Pdyn (prodynorphin), a gene encoding an opioid neuropeptide implicated in motivated behavior and neuropsychiatric disease states. CRISPR-based functional assays demonstrated that this enhancer is both necessary and sufficient for Pdyn transcription. This regulatory element is also conserved at the human PDYN locus, where its activation is sufficient to drive PDYN transcription in human cells. These results suggest that IEGs participate in chromatin remodeling at enhancers and identify a conserved enhancer that may act as a therapeutic target for brain disorders involving dysregulation of Pdyn.Neuronal and behavioral adaptations to novel stimuli are regulated by temporally dynamic waves of transcriptional activity, which shape neuronal function and guide enduring plasticity. Neuronal activation promotes expression of an immediate early gene (IEG) program comprised primarily of activity-dependent transcription factors, which are thought to regulate a second set of late response genes (LRGs). However, while the mechanisms governing IEG activation have been well studied, the molecular interplay between IEGs and LRGs remain poorly characterized. Here, we used transcriptomic and chromatin accessibility profiling to define activity-driven responses in rat striatal neurons. As expected, neuronal depolarization generated robust changes in gene expression, with early changes (1 hr) enriched for inducible transcription factors and later changes (4 hr) enriched for neuropeptides, synaptic proteins, and ion channels. Remarkably, while depolarization did not induce chromatin remodeling after 1 hr, we found broad increases in chromatin accessibility at thousands of sites in the genome at 4 hr after neuronal stimulation. These putative regulatory elements were found almost exclusively at non-coding regions of the genome, and harbored consensus motifs for numerous activity-dependent transcription factors such as AP-1. Furthermore, blocking protein synthesis prevented activity-dependent chromatin remodeling, suggesting that IEG proteins are required for this process. Targeted analysis of LRG loci identified a putative enhancer upstream of  Fos (aka c-Fos), Npas4, and Nr4a2. These genes follow a temporally dynamic profile, with elevated expression within 1 hr of a stimulus and rapid return to baseline levels. In contrast, the same stimuli promote expression of a more delayed gene expression program, termed late response genes (LRGs)  . This seFos and Jun family members, directly contributes to chromatin remodeling at LRG enhancers  gene locus that is conserved in the human genome. CRISPR-based activation and repression experiments provided functional validation that this region serves as a Pdyn enhancer in both rat neurons and dividing human cell lines. Collectively, these results highlight the mechanisms through which neuronal activity promotes gene expression changes to modify neuronal function, and have relevance for brain disease states characterized by alterations to this process.Here, we used next-generation sequencing approaches to characterize temporally distinct, activity-dependent transcriptomic and epigenomic reorganization in cultured rat embryonic striatal neurons, an in vitro model containing many cell types implicated in learning, motivation, reward, and substance use disorders . These eNpas4, Fos, Fosb, Fosl2, and Nr4a1  and a representative LRG (Pdyn) revealed that while Fos mRNA began to return to baseline in the wash off (1 hr stimulation) condition, Pdyn mRNA was equally elevated in response to 1 hr KCl (followed by 3 hr wash off) and 4 hr KCl stimuli  or no wash off for 3 hr . Reverse stimuli . Additio stimuli , includiplicates , suggestIn non-neuronal systems, LRG induction is dependent on activity-dependent chromatin remodeling at genomic enhancers . To idenEnhancers are marked by distinctive histone modifications, including histone acetylation (at H3K27) and increased ratios of histone monomethylation to trimethylation at H3K4 (H3K4me1:H3K4me3). In contrast, while promoters are also marked by H3K27ac, they tend to exhibit a lower H3K4me1:H3K4me3 ratio . To inveThe combination of RNA- and ATAC-seq results suggests a temporal decoupling between induction of IEG transcription factors and activity-dependent chromatin remodeling. IEG transcription factors were activated following 1 hr of depolarization , but actTo test this possibility, we searched for enriched motifs using a database of experimentally validated transcription factor-binding motifs with HOMER . AdditioThe HOMER database allowed us to investigate the enrichment of over 400 transcription factor motifs in over 5300 DARs. To understand if DARs could be separated based on the transcription factor motifs present within these regions, we used uniform manifold approximation and projection   and densThe significant enrichment of IEG transcription factor motifs within 4 hr DARs suggests that these inducible transcription factors may be required for subsequent activity-dependent chromatin remodeling. Furthermore, the temporal decoupling between IEG expression and activity-dependent chromatin remodeling suggests that IEGs must be translated before acting on genomic regions to induce an open chromatin state. To test the hypothesis that de novo protein translation is required for activity-dependent chromatin remodeling, we repeated ATAC-seq after 4 hr of depolarization in combination with protein synthesis inhibition. Cultured rat embryonic striatal neurons were pretreated with 40 μM anisomycin, a translation inhibitor, for 30 min followed by 10 mM KCl for 4 hr . As expeFos locus), which are accessible at baseline and do not undergo activity-dependent chromatin remodeling  located in non-coding regions of the genome, (2) inaccessible at baseline and accessible following depolarization, and (3) inaccessible when depolarization was paired with protein synthesis inhibition. These regions would be fundamentally different from enhancers regulating IEGs  resulted in significant upregulation of Pdyn mRNA  fused to the transcriptional activator VPR (CRISPRa) or the transcriptional repressor KRAB-MeCP2 (CRISPRi)  to target dCas9-KRAB-MeCP2 to the DAR in the presence of depolarization. As a negative control, we transduced cultured striatal neurons with a sgRNA for lacZ, a bacterial gene not found in the mammalian genome. As expected, activity-dependent Pdyn upregulation was observed in neurons transduced with lacZ sgRNAs and dCas9-KRAB-MeCP2 (Pdyn upregulation was blocked in neurons transduced with DAR-targeting sgRNAs and dCas9-KRAB-MeCP2 (Pdyn mRNA levels were attenuated by CRISPRi of the Pdyn DAR (Pdyn TSS is necessary for activity-dependent Pdyn transcription.The observation that CRISPRi)  to activCRISPRi) . To testAB-MeCP2 . HoweverAB-MeCP2 . AdditioPdyn DAR , suggestlacZ or the Pdyn DAR and dCas9-VPR (CRISPRa) to test if activation of this DAR is sufficient to promote Pdyn transcription. Pdyn mRNA levels were significantly increased in neurons transduced with DAR-targeting sgRNAs  were co-housed in pairs in plastic filtered cages with nesting enrichment in an Association for Assessment and Accreditation of Laboratory Animal Care-approved animal care facility maintained between 23 and 24°C on a 12-hr light/12-hr dark cycle with ad libitum food (Lab Diet Irradiated rat chow) and water. Bedding and enrichment were changed weekly by animal resources program staff. Animals were randomly assigned to experimental groups. All experiments were approved by the University of Alabama at Birmingham Institutional Animal Care and Use Committee (IACUC) under animal protocol number 20118 or 21306. For primary neuronal cultures, timed pregnant Sprague-Dawley dams were individually housed until embryonic day 18 when striatal cultures were generated as previously described .A receptor antagonist gabazine . All treatments were applied for 4 hr followed by RNA extraction and RT-qPCR.Cells were maintained in neurobasal media supplemented with B27 and LG for 11–12 days in vitro (DIV) with half media changes on DIV 1, 5–6, and 9–10. For depolarization experiments, neurons were treated with KCl dissolved in neurobasal media to a final concentration of 10 mM for 1 or 4 hr. To test the necessity of protein translation in mediating activity-dependent transcription and chromatin remodeling, anisomycin was dissolved in DMSO and treated to a final concentration of 40 μM for 30 min prior to depolarization. Additional stimuli outlined in CVCL_0063) were maintained in Dulbecco’s modified Eagle medium + 10% fetal bovine serum and plated at 80,000 cells/well in 24-well plates. 24 hr later cells were transfected with Fugene HD (Promega) and constructs containing dCas9-VPR and gRNA vectors targeting the conserved DAR region (500 ng plasmid DNA in molar ratios sgRNA:dCas9-VPR). Forty-eight hours later, cells were lysed and RNA was extracted.HEK-293T cells  )and β-tubulin primary antibody  overnight at 4°C. Following primary antibody incubation, secondary antibodies  for 1 hr at room temperature. Imaging was performed using a Licor Odyssey imager.DIV 11–12 cultured rat embryonic striatal neurons were treated with 40 μM anisomycin followed by 4 hr of depolarization with 10 mM KCl in 12-well plates. Following depolarization, media was removed, and wells were washed with 1× Tris-buffered saline (TBS). Cells were lysed using radio-immunoprecipitation assay (RIPA) lysis buffer . Following protein separation and transfer, Polyvinylidene difluoride (PVDF) membrane was incubated with FOS primary antibody  and submitted to the Genomics core lab at the Heflin Center for Genomic Sciences at the University of Alabama at Birmingham for library preparation as previously described . RNA-seqCTGTCTCTTATA-3′) were identified and removed. For rat striatal neuron experiments, FASTQ files were then aligned using Bowtie2 (ATAC-seq libraries were prepared as previously described . ATAC-se Bowtie2  (v2.4.4) Bowtie2  (v2.26.2 Bowtie2  (v1.13). Bowtie2  (v3.5.1) Bowtie2  (v1.11). Bowtie2  (v 2.1.1 Bowtie2  (v3.8.4) Bowtie2  , homogenized using a motorized homogenizer and RNase-free pestle, and incubated on ice for 5 min. Samples were then pipette mixed 15× and incubated on ice for an additional 10 min. Following lysis, four samples from same sex and treatment were grouped into a single sample, and 2 ml chilled wash buffer  was added. Samples were then passed through 70 and 40 μM Scienceware Flowmi Cell Strainers. Samples were then centrifuged at 250 rcf for 5 min at 4°C. Supernatant was removed and the pellet was resuspended in 1 ml of chilled wash buffer . 