ImmunoFusion: A Unified Platform for Investigating RNA-seq-Derived Gene Fusions in Cancer and Immunotherapy

ImmunoFusion: A Unified Platform for Investigating RNA-seq-Derived Gene Fusions in Cancer and Immunotherapy | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (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];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results ImmunoFusion: A Unified Platform for Investigating RNA-seq-Derived Gene Fusions in Cancer and Immunotherapy View ORCID Profile YanKun Zhao , View ORCID Profile Shixiang Wang , View ORCID Profile Shensuo Li , Minjun Chen , Su-han Jin , View ORCID Profile Udo S. Gaipl , Hu Ma , View ORCID Profile Jian-Guo Zhou doi: https://doi.org/10.1101/2025.06.05.658082 YanKun Zhao 1 Department of Oncology, The Second Affiliated Hospital of Zunyi Medical University , Zunyi, 563000, P. R. China Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for YanKun Zhao Shixiang Wang 2 Department of Biomedical Informatics, School of Life Sciences, Central South University , Changsha, PR China Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Shixiang Wang For correspondence: wangshx{at}csu.edu.cn mahuab{at}163.com jianguo.zhou{at}zmu.edu.cn Shensuo Li 3 West China School of Public Health and West China Fourth Hospital, and State Key Laboratory of Biotherapy, Sichuan University , Chengdu, 610041, P. R. China Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Shensuo Li Minjun Chen 1 Department of Oncology, The Second Affiliated Hospital of Zunyi Medical University , Zunyi, 563000, P. R. China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Su-han Jin 4 Department of Orthodontics, Affiliated Stomatological Hospital of Zunyi Medical University , Zunyi, 563000, P. R. China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Udo S. Gaipl 5 Translational Radiobiology, Department of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg , Erlangen, 91052, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Udo S. Gaipl Hu Ma 1 Department of Oncology, The Second Affiliated Hospital of Zunyi Medical University , Zunyi, 563000, P. R. China 4 Department of Orthodontics, Affiliated Stomatological Hospital of Zunyi Medical University , Zunyi, 563000, P. R. China Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: wangshx{at}csu.edu.cn mahuab{at}163.com jianguo.zhou{at}zmu.edu.cn Jian-Guo Zhou 1 Department of Oncology, The Second Affiliated Hospital of Zunyi Medical University , Zunyi, 563000, P. R. China 5 Translational Radiobiology, Department of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg , Erlangen, 91052, Germany 6 Key Laboratory for Cancer Prevention and treatment of Guizhou Province, Zunyi Medical University , Zunyi, 563000, P. R. China Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Jian-Guo Zhou For correspondence: wangshx{at}csu.edu.cn mahuab{at}163.com jianguo.zhou{at}zmu.edu.cn Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF ABSTRACT Background Gene fusions play a critical role in cancer development by persistently activating kinases or inactivating tumor suppressor genes, leading to altered signal transduction and gene expression regulation. However, their impact on treatment responses remains poorly understood. Although existing cancer databases catalog numerous fusion events or immune checkpoint blockade (ICB) studies, no unified platform integrates gene fusion data across cancer types while linking them to the tumor microenvironment (TME) and patient outcomes. Such integration is essential for elucidating how fusions shape immune responses and for developing improved biomarkers for personalized cancer therapies. Methods To address this gap, we constructed the Fusion Immune Atlas (ImmunoFusion), a platform integrating data from TCGA, TARGET, CPTAC, and 29 ICB cohorts (including ICB treatments and other treatment modalities) across diverse cancer types. We identified fusion events from raw RNA-seq data using Arriba and STAR-Fusion, and standardized fusion calls by adapting MetaFusion to the GRCh38 reference genome. Additionally, we curated clinical data and estimated TME signatures and cell fractions using the Immuno-Oncology Biological Research (IOBR) approach. ImmunoFusion was developed using the high-quality Rhino framework for Shiny applications, built on R. Results ImmunoFusion ( https://shiny.zhoulab.ac.cn/ImmunoFusion ) encompasses 21,014 clinical samples, serving as a comprehensive RNA-seq-derived gene fusion database and analytical tool. It offers functionalities to investigate fusion breakpoints, confidence scores, frequency patterns, and associations with clinical variables. The platform also enables TME evaluation and interactive exploration of fusions for analyzing tumor immunophenotypes in cancer and immunotherapy contexts. As an illustration, analysis