Oligoclonal B cell Expansion and Passenger Fusion Genes Predict Response to Nivolumab in Recurrent Ovarian Cancer: Phase II Kyoto Trial

Oligoclonal B cell Expansion and Passenger Fusion Genes Predict Response to Nivolumab in Recurrent Ovarian Cancer: Phase II Kyoto Trial | 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 Research Article Oligoclonal B cell Expansion and Passenger Fusion Genes Predict Response to Nivolumab in Recurrent Ovarian Cancer: Phase II Kyoto Trial Ryusuke Murakami, Junzo Hamanishi, J. B. Brown, Yuko Hosoe, Taito Miyamoto, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7492478/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 27 Jan, 2026 Read the published version in Cancer Immunology, Immunotherapy → Version 1 posted 10 You are reading this latest preprint version Abstract Background : We previously reported a phase II Kyoto trial for platinum-resistant ovarian cancer (n = 20) using nivolumab (anti-programmed cell death-1 [PD-1] antibody). We evaluated the associations between clinical outcomes and transcriptomics and T and B cell clonality from tumor and blood cells. Methods : We analyzed gene expression microarray with pre- and post-treatment peripheral blood mononuclear cells, α- and β-chain of T cell receptor (TCR) repertoires, and immunoglobulin G (IgG) and M of B cell receptor (BCR) repertoires in 61 samples from 19 patients. Shannon-Weaver diversity scores of the TCR and BCR repertoires were compared between responders and non-responders. RNA sequencing analyzed gene expression and fusion genes in tumor samples (n=17). Results : BCR repertoire analyses of post-/pre-treatment ratios in four responders (two patients with complete response (CR), one with partial response, and one with stable disease near to CR) revealed significantly decreased BCR-IgG repertoires diversity versus non-responders (Shannon-Weaver index, median 0.84 vs. 1.04, p<0.05); the diversity of BCR-IgG repertoires recovered over 100 days. More than two passenger fusion genes were detected in six of the seven responders, whereas eight of the ten non-responders lacked fusion genes. The antitumor response significantly correlated with the number of fusion genes (p=0.0003). Pathway analyses consistently identified immune-related processes, including cytokine-cytokine receptor interactions, neutrophil degranulation, and immunoregulatory interactions in both responders and tumors with high fusion gene counts. Conclusion : Transient oligoclonal expansion of B cells and passenger fusion genes might serve as predictive biomarkers of response to PD-1 blockade in ovarian cancer. ovarian cancer anti PD-1 antibody B cell repertoire fusion genes Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Ovarian cancer is the leading cause of death among gynecologic malignancies, causing approximately 140,000 deaths annually worldwide [ 1 , 2 ]. Programmed cell death-1 (PD-1), an immune checkpoint receptor expressed by T cells, binds to PD-1 ligands (PD-Ls: PD-L1 [B7-H1] and PD-L2 [B7-H2]), and suppresses antigen-specific immune responses to cancer cells[ 3 – 7 ]. Previously, we demonstrated the efficacy of nivolumab, an anti-PD-1 antibody, in a phase II clinical trial involving 20 patients with recurrent platinum-resistant ovarian cancer at the Kyoto University Hospital. The best overall response rate was 15%, including two patients with complete response (CR), and the disease control rate was 45% [ 8 ]. However, reliable biomarkers for predicting the response to immune checkpoint inhibitors in patients with ovarian cancer remain elusive despite several clinical trials having been conducted. Moreover, PD-1/PD-L1 inhibitors are yet to be implemented as standard treatments for ovarian cancer [ 9 – 13 ]. Several groups have identified potential biomarkers that may predict response to PD-1 inhibitors in various cancer types [ 14 ]. The expression of PD-L1 on tumor cells or T cells [ 15 – 17 ], PD-1(+) or CD8(+) tumor-infiltrating lymphocytes [ 18 , 19 ], mismatch repair deficiency [ 20 ], and higher tumor mutation burden [ 21 ] have been utilized as predictive biomarkers in several cancer types. Furthermore, research efforts aimed at identifying predictive biomarkers based on T-cell and B-cell repertoires associated with anti-PD-1 and anti-PD-L1 blockade therapies are underway [ 19 , 22 – 24 ]. This study aimed to identify potential predictive biomarkers of clinical response to nivolumab in ovarian cancer using peripheral blood mononuclear cells (PBMCs) [ 25 ] and formalin-fixed paraffin-embedded (FFPE) tumor tissues. We employed unsupervised hierarchical clustering to analyze PBMC transcriptome profiles, which guided our investigation toward B-cell rather than T-cell repertoire analyses as predictive biomarkers for anti-PD-1 therapy for ovarian cancer management. Materials and methods Patient samples Twenty patients with platinum-resistant ovarian cancer were enrolled in an anti-PD-1 clinical trial conducted at Kyoto University Hospital from 2011 to 2015 (UMIN000005714). This study was approved by the institutional ethics committee, and donors provided written informed consent in accordance with institutional and national guidelines [ 8 ]. One patient was excluded from the analysis because of early discontinuation after the first administration of nivolumab (not evaluable). Nineteen patients (patients 1–19) were enrolled in the subsequent analyses. Blood samples were collected during the first course of anti-PD-1 treatment at three timepoints: prior to treatment (pre-treatment), 14 days after treatment initiation, and at the subsequent follow-up. PBMCs were isolated using density gradient centrifugation with Lymphocyte Separation Medium (Nakarai, Kyoto, Japan) and Leucosep tubes (Greiner, Frickenhausen, Germany) (Fig. 1 a). Total RNA was extracted from the isolated PBMCs using the QIAamp RNA Blood Mini Kit (Qiagen, Valencia, CA, USA). Tumor samples were obtained through surgery and stored as FFPE tissues following the recommendations of best practices for FFPE-based gene expression measurement [ 26 ]. Three patients were excluded from the overall response and RNA analyses; hence, 17 patients were included in the statistical analysis. According to the Response Evaluation Criteria in Solid Tumors (version 1.1), clinical responses of the patients were as follows: 2 CR, one partial response (PR), six stable disease (SD), and 10 progressive disease (PD). One patient demonstrated complete elimination of the primary target lesion, but had para-aortic metastasis. Therefore, this patient was given a special designation of “SD-CR”[ 27 ] Gene expression microarray analysis with PBMC Gene expression analysis was performed using Affymetrix U133 Plus 2.0 GeneChips according to the manufacturer's protocol. Robust Multi-Average was performed, and filtering was applied to focus on average expression levels > 50% and subsequently on standard deviations > 50%, identifying 10,478 gene probes as candidates for predictive genes across patients treated with anti-PD-1 antibody [ 28 ]. We calculated the ratio of post-treatment (day 14) to pre-treatment gene expression profiles of PBMC and tumor samples. This ratio reflects the activity of immune cells in the PBMC in response to PD-1 antibody treatment, with > 1.0 signifying an increase in gene expression levels and < 1.0 signifying a reduction in gene expression levels. We performed Uniform Manifold Approximation and Projection (UMAP) visualization of PBMC gene expression profiles, showing the distribution of patient samples based on treatment response. Immune cell marker expression was analyzed in PBMC samples collected before and after nivolumab treatment. mRNA expression levels were measured for multiple immune cell markers, including B cell markers (CD24, CD79A, and CD79B), T cell markers (CD4, CD8A, and CD8B), a macrophage marker (CD163), and a regulatory T cell marker (FOXP3). Patients were grouped based on their treatment response (CR, PR, SD vs. PD), and expression patterns were compared between the groups using statistical analysis. T cell receptor (TCR) and B cell receptor (BCR) repertoire analyses To assess T and B cell immune responses, we analyzed the clonal diversity of T cell receptor (TCR) repertoires (TRA and TRB chains) and B cell receptor (BCR) repertoires (IgG and IgM) in PBMCs from 19 patients treated with nivolumab. We used unbiased next-generation sequencing-based immune repertoire analysis with an original adaptor-ligation polymerase chain reaction (PCR) technique from Repertoire Genesis Inc. (Osaka, Japan) [ 29 ] throughout the time course. The all-frame and in-frame (productive) repertoire were analyzed, as in-frame represents functionally expressed receptors capable of antigen recognition, consistent with standard immune repertoire analysis protocols [ 30 , 31 ]. The diversity and dominance of TCR and BCR repertoires were compared using the Shannon-Weaver index [ 32 ] between clinical super-responders (two CR, one PR, and one SD-CR; n = 4) and poor-responders (five SD and 10 PD; n = 15). The diversity score ratio of post-treatment to pre-treatment of the Shannon-Weaver index scores was compared between super-responders and poor responders. Longitudinal tracking of the B-cell IgG repertoire was assessed in individual patients at multiple timepoints during treatment. The post/pre-treatment ratios of the Shannon-Weaver index scores were plotted over time (0-700 days) for both super-responders and poor responders. RNA sequencing from FFPE tumor tissues Total RNA was extracted from the FFPE tumor samples using a DNA/RNA FFPE Kit (Qiagen, Valencia, CA, USA). RNA sequencing and candidate gene fusion detection analyses were performed by Illumina (Japan). After adjusting the extracted RNA to align with quality standards (30–200 ng), an RNA-seq library focusing on RNA coding regions was prepared using Illumina's TruSeq RNA Access Library Prep Kit®. The library was enriched twice, yielding a concentration range of 2.6–92 ng/µL. RNA sequencing was performed using Illumina NextSeq 500 [High Output v2]. The insert size was 70–200 bp, with over 75% of the reads aligned to the coding regions. Although partial degradation of transcripts was observed in some samples, the 5'-3' coverage was maintained at a consistent accuracy across most cases. RNA quality control was performed using a TapeStation 2200 (Agilent Technologies), which provided RNA integrity number equivalent scores and RNA fragment percentages above 200 nucleotides (DV200). Seventeen patients were included in the gene expression analysis after quality control of the RNA samples. Two samples were excluded due to insufficient RNA quality. RNA-Seq Analysis Pipeline Using BaseSpace TopHat Alignment v1.0 We implemented an RNA-seq analysis pipeline using BaseSpace TopHat Alignment v1.0, integrating TopHat2 (v2.0.7), Bowtie (v0.12.9), Cufflinks (v2.1.1), and other essential bioinformatics tools. Raw sequencing reads were initially filtered to remove adapter sequences, PhiX, and mitochondrial DNA; this was followed by alignment with the hg19 reference genome using TopHat2 with parameters optimized for RNA-seq analysis. This pipeline incorporates quality control measures, including duplicate PCR removal and fusion gene detection, with stringent filtering criteria. Variant calling was performed chromosome-wise using Illumina's starling2 algorithm with optimized parameters (maximum input depth: 100,000 reads; minimum paired/single alignment scores: 40/10). The resulting variant calls were merged and indexed for downstream analyses. Expression was quantified using Cufflinks with strand-specific parameters and abundant sequence masking. All analyses utilized the hg19 human reference genome and the corresponding annotations with tools specifically modified for the BaseSpace environment to ensure optimal performance in high-throughput sequencing analysis (technical services provided by Illumina, Japan) [ 33 ]. Differential Expression Analysis Two samples were excluded due to insufficient RNA quality, resulting in 17 samples for the final analysis. Expression data were filtered to include genes with FPKM > 1 in at least three samples and were log2-transformed (log2[FPKM + 1]). High-variance genes were selected by retaining the top 75th percentile based on the expression variance, and 10548 genes were used for subsequent analyses. Differential gene expression analysis was performed using