100 μl of sample was then stained with 7-aminoactinomycin D and used to set a representative gate for fluorescence-activated cell sorting (FACS). This gate was then used to purify nuclei further. Following FACS, samples were spun at 250 rcf for 10 min at 4°C to remove any remaining debris. Samples were then loaded into individual wells of the Chromium NextGem Single Cell Chip using four of the eight available wells.Flash-frozen NAc tissue was added to a tube containing 500 μl lysis buffer . The groups were then merged into one object based on a common set of features and the standard Signac dimensionality reduction and clustering workflow was performed using 2:30 dimensions and a resolution value of 0.2. Data were then integrated with the snRNA-seq data from the same samples. Common anchors between the two were used to predict and label the cell types present in the snATAC-seq data.10× Genomics snATAC-seq libraries were generated from male and female Sprague-Dawley rats exposed injected with 20 mg/kg cocaine  once daily for 7 days. snATAC-seq data were aligned to the mRatBN7.2/Rn7 reference assembly using cellranger-atac v2.1.0 and the procedure outlined by 10× Genomics . AnalysiDetection of cis-coaccessible sites in the snATAC-seq data was performed using Cicero v1.3.9  and Monohttps://github.com/Jeremy-Day-Lab/Phillips_etal_2023, (copy archived at All relevant data that support the findings of this study are available by request from the corresponding author (J.J.D.). Custom code can be found at  important study that uses chromatin accessibility as a measure to determine the impact of neuronal activity on the state of chromatin regulatory elements in striatal neurons. The authors provide convincing evidence of how Pdyn gene expression is highly dependent on a distal regulatory genomic region both at basal and upon neuronal activation in this particular system, a mechanism conserved as well in human neuronal cells. Although the basic idea of accessibility changes have been studied before, this paper ties previous findings all together in one place and uses the analysis to identify a functionally relevant and conserved enhancer for the prodynorphin gene with potential relevance for neuropsychiatric disorders beyond basic cellular neuroscience. The study will be of interest to neuroscientists studying the striatum, neuronal plasticity, or related neuropsychiatric disorders.This is an  Summary:In this manuscript the authors use ATAC-seq to find regions of the genome of rat embryonic striatal neurons in culture that show changes in regulatory element accessibility following stimulation by KCl-mediated membrane depolarization. The authors compare 1hr and 4hr transcriptomes to see both rapid and late response genes. When they look at ATAC-seq data they see no changes in accessibility at 1hr but strong changes at 4hr. The differentially accessible sites were enriched for the AP-1 site, suggesting regulation by Fos-Jun family members, and consistent with the requirement for IEG expression, anisomycin blocked the increase in accessibility. To test the functional importance of this regulation the authors focus on a putative enhancer 45kb upstream of the activity-induced gene encoding the neuromodulator dynorphin (Pdyn). To test the function of this region, the authors recruited CRISPRi to the site, which blocked KCL-dependent Pdyn induction, or CRISPRa, which selectively increased Pdyn expression in the absence of KCl. Finally the authors reanalyze other human and rat datasets to show cell-type specific function of this enhancer correlated to Pdyn expression.The idea that stimuli that induce expression of Fos in neurons can change accessibility of regulatory elements bound from Fos has been shown before, but almost all the data are from hippocampal neurons so it is nice to see the different cell type used here. The most interesting part of the study is the identification of the Pdyn enhancer because of the importance of this gene product in the function of striatal neurons. Overall the conclusions appear to be well supported by the data. This study aims to characterize transcriptional and epigenetic activity-dependent striatal neuronal adaptations using rat primary cultures, a model still poorly characterized up to date. In addition, the authors aim to interrogate regulatory mechanisms that could modulate the expression of a highly-striatal enriched gene responding to neuronal activation in striatal neuronal cells, the Pdyn gene.Among the major strengths of the article there is the generation of high quality neuronal RNA-seq and ATAC-seq data in rat striatal neuronal cells in basal level and upon neuronal activity, a experimental setup that has not been so characterized as other more common ones such as mouse hippocampal neuronal cells. In this model, the authors clearly demonstrate the need of protein translation to induce the transcriptional waves of late response genes. In addition, the functional characterization of an enhancer of the Pdyn gene might be of great interest for translational applications in which alterations of this gene might be occurring in neurological disorders.On the other hand, the manuscript presents some limitations to be considered. One of the major points in this regard is that, at least in part, some of the conclusions reached by the study related to the induction of particular transcriptional programs upon neuronal activation, the changes in chromatin state, and the need of protein translation for proper induction of LRGs have been already previously described in the literature, affecting the novelty of the study. However, it is needed to be mentioned, that these previous studies were not conducted using the same model , which can make some differences in the final outputs. The other major cautionary point in the study is the selection of the time point for distinguishing early versus late response genes, as the short difference in time and the overlap of part of the transcriptional signature between them suggest that the transcriptional waves are somehow partially overlapping , which could result in missing part of the late-response genes.Despite this, the conclusions raised in the study are well supported by the data generated in it.In summary, the study presents a useful set of transcriptomic and epigenomic data of activity-dependent striatal neuronal programs in rats, which will be of great use for the scientific community working in this not so well characterized model. In addition, the characterization of a Pdyn distal regulatory genomic region involved in its transcriptional regulation, both at basal levels and upon neuronal activation in this particular system, can present translational relevance for striatal disorders such as Huntington's disease or other neuropsychiatric disorders. This work contributes to the literature characterizing early and late waves of transcription and associated chromatin remodeling following neuronal depolarization, here in cultured embryonic striatum. While they find IEG transcription 1 h after depolarization, they find chromatin remodeling is slower (opening at the 4 h time point). While this is not the first paper to describe chromatin changes in response to neuronal activity, this paper ties previous findings all together in one place using novel sequencing analyses and visualizations. Previous work has found remodeling occurring at the 1 h time point, so the lack of differences at that early time point in the current study needs to be better understood and the "temporal decoupling" described by authors should be further explored. Differences may be due to chromatin at IEG regulatory regions already being open in embryonic tissue (here) vs generally more closed in adult tissue (previous), or due to previous studies using protocols to specifically silence neurons prior to activation. The authors next show that the chromatin remodeling that occurs at the late (4 h) stage is largely in putative regulatory regions of the genome (rather than gene bodies), and is dependent on translation, which validates and extends the prior literature. The authors then transition from genome-wide basic neuroscience to focus on a specific gene of interest, prodynorphin (Pdyn), and a putative enhancer they identify from their chromatin analysis. They target CRISPR-activating and -inhibiting complexes to the putative enhancer and demonstrate that accessibility of this locus is necessary and sufficient for Pdyn transcription. They then show that at least one PDYN enhancer is conserved from rodents to humans, and is only activity-regulated in human GABAergic but not glutamatergic neurons. Finally, the authors generate snATAC-seq and show Pdyn gene and enhancer activity is also cell-type-specific in rat striatum. The Pdyn work, in particular, is thorough and novel, and demonstrates a translational aspect of this work. The following is the authors’ response to the original reviews.We thank all the reviewers for their comments and constructive feedback regarding our manuscript. We have made many changes to strengthen the manuscript, including addition of two new experiments  that help to clarify the nature and scope of activation of late response genes in striatal neurons. Our specific responses to individual reviewer comments are provided below.Reviewer #1Public reviewWeaknesses: The timing and the location of the accessibility changes are meaningfully different from other similar studies, which should be discussed. The authors provide good data for the function of a single enhancer near Pdyn, but could contextualize this with respect to other regulatory elements nearby.In the revised manuscript, we have expanded our discussion of the differences between chromatin accessibility changes observed in this study and those found in prior reports in different systems. These differences are also addressed in extended detail below. Unfortunately, limitations on resources and time prevented a deeper exploration of additional candidate enhancers near the Pdyn locus. However, we believe our efforts to characterize an activity-dependent enhancer in the Pdyn locus provides a useful starting point, and future studies may seek to more completely define the contributions of nearby regulatory elements.Recommendations For The Authors1. At 1hr after stimulation in previous papers : 1718-1730.) there are large increases in accessibility directly over the IEGs, consistent with the concerted transcription of these genes following stimulation. It is surprising that the authors do not see this here, either at 1hr or at 4hr. This difference in results needs to be addressed.We thank the reviewer for bringing this discrepancy to our attention. Indeed, Su et al. 2017 and Fernandez-Albert et al. 2019 both describe increases in chromatin accessibility at IEG promoters. There are several experimental differences that could be contributing to differences between our study and previously published studies. Two major reasons include the developmental timepoint of the tissue/cells and the cell type/brain region that is being assayed. Su et al. assayed chromatin accessibility in ex vivo slices containing the dentate gyrus from adult mice, while Fernandez-Albert et al. assayed chromatin accessibility in forebrain principal neurons of adult mice following kainic acid injection. Bulk ATAC-Seq experiments described in the present manuscript were generated from cultured embryonic rat striatal neurons. Additionally, baseline chromatin accessibility seems to be significantly different between forebrain principal neurons studied in Fernandez-Albert et al. 2019 and the current study. For example, in Figure 3a of Fernandez-Albert et al. 2019, the Npas4 gene body is not accessible in a saline treated animal. In vehicle treated, cultured embryonic rat striatal neurons, the Fos gene body and associated enhancers are accessible at baseline , and do not increase with KCl depolarization.We have expanded our discussion of this discrepancy in the discussion section of the revised manuscript, and included additional citations addressing this difference.1. It is also somewhat surprising that the authors see almost no regions that show changes in accessibility at 1hr and then a very large number of differentially accessible regions at 4hr. This is quite different from the more rapid changes shown for example in Figure 7f in the human GABA neurons even though these are also studies in culture with rapid calcium channel opening. Can the authors speculate on the reason for the difference?Many previously published studies that use cultured neurons include a pre-treatment in which spontaneous neuronal activity is inhibited with the sodium channel blocker tetrodotoxin . The Sanchez-Priego et al. Cell Reports manuscript also blocked NMDA receptor activity with the competitive NMDAR antagonist D-AP5 for 12 hours prior to depolarization. Rapid changes in chromatin accessibility observed in other studies at <1 hour timepoints could be due to prior silencing of the cells and subsequent reduction in the accessibility and transcriptional activity of IEGs. Decreased baseline accessibility and transcriptional activity of IEGs can be observed in Figure 1a of Malik et al. 2014, which displays ChIP-Seq tracks for both RNA pol II and H3K27ac. At baseline, H3K27ac and RNA pol II enrichment is low throughout the Fos locus. Subsequent depolarization of silenced neurons drives accessibility