of MTAP fusions in lung cancer cohorts revealed their association with a metabolically depleted, “cold” (immune-suppressive) tumor environment and poorer patient outcomes, pinpointing MTAP fusions as a novel biomarker for treatment selection. Conclusions ImmunoFusion represents a significant advancement, delivering a unified platform to explore the influence of gene fusions on cancer and immune responses. It offers particular value in understanding fusion-driven immunotherapy outcomes, paving the way for more effective therapeutic strategies. Introduction Oncogenic gene fusions have transformed cancer diagnostics and therapeutics. Over the past century, from the pivotal role of BCR :: ABL1 in chronic myeloid leukemia to ALK , ROS1 , and RET fusions guiding targeted therapies in non-small cell lung cancer (NSCLC), these chimeric products drive carcinogenesis by aberrant transcription or generating oncoproteins 1 . An analysis of available data shows that gene fusions occur in all malignancies, and that they account for 20% 2 , but their functional heterogeneity is driven by complex genomic instability, necessitates transcriptomic validation to differentiate driver events 3 , 4 from biological noise (e.g., physiological chimeric RNAs 5 , readthrough transcripts 6 , conjoined/duplicated genes 7 , 8 and tandem arrangements 4 ). Recent evidence suggests that distinct fusion events may cooperatively define molecular subtypes, as seen with RET and NTRK fusions in pediatric thyroid cancers 9 . Existing gene fusion databases primarily catalog key fusion genes or events rather than comprehensively scanning fusions, thus failing to fully address the complexity of cancer gene fusions. RNA sequencing (RNA-seq) offers superior sensitivity and resolution over traditional methods for detecting gene fusions, yet it poses challenges in prioritizing pathogenic fusions among millions of candidates. Accurate identification of fusion isoforms in the transcriptome and distinguishing driver fusions are critical for precision oncology. Arriba 10 and STAR-Fusion 11 , two established fusion detection tools, have been widely applied to large cancer cohorts, including The Cancer Genome Atlas (TCGA). Large-scale fusion databases, including TumorFusions 12 and ChimerDB4.0 13 , primarily integrate TCGA RNA-seq data to identify fusion candidates across cancer types, but most rely on a single fusion-calling algorithm. Given frequent discrepancies among callers, integrated tools combining their strengths are essential for improved accuracy 14 . Moreover, well-known projects, such as the Clinical Proteomic Tumor Analysis Consortium (CPTAC) and Therapeutically Applicable Research to Generate Effective Treatments (TARGET), lack dedicated fusion databases, restricting their utility for fusion analysis. Immune checkpoint blockade (ICB) has transformed cancer treatment, yet many tumors remain unresponsive, with durable benefits limited to a small subset of patients. Current biomarkers, such as PD-L1 15 and tumor mutation burden (TMB) 16 , provide limited predictive accuracy, underscoring the need for novel biomarkers derived from other molecular alterations. Although gene fusions are established oncogenic drivers, their role in predicting ICB response remains largely underexplored. Recent studies have found that ALK fusions are associated with poor ICB response in NSCLC patients 17 , whereas CDK12 fusions correlate with improved ICB response in prostate cancer 18 . However, no systematic platform currently exists to investigate the interplay between gene fusions and ICB response. To address these unmet needs, we developed ImmunoFusion, a comprehensive database and analysis platform integrating RNA-seq-derived gene fusions from TCGA, TARGET, CPTAC, and 29 ICB cohorts. By integrating fusion calls from Arriba and STAR-Fusion using MetaFusion, ImmunoFusion creates, to our knowledge, the largest cancer gene fusion database. This platform facilitates exploration of interactions among fusion events, clinical features, and the TME, enabling systematic identification of gene fusions as biomarkers for cancer prognosis and treatment response. By elucidating the role of gene fusions in cancer and the TME, ImmunoFusion enhances our understanding of molecular alterations and advances insights into immuno-oncology. Results Construction of the cancer gene fusion catalogue ImmunoFusion (accessible at https://shiny.zhoulab.ac.cn/ImmunoFusion/ ) was developed through a streamlined data processing pipeline encompassing data collection, preprocessing, and integration. We collected TCGA, TARGET, and CPTAC data (N=16,324) from the GDC data portal and 29 ICB cohorts with RNA-seq data sourced from genome sequence archive repositories (N=4,690) ( Figure 1A, B ; see Methods). Gene fusions were detected from normal (N=1,807) and tumor samples (N=19,267) using two top-performing algorithms Arriba 10 and STAR-fusion 11 to detect RNA-seq fusion calls 19 . We utilized MetaFusion 20 to standardize the outputs of Arriba and STAR-Fusion, identifying 69,836 fusions in normal samples and 586,607 fusions in tumor samples. Following rigorous preprocessing (see Supplementary Methods), we identified 2,097 high-confidence (confident) fusions in normal tissues and 93,310 confident fusions in tumor tissues ( Figure 1B ). Clinical data, patient outcomes, and cancer and TME signatures were cleaned and standardized to enhance cross-cohort consistency and comparability. ImmunoFusion encompasses 39 cancer types and provides a unified database and analysis platform to facilitate exploration of gene fusions in cancer and ICB ( Figure 1C, D ). Unlike existing databases 12 , 13 , 21 – 31 , which primarily focus on fusion events, annotations, or ICB cohorts (Supplementary Table 1), ImmunoFusion integrates these elements comprehensively. Download figure Open in new tab Figure 1. Overview of ImmunoFusion. ( A ) Workflow for acquiring and processing patient data, cancer signatures, and tumor microenvironment (TME) signatures. ( B ) Pipeline for identifying, integrating, and scoring gene fusions. ( C ) Example analysis page on the ImmunoFusion platform, illustrating analysis steps and results, with options to save results as PDF or PNG images in user-specified dimensions. ( D ) ImmunoFusion offers four web pages and five analytical modules, including survival analysis, cancer and TME signatures, fusion frequency, fusion associations, and group analysis. Statistics of ImmunoFusion The distribution of gene fusions is categorized as follows ( Figure 2A ): (1) Sample type: normal samples yielded 114,483 fusions, while tumor samples yielded 986,816 fusions. (2) Fusion type: MetaFusion classified fusions into six subtypes (Supplementary Figure 1): CodingFusion (normal: 54.7%, tumor: 53.9%), ReadThrough (15.8%, 18.9%), TruncatedNoncoding (14.1%, 13.4%), TruncatedCoding (12.8%, 11.6%), SameGene (2.2%, 2.1%), and NoHeadGene (0.3%, 0.2%). (3) Fusion confidence score: A stringent scoring system was implemented to evaluate reliability of fusion candidates (Supplementary Methods), with approximately 81.9% of fusions scoring below 2, defined unconfident in this study and thus filter them out for further exploration. We annotated unique fusions using FusionAnnotator, revealing that 96.5% of fusions in normal samples and 18.4% in tumor samples lacked annotations ( Figure 2C, D ). We observed high variability in fusion abundance, measured as record counts, across cancer types ( Figure 2B ). Download figure Open in new tab Figure 2. Distribution of MetaFusion integrated gene fusions. (A) Sankey diagram illustrating the stratification of fusion records counts. The leftmost node represents sample types. The second-layer node displays fusion classifications by MetaFusion based on 5’/3’ gene breakpoints and coding region annotations. Subsequent layers depict filtration steps in the ImmunoFusion workflow, with the final layer categorizing fusion pairs as confident (score ≥2, high-confidence) or unconfident (score <2, low-confidence). (B) Number of confident cancer fusions across cancer types. Note: Pan-cancer cohort detail() (C) Distribution of confident fusions in normal samples annotated by FusionAnnotator, potentially unrelated to cancer or representing false positives. Horizontal bars (right) indicate fusion counts per annotation. Dots and lines represent fusion subsets. Vertical histograms show gene counts per subset. Group_Greger: tandem genes from Greger et al. 32 ; Group_HGNC: HGNC gene family membership; Group_GTEx: recurrent fusions in GTEx normal samples detected by STAR-Fusion 11 ; Group_DGD: Duplicated genes from Ouedraogo et al. 8 ; Group_Not_Reported: confident fusions not reported previously identified in normal samples. (D) Distribution of confident fusions in tumor samples annotated by FusionAnnotator. Horizontal bars (right) denote fusion counts per annotation. Group_Karyotype: Known fusions from the Mitelman Database 27 ; Group_Reviews: 2019 COSMIC reported fusions 33 + literature-curated cancer fusions by FusionAnnotator; Group_CCLE: Fusions in DepMap cancer cell lines 34 + Klijn et al. study 35 + TCGA cell line data (STAR-Fusion) 11 ; Group_Chimer: ChimerDB-reported fusions 36 ; Group_Not_Reported: Unannotated confident cancer fusions; Group_TCGA: Fusions from DEEPEST-Fusion 37 + Guo et al. 14 + TumorFusions 12 + TCGA RNA-seq (STAR-Fusion) 11 + Alaei-Mahabadi et al. 38 + YOSHIHARA et al. 3 . To provide a comprehensive overview of gene fusion frequency and the contribution of dominant fusions across cancer and sample types, we constructed a pan-cancer landscape of confident gene fusions and