Super-enhancer-driven HAVCR1 defines cisplatin resistance yet immunotherapy responsiveness in non-small-cell lung cancer

Super-enhancer-driven HAVCR1 defines cisplatin resistance yet immunotherapy responsiveness in non-small-cell lung cancer | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Super-enhancer-driven HAVCR1 defines cisplatin resistance yet immunotherapy responsiveness in non-small-cell lung cancer Lehang Lin, Zhuojian Shen, Baishen Chen, Honglve Dai, Wei Zhang, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9304377/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 12 You are reading this latest preprint version Abstract Cisplatin (DDP) resistance remains a major obstacle in the treatment of non-small-cell lung cancer (NSCLC), underscoring the need to identify robust prognostic biomarkers and therapeutic strategies. Super-enhancers (SEs) play central roles in orchestrating oncogenic transcriptional programs and cellular adaptation to therapeutic stress. In this study, we integrated H3K27ac ChIP-seq and RNA-seq analyses in DDP-sensitive and -resistant NSCLC cells to define SE-associated transcriptional alterations underlying chemoresistance. DDP-resistant cells exhibited extensive SE reprogramming, accompanied by activation of transcriptional programs related to cell adhesion and junction, cell cycle regulation, and cancer-associated signaling. Further analyses identified HAVCR1 as a key SE-associated gene whose upregulation correlated with DDP resistance and poorer survival in platinum-relevant NSCLC cohorts. Notably, however, elevated HAVCR1 expression was associated with improved clinical outcomes in immune checkpoint blockade (ICB) datasets. Mechanistically, HAVCR1-high tumors displayed a lymphocyte-inflamed tumor microenvironment (TME), characterized by increased CD8 + T cell infiltration and reduced myeloid-driven immunosuppression. Together, our findings suggest that SE-driven HAVCR1 defines a transcriptional and immunological state marked by enhanced chemoresistance yet increased susceptibility to immunotherapy, highlighting its potential as a biomarker for guiding chemo-immunotherapy strategies in NSCLC. Biological sciences/Cancer/Lung cancer/Non-small-cell lung cancer Biological sciences/Immunology/Immunotherapy/Immunosuppression Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Non-small-cell lung cancer (NSCLC) accounts for more than 85% of lung cancer cases worldwide and remains a leading cause of cancer-related mortality 1 . Despite substantial advances in targeted therapies and immunotherapy, a large proportion of patients with advanced NSCLC—particularly those lacking actionable driver mutations or exhibiting low PD-L1 expression—continue to rely on platinum-based chemotherapy as a cornerstone of systemic therapy, either alone or in combination with immunotherapy 2 . However, the clinical efficacy of cisplatin (DDP) is frequently undermined by intrinsic or acquired resistance, ultimately leading to treatment failure and disease progression 3 . Elucidating the molecular basis underlying DDP resistance, as well as identifying biomarkers to guide treatment selection and the optimal integration of chemotherapy and immunotherapy, therefore remains an urgent clinical priority. Accumulating evidence highlights epigenetic reprogramming as a key driver of chemotherapy resistance. Among epigenetic regulatory elements, super-enhancers (SEs)—large clusters of enhancers characterized by exceptionally high levels of H3K27ac and dense occupancy of transcriptional co-activators—have emerged as central regulators of lineage-specific and oncogenic transcriptional programmes 4 . SEs orchestrate gene expression networks that promote tumor growth, metastasis, and therapeutic resistance across multiple cancer types 5 – 7 . Notably, recent studies suggest that anticancer therapies, including chemotherapy, can dynamically reshape the SE landscape, thereby enabling tumor cells to adapt to genotoxic stress and acquire drug tolerance 7 , 8 . In addition, SE-driven transcriptional programs have been implicated in modulating cytokine signaling 9 , immune evasion 10 , and stromal–immune interactions 11 , raising the possibility that SE reprogramming may coordinately regulate both drug resistance and the tumor immune microenvironment. However, whether SE reprogramming constitutes a central regulatory mechanism underlying DDP resistance in NSCLC remains unclear. In recent years, immune checkpoint blockade (ICB) has revolutionized the treatment landscape of NSCLC, yielding durable clinical responses in a subset of patients. Nevertheless, substantial inter-patient heterogeneity in therapeutic response underscores the limitations of current biomarkers and highlights the need for more refined molecular stratification strategies 12 – 14 . Hepatitis A virus cellular receptor 1 (HAVCR1), also known as T-cell immunoglobulin and mucin structural domain 1 (TIM-1) or kidney injury molecule 1 (KIM-1), is a transmembrane protein involved in immune regulation 15 . Aberrant HAVCR1 expression has been reported across multiple cancer types and is associated with tumor progression and remodeling of the TME 16 . However, the upstream regulatory mechanisms governing HAVCR1 expression, as well as its potential role in chemotherapy resistance and immunotherapy responsiveness in NSCLC, remain largely undefined. In this study, we performed integrative epigenomic and transcriptomic analyses to systematically characterized SE reprogramming and SE–driven transcriptional programmes associated with DDP resistance in NSCLC. By integrating H3K27ac ChIP-seq and RNA-seq data from DDP-sensitive and -resistant A549 cells, we identified extensive SE landscape reprogramming accompanying the resistant phenotype and defined a high-confidence set of SE-associated, transcriptionally upregulated genes, among which HAVCR1 emerged as a key candidate. We further demonstrated that HAVCR1 high expression is linked to DDP resistance while concurrently associated with a lymphocyte-inflamed tumor microenvironment (TME) and favourable immunotherapy-related features. Collectively, our findings identify SE-driven HAVCR1 as a potential biomarker to inform the rational integration and sequencing of chemotherapy and immunotherapy in NSCLC. Materials and Methods Cell lines and culture Human NSCLC A549 cells and their DDP-resistant derivative, A549-DDP, were cultured in RPMI-1640 medium (Gibco, Thermo Fisher Scientific, Waltham, MA, USA) supplemented with 10% fetal bovine serum (FBS; Gibco), 100 U/mL penicillin, and 100 µg/mL streptomycin (Gibco). Cells were maintained at 37°C in a humidified incubator with 5% CO 2 . To preserve the DDP-resistant phenotype, A549-DDP cells were routinely cultured in medium containing 1 µM DDP (Sigma-Aldrich, St. Louis, MO, USA). Prior to all experiments, A549-DDP cells were cultured in drug-free medium for at least one week to eliminate transient effects of DDP exposure. Identifying ChIP-seq enriched regions ChIP-seq libraries were sequenced in 150 bp paired-end mode on an Illumina NovaSeq 6000 platform (Illumina, San Diego, CA, USA). Raw sequencing reads were trimmed to remove adapter sequences and low-quality bases using Cutadapt (v2.5). The resulting clean reads were aligned to the human reference genome (GRCh38/hg38) using Bowtie2 (v2.3.5.1) with default parameters. Peaks of H3K27ac enrichment were identified using MACS2 (v2.1.2) with a q-value threshold of 0.05. These MACS2-defined peaks were subsequently used as constituent enhancers for SE identification. Definition of enhancers and super-enhancers Enhancers and SEs were identified using the ROSE (Rank Ordering of Super-Enhancers) algorithm as previously described 17 . Briefly, constituent H3K27ac peaks were stitched if the genomic distance between adjacent peaks was ≤ 12.5 kb. All stitched enhancer regions were then ranked according to their input-subtracted H3K27ac signal intensity. SEs were defined as the subset of stitched enhancers located above the inflection point in the ranked H3K27ac signal curve. To exclude promoter-associated regions, peaks located within ± 2.5 kb of annotated transcription start sites (TSSs) were excluded from the stitching process. For gene annotation, stitched enhancer and SE regions were assigned to their nearest genes using the annotatePeaks.pl utility from HOMER (v4.10.4). RNA-seq analysis Total RNA was extracted from A549 and A549-DDP cells and used for poly(A)-selected library construction, followed by paired-end sequencing. Sequencing reads were aligned to the human reference genome (GRCh38/hg38) using STAR, and gene-level read counts were generated with featureCounts. Gene expression levels were normalised as fragments per kilobase of transcript per million mapped reads (FPKM). Differentially expressed genes (DEGs) between A549-DDP and parental A549 cells were identified using DESeq2, with a threshold of |log₂ fold change| > 1 and a false discovery rate (FDR) < 0.05. Gene ontology and pathway analysis To identify pathways associated with DDP resistance driven by SE reprogramming, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed using the clusterProfiler R package (v3.6.0). Statistical significance was defined by FDR < 0.05. Enrichment analyses were conducted on four gene sets: (1) SE-associated genes in A549 cells; (2) SE-associated genes in A549-DDP cells; (3) genes significantly upregulated in A549-DDP relative to parental A549 cells based on RNA-seq; and (4) a 46-gene SE-associated core module, representing high-confidence candidates that are both SE–associated and transcriptionally upregulated in the DDP-resistant state. Clinical datasets and survival analysis For chemotherapy-related survival analyses, microarray expression data and corresponding clinical information were obtained from the JBR.10 trial (GSE14814), a randomized study comparing adjuvant DDP-based chemotherapy with observation in patients with NSCLC 18 . Only patients with available HAVCR1 expression data and overall survival (OS) data were included. Patients who died from causes unrelated to lung cancer were excluded. Univariate Cox proportional hazards models were applied to the 46-gene core module to estimate hazard ratios (HRs) for OS. Kaplan–Meier (KM) survival curves were generated for the adjuvant chemotherapy subgroup, and differences between HAVCR1-high and HAVCR1-low groups were assessed using the log-rank test. OS was further evaluated in an independent lung adenocarcinoma (LUAD) cohort 19 stratified by median HAVCR1 expression. To assess the relevance of HAVCR1 in immunotherapy, three independent datasets were analyzed. First, GSE126044 includes NSCLC patients treated with anti-PD-1 therapy, with available pre-treatment transcriptomic profiles and clinical response annotations (complete or partial response versus stable or progressive disease), allowing comparison of HAVCR1 expression between responders and non-responders 20 . Second, pan-cancer immunotherapy cohorts 21 were analyzed using the KM plotter immunotherapy platform, including datasets of patients treated with anti-CTLA-4, anti-PD-1, and anti-PD-L1 therapies. Third, the IMvigor210 cohort of metastatic urothelial carcinoma treated with atezolizumab ( http://research-pub.gene.com/IMvigor210CoreBiologies ) was used as an external