Nature Communications 10.1038/s41467-021-24364-6, published online 30 June 2021Correction to: MSH2 inserted; instead, the inserted MSH2 lacked the stop codon and encoded 24 extra amino acids at the 3’ end. Specifically, three of the 13 original second-order MA lines  had the correct MSH2 inserted, while the remaining 10 had the MSH2 that lacked the stop codon. To correct this error, the wild-type MSH2 was inserted into all lines after the first round of MA, the inserted MSH2 sequence was confirmed by Sanger sequencing, and the downstream experiments were repeated, including fluctuation test, second round of MA, and whole-genome sequencing after the second round of MA. The newly generated data have been reanalyzed and presented in the corrected version of this Article. The following changes have been made:In the Results subsection ‘Mutagenesis frequently reduces μ’, the third and fourth paragraphs have been modified from:Ne of the MA lines was about 10 (see “Methods”) while most mutations are expected to have a fitness effect on the order of 1% or smaller21, selection should be infrequent during the MA and was indeed the case (see “Methods”). To assess the impact of MA on μ, we first inserted MSH2 back to the MA lines, which was accomplished in 66 of the 93 lines . Using the classic fluctuation test based on the reporter gene CAN1 (see “Methods”), we successfully measured μ in the progenitor as well as 48 of the above 66 MA lines, all carrying an intact MSH2 . We found μ of the 48 MA lines to range from 0.01 to 27 times that of the progenitor , including 28 lines with significantly higher μ and 12 lines with significantly lower μ than the progenitor (see “Methods”). Furthermore, 10 of the 12 lines with significantly decreased μ had μ reduced by at least 50%, while the remaining two lines had μ reduced by 46% and 38%, respectively . That 30% of MA lines with significantly altered μ exhibit such drastic reductions in μ is inconsistent with the DBH, because when μ is near the drift barrier, mutations are expected to be strongly biased toward increasing μ and are not expected to cause such large reductions of μ so frequently . Our finding suggests that the progenitor’s μ is well above the drift barrier . We found no significant correlation between μ and the number of mutations accumulated during the MA or the growth rate of the MA line .“Because the MSH2 . 18–20 parallel lines were established from each strain, and on average 695 generations of MA were performed in the medium similar to that used in the fluctuation test . The MA + WGS results were generally consistent with those from the fluctuation test. For instance, compared with the progenitor, all five lines with higher CAN1-based μ exhibited higher MA+ WGS-based SNV  and indel  rates. Among the eight lines with lower CAN1-based μ, four exhibited significantly lower MA + WGS-based SNV and/or indel rates; the reduced consistency here is likely due to larger errors of lower CAN1-based μ estimates . CAN1-based μ is significantly correlated with the MA + WGS-based SNV rate  but not with the MA+ WGS-based indel rate . This observation is not unexpected given that most loss-of-function mutations in CAN1 are SNVs instead of indels22.”Because the above estimation of μ was based on loss-of-function mutations in one gene, we attempted to verify these results by performing another round of MA followed by WGS in 13 of the above 48 MA lines as well as the progenitor, all with an intact to now read:Ne of the MA lines was about 10 (see “Methods”) while most mutations are expected to have a fitness effect on the order of 1% or smaller21, selection should be infrequent during the MA and was indeed the case (see “Methods”). To assess the impact of MA on μ, we first inserted MSH2 back to the MA lines, which was accomplished in 60 of the 93 lines . Using the classic fluctuation test based on the reporter gene CAN1 (see “Methods”), we successfully measured μ in the progenitor as well as 49 of the above 60 MA lines, all carrying an intact MSH2 . We subsequently found that five of the 49 lines were likely diploidized upon the insertion of MSH2 (see the following paragraph) and excluded them  from all CAN1-based analyses. We found μ of the 44 remaining MA lines to range from 0.01 to 26 times that of the progenitor , including 19 lines with significantly higher μ and 13 lines with significantly lower μ than the progenitor (see “Methods”). Furthermore, 10 of the 13 lines with significantly decreased μ had μ reduced by at least 50%, while the remaining three lines had μ reduced by 40% to 43% . That over 40% of MA lines with significantly altered μ exhibit such drastic reductions in μ is inconsistent with the DBH, because when μ is near the drift barrier, mutations are expected to be strongly biased toward increasing μ and are not expected to cause such large reductions of μ so frequently . Our finding suggests that the progenitor’s μ is well above the drift barrier . We found a significant positive correlation between μ and the number of mutations accumulated during the MA, but μ was not significantly correlated with the growth rate of the MA line .“Because the MSH2 . 4–20 parallel lines were established from each strain, and on average 684 generations of MA were performed in the medium similar to that used in the fluctuation test . Four of the 16 MA lines were apparently diploid, because the majority of the mutations observed in MA+WGS were in heterozygous state. Diploids should not produce mutant colonies in the fluctuation test. To be conservative in inferring mutation rate reductions in the first round of MA, we additionally regarded a line that was not subject to MA+WGS but had only two mutant colonies in the fluctuation test as putatively diploid . We excluded these five diploid lines from all CAN1-based analyses. Because haploid and diploid progenitors showed similar mutation rates , all 16 lines with MA+WGS were included in analyses based solely on MA+WGS. The MA + WGS results were generally consistent with those from the fluctuation test. For instance, compared with the progenitor, all eight lines with higher CAN1-based μ exhibited higher MA+ WGS-based SNV  or indel  rates. Among the four lines with lower CAN1-based μ, three exhibited significantly lower MA + WGS-based SNV or indel rates; the reduced consistency here is likely due to larger errors of lower CAN1-based μ estimates . CAN1-based μ is significantly correlated with both the MA + WGS-based SNV rate  and the MA+ WGS-based indel rate , although the latter correlation is weaker than the former. This observation is not unexpected given that most loss-of-function mutations in CAN1 are SNVs instead of indels22.”Because the above estimation of μ was based on loss-of-function mutations in one gene, we attempted to verify these results by performing another round of MA followed by WGS in 16 of the above 49 MA lines as well as the progenitor (and a diploid version of the progenitor), all with an intact In the Results subsection ‘Stabilizing selection of μ’, the first paragraph sentenceVm that is comparable with Vg, we used the CAN1-based μ estimates from the 48 MA lines, but corrected for the increased mutagenesis in the MA induced by deleting MSH2.”“To estimate has been corrected to read ‘44 haploid’ in place of ‘48’.The second paragraph has been modified from:Vg/Vm to be at least 800 times lower than the neutral expectation of 4Ne ≈ 4 × 107 (see “Methods”), regardless of the particular Vm used (Table 1), indicating strong stabilizing selection of μ. This signal of stabilizing selection is not an artifact of the physical limits of μ, because the range of μ among the natural strains is even smaller than that of the MA lines . To investigate whether the stabilizing selection prohibits the evolution of higher μ, lower μ, or both, we separated Vm into two components that respectively measure the variance of μ created by mutations decreasing μ (VmL) and increasing μ (VmH). If there is no selection against a reduction in μ, Vg should be at least as large as 4NeVmL. However, we found Vg to be at least 700 times lower than 4NeVmL (Table 1), indicating the action of selection prohibiting a reduction of μ in evolution. Similarly, Vg was at least 150 times lower than 4NeVmH (Table 1), indicating the action of selection prohibiting a rise of μ in evolution. In the above tests, the smallest difference observed between Vg and a neutral expectation was 150 times, based on Vm2 that corresponds to a conservative test. Therefore, it is exceedingly unlikely that our test results are due to confounding factors such as mutation spectrum differences between wild-type and MSH2-lacking strains or the inaccuracy of yeast’s Ne estimate (see “Methods”). Together, the above results demonstrate that μ has been selectively maintained at an intermediate level in S. cerevisiae.”“We found to now read:Vg/Vm to be at least 540 times lower than the neutral expectation of 4Ne ≈ 4 × 107 (see “Methods”), regardless of the particular Vm used (Table 1), indicating strong stabilizing selection of μ. This signal of stabilizing selection is not an artifact of the physical limits of μ, because the range of μ among the natural strains is even smaller than that of the MA lines . To investigate whether the stabilizing selection prohibits the evolution of higher μ, lower μ, or both, we separated Vm into two components that respectively measure the variance of μ created by mutations decreasing μ (VmL) and increasing μ (VmH). If there is no selection against a reduction in μ, Vg should be at least as large as 4NeVmL. However, we found Vg to be at least 300 times lower than 4NeVmL (Table 1), indicating the action of selection prohibiting a reduction of μ in evolution. Similarly, Vg was at least 230 times lower than 4NeVmH (Table 1), indicating the action of selection prohibiting a rise of μ in evolution. In the above tests, the smallest difference observed between Vg and a neutral expectation was 230 times, based on Vm2 that corresponds to a conservative test. Therefore, it is exceedingly unlikely that our test results are due to confounding factors such as mutation spectrum differences between wild-type and MSH2-lacking strains or the inaccuracies of Vm, Vg, and Ne estimates (see “Methods”). Together, the above results demonstrate that μ has been selectively maintained at an intermediate level in S. cerevisiae. Note that the selective forces to increase and to suppress μ are not equally strong, because the mean μ of the MA lines is higher than the progenitor .”