the Limma package in R. Patients were categorized into two groups: responders (CR, PR, SD-CR, SD) and non-responders (PD). Genes were considered differentially expressed if they met the following criteria: adjusted p-value 1. The results were visualized using volcanic plots. Correlation Analysis with Fusion Gene Counts We analyzed the correlation between the gene expression levels and fusion gene counts for each expressed gene. Highly correlated genes were identified using the threshold of the mean plus one standard deviation of the correlation coefficient distribution (r = 0.53). The normality of the correlation coefficients was assessed using density plots. Validation analysis Gene expression microarray data for the same RNA derived from previously published FFPE tumors (n = 19) were used to identify a good responder signature (CR, n = 2 vs. non-CR, n = 17) [ 27 ]. Gene expression analysis was performed using R version 3.1.1 and the samroc function from Bioconductor’s ‘SAGx' package. Significant differential expression was defined using a false discovery rate q-value threshold of 0.05 and log2 fold change > 1. Pathway Analysis Genes showing a high correlation with fusion gene counts were further analyzed using multiple pathway analysis approaches. Gene symbols were converted into Entrez IDs using the org.Hs.eg.db database. We performed Gene Ontology (GO) enrichment analysis for biological processes, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, and Reactome pathway analysis using ClusterProfiler and ReactomePA packages in R version 4.4.1 (R Foundation for Statistical Computing, Vienna, Austria). The Benjamini–Hochberg method was applied for multiple testing corrections. The results were visualized using dotplots and bar plots, displaying the most enriched pathways based on adjusted p-values. Statistical Analysis All PBMC microarray analyses were performed using the R statistical environment, R version 4.4.1. UMAP was performed using the 'umap' R package. The Mann–Whitney U test, paired t-test, and Fisher's exact test were performed using GraphPad Prism version 9.3.1 (GraphPad Software, San Diego, CA, USA). The threshold for statistical significance was set at 0.05. The null hypothesis that response and gene fusion counts were not differential was rejected when the probability of such (p-value) was < 0.05. Results Gene expression microarray analysis for blood samples The UMAP visualization of PBMC gene expression post/pre-treatment ratio profiles showed the distribution of patient samples with some trend observed between different response categories (Fig. 1 b). Analysis of immune cell marker expression post/pre-treatment ratios revealed differences between responders (CR, PR, and SD) and non-responders (PD). Among the B-cell markers, CD24 and CD79B showed significantly higher ratios in responders than in non-responders (p = 0.017 and 0.017, respectively), whereas CD79A showed no significant difference. The macrophage marker CD163, which is typically associated with M2 (immunosuppressive) macrophages, exhibited significantly lower ratios in responders (p = 0.017). T cell-related markers, including CD4, CD8A, and CD8B, and the regulatory T cell marker, FOXP3, showed no significant differences between the response groups. (Fig. 1 c). Analysis of immune cell marker gene expression showed changes between pre-treatment and post-treatment timepoints that differed between response groups. Using paired t-test analysis, CD8A and CD8B demonstrated significant increases in responders (p = 0.017 and p = 0.008, respectively), whereas no significant changes were observed in non-responders. CD163 expression was significantly decreased in responders (p = 0.011), and CD79B expression showed a significant increase following treatment (p = 0.034). These findings indicate that certain immune cell markers undergo differential temporal changes that may be associated with response to nivolumab treatment (supplementary Fig. 1). TCR and BCR repertoire analyses To verify the immunoreaction of T cells and/or B cells before (pre-treatment) and after (post-treatment) nivolumab treatment, we analyzed the repertoire profiles of TCR (TCA and TCB) and BCR (IGG and IGM) using PBMCs from our nivolumab trial. A strong correlation was observed between all-frame and in-frame repertoires in T cell receptor alpha chain (TRA), T cell receptor beta chain (TRB), IGG, and IGM (r = 0.9989, 0.9997, 1.000, and 0.9995, respectively) (supplementary Fig. 2). Each dot represents the diversity score from 61 sporadic checks in 19 patients over the course of anti-PD-1 treatment. We compared the post-treatment to pre-treatment diversity score ratios at the nearest 1-month time point (mean 23.7 days, range 14–56 days). The diversity scores resulting from PD-1 antibody treatment were compared between clinical super-responders (CR, PR, and SD-CR) and poor responders (SD and PD). For the IgG repertoire, the ratios were significantly lower in clinical super-responders than in poor responders (p = 0.013). Contrastingly, no significant differences were found in other repertoire analyses, including TRA, TRB, and IgM. (Fig. 2 a). B-cell IgG repertoire analysis revealed distinct patterns of clonal expansion across four representative patients with a clinical super-response (two CR, one PR, and one SD-CR), where specific IgG clones emerged and expanded substantially within the first 100 days post-treatment. In patient 11 (CR), the IGHV3-7—IGHJ3 clone expanded from 4.01% (pre-treatment) to 15.69% of the total repertoire by day 29, representing a 3.9-fold increase. Similarly, patient 14 (CR) showed significant expansion of two distinct clones by day 19 after pre-treatment: IGHV3-7—IGHJ4 increased from 2.43–17.50% (7.2-fold expansion), and IGHV3-74—IGHJ4 increased from 1.18–16.88% (14.3-fold expansion). Patients who experienced PR and SD-CR demonstrated comparable patterns, with patient 4 (PR) showing expansion of IGHV3-23—IGHJ4 from 6.56–11.03% by day 70 (1.7-fold increase), and patient 6 (SD-CR) exhibiting IGHV3-15—IGHJ6 expansion from 0.24–15.35% by day 71 (64.0-fold increase). These expanded clones contracted at later timepoints (Fig. 2 b). Comparative analysis of the TCR (TRA and TRB) and BCR (IgM) repertoires across the same timepoints revealed relatively stable patterns in these populations, in contrast to the dynamic changes observed in the IgG repertoire (supplementary Fig. 3). Longitudinal analysis of the Shannon-Weaver diversity index patterns revealed distinct immune dynamic signatures between the response groups. Super-responders demonstrate a monophasic recovery pattern characterized by initial decreased IgG repertoire diversity (ratio < 0.9) followed by gradual restoration toward baseline levels within the first 100 days. This decrease was more pronounced in super responders (Fig. 2 c) than in poor responders (Fig. 2 d). This analysis was extended across multiple immune repertoires (TRA, TRB, and IgM), demonstrating that while similar patterns were observed in these populations, the magnitude of change was less dramatic than that in the IgG repertoire. (supplementary Fig. 4). RNA sequencing of the tumors and fusion gene detection Using viable FFPE tissues, RNA transcripts were analyzed with a focus on identifying fusion genes. Patients with CR, PR, and SD were grouped as responders, and those exhibiting PD were grouped as non-responders. The analysis revealed that at least two fusion genes were detected in six of the seven responders, whereas eight of the 10 non-responders lacked fusion genes. The remaining pairs of non-responders had only one fusion gene (all responders: mean = 6.3, range = 0–15; all non-responders: mean = 0.2, range = 0–1). Statistically, the antitumor response to nivolumab strongly correlated with the number of fusion genes in ovarian cancer (p = 0.0003 with Fisher's exact test, sensitivity: 86% [6/7], specificity: 100% [10/10]) (Fig. 3 a). The presence of fusion transcripts was confirmed using reverse transcription-PCR and Sanger sequencing, as exemplified by the ERI1-NCOA2 fusion junction (Fig. 3 b). Analysis of the fusion genes across different clinical response groups revealed significant patterns associated with nivolumab treatment outcomes (supplementary table 1 ). Patients who experienced CR exhibited multiple fusion genes (five in Patient 11 and five in Patient 14), while those who experienced PR (Patient 4) showed the highest number of 15 fusion genes. Patient 6 (SD-CR) had three fusion genes, and other patients with SD demonstrated variable numbers (4 in Patient 7 and 12 in Patient 13). Notably, patients with PD harbored only one fusion gene (Patients 5 and 12). Several recurrent fusion events were identified across different response groups: IGLV2-14-IGLL5 fusion detected Patient 11 (CR) and Patient 6 (SD-CR), GRIP1-ENSG00000256248 fusion in Patient 11 (CR), Patient 7 (SD), and Patient 13 (SD), CPLV-CHN2 fusions in Patient 14 (CR) and Patient 13 (SD), and CCDC7-C10orf68 fusions in Patient 4 (PR) and Patient 13 (SD). To validate these findings and exclude potential artifacts, we performed cDNA transcription from RNA extracts and designed specific PCR primers targeting each fusion breakpoint. The presence of the fusion transcripts was confirmed using gel electrophoresis and Sanger sequencing to establish the biological authenticity of the detected fusion genes. Analysis of validation results for the 42 fusion gene candidates revealed that 26 (61.9%) were "validated," 3 (7.1%) were "partially validated," 8 (19.0%) were "not validated," and 5 (11.9%) were "not validated (no band)." The high validation rate of approximately 69% (fully or partially validated) supports the biological authenticity of the detected fusion genes and the reliability of our RNA-seq-based fusion gene detection approach. (supplementary table 1 ). The strong correlation between fusion gene number and clinical outcomes suggests that the fusion gene burden may serve as a potential predictive biomarker for nivolumab response in patients with ovarian cancer, with a higher number of fusion events associated with better treatment outcomes. Differential gene expression analysis Differential gene expression analysis was performed to compare the multiple response group classifications. Four different group comparisons were analyzed: responders (CR, PR, SD-CR, SD, n = 7) versus non-responders (PD, n = 8) (Fig. 4 a), responders (CR, n = 2) versus non-responders (PR, SD-CR, SD, PD, n = 13) (Fig. 4 b), responders (CR, PR, n = 3) versus non-responders (SD-CR, SD, PD, n = 12) (supplementary Fig. 5a), and responders (CR, PR, SD-CR, n = 4) versus non-responders (SD, PD, n = 11) (supplementary Fig. 5b). To validate the RNA sequencing data, we performed a comparative analysis with gene expression microarray data from the same patient cohort to examine the differences between patients with CR (n = 2) and those without CR (n = 17) [ 27 ]. Microarray analysis identified 22 genes that were significantly upregulated in patients with CR. RNA sequencing revealed a broad signature of 82 upregulated genes in patients with CR. Notably, eight genes were consistently identified across both platforms (BAIAP2L2, EEF1A2, EPHA7, HNF1B, MMP24, PTHLH, SAA2, and VNN1) and were highly significant (hypergeometric test, p-value = 1.97 × 10⁻¹², 89-fold enrichment over expected by chance), validating the consistency of our findings across different technological platforms (supplementary table 2 ). Pathway analysis A comparison between responders (CR, PR, SD-CR, SD, n = 7) and non-responders (PD, n = 8) identified 812 upregulated genes in responders. Additionally, 1,506 genes were positively correlated with fusion gene counts (n = 15) (supplementary table 2 ). These two gene signatures were analyzed using KEGG, GO, and Reactome pathway analyses, which revealed distinct immunological signatures associated with the nivolumab response. KEGG pathway analysis identified significant enrichment in cytokine-cytokine receptor interactions and rheumatoid arthritis pathways in responders (Fig. 4 c). Analysis of genes that positively correlated with fusion gene counts showed similar enrichment patterns (Fig. 4 d). GO enrichment analysis revealed significant biological processes associated with immune response, including leukocyte-mediated immunity, leukocyte cell-cell adhesion, and regulation of immune effector processes (supplementary Fig. 6a). Analysis of