PTBP1 drives immune dysfunction and predicts immunotherapy response in metastatic triple-negative breast cancer

PTBP1 drives immune dysfunction and predicts immunotherapy response in metastatic triple-negative breast cancer | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article PTBP1 drives immune dysfunction and predicts immunotherapy response in metastatic triple-negative breast cancer Diego Marzese, Miquel Ensenyat-Mendez, Pere Llinas-Arias, Javier Orozco, and 11 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7355872/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract Triple-negative breast cancer (TNBC) is a subtype with limited treatment options and poor outcomes, particularly in the metastatic setting. Although immunotherapy has shown efficacy in early-stage disease, its benefit remains suboptimal in women with locally advanced and metastatic TNBC. Here, we identify the splicing factor PTBP1 as a tumor-intrinsic regulator of immune evasion in metastatic TNBC. By integrating clinical, single-cell, and bulk transcriptomic data with multiplex immunohistochemistry, CRISPR-Cas9 genome editing, and functional assays, we show that PTBP1 impairs antigen presentation, promotes T cell dysfunction, and is associated with worse outcomes, independent of tumor-infiltrating lymphocyte levels. Furthermore, CRISPR-mediated silencing of PTBP1 restores HLA expression and reactivates antigen presentation pathways in TNBC. PTBP1 expression is elevated in metastatic compared to primary TNBC tumors and correlates with immune dysfunction signatures. Consistently, in the phase II TONIC clinical trial, metastatic TNBC patients with PTBP1-high tumors had poor response and shorter survival following PD-1 blockade, and PTBP1 expression showed a predictive performance comparable to PD-L1 and TILs in this cohort. These findings position PTBP1 as a tumor-intrinsic regulator of immune evasion and a potential biomarker to inform immunotherapy strategies in metastatic TNBC. Biological sciences/Cancer/Breast cancer Health sciences/Oncology/Cancer/Breast cancer Immunotherapy RNA splicing metastatic breast cancer Triple-negative breast cancer immune evasion antigen presentation immune checkpoint blockade predictive biomarkers Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 BACKGROUND Triple-negative breast cancer (TNBC) represents 15–20% of all breast cancers and exhibits early relapses and a poorer survival rate in metastatic settings compared to other subtypes 1 – 3 . This is partially due to its aggressive behavior, early relapses, and the absence of actionable therapeutic targets, such as estrogen receptor (ER) or HER2. The molecular heterogeneity of this disease 4 – 7 further challenges the development of effective targeted therapies. Notably, TNBC tumors display higher levels of tumor-infiltrating lymphocytes (TILs) and elevated expression of the programmed death ligand 1 (PD-L1) compared to hormone receptor-positive tumors, making immunotherapy (IT) an increasingly attractive therapeutic alternative for patients with TNBC 8 – 11 . Consequently, phase 3 clinical trials, such as KEYNOTE-355 and KEYNOTE-522, have consistently demonstrated improved survival outcomes with chemotherapy combined with IT compared to chemotherapy alone, leading to the approval of IT in both early-stage and metastatic TNBC 8 , 12 . However, despite the promise of IT, its efficacy remains modest among patients with TNBC, especially those with advanced disease, due to tumor-intrinsic factors and host immune system-specific molecular determinants. Moreover, beyond PD-L1 expression and, to some extent, TILs, no robust biomarkers currently exist to predict IT response or benefit in this population reliably 13 . Variability in the tumor microenvironment, antigen processing and presentation capacity, neoantigen burden, and immune cell infiltration can significantly impact tumor recognition and immune-mediated killing of cancer cells 14 , 15 . Moreover, the dynamic and redundant nature of tumor adaptation suggests the existence of additional, underexplored pathways of immune escape. Different studies have described how TNBC cells that evade the immune system and metastasize to regional and distant sites often exhibit phenotypic plasticity, allowing them to adapt to hostile microenvironments and survive aggressive treatments 16 , 17 . This plasticity is partially sustained by alternative RNA splicing (AS), a process that generates functionally diverse protein variants from a single gene and is mediated by splicing factors (SFs) 18 . Alterations in AS have been extensively reported in breast cancer, where they contribute to oncogenic signaling, therapy resistance, immune evasion, and the formation of a supportive tumor microenvironment 19 , 20 . Recent studies have shown that defects in antigen presentation, such as loss of HLA-I expression or β2-microglobulin mutations, can lead to primary or acquired resistance to immune checkpoint blockade 21 . In addition, expression of major histocompatibility complex (MHC) class II molecules on tumor cells has been associated with improved response to anti-PD1/L1 therapies in HER2-negative breast cancer 22 . However, the role of SFs in regulating these pathways remains underexplored in TNBC. These observations underscore the need to efficiently identify active immune evasive mechanisms in metastatic TNBC, understand their role in modulating IT response, and how SFs drive these mechanisms. In this study, we sought to identify SFs involved in tumor-intrinsic modulation of the immune response in metastatic TNBC. Through integrative transcriptomic, spatial, and functional analyses, we identified PTBP1 as a candidate regulator associated with poor survival and features of immune evasion. PTBP1 expression was significantly higher in metastatic compared to primary tumors and correlated with transcriptional signatures of immune dysfunction across multiple datasets. In TNBC cell models, disruption of PTBP1 led to upregulation of antigen presentation machinery, including increased expression of HLA class I and II genes and elevated surface HLA protein levels. Transcriptomic analysis of longitudinal tumor biopsies from patients enrolled in the phase II adaptive TONIC clinical trial 23 further revealed that elevated PTBP1 expression was associated with reduced response to PD-1 blockade, showing a similar accuracy to other factors known to predict response to IT, such as PD-L1 and TIL density. In contrast, no associations were observed in patients with early-stage TNBC treated with IT in the I-SPY2 clinical trial. Taken together, these findings support PTBP1 as a potential regulator of immune escape and a candidate biomarker with relevance for IT stratification in metastatic TNBC. RESULTS Dysregulation of splicing factors is associated with impaired immune responses and poor survival in TNBC To investigate the role of splicing dysregulation in TNBC, we analyzed the expression of 243 genes involved in mRNA processing, comparing normal breast tissues ( n = 101) and primary TNBC tumors ( n = 95) from the TCGA cohort. This analysis revealed widespread alterations with 104 SFs significantly upregulated and 11 downregulated in TNBC tumors (p adj 0.5; Fig. 1 A). We next examined whether these dysregulated SFs might influence antitumor immunity by correlating their expression with a T cell-mediated immune response score derived from single-sample gene set enrichment analysis (ssGSEA). Importantly, we identified 15 SFs whose expression significantly correlated with this score across TCGA ( n = 95) and SCAN-B ( n = 655) cohorts, suggesting their involvement in modulating T cell activity in TNBC (p 0.30; Fig. 1 B). We additionally explored the involvement of these dysregulated SFs in TNBC clinical outcomes by assessing their association with progression-free survival (PFS) and overall survival (OS) intervals (Fig. 1 C). Interestingly, we identified one upregulated factor, PTBP1, as the one most significantly associated with both shorter PFS (Hazard Ratio [HR] = 1.61, p = 0.019) and OS (HR = 1.81, p < 0.001; Fig. 1 C). These associations were further validated by analysis of survival curves, which confirmed the risk stratification using PTBP1 levels in patient with TNBC (Fig. 1 D). Altogether, these findings suggest that PTBP1 is a potential tumor-intrinsic regulator of immune suppression, which is involved in poor outcomes and faster progression to metastatic disease. PTBP1 disruption induces transcriptomic reprogramming, enhancing antigen presentation pathways in TNBC cells To further investigate the role of PTBP1 in immune evasion, we initially selected TNBC cell lines based on PTBP1 expression from the Cancer Cell Line Encyclopedia (CCLE) project (Fig. 2 A) 24 . MDA-MB-231 and BT-549, which express high levels of PTBP1 , were chosen for functional studies. Using these models, we generated PTBP1 