Loss of KDM6A-mediated genomic instability and metabolic reprogramming regulates response to therapeutic perturbations in bladder cancer

Domain: biology
Published: 2026-01-07
Editorial outcome: major_revision

Abstract
Mutations in epigenetic regulators are common in bladder cancer, yet their impact on therapeutic responses remains unclear. Here, we identify that loss-of-function mutations in KDM6A, a histone demethylase altered in about 26% of advanced bladder cancers, are associated with poor survival after cisplatin chemotherapy, whereas they correlate with improved outcomes with anti-PD-1 therapy. Using CRISPR-Cas9-engineered murine and human bladder cancer models, we show that KDM6A deficiency increases formation of extra-chromosomal circular DNA carrying chemoresistance loci, promoting cisplatin resistance. In parallel, KDM6A loss impairs DNA repair and rewires tumor metabolism, reducing glycolysis and lactate output. This metabolic shift diminishes histone lactylation in regulatory T cells, suppressing immunoregulatory genes and limiting expansion of PD-1hi regulatory T cells. Collectively, our findings establish KDM6A mutation as a key regulator of therapeutic responses, providing a foundation for its use in guiding precision therapy in advanced bladder cancer.

Introduction
Bladder cancer is the sixth most commonly occurring cancer in the United States, with the five-year overall survival (OS) for advanced bladder cancer being less than 10%1. Cisplatin-based chemotherapy has been a mainstay of therapy for many decades; however the advent of immune checkpoint therapy (ICT) and targeted therapy has changed the current landscape of treatment for bladder cancer2,3. While the availability of different therapeutic agents such as chemotherapy, ICT and targeted therapy, either as single agents or in combination, has significantly improved outcomes, there remains a lack of biological insight into selecting and sequencing these therapies based on patient attributes to develop a personalized treatment algorithm.

The genes encoding key epigenetic regulators are frequently mutated in bladder cancer4,5. These epigenetic factors orchestrate gene expression, impacting multiple pathways governing cellular phenotype and function6,7. While the impact of mutations in epigenetic factors on initiation and progression of bladder tumorigenesis has been studied8–10, the mechanistic insight into the roles of these mutations in regulating response to therapeutic agents remains largely unexplored. Lysine Demethylase 6A (KDM6A) is a commonly mutated gene in bladder cancer, and approximately 26% of patients with muscle-invasive bladder cancer harbor KDM6A mutations5. KDM6A catalyzes the demethylation of H3K27 di- and tri-methyl (H3K27me2/me3) marks which are repressive histone post-translational modifications (PTMs), thereby promoting transcriptional activation11,12.

Here, we investigate how inactivating mutations in KDM6A, a frequently occurring mutation in advanced bladder cancer, regulate responses to therapeutic perturbations including chemotherapy and ICT in bladder cancer. Retrospective analyses of bladder cancer patient cohorts reveal that loss-of-function mutations in KDM6A, a histone demethylase frequently mutated in approximately 26% of advanced bladder cancer cases, are significantly associated with reduced overall survival (OS) following cisplatin-based chemotherapy.

Extrachromosomal circular DNA (eccDNA) has recently emerged as a key mediator of chemoresistance across cancer types. Analysis of the TCGA-BLCA cohort13 reveals that tumors harboring KDM6A mutations contained a higher abundance of circular amplicons and upregulated the expression of POLQ14,15, a key enzyme in eccDNA biogenesis. Consistent with these observations, whole-genome sequencing (WGS) of human KDM6A-knockout bladder cancer cells demonstrate genome-wide copy number gains, particularly across chromosomes 2, 3, and 7, indicating increased regional amplifications upon KDM6A loss. Moreover, WGS identify multiple eccDNA amplicons encompassing oncogenes and chemoresistance-associated loci, including TP6316–18, CLDN419,20, GLI221–24, LUC7L325,26, ERCC427, and SHCBP128–30. Together, our data identify KDM6A loss as a key driver of eccDNA accumulation and oncogene amplification, establishing a mechanistic link between chromatin dysregulation and cisplatin resistance in bladder cancer, and highlighting KDM6A as a potential biomarker for predicting response to cisplatin-based chemotherapy.

To investigate whether KDM6A loss confers differential sensitivity to other therapeutic modalities, we analyze outcomes in patients treated with immune checkpoint blockade. In contrast to the poor response to cisplatin, patients harboring KDM6A mutations exhibit improved OS following anti-PD-1/anti-PD-L1 therapy, suggesting a context-dependent role for KDM6A in modulating therapeutic efficacy. Supporting this observation, KDM6A-mutant tumors display elevated tumor mutation burden (TMB). Mechanistic interrogation in both human and murine models reveal that KDM6A directly regulates the transcription of genes involved in mismatch repair (MMR) and double strand break repair (DSBR). Loss of KDM6A result in transcriptional repression of DNA repair programs, potentially augmenting neoantigen load and enhancing tumor immunogenicity, thereby contributing to increased responsiveness to immune checkpoint inhibitors.

In parallel, our work reveals that KDM6A is critical for tumor metabolic shift that ultimately modifies the tumor immune microenvironment in bladder cancer. Loss of KDM6A suppress glycolytic flux and decrease intratumoral lactate accumulation, thereby attenuating histone lactylation (H3K9la and H3K18la) in regulatory T cells (Tregs). This epigenetic modification results in downregulation of key immunosuppressive genes, including Foxp3, Tgfb, and Pdcd1, and impair PD-1 expression on Tregs. Consequently, the expansion of PD-1hi Tregs was limited following anti–PD-1 therapy, enhancing the effector CD8+ T cell-to-Treg ratio and improving anti-tumor immune responses in Kdm6a-deficient tumor-bearing mice. Building on the recognized role of lactate in Treg metabolism, our study links KDM6A-driven metabolic remodeling to histone lactylation–mediated suppression of Treg function within the tumor microenvironment.

Collectively, these findings establish KDM6A as a central epigenetic regulator integrating genomic stability, metabolic state, and immunologic context to govern therapeutic responsiveness in bladder cancer. By delineating distinct mechanisms through which KDM6A mutations mediate resistance to cisplatin-based chemotherapy and enhanced sensitivity to anti-PD-1/anti-PD-L1 therapy, this study offers a rational framework for biomarker-driven patient stratification and the development of precision therapeutic strategies in KDM6A-deficient bladder cancer.

Results
Inactivating KDM6A mutations are associated with reduced overall survival following cisplatin therapy with concurrent accumulations in extrachromosomal DNA formation
To understand how inactivating mutations in KDM6A impact responses to therapeutic perturbations in bladder cancer, we performed retrospective analyses of data from patients with resectable and advanced bladder cancer receiving cisplatin-based chemotherapy31,32. Our analysis showed that patients with KDM6A mutations (KDM6A-Mut) have reduced OS compared to those without the mutation (KDM6A-WT) (Fig. 1A). To delineate the role of KDM6A in regulating responses to cisplatin therapy, we used CRISPR-Cas9 to excise Kdm6a from the murine MB49 bladder cancer cell line and generated independent KDM6A-KO cell clones (sgKdm6a C1-C3) (Supplementary Fig. 1A). Consistent with the findings from the clinical cohorts, mice harboring sgKdm6a tumors showed resistance to cisplatin-based chemotherapy compared to mice harboring control (sgScramble) tumors (Fig. 1B, Supplementary Fig. 1C). To further investigate the differential impact of cisplatin on sgScramble versus sgKdm6a cell lines, we treated the cell lines with cisplatin in-vitro and noted significantly higher cisplatin-induced cytotoxicity in sgScramble cells compared to sgKdm6a cells (Fig. 1C, Supplementary Fig. 1D). Additionally, sgKdm6a cells demonstrated higher invasion, migration and spheroid forming potential in response to cisplatin, compared to sgScramble cells, indicating reduced sensitivity of Kdm6a-deleted murine bladder cancer cell line to cisplatin (Fig. 1D, F, Supplementary Fig. 1F, H, J). To validate the cisplatin-resistant phenotype of KDM6A-mutant murine bladder cancer cells, we extended our analysis to two human bladder cancer cell lines, RT4 and ScaBER. We performed CRISPR-Cas9 mediated knockout of KDM6A in the cell lines, with the resulting isogenic counterparts designated as K2 and B7, respectively, for subsequent analysis (Supplementary Fig. 1B). In line with our findings in murine cells, K2 and B7 cells also exhibited reduced cisplatin-induced cytotoxicity along with greater invasion, migration and spheroid formation than RT4 and ScaBER cells, respectively (Fig. 1C, E, G, Supplementary Fig. 1E, G, I, K). Importantly, we observed no change in cellular proliferation rate between the control and KDM6A knockout cells over the treatment period (Supplementary Fig. 1L).

Notably, extrachromosomal circular DNA (eccDNA) has recently emerged as a key player driving resistance to cisplatin-based chemotherapy33–35. Therefore, we compared eccDNA levels between sgScramble and sgKdm6a cells. Purification of eccDNA followed by rolling-circle amplification demonstrated a higher abundance of eccDNA in sgKdm6a cells (Supplementary Fig. 2A). Importantly, analysis of chromosomal structural variants in the TCGA dataset13 also demonstrated that patients harboring KDM6A mutation exhibited more circular amplicons (eccDNA) (Supplementary Fig. 2B), with consistent upregulation of POLQ expression14,15 (Supplementary Fig. 2C). Importantly, we noted that eccDNA derived from patients with KDM6A mutation selectively amplify SPP1, SHCBP1 and EGFR genes, strongly associated with cisplatin-resistance28,29,36–38 (Supplementary Fig. 2D).

To further substantiate the role of KDM6A loss in eccDNA generation and cisplatin resistance, we performed Whole Genome Sequencing (WGS) of RT4 and KDM6A KO RT4 (K2) cell lines. We identified genome wide increase in copy numbers in various regions specifically in chromosomes 2, 3 and 7 in K2 cells (Fig. 1H). Analysis of the circular-amplicons using Circle-map showed multiple circular amplicons carrying critical genes implicated in promoting cisplatin resistance including TP6316–18, CLDN419,20, GLI221–24, LUC7L325,26, ERCC427 and SHCBP128–30 (Fig. 1I, Supplementary Fig. 2E). Further scatter plots for the eccDNA gene loci in these chromosomes demonstrated increased copy numbers in K2 cells than RT4 cells (Fig. 1I, Supplementary Fig. 2F). Cumulatively, these findings indicate that the loss of KDM6A confers resistance to cisplatin-based chemotherapy through accumulation of eccDNA harboring oncogenes linked to cisplatin resistance and may serve as a potential biomarker of cisplatin-based chemotherapy resistance in advanced bladder cancer.

Loss of KDM6A impairs the MMR machinery and correlates with improved overall survival following immune checkpoint therapy
Given the association between KDM6A loss and cisplatin resistance, we next investigated whether KDM6A mutations impact the response to immune checkpoint therapy (ICT), a key therapeutic modality for patients with advanced bladder cancer. Analyses of the IMVigor210 cohort (N=275) where patients with advanced bladder cancer received anti-PD-L1 therapy39 demonstrated that patients harboring the KDM6A mutation had significantly improved OS in response to anti-PD-L1 therapy compared to patients without KDM6A mutation (Fig. 2A). Analyses of another cohort40 of patients with advanced bladder cancer receiving ICT (MSK_2018, N=148) also showed improved OS in KDM6A-Mut patients (Supplementary Fig. 3A). Further, consistent with the findings from the clinical cohorts, deletion of Kdm6a in murine bladder cancer cell line attenuated tumor growth following anti-PD-1 therapy in tumor-bearing mice (Fig. 2B, Supplementary Fig. 3B) confirming the association of inactivating KDM6A mutations with improved response to anti-PD-1 therapy.

Additionally, we noted higher tumor mutation burden (TMB) in patients with KDM6A mutations, (Fig. 2C). Since higher TMB is frequently driven by mutations in genes encoding enzymes involved in DNA MMR41, we investigated the frequency of MMR gene mutations in the TCGA bladder cancer patient cohort (N=412). However, we did not note any co occurrence of the KDM6A mutation with mutations in the genes associated with the MMR pathway in these patients (Fig. 2D). Notably, analyses of the HCRN dataset42 demonstrated decreased expression of several critical genes involved in the MMR pathway including MSH2 and MSH6 in patients harboring the KDM6A mutation indicating an attenuated MMR machinery in these patients (Supplementary Fig. 3C). This highlighted a distinct KDM6A-mediated pathway regulating genes associated with the MMR machinery and TMB.

Next, to investigate the mechanisms by which KDM6A regulates the expression of genes involved in the MMR pathway in bladder cancer, we performed chromatin immunoprecipitation sequencing (ChIP-seq) of sgScramble and sgKdm6a cells. We noted direct KDM6A binding to multiple MMR genes including Msh2 and Msh6, in sgScramble cells with lack of binding of KDM6A to these genes following the loss of Kdm6a in sgKdm6a cells (Fig. 2E, Supplementary Fig. 3D, F, G, I). Importantly, we also observed enrichment of H3K27me3 marks with concurrent attenuation of H3K4me3 enrichment in these genes in the absence of KDM6A (Fig. 2E, F, Supplementary Fig. 3E–I).

We used RNA-sequencing (RNA-seq) based gene expression studies to confirm the downregulation of the MMR genes in sgKdm6a cells (Fig. 2G, Supplementary Fig. 3J), mirroring the findings from the patient cohorts, suggesting KDM6A- and H3K4me3-mediated regulation of these genes. To confirm the reduction in MMR enzyme expression at the protein level, we performed flow cytometry. This analysis revealed decreased expression of MSH2 and MSH6 in sgKdm6a cells compared to sgScramble cells (Fig. 2H). Consistent with these observations, both K2 and B7 cell lines exhibited an attenuation of MSH2 and MSH6 in both transcript and protein levels (Fig. 2I, Supplementary Fig. 4A-C). Further investigation using ChIP-qPCR in the RT4 and K2 cell lines demonstrated a reduced binding of KDM6A to specific MMR genes including MLH1 and MSH6 in K2 cells (Supplementary Fig. 4D), Additionally, we noted a reduced enrichment of the H3K4me3 in MLH1, MSH6 and MSH2 genes in absence of KDM6A (Supplementary Fig. 4E) which further solidified our data in murine sgKdm6a clones.

Disrupted MMR pathway is associated with microsatellite instability and an MSI-high phenotype43,44. Therefore, to determine the functional impact of the decreased expression of MMR genes in sgKdm6a tumor cells, we compared microsatellite instability (MSI) in sgScramble versus sgKdm6a MB49 tumors. We used fluorescent fragment length analysis (FFLA) to compare the microsatellite instability in sgScramble (N=7) and sgKdm6a (N=7) tumors. FFLA involves labeling DNA fragments with fluorescent dyes and analyzing their sizes and distribution via capillary electrophoresis. Importantly, analysis of electropherogram peaks revealed a significant left shift of 5 nucleotides in the modal (tallest) peak in sgKdm6a tumors compared to sgScramble tumors indicating contraction of the mBAT-64 microsatellite length and genetic instability (Fig. 2J, Supplementary Fig. 4F). Similarly, we noted a right shift of 5 nucleotides in the modal peak in K2 cells compared to RT4 cells indicating expansion of the NR21 microsatellite (Supplementary Fig. 4G, H). Cumulatively, these findings indicate that KDM6A modulates the epigenetic landscape by altering the enrichment of repressive H3K27me3 and active H3K4me3 marks at the promoter regions of DNA MMR genes thus regulating their expression and microsatellite instability in murine and human bladder cancer cells.

The MMR-deficient, MSI-h phenotype has been associated with improved responses to ICT45,46 and reduced sensitivity to platinum-based chemotherapy47–50. Therefore, attenuated MMR activity following the loss of KDM6A could account for improved response to ICT while decreasing sensitivity to cisplatin.

Loss of KDM6A impairs the double-stranded break repair pathway
In addition to reduced expression of genes involved in the MMR pathway following the loss of KDM6A, we observed lower expression of EXO1, a gene involved in DSBR in bladder cancer patients harboring KDM6A mutations42 (Supplementary Fig. 5A). Importantly, we did not observe any co occurrence of the KDM6A mutation with mutations in the DSBR genes in these patients suggesting an independent role of KDM6A in regulating the expression of DSBR genes (Supplementary Fig. 5B).

Next, we analyzed the murine ChIP-seq data to investigate the impact of KDM6A loss on genes involved in DSBR. Our results showed a reduction in KDM6A binding in multiple genes involved in DSBR pathways including Exo1 and Lig3 in sgKdm6a cells indicating KDM6A-mediated regulation of these genes (Fig. 3A, Supplementary Fig. 5C, D, F, G). We also observed an increase in H3K27me3 (Supplementary Fig. 5E) with concurrent decrease in H3K4me3 enrichment across these genes (Fig. 3A, B, Supplementary Fig. 5C, D, F, G). RNA-seq analysis also demonstrated a reduction in the expression of these DSBR genes in sgKdm6a cells, confirming the findings from the ChIP-seq data (Fig. 3C, Supplementary Fig. 5H). Further, K2 and B7 cells demonstrated significantly lower expression of several genes involved in DSBR, compared to RT4 and ScaBER cells respectively (Supplementary Fig. 5I, J). Additionally, ChIP qPCR analysis in RT4 and K2 showed reduced KDM6A binding in multiple DSBR related genes like BAZ1B, EXO1 and LIG3 (Supplementary Fig. 5K) with simultaneous reduction in H3K4me3 enrichment at these gene promoters (Supplementary Fig. 5L). We also noted no significant differences in cell cycle distribution and cell proliferation rate between the control and the KDM6A knockout cell lines, suggesting that the reduction in DSBR gene expression is not secondary to altered proliferation or cell cycle phase distribution (Supplementary Figs. 1L, 5M).

We also assessed EXO1 protein expression levels in sgScramble and sgKdm6a tumors through immunohistochemical (IHC) staining and imaging of murine tumor sections. This revealed a significant decrease in EXO1 protein expression in sgKdm6a tumors (Fig. 3D). To elucidate the functional implications of reduction in EXO1 expression, we conducted several studies to compare genomic integrity between sgScramble and sgKdm6a cells. We first assessed the levels of Gamma (phosphorylated)-H2AX (gamma-H2AX), between sgScramble and sgKdm6a cells. Gamma-H2AX is a surrogate marker for DNA damage and forms foci at sites of DNA double-strand breaks51. The phosphorylation of H2AX at these sites is an early and sensitive indicator of DNA damage response. Confocal microscopy and flow cytometry studies revealed a significantly higher abundance of gamma-H2AX foci in both untreated and Etoposide (DNA damage inducer)-treated sgKdm6a cells compared to treated sgScramble cells (Fig. 3E, Supplementary Fig. 5N). These findings were also validated in sgKdm6a tumor samples (Fig. 3F) as well as in the human cell lines RT4 and K2 (Fig. 3H). Additionally, the COMET assay, which detects DNA damage in individual cells by measuring the migration of fragmented DNA during electrophoresis resulting in a comet-like appearance with a head (intact DNA) and tail (damaged DNA) was used to directly assess DNA breaks52. The results indicated significantly higher tail DNA percentage, olive tail moment, and tail moment in sgKdm6a cells and K2 cell with KDM6A loss, demonstrating increased DNA damage (Fig. 3G, I). Collectively, these data demonstrated that loss of KDM6A impairs DSBR, thereby compromising DNA integrity in bladder cancer cells.