their original cohort resources ( Figure 3A-C ). Our data reveal distinct patterns of fusion burden across cancer types, with significant variability in sample size and fusion burden. Hematologic malignancies like LAML (N=2,772; 46,601 fusions) exhibit near-universal fusion positivity (98.7%) but low per-sample fusion rates (∼17/sample), suggesting reliance on a limited set of driver events. In contrast, solid tumors such as stomach adenocarcinoma (STAD) (N=457; 11,072 fusions; ∼25/sample) and ovarian serous cystadenocarcinoma (OV) (N=428; 11,844 fusions; ∼23/sample) demonstrate disproportionately high fusion burdens despite smaller cohort sizes—likely driven by genomic instability (OV’s propensity for structural variations 39 , STAD’s complication in histological and etiological heterogeneity 40 ). Consistent with prior reports 14 , 41 , kidney renal clear cell carcinoma (KIRC) (N=2,006; 8,317 fusions; ∼5/sample) exhibits a markedly lower fusion burden. Download figure Open in new tab Figure 3. Data landscape of ImmunoFusion. (A) An overview of confident gene fusion events detected in each cancer type or normal samples. For a given cancer type, subtypes are indicated with a ring around the corresponding inset organ view. Breaks in each ring distinguish whether fusions were detected in the samples. (The Pancer-cohort 42 was excluded from visualization due to unavailability of tumor type annotations for samples and its predominant reliance on blood transcriptomes data; Normal samples from patients with diverse cancer types were consolidated into a single category and represented as a circle; tumor cell lines, derived from the TARGET-AML project cell line subset, were visualized separately.) (B, C) Inclusion of cancer types, cohort and publication years in this study between ICB cohort to GDC. (D, E) These forest plots illustrate the hazard ratios (HR) and 95% confidence intervals (CI) for tumor-type-stratified analyses of OS and PFS in the Cox proportional hazards model, restricted to ICB-treated patients with pre-treatment samples.The aggregate of all cohorts is denoted as “Overall”. Associations with p < 0.001 are indicated as ‘p < 0.001’. Gene Fusions associated survival landscape For ICB treated subgroups, in IPD meta-analysis, while fusion-detected (Fusion+) patients exhibited significantly shorter overall survival (OS) (HR=1.67 Fusion+ vs Fusion- [1.3-2.1], p<0.001; Median=19.1 months(m) vs 27 m) ( Figure 3D ), no statistical difference was observed in progression-free survival (PFS) (p=0.152) ( Figure 3E ). In tumor type-specific analyses, Fusion+ cases in NSCLC showed a trend toward reduced overall survival (OS; HR=1.45 [95% CI: 0.96–2.20], p=0.08) and progression-free survival (PFS; HR=1.37 [95% CI: 0.97–1.94], p=0.07). In SKCM, Fusion+ cases were associated with significantly worse OS (HR=1.58 [95% CI: 1.04–2.40], p=0.031), but PFS showed minimal change (p=0.880). Similar trends were observed across other tumor types. Similarly, GDC IPD meta-analysis revealed significantly elevated risks for both OS (HR=1.48 [1.28-1.7], p<0.001) (Supplementary Figure 2A) and PFS (HR=1.23 [1.08-1.4], p=0.002) (Supplementary Figure 2B) in Fusion+ patients. GDC data also revealed cancer specific divergent prognostic impacts: colon adenocarcinoma (COAD) Fusion+ patients showed worse OS and PFS. In comparison, skin cutaneous melanoma (SKCM) Fusion+ patients exhibited improved OS and PFS; Lung adenocarcinoma (LUAD) displayed non-significant OS differences (Supplement Figure 2A) but favorable PFS (HR=0.63 [0.42-0.95], p=0.027) (Supplement Figure 2B); LAML(HR=0.51 [0.27-0.95], p=0.033; Median=17.9 vs 8.9) (Supplement Figure 2A); neuroblastoma(NBL) (HR Fusion+ vs Fusion- =0.34 [0.14-0.86], p=0.022; Median=54.5 vs 10.8) (Supplement Figure 2A) demonstrated significant OS benefits in Fusion+ patients (Other cohort details in Supplement Figure 2A, B). These findings underscore context dependent and cancer specific biological roles of fusions, necessitating integrated functional annotation and TME profiling to elucidate underlying mechanisms. Web functionality of ImmunoFusion ImmunoFusion offers exploratory and advanced analytical tools, enabling researchers to explore complex relationships and gain actionable insights ( Figure 1D ). Modules are organized across five pages: Home, Fusion, Cohort, Analysis, and Help. Users can explore associations between fusion events, clinical variables, TME features, molecular signatures, therapeutic responses, and prognosis. Researchers can identify fusions tied to prognosis or immunotherapy response and validate findings across cohorts or tumor types. A standardized, user-friendly workflow is provided ( Figure 1C ). Below, we detail the core modules. Fusion page: The ImmunoFusion Fusion page offers a comprehensive resource for exploring