“We found The third paragraph sentences:Vm based on the 13 MA lines with MA+ WGS-based estimates of SNV rates and corrected the impact of deleting MSH2 as in the above analysis. We found D2/Vm to be at least 104 times smaller than T (Table 1). We also respectively estimated VmL and VmH using the 13 MA lines with MA+ WGS-based estimates of μ. Again, we found D2/VmL to be at least 300 times smaller and D2/VmH at least 6000 times smaller than T (Table 1), demonstrating selection against lowering as well as increasing μ in the divergence of Saccharomyces species.”“We obtained have been modified to now read:Vm based on the 16 MA lines with MA+ WGS-based estimates of SNV rates and corrected the impact of deleting MSH2 as in the above analysis. We found D2/Vm to be at least 4000 times smaller than T (Table 1). We also respectively estimated VmL and VmH using the 16 MA lines with MA+ WGS-based estimates of μ. Again, we found D2/VmL to be at least 400 times smaller and D2/VmH at least 2400 times smaller than T (Table 1), demonstrating selection against lowering as well as increasing μ in the divergence of Saccharomyces species.”“We obtained PSP2 as a mutator gene’, the first paragraph sentences:In the Results subsection ‘Discovery of CAN1-based μ estimates (Supplementary Table 5). We then picked four candidates  based on their ranks from the screening and annotated functions for a follow-up study.”“To this end, we first identified candidate mutator genes by screening genes that were more frequently mutated in low-μ lines than in high-μ lines among the 48 MA lines with have been modified to now read:CAN1-based μ estimates (Supplementary Table 5), and picked four candidates  based on their ranks from the screening and annotated functions for a follow-up study. We subsequently found that some MA lines had erroneous MSH2, so reinserted MSH2 followed by re-estimation of CAN1-based μ. Based on these new estimates of μ , the four genes are ranked 1362, 911, 58, and 76, respectively.”“We originally identified candidate mutator genes by screening genes that were more frequently mutated in low-μ lines than in high-μ lines among MA lines with In the Results subsection ‘Mutation spectrum has been shaped by selection’, the first paragraph sentences:Vg) in a component of the mutation spectrum among five divergent natural yeast strains having published MA+WGS data , with the corresponding mutational variance per generation estimated from the 13 MA lines with MA+WGS data. For instance, even under the most generous calculation, Vg/Vm (4.48 × 104) of the proportion of mutations that are SNVs is orders of magnitude smaller than the neutral expectation of 4 × 107 (Supplementary Table 6). In fact, the variance of the proportion of SNVs is smaller among the five natural strains than among the 13 MA lines , despite that the numbers of generations separating the natural strains are much greater than those separating the MA lines even after the correction for the increased mutagenesis of MA lines induced by deleting MSH2. Similar results were found regarding the proportion of insertions  and that of deletions  .”“To investigate the potential role of natural selection in shaping yeast’s mutation spectrum, we compared the variance (have been modified to now read:Vg) in a component of the mutation spectrum among five divergent natural yeast strains having published MA+WGS data , with the corresponding mutational variance per generation estimated from the 16 MA lines with MA+WGS data. Because haploid and diploid progenitors show similar mutational spectrums , we analyze the MA lines and natural strains regardless of their ploidy. Even under the most generous calculation, Vg/Vm (3.07 × 104) of the proportion of mutations that are SNVs is orders of magnitude smaller than the neutral expectation of 4 × 107 (Supplementary Table 6). In fact, the variance of the proportion of SNVs is smaller among the five natural strains than among the 16 MA lines , despite that the numbers of generations separating the natural strains are much greater than those separating the MA lines even after the correction for the increased mutagenesis of MA lines induced by deleting MSH2. Similar results were found regarding the proportion of insertions  and that of deletions  .”“To investigate the potential role of natural selection in shaping yeast’s mutation spectrum, we compared the variance .”“There are six different types of SNVs  and we found evidence for stabilizing selections on each of the six fractions . In particular, Ts/Tv is higher than that of the progenitor in 10 of the 13 MA lines, and Ts/Tv is increased in all 7 MA lines with a significantly altered Ts/Tv  .”“We found evidence for stabilizing selection of Ts/Tv ; the maximal have been modified to now read:Vg/Vm equals 9.13 × 104 (Supplementary Table 6). In particular, Ts/Tv is significantly higher (or lower) than that of the progenitor in two (or zero) MA lines .”“We found evidence for stabilizing selection of Ts/Tv ; the maximal The fourth paragraph sentences:Vg/Vm for the AT mutational bias is at least 700 times lower than the neutral expectation (Supplementary Table 6), indicating that the bias has been maintained by stabilizing selection.”“We found that the ratio of the number of GC→AT mutations to the number of AT→GC mutations is significantly different in 7 of the 13 MA lines when compared with the progenitor, including 6 lines that increased the bias and one line that reversed the bias to the opposite direction . Clearly, the AT mutational bias is subject to genetic control and is not a chemical necessity. Furthermore, have been modified to now read:Vg/Vm for the AT mutational bias is at least 120 times lower than the neutral expectation (Supplementary Table 6), indicating that the bias has been maintained by stabilizing selection.”“We found that, in one MA line, the ratio of the number of GC→AT mutations to the number of AT→GC mutations is significantly different from that in the progenitor, and is reversed from >1 to <1 . Clearly, the AT mutational bias is subject to genetic control and is not a chemical necessity. Furthermore, In the Methods subsection ‘Strains and genetic manipulations’, the first paragraph sentences:MSH2 was successful in only 66 of the 93 MA lines, probably due to reduced transformation efficiencies in the MA lines.”“Transformation was performed in each MA line for up to three times. Restoration of have been modified to now read:MSH2 was successful in only 60 of the 93 MA lines, probably due to reduced transformation efficiencies in the MA lines.”“Transformation was performed in each MA line for up to three times and was confirmed by Sanger sequencing of the reinserted locus. Restoration of In the Methods subsection ‘MA and whole-genome sequencing’, the second paragraph sentences:MSH2, were subject to the second round of MA. Eighteen to 20 replicate lines were established for each strain. Cells were propagated at 30 °C on SC (synthetic complete) plates, similar to that used in the fluctuation test. The total time in the second round of MA for all lines was kept at ~100 days and the number of generations between bottlenecks was kept at ~20. The between-bottleneck duration was different among these 14 strains because of their different generation times. It was 48 h in BY4741, MA28, MA29, and MA38, 72 h in MA13, MA23, MA25, MA33, MA77, MA78, and MA85, and 96 h in MA45, MA59, and MA95. The genomes of 272 MA lines at the end of the second round of MA and their 14 ancestral strains were sequenced.”“A total of 14 strains, including 13 of the above 93 MA lines and BY4741, all with intact have been modified to now read:MSH2, were subject to the second round of MA. Four to 20 replicate lines were established for each strain. Cells were propagated at 30 °C on SC (synthetic complete) plates, similar to that used in the fluctuation test. The total time in the second round of MA for all lines was kept at ~100 days and the number of generations between bottlenecks was kept at ~20. The between-bottleneck duration was different among these 18 strains because of their different generation times. It was 48 h in BY4741, BY4743, MA28, and MA38, 72 h in MA15, MA21, MA23, MA25, MA29, MA33, MA44, MA63, and MA92, and 96 h in MA45, MA51, MA56, MA64, and MA94. The genomes of 209 MA lines at the end of the second round of MA and their 18 ancestral strains were sequenced.”“A total of 18 strains, including 16 of the above 93 MA lines, BY4741 (haploid), and BY4743 (diploid), all with intact In the Methods subsection ‘Fluctuation test’, the first paragraph sentence:MSH2 to the fluctuation test, but only 48 of them grew in the medium.”“We subjected all 66 MA lines with reinserted has been corrected to read ‘60’ in place of ‘66’ and ‘49’ in place of ‘48’.The second paragraph sentence:CAN1 mutant was observed in 4 MA lines, and their μ values were calculated by assuming the observation of one mutant colony to allow plotting μ in a logarithmic scale.”