knockdown (KD) cell lines via short hairpin RNA (shRNA)-mediated silencing and confirmed the reduction in PTBP1 expression by qPCR (Fig. 2 B). Using flow cytometry, we determined that PTBP1 KD significantly increased the levels of MHC class I molecules on the surface of TNBC cells in both models, suggesting an enhancement of antigen presentation capability and potential facilitation of immune recognition (Fig. 2 C). To deepen our understanding of the relationship between PTBP1 expression and TNBC immune evasive phenotype, we used CRISPR-Cas9 technology to generate a PTBP1 knockout (KO) model in MDA-MB-231 cells. The selected gRNAs targeted exons 6 and 11 to produce a deletion affecting the four RNA recognition motifs of the protein ( Suppl. Figure 1A-B ). The efficiency of the CRISPR-guided disruption was assessed by qPCR (Fig. 2 D) and immunofluorescence, which, in addition to confirming a depletion of PTBP1 protein levels, showed a significant increase in HLA-ABC protein expression on TNBC cells (Fig. 2 E-F). Flow cytometry analysis further confirmed our previous results, showing a notable increase in MHC class I levels on the cancer cell surface of PTBP1-disrupted TNBC cells (p < 0.001; Fig. 2 G). Then, we used RNA sequencing (RNA-seq) to characterize global transcriptomic changes associated with PTBP1 disruption in PTBP1-KO and PTBP1 wild-type (WT) cells. First, we verified PTBP1 disruption by finding a significant downregulation of PTBP1 expression. This was validated by the deletion of the exons 6 to 11 ( Suppl. Figure 1A ). As expected, PTBP1-KO and PTBP1-WT cells presented a significantly different transcriptomic profile ( Suppl. Figure 1C ). Specifically, we identified 1,770 upregulated and 1,002 downregulated genes between the two models ( Suppl. Figure 1D-E ). Among the differentially expressed genes (DEGs), we found a significant enrichment in genes related to the cancer hallmarks, including “ evading immune destruction” , “ tumor-promoting inflammation” , and “ sustained angiogenesis” , three processes with a high impact on immune evasion and IT response (Fig. 2 H) 25 , 26 . Analysis of Kyoto Encyclopedia of Genes and Genomes (KEGG) signatures revealed that upregulated genes were enriched in several immune-related mechanisms, including “ antigen processing and presentation” among the top ten most significant pathways ( Suppl. Figure 1F ). This finding was supported by gene set enrichment analysis (GSEA), which demonstrated a significant upregulation of genes responsible for presenting antigens to immune cells (p = 0.002; Fig. 2 I). Further transcriptomic analysis revealed a significant upregulation of key components of both MHC Class I and Class II gene families in PTBP1-KO compared to WT cells (Fig. 2 J). These results indicate that PTBP1 suppresses antigen presentation in TNBC, and its disruption restores the expression of multiple MHC Class I and II molecules at both transcriptional and protein levels, potentially increasing TNBC tumor immunogenicity. Furthermore, we analyzed publicly available gene expression datasets where the PTBP1 gene had been disrupted across various cancer cell lines to validate these findings. Consistent with our results in CRISPR-Cas9-mediated KO and shRNA-mediated KD, PTBP1 silencing using a doxycycline-inducible lentiviral system led to a significant upregulation of multiple HLA genes from the MHC Class I and Class II genes in TNBC ( Suppl. Figure 2 ). Interestingly, while a partial reactivation of HLA genes was detected in a glioblastoma cell line (U251), the effect was attenuated in ER + breast cancer (MCF-7), ovarian carcinoma (A2780), hepatocellular carcinoma (HepG2), and erythroid cell lines ( Suppl. Figure 2 ). PTBP1 mediates downregulation of antigen presentation in clinical tissues We investigated the tumor-intrinsic role of PTBP1 in TNBC using single-cell RNA-seq (scRNA-seq) data from tumor biopsies of 20 patients with primary, treatment-naïve TNBC enrolled in the phase Ib/II clinical trial NCT03366844, which tested preoperative pembrolizumab and stereotactic radiotherapy 27 . Following quality control and preprocessing of the scRNA-seq data, we used canonical lineage markers to classify all cells into nine major compartments representing immune (e.g., T and NK cells, B cells, myeloid cells) and stromal populations (e.g., fibroblasts, endothelial cells), in addition to epithelial cells (Fig. 3 A). The resulting unsupervised clustering showed these cellular types and revealed epithelial subtypes, including neoplastic tumor cells and normal luminal or myoepithelial populations (Fig. 3 B-C). We then selected the epithelial population and focused our analysis on neoplastic TNBC cells, stratifying them based on PTBP1 expression levels (Fig. 3 C). Comparative analysis revealed that PTBP1-expressing tumor cells exhibited significant downregulation of immune-related pathways, including “ antigen processing and presentation ” (Fig. 3 D). Moreover, these cells had a significant decrease in the expression levels of MHC Class I and II genes (Fig. 3 E). Taken together, these findings confirm that PTBP1 modulates antigen presentation in TNBC, with evidence from in vitro models and surgical specimens supporting its role as an immune suppressive factor. PTBP1 is associated with T cell dysfunction but not with altered immune cell infiltration in TNBC After establishing that PTBP1 modulates antigen presentation pathways in vitro , we next evaluated its relationship with immune infiltration and T cell function in TNBC tumors. First, we performed multiplex immunohistochemistry (multi-IHC) on tissue samples from 60 breast cancer patients, including 44 TNBC cases (2% Stage I, 80% Stage II, and 18% Stage III; 0% Grade 1, 57% Grade 2, and 43% Grade 3, respectively). We quantified the density of CD4⁺, CD8⁺, CD20⁺, and PTBP1⁺ cells in both stromal and epithelial (PANCK⁺) compartments (Fig. 4 A). Tumors were stratified into low, medium, and high PTBP1 expression groups based on the percentage of PTBP1⁺ cells in the epithelial compartment. Notably, we found no significant differences in the density of infiltrating immune cells across PTBP1 expression groups in either the stroma or tumor nests (Fig. 4 B), suggesting that PTBP1 does not significantly impair immune cell recruitment. However, together with the pathway enrichment analysis in cell models reflecting poor antigen processing and presentation on tumor cells with high PTBP1 expression, this result raised the possibility that PTBP1 may instead modulate the functional state of infiltrating lymphocytes rather than their overall abundance. To further investigate this hypothesis, we leveraged the cell types identified in the tumor microenvironment of the biopsied profiled with scRNA-seq from the NCT03366844 trial (Fig. 3 B) 27 . We specifically focused on T cells for further detailed analysis, resolving 11 transcriptionally distinct T cell subpopulations, as previously described by Wu et al. 28 . This approach allowed us to stratify T cells according to CD4, CD8, and other markers into different subsets. CD4 + subsets included regulatory (FOXP3 + ), follicular helper (CXCL13 + ), naive/central memory (CCR7 + ), and type 1 helper effector memory T cells (IL7R + ). Among CD8 + clusters, we identified exhausted (LAG3 + ), effector (IFNG + ), and quiescent (ZFP36 + ) T cells. Additionally, two other T cell subpopulations, comprising both CD4 + and CD8 + cells, were identified: one driven by a type 1 interferon signature (IFIT1 + ) and another associated with proliferation (MKI67 + ). We also identified natural killer cells (NK, AREG + ) and NK T-like cells (FCGR3A + ; Fig. 4 C). To assess the functional state of the T cell compartment, we assigned dysfunction scores to each subset based on a validated exhaustion signature 28 . We then calculated a patient-level T cell dysfunction score by considering the relative abundance of each dysfunctional subtype (Fig. 4 D). Notably, the proportion of PTBP1-positive tumor cells (Fig. 3 C) was significantly correlated with the T cell dysfunction score (r = 0.52, p = 0.02; Fig. 4 E), suggesting that elevated PTBP1 expression is associated with a shift in the intratumoral T cell landscape toward more dysfunctional or exhausted states. We additionally examined the relationships between PTBP1 expression and the abundance of specific T cell populations. The percentage of PTBP1-positive tumor cells was not significantly correlated with the proportion of any individual cell type. However, it showed a trend towards being negatively correlated with CD4 + CCR7 T cells (r=-0.42, p = 0.06, Suppl. Figure 3 ), the subset of immune cells with the lowest dysfunctionality score, and a trend towards a positive correlation with Ki67 T cells (r = 0.41, p = 0.07, Suppl. Figure 3 ) and CD8 + LAG3 T cells (r = 0.36, p = 0.12, Suppl. Figure 3 ), two groups of cells with a high dysfunction