements.We have constructed models incorporating TERT mRNA expression, TERC mRNA expression, cell line primary site, TERTp status, donor age, and calculated doubling time for predicting CCLE WGS and GDSC WES telomere content separately. The outputs of these models are detailed in Table S2.Of the CNS lines for which we found ALT status in the literature, we know that PFSK1 and DAOY are ALT+, and we also know that A172, KNS42, U251MG, MOGGUVW, and SW1088 are not. The majority of CNS tumors in this dataset from the CCLE are TERT+.e. Some correlations with TC  are likely to be biased by the important TC differences between tissue types. Can the authors perform this analysis on each tissue separately, or normalize TC to use relative TC within a tissue type?This is an interesting suggestion. We have updated the analysis to include tissue type as a covariate .4. About figures.a. Figure 1.– This is an informative figure. Can a clarification of the total number of cell lines be added?– It says it is 683 but from the main text it seems that many more were taken forward for analysis ? Why are the others not depicted here?We thank the reviewer for this positive feedback. In this figure, we omitted cell lines that lack expression and copy number annotations for TERT and TERC, merged (CCLE WGS and GDSC WES) telomere content estimates, and ATRX and DAXX mutation profiling for clarity. We also omitted tissue types with less than 10 cell lines present after this preliminary filtering was applied. This filtering was done for clarity, as it would have been difficult to display cell lines with missing values in a concise format. (We have since made an exception for non-cancerous cell lines as per the recent comments). In total, 738 cell lines are shown in this figure.– If the outlier U2-OS cell line is removed from the 44 bone cell lines, is the bone cell line average still the highest?If the U2-OS cell line is removed, the bone cell line average is no longer the highest (blood becomes the highest). To account for the impact of cell lines with particularly high telomere contents, we have revised this figure to sort by median, rather than mean, telomere content.– Can the ATRX/DAXX expression be included?We have now added ATRX and DAXX expression levels to the bottom of the Figure 1.b. Figure 2.– Figure 2a-b: the authors claim that dependency toward TRF1 and the CST complex is higher in cells with short telomeres. However, they show a positive correlation between TC and AVANA dependency. This may be interpreted that cells with longer telomeres are more sensitive to loss of TRF1 or the CST complex. That could be explained by their role during telomere replication, and the likely higher replication stress at longer telomeres. If this is not the correct explanation, can the explanation be reworded, as it is confusing?We thank the Reviewer for this helpful suggestion. We have reworded our discussion of the association between telomere content and CST complex dependencies to make this point clearer. In particular, the numerical dependency scores that we use are calculated such that a lower score indicates a stronger (or higher) dependency. Therefore, we meant to say that cells with longer telomeres are less sensitive to loss of TRF1 and the CST complex, as evidenced by a higher raw dependency score.– Figure 2d-e: Does each dot represent a cell line? If so, are the conclusions (model in 2f) drawn out of only 1 cell with a mutation in a hotspot?Here, each dot represents a mutation, and we aim to show the mutations that are associated with the respective dependencies of telomere-related proteins. We have remade this figure in the format of a volcano plot to make this point more clear.c. Figure 3a.– Here, it seems that cells with lower PML expression are more sensitive to DAXX suppression. Could the analysis be done with telomerase positive cells only? Similarly, cells with higher levels of ZMYM3 are more sensitive to DAXX suppression. What does that mean biologically? Can you please specify what are the dots?, do they refer to cell lines? So these all represent the top score of their corresponding cell line?In response to previous concerns regarding the low contribution of this figure to the paper, we have removed this figure from the text.5. Other pointsa. The introduction and the narrative tell us that telomerase is activated, that telomeres are usually longer in cancer, etc. Therefore, the phrase "The 55 non-cancerous samples profiled as part of the GDSC also displayed relatively high telomere contents , consistent with previous reports of widespread telomere shortening in cancer ." seems to contradict what has been said. So, is it expected that the normal samples would have long telomeres? Would this be expected physiologically or is it an effect from these being cell lines? Also – is the information for these normal cell lines displayed in Figure 1? Reviewers recommend to add the normals to this figure so it is easier to compare between normal and cancer tissues.We appreciate this comment and agree it enables a better comparison and have now added the normal cell lines to this figure (they were previously excluded due to a lack of expression and copy number profiling in the CCLE). We have also revised the introduction to state that cancers in general tend to have shorter telomeres than normal tissues, perhaps as a result of telomere maintenance mechanisms emerging only after telomeres have become sufficiently short in a crisis or because there is some fitness advantage to shorter telomeres.b. Could the definition of what a dependency means, exactly, be included? Does it assess correlated gene expression? Does it assess activation of one gene upon knockout of the other? It would be helpful to spell this out in the main text. This may help understand the observed dependencies between the members of the shelterin complex and those of the CST complex.We thank the Reviewer for this question. We have now added an explanation of the numerical value of a gene dependency to the “Telomere content associates with CST complex dependencies” section. In particular, a dependency score reflects how sensitive a particular cell line is to the inactivation of a particular gene, with more negative scores reflecting increased sensitivity.c. Page 6 says "increased sensitivities to knockout of the CST complex components, which