“No has been corrected to read ‘two’ in place of ‘4’.Vm, Vg, and D2’, a new paragraph was appended to the end:In the Methods subsection ‘Estimation of In the original version of this Article, some mutation accumulation (MA) lines did not have the correct Vg and Vm calculated here are phenotypic variances, including the genetic component and estimation error, because the environment is fixed. The phenotypic variance caused by estimation error should be similar for natural strains and MA lines because of the use of the same phenotyping method. Because the phenotypic variance is greater for MA lines than for natural strains, the fraction of phenotypic variance contributed by genetics is greater for MA lines than for natural strains. Hence, Vg/Vm is overestimated when computed using phenotypic variance instead of genetic variance, which renders our conclusion that Vg/Vm is smaller than the neutral expectation conservative.”In Figure 1d, the number of lines has been corrected.The previous incorrect version of Figure 2:“The Fig. 2 Mutation frequencies and rates of the MA lines.aCAN1 mutation frequencies of the progenitor (green), 48 MA lines (gray), and 7 natural yeast strains (yellow), determined by the fluctuation test based on 72 (green and gray) or 288 (yellow) biologically independent cell cultures from each strain. The data from the seven natural strains came from ref. 14. Error bars indicate 95% confidence intervals of the mean. Mutation frequency is the probability of loss-of-function mutation in CAN1 per cell division, so is not directly comparable with the mutation rates estimated by MA+ WGS. SNV (b) or indel (c) mutation rate per site per generation in the progenitor (green) and 13 MA lines estimated by MA+ WGS based on 18 to 20 biologically independent replicates. Numbers on the X-axis refer to IDs of MA lines, while “Prog” refers to the progenitor. Circles represent mean values while error bars show 95% confidence intervals predicted from Poisson distributions. Significant rate differences from the progenitor are indicated by blue (lower than the progenitor) or red (higher than the progenitor) with different shades for different nominal P values from Wilcoxon rank-sum tests. White circles show no significant rate difference from the progenitor. Correlation between SNV (d) or indel (e) mutation rate measured by MA+ WGS and CAN1 mutation frequency measured in fluctuation test among the 13 MA lines and the progenitor (green). Pearson’s r  and associated P value are shown. The green dot shows the progenitor.”“has been replaced with the correct version of Figure 2Fig. 2 Mutation frequencies and rates of the MA lines.aCAN1 mutation frequencies of the progenitor (green), 49 MA lines (gray), and 7 natural yeast strains (yellow), determined by the fluctuation test based on 72 (green and gray) or 288 (yellow) biologically independent cell cultures from each strain. The data from the seven natural strains came from ref. 14. Error bars indicate 95% confidence intervals of the mean. “*” on the X-axis indicates diploid or putatively diploid strains. Mutation frequency is the probability of loss-of-function mutation in CAN1 per cell division, so is not directly comparable with the mutation rates estimated by MA+ WGS. SNV (b) or indel (c) mutation rate per site per generation in the progenitor in both haploid and diploid form (green) and 16 MA lines estimated by MA+ WGS based on 4 to 20 biologically independent replicates. Numbers on the X-axis refer to IDs of MA lines, while “Hap” refers to the progenitor in the haploid form and “Dip” refers to the progenitor in the diploid form. Circles represent mean values while error bars show 95% confidence intervals predicted from Poisson distributions. Significant rate differences from the progenitor are indicated by blue (lower than the progenitor) or red (higher than the progenitor) with different shades for different nominal P values from Wilcoxon rank-sum tests. White circles show no significant rate difference from the progenitor. Correlation between SNV (d) or indel (e) mutation rate measured by MA+ WGS and CAN1 mutation frequency measured in fluctuation test among the 12 haploid MA lines and the haploid progenitor (green). Pearson’s r  and associated P value are shown. The green dot shows the progenitor.The previous incorrect version of Figure 4:Fig. 4 Molecular spectra of mutations in 13 MA lines and 5 natural yeast strains estimated by MA + WGS.a Proportions of SNVs, insertions, and deletions among all mutations. b Relative proportions of the six types of SNVs. c Number of transition mutations relative to the number of transversion mutations (Ts/Tv). d Number of GC→AT mutations relative to the number of AT→GC mutations. In c and d, the gray dotted line shows the random expectation while the green dotted line shows the value in the progenitor. In all panels, the small letters on the X-axis refer to natural strains , “Prog” refers to the progenitor, and the numbers refer to IDs of MA lines. Two-tailed chi-squared test is performed between each strain and the progenitor: *, P < 0.05; **, P < 0.01; ***, P < 0.001.”“has been replaced with the correct version of Figure 4Fig. 4 Molecular spectra of mutations in 16 MA lines and 5 natural yeast strains estimated by MA + WGS.a Proportions of SNVs, insertions, and deletions among all mutations. b Relative proportions of the six types of SNVs. c Number of transition mutations relative to the number of transversion mutations (Ts/Tv). d Number of GC→AT mutations relative to the number of AT→GC mutations. In c and d, the gray dotted line shows the random expectation. In all panels, the small letters on the X-axis refer to natural strains , “Hap” refers to the progenitor in the haploid form, “Dip” refers to the progenitor in the diploid form, and the numbers refer to IDs of MA lines. Two-tailed chi-squared test is performed between each strain and the progenitor: *, P < 0.05; **, P < 0.01; ***, P < 0.001.”“The previous incorrect version of Table 1: has been replaced with the correct version of Table 1 The original version of the Supplementary Information associated with this Article contained errors in Supplementary Figure 2 and Supplementary Tables 2, 4, 6, 7 and 8. The HTML has been updated to include a corrected version of the Supplementary Information; the original incorrect versions of this Figure and these Tables can be found as Supplementary Information associated with this Correction.The original version of the Supplementary Information associated with this Article included an incorrect Supplementary Data The original version of the Source Data associated with this Article contained errors reflecting the changes made to the data and figures described above. The HTML has been updated to include a corrected version of the Updated Supplementary InformationIncorrect Supplementary InformationUpdated Supplementary Data 1Incorrect Supplementary Data 1Updated Source DataIncorrect Source Data


Multidrug resistance renders treatment failure in a large proportion of head and neck squamous cell carcinoma (HNSCC) patients that require multimodal therapy involving chemotherapy in conjunction with surgery and/or radiotherapy. Molecular events conferring chemoresistance remain unclear. Through transcriptome datamining, 28 genes were subjected to pharmacological and siRNA rescue functional assays on 12 strains of chemoresistant cell lines each against cisplatin, 5-fluorouracil (5FU), paclitaxel (PTX) and docetaxel (DTX). Ten multidrug chemoresistance genes  were identified. Of these, four genes  were upregulated in an HNSCC patient cohort (n = 221). Silencing NEK2 abrogated chemoresistance in all drug-resistant cell strains. INHBA and TOP2A were found to confer chemoresistance in majority of the drug-resistant cell strains whereas DNMT1 showed heterogeneous results. Pan-cancer Kaplan-Meier survival analysis on 21 human cancer types revealed significant prognostic values for INHBA and NEK2 in at least 16 cancer types. Drug library screens identified two compounds (Sirodesmin A and Carfilzomib) targeting both INHBA and NEK2 and re-sensitised cisplatin-resistant cells. We have provided the first evidence for NEK2 and INHBA in conferring chemoresistance in HNSCC cells and siRNA gene silencing of either gene abrogated multidrug chemoresistance. The two existing compounds could be repurposed to counteract cisplatin chemoresistance in HNSCC. This finding may lead to novel personalised biomarker-linked therapeutics that can prevent and/or abrogate chemoresistance in HNSCC and other tumour types with elevated NEK2 and INHBA expression. Further investigation is necessary to delineate their signalling mechanisms in tumour chemoresistance.The online version contains supplementary material available at 10.1186/s12943-023-01846-3. Head and neck squamous cell carcinomas (HNSCCs) constitute 90% of all head and neck cancers. Whilst a minority of HNSCCs are caused by human papilloma virus (HPV) infection the majority of the more aggressive HPV-negative HNSCCs (75%) are associated with tobacco and alcohol use . AlthougFor HNSCC patients requiring multimodal therapy involving chemotherapy, cisplatin, 5-fluorouracil (5FU) and paclitaxel (PTX) or docetaxel (DTX) are amongst the most commonly used chemotherapeutic agents often used in combinations , 2. UnfoA number of key mechanisms for conferring intrinsic chemoresistance in HNSCC tumour cells have been studied and these include perturbations of pathways regulating apoptosis/cell death, DNA damage repair, epithelial mesenchymal transition, cell cycle, cancer stem cell, chromatin/epigenetic, miRNA processing, autophagy and stroma/matrix, immune cell interactions . AlthougThis study explored using a combination of bioinformatics transcriptome data mining, differential gene expression analysis in chemoresistant cell line models and validation in clinical HNSCC tumour specimens, pharmacological dose-response drug library screen and cell culture models with the aim to identify key multidrug-resistant biomarker genes and repurpose existing drugs to counteract chemoresistance.All details of materials and methods can be found in Additional File www.oncomine.org) to identify differentially expressed genes in studies comparing HNSCC with normal oral mucosa. Initially, top 40 differentially expressed genes were selected based on