and retrieving gene fusions identified across cancer cohorts. It integrates essential details such as gene symbols, genomic positions (hg38), cohort of origin, fusion type, breakpoint exon regions, confidence metrics (including ImmunoFusion score, prior reporting status, and read counts), and distribution patterns across cohorts. Users can easily locate specific fusions using search filters based on gene symbols or any text for regular expression matching. Cohort page: The Cohort page provides an intuitive platform for accessing datasets, allowing users to filter cohorts by origin, cancer type, treatment modality, therapeutic agents, and cohort size. Detailed cohort-level metadata is presented in comprehensive tables, facilitating informed cohort selection. Analysis page: The Analysis page enables multi-tiered exploration of fusion events at the gene, fusion, and record levels, facilitating in-depth investigation of their roles in tumor progression, immune modulation, and immunotherapy outcomes. This framework identifies differential biological impacts of distinct fusion partners, revealing potential biomarkers. Key sub-modules include: Distribution , which employs stacked bar plots to display fusion gene frequencies across tumor types and cohorts, highlighting specific or pan-cancer prevalence; Comparison , which identifies significant changes in TME cell type infiltrations or 255 cancer-associated signatures—categorized into TME-related, tumor metabolism, and tumor-intrinsic groups—associated with specific fusion events; Association , which uses Fisher’s exact test to generate heatmap plots visualizing co-occurrence or mutual exclusivity of fusion events, elucidating their correlations; Kaplan-Meier (KM) and Cox Analysis , where KM generates survival curves for comparative studies and Cox evaluates prognostic associations of fusions; and Group Analysis , which enables nuanced comparisons of fusion events across multiple groups within a cohort, uncovering intricate patterns through dual-mode analysis. Case studies validating and extending data of MTAP Fusion Emerging evidence establishes MTAP expression which represents the most frequently homozygously deleted region 43 as an independent prognostic factor with greater clinical significance than CDKN2A in NSCLC 44 . Recent multi-cancer analyses demonstrate that combined CDKN2A / MTAP loss correlates with immunologically “cold” TME characterized and inferior responses to ICI patient 45 . However, these studies did not investigate MTAP fusion events. MTAP fusions primarily result from deletion events, with detailed fusion characteristics from Arriba provided in Supplementary Table 2. As a use case, we utilized ImmunoFusion to investigate MTAP fusion events in NSCLC cohorts. In the POPLAR cohort (N = 192; EGAD00001008548), MTAP fusion (2.6% incidence, Figure 4A ) predicted reduced survival (OS HR MTAP+ vs MTAP- =2.52 [0.63-10.07, p=0.034; 6.4 vs 11.4; Figure 4B ). Similar patterns emerged in the OAK cohort (N=699; EGAD00001008549) with 1.6% fusion frequency ( Figure 4D ) showing worse outcomes (OS HR=2.05 [0.85-5.05], p=0.02; Median=6.4 vs 11.5; Figure 4E ; PFS HR=1.86 [0.83-4.18], p=0.035; Median=2.2 vs 2.9; Figure 4F ). TCGA-LUAD cohort (N=506; 3.1% incidence, Figure 4G ) confirmed the poor OS outcome (HR=1.01 [0.83-5.66] p=0.020; Median=26.9 vs 50; Figure 4H ), though PFS lacked significance ( Figure 4I ). Furthermore, to explore differential treatment effects in NSCLC, we performed treatment-stratified analyses, In the atezolizumab-treated subgroups (Supplementary Figures 3A, B, E, F), the POPLAR cohort demonstrated significantly elevated OS risk (HR=3.2, p=0.015, Median=5.7 vs 14.5; Supplementary Figures 3A]), although not statistically significant, the OAK cohort showed a similar tendency (OS HR=2.3; Median=9.4 vs 12.8). Meanwhile, the OAK docetaxel-treated subgroup exhibited significantly inferior OS (HR=1.87; Median=4.7 vs 10.7; Supplementary Figure 3I). MTAP fusion patients tend to have nonresponse (Supplementary Figure 3C, D, G, H). MTAP Fusion+ NSCLC is associated with progression following atezolizumab monotherapy versus docetaxel monotherapy and tend to result in primary resistance to anti-PD-L1 therapy, as indicated by the lack of response in the majority of Fusion+ cases. Download figure Open in new tab Figure 4. MTAP fusion prevalence and prognostic impact in NSCLC cohorts (Pail et al(POPLAR, OAK) 51 , TCGA-LUAD). (A, D, G) Pie charts depict the proportion of MTAP fusion-detected (Fusion+: orange) and fusion-not detected (Fusion−: blue) patients in the POPLAR (N = 192; Fusion+ = 5, 2.6%), OAK (N = 699; Fusion+ = 11, 1.6%), and TCGA-LUAD (N = 506; Fusion+ = 16, 3.1%) cohorts. (B, C, E, F) (Fusion+: red; Fusion-: blue) Kaplan-Meier curves for overall survival (OS) and progression-free