genes positively correlated with fusion gene counts demonstrated enrichment in comparable immune-related processes (supplementary Fig. 6b). Reactome pathway analysis further supports these findings by highlighting several immune-related processes. Significantly enriched pathways in responders included neutrophil degranulation, immunoregulatory interactions between lymphoid and non-lymphoid cells, and interleukin-10 signaling (supplementary Fig. 6c). Similarly, genes positively correlated with fusion gene counts were enriched in neutrophil degranulation, immunoregulatory interactions, and various cytokine signaling pathways (supplementary Fig. 6d). The consistency between the differential expression analysis and fusion gene correlation analysis in identifying similar immune-related biological processes further supports the role of immune activation in response to nivolumab treatment. Discussion The gene expression profiling of PBMCs collected at pre-treatment and 14 days post-treatment revealed significantly elevated expression of B cell-related genes in patients with disease control compared to non-responders. This early B cell activation signature aligns with immunological patterns observed in viral vaccination studies, where robust B cell responses typically emerge 7–14 days after antigenic stimulation [ 34 , 35 ]. These results suggest that B-cell reactivity approximately 2 weeks after anti-PD-1 antibody administration could serve as an early predictive biomarker of clinical benefit. Our TCR and BCR repertoire analyses confirmed enhanced B-cell activity in clinical responders to nivolumab therapy. Most notably, we observed a characteristic decrease in the IgG B cell repertoire diversity among responders within 60–100 days post-treatment, followed by restoration to baseline levels. This transient oligoclonal expansion pattern aligns with the findings of Nakahara et al. in patients with EGFR/ALK wild-type non-small cell lung cancer [ 23 ], where responders exhibited decreased B-cell repertoire diversity at 6 weeks post-treatment, correlating with prolonged progression-free survival. While both the T-cell and B-cell repertoires showed similar trends, the magnitude of change was substantially more pronounced in the B-cell IgG repertoires. B cells possess a greater capacity to generate and maintain antigen-specific antibodies throughout their lifespan, potentially allowing for a more distinct visualization of transient oligoclonal expansion patterns in IgG repertoires [ 36 ]. A particularly novel finding was the strong correlation between the fusion gene burden and treatment response. Patients with multiple fusion genes (≥ 2) showed significantly better clinical outcomes compared to those with one or no fusion genes. This association suggests that genomic rearrangements that result in the fusion of genes may generate neoantigens that enhance immune recognition. Unlike previous studies, where tumor mutation burden or frameshift indels correlated with immunotherapy response [ 37 – 39 ], findings from the present study indicate that fusion genes may serve as a more relevant source of immunogenic neoantigens in ovarian cancer. The fusion genes we identified appeared to be passenger mutations rather than driver mutations, as evidenced by their diverse nature and lack of consistent oncogenic signatures. This distinction may explain why our findings contrast with the existing literature on oncogenic fusion genes. Previous studies have shown that tumors harboring driver fusion genes (such as ALK and RET) typically exhibit immunosuppressive tumor microenvironments and demonstrate limited responsiveness to anti-PD-1/PD-L1 inhibitors[ 40 , 41 ]. Our findings suggest that passenger fusion genes in ovarian cancer may contribute to enhanced immunogenicity and an improved response to immunotherapy. This contrasts with established driver fusion genes in ovarian cancer, such as CDKN2D-WDFY2 (20% of cases) and BCAM-AKT2 (7% of cases) in high-grade serous carcinoma [ 42 , 43 ] and UBAP1-TGM7 (10% of clear cell cases) in clear cell carcinoma [ 44 ], which primarily function through oncogenic pathway activation rather than immunomodulation. Notably, these common driver fusion genes were not detected in the present study. According to Earp et al. [ 44 ], the frequency of fusion genes varies significantly according to the histological subtype, with clear cell carcinomas harboring the highest number (mean: 7.4 fusions per tumor) compared to high-grade serous carcinomas (mean: 2.0 fusions per tumor), while endometrioid and mucinous subtypes contain even fewer fusions (means: 0.24 and 0.25, respectively). In our cohort, responders exhibited significantly more fusion genes (mean: 6.3, range: 0–15) compared to non-responders (mean: 0.2, range: 0–1), suggesting that the accumulation of passenger fusion events, rather than specific oncogenic driver fusions, may contribute to an enhanced response to immunotherapy. Our pathway analyses revealed remarkable concordance between differentially expressed genes in responders and non-responders, and genes positively correlated with fusion gene counts. Both analyses consistently identified immune-related processes, including cytokine-cytokine receptor interactions, neutrophil degranulation, and immunoregulatory interactions. This significant overlap suggests that tumors with a higher fusion gene burden possess inherently greater immunogenicity, likely due to the generation of novel epitopes that are recognized by the immune system. Our study has several limitations, including the small sample size (n = 19). Previous research has demonstrated that BCR-IgG repertoires in breast cancer tissues show pronounced oligoclonality [ 45 ] and that patients with melanoma responding to immunotherapy often exhibit more clonal TCR repertoires in pre-treatment tumor samples [ 19 ]. These findings suggest that an integrated analysis incorporating both the tumor and peripheral immune compartments would provide a more robust understanding of the immunological mechanisms underlying the response to nivolumab. Future studies with larger patient cohorts and multicompartment immune profiling are needed to validate our findings and further explore the relationship between fusion gene burden, immune repertoire dynamics, and clinical outcomes. The integration of B-cell repertoire dynamics, fusion gene burden, and immune activation markers may provide a multi-dimensional approach for predicting nivolumab response in ovarian cancer patients. Abbreviations BCR, B cell receptor CR, Complete response FFPE, fixed paraffin-embedded GO, Gene Ontology IgG, Immunoglobulin G IgM, Immunoglobulin M KEGG, Kyoto Encyclopedia of Genes and Genomes PD, Progressive disease PD-1, Programmed cell death-1 PD-L, Programmed Cell Death Ligand 1 PMBCs, peripheral blood mononuclear cells PR, Partial response SD, Stable disease TCR, T cell receptor TRA, T cell receptor alpha chain TRB, T cell receptor beta chain Declarations Conflicts of interest JH reports research grants from Eizai, Ono, Chugai, Sumitomo Pharma, and Kinopharma, and lecture fees from MSD and Eizai. RM reports research grants from Sumitomo Pharma. KY and MM reported receiving personal fees from Dumsco Inc. The other authors have no conflict of interest to declare. Ethics approval This study was approved by the Ethics Committee of the Kyoto University Graduate School and Faculty of Medicine (approval number: R1000-3 and G531). Consent to participate Written informed consent was obtained from all participants. Funding This work was supported by the Project for the Development of Innovative Research on Cancer Therapeutics (P-DIRECT) of the Japan Agency for Medical Research and Development (AMED) grant (15 cm0106133h0002) and the Japanese Society for the Promotion of Science (grant 17K19591 and 24K12601). Author Contribution Clinical trial planning and execution: JH, MM; Patient sample biochemical analyses: RM, YH; Data analysis: RM, JH, JB; Manuscript authoring: RM, JH, JB; Contextualization of the findings of this work: RM, JH, JB, TM, RM, KY, MT, KY, and MM. All the authors have read and approved the final manuscript. Acknowledgement This work was supported by the Project for the Development of Innovative Research on Cancer Therapeutics (P-DIRECT) of the Japan Agency for Medical Research and Development (AMED) grant (15 cm0106133h0002) and the Japanese Society for the Promotion of Science (grant 17K19591 and 24K12601). Data Availability The datasets generated and analyzed during the current study are available from the corresponding author upon reasonable request. References Bray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I, Jemal A (2024) Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 74: 229-63. doi: 10.3322/caac.21834 Nakai H, Higashi T, Kakuwa T, Matsumura N (2024) Trends in gynecologic cancer in Japan: incidence from 1980 to 2019 and mortality from 1981 to 2021. 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(2018) Nivolumab plus Ipilimumab in Lung Cancer with a High Tumor Mutational Burden. N Engl J Med. 378: 2093-104. doi: 10.1056/NEJMoa1801946 Kato T, Kiyotani K, Tomiyama E et al. (2021) Peripheral T cell receptor repertoire features predict durable responses to anti-PD-1 inhibitor monotherapy in advanced renal cell carcinoma. Oncoimmunology. 10: 1862948. doi: 10.1080/2162402x.2020.1862948 Nakahara Y, Matsutani T, Igarashi Y et al. (2021) Clinical significance of peripheral TCR and BCR repertoire diversity in EGFR/ALK wild-type NSCLC treated with anti-PD-1 antibody. Cancer Immunol Immunother. 70: 2881-92. doi: 10.1007/s00262-021-02900-z Vos JL, Burman B, Jain S et al. (2023) Nivolumab plus ipilimumab in advanced salivary gland cancer: a phase 2 trial. Nat Med. 29: 3077-89. doi: 10.1038/s41591-023-02518-x Ulmer AJ, Scholz W, Ernst M, Brandt E, Flad HD (1984) Isolation and subfractionation of human peripheral blood mononuclear cells (PBMC) by density gradient centrifugation on Percoll. 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Mol Cell Biol. 2: 829-36. doi: 10.1128/mcb.2.7.829-836.1982 Lefranc MP (2014) Immunoglobulin and T Cell Receptor Genes: IMGT(®) and the Birth and Rise of Immunoinformatics. Front Immunol. 5: 22. doi: 10.3389/fimmu.2014.00022 Venturi V, Kedzierska K, Turner SJ, Doherty PC, Davenport MP (2007) Methods for comparing the diversity of samples of the T cell receptor repertoire. J Immunol Methods. 321: 182-95. doi: 10.1016/j.jim.2007.01.019 Kim D, Salzberg SL (2011) TopHat-Fusion: an algorithm for discovery of novel fusion transcripts. Genome Biol. 12: R72. doi: 10.1186/gb-2011-12-8-r72 Galson JD, Pollard AJ, Truck J, Kelly DF (2014) Studying the antibody repertoire after vaccination: practical applications. Trends Immunol. 35: 319-31. doi: 10.1016/j.it.2014.04.005 Wang C, Liu Y, Xu LT et al. (2014) Effects of aging, cytomegalovirus infection, and EBV infection on human B cell repertoires. J Immunol. 192: 603-11. doi: 10.4049/jimmunol.1301384 Theaker SM, Rius C, Greenshields-Watson A et al. (2016) T-cell libraries allow simple parallel generation of multiple peptide-specific human T-cell clones. J Immunol Methods. 430: 43-50. doi: 10.1016/j.jim.2016.01.014 Rooney MS, Shukla SA, Wu CJ, Getz G, Hacohen N (2015) Molecular and genetic properties of tumors associated with local immune cytolytic activity. Cell. 160: 48-61. doi: 10.1016/j.cell.2014.12.033 Riaz N, Havel JJ, Makarov V et al. (2017) Tumor and Microenvironment Evolution during Immunotherapy with Nivolumab. Cell. 171: 934-49.e16. doi: 10.1016/j.cell.2017.09.028 Alban TJ, Riaz N, Parthasarathy P et al. (2024) Neoantigen immunogenicity landscapes and evolution of tumor ecosystems during immunotherapy with nivolumab. Nat Med. 30: 3209-22. doi: 10.1038/s41591-024-03240-y Gainor JF, Shaw AT, Sequist LV et al. (2016) EGFR Mutations and ALK Rearrangements Are Associated with Low Response Rates to PD-1 Pathway Blockade in Non-Small Cell Lung Cancer: A Retrospective Analysis. Clin Cancer Res. 22: 4585-93. doi: 10.1158/1078-0432.Ccr-15-3101 Offin M, Guo R, Wu SL et al. (2019) Immunophenotype and Response to Immunotherapy of RET-Rearranged Lung Cancers. JCO Precis Oncol. 3. doi: 10.1200/po.18.00386 Kannan K, Coarfa C, Rajapakshe K, Hawkins SM, Matzuk MM, Milosavljevic A, Yen L (2014) CDKN2D-WDFY2 is a cancer-specific fusion gene recurrent in high-grade serous ovarian carcinoma. PLoS Genet. 