score (Fig. 4 D). In agreement with our observations in multi-IHC, we found no significant correlation between PTBP1 expression and the proportion of TILs (r = 0.18, p = 0.23, Fig. 4 F). These results suggest that PTBP1 expression is associated with a distinct immune phenotype characterized by T cell dysfunction rather than by changes in total immune cell abundance, indicating that PTBP1 may play a role in the efficient response to immune checkpoint inhibition in TNBC tumors. PTBP1 increases with TNBC progression and is associated with immune evasion in metastatic disease Having established that PTBP1 expression is associated with an immune-evasive phenotype in TNBC, we next investigated whether its expression increases with clinical disease progression. Analysis of patients included in the TCGA and SCAN-B datasets revealed significantly higher PTBP1 expression in tumors from patients with advanced AJCC stage (TCGA: p = 0.03; SCAN-B: p 2 cm, T2/T3) compared to smaller ones (< 2 cm, T1; TCGA: p = 0.03; SCAN-B: p = 0.003; Fig. 5 B). Additionally, PTBP1 was significantly associated with nodal involvement in the SCAN-B cohort (p = 0.002), though this relationship was not observed in TCGA (p = 0.69; Fig. 5 C). Building upon these observations, we analyzed primary ( n = 18) and metastatic ( n = 34) TNBC samples from the AURORA US Metastasis Project to evaluate whether PTBP1 expression further increases in distant disease. Indeed, metastatic lesions exhibited significantly higher PTBP1 expression than matched primary tumors (p = 0.001; Fig. 5 D), reinforcing its association with advanced disease states. Since PTBP1 upregulation was linked to reduced antigen presentation in early-stage TNBC, we investigated whether it also contributes to immune dysregulation in advanced TNBC. Transcriptomic data from the AURORA cohort revealed substantial downregulation of HLA class I and II genes (Fig. 5 E), and multiple immune response pathways (Fig. 5 F), including components of the antigen processing machinery (Fig. 5 G) in metastatic tumors compared to primaries. To further evaluate the association of PTBP1 with immune dysfunction in metastatic TNBC, we applied the TIDE (Tumor Immune Dysfunction and Exclusion) framework, a validated computational method for predicting immune checkpoint blockade response based on transcriptional signatures of T cell dysfunction and exclusion 29 . Importantly, PTBP1 expression levels in metastatic TNBC were significantly and positively correlated with TIDE scores (r = 0.5, p = 0.036; Fig. 5 H), suggesting that tumors with high PTBP1 are more likely to exhibit T cell dysfunction and resistance to immune checkpoint blockade. Taken together, these findings indicate that PTBP1 expression increases during TNBC progression and metastasis and is likely linked to a transcriptional program of tumor immune dysfunction and exclusion in metastatic TNBC. PTBP1 expression associates with immunotherapy response in patients with metastatic TNBC Given the variable clinical benefit of IT across TNBC stages, we hypothesized that PTBP1 may influence treatment response. To evaluate this, we analyzed gene expression profiles of pre-treatment biopsies from patients with early-stage TNBC enrolled in the phase II I-SPY2 clinical trial (NCT01042379; n = 50) 30,31 . PTBP1 expression did not differ significantly between responders and non-responders to neoadjuvant IT (Fig. 6 A). Although negative, this finding agrees with our prior observations, indicating that while PTBP1 impairs the antigen presentation machinery in different TNBC models, its expression significantly increases in locally advanced and metastatic disease (Fig. 5 A-D). Leveraging on our findings, we examined PTBP1 expression in 53 biopsies from patients with metastatic TNBC enrolled in the phase II TONIC clinical trial (NCT02499367) randomized to receive one of five short-course induction therapies (control, irradiation, cyclophosphamide, cisplatin, or doxorubicin) followed by anti–PD-1 therapy (nivolumab) 23 . In this setting, patients with low PTBP1 expression in pre-nivolumab biopsies were significantly more likely to respond to treatment, regardless of the trial arm (p = 0.03; Fig. 6 B). This difference is similar to what has been reported in the original molecular study of this cohort for TILs and the percentage of PD-L1 in immune cells 23 . Among the TONIC trial arms, patients who received low-dose doxorubicin as induction therapy exhibited the most promising clinical objective response rate (ORR = 35%, n = 17) 23 . We therefore compared the expression levels of PTBP1 in paired tumor biopsies collected before and after induction treatments. Notably, metastatic TNBC tumors exposed to low-dose doxorubicin experienced a significant reduction in PTBP1 expression (p = 0.03; Fig. 6 C), a phenomenon not observed in any of the other induction strategies (p > 0.05; Suppl. Figure 4A ). Interestingly, this PTBP1 reduction was accompanied by increased expression of genes involved in antigen processing and presentation (p < 0.001; Suppl. Figure 4B ), a transcriptional program also enriched in tumors from patients who responded to nivolumab, regardless of the induction treatment strategy ( Suppl. Figure 4C ). To evaluate PTBP1 modulation in a controlled setting, we treated TNBC cell lines with a low-dose doxorubicin regime. Concordant with the data from tumor biopsies from patients treated in the TONIC trial, we observed a significant reduction of PTBP1 expression after treatment of the cells with this regimen (p < 0.001; Fig. 6 D). These findings suggest a role for PTBP1 as a modulator of immune evasion in metastatic TNBC and that its downregulation, whether endogenous or therapy-induced, may restore antigen presentation and sensitize tumors to IT, potentially offering both predictive and therapeutic value beyond conventional biomarkers such as TILs and PD-L1. PTBP1 may complement current immunotherapy response markers for patients with metastatic TNBC Consistent with our scRNA-seq and multi-IHC findings, showing that PTBP1 does not alter overall immune cell infiltration but instead modulates antigen presentation and T cell activation, we found no significant correlation between PTBP1 expression and CD8 + T cell density, TIL percentage, PD-L1 expression, or tumor mutational burden in metastatic biopsies from patients enrolled in the TONIC trial, either before or after induction treatment ( Suppl. Figure 5A-B ). Given this lack of association with canonical immune biomarkers, we investigated whether PTBP1 might offer independent or complementary predictive value for IT response. We observed that PTBP1 expression in pre-nivolumab biopsies showed modest predictive performance for objective response of patients with metastatic TNBC (AUC = 0.74; 95% CI: 0.53–0.95; Fig. 6 E), comparable to established IT markers, such as TILs (AUC = 0.67) and PD-L1 levels (AUC = 0.66), as reported in the original study of this cohort 23 , 32 . Moreover, combining PTBP1 levels with PD-L1 score modestly, but not significantly (p = 0.55) improved the predictive accuracy (AUC = 0.82; 95% CI: 0.66–0.99; Fig. 6 F) of PTBP1 alone. These results suggest that PTBP1 may serve as a complementary biomarker to refine IT stratification in metastatic TNBC. Importantly, patients with high PTBP1 expression in pre-IT treatment biopsies had significantly shorter PFS (HR = 2.41, p = 0.01; Fig. 6 G) and OS (HR = 2.48, p = 0.03; Fig. 6 H), supporting its potential prognostic value in the context of IT. Although based on a relatively small, unadjusted cohort, the magnitude and consistency of the effect support a potential prognostic relevance of this PTBP1 in patients with metastatic TNBC undergoing IT. To assess whether this association extends to other cancer types treated with IT, we analyzed gene expression and survival data from 625 patients with cutaneous melanoma (n = 251), urothelial carcinoma (n = 348), or glioblastoma (n = 26). In cutaneous melanoma, high PTBP1 expression significantly associated with reduced PFS (HR = 2.0, p < 0.001; Suppl. Figure 5C ). In glioblastoma, although in a small cohort, the association was even stronger (HR = 5.04, p = 0.001; Suppl. Figure 5D ) and in urothelial carcinoma, we found a non-significant trend toward worse OS in patients with higher PTBP1 levels (HR = 1.27, p = 0.07; Suppl. Figure 5E ). Together, these findings support PTBP1 as a tumor-intrinsic modulator of immune resistance across cancer types. While it does not predict response in early-stage TNBC, PTBP1 expression consistently associates with immune evasion signatures, inferior survival, and IT response failure in metastatic TNBC and other cancers, highlighting its potential as a complementary biomarker to guide IT strategies. DISCUSSION Approximately 23–33% of patients with primary TNBC will develop metastases within the first five years after diagnosis 33 , 34 . The prognosis