survival (PFS) stratified by MTAP fusion status in patients treated with chemotherapy (docetaxel) or anti-PD-L1 therapy (atezolizumab) (POPLAR: B, C; OAK: E, F). (H, I) Survival analyses in the TCGA-LUAD cohort (non-treatment-specific). Hazard ratios (HR) with 95% confidence intervals were calculated using Log-Rank Test. Mechanistically, loss of MTAP leads to extracellular accumulation of its substrate methylthioadenosine (MTA), a metabolite with potent immunomodulatory effects. Tumor-derived MTA suppresses T-cell function by engaging the adenosine A2B receptor (A2BR) 46 – 48 , contributing to an immunosuppressive TME. Additionally, MTAP deletion influences the occurrence and progression of tumors through multiple metabolic pathways 49 . Emerging evidence further implicates MTA in broader T-cell dysregulation 50 , fostering a “cold” TME and primary resistance to ICB 45 . Our multi-cohort TME analysis of MTAP fusion-positive (Fusion+) tumors reveals conserved metabolic vulnerabilities with context-dependent immunosuppressive features. Fusion+ tumors exhibit elevated hypoxia signatures ( Figure 5A ), reflecting a metabolic adaptation to oxygen scarcity ( Figure 5A ). Enhanced cardiolipin metabolism ( Figure 5B ), methionine cycle hyperactivity ( Figure 5D ) and elevated polyamine levels ( Figure 5F ) further indicate mitochondrial stress. Paradoxically, despite higher tumor antigen release ( Figure 5E ), Fusion+ tumors display an immune-cold TME characterized by MDSC enrichment ( Figure 5G ) and reduced MHC Class II expression ( Figure 5C ), suggesting defective antigen presentation and myeloid-driven suppression override antigen availability. This dichotomy implies that MTAP fusions establish a dual barrier to immunity: metabolic suppression of effector T cells combined with defective antigen recognition. Download figure Open in new tab Figure 5. MTAP fusion+ tumors exhibit characteristics of metabolic vulnerability and an immunologically cold TME. (A) Metabolism Hypoxia (zscore): Hypoxia scores are elevated in MTAP Fusion+ tumors (EGAD00001008548-Atezolizumab and TCGA-LUAD cohorts). (B) Cardiolipin Metabolism (ssGSEA): Mitochondrial dysfunction is enhanced in MTAP Fusion+ tumors (EGAD00001008548-Atezolizumab and TCGA-LUAD cohorts); (D) Methionine Cycle (PCA): Hyperactivated methionine metabolism in MTAP Fusion+ tumors (EGAD00001008548-Atezolizumab cohort); (F) Polyamine Biosynthesis (zscore): Elevated polyamine levels in MTAP Fusion+ tumors (EGAD00001008548-Atezolizumab cohort); (C) MHC Class II (ssGSEA): Paradoxically elevated MHC-II expression in MTAP Fusion+ tumors (EGAD00001008548-Atezolizumab cohort); (E) TIP Release of cancer cell antigens (ssGSEA): Increased cancer cell antigen release in MTAP Fusion+ tumors (EGAD00001008549-Atezolizumab cohort); (G) MDSC (zscore): MDSC enrichment in MTAP Fusion+ tumors (EGAD00001008549-Atezolizumab cohort); p values were calculated by two-sided Wilcoxon signed-rank test. Number of samples: POPLAR [Fusion+ N=4, Fusion- N=88], OAK [Fusion+ N=3, Fusion- N=341], TCGA-LUAD [Fusion+ N=16, Fusion- N=514]. Boxes indicate the median ± interquartile range; whiskers, 1.5× the interquartile range; centre line, median. Only contains NSCLC patients treated with anti-PD-L1. Discussion Gene fusions are a defining feature in certain cancers 37 , 52 , offering substantial potential for clinical research and precision oncology. However, existing frameworks for studying fusion-related cancer biology face critical limitations, including restricted sample sizes and diversity, which hinder the generalizability of findings. Challenges in exploring rare fusions—stemming from functional variability in breakpoint locations, interpretative complexities in fusion detection datasets, and barriers to clinical trial recruitment—further complicate therapeutic prioritization when multiple actionable genetic alterations are present. To address these gaps, we developed ImmunoFusion, an integrated data platform that aggregates fusion events from three large pan-cancer projects and 29 immunotherapy cohorts, encompassing 19,267 cancer samples across 39 cancer types and pan-cancer cohort. ImmunoFusion provides user-friendly visualization, customizable options, and unrestricted access without mandatory registration, making it a cost-effective resource for researchers and clinicians. In this study, we describe the data sources, collection, and standardization procedures for ImmunoFusion, followed by an overview of its statistics, functionalities and website analysis modules. We also provide a detailed, step-by-step guide to operating the platform. Compared with other tools, ImmunoFusion offers several key advantages. First, it constitutes the largest database for gene fusion exploration in cancer, incorporating previously unavailable data from TARGET and