their reported P-values (> 0.001). We performed RT-qPCR to quantify each of the 40 genes in a panel of eight primary normal human oral keratinocytes  and ten HNSCC cell lines  to identify and validate differentially expressed genes. Of the 40 genes, 28 were found to be differentially expressed in our cell line panels and have been implicated in the regulation of matrix remodelling, immune modulation, cell proliferation & differentiation, stem cell renewal, epigenetic programming and genomic instability , carcinogen (nicotine)-transformed malignancy (SVFN8) and a patient HNSCC tumour-derived malignancy (CaLH2). In order to identify common multidrug-resistant genes, we generated four drug-resistant cell strains  for each of the three different cell lines  giving rise to a total of 12 cell/drug-resistant combination strains Fig. -S12. We − 5; Fig. In order to confirm that these four genes  were indeed upregulated in HNSCC tumours, we performed RT-qPCR on a UK HNSCC tissue cohort to quantify their relative gene expression levels in adjacent margin (n = 98) and HNSCC core tumour tissues (n = 123). All four genes were confirmed to be significantly upregulated in HNSCC tumour compared to margin tissues . Abrogation of chemoresistance would result in a shift in IC50 values of resistant cells towards IC50 of WT cells . We included untransfected  and control siRNA (siCTRL) as controls and confirmed gene-specific siRNA silencing by RT-qPCR  of all the 12 cell strains are summarised in Fig. 50 values in siCTRL transfected cells were very similar to untransfected cells indicating that siCTRL did not induce any non-specific or off-target effects. In SVpgC2a cells, siTOP2A and siNEK2 both completely reversed chemoresistance (P < 0.001) in all four drug-resistant strains. siDNMT1 reversed only PTX- and DTX-resistant strains whilst, siINHBA reversed only cisplatin and PTX-resistant strains. In SVFN8 cells, similar to results from SVpgC2a cells, siTOP2A and siNEK2 completely reversed drug resistance in all four drug-resistant strains. However, unlike results for SVpgC2a, siDNMT1 and siINHBA showed partial reversal of resistance in all four different drug-resistant strains. Interestingly, in CaLH2 cells (HNSCC tumour-derived cell line), siNEK2 and siINHBA showed complete reversal of drug resistance in all four drug-resistant strains, whilst siDNMT1 and siTOP2A showed only partial reversal of chemoresistance.To investigate if the four genes  identified above were conferring multidrug resistance across the 12 different cell strains, we performed gene silencing using siRNA to knockdown each of these genes in both the WT and drug-resistant cells in response to each corresponding drug. We hypothesised if the genes were necessary to sustain drug resistance, knockdown of the genes would abrogate chemoresistance. To test this hypothesis, we transfected gene-specific siRNA and treated both WT and drug-resistant cells to serial-dilutions of corresponding drug to determine their IChttp://km-plotter.com/) containing 54,675 genes with survival outcome for 21 different human cancer types  differential expression and if they have any prognostic value in different human cancer types, we performed data mining on publicly available pan-cancer databases Oncomine and KM Plotter with RNA-seq transcriptome (50) of D4, D5 and D7 on gene inhibition were found to range from 1.1 × 10− 8 M to 6.3 × 10− 7. As D5 only inhibited INHBA and not NEK2, this compound was not further investigated. The chemical structures and identities of the two selected compounds D4 (Sirodesmin A) and D7 (Carfilzomib) are shown in Fig. 50) of cisplatin from 15.10 ± 1.59 µM to 0.52 ± 0.12 µM  or to 0.54 ± 0.07 µM , respectively. This demonstrated that cisplatin-resistant cells could be significantly re-sensitised to cisplatin by addition of either D4 or D7  and 390 natural products (Set V) in WT and cisplatin-resistant CR) CaLH2 with an aim to identify and repurpose existing drugs that suppress INHBA and/or NEK2 gene expression to counteract chemoresistance in HNSCC. We chose the CaLH2 cell line to investigate the effect of candidate drugs as both INHBA and NEK2 genes were not found to be differentially expressed between WT and CR cells in respond to cisplatin Fig. . To inveR CaLH2 wCurrently there are no specific molecular biomarkers that can indicate which HNSCC patient is susceptible to developing drug resistance. This study presented a series of experiments using a combination of bioinformatics data mining, cell culture model and pharmacological approaches to identify key transcriptome biomarkers that confer multidrug chemoresistance. We have identified ten candidate genes  of which, four  were confirmed to be significantly upregulated in our UK HNSCC tumour cohort and also consistent with TCGA/GTEx data. NEK2, DNMT1 and FOXM1B had been previously shown to be upregulated in HNSCC tumour cohorts from UK, Norway and China , and theHere, we uncovered an essential role for a cell cycle gene, NEK2 (Never in mitosis gene A-related kinase 2), in conferring multidrug chemoresistance in HNSCC cells whereby targeted siRNA gene silencing against NEK2 led to complete abrogation of chemoresistance in all 12 chemoresistant cell strains . Consistently, our pan-cancer Kaplan-Meier survival analysis showed that upregulation of NEK2 predicted poor prognosis in HNSCC patients. Although NEK2 has been previously reported to confer chemoresistance in multiple human malignancies, to our knowledge we presented the first evidence for NEK2 in conferring multidrug chemoresistance in HNSCC. We have also presented evidence for INHBA (inhibin subunit beta A), TOP2A  and DNMT1 (DNA methyltransferase 1) in conferring chemoresistance in the majority of drug-resistant cell strains. Targeted siRNA on each of these genes showed mixed responses across the 12 chemoresistance cell strains perhaps due to inherent heterogeneity of the different parental cell lines. Further investigation is required to delineate and differentiate their molecular pathways in these cell strains.In support of our HNSCC cohort data, previous studies have also demonstrated differential upregulation of INHBA in HNSCC . Our panWith an aim to repurpose the use of licensed drugs for counteracting cisplatin resistance in HNSCC, from our drug-gene interaction library screens, we identified two drugs (Sirodesmin A and Carfilzomib) targeted both INHBA and NEK2 in a dose-dependent manner and re-sensitised cisplatin resistant cells. Sirodesmin A is a natural metabolite produced by the fungus Sirodesmium diversum (ascomycete fungi)  and littWe presented the first evidence for NEK2 in conferring multidrug chemoresistance in HNSCC cells and that targeted siRNA gene silencing led to complete reversal of drug resistance to cisplatin, 5FU, PTX and DTX. INHBA and TOP2A were found to confer chemoresistance in the majority of drug-resistant cell strains whereas DNMT1 showed heterogeneous effects on chemoresistance. Pan-cancer Kaplan-Meier survival analysis on 21 human cancer types revealed significant prognostic values for NEK2 and INHBA in the majority of cancer types. We further identified a naturally occurring fungal derivative Sirodesmin A and a licensed anticancer drug Carfilzomib, both targeting NEK2 and INHBA, could re-sensitise resistant HNSCC cells to cisplatin. This finding requires further investigations into the potential of repurposing licensed drugs for reversing chemoresistance in HNSCC patients.Below is the link to the electronic supplementary material.Supplementary Material 1Supplementary Material 2Supplementary Material 3


Intra-tumour heterogeneity (ITH) presents a significant obstacle in formulating effective treatment strategies in clinical practice. Single-cell RNA sequencing (scRNA-seq) has evolved as a powerful instrument for probing ITH at the transcriptional level, offering an unparalleled opportunity for therapeutic intervention.Drug response prediction at the single-cell level is an emerging field of research that aims to improve the efficacy and precision of cancer treatments. Here, we introduce DREEP (Drug Response Estimation from single-cell Expression Profiles), a computational method that leverages publicly available pharmacogenomic screens from GDSC2, CTRP2, and PRISM and functional enrichment analysis to predict single-cell drug sensitivity from transcriptomic data. We validated DREEP extensively in vitro using several independent single-cell datasets with over 200 cancer cell lines and showed its accuracy and robustness. Additionally, we also applied DREEP to molecularly barcoded breast cancer cells and identified drugs that can selectively target specific cell populations.https://github.com/gambalab/DREEP.DREEP provides an in silico framework to prioritize drugs from single-cell transcriptional profiles of tumours and thus helps in designing personalized treatment strategies and accelerating drug repurposing studies. DREEP is available at The online version contains supplementary material available at 10.1186/s12916-023-03182-1. Intra-tumour heterogeneity (ITH) is a recurrent issue in designing effective treatment strategies in clinical practice. It refers to heterogeneous cancer cell populations present in the same tumour that exhibit high cellular plasticity and phenotypic heterogeneity. Accumulating evidence from independent studies shows that ITH gives rise to rare sub-populations of malignant cells characterized by a different epigenetic and transcriptional state that renders them refractory to a variety of anticancer drugs, protecting the whole population from complete eradication –4.Single-cell RNA sequencing (scRNA-seq) has now become a powerful tool to explore ITH at the transcriptional level  and thusTo address these challenges, we introduce DREEP (Drug Response Estimation from single-cell Expression Profiles), a novel bioinformatics tool designed to augment the precision and efficacy of therapeutic strategies by addressing