validation dataset, in which patients were stratified into HAVCR1-high and HAVCR1-low groups based on HAVCR1 median expression, and survival differences were evaluated using KM analysis. Immune characteristics and tumor microenvironment analysis The immune landscape associated with HAVCR1 expression— including TME immune cell type signatures, immune suppression-related signatures, immune exclusion-related signatures, and immunotherapy-related biomarker signatures—was systematically evaluated using the Immuno-Oncology Biological Research (IOBR) R package 22 . To characterize immune functional states, the TIP (tracking tumor immunophenotype) meta-server ( http://biocc.hrbmu.edu.cn/TIP/ ) was employed 23 . TIP integrates "ssGSEA" and "CIBERSORT" algorithms to infer and visualize anticancer immune activity across the seven steps of the cancer-immunity cycle using RNA-seq or microarray data. Spearman correlation analyses were then performed to assess associations between HAVCR1 expression and individual steps of the cancer–immunity cycle. Statistical analysis All data processing and statistical analyses were performed using R software. Continuous variables were compared using either Student’s t -test or the Wilcoxon rank-sum test, depending on data distribution. Categorical variables were analyzed using the χ 2 test or Fisher’s exact test, as appropriate. Correlations between variables were assessed using Spearman’s rank correlation analysis. Survival curves were generated using the Kaplan–Meier method and compared using the log-rank test. HRs were estimated using univariate Cox proportional hazards models. For analyses involving multiple signatures or enrichment terms, P values were adjusted using the Benjamini–Hochberg method where applicable. A P value < 0.05 was considered statistically significant for all analyses. Results Super-enhancer landscape reprogramming in A549-DDP cells To elucidate the epigenetic basis underlying DDP resistance in NSCLC, we performed H3K27ac ChIP-seq in parental A549 cells and their DDP-resistant derivative, A549-DDP. SEs and typical enhancers were subsequently defined using the ROSE algorithm based on H3K27ac signal intensity. Compared with parental A549 cells, A549-DDP cells exhibited a marked expansion of the SE repertoire, with SE numbers increasing from 392 to 681, indicating extensive enhancer reprogramming during the acquisition of DDP resistance (Fig. 1 A). Consistently, aggregate H3K27ac signal profiles centered on enhancer regions showed stronger enhancer-associated H3K27ac occupancy in A549-DDP cells (Supplementary Fig. S1 ). Annotating SEs to their nearest genes further revealed substantial remodelling of SE-associated transcriptional programmes. Among all SE-associated genes, only 235 were shared between the two cell lines, whereas 123 and 394 genes were uniquely associated with SEs in A549 and in A549-DDP cells, respectively (Fig. 1 B; Supplementary Datasets S1 and S2). Functional annotation showed that SE-associated genes in A549-DDP cells were significantly enriched in adhesion- and junction-related processes by GO analysis, while KEGG analysis highlighted focal adhesion, cell cycle regulation, and cancer-associated pathways—processes previously implicated in chemotherapy resistance 19 , 24 – 26 (Fig. 1 C and D). Collectively, these findings demonstrate extensive SE landscape reprogramming in DDP-resistant NSCLC cells, accompanied by the activation of adhesion-related transcriptional networks. This SE-driven transcriptional rewiring likely provides an epigenetic basis for tumor cells to evade DDP-induced apoptosis and sustain stable chemoresistance. A 46-gene super-enhancer-associated core module linked to DDP resistance in A549-DDP cells Given that SEs drive high-level transcription of target genes, we next sought to identify SE-associated genes contributing to DDP resistance in A549-DDP cells. RNA-seq analysis comparing A549 and A549-DDP cells identified a subset of genes significantly upregulated in DDP resistant cells (Supplementary Dataset S3). By intersecting the 394 A549-DDP-specific SE-associated genes with the 1,752 genes upregulated in A549-DDP cells, we identified a set of 46 overlapping genes (Fig. 2 A; Supplementary Dataset S4). Functional enrichment analysis showed that these genes recapitulated key features of the broader SE-associated landscape. Specifically, GO terms were predominantly related to cell adhesion and junction organization (Fig. 2 B), while KEGG pathways included focal adhesion, pathways in cancer, p53 signaling, actin cytoskeleton regulation, and ECM-receptor interaction (Fig. 2 C). Notably, several genes within this set, including BMAL2 27 , NEK6 28 , CTPS1 29 , and PLOD2 30 , have been reported to contribute to chemotherapy resistance across multiple cancer types. Consistent with their SE association, H3K27ac tracks showed increased signal intensity at their loci in A549-DDP cells relative to A549, supporting enhancer activation (Fig. 2 D; Supplementary Fig. S2). Together, the consistent enrichment of adhesion-, cytoskeleton-, and cell cycle-related signatures across both global SE-associated genes and the refined gene set suggests that these 46 SE-driven genes constitute a core transcriptional module underlying SE-mediated DDP resistance in A549-DDP cells. Clinical associations of HAVCR1 in chemotherapy and immunotherapy settings To assess the clinical relevance of these 46 SE-driven core genes, we analyzed data from the JBR.10 trial (GSE14814), which includes 133 patients with early-stage NSCLC treated with adjuvant DDP/vinorelbine or observation following surgical resection 18 . After excluding 10 patients who died from non-lung cancer causes, univariate Cox regression analysis identified HAVCR1 as the only gene significantly associated with poorer patient overall survival (HR = 2.56, 95% CI 1.00-6.53; P < 0.05), suggesting its potential as an adverse prognostic marker (Fig. 3 A; Supplementary Fig. S3). Consistent with its SE regulation in the chemoresistant state, H3K27ac signal at the HAVCR1 locus was elevated in A549-DDP cells relative to A549 (Fig. 2 D). Within the adjuvant chemotherapy subgroup, patients with high HAVCR1 expression showed a trend towards worse overall survival compared with those with low HAVCR1 expression, although this did not reach statistical significance (HR = 2.08, 95% CI 0.86-5.00; P = 0.170; Fig. 3 B). Similarly, analysis using the Kaplan–Meier Plotter in an independent LUAD cohort 19 demonstrated that elevated HAVCR1 expression was significantly associated with poorer overall survival (HR = 1.46, 95% CI 1.09–1.95; P = 0.011; Fig. 3 C), in agreement with prior pan-cancer observations 31 . Together, these findings support that SE-driven upregulation of HAVCR1 is associated with poor outcomes in NSCLC patients receiving platinum-based chemotherapy. Interestingly, in another independent NSCLC cohort treated with anti-PD-1 therapy, HAVCR1 expression tended to be higher in responders than in non-responders, although the difference was not statistically significance ( P = 0.15; Fig. 3 D), likely due to limited sample size. Furthermore, in pan-cancer immunotherapy datasets, elevated HAVCR1 expression was associated with improved overall survival in patients receiving CTLA-4 blockade (HR = 0.48, 95% CI 0.29–0.8; P = 0.0042) and PD-L1 blockade (HR = 0.72, 95% CI 0.56–0.93; P = 0.011), with a similar trend observed for PD-1 blockade (HR = 0.74, 95% CI 0.54–1.01; log-rank, P = 0.059) (Fig. 3 E-G). These results suggest a context-dependent role for HAVCR1: while its high expression predicts poor prognosis in patients receiving platinum-based chemotherapy, it is associated with improved clinical benefit in immunotherapy settings. Immune microenvironment characteristics associated with HAVCR1 expression To further characterize the immune context associated with HAVCR1, we compared TME features between HAVCR1-high and HAVCR1-low LUAD samples from the TCGA dataset. Of note, HAVCR1-high tumors exhibited a more lymphocyte-inflamed phenotype, with increased enrichment of follicular helper T cells, CD8 + T cells, B cells, and plasma cells. In contrast, HAVCR1-low tumors showed stronger enrichment of myeloid and stromal components, including exhausted T cells, macrophages, fibroblasts, monocytes, neutrophils, and myeloid-derived suppressor cells (MDSC), indicative of an immunosuppressive microenvironment (Fig. 4 A). Consistent with this pattern, immune-suppressive signatures, including immune checkpoint pathways, were more enriched in HAVCR1-low tumors (Fig. 4 B). Additionally, immune exclusion-related programs, such as EMT and TGF-β signatures, were also more prominent in the HAVCR1-low group (Fig. 4 C), aligning with previously reported features associated with reduced response to ICB 32 – 35 . Although several immunotherapy-related biomarkers showed minimal differences between groups, HAVCR1-high tumors displayed significantly lower TMEscoreB values, a stromal-relevant signature negatively correlated with immunotherapy response 36 (Fig. 4 D). Overall, these findings suggest that HAVCR1-high tumors are more likely to exhibit an immune-activated ("hot") microenvironment conductive to ICB response, whereas HAVCR1-low tumors display an immune-excluded ("cold") phenotype that may limit ICB efficacy. To further explore the immunological implications of HAVCR1, we analyzed the cancer-immunity cycle using the TIP meta-server 23 , 37 . Spearman correlation analysis revealed that in TCGA-LUAD cohort, HAVCR1 expression was positively correlated with Step 6 (recognition of cancer cells by T cells), a key feature of effective anti-tumor immunity 38 , 39 , and negatively correlated with Step 4 (macrophage recruiting), supporting enhanced immune recognition alongside reduced myeloid infiltration in HAVCR1-high tumors (Supplementary Fig. S4). Consistently, in the IMvigor210 cohort of metastatic urothelial carcinoma treated with atezolizumab, patients with HAVCR1-high expression showed improved overall survival compared with those with HAVCR1-low expression, with separation of survival curves becoming evident after approximately six months of therapy ( P = 0.009; Supplementary Fig. S5). Although derived from a different cancer type, this dataset provides independent support for the association between elevated HAVCR1 expression and favorable response to ICB. Discussion In this study, we delineate a comprehensive epigenomic and transcriptomic framework underlying DDP resistance in NSCLC and identify HAVCR1 as a SE–driven effector linking chemoresistance-associated transcriptional programs to immunotherapy-relevant tumor features. Through integrative analyses, our findings suggest that enhancer reprogramming, coordinated transcriptional activation, and TME remodelling converge to shape therapeutic vulnerabilities in resistant tumors. Our epigenomic profiling revealed extensive reprogramming of the SE landscape in DDP-resistant A549-DDP cells, characterized by expansion of the SE repertoire and preferential activation of adhesion-, junction-, and proliferation-related pathways 40 , 41 (Fig. 1 ; Supplementary Fig. S1 ). These observations, together with the identification of a functionally convergent SE-associated gene module (Fig. 2 ), support a model in which enhancer rewiring drives