CPTAC. Second, it is the first web server to integrate fusion genes with ICB cohorts, offering enhanced tumor microenvironment characterization compared to databases like FPIA 23 , thereby advancing the study of gene fusions in immuno-oncology. As an example, we systematically analyzed MTAP gene fusion events in 1,427 NSCLC patients, revealing their clinical relevance and associations with TME characteristics—findings supported by prior research 45 , 53 on similar mechanisms. By elucidating the interplay between fusion events and the TME and establishing a biomarker exploration framework, ImmunoFusion is poised to become an indispensable resource in ICB research. Nevertheless, ImmunoFusion faces several data limitations. Sample sizes for certain cancer types, such as BRCA and HNSC, remain limited, and access restrictions to raw RNA-seq datasets 54 , 55 further constrain data comprehensiveness. Integrating data is challenging due to the inherent heterogeneity in fusion events and phenotypic data across cohorts, complicating unified analyses; however, the platform’s flexibility allows users to combine cohorts for customized exploration. Moreover, despite rigorous data processing, integration, and scoring, the detected fusions are computational predictions and lack large-scale laboratory validation. Additionally, ImmunoFusion’s functional scope is focused on ICB, and its analytical cohorts do not include those primarily designed for studying targeted therapies. We are dedicated to continually enhancing ImmunoFusion by incorporating data from diverse and emerging cohorts in cancer, immunotherapy, and targeted therapies, while integrating new features driven by user feedback. Its distinctive capabilities, robust analytical power, and scalability establish ImmunoFusion as an essential tool for elucidating fusion-driven insights in cancer and informing personalized therapeutic approaches. Methods Data acquisition from GDC portal We obtained Arriba and STAR-Fusion results from the Genomic Data Commons (GDC) data portal ( https://portal.gdc.cancer.gov/ ) (accessed on 2023-08-13). Specifically, we downloaded gene fusion datasets from TCGA, TARGET and CPTAC projects. These datasets encompass a total of 14,521 tumor samples and 1807 normal tissues across 36 different tumor types. It is important to note that these cohorts are not specifically related to immunotherapy; rather, they represent publicly available cancer datasets. Their inclusion in our study serves to complement and contrast with the checkpoint immunotherapy cohorts, thereby enabling a more in-depth exploration of the role of fusion genes in tumors. Systematic search bulk RNA-seq data from ICB related cohorts Referring to our previous cohort collection process in TCCIA 56 , we performed a systematic search using PubMed to identify articles related to bulk RNA-seq data from solid cancer patients treated with ICB. In total, we collected 29 studies 51 , 57 – 74 related to checkpoint immunotherapy that provided raw RNA-seq datasets. Details are described in Supplementary Methods. This analysis included over 4,746 clinical samples from 29 cohorts treated with ICBs including PD-1/PD-L1 and CTLA-4 inhibitors, as well as other treatments. The analysis considered both pre-treatment and on-treatment responses. Fusion identification and integration Raw RNA-seq data were aligned to the human reference genome (GRCh38) using STAR aligner (v2.7.10a). We employed a dual-caller framework, utilizing Arriba (IO: v2.0.0; GDC: v1.1.0, https://github.com/suhrig/arriba ) and STAR-Fusion (GDC: v2.0.0; IO: v1.6.0, https://github.com/STAR-Fusion/STAR-Fusion ), to identify RNA-derived gene fusion candidates, with results integrated via MetaFusion. A scoring system was developed to assess the credibility of fusions detected in tumor and normal samples. See Supplementary Methods for more details. Clinical data preprocessing We retrieved clinical data from the Genomic Data Commons (GDC) and UCSCXenaShiny 75 platform, followed by data cleaning and standardization, including filling missing values, removing duplicates, renaming columns, and processing the gender field. Survival, purity, genomic instability, and subtype data were merged using Sample ID. We confirmed that most valid rows in GDC clinical data aligned with those from UCSCXenaShiny, and used UCSCXenaShiny data to impute missing values in the GDC dataset, merging by Sample ID. See Supplementary Methods for more details. ImmunoFusion implementation The ImmunoFusion database is developed as a Web application leveraging R Shiny ( https://shiny.posit.co/ ) and utilized the high-performance and state-of-the-art Rhino ( https://appsilon.github.io/rhino/ ) frame to optimize our Shiny framework through The Appsilon Way. ImmunoFusion, is developed solely for research purposes