are key mediators of telomere capping and elongation termination , were correlated with lower telomere content." However, it seems that supplementary figure 3b indicates that the CST genes are positively correlated with telomere content? If this is an index of sensitivity, could this be indicated somewhere? . Could TERF1 be highlighted in Figure S3b please, DRIVE dependencies?Consistent with the above comment, we have revised our wording to clarify our results here. A negative dependency score reflects increased sensitivity, so a positive correlation between telomere content and dependency scores suggests that cell lines with higher telomere content are less sensitive to inactivation of the CST complex members.We have now highlighted TERF1 in Figure S3b.d. About methylation studies. In page 11 it says "TERTp mutants exhibited strong and significant (P < 0.01) increases in ASM  in the promoter (n = 485), remaining gene body (n = 493), and exon 1 (n = 478) regions", but then in page 12, line 292 it says "The observation that TERT promoter mutants display a hypomethylated TERT locus…" – does this not directly contradict the statement above? Or is it referring to methylation on a specific region? Could this be clarified please?We appreciate the opportunity to make this clarification. We state that TERTp mutants exhibited increases in ASM  yet show a hypomethylated TERT locus. This statement is consistent, because the increase in ASM is due to the hypomethylation (loss of methylation) at the TERT locus on a single allele. We have reworded this section to make this point clearer to the reader.


MSN with an average size of 108 d.nm, a zeta potential of −42 mV and a PDI of 0.150 were synthesized and were impregnated with CurNQ to form the novel nanosystem MSN_CurNQ. MSN_CurNQ was demonstrated to have pH-responsivity whereby after 96 h, at pH 7.4, 31.5% of CurNQ was released from the MSN compared to 57% release at pH 6.8, corresponding to an increase of 25.5% in release with a 0.6 pH drop. The innate fluorescence was then characterized through confocal and fluorescence microscopy. Microscopy images illustrated the distinct, high intensity innate fluorescence with a high background to target ratio, thus confirming detection capabilities and potentially extending MSN_CurNQ’s application to molecular imaging purposes. Moreover, the chemotherapeutic potential of MSN_CurNQ was demonstrated as cell viability was reduced to below 50% in OVCAR-5, CACO-2, CHLA, and MCF-7 cell lines. Furthermore, MSN_CurNQ displayed tumor specific toxicity whereby the cell viability was reduced to a far greater extent in the cancer cell lines compared to a healthy fibroblast cell line (p = 0.000). Indeed, the novel MSN_CurNQ nanosystem has potential for applications in cancer targeting, detection and treatment.A novel theranostic molecule, derived from curcumin (Cur) and naphthoquinone (NQ), allowing for cancer targeting, detection and treatment was previously described and termed CurNQ. To allow for enhanced theranostic capabilities, advanced drug delivery techniques are required. To this end, mesoporous silica nanoparticles (MSN) were synthesized and CurNQ was loaded into its pores to form the novel nanosystem MSN_CurNQ. The formation of the nanosystem aimed to augment the drug delivery of CurNQ through the EPR effect and sustained release. Moreover, the loading of CurNQ into its pores, formed a fluorescent nanoparticle that can be tracked, detected and visualized. Herein, the synthesis of a novel nanosystem is described and its theranostic potential are explored  Cancer is set to become the leading cause of death globally and is the biggest impediment to increased life expectancy in the 21st century as the global incidences and mortality rates are forever increasing . Even wiThe search for new chemical entities for cancer treatment is ongoing. New cancer therapies should circumvent the issues that plague current chemotherapeutics, some of which include, high toxicity, non-specificity, cancer resistance and late diagnosis . One of Theranostic intervention is often achieved using nanotechnology. Nanomedicine has become of great interest in the field of cancer research on account of enhancements to drug delivery mechanisms . A signiLeaky tumor vasculature is hallmark feature of cancer. This induces nanoparticles to leak out of tumor blood vessels . This phin vitro (Mesoporous silica nanoparticles (MSN) are a class of nanoparticles that have been widely studied for drug delivery applications. Silica is one of the most abundant natural resources and has been deemed safe and is widely used in food additives, pharmaceutical products and cosmetics . With thin vitro . Moreovein vitro . The larin vitro . These pOur previous research demonstrated the development of a novel theranostic molecule for cancer intervention. The novel molecule termed CurNQ, was synthesized through a Williamson ether synthesis reaction between curcumin (Cur) and lawsone . Cur andThe aim of this research was to further the theranostic capabilities of the previously described novel molecule CurNQ through the synthesis of mesoporous silica nanoparticles and the loading of CurNQ into its pores (MSN_CurNQ). This aimed to form a detectable fluorescent nanoparticle that has additional targeting and therapeutic properties.CTAB—Hexadecyltrimethylammonium bromide (MW−364.45), Pluronic F-127 (MW–12600), TEOS—Tetraethyl orthosilicate (MW–208.33), Phalloidin-Atto 488 (MW- 1472) and DAPI- 4′,6-Diamidine-2′-phenylindole dihydrochloride (MW- 350.25) were purchased from Merck/Sigma Aldrich  and were used as received. High purity solvents (> 98%) were purchased and used as received from ACE chemicals . Cell lines were all acquired from Cell Biolabs Inc.  and from Fox Chase Cancer Center . Cell culture consumables, MTT–3- -2,5-diphenyl tetrazolium bromide, and other cell culture reagents were sourced from Thermo Fischer Scientific .The synthesis of mesoporous silica nanoparticles (MSN) was performed using a modified method establish by Yang et al. and the synthesis scheme is displayed in Size and zeta potential analyses were performed on the Zetasizer Nano ZS . Dilute 10 mL suspensions of nanoparticles were sonicated for 30 min at room temperature prior to analysis to reduce particle aggregation. 