10: e1004216. doi: 10.1371/journal.pgen.1004216 Kannan K, Coarfa C, Chao PW et al. (2015) Recurrent BCAM-AKT2 fusion gene leads to a constitutively activated AKT2 fusion kinase in high-grade serous ovarian carcinoma. Proc Natl Acad Sci U S A. 112: E1272-7. doi: 10.1073/pnas.1501735112 Earp MA, Raghavan R, Li Q et al. (2017) Characterization of fusion genes in common and rare epithelial ovarian cancer histologic subtypes. Oncotarget. 8: 46891-9. doi: 10.18632/oncotarget.16781 Coronella JA, Spier C, Welch M, Trevor KT, Stopeck AT, Villar H, Hersh EM (2002) Antigen-driven oligoclonal expansion of tumor-infiltrating B cells in infiltrating ductal carcinoma of the breast. J Immunol. 169: 1829-36. doi: 10.4049/jimmunol.169.4.1829 Additional Declarations No competing interests reported. Supplementary Files Supplementarymaterials20250830.docx SupFig1202508301.jpeg SupFig2202508302.jpeg SupFig3202508303.jpeg SupFig4202508304.jpeg SupFig5202508305.jpeg SupFig6A6B202508306.jpeg SupFig6C6D202508307.jpeg Cite Share Download PDF Status: Published Journal Publication published 27 Jan, 2026 Read the published version in Cancer Immunology, Immunotherapy → Version 1 posted Editorial decision: Revision requested 02 Oct, 2025 Reviews received at journal 19 Sep, 2025 Reviews received at journal 15 Sep, 2025 Reviewers agreed at journal 03 Sep, 2025 Reviewers agreed at journal 03 Sep, 2025 Reviewers agreed at journal 03 Sep, 2025 Reviewers invited by journal 03 Sep, 2025 Editor assigned by journal 30 Aug, 2025 Submission checks completed at journal 30 Aug, 2025 First submitted to journal 29 Aug, 2025 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-7492478","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":511283951,"identity":"a6c1e721-1ecc-4d04-a880-09a5f4a94804","order_by":0,"name":"Ryusuke Murakami","email":"","orcid":"","institution":"Kyoto University Graduate School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Ryusuke","middleName":"","lastName":"Murakami","suffix":""},{"id":511283952,"identity":"55f6aabf-a05b-4e12-a267-73c71b26d39e","order_by":1,"name":"Junzo Hamanishi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCUlEQVRIiWNgGAWjYBACxgY2NhAtJ8HAA6QOoEjh12IM18JDSAsDA0RL4gwMLbgA8+y2tEc3d9Slz5yRe+wBw5nDcvbsDWwSDDV2QCns1jDOOXbcOPfM4dzZEnnpBgw3Dhvz8BwAajmWDJQ6gF3LjPQ26dy2A7nzJHLMJBg+HE7skcj/JsHAdgAolYBPS126HEJLAtCWf/i0pB0DamFOkAZruQHVwtiGV0saUMthw5k9b8wNEs6kG/OcOcBskdiXzIPLL4Yz0sxADpOXOJ5j9uDDMWs59vYGxhsfvtnJGeIIMUMkYTaGBIZmCBPoJB7DGVh1MMgjsUFxWockJYFdyygYBaNgFIw4AADsllpDQVsjhgAAAABJRU5ErkJggg==","orcid":"","institution":"Kyoto University Graduate School of Medicine","correspondingAuthor":true,"prefix":"","firstName":"Junzo","middleName":"","lastName":"Hamanishi","suffix":""},{"id":511283953,"identity":"a1145e35-d541-45bd-bfad-3a5e16fc6e0a","order_by":2,"name":"J. B. Brown","email":"","orcid":"","institution":"Kyoto University Graduate School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"J.","middleName":"B.","lastName":"Brown","suffix":""},{"id":511283954,"identity":"4aaaf08f-54ec-43ab-96a1-0890146cf593","order_by":3,"name":"Yuko Hosoe","email":"","orcid":"","institution":"Kyoto University Graduate School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Yuko","middleName":"","lastName":"Hosoe","suffix":""},{"id":511283955,"identity":"913da3b3-b8ce-49d1-8950-5e7745108ab3","order_by":4,"name":"Taito Miyamoto","email":"","orcid":"","institution":"Kyoto University Graduate School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Taito","middleName":"","lastName":"Miyamoto","suffix":""},{"id":511283956,"identity":"90a9815b-f815-4400-9f78-9d1ef12e09a7","order_by":5,"name":"Rin Mizuno","email":"","orcid":"","institution":"Kyoto University Graduate School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Rin","middleName":"","lastName":"Mizuno","suffix":""},{"id":511283957,"identity":"4e04aa0e-0a51-441b-818e-509898ae9233","order_by":6,"name":"Mana Taki","email":"","orcid":"","institution":"Kyoto University Graduate School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Mana","middleName":"","lastName":"Taki","suffix":""},{"id":511283958,"identity":"d0d8cfd5-910a-45b1-b9c0-54caf6f1a078","order_by":7,"name":"Koji Yamanoi","email":"","orcid":"","institution":"Kyoto University Graduate School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Koji","middleName":"","lastName":"Yamanoi","suffix":""},{"id":511283959,"identity":"6597430d-cbc2-44ac-bb9f-e2a01d8abb5d","order_by":8,"name":"Ken Yamaguchi","email":"","orcid":"","institution":"Kyoto University Graduate School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Ken","middleName":"","lastName":"Yamaguchi","suffix":""},{"id":511283961,"identity":"422b34bc-353e-4849-b0d2-c1fbc5038519","order_by":9,"name":"Masaki Mandai","email":"","orcid":"","institution":"Kyoto University Graduate School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Masaki","middleName":"","lastName":"Mandai","suffix":""}],"badges":[],"createdAt":"2025-08-30 03:38:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7492478/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7492478/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00262-025-04289-5","type":"published","date":"2026-01-27T15:59:10+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":91067656,"identity":"7fb6380a-ac29-4400-a2b9-717bf07eadbb","added_by":"auto","created_at":"2025-09-11 10:13:26","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":492610,"visible":true,"origin":"","legend":"\u003cp\u003eGene expression analysis of peripheral blood mononuclear cells (PBMCs) reveals distinct immune signatures associated with nivolumab response.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea\u003c/strong\u003e. Schematic overview of the initial analytical approach. PBMCs were collected before nivolumab treatment (pre) and 14 days after initial treatment (day 14). Gene expression profiles were analyzed using microarray, and post/pre-treatment ratios were calculated to assess treatment-induced changes in immune cell activity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eb\u003c/strong\u003e. Uniform Manifold Approximation and Projection (UMAP) visualization of PBMC gene expression post/pre ratios showing the distribution of patient samples according to treatment response. Each point represents an individual patient, colored by clinical response category: Complete Response (CR, coral), Partial Response (PR, lime green), Stable Disease with CR tendency (SD-CR, light magenta), Stable Disease (SD, cyan blue), and Progressive Disease (PD, gold). The visualization reveals distinct clustering patterns that partially separate responders from non-responders.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ec\u003c/strong\u003e. Comparison of post/pre-treatment expression ratios of key immune cell markers between responders (CR_PR_SD, red) and non-responders (PD, blue). B cell markers (CD24, CD79B) showed significantly higher ratios in responders compared to non-responders (p\u0026lt;0.05), indicating enhanced B cell activation. The immunosuppressive macrophage marker CD163 exhibited significantly lower ratios in responders (p\u0026lt;0.05), suggesting diminished immunosuppressive myeloid activity. T cell markers (CD4, CD8A, and CD8B) and the regulatory T cell marker FOXP3 showed no significant differences between response groups. Horizontal lines represent median values.\u003c/p\u003e","description":"","filename":"Figure1202508301.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7492478/v1/f5642bbd5fcfc978a71769b5.jpeg"},{"id":91067661,"identity":"95fefac0-20ff-4ffc-9379-9157be783f33","added_by":"auto","created_at":"2025-09-11 10:13:26","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":950970,"visible":true,"origin":"","legend":"\u003cp\u003eB cell immunoglobulin G (IgG) repertoire dynamics following nivolumab treatment reveal distinct patterns in responders versus non-responders.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea\u003c/strong\u003e. Comparison of post/pre-treatment Shannon-Weaver diversity index ratios for T cell receptor alpha chain (TRA), T cell receptor beta chain (TRB), B cell IgG, and B cell immunoglobulin M (IgM) repertoires between super-responders (CR, PR, SD-CR, n=4) and poor-responders (SD, PD, n=15). Horizontal lines indicate median values. The IgG repertoire shows significantly decreased diversity ratio in super-responders compared to poor-responders (*p\u0026lt;0.05), indicating oligoclonal expansion, while no significant differences (NS) were observed in TRA, TRB, or IgM repertoires.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eb\u003c/strong\u003e. Longitudinal tracking of B cell IgG repertoire composition in four representative super-responders. Red arrows indicate timepoints with maximal clonal expansion. Specific IgG clones showing substantial expansion are highlighted, with their frequency increase from baseline to peak: Patient 11 (CR): IGHV3-7 ---IGHJ3 expanded 3.9-fold; Patient 14 (CR): IGHV3-7 ---IGHJ4 expanded 7.2-fold and IGHV3-74 ---IGHJ4 expanded 14.3-fold; Patient 4 (PR): IGHV3-23 ---IGHJ4 expanded 1.4-fold; and Patient 6 (SD-CR): IGHV3-15---IGHJ6 expanded 64.0-fold. All expanded clones subsequently contracted at later timepoints.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ec\u003c/strong\u003e. Temporal changes in relative diversity (Shannon-Weaver index ratio) of IgG repertoire in super-responders over time (0–700 days). Super-responders demonstrate a monophasic recovery pattern characterized by initial decreased IgG repertoire diversity (ratio \u0026lt;0.9) followed by gradual restoration toward baseline levels within the first 100 days.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ed\u003c/strong\u003e. Temporal changes in IgG repertoire diversity in poor-responders, showing more heterogeneous patterns without the consistent reduction in diversity seen in super-responders.\u003c/p\u003e\n\u003cp\u003eCR, Complete response; PR, Partial response; SD, Stable disease; PD, Progressive disease\u003c/p\u003e","description":"","filename":"Figure2202508302.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7492478/v1/cd6760f2ebe7287c505cec22.jpeg"},{"id":91068478,"identity":"8360b049-9e27-474e-87d4-b82c56f038ab","added_by":"auto","created_at":"2025-09-11 10:21:26","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":977100,"visible":true,"origin":"","legend":"\u003cp\u003eFusion gene burden in tumor tissues strongly correlates with response to nivolumab in ovarian cancer.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea\u003c/strong\u003e. Association between fusion gene burden status and clinical response. Patients were classified into high (≥2 fusion genes) and low (\u0026lt;2 fusion genes) fusion gene burden groups. All patients with high fusion gene counts (n=6) were clinical responders, while most patients with low fusion gene counts (8/10) were non-responders (***p\u0026lt;0.001, Fisher's exact test).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eb\u003c/strong\u003e. Representative example of a fusion gene breakpoint identified and validated in our analysis, showing the junction sequence between ER1 (chromosome 8) and NCOA2 (chromosome 8). Flanking sequences and alignment of sequencing reads spanning the fusion junction confirm the genomic rearrangement event.\u003c/p\u003e\n\u003cp\u003eCR, Complete response; PR, Partial response; SD, Stable disease; PD, Progressive disease\u003c/p\u003e","description":"","filename":"Figure3202508303.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7492478/v1/1f720e3e93d69fc261a6759a.jpeg"},{"id":91069726,"identity":"d266d25b-14f0-4012-bafc-e435093fd024","added_by":"auto","created_at":"2025-09-11 10:37:26","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":676477,"visible":true,"origin":"","legend":"\u003cp\u003eFusion gene burden correlates with immune activation signatures in nivolumab-responsive ovarian cancer\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea-b. \u003c/strong\u003eDifferential gene expression analysis identifies response-associated transcriptomic signatures\u003cstrong\u003e.\u003c/strong\u003eVolcano plots display differential gene expression between response groups, with log2 fold change (x-axis) versus log10 adjusted p-values (y-axis). Significantly upregulated genes (adjusted p \u0026lt; 0.25, |log2FC| \u0026gt; 1) are shown in red, downregulated genes in blue, and non-significant genes in gray.