for patients with advanced and metastatic TNBC remains dismal, with median PFS and OS below six and 17 months, respectively 35 , 36 . Although pembrolizumab has improved outcomes in metastatic TNBC with a PD-L1 combined positive score (CPS) ≥ 10, more than 60% of these patients still relapse within a year. Importantly, while other biomarkers such as TILs have shown promising potential in predicting response to IT 23 , 37 , PD-L1 remains the only clinically accepted biomarker to date. PTBP1 is a well-characterized SF with a widespread impact on mRNA processing, being able to promote exon retention or skipping and influencing gene expression programs 38 . It is involved in pro-tumorigenic processes across multiple cancer types, including the modulation of tumor immunity 39 – 41 . By integrating bulk and single-cell transcriptomics from patient samples, spatial multi-IHC profiling, and functional data across TNBC cell models, in this study, we identified that PTBP1 expression correlates with features of T cell dysfunction and transcriptional repression of antigen presentation pathways. These findings support a tumor-intrinsic immune suppressive phenotype, potentially driven by ongoing antigen exposure and chronic pro-inflammatory signaling, both of which can weaken cytotoxic T cell function 26 . Disruption of the PTBP1 gene expression in TNBC cell models correlates with a significant upregulation of HLA class I and II genes, which was supported by findings in independent cell lines and clinical scRNA-seq datasets. Interestingly, PTBP1 expression was elevated in metastatic lesions when compared to primary tumors, suggesting a potential role in immune escape during disease progression. Mechanistically, tumors from patients receiving low-dose doxorubicin, the induction arm of the TONIC trial with the most promising overall response rate, were associated with a significant reduction in PTBP1 expression. This observation was corroborated in our TNBC cell models, where doxorubicin exposure led to transcriptional downregulation of PTBP1 . Our analysis of gene expression and clinical outcomes of patients enrolled in the TONIC clinical trial showed that metastatic TNBC patients with lower PTBP1 expression following induction therapy had better response rates and longer survival after PD-1 blockade. This effect was not explained by differences in CD8 + T cell density or TIL levels, supporting the idea that PTBP1 modulates immune evasion through alternative pathways. Notably, the expression of PTBP1 alone was a good predictive feature for patients in the trial (AUC = 0.74). This performance is similar to that of two established IT predictive markers, such as TILs (AUC = 0.67) and PD-L1 levels (AUC = 0.66), reported in the original study of this cohort 23 , 32 . Recent studies show that combining gene expression with clinicopathological features can enhance survival prediction in TNBC 42 . In line with these efforts, we explored the integration of PTBP1 with PD-L1 scores and found a modest but non-significant increase in predictive accuracy (AUC = 0.82). While these findings support PTBP1 as a potentially useful feature in predictive models, our study was not specifically designed or powered to develop biomarkers for clinical use, as we previously pursued with the TNBC-ICI classifier for early-stage disease 43 . Beyond TNBC, our analysis suggests that PTBP1 may have broader relevance in immune modulation, as higher expression was associated with worse clinical outcomes in patients with melanoma, glioblastoma, and urothelial carcinoma treated with IT. These observations align with growing evidence that RNA regulatory proteins contribute to immune resistance across different cancer types 44 , 45 . However, in patients with early-stage TNBC enrolled in the I-SPY2 trial, PTBP1 expression was not associated with response to neoadjuvant IT, suggesting that its role may be context-dependent and influenced by disease stage or treatment timing. Certain limitations should be considered when interpreting these results from this study. First, although the findings were supported across multiple independent datasets (TCGA, SCAN-B, multi-IHC, AURORA US, I-SPY2, TONIC, scRNA-seq; total TNBC patients n = 969), most individual cohorts are relatively small and heterogeneous in terms of study design, treatment regimens, and data collection procedures. This limits the statistical power of some comparisons and precludes multivariate adjustment for potential confounding variables. Second, the TONIC trial includes pre-IT induction regimens (chemotherapy or radiation), which may confound the interpretation of IT-specific effects. Nevertheless, this study design resembles real-world treatment paradigms, where IT is commonly administered alongside or following chemotherapy in TNBC. Importantly, the association between PTBP1 expression and clinical outcomes was observed across all TONIC arms. Together, the consistency of these results across bulk, single-cell, and functional datasets supports the relevance of PTBP1 in metastatic TNBC, although validation in larger prospective cohorts will be necessary to determine its clinical utility. In summary, this study identifies PTBP1 as a tumor-intrinsic regulator of immune evasion and a potential biomarker of immune dysfunction and resistance to IT in metastatic TNBC. While directly targeting PTBP1 is not currently feasible due to its widespread biological roles, its expression may help guide patient stratification strategies in clinical trials, and the modulation of its downstream regulatory network could be explored for therapeutic intervention in future studies. Our findings also highlight the value of integrating splicing-related biomarkers with established immuno-oncology markers to enhance precision in patient selection for IT. Ultimately, these results support the consideration of PTBP1 in future predictive models aimed at optimizing IT stratification and advancing personalized treatment strategies for patients with metastatic TNBC. METHODS Clinical and molecular data access, collection, and curation Clinical data from The Cancer Genome Atlas – Breast Cancer (TCGA-BRCA; n = 1,006) study cohort were downloaded from the National Cancer Institute (NCI) Genomic Data Commons portal. The data were manually curated using the available pathological reports. Inclusion criteria for this study involved: Female patients 18 years of age or older with invasive ductal carcinoma, TNBC with confirmation of estrogen and progesterone receptor, and HER2 negativity (ER < 10% and HER2 0, 1, or 2 in the absence of amplification as determined by in situ hybridization). TCGA-BRCA gene expression data were downloaded using the R/Bioconductor TCGAbiolinks v2.30.4 package. TNBC specimens with a tumor purity above 60%, determined by the consensus measurement of purity estimation (CPE) 46 method ( n = 95), and normal breast tissue samples ( n = 101) from the same cohort were included in the gene expression analyses. Clinical and gene expression data from the Sweden Cancerome Analysis Network – Breast (SCAN-B; n = 8,168) study cohort, including TNBC invasive ductal carcinoma cases ( n = 655), were downloaded from the Mendeley repository (DOI: 10.17632/yzxtxn4nmd.4 ) 47 . Cell line gene expression data were downloaded from the Cancer Cell Line Encyclopedia (CCLE, TPM22Q2) 24 through the R/depmap v1.16.0 and ExperimentHub v2.10.0 packages. Gene expression datasets following PTBP1 knockdown across various cancer types were retrieved from the NCBI Gene Expression Omnibus (GEO) database. The included datasets comprised the TNBC cell line MDA-MB-231 (GSE52493, shRNA 48 ), glioblastoma cell line U251 (GSE189816, shRNA 49 ), hepatocellular carcinoma cell line HepG2 (GSE232354, siRNA), primary erythroid cell cultures (GSE106566, shRNA 50 ), hormone receptor-positive breast cancer cell line MCF-7 (GSE206142, siRNA 51 ), and ovarian carcinoma cell line A2780 (GSE52493, shRNA 48 ). Data from the AURORA US Metastasis cohort ( n = 129) comprising 52 TNBC tumors (18 primary tumors and 34 metastatic tumors) were retrieved from the NCBI GEO (GSE209998) 52 . Publicly available clinical and single-cell transcriptomic data from 36 patients enrolled in the phase Ib/II trial NCT03366844, including 140,828 analyzed cells, were retrieved from the NCBI GEO (GSE246613) 27 . We also obtained clinical and transcriptomic data from the I-SPY2 clinical trial, including 50 patients subjected to the IT neoadjuvant regimen (NCT01042379; GSE173839, GSE194040) 30 , 31 . Additionally, we included clinical and transcriptomic data from 53 patients enrolled in the TONIC trial (NCT02499367) 23 , a phase II study evaluating response to nivolumab following short-course induction treatments (two weeks of low-dose cisplatin, doxorubicin, cyclophosphamide, or irradiation) in metastatic TNBC. The list of all analyzed cohorts, including clinical