2 mL of the dispersed samples were filtered into disposable cuvettes and the size, polydispersity index (PDI) and zeta potential values were recorded at 25°C on the ZetaSizer Nano ZS.X-ray diffraction (PXRD) utilizing a variable and fixed slit system was used to determine the crystallinity and atomic spacing and unit cell dimensions of the MSN. This characterization technique was performed on the Rigaku MiniFlex 600 Benchtop X-ray Diffractometer . A powdered sample was packed onto the sample holder and was analyzed at a scanning rate of 15° per minute at a diffraction angle range of 3–90° with a degree step of 0.02, a voltage of 40 kV and a current of 15 mA.Surface area and porosity analysis was performed on MSN using Porositometric Analyzer . Surface moisture and contaminants were removed through degassing prior to analysis through the insertion of a glass filter rod. Degassing occurred over a period of 9 h and after the degassing process, the MSN were transferred to the analysis port where surface area, pore volume, and pore size data were obtained in accordance with standard BJH and BET gas adsorption method computations .The FEI Nova Nanolab 600 SEM was utilized for scanning electron microscopy (SEM) which was used to determine nanoparticle morphology and offer particle size confirmation through dynamic light scattering. Powdered sample of the nanoparticles was placed onto an aluminum specimen stub covered with a double-sided carbon adhesive disk and placed under a vacuum, flushed with argon, evacuated and sputter coated with gold for 4 min at 20 kV. SEM images were obtained on a FEI Nova Nanolab 600 FEG-SEM/FIB.via centrifugation at 5,000 rpm for 10 min. The particles were then dried in an oven overnight at 37°C. The above procedure was repeated with Cur to produce MSN_Cur, which was to be used for comparative analyses.Mesoporous silica nanoparticles were impregnated with CurNQ and were designated MSN_CurNQ. To achieve this, the methodology previously utilized by Jambhrunkar et al. to load Cur into MSN was followed owing to similarity . Here 40R2 = 0.99).A mixture containing 40 mg of CurNQ and 160 mg of MSN was added to methanol and was stirred rapidly, allowing for loading to occur . The UV Entrapment efficiency values of 54.54 and 32.5% were computed for CurNQ and Cur, respectively. A decrease in UV absorbance indicated successful entrapment owing to CurNQ being impregnated into MSN thereby being removed from the solution.R2 = 0.99) for both CurNQ and Cur, respectively. The experiment was performed in triplicate to ensure statistical reliability (N = 3). The experiment was concluded after 96-h as no more CurNQ was released after this time.CurNQ release from the MSN nanoparticles was measured at pH 7.4 and 6.8 at 37°C for both MSN_CurNQ and MSN_Cur. One mg/mL of CurNQ-loaded nanoparticles were immersed in a PBS/CTAB solution and the pH of each sample was adjusted to mimic both physiological conditions (pH 7.4) and at the tumor microenvironment (pH 6.8). Samples were incubated and stirred on an orbital shaker incubator at a rate of 50 rpm at 37°C. At 12-h intervals, 1 mL of buffer was removed, and UV absorbance was measured through UV/Vis spectroscopy at 429 nm. Total CurNQ release was then calculated using a standard curve  and EDTA (0.01% w/v).Multiple cell lines were used to determine the cytotoxic effects of MSN_CurNQ and MSN_Cur on a variety of cancer types. The following cell lines were cultured namely: OVCAR-5, 3T3, MCF-7, HeLa, CACO-2, HT-29 and CHLA. The cell lines were cultured using standard tissue culture techniques. Cells were either grown in RMPI 1,640 or DMEM, depending on the guidelines specified by the cell line supplier. All media was supplemented with 10% foetal bovine serum (FBS) and 1% Streptomycin and penicillin . Cells w4 cells were counted and distributed equally in a 96 well dish and were incubated at 37°C in 5% CO2 for 24 h, allowing for cell attachment. Subsequently, cells were treated with varying concentrations of MSN_CurNQ and MSN-Cur. Cells were incubated in treatments for 24, 48, and 72-h time period. After which, 3- -2,5-diphenyl tetrazolium bromide (MTT) reagent was pipetted into each well and was incubated 4 h. DMSO was then used to solubilize the formazan crystals. All samples were performed in quadruplicate for statistical reliability. Absorbance readings were then recorded on a Perkin Elmer Victor X3 2030 multilabel reader at 570 nm and 690 nm.Seven cell lines were cultured and the cytotoxicity of MSN_CurNQ and MSN_Cur was evaluated on all seven cell lines, thus allowing for the cytotoxic effect of MSN_CurNQ to be evaluated on several differing cancer types. Cells were cultured as per above conditions. Cell confluency was maintained at 70–80% through standard sub-culturing techniques utilizing trypsin/EDTA harvesting. Thereafter, 1.5 × 10The OVCAR-5 cell line was cultured on coverslips until reaching 70% confluency. Cells were then treated with MSN_CurNQ for 24 h, thereby allowing for cellular uptake to occur. After this, Phalloidin-Atto 488 was added, and staining occurred over 30 min. Cells were then washed and fixed through incubation in 5% formaldehyde in PBS for 20 min. After fixation, DAPI stain was added at a concentration of 1 μg/mL and samples were left to incubate for 15 min in the dark. Hereafter, samples were washed thoroughly with PBS to remove unstained DAPI. Coverslips were stored in the dark in PBS. Coverslips were viewed on the Zeiss Laser Scanning Confocal Microscope (LSM) 780 . Super resolution microscopy was performed using Airyscan technology as an added on feature to the LSM confocal microscope.