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea\u003c/strong\u003e. Comparison between Responders (CR, PR, SD-CR, SD, n=7) and non-responders (PD, n=8), revealing numerous differentially expressed genes with predominant upregulation in responders.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eb\u003c/strong\u003e. Comparison between Complete Responders (CR, n=2) and all other patients (PR, SD-CR, SD, PD, n=13), identifying genes specifically associated with complete response, including EEF1A2, KCNQ2, and HNF1B.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ec-d. \u003c/strong\u003ePathway enrichment analysis demonstrates immune activation convergence\u003cstrong\u003e.\u003c/strong\u003e KEGG pathway analysis reveals consistent immune-related enrichment patterns across independent analytical approaches. Dot plots display the most enriched pathways with adjusted p-values (color gradient) and gene counts (dot size).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ec\u003c/strong\u003e. KEGG pathway analysis of differentially upregulated genes in responders (CR, PR, SD-CR, SD; n=7) compared to non-responders (PD; n=8). The most enriched pathways included cytokine-cytokine receptor interactions, rheumatoid arthritis, and cell adhesion molecules, highlighting immune system activation in nivolumab-responsive patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ed\u003c/strong\u003e. KEGG pathway analysis of genes positively correlated with the fusion gene count (n=15). Similar to panel A, cytokine-cytokine receptor interactions and rheumatoid arthritis pathways were significantly enriched, suggesting a mechanistic link between fusion gene burden and immune activation.\u003c/p\u003e\n\u003cp\u003eCR, Complete response; PR, Partial response; SD, Stable disease; PD, Progressive disease; KEGG, Kyoto Encyclopedia of Genes and Genomes\u003c/p\u003e","description":"","filename":"Figure4202508304.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7492478/v1/3f0a13639b3a9c56bd973caf.jpeg"},{"id":101690563,"identity":"a023082f-3922-4c79-a184-338470b2a521","added_by":"auto","created_at":"2026-02-02 16:05:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3919282,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7492478/v1/ffc4afe8-c634-48c4-bab1-9e4b6898d802.pdf"},{"id":91068474,"identity":"998f3a66-5f84-4545-bc11-d733ba5f4a96","added_by":"auto","created_at":"2025-09-11 10:21:26","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":453320,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterials20250830.docx","url":"https://assets-eu.researchsquare.com/files/rs-7492478/v1/e082f4838daf6b79bf85b315.docx"},{"id":91067659,"identity":"69bdac7c-ac43-4b10-972e-8de4fd4e176c","added_by":"auto","created_at":"2025-09-11 10:13:26","extension":"jpeg","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":371569,"visible":true,"origin":"","legend":"","description":"","filename":"SupFig1202508301.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7492478/v1/14745a89b6a61ae32b23a501.jpeg"},{"id":91069415,"identity":"73cd5dcb-0e5a-4fe8-9f8d-74e4ba966119","added_by":"auto","created_at":"2025-09-11 10:29:26","extension":"jpeg","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":309924,"visible":true,"origin":"","legend":"","description":"","filename":"SupFig2202508302.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7492478/v1/855753e6c34bf0694ed2a64a.jpeg"},{"id":91068479,"identity":"d5d7becb-2fda-4669-8c93-e24982ab85ca","added_by":"auto","created_at":"2025-09-11 10:21:26","extension":"jpeg","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":902678,"visible":true,"origin":"","legend":"","description":"","filename":"SupFig3202508303.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7492478/v1/3ff0adfed2e6c7c6c147e108.jpeg"},{"id":91069418,"identity":"0fa7956a-0ea1-44d3-80f1-29b4f5454b9c","added_by":"auto","created_at":"2025-09-11 10:29:26","extension":"jpeg","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":484895,"visible":true,"origin":"","legend":"","description":"","filename":"SupFig4202508304.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7492478/v1/b5132ab41f4e68a35f1713e4.jpeg"},{"id":91069416,"identity":"72151ab2-807a-4e47-8e2e-26c2e6c369cb","added_by":"auto","created_at":"2025-09-11 10:29:26","extension":"jpeg","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":302333,"visible":true,"origin":"","legend":"","description":"","filename":"SupFig5202508305.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7492478/v1/7df3921a0dfb3d287875ec9a.jpeg"},{"id":91068484,"identity":"037fab10-9d99-4315-8a82-ab1fa3fe5d66","added_by":"auto","created_at":"2025-09-11 10:21:26","extension":"jpeg","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":503531,"visible":true,"origin":"","legend":"","description":"","filename":"SupFig6A6B202508306.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7492478/v1/33c374862f7d449e6958f1dd.jpeg"},{"id":91067667,"identity":"cdb70873-8f62-48b9-8776-be85a55a99ab","added_by":"auto","created_at":"2025-09-11 10:13:26","extension":"jpeg","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":465984,"visible":true,"origin":"","legend":"","description":"","filename":"SupFig6C6D202508307.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7492478/v1/d8b2c26e0cb1b0702dcbdb4c.jpeg"}],"financialInterests":"No competing interests reported.","formattedTitle":"Oligoclonal B cell Expansion and Passenger Fusion Genes Predict Response to Nivolumab in Recurrent Ovarian Cancer: Phase II Kyoto Trial","fulltext":[{"header":"Introduction","content":"\u003cp\u003eOvarian cancer is the leading cause of death among gynecologic malignancies, causing approximately 140,000 deaths annually worldwide [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Programmed cell death-1 (PD-1), an immune checkpoint receptor expressed by T cells, binds to PD-1 ligands (PD-Ls: PD-L1 [B7-H1] and PD-L2 [B7-H2]), and suppresses antigen-specific immune responses to cancer cells[\u003cspan additionalcitationids=\"CR4 CR5 CR6\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Previously, we demonstrated the efficacy of nivolumab, an anti-PD-1 antibody, in a phase II clinical trial involving 20 patients with recurrent platinum-resistant ovarian cancer at the Kyoto University Hospital. The best overall response rate was 15%, including two patients with complete response (CR), and the disease control rate was 45% [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. However, reliable biomarkers for predicting the response to immune checkpoint inhibitors in patients with ovarian cancer remain elusive despite several clinical trials having been conducted. Moreover, PD-1/PD-L1 inhibitors are yet to be implemented as standard treatments for ovarian cancer [\u003cspan additionalcitationids=\"CR10 CR11 CR12\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSeveral groups have identified potential biomarkers that may predict response to PD-1 inhibitors in various cancer types [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The expression of PD-L1 on tumor cells or T cells [\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], PD-1(+) or CD8(+) tumor-infiltrating lymphocytes [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], mismatch repair deficiency [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], and higher tumor mutation burden [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] have been utilized as predictive biomarkers in several cancer types. Furthermore, research efforts aimed at identifying predictive biomarkers based on T-cell and B-cell repertoires associated with anti-PD-1 and anti-PD-L1 blockade therapies are underway [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThis study aimed to identify potential predictive biomarkers of clinical response to nivolumab in ovarian cancer using peripheral blood mononuclear cells (PBMCs) [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] and formalin-fixed paraffin-embedded (FFPE) tumor tissues. We employed unsupervised hierarchical clustering to analyze PBMC transcriptome profiles, which guided our investigation toward B-cell rather than T-cell repertoire analyses as predictive biomarkers for anti-PD-1 therapy for ovarian cancer management.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003ePatient samples\u003c/h2\u003e\u003cp\u003eTwenty patients with platinum-resistant ovarian cancer were enrolled in an anti-PD-1 clinical trial conducted at Kyoto University Hospital from 2011 to 2015 (UMIN000005714). This study was approved by the institutional ethics committee, and donors provided written informed consent in accordance with institutional and national guidelines [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. One patient was excluded from the analysis because of early discontinuation after the first administration of nivolumab (not evaluable). Nineteen patients (patients 1\u0026ndash;19) were enrolled in the subsequent analyses.\u003c/p\u003e\u003cp\u003eBlood samples were collected during the first course of anti-PD-1 treatment at three timepoints: prior to treatment (pre-treatment), 14 days after treatment initiation, and at the subsequent follow-up. PBMCs were isolated using density gradient centrifugation with Lymphocyte Separation Medium (Nakarai, Kyoto, Japan) and Leucosep tubes (Greiner, Frickenhausen, Germany) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). Total RNA was extracted from the isolated PBMCs using the QIAamp RNA Blood Mini Kit (Qiagen, Valencia, CA, USA).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTumor samples were obtained through surgery and stored as FFPE tissues following the recommendations of best practices for FFPE-based gene expression measurement [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Three patients were excluded from the overall response and RNA analyses; hence, 17 patients were included in the statistical analysis.\u003c/p\u003e\u003cp\u003eAccording to the Response Evaluation Criteria in Solid Tumors (version 1.1), clinical responses of the patients were as follows: 2 CR, one partial response (PR), six stable disease (SD), and 10 progressive disease (PD). One patient demonstrated complete elimination of the primary target lesion, but had para-aortic metastasis. Therefore, this patient was given a special designation of \u0026ldquo;SD-CR\u0026rdquo;[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eGene expression microarray analysis with PBMC\u003c/h3\u003e\n\u003cp\u003eGene expression analysis was performed using Affymetrix U133 Plus 2.0 GeneChips according to the manufacturer's protocol. Robust Multi-Average was performed, and filtering was applied to focus on average expression levels\u0026thinsp;\u0026gt;\u0026thinsp;50% and subsequently on standard deviations\u0026thinsp;\u0026gt;\u0026thinsp;50%, identifying 10,478 gene probes as candidates for predictive genes across patients treated with anti-PD-1 antibody [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eWe calculated the ratio of post-treatment (day 14) to pre-treatment gene expression profiles of PBMC and tumor samples. This ratio reflects the activity of immune cells in the PBMC in response to PD-1 antibody treatment, with \u0026gt;\u0026thinsp;1.0 signifying an increase in gene expression levels and \u0026lt;\u0026thinsp;1.0 signifying a reduction in gene expression levels.\u003c/p\u003e\u003cp\u003eWe performed Uniform Manifold Approximation and Projection (UMAP) visualization of PBMC gene expression profiles, showing the distribution of patient samples based on treatment response.\u003c/p\u003e\u003cp\u003eImmune cell marker expression was analyzed in PBMC samples collected before and after nivolumab treatment. mRNA expression levels were measured for multiple immune cell markers, including B cell markers (CD24, CD79A, and CD79B), T cell markers (CD4, CD8A, and CD8B), a macrophage marker (CD163), and a regulatory T cell marker (FOXP3). Patients were grouped based on their treatment response (CR, PR, SD vs. PD), and expression patterns were compared between the groups using statistical analysis.