KDM6A drives glycolysis and lactate production in bladder cancer cells
Previous research has shown that activation of DDR pathways can induce metabolic shifts, including enhanced glycolytic activity to support the Pentose-Phosphate-Pathway and generate nucleotide precursors53–55. In alignment with reduced expression of genes associated with DDR pathways in KDM6A-Mut patients, we noted attenuated expression of several genes involved in glycolysis and lactate production, including LDHA, PGAM1, and HK2 in KDM6A-Mut patients from IMvigor210 cohort along with LDHA and ENO1 from TCGA-BLCA cohort dataset5 (Fig. 4A, Supplementary Fig. 6A). Further, murine ChIP-seq data demonstrated reduced KDM6A binding, increased H3K27me3 enrichment and concurrent depletion of H3K4me3 enrichment at glycolysis associated genes in sgKdm6a cells (Fig. 4B, C, Supplementary Fig. 6B-F). Geneset enrichment analysis (GSEA) of H3K4me3 peaks differentially enriched between sgScramble and sgKdm6a cells also showed significant downregulation of the glycolysis pathway with concurrent upregulation of the oxidative phosphorylation (OXPHOS) pathway (Supplementary Fig. 6G). RNA-seq further corroborated these findings by showing decreased expression of these glycolysis genes in sgKDM6A cells compared to sgScramble cells (Fig. 4D, Supplementary Fig. 6H). Consistent decrease in expression of crucial glycolytic pathway genes HK2, LDHA and ENO2 was observed in human bladder cancer cells K2 and B7 with KDM6A excision (Fig. 4E, Supplementary Fig. 6J). ChIP-qPCR analysis in RT4 and K2 cells further demonstrated reduced KDM6A binding and decreased enrichment of H3K4me3 at the promoters of these genes in K2 cells (Supplementary Fig. 6K, L). Together, these findings indicate downregulation of the glycolysis pathway in tumor cells following Kdm6a deletion.

To delineate the functional impact of decreased expression of glycolysis genes in sgKdm6a bladder cancer cells, we compared the metabolic profiles of control and KDM6A Knockout cells using the Seahorse Extracellular Flux Analyzer-based ATP Rate Determination Assay. The data demonstrated reduced glycolytic ATP production rates in sgKdm6a cells as well as in K2 cells with KDM6A deletion compared to sgScramble and RT4 cells respectively with concurrent upregulation of mitochondrial OXPHOS (Fig. 4F-I). Given that downregulation of aerobic glycolysis attenuates lactate production, we measured lactate output in the culture supernatants of sgScramble, sgKdm6a, RT4, and K2 cells. Our results showed reduced lactate accumulation in the sgKdm6a and K2 cell lines (Fig. 4J, K). To verify whether these in-vitro findings are recapitulated in-vivo, we quantified lactate levels in the tumor interstitial fluid (TIF) of sgScramble and sgKdm6a tumors. We noted significantly lower accumulation of lactate in the sgKdm6a TIF, normalized to tumor weight (Fig. 4L, Supplementary Fig. 6I), demonstrating that the absence of KDM6A attenuates glycolysis and lactate production in bladder tumors. Importantly, the observed upregulation of the OXPHOS pathway in the cisplatin resistant sgKdm6a cells is in alignment with previous studies associating a switch from glycolysis to OXPHOS pathways with cisplatin resistance56,57. Cumulatively, these findings highlight the role of KDM6A as a major driver of metabolic pathways in bladder cancer and underscore the association between KDM6A deficiency and reduced lactate production by tumor cells.

Loss of KDM6A-mediated reduction in intratumoral lactate levels reduce Treg abundance and function
Lactate produced by tumor cells has been associated with immune suppression across various tumor types58. Consequently, we investigated how reduced lactate accumulation in sgKdm6a tumors affect the tumor immune microenvironment and response to anti-PD-1 therapy.

Mass cytometry (CyTOF)-based immunophenotyping of sgScramble and sgKdm6a tumors followed by unsupervised clustering and t-SNE analyses of CD45+ immune cell subsets revealed the presence of distinct T cells, B cells, NK cells, and immune-suppressive myeloid cell clusters in the TME (Fig. 5A, Supplementary Fig. 7A). Further, this analysis revealed that sgScramble tumors, which have higher levels of intratumoral lactate, harbor a higher abundance of TGFβ+CD44+ LY6C+ immune-suppressive myeloid cells compared to sgKdm6a tumors (Fig. 5B). Additionally, sgScramble tumors showed a higher abundance of Tregs and increased expression of PD-1 in Tregs compared to sgKdm6a tumors (Fig. 5B, D). Notably, following anti-PD-1 therapy, the Treg population significantly expanded in sgScramble tumors, while this expansion was not observed in sgKdm6a tumors (Fig. 5C). This differential expansion of Tregs resulted in a higher T-effector to Treg ratio in sgKdm6a tumors, indicating a preferential pro-inflammatory shift in the tumor immune microenvironment following anti-PD-1 therapy thus improving responses to ICT (Fig. 5E, F).

Next, to determine the impact of lactate specifically on Tregs, we performed a pHRodo-based lactic acid uptake assay which confirmed that Tregs can uptake exogenous lactic acid (La) (Supplementary Fig. 7B). This is in alignment with a previous study that demonstrated that Tregs can utilize exogenous lactate to maintain its functions59. We also noted that treatment with exogenous sodium lactate (Na-La) increased PD-1 expression in Tregs (Fig. 5G, Supplementary Fig. 7C, D). Importantly, when Tregs were treated with supernatants from sgScramble and sgKdm6a cells, those treated with sgKdm6a supernatants had lower PD-1 expression and fewer PD-1+ Tregs linked to the lower lactate levels in the sgKdm6a supernatants (Fig. 5H, Supplementary Fig. 7E, F). Together, these findings demonstrated that loss of KDM6A decreased glycolysis and lactate production in tumor cells which subsequently reduced PD-1 expression and expansion of PD-1hi intratumoral Tregs following anti-PD1 therapy thus improving the efficacy of anti-PD-1 therapy. In addition to reduction in PD-1 expression, TGFβ expression was also notably reduced in sgKdm6a supernatant treated Tregs (Fig. 5H). Production of TGFβ is one of the mechanisms through which Tregs exert their immunosuppression60. Therefore, to determine the impact of extracellular lactate levels on the immune-suppressive function of Tregs, we conducted in-vitro T cell suppression assays. We noted decreased proliferation of CD4+ CD25- T conventional cells (Tconv) when co-cultured with sgScramble supernatant-treated Tregs compared to sgKdm6a supernatant-treated Tregs indicating attenuated suppressive potential of the sgKdm6a Tregs (Fig. 5I, Supplementary Fig. 7G). Cumulatively, these findings demonstrate how tumor cell-specific KDM6A regulates the intratumoral metabolic milieu, which in turn alters the tumor immune microenvironment, including PD-1 expression in Tregs and their immune-suppressive function regulating efficacy of anti-PD-1 therapy in bladder cancer.

Loss of KDM6A in tumor cells impairs lactate mediated histone lactylation and function of regulatory T cells
We and others have demonstrated the role of lactate-derived histone lactylation in the regulation of phenotype and function of immune cell subsets including macrophages and CD8 T cells61,62. To determine lactate-mediated epigenetic changes in Tregs, we performed high-performance liquid chromatography (HPLC)–tandem mass spectrometry (MS/MS) analysis of Lys-C digested core histones isolated from murine Tregs. We noted that addition of 13C labelled Na-La increased the abundance of H3 histone lysine lactylation (Supplementary Fig. 8A). Additionally, we observed 13C incorporation in H3K18la confirming the role of exogenous lactate in driving histone lactylation in Tregs (Supplementary Fig. 8B, C). We have previously highlighted the importance of two specific histone H3 lactylation sites - H3K18la and H3K9la in regulating CD8 T cell function and phenotype62. To understand the relevance of H3K9la and H3K18la in Tregs, we performed flow-cytometry assays to determine the impact Na-La on H3K9la and H3K18la levels which demonstrated dose dependent increase in enrichment of H3K9la and H3K18la in Tregs following treatment with Na-La (Fig. 6A, Supplementary Fig. 8D-F). Further, to determine the impact of H3K9la and H3K18la on chromatin states in Tregs, we performed ChIP-seq studies followed by ChromHMM analysis. This analysis revealed that chromatin regions marked by H3K9la and H3K18la are also enriched for other transcription initiation marks including H3K4me3, H3K9ac and H3K27ac in Tregs (Supplementary Fig. 8G). Further, genomic regions enriched with H3K9la and H3K18la marks were identified as transcription start sites (TSS), TSS proximal regions and CpG islands (Supplementary Fig. 8G). Importantly, treatment with Na-La resulted in the enrichment of H3K9la and H3K18la marks at the TSS indicating a role of exogenous lactate-derived H3K9la and H3K18la in activating gene transcription (Fig. 6B). However, treatment with Na-La did not induce H3K4me3 or H3K27ac enrichment in the promoter regions, indicating selective regulation of H3K9la and H3K18la by exogenous lactate (Fig. 6B). Next, we annotated the genes showing differential enrichment of H3K9la and H3K18la following treatment with exogenous lactate (Fig. 6C). We noted H3K9la and H3K18la enrichment in multiple genes implicated in the regulation of Treg identity and function including Tgfb, Il10, Helios, Pdcd1, Lag3, Havcr2 and Irf4 in Na-La treated Tregs (Fig. 6C, Supplementary Fig. 8H). ChIP-qPCR and qPCR studies on Na-La treated Tregs also demonstrated higher H3K9la and H3K18la enrichment with concurrent increase in the expression of genes regulating Treg identity and function, including Foxp3, Pdcd1 and Tgfβ1, corroborating a role of exogenous lactate-derived H3K9la and H3K18la in regulating gene expression in Tregs (Fig. 6D, Supplementary Fig. 8I).

Next, considering the low intratumoral lactate content in sgKdm6a tumors (Supplementary Fig. 9A), we interrogated the levels of histone lactylation in intratumoral Tregs and noted significantly lower H3K9la and H3K18la in Tregs derived from sgKdm6a tumors (Fig. 6E, Supplementary Fig. 9B). Further, to directly implicate differences in lactate production by sgScramble and sgKdm6a bladder cancer cells as a major regulator of H3K9la and H3K18la in Tregs, we cultured Tregs in the presence of culture supernatants derived from sgScramble and sgKdm6a cells. Flow cytometry demonstrated significantly lower enrichment of H3K9la and H3K18la in Tregs cultured with KDM6A-deficient supernatants compared to control supernatants, linked to their respective lactate content (Fig. 6F, Supplementary Fig. 9C, D). Importantly, ChIP-qPCR studies demonstrated attenuated H3K9la and H3K18la enrichment in Foxp3, Tgfβ1 and Pdcd1 in Tregs cultured with sgKdm6a cell supernatant (Supplementary Fig. 9E). Additionally, qPCR studies confirmed reduced expression of these genes, establishing a direct association between extracellular lactate content, H3K9la/H3K18la enrichment, and gene expression including that of Pdcd1 in Tregs (Fig. 6G). Overall, these findings identified a critical role of the exogenous lactate derived histone PTM-histone lactylation in regulating the expression of critical Treg specific genes, thus regulating their function and response to immunotherapy in sgKdm6a tumors.

Methods
Cell lines (CRISPR-Cas9 mediated deletion of Kdm6a)
For knockout murine cell line generation:
CRISPR-Cas9 technology was used to establish Kdm6a knockout (KO) lines in MB49 cells (MDACC Functional Genomics Core). Single guide RNA was subcloned into LentiCRISPR v2 vector (AddGene #52961). The following mKdm6a gRNA sequence: TAGCATTATCTGCATACCAG, TTCCTCATCACCGAAAGCGG and TTCGTAGCAGCGAA CAGCCT were used. LentiCRISPR v2 gRNA was transfected into MB49 cells using the Jet Prime transfection reagent (VWR, 89129-924). 2 days after transfection, 2ug/ml puromycin was used to remove non-transfected cells. Multiple single cell mKdm6a KO clones (C1-C3) were screened by western blots and genomic DNA sequencing and authenticated by ATCC mouse cell line STR testing.

For control murine cell line generation:
LentiCRISPR V2 vector only was co-transfected with pMD2.G and psPAX2-D64V into 293T cells and the virus containing medium were collected 48h after transfection and used for cell transduction.
Cells were transduced with lentivirus and replaced with medium containing 2ug/mL puromycin after 72h of transduction. Cells were cultured with puromycin medium for 5 days and replaced medium every two days. Once reached confluent in 10cm dish, cells were harvested for frozen stock.

Murine cell line maintenance: Cells were maintained in DMEM medium supplemented with 10% FBS at 37°C and 5% CO2.

Human cell lines:
CRISPR-Cas9 mediated knockout in RT4 and ScaBER cell lines were performed following previously described protocol8. Briefly, KDM6A knockout sgRNAs (GGTATGCAGATAATGCTGAA, ACAGTTTA CAGTCTGACTAC) were cloned into lentiCRISPR v2 nickase vector. Lentiviral plasmids were cotransfected with psPAX2 and pMD2.G into HEK293T cells to generate lentiviral particles and used for target cell transduction. Transduced cells bearing CRISPR vectors were cultured in a selective growth medium containing puromycin and single clones were screened for KDM6A expression by immunoblot. Clones with no detectable KDM6A expression were then selected for subsequent experiments. The RT4 and ScaBER cell lines were authenticated by STR testing.

Human cell line maintenance: RT4 and K2 were cultured in MEM media and SCaBER and B7 were cultured in DMEM media supplemented with 10% FBS at 37°C and 5% CO2.

Mice
This study used age and sex matched 5–7-weeks old C57BL/6 mice purchased from National Cancer Institute (NCI) for experiments. Mice were housed and sustained in pathogen-free conditions, 20-25°C, 30-70% humidity and on a 12-hour light/12-hour dark cycle at the Animal Resource Center, The University of Texas MD Anderson Cancer Center (MDACC) Institutional Animal Care and Use Committee approved all animal protocols (Protocol number 00002153-RN01). Sex was not considered in the study design. The study involves understanding the role of KDM6A in the regulation of response to therapeutic perturbations in bladder cancer. Therefore sex and gender based analyses were not performed.

Overall Survival (OS) studies
The OS was compared between bladder cancer patients with and without KDM6A mutations (KDM6A-Mut and KDM6A-WT) utilizing publicly available clinical and genomic datasets. The data for IMviogor210 study which evaluated atezolizumab in advanced bladder cancer (NCT02108652, NCT02951767) was accessed through IMvigor210CoreBiologies R package39. Additionally, the OS data for advanced bladder cancer patient treated with at least one dose ICT (atezolizumab, avelumab, durvalumab, ipilimumab, nivolumab, pembrolizumab, or tremelimumab) was retrieved from cBioPortal40. The clinical and genomic data for patients with resectable and advanced bladder cancer receiving cisplatin-based chemotherapy was retrieved from two independent studies, blca_msk_tcga_2020 and paired_bladder_2022 in cBioPortal31,32.
Survival curve for above mentioned patient data was estimated with Kaplan-Meier method using “survfit” function from “survival” R package. The survival curves were plotted using “ggsurvplot” function from “survminer” R package. The statistical analyses were conducted in R v4.1.2.

Identification of eccDNA in bladder cancer patients
We obtained previously reported amplicon intervals identified from WGS data of TCGA-BLCA cohort13. We quantified distribution of structural-class categories across patients stratified by KDM6A mutation status. Within each KDM6A stratum (WT: n=85, Mut: n=27), we counted samples per classification and converted counts to percentages of the stratum total. The resulting proportions for circular amplicons were visualized with barplot created using ggplot2. Amplicon intervals were annotated to proximal gene symbols using Ensemble via the biomaRTR package. The annotated amplicon intervals were manually inspected for presence of cisplatin resistant genes reported in literature. The representative figures for eccDNA amplicons in Supplementary Fig. 2D were prepared using Biorender and (Integrative Genomics Viewer) IGV84,85.

Tumor mutation burden and somatic interactions
Tumor mutation burden for patients with advanced bladder cancer was retrieved from IMvigor210 study and The Cancer Genome Atlas (Stage IV) (TCGA-BLCA, n=136)5,39. The mutational burden was compared between patients with and without KDM6A mutations. Somatic variants summary for all bladder cancer patients in the TCGA-BLCA (n=412) were downloaded in Mutation Annotation Format (maf) using TCGAbiolinks R package. The co-occurrence or mutual exclusivity of KDM6A mutations with mutations in MMR and DSBR-related genes was computed using the “somaticInteractions” function in maftools v2.10.05. Significance of co-occurrence and mutual exclusivity was estimated with two-tailed Pairwise Fisher’s exact test and derived P values were adjusted with Benjamini–Hochberg method (-log10(-pAdj)). The somatic interactions plots were prepared in ggplot2. The statistical analyses were conducted using R v4.1.2 and GraphPad Prism 10.0.

mRNA expression levels for patient cohorts
The RNA-sequencing expression levels for genes involved in MMR and DSBR pathways were obtained from publicly available transcriptomic and genomic data at cBioPortal for advanced bladder cancer patients treated with ICT (HCRN)42. The mRNA expression levels of genes involved in glycolysis pathways were retrieved from IMvigor21039. Additionally, mRNA expression levels for genes were retrieved for TCGA-BLCA cohort were obtained cBioPortal. The mRNA expression data for TCGA-BLCA and HCRN cohorts were obtained prior to June 2024. The expression levels of gene involved in MMR, DSBR and glycolytic pathways were compared between KDM6A-Mut and KDM6A-WT. The statistical analyses were conducted using R v4.1.2 and GraphPad Prism 10.0.

Western Blot
KDM6A expression was accessed in all the murine and human cell lines. Briefly, cells were lysed using freshly prepared lysis buffer (2% SDS, 10mM Tris pH 7.5, supplemented with 1X protease inhibitors) by frequent vortexing and incubated on ice for 20 min. This was followed by centrifugation at 17000×g for 2 min using QIAshredder and the protein in the collected supernatant was quantified using the Pierce BCA Protein Assay kit according to the manufacturer’s protocol. Samples were mixed with 2X reducing Laemmli buffer and incubated at 95°C for 10 min. 15μg of samples were loaded into a PROTEAN TGX gel and underwent electrophoresis. After electrophoresis, proteins were transferred to an Immobilon membrane for 1h. Then, the membrane with the transferred protein was blocked for 1h in Every Blot Blocking buffer, then incubated overnight at 4°C in 1:2000 of anti-KDM6A primary antibody diluted in Every Blot Blocking Buffer. The following morning, the membrane was washed 5 times in PBST before being incubated with 1:2000 of anti-Rabbit IgG HRP linked secondary antibody diluted in Every Blot Blocking Buffer for 1h. Lastly, the membrane was washed in PBST six times followed by chemiluminescent imaging in a Bio Rad ChemiDoc imaging instrument.