and transcription of the Fos gene and associated enhancers. In contrast, we found accessible chromatin at Fos enhancer elements at baseline .The experiments described in the current study do not include any pre-treatment with tetrodotoxin or D-AP5, and thus the neurons are expected to be spontaneously active. This baseline electrophysiological activity may result in increased accessibility and transcription at IEG loci, which ultimately makes it difficult to identify activity-dependent increases in IEG accessibility at timepoints <1 hour. Furthermore, a previously published manuscript from our lab  conducted ATAC-seq on cultured embryonic rat cortical, hippocampal, and striatal neurons and found that transcribed enhancers for IEG loci (including Fos) had decreased chromatin accessibility following depolarization when compared to vehicle treatment. These differences in experimental design  may all contribute to differences in the temporal dynamics of chromatin remodeling between the current manuscript and previously published studies.1. Experimentally it can be challenging to repress a single enhancer and show a significant effect on gene regulation which makes the repression in Fig 6c somewhat unexpected. There are several regions near Pdyn that show activity-dependent changes in accessibility in the human cells  and presumably in the rat neurons too . Did the authors target any of these other regions?We chose the identified regulatory element upstream of the Pdyn TSS because it met several criteria that we determined are important for characterizing LRG enhancers. These criteria are outlined in the Results: “(1) located in non-coding regions of the genome, (2) inaccessible at baseline and accessible following depolarization, and (3) inaccessible when depolarization was paired with protein synthesis inhibition.” Indeed, ATAC-seq experiments presented in the current study demonstrate that thousands of genomic regions undergo reprogramming, and many of these regions meet these criteria . However, we lacked the time and resources to systematically investigate all other enhancers, and did not target any other regions within the Pdyn locus. While many enhancers may regulate a single gene, the identified enhancer seems to be particularly important for activity-dependent Pdyn gene expression. Importantly, CRISPRi-based repression of this enhancer  did not reduce basal Pdyn expression as compared to a non-targeting control, but completely blocked stimulus-dependent induction of Pdyn transcription. We believe this is a useful starting point, and future studies may seek to more completely define the contributions of nearby regulatory elements.1. The authors should clarify in the methods or figure legends the number of independent replicate libraries for each experiment and were the RNA and ATAC libraries made from the same or different experiments.We thank the reviewer for bringing this to our attention. We have clarified the number of replicates in the methods as outlined below. Additionally, RNA and ATAC libraries were generated from different experiments, and this information is also now included in the methods.Within the ATAC-Seq library preparation and analysis methods section: “ATAC-seq libraries were generated from experiments independent of the RNA-seq experiments. For the ATAC-seq experiment of neurons treated with vehicle or KCl for 1 h, there were 3 replicates within each treatment group . For the ATAC-seq experiment of neurons treated with vehicle or KCl for 4 h, there were 3 replicates within each treatment group . For the ATAC-seq experiment of neurons pre-treated with DMSO or Anisomycin, there were 4 replicates within each treatment group .”Within the RNA-seq library preparation and analysis methods section: “RNA-seq libraries were generated from experiments independent of the ATAC-seq experiments. For the RNA-seq experiment of neurons treated with vehicle or KCl for 1 h, there were 3 replicates within the KCl group and 4 replicates within the vehicle group. For the RNA-seq experiment of neurons treated with vehicle or KCl for 4 h, there were 4 replicates within each group .”Reviewer #2Public reviewFirst of all, at a conceptual level, most of the findings related to the induction of particular transcriptional programs upon neuronal activation the changes in chromatin state, and the need for protein translation for proper induction of LRGs have been broadly characterized previously in the literature . In addition, it is not so obvious why to focus on Pdyn gene regulatory regions among the thousands of genes upregulated and with modified chromatin landscape after neuronal activation. The authors highlight three particular traits of this gene as the reason to choose it, but those traits are probably shared by most of the genes that are part of the LRGs set.We thank the reviewer for these comments, and have included these important publications as citations in our manuscript. With over 5,000 differentially accessible chromatin regions following KCl stimulation, it was not possible to follow up on all regulatory regions or linked genes in a rigorous way. Therefore, we selected a target candidate enhancer near the Pdyn locus for mechanistic studies. In addition to the criteria highlighted in the manuscript, we chose this locus due to decades of literature establishing the importance of prodynorphin in the striatum, and the role of this gene in human neuropsychiatric diseases. We would argue that this increases the relevance of more detailed exploration of this gene, and makes our results applicable to a broader pre-existing literature.At the methodological level, some attention should be put into the timings chosen for generating the data. The authors claim that these time points (1h and 4hrs) identify the first (i.e IEGs) and second (i.e LRGs) waves of transcription. However, at 4hrs the highest over-expressed genes are still IEGs, as shown in the volcano plots of Figure 1B and 1C, showing a high overlap with up-regulated genes found at 1h . This might suggest that the 4hrs time point is somewhere in between the first and second wave of transcription, probably missing some of the still-to-be-induced LRGs of the latest one.Given that the depolarization applied in RNA-seq and ATAC-seq experiments is continuous, it was not unexpected to find IEGs present at both 1 h and 4 h timepoints. The revised manuscript contains a new experiment  demonstrating that a shorter depolarization period (1 h KCl followed by a 3 h wash off period) also induces Fos mRNA, but to a much lower extent than 4 h continuous stimulation. In contrast, both short (1 h) and long (4 h) depolarization periods induce Pdyn to equivalent levels when measured at 4 h after the onset of the stimulus. These experiments support our conclusion that LRGs require a temporal delay, and not simply extended stimulation. Nevertheless, the reviewer is correct that a 4 h timepoint may potentially miss some LRGs that are induced even later. We plan to explore the full timecourse of LRG induction in future studies.Finally, while only prosed as a suggestion, the assumption that from the data generated in this article, we can envision a mechanism by which AP-1 family of transcription factors interacts with the SWI/SNF chromatin remodeling complex is going too far, as no evidence is provided implicated SWI/SNF in the data presented in the manuscript.While speculative in the current context, we felt that it was important to highlight this prior literature to identify potential mechanisms that may link IEGs  to chromatin remodeling machinery. We have altered this section of the discussion to emphasize that this link is speculative in the context of neuronal chromatin remodeling.Recommendations For The Authors1. I couldn't find the number of replicates generated for each dataset, neither for RNA nor for ATAC-seq. It could be worth adding these data to the figure legends or in the material and methods.We thank the reviewer for bringing this to our attention. The number of replicates generated for each dataset are now included in the methods section .1. In Figure 1D, Gene Ontology terms appear significant only for each of the individual datasets. While this might be expected for the 1h time-point, the 4hrs time-point comprises a big extent of the genes up-regulated at 1h as well, and it is surprising no term related to chromatin or transcription regulation appears as significant. Is this due to the fact that the analysis has been conducted with two separated lists of genes and only the top terms are shown without crossing the data? This could be misleading for the reader and maybe a comparative GO term analysis might be better such as using CluterProfiler or similar tools, that might allow for real comparison of terms enriched in each dataset.We thank the reviewer for pointing this out. For Figure 1d, GO term analysis was conducted with two separated gene lists, each consisting of timepoint-specific upregulated DEGs. Thus, 772 genes were included for the analysis of 4 h GO terms and 39 genes were included for the analysis of 1 h GO terms. Previously, comparisons of cellular component GO terms included in the current study only included the top 10 GO terms. The revised manuscript contains an updated analysis that compares all enriched GO terms and identifies that three of the top 10 cellular component GO terms for the 1 h gene set are also identified as significantly enriched in the 4 h gene set. We have revised the graph in Fig. 1f to reflect this updated analysis. Overall, our conclusions  remains supported by this analysis.1. In Figure 3D, the graphs show the density of motifs within the DARs in units of"Motifs/Kb/peak" while the x-axis represents the peaks coordinates from -500bp to +500bp. It is not clear to me how this graph is generated and how within 1000bp the profiles can reach values of 18-20 Motifs/Kb/peak. Could this be clarified?The motif enrichment score was calculated by identifying the number of total motifs within defined 50bp genomic bins surrounding the center of the DAR regions. HOMER builds enrichment histograms that normalize motif presence to set size , and also to genomic space (base pairs). While HOMER’s default histogram represents motifs/bp/peak, we converted this to motifs/kb/peak for ease of interpretation. However, to avoid confusion we have returned the y axis labels to the default HOMER settings (motifs/bp/peak). The normalization and units for this graph have been clarified in the methods section.1. In Figure 4C the newly generated ATAC-seq data is just "targeted" analyzed, showing global tendencies are maintained between the initial generated data and this one. It could be interesting, however, to see the number of DARs obtained in these conditions, especially to see if some DARs are observed in the Anisomycin condition that might be translation-independent.The experiment described in Figure 4 was designed to both validate the 5,312 DARs and understand the role of protein translation in activity-dependent chromatin remodeling. One way to begin identifying translation-independent DARs is to compare the DMSO + Vehicle group to the Anisomycin + KCl group. With this comparison, any 4 h DAR that has increased accessibility in the Anisomycin + KCl group may be translation-independent as pretreatment with anisomycin did not prevent chromatin remodeling. After conducting this analysis, we identified a very small percentage (3.44%) of 5,312 4 h DARs that still exhibited significantly increased accessibility when pre-treated with Anisomycin. This small number is consistent with the robust effects of anisomycin on KCl-dependent remodeling shown in Fig. 4c-d. However, to confirm that these were in fact translation-independent activity-regulated DARs, we would need to perform direct comparison of chromatin accessibility between neurons pre-treated with Anisomycin and then treated with either vehicle or KCl. Since we did not include an anisomycin only group in experiments in Fig. 