specimens, can be accessed in Supplementary Table 1 . Selection of splicing-associated genes A total of 280 genes involved in RNA splicing and pre-mRNA processing were selected using the R/TIN package v1.34.0 with the dataset access code ‘splicingFactors’ 53 . Genes with an average expression below 100 RSEM-normalized counts in tumor tissues were excluded from downstream analyses. This cutoff was applied to remove genes with negligible expression, ensuring that only the most relevant and significantly expressed genes were included. Differentially expressed SFs between TNBC tumors and normal tissue were defined as an absolute log 2 FC > 0.5 and an adjusted p-value < 0.01. Cell culture conditions, CRISPR-Cas9 knockout, and shRNA knockdown experiments TNBC cell lines were cultured in RPMI 1640 medium supplemented with 10% fetal bovine serum (Fisher Scientific, Madrid, Spain) and 1% penicillin-streptomycin (ThermoFisher Scientific, Madrid, Spain). TNBC cells (MDA-MB-231 and BT-549) were obtained from ATCC and maintained in a humidified incubator at 37°C with 5% CO 2 . For low-dose doxorubicin (MedChem Express, Sollentuna, Sweden; #15142A) treatments, cells were incubated at a sublethal dose (IC10, 2 nM) for 72 h. To generate the PTBP1 -KO, a lentiviral vector containing the S. pyogenes Cas9 gene and a blasticidin resistance gene (VCAS10125; Dharmacon, Lafayette, CO, USA) was transduced into cells using lentiviral particles. Cas9-expressing cells were clonally selected and expanded. Two independent guide RNAs (gRNA; Synthego, Menlo Park, CA, USA; Supplementary Table 2 ), targeting PTBP1 exons 6 and 11, were transfected into Cas9-expressing cells during the logarithmic growth phase (50–70% confluency). Single clones were isolated by serial dilutions and expanded. For PTBP1 knockdown, shRNAs targeting PTBP1 were designed (IDT Technologies, IA, US). A control shRNA against SY14 ( S. cerevisiae ) was used as a scramble (SCR) control. The construct containing the shRNA was cloned into the pLVX-shRNA2-ZsGreen plasmid (Clontech, Mountain View, CA, USA). 10 µg of each plasmid were mixed with 7.5 µg of PPAX, 2.5 µg of PDM2, 64 µL of P3000 reagent, 40 µL of lipofectamine 3000 reagent (Invitrogen, Waltham, MA, USA; L3000001), and 1 mL of Opti-MEM (Gibco, 31985062) and incubated at room temperature (RT) for 15 minutes. The resulting mix was then transfected into HEK293 cells to produce lentiviral particles. After 48 and 72 hours, the medium containing high-titer lentiviral particles was collected and filtered through a 0.45 µm filter. 500,000 cells were seeded in six-well plates with virus-containing media and 1:1,000 polybrene solution. Then, the cells were spun down at 1,000 x g for 90 minutes at 32 ºC and maintained with the virus-containing media for 8 hours. After five passages, the cells were selected based on high ZsGreen expression using a BD FACSAria Fusion cell sorter. Immunofluorescence and confocal microscopy Cells were fixed in 4% paraformaldehyde in PBS for 10 minutes at RT, followed by permeabilization with 0.25% Triton X-100 in PBS for 10 minutes. Blocking was performed for 30 minutes at RT using 1% BSA with 22.52 mg/ml glycine in PBS-T (0.1% Tween 20) and 0.25% Triton X-100. Primary antibodies were diluted in 1% BSA in PBS-T and incubated for 1 hour at RT. The following primary antibodies were used: anti-PTBP1 (mouse, 1:200, ThermoFisher Scientific, Madrid, Spain, #32-4800) and anti-HLA-ABC (rabbit, 1:200, ThermoFisher Scientific, #PA5-98355). Secondary antibodies were incubated for 1 hour in the dark using wet chambers. These included goat anti-mouse IgG Alexa Fluor 488 (1 µg/mL; ThermoFisher Scientific, #A28175) and goat anti-rabbit IgG Alexa Fluor 568 (2 µg/mL; ThermoFisher Scientific, #A-11011). Nuclei were counterstained with DAPI (1:10,000; ThermoFisher Scientific, #62248) in PBS-T with 0.25% Triton X-100 for 5 minutes in wet chambers. Finally, cells were mounted using Fluoromount-G (ThermoFisher Scientific, #00-4958-02) and imaged using a Leica TCS SPE confocal microscope (Leica Microsystems, Wetzlar, Germany). Quantification of fluorescence intensity was performed using ImageJ. Flow cytometry workflow Cells were dissociated by scraping in ice-cold PBS 1X into a single-cell suspension. After two washes with PBS containing 0.1% BSA, cells were blocked in 1% BSA for 20 minutes. Cells were then incubated with 5 µL of anti-HLA-ABC Monoclonal Antibody (W6/32, APC conjugated, eBioscience, #17-9983-42) for 45 minutes. Following incubation, cells were washed twice with 0.1% BSA and analyzed using the FACSVerse flow cytometer (BD Biosciences, Franklin Lakes, NJ, USA). The gating strategy can be checked in Supplementary Fig. 6 . RNA-Sequencing, RT-PCR, and qPCR Total RNA was extracted using the E.Z.N.A. Total RNA Kit I (Omega Bio-Tek, Norcross, GA, USA; R6834). RNA samples with high integrity (RIN ≥ 9.5) and high purity (OD 260/280 = 1.8-2.0) were used to generate libraries using Illumina® TruSeq Stranded mRNA Library Prep (Illumina Inc., San Diego, CA, USA). mRNA libraries were sequenced on the Illumina NovaSeq 6000 platform in paired-end mode with a read length of 2 x 100bp at NIMGenetics (Madrid, Spain). An average of 62M paired-end reads was processed for each sample. Fastp software (v0.21.0) was used to trim adapters from FASTQ files, and sequences were aligned to the Homo Sapiens GRCh38 reference genome using HISAT2 (v2.2.0). Resulting alignments were sorted with Samtools (v1.10). The generated BAM files were used to assemble transcripts and genes before generating the read counts table with StringTie (v2.1.4). For qPCR experiments, RNA was retrotranscribed into cDNA using the SensiFAST cDNA Synthesis Kit (Meridian Bioscience, Cincinnati, OH, USA). Quantitative amplification was performed using SensiFAST™ SYBR No-ROX (Bioline #BIO-98005) on the CFX Opus 96 Real-Time PCR System (Bio-Rad Laboratories, Hercules, CA, USA, #12011319). Gene expression levels were normalized to SDHA using the ΔΔ-comparative quantitation method. Full list of primers can be retrieved from Supplementary Table 2. Gene expression data processing and analysis DEGs from RNA-seq raw counts were calculated using the R/Bioconductor DESeq2 v1.42.1 package. Gene set data from the Kyoto Encyclopedia of Genes and Genomes (KEGG) were sourced from the Molecular Signatures Database (MSigDB) via the R/msigdbr v7.5.1 package. GSEA and pathway enrichment analysis of DEGs were calculated using the R/clusterProfiler v4.10.1 package, and hallmark enrichment analysis was conducted following the approach described by Menyhart et al. 54 . To evaluate the activity of the T cell-mediated immune response, we selected the GO biological process “positive regulation of T cell-mediated immune response to tumor cell”. ssGSEA was performed using the R/GSVA v1.50.5 package. The gene set was obtained from the MSigDB via the R/msigdbr v7.5.1 package. For the TCGA-BRCA dataset, gene expression values were standardized using a variance-stabilizing transformation (VST) prior to ssGSEA computation. For the SCAN-B dataset, where VST could not be applied, a log2(x + 1) transformation was used instead. The TIDE score was calculated using the TIDEpy Python package in Spyder software (Python 3.11.5) 29 . Multiplex immunohistochemistry A tissue microarray including two tissue cores per patient of 60 patients with breast cancer was obtained from TissueArray.com (Derwood, MD, USA; BR1201a). Four µm-thick formalin-fixed paraffin-embedded (FFPE) tissue sections were baked at 60 ºC for 1 hour, deparaffinized with xylene, rehydrated through decreasing ethanol gradients (100%, 95%, 70%), and fixed with 10% neutral buffer formalin for 20 minutes. Antigen retrieval was performed using 1X antigen retrieval buffer, pH 6 (Opal 7-color Automation IHC kit; NEL821001KT, PerkinElmer, Waltham, MA, USA) by microwave heating. All tissue sections were blocked with the manufacturer-supplied antibody diluent/block solution for 10 minutes. Slides were first incubated with the primary antibodies, followed by incubation with a secondary antibody working solution (HRP, 1X Opal Anti-Ms + Rb HRP, Akoya Biosciences, Marlborough, MA, USA; #SKU ARH1001EA) for 10 minutes at RT, and subsequently with Opal fluorophores (PerkinElmer Opal 7 Immunology Discover Kit, 1:100 dilution) for 10 minutes at RT. The antibody/fluorophore cycling order was as follows: 1. Anti-CD4 (EPR6855; ab133616, 1:100, Abcam, Cambridge, UK), Opal 520, 2. Anti-PTBP1 (EPR9048(B), ab133734 1:00 Abcam, Cambridge, UK), Opal 620, 3. Anti-CD8 (SP57, #7904460, ND, Roche Diagnostics, Indianapolis, USA), Opal 540, 4. Anti-CD20 (EP459Y, ab78237, 1:100, Abcam, Cambridge), Opal 570, and 5. Anti-pan Keratin (PANCK, AE1/AE3/PCK26; #7602595, ND, Roche Diagnostics, Indianapolis, USA), Opal 690. Finally, nuclei were counterstained with DAPI for 3 minutes at RT. Slides were imaged using the Mantra Quantitative Pathology