In-vivo tumor model and tumor processing
sgScramble and sgKdm6a C1 cells were collected from culture via trypsinization and then washed with PBS. C57BL/6 mice were injected subcutaneously in the right flank with 2×105 sgScramble or sgKdm6a C1 cells resuspended in 100μl of PBS. The tumor volume was monitored on day 3, 4, 7 and 10 using digital calipers post-tumor inoculation. On Day 11 post tumor implantation, mice were sacrificed, and the tumors were isolated and weighed. The maximum tumor size for subcutaneous tumors permitted by the Institutional Animal Care and Use Committee (IACUC protocol no. 00002153-RN01) at The University of Texas MD Anderson Cancer Center (MDACC) is 15mm. However, mice were sacrificed, and tumors were isolated on Day 11, prior to reaching this size limit.
Collected tumors were transferred to Eppendorf tubes containing FBS-free media supplemented with 0.66mg/ml of Liberase and 20mg/ml of DNAse. Next, tumors were chopped using scissors, and the sample tubes were transferred to a thermal shaker for tumor digestion at 37°C at 1000 rpm for 30 min. Following tumor digestion, a single-cell suspension was obtained by mushing tumors through a 70μm strainer. The single-cell suspension was subsequently washed in PBS at 200×g for 5 min at 4°C, the cell pellet was subjected to RBC lysis by resuspending in ACK Lysis Buffer and then washed in PBS before proceeding to downstream assays. For some experiments, the cell pellet was resuspended in freezing media comprising of 90% FBS and 10% DMSO before storage at −80°C for downstream use.

Anti-PD-1 in-vivo treatment regimen
Tumor-bearing mice were injected intraperitoneally with 200μg, 100μg, and 100μg of anti-PD1 diluted in 100μl PBS on Days 3, 6 and 9 post-tumor implantations. The tumor volume was monitored on day 3, 4, 7 and 10 using digital calipers post-tumor inoculation and was calculated using the formula, Tumor Volume = (Length × Width2)/2. The tumors were isolated, weighed, and subjected to tumor processing on Day 11 post-tumor implantation as described previously.

In-vivo cisplatin-based chemotherapy regimen
Tumor-bearing mice were injected intraperitoneally with 2.5mg/kg of gemcitabine plus 6mg/kg of cisplatin on Days 1, 4 and 7 post-tumor implantations. The tumor volume was monitored on day 3, 4, 7 and 10 using digital calipers post-tumor inoculation and was calculated using the formula, Tumor Volume = (Length × Width2)/2. Tumors were isolated, weighed and subjected to tumor processing on Day 11 post-tumor implantation as described previously.

Murine splenocyte isolation and in-vitro Treg generation
Spleens were harvested from C57BL/6 mice, mushed and single-cell suspension was obtained by passing through a 70μm strainer. The resulting single-cell suspension was treated with ACK Lysis Buffer to lyse the RBCs. The cell counts were obtained using an automated cell counter before proceeding to Magnetic Assisted Cell Sorting (MACS) of naïve CD4 T cells.
Naïve CD4 T cells were magnetically isolated using the Murine Naïve CD4 T Cell Isolation Kit, as per manufacturer’s guidelines. Briefly, splenocytes were resuspended in MACS buffer (1X PBS supplemented with 0.5% BSA and 2mM EDTA) on ice. Naïve CD4 Biotin Antibody Cocktail was added, mixed thoroughly, and incubated at 4°C for 15 min. Next, Anti-Biotin Microbeads were added to the samples, mixed thoroughly, and incubated on ice for an additional 15 min. CD44 Microbeads were then added, followed by a final incubation for 20 min on ice. The samples were subsequently washed by centrifugation at 200×g for 5 min at 4°C in surplus MACS Buffer. The resulting cell pellets were resuspended in MACS buffer, passed through LS columns and the naïve CD44- CD4 T cells were collected in the flow-through. Subsequently, to generate Tregs, naïve CD4 T cells (1-1.5×106 cells per well) were stimulated with 3μg/ml of anti-CD3, 2μg/ml of anti-CD28, 10ng/ml of murine recombinant IL-2, and 10ng/ml of recombinant mouse TGF-β in complete RPMI media and plated in 24-well plates. The cells were then incubated for 5 days at 37°C with 5% CO2.

Genomic DNA isolation for Whole Genome Sequencing (WGS)
Genomic DNA was extracted from 2 ×106 RT4 and K2 cells using QIAamp DNA Micro Kit, according to the manufacturer’s instructions. For cell lysis, the cell pellet was resuspended in buffer ATL, proteinase K and buffer AL was added and pulse-vortexed for 15s, followed by 10 min incubation at 56°C in a thermoshaker. After incubation, 100% ethanol was added, pulse-vortexed for 15s and incubated at room temperature for 3 min. The lysates were then transferred to QIAamp MinElute columns and centrifuged at 6000×g for 1 min at room temperature, followed by washing with buffer AW1 and AW2. Then, the columns were centrifuged at 20,000×g to remove excess buffer, nuclease-free water was added, incubated for 5 min at room temperature and centrifuged at 20,000×g to elute the genomic DNA. The concentration of genomic DNA was measured with a Qubit Flex Fluorometer using the dsDNA HS Assay kit according to the manufacturer’s instruction. 500μg of DNA with an DNA Integrity Number value >7 from each condition was sent to the MDACC Advanced Technology Genomics Core for library preparation and WGS sequencing on the Illumina NovaSeq instrument.

Whole Genome Sequencing data analysis
The raw paired-end FASTQ reads were generated from whole genome sequencing of RT4 and K2 cells. We trimmed the raw sequences with Trimmomatic v.0.39 to remove adapters and low-quality bases. The reads were mapped to human reference genome (hg19) using BWA-MEM of Burrows-Wheeler Aligner (BWA) v0.7.17-r1188 and mapped “bam” reads were generated. Further processing including duplicate removal, sorting and indexing was performed with SAMTOOLS v1.1386. We estimated copy number variants for each sample using CNVkit and genome wide heatmap showing copy number ratio was generated with “cnvkit.py heatmap” module. As our study was focused on identification of eccDNA, we used Circle-Map v.1.1.2 to identify circular amplicons87. All soft clipped, hard clipped and discordant reads were extracted to a new “bam” file using “ReadExtractor” module and “realignment” module was executed with default parameters of Circle-Map. We used standard threshold of minimum 2 split reads and circle score above 50 to obtain robust set of eccDNA. Circlize v 0.4.15, Biorender and ggplot2 were used to generate circular representations of identified eccDNA amplicons and copy number variation for specific regions between different groups were visualized with “cnvkit.py scatter” module.

ChIP Sequencing
ChIP assays were performed using pooled samples from MB49 sgScramble and two biological clones of sgKdm6a (C1, C2) cell lines, as well as in-vitro generated Tregs, treated with or without 25mM Na-La for 48h, using the MAGnify Chromatin Immunoprecipitation System kit as per the manufacturer’s instructions. Briefly, cells were incubated with 37% formaldehyde for 10 min at room temperature to crosslink the chromatin. Then to stop the crosslinking reaction, the samples were treated with 1.5M glycine and incubated for 5 min at room temperature. Next, the samples were washed thrice with ice-cold PBS through centrifugation at 200×g at 4°C for 10 min. The resulting cell pellets were then resuspended in lysis buffer supplemented with protease inhibitors and subjected to sonication to fragment the DNA into 150-300 kb fragments. For every immunoprecipitation reaction, 10μg of specific antibodies as detailed in the Figure legends and Supplementary Table 1, were first coupled with Protein A/G Dynabeads and then added to the sheared samples and incubated overnight at 4°C. The subsequent day, unbound antibodies were removed by washing thrice with Immunoprecipitation (IP) Buffer 1 and twice with IP Buffer 2. The crosslinking was then reversed through heat treatment, using the Reverse crosslinking buffer supplemented with Proteinase K. Next, the target DNA was purified using the DNA purification magnetic beads resuspended in DNA purification buffer and incubated at room temperature for 30 min. The bead-bound DNA was then washed twice with DNA wash buffer to remove any residual contaminants. Finally, the purified target DNA was eluted out by incubating the bead-bound DNA with DNA elution buffer at 55°C for 20 min in a thermocycler. The immunoprecipitated DNA was then quantified with the dsDNA HS Assay kit using the Qubit Flex Fluorometer. 10μg of DNA from each condition was sent to the MDACC ATGC for library preparation and sequencing which was carried out using the Illumina NextSeq500 instrument.

ChIP Sequencing data analysis
The quality of ChIP-seq FASTQ sequences generated as described above, were assessed using FastQC v0.11.9, followed by mapping by bowtie2 v2.3.5.188 with mouse reference genome mm10. The bam files obtained from mapping were further processed using SAMBLASTER v0.1.2689 and SAMTOOLS v1.1386, for duplicate removal, sorting and indexing. Further, SAMBAMBA v0.6.690 was used to normalize the bam files per read counts by performing random sampling. The ChIP-seq signal enrichment over “Input control” background was identified using Model based analysis of ChIP-seq (MACS) 2 v2.2.891. The peaks with significant p values (<0.05) were considered for further annotation with using CHIPseeker v1.30.392 and clusterProfiler93 packages in R v4.3.2. The quantitative pairwise comparisons of different datasets were performed using Manorm v1.1.494 using both peak (bed) and read (bam) coordinates for respective sample. The significant enrichment of target genes was estimated of the basis of M value which describes the log2 fold change and plotted with ggplot2. The differential peaks for specific genes were visualized using Integrative Genomics Viewer (IGV)84,85. Differential pathways enriched among datasets were identified using GSEA v4.2.395. The bigwig files were generated from aligned bam reads with “bamCoverage” function of deepTools v3.5.196 and profile plots for specific genes were plotted using the computeMatrix and plotProfile programs of deepTools. We obtained differential peaks for set of genes involved in MMR, DSBR and glycolysis to prepare consolidated BED for each pathway between groups. Signal intensity over these regions was quantified with deepTools using computeMatrix. The resulting matrix was visualized with plotProfile to obtain average signal profiles for consolidated set of genes involved in MMR, DSBR and glycolysis for different groups. For profile plots of individual genes, we extracted the differential peaks mapping to genes involved in MMR, DSBR and glycolysis as BED regions for each gene individually (reference-point mode, TSS, +/−1 kb flanking regions). Signal intensity over these regions was quantified with deepTools using computeMatrix (reference-point mode, TSS, +/−1 kb flanking regions). The resulting matrix was visualized with plotProfile to obtain average signal profiles for signals for genes of interest for different groups. The coverage (bw) data were used to generate heatmaps showing the distribution of promoters (TSS, +/−5 kb) in Treg with and without sodium lactate treatment for different histone post-translational using “computeMatrix” and “plotHeatmap” functions of deepTools. The genome wide annotation of aligned reads was performed using ChromHMM v1.2497 which applies multivariate hidden Markov model (HMM) to assign states by modeling combinatorial presence and absence of each mark. The reads were subjected to “BinarizeBam” followed by “LearnModel” using mm10 assembly study enrichment and functional annotation of each mark in 7-state model.

RNA sequencing
MB49 sgScramble and two biological clones of sgKdm6a (C1, C2) cells were collected from culture by trypsinization, washed once in PBS, and then snap-frozen in liquid nitrogen before total RNA extraction performed by the MDACC Biospecimen Extraction Core Facility. A minimum of 120ng of extracted RNA with an RNA Integrity Number value >7 was sent to the MDACC ATGC for stranded, paired-end mRNA sequencing in a NextSeq500 instrument.

RNA sequencing data analysis
For RNA-seq analysis, the paired-end raw FASTQ sequences were subjected to quality control with FastQC v0.11.9, followed by adapter removal using Trimmomatic v0.3978. The trimmed fastq reads mapped to mm10 reference genome using STAR v2.7.3a 79 and resulting bam reads were sorted and indexed with SAMTOOLS v1.13. The gene counts for each sample were estimated with HTseq v2.0.2. The raw counts from HTSeq were imported into R and analyzed with edgeR to compare different groups. Count matrices were assembled and labeled accordingly, then converted to DGE list. CPM were computed for quality checks and dispersion was visualized with plotBCV. As this comparison did not include biological replicates, differential expressions were assessed with edgeR’s exact test with a fixed biological coefficient of variation as recommended by published protocol98. We adjusted the resulting P-values obtained from differential gene expression analysis between groups for multiple testing using the Benjamini–Hochberg false discovery rate (FDR). The volcano plots showing differential gene expression between groups were plotted with ggplot2 in R.

Isolation of CD4+CD25+ Tregs and CD4+CD25− Tconv cells
Murine CD4+CD25+ Tregs, and CD4+CD25− Tconv cells were magnetically isolated using murine CD4+CD25+ Regulatory T Cell Isolation Kit, according to the manufacturer’s protocol. Briefly, cells were resuspended in MACS buffer and CD4+CD25+ Regulatory T Cell Biotin-Antibody Cocktail was added and incubated on ice for 15 min. Next, the Anti-Biotin Microbeads were added, incubated at 4°C for 15 min, following which the samples were washed in surplus MACS buffer by centrifugation. The resulting cell pellet was passed through the LD columns, flow through was collected and centrifuged at 200×g for 5 min at 4°C. The cell pellet was then resuspended in MACS buffer, CD25-PE antibody was added, mixed well, and incubated for another 15 min on ice. The samples were incubated for an additional 15 min with Anti-PE Microbeads and washed in excess MACS buffer by centrifugation. The pellets were resuspended and run through the MS Columns. CD4+CD25− Tconv cells were collected in the flow-through, while CD4+CD25+ Tregs were eluted from the column using a plunger.

Flow cytometry
Flow cytometry was conducted on the cells as mentioned in the figure legend. 2-3×106 cells were briefly rinsed in FACS buffer (PBS supplemented with 5% FBS) by centrifugation at 200×g at 4°C for 5 min. After washing, the cells were incubated on ice for 15 min in a blocking buffer containing murine, bovine, hamster, rat, and rabbit serum along with 25μg/mL 2.4G2 (Fc block) antibody in PBS. The cells were then stained with the surface antibody cocktail as detailed in the Supplementary Table 1 for 30 min at 4°C. After, two subsequent washes with FACS buffer, the cells were fixed and permeabilized on ice for 45 min. Following another wash with 1X permeabilization buffer, the cells were incubated in the intracellular antibody cocktail as mentioned in Supplementary Table 1 for 20 min at room temperature. Finally, the samples were washed with FACS buffer, fixed with 1% paraformaldehyde. The samples were then acquired using a BD LSR Fortessa flow cytometer and FlowJo v10.10 software was used for analysis of the flow cytometry data.

Annexin V based cytotoxicity assay
To evaluate cisplatin-induced cytotoxicity, between MB49 sgScramble, RT4 and SCaBER cells with their corresponding KDM6A KO clones, cells were treated with or without 5µM cisplatin for 48h. Following that the cells were harvested and stained with Annexin V-Pacific Blue diluted in 1X Annexin Binding Buffer for 15 min at room temperature. The stained cells were then washed with 1X Annexin Binding Buffer and acquired immediately on the BD LSR Fortessa flow cytometer and analyzed using FlowJo v10.10 software.

Spheroid generation assay
To compare the spheroid formation ability between MB49 sgScramble, RT4 and SCaBER cells with their corresponding KDM6A KO clones in presence of cisplatin, cells were allowed to form spheroids on top of agarose. Briefly, 100µl of 1.5% molten agarose was transferred to each well of a 96-well flat-bottom plate and allowed to solidify. Then 2×104 cells resuspended in 200µl of complete media containing 5µM cisplatin were transferred to the top of the agarose. To allow spheroid formation, the plates were centrifuged at 220×g for 10 min and incubated for 48h at 37°C. After 48h, spheroids were imaged in a EVOS FL imaging system under a 4X objective. The ImageJ software was used to measure the diameter of the spheroids using the scale in the microscopy images as reference.

Wound-healing assay
To compare the migration ability of MB49 sgScramble, and SCaBER with their respective KDM6A KO clones in presence of cisplatin, the wound-healing assay was performed. Briefly, 2×105 cells were seeded into each well of a 24-well plate and incubated until cells reached confluency. Using a sterile 200µl pipette tip a scratch was made through the middle of the well and imaged at 0h in a EVOS FL imaging system under a 4X objective. The cells were then treated with or without 5µM cisplatin in serum free media followed by 48h incubation and imaged as described previously. The images were analyzed in ImageJ Software using the Wound healing size tool to determine the wound closure percentage. The wound closure was calculated as the percentage reduction in scratch area between 0 and 48h.

Cell Transwell Invasion assay
To determine cellular invasiveness, the MB49 sgScramble, RT4 and SCaBER with their respective KDM6A KO clones were subjected to starvation by culturing in serum free media for 24h. Following serum starvation, 5×104 cells were seeded in FBS free media, either with or without 5µM cisplatin, onto the transwell inserts with 8μm pore membrane coated with Matrigel (1:8 dilution in serum free media). The inserts were then placed into wells of 24-well plate containing 30% FBS media. Following 48h incubation, the invaded cells on the lower side of the membrane were washed with PBS and fixed in 4% PFA for 7 min at RT. The membranes were then excised out using scalpel, mounted on a glass slide using VECTASHIELD mounting media containing DAPI and imaged using the EVOS FL imaging system at either 10X or 20X magnification. The cell numbers were quantified by counting 4 independent visual fields using the ImageJ Software.

Cell proliferation assay
1×106 cells of interest were stained with CellTrace™ CFSE according to the manufacturer’s instructions. Briefly, cells were harvested and resuspended in 1ml PBS, then 1µl of 5mM CFSE dye was added, and the cells were incubated in dark at RT for 20 min. Following incubation, the cells were resuspended in complete media and incubated for another 5 min. Cells were then centrifuged and washed again with complete medium to remove any excess dye. At 0h, a baseline measurement of CFSE intensity was acquired in BD LSR Fortessa flow cytometer in FITC channel. The cells were then cultured for an additional 48h, after which they were acquired again in flow cytometer. FlowJo v10.10 software was employed to analyze the differences in the CFSE dilution which is used as a measure of cell proliferation.