4, we cannot confidently claim whether this 3.4% of DARs are translationindependent. Nevertheless, we agree with the reviewer that this is an interesting avenue of future exploration.Reviewer #3Public review1. Throughout the paper, the authors emphasize a "temporal decoupling" of transcriptional and chromatin response to depolarization, based on a lack of significant chromatin changes at 1h, despite IEG transcription. However, previous publications show significant chromatin remodeling at 1h  or 2h . The discussion briefly mentions this contrast, but it remains difficult to conclude decisively whether there is temporal decoupling when such decoupling is not found consistently. If one is to make broad conclusions about basic neural chromatin response to depolarization, it would be ideal to know under which conditions there is temporal decoupling, or if this is a region-specific phenomenon.Indeed, prior studies referred to in our manuscript have identified chromatin remodeling at earlier timepoints than we identified here. As addressed above , it is possible that this discrepancy arises due to the difference in experimental model system, differences in the type of stimulation applied, pretreatment protocols used to silence neurons prior to activation, or even differences in developmental stage. Differences in each of these parameters make it difficult to make straightforward comparisons between datasets and results in this manuscript. It is possible that other cell types induce IEGs more quickly (or more robustly) in response to stimulation, which could lead to earlier chromatin remodeling. However, the common patterns of chromatin reorganization  lend support for the idea that the transcriptional waves identified here can also be found in other cell types and in other contexts.1. The UMAP analysis is a novel way to probe transcription factor enrichment, but it's unclear what this is actually showing. The authors sought to ask whether "DARs could be separated based on transcription factor motifs in these regions." However, the motifs present in any genomic stretch are fixed based on genomic sequence, so it seems like this analysis might be asking whether certain motifs are more likely to be physically clustered together in the genome, in activity-regulated regions . While still potentially interesting, this analysis does not seem to give much additional insight into activity-dependent chromatin remodeling beyond the motif enrichment analysis already performed. Nevertheless, to draw stronger conclusions, it would be necessary to compare clustering to a random set of genomic regions of the same length/size to interpret the clustering here. It would also be useful to know whether the ISL1 motif is also enriched in ubiquitously accessible genomic regions in the striatum (and not just DARs).We agree that additional analysis is needed to explore enrichment of various transcription factor motifs and activity at differently accessible regions of the genome. The motif enrichment analysis in Figure 3 demonstrated the types of motifs that were enriched in DARs , the overall degree of enrichment , and the distribution of those motifs across DAR sites . This analysis allowed us to understand whether motifs for cell-defining transcription factors like ISL1 are enriched uniquely in DARs, or are also found in other regions that are accessible at baseline . However, these approaches represent enrichment across all DARs as group, and do not show TF presence/absence at any specific DAR. The UMAP analysis presented in Figure 3e allowed identification of DAR clusters based on the presence or absence of specific transcription factor motifs, and allowed us to represent specific DARs in a reduced two-dimensional space. Because this analysis identifies the existence of distinct motifs within single DARs, it allowed us to speculate as to the possibility of transcription factor cooperation within DARs, or the meaning of DAR clusters that appear to be defined by specific motifs . Given the information that this adds to the initial analyses, we argue that its inclusion in the manuscript is useful and potentially informative for generating follow-up hypotheses.1. The authors identify late-response gene enhancers by 3 criteria. However, only Pdyn was highlighted thereafter. How many putative DARs met these three criteria in striatum? Only Pdyn?As illustrated in Figures 2 and 4, nearly all of the DARs in our dataset met these criteria, which included presence in non-coding genomic regions, increase in accessibility following stimulation, and prevention of chromatin accessibility changes by protein synthesis inhibition. We did not mean to indicate that the Pdyn locus was unique in this way. In addition to the criteria highlighted in the manuscript, we chose this locus due to decades of literature establishing the importance of prodynorphin in the striatum, and the role of this gene in human neuropsychiatric diseases. We would argue that this increases the relevance of more detailed exploration of the regulator mechanisms that control expression of this gene, and makes our results applicable to a broader pre-existing literature. The revised manuscript includes additional experiments that examine Pdyn expression changes in response to different stimuli, which help to justify the focus on this gene from the beginning of the manuscript.Recommendations For The Authors1. Figure 1 volcano plots show a scatter primarily in the up-regulated portion at both the 1-h and 4-h time points. However, the Venn diagrams show largely similar numbers of up- and downregulated genes at the 4-h time point. Is the clustering of down-regulated genes tighter/more overlapping? If so, semi-translucent volcano dots or some acknowledgment of the visual discrepancy would be useful.We thank the reviewer for bringing this to our attention. Down-regulated genes are clustering tighter on the volcano plot due to smaller fold changes. This visual discrepancy is acknowledged by the numeric indicators of up- and down-regulated genes in the upper left-hand corner of the volcano plot.1. Methods for RNA and ATAC seq analysis align to human genome Hg38, rather than rat?RNA- and ATAC-Seq analyses from rat neurons were aligned to the mRatBn7.2/Rn7 rat genome. RNA- and ATAC-Seq analyses from human neurons were aligned to the Hg38 human genome. We have updated the methods to make this clear.1. The introduction states that different classes of neurons induce distinct LRGs. Please add a citation. Citations are also needed for the last statement WRT consequences of chromatin remodeling near LRGs not being concretely linked to LRG transcription.We thank the reviewer for pointing this out. The revised manuscript now includes additional citations supporting each of these statements.1. Specify somewhere in Methods that DEGs were compared to vehicle for both 1-h and 4-h (and not 4 vs 1 h).We thank the reviewer for bringing this to our attention. We have updated the methods to include: “DEGs were calculated by comparing the KCl and Vehicle treatment groups at each respective timepoint.”1. In Figure 2E, why are the enrichments exactly opposite, especially given these are two different types of input ?Odds ratios were calculated by comparing baseline peaks  to KCl-induced DARs. This allowed us to identify the enrichment of DARs in specific genomic annotations in comparison to the genomic features that are accessible at baseline, rather than making comparisons to random probe sets or genomic space dedicated to these distinct annotations. This analysis identified that relative to baseline peaks, DARs are significantly depleted in coding regions of the genome and enriched in non-coding regions of the genome. However, given this analysis we agree that it does not make sense to graph both the vehicle (baseline) and DARs on this graph, given that enrichment of each set is determined relative to the other . We have updated Fig. 2e to only include points for 4 h DARs.1. Some references are off. One that I noted was "...chromatin remodeling in the mouse dentate gyrus following 1 h of electricoconvulsive stimulation" should be Su et al 2017 not Malik 2014. For the statement that IEGs are critical regulators of non-neuronal IEGs, the authors may want to add Hrvatin 2017 ref.We thank the reviewer for bringing this to our attention. We have revised the manuscript to include the correct citation for this claim, and also to incude the Hrvatin, et al reference.1. It would be helpful for the authors to write out the whole gene name for Pdyn somewhere.We have updated the text to include the gene name for Pdyn, both in the abstract and also in the introduction of the manuscript.1. Figure 5f: For ease, please include what is high vs low in the figure caption in addition to the main text.We thank the reviewer for bringing this to our attention. We have updated the figure caption and main text to include what is high vs low in Pseudotime estimates in Fig. 5f.1. How are the tracks ordered in Fig8c?Tracks within Fig. 8c demonstrate snATAC-seq signal at the Pdyn gene locus for transcriptionally distinct cell types within the NAc. The tracks are ordered by cluster size (nuclei number) in the snATAC-seq dataset.


Policy1 modelling platforms to predict outcomes for potential disruptions to cancer screening participation, covering periods of 3, 6, 9, and 12 mo. We estimated missed screens, clinical outcomes , and various diagnostic service impacts. We found that a 12-mo screening disruption would reduce breast cancer diagnoses (9.3% population-level reduction over 2020–2021) and colorectal cancer (up to 12.1% reduction over 2020–21), and increase cervical cancer diagnoses (up to 3.6% over 2020–2022), with upstaging expected for these cancer types . Findings for 6–12-mo disruption scenarios illustrate that maintaining screening participation is critical to preventing an increase in the burden of cancer at a population level. We provide programme-specific insights into which outcomes are expected to change, when changes are likely to become apparent, and likely downstream impacts. This evaluation provided evidence to guide decision-making for screening programmes and emphasises the ongoing benefits of maintaining screening in the face of potential future disruptions.Australia introduced COVID-19 infection prevention and control measures in early 2020. To help prepare health services, the Australian Government Department of Health commissioned a modelled evaluation of the impact of disruptions to population breast, bowel, and cervical cancer screening programmes on cancer outcomes and cancer services. We used the  Population cancer screening programmes aim to reduce cancer-specific mortality, complications, and side effects associated with the treatment of advanced-stage neoplasms and, in the case of bowel and cervical screening, reduce the incidence, illness, and mortality of cancers through the detection and treatment of precancerous abnormalities . Breast,Australia established national screening programmes for breast and cervical cancer in 1991 and colorectal cancer in 2006 . FollowiModelled evaluations of COVID-impact scenarios have been undertaken by different countries to support their cancer screening programmes as they respond to the pandemic . In paraThis article presents a within-country comparison of key findings, at a national level, between three established population cancer screening programmes, using comparable time periods and metrics. Although some of the scenarios explored here have been harnessed in prior cross-country evaluation studies , modellePolicy1 modelling platform (https://www.policy1.org/)  . ScenariPolicy1 which has evaluated the clinical benefits and harms of a wide range of screening protocols, new technologies, and risk-based approaches aiming to optimise population cancer screening programmes  and degree (complete pauses or reduced throughput) .The BreastScreen Australia (BSA) programme offers biennial  mammographic screening to