coordinated transcriptional programs that promote cell survival and adaptation under platinum-induced stress. Such epigenetic plasticity has been increasingly recognized as central mechanism of chemoresistance in cancers including NSCLC, whereby enhancer activation sustains oncogenic transcriptional states that buffer tumor cells against DNA damage and apoptosis 26 . Within this SE–driven transcriptional context, HAVCR1 emerged as a clinically relevant candidate. Elevated HAVCR1 expression was associated with poorer outcomes in platinum-treated NSCLC cohorts (Fig. 3 B and C), supporting its potential role in treatment resistance. Intriguingly, however, HAVCR1 expression showed an opposite trend in the context of immunotherapy: higher HAVCR1 expression was observed in NSCLC patients responding to anti-PD-1 treatment (Fig. 3 D), and HAVCR1-high tumors exhibited transcriptional features characteristic of a lymphocyte-inflamed TME (Fig. 4 ). These included increased infiltration of CD8 + T cells, follicular helper T cells, B cells, and plasma cells, along with enhanced antigen presentation and co-stimulatory signalling, whereas HAVCR1-low tumors were enriched for myeloid-dominant and immune-excluded signatures. This apparent context-dependent role of HAVCR1 may reflect its dual association with tumor-intrinsic transcriptional programs and the extrinsic immune microenvironment. While SE-driven HAVCR1 upregulation may contribute to chemoresistance through its linkage to survival-associated transcriptional networks, it is simultaneously associated with an immune-activated state that is permissive to effective anti-tumor immunity. This duality is consistent with emerging paradigms in which tumors with robust T-cell infiltration and inflammatory signaling exhibit improved responsiveness to ICB 38 , 39 . Importantly, given the limitations of current biomarkers such as PD-L1 expression, HAVCR1 may provide complementary value for stratifying patients, particularly in the context of combined or sequential chemo-immunotherapy strategies. Based on these findings, we propose a working model in which SE-mediated upregulation of HAVCR1 defines a transcriptional and immunological state characterized by enhanced chemoresistance but increased susceptibility to immunotherapy. Within this framework, HAVCR1 and its associated SE network may represent promising biomarkers and potential therapeutic targets for optimizing treatment sequencing and chemo-immunotherapy combination strategies in NSCLC. This study is primarily based on integrative analyses of cell line models and publicly available patient cohorts. Although the associations between HAVCR1 expression, SE activation, and immunotherapeutic response were consistent across multiple datasets, causal validation is required. Future studies employing targeted perturbation of SE elements and HAVCR1, coupled with functional assays assessing tumor cell survival, immune activation, and response to ICB, will be essential to further strengthen the mechanistic interpretation and therapeutic relevance of this regulatory axis. Data Availability Statement: The public datasets analyzed in this study are available from GEO (GSE14814 and GSE126044) and the IMvigor210CoreBiologies resource. Additional data supporting the findings of this study are available from the corresponding author upon reasonable request. Declarations Ethics approval This study did not involve any experiments on human participants, human tissues, or animals conducted by the authors. All data used in this research were obtained from publicly available and fully anonymized databases (e.g., GEO and TCGA). According to the policies of our institutional ethics committee, ethical review and informed consent were not required for studies based solely on publicly available data. Competing interests The authors declare that they have no competing interests. Funding: This study was supported by funding from the Guangdong Province Natural Science Foundation(Grant No. 2021A1515011135); Guangdong Basic and Applied Basic Research Foundation(2026A1515012833). Final approval of manuscript: All authors. Authors' contributions Conception and design: Zhuojian Shen, Lehang Lin and Minghui Wang. Acknowledgments None. References Bade BC, Dela Cruz CS. Lung Cancer 2020: Epidemiology, Etiology, and Prevention. 2020. Ruiz-Cordero R, Devine WP. Targeted Therapy and Checkpoint Immunotherapy in Lung Cancer. Surgical pathology clinics 2020; 13(1): 17–33. Galluzzi L, Vitale I, Michels J, Brenner C, Szabadkai G, Harel-Bellan A et al. . Systems biology of cisplatin resistance: past, present and future. CELL DEATH DIS 2014; 5(5): e1257. Creyghton MP, Cheng AW, Welstead GG, Kooistra T, Carey BW, Steine EJ et al. . Histone H3K27ac separates active from poised enhancers and predicts developmental state. P NATL ACAD SCI USA 2010; 107(50): 21931–21936. Liu S, Dai W, Jin B, Jiang F, Huang H, Hou W et al. . Effects of super-enhancers in cancer metastasis: mechanisms and therapeutic targets. MOL CANCER 2024; 23(1): 122. Cheng Y, Pei G, Zhang H, Hou Y, Sun L, Xu H et al. . Super enhancers as key drivers of gene regulatory networks in normal and malignant hematopoiesis. Frontiers in cell and developmental biology 2025; 13: 1674470. Li G, Qu Q, Qi T, Teng X, Zhu H, Wang J et al. . Super-enhancers: a new frontier for epigenetic modifiers in cancer chemoresistance. Journal of experimental & clinical cancer research: CR 2021; 40(1): 174. Bhattacharjee R, Dey T, Kumar L, Kar S, Sarkar R, Ghorai M et al. . Cellular landscaping of cisplatin resistance in cervical cancer. Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie 2022; 153: 113345. Zhou RW, Xu J, Martin TC, Zachem AL, He J, Ozturk S et al. . A local tumor microenvironment acquired super-enhancer induces an oncogenic driver in colorectal carcinoma. NAT COMMUN 2022; 13(1): 6041. Xu Y, Wu Y, Zhang S, Ma P, Jin X, Wang Z et al. . A Tumor-Specific Super-Enhancer Drives Immune Evasion by Guiding Synchronous Expression of PD-L1 and PD-L2. CELL REP 2019; 29(11): 3435–3447. Ma P, Jin X, Fan Z, Wang Z, Yue S, Wu C et al. . Super-enhancer receives signals from the extracellular matrix to induce PD-L1-mediated immune evasion via integrin/BRAF/TAK1/ERK/ETV4 signaling. CANCER BIOL MED 2021; 19(5): 669–684. Rizvi NA, Hellmann MD, Snyder A, Kvistborg P, Makarov V, Havel JJ et al. . Cancer immunology. Mutational landscape determines sensitivity to PD-1 blockade in non-small cell lung cancer. Science (New York, N.Y.) 2015; 348(6230): 124–128. Mellman I, Chen DS, Powles T, Turley SJ. The cancer-immunity cycle: Indication, genotype, and immunotype. IMMUNITY 2023; 56(10): 2188–2205. Hugo W, Zaretsky JM, Sun L, Song C, Moreno BH, Hu-Lieskovan S et al. . Genomic and Transcriptomic Features of Response to Anti-PD-1 Therapy in Metastatic Melanoma. CELL 2016; 165(1): 35–44. Cao J, Qing J, Zhu L, Chen Z. Role of TIM-1 in the development and treatment of tumours. Frontiers in cell and developmental biology 2024; 12: 1307806. Liu S, Tang W, Cao J, Shang M, Sun H, Gong J et al. . A Comprehensive Analysis of HAVCR1 as a Prognostic and Diagnostic Marker for Pan-Cancer. FRONT GENET 2022; 13: 904114. Whyte WA, Orlando DA, Hnisz D, Abraham BJ, Lin CY, Kagey MH et al. . Master transcription factors and mediator establish super-enhancers at key cell identity genes. CELL 2013; 153(2): 307–319. Zhu C, Ding K, Strumpf D, Weir BA, Meyerson M, Pennell N et al. . Prognostic and predictive gene signature for adjuvant chemotherapy in resected non-small-cell lung cancer. Journal of clinical oncology: official journal of the American Society of Clinical Oncology 2010; 28(29): 4417–4424. Gy Rffy B. Integrated analysis of public datasets for the discovery and validation of survival-associated genes in solid tumors. Innovation (Cambridge (Mass.)) 2024; 5(3): 100625. Cho J, Hong MH, Ha S, Kim Y, Cho BC, Lee I et al. . Genome-wide identification of differentially methylated promoters and enhancers associated with response to anti-PD-1 therapy in non-small cell lung cancer. Experimental & molecular medicine 2020; 52(9): 1550–1563. Kovács SA, Fekete JT, Gy Rffy B. Predictive biomarkers of immunotherapy response with pharmacological applications in solid tumors. 2023. Zeng D, Ye Z, Shen R, Yu G, Wu J, Xiong Y et al. . IOBR: Multi-Omics Immuno-Oncology Biological Research to Decode Tumor Microenvironment and Signatures. FRONT IMMUNOL 2021; 12: 687975. Xu L, Deng C, Pang B, Zhang X, Liu W, Liao G et al. . TIP: A Web Server for Resolving Tumor Immunophenotype Profiling. CANCER RES 2018; 78(23): 6575–6580. Geijerman E, Terrana F, Peters GJ, Deng D, Diana P, Giovannetti E et al. . Targeting a key FAK-tor: the therapeutic potential of combining focal adhesion kinase (FAK) inhibitors and chemotherapy for chemoresistant non-small cell lung cancer. EXPERT OPIN INV DRUG 2024; 33(11): 1103–1118. Chandra Jena B, Kanta Das C, Banerjee I, Das S, Bharadwaj D, Majumder R et al. . Paracrine TGF-β1 from breast cancer contributes to chemoresistance in cancer associated fibroblasts via upregulation of the p44/42 MAPK signaling pathway. BIOCHEM PHARMACOL 2021; 186: 114474. Sandu K, Warta R, Biswas U, Zhang W, Michl P, Herold-Mende C et al. . Cisplatin resistance in head and neck squamous cell carcinoma is linked to DNA damage response and cell cycle arrest transcriptomics rather than poor drug uptake. Cancer drug resistance (Alhambra, Calif.) 2025; 8: 51. Yang J, Lin D, Huang Y, Yin S, Chen M, Sun H et al. . Clock gene ARNTL2 enhances 5-fluorouracil resistance in colon cancer by upregulating SLC7A11 to suppress ferroptosis. REDOX BIOL 2025; 86: 103798. Liu J, Wang H, Wan H, Yang J, Gao L, Wang Z et al. . NEK6 dampens FOXO3 nuclear translocation to stabilize C-MYC and promotes subsequent de novo purine synthesis to support ovarian cancer chemoresistance. CELL DEATH DIS 2024; 15(9): 661. Sun X, Wei Z, Liao X, Zheng D, Li F, Chen L et al. . INHBA promotes chemoresistance in pancreatic cancer by enhancing CTPS1 stability and mediating pyrimidine metabolism. CANCER CELL INT 2025; 25(1): 403. Okumura Y, Noda T, Eguchi H, Sakamoto T, Iwagami Y, Yamada D et al. . Hypoxia-Induced PLOD2 is a Key Regulator in Epithelial-Mesenchymal Transition and Chemoresistance in Biliary Tract Cancer. ANN SURG ONCOL 2018; 25(12): 3728–3737. Li G, Javed M, Rasool R, Abdel-Maksoud MA, Mubarak AS, Studenik CR et al. . A pan-cancer analysis of HAVCR1 with a focus on diagnostic, prognostic and immunological roles in human cancers. AM J TRANSL RES 2023; 15(3): 1590–1606. Mariathasan S, Turley SJ, Nickles D, Castiglioni A, Yuen K, Wang Y et al. . TGFβ attenuates tumour response to PD-L1 blockade by contributing to exclusion of T cells. NATURE 2018; 554(7693): 544–548. Tauriello DVF, Palomo-Ponce S, Stork D, Berenguer-Llergo A, Badia-Ramentol J, Iglesias M et al. . TGFβ drives immune evasion in genetically reconstituted colon cancer metastasis. NATURE 2018; 554(7693): 538–543. Jiang Y, Zhan H. Communication between EMT and PD-L1 signaling: New insights into tumor immune evasion. CANCER LETT 2020; 468: 72–81. Li M, Long S, Liu W, Long K, Gao X. EMT-related gene classifications predict the prognosis, immune infiltration, and therapeutic response of osteosarcoma. FRONT PHARMACOL 2024; 15: 1419040. Zeng D, Wu J, Luo H, Li Y, Xiao J, Peng J et al. . Tumor microenvironment