Extrachromosomal circular DNA isolation and rolling circle amplification
Nuclei extraction was performed on 7 million sgScramble and sgKdm6a C1 cells per reaction, using CUTANA nuclei extraction buffer supplemented with spermidine and Halt protease inhibitor. The samples were incubated for 10 min on ice, and then centrifuged at 600×g for 5 min at 4°C. Following nuclei extraction, lysis and circular DNA isolation was performed using the QIAprep spin miniprep kit according to the manufacturer’s instructions. Briefly, the nuclei were resuspended in P1 buffer, following which proteinase K was added to the cell suspension. The samples were incubated for 17h on a 50°C heating block with agitation at 700 rpm. Then, buffer P2 was added to the samples and mixed thoroughly by inversion. To stop the lysis reaction, buffer N3 was added and mixed thoroughly by inversion. The sample was spun at 17,900×g for 1 min. The supernatant was collected, added in the QIAprep spin column, and spun at 17,900×g for 1 min. The spin column was washed using buffer PB, and spun at 17,900×g for 1 min followed by a wash with buffer PE and spun at 17,900×g for 1 min. An additional spin was performed to remove residual wash buffer, then the column was placed on a fresh 1.5 mL Eppendorf tube. Nuclease free water was added to the column and left to stand for 5 min at room temperature and eluted by centrifugation at 17,900×g for 1 min.
Subsequently the isolated DNA was subjected to linear DNA using the Plasmid safe ATP dependent DNase kit at 37°C for 1h and the reaction was deactivated at 70°C for 30 min, as per the manufacturer’s instruction. Next, the sample was purified using the AMPure XP Reagent according to the manufacturer’s instructions.
Subsequently the purified DNA was subjected to rolling circle DNA amplification. Briefly, a denaturation mix was prepared using the phi DNA polymerase kit according to the manufacturer’s instructions. The sample was amplified for 21h at 30°C, then inactivated for 10 min at 65°C. The PCR products were visualized via electrophoresis on a 1% agarose gel.

Immunohistochemistry (IHC)
IHC staining was performed on formalin-fixed, paraffin-embedded tissue sections of sgScramble and sgKdm6a C1 tumors to detect EXO1 levels. sgScramble and sgKdm6a C1 tumors were isolated as described previously, fixed in 10% formalin for 4h and sent to MDACC Research Histology Core Laboratory (RHCL) for sectioning and staining. Fixed tumors were embedded in paraffin and sectioned at 4-micron thickness. Next, sections were deparaffinized, hydrated and antigen retrieved with BOND Epitope Retrieval Solution 1 followed by washing with BOND wash buffer. For staining BOND Polymer Refine Detection kit was used, sections were blocked with hydrogen peroxide for 15 min, stained with rabbit anti-mouse EXO1 antibody at a 1:200 dilution for 30 min, followed by staining with anti-rabbit Poly-HRP-IgG secondary antibody for 15 min. Slides were visualized using 3′-3-Diaminobenzidine substrate as a chromogen, followed by hematoxylin counterstaining for 3 min. Slides were scanned into Aperio eSlide manager for visualization. The quantification of the Exo1 expression was performed using the color deconvolution 2 plugin on the ImageJ software.

Microsatellite Instability detection
To detect Microsatellite instability, the following markers were used- mBAT64, mBAT24, mBAT37 and mBAT59 for the mouse genome and NR21, NR27, NR24, BAT25 and BAT26 for the human genome. These microsatellite markers were PCR amplified from the respective genomes, and the size of the marker was analyzed by fluorescent fragment length analysis (FFLA). Briefly, sgScramble and sgKdm6a C1 tumors were processed as described previously to obtain sgScramble and sgKdm6a C1 single cell suspensions, while RT4 and K2 cells were harvested via trypsinization. Genomic DNA was extracted from 2×106 cells using QIAamp DNA Micro Kit as previously detailed. These microsatellite markers were PCR amplified from 20ng genomic DNA with primers as detailed in the Supplementary Table 1, using the Type-it Microsatellite PCR Kit as per the manufacturer’s instructions. PCR conditions were as follows for murine microsatellite markers: initial activation at 95°C for 5 min; followed by 27 cycles of 95°C for 30s, 60°C for 90s and 72°C for 30s; then a final extension step at 60°C for 30 min. For human microsatellite markers, the following PCR conditions were used: initial activation at 95°C for 15 min; followed by 35 cycles of 95°C for 30s, 55°C for 90s and 72°C for 30s; then a final extension step at 72°C for 10 min. The size and distribution of the PCR amplicons were determined by capillary electrophoresis performed on a 3730 Genetic Analyzer instrument followed by analysis of data using the Peak Scanner software. The microsatellite-based amplicons were sent to MDACC ATGC for FFLA. The amplicons were mixed with formamide and Liz 500 size standard in a 96-well PCR plate, loaded on the mentioned instrument and run for 45 min. The results (.fcs files) were imported to Peak Scanner software, size standard and analysis method were set to GS500 LIZ and Sizing Default PP, respectively and analyzed. The modal peaks of the microsatellite markers in the electropherogram were identified between 75-195 bp. The modal peak position in base pairs of mBAT-64 was compared between sgScramble and sgKdm6a C1 tumor cells and the modal peak position for NR21 was compared between RT4 and K2 cells.

Gamma-H2AX assays
To detect dsDNA breaks in cell lines, presence of gamma-H2AX foci were detected using flow cytometry or microscopy-based studies.
For flow cytometry experiments, sgScramble and sgKdm6a C1 cells were seeded in a 24-well plate and incubated overnight at 37°C. The following day, cells were treated with 100µM Etoposide for 1h followed by 4h recovery or no recovery. Cells resuspended in PBS were transferred to a 96-well round bottom plate, centrifuged at 900×g for 5 min at 4°C and resuspended in fixation and permeabilization buffer for 40 min on ice. Following fixation and permeabilization, the cells were centrifuged at 900×g for 5 min at 4°C and the cell pellet was subsequently stained with Anti-Phospho-Histone H2A.X (Ser 139) primary antibody for 20 min at room temperature. After incubation, cells were washed with PBS by centrifugation at 1400×g for 5 min at 4°C and subsequently stained with anti-mouse FITC-conjugated secondary antibody for 15 min in dark at room temperature. Then, the cells were washed with PBS and fixed with 1% paraformaldehyde. The samples were acquired using a BD LSR Fortessa flow cytometer and analyzed using FlowJo v10.10 to determine the percentage of gamma-H2AX positive cells.
For microscopy experiments, 1 ×106 sgScramble, sgKdm6a C1, RT4, and K2 cells were seeded on poly-D-lysine coated coverslips and incubated for 24h at 37°C. After the cells properly adhered, they were treated as described previously, then fixed with 3.7% paraformaldehyde for 20 min at room temperature, permeabilized with 1% Triton X-100 for 10 min at room temperature and subsequently blocked for 30 min with blocking buffer (1% BSA in PBS) at room temperature. After blocking, the cells were incubated with the H2AX primary antibody as mentioned previously for 90 min at room temperature, washed with PBST (0.05% Tween-20 in PBS) and then incubated with anti-mouse secondary antibody in the dark for 1h at room temperature. Then, the cells were washed with PBST followed by DAPI staining in the dark for 15 min. To remove excess dye cells were washed with PBS and coverslips were mounted onto slides using mounting media. The slides were imaged using the Leica SP8 confocal system or the Zeiss LSM880 confocal system at the MDACC Advanced Microscopy Core (AMC) and the raw image files were imported to ImageJ software for analysis. The gamma value was corrected uniformly within each comparison group and the number of distinctly visible gamma-H2AX foci were counted manually for each cell.

Gamma-H2AX assay of in-vivo tumor cells
sgScramble and sgKdm6A C1 tumors were processed as described previously to obtain sgScramble and sgKdm6a C1 tumor single cell suspensions. 3 ×106 sgScramble and sgKdm6a C1 tumor cells were transferred to a 96-well round bottom plate, centrifuged at 900×g for 5 min at 4°C and the cell pellet was stained with PE conjugated anti-CD45+ antibody followed by 15 min incubation at room temperature. The cells were washed with PBS followed by fixation and permeabilization and then stained with H2AX primary antibody and anti-mouse secondary antibody as described previously. The cells were then washed with PBS and incubated with PE conjugated tumor cell marker anti-E-Cadherin antibody for 15 min at room temperature. Cells were washed, fixed, acquired and analyzed as described previously to determine the MFI of gamma-H2AX in tumor cells and percentage of gamma-H2AX positive tumor cells.

Comet assay
To compare DNA damage in MB49 sgScramble and RT4 cell lines with their respective KDM6A KO cell lines, comet assay kit was used for single cell gel electrophoresis and DNA staining according to the manufacturer’s protocol. 2×105 cells were seeded in a 24-well plate and incubated overnight at 37°C. The following day, cells were treated with 100µM Etoposide for 2h followed by 1h recovery. Following the treatment, cells were collected, resuspended in cold 1X PBS to achieve a final of 500 cells/μl and mixed with molten comet agarose at 37°C in the ratio of 1:10 (v/v). A base layer of agarose was prepared on comet slides and allowed to solidify, then the cell-agarose mixture was transferred on top of the base layer. Following solidification, alkaline lysis of cells was performed by immersing the slides in pre-chilled lysis buffer for 45 min at 4°C in the dark, followed by a 45-minute immersion in pre-chilled alkaline solution. Then single cell alkaline electrophoresis was performed by placing the slides in an electrophoresis chamber containing cold alkaline electrophoresis solution, at 1 volt/cm for 25 min at 310 mA. The slides were then washed three times with cold autoclaved water, followed by cold 70% ethanol for 5 min. Slides were allowed to air dry at room temperature and stained with Vista green DNA dye for 15 min in dark at room temperature. Excess dye was removed, and slides were imaged using Leica SP8 confocal system at the MDACC Advanced Microscopy Core. The raw image files were imported to ImageJ software, and gamma value was corrected uniformly within each comparison group for further analysis. Using the OpenComet plugin, profile analysis was done with background correction off for comet finding and head finding. The output of the analysis provided the corresponding tail DNA percentage, tail moment and olive moment of the comets.

Cell cycle analysis
Cell population in each cell cycle stage was determined by using the Vybrant DyeCycle Violet DNA binding Stain according to the manufacturer’s instruction. Briefly, 2×105 cells were incubated with 10µM of the DNA binding dye in complete media at 37°C in dark for 30 min. The cells were then directly acquired in BD LSR Fortessa flow cytometer using the BV421 channel. FlowJo v10.10 software was used for analysis of the flow cytometry data. The univariate cell cycle model using the Watson (Pragmatic) algorithm in FlowJo was used to analyze the distribution of cells across different cell cycle stages based on their DNA content.

Seahorse assay (ATP Real-time Rate Assay)
To compare the ATP production rates from glycolytic and mitochondrial pathways between murine MB49 sgScramble and sgKdm6a C1 cell lines as well as between human RT4 and its KDM6A KO Clone K2, the Seahorse XF Real-Time ATP rate assay was performed following the manufacturer’s instructions by the MDACC Metabolomics Core. The cells were seeded overnight in the XF-96 well-plate. The assay was performed using an XF-96 Analyzer which measured the extracellular acidification rate (ECAR) under basal conditions as well as after sequential injections of 1.5μM oligomycin and 0.5μM Rotenone/Antimycin A. Real-time rates of ATP production were recorded and assessed using the Wave software.

Extracellular lactate measurement assay
To distinguish differences in concentration of L-Lactate produced, culture supernatants were collected from murine MB49 sgScramble and sgKdm6a C1 cell lines after 24h of culture, and from human RT4 and its KDM6A KO Clone K2 cells following 48h of culture.
To compare the intratumoral concentration of L-Lactate in sgScramble and sgKdm6a C1 tumors, tumor interstitial fluid (TIF) was collected by resting tumors on a 40μm nylon filter, then centrifuging the sample tubes at 106×g for 10 min at 4°C. The collected TIF samples were transferred to separate Eppendorf tubes, snap-frozen in liquid nitrogen, and then stored at −80°C before assaying.
TIF and culture supernatant samples were processed for extracellular lactate quantification using the L-Lactate Assay Kit. Briefly, samples were subjected to deproteination using cold 0.5M MPA on ice for 5 min. After deproteination, samples were centrifuged at 10,000×g for 5 min at 4°C. Next, the supernatant was collected, transferred to a new tube, and neutralized using potassium carbonate and centrifuged at 10,000×g for 5 min at 4°C, after which the supernatant was collected. The assay plating was conducted following the manufacturer’s instructions, and the absorbance was read using a BioTek Synergy H1 microplate reader at 535 nm.

Mass cytometry (CyTOF) for immune profiling of tumor microenvironment
Tumor samples processed and stored as previously described were revived in complete DMEM media. Then, 3×106 cells were collected from each sample and washed with PBS, resuspended in 194Pt monoisotopic cisplatin at a final concentration of 5μM in FACS buffer and incubated for 3 min at room temperature. Subsequently, samples were washed three times with FACS buffer followed by fixation and permeabilization for 15 min at room temperature using Maxpar barcode fixation and permeabilization buffer. Then, samples were labeled using palladium barcoding, according to the manufacturer’s protocol, and incubated for 30 min at room temperature. After labeling, samples were washed three times in FACS buffer followed by surface staining was performed by resuspending samples in the antibody mixture (outlined in Supplementary Table 1) prepared in blocking buffer at 4°C for 30 min. After surface staining, samples were washed twice with FACS buffer and then subjected to fixation and permeabilization for 1h at 4°C. Next, samples were washed twice in permeabilization buffer and resuspended in the intracellular antibody mixture prepared in permeabilization buffer (described in Supplementary Table 1) for 30 min at 4°C. After washing with FACS three times, samples were fixed using 1.6% paraformaldehyde in PBS supplemented with 100nM iridium nucleic acid intercalator and left overnight at 4°C. The following day, samples were washed with PBS three times and resuspended in nuclease-free water before acquisition using a Helios mass cytometer. We analyzed the CyTOF data using Premessa, FlowCore, and FlowSOM packages as described in a previously published protocol99.

pHRodo assay for lactic-acid uptake
Tregs were collected from culture and 1×106 cells were plated per well in a 96 well plate, then washed with 20mM HEPES in PBS. After washing, cells were resuspended in the pHrodo staining solution prepared according to manufacturer’s instructions in 20mM HEPES and incubated at 37°C for 30 min. Following staining, cells were washed twice in 20mM HEPES. Following the washes, lactic acid was added to each sample well at final concentrations of 5mM and 15mM and then incubated at 37°C for 30 min, following which samples were acquired on a BD LSR Fortessa flow cytometer and analyzed using FlowJo v10.10.

In-vitro suppression assay
Tregs were generated in-vitro, magnetically sorted for CD4+CD25+ Tregs as described previously and then treated with the supernatant of sgScramble or sgKdm6a C1 cell lines for 36h. For Tconv cells, single cell suspensions of murine splenocytes were obtained and magnetically isolated for CD4+CD25− Tconv cells as previously outlined.
Tconv and Tregs were labelled with CellTrace Violet (CTV) and Far Red (FR) respectively according to the manufacturer’s protocol. Tconv (5×104) cells were co-cultured with Tregs (5×104) at varying ratios (1:1, 2:1 and 1:2, Tconv:Treg ratio) and stimulated using anti-mouse CD3/CD28 Dynabeads and 10ng/ml of murine recombinant IL-2 per the manufacturer’s guidelines. The cells were co-cultured in 96 well round bottom plates with 200μl RPMI for 4 days at 37°C and 5% CO2. On Day 4, cells were acquired using the BD LSR Fortessa. FlowJo v10.10 software was employed to analyze the differences in the CTV dilution which is used as a measure of T cell proliferation in response to co-culture with Tregs pre-treated with sgScramble supernatant or sgKdm6a supernatant.

Isolation and enrichment of histones for high-performance liquid chromatography (HPLC)–tandem mass spectrometry (MS/MS)
Tregs were generated in-vitro as described above and then treated with or without 25mM 13C labelled Na-La for 48h. Following treatment, histones were isolated using the Active Motif Histone Purification Mini Kit according to the manufacturer’s instructions. Briefly cells were harvested and washed by centrifugation at 200×g for 5 min at 4°C. The pellet was resuspended in pre-chilled extraction buffer and incubated overnight at 4°C with end-to-end rotation. The subsequent day, the samples were centrifuged at maximum speed for 5 min at 4°C, the supernatant containing the crude histones was collected and neutralized with 5X Neutralization Buffer. The neutralized histone samples were then loaded onto a spin column and centrifuged at 2000×g for 5 min at 4°C. The column was washed thrice with Histone wash buffer and the histones were eluted out in the flow through by centrifugation at 2000×g for 5 min at 4°C with the elution buffer. Next, the eluted histones were precipitated using 4% perchloric acid by overnight incubation at 4°C. The subsequent day, samples were centrifuged at maximum speed at 4°C for an hour. The pelleted histones were then washed twice with cold 4% perchloric acid followed by two washes with ice-cold acetone (with 0.2% HCl) and pre-chilled acetone. The pellets were air-dried for 15 min and then resuspended in 100mM tetraethylammonium bromide (pH 8) by incubation for 15 min at room temperature with periodic vortexing.
The extracted histones were then quantitated using the Pierce BCA Assay Kit as outlined previously. 1000µg of purified histones were then subjected to Lys-C digestion overnight at 1:50 (Lys-C to histone) ratio following the manufacturer’s guidelines. The digested samples were subsequently reduced and alkylated using 5mM Dithiothreitol at 37°C for an hour and 11mM Iodoacetamide for 50 min at room temperature respectively.
The Lys-C digested histones were then immunoprecipitated using a Pan anti-Kla antibody using Pierce Classic IP Kit following the manufacturer’s protocol. 10µg of Pan anti-Kla antibody was incubated overnight with 1000µg of digested histones at 4°C with end-to-end rotation. The next day, Pierce Protein A/G Agarose slurry solution was added into the Pierce Spin Column and centrifuged for 1 min at 1000×g. Then the antibody/ digested peptide mixture was added onto the Protein A/G Plus Agarose in the spin column and incubated over end-over-end rotation for 3.5h. The column was then washed thrice using 1 × 0.025M TBS Buffer and once with 1X Conditioning Buffer. The immune complex was eluted out of the column following incubation with low-pH elution buffer for 15 min and then centrifugation at 1000×g for 1 min at 4°C. The enriched peptides were then quantitated by BCA assay as described previously and sent to the MDACC Proteomics Core for LC–MS/MS analysis.

