Multicellular organisms potentially show a large degree of diversity in reproductive strategies, producing offspring with varying sizes and compositions compared to their unicellular ancestors. In reality, only a few of these reproductive strategies are prevalent. To understand why this could be the case, we develop a stage-structured population model to probe the evolutionary growth advantages of reproductive strategies in incipient multicellular organisms. The performance of reproductive strategies is evaluated by the growth rates of the corresponding populations. We identify the optimal reproductive strategy, leading to the largest growth rate for a population. Considering the effects of organism size and cellular interaction, we found that distinct reproductive strategies could perform uniquely or equally well under different conditions. If a single reproductive strategy is optimal, it is binary splitting, dividing into two parts. Our results show that organism size and cellular interaction can play crucial roles in shaping reproductive strategies in nascent multicellularity. Our model sheds light on understanding the mechanism driving the evolution of reproductive strategies in incipient multicellularity. Beyond multicellularity, our results imply that a crucial factor in the evolution of unicellular species’ reproductive strategies is organism size. With The origin and the evolution of reproductive strategies are not well understood. Only a few reproductive strategies have been considered in previous work. The fragmentation mode of producing many-celled propagules has been investigated, in order to understand cell death in yeast  or to unOrganism size confers various advantages to organisms ,23, suchSaccharomyces cerevisiae [Volvox, along with the germ–soma differentiation [Previous studies have shown that cellular interactions can change reproductive modes ,23,28. Frevisiae . Phenotyrevisiae ,35. In tntiation , 1–20 gentiation .Thus, the size and composition could affect growth in phenotypically heterogeneous multicellular organisms. We develop a theoretical model to address the evolution of reproductive strategies considering the effects of organism size and thresholds for the number of different cell types. The size effects could increase or decrease organism growth, with the organism growing faster when the cell number of a particular phenotype reaches a given threshold. Organisms in a population share one common reproductive strategy while populations differ in reproductive strategies. Reproductive strategies thus compete with each other via population growth rates. The optimal reproductive strategy maximizes the population growth rate. We found that reproductive strategies can coexist or dominate others under different conditions. The uniquely optimal reproductive strategy always produces two offspring units.. 2a). The organisms in each population have a unique reproductive strategy. For example, a population with maturity size N = 3 must have the reproductive strategy either 1 + 1 + 1 or 2 + 1. In a population with 2 + 1, mature organisms with three cells produce a single-celled newborn organism and a two-celled newborn organism. The reproductive strategy determines the organism size at which an organism is born and at which size it is mature and reproduces. For the reproductive strategy n1 + n2 + · · · + nM, newborn organisms have cell number ni  and maturity size n1 ≥ n2 ≥ · · · ≥ nM. We consider organisms consisting of two cell types: type A and type B. Newborn organisms may differ in their size and composition in a population. For example, a population with reproductive strategy 2 + 1 can have five types of newborn organisms: , , ,  and , where  shows the number of type A cells and type B cells in an organism (d). Each organism grows incrementally by one cell at a time. During each increment, a cell is selected to divide, and two daughter cells are produced. Each daughter cell can switch to another phenotype independently with a cell-type switching probability, which is typically m = 0.01 in our model . After reaching their maturity size N, organisms reproduce via random fragmentation in terms of organism composition. The probabilities of forming different newborn organisms are calculated in electronic supplementary material, appendix S1. The newborn organism follows the same life cycle, growing from newborn to mature (see a).We consider multiple populations in which organisms grow and fragment into smaller pieces a. The ororganism d. Each oture see a.Figureλ, depending on the number of offspring and the growth time of organisms [tn is the time for organisms growing from size n to (n + 1). The growth time depends on the cell size at the newborn stage and the cell size at the maturity stage. The time tn is determined by the organism size and composition astsn and tgn are the size component and the threshold component of tn. Next, we discuss how we model tsn and tgn.We assume that organisms in populations grow independently without density dependence. Thus, populations follow exponential growth . The poprganisms ,38, can tsn depends only on the cell number n = nA + nB of an organism during growth, but not on nA or nB individually. Under the neutral condition b). Without loss of generality, we choose γ = 1 and treat tsn around the neutral condition (a). We call n = 1, …, N. By perturbing all normalized cell increment components χn for n = 1, …, 7, we can capture all possible effects of size. For χn = 1, we recover the neutral condition.The size component ism size . Thus, osn0) see a. We caltgn is determined by the number of the cell type of interest. Without loss of generality, we choose the type B. We assume the type B providing a benefit to the organism while bearing an individual