evaluation promotes precise checkpoint immunotherapy of advanced gastric cancer. J IMMUNOTHER CANCER 2021; 9(8): e2467. Chen DS, Mellman I. Oncology meets immunology: the cancer-immunity cycle. IMMUNITY 2013; 39(1): 1–10. Ma K, Xu Y, Cheng H, Tang K, Ma J, Huang B. T cell-based cancer immunotherapy: opportunities and challenges. SCI BULL 2025; 70(11): 1872–1890. Sim MJW, Sun PD. T Cell Recognition of Tumor Neoantigens and Insights Into T Cell Immunotherapy. FRONT IMMUNOL 2022; 13: 833017. Ruan Y, Chen L, Xie D, Luo T, Xu Y, Ye T et al. . Mechanisms of Cell Adhesion Molecules in Endocrine-Related Cancers: A Concise Outlook. FRONT ENDOCRINOL 2022; 13: 865436. Damiano JS, Cress AE, Hazlehurst LA, Shtil AA, Dalton WS. Cell adhesion mediated drug resistance (CAM-DR): role of integrins and resistance to apoptosis in human myeloma cell lines. BLOOD 1999; 93(5): 1658–1667. Additional Declarations There is NO conflict of interest to disclose. Supplementary Files SupplementaryInformation.pdf Supplementary figure and legends SupplementaryDataset.xls Dataset 1 Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: revise 01 May, 2026 Review # 2 received at journal 29 Apr, 2026 Review # 1 received at journal 25 Apr, 2026 Review # 3 received at journal 17 Apr, 2026 Reviewer # 3 agreed at journal 17 Apr, 2026 Reviewer # 2 agreed at journal 16 Apr, 2026 Reviewer # 1 agreed at journal 16 Apr, 2026 Reviewers invited by journal 16 Apr, 2026 Submission checks completed at journal 14 Apr, 2026 First submitted to journal 10 Apr, 2026 Unknown event 10 Apr, 2026 Editor assigned by journal 02 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9304377","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":624137448,"identity":"fc914a84-aeab-43e9-9751-4b4d77c3c701","order_by":0,"name":"Lehang Lin","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3ElEQVRIiWNgGAWjYBACPgYeEGXBw8DeAKQNLAhrYYNokeBh4DkA0iJBvBYgSoAyCGph7z34ueCXhIy55POrG34USDDwt3cn4NfCcy5ZemafBI/l7Jyymz1Ah0mcObsBvxaJHANp3h4JHoPbOWk3eIBaDCRyCWiRf2P8G6zl5pm0m3+I0iLBYybN8wOo5Qb7sdvE2cKTY2bN2wDUciaH7baMgQQPQb/ws58xvs3zx8be4PjxZzff/LGR42/vxa8FDBjbQCSPAZgkrBwM/oAI9gdEqh4Fo2AUjIKRBgBSvz6eS8Z1HwAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0003-2005-9707","institution":"Sun Yat-Sen Memorial Hospital","correspondingAuthor":true,"prefix":"","firstName":"Lehang","middleName":"","lastName":"Lin","suffix":""},{"id":624137449,"identity":"7e7260a3-f897-420a-8454-fcd991de1e59","order_by":1,"name":"Zhuojian Shen","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Zhuojian","middleName":"","lastName":"Shen","suffix":""},{"id":624137450,"identity":"edd5d419-f7e8-4944-aa1f-fe2f20c106ec","order_by":2,"name":"Baishen Chen","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Baishen","middleName":"","lastName":"Chen","suffix":""},{"id":624137451,"identity":"0f97f38a-2d30-4a74-b4fd-b3c7cfe02cac","order_by":3,"name":"Honglve Dai","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Honglve","middleName":"","lastName":"Dai","suffix":""},{"id":624137452,"identity":"54ca834f-3bd4-454b-9baa-bf39263f6197","order_by":4,"name":"Wei Zhang","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Zhang","suffix":""},{"id":624137453,"identity":"c79cce62-2942-4e43-b691-483faff986e7","order_by":5,"name":"Xu Cheng","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Xu","middleName":"","lastName":"Cheng","suffix":""},{"id":624137454,"identity":"eda73770-a7a8-4081-b6b8-60f69ed72a7f","order_by":6,"name":"Jiaxi Sun","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Jiaxi","middleName":"","lastName":"Sun","suffix":""},{"id":624137455,"identity":"e1b57eb4-67d3-42e6-b4be-4e3846fe9498","order_by":7,"name":"Shiru Tang","email":"","orcid":"https://orcid.org/0009-0001-4242-0244","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Shiru","middleName":"","lastName":"Tang","suffix":""},{"id":624137456,"identity":"91614ce4-2365-4557-a439-e90662051cac","order_by":8,"name":"Minghui Wang","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Minghui","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2026-04-02 14:26:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9304377/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9304377/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107753853,"identity":"8d315383-73cd-4287-aa91-7878fa5c5fad","added_by":"auto","created_at":"2026-04-24 18:18:23","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":4904295,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIdentification of super-enhancers and functional analysis of SE-associated genes in A549 and A549-DDP cells.\u003c/strong\u003e (A) Identification of super-enhancers (SEs) in A549 and A549-DDP cells using the ROSE algorithm. (B) Venn diagram showing the cell line-specific and intersections of SE-associated genes in A549 and A549-DDP cells. (C) Gene Ontology (GO) enrichment analysis and (D) Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis of SE-associated genes in A549 and A549-DDP cells.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-9304377/v1/838667c1844a038751e294b8.png"},{"id":107868919,"identity":"8088513b-3d17-48b2-ae27-f0a783188814","added_by":"auto","created_at":"2026-04-27 07:34:58","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":9005007,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIdentification and functional enrichment analysis of 46 super-enhancer-driven core genes associated with cisplatin resistance in A549-DDP cells.\u003c/strong\u003e (A) Venn diagram showing the intersection between A549-DDP-specific super-enhancer-associated genes and genes upregulated in A549-DDP relative to parental A549 cells. (B) GO enrichment analysis and (C) KEGG pathway enrichment analysis of the 46 overlapping genes. (D) Genome browser tracks illustrating H3K27ac enrichment at chemoresistance-related genes in A549-DDP versus A549 cells.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-9304377/v1/049f645dc7321a067bb05a8a.png"},{"id":107868988,"identity":"6bd469e0-98ec-489b-b6a0-d94ff6732fd4","added_by":"auto","created_at":"2026-04-27 07:35:32","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":5743816,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAssociations between HAVCR1 expression and clinical outcomes in chemotherapy- and immunotherapy-treated cancer cohorts.\u003c/strong\u003e(A) Univariate Cox regression analysis of 46 genes in early-stage NSCLC patients receiving adjuvant chemotherapy (ACT) in the JBR.10 cohort. (B) Kaplan-Meier (KM) survival analysis comparing overall survival between HAVCR1-high and HAVCR1-low groups in early-stage NSCLC patients receiving ACT in the JBR.10 cohort. (C) KM survival analysis of overall survival stratified by HAVCR1 expression in the TCGA-LUAD cohort. (D) Comparison of HAVCR1 expression between responders and non-responders in the GSE126044 non-small-cell lung cancer cohort treated with anti-PD-1 therapy. (E–G) KM survival analyses comparing overall survival between HAVCR1-high and HAVCR1-low groups in patients receiving (E) CTLA-4 blockade, (F) PD-1 blockade, and (G) PD-L1 blockade in the pan cancer cohort from online Kaplan-Meier plotter.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-9304377/v1/8ec49443e0cb562024af26ed.png"},{"id":107869179,"identity":"f8a3530c-f2af-4d2e-bbd0-cafd587cf66d","added_by":"auto","created_at":"2026-04-27 07:36:20","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":12609701,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eImmune microenvironment characteristics associated with HAVCR1 expression in non-small-cell lung cancer.\u003c/strong\u003e(A–D) Comparisons of (A) tumor microenvironment immune cell type signatures, (B) immune suppression-related signatures, (C) immune exclusion-related signatures, and (D) immunotherapy-related biomarkers between HAVCR1-high and HAVCR1-low tumors.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-9304377/v1/f9e25eae0c5dc826de1d7476.png"},{"id":108803389,"identity":"57ae9c5e-f412-4e23-bed3-80d9f632a3ef","added_by":"auto","created_at":"2026-05-08 14:50:17","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":27828674,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9304377/v1/0ef4ec4a-44cf-4408-9513-83a05a6166f6.pdf"},{"id":107753854,"identity":"45ab8c13-b951-4bbf-af89-1806d104897a","added_by":"auto","created_at":"2026-04-24 18:18:23","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":4431725,"visible":true,"origin":"","legend":"Supplementary figure and legends","description":"","filename":"SupplementaryInformation.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9304377/v1/20ce0e86457e7dbd3be1a8ea.pdf"},{"id":107753856,"identity":"1b6daecb-af87-473d-a6c8-3b61c92fe483","added_by":"auto","created_at":"2026-04-24 18:18:23","extension":"xls","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":1221632,"visible":true,"origin":"","legend":"Dataset 1","description":"","filename":"SupplementaryDataset.xls","url":"https://assets-eu.researchsquare.com/files/rs-9304377/v1/feb89b82016957c825603bec.xls"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e conflict of interest to disclose.","formattedTitle":"Super-enhancer-driven HAVCR1 defines cisplatin resistance yet immunotherapy responsiveness in non-small-cell lung cancer","fulltext":[{"header":"Introduction","content":"\u003cp\u003eNon-small-cell lung cancer (NSCLC) accounts for more than 85% of lung cancer cases worldwide and remains a leading cause of cancer-related mortality\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Despite substantial advances in targeted therapies and immunotherapy, a large proportion of patients with advanced NSCLC\u0026mdash;particularly those lacking actionable driver mutations or exhibiting low PD-L1 expression\u0026mdash;continue to rely on platinum-based chemotherapy as a cornerstone of systemic therapy, either alone or in combination with immunotherapy\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. However, the clinical efficacy of cisplatin (DDP) is frequently undermined by intrinsic or acquired resistance, ultimately leading to treatment failure and disease progression\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Elucidating the molecular basis underlying DDP resistance, as well as identifying biomarkers to guide treatment selection and the optimal integration of chemotherapy and immunotherapy, therefore remains an urgent clinical priority.\u003c/p\u003e \u003cp\u003eAccumulating evidence highlights epigenetic reprogramming as a key driver of chemotherapy resistance. Among epigenetic regulatory elements, super-enhancers (SEs)\u0026mdash;large clusters of enhancers characterized by exceptionally high levels of H3K27ac and dense occupancy of transcriptional co-activators\u0026mdash;have emerged as central regulators of lineage-specific and oncogenic transcriptional programmes\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. SEs orchestrate gene expression networks that promote tumor growth, metastasis, and therapeutic resistance across multiple cancer types\u003csup\u003e\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Notably, recent studies suggest that anticancer therapies, including chemotherapy, can dynamically reshape the SE landscape, thereby enabling tumor cells to adapt to genotoxic stress and acquire drug tolerance\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. In addition, SE-driven transcriptional programs have been implicated in modulating cytokine signaling\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e, immune evasion\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e, and stromal\u0026ndash;immune interactions\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e, raising the possibility that SE reprogramming may coordinately regulate both drug resistance and the tumor immune microenvironment. However, whether SE reprogramming constitutes a central regulatory mechanism underlying DDP resistance in NSCLC remains unclear.