\u003c/p\u003e\n\u003ch3\u003eT cell receptor (TCR) and B cell receptor (BCR) repertoire analyses\u003c/h3\u003e\n\u003cp\u003eTo assess T and B cell immune responses, we analyzed the clonal diversity of T cell receptor (TCR) repertoires (TRA and TRB chains) and B cell receptor (BCR) repertoires (IgG and IgM) in PBMCs from 19 patients treated with nivolumab. We used unbiased next-generation sequencing-based immune repertoire analysis with an original adaptor-ligation polymerase chain reaction (PCR) technique from Repertoire Genesis Inc. (Osaka, Japan) [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] throughout the time course. The all-frame and in-frame (productive) repertoire were analyzed, as in-frame represents functionally expressed receptors capable of antigen recognition, consistent with standard immune repertoire analysis protocols [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe diversity and dominance of TCR and BCR repertoires were compared using the Shannon-Weaver index [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] between clinical super-responders (two CR, one PR, and one SD-CR; n\u0026thinsp;=\u0026thinsp;4) and poor-responders (five SD and 10 PD; n\u0026thinsp;=\u0026thinsp;15). The diversity score ratio of post-treatment to pre-treatment of the Shannon-Weaver index scores was compared between super-responders and poor responders. Longitudinal tracking of the B-cell IgG repertoire was assessed in individual patients at multiple timepoints during treatment. The post/pre-treatment ratios of the Shannon-Weaver index scores were plotted over time (0-700 days) for both super-responders and poor responders.\u003c/p\u003e\n\u003ch3\u003eRNA sequencing from FFPE tumor tissues\u003c/h3\u003e\n\u003cp\u003eTotal RNA was extracted from the FFPE tumor samples using a DNA/RNA FFPE Kit (Qiagen, Valencia, CA, USA). RNA sequencing and candidate gene fusion detection analyses were performed by Illumina (Japan). After adjusting the extracted RNA to align with quality standards (30\u0026ndash;200 ng), an RNA-seq library focusing on RNA coding regions was prepared using Illumina's TruSeq RNA Access Library Prep Kit\u0026reg;. The library was enriched twice, yielding a concentration range of 2.6\u0026ndash;92 ng/\u0026micro;L. RNA sequencing was performed using Illumina NextSeq 500 [High Output v2]. The insert size was 70\u0026ndash;200 bp, with over 75% of the reads aligned to the coding regions. Although partial degradation of transcripts was observed in some samples, the 5'-3' coverage was maintained at a consistent accuracy across most cases. RNA quality control was performed using a TapeStation 2200 (Agilent Technologies), which provided RNA integrity number equivalent scores and RNA fragment percentages above 200 nucleotides (DV200). Seventeen patients were included in the gene expression analysis after quality control of the RNA samples. Two samples were excluded due to insufficient RNA quality.\u003c/p\u003e\n\u003ch3\u003eRNA-Seq Analysis Pipeline Using BaseSpace TopHat Alignment v1.0\u003c/h3\u003e\n\u003cp\u003eWe implemented an RNA-seq analysis pipeline using BaseSpace TopHat Alignment v1.0, integrating TopHat2 (v2.0.7), Bowtie (v0.12.9), Cufflinks (v2.1.1), and other essential bioinformatics tools. Raw sequencing reads were initially filtered to remove adapter sequences, PhiX, and mitochondrial DNA; this was followed by alignment with the hg19 reference genome using TopHat2 with parameters optimized for RNA-seq analysis.\u003c/p\u003e\u003cp\u003eThis pipeline incorporates quality control measures, including duplicate PCR removal and fusion gene detection, with stringent filtering criteria. Variant calling was performed chromosome-wise using Illumina's starling2 algorithm with optimized parameters (maximum input depth: 100,000 reads; minimum paired/single alignment scores: 40/10). The resulting variant calls were merged and indexed for downstream analyses.\u003c/p\u003e\u003cp\u003eExpression was quantified using Cufflinks with strand-specific parameters and abundant sequence masking. All analyses utilized the hg19 human reference genome and the corresponding annotations with tools specifically modified for the BaseSpace environment to ensure optimal performance in high-throughput sequencing analysis (technical services provided by Illumina, Japan) [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eDifferential Expression Analysis\u003c/h2\u003e\u003cp\u003eTwo samples were excluded due to insufficient RNA quality, resulting in 17 samples for the final analysis. Expression data were filtered to include genes with FPKM\u0026thinsp;\u0026gt;\u0026thinsp;1 in at least three samples and were log2-transformed (log2[FPKM\u0026thinsp;+\u0026thinsp;1]). High-variance genes were selected by retaining the top 75th percentile based on the expression variance, and 10548 genes were used for subsequent analyses.\u003c/p\u003e\u003cp\u003eDifferential gene expression analysis was performed using the Limma package in R. Patients were categorized into two groups: responders (CR, PR, SD-CR, SD) and non-responders (PD). Genes were considered differentially expressed if they met the following criteria: adjusted p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.25 and absolute log2 fold change\u0026thinsp;\u0026gt;\u0026thinsp;1. The results were visualized using volcanic plots.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eCorrelation Analysis with Fusion Gene Counts\u003c/h3\u003e\n\u003cp\u003eWe analyzed the correlation between the gene expression levels and fusion gene counts for each expressed gene. Highly correlated genes were identified using the threshold of the mean plus one standard deviation of the correlation coefficient distribution (r\u0026thinsp;=\u0026thinsp;0.53). The normality of the correlation coefficients was assessed using density plots.\u003c/p\u003e\n\u003ch3\u003eValidation analysis\u003c/h3\u003e\n\u003cp\u003eGene expression microarray data for the same RNA derived from previously published FFPE tumors (n\u0026thinsp;=\u0026thinsp;19) were used to identify a good responder signature (CR, n\u0026thinsp;=\u0026thinsp;2 vs. non-CR, n\u0026thinsp;=\u0026thinsp;17) [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Gene expression analysis was performed using R version 3.1.1 and the samroc function from Bioconductor\u0026rsquo;s \u0026lsquo;SAGx' package. Significant differential expression was defined using a false discovery rate q-value threshold of 0.05 and log2 fold change\u0026thinsp;\u0026gt;\u0026thinsp;1.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003ePathway Analysis\u003c/h2\u003e\u003cp\u003eGenes showing a high correlation with fusion gene counts were further analyzed using multiple pathway analysis approaches. Gene symbols were converted into Entrez IDs using the org.Hs.eg.db database. We performed Gene Ontology (GO) enrichment analysis for biological processes, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, and Reactome pathway analysis using ClusterProfiler and ReactomePA packages in R version 4.4.1 (R Foundation for Statistical Computing, Vienna, Austria). The Benjamini\u0026ndash;Hochberg method was applied for multiple testing corrections. The results were visualized using dotplots and bar plots, displaying the most enriched pathways based on adjusted p-values.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eAll PBMC microarray analyses were performed using the R statistical environment, R version 4.4.1. UMAP was performed using the 'umap' R package. The Mann\u0026ndash;Whitney U test, paired t-test, and Fisher's exact test were performed using GraphPad Prism version 9.3.1 (GraphPad Software, San Diego, CA, USA). The threshold for statistical significance was set at 0.05. The null hypothesis that response and gene fusion counts were not differential was rejected when the probability of such (p-value) was \u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eGene expression microarray analysis for blood samples\u003c/h2\u003e\u003cp\u003eThe UMAP visualization of PBMC gene expression post/pre-treatment ratio profiles showed the distribution of patient samples with some trend observed between different response categories (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb).\u003c/p\u003e\u003cp\u003eAnalysis of immune cell marker expression post/pre-treatment ratios revealed differences between responders (CR, PR, and SD) and non-responders (PD). Among the B-cell markers, CD24 and CD79B showed significantly higher ratios in responders than in non-responders (p\u0026thinsp;=\u0026thinsp;0.017 and 0.017, respectively), whereas CD79A showed no significant difference. The macrophage marker CD163, which is typically associated with M2 (immunosuppressive) macrophages, exhibited significantly lower ratios in responders (p\u0026thinsp;=\u0026thinsp;0.017). T cell-related markers, including CD4, CD8A, and CD8B, and the regulatory T cell marker, FOXP3, showed no significant differences between the response groups. (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec).\u003c/p\u003e\u003cp\u003eAnalysis of immune cell marker gene expression showed changes between pre-treatment and post-treatment timepoints that differed between response groups. Using paired t-test analysis, CD8A and CD8B demonstrated significant increases in responders (p\u0026thinsp;=\u0026thinsp;0.017 and p\u0026thinsp;=\u0026thinsp;0.008, respectively), whereas no significant changes were observed in non-responders. CD163 expression was significantly decreased in responders (p\u0026thinsp;=\u0026thinsp;0.011), and CD79B expression showed a significant increase following treatment (p\u0026thinsp;=\u0026thinsp;0.034). These findings indicate that certain immune cell markers undergo differential temporal changes that may be associated with response to nivolumab treatment (supplementary Fig.\u0026nbsp;1).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eTCR and BCR repertoire analyses\u003c/h2\u003e\u003cp\u003eTo verify the immunoreaction of T cells and/or B cells before (pre-treatment) and after (post-treatment) nivolumab treatment, we analyzed the repertoire profiles of TCR (TCA and TCB) and BCR (IGG and IGM) using PBMCs from our nivolumab trial. A strong correlation was observed between all-frame and in-frame repertoires in T cell receptor alpha chain (TRA), T cell receptor beta chain (TRB), IGG, and IGM (r\u0026thinsp;=\u0026thinsp;0.9989, 0.9997, 1.000, and 0.9995, respectively) (supplementary Fig.\u0026nbsp;2). Each dot represents the diversity score from 61 sporadic checks in 19 patients over the course of anti-PD-1 treatment.\u003c/p\u003e\u003cp\u003eWe compared the post-treatment to pre-treatment diversity score ratios at the nearest 1-month time point (mean 23.7 days, range 14\u0026ndash;56 days). The diversity scores resulting from PD-1 antibody treatment were compared between clinical super-responders (CR, PR, and SD-CR) and poor responders (SD and PD). For the IgG repertoire, the ratios were significantly lower in clinical super-responders than in poor responders (p\u0026thinsp;=\u0026thinsp;0.013). Contrastingly, no significant differences were found in other repertoire analyses, including TRA, TRB, and IgM. (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eB-cell IgG repertoire analysis revealed distinct patterns of clonal expansion across four representative patients with a clinical super-response (two CR, one PR, and one SD-CR), where specific IgG clones emerged and expanded substantially within the first 100 days post-treatment. In patient 11 (CR), the IGHV3-7\u0026mdash;IGHJ3 clone expanded from 4.01% (pre-treatment) to 15.69% of the total repertoire by day 29, representing a 3.9-fold increase. Similarly, patient 14 (CR) showed significant expansion of two distinct clones by day 19 after pre-treatment: IGHV3-7\u0026mdash;IGHJ4 increased from 2.43\u0026ndash;17.50% (7.2-fold expansion), and IGHV3-74\u0026mdash;IGHJ4 increased from 1.18\u0026ndash;16.88% (14.3-fold expansion). Patients who experienced PR and SD-CR demonstrated comparable patterns, with patient 4 (PR) showing expansion of IGHV3-23\u0026mdash;IGHJ4 from 6.56\u0026ndash;11.03% by day 70 (1.7-fold increase), and patient 6 (SD-CR) exhibiting IGHV3-15\u0026mdash;IGHJ6 expansion from 0.24\u0026ndash;15.35% by day 71 (64.0-fold increase). These expanded clones contracted at later timepoints (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb).\u003c/p\u003e\u003cp\u003eComparative analysis of the TCR (TRA and TRB) and BCR (IgM) repertoires across the same timepoints revealed relatively stable patterns in these populations, in contrast to the dynamic changes observed in the IgG repertoire (supplementary Fig.\u0026nbsp;3).