® X High Content Imaging System which obtained rapid fluorescent microscopy images of each well, and quantitative and qualitative data was obtained from these images through the use of their proprietary software .Exploiting the use of advanced technological systems such as machine learning, and AI has rapidly advanced the field of cell biology and enables quantitative and qualitative data to be extracted through microscopy images . In-deptMesoporous silica nanoparticles (MSN) were synthesized and were aimed to act as a delivery platform for CurNQ . Small pAs shown in The particle size was further confirmed, and the morphology determined through scanning electron microscopy (SEM). This allowed for the surface of the nanosystem to be characterized and to ascertain if the morphology of the nanosystem was uniform. As seen in Following MSN synthesis and calcination, MSN was impregnated with CurNQ and Cur individually to create the nanosystems MSN_CurNQ and MSN_Cur, respectively. This was achieved through rapid mixing of the MSN and CurNQ in a small volume of methanol protected from light at room temperature for 48 h, allowing for CurNQ to be impregnated in the pores of the MSN. Successful loading of MSN with CurNQ was confirmed through UV/Vis absorbance readings. The absorbance readings were taken before, during and after the 48-h period. A decline in absorbance readings corresponded to the uptake of the drug into the pores of the nanoparticles, thereby leaving the solution and reducing the absorbance of the solution. This uptake was quantified, and the encapsulation efficiency was calculated. Moreover, to further confirm successful CurNQ encapsulation the size of the loaded nanoparticles was measured and compared to the empty nanoparticles. As expected, the size of the CurNQ loaded particles increased upon drug loading from an average of 108 to 214 d.nm. This increase in size was owing to the pores of the particles being impregnated with the product. The above process was repeated for the loading of Cur into MSN, thereby acting as a control. The entrapment efficiencies were calculated to be 54,54 and 32,5% for CurNQ and Cur, respectively.To support constant cell growth and proliferation, a cancer cell increases glycolysis and bypasses oxidative phosphorylation, this allows for the rapid production of ATP and for cellular proliferation to continue unabated . This php = 0.0001). The release of CurNQ at pH 6.8 is just less than double its release at pH 7.4. At physiological pH (7.4), there was only 10% release within the first 12 h, with it reaching 20% after 24 h and peaking at 31,5% at 96 h.It was of interest to determine if this molecular switch occurred when CurNQ was loaded onto MSN and if this molecular switch could be exploited for cancer targeting; therefore, a release assay of CurNQ from the MSN was performed at pH 7.4 and pH 6.8. This assay again included MSN_Cur acting as a comparison and allowed for the novelty of this pH switch to be highlighted. As seen in p-value confirms the effects of pH on the release of CurNQ from the MSN, verifying the pH responsivity and specificity of the nanosystem. Moreover, this highly statistically significant p-value emphasizes the robust nature of CurNQ pH-responsivity. Clearly, the change in pH associated with malignant transformation acts as a solubility switch for CurNQ. This pH specificity was not observed for MSN_Cur (p = 0.31), and notably MSN_Cur exhibited a slightly greater release at physiological pH, thereby indicating its non-specific pH solubility and its inability to act as a tumor targeting agent.This was in stark contrast to the release profile of MSN_CurNQ at pH 6.8. With this small reduction in pH, the novel nanosystem released 43, 45% after 48 h, with the release peaking at 57% at 96 h. This equates to a 25, 5% increase in release occurring in response to a 0.6 reduction in pH. Consequently, the release of CurNQ at pH 6.8 was almost double the release at pH 7.4. This difference in release between the two pH values was highly statistically significant with a corresponding p-value of 0.000. This highly significant The release profile aligns with the saturation solubility results presented in our previous research . At pH 7The synthesis of CurNQ saw the addition of two quinone moieties and the substantial pH dependent release profile difference between Cur and CurNQ can be attributed to the addition of these quinone moieties. Quinones undergo a reversible two-electron redox reaction and the corresponding redox potential is strongly pH dependent. At acidic pH, reduction occurs and is a single step two-electron two-proton process and this reduction forms a hydroquinone and this hydroquinone form has a greater solubility than the quinone form. Thus, this redox reaction accounts for the switch in solubility and the pH dependent release.Furthermore, Targeting the slightly acidic tumor microenvironment for tumor targeting, detection and specific drug release has been of great interest for cancer theranostic applications. Several strategies have been investigated by several researchers to achieve this. Such strategies have included the modification of nanoparticles whereby their surface was modified with a pH sensitive compound such as glycol chitosan or through the introduction of protonatable groups. Other avenues include using polymers whereby acid labile bonds are introduced or through an amphiphilic polymer triggered prodrug. Other researchers have used calcium carbonate nanocrystals loaded with cancer drugs that disassociate at the tumor microenvironment, thereby releasing the drug at the desired location . MoreoveThe realization of pH-responsiveness in this study differs to all the strategies mentioned above. To best of our knowledge we are not aware of any active compound that intrinsically offers robust pH sensitivity without the addition of nanomaterials or pH sensitive groups. The simplicity of the pH-responsive solubility of the active compound is unique and is highly novel, especially owing to the sensitivity and alignment to the pH change that occurs during malignancy. Not only is the molecule highly pH specific which is rare, but it offers this specificity at the exact change in pH that is stimulated in response to oncogenesis. Clearly, this is an exceptional finding and can be exploited for tumor targeting and detection applications.in vivo imaging and detection devices can be applied in numerous and varied applications. The synthesis of a fluorescent compound that is ideal for imaging applications is a large area of research  respectively. The absorption peak was determined to be 429 nm, the excitation peak 595 nm and the emission peak at 670 nm.in vitro and in vivo. Therefore, the loading of CurNQ onto a delivery vehicle is crucial to exploit its fluorescent capabilities for detection applications. In order to explore these fluorescent capabilities and to determine if MSN_CurNQ can be used for detection and imaging applications, confocal microscopy utilizing super resolution Airyscan technology and fluorescent microscopy on the CELENA® X High Content Imaging System were performed.CurNQ has a high fluorescence intensity and its pH-responsive solubility would allow it to release a fluorescent signal at the pH of the tumor microenvironment. However, without the loading of CurNQ onto a nanoparticle, the fluorescence of CurNQ cannot properly be traced and visualized, as generalized fluorescence is difficult to pinpoint These assays were used to verify fluorescence and to determine whether the loaded nanoparticles could be accurately detected and visualized using the innate fluorescent properties of CurNQ; moreover, cellular uptake could be evaluated. Cellular uptake is important as for an active compound to exert its desired effect, it is required to enter the cell. The ovarian cancer cell line OVCAR-5 was utilized for this assay.