\u003c/p\u003e \u003cp\u003eIn recent years, immune checkpoint blockade (ICB) has revolutionized the treatment landscape of NSCLC, yielding durable clinical responses in a subset of patients. Nevertheless, substantial inter-patient heterogeneity in therapeutic response underscores the limitations of current biomarkers and highlights the need for more refined molecular stratification strategies\u003csup\u003e\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Hepatitis A virus cellular receptor 1 (HAVCR1), also known as T-cell immunoglobulin and mucin structural domain 1 (TIM-1) or kidney injury molecule 1 (KIM-1), is a transmembrane protein involved in immune regulation\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Aberrant HAVCR1 expression has been reported across multiple cancer types and is associated with tumor progression and remodeling of the TME \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. However, the upstream regulatory mechanisms governing HAVCR1 expression, as well as its potential role in chemotherapy resistance and immunotherapy responsiveness in NSCLC, remain largely undefined.\u003c/p\u003e \u003cp\u003eIn this study, we performed integrative epigenomic and transcriptomic analyses to systematically characterized SE reprogramming and SE\u0026ndash;driven transcriptional programmes associated with DDP resistance in NSCLC. By integrating H3K27ac ChIP-seq and RNA-seq data from DDP-sensitive and -resistant A549 cells, we identified extensive SE landscape reprogramming accompanying the resistant phenotype and defined a high-confidence set of SE-associated, transcriptionally upregulated genes, among which HAVCR1 emerged as a key candidate. We further demonstrated that HAVCR1 high expression is linked to DDP resistance while concurrently associated with a lymphocyte-inflamed tumor microenvironment (TME) and favourable immunotherapy-related features. Collectively, our findings identify SE-driven HAVCR1 as a potential biomarker to inform the rational integration and sequencing of chemotherapy and immunotherapy in NSCLC.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eCell lines and culture\u003c/h2\u003e \u003cp\u003eHuman NSCLC A549 cells and their DDP-resistant derivative, A549-DDP, were cultured in RPMI-1640 medium (Gibco, Thermo Fisher Scientific, Waltham, MA, USA) supplemented with 10% fetal bovine serum (FBS; Gibco), 100 U/mL penicillin, and 100 \u0026micro;g/mL streptomycin (Gibco). Cells were maintained at 37\u0026deg;C in a humidified incubator with 5% CO\u003csub\u003e2\u003c/sub\u003e. To preserve the DDP-resistant phenotype, A549-DDP cells were routinely cultured in medium containing 1 \u0026micro;M DDP (Sigma-Aldrich, St. Louis, MO, USA). Prior to all experiments, A549-DDP cells were cultured in drug-free medium for at least one week to eliminate transient effects of DDP exposure.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eIdentifying ChIP-seq enriched regions\u003c/h3\u003e\n\u003cp\u003eChIP-seq libraries were sequenced in 150 bp paired-end mode on an Illumina NovaSeq 6000 platform (Illumina, San Diego, CA, USA). Raw sequencing reads were trimmed to remove adapter sequences and low-quality bases using Cutadapt (v2.5). The resulting clean reads were aligned to the human reference genome (GRCh38/hg38) using Bowtie2 (v2.3.5.1) with default parameters. Peaks of H3K27ac enrichment were identified using MACS2 (v2.1.2) with a q-value threshold of 0.05. These MACS2-defined peaks were subsequently used as constituent enhancers for SE identification.\u003c/p\u003e\n\u003ch3\u003eDefinition of enhancers and super-enhancers\u003c/h3\u003e\n\u003cp\u003eEnhancers and SEs were identified using the ROSE (Rank Ordering of Super-Enhancers) algorithm as previously described\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Briefly, constituent H3K27ac peaks were stitched if the genomic distance between adjacent peaks was \u0026le;\u0026thinsp;12.5 kb. All stitched enhancer regions were then ranked according to their input-subtracted H3K27ac signal intensity. SEs were defined as the subset of stitched enhancers located above the inflection point in the ranked H3K27ac signal curve. To exclude promoter-associated regions, peaks located within \u0026plusmn;\u0026thinsp;2.5 kb of annotated transcription start sites (TSSs) were excluded from the stitching process. For gene annotation, stitched enhancer and SE regions were assigned to their nearest genes using the annotatePeaks.pl utility from HOMER (v4.10.4).\u003c/p\u003e\n\u003ch3\u003eRNA-seq analysis\u003c/h3\u003e\n\u003cp\u003eTotal RNA was extracted from A549 and A549-DDP cells and used for poly(A)-selected library construction, followed by paired-end sequencing. Sequencing reads were aligned to the human reference genome (GRCh38/hg38) using STAR, and gene-level read counts were generated with featureCounts. Gene expression levels were normalised as fragments per kilobase of transcript per million mapped reads (FPKM). Differentially expressed genes (DEGs) between A549-DDP and parental A549 cells were identified using DESeq2, with a threshold of |log₂ fold change| \u0026gt; 1 and a false discovery rate (FDR)\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\n\u003ch3\u003eGene ontology and pathway analysis\u003c/h3\u003e\n\u003cp\u003eTo identify pathways associated with DDP resistance driven by SE reprogramming, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed using the clusterProfiler R package (v3.6.0). Statistical significance was defined by FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Enrichment analyses were conducted on four gene sets: (1) SE-associated genes in A549 cells; (2) SE-associated genes in A549-DDP cells; (3) genes significantly upregulated in A549-DDP relative to parental A549 cells based on RNA-seq; and (4) a 46-gene SE-associated core module, representing high-confidence candidates that are both SE\u0026ndash;associated and transcriptionally upregulated in the DDP-resistant state.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eClinical datasets and survival analysis\u003c/h2\u003e \u003cp\u003eFor chemotherapy-related survival analyses, microarray expression data and corresponding clinical information were obtained from the JBR.10 trial (GSE14814), a randomized study comparing adjuvant DDP-based chemotherapy with observation in patients with NSCLC\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Only patients with available HAVCR1 expression data and overall survival (OS) data were included. Patients who died from causes unrelated to lung cancer were excluded. Univariate Cox proportional hazards models were applied to the 46-gene core module to estimate hazard ratios (HRs) for OS. Kaplan\u0026ndash;Meier (KM) survival curves were generated for the adjuvant chemotherapy subgroup, and differences between HAVCR1-high and HAVCR1-low groups were assessed using the log-rank test. OS was further evaluated in an independent lung adenocarcinoma (LUAD) cohort\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e stratified by median HAVCR1 expression.\u003c/p\u003e \u003cp\u003eTo assess the relevance of HAVCR1 in immunotherapy, three independent datasets were analyzed. First, GSE126044 includes NSCLC patients treated with anti-PD-1 therapy, with available pre-treatment transcriptomic profiles and clinical response annotations (complete or partial response versus stable or progressive disease), allowing comparison of HAVCR1 expression between responders and non-responders \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Second, pan-cancer immunotherapy cohorts\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e were analyzed using the KM plotter immunotherapy platform, including datasets of patients treated with anti-CTLA-4, anti-PD-1, and anti-PD-L1 therapies. Third, the IMvigor210 cohort of metastatic urothelial carcinoma treated with atezolizumab (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://research-pub.gene.com/IMvigor210CoreBiologies\u003c/span\u003e\u003cspan address=\"http://research-pub.gene.com/IMvigor210CoreBiologies\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used as an external validation dataset, in which patients were stratified into HAVCR1-high and HAVCR1-low groups based on HAVCR1 median expression, and survival differences were evaluated using KM analysis.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eImmune characteristics and tumor microenvironment analysis\u003c/h3\u003e\n\u003cp\u003eThe immune landscape associated with HAVCR1 expression\u0026mdash; including TME immune cell type signatures, immune suppression-related signatures, immune exclusion-related signatures, and immunotherapy-related biomarker signatures\u0026mdash;was systematically evaluated using the Immuno-Oncology Biological Research (IOBR) R package\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTo characterize immune functional states, the TIP (tracking tumor immunophenotype) meta-server (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://biocc.hrbmu.edu.cn/TIP/\u003c/span\u003e\u003cspan address=\"http://biocc.hrbmu.edu.cn/TIP/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was employed\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. TIP integrates \"ssGSEA\" and \"CIBERSORT\" algorithms to infer and visualize anticancer immune activity across the seven steps of the cancer-immunity cycle using RNA-seq or microarray data. Spearman correlation analyses were then performed to assess associations between HAVCR1 expression and individual steps of the cancer\u0026ndash;immunity cycle.\u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eAll data processing and statistical analyses were performed using R software. Continuous variables were compared using either Student\u0026rsquo;s \u003cem\u003et\u003c/em\u003e-test or the Wilcoxon rank-sum test, depending on data distribution. Categorical variables were analyzed using the χ\u003csup\u003e2\u003c/sup\u003e test or Fisher\u0026rsquo;s exact test, as appropriate. Correlations between variables were assessed using Spearman\u0026rsquo;s rank correlation analysis. Survival curves were generated using the Kaplan\u0026ndash;Meier method and compared using the log-rank test. HRs were estimated using univariate Cox proportional hazards models. For analyses involving multiple signatures or enrichment terms, \u003cem\u003eP\u003c/em\u003e values were adjusted using the Benjamini\u0026ndash;Hochberg method where applicable. A \u003cem\u003eP\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant for all analyses.