\u003c/p\u003e\u003cp\u003eLongitudinal analysis of the Shannon-Weaver diversity index patterns revealed distinct immune dynamic signatures between the response groups. Super-responders demonstrate a monophasic recovery pattern characterized by initial decreased IgG repertoire diversity (ratio\u0026thinsp;\u0026lt;\u0026thinsp;0.9) followed by gradual restoration toward baseline levels within the first 100 days. This decrease was more pronounced in super responders (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec) than in poor responders (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed). This analysis was extended across multiple immune repertoires (TRA, TRB, and IgM), demonstrating that while similar patterns were observed in these populations, the magnitude of change was less dramatic than that in the IgG repertoire. (supplementary Fig.\u0026nbsp;4).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eRNA sequencing of the tumors and fusion gene detection\u003c/h2\u003e\u003cp\u003eUsing viable FFPE tissues, RNA transcripts were analyzed with a focus on identifying fusion genes. Patients with CR, PR, and SD were grouped as responders, and those exhibiting PD were grouped as non-responders.\u003c/p\u003e\u003cp\u003eThe analysis revealed that at least two fusion genes were detected in six of the seven responders, whereas eight of the 10 non-responders lacked fusion genes. The remaining pairs of non-responders had only one fusion gene (all responders: mean\u0026thinsp;=\u0026thinsp;6.3, range\u0026thinsp;=\u0026thinsp;0\u0026ndash;15; all non-responders: mean\u0026thinsp;=\u0026thinsp;0.2, range\u0026thinsp;=\u0026thinsp;0\u0026ndash;1). Statistically, the antitumor response to nivolumab strongly correlated with the number of fusion genes in ovarian cancer (p\u0026thinsp;=\u0026thinsp;0.0003 with Fisher's exact test, sensitivity: 86% [6/7], specificity: 100% [10/10]) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). The presence of fusion transcripts was confirmed using reverse transcription-PCR and Sanger sequencing, as exemplified by the ERI1-NCOA2 fusion junction (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAnalysis of the fusion genes across different clinical response groups revealed significant patterns associated with nivolumab treatment outcomes (supplementary table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Patients who experienced CR exhibited multiple fusion genes (five in Patient 11 and five in Patient 14), while those who experienced PR (Patient 4) showed the highest number of 15 fusion genes. Patient 6 (SD-CR) had three fusion genes, and other patients with SD demonstrated variable numbers (4 in Patient 7 and 12 in Patient 13). Notably, patients with PD harbored only one fusion gene (Patients 5 and 12).\u003c/p\u003e\u003cp\u003eSeveral recurrent fusion events were identified across different response groups: IGLV2-14-IGLL5 fusion detected Patient 11 (CR) and Patient 6 (SD-CR), GRIP1-ENSG00000256248 fusion in Patient 11 (CR), Patient 7 (SD), and Patient 13 (SD), CPLV-CHN2 fusions in Patient 14 (CR) and Patient 13 (SD), and CCDC7-C10orf68 fusions in Patient 4 (PR) and Patient 13 (SD). To validate these findings and exclude potential artifacts, we performed cDNA transcription from RNA extracts and designed specific PCR primers targeting each fusion breakpoint. The presence of the fusion transcripts was confirmed using gel electrophoresis and Sanger sequencing to establish the biological authenticity of the detected fusion genes. Analysis of validation results for the 42 fusion gene candidates revealed that 26 (61.9%) were \"validated,\" 3 (7.1%) were \"partially validated,\" 8 (19.0%) were \"not validated,\" and 5 (11.9%) were \"not validated (no band).\" The high validation rate of approximately 69% (fully or partially validated) supports the biological authenticity of the detected fusion genes and the reliability of our RNA-seq-based fusion gene detection approach. (supplementary table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe strong correlation between fusion gene number and clinical outcomes suggests that the fusion gene burden may serve as a potential predictive biomarker for nivolumab response in patients with ovarian cancer, with a higher number of fusion events associated with better treatment outcomes.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eDifferential gene expression analysis\u003c/h2\u003e\u003cp\u003eDifferential gene expression analysis was performed to compare the multiple response group classifications. Four different group comparisons were analyzed: responders (CR, PR, SD-CR, SD, n\u0026thinsp;=\u0026thinsp;7) versus non-responders (PD, n\u0026thinsp;=\u0026thinsp;8) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea), responders (CR, n\u0026thinsp;=\u0026thinsp;2) versus non-responders (PR, SD-CR, SD, PD, n\u0026thinsp;=\u0026thinsp;13) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb), responders (CR, PR, n\u0026thinsp;=\u0026thinsp;3) versus non-responders (SD-CR, SD, PD, n\u0026thinsp;=\u0026thinsp;12) (supplementary Fig.\u0026nbsp;5a), and responders (CR, PR, SD-CR, n\u0026thinsp;=\u0026thinsp;4) versus non-responders (SD, PD, n\u0026thinsp;=\u0026thinsp;11) (supplementary Fig.\u0026nbsp;5b).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTo validate the RNA sequencing data, we performed a comparative analysis with gene expression microarray data from the same patient cohort to examine the differences between patients with CR (n\u0026thinsp;=\u0026thinsp;2) and those without CR (n\u0026thinsp;=\u0026thinsp;17) [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Microarray analysis identified 22 genes that were significantly upregulated in patients with CR. RNA sequencing revealed a broad signature of 82 upregulated genes in patients with CR. Notably, eight genes were consistently identified across both platforms (BAIAP2L2, EEF1A2, EPHA7, HNF1B, MMP24, PTHLH, SAA2, and VNN1) and were highly significant (hypergeometric test, p-value\u0026thinsp;=\u0026thinsp;1.97 \u0026times; 10⁻\u0026sup1;\u0026sup2;, 89-fold enrichment over expected by chance), validating the consistency of our findings across different technological platforms (supplementary table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003ePathway analysis\u003c/h2\u003e\u003cp\u003eA comparison between responders (CR, PR, SD-CR, SD, n\u0026thinsp;=\u0026thinsp;7) and non-responders (PD, n\u0026thinsp;=\u0026thinsp;8) identified 812 upregulated genes in responders. Additionally, 1,506 genes were positively correlated with fusion gene counts (n\u0026thinsp;=\u0026thinsp;15) (supplementary table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). These two gene signatures were analyzed using KEGG, GO, and Reactome pathway analyses, which revealed distinct immunological signatures associated with the nivolumab response.\u003c/p\u003e\u003cp\u003eKEGG pathway analysis identified significant enrichment in cytokine-cytokine receptor interactions and rheumatoid arthritis pathways in responders (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec). Analysis of genes that positively correlated with fusion gene counts showed similar enrichment patterns (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed). GO enrichment analysis revealed significant biological processes associated with immune response, including leukocyte-mediated immunity, leukocyte cell-cell adhesion, and regulation of immune effector processes (supplementary Fig.\u0026nbsp;6a). Analysis of genes positively correlated with fusion gene counts demonstrated enrichment in comparable immune-related processes (supplementary Fig.\u0026nbsp;6b). Reactome pathway analysis further supports these findings by highlighting several immune-related processes. Significantly enriched pathways in responders included neutrophil degranulation, immunoregulatory interactions between lymphoid and non-lymphoid cells, and interleukin-10 signaling (supplementary Fig.\u0026nbsp;6c). Similarly, genes positively correlated with fusion gene counts were enriched in neutrophil degranulation, immunoregulatory interactions, and various cytokine signaling pathways (supplementary Fig.\u0026nbsp;6d).\u003c/p\u003e\u003cp\u003eThe consistency between the differential expression analysis and fusion gene correlation analysis in identifying similar immune-related biological processes further supports the role of immune activation in response to nivolumab treatment.\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe gene expression profiling of PBMCs collected at pre-treatment and 14 days post-treatment revealed significantly elevated expression of B cell-related genes in patients with disease control compared to non-responders. This early B cell activation signature aligns with immunological patterns observed in viral vaccination studies, where robust B cell responses typically emerge 7\u0026ndash;14 days after antigenic stimulation [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. These results suggest that B-cell reactivity approximately 2 weeks after anti-PD-1 antibody administration could serve as an early predictive biomarker of clinical benefit.\u003c/p\u003e\u003cp\u003eOur TCR and BCR repertoire analyses confirmed enhanced B-cell activity in clinical responders to nivolumab therapy. Most notably, we observed a characteristic decrease in the IgG B cell repertoire diversity among responders within 60\u0026ndash;100 days post-treatment, followed by restoration to baseline levels. This transient oligoclonal expansion pattern aligns with the findings of Nakahara et al. in patients with EGFR/ALK wild-type non-small cell lung cancer [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], where responders exhibited decreased B-cell repertoire diversity at 6 weeks post-treatment, correlating with prolonged progression-free survival. While both the T-cell and B-cell repertoires showed similar trends, the magnitude of change was substantially more pronounced in the B-cell IgG repertoires. B cells possess a greater capacity to generate and maintain antigen-specific antibodies throughout their lifespan, potentially allowing for a more distinct visualization of transient oligoclonal expansion patterns in IgG repertoires [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eA particularly novel finding was the strong correlation between the fusion gene burden and treatment response. Patients with multiple fusion genes (\u0026ge;\u0026thinsp;2) showed significantly better clinical outcomes compared to those with one or no fusion genes. This association suggests that genomic rearrangements that result in the fusion of genes may generate neoantigens that enhance immune recognition. Unlike previous studies, where tumor mutation burden or frameshift indels correlated with immunotherapy response [\u003cspan additionalcitationids=\"CR38\" citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], findings from the present study indicate that fusion genes may serve as a more relevant source of immunogenic neoantigens in ovarian cancer.\u003c/p\u003e\u003cp\u003eThe fusion genes we identified appeared to be passenger mutations rather than driver mutations, as evidenced by their diverse nature and lack of consistent oncogenic signatures. This distinction may explain why our findings contrast with the existing literature on oncogenic fusion genes. Previous studies have shown that tumors harboring driver fusion genes (such as ALK and RET) typically exhibit immunosuppressive tumor microenvironments and demonstrate limited responsiveness to anti-PD-1/PD-L1 inhibitors[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Our findings suggest that passenger fusion genes in ovarian cancer may contribute to enhanced immunogenicity and an improved response to immunotherapy. This contrasts with established driver fusion genes in ovarian cancer, such as CDKN2D-WDFY2 (20% of cases) and BCAM-AKT2 (7% of cases) in high-grade serous carcinoma [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e] and UBAP1-TGM7 (10% of clear cell cases) in clear cell carcinoma [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e], which primarily function through oncogenic pathway activation rather than immunomodulation.