® X High Content Imaging System , indicating MSN_CurNQ treatment does result in a statistically significant reduction in cell viability to a variety of cancer types. Accordingly, it is recommended that in an in vivo setting, that a treatment regime is instigated, whereby a patient would receive several doses of the nanosystem, as to ensure malignant cell recovery does not occur.MCF-7 and CHLA cell lines also demonstrated a reduction in cell viability but this reduction was slightly less compared to OVCAR-5 and CACO-2, yet cell viability was still reduced to below 50%, hence MSN_CurNQ shows promise as a broad chemotherapeutic agent that has the ability to act against a variety of cancer types. Interestingly, the MCF-7 and CHLA cell lines experienced their greatest reduction in cell viability after 24 h, differing from the response observed for OVCAR-5 and CACO-2 cell lines. While the release from MSN peaks after 96 h, there is not a large difference in release between 24 and 48 h on account of the sustained release from the nanoparticles. The MCF-7 and CHLA cell lines had an immediate response to treatment, with cell viability reducing to below 50% in cells receiving 200 ng/mL or higher of MSN_CurNQ. However, MCF-7 and CHLA cells recovered subsequent to their initial reduction in cell viability, and this also occurred in cells receiving the highest concentration of MSN_CurNQ; yet cells do not recover to full viability suggesting treatment was still effective in reducing the overall cell population. Although cell viability reduction was not as high as OVCAR-5 and CACO-2, the reduction in cell viability of MCF-7 and CHLA was statistically significantly different to the control cell line 3T3 . Administered concentration was also a statistically significant factor in cell viability reduction (p = 0.0000). OVCAR-5 cells were the most responsive to MSN_CurNQ treatment, and induced cell death was significantly different to each other cell line tested . Importantly, there was a significant difference in the reduction in cell viability of the healthy cell line 3T3 to all other cancer cell lines investigated, barring HeLa cells . This means that MSN_CurNQ reduced cancer cell viability to a greater extent than it did to a healthy fibroblast cell line. The p-values of 0.000 and 0.004 indicate a high degree of statistically significance and verify the selective toxicity of MSN_CurNQ to cancer cells, meaning MSN_CurNQ has a higher potency toward cancerous cells than to healthy fibroblast cells. This selective toxicity further extends the cancer targeting capabilities of the nanosystem and further cements its novelty and applicability in the field of cancer theranostics.Statistical analyses were performed to ascertain the effect of MSN_CurNQ and MSN_Cur on a variety of cancer cell types and this reduction was compared to the healthy fibroblast cell line 3T3. Statistical analyses were performed using Stata/IC 15.1 statistical software. The cell line was indeed a statistically significant factor for the reduction of cell viability (The obtained results indicate a high concentration of MSN_CurNQ is required for effective and sustained reductions in cell viability. This high required dosage would not be a limiting factor to successful treatment on account of the observed cancer selective toxicity. Cell viability remained above 80% for the healthy fibroblast cell line 3T3, after treatment with 400 mg/mL of MSN_CurNQ, thereby indicating that MSN_CurNQ is non-toxic to these healthy fibroblast cells. This suggests that it would be safe to administer high concentrations of MSN_CurNQ to patients, as this should induce large reductions in cancer cell viability and a very low reduction to healthy cell viability.Indeed, this research demonstrated that CurNQ requires to be loaded onto a nanoplatform to allow for all its theranostic capabilities to be realized. MSN_CurNQ thereby offers a three-pronged approach for cancer targeting namely its pH-responsive solubility, the EPR effect and its selective toxicity toward cancer cells. This makes MSN_CurNQ a highly advanced and novel nanosystem for cancer theranostics.in vitro and in vivo imaging and detection. To this end, MSN were synthesized and were impregnated with CurNQ to form the novel nanosystem MSN_CurNQ. CurNQ demonstrated pH responsive release from MSN, specific to the tumor microenvironment whereby after 96 h 31.5% of CurNQ was released at pH 7.4 compared to 57% release at pH 6.8, which thereby may allow for tumor targeting applications owing to pH responsivity. Moreover, MSN_CurNQ exhibited high intense, clear and distinctive innate fluorescence enabling detection and possible imaging applications. Moreover, MSN_CurNQ induced cytotoxicity culminating in a reduction to below 50% cell viability in OVCAR-5, CACO-2, CHLA, and MCF-7 cell lines thereby demonstrating its chemotherapeutic potential to a variety of cancer cell types. Furthermore, MSN_CurNQ treatment did not induce cytotoxicity to the healthy fibroblast cell line 3T3, demonstrating cancer selective toxicity, furthering the nanosystem’s novelty and potential applications. Clearly, the novel nanosystem MSN_CurNQ has potential applications in cancer theranostics.The loading of CurNQ onto the MSN platform aimed to allow for enhanced delivery and enable The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.PK, PP, and YC conceptualized the research. LF, TM, and PP contributed to methods and experimentation. LF wrote the first draft. PK and YC reviewed and revised the manuscript. All authors approved the final draft.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