cost. An organism grows faster if the number of its type B cells meets a given threshold k (c). There are many methods to construct such a compositional threshold effect. Here, we choose a volunteer dilemma game [n cells with nA type A cells and nB type B cells. When nB meets a contribution threshold k, each cell of the organism gets a benefit b from an increased organism growth. Each B cell pays an individual cost c and A cells pay no costs (d):pA and pB are the division probabilities for A cells and B cells, respectively, and w is the intensity of selection [tgn is the inverse of average fitness of the organism:k.The threshold component eshold k c. There mma game . Consideno costs d:2.2PA. If the organisms of a population are going through a perturbed state at size n, i.e. n1 + n2 + · · · + nM) can deviate from the one under the neutral condition. Since the population growth rate is inversely proportional to growth time, a perturbation is either advantageous  or disadvantageous  for population growth. A reproductive strategy is referred to as being promoted (suppressed) when its population growth rate is greater  than the neutral growth rate 1. A single advantageous perturbation (χn < 1) promotes the reproductive strategy of any population with organisms going through the state n of the perturbation, i.e. the strategies are satisfying nM ≤ n ≤ N (b). The performance of reproductive strategies is unaffected when their populations’ organisms do not go through the size under perturbations, i.e. n < nM or n > N. A single adverse perturbation χn > 1 suppresses reproductive strategies that satisfy nM ≤ n ≤ N. Among these affected populations, we found that the reproductive strategy n + 1 is most affected by perturbations at size n. Since the population with reproductive strategy n + 1 contains n-celled newborn organisms, which mature at size n + 1, its growth time depends on χn. Therefore, under the condition of χn < 1 and χk = 1 , the reproductive strategy n + 1 is uniquely optimal. Under the condition of χn > 1, the reproductive strategy n + 1 is most suppressed (see b). Analogous to the reproductive strategy n + 1, the reproductive strategy n + 2 is the second most affected reproductive strategy. Similarly, for the rest of the reproductive strategies, their population composition determines whether the growth rates are affected or not. The growth rates then determine the performance of reproductive strategies.To focus on size effects only, we first assume no threshold effect,  1, …, 7 a. If the ≤ n ≤ N b. The pessed see b. Analogn = 1, …, 7, which forms a discrete size function with respect to n. As each χn is independently perturbed from the neutral condition, the discrete function can cover all possible shapes. We investigate the general features of size functions that promote a given reproductive strategy, but ignore the specific shape of the size functions. As expected, we observed that the populations of optimal reproductive strategies contain organisms that mostly go through sizes with smaller χn. This is illustrated in figure 2c and an analytical proof is given in electronic supplementary material, appendix S3, for reproductive strategies with N ≤ 3. We found that only the binary-splitting reproductive strategy (producing two offspring) can be uniquely optimal . Intuitively, this is because the fastest-growing newborn organisms in a population with a multiple-splitting reproductive strategy can also be found in another population with a binary-splitting reproductive strategy. For example, the population growth rate of 2 + 1 + 1 cannot be greater than that of 1 + 1 and 2 + 2 at the same time. Additionally, 1 + 1 is the most frequently observed reproductive strategy in binary-splitting reproductive strategies (figure 2d) because 1 + 1 is the only reproductive strategy that depends on a single cell increment component χ1. Therefore, for a randomly chosen χn , 1 + 1 has a higher probability of being optimal compared to other strategies. Generally, reproductive strategies have lower chances to be optimal when binary splitting makes organisms go through many cell increment stages.We can analyse all possible size effects by combining single perturbations for . 3.2χn = 1, such that b − c = 0, cells of type B would never lead to any growth advantage; thus we focus on b − c > 0. With a threshold at size k, newborn organisms of a population with nB ≥ k have larger payoffs and thus have shorter growth time; see equations (d). With the contribution threshold k = 2,  grows fastest as it has two B cells.  is the second-fastest-growing newborn organism as it most likely gains benefits by producing a second B cell during growth. ,  and  grow relatively slowly because they are less likely to produce at least two B cells during growth. For convenience, we refer to newborn organisms in a population as ‘beneficial’ if nB ≥ k and ‘intermediate beneficial’ if nA = 0, nB < k and N > k, where N is maturity size. All other newborn organisms are unlikely to reap the benefits of cooperation before maturity. The growth rate of a population depends primarily on its beneficial newborn organisms and secondly on its intermediate beneficial newborn organisms. Moreover, among all beneficial newborn organisms, the one with exactly k type B cells has the largest payoff. Thus, they have growing advantages under no or low cell-type switching probability. For a low cell-type switching probability, e.g. m = 0.01, homogeneous newborn organisms are more abundant than heterogeneous ones. In the long run, we expect that populations mostly contain homogeneous newborn organisms.To investigate threshold effects exclusively, we assume the size effect to be neutral: quations  and (2.4quations d. With ta). The optimal reproductive strategies