Workstation and analyzed using inForm v2.4 software (Akoya Biosciences). Cell classification and tumor/stromal tissue stratification categorization, based on proximity to PANCK expression-positive cells, were performed using QuPath v0.4.3 55 . After machine learning-based cell classification, hematoxylin and eosin-stained tissues were used to correct misclassified tumor versus stromal regions. Single-cell RNA-seq analysis For the Shiao database 27 , data were processed using the standard R/Seurat v5.2.1 pipeline 56 . Briefly, quality control steps were performed to remove low-quality cells, and a total of 47,894 cells from treatment-naïve biopsies were retained for downstream analyses. After quality control, the data were log-normalized, scaled, and the most variable genes were identified. Canonical marker genes were used to determine cell types, following the methodology outlined by Wu et al. 28 . Malignant and normal epithelial cells were selected based on copy number variation (CNV) profiles inferred using the inferCNV algorithm. Tumors containing fewer than 50 cancer cells were excluded from the study. The FindMarkers function was used to identify DEGs (p adj 0.2) between PTBP1-positive and PTBP1-negative groups. Enrichment analyses were performed using the enrichKEGG function from the R/clusterprofiler package 57 . The T cell dysfunction score for each case was calculated by evaluating the percentage of immune cell subsets, using the original scoring methodology 28 . Data visualization and management For data representation, the R/ggplot2 v.3.5.1, the R/pheatmap v.1.0.12, and R/ggpubr v.0.6.0 packages were used. Forest plots were generated using the R/forestploter v1.1.2 package. Sashimi plots were produced using the Integrative Genomics Viewer (IGV) with the Sashimi Plot function. The PTBP1 protein structure was generated using the AF-P265299-F1 AlphaFold predicted structure and visualized using the RCSB-PDB tool ( https://www.rcsb.org/3d-view ). Principal component analysis (PCA) was performed using the R/M3C v1.24.0 package, and heatmaps were created using the R/gplots package v3.1.3.1 to visualize hierarchical clustering using the Euclidean metric distances between gene expression profiles. For single-cell transcriptomic data visualizations, the R/Seurat v5.2.1 and R/scCustomize v3.0.1 packages were used. The R/ROCR v1.0-11 package was employed to compute the Receiver Operating Curves (ROC) and the corresponding AUC values. The R/tidyverse v2.0.0 package was used for data manipulation. Statistics and reproducibility The distribution of data for each variable was assessed for normality using the Shapiro-Wilk test. Unless otherwise specified, the two-sided Wilcoxon rank sum test was applied to evaluate the statistical significance of differences in non-parametric variables, whereas parametric variables were analyzed using the two-sided Student’s t-test. The Kruskal-Wallis test was used for comparison between multiple groups for non-parametric variables. When necessary, p-values were adjusted for multiple comparisons using the Benjamini-Hochberg (BH) procedure to control the false discovery rate and reduce the likelihood of Type I errors. For paired data, the paired Student’s t-test was applied. Correlations between continuous variables were calculated using either Spearman’s rank correlation ( rho ) coefficient for non-normally distributed data or Pearson’s correlation coefficient (r) for variables following a normal distribution, as determined by the Shapiro-Wilk test. Kaplan-Meier curves were generated using the Kaplan-Meier plotter tool 58 , and the log-rank test was applied to assess the statistical significance of differences in survival rates. Unless otherwise specified, all computational analyses were performed using R software (v.4.3.3). To correct PTBP1 using PD-L1, PTBP1 expression was scaled to a uniform scale from 1 to 10, and this score was doubled when the PD-L1 percentage was below 5%. Declarations Acknowledgements This work was supported by the Instituto de la Salud Carlos III (ISCIII) AES2022 (#PI22/01496), co-funded by the European Union, and the Sara Borrell project (#CD22/00026), the Fundación CONTIGO Contra el Cáncer de la Mujer (#MERIT project), the Institut d’Investigació Sanitària Illes Balears Financiación Grupos Emergentes (INSE) program, the Servei d'Ocupació de les Illes Balears (SOIB) Jove Qualificats program, and the Scientific Foundation of the Spanish Association Against Cancer – Illes Balears. Flow cytometry, cell sorting, and cell culture experiments were performed with the support of the Cytometry and Cell Culture Core Facility at the Institut d’Investigació Sanitària Illes Balears (IdISBa). Author Contributions (CRediT taxonomy) Conceptualization: D.M.M., J.I.J.O., Methodology: M.E.M., J.I.J.O., P.L.A., D.M.M., Investigation: P.L.A., A.M.P., S.I.M. (CRISPR and shRNA experiments); B.V., J.I.J.O. (multiplex IHC); M.E.M., J.I.J.O. (multiplex IHC image analysis), Formal analysis: M.E.M., A.F.B.L., M.P.S. (bioinformatics, RNA-seq processing, public dataset interrogation); M.E.M., A.F.B.L. (statistical analysis, data visualization), Data curation: M.E.M., A.F.B.L., M.P.S., Resources: M.K., D.M.M., J.I.J.O. (TONIC trial compliance, data transfer agreements), Visualization: M.E.M., A.F.B.L. (figures and tables), Validation: P.G.E. (immune biomarker interpretation); S.G.M., M.G., J.C., M.K., M.L.D. (clinical interpretation and translational contextualization), Supervision: D.M.M., J.I.J.O., Project administration: D.M.M., Writing the original draft: M.E.M., J.I.J.O., P.L.A., A.F.B.L., S.I.M., D.M.M., Writing, reviewing & editing: All authors. Competing interests The authors declare no competing interests. Ethics Statement All publicly available cohort data analyzed in this study were collected under the respective institutional review board (IRB) approvals, in accordance with human subjects protection and data access policies, and with written informed consent from all participants. All samples were de-identified and coded according to the Health Insurance Portability and Accountability Act (HIPAA) guidelines. All datasets were obtained from retrospective databases, and patients were recruited at their host institutions. Data from patients enrolled in the TONIC clinical trial were included in a de-identified manner under Data Transfer Agreement 10680 DTA 15112019, linked to IRBd19-233. Based on the documents registered on 18 September 2019 in IRB ART (IRBd19-233), the Netherlands Cancer Institute – Antoni van Leeuwenhoek (NKI-AVL) IRB determined that the project entitled RNA splicing factors and the association with response to PD-1 blockade in patients with metastatic triple-negative breast cancer treated in the TONIC trial does not meet the criteria of the Medical Research Involving Human Subjects Act (WMO) and issued a non-WMO statement. Materials & Correspondence Correspondence and requests for materials should be addressed to D.M.M. Data availability RNA-seq data of CRISPR–Cas9 models generated in this study are available at the European Bioinformatics Institute (ArrayExpress) under accession number E-MTAB-15284 . All other data sets used are publicly available and referenced in the Methods section ( Supplementary Table 1 ). Code availability No custom code was generated for this project. 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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-7355872","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":503675108,"identity":"5d4f5622-cf4f-4181-b2a6-ddb5c39aeeb5","order_by":0,"name":"Diego Marzese","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA90lEQVRIiWNgGAWjYDCCwyi8CmYGCRDNQ7yWM8RoOYDMYWwjQgvfcebDLz4wbJM37z98+MPHedaJM9sbGB+8bcOtRfIwW5rlDIbbhnMOHEuTnLktPXE2zwFmw7l4tBgc5jEz5mG4zTiDsceMmXfb4cR5Egls0rx4tfB/A2mxn8HM//nz3zlALfIP2H/j18LD/BioJXEGGw+DNGPD4cTZEgxszPi0AP1ixjjD4HbyDB42M8meY+nGM3sSmyXnnMOthe/84ccfPlTctp3BD2T8qLGWnXH88MEPb8pwawECNgkGAxQBxga86oGA+QMhFaNgFIyCUTDCAQAkQlJdTev8qgAAAABJRU5ErkJggg==","orcid":"","institution":"Duke University School of Medicine","correspondingAuthor":true,"prefix":"","firstName":"Diego","middleName":"","lastName":"Marzese","suffix":""},{"id":503675109,"identity":"e8fd3949-f78a-451e-bb83-3eaa82d41f55","order_by":1,"name":"Miquel Ensenyat-Mendez","email":"","orcid":"","institution":"Duke University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Miquel","middleName":"","lastName":"Ensenyat-Mendez","suffix":""},{"id":503675110,"identity":"9f726831-30b9-413c-b049-6a317dfe93c7","order_by":2,"name":"Pere Llinas-Arias","email":"","orcid":"","institution":"Cancer Epigenetics Laboratory at the Cancer Cell Biology Group, Health Research Institute of the Balearic Islands (IdISBa), 07120 Palma, Spain","correspondingAuthor":false,"prefix":"","firstName":"Pere","middleName":"","lastName":"Llinas-Arias","suffix":""},{"id":503675111,"identity":"062055e4-d99f-4907-9bab-0cdf074ef243","order_by":3,"name":"Javier