women from the age of 40 y, targeted to women aged 50–74 y . The prohttps://www.policy1.org/models/breast.Additional information on the Policy1-Breast model can be found at The National Bowel Cancer Screening Program (NBCSP) provides biennial primary screening, using an immunochemical faecal occult blood test (iFOBT), to all Australians aged 50–74 y via the national postal service. The screening kit is self-administered at home and, if positive, a follow-up colonoscopy is recommended for diagnostic investigation. Prior to COVID-19, participation was approximately 44%, with 62% of individuals with a positive iFOBT reported as completing a colonoscopy . Using tThe status quo scenario is modelled based on no disruption to the NBCSP using observed participation rates (~40%), with all rates from 2017 onwards extrapolated from the reported data for 2017, unless otherwise noted. Based on the observed data, the model assumes ~70% of individuals with a positive iFOBT complete a diagnostic follow-up colonoscopy. Depending on the follow-up colonoscopy findings, individuals are referred to either return to the NBCSP for iFOBT screening after an interval of 4 y or to repeat colonoscopic assessments (referred to as surveillance colonoscopies) after 1–5 y based on the 2011 guidelines recommendations. Combined, both follow-up and surveillance colonoscopies are referred to as NBCSP-related colonoscopies.Outcomes have been estimated for the affected cohorts only, that is, among people aged 50–74 y eligible for screening in 2020 and 2021. This cohort comprises 7.1 million people; 3.5 million men and 3.6 million women.Based on the GESA recommendations from 24 March 2020, most ‘elective’ colonoscopies were suspended for most of March and April 2020 unless considered to be ‘urgent’. Recommendations suggested that colonoscopies for the investigation of a positive iFOBT be considered on a case-by-case basis only if the patient has not had a high-quality colonoscopy within the previous 4 y. The continuation of urgent colonoscopies was not incorporated in these results, nor was rescheduling of elective colonoscopies. We note that recommendations to recommence colonoscopy services were announced at the end of the April 2020 but have not been incorporated into these results.https://www.policy1.org/models/bowel.Additional information on the Policy1-Bowel model can be found at The National Cervical Screening Program (NSCP) is provided to women aged 25–74 y and usually involves a visit to primary care. In mid-March 2020, the NCSP was approximately 27 mo into a transition from 2-yearly Pap testing to 5-yearly primary HPV screening, where a woman’s first HPV test was due 2 y after her previous Pap test.As part of this evaluation, we updated modelled screening participation to reflect National Cancer Screening Register data , indicatAll modelled scenarios assume the NCSP transitioned from the pre-renewed NCSP (2-yearly cytology for women aged 18–69) to the renewed programme (5-yearly primary HPV screening for women aged 25 to 70–74 y) at the beginning of 2018, including clinical management guidelines with women with detected abnormalities . The modhttps://www.policy1.org/models/cervix). Re-screening patterns reflecting a recommended 5-yearly interval do not apply to women until after they have attended for their first HPV test. Assumptions for re-screening attendance (routine testing) and follow-up under all modelled scenarios are outlined in Similar re-screening patterns as in the pre-renewed NCSP were assumed to apply until women have attended for their first HPV test in the renewed NCSP (i.e. screening behaviour reflects that women are recommended to attend for their first HPV test 2 y after their last cytology test). These screening patterns for women’s first HPV test differ slightly from assumed adherence to the 2-yearly interval in the pre-renewed NCSP in order to directly reflect NCSR data on observed behaviour from December 2017 onwards  between a 6-mo pause with pre-COVID throughout during recovery and a 12-mo pause with 150% throughput during recovery .All scenarios would lead to reduced population-level rates of screen-detected cancers and interval cancers, reduced programme sensitivity, and more advanced tumour stage at diagnosis. Longer-term fluctuations in clinical and programme outcomes are also expected; for example, with a 12-mo pause we estimate a 10% difference in population-level invasive breast cancer diagnoses among women aged 50–74 between 2020 and 2021  and 2022–2023  .We assumed higher priority for women who missed scheduled screens during the pause and report the estimated distribution of these women compared to other client groups . EstimatAll three scenarios illustrated a decrease in cancer diagnoses and cancers being diagnosed at a later stage in the screening cohort compared to the status quo. Across the modelled scenarios, colorectal cancer diagnoses decreased in 2020–2021 by 583–2549 cases (2.8–12.1% decrease). The modelled disruptions resulted in a stage shift, with up to 891 cases which would have been diagnosed but instead progressed to a later stage with lower survival.Due to the screening disruptions, fewer positive iFOBT would lead to fewer diagnostic follow-up colonoscopies, and additionally no surveillance colonoscopies would be conducted. The NBCSP-related colonoscopies that would not be conducted in 2020 due to the 3-, 6-, and 12-mo disruptions would be 19,151, 38,335, and 76,125 respectively.This analysis found that a 12-mo pause to screening would lead to an additional 7140 colorectal cancer cases over 2020–2050. This is due to both changes in detection of both cancers and precancerous lesions during the 2020–2021 period, as well as the long-term effect on changes to screening participation and behaviours estimated to be attributable to the disruption.Additional outcomes for diagnostic assessments and short-term adenoma outcomes are presented in All three scenarios resulted in an increase in cancer diagnoses and cancers being diagnosed at a later stage among screening-age women compared to what would have been expected in the absence of any disruption. The increase in cancer cases over 2020–2022 ranged from 21 to 69 cases (1.1–3.6% increase). Most (57–63%) of the additional cancers were diagnosed among women 30–49 y. Women aged 30–39 y and 40–49 y were also the age groups where the percentage increase in cancers was largest . The model predicted that disruptions to routine primary screening would lead to 6–18 cervical cancers being diagnosed at regional stage in 2021–2022, rather than as localised cancers in 2020; and 3–9 cervical cancers being diagnosed at distant stage in 2021–2022, rather than at regional stage in 2020. Considering both additional cervical cancers and those which were diagnosed at a later stage due to disruptions to cervical screening, an estimated 30–97 women would be affected by delays in diagnosis due to disruptions to routine screening in 2020. The longer-term impact of these additional and upstaged cancers on cervical cancer deaths is presented in Disruptions to primary screening in 2020 are estimated to result in 17,680–47,868 fewer women attending for a colposcopy in 2020 (17.4–47.2% reduction) as a result of their primary screening or triage test. The impact of the disruptions on expected colposcopy utilisation between 2020 and 2022 is presented in Additional results can also be found in the online reports produced as part of this project for the Australian Government, Department of Health .We present outcomes estimated in 2020 for a range of potential disruptions to national population cancer screening programmes for breast, cervical, and colorectal cancer, including population-level changes in cancer diagnoses and staging. For similar disruption scenarios, we estimate markedly different impacts for each programme. We estimated that a 12-mo screening disruption from early 2020 would in the short term lead to reduced diagnoses of breast cancer  and colorectal cancer (up to 12.1% reduction over 2020–21) and increased cervical cancer diagnoses (up to 3.6% over 2020–2022), with upstaging expected for all three cancer types. For a 6-mo screening disruption we estimated reduced diagnoses of breast cancer  and colorectal cancer (up to 5.8% reduction over 2020–21) and increased cervical cancer diagnoses (up to 1.1% over 2020–2022), with less marked upstaging compared to a 12-mo disruption.Findings were most marked for bowel screening, for which short-term upstaging and reduced cancer incidence was expected to lead to a significant number of additional colorectal cancer cases over the lifetime of the affected cohort. For breast screening, we estimated smaller but important reductions in cancer diagnoses at a population level over 2021 with fluctuating rates in subsequent years and upstaging of cancers at diagnosis . For cervical screening, relative and absolute increases in cancer are expected to be less than for breast and colorectal cancer. This is likely due to COVID-19 impacts coinciding with lower participation under status quo because the programme recently transitioned to a longer screening interval, along with the lower burden of disease due to prevention through both screening and widespread uptake of HPV vaccination  .We used established modelling platforms for breast, colorectal, and HPV/cervical cancer natural history and screening in Australia, which were already well calibrated and validated to the Australian context using high-quality published data, including detailed governmental screening reports. These models describe the Australian national age and sex distribution and the incidence and natural histories of breast, cervical, and colorectal cancer, and can be used to simulate a range of scenarios for cancer screening. Prior investment into these modelling platforms facilitated the rapid response modelling required in the face of a crisis; the evaluations reported highlight the importance of ongoing investment into comprehensive, well-calibrated and validated population predictive models that can be quickly harnessed for critical decision-making as needed for disruptions such as the COVID-19 pandemic and other potential disruptions due to major social, public health, or natural disasters as are increasingly common due to climate change .This evaluation included detailed reporting of outcomes for different subgroups where this was requested by key stakeholders (e.g. breast screening outcomes according to whether clients were directly or indirectly impacted by screening disruptions).Each modelled evaluation drew on the most contemporary data available, employing projections as required. In addition, the cervical screening model incorporated the effects of recent policy changes that were expected to reduce the impact of COVID-19 disruptions, drawing on unpublished data from the NCSR.Scenarios were by necessity defined in simple terms. For example, we modelled single, continuous periods of national disruption but, in practice, screening participation would likely fluctuate by time and place in line with local outbreaks and pandemic control measures. By deliberately focusing on a high-level effect – a wide range of possible effects on attendance for routine screening – the analysis was agnostic to the cause, and therefore this could reflect any or many of a wide range of possible factors, including changes in behaviour and access to screening, reduced capacity of services involved in providing screening, or reduced saliency or delivery delays of reminder letters or home-delivered testing kits. These effects will be incorporated in future modelled evaluations, after careful calibration and validation to observed data, with a view to extending the modelled outcomes to mortality estimates.Modelled estimates will likely include some biases. We do not account for changes in diagnostic and treatment services due to COVID-19 which have since been observed . For exaThis modelled evaluation was conceived