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eSuper-enhancer landscape reprogramming in A549-DDP cells\u003c/h2\u003e \u003cp\u003eTo elucidate the epigenetic basis underlying DDP resistance in NSCLC, we performed H3K27ac ChIP-seq in parental A549 cells and their DDP-resistant derivative, A549-DDP. SEs and typical enhancers were subsequently defined using the ROSE algorithm based on H3K27ac signal intensity. Compared with parental A549 cells, A549-DDP cells exhibited a marked expansion of the SE repertoire, with SE numbers increasing from 392 to 681, indicating extensive enhancer reprogramming during the acquisition of DDP resistance (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Consistently, aggregate H3K27ac signal profiles centered on enhancer regions showed stronger enhancer-associated H3K27ac occupancy in A549-DDP cells (Supplementary Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAnnotating SEs to their nearest genes further revealed substantial remodelling of SE-associated transcriptional programmes. Among all SE-associated genes, only 235 were shared between the two cell lines, whereas 123 and 394 genes were uniquely associated with SEs in A549 and in A549-DDP cells, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB; Supplementary Datasets S1 and S2). Functional annotation showed that SE-associated genes in A549-DDP cells were significantly enriched in adhesion- and junction-related processes by GO analysis, while KEGG analysis highlighted focal adhesion, cell cycle regulation, and cancer-associated pathways\u0026mdash;processes previously implicated in chemotherapy resistance\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC and D).\u003c/p\u003e \u003cp\u003eCollectively, these findings demonstrate extensive SE landscape reprogramming in DDP-resistant NSCLC cells, accompanied by the activation of adhesion-related transcriptional networks. This SE-driven transcriptional rewiring likely provides an epigenetic basis for tumor cells to evade DDP-induced apoptosis and sustain stable chemoresistance.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eA 46-gene super-enhancer-associated core module linked to DDP resistance in A549-DDP cells\u003c/h2\u003e \u003cp\u003eGiven that SEs drive high-level transcription of target genes, we next sought to identify SE-associated genes contributing to DDP resistance in A549-DDP cells. RNA-seq analysis comparing A549 and A549-DDP cells identified a subset of genes significantly upregulated in DDP resistant cells (Supplementary Dataset S3). By intersecting the 394 A549-DDP-specific SE-associated genes with the 1,752 genes upregulated in A549-DDP cells, we identified a set of 46 overlapping genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA; Supplementary Dataset S4). Functional enrichment analysis showed that these genes recapitulated key features of the broader SE-associated landscape. Specifically, GO terms were predominantly related to cell adhesion and junction organization (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB), while KEGG pathways included focal adhesion, pathways in cancer, p53 signaling, actin cytoskeleton regulation, and ECM-receptor interaction (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). Notably, several genes within this set, including \u003cem\u003eBMAL2\u003c/em\u003e\u003csup\u003e27\u003c/sup\u003e, \u003cem\u003eNEK6\u003c/em\u003e\u003csup\u003e28\u003c/sup\u003e, \u003cem\u003eCTPS1\u003c/em\u003e\u003csup\u003e29\u003c/sup\u003e, and \u003cem\u003ePLOD2\u003c/em\u003e\u003csup\u003e30\u003c/sup\u003e, have been reported to contribute to chemotherapy resistance across multiple cancer types. Consistent with their SE association, H3K27ac tracks showed increased signal intensity at their loci in A549-DDP cells relative to A549, supporting enhancer activation (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD; Supplementary Fig. S2). Together, the consistent enrichment of adhesion-, cytoskeleton-, and cell cycle-related signatures across both global SE-associated genes and the refined gene set suggests that these 46 SE-driven genes constitute a core transcriptional module underlying SE-mediated DDP resistance in A549-DDP cells.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eClinical associations of HAVCR1 in chemotherapy and immunotherapy settings\u003c/h2\u003e \u003cp\u003eTo assess the clinical relevance of these 46 SE-driven core genes, we analyzed data from the JBR.10 trial (GSE14814), which includes 133 patients with early-stage NSCLC treated with adjuvant DDP/vinorelbine or observation following surgical resection\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. After excluding 10 patients who died from non-lung cancer causes, univariate Cox regression analysis identified \u003cem\u003eHAVCR1\u003c/em\u003e as the only gene significantly associated with poorer patient overall survival (HR\u0026thinsp;=\u0026thinsp;2.56, 95% CI 1.00-6.53; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), suggesting its potential as an adverse prognostic marker (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA; Supplementary Fig. S3). Consistent with its SE regulation in the chemoresistant state, H3K27ac signal at the \u003cem\u003eHAVCR1\u003c/em\u003e locus was elevated in A549-DDP cells relative to A549 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). Within the adjuvant chemotherapy subgroup, patients with high HAVCR1 expression showed a trend towards worse overall survival compared with those with low HAVCR1 expression, although this did not reach statistical significance (HR\u0026thinsp;=\u0026thinsp;2.08, 95% CI 0.86-5.00; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.170; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). Similarly, analysis using the Kaplan\u0026ndash;Meier Plotter in an independent LUAD cohort\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e demonstrated that elevated HAVCR1 expression was significantly associated with poorer overall survival (HR\u0026thinsp;=\u0026thinsp;1.46, 95% CI 1.09\u0026ndash;1.95; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.011; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC), in agreement with prior pan-cancer observations\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Together, these findings support that SE-driven upregulation of HAVCR1 is associated with poor outcomes in NSCLC patients receiving platinum-based chemotherapy.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eInterestingly, in another independent NSCLC cohort treated with anti-PD-1 therapy, HAVCR1 expression tended to be higher in responders than in non-responders, although the difference was not statistically significance (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.15; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD), likely due to limited sample size. Furthermore, in pan-cancer immunotherapy datasets, elevated HAVCR1 expression was associated with improved overall survival in patients receiving CTLA-4 blockade (HR\u0026thinsp;=\u0026thinsp;0.48, 95% CI 0.29\u0026ndash;0.8; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0042) and PD-L1 blockade (HR\u0026thinsp;=\u0026thinsp;0.72, 95% CI 0.56\u0026ndash;0.93; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.011), with a similar trend observed for PD-1 blockade (HR\u0026thinsp;=\u0026thinsp;0.74, 95% CI 0.54\u0026ndash;1.01; log-rank, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.059) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE-G). These results suggest a context-dependent role for HAVCR1: while its high expression predicts poor prognosis in patients receiving platinum-based chemotherapy, it is associated with improved clinical benefit in immunotherapy settings.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eImmune microenvironment characteristics associated with HAVCR1 expression\u003c/h2\u003e \u003cp\u003eTo further characterize the immune context associated with HAVCR1, we compared TME features between HAVCR1-high and HAVCR1-low LUAD samples from the TCGA dataset. Of note, HAVCR1-high tumors exhibited a more lymphocyte-inflamed phenotype, with increased enrichment of follicular helper T cells, CD8\u003csup\u003e+\u003c/sup\u003e T cells, B cells, and plasma cells. In contrast, HAVCR1-low tumors showed stronger enrichment of myeloid and stromal components, including exhausted T cells, macrophages, fibroblasts, monocytes, neutrophils, and myeloid-derived suppressor cells (MDSC), indicative of an immunosuppressive microenvironment (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Consistent with this pattern, immune-suppressive signatures, including immune checkpoint pathways, were more enriched in HAVCR1-low tumors (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Additionally, immune exclusion-related programs, such as EMT and TGF-β signatures, were also more prominent in the HAVCR1-low group (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC), aligning with previously reported features associated with reduced response to ICB\u003csup\u003e\u003cspan additionalcitationids=\"CR33 CR34\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. Although several immunotherapy-related biomarkers showed minimal differences between groups, HAVCR1-high tumors displayed significantly lower TMEscoreB values, a stromal-relevant signature negatively correlated with immunotherapy response\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). Overall, these findings suggest that HAVCR1-high tumors are more likely to exhibit an immune-activated (\"hot\") microenvironment conductive to ICB response, whereas HAVCR1-low tumors display an immune-excluded (\"cold\") phenotype that may limit ICB efficacy.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo further explore the immunological implications of HAVCR1, we analyzed the cancer-immunity cycle using the TIP meta-server\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. Spearman correlation analysis revealed that in TCGA-LUAD cohort, HAVCR1 expression was positively correlated with Step 6 (recognition of cancer cells by T cells), a key feature of effective anti-tumor immunity \u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e, and negatively correlated with Step 4 (macrophage recruiting), supporting enhanced immune recognition alongside reduced myeloid infiltration in HAVCR1-high tumors (Supplementary Fig. S4). Consistently, in the IMvigor210 cohort of metastatic urothelial carcinoma treated with atezolizumab, patients with HAVCR1-high expression showed improved overall survival compared with those with HAVCR1-low expression, with separation of survival curves becoming evident after approximately six months of therapy (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.009; Supplementary Fig. S5). Although derived from a different cancer type, this dataset provides independent support for the association between elevated HAVCR1 expression and favorable response to ICB.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we delineate a comprehensive epigenomic and transcriptomic framework underlying DDP resistance in NSCLC and identify HAVCR1 as a SE\u0026ndash;driven effector linking chemoresistance-associated transcriptional programs to immunotherapy-relevant tumor features. Through integrative analyses, our findings suggest that enhancer reprogramming, coordinated transcriptional activation, and TME remodelling converge to shape therapeutic vulnerabilities in resistant tumors.