LC–MS/MS Analysis
For the analysis of histone lactylation, vacuum-dried tryptic peptides were dissolved in solvent A (0.1% formic acid in water) and directly loaded onto a nano reverse-phase C18 column (75μm ID×25cm L, 1.7μm particle size). Peptides were separated using a linear gradient of solvent A (0.1% formic acid in water) and solvent B (0.1% formic acid in acetonitrile). The gradient was as follows: 0% to 10% solvent B over 2 min, 10% to 60% solvent B over 30 min, 60% to 100% solvent B over 1 min, and held at 100% solvent B for 11 min. The eluate was electrosprayed into an Orbitrap Astral Mass Spectrometer (Thermo Fisher Scientific) operating in positive ion mode, with an electrospray voltage of 2kV and an ion transfer tube temperature of 280°C, flow rate 400nl/min. Data were acquired in DDA mode with a fixed cycle time of 2s. Full MS scans were performed in the range of 350 to 1500 m/z at resolution of 240,000, with an AGC target of 500% and a maximum injection time of 100ms. Precursor ion selection width was set to 2 Th. Peptide fragmentation was carried out using HCD with a normalized collision energy of 27%. Fragment ion scans were recorded at resolution of 80,000, with a maximum injection time of 30ms and a normalized AGC target of 300%. Dynamic exclusion was enabled and set to 15s.
For data analysis, the MS/MS spectra were processed using Proteome Discoverer v2.4 with a mouse protein sequence database from UniProt (downloaded November 2023, 17,184 entries). Peptide searching was performed with Sequest HT using the following parameters: Protease- Trypsin, allowing up to two missed cleavages, minimum peptide length of 5, a precursor mass tolerance of 10 ppm, and a production tolerance of 0.6 Da and applied score cutoff- none. Carboxyamidomethylation of cysteine was set as a fixed modification, while oxidation of methionine and lactylation of lysine were set as variable modifications. A total of eight samples were analysed. These included two technical replicates of in-vitro generated untreated Tregs and two technical replicates of Tregs labeled for 48h with 25mM 13C sodium L-lactate. For each experimental condition, two corresponding input controls (5% of the peptide collected prior to IP) were also included.

ChIP-qPCR
ChIP assay was performed as previously detailed on the following samples- Human RT4 and K2 cells using antibodies against KDM6A and H3K4me3 and in-vitro generated, CD4+CD25+ sorted Tregs treated with and without 25mM Na-La for 48h or treated with supernatants from sgScramble or sgKdm6a C1 cells for additional 24h against anti-H3K9la and H3K18la antibodies (detailed in Supplementary Table 1). For each condition, 100ng of immunoprecipitated DNA was used as input for real-time PCR (qPCR) to evaluate the gene expression utilizing the SYBR Green Master Mix according to the manufacturer’s instructions and using the primer sequences listed in Supplementary Table 1.

RNA Isolation, cDNA preparation and qPCR
RT4 with its KDM6A KO K2; SCaBER with its KDM6A KO B7 and in-vitro generated, CD4+CD25+ sorted Tregs treated with and without 25mM Na-La for 48h or treated with the supernatant of sgScramble and sgKdm6a C1 cells for 24h were lysed using the TriZol reagent and one-fifth volume of chloroform was added. After vigorous mixing and a 15-minute incubation at room temperature the samples were centrifuged at 12,000×g for 15 min at 4°C. The aqueous layer on top was carefully collected and incubated with equal volume of isopropanol for 10 min at room temperature. Following this, the samples were centrifuged at 12,000×g for 15 min at 4°C. The pellets were then washed with 75% ethanol by centrifugation. The resulting RNA pellets were air-dried, resuspended in nuclease-free water and quantified using a Nanodrop spectrophotometer. Subsequently, cDNA synthesis was performed by reverse transcribing 1µg of RNA using the Superscript III cDNA synthesis kit, according to the manufacturer’s instructions containing reverse transcriptase (RT) buffer, dNTP mix, RT Random primers, and Reverse transcriptase enzyme. 1000-2000ng of cDNA was then utilized for downstream qPCR analysis to measure gene expression using the SYBR Green Master Mix according to the manufacturer’s instructions and the primer sequences detailed in Supplementary Table 1.

Statistics & Reproducibility
R v4.0.2 and GraphPad Prism software V10 were used for statistical analyses. The specific statistical tests used have been indicated in the respective figure legends.

Ethical Statement
The study complies with all relevant ethical regulations, and all animal experiments were approved by the Institutional Animal Care and Use Committee (IACUC protocol no. 00002153-RN01) at The University of Texas MD Anderson Cancer Center (MDACC).