New insights into the pathophysiology of GVHD garnered from our understanding of the immunological pathways within animal models have been pivotal in driving new therapeutic paradigms in the clinic. Successful clinical translations include histocompatibility matching, GVHD prophylaxis using cyclosporine and methotrexate, posttransplant cyclophosphamide, and the use of broad kinase inhibitors that inhibit cytokine signaling (e.g. ruxolitinib). New approaches focus on naïve T cell depletion, targeted cytokine modulation and the inhibition of co-stimulation. This review highlights the use of animal transplantation models to guide new therapeutic principles.Allogeneic hematopoietic cell transplantation (HCT) is a curative treatment for hematologic malignancies, bone marrow failure syndromes, and inherited immunodeficiencies and metabolic diseases. Graft- In 1963, Mathé and colleagues reported the first case of a human allogeneic bone marrow transplantation (BMT) recipient that survived beyond a year. This patient had complete engraftment and the development of a lethal “secondary disease” was described  and administered allogeneic splenocytes survived for a shorter time than those transplanted with syngeneic splenocytes . The recescribed . These pescribed .Although many investigators lost their enthusiasm for BMT, several groups increasingly utilized animal models to gain a better understanding of the allogeneic barrier with regard to both GVHD and graft rejection. Studies of allogeneic BMT in Seattle using dog models in the 1980s provided the scientific groundwork for the field leading to the concepts of histocompatibility matching, conditioning regimens and pharmacological GVHD prophylaxis . These fFor a detailed discussion of the various mouse models of GVHD currently available and the penetrance of disease therein, we refer the reader to some of the excellent reviews on this subject –17.versus CD8) responsible for the induction of GVHD in each model  effects. In 1956, Barnes et al. reported that leukemia-bearing mice receiving allogeneic cells eventually died of GVHD without evidence of leukemia  defined by the transcription factor FoxP3 are pivotal for the maintenance of self-tolerance and the induction of tolerance after allogeneic hematopoietic stem cell transplantation (HCT). Depletion of CD25 in mice –46. Moga in mice . Pretran in mice . Followi in mice –54 but d in mice . Promisi in mice . Additio in mice .GVHD prophylaxis using calcineurin inhibitors (cyclosporin or tacrolimus) reduce the expansion of effector T cells (Teff) by blocking IL-2 and prevent acute GVHD, but fail to reduce chronic GVHD –60. CalcIn the setting of BMT, donor T cells are infused into recipients that have potentially experienced tissue injury by prior treatments of the underlying malignancy, infections, and more immediately, pre-transplant conditioning. The inflammatory environment invoked by these therapies predispose to a state of enhanced alloantigen-presentation. Johnson and Truitt demonstrated that delayed infusion of donor T cells induced less severe GVHD . The Fer+ or CD11b+ APCs do not prevent GVHD  were responsible for donor T cell activation and the induction of acute GVHD . They suent GVHD , 87.γ to mediate these effects by IEC  and a differentiation signal in the form of cytokine as defined above. The recognition of the importance of cognate costimulatory signals has led to the investigation of the relative ability of inhibitory antibodies that block these pathways in preclinical models. Thus CTLA-4-Ig that inhibits CD80 and CD86, anti-CD28 and anti-CD40 have all been shown to attenuate GVHD in preclinical models –97. The γ, GM-CSF, IL-17) can in turn invoke target tissue apoptosis and secondary myeloid cell migration to amplify GVHD [reviewed in  are generated in response to chemoradiotherapy during conditioning and promote the activation of APCs. Other cytokines act in a costimulatory role to promote pathogenic T cell differentiation . Finally pivotal T cell derived cytokines . Some of these limitations can be overcome by more thorough study of these variables in mice  and/or the use of non-human primates or dog models . Additional limitations include the widespread use of cell lines to study graft-versus-leukemia effects and the lack of relevant models to study pathogen-specific immunity in the context of new therapies, at least until recently. Nevertheless, to date almost all effective therapeutic paradigms that are now established in humans have their genesis in animal models, suggesting that these systems will continue to provide valuable insights and therapeutic advances to the field. Importantly, it would seem critical that well-established preclinical systems are utilized to analyze the effects of various therapeutic interventions before they are translated into early phase clinical trials.Mouse models of GVHD faithfully recapitulate the pathological lesions seen in clinical disease and allow the dissection of pathogenic TT and GH, wrote the manuscript. All authors contributed to the article and approved the submitted version.TT is supported by JSPS KAKENHI . GRH is supported by NIH R01 HL148164. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.TT: Grants from Kyowa Kirin, Chugai, Sanofi, Astellas, TEIJIN PHARMA, Fuji Pharma, NIPPON SHINYAKU, Personal Fees from Novartis, Merck, Kyowa Kirin, Takeda, Pfizer, Bristol-Myers Squibb, Non-Financial Support from Janssen, Novartis.GRH has consulted for Generon Corporation, NapaJen Pharma, Neoleukin Therapeutics, iTeos Therapeutics and has received research funding from Roche Pharmaceuticals, Compass Therapeutics, Syndax Pharmaceuticals, Applied Molecular Transport and iTeos Therapeutics.