\u003c/p\u003e \u003cp\u003eOur epigenomic profiling revealed extensive reprogramming of the SE landscape in DDP-resistant A549-DDP cells, characterized by expansion of the SE repertoire and preferential activation of adhesion-, junction-, and proliferation-related pathways\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e; Supplementary Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). These observations, together with the identification of a functionally convergent SE-associated gene module (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), support a model in which enhancer rewiring drives coordinated transcriptional programs that promote cell survival and adaptation under platinum-induced stress. Such epigenetic plasticity has been increasingly recognized as central mechanism of chemoresistance in cancers including NSCLC, whereby enhancer activation sustains oncogenic transcriptional states that buffer tumor cells against DNA damage and apoptosis\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWithin this SE\u0026ndash;driven transcriptional context, HAVCR1 emerged as a clinically relevant candidate. Elevated HAVCR1 expression was associated with poorer outcomes in platinum-treated NSCLC cohorts (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB and C), supporting its potential role in treatment resistance. Intriguingly, however, HAVCR1 expression showed an opposite trend in the context of immunotherapy: higher HAVCR1 expression was observed in NSCLC patients responding to anti-PD-1 treatment (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD), and HAVCR1-high tumors exhibited transcriptional features characteristic of a lymphocyte-inflamed TME (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). These included increased infiltration of CD8\u003csup\u003e+\u003c/sup\u003e T cells, follicular helper T cells, B cells, and plasma cells, along with enhanced antigen presentation and co-stimulatory signalling, whereas HAVCR1-low tumors were enriched for myeloid-dominant and immune-excluded signatures. This apparent context-dependent role of HAVCR1 may reflect its dual association with tumor-intrinsic transcriptional programs and the extrinsic immune microenvironment. While SE-driven HAVCR1 upregulation may contribute to chemoresistance through its linkage to survival-associated transcriptional networks, it is simultaneously associated with an immune-activated state that is permissive to effective anti-tumor immunity. This duality is consistent with emerging paradigms in which tumors with robust T-cell infiltration and inflammatory signaling exhibit improved responsiveness to ICB\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. Importantly, given the limitations of current biomarkers such as PD-L1 expression, HAVCR1 may provide complementary value for stratifying patients, particularly in the context of combined or sequential chemo-immunotherapy strategies.\u003c/p\u003e \u003cp\u003eBased on these findings, we propose a working model in which SE-mediated upregulation of HAVCR1 defines a transcriptional and immunological state characterized by enhanced chemoresistance but increased susceptibility to immunotherapy. Within this framework, HAVCR1 and its associated SE network may represent promising biomarkers and potential therapeutic targets for optimizing treatment sequencing and chemo-immunotherapy combination strategies in NSCLC.\u003c/p\u003e \u003cp\u003eThis study is primarily based on integrative analyses of cell line models and publicly available patient cohorts. Although the associations between HAVCR1 expression, SE activation, and immunotherapeutic response were consistent across multiple datasets, causal validation is required. Future studies employing targeted perturbation of SE elements and HAVCR1, coupled with functional assays assessing tumor cell survival, immune activation, and response to ICB, will be essential to further strengthen the mechanistic interpretation and therapeutic relevance of this regulatory axis.\u003c/p\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eData Availability Statement:\u003c/h2\u003e \u003cp\u003eThe public datasets analyzed in this study are available from GEO (GSE14814 and GSE126044) and the IMvigor210CoreBiologies resource. Additional data supporting the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eEthics approval\u003c/h2\u003e \u003cp\u003eThis study did not involve any experiments on human participants, human tissues, or animals conducted by the authors. All data used in this research were obtained from publicly available and fully anonymized databases (e.g., GEO and TCGA). According to the policies of our institutional ethics committee, ethical review and informed consent were not required for studies based solely on publicly available data.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCompeting interests\u003c/strong\u003e \u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eThis study was supported by funding from the Guangdong Province Natural Science Foundation(Grant No. 2021A1515011135); Guangdong Basic and Applied Basic Research Foundation(2026A1515012833).\u003c/p\u003e \u003cp\u003eFinal approval of manuscript: All authors.\u003c/p\u003e\u003ch2\u003eAuthors' contributions\u003c/h2\u003e \u003cp\u003eConception and design: Zhuojian Shen, Lehang Lin and Minghui Wang.\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e \u003cp\u003eNone.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBade BC, Dela Cruz CS. Lung Cancer 2020: Epidemiology, Etiology, and Prevention. 2020.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRuiz-Cordero R, Devine WP. Targeted Therapy and Checkpoint Immunotherapy in Lung Cancer. \u003cem\u003eSurgical pathology clinics\u003c/em\u003e 2020; 13(1): 17\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGalluzzi L, Vitale I, Michels J, Brenner C, Szabadkai G, Harel-Bellan A \u003cem\u003eet al.\u003c/em\u003e. Systems biology of cisplatin resistance: past, present and future. \u003cem\u003eCELL DEATH DIS\u003c/em\u003e 2014; 5(5): e1257.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCreyghton MP, Cheng AW, Welstead GG, Kooistra T, Carey BW, Steine EJ \u003cem\u003eet al.\u003c/em\u003e. Histone H3K27ac separates active from poised enhancers and predicts developmental state. \u003cem\u003eP NATL ACAD SCI USA\u003c/em\u003e 2010; 107(50): 21931\u0026ndash;21936.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu S, Dai W, Jin B, Jiang F, Huang H, Hou W \u003cem\u003eet al.\u003c/em\u003e. Effects of super-enhancers in cancer metastasis: mechanisms and therapeutic targets. \u003cem\u003eMOL CANCER\u003c/em\u003e 2024; 23(1): 122.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCheng Y, Pei G, Zhang H, Hou Y, Sun L, Xu H \u003cem\u003eet al.\u003c/em\u003e. Super enhancers as key drivers of gene regulatory networks in normal and malignant hematopoiesis. \u003cem\u003eFrontiers in cell and developmental biology\u003c/em\u003e 2025; 13: 1674470.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi G, Qu Q, Qi T, Teng X, Zhu H, Wang J \u003cem\u003eet al.\u003c/em\u003e. Super-enhancers: a new frontier for epigenetic modifiers in cancer chemoresistance. \u003cem\u003eJournal of experimental \u0026amp; clinical cancer research: CR\u003c/em\u003e 2021; 40(1): 174.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBhattacharjee R, Dey T, Kumar L, Kar S, Sarkar R, Ghorai M \u003cem\u003eet al.\u003c/em\u003e. Cellular landscaping of cisplatin resistance in cervical cancer. \u003cem\u003eBiomedicine \u0026amp; pharmacotherapy\u0026thinsp;=\u0026thinsp;Biomedecine \u0026amp; pharmacotherapie\u003c/em\u003e 2022; 153: 113345.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou RW, Xu J, Martin TC, Zachem AL, He J, Ozturk S \u003cem\u003eet al.\u003c/em\u003e. A local tumor microenvironment acquired super-enhancer induces an oncogenic driver in colorectal carcinoma. \u003cem\u003eNAT COMMUN\u003c/em\u003e 2022; 13(1): 6041.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu Y, Wu Y, Zhang S, Ma P, Jin X, Wang Z \u003cem\u003eet al.\u003c/em\u003e. A Tumor-Specific Super-Enhancer Drives Immune Evasion by Guiding Synchronous Expression of PD-L1 and PD-L2. \u003cem\u003eCELL REP\u003c/em\u003e 2019; 29(11): 3435\u0026ndash;3447.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMa P, Jin X, Fan Z, Wang Z, Yue S, Wu C \u003cem\u003eet al.\u003c/em\u003e. Super-enhancer receives signals from the extracellular matrix to induce PD-L1-mediated immune evasion via integrin/BRAF/TAK1/ERK/ETV4 signaling. \u003cem\u003eCANCER BIOL MED\u003c/em\u003e 2021; 19(5): 669\u0026ndash;684.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRizvi NA, Hellmann MD, Snyder A, Kvistborg P, Makarov V, Havel JJ \u003cem\u003eet al.\u003c/em\u003e. Cancer immunology. Mutational landscape determines sensitivity to PD-1 blockade in non-small cell lung cancer. \u003cem\u003eScience (New York, N.Y.)\u003c/em\u003e 2015; 348(6230): 124\u0026ndash;128.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMellman I, Chen DS, Powles T, Turley SJ. The cancer-immunity cycle: Indication, genotype, and immunotype. \u003cem\u003eIMMUNITY\u003c/em\u003e 2023; 56(10): 2188\u0026ndash;2205.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHugo W, Zaretsky JM, Sun L, Song C, Moreno BH, Hu-Lieskovan S \u003cem\u003eet al.\u003c/em\u003e. Genomic and Transcriptomic Features of Response to Anti-PD-1 Therapy in Metastatic Melanoma. \u003cem\u003eCELL\u003c/em\u003e 2016; 165(1): 35\u0026ndash;44.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCao J, Qing J, Zhu L, Chen Z. Role of TIM-1 in the development and treatment of tumours. \u003cem\u003eFrontiers in cell and developmental biology\u003c/em\u003e 2024; 12: 1307806.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu S, Tang W, Cao J, Shang M, Sun H, Gong J \u003cem\u003eet al.\u003c/em\u003e. A Comprehensive Analysis of HAVCR1 as a Prognostic and Diagnostic Marker for Pan-Cancer. \u003cem\u003eFRONT GENET\u003c/em\u003e 2022; 13: 904114.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWhyte WA, Orlando DA, Hnisz D, Abraham BJ, Lin CY, Kagey MH \u003cem\u003eet al.\u003c/em\u003e. Master transcription factors and mediator establish super-enhancers at key cell identity genes. \u003cem\u003eCELL\u003c/em\u003e 2013; 153(2): 307\u0026ndash;319.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhu C, Ding K, Strumpf D, Weir BA, Meyerson M, Pennell N \u003cem\u003eet al.\u003c/em\u003e. Prognostic and predictive gene signature for adjuvant chemotherapy in resected non-small-cell lung cancer. \u003cem\u003eJournal of clinical oncology: official journal of the American Society of Clinical Oncology\u003c/em\u003e 2010; 28(29): 4417\u0026ndash;4424.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGy Rffy B. Integrated analysis of public datasets for the discovery and validation of survival-associated genes in solid tumors. \u003cem\u003eInnovation (Cambridge (Mass.))\u003c/em\u003e 2024; 5(3): 100625.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCho J, Hong MH, Ha S, Kim Y, Cho BC, Lee I \u003cem\u003eet al.