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Singh et al. report a series of studies regarding potential mechanisms through which KDM6A inactivation could contribute to bladder cancer. The authors use a variety of models including established human and mouse bladder cancer cell lines and tumor tissue data. There are several partially connected components to the work but there is a certain lack of coherence to the whole thing, in my opinion, in part due to the rather superficial analysis of some of these components. The authors start by commenting on the relationship between KDM6A mutations and sensitivity to cisplatin and extrachromosomal DNA, then they go on to discuss impact of KDM6A mutations on the mismatch repair machinery and response to immunotherapy, then go on to assess the impact on the double-strand break pathway and finally go to discuss the metabolic impact on T cells and on T cell abundance and function, including the impact on histone lactylation. Loose connection and lack of depth characterize the work. Overall, the authors also fail to consider the fact that KDM6A is an early bladder cancer driver that is mainly mutated in non muscle-invasive bladder cancer while all their work is based on muscle-invasive/metastatic cancer.\nImportant general methodological considerations:\n- the number of replicates in the experiments is not always indicated; the use of controls is not always described; the scales in the graph do not have \"0\" as background level.\n- the correction of p-values for multiple testing is not indicated.\n- statistical tests are not properly used. Student's t test can only be applied to data with a normal distribution, which is not the case in many of the comparisons.\nSpecific comments\n1. Figure 1: to be conclusive on the association with resistance to cisplatin, robust data in more than one series should be provided. The data on final tumor weight in B should be accompanied by growth curves. The migration data does not take into consideration the effect of cell growth. The use of a single sgRNA is not appropriate. A western blot to convincingly show down-regulation of KDM6A protein expression is not shown. The impact on extrachromosomal DNA is not robustly demonstrated, the analysis is very superficial and inconclusive.\n2. Effects on the MMR machinery are not convincing, as is the case for the association with response to immunotherapy. They should consider multiple independent series and perform multivariable analysis. Co-occurrence and mutual exclusivity are probably not corrected for multiple testing. The association with tumor mutational burden should be analysed if there is a real effect on MMR. The standard MMR analysis included several microsatellite sites, rather than just one. The effects are small in several of the shown comparisons. In panel H comparing one cell line to another is just inappropriate. More detais is required in the description of multiple results in Figure 2. Reduced expression of MMR proteins should be shown using western blotting, with appropriate controls.\n3. The effects on the DBS break pathway are poorly analyzed. Are the differences in gene expression shown in Figure 3 related to differences in cell proliferation or cell cycle distribution? Data in panel C do not have any statistical analysis. Panels F and H in Figure 3 lack baseline comparisons.\n4. Figure 4 shows very minor, and not always significant, differences in gene expression which render the conclusions less solid. The RNA-seq analysis is not properly described, nor the multiple testing applied. It is kind of surprising that the effects on cell proliferation are associated with reduced glycolysis. Panel E lacks statistics.\n5. The concentrations of lactate used are very high compared to physiological levels and the effects in Figure 5-6 are often quite small. The genomic analysis is not properly controlled nor does it have sufficient explanation about replicates, statistics, etc.", "reviewer_2": "This study aims to elucidate an innovative model in which loss of KDM6A regulates therapeutic response in bladder cancer through two distinct mechanisms: accumulation of extrachromosomal circular DNA (eccDNA), contributing to platinum resistance, and lactate-mediated histone lactylation, which modulates Treg function and enhances immunotherapy sensitivity. The authors integrate multi-omics analyses with comprehensive in vitro and in vivo functional assays, and the conceptual framework holds promising translational potential. However, several important issues remain to be addressed:\nMajor Comments\n1.In Figure 1D, the figure legend states that the wound-healing assays under cisplatin treatment were performed with n = 9 biologically independent samples per group; however, the box-and-whisker plot shows approximately 12 data points for the sgScramble group and 15 for the sgKdm6a group, which is inconsistent with the stated sample size. A similar discrepancy appears in Figure 1C (“sgScramble + cisplatin” legend: n = 3 per group, yet only 2 data points are shown) and in Figure 3E, where the MFI of γ-H2AX shows 3 points for sgScramble but the percentage of γ-H2AX shows 6 points, while sgKdm6a shows 5 versus 6 points—contradicting the figure legend’s claim of n = 3 for the left panel and n = 5 for the right. Such inconsistencies between declared and plotted sample sizes undermine confidence in the statistical analyses and, by extension, the study’s mechanistic conclusions.\n2.On line 89, the authors state, “we uncovered a previously uncharacterized role for KDM6A in tumor metabolic reprogramming. Loss of KDM6A suppressed glycolytic flux and decreased intratumoral lactate accumulation.” However, Yang et al. (PMID: 36215227) have already reported that KDM6A regulates tumor glycolysis. The authors should temper their language, cite Yang et al., and discuss how their findings extend or differ from that prior work.\n3.While the wound-healing assay in Figure 1 is informative, it assesses only two-dimensional migration and can be confounded by proliferation and scratch width variability. Incorporating Transwell migration and invasion assays would provide more quantitative and mechanistic insight into how KDM6A loss affects bladder cancer cell motility and invasiveness.\n4.Comparing J82 (KDM6A-mutant) and RT4 (KDM6A-wildtype) cell lines offers supportive evidence, but differences in their genetic backgrounds, signaling pathways, and chromosomal alterations may also influence cisplatin sensitivity. To attribute the phenotype specifically to KDM6A, the authors should perform isogenic manipulations—for example, re-expressing KDM6A in J82 and knocking out KDM6A in RT4—and assess cisplatin response in these matched backgrounds.\nMinor Comments\n1.Lines 252–253: In “in J82 cells compared to RT4 cells” (Figure 4C), should this reference be to Extended Data Figure 4C?\n2.All histological images lack scale bars; in Figure 3D, the white arrows are unexplained—please add scale bars and clarify the meaning of these arrows.\n3.In Figure 4B, correct “Chip-seq” to “ChIP-seq.”\n4.The figure legend numbering for Figures 3E and 3F does not match the order shown in the panels.\n5.In Figure 4F, the right-hand ECAR y-axis is labeled “mpH/min/Norm. Unit.” Please clarify whether “Norm. Unit” refers to normalization by cell number or by protein content. Additionally, the points at which oligomycin and rotenone/antimycin A were added should be indicated on the graph.", "reviewer_3": "In this manuscript, Singh et al report that KDM6A loss in bladder cancer is associated with altered response to chemotherapy and immune checkpoint blockade treatments, by performing retrospective analysis in published data from advanced bladder cancer patients. They utilize murine cell lines and allograft tumors, as well as human cell lines, and report that KDM6A regulates key genes in several molecular pathways, including genome integrity and metabolism. The authors link the presence of oncogene copies in extrachromosomal DNA to chemotherapy resistance, and metabolic rewiring to improved immune checkpoint blockade (ICB) efficacy. Additionally KDM6A is shown to dysregulate DNA repair pathways that could also contribute to altered therapy response.\nOverall, there is considerable novelty in their findings, but several mechanistic connections lack sufficient evidence. To address this issue, the authors should perform functional experiments and change the language in text so that in the revised manuscript it is clear which transcriptional effects of KDM6A loss contribute to altered therapy response and to what degree.\nMajor comments:\n1. Overall the evidence that implicate eccDNA as a cause of chemotherapy resistance is weak (Figures 1G-J, Lines 386-387). The authors should provide experiments that show presence of oncogenes and/or chemoresistance-associated genes in eccDNA of KDM6A KO/MUT cells (e.g. by FISH). In addition, the authors should provide evidence of specific eccDNA involvement in chemoristance, e.g. by ablation of candidate genes in KDM6A MUT cells to restore chemotherapy sensitivity.\n2. Comparison of only two human cell lines is not sufficient to draw conclusions about conservation of findings in human cells (Figures 1F, 2H, 3G, Extended Data Figure 4C). The authors should either use more KDM6A WT and KDM6A MUT cells (at least 3 per condition), or perform KO/KD experiments in KDM6A WT cells and/or KDM6A rescue experiments, e.g. by OE, in KDM6A MUT cells.\n3. Related to Lines 171-175, Figures 2E,G and Extended Data Figures 2C-D, and Lines 212-215 3A,C, Extended Data Figures 3C-D, and Figures 4B-C and Extended Data Figure 6E: The authors should provide IGV track images (or similar) for visualization of KDM6A and H3K4me3 peaks. Also to strengthen causal relationship of KDM6A binding and Msh2 Msh6 downregulation they could show H3K27me3 ChIP-seq at these loci. Similarly, for Figure 6C, the enrichment of H3K9la and H3K18la at specific genes should be vizualized in IGV.\n4. The authors provide evidence for multiple pathways that could contribute to improved ICB efficacy (Figures 2-5), yet they do not provide mechanistic evidence to assess the degree of contribution for each one. For example, overexpression of Mlh1/Msh2, Exo1/Lig3, and Ldha/HK2/Eno2 in KDM6A KO cells could lead to ICB resistance.\n5. For Figures 1B and 2B, tumor growth curves should be provided.\nMinor:\n6. Lines 138-139 and Extended Data Figure 1B: The eccDNA study in KMD6A WT and MUT patients is incompletely described in the text, methods and the related figure. Are the levels and heterogeneity of eccDNA different between WT and MUT patients?\n7. Figure 2B description is incorrect, wrong treatment is mentioned.\n8. Figure 3D: how many tumors analyzed? Provide quantitation\n9. Lines 230 S3E reference irrelevant\n10. Lines 228-230 3E: please also provide representative images of gH2AX staining’\n11. Lines 252-253 fix reference to Extended Data Figure 4C\n12. Cell line suppernatant experiments in human cell lines\n13. Lines 266-269: please reference the studies mentioned.", "reviewer_4": "This study describes a mechanistic framework by which loss-of-function mutations in the histone demethylase KDM6A modulate therapeutic responses in bladder cancer. The authors present analyses of numerous clinical cohorts and data from model systems indicating that KDM6A deficiency is associated with worse overall survival following cisplatin-based chemotherapy, but improved outcomes with immune checkpoint blockade (ICB); a noteworthy and potentially important result. Mechanistically, the authors show data that KDM6A loss promotes the accumulation of extrachromosomal circular DNA (eccDNA), contributing to cisplatin resistance. The authors show that KDM6A directly regulates DNA mismatch repair (MMR) and double-strand break repair (DSBR) genes, and that its loss impairs these pathways, resulting in genomic instability, elevated tumor mutational burden, and enhanced immunogenicity. In parallel, KDM6A deficiency suppresses intratumoral lactate production, which leads to diminished histone lactylation (H3K9la, H3K18la) in regulatory T cells (Tregs). This epigenetic alteration attenuates the expression of immunosuppressive genes, thereby enhancing effector T cell activity in the tumor microenvironment. Collectively, the authors conclude that the findings position KDM6A mutation status as a potential predictive biomarker for therapeutic stratification in bladder cancer.\nOverall this paper combines epigenetic, metabolic, and the immune experiments to create a mechanistically rich study linking KDM6A loss to divergent therapeutic responses in bladder cancer. However, the limited validation of some key claims needs to be addressed to solidify its mechanistic and clinical impact. I recommend the following points be addressed prior to acceptance.\n1. The association between KDM6A inactivating mutations and eccDNA is very intriguing but the mechanistic experiments rely heavily on a single murine bladder cancer cell line (MB49)+/- CRISPR-Cas9 knockout and a limited number of human cell lines. In particular the authors attempted to validate the cisplatin-resistant phenotype of KDM6A-mutant murine bladder cancer cells with the human cell lines RT4 and J82 (Fig 1F). Comparing two different human cell lines, even from the same tumor type, is prone to confounding as cell specific copy number alterations or epigenetic differences may strongly influence therapeutic response. (References “Oncogene volume 36, pages35–46 (2017); Scientific Reports volume 6, Article number: 38531 (2016)” show some of the known cell line differences.) The authors need to provide cisplatin resistance data from either a larger panel of bladder cancer cell lines with known KDM6A status or knockdown KDM6A in the T24 line to show validation in a human system comparable to the murine sgScramble and sgKdm6a cells.\n2. While the study clearly demonstrates increased eccDNA in the murine sgKdm6a cells compared with sgScramble (Fig 1G), a more detailed characterization of these amplifications is needed. At the very least genome wide copy number plots of the sgScramble and sgKdm6a cells should be generated and presented. This along with sequencing the eccDNA will enable eccDNA differences to be clearly observed and specific genetic loci identified. It will significantly strengthen the association the authors are trying to show if any of the eccDNA loci correspond to some of the key oncogenes already associated with cisplatin resistance or identify new resistance genes.\n3. The Chip-seq results need to be clarified. The authors present numerous “Profile Plots” of their ChIP-seq data. I cannot find an explanation in the Methods, but it seems to be a plot of the Chip-seq signal averaged across samples (e.g. Fig. 3C) and/or gene sets (e.g. Fig. 4C). This needs to be clarified for each plot; exactly what genes or gene sets and how many samples were averaged in the plot needs to be stated. Also, all the replicates for the individual gene Profile plots need to be shown in supplementary to evaluate the variability in the raw data."}, "author_response": "We thank the Reviewer for the assessment of our manuscript. While KDM6A mutation is more common in non-muscle-invasive bladder cancer, we would like to highlight that around 26% of patients with muscle invasive bladder cancer harbor mutations in KDM6A1.\nSpecific comments\n1. Figure 1: to be conclusive on the association with resistance to cisplatin, robust data in more than one series should be provided. The data on final tumor weight in B should be accompanied by growth curves. The migration data does not take into consideration the effect of cell growth. The use of a single sgRNA is not appropriate. A western blot to convincingly show down-regulation of KDM6A protein expression is not shown. The impact on extrachromosomal DNA is not robustly demonstrated, the analysis is very superficial and inconclusive.\nAuthor’s Response: We thank the Reviewer for the comments.\n• To address the Reviewer’s concerns and to strengthen our conclusions, we have provided additional data to demonstrate the association of KDM6A mutation with cisplatin resistance. We performed experiments with an expanded panel of both murine and human bladder cancer cell lines. We generated independent CRISPR-Cas9 Kdm6a knockout clones using multiple sgRNAs in MB49 cells. Additionally, we used two human bladder cancer cell lines RT4 and ScaBER2 to generate KDM6A knockout cells K2 and B7 respectively. Western blotting confirms effective loss of KDM6A protein (Figure 1a and Figure 2a in the point-by-point response letter and Extended Data Fig. 1A, B in revised manuscript). Consistent with our previous findings, assays of cisplatin cytotoxicity, spheroid formation, transwell invasion and wound healing assay uniformly demonstrated enhanced cisplatin resistance in sgKdm6a clones as well as in K2 and B7 cells compared with control clones in both murine and human cell lines (Figure 1b-e and Figure 2b-f in the point-by-point response letter and Fig. 1C-G and Extended Data Fig. 1D-K in revised manuscript).\n• To address the Reviewer’s comment on growth curves accompanying the final tumor weight in Fig. 1B, we have now provided both tumor growth curve and endpoint weights. (Figure 3 in the point-by-point response letter and Fig. 1B and Extended Data Fig.1C in revised manuscript). Consistent with the tumor weight, growth curves also demonstrated that cisplatin-based chemotherapy showed greater benefit in mice with sgScramble tumors compared to those with sgKdm6a tumors, highlighting cisplatin resistance following Kdm6a loss.\n• To address the concern regarding cell proliferation in migration assays, we performed wound-healing and transwell experiments under low-serum conditions, thereby minimizing confounding growth effects (Figure 4a, b in the point-by-point response letter and Fig.1D and Extended data Fig.1F, H in revised manuscript). This has been clarified in the Methods. Additionally, we have performed CFSE based labelling and flowcytometry assay using these cell lines which demonstrated that the proliferation rates for both sgKdm6a and sgScramble cells were similar over the treatment period (Figure 4c in the point-by-point response letter and Extended data Fig.1L in revised manuscript).\n• To address the Reviewer’s concern regarding the impact on extrachromosomal DNA in cisplatin resistance, we performed whole genome sequencing (WGS) on human bladder cancer cell lines RT4 and K2 (KDM6A-KO RT4 cells). Our WGS analysis demonstrated the following:\nThe genome-wide copy number heatmap of RT4 and K2 cells showed increased copy number gains in various genomic regions specifically in chromosomes 2, 3, and 7, indicating increased amplifications in these regions upon KDM6A loss. (Figure 5 in the point-by-point response letter and Fig.1H in revised manuscript).\nAnalysis of the circular-amplicons using Circle-map showed multiple circular amplicons carrying critical genes implicated in promoting cisplatin resistance including TP633, 4, 5, , CLDN46, 7, GLI28, 9, 10, 11, LUC7L312, 13, ERCC414, 15and SHCBP116, 17, 18 in KDM6A KO cells (K2 cells) (Figure 6 in the point-by-point response letter and Fig 1I and Extended data Fig. 2E in revised manuscript). We used the standard thresholds of split reads greater than 2 and circle score higher than 50 to ensure the reliability of observations which has been described in the Methods section of the revised manuscript.\nAs the genome wide copy number heatmap showed increased amplifications in chromosomes 2, 3, and 7, we evaluated copy numbers for the eccDNA gene loci. Coverage plot of the genes showed increased copy numbers in K2 cells than RT4 cells. (Figure 7 in the point-by-point response letter and Fig 1I and Extended data Fig. 2F in revised manuscript).\nTo summarize, these results show KDM6A loss mediated formation of eccDNA as a vehicle for multiple critical genes that drive cisplatin resistance in bladder cancer cells.\n2. Effects on the MMR machinery are not convincing, as is the case for the association with response to immunotherapy. They should consider multiple independent series and perform multivariable analysis. Co-occurrence and mutual exclusivity are probably not corrected for multiple testing. The association with tumor mutational burden should be analysed if there is a real effect on MMR. The standard MMR analysis included several microsatellite sites, rather than just one. The effects are small in several of the comparisons shown. In panel H comparing one cell line to another is just inappropriate. More details is required in the description of multiple results in Figure 2. Reduced expression of MMR proteins should be shown using western blotting, with appropriate controls.\nAuthor’s Response: We thank the Reviewer for these important comments.\n• The MSI-High phenotype is primarily driven by attenuation of expression of genes associated with MMR machinery secondary to mutations in the DNA MMR system, which in turn leads to a higher tumor mutation burden (TMB). Our prior analysis demonstrated a higher TMB in patients harboring KDM6A mutation across two independent datasets (IMvigor21019 and TCGA-BLCA1) (Figure 8a in the point-by-point response letter and Fig. 2C in the revised manuscript). Based on the Reviewer's suggestion, we reanalyzed all co-occurrence and mutual exclusivity analyses, correcting for multiple testing using the Benjamini–Hochberg FDR20 (Figure 8b in the point-by-point response letter and Fig. 2D in the revised manuscript). This confirmed no significant correlation between cooccurrence of KDM6A mutations and mutations in MMR machinery genes.\n• To directly link KDM6A loss with MMR machinery disruption, we analyzed RNA sequencing data from two sgKdm6a cell clones following KDM6A knockout (Figure 8c in the point-by-point response letter and, Fig. 2G and Extended Data Fig. 3J in the revised manuscript). We noted a significant attenuation of the MMR pathway, evidenced by a reduction in the expression of multiple MMR pathway genes. To establish a mechanistic link between KDM6A loss and MMR pathway disruption, ChIP seq analysis was performed. We found that the loss of KDM6A led to reduced KDM6A binding, enrichment of H3K27me3 repressor marks and a concomitant reduction in the enrichment of H3K4me3 activation marks at the promoters of key MMR genes, including MSH2 and MSH6 (Figure 8d in the point-by-point response letter and Fig. 2F and Extended Data Fig. 3G in the revised manuscript).\n• Extending our previous findings, we performed CRISPR-Cas9 mediated knockout of KDM6A in two additional human bladder cancer cell lines, RT4 and ScaBER. The knockout cells for the RT4 and ScaBER cell lines are designated as K2 and B7 respectively. We validated the downregulation of KDM6A using western blot (Figure 9a in the point-by-point response letter and Extended Data Fig. 1B in the revised manuscript). Subsequent qRT-PCR analysis demonstrated a significant reduction in key MMR machinery genes like MSH2 and MSH6 (Figure 9b in the point-by-point response letter and, Fig. 2I and Extended Data Fig. 4A in the revised manuscript). Additionally, to validate our previous mechanistic studies in murine cell lines, we performed ChIP-qPCR analysis which confirmed the direct binding of KDM6A to MMR pathway genes in RT4 cells. Reduction in KDM6A binding to MSH6 and MLH1 alongside a reduction in H3K4me3 enrichment at MSH6, MLH1 and MSH2 gene promoters supported a direct role for KDM6A in maintaining the epigenetic landscape of these critical repair genes (Figure 9c, d in the point-by-point response letter and Extended Data Fig. 4D, E in the revised manuscript).\nThese data suggest that the attenuation of MMR-associated gene expression results from direct transcriptional regulation by KDM6A rather than from co-occurring mutations in canonical MMR genes, underscoring the role of KDM6A as an upstream epigenetic regulator of genomic stability.\n• To address the Reviewer’s comment that standard MMR analysis should assess multiple microsatellite loci rather than a single site, we performed Fluorescent Fragment Length Analysis (FFLA) to evaluate microsatellite instability (MSI) across murine and human bladder cancer models. Specifically, we analyzed murine sgScramble (N=7) and sgKdm6a (N=7) tumors using markers mBAT64, mBAT24, mBAT37, and mBAT59, and human bladder cancer cell lines RT4 (wild-type KDM6A) and K2 (KDM6A knockout) using NR27, NR21, NR24, BAT25, and BAT26. Among these markers, we observed reproducible modal peak shifts associated with KDM6A loss. The mBAT64 locus showed 5-nucleotide leftward shift in sgKdm6a murine tumors compared with sgScramble controls, while the NR21 locus in K2 human cells exhibited a corresponding 5-nucleotide rightward shift. Although these changes were limited to specific loci, they are consistent with microsatellite instability and support impaired MMR fidelity following KDM6A loss (Figure 10a, b in the point-by-point response letter and Fig. 2J and Extended Data Fig. 4F-H in the revised manuscript).\n• To address the Reviewer’s concern regarding the comparison of one cell line to another, we have expanded our analysis by generating CRISPR-Cas9 mediated KDM6A knockout cell lines K2 and B7 in two additional human bladder cancer cell lines, RT4 and ScaBER respectively. We validated the loss of KDM6A using western blot (Figure 9a in the point-by-point response letter and Extended Data Fig. 1B in the manuscript). Consistent to our murine RNA-seq data with the sgScramble and sgKdm6a clones, we observed a significant reduction in expression of key MMR machinery genes like, MSH2 and MSH6 (Figure 9b in the point-by-point response letter and Fig. 2I and Extended Data Fig. 4A in the revised manuscript). Further ChIP-qPCR data in RT4 and K2 cell lines demonstrated reduced KDM6A binding at MLH1 and MSH6 genes alongside reduced H3K4me3 enrichment at MLH1, MSH6 and MSH2 corroborating our previous mechanistic studies. (Figure 9c, d in the point-by-point response letter and Extended Data Fig. 4D, E in the revised manuscript)\n• To address the Reviewer’s concern regarding reduced expression of MMR proteins, we used flow cytometry to assess the protein expression levels of MSH2 and MSH6 in these cells (Figure 11a, b in the point-by-point response letter and Extended Data Fig. 4B, C in the manuscript). Consistent with our gene expression data, we observed a decrease in protein expression in both human cells in absence of KDM6A expression. We selected flow cytometry as it provides precise and quantitative measurement of protein expression over western blot which offers semi quantitative analysis.