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Streptomyces (S.) griseus isolate KJ623766, a recovered soil bacterium previously screened in our lab that showed promising cytotoxic activities against various cancer cell lines. Methods: Production of cytotoxic metabolites from S. griseus isolate KJ623766 was carried out in a 14L laboratory fermenter under specified optimum conditions. Using a 3--2,5-diphenyl tetrazolium-bromide assay, the cytotoxic activity of the ethyl acetate extract against Caco2 and Hela cancer cell lines was determined. Bioassay-guided fractionation of the ethyl acetate extract using different chromatographic techniques was used for cytotoxic metabolite purification. Chemical structures of the purified metabolites were identified using mass, 1D, and 2D NMR spectroscopic analysis. Results: Bioassay-guided fractionation of the ethyl acetate extract led to the purification of two cytotoxic metabolites, R1 and R2, of reproducible amounts of 5 and 1.5 mg/L, respectively. The structures of R1 and R2 metabolites were identified as β- and γ-rhodomycinone with CD50 of 6.3, 9.45, 64.8 and 9.11, 9.35, 67.3 µg/mL against Caco2, Hela and Vero cell lines, respectively. Values were comparable to those of the positive control doxorubicin. Conclusions: This is the first report about the production of β- and γ-rhodomycinone, two important scaffolds for synthesis of anticancer drugs, from S. griseus.Background: This study aimed to produce, purify, structurally elucidate, and explore the biological activities of metabolites produced by  Worldwide cancer incidence and mortality have always been a concern to the community. The cancer mortality rate has generally declined over the years; however, there is still an increased mortality rate in poorer countries that receives considerable attention from healthcare professionals . This suMicrobial metabolites can be utilized as novel anticancer agents; with fewer side effects . StreptoStreptomyces have made imperative contributions to human health with their capabilities to produce various important active metabolites, including anticancer agents [Streptomyces peucetius bacterium. It has widespread use as a chemotherapeutic agent since the 1960s [2 [Various members of r agents . Many drr agents . Doxorubhe 1960s , Unfortu1960s [2 . Doxorub1960s [2 .Streptomyces spp. and used in the treatment of various types of cancers, including leukemias, lymphomas, breast, stomach, uterine, ovarian, bladder, and lung cancers [Streptomyces spp. [S. griseus strains including two new benzoxazines, the chandrananimycin E and dandamycin, [S. griseus, and biosynthetic genes of S. griseus have been heterologously cloned, expressed, and had their biological activities evaluated in vivo [S. griseus isolate KJ623766, a soil bacterial isolate previously screened in our lab that showed promising cytotoxic activities against various cancer cell lines.Anthracyclines are drugs extracted from  cancers . Regardl cancers . The mos cancers . Daunoru cancers . Anthracces spp. . These aces spp. . Severaldamycin,  chromomydamycin, . In addi in vivo . A recen in vivo . TherefoCaco2 (CD50 14 µg/mL) than Hela cell line (CD50 20 µg/mL), which indicates the presence of metabolites with higher selectivity to human colorectal adenocarcinoma  were obtained. All the fractions were subjected to 3--2,5-diphenyl tetrazolium-bromide (MTT) assay to measure the cytotoxic activity of each. The fraction SG-3 (70% Hexane) showed potential cytotoxic activity against ell line .m/z 385.05 (M − H)− corresponding to the molecular formula C20H18O8 (The metabolite R1 (5mg) was isolated as a reddish powder, ESI-MS revealed pseudomolecular ion peak at C20H18O8 . As showC20H18O8 .This is confirmed by COSY  correlatm/z 369.09 (M−H)− corresponding to the molecular formula C20H18O7 (The metabolite R2 (1.5 mg), a red colored metabolite, showed an identical 1H NMR spectrum to R1 except in the replacement of H-7 at δH 5.25  in R1 with the methylene protons δH 2.98  and 2.90 , corresponding to H-7a and H-7b in R2. A comparison of 1H NMR spectrum of R2  with thaC20H18O7 . TherefoC20H18O7 .S. griseus isolate KJ623766, a recovered soil isolate previously screened in our lab that exhibited promising cytotoxic activity against various cancer cell lines [Natural products are likely to provide many of the lead structures, and these will be used as templates for the construction of novel compounds with enhanced biological properties, so natural products have been the mainstay of cancer chemotherapy for the past 30 years . These nll lines . The proll lines  in orderCaco2 and Hela cell lines, the CD50 were 6.3 and 9.45 µg/mL, respectively, while lower cytotoxic activity was shown against the Vero cell line (CD50 = 64.8 µg/mL). Being cancer cell lines, Caco2 and Hela cell lines have lost their normal regulation of growth or cell death and were more sensitive to the effect of cytotoxic agents than the Vero cell line. The lower cytotoxic effect of certain metabolites on the Vero cell lines usually reflects safety and potential of these metabolites to be used in future treatment of cancer. In addition, the Vero cell line is a continuous non-tumourigenic one when a cell passage is not prolonged [For, metabolite R1, it showed potent cytotoxic activity against rolonged . Thereforolonged . TherefoCaco2 and Hela cell lines, the CD50 were 7.11 µg/mL and 9.35 µg/mL, respectively. While lower cytotoxic activity was shown against Vero cell line [CD50 = 67.3 µg/mL). Accordingly, both β- (R1) and γ-rhodomycinone (R2) showed potent cytotoxic activities against Caco2 and Hela cell lines as compared to their activities on the Vero cell line, confirming their potential applicability to be used as potential anticancer agents in humans. These results matched with those of Tsuji et al. [Caco2 cell line matched with results obtained by Tan et al. [Streptomyces sp. MUM256 crude extract against Colon Cancer Cell HCT116. Results revealed that the ethyl acetate fraction of MUM256 extract was shown to exhibit the highest cytotoxicity towards HCT116 among the fractions tested, followed by the hexane fraction and the aqueous fraction as the least toxic against HCT116 cells. Lesser toxicity was observed when the extract was evaluated against a normal colon cell line CCD-18Co at all the concentrations tested in the study [Hela cell line matched with results obtained by Kim et al. [Streptomyces bulli GJA1, against ovarian cancer cell lines, OV90 and ES2. Results revealed potent antiproliferative activities against both cell lines [Comparison of the 1H NMR spectrum of R2 with that of known anthracyclins in the literature showed strong similarity to the known metabolite γ-rhodomycinone . Like β-i et al.  which shn et al.  who examhe study . On the m et al.  who evalll lines . MoreoveStreptomyces spp. used in the treatment of various types of cancers [Streptomyces cultures that exhibited potent cytotoxic activities [Streptomyces broth yielded twelve new anthracycline antibiotics including three new ε-rhodomycinone