during the first wave of COVID-19 restrictions, and in the context of needing to provide safe health services including population screening for breast, cervical, and colorectal cancer. At the time of the evaluation, the current analysis provided government with timely background estimates of potential impacts. The Australian cervical and bowel screening programmes continued to operate continuously through 2020 (and since), while BreastScreen services paused nationally from March to late April/early May 2020, resuming thereafter. Monthly screening tests over the period January–September 2020 fluctuated as shown in Observed screening reductions were driven by a range of factors such as screening service closure followed by reduced throughput capacity for breast screening, reduced primary care visits , as well individuals likely de-prioritising screening in the context of the pandemic . ScreeniOur predicted clinical implications of reduced participation cannot be confirmed for some time; however, early signs suggest an observed reduction in cancer diagnosis in 2020 . While CWe quantified resource requirements for programmes and related health services in the face of disruption, with requirements for breast cancer screening assessments, colonoscopies, and colposcopies affected to varying degrees. Variations in breast and colorectal cancer incidence are expected to have a significant flow-on effect on the demand for treatment services, compounded by a changing case-mix with shifts to later-stage diagnoses to varying degrees over time.Collectively, these findings from the 6–12-mo disruption scenarios illustrate that maintaining screening participation is critical to preventing an increase in the burden of cancer at a population level. We enumerate the extent to which COVID-19 disruptions are likely to impact short- and long-term cancer outcomes, resource requirements, and usual indicators used in programme monitoring and evaluation, providing critical evidence to guide cancer screening programmes as they continue to adapt to the ongoing impacts of the COVID-19 pandemic, and as they prepare for other major disruptions that may arise in the future .The estimated impacts described here may be reduced if screening programmes sustain or revisit planned improvements that commenced prior to the pandemic, some of which may also directly assist programme recovery. For example, from 1 July 2022, all women now have the option to use self-collection and this could facilitate continuation or recovery of cervical screening programmes .As programmes work to bring screening participants back on schedule, well-planned reactive risk-based approaches may help direct limited services to those who will benefit the most. For HPV screening, this could mean more refinements to triaging and surveillance of screen-positive women, and optimising screening for women protected by HPV vaccination. For breast screening, this could involve prioritising women at higher risk of breast cancer, such as women usually offered annual screening and women assessed as higher risk using routine risk prediction models incorporating breast density . For bow This study presents important results on predicted impact of cancer screening disruptions in Australia during the COVID-19 pandemic based on rapid-response consultation with public health stakeholders. The evidence presented is solid as simulations were based on several previously validated breast, cervical, and bowel cancer screening decision models. Although the scenarios were based on hypothetical disruptions that do not always match experienced disruptions, the work will be of interest to local policymakers, public health specialists, and cancer epidemiologists. public reviews designed to be posted alongside the preprint for the benefit of readers; (ii) feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.Our editorial process produces two outputs: (i) Decision letter after peer review:eLife. Your article has been reviewed by 3 peer reviewers, and the evaluation has been overseen by a Reviewing Editor and a Senior Editor. The reviewers have opted to remain anonymous.Thank you for submitting your article "The impact of COVID-19 on population cancer screening programs in Australia: modelled evaluations for breast, bowel and cervical cancer" 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.Essential revisions:– Please explain the novelty of this study in relation to previous papers from the same research group.– Address all requests for clarifications made by reviewers.– Place figures currently in the Discussion in the Appendix.– Temper claims regarding the impact of screening disruptions on cancer burden, as the results also suggest that relatively short (~3 month) disruptions would likely not have a large impact on cancer burden.– Provide some further explanation on why the authors consider that model-estimated reductions in screening tests in the first year of disruption overlapped with observed rates, as Reviewer #1 is correct that empirical disruptions do not appear to always match hypothetical modelled disruptions.– Provide confidence or uncertainty intervals for model predictions, or provide an explanation as to why confidence intervals are unavailable.– The definition of interval cancers and findings regarding interval cancers need to be better explained, as the results were confusing and counter-intuitive to reviewers.– While I understand there are reasons why the authors did not include cancer mortality outcomes as the models did not include the impacts of treatment disruptions, the lack of mortality outcomes was noted by two reviewers so it is likely that many readers would similarly wonder why cancer death outcomes were not included. The authors may want to consider including screening disruption-related predicted additional deaths if available, specifying that these deaths do not include the impact of treatment disruptions.Reviewer #1 (Recommendations for the authors):Thank you for the nicely written paper. I still have a few comments.Overall:1. I recently read two other papers which used the Policy1 modelling platforms to estimate the effect of disruptions in the bowel and cervical screening program on cancer outcomes. I assume that you are familiar with those studies as some of your co-auteurs are also listed on them. I am talking about the following two papers: "Impact of the COVID-19 pandemic on faecal immunochemical test-based colorectal cancer screening programmes in Australia, Canada, and the Netherlands: a comparative modelling study" and "Impact of disruptions and recovery for established cervical screening programs across a range of high-income country program designs, using COVID-19 as an example: A modelled analysis".Could you please explain the difference between the current study and the two studies I listed above? Why did the current study need to be performed? As you already had the other two studies, which look rather similar to the current study.2. Make sure you write percentages the same throughout the article. Sometimes I see 9.3 and then I see 9·3 (dot in the middle instead of on the bottom).Abstract:1. I miss some information about upstaging. How many percent of the tumours are expected to be upstaged?2. I believe that a suspension of the screening program for 3 months will not lead to significant upstaging. The first sentence of the conclusion might be a bit too harsh, as suspension for only three months might be wise in situations with limited resources.Introduction:1. Nicely written introduction. However, I would like you to mention here the significance of this paper, as there are already two modelling studies using the Policy1 modelling platform.Methods:1. I miss a description of the restart strategy of the screening program. Did the persons with a suspended screen still received their screening when the program restarted? Did the program tried to catch-up on the missed screens by increasing their capacity? The current article nicely describes different restart strategies: Kregting, L. M., Kaljouw, S., de Jonge, L., Jansen, E. E., Peterse, E. F., Heijnsdijk, E. A., … and de Kok, I. M. (2021). Effects of cancer screening restart strategies after COVID-19 disruption. British journal of cancer, 124(9), 1516-1523.2. In line 109 you mention: "60-84% of clients screened on schedule (first to third and subsequent round)". What is meant by this?3. In line 178 you mention a "cervical screening test (CST)". Do you mean a HPV-test?Results:1. Table 3 shows the reduction in the number of screen-detected invasive cancers per 100,000 women. I am quite surprised to see that the number of screen-detected tumors after a disruption of 12 months only decreased by 24%, when there was no screening for an entire year and the screening program restarted gradually over the next 6 months. Is there a catch-up in those last 6 months when the normal screening program was again in place?2. Table 3 shows the reduction in the number of interval-detected invasive cancers. Does this concern the interval-detected invasive cancers of women screened in 2020/2021? Or does it concern the number of interval-detected invasive cancers diagnosed in 2020-2021?3. In line 259-260 you write that an estimated 759-3022 people would be affected by delays in diagnosis for bowel cancer. Where can I find those numbers?Discussion:1. I was quite surprised to see some figures in the Discussion. I would place figure 1 in the Results section. Also it would be very interesting to add an analysis showing the expected effect of what really happened with the screening program. This would also distinguish your paper from the other two papers I mentioned above.2. I would place figure 2 in the appendix, since you already have Table 2.3. Could you please explain your reasoning in line 376-378? I am not sure if I agree with the statement that your model-estimated reductions in screening tests in the first year of disruption overlapped with observed rates. Table 2 show that in Q1-Q3 still 82% of the tests were performed for breast cancer, while with a 3 months disruption only 53% of the test will be performed in April 2020-March 2021. I do not think those numbers overlap. The numbers are also not very comparable as the observed numbers are presented for Jan-Sept 2020 and the modelled numbers for April 2020-March 2021. It would be nice if you can take the same periods.4. In line 413-414 you mention that your findings illustrate that maintaining screening participation is critical to reducing the burden of cancer at a population level. Could you please explain your reasoning? I believe that the numbers show that screening can safely be suspended for a few months if there are limited resources available. Only if you suspended the program for a year, problems may occur.Reviewer #3 (Recommendations for the authors):While the presented results by assessing various disrupted screening schedules  through statistical modelling are very helpful for guiding policy-makers to avert long-term impact on cause-specific mortality and cost for example in the post-pandemic COVID-19 era, the manuscript can be substantially improved in the light of the following suggestions.1. It is very important to report the influences of COVID-19 pandemic on long-term mortality resulting from the disrupted screening schedule although the reported outcomes here have already included the reduction of screen-detected cancers and the assessment of upstaging. The author has already indicated the unfeasibility of reporting mortality estimates because of unavailable information on COVID-19 disruptions to cancer treatment. However, since your modelling has assumed many situations on various delayed screening schedule it would not be difficult to model mortality estimates by setting various circumstances on COVID-19 disruptions to cancer treatment.2. While modelling screening episodes by various disrupted screening schedules plays an important role in policy analysis it is even important to report the