the innate heterogeneity of cancer cell populations. DREEP’s primary function is to assess the complexity of tumour therapeutics and recommend specific drugs tailored to target distinct cell subpopulations. DREEP can predict a cell’s vulnerability to more than 2000 drugs by simplifying the whole process with enrichment analysis, eliminating the need for complex parameter adjustments and thus making this type of analysis accessible to a broad audience.https://github.com/gambalab/DREEP.We validate DREEP performance extensively using in vitro drug response data of over 200 cancer cell lines covering 22 distinct cancer types from publicly available single-cell and drug viability datasets. We show that our method can effectively identify recurring drug vulnerabilities across cells of diverse tumour types and establishes a clear relationship between drug response and the functional status of the cells. We show that DREEP can detect changes in drug sensitivity over time in the breast cancer cell line MCF7 and predict the emergence of drug resistance. Additionally, using molecular barcoded MDA-MB-468 cells . In conchttps://github.com/gambalab/gficf) [https://github.com/gambalab/DREEP. Enrichment Scores (ES) and associated p-values were computed using the fgsea package [fgsea package utilizes an adaptive and efficient multi-level split Monte Carlo approach [ p-value of an ES. Subsequently, the p-values for each drug across the cells are corrected for the false discovery rate (FDR) using the Benjamini–Hochberg correction method, implemented within the function p.adjust in the base package of the R statistical environment. Finally, only ES values with an FDR < 0.1 were deemed significant (unless otherwise stated) and used to predict the effect of a drug on a single cell. The percentage of sensitive and resistant cells in a sample is finally computed using only significant ES scores.To predict drug response at the single-cell level, we first used gf-icf normalization (b/gficf)  to extrab/gficf)  against  package . The fgsapproach  to estimhttps://depmap.org). Cell lines associated with haematopoietic tumours were excluded. Raw counts were normalized with edgeR [entropy. After these filtering steps, the final expression matrix comprised 1117 cell lines and 13,849 genes.Raw counts and associated metadata of the bulk RNA-seq data of cancer cell lines used to build GPDS signature were obtained from the depmap portal (th edgeR  and tranGenomic Profiles of Drug Sensitivity (GPDS) ranked lists were built by computing the Pearson correlation coefficient (PCC) between the expression of each gene and the potency of a drug across the cell lines for which drug potency was measured. At the end of this process, each GPDS is a ranked list of 13,849 expression-based biomarker genes whose degree of PCC reflects their importance in predicting the effect of a small molecule. This is because the AUC value of a drug reflects its cell line growth inhibition, with lower values indicating sensitivity and higher values implying tolerance to the drug. Thus, a gene whose expression positively correlates with the AUC of a drug is predictive of resistance to that drug; conversely, a negatively correlated gene is predictive of sensitivity to the drug.z-score percentiles from the AUC of each drug across the cell lines for which AUC was available and then defined a cell line sensitive to a drug only if its z-score was in the 5% percentile. Next, we applied DREEP to scRNA-seq data of each cell line and computed its median enrichment score for each drug. Finally, precision-recall and ROC curves on each cell-viability screening dataset were computed with the precrec R package  using the corresponding “gold standard” and sorting DREEP drug/cell line pairs according to the estimated median cell line enrichment score multiplied by minus one in order to have drugs predicted to be sensitive at the top.To estimate DREEP’s precision and sensitivity in predicting drug response from scRNA-seq data of cancer cell lines, we used the three cell-viability screening datasets from CTRP2, GDSC, and PRISM described above. For each cell-viability screening dataset, the corresponding “gold standard” is composed of pairs of cell lines and drugs for which the corresponding cell line is sensitive. To build the “gold standard” of each cell-viability screening dataset, we first computed the Fuzzy Jaccard Distance defined as follows:DREEP output can be arranged in a drug response matrix umap function of the uwot R package (https://github.com/jlmelville/uwot). Cell embedding from DREEP predictions has been implemented in the function runDrugReduction of the DREEP package.https://doi.org/10.6084/m9.figshare.15022698. scRNA-seq data of barcoded MDA-MB-468 cells were obtained from GEO (GSE228154). Raw UMI count data of nutlin treated and untreated cells was downloaded from figshare at https://figshare.com/s/139f64b495dea9d88c70 and only cells with at least 5000 UMI and with less than 10% of UMI counts in mitochondrial genes were used. PDCs TPM counts were instead retrieved from supplementary data of the original work [The scRNA-seq data of the cell line pan-cancer dataset from Kinker et al.  was obtanal work .https://depmap.org). Only drugs for which the in vitro response was available for at least 100 cell lines out of the 1117 for which gene expression data was available were retained. After this filtering, the final drug sensitivity dataset comprised 478 drugs from the GDSC dataset, 511 drugs from the CTRP2 dataset, and 1445 drugs from the PRISM dataset. For all three cell viability datasets, drug potency was expressed in terms of AUC (area under the curve) of the corresponding dose–response graph.The drug sensitivity dataset of CTRP2 , GDSC (Genomics of Drug Sensitivity in Cancer), and PRISM (Profiling Relative Inhibition in Mixtures) were all obtained from the depmap portal (P < 0.05). At the end of this process, we obtained a matrix dist function of the R statistical environment. Clustering of both, cell lines and drugs, was performed using the hclust function and resulting dendrograms cut with the cutree function of R statistical environment.We first used DREEP to predict the effect of the 2434 drugs on each of the 53,513 single-cell transcriptional profiles of the Kinker et al. dataset . Then, wp-values using the Benjamini–Hochberg method to identify drugs with differential sensitivity between afatinib-sensitive and afatinib-tolerant subpopulations. Finally, a drug was considered specific for a subpopulation if its FDR was less than 0.1 and the median enrichment score across the other subpopulations was greater than zero. This analysis has been implemented in the runDiffDrugAnalysis function of the DREEP package.We used DREEP to predict the effects of 2434 drugs on 1541 sequenced cells of the barcoded MDA-MB-468 cell line [4 MDA-MB-468 cells were seeded in a 96-well plate and incubated overnight at 37 °C. The two drugs to test were prepared in different dilutions and then combined in all possible drug pairs to generate a 5 × 5 combination matrix. Then, cells were exposed to either single agent drug or to the drug pairs of the drug combination matrix, while negative controls were treated with DMSO (each treatment was performed in triplicate). Following 72-h incubation at 37 °C, cell viability was measured with CellTiter (Promega) and the absorbance was read at 490 nm with the plate reader GloMax® Discover instrument. The drug interactions and expected drug responses were calculated with the Combenefit tool [4 × 10fit tool  using thDSEA was employed by using the online drugEnrichr tool  using thAll methods were run using default parameters and when required single-cell expression was normalized using the Seurat package. Common drugs between DREEP and each method were found by simply intersecting the drug names.PDCs drug response data were retrieved from supplementary data of the original work . Specifipre-defined collection of Genomic Profiles of Drug Sensitivity (GPDS); these are lists of genes ranked according to their contribution in correctly predicting the effect of a small molecule. To build GPDS, we integrated several publicly available RNA-seq datasets and cell-line viability screens, including the following datasets: GDSC [DREEP (Drug Response Estimation from single-cell Expression Profiles) is a bioinformatics tool that leverages results from large-scale cell-line viability screens and enrichment analysis to predict drug vulnerability from the transcriptional state of a cell. It requires a ts: GDSC , CTRP2 [ts: GDSC , 33, andts: GDSC   across all the cancer cell lines for which the drug potency was measured see Fig. ” sectionUsing this approach, we generated a collection of 2434 GPDS signatures  and used them to predict the single-cell-level drug response with the strategy depicted in Fig. We found 90 small molecules shared among GDSC, CTRP2, and PRISM datasets. To assess the consistency of GPDS ranked lists, we calculated Spearman correlation coefficients (SCC) between GPDS representations of the same drug but constructed from distinct drug viability datasets. Figure S1  . Indeed,Cluster C3 includes 69% of ovarian cancer cell lines (9 out of 13) and 70% of endometrial/uterine ones (7 out of 10). Cell lines in this cluster are predicted to be sensitive to drugs in the D2 cluster, which is enriched for topoisomerase, AURORA, HDAC, and CDK inhibitors. Interestingly, ovarian and uterine cancers are usually due to inefficient DNA repair . The staCluster C4 includes all skin cancer cell lines (16 out of 16), all kidney (6 out of 6), all brain (12 out of 12), and 13 lung cancer cell lines. Cell lines in cluster C4 are predicted to be sensitive to multiple families of drugs, including RAF, KIT, PDGFR, and PI3K/mTOR inhibitors. This agrees with current clinical practice which uses mTOR and BRAF inhibitors as first-line therapy for kidney cancer and melanoma, respectively , 64, sinTo