can be classified into three categories: multiple optima, symmetric binary splitting (N/2) + (N/2) (or ((N + 1)/2) + ((N − 1)/2)) and asymmetric binary splitting with a k-celled newborn organism (N − k) + k. When k = 1, multiple reproductive strategies are optimal at the same time (see a–c). Since every population contains beneficial newborn organisms, the performances of different reproductive strategies are similar. As k increases, the symmetric binary-splitting reproductive strategies (N/2) + (N/2) (or ((N + 1)/2) + ((N − 1)/2)) are optimal for 1 < k ≤ (1/2)N . Newborn organisms with size equal to or greater than k have growth advantages; thus, intuitively (N/2) + (N/2) and k + (k + 1) should have the same performance in population growth. However, we found that only (N/2) + (N/2) (or ((N + 1)/2) + ((N − 1)/2)) is optimal. The intrinsic composition of the population and the effects of cell-type switching probability m = 0.01 determine the results. To understand the growth advantages of the symmetric binary-splitting reproductive strategies with the maximal maturity size, we take 4 + 4 and 3 + 3 at k = 3 as an example. For k = 3, the population of 4 + 4 contains the beneficial newborn organisms  and . The population of 3 + 3 only contains beneficial newborn organisms . When a cell-type switching event happens during growth,  produces a beneficial newborn organism , while  reproduces a non-beneficial newborn organism . Populations with larger maturity sizes are less affected by the cell-type switching probability as they contain multiple types of beneficial newborn organisms. Finally, when N − k) + k becomes optimal (see a). When N − k) + k rather than other reproductive strategies such as N − k − 1) + k + 1. Because of N − k < k, organisms with N − k cells can only form intermediate beneficial newborn organisms—and only when they are pure B cells. Larger intermediate beneficial newborns grow faster than smaller ones. We take 3 + 1 + 1 and 3 + 2 for k = 3 as an example. 3 + 1 + 1 has the intermediate beneficial newborn organism  and 3 + 2 has the intermediate beneficial newborn organism . During organism growth,  undergoes two cell increment stages with longer time N see a,b. Newbimal see a. When kquations  and 2.2a. The opk, resulting from reducing the number of beneficial and intermediate beneficial newborn organisms. Especially when k ≥ N, no reproductive strategies will obtain the benefits of cooperation, and their populations grow slower due to the associated costs . Increasing maturity size N increases population growth rates of the optimal reproductive strategies because the number of beneficial or intermediate beneficial newborn organisms increases. As expected, population growth rates also increase with the benefit to cost ratio (see b–e).Population growth rates decrease with increasing osts see a,b. Incratio see b–e.m on the reproductive strategy for N = 8. We found that it is robust for the conclusion: the optimal reproductive strategy is binary splitting with maximum maturity size. At high m and high k, the asymmetric binary-splitting strategy 7 + 1 invades other asymmetric binary-splitting ones . Since 7 + 1 can keep the largest number of B cells in a newborn organism, this organism could gain growth advantages for high cell-type switching probability. A higher cell-type switching probability increases the heterogeneity of organisms, especially for large organisms. This leads to fewer B cells in an organism—thus, the population growth rate generally decreases with increasing m; see electronic supplementary material, figure S2b. For some reproductive strategies, a change in the cell-type switching probability leads to complex effects on population growth . This is the outcome of several factors: the cell-type switching probability m, the contribution threshold k and the structure of the population. We take the reproductive strategy 1 + 1 + 1 as an example to discuss the potential reason: when k = 2, the population has a lower growth rate when m = 0.5 because organisms cannot reach the threshold during their growth.Additionally, we investigated the effect of varying . 3.3ni + nj can be uniquely optimal, where ni and nj are positive integers, and ni + nj ≤ N . With the combined effects of size and threshold, we found new optimal binary-splitting reproductive strategies that are neither optimal in the effects of single perturbation only nor for thresholds only, including 2 + 2, 3 + 2, 4 + 2, 5 + 2, 3 + 3 and 4 + 3. Furthermore, under a beneficial size perturbation, we found n + 1  can be optimal both at small and large contribution threshold k . This is due to the fact that the threshold effects lead to a similar performance of reproductive strategies either at small k and at large k (b). Therefore, for combined size and threshold effects, the size effect has a larger impact on the performance of reproductive strategies . Consequently, the reproductive strategy n + 1 becomes optimal under an advantageous perturbation, where n = 1, …, 7. Newly emerged binary-splitting reproductive strategies have advantages for intermediate contribution thresholds k, suggesting that it is an outcome of the trade-off between the effect of size and threshold. For an adverse size perturbation, we found that the reproductive strategy n + 1 cannot be optimal (b), because the adverse size perturbation leads to poor performance of reproductive strategies that are influenced by the perturbation (b and 4d). The reproductive strategy 7 + 1 is an exception to this rule, as the threshold effect strongly influences it at k = 7. The optimal reproductive strategies observed are those that can obtain growth benefits from threshold effects and avoid the disadvantages from the adverse size effect. For example, 3 + 3 