empirical results of the delayed screening schedule until 31 March 2021 instead of only reporting the observed number from January to September 2020. You can still report your simulated results for policy analysis but report which scenario, on average, is close to your empirical estimate. So doing is very helpful for predicting the best-case outcomes after 2021.3. It is quite unreasonable to have seen reduced interval cancers and the reduced program sensitivity with the disruption scenario from the aspect of screening theory. If disruption to screening happen those cancers would be delayed to have diagnosis. It is therefore inadequate to define interval cancer in this manner in the face of disruption scenario, saying an artefact of reduced screening participation. The reader is very confused with such a kind of results. As interval cancer is often a negative outcome in the theory of screening, the results of reducing interval cancer as a result of disruption to screening would be very misleading. The longer disruption the less likely to have interval cancer. This part should be rectified to have adequate presentation.4. Instead of reporting absolute number of upstaging with different expressions for various cancers, it is indispensable to report relative risk of having upstaging for various disruption scenarios as opposed to status quo by breast cancer, colorectal cancer, and cervical cancer base on the modelling results. So doing this can be also of assistance to evaluate statistical significance by reporting 95% confidence interval and also P value for hypothesis testing.5. The results of upstaging are so important for this manuscript particularly when mortality estimates have been not reported. However, the modelling results on upstaging seems to vary with disruption scenarios. This part has to be emphasized in the main conclusions for different cancer. Again, using the relative risk estimates would make the comparisons of the finding across three sites of cancer comparable.6. The point 2 mentioned above is also important for validating the simulated results of screen-detected cases, upstaging and other outcomes with the scenario in commensuration with the empirical scenario of disruption to screening. Reviewer #1 (Recommendations for the authors):Thank you for the nicely written paper. I still have a few comments.Overall:1. I recently read two other papers which used the Policy1 modelling platforms to estimate the effect of disruptions in the bowel and cervical screening program on cancer outcomes. I assume that you are familiar with those studies as some of your co-auteurs are also listed on them. I am talking about the following two papers: "Impact of the COVID-19 pandemic on faecal immunochemical test-based colorectal cancer screening programmes in Australia, Canada, and the Netherlands: a comparative modelling study" and "Impact of disruptions and recovery for established cervical screening programs across a range of high-income country program designs, using COVID-19 as an example: A modelled analysis".Could you please explain the difference between the current study and the two studies I listed above? Why did the current study need to be performed? As you already had the other two studies, which look rather similar to the current study.Modelled scenarios in the current paper (as noted in the first paragraph of the methods section) were developed after consultation with the Australian Government Department of Health and input from stakeholders to ensure they are relevant to the local context. Although some of the scenarios explored in the current paper have been harnessed in prior evaluation studies, as mentioned by the reviewer, they were aimed at exploring how the effect of standardised disruptions scenarios might vary depending on country-specific factors, such as program design. Additionally, the current paper provides a within-country comparison between three established population cancer screening programs, using comparable time periods and metrics to enable comparisons not possible based on previously published findings. Modelled estimates are also presented against subsequent observed national data. We have addressed these points in the last paragraph of the introduction with the following text:“The current article presents a within-country comparison of key findings, at a national level, between three established population cancer screening programs, using comparable time periods and metrics. Although some of the scenarios explored here have been harnessed in prior cross-country evaluation studies , modelled scenarios in the current paper were developed after consultation with the Australian Government Department of Health and input from stakeholders to ensure they were relevant to the local context.”2. Make sure you write percentages the same throughout the article. Sometimes I see 9.3 and then I see 9·3 (dot in the middle instead of on the bottom).Thank you for pointing that out. This has now been amended.Abstract:1. I miss some information about upstaging. How many percent of the tumours are expected to be upstaged?Additional information on upstaging has been added to Table 3 for bowel and cervical screening (please refer to main article). We have also amended the abstract with the text as shown below:We estimated that a 12-month screening disruption would reduce breast cancer diagnoses (9.3% population-level reduction over 2020-2021) and colorectal cancer (up to 12.1% reduction over 2020-21), and increase cervical cancer diagnoses (up to 3.6% over 2020-2022), with upstaging expected for these cancer types .2. I believe that a suspension of the screening program for 3 months will not lead to significant upstaging. The first sentence of the conclusion might be a bit too harsh, as suspension for only three months might be wise in situations with limited resources.We agree with the reviewer that more significant impacts were predicted for all screening programs when the disruption period was longer than 3 months. We have amended the first line of the Abstract’s conclusion section as shown by the text below:“Findings for 6-12-month disruption scenarios illustrate that maintaining screeningparticipation is critical to preventing an increase in the burden of cancer at a population level.”We would, however, like to point out that for individuals who missed their scheduled bowel screen during the pause, this screen would not be occurring until the next scheduled round. This point has now been added to the methods section under ‘Bowel screening’ as follows:“Individuals with undetected polyps or colorectal cancers (CRC) who missed screening due to the pauses would therefore have these detected in a later year, either symptomatically or at a later screening round. These may be at detected as more advanced disease.”Introduction:1. Nicely written introduction. However, I would like you to mention here the significance of this paper, as there are already two modelling studies using the Policy1 modelling platform.This point has been addressed above (see point 1). The last paragraph of the introduction has been amended to:“The current article presents a within-country comparison of key findings, at a national level, between three established population cancer screening programs, using comparable time periods and metrics. Although some of the scenarios explored here have been harnessed in prior site-specific cross-country evaluation studies , modelled scenarios in the current paper were developed after consultation with the Australian Government Department of Health and input from stakeholders to ensure they were relevant to the local context. We demonstrate what could be estimated by a rapid response evaluation based on information available early in the pandemic, and discuss how these estimates relate to subsequent observed disruptions to screening.”Methods:1. I miss a description of the restart strategy of the screening program. Did the persons with a suspended screen still received their screening when the program restarted? Did the program tried to catch-up on the missed screens by increasing their capacity? The current article nicely describes different restart strategies: Kregting, L. M., Kaljouw, S., de Jonge, L., Jansen, E. E., Peterse, E. F., Heijnsdijk, E. A., … and de Kok, I. M. (2021). Effects of cancer screening restart strategies after COVID-19 disruption. British journal of cancer, 124(9), 1516-1523.For BreastScreen Australia, the restart strategy is determined by the prioritisation module, described in the Table entitled Supplementary file 1A, where clients are prioritised (ranked) according to (i) whether they are newly invited or existing clients, (ii) their age; (iii) in which period their appointment fell (i.e. during the pause or during the recovery period), and (iv) the time elapsed since their originally scheduled appointment. Clients with the highest priority of being allocated an appointment after the pause, were those who were newly invited to join BreastScreen, aged 50-74 years, and most overdue for screening. In contrast, clients with least priority were existing clients who reached the target age range (50-74 years) and whose scheduled appointment fell during the recovery period.The following text has been added to the manuscript, in the methods section, under the section entitled ‘Breast screening’:“Following service resumption the restart strategy involved the allocation of available screens according to (i) whether clients were newly invited or existing, (ii) their age; (iii) the period in which their appointment fell (i.e. during the pause or during the recovery period), and (iv) the time elapsed since their originally scheduled appointment. Appointments were prioritised for women in the target age range of 50-74 years and to clients most overdue for screening.For bowel screening, individuals missing a screen during the pause would be screened at the next screening round, 2 years later. The following text has also been added to the methods section, under ‘Bowel screening’ as follows:“It was assumed that individuals missing a screen during the pause would be screened at the next screening round, 2 years later.”For cervical screening, as noted in the Methods section, we assumed that women who missed screening in 2020, were instead screened in 2021-2022. To further clarify, we have adapted the text in the methods section, under ‘Cervical screening’ by adding the text:“We explored two recovery scenarios, one where women who missed screening in 2020 all attended in 2021 (used with the 9-month disruption scenario), and another where women who missed screening in 2020 attended gradually over 2021-2022 (used with 6- and 12-month disruption scenarios)”.The existing footnote in Table 1 has also been adjusted to further clarify by adding the text:“Scenarios assumes women who miss screening in 2020, instead attend over 2021-2022"2. In line 109 you mention: "60-84% of clients screened on schedule (first to third and subsequent round)". What is meant by this?st -3rd screen, or a screen following that. The sentence has been amended as follows:Thank you for identifying that this is a confusing statement. National BreastScreen data is usually reported according to screening round as follows: first round, second round, third round and subsequent rounds (>3+ rounds). The statement means that 60-84% of women participating in the national screening program, attended their scheduled appointment, whether that appointment corresponded to their 1“Prior to COVID-19, participation by women aged 50-74 was approximately 55%, with 51% of national breast cancers detected through BreastScreen, while the proportion of clients who attended screening on schedule ranged from 60% to 84%, depending on the screening round.”3. In line 178 you mention a "cervical screening test (CST)". Do you mean a HPV-test?Yes, CST is used in Australia to refer to an HPV test but for improved clarity for an international audience, we have replaced CST/ Cervical