and does not utilize any cookies or collect any personal identifiable information. ImmunoFusion is free available in https://shiny.zhoulab.ac.cn/ImmunoFusion/ . Dissecting the TME and quantifying cancer gene signatures Consistent with our prior TME decomposition and cancer gene signature scoring methodology in TCCIA, we likewise employed IOBR 76 which incorporates eight widely used open-source deconvolution methods. IOBR integrates a comprehensive compilation of 255 established cancer signatures. These signatures are categorized into three distinct groups: those associated with the TME, tumor metabolism, and tumor-intrinsic features. This organization enables a systematic investigation into the landscape of fusion genes and the immune microenvironment. Statistical analysis We performed Kaplan-Meier survival analysis to generate and compare survival curves. The log-rank test was used for comparison. We also conducted multivariate survival analysis using the Cox regression model. All reported P -values are two-tailed, and a significance level of p≤0.05 was used unless otherwise specified. All statistical analyses and visualization were conducted using R v4.4.1. Contributions YZ: Software, methodology, formal analysis, visualization, conceptualization, funding acquisition, writing original draft, review and editing. SW: Conceptualization, project administration, software, methodology, visualization, supervision, funding acquisition, writing original draft, review and editing. SL: Software, methodology, visualization. MC: Visualization. JGZ: Conceptualization, methodology, resources, supervision, funding acquisition, writing original draft, review and editing. Competing interests No, there are no competing interests. Data availability All relevant data reported in the study can be found in the article or on the ImmunoFusion website (accessible at https://shiny.zhoulab.ac.cn/ImmunoFusion/ ). For any other data requests, please contact the leader of this project Prof. Jian-Guo Zhou. Code availability The fusion identification pipeline with an ensemble approach and corresponding scripts and logs for handling ImmunoFusion cohorts are available at https://github.com/OncoHarmony-Network/fusion-pipeline . Ethics statements In this study, we conducted a secondary analysis of trial datasets, which was determined to carry minimal risk. The Institutional Review Board of the Second Affiliated Hospital, Zunyi Medical University (No. YXLL(KY-R)-2021-010) approved the study protocol. As per national legislation and institutional guidelines, written informed consent for participation was not deemed necessary for this particular study. Acknowledgments We are grateful for resources from the Bioinformatics Platform, Furong Laboratory and Bioinformatics Center, Xiangya Hospital, Central South University. This work was supported by the Guizhou Province College Students Innovation and Entrepreneurship Training Program (Grant No. ZHCX2023005), the Central South University Startup Funding, National Natural Science Foundation of China (Grant No. 82303953, 82060475), the Hunan Provincial Natural Science Foundation of China (Grant No. 2025JJ40079), Noncommunicable Chronic Diseases-National Science and Technology Major Project (Grant No. 2023ZD0502105), Chunhui program of the MOE (Ministry of Education in China) (Grant No. HZKY20220231), MOE Liberal arts and Social Sciences Foundation (Grant No. 24YJCZH462), Youth Science and Technology Elite Talent Project of Guizhou Provincial Department of Education (Grant No.QJJ-2024-333), Excellent Young Talent Cultivation Project of Zunyi City (Zunshi Kehe HZ (2023) 142), Future Science and Technology Elite Talent Cultivation Project of Zunyi Medical University (ZYSE 2023-02), and the Key Program of the Education Sciences Planning of Guizhou Province (Grant No.7). Funder Information Declared Guizhou Province College Students Innovation and Entrepreneurship Training Program , ZHCX2023005 Central South University Startup Funding, National Natural Science Foundation of China , 82303953 , 82060475 the Hunan Provincial Natural Science Foundation of China , 2025JJ40079 Noncommunicable Chronic Diseases-National Science and Technology Major Project , 2023ZD0502105 Chunhui program of the MOE (Ministry of Education in China) , HZKY20220231 MOE Liberal arts and Social Sciences Foundation , 24YJCZH462 Youth Science and Technology Elite Talent Project of Guizhou Provincial Department of Education , QJJ-2024-333 Excellent Young Talent Cultivation Project of Zunyi City , Zunshi Kehe HZ (2023) 142 Future Science and Technology Elite Talent Cultivation Project of Zunyi Medical University , ZYSE 2023-02 the Key Program of the Education Sciences Planning of Guizhou Province , 7 Footnotes ↵ # Co-first author. 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