derivatives [Streptomyces strains producing aglycones and nucleotide deoxysugars to yield more potent cytotoxic derivatives with high potential to be used in the treatment of various cancers in humans [Streptomyces venezuelae-based combinatorial biosynthetic systems have previously been developed for the production of glycosylated derivatives of doxorubicin and its biosynthetic intermediates [Both β- and γ-rhodomycinone belong to the antharcycline family of drugs extracted from various  cancers . They artivities ,28. Purin humans . Anthracmediates . This symediates .S. griseus isolate KJ623766. To the best of our knowledge, this the first report of β- and γ-rhodomycinone production from S. griseus that can be used as precursors for the construction of new derivatives with more potent cytotoxic activities. Further studies should be conducted to explore the biosynthetic pathways of the respective metabolites, as well as to produce new derivatives of the anthracycline family with higher antitumor activities and lower side effects. The latter can be achieved by structural modifications or microbial transformation of the recovered metabolites.Bacteria and bacterially produced bioactive compounds have recently emerged as a promising alternative for cancer therapeutics . BacteriStreptomyces strain from Egyptian soil sample, S. griseus KJ623766, was identified using 16S ribosomal RNA gene sequences  [http://www.wfcc.info/ccinfo/collection/col_by_country/e/20/, accessed on 14 September 2018) belonging to the world data center of microorganisms (WDCM) . This isolate was preserved onto starch nitrate agar slants  and sub-cultured every month, while slant-medium (50:50)  was used for long term preservation.Locally isolated J623766) . It was Vero cell line, ATCC No.CCL-81), colorectal adenocarcinoma derived from the human colon (Caco2 cell line), and epithelioid carcinoma derived from the human cervix (Hela cell line) were obtained from VACSERA, Egypt. The Caco2 and Hela cell lines were used to study the cytotoxic activities of the crude extract and isolated metabolites; stock cultures of these cell lines were grown in T-75 tissue culture flasks containing 20 mL of RPMI-1640 medium with 1% antibiotic antimycotic solution and 10% fetal bovine serum. The medium was changed at 48 h intervals and cells dissociated with trypsin solution . Vero cell line is a continuous non-tumourigenic when a cell passage was not prolonged and was used to analyze the cytotoxic activities of isolated metabolites [Three cell lines, including kidney and epithelial cells derived from the African green monkey (abolites . It was abolites .3 1 g per 1 L of distilled water in 100 mL flask) and incubated at 28 °C with shaking 200 RPM for 72 h in a C5KC shaking incubator . The culture obtained was used to inoculate the main culture.This was carried out according to Peng et al.  with somv/v) in subsequent manner [Caco2 and Hela cancer cell lines using MTT assay [50 value is ≤30 μg/mL [1 L of CFCS was extracted with ethyl acetate at the level of 1:1  on Kromasil ODS preparative column (10 mm × 250 mm) at flow rates of 4 mL/min and UV detection at 254, yielding metabolites R1 and R2  consisting of a mixing HPLC quaternary pump, a PDA-UV-Vis detector 190–840 nm, a fraction collector, and a sample loading module. For system controlling and process monitoring, Interchim Software 5.0 was used. 360 mg sample was dissolved in 50 mL of methanol then introduced into the column via dry load using 4 gm silica. Elution started with hexane:ethyl acetate (100%:0%) followed by ethyl acetate: methanol (100%:0%) gradient to yield 117 fractions. Elution was monitored using TLC  with UV-detection and spraying with vanillin sulphuric acid spray reagent to yield 12 major fractions (SG1-SG12); these fractions were subjected to MTT assay to measure the cytotoxic activity of each against 1 and R2 . MTT assv/v) under aseptic conditions, the run started under the previously stated conditions. The fermentation process was left for 72 h, during which foam was suppressed using silicon oil. The culture obtained was centrifuged at 6000 RPM for 10 min using an EBA20 Centrifuge , and the cell free culture supernatant (CFCS) obtained was collected.This was done according to Radwan et al.  with som®xevo-tqd®  and NMR spectroscopic analysis, which was recorded on a Bruker AVANCE HD III 400 MHz spectrometer . NMR samples were dissolved in methanol-d4 or chloroform-d3  and transferred to 3 mm NMR tubes .Structures of the isolated metabolites were determined based on LC-ESI-MS analysis performed using waters50 for each. Control wells contained two aliquots of 100 μL of ethyl acetate extract of soybean meal medium (1 mg dissolved in 5% DMSO and tissue culture medium) and 100 μL of tissue culture medium, followed by two-fold serial dilutions. Values were compared to those of the positive control doxorubicin. After 24 h incubation period at 37 °C in a CO2 incubator, the wells were washed with PBS, followed by incubation with 100 μL MTT solution (1 mg/mL) per well for 1 h at 37 °C. Supernatants were obtained by decantation and the cells were mixed with 100 μL DMSO per well to dissolve formazan particles. Elutes of the 12 wells of each tested metabolite were obtained and measured spectroscopically at 540 nm using differential wavelengths of 630 nm in a Plato R496 Microplate reader AMD diagnostics, Graz, Austria. Control wells were similarly handled, and the percentage cytotoxicity was calculated by the following formula [An MTT assay was carried out as previously reported  with som formula .CytotoxS. griseus isolate KJ623766 in a 14L fermenter and studied the different recovery methods for downstream processing. The structures of isolated metabolites were determined based on the mass, 1D, 2D NMR, COSY, and HSQC BMR spectroscopic analysis and identified as β- and γ-rhodomycinone, respectively. The respective metabolites were biologically analyzed using various cancer cell lines for their cytotoxic actives and exhibiting promising cytotoxic activities. Accordingly, S. griseus isolate KJ623766 could be used as a potential industrial strain for the commercial production of β- and γ-rhodomycinone to be used for production of different derivatives of antitumor antibiotics of the anthracycline family.This study targeted large scale production of cytotoxic metabolites by