Orozco","email":"","orcid":"https://orcid.org/0000-0003-2585-358X","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Javier","middleName":"","lastName":"Orozco","suffix":""},{"id":503675112,"identity":"26fbce86-f180-4035-8b8e-ef3c5585dee3","order_by":4,"name":"Andres Bedoya-López","email":"","orcid":"","institution":"Health Research Institute of the Balearic Islands (IdISBa)","correspondingAuthor":false,"prefix":"","firstName":"Andres","middleName":"","lastName":"Bedoya-López","suffix":""},{"id":503675113,"identity":"5b693aef-e9a2-4ede-9452-c8c7afed6d30","order_by":5,"name":"Ayla Manughian-Peter","email":"","orcid":"","institution":"University of California-Irvine","correspondingAuthor":false,"prefix":"","firstName":"Ayla","middleName":"","lastName":"Manughian-Peter","suffix":""},{"id":503675114,"identity":"6148e518-ca07-4550-9478-25a3db068534","order_by":6,"name":"Betsy Valdez","email":"","orcid":"","institution":"Providence Saint John's Health Center","correspondingAuthor":false,"prefix":"","firstName":"Betsy","middleName":"","lastName":"Valdez","suffix":""},{"id":503675115,"identity":"fe8f7b6b-b9c5-422e-b48d-4899f341dfe6","order_by":7,"name":"Sandra Íñiguez-Muñoz","email":"","orcid":"","institution":"Health Research Institute of the Balearic Islands (IdISBa)","correspondingAuthor":false,"prefix":"","firstName":"Sandra","middleName":"","lastName":"Íñiguez-Muñoz","suffix":""},{"id":503675116,"identity":"70a44942-7992-47e4-9962-c5af25bd2619","order_by":8,"name":"Paula Gonzalez-Ericsson","email":"","orcid":"https://orcid.org/0000-0002-6292-6963","institution":"Vanderbilt-Ingram Cancer Center","correspondingAuthor":false,"prefix":"","firstName":"Paula","middleName":"","lastName":"Gonzalez-Ericsson","suffix":""},{"id":503675117,"identity":"f0a1b9b4-e0eb-4bab-9c4f-93880c4db057","order_by":9,"name":"Matthew Salomon","email":"","orcid":"","institution":"Keck School of Medicine, University of Southern California","correspondingAuthor":false,"prefix":"","firstName":"Matthew","middleName":"","lastName":"Salomon","suffix":""},{"id":503675118,"identity":"81768184-6e80-4eb3-acd8-4cd784828058","order_by":10,"name":"Silvia González-Martínez","email":"","orcid":"","institution":"Ramón y Cajal Health Research Institute (IRYCIS)","correspondingAuthor":false,"prefix":"","firstName":"Silvia","middleName":"","lastName":"González-Martínez","suffix":""},{"id":503675119,"identity":"324b98ca-2262-4000-8648-4699f60b570e","order_by":11,"name":"María Gion","email":"","orcid":"","institution":"Hospital Universitario Ramón y Cajal","correspondingAuthor":false,"prefix":"","firstName":"María","middleName":"","lastName":"Gion","suffix":""},{"id":503675120,"identity":"99689fd9-c12e-48d6-a389-a99cb29a2082","order_by":12,"name":"Javier Cortés","email":"","orcid":"https://orcid.org/0000-0001-7623-1583","institution":"IBCC, Pangaea Oncology, Quiron Group; Medica Scientia Innovation Research (MedSIR); Universidad Europea de Madrid; IOB Madrid, Hospital Beata María Ana; Hospital Universitario","correspondingAuthor":false,"prefix":"","firstName":"Javier","middleName":"","lastName":"Cortés","suffix":""},{"id":503675121,"identity":"588094a2-f5a2-45ad-8284-0393b31211c8","order_by":13,"name":"Marleen Kok","email":"","orcid":"https://orcid.org/0000-0001-9043-9815","institution":"NKI","correspondingAuthor":false,"prefix":"","firstName":"Marleen","middleName":"","lastName":"Kok","suffix":""},{"id":503675122,"identity":"78155f76-ea4b-4e9b-9f2e-8adf23ac887e","order_by":14,"name":"DiNome Maggie","email":"","orcid":"https://orcid.org/0000-0002-1926-292X","institution":"Department of Surgery, Duke University School of Medicine, Durham, NC, USA","correspondingAuthor":false,"prefix":"","firstName":"DiNome","middleName":"","lastName":"Maggie","suffix":""}],"badges":[],"createdAt":"2025-08-12 12:45:33","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7355872/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7355872/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":91199320,"identity":"e97bd34a-22b4-41f8-8d70-8c54c9707295","added_by":"auto","created_at":"2025-09-12 15:19:13","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":627517,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003ePTBP1\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e is overexpressed in TNBC and associated with poor clinical outcomes. A)\u003c/strong\u003eVolcano plot displaying upregulated (\u003cem\u003en\u003c/em\u003e=104) and downregulated (\u003cem\u003en\u003c/em\u003e=11) splicing factors (SFs) in TNBC compared to normal breast tissue. \u003cstrong\u003eB)\u003c/strong\u003eSpearman’s \u003cem\u003erho\u003c/em\u003e correlation of \u003cem\u003ePTBP1\u003c/em\u003e expression with a T cell-mediated immune response score from ssGSEA in TCGA (y-axis; \u003cem\u003en\u003c/em\u003e=95) and SCAN-B (x-axis,\u003cem\u003e n\u003c/em\u003e=655). \u003cstrong\u003eC)\u003c/strong\u003e Forest plot showing progression-free survival (PFS; left) and overall survival (OS; right) for the 15 SFs selected based on their correlation with T cell–mediated immune responses in both cohorts. \u003cstrong\u003eD)\u003c/strong\u003e Kaplan-Meier plot representing the PFS (left) and OS (right) in patients expressing higher and lower levels of \u003cem\u003ePTBP1\u003c/em\u003e.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7355872/v1/2219465ef2d6f5eee6fb417a.png"},{"id":91199317,"identity":"68658eb0-99ca-4d5f-a2b0-810a65849ba4","added_by":"auto","created_at":"2025-09-12 15:19:13","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":460425,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePTBP1 disruption restores antigen presentation via HLA class I and II gene upregulation in TNBC cell models. A) \u003c/strong\u003e\u003cem\u003ePTBP1\u003c/em\u003e mRNA expression in breast cancer cell lines from the Cancer Cell Line Encyclopedia, stratified by breast cancer subtype. \u003cstrong\u003eB)\u003c/strong\u003e \u003cem\u003ePTBP1\u003c/em\u003e mRNA expression assessed by qPCR in MDA-MB-231 and BT-549 PTBP1-KD models. \u003cstrong\u003eC)\u003c/strong\u003e HLA protein expression in the membrane of PTBP1-KD models, assessed by flow cytometry. \u003cstrong\u003eD)\u003c/strong\u003e \u003cem\u003ePTBP1\u003c/em\u003e mRNA expression assessed by qPCR in MDA-MB-231 PTBP1-KO model. \u003cstrong\u003eE-F)\u003c/strong\u003e Immunofluorescence displaying PTBP1 depletion and HLA protein overexpression in MDA-MB-231 PTBP1-KO cells. \u003cstrong\u003eG)\u003c/strong\u003e HLA protein expression in the membrane of the PTBP1-KO model, assessed by flow cytometry. \u003cstrong\u003eH)\u003c/strong\u003e Radar plot displaying the enrichment of different cancer hallmarks using the differentially expressed genes between PTBP1-KO and PTBP1-WT cells. \u003cstrong\u003eI) \u003c/strong\u003eGSEA highlighting the upregulation of antigen processing and presentation genes in the KO model. \u003cstrong\u003eJ)\u003c/strong\u003e Log\u003csub\u003e2\u003c/sub\u003e fold change of MHC Class I and Class II genes in PTBP1-KO TNBC cells compared to PTBP1-WT.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7355872/v1/2346412c0ea8f939575c5db5.png"},{"id":91200534,"identity":"94d38197-ccf1-4688-aaf4-4d3a69a7e2a2","added_by":"auto","created_at":"2025-09-12 15:27:14","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":485363,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSingle-cell transcriptomic analysis of primary TNBC tumors reveals tumor-intrinsic PTBP1-associated suppression of antigen processing and presentation pathways. A) \u003c/strong\u003eLog-normalized expression of canonical markers for cluster annotation, from scRNA-seq data of 20 primary TNBC tumors. \u003cstrong\u003eB) \u003c/strong\u003eUMAP visualization of the nine major cell populations, with cluster labels based on cell types, determined using canonical markers. \u003cstrong\u003eC) \u003c/strong\u003eUMAP representation displaying the \u003cem\u003ePTBP1\u003c/em\u003e expression of tumor epithelial cells. \u003cstrong\u003eD)\u003c/strong\u003e GSEA comparing PTBP1-positive to PTBP1-negative tumor cells demonstrated a significant downregulation of immune-related pathways, notably those involved in antigen processing and presentation. \u003cstrong\u003eE) \u003c/strong\u003eLog\u003csub\u003e2 \u003c/sub\u003efold change of MHC Class I and Class II genes in PTBP1-positive compared to PTBP1-negative TNBC cells\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7355872/v1/7324e0e738ff7ab1d0d35647.png"},{"id":91200540,"identity":"34243747-b6fb-4e59-87d0-69f515eb08b2","added_by":"auto","created_at":"2025-09-12 15:27:14","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":794603,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eUpregulation of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003ePTBP1\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e is associated with T cell dysfunction but not with variations in the immune infiltration landscape. A) \u003c/strong\u003eRepresentative images of the multi-immunohistochemistry, assessing expression of CD4, CD8, CD20, PTBP1, and PANCK in TNBC tissues. \u003cstrong\u003eB) \u003c/strong\u003eNumber of CD4 (left), CD8 (middle), and CD20-positive cells (right) per mm\u003csup\u003e2 \u003c/sup\u003ein stroma (top) and tumor tissue (bottom) in tumors expressing low, medium, or high levels of PTBP1. PTBP1 categories were selected according to the percentage of PTBP1-expressing tumor cells and stratified into tertiles. \u003cstrong\u003eC) \u003c/strong\u003eHeatmap showing the expression of canonical markers in each of the 11 identified immune subpopulations from scRNA-seq data. \u003cstrong\u003eD)\u003c/strong\u003e Distribution of the 11 immune subpopulations in each patient, and representation of the normalized dysfunction score and percentage of PTBP1-positive cancer cells. \u003cstrong\u003eE-F)\u003c/strong\u003e Correlation of the percentage of PTBP1-positive cancer cells with \u003cstrong\u003e(E) \u003c/strong\u003ethe T cell dysfunction score and \u003cstrong\u003e(F)\u003c/strong\u003e TIL percentage in each patient.