Screening Test throughout the article with HPV test.Results:1. Table 3 shows the reduction in the number of screen-detected invasive cancers per 100,000 women. I am quite surprised to see that the number of screen-detected tumors after a disruption of 12 months only decreased by 24%, when there was no screening for an entire year and the screening program restarted gradually over the next 6 months. Is there a catch-up in those last 6 months when the normal screening program was again in place?We thank the reviewer for bringing this to our attention. We assumed higher throughput following a 12-month disruption (150% of status quo) than following shorter disruptions (status quo throughput). This was not described previously. We have now amended the methods section of manuscript, under ‘Breast screening’ as follows:“We assumed a gradual restoration of service following pause periods, returning by the seventh month to pre-COVID throughput (screening capacity) for shorter-term pauses (3- and 6 -months) and 150% of pre-COVID throughput for a longer pause (12 months), as a year-long pause would likely involve expanded recovery strategies.”We have amended the relevant text in Table 1 for the Breast screening program as indicated by highlighted text below:12 months: 12-month pause assuming gradual recovery over 6 months, to 50% higher screening capacity than status-quo6 months: 6 month-pause assuming gradual recovery over 6 months, to status-quo screening capacity3 months: 3-month pause assuming gradual recovery over 6 months, to status-quo screening capacityIn the Results section, under ‘Breast cancer screening’ to make this distinction in throughput between scenarios, we added the text as follows:For the two-year period 2020-2021, we estimate that screening pauses of any duration would lead to an overall reduction in population-level invasive breast cancer diagnosis for women aged 50-74 years, noting that the estimated reduction in cancer incidence was comparable (~9%) between a 6-month pause with pre-COVID throughout during recovery and a 12-month pause with 150% throughput during recovery.Similarly, the following additional text was added to the first paragraph of the discussion:For a 6-month screening disruption we estimated reduced diagnoses of breast cancer  and colorectal cancer (up to 5.8% reduction over 2020-21) and increased cervical cancer diagnoses (up to 1.1% over 2020-2022), with less marked upstaging compared to a 12-month disruption.2. Table 3 shows the reduction in the number of interval-detected invasive cancers. Does this concern the interval-detected invasive cancers of women screened in 2020/2021? Or does it concern the number of interval-detected invasive cancers diagnosed in 2020-2021?The number of interval-detected invasive cancers in Table 3 refers to cancers diagnosed in 2020-2021. Footnote c, under Table 3, has now been amended as shown below:c) Breast screening: invasive breast cancers arising within 12 or 27 months of a negative screening episode. Figures reflect interval cancers diagnosed in 2020, or 2020-2021, respectively.”3. In line 259-260 you write that an estimated 759-3022 people would be affected by delays in diagnosis for bowel cancer. Where can I find those numbers?These numbers are the combination of the delayed and upstaged diagnoses, though it differs from the sum of these to avoid double counting those affected by both a delay and an upstage. We have removed this for clarity as we do not feel this output is useful and may be confusing for this manuscript.Discussion:1. I was quite surprised to see some figures in the Discussion. I would place figure 1 in the Results section. Also it would be very interesting to add an analysis showing the expected effect of what really happened with the screening program. This would also distinguish your paper from the other two papers I mentioned above.Figure 1 draws on national data that became publicly available in 2021, well after our evaluation was conducted. Since this figure is not based on results from our study, and to prevent confusion, Figure 1 was placed in the Discussion. Although uncommon, in this particular case we believe the figure should remain in place to enable a better understanding of the timeline the different data became available. We are planning a detailed comparison of modelled and observed outcomes as more observed data becomes available. While this would be interesting, our paper is sufficiently unique.2. I would place figure 2 in the appendix, since you already have Table 2.Figure 2 has now been removed from the main manuscript and converted into supplementary figure 8. .3. Could you please explain your reasoning in line 376-378? I am not sure if I agree with the statement that your model-estimated reductions in screening tests in the first year of disruption overlapped with observed rates. Table 2 show that in Q1-Q3 still 82% of the tests were performed for breast cancer, while with a 3 months disruption only 53% of the test will be performed in April 2020-March 2021. I do not think those numbers overlap. The numbers are also not very comparable as the observed numbers are presented for Jan-Sept 2020 and the modelled numbers for April 2020-March 2021. It would be nice if you can take the same periods.We agree that this statement is not sufficiently supported, and have removed it from the discussion. We will consider generating data for matching periods in a future more detailed comparison of observed and simulated data.4. In line 413-414 you mention that your findings illustrate that maintaining screening participation is critical to reducing the burden of cancer at a population level. Could you please explain your reasoning? I believe that the numbers show that screening can safely be suspended for a few months if there are limited resources available. Only if you suspended the program for a year, problems may occur.This comment is similar to comment 4, which has been addressed above. We have now amended the relevant sentence in the discussion as follows:“Collectively, findings from the 6-12 month disruption scenarios illustrate that maintaining screening participation is critical to preventing an increase in the burden of cancer at a population level.”Reviewer #3 (Recommendations for the authors):While the presented results by assessing various disrupted screening schedules  through statistical modelling are very helpful for guiding policy-makers to avert long-term impact on cause-specific mortality and cost for example in the post-pandemic COVID-19 era, the manuscript can be substantially improved in the light of the following suggestions.1. It is very important to report the influences of COVID-19 pandemic on long-term mortality resulting from the disrupted screening schedule although the reported outcomes here have already included the reduction of screen-detected cancers and the assessment of upstaging. The author has already indicated the unfeasibility of reporting mortality estimates because of unavailable information on COVID-19 disruptions to cancer treatment. However, since your modelling has assumed many situations on various delayed screening schedule it would not be difficult to model mortality estimates by setting various circumstances on COVID-19 disruptions to cancer treatment.We agree with the reviewer that estimated mortality is important, and while this was not within the scope of the rapid-response modelling reporting here, these have now been published for some of the scenarios reported here for bowel and cervical screening, as part of cross-country modelling exercises . We have added the following sentence to the methods section under ‘Reported Outcomes’.12,14Mortality estimates are not reported, however, estimates for some of the scenarios have since been published separately for cervical and bowel cancer screening2. While modelling screening episodes by various disrupted screening schedules plays an important role in policy analysis it is even important to report the empirical results of the delayed screening schedule until 31 March 2021 instead of only reporting the observed number from January to September 2020. You can still report your simulated results for policy analysis but report which scenario, on average, is close to your empirical estimate. So doing is very helpful for predicting the best-case outcomes after 2021.We have included extensive discussion of the subsequent real-world decisions and developments in the relation to all three screening programs as part of the Discussion section. To address the reviewer’s point, we have now added the following text in the discussion under the section entitled ‘Subsequent observed outcomes’ to indicate that the most conservative modelled scenarios were closest to observed data:26, an estimated 7% reduction in bowel screening participation,26 and for cervical screening, based on reimbursement claims data, a 10% reduction in expected HPV tests.27-28’‘For the year 2020, observed reductions in participation were lower than the most conservative estimates modelled for each program, with 14% fewer breast screening tests3. It is quite unreasonable to have seen reduced interval cancers and the reduced program sensitivity with the disruption scenario from the aspect of screening theory. If disruption to screening happen those cancers would be delayed to have diagnosis. It is therefore inadequate to define interval cancer in this manner in the face of disruption scenario, saying an artefact of reduced screening participation. The reader is very confused with such a kind of results. As interval cancer is often a negative outcome in the theory of screening, the results of reducing interval cancer as a result of disruption to screening would be very misleading. The longer disruption the less likely to have interval cancer. This part should be rectified to have adequate presentation.We agree that this can be confusing, but it would not be appropriate for us to introduce an alternative definition of interval cancers, particularly given that BreastScreen did not change its definitions. We do explain this complication clearly in the second paragraph of the discussion .4. Instead of reporting absolute number of upstaging with different expressions for various cancers, it is indispensable to report relative risk of having upstaging for various disruption scenarios as opposed to status quo by breast cancer, colorectal cancer, and cervical cancer base on the modelling results. So doing this can be also of assistance to evaluate statistical significance by reporting 95% confidence interval and also P value for hypothesis testing.As these are modelled results rather than observed outcomes, we would not apply the measures of error suggested. Therefore, we have not made the suggested change.5. The results of upstaging are so important for this manuscript particularly when mortality estimates have been not reported. However, the modelling results on upstaging seems to vary with disruption scenarios. This part has to be emphasized in the main conclusions for different cancer. Again, using the relative risk estimates would make the comparisons of the finding across three sites of cancer comparable.We have now provided additional information on upstaging of cancers for all disruption scenarios for bowel and cervical cancer screening in Table 3. However, we have not added relative risk estimates as these metrics would differ between cancer types; due to differences in model structures, the breast and bowel models report the proportion of high-grade cancers, and cervical cancer reports the proportion of cancers that were upstaged.6. The point 2 mentioned above is also important for validating the simulated results of screen-detected cases, upstaging and other outcomes with the scenario in commensuration with the empirical scenario of disruption to screening.We agree that further validation of simulated and observed outcomes would be of value, and this will be the subject of a future evaluations.