\u003c/p\u003e\u003cp\u003eNotably, these common driver fusion genes were not detected in the present study. According to Earp et al. [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e], the frequency of fusion genes varies significantly according to the histological subtype, with clear cell carcinomas harboring the highest number (mean: 7.4 fusions per tumor) compared to high-grade serous carcinomas (mean: 2.0 fusions per tumor), while endometrioid and mucinous subtypes contain even fewer fusions (means: 0.24 and 0.25, respectively). In our cohort, responders exhibited significantly more fusion genes (mean: 6.3, range: 0\u0026ndash;15) compared to non-responders (mean: 0.2, range: 0\u0026ndash;1), suggesting that the accumulation of passenger fusion events, rather than specific oncogenic driver fusions, may contribute to an enhanced response to immunotherapy.\u003c/p\u003e\u003cp\u003eOur pathway analyses revealed remarkable concordance between differentially expressed genes in responders and non-responders, and genes positively correlated with fusion gene counts. Both analyses consistently identified immune-related processes, including cytokine-cytokine receptor interactions, neutrophil degranulation, and immunoregulatory interactions. This significant overlap suggests that tumors with a higher fusion gene burden possess inherently greater immunogenicity, likely due to the generation of novel epitopes that are recognized by the immune system.\u003c/p\u003e\u003cp\u003eOur study has several limitations, including the small sample size (n\u0026thinsp;=\u0026thinsp;19). Previous research has demonstrated that BCR-IgG repertoires in breast cancer tissues show pronounced oligoclonality [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e] and that patients with melanoma responding to immunotherapy often exhibit more clonal TCR repertoires in pre-treatment tumor samples [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. These findings suggest that an integrated analysis incorporating both the tumor and peripheral immune compartments would provide a more robust understanding of the immunological mechanisms underlying the response to nivolumab. Future studies with larger patient cohorts and multicompartment immune profiling are needed to validate our findings and further explore the relationship between fusion gene burden, immune repertoire dynamics, and clinical outcomes. The integration of B-cell repertoire dynamics, fusion gene burden, and immune activation markers may provide a multi-dimensional approach for predicting nivolumab response in ovarian cancer patients.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eBCR, B cell receptor\u003c/p\u003e\n\u003cp\u003eCR, Complete response\u003c/p\u003e\n\u003cp\u003eFFPE, fixed paraffin-embedded\u003c/p\u003e\n\u003cp\u003eGO, Gene Ontology\u003c/p\u003e\n\u003cp\u003eIgG, Immunoglobulin G\u003c/p\u003e\n\u003cp\u003eIgM, Immunoglobulin M\u003c/p\u003e\n\u003cp\u003eKEGG, Kyoto Encyclopedia of Genes and Genomes\u003c/p\u003e\n\u003cp\u003ePD, Progressive disease\u003c/p\u003e\n\u003cp\u003ePD-1, Programmed cell death-1\u003c/p\u003e\n\u003cp\u003ePD-L, Programmed Cell Death Ligand 1\u003c/p\u003e\n\u003cp\u003ePMBCs, peripheral blood mononuclear cells\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePR, Partial response\u003c/p\u003e\n\u003cp\u003eSD, Stable disease\u003c/p\u003e\n\u003cp\u003eTCR, T cell receptor\u003c/p\u003e\n\u003cp\u003eTRA, T cell receptor alpha chain\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTRB, T cell receptor beta chain\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eConflicts of interest\u003c/h2\u003e\u003cp\u003eJH reports research grants from Eizai, Ono, Chugai, Sumitomo Pharma, and Kinopharma, and lecture fees from MSD and Eizai. RM reports research grants from Sumitomo Pharma. KY and MM reported receiving personal fees from Dumsco Inc. The other authors have no conflict of interest to declare.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eEthics approval\u003c/h2\u003e\u003cp\u003e This study was approved by the Ethics Committee of the Kyoto University Graduate School and Faculty of Medicine (approval number: R1000-3 and G531).\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003cp\u003e Written informed consent was obtained from all participants.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis work was supported by the Project for the Development of Innovative Research on Cancer Therapeutics (P-DIRECT) of the Japan Agency for Medical Research and Development (AMED) grant (15 cm0106133h0002) and the Japanese Society for the Promotion of Science (grant 17K19591 and 24K12601).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eClinical trial planning and execution: JH, MM; Patient sample biochemical analyses: RM, YH; Data analysis: RM, JH, JB; Manuscript authoring: RM, JH, JB; Contextualization of the findings of this work: RM, JH, JB, TM, RM, KY, MT, KY, and MM. All the authors have read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThis work was supported by the Project for the Development of Innovative Research on Cancer Therapeutics (P-DIRECT) of the Japan Agency for Medical Research and Development (AMED) grant (15 cm0106133h0002) and the Japanese Society for the Promotion of Science (grant 17K19591 and 24K12601).\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated and analyzed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I, Jemal A (2024) Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. 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Nat Med. 29: 3077-89. doi: 10.1038/s41591-023-02518-x\u003c/li\u003e\n\u003cli\u003eUlmer AJ, Scholz W, Ernst M, Brandt E, Flad HD (1984) Isolation and subfractionation of human peripheral blood mononuclear cells (PBMC) by density gradient centrifugation on Percoll. Immunobiology. 166: 238-50. doi: 10.1016/s0171-2985(84)80042-x\u003c/li\u003e\n\u003cli\u003eBelder N, Coskun O, Doganay Erdogan B, Ilk O, Savas B, Ensari A, Ozdag H (2016) From RNA isolation to microarray analysis: Comparison of methods in FFPE tissues. Pathol Res Pract. 212: 678-85. doi: 10.1016/j.prp.2015.11.008\u003c/li\u003e\n\u003cli\u003eMurakami R, Hamanishi J, Brown JB et al. (2021) Combination of gene set signatures correlates with response to nivolumab in platinum-resistant ovarian cancer. Sci Rep. 11: 11427. doi: 10.1038/s41598-021-91012-w\u003c/li\u003e\n\u003cli\u003eMurakami R, Matsumura N, Brown JB et al. 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Clin Cancer Res. 22: 4585-93. doi: 10.1158/1078-0432.Ccr-15-3101\u003c/li\u003e\n\u003cli\u003eOffin M, Guo R, Wu SL et al. (2019) Immunophenotype and Response to Immunotherapy of RET-Rearranged Lung Cancers. JCO Precis Oncol. 3. doi: 10.1200/po.18.00386\u003c/li\u003e\n\u003cli\u003eKannan K, Coarfa C, Rajapakshe K, Hawkins SM, Matzuk MM, Milosavljevic A, Yen L (2014) CDKN2D-WDFY2 is a cancer-specific fusion gene recurrent in high-grade serous ovarian carcinoma. PLoS Genet. 10: e1004216. doi: 10.1371/journal.pgen.1004216\u003c/li\u003e\n\u003cli\u003eKannan K, Coarfa C, Chao PW et al. (2015) Recurrent BCAM-AKT2 fusion gene leads to a constitutively activated AKT2 fusion kinase in high-grade serous ovarian carcinoma. Proc Natl Acad Sci U S A. 112: E1272-7. doi: 10.1073/pnas.1501735112\u003c/li\u003e\n\u003cli\u003eEarp MA, Raghavan R, Li Q et al. (2017) Characterization of fusion genes in common and rare epithelial ovarian cancer histologic subtypes. 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J Immunol. 169: 1829-36. doi: 10.4049/jimmunol.169.4.1829\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"cancer-immunology-immunotherapy","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ciim","sideBox":"Learn more about [Cancer Immunology, Immunotherapy](http://link.springer.com/journal/262)","snPcode":"262","submissionUrl":"https://submission.nature.com/new-submission/262/3","title":"Cancer Immunology, Immunotherapy","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"ovarian cancer, anti PD-1 antibody, B cell repertoire, fusion genes","lastPublishedDoi":"10.21203/rs.3.rs-7492478/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7492478/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e:\u003c/strong\u003e We previously reported a phase II Kyoto trial for platinum-resistant ovarian cancer (n = 20) using nivolumab (anti-programmed cell death-1 [PD-1] antibody). We evaluated the associations between clinical outcomes and transcriptomics and T and B cell clonality from tumor and blood cells.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e:\u003c/strong\u003e We analyzed gene expression microarray with pre- and post-treatment peripheral blood mononuclear cells, α- and β-chain of T cell receptor (TCR) repertoires, and immunoglobulin G (IgG) and M of B cell receptor (BCR) repertoires in 61 samples from 19 patients. Shannon-Weaver diversity scores of the TCR and BCR repertoires were compared between responders and non-responders. RNA sequencing analyzed gene expression and fusion genes in tumor samples (n=17).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e: \u003c/strong\u003eBCR repertoire analyses of post-/pre-treatment ratios in four responders (two patients with complete response (CR), one with partial response, and one with stable disease near to CR) revealed significantly decreased BCR-IgG repertoires diversity versus non-responders (Shannon-Weaver index, median 0.84 vs. 1.04, p\u0026lt;0.05); the diversity of BCR-IgG repertoires recovered over 100 days. More than two passenger fusion genes were detected in six of the seven responders, whereas eight of the ten non-responders lacked fusion genes. The antitumor response significantly correlated with the number of fusion genes (p=0.0003). Pathway analyses consistently identified immune-related processes, including cytokine-cytokine receptor interactions, neutrophil degranulation, and immunoregulatory interactions in both responders and tumors with high fusion gene counts.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e: \u003c/strong\u003eTransient oligoclonal expansion of B cells and passenger fusion genes might serve as predictive biomarkers of response to PD-1 blockade in ovarian cancer.\u003c/p\u003e","manuscriptTitle":"Oligoclonal B cell Expansion and Passenger Fusion Genes Predict Response to Nivolumab in Recurrent Ovarian Cancer: Phase II Kyoto Trial","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-11 10:13:21","doi":"10.21203/rs.3.rs-7492478/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-03T00:50:52+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-20T02:19:49+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-15T10:44:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"26142371852228815331571475676273419408","date":"2025-09-04T03:04:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"228181450020377422310460135336405449663","date":"2025-09-03T17:19:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"141830809108986752091470580890796350579","date":"2025-09-03T15:10:32+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-03T12:38:21+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-30T05:27:50+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-30T05:26:30+00:00","index":"","fulltext":""},{"type":"submitted","content":"Cancer Immunology, Immunotherapy","date":"2025-08-30T03:30:30+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"cancer-immunology-immunotherapy","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ciim","sideBox":"Learn more about [Cancer Immunology, Immunotherapy](http://link.springer.com/journal/262)","snPcode":"262","submissionUrl":"https://submission.nature.com/new-submission/262/3","title":"Cancer Immunology, Immunotherapy","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"cd8cc28a-8f2f-43f1-a6eb-ea7876b1e18e","owner":[],"postedDate":"September 11th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-02-02T16:02:00+00:00","versionOfRecord":{"articleIdentity":"rs-7492478","link":"https://doi.org/10.1007/s00262-025-04289-5","journal":{"identity":"cancer-immunology-immunotherapy","isVorOnly":false,"title":"Cancer Immunology, Immunotherapy"},"publishedOn":"2026-01-27 15:59:10","publishedOnDateReadable":"January 27th, 2026"},"versionCreatedAt":"2025-09-11 10:13:21","video":"","vorDoi":"10.1007/s00262-025-04289-5","vorDoiUrl":"https://doi.org/10.1007/s00262-025-04289-5","workflowStages":[]},"version":"v1","identity":"rs-7492478","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7492478","identity":"rs-7492478","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}