\u003c/em\u003e. Genome-wide identification of differentially methylated promoters and enhancers associated with response to anti-PD-1 therapy in non-small cell lung cancer. \u003cem\u003eExperimental \u0026amp; molecular medicine\u003c/em\u003e 2020; 52(9): 1550\u0026ndash;1563.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKov\u0026aacute;cs SA, Fekete JT, Gy Rffy B. Predictive biomarkers of immunotherapy response with pharmacological applications in solid tumors. 2023.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZeng D, Ye Z, Shen R, Yu G, Wu J, Xiong Y \u003cem\u003eet al.\u003c/em\u003e. IOBR: Multi-Omics Immuno-Oncology Biological Research to Decode Tumor Microenvironment and Signatures. \u003cem\u003eFRONT IMMUNOL\u003c/em\u003e 2021; 12: 687975.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu L, Deng C, Pang B, Zhang X, Liu W, Liao G \u003cem\u003eet al.\u003c/em\u003e. TIP: A Web Server for Resolving Tumor Immunophenotype Profiling. \u003cem\u003eCANCER RES\u003c/em\u003e 2018; 78(23): 6575\u0026ndash;6580.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGeijerman E, Terrana F, Peters GJ, Deng D, Diana P, Giovannetti E \u003cem\u003eet al.\u003c/em\u003e. Targeting a key FAK-tor: the therapeutic potential of combining focal adhesion kinase (FAK) inhibitors and chemotherapy for chemoresistant non-small cell lung cancer. \u003cem\u003eEXPERT OPIN INV DRUG\u003c/em\u003e 2024; 33(11): 1103\u0026ndash;1118.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChandra Jena B, Kanta Das C, Banerjee I, Das S, Bharadwaj D, Majumder R \u003cem\u003eet al.\u003c/em\u003e. Paracrine TGF-β1 from breast cancer contributes to chemoresistance in cancer associated fibroblasts via upregulation of the p44/42 MAPK signaling pathway. \u003cem\u003eBIOCHEM PHARMACOL\u003c/em\u003e 2021; 186: 114474.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSandu K, Warta R, Biswas U, Zhang W, Michl P, Herold-Mende C \u003cem\u003eet al.\u003c/em\u003e. Cisplatin resistance in head and neck squamous cell carcinoma is linked to DNA damage response and cell cycle arrest transcriptomics rather than poor drug uptake. \u003cem\u003eCancer drug resistance (Alhambra, Calif.)\u003c/em\u003e 2025; 8: 51.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang J, Lin D, Huang Y, Yin S, Chen M, Sun H \u003cem\u003eet al.\u003c/em\u003e. Clock gene ARNTL2 enhances 5-fluorouracil resistance in colon cancer by upregulating SLC7A11 to suppress ferroptosis. \u003cem\u003eREDOX BIOL\u003c/em\u003e 2025; 86: 103798.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu J, Wang H, Wan H, Yang J, Gao L, Wang Z \u003cem\u003eet al.\u003c/em\u003e. NEK6 dampens FOXO3 nuclear translocation to stabilize C-MYC and promotes subsequent de novo purine synthesis to support ovarian cancer chemoresistance. \u003cem\u003eCELL DEATH DIS\u003c/em\u003e 2024; 15(9): 661.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSun X, Wei Z, Liao X, Zheng D, Li F, Chen L \u003cem\u003eet al.\u003c/em\u003e. INHBA promotes chemoresistance in pancreatic cancer by enhancing CTPS1 stability and mediating pyrimidine metabolism. \u003cem\u003eCANCER CELL INT\u003c/em\u003e 2025; 25(1): 403.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOkumura Y, Noda T, Eguchi H, Sakamoto T, Iwagami Y, Yamada D \u003cem\u003eet al.\u003c/em\u003e. Hypoxia-Induced PLOD2 is a Key Regulator in Epithelial-Mesenchymal Transition and Chemoresistance in Biliary Tract Cancer. \u003cem\u003eANN SURG ONCOL\u003c/em\u003e 2018; 25(12): 3728\u0026ndash;3737.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi G, Javed M, Rasool R, Abdel-Maksoud MA, Mubarak AS, Studenik CR \u003cem\u003eet al.\u003c/em\u003e. A pan-cancer analysis of HAVCR1 with a focus on diagnostic, prognostic and immunological roles in human cancers. \u003cem\u003eAM J TRANSL RES\u003c/em\u003e 2023; 15(3): 1590\u0026ndash;1606.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMariathasan S, Turley SJ, Nickles D, Castiglioni A, Yuen K, Wang Y \u003cem\u003eet al.\u003c/em\u003e. TGFβ attenuates tumour response to PD-L1 blockade by contributing to exclusion of T cells. \u003cem\u003eNATURE\u003c/em\u003e 2018; 554(7693): 544\u0026ndash;548.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTauriello DVF, Palomo-Ponce S, Stork D, Berenguer-Llergo A, Badia-Ramentol J, Iglesias M \u003cem\u003eet al.\u003c/em\u003e. TGFβ drives immune evasion in genetically reconstituted colon cancer metastasis. \u003cem\u003eNATURE\u003c/em\u003e 2018; 554(7693): 538\u0026ndash;543.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJiang Y, Zhan H. Communication between EMT and PD-L1 signaling: New insights into tumor immune evasion. \u003cem\u003eCANCER LETT\u003c/em\u003e 2020; 468: 72\u0026ndash;81.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi M, Long S, Liu W, Long K, Gao X. EMT-related gene classifications predict the prognosis, immune infiltration, and therapeutic response of osteosarcoma. \u003cem\u003eFRONT PHARMACOL\u003c/em\u003e 2024; 15: 1419040.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZeng D, Wu J, Luo H, Li Y, Xiao J, Peng J \u003cem\u003eet al.\u003c/em\u003e. Tumor microenvironment evaluation promotes precise checkpoint immunotherapy of advanced gastric cancer. \u003cem\u003eJ IMMUNOTHER CANCER\u003c/em\u003e 2021; 9(8): e2467.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen DS, Mellman I. Oncology meets immunology: the cancer-immunity cycle. \u003cem\u003eIMMUNITY\u003c/em\u003e 2013; 39(1): 1\u0026ndash;10.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMa K, Xu Y, Cheng H, Tang K, Ma J, Huang B. T cell-based cancer immunotherapy: opportunities and challenges. \u003cem\u003eSCI BULL\u003c/em\u003e 2025; 70(11): 1872\u0026ndash;1890.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSim MJW, Sun PD. T Cell Recognition of Tumor Neoantigens and Insights Into T Cell Immunotherapy. \u003cem\u003eFRONT IMMUNOL\u003c/em\u003e 2022; 13: 833017.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRuan Y, Chen L, Xie D, Luo T, Xu Y, Ye T \u003cem\u003eet al.\u003c/em\u003e. Mechanisms of Cell Adhesion Molecules in Endocrine-Related Cancers: A Concise Outlook. \u003cem\u003eFRONT ENDOCRINOL\u003c/em\u003e 2022; 13: 865436.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDamiano JS, Cress AE, Hazlehurst LA, Shtil AA, Dalton WS. Cell adhesion mediated drug resistance (CAM-DR): role of integrins and resistance to apoptosis in human myeloma cell lines. \u003cem\u003eBLOOD\u003c/em\u003e 1999; 93(5): 1658\u0026ndash;1667.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"cancer-gene-therapy","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"cgt","sideBox":"Learn more about [Cancer Gene Therapy](http://www.nature.com/cgt/)","snPcode":"41417","submissionUrl":"https://mts-cgt.nature.com/cgi-bin/main.plex","title":"Cancer Gene Therapy","twitterHandle":"@cgtnature","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-9304377/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9304377/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCisplatin (DDP) resistance remains a major obstacle in the treatment of non-small-cell lung cancer (NSCLC), underscoring the need to identify robust prognostic biomarkers and therapeutic strategies. Super-enhancers (SEs) play central roles in orchestrating oncogenic transcriptional programs and cellular adaptation to therapeutic stress. In this study, we integrated H3K27ac ChIP-seq and RNA-seq analyses in DDP-sensitive and -resistant NSCLC cells to define SE-associated transcriptional alterations underlying chemoresistance. DDP-resistant cells exhibited extensive SE reprogramming, accompanied by activation of transcriptional programs related to cell adhesion and junction, cell cycle regulation, and cancer-associated signaling. Further analyses identified \u003cem\u003eHAVCR1\u003c/em\u003e as a key SE-associated gene whose upregulation correlated with DDP resistance and poorer survival in platinum-relevant NSCLC cohorts. Notably, however, elevated HAVCR1 expression was associated with improved clinical outcomes in immune checkpoint blockade (ICB) datasets. Mechanistically, HAVCR1-high tumors displayed a lymphocyte-inflamed tumor microenvironment (TME), characterized by increased CD8\u003csup\u003e+\u003c/sup\u003e T cell infiltration and reduced myeloid-driven immunosuppression. Together, our findings suggest that SE-driven HAVCR1 defines a transcriptional and immunological state marked by enhanced chemoresistance yet increased susceptibility to immunotherapy, highlighting its potential as a biomarker for guiding chemo-immunotherapy strategies in NSCLC.\u003c/p\u003e","manuscriptTitle":"Super-enhancer-driven HAVCR1 defines cisplatin resistance yet immunotherapy responsiveness in non-small-cell lung cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-24 18:18:18","doi":"10.21203/rs.3.rs-9304377/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"revise","date":"2026-05-01T14:04:17+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"This content is not available.","date":"2026-04-29T16:24:41+00:00","index":2,"fulltext":"This content is not available."},{"type":"editorInvitedReview","content":"This content is not available.","date":"2026-04-25T07:49:49+00:00","index":1,"fulltext":"This content is not available."},{"type":"editorInvitedReview","content":"This content is not available.","date":"2026-04-17T12:15:38+00:00","index":3,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2026-04-17T07:22:31+00:00","index":3,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2026-04-16T08:29:57+00:00","index":2,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2026-04-16T08:20:14+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewersInvited","content":"","date":"2026-04-16T08:05:04+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-14T09:22:41+00:00","index":"","fulltext":""},{"type":"submitted","content":"Cancer Gene Therapy","date":"2026-04-10T13:26:07+00:00","index":"","fulltext":""},{"type":"checksFailed","content":"","date":"2026-04-10T10:02:44+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-02T14:15:30+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"cancer-gene-therapy","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"cgt","sideBox":"Learn more about [Cancer Gene Therapy](http://www.nature.com/cgt/)","snPcode":"41417","submissionUrl":"https://mts-cgt.nature.com/cgi-bin/main.plex","title":"Cancer Gene Therapy","twitterHandle":"@cgtnature","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"7acd6f39-3d90-4a32-915f-41c542e9cd0e","owner":[],"postedDate":"April 24th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"revise","date":"2026-05-01T14:04:17+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"This content is not available.","date":"2026-04-29T16:24:41+00:00","index":2,"fulltext":"This content is not available."}],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[{"id":66429133,"name":"Biological sciences/Cancer/Lung cancer/Non-small-cell lung cancer"},{"id":66429134,"name":"Biological sciences/Immunology/Immunotherapy/Immunosuppression"}],"tags":[],"updatedAt":"2026-05-01T14:06:23+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-24 18:18:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9304377","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9304377","identity":"rs-9304377","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}