further demonstrate DREEP’s ability to identify recurring drug vulnerabilities across cancer cells of different tumour types with shared genetic backgrounds, we applied our method to the dataset from McFarland et al. . This daAltogether, these results show that DREEP can find recurrent drug sensitivity patterns shared by cell lines from multiple cancer types, as well as the relationship between drug response and functional cell status.Next, to evaluate DREEP’s ability to recognize adaptation to drug treatment over time, we applied it to the Ben-David et al. dataset . As Fig.As shown in Fig. In summary, these analyses demonstrate DREEP’s ability to recapitulate drug response and variability over time.Next, we evaluated DREEP’s ability to identify innate drug-resistant cell populations and to suggest drug co-treatments. To this end, we used single-cell data of the untreated barcoded MDA-MB-468 breast cancer cell line that we recently generated [We applied DREEP to each of the two untreated MDA-MB-468 cell subpopulations (i.e. afatinib-tolerant and afatinib-sensitive) and predicted drugs able to selectively inhibit the growth of either. We thus found 156 drugs predicted to preferentially inhibit the afatinib-tolerant subpopulation and 80 drugs  and three AKT/MTOR/PI3K inhibitors  from the list of drugs predicted to selectively inhibit the afatinib-tolerant subpopulation. We then measured experimentally if these drugs had an additive, synergistic, or antagonistic effect on MDA-MB-468 cells in combination with afatinib  from both primary and metastatic sites of individuals suffering from head and neck squamous cell carcinoma . These mAs illustrated in Fig. p-value = 3.6e − 09) between DREEP predictions and the experimentally observed drug responses for five specific drugs: Docetaxel, Doxorubicin, Epothilone B, Gefitinib, and Vorinostat , followed by CTRP2 with an SCC of 0.591 , and PRISM with an SCC of 0.512 .Next, to assess the predictive performance of DREEP on this dataset, we first applied it to the single-cell profiles of each patient and then converted these predictions from the single-cell level to the patient level by computing the median enrichment score for each drug across the cells within each individual patient. As depicted in Fig. Intra-tumour heterogeneity (ITH) presents a significant challenge in developing effective cancer treatments and achieving precision oncology. ITH refers to the variation of tumour cells both within and between tumours. As such, it is crucial to have methods to accurately identify the drug sensitivities of different tumour clones and determine the most effective treatment for each cancer type and the individual patient. To address this challenge, we introduced DREEP, a novel computational method that predicts drug sensitivity at the single-cell level and can propose cell population-specific treatments. To achieve this, DREEP uses a set of drug-sensitivity signatures obtained by correlating gene expression and drug sensitivity profiles across hundreds of tumorigenic cell lines. It then uses these signatures to compute an enrichment score reflecting the degree of sensitivity of a cell to a specific drug.https://github.com/gambalab/DREEP.To test the predictive performance and reliability of DREEP, we applied it to two independent datasets: (i) the pan-cancer cell line dataset from Kinker et al. comprising 198 tumorigenic cell lines from 22 distinct cancer types  and (ii)Our tool relies heavily on the quality and predictive power of GPDS, which were trained by linking gene expression to drug response data from pharmacogenomic screens. However, these screens may possess inherent biases and limitations in covering the broad spectrum of cancer types. We applied several evaluation metrics on multiple showcases to assess DREEP’s predictive accuracy and ability to generalize across different cancer types, encompassing approximately one hundred thousand cells originating from more than 20 distinct cancer types.While our results have demonstrated DREEP’s ability to generalize across various cancer types effectively, we recognize that potential biases in our training data could limit DREEP’s applicability to underrepresented cancer types. Therefore, it is crucial to interpret DREEP’s performance within the context of the available training data. We advocate for ongoing refinement and expansion of pharmacogenomic screens to improve the diversity and coverage of cancer types. Addressing these biases in future data collection efforts can enhance DREEP’s utility in advancing personalized cancer treatment strategies. Moreover, in the future, it would be crucial to evaluate how the integration of other single-cell -omics measurements, such as genomic, proteomic, and epigenetic cell states, can improve the identification of variations in drug responses among cancer cell populations. Finally, since tumour heterogeneity is linked to clonal evolution processes, it would be also important to consider the dynamics of clonal populations under therapy pressure to improve the performance of future computational methods that will be developed for personalized diagnosis and treatment of cancer patients.Project name: DREEP.https://github.com/gambalab/DREEPProject home page: Operating system(s): Platform independent.Programming language: R and C +  + Other requirements: R version ≥ 4.3Licence: MIT License.Any restrictions to use by non-academics: Licence needed.Additional file 1: Figure S1. GPDS consistency across viability datasets. Figure S2. DREEP’s ROC AUC distribution across cancer types. Figure S3. DREEP Method Performance on Pan-Cancer Dataset using IC50-Derived GPDS. Figure S4. Drug prediction performance using absolute expression to extract relevant genes of a cell. Figure S5. Performance comparison between DREEP and other single cell drug prediction tool. Figure S6. DREEP's predictions for McFarland et al. Dataset.Additional file 2: Table S1. Accurately and erroneously predicted drugs. Table S2. MoA enrichment analysis of accurately and erroneously predicted drugs. Table S3. Cell-lines therapeutic clusters. Table S4. MoA enrichment analysis of drug clusters. Table S5. Differential Drug Analysis of MDAMB468 cells. Table S6. Drug Combination results. Table S7. Cell Viability of patient-derived cultures (PDCs). Table S8. AUC of ROC and PPV curves for the comparison of DREEP vs BeyonCell (Figure S Table S9. AUC of ROC and PPV curves for the comparison of DREEP vs scDEAL (Figure S Table S10. AUC of ROC and PPV curves for the comparison of DREEP vs scDRUG (Figure S


Both flavonoids, as well as cisplatin as a reference anticancer drug, were tested in vivo against ten human cancer cell lines , colon cancer , prostate cancer , lung cancer (A549) and leukemia (MV-4-11) and two normal cell lines ) and murine embryonic fibroblasts (BALB/3T3). Chalcone 1 and aurone 2 demonstrated potent to moderate anticancer activity against nine tested cancer cell lines (including drug-resistant ones). The antiproliferative activity of all the tested compounds against cancer and the normal cell lines was compared to determine their selectivity of action. Prenylated flavonoids, especially the semisynthetic derivative of xanthohumol (1), aurone 2, were found as selective antiproliferative agents in most of the used cancer cell lines, whereas the reference drug, cisplatin, acted non-selectively. Our findings suggest that the tested flavonoids can be considered strong potential candidates for further studies in the search for effective anticancer drugs.The antiproliferative activity of xanthohumol ( Cancer is a serious group of diseases involving uncontrolled cell proliferation, which causes millions of deaths every year, making it the second leading cause of death in low- and middle-income countries . Therefo1).Flavonoids, such as chalcones, exhibit numerous biological properties that are beneficial to human health, and most importantly, they are relatively non-toxic. One of the most biologically active natural chalcones is xanthohumol (1) , the major prenylflavonoid found in the hop (Humulus lupulus L.), is known for its various biological properties, such as anti-inflammatory, antioxidant, antibacterial, antifungal, antiplasmodial and antiviral properties [1) significantly suppresses the development of a wide variety of human cancer cells in vitro during the initiation, promotion and progression of carcinogenesis. Hence, it has been characterized as a “broad spectrum” cancer chemopreventive agent [1), only a few papers concerning the antiproliferative activity of xanthohumol derivatives, metabolites or analogues have been published so far [Xanthohumol  and its aurone derivative (2), as well as cisplatin as a reference anticancer drug, were examined against ten human cancer and two normal cell lines and then compared to determine their selectivity of action. The studies demonstrate how the modification of a prenylated chalcone into its aurone derivative may affect anticancer activity and selectivity.In continuation of our research on xanthohumol derivatives as potential therapeutic agents, in our previous studies, we synthesized an “aurone type” derivative, which was the starting material for obtaining glycosylated derivatives, and then the influence of the skeleton type of the prenylated flavonoids and the presence of sugar moiety in the flavonoid structure on the binding to human serum albumin and the inhibition of the cyclooxygenases COX-1 and COX-2 were investigated . In this2), towards human cancers as well as normal cells have not been published so far.To the best of our knowledge, the antiproliferative activities of xanthohumol, an aurone-type derivative (1), its aurone (2) (Xanthohumol (rone (2)  and cispAdditionally, the cytotoxicity of these compounds was also tested against normal cell lines: murine embryonic fibroblasts (BALB/3T3) and human lung microvascular endothelium (HLMEC). One of the used cancer cell lines was