outcompetes 4 + 4 for k = 2 when a disadvantageous size perturbation occurs at n = 7. Both strategies can obtain growth advantages from threshold effects. However, adverse size perturbation decreases the population growth rate of 4 + 4, but it has no impact on 3 + 3. Thus the performance of reproductive strategies is the outcome of the trade-off between the effects of size and threshold. Our results suggest that all binary-splitting reproductive strategies can evolve under an appropriate choice of size effects (at a single size) and threshold effects.Finally, we investigate the optimal reproductive strategies under size and threshold effects combined. For simplicity, we only consider the size effects in the form of a single perturbation. All binary-splitting reproductive strategies + nj ≤ N a,b. Withld k see a,b. This large k b. Therefgies see a,c. Cons optimal b, becausurbation b and 4d.. 4Numerous reproductive strategies are conceivable for multicellular organisms, but only recently more attention has been paid to the evolution of such reproductive strategies ,20,41,42n + 1 resembles organisms passing through a unicellular phase, which is a striking feature for most multicellular organisms [n + 1 can evolve under size or threshold effects. This indicates an alternative way for evolving organisms with a single cell bottleneck. Our models also show the conditions where multiple reproductive strategies can evolve simultaneously. Each reproductive strategy can evolve when the size effects are the same for each cell size (χn are the same) or when the threshold is low . Many reproductive strategies have beneficial newborn organisms (nB ≥ k) when the threshold is low, leading to a large population growth rate. This result indicates that multiple reproductive strategies can coexist for an organism. Notably, the results resonate with the observation that one species can possess several reproductive strategies simultaneously in nature [The reproductive strategy rganisms . Previourganisms ,17. Inten nature ,15,45,46χn), we found the frequently observed optimal reproductive strategy 1 + 1. The finding suggests that 1 + 1 is the best reproductive strategy unless explicit advantages exist for a larger organism. The size effects considered in our model are functions of cell numbers rather than the task allocation of each cell. Previous work on the division of labour has instead focused on continuous functions [In our model, we can choose a flexible impact of size on organism growth. We investigated the size effects by first investigating the effects of single size perturbations. Although single size perturbations seem unrealistic for organism growth in nature, they provide a theoretically convenient approach towards understanding size effects. Size effects that are present by combing single size perturbations could have positive, negative or neutral effects on growth at each cell increment. The model assumption corresponds to studies concerning size effect on growth –34. The unctions ,48–50.We delineated the threshold effect of cellular interactions in a multiplayer volunteer game given the utility of game theory in depicting biological interactions ranging from social foraging to cancer development –58. We dm = 0.01. This focus assumed that cell-type switching is uncommon and mostly happens under environmental pressure in nature [nB ≥ k) ) newborn organisms, then homogeneous beneficial  newborn organisms dominate the population. Although heterogeneous beneficial newborn organisms grow fastest, they are not most abundant. First, heterogeneous beneficial newborn organisms do not produce themselves fast after reproduction. The reason is that A cells have higher payoffs; thus, they produce more A cells during the growth of heterogeneous beneficial newborn organisms. When the organisms reproduce, they are unlikely to produce more than one heterogeneous beneficial newborn organism. Second, although heterogeneous beneficial newborn organisms can be replenished from the reproduction of beneficial newborn organisms (via cell-type switching), the replenishment is slow as beneficial newborn organisms have lower growth advantages. Thus, it is also theoretically intriguing to investigate reproductive strategies under high cell-type switching probability. Such an investigation shows that the conclusion is robust across cell-type switching probability parameter space: the optimal reproductive strategy is the binary-splitting one with maximum maturity size . Although high cell-type switching probability mainly decreases the population growth rate, nevertheless it does not affect the relative performance of reproductive strategies. Within these binary-splitting strategies, the binary-splitting reproductive strategy producing the largest newborn invades others at high cell-type switching probability.We mainly investigated reproductive strategies with a small cell-type switching probability, n nature ,60. The Stanieria, Pleurocapsa and Dermocarpella [Our model investigated the competition of reproductive strategies via the growth rates of isolated populations. In many situations, this correlates with the ability to invade other populations—simply by outgrowing existing types. We can extend the model to a density-dependent population consisting of organisms with different reproductive strategies. Given the size and threshold effects, the invasive strategies would reflect the reproductive strategies observed in our model. Furthermore, our model considered the size effect via the number of cells in an organism, so the size function is discrete. In nature, some unicellular organisms grow by enlarging themselves rather than by cell divisions, such as carpella . Insteadructures ,62.


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.