\n3. The effects on the DBS break pathway are poorly analyzed. Are the differences in gene expression shown in Figure 3 related to differences in cell proliferation or cell cycle distribution? Data in panel C do not have any statistical analysis. Panels F and H in Figure 3 lack baseline comparisons.\nAuthor’s Response: We thank the Reviewer for these thoughtful comments.\n• Our murine RNA-seq data demonstrated reduced expression of DSBR pathway genes such as Exo1, Lig3 and Baz1b in sgKdm6a cells as compared to sgScramble cells (Figure 12a in the point-by-point response letter and Fig. 3C and Extended data Fig. 5H in the revised manuscript). Additionally, our ChIP seq analysis demonstrated reduction in KDM6A binding at the promoter of genes associated with DSBR pathway in sgKdm6a cells with a reduction in enrichment of H3K4me3 at these gene promoters (Fig. 3A in the revised manuscript). To address the Reviewer’s concern regarding the impact on DSBR pathway, we expanded the analysis to further validate our data, incorporating an alternative sgKdm6a clone (sgKdm6a C2) and two human bladder cancer cell lines (RT4 and ScaBER).\n• Consistent with our prior findings, analysis of the additional murine sgKdm6a clone C2 revealed reduced expression of genes such as Exo1, Lig3, and Baz1b, accompanied by diminished KDM6A binding and attenuated H3K4me3 enrichment at their promoter regions (Figure 12a, b in the point-by-point response letter and Extended data Fig. 5D, H in revised manuscript). Similarly, with the loss of KDM6A in K2 and B7 cell lines, significant decrease in the expression of gene like BAZ1B, EXO1 and LIG3 were observed (Figure 12c, d in the point-by-point response letter and Extended Data Fig. 5I, J in the revised manuscript). ChIP-qPCR analysis further showed decreased KDM6A occupancy and H3K4me3 enrichment at these loci in K2 cells with KDM6A loss, consistent with our murine data (Figure 12e, f in the point-by-point response letter and Extended Data Fig. 5K, L in the revised manuscript).\n• To address the Reviewer’s concern regarding potential confounding by cell proliferation or cell cycle distribution, we performed flow cytometry-based cell proliferation and cell cycle analysis using cell permeable labeling dyes. We observed no significant differences between the control and KDM6A knockout cells (Figure 13a-c in the point-by-point response letter and Extended Data Fig. 1L, 5M in the revised manuscript). This suggests that the reduction in the expression of genes associated with DSBR pathway is not secondary to altered proliferation or cell cycle phase distribution in murine and human bladder cancer cell lines.\n• To address the Reviewer’s comment regarding Extended data Fig. 5F in the revised manuscript, we would like to clarify that the profile plots represent chromatin signals, whereas the accompanying bar plots show RNA expression levels (counts per million) for genes identified in the differential ChIP-seq and RNA-seq analyses of sgScramble and sgKdm6a C1 cells (Fig. 3A, C in the revised manuscript). For the profile plots, we extracted peak regions from the differential ChIP-seq analysis of the respective genes and calculated binding scores, which were visualized over a ±1 kb region surrounding the TSS to compare signal intensity between sgKdm6a and sgScramble. To further strengthen these findings, we repeated the analysis in an independent sgKdm6a clone (sgKdm6a C2). Consistent with our previous data, we observed a decrease in expression of genes associated with DSBR pathway along with decreased binding of KDM6A and corresponding reduction in H3K4me3 enrichment in these gene promoter indicating role of KDM6A in regulating DSBR (Figure 12b in the point-by-point response letter and Extended data Fig. 5D in the revised manuscript).\n• To address the Reviewer’s comment regarding the baseline comparisons in Fig. 3E and 3G in the revised manuscript, we have included baseline γ-H2AX foci measurements (without Etoposide treatment), from confocal microscopy, which showed a higher abundance of γ-H2AX foci in sgKdm6a C1 cells as compared to sgScramble cells (Figure 14a in the point-by-point response letter and Fig. 3E in the revised manuscript). We would also like to clarify that the COMET assay presented in Fig. 3G in the revised manuscript was performed without the addition of DNA damage inducer (Etoposide). Consistent results were observed in the RT4 and K2 cell lines, where γ-H2AX foci and COMET assay parameters (tail DNA percentage, Olive Tail moment, and tail moment) were all significantly increased upon KDM6A loss in K2 cells (Figure 14b, c in the point-by-point response letter and Fig. 3H, I in the revised manuscript).\nOverall, we would like to highlight that among the genes downregulated following KDM6A loss, EXO1 and LIG3 functions at the interface of the MMR and DSBR pathways. EXO121, 22, 23, 24 orchestrates the excision step of mismatch repair and contributes to DNA end resection during homologous recombination, whereas LIG325 mediates repair-gap ligation and supports alternative end-joining under replicative stress. Downregulation of these genes, alongside reduction in the expression of other MMR associated genes, compromises mismatch repair pathway and elements of DSBR pathway, collectively promoting genomic instability. Notably, the repair deficiency arising from KDM6A loss is mechanistically distinct from classical MSI-high or DSBR-defective tumors, as it stems from epigenetic transcriptional repression rather than direct mutational inactivation of canonical repair genes, defining a unique, chromatin-driven form of genomic instability. We have included this in the Discussion section in the revised manuscript.\n4. Figure 4 shows very minor, and not always significant, differences in gene expression which render the conclusions less solid. The RNA-seq analysis is not properly described, nor the multiple testing applied. It is kind of surprising that the effects on cell proliferation are associated with reduced glycolysis. Panel E lacks statistics.\nAuthor’s Response: We thank the Reviewer for the thoughtful comments.\n• We acknowledge that certain genes such as PFKFB4 and PGK1 did not meet statistical significance in the IMvigor21019 dataset (n=275), however, other critical genes in the glycolytic pathway such as LDHA, ENO1, BPGM, HK2, PKM and PGM1 expression were significantly attenuated in patients harboring KDM6A mutation (Fig. 4A in the revised manuscript). To address the Reviewer’s concern, we extended our analysis to the TCGA-BLCA dataset (n=293)1, and consistent to our previous analysis we saw significant attenuation in LDHA and ENO1 in KDM6A mutant patients (Figure 15a in the point-by-point response letter and Extended data Fig. 6A in revised manuscript).\nAdditionally, to address the Reviewer’s concern regarding the RNA seq data, we have revised our manuscript describing the RNA seq analysis. We have reanalyzed our RNA seq data applying Benjamini–Hochberg FDR correction for multiple testing. To increase the robustness of the data, we have performed RNA seq analysis in another independent sgKdm6a clone (sgKdm6a C2). We observed a consistent decrease in glycolytic genes in both the sgKdm6a clones C1 and C2 as compared to sgScramble showing downregulation in glycolytic pathway in absence of KDM6A (Figure 15b in the point-by-point response letter and Fig.4D and Extended data Fig. 6H in revised manuscript).\nFurther, to validate these findings in human bladder cancer cells with KDM6A knockout, we performed qPCR and ChIP-qPCR. Reduced glycolytic gene expression was observed in K2 and B7 with KDM6A deletion (Figure 16a, b in the point-by-point response letter and Fig. 4E and Extended data Fig. 6J in revised manuscript). We also observed a decreased KDM6A occupancy and H3K4me3 enrichment at glycolytic promoters in the K2 cells as compared to RT4 cells from our ChIP-qPCR data (Figure 16c, d in the point-by-point response letter and Extended data Fig. 6K, L in revised manuscript).\nTogether, these results demonstrate that KDM6A directly regulates glycolytic pathways and that its loss drives metabolic reprogramming.\n• We acknowledge the Reviewer’s comment regarding glycolysis and cell proliferation. In both murine and human cell lines, glycolytic ATP production was significantly reduced, accompanied by a compensatory metabolic shift toward oxidative phosphorylation (OXPHOS) following KDM6A loss (Figure 17a, b in the point-by-point response letter and Fig. 4F-I in revised manuscript).This is consistent with prior reports showing that cancer cells adapt to glycolysis inhibition by upregulating mitochondrial respiration26 and activating the TCA cycle27.\n• To address the Reviewer’s concern regarding Extended data Fig. 6E in the revised manuscript, we would like to clarify that the profile plots represent chromatin signals at gene loci (e.g., Ldha, Hk2, Eno2), whereas the accompanying bar plots show RNA expression levels (counts per million) for genes identified in the differential ChIP-seq and RNA-seq analyses. For the profile plots, we extracted peak regions from the differential ChIP-seq analysis of the respective genes and calculated binding scores, which were visualized over a ±1 kb region surrounding the TSS to compare signal intensity between groups. We have revised the Methods section to include necessary details for ChIP-seq analysis.\n5. The concentrations of lactate used are very high compared to physiological levels and the effects in Figure 5-6 are often quite small. The genomic analysis is not properly controlled, nor does it have sufficient explanation about replicates, statistics, etc.\nAuthor’s Response: We thank the Reviewer for this important comment. For our experiments, we used lactate concentrations that have been widely used in prior studies to assess functional effects on immune subsets28, 29, 30, 31. While physiological lactate levels in immune cells typically range from 1–2 mM, pathological states such as chronic inflammation and cancer create a microenvironment where lactate concentrations rise substantially due to rapid glycolysis and the “Warburg effect”.\n• To address the Reviewer’s concern, we tested the impact of exogenous lactate on T regulatory (Treg) cell function using sodium lactate at 5, 10, 15, and 25 mM. We observed a dose-dependent increase in H3K18la and H3K9la histone lactylation, along with elevated PD-1 expression in Tregs even at lower concentrations. (Figure 18a, b and 19 in the point-by-point response letter and Extended data Fig. 7C and 8E, F in revised manuscript).\n• The details regarding replicates, controls, and statistical analyses for the genomic data presented in Fig. 6B, Fig. 6C and Extended data Fig. 8G in the revised manuscript, have now been clarified in both the Methods and figure legends. Briefly, quality control, read mapping, and downstream processing of ChIP-seq data were performed using standard tools and approaches. In addition, matched “Input controls” were included to ensure accurate identification of significant ChIP-seq signal enrichment peaks.\nWe compared genomic occupancy and differential enrichment of histone PTMs (H3K9la, H3K18la, H3K4me3, and H3K27ac) in Tregs treated with sodium lactate (Treg + Na-La) versus untreated controls (Tregs). Notably, Na-La–treated Tregs exhibited higher enrichment of H3K9la and H3K18la, accompanied by increased expression of key genes regulating Treg identity and function, including Foxp3, Pdcd1, and Tgfβ1 (Fig. 6C, D and Extended data Fig. 8I in revised manuscript). These findings support a role for exogenous lactate–derived H3K9la and H3K18la in modulating gene expression in Tregs. The Methods section of the revised manuscript has been updated to provide critical details of the genomic analyses.\nReviewer #2 (Remarks to the Author): with expertise in cancer, immunology\nThis study aims to elucidate an innovative model in which loss of KDM6A regulates therapeutic response in bladder cancer through two distinct mechanisms: accumulation of extrachromosomal circular DNA (eccDNA), contributing to platinum resistance, and lactate-mediated histone lactylation, which modulates Treg function and enhances immunotherapy sensitivity. The authors integrate multi-omics analyses with comprehensive in vitro and in vivo functional assays, and the conceptual framework holds promising translational potential. However, several important issues remain to be addressed:\nWe sincerely thank the Reviewer for the assessment of our manuscript and for recognizing the translational potential of our conceptual framework.\nMajor Comments\n1.In Figure 1D, the figure legend states that the wound-healing assays under cisplatin treatment were performed with n = 9 biologically independent samples per group; however, the box-and-whisker plot shows approximately 12 data points for the sgScramble group and 15 for the sgKdm6a group, which is inconsistent with the stated sample size. A similar discrepancy appears in Figure 1C (“sgScramble + cisplatin” legend: n = 3 per group, yet only 2 data points are shown) and in Figure 3E, where the MFI of γ-H2AX shows 3 points for sgScramble but the percentage of γ-H2AX shows 6 points, while sgKdm6a shows 5 versus 6 points—contradicting the figure legend’s claim of n = 3 for the left panel and n = 5 for the right. Such inconsistencies between declared and plotted sample sizes undermine confidence in the statistical analyses and, by extension, the study’s mechanistic conclusions.\nAuthor’s Response: We thank the Reviewer for this important comment. We would like to emphasize that the sample sizes presented are not inconsistencies. Rather, they reflect the reporting format we were instructed to follow during a prior rebuttal for Nature Immunology (Raychaudhuri, Singh et al. 28), where we were advised to report the lowest n within the total sample group rather than providing a range. In preparing this manuscript for Nature Communications, we adhered to the same formatting approach. Importantly, all plotted data points correspond to biological replicates, no data has been excluded, and all statistical analyses were performed on the complete dataset.\nHowever, we are happy to revise the figure legends to explicitly indicate the full range of sample sizes if preferred by the Reviewer and the editorial team.\n2.On line 89, the authors state, “we uncovered a previously uncharacterized role for KDM6A in tumor metabolic reprogramming. Loss of KDM6A suppressed glycolytic flux and decreased intratumoral lactate accumulation.” However, Yang et al. (PMID: 36215227) have already reported that KDM6A regulates tumor glycolysis. The authors should temper their language, cite Yang et al., and discuss how their findings extend or differ from that prior work.\nAuthor’s Response: We thank the Reviewer for drawing our attention to the study by Yang et al.32 which demonstrated that in glioblastoma HOXA3 activates KDM6A to remove the repressive H3K27me3 mark at glycolytic gene loci, thereby promoting glycolysis and tumor progression. We modified the sentence and acknowledged this study in our discussion section of the revised manuscript.\nOur findings extend this concept by showing that KDM6A loss suppresses glycolysis and reduces intratumoral lactate production, thereby reshaping the tumor immune microenvironment in bladder cancer. Additionally, CRISPR-mediated KDM6A deletion attenuated H3K4me3 enrichment at glycolytic promoters linking the loss of a permissive histone mark to impaired glycolytic gene expression. This metabolic reprogramming shifted cells toward OXPHOS, lowered lactate levels, and reduced lactate-driven histone lactylation in Tregs, ultimately improving the CD8 T cells:Treg ratio and enhancing responsiveness to anti-PD-1 therapy.\nThus, while Yang et al. established a HOXA3–KDM6A–H3K27me3 axis in glioblastoma, our work demonstrates that KDM6A loss disrupts both H3K27me3 and H3K4me3 regulation, thereby linking metabolic reprogramming with immune modulation and therapeutic response in bladder cancer.\n3.While the wound-healing assay in Figure 1 is informative, it assesses only two-dimensional migration and can be confounded by proliferation and scratch width variability. Incorporating Transwell migration and invasion assays would provide more quantitative and mechanistic insight into how KDM6A loss affects bladder cancer cell motility and invasiveness.\nAuthor’s Response: We thank the Reviewer for this insightful comment. Based on the Reviewer's suggestions, we measured cell proliferation and performed transwell invasion assays in both control and KDM6A knockout cells. We performed CRISPR-Cas9 mediated knockout of KDM6A in two human bladder cancer cell lines, RT4 and ScaBER and used multiple sgRNAs to generate independent Kdm6a knockout clones (sgKdm6a C1-C3) in murine MB49 cells. K2 and B7 are the respective KDM6A knockout cells of RT4 and ScaBER2. Western blot analysis was used to validate successful deletion of KDM6A in human as well as the murine cell lines (Figure 20a, b in the point-by-point response letter and Extended data Fig.1A, B in revised manuscript).\nWe performed cell proliferation assay for the treatment period of 48 hours. Proliferation rates were comparable between the control and KDM6A knockout groups across the cell lines, ruling out proliferation as a confounder (Figure 20c in the point-by-point response letter and Extended data Fig.1L in revised manuscript).\nAdditionally, KDM6A knockout cells (K2 and B7) from both the human cell lines and the murine sgKdm6a clones exhibited higher transwell invasion under cisplatin treatment compared to controls (Figure 20d-f in the point-by-point response letter and Fig. 1D, E and Extended data Fig.1F, G in revised manuscript).\nTo further validate our observations, we repeated wound-healing, spheroid formation and cisplatin cytotoxicity assays in additional murine and human KDM6A knockout cell lines, all of which confirmed that KDM6A loss is associated with enhanced cell motility and cisplatin resistance (Figure 21a-h in the point-by-point response letter and Fig.1C, F, G and Extended data Fig.1D, E, H-K in revised manuscript).\n4.Comparing J82 (KDM6A-mutant) and RT4 (KDM6A-wildtype) cell lines offers supportive evidence, but differences in their genetic backgrounds, signaling pathways, and chromosomal alterations may also influence cisplatin sensitivity. To attribute the phenotype specifically to KDM6A, the authors should perform isogenic manipulations—for example, re-expressing KDM6A in J82 and knocking out KDM6A in RT4—and assess cisplatin response in these matched backgrounds.\nAuthor’s Response: We thank the Reviewer for the comment regarding the potential impact of genetic background of J82 (KDM6A-mutant) and RT4 (KDM6A-wildtype) cell lines on cisplatin sensitivity. We acknowledge that differences in their genetic makeup, alongside other associated factors, could impact the observed results. To address this concern, we performed CRISPR-Cas9 mediated KDM6A knockout in two distinct human bladder cancer cell lines: RT4 and ScaBER. The knockout cell lines for RT4 and ScaBER are designated as K2 and B7, respectively. KDM6A knockout in the K2 and B7 cell lines was confirmed using western blot (Figure 22a in the point-by-point response letter and Extended data Fig.1B in revised manuscript).\nWe repeated all the previous experiments, including transwell invasion, spheroid formation, wound healing assay and cisplatin-mediated cytotoxicity assays, to assess the impact of cisplatin treatment. Consistent with our original findings, the KDM6A knockout human bladder cancer cells exhibited increased invasion, migration and spheroid formation, as well as reduced cisplatin-mediated cytotoxicity compared to their respective control cells (Figure 22b-h in the point-by-point response letter, Fig. 1C, E, G and Extended data Fig.1E, G, I, K in revised manuscript).\nThese results, obtained in multiple isogenic systems, confirm that the loss of KDM6A contributes to cisplatin resistance in bladder cancer cells, independent of broader genetic background differences.\nMinor Comments\n1.Lines 252–253: In “in J82 cells compared to RT4 cells” (Figure 4C), should this reference be to Extended Data Figure 4C?\nAuthor’s Response: We thank the Reviewer for the comment. To eliminate the potential confounding effect of genetic background between distinct cell lines, we have performed CRISPR-Cas9 mediated knockout in two human bladder cancer cell lines RT4 and ScaBER. The knockout cell lines for RT4 and ScaBER are designated as K2 and B7, respectively. We have updated the manuscript to incorporate all experimental data generated using these cell lines. Importantly, consistent to our data from murine KDM6A knockout cells, human cell lines with KDM6A loss also demonstrated significantly increased cisplatin resistance, which was mediated by the amplification of circular eccDNA amplicons carrying genes associated with cisplatin resistance along with an impairment in MMR, DSBR, and Glycolysis pathways.\n2.All histological images lack scale bars; in Figure 3D, the white arrows are unexplained—please add scale bars and clarify the meaning of these arrows.\nAuthor’s Response: We thank the Reviewer for their careful review. We apologize for the oversight. The white arrows were mistakenly duplicated from another image and incorrectly placed in Figure 3D. We have rectified this mistake and uploaded the corrected figure in the revised manuscript (Fig 3D in the revised manuscript). We have also added scale bars to all relevant microscopy images throughout the manuscript.\n3.In Figure 4B, correct “Chip-seq” to “ChIP-seq.”\nAuthor’s Response: We have made the necessary corrections in the updated manuscript.\n4.The figure legend numbering for Figures 3E and 3F does not match the order shown in the panels.\nAuthor’s Response: We have made the necessary corrections in the updated manuscript.\n5.In Figure 4F, the right-hand ECAR y-axis is labeled “mpH/min/Norm. Unit.” Please clarify whether “Norm. Unit” refers to normalization by cell number or by protein content. Additionally, the points at which oligomycin and rotenone/antimycin A were added should be indicated on the graph.\nAuthor’s Response: We thank the Reviewer for the comment. We had used “cell number” as the Normalization unit. We have updated the y-axis label in the figure. Additionally, we have added the points at which oligomycin and rotenone/antimycin A were added on the graph (Fig 4G, I in the revised manuscript).\nReviewer #3 (Remarks to the Author): with expertise in bladder cancer, epigenetics\nIn this manuscript, Singh et al report that KDM6A loss in bladder cancer is associated with altered response to chemotherapy and immune checkpoint blockade treatments, by performing retrospective analysis in published data from advanced bladder cancer patients. They utilize murine cell lines and allograft tumors, as well as human cell lines, and report that KDM6A regulates key genes in several molecular pathways, including genome integrity and metabolism. The authors link the presence of oncogene copies in extrachromosomal DNA to chemotherapy resistance, and metabolic rewiring to improved immune checkpoint blockade (ICB) efficacy. Additionally KDM6A is shown to dysregulate DNA repair pathways that could also contribute to altered therapy response.\nOverall, there is considerable novelty in their findings, but several mechanistic connections lack sufficient evidence. To address this issue, the authors should perform functional experiments and change the language in text so that in the revised manuscript it is clear which transcriptional effects of KDM6A loss contribute to altered therapy response and to what degree.\nWe sincerely thank the Reviewer for the thoughtful assessment of our work and for recognizing the novelty and translational relevance of our findings.\nMajor comments:\n1. Overall the evidence that implicate eccDNA as a cause of chemotherapy resistance is weak (Figures 1G-J, Lines 386-387). The authors should provide experiments that show presence of oncogenes and/or chemoresistance-associated genes in eccDNA of KDM6A KO/MUT cells (e.g. by FISH). In addition, the authors should provide evidence of specific eccDNA involvement in chemoristance, e.g. by ablation of candidate genes in KDM6A MUT cells to restore chemotherapy sensitivity.\nAuthor’s Response:\n• We thank the Reviewer for this important comment. In our prior analysis of chromosomal structural variants in the TCGA-BLCA cohort33, we showed that patients harboring KDM6A mutation exhibited more circular amplicons (eccDNA) along with elevated POLQ expression, consistent with a role for KDM6A in regulating eccDNA formation (Figure 23a, b in the point-by-point response letter and Extended Data Fig. 2B, C in the revised manuscript). To build on these findings and to directly examine the presence of oncogenes and cisplatin resistance–associated genes within eccDNA, we first generated a KDM6A-knockout of the human RT42 bladder cancer cell line (designated K2) using CRISPR-Cas9 gene editing. Western blot analysis confirmed successful deletion of KDM6A in K2 cells (Figure 23c in the point-by-point response letter and Extended Data Fig. 1B in the revised manuscript). We then performed whole-genome sequencing (WGS) on RT4 and K2 cell lines, which enabled us to characterize eccDNA content and identify oncogene and resistance-associated loci enriched upon KDM6A loss.\n• The genome-wide copy number analysis showed increased copy number gains in various genomic regions specifically in chromosomes 2, 3, and 7 in K2 cells, indicating increased amplifications in these regions upon KDM6A loss (Figure 24 in the point-by-point response letter and Fig.1H in revised manuscript).\n• Analysis of the circular-amplicons using Circle-map showed multiple circular amplicons carrying critical genes implicated in promoting cisplatin resistance including TP633, 4, 5, , CLDN46, 7, GLI28, 9, 10, 11, LUC7L312, 13, ERCC414, 15and SHCBP116, 17, 18 in KDM6A KO cells (K2 cells) (Figure 25 in the point-by-point response letter and Fig 1I and Extended data Fig. 2E in revised manuscript). We used the standard thresholds of split reads greater than 2 and circle score higher than 50 to ensure the reliability of observations.\nAs the genome wide copy number heatmap showed increased amplifications in chromosomes 2, 3, and 7, we evaluated copy numbers for the eccDNA gene loci. Coverage plot of the genes showed increased copy numbers in K2 cells than RT4 cells. (Figure 26 in the point-by-point response letter and Fig 1I and Extended data Fig. 2F in revised manuscript).\nTo summarize, these results show KDM6A loss mediated formation of eccDNA as a vehicle for multiple critical genes that drive in cisplatin resistance in bladder cancer cells.