resistant to doxorubicin (LoVo/Dx). The concentrations of the tested compounds (µM) at which 50% cell growth inhibition was observed are summarized in 1) and its aurone (2) showed potent  to moderate  antiproliferative activity against the examined cancer cell lines, except colon cancer cells (HT-29), in which case the compounds were rather weakly active (IC50 > 50 µM). Xanthohumol (1) and aurone (2), as well as cisplatin, displayed no significant differences in their antiproliferative effect towards the MCF-7, SK-BR-3, T47D, LoVo, LoVo/Dx, PC-3 and A549 cancer cell lines (p > 0.05). Among all the used cancer cell lines, cisplatin, which is commonly used as a drug in anticancer therapy, showed significantly higher activity (p < 0.05) only in the case of two cancer cell lines: colon cancer (HT-29) and leukemia (MV-4-11). In the case of the colon cancer cell line, both flavonoids showed relatively low antiproliferative activity (91.31 ± 8.92 µM and 62.09 ± 16.52 µM), whereas cisplatin was 4.8-fold and 3.3-fold more active, respectively. In the case of the MV-4-11 leukemia cancer cell line, both flavonoids displayed high antiproliferative activities . However, cisplatin was significantly more active . The significant differences in the antiproliferative activities between all the tested compounds (p < 0.05) were recorded. In the case of the prostate cancer cell line (Du145), cisplatin was the most active, xanthohumol (1) was only slightly less active and aurone (2), with a value of IC50 = 14.71 ± 4.42 µM, was the least effective at inhibiting the growth of the prostate cancer line. The obtained results of the antiproliferative activity of the tested flavonoids against the human cancer cell lines often revealed no statistical difference in activity compared to cisplatin ), which may indicate that both xanthohumol 1 and its aurone 2 may be potential candidates as compounds for use in anticancer therapy.Both xanthohumol  to the IC50 value for the corresponding tumor cell line using the following equation:The selective effect of a drug can be expressed by the selectivity index (SI), which is determined by comparing the cytotoxic activity of each compound against each cancer cell line with the activity against a normal cell line. The SI was calculated as the ICThe SI values higher than 1.00 indicate that the tested compound exhibited higher selectivity towards cancer cells than normal ones . The selB, whereas the selective indexes for human lung microvascular endothelium (HLMEC), SIB, shows nonselective action of cisplatin against all the tested human cancer cell lines. These data confirm the well-known fact that many of the commonly used chemotherapeutics, including cisplatin, are toxic not only to malignant cells but also to normal cells.Due to the different antiproliferative activities of the tested compounds against two normal cell lines, human (HLMEC) and murine (BALB/3T3), the values of the selectivity indexes SIA and SIB for a given compound against a respective cancer line differ. However, without a doubt, the calculated selectivity indexes of the tested compounds clearly indicate that both tested flavonoids show high selectivity of action against all the tested cancer cell lines except colon cancer (HT-29), while cisplatin was selective only for colon cancer (LoVo cell line) and leukemia (MV-4-11 cell line). However, this result is not clear, since the selective action against the above-mentioned cancers is confirmed only by the selectivity indexes calculated for the normal murine embryonic fibroblast (BALB3/T3), SI2), which exhibited very high selectivity towards all the breast cancer cell lines tested  and the colon cancer lines LoVo and LoVo/Dx . These results may be very interesting in the context of possible future applications of this compound, due to the resistance of the LoVo/Dx line to the commonly used anticancer drug doxorubicin. In the case of both prostate cancers , as well as lung cancer (A549), aurone 2 was as selective as xanthohumol (1) considering the values of SIA and slightly more selective when comparing the antiproliferative activity against cancer cell lines to the activity towards normal murine embryonic fibroblasts cells (SIB). The selectivity of both flavonoids against the above-mentioned cancer cell lines can be considered high . Very high and high selectivity of the tested prenylated flavonoids towards most of the cancer lines used indicates that both compounds can be recognized as potent and selective potential anticancer agents. Moreover, the higher selectivity of aurone (2) in comparison to xanthohumol (1) clearly shows that the modification of the flavonoid skeleton, prenylated chalcone, into its aurone derivative significantly increases the selectivity against almost all of the used cancer cell lines. The most selective among of all the investigated compounds was aurone , a prenylated chalcone naturally occurring in hops, is known for its various biological activities, including anticancer activity [1) inhibited the proliferation, migration and invasion, as well as induced apoptosis and cell cycle arrest, in many types of cancer-inhibiting carcinogenesis and metastasis [1), including Akt, NF-kB, ROS and ERK1/2 [1) can also enhance apoptosis induced by TRAIL in cancer cells [1) raises concerns due to its conversion into the most potent phytoestrogen known, 8-prenylnaringenin (8-PN), which may promote the growth of estrogen-dependent cancers [Natural dietary compounds, such as flavonoids and synthetic pharmacological agents, can arrest and/or reverse carcinogenesis—this process is often called chemoprevention. Recently, there has been much research focused on the biological activities of prenylated flavonoids, which very often exhibit stronger properties than those of non-prenyl parent flavonoids ,28,29,30activity ,31. Resetastasis ,6,9. Muld ERK1/2 ,6. Studier cells ,33,34. D cancers .1) to isoxanthohumol (3) and O-demethylation, catalyzed by the host’s cytochrome P450 enzyme (CYP1A2) and gut microbiota, resulting in the formation of 8-prenylnaringenin (4) (4), are strongly sought. This two-step reaction involves the spontaneous isomerization of xanthohumol (enin (4)  [36,37,31) to α,β-dihydroxanthohumol (5) prevents isomerization to isoxanthohumol (3) and, therefore, conversion to the potentially dangerous 8-prenylnaringenin (5) might be as effective an antiproliferative agent against human breast cancer cell lines  and an ovarian cancer cell line (A2780) as xanthohumol (1). Both prenylated flavonoids exhibited a comparable growth-inhibiting effect, whereas structural modifications, such as the isomerization of xanthohumol (1) to isoxanthohumol (3) or the demethylation of isoxanthohumol (3) to 8-prenylnaringenin (4), led to a reduction in the cytostatic activity towards the cancer cell lines tested [4) in the body, can affect the growth inhibition and selectivity against human cancer cell lines in vitro. Therefore, the antiproliferative properties and selectivity of the aurone derivative of xanthohumol (2) against ten human cancer cell lines in comparison to xanthohumol (1) and a reference, cisplatin (a drug commonly used in anticancer treatment), was investigated. Our studies have shown that the modification of xanthohumol (1) to an aurone derivative does not adversely affect the antiproliferative activity against the human cancer cell lines tested. Both xanthohumol (1) and its aurone (2) showed potent to moderate antiproliferative activity against the examined cancer cell lines. This is a remarkable finding since modifications of xanthohumol (1) to other analogous flavonoids with a flavanone or flavone skeleton reduce its potent anticancer activity [1) into an aurone derivative (2) significantly (p < 0.05) reduced the cytotoxic effect against normal cells: 1.5-fold in the case of the human lung microvascular endothelial (HLMEC) cell line and 2.3-fold in the case of the murine embryonic fibroblasts (BALB/3T3) cell line, respectively. The comparable antiproliferative activities of flavonoids 1 and 2, as well as the significantly lower cytotoxicity of aurone 2 towards normal cells lines, makes this compound a more selective anticancer agent than xanthohumol (1) against breast cancers , doxorubicin-resistant colon cancer (LoVo/Dx) and leukemia (MV-4-11), whereas in the case of prostate cancer  and lung cancer (A549), aurone 2 exhibited a comparable selectivity of action as xanthohumol (1). Since aurone 2 is not able to be transformed into potentially dangerous 8-prenylnaringenin, as happens in the case of xanthohumol (1), the obtained results may be very interesting in the context of possible future applications of this compound as a potential anticancer drug, including estrogen-dependent ones.Modification of the chalcone to the dihydrochalcone skeleton by the hydrogenation of the α,β-olefin bond counteracts spontaneous chalcone—flavanone isomerization. Thus, the saturation of the double bond at the α,β-position in xanthohumol , RPMI 1640 and Opti-MEM media were purchased from Gibco, Scotland, UK. All the solvents used for the syntheses and purification were of analytical grade, and those used for HPLC analysis were of HPLC grade .Mercury (II) acetate , sodium bicarbonate, magnesium sulfate, L-glutamine, sodium pyruvate, amino acids, doxorubicin, insulin, sulforhodamine B, 3--2,5-diphenyl tetrazolium bromide, Tris base, hydrochloric acid, acetic acid, formic acid, dimethylformamide, sodium dodecyl sulfate and dimethyl sulfoxide-dv/v), 3–32 min linear gradient from A:B (55:45 v/v) to A:B (5:95 v/v), 32–37 min isocratic A:B (5:95 v/v), 37–39 min linear gradient from A:B (5:95 v/v) to A:B (55:45 v/v), with 10 min of re-equilibration of the column before the next run. High-resolution ESI-MS spectra were obtained on a Bruker micrOTOF-Q spectrometer. The direct infusion ESI-MS parameters were as follows: The mass spectrometer was operated in negative ion mode with the potential between the spray needle and the orifice at 4.5 kV, a nebulizer pressure of 0.4 bar and a drying gas flow rate of 4 L/min at 200 °C. The sample flow rate was 180 μL/min. Ionization mass spectra were collected at the range m/z 150–3000. The instrument was calibrated with an Agilent electrospray calibration solution  that was diluted with acetonitrile. NMR spectra , 1H–13C NMR (HSQC)) were recorded at 600 and 151 MHz on a DRX Bruker Avance instrument using DMSO-d6 as a solvent. The chemical shifts are provided in parts per million (ppm) . UV spectra were collected on a Cintra 303 Spectrophotometer (GBC) in methanol.Column chromatography was performed using Sephadex LH-20  or silica gel 60  with the solvent mixtures specified in each experiment. The HPLC analyses were performed on a Waters 2695 Alliance instrument equipped with a Waters 2996 photodiode array detector (detection at wavelengths within the range of 200 to 600 nm) and using an Agilent Zorbax XDB C-18 analytical HPLC column . The elution was achieved at a flow rate of 1 mL/min; eluent A: 0.05% formic acid in redistilled water; eluent B: 0.05% formic acid in methanol, using the following elution program: 0–3 min isocratic A:B (55:45 1) was isolated from supercritical CO2 extracted hops  obtained from Łukasiewicz—Institute of New Chemical Syntheses . The method of isolation is described in the Xanthohumol  (Z)-6,4′-dihydroxy-4-methoxy-7-prenylaurone (2) was prepared from xanthohumol (1) according to the method described in our previous studies using mercury (II) acetate as a catalyst of the reaction [(reaction . The met1) and (Z)-6,4′-dihydroxy-4-methoxy-7-prenylaurone (2) are available in the The NMR, mass spectroscopy and spectrophotometry data of xanthohumol  medium was supplemented with 2 mM L-glutamine, 5% fetal bovine serum  and 0.8 mg L−1 insulin (T47D) or 1 mM sodium pyruvate  and 0.1 ug/mL doxorubicin (LoVo/Dx). The media contained antibiotics: 100 µg mL−1 streptomycin and 100 U mL−1 penicillin. All the cell lines were incubated at 37 ºC in a humid atmosphere saturated with 5% CO2. The following established in vitro cell lines were applied: human breast adenocarcinoma (MCF-7 and SK-BR-3), human breast ductal carcinoma (T47D), human prostate adenocarcinoma (PC-3), human prostate carcinoma (Du145), human colorectal adenocarcinoma (HT-29 and LoVo), doxorubicin-resistant human colorectal adenocarcinoma (LoVo/Dx), human myelomonocytic leukemia (MV-4-11), human lung adenocarcinoma (A549), normal murine embryonic fibroblasts (BALB/3T3) and human lung microvascular endothelial (HLMEC). All the cell lines were obtained from the American Type Culture Collection  except for LoVo and LoVo/DX, which were received from the Technical University of Gdańsk, Poland, courtesy of Prof. E. Borowski. All the cell lines were maintained in the Institute of Immunology and Experimental Therapy, Wroclaw, Poland. The cells were cultured in the following media: DMEM medium (BALB/3T3), Eagle medium , RPMI medium  or RPMI 1640 + Opti-MEM (1:1) medium . The DMEM medium was supplemented with 2 mM L-glutamine and 10% fetal bovine serum. The Eagle medium was supplemented with 4 mM L-glutamine, 10% fetal bovine serum, 1 mM sodium pyruvate for Du145 cells or 2 mM L-glutamine, 1% amino acids and 0.8 mg LThe details of this technique were described by Skehan et al. . After 7The details of this technique were described by Antoszczak et al. . The pro4 cells per well (103 cells per well for HLMEC) in 100 µL of culture medium. The analysis of the antiproliferative activity of the compounds was performed using sulforhothamine B (SRB)  or 3--2,5-diphenyl tetrazolium bromide (MTT) (for the leukemia cells—MV-4-11). The details of this technique have been previously described by Skehan et al. [−1) of xanthohumol (1), aurone (2) and the reference drug cisplatin. Each compound at each concentration was tested in a single experiment, which was repeated at least 3 times. DMSO, which was used as a solvent in a dilution corresponding to its highest concentration applied to the tested compounds, did not exert any inhibitory effect on cell proliferation. The results were calculated as the IC50 (μM) , i.e., the concentration of the tested agent that inhibits the proliferation of the cancer cell population by half [50 values and the mean values ± SD calculated for each experiment separately using GraphPad Prism software  are presented in Twenty-four hours before incubation with the tested compounds, the cells were seeded in 96-well plates  at a density of 10n et al.  and Anton et al. . An assa by half . The IC5p value < 0.05.The data are presented as the mean value ± standard error of the mean (SD). Statistical analysis was performed with the one-way ANOVA with Tukey’s post hoc test . The significance was accepted at a 1), has remarkable biological properties. The anticancer activity of xanthohumol (1) has been the subject of studies for more than 20 years. It is known that xanthohumol (1) may induce cell cycle arrest and apoptosis in cancer cells. Numerous cell signaling pathways, such as NF-κB, Akt, ERK, ROS, STAT3, Notch and AMPK, are involved in the anticancer activity of this prenylated chalcone [1) an excellent starting material for obtaining other biologically active compounds. Our studies have shown that both xanthohumol (1) and its semisynthetic aurone derivative (2) have significant antiproliferative activity against nine out of ten cancer cell lines, including doxorubicin-resistant human colon cancer (LoVo/Dx). Moreover, they displayed much higher cytotoxicity against almost all the tested cancer cell lines compared to the normal cell lines. Plants are a great source of many natural compounds that have potential in the prevention and treatment of numerous diseases, including cancer. The major prenylated flavonoid of hops, xanthohumol  in comparison to xanthohumol (1) clearly shows that the chalcone–aurone type modification of the flavonoid skeleton in prenylated flavonoids may significantly increase the selectivity of action against tumor cell lines in vitro. In addition, aurone 2 is unable to convert into the potentially dangerous 8-prenylnaringenin (4) in the body, which could be a major advantage for its use over xanthohumol (1) in the context of possible future applications as a potential anticancer drug, including estrogen-dependent ones. Nevertheless, it is crucial to conduct additional research into the mechanisms of action of aurone 2 to confirm its therapeutic potential and possible use in future cancer treatment.The higher selectivity of aurone (1) and its aurone derivative (2) are potent and selective antiproliferative agents. However, further research is required to determine the molecular mechanisms of action and confirm the obtained results using in vivo cancer models.Our studies have shown that xanthohumol (


Cytidine analogs, such as decitabine (DAC) and cytarabine (ara-C), have been widely used in the clinical treatment for several cancer types, including myelodysplastic syndrome and acute myeloid leukemia AML; . HoweverCDA in these cell lines, we found that CDA is expressed in only two 5hmdC-sensitive and 14 5fdC-sensitive cell lines. Interestingly, a subset of cell lines lacking CDA expression, including SEM (a human acute lymphoblastic leukemia cell line) and Raji (a human B lymphoblastoid cell line), was strongly inhibited by either or both oxidized methylcytidines  of 5hmdC and 5fdC in 45 human cancer cell lines . The surytidines , suggestytidines , and thaytidines . 5hmdC- ytidines , indicatytidines  and an iytidines  of SEM cEµ-Myc p19Arf−/− mouse B-cell lymphoma cell line, which was shown to be an ideal model system for analyzing gene–drug interactions  and 2ʹ-deoxynucleoside 5ʹ-phosphate N-hydrolase 1 (Dnph1), a nucleotide salvage factor recently found to be responsible for removing 5hmdUMP (Due to the inability of cytidine monophosphate kinase 1 (CMPK1) to phosphorylate modified cytidine monophosphates, the direct incorporation of 5mdC, 5hmdC and 5fdC into the genome is prevented . A deamiractions  and doesractions . Cas9-ex library , followe library . Bioinfoand Dctd , suggest 5hmdUMP . Thus, win vitro deamination assay indicated that dCMP could be converted to dUMP by DCTD and CDA (CDA than not (14/19), while most 5hmdC-sensitive cells have no CDA expression (8/10)  .DCTD KO SEM and Raji cells , reaching about 600 μmol/L in 10 min and being eliminated from peripheral blood within 4 h  for 11 days. Xenograft tumor growth of wild-type Raji cells was significantly restricted by 5hmdC treatment while that of DCTD-deficient cells was not affected (DCTD KO tumors (DCTD KO tumors (Wild-type and affected . ConsistO tumors  and S8C.O tumors . SignifiO tumors .−Sca-1+c-Kit+ (LSK) hematopoietic stem/progenitor cells and Gr-1+Mac-1+ myeloid cells in the bone marrow of 5hmdC-treated mice were not changed either  and spleen size in 5hmdC-treated animals . The perd either . The cond either . Taken tDck, Dctd, Cda, and Dnph1  derived from normal cord blood using colony formation assay. Treating hematopoietic stem cells (CD34+) with up to 100 μmol/L 5hmdC exerted little inhibitory effect on their colony forming abilities  derived from all patient samples except patients 5 and 6 due to insufficient cell numbers. Importantly, DCK was found to be highly expressed only in the bone marrow cells of patients 1–4 but not in those of patients 7–10, whereas no significant difference was observed in the expression of DCTD, CDA and DNPH1 between patients 1–4 and patients 7–10  and nucleoside diphosphate kinases (NDPKs), generating 5hmdUTP and 5fdUTP that can be incorporated into the genome by DNA polymerase and eventually result in DNA damage and cell death . Accordihttps://doi.org/10.1093/procel/pwac028.The online version contains supplementary material available at pwac028_suppl_Supplementary_Material_S1Click here for additional data file.pwac028_suppl_Supplementary_Material_S2Click here for additional data file.