\nRegarding functional validation, we carefully considered the Reviewer’s suggestion to ablate individual candidates. However, because multiple resistance genes co-occur on the same eccDNA amplicons, targeting a single locus would not recapitulate the collective oncogenic pressure mediated by eccDNA amplification. Our data instead support a model in which the cumulative amplification of several resistance-associated genes within eccDNA underlies the cisplatin-resistant phenotype of KDM6A-deficient tumors.\n2. Comparison of only two human cell lines is not sufficient to draw conclusions about conservation of findings in human cells (Figures 1F, 2H, 3G, Extended Data Figure 4C). The authors should either use more KDM6A WT and KDM6A MUT cells (at least 3 per condition) or perform KO/KD experiments in KDM6A WT cells and/or KDM6A rescue experiments, e.g. by OE, in KDM6A MUT cells.\nAuthor’s Response: We thank the Reviewer for this important comment. To directly address the concern, we expanded our analyses beyond the initial comparison of two human bladder cancer cell lines. Specifically, we generated CRISPR-Cas9–mediated KDM6A knockout in RT4 and ScaBER2 cells, hereafter referred to as K2 and B7, respectively. Western blot analysis verified the successful knockout of KDM6A in K2 and B7.(Figure 27a in the point-by-point response letter and Extended data Fig. 1B in revised manuscript).In addition, we validated our results using multiple independent murine Kdm6a-knockout clones.\n• To corroborate our previous findings of cisplatin resistance in murine Kdm6a knockout MB49 cell lines, we performed similar series of experiments on the human cell lines. We conducted transwell invasion, spheroid formation, cisplatin-mediated cell cytotoxicity and wound healing assays. Consistent with our murine cell line data, the human KDM6A knockout cells showed a significant increase in migration and spheroid formation (Figure 27b-f in the point-by-point response letter and Fig.1E, 1G and Extended data Fig. 1G, I, K in revised manuscript). We also observed a notable reduction in cisplatin-mediated cell death in the cells with KDM6A ablation (Figure 27g, h in the point-by-point response letter and Fig.1C and Extended data Fig.1E in revised manuscript).\n• Additionally, we observed a significant decrease in the expression of genes associated with the MMR pathway KDM6A knockout human bladder cancer cells, which is in line with our findings in murine cells lacking KDM6A. Quantitative RT-PCR and Flow cytometry were used to confirm the reduction in expression of MSH2 and MSH6 in the human KDM6A knockout cells K2 and B7(Figure 28a-d in the point-by-point response letter and Fig.2I and Extended data Fig.4A-C in revised manuscript).\n• To further investigate whether KDM6A directly regulates MMR pathway genes, we performed ChIP-qPCR in RT4 and K2 cells. Our results were consistent with our murine ChIP-seq data, showing reduced KDM6A binding at these MMR-associated genes (Figure 28e in the point-by-point response letter and Extended data Fig.4D in revised manuscript). Additionally, we found a corresponding decrease in the enrichment of H3K4me3, a histone mark associated with active gene transcription, at these same genes in the K2 cells with KDM6A knockout (Figure 28f in the point-by-point response letter and Extended data Fig. 4E in revised manuscript).\n• Next, we assessed the impact of loss of KDM6A in human bladder cancer cell lines on the expression of genes associated with DSBR pathway. Consistent with our mouse data, we observed decreased expression of specific genes, such as BAZ1B, EXO1 and Lig3, in the human cells K2 and B7 with KDM6A knockout (Figure 29a, b in the point-by-point response letter and Extended data Fig.5I, J in revised manuscript). Our ChIP-seq data further validated these findings, showing reduced binding of KDM6A and a decrease in the enrichment of the H3K4me3 histone mark at these same genes in K2 cells with KDM6A downregulation (Figure 29c, d in the point-by-point response letter and Extended data Fig. 5K, L in revised manuscript).\n• We also observed a reduction in the expression of glycolytic genes like HK2, LDHA and ENO2 in the human KDM6A knockout cell lines, which we had previously demonstrated in our murine cell lines in absence of KDM6A (Figure 30a, b in the point-by-point response letter and Fig. 4E and Extended data Fig 6J in revised manuscript). Our ChIP qPCR results in the human cell lines also demonstrated decreased KDM6A binding and reduced enrichment of the H3K4me3 histone mark at glycolysis-associated genes like HK2 and LDHA. (Figure 30c, d in the point-by-point response letter and Extended data Fig.6K, L in revised manuscript). Furthermore, these transcriptional changes correlated with a shift in cellular metabolism. Consistent with our murine data, the human KDM6A knockout cells showed reduced glycolytic ATP production and a compensatory increase in mitochondrial ATP production. (Figure 30e in the point-by-point response letter and Fig.4H, I in revised manuscript).\n3. Related to Lines 171-175, Figures 2E,G and Extended Data Figures 2C-D, and Lines 212-215 3A,C, Extended Data Figures 3C-D, and Figures 4B-C and Extended Data Figure 6E: The authors should provide IGV track images (or similar) for visualization of KDM6A and H3K4me3 peaks. Also to strengthen causal relationship of KDM6A binding and Msh2 Msh6 downregulation they could show H3K27me3 ChIP-seq at these loci. Similarly, for Figure 6C, the enrichment of H3K9la and H3K18la at specific genes should be vizualized in IGV.\nAuthor’s Response: We thank the Reviewer for this constructive comment. Based on the Reviewer’s recommendations,\n• The IGV track images for visualization of KDM6A and H3K4me3 differential peaks for critical genes involved in MMR, DSBR and glycolysis are included in the manuscript (Figure 31a-c in the point-by-point response letter and Fig. 2F, 3B, 4C in revised manuscript). Consistent with our findings, sgKdm6a cells showed a decreased enrichment of both KDM6A and H3K4me3 at the promoter regions of key genes involved in MMR, DSBR and glycolysis.\n• Additionally, to address the Reviewer’s comment regarding the causal relationship of KDM6A binding and MMR pathway gene downregulation, we have analyzed and provided the IGV plots for H3K27me3 enrichment of in the revised manuscript. The differential ChIP-seq analysis between sgKdm6a and sgScramble showed increased enrichment of H3K27me3 within +/- 5kb TSS regions of Msh2 and Msh6 genes. Additionally, we observed similar trends of increased H3K27me3 enrichment in genomic regions of Baz1b, Ldha and Eno2 (Figure 32 in the point-by-point response letter and Extended data Fig. 3G, 5E, 6D in revised manuscript).\nOverall, these data demonstrated that loss of KDM6A results in reduced H3K4me3 at the gene loci with concurrent enrichment of H3K27me3 resulting in the attenuation of the expression of key genes associated with the MMR, DSRB and glycolysis pathways.\n• The IGV browser plots for Figure 6C, showing enrichment of H3K9la and H3K18la at Pdcd1 and Tgfb1 are also included in the revised manuscript (Figure 33 in the point-by-point response letter and Extended data Fig. 8H in revised manuscript). Consistent with genomic data reported in the browser plots demonstrated that Na-La–treated Tregs exhibited higher enrichment of H3K9la and H3K18la, accompanied by increased expression of key genes regulating Treg identity and function, including Foxp3, Pdcd1, and Tgfβ1. These findings support a role for exogenous lactate–derived H3K9la and H3K18la in modulating gene expression in Tregs.\n4. The authors provide evidence for multiple pathways that could contribute to improved ICB efficacy (Figures 2-5), yet they do not provide mechanistic evidence to assess the degree of contribution for each one. For example, overexpression of Mlh1/Msh2, Exo1/Lig3, and Ldha/HK2/Eno2 in KDM6A KO cells could lead to ICB resistance.\nAuthor’s Response: We thank the Reviewer for this thoughtful comment. Prior studies have shown that defective DNA repair pathways, including MMR and DSBR, promote the accumulation of mutations and neoantigens, rendering tumors more sensitive to immune checkpoint therapy (ICT), while hyperactive glycolysis creates an immunosuppressive environment through lactate accumulation.\nIn our study, we demonstrate that KDM6A loss functions as an upstream regulator of these pathways. Loss of KDM6A impaired MMR and DSBR gene expression and reduced glycolytic activity, collectively reshaping the tumor microenvironment. To further address the Reviewer’s concern, we performed multivariable Cox regression incorporating TMB and KDM6A status. This analysis showed that the survival advantage conferred by KDM6A mutations persisted even after adjusting for TMB (Figure 34a in the point-by-point response letter). Moreover, within the subgroup of patients with low MMR signatures in the IMvigor21019 , KDM6A-mutant tumors exhibited improved survival compared with KDM6A-WT tumors (Figure 34b in the point-by-point response letter). These findings suggest that additional mechanisms beyond MMR loss contribute to ICT sensitivity, likely through the combined effects of increased tumor immunogenicity and a more favorable immune microenvironment driven by reduced lactate production. These data were generated for the Reviewer’s perusal and have not been included in the revised manuscript.\nWe also emphasize that dissecting each pathway in isolation would not accurately reflect KDM6A’s role as an upstream regulator. For example, knocking down LDHA to study glycolysis alone would impair anti-tumor immunity, as we and others have previously shown (Raychaudhuri, Singh et al. 28 ). Similarly, selective perturbation of MMR or DSBR genes alters genomic stability in ways that do not capture the integrated regulatory role of KDM6A. Thus, isolating these pathways risks oversimplifying the biology. Our study instead highlights the convergent and cumulative effects of KDM6A loss, which more faithfully represent its impact on tumor–immune interactions.\nTogether, these findings establish KDM6A as an upstream regulator that coordinates DNA repair and metabolic pathways to reshape the tumor immune landscape and enhance ICT sensitivity. Importantly, they also underscore the potential of KDM6A as a biomarker to stratify bladder cancer patients for therapeutic interventions, particularly in guiding the use of chemotherapy and ICT.\n5. For Figures 1B and 2B, tumor growth curves should be provided.\nAuthor’s Response: We thank the reviewer for the comment. We have added the tumor growth curves in the revised manuscript. (Figure 35a-d in the point-by-point response letter and Fig 1B, 2B and Extended data Fig. 1C, 3B in revised manuscript).\nMinor:\n6. Lines 138-139 and Extended Data Figure 1B: The eccDNA study in KMD6A WT and MUT patients is incompletely described in the text, methods and the related figure. Are the levels and heterogeneity of eccDNA different between WT and MUT patients?\nAuthors’ response: Thank you for the comment. We apologize for the lack of clarity in the text and other sections. We have revised the manuscript to include the methods used for eccDNA analysis in the patient cohort. We observed eccDNA amplicons for SHCBP1, SPP1 and EGFR were only present in patients harboring KDM6A mutation (Extended data Fig. 2D in revised manuscript). Further, our WGS studies in human cell lines showed multiple eccDNA amplicons mapping critical cisplatin resistant genes including SHCBP1, TP63 and CLDN4.\nAdditionally, our results show elevated levels of copy number amplification in K2 (KDM6A knocked out) than parental RT4 cells (Figure 25, 26 in the point-by-point response letter and Fig. 1I and Extended data Fig. 2E, F in revised manuscript).\n7. Figure 2B description is incorrect, wrong treatment is mentioned.\nAuthors’ response: We apologize for the oversight. We have included the corrected treatment regimen in the revised manuscript.\n8. Figure 3D: how many tumors analyzed? Provide quantitation\nAuthors’ response: Thank you for the comment. We have quantified Exo1 expression in n=5 tumors. We have included the quantification data in the manuscript (Figure 36a, b in the point-by-point response letter and Fig. 3D in the revised manuscript).\n9. Lines 230 S3E reference irrelevant\nAuthors’ response: We thank the Reviewer for pointing this out. We have corrected this in the revised manuscript.\n10. Lines 228-230 3E: please also provide representative images of gH2AX staining’\nAuthors’ response: We thank the Reviewer for this comment. We have added all the representative images of gamma-H2AX confocal microscopy in the updated manuscript.\n11. Lines 252-253 fix reference to Extended Data Figure 4C\nAuthors’ response: We thank the Reviewer for bringing it to our attention. We have modified it in our revised manuscript.\n12. Cell line supernatant experiments in human cell lines\nAuthors’ response: We thank the Reviewer for this comment. We have performed cell supernatant experiments with RT4 and K2 (KDM6A-KO) human bladder cancer cell lines. We have noted that following KDM6A loss, the glycolytic ATP production was significantly reduced, accompanied by a compensatory metabolic shift toward oxidative phosphorylation (OXPHOS) and reduced lactate production. We have added the results and figures in the updated manuscript (Figure 37a, b in the point-by-point response letter and Fig. 4H, I, K in revised manuscript).\n13. Lines 266-269: please reference the studies mentioned.\nAuthors’ response: We thank the Reviewer for bringing it to our attention. We have added the reference in our revised manuscript.\nReviewer #4 (Remarks to the Author): with expertise in cancer epigenetics\nThis study describes a mechanistic framework by which loss-of-function mutations in the histone demethylase KDM6A modulate therapeutic responses in bladder cancer. The authors present analyses of numerous clinical cohorts and data from model systems indicating that KDM6A deficiency is associated with worse overall survival following cisplatin-based chemotherapy, but improved outcomes with immune checkpoint blockade (ICB); a noteworthy and potentially important result. Mechanistically, the authors show data that KDM6A loss promotes the accumulation of extrachromosomal circular DNA (eccDNA), contributing to cisplatin resistance. The authors show that KDM6A directly regulates DNA mismatch repair (MMR) and double-strand break repair (DSBR) genes, and that its loss impairs these pathways, resulting in genomic instability, elevated tumor mutational burden, and enhanced immunogenicity. In parallel, KDM6A deficiency suppresses intratumoral lactate production, which leads to diminished histone lactylation (H3K9la, H3K18la) in regulatory T cells (Tregs). This epigenetic alteration attenuates the expression of immunosuppressive genes, thereby enhancing effector T cell activity in the tumor microenvironment. Collectively, the authors conclude that the findings position KDM6A mutation status as a potential predictive biomarker for therapeutic stratification in bladder cancer.\nOverall this paper combines epigenetic, metabolic, and immune experiments to create a mechanistically rich study linking KDM6A loss to divergent therapeutic responses in bladder cancer. However, the limited validation of some key claims needs to be addressed to solidify its mechanistic and clinical impact. I recommend the following points be addressed prior to acceptance.\nWe are grateful to the Reviewer for the thorough and insightful evaluation of our work, and for recognizing the integrative framework that connects epigenetic, metabolic, and immune mechanisms to therapeutic response in bladder cancer.\n1. The association between KDM6A inactivating mutations and eccDNA is very intriguing but the mechanistic experiments rely heavily on a single murine bladder cancer cell line (MB49)+/- CRISPR-Cas9 knockout and a limited number of human cell lines. In particular the authors attempted to validate the cisplatin-resistant phenotype of KDM6A-mutant murine bladder cancer cells with the human cell lines RT4 and J82 (Fig 1F). Comparing two different human cell lines, even from the same tumor type, is prone to confounding as cell specific copy number alterations or epigenetic differences may strongly influence therapeutic response. (References “Oncogene volume 36, pages35–46 (2017); Scientific Reports volume 6, Article number: 38531 (2016)” show some of the known cell line differences.) The authors need to provide cisplatin resistance data from either a larger panel of bladder cancer cell lines with known KDM6A status or knockdown KDM6A in the T24 line to show validation in a human system comparable to the murine sgScramble and sgKdm6a cells.\nAuthor’s Response: We thank the Reviewer for this thoughtful comment. We agree that comparing two distinct human bladder cancer cell lines, RT4 (KDM6A-WT) and J82 (KDM6A-Mut), could be confounded by differences in their genetic backgrounds. To address this concern, we performed CRISPR-Cas9 mediated knockout of KDM6A in two human bladder cancer cell lines, RT4 and ScaBER2. K2 and B7 are the respective KDM6A knockout cells of RT4 and ScaBER. Western blot was used to verify KDM6A deletion in both K2 and B7 cells (Figure 38a in the point-by-point response letter and Extended data Fig. 1B in revised manuscript). Subsequently, we repeated key functional assays, including cisplatin-mediated cytotoxicity, spheroid formation, transwell invasion and wound healing assays (Figure 38b-f in the point-by-point response letter and Fig 1C, E, G and Extended data Fig. 1E, G, I, K in revised manuscript). Consistent with our original findings, KDM6A knockout human bladder cancer cells demonstrated increased migration, enhanced spheroid formation, and reduced sensitivity to cisplatin.\nTo further strengthen these results, we also generated independent sgKdm6a clones in murine MB49 cells using alternative sgRNAs. Western blot confirmed Kdm6a deletion in the sgKdm6a cell clones (sgKdm6a C1-C3) (Figure 39a in the point-by-point response letter and Extended data Fig. 1A in revised manuscript). These clones exhibited enhanced migration, spheroid formation, and cisplatin resistance, reinforcing the reproducibility of our findings (Figure 39b-e in the point-by-point response letter and Fig.1C, D, F and Extended data Fig.1D, F, H, J in revised manuscript). Together, these additional experiments in both murine and human systems confirm that KDM6A loss contributes to cisplatin resistance in bladder cancer cells.\n2. While the study clearly demonstrates increased eccDNA in the murine sgKdm6a cells compared with sgScramble (Fig 1G), a more detailed characterization of these amplifications is needed. At the very least genome wide copy number plots of the sgScramble and sgKdm6a cells should be generated and presented. This along with sequencing the eccDNA will enable eccDNA differences to be clearly observed and specific genetic loci identified. It will significantly strengthen the association the authors are trying to show if any of the eccDNA loci correspond to some of the key oncogenes already associated with cisplatin resistance or identify new resistance genes.\nAuthor Response: We thank the Reviewer for this important comment. In our prior chromosomal structural variants analysis of the TCGA-BLCA cohort1 , we showed that KDM6A-mutant bladder cancer patients exhibited more circular amplicons (eccDNA) along with elevated POLQ34 expression which is implicated in eccDNA formation (Extended data Fig. 2B, C in revised manuscript). Building on these results and to address the Reviewer’s comment regarding the presence of oncogenes and/or cisplatin resistance associated genes in eccDNA of KDM6A KO cells, we performed whole genome sequencing (WGS) on human bladder cancer cell line, RT4 and K2 (KDM6A-KO RT4 cells). WGS analysis demonstrated the following:\n• The genome-wide copy number analysis showed increased copy number gains in various genomic regions specifically in chromosomes 2, 3, and 7 in K2 cells, indicating increased amplifications in these regions upon KDM6A loss (Figure 40 in the point-by-point response letter and Fig. 1H in revised manuscript).\n• Analysis of the circular-amplicons using Circle-map showed multiple circular amplicons carrying critical genes implicated in promoting cisplatin resistance including TP633, 4, 5, , CLDN46, 7, GLI28, 9, 10, 11, LUC7L312, 13, ERCC414, 15and SHCBP116, 17, 18 in KDM6A KO cells (K2 cells) (Figure 41 in the point-by-point response letter and Fig 1I and Extended data Fig. 2E in revised manuscript). We used the standard thresholds of split reads greater than 2 and circle score higher than 50 to ensure the reliability of observations.\n• As the genome wide copy number heatmap showed increased amplifications in chromosomes 2, 3, and 7, we evaluated copy numbers for the eccDNA gene loci. Coverage plot of the genes showed increased copy numbers in K2 cells than RT4 cells. (Figure 42 in the point-by-point response letter and Fig 1I and Extended data Fig. 2F in revised manuscript).\nTo summarize, these results show KDM6A loss mediated formation of eccDNA as a vehicle for multiple critical genes that drive in cisplatin resistance in bladder cancer cells.\n3. The Chip-seq results need to be clarified. The authors present numerous “Profile Plots” of their ChIP-seq data. I cannot find an explanation in the Methods, but it seems to be a plot of the Chip-seq signal averaged across samples (e.g. Fig. 3C) and/or gene sets (e.g. Fig. 4C). This needs to be clarified for each plot; exactly what genes or gene sets and how many samples were averaged in the plot needs to be stated. Also, all the replicates for the individual gene Profile plots need to be shown in supplementary to evaluate the variability in the raw data.\nAuthor’s Response: We thank the Reviewer for the comment. We have revised the Methods section to include the details of downstream analysis to ensure clarity.\n• For the profile plots of individual genes shown in Extended data Fig.3H, 5F, 6E in revised manuscript, we extracted peak regions from the differential ChIP-seq analysis for the respective genes and calculated binding scores. These scores were visualized as profile plots over a ±1 kb region surrounding the TSS to compare signal intensity between sgKdm6a and sgScramble. We consistently observed reduced enrichment of KDM6A and H3K4me3 in sgKdm6a cells at critical loci involved in MMR, DSBR, and glycolysis, concordant with the decreased expression of these genes detected in our RNA-seq analysis (Fig. 2E, 2G, 3A, 3C, 4B, 4D in revised manuscript).\n• For the profile plots of the MMR pathway, DSBR pathway and glycolytic pathway, we extracted differential peak regions for the corresponding gene sets from the ChIP-seq analysis comparing sgKdm6a C1 and sgScramble cells for KDM6A and H3K4me3 (Extended Data Fig. 3D, 3E, 5C, 6B in the revised manuscript). For each pathway, signal intensities across these regions were consolidated, quantified, and visualized to compare enrichment. Consistent with the individual gene plots, we observed reduced enrichment of both KDM6A and H3K4me3 in sgKdm6a cells, highlighting a consolidated effect of KDM6A loss on MMR, DSBR, and glycolytic pathways.\n• Previously, we reported that KDM6A regulates the expression of genes involved in the MMR, DSBR, and glycolysis pathways in bladder cancer by performing ChIP-seq on CRISPR-Cas9–deleted Kdm6a murine MB49 bladder cancer cells (sgKdm6a C1) and control cells (sgScramble) (Fig. 2E, 3A, 4B, in revised manuscript). In response to the Reviewer’s suggestion and to provide an additional biological replicate, we extended ChIP-seq on an additional clone of CRISPR-Cas9–excised Kdm6a cells (sgKdm6a C2). We extracted peaks associated with key genes in MMR, DSBR, and glycolysis from the differential comparison between sgKdm6a C2 and sgScramble, and calculated binding scores. These were visualized as profile plots across a ±1 kb region surrounding the TSS to compare signal intensity between groups (Figure 43 in the point-by-point response letter and Extended data Fig. 3I, 5G, 6F in revised manuscript). As observed previously, sgKdm6a C2 cells displayed reduced enrichment of KDM6A and H3K4me3 at gene loci, in line with decreased expression of these genes in sgKdm6a C2 cells."}, {"round": 2, "reviewer_comments": {"reviewer_2": "Thank you for the careful and thorough revision. Your changes address my previous concerns and the manuscript is now acceptable for publication.", "reviewer_3": "The authors addressed the majority of my comments adequately. The only exception is major comment 3: the authors presented genome viewer plots that are (a) low quality since the range is too small to be discerned, and (b) too zoomed in, meaning they show a very narrow view of the gene locus that does not allow proper evaluation of peak signal compared to background."}, "author_response": "We thank the Reviewer for the thoughtful review of the manuscript and appreciate the suggestion to include all replicates/samples in a single plot to allow evaluation of consistency between replicates. We have updated the relevant figure to include all samples in a single plot (Figure 2a from point-by-point response letter and Extended Data Fig. 3I, 5G, 6F in revised manuscript). We would like to add here that ChIP sequencing was performed using pooled samples from sgScramble and two biological replicates of sgKdm6a murine cell lines (sgKdm6a C1 and C2). Additionally, we used CRISPR-Cas9 mediated KDM6A knockout in human bladder cancer cell line RT4 and performed ChIP qPCR to validate our findings. The data showing all the replicates are presented in Figure 2b of the point-by-point response letter and Extended Data Fig. 4D, 4E, 5K, 5L, 6K, 6L in revised manuscript."}, {"round": 3, "reviewer_comments": {"reviewer_3": "The authors addressed the requests properly. I would note that the zoomed out genomic visualization of ChIP-seq reveals that the peaks evaluated are quite small and their significance is questionable. Since this is clearly shown in the main figure, the manuscript is acceptable for publication. The authors might want to be more reserved in their claims concerning changes in H3K4me3."}, "author_response": "We appreciate the Reviewer for careful evaluation of the manuscript and for acknowledging that the revisions adequately address the prior concerns. As per the Reviewer’s recommendation, we have now refined the wordings concerning the changes in H3K4me3 marks in the manuscript (Discussion section) to present a more measured interpretation of the data."}]

Decision letter