\u0026nbsp;\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7355872/v1/d982663a403e7c2cda090d5a.png"},{"id":91201562,"identity":"bdde508d-b7ce-4293-8d30-f202122f8ab0","added_by":"auto","created_at":"2025-09-12 15:43:14","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":454666,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003ePTBP1\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e expression increases with TNBC progression and correlates with immune evasion in metastatic disease. A) \u003c/strong\u003e\u003cem\u003ePTBP1\u003c/em\u003e expression in Stage I and Stage II-III TNBC tumors in the TCGA (left) and SCAN-B (right) cohorts. \u003cstrong\u003eB)\u003c/strong\u003e \u003cem\u003ePTBP1\u003c/em\u003e expression in T1 and T2-T3 TNBC tumors in the TCGA (left) and SCAN-B (right) cohorts. \u003cstrong\u003eC)\u003c/strong\u003e \u003cem\u003ePTBP1\u003c/em\u003e expression in tumors with and without node-positive TNBC disease in the TCGA (left) and SCAN-B (right) cohorts. \u003cstrong\u003eD)\u003c/strong\u003e \u003cem\u003ePTBP1\u003c/em\u003e expression in primary (\u003cem\u003en\u003c/em\u003e=18) and metastatic (\u003cem\u003en\u003c/em\u003e=34) TNBC tumors in the AURORA US cohort. \u003cstrong\u003eE)\u003c/strong\u003e Log\u003csub\u003e2 \u003c/sub\u003efold change of MHC Class I and Class II genes in metastatic TNBC tumors compared to primary tumors from the AURORA US cohort. \u003cstrong\u003eF) \u003c/strong\u003ePathway enrichment analysis of genes downregulated in metastatic TNBC compared to primary tumors in the AURORA US cohort. \u003cstrong\u003eG) \u003c/strong\u003eGSEA analysis showing the downregulation of antigen processing and presentation genes in metastatic TNBC. \u003cstrong\u003eH) \u003c/strong\u003eCorrelation of the PTBP1 expression in the AURORA US cohort with the Tumor Immune Dysfunction and Exclusion (TIDE) score.\u0026nbsp;\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7355872/v1/bd4eb7af992cfe9312db8cab.png"},{"id":91200865,"identity":"4756ed01-0c16-43ed-a0c5-a9c75d4cf2eb","added_by":"auto","created_at":"2025-09-12 15:35:14","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":383858,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePTBP1 predicts immunotherapy response and survival in metastatic TNBC. A) \u003c/strong\u003ePTBP1 expression in early-stage TNBC patients treated with IT during the I-SPY2 clinical trial, stratified by response status.\u003cstrong\u003e B) \u003c/strong\u003ePre-nivolumab \u003cem\u003ePTBP1\u003c/em\u003e expression in metastatic TNBC patients from the TONIC clinical trial, stratified by response to nivolumab. \u003cstrong\u003eC) \u003c/strong\u003e\u003cem\u003ePTBP1\u003c/em\u003e expression before and after doxorubicin induction. \u003cstrong\u003eD) \u003c/strong\u003e\u003cem\u003ePTBP1\u003c/em\u003e expression in TNBC cells treated with vehicle and sublethal dose of doxorubicin (2 nM, 72 h). \u003cstrong\u003eE-F) \u003c/strong\u003eROC Curves displaying the Area Under the Curve (AUC) for the prediction of response to IT of pre-nivolumab \u003cstrong\u003e(E)\u003c/strong\u003e \u003cem\u003ePTBP1\u003c/em\u003e expression and \u003cstrong\u003e(F) \u003c/strong\u003e\u003cem\u003ePTBP1\u003c/em\u003eexpression corrected by PD-L1. \u003cstrong\u003eG-H) \u003c/strong\u003eKaplan-Meier plot highlighting the association of \u003cem\u003ePTBP1\u003c/em\u003e expression with \u003cstrong\u003e(G)\u003c/strong\u003e progression-free survival (HR=2.41) and \u003cstrong\u003e(H)\u003c/strong\u003e overall survival (HR=2.48).\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7355872/v1/4ccc43e956b117ec00f2881c.png"},{"id":91332547,"identity":"74175845-9881-4485-a66b-96068f0f8464","added_by":"auto","created_at":"2025-09-15 11:12:16","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5030704,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7355872/v1/d47120bd-7f5d-4abd-b98f-994266226b6d.pdf"},{"id":91200532,"identity":"6d2b26e5-5444-4681-ac1f-4036885ea5e1","added_by":"auto","created_at":"2025-09-12 15:27:14","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1759419,"visible":true,"origin":"","legend":"Supplementary Figures and Tables","description":"","filename":"EnsenyatMendezetalSupplementaryFiguresandTables.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7355872/v1/ada220e6467ef70b48ac83f7.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"PTBP1 drives immune dysfunction and predicts immunotherapy response in metastatic triple-negative breast cancer","fulltext":[{"header":"BACKGROUND","content":"\u003cp\u003eTriple-negative breast cancer (TNBC) represents 15\u0026ndash;20% of all breast cancers and exhibits early relapses and a poorer survival rate in metastatic settings compared to other subtypes\u003csup\u003e\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. This is partially due to its aggressive behavior, early relapses, and the absence of actionable therapeutic targets, such as estrogen receptor (ER) or HER2. The molecular heterogeneity of this disease\u003csup\u003e\u003cspan additionalcitationids=\"CR5 CR6\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e further challenges the development of effective targeted therapies. Notably, TNBC tumors display higher levels of tumor-infiltrating lymphocytes (TILs) and elevated expression of the programmed death ligand 1 (PD-L1) compared to hormone receptor-positive tumors, making immunotherapy (IT) an increasingly attractive therapeutic alternative for patients with TNBC\u003csup\u003e\u003cspan additionalcitationids=\"CR9 CR10\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Consequently, phase 3 clinical trials, such as KEYNOTE-355 and KEYNOTE-522, have consistently demonstrated improved survival outcomes with chemotherapy combined with IT compared to chemotherapy alone, leading to the approval of IT in both early-stage and metastatic TNBC\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eHowever, despite the promise of IT, its efficacy remains modest among patients with TNBC, especially those with advanced disease, due to tumor-intrinsic factors and host immune system-specific molecular determinants. Moreover, beyond PD-L1 expression and, to some extent, TILs, no robust biomarkers currently exist to predict IT response or benefit in this population reliably\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Variability in the tumor microenvironment, antigen processing and presentation capacity, neoantigen burden, and immune cell infiltration can significantly impact tumor recognition and immune-mediated killing of cancer cells\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Moreover, the dynamic and redundant nature of tumor adaptation suggests the existence of additional, underexplored pathways of immune escape. Different studies have described how TNBC cells that evade the immune system and metastasize to regional and distant sites often exhibit phenotypic plasticity, allowing them to adapt to hostile microenvironments and survive aggressive treatments\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. This plasticity is partially sustained by alternative RNA splicing (AS), a process that generates functionally diverse protein variants from a single gene and is mediated by splicing factors (SFs)\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Alterations in AS have been extensively reported in breast cancer, where they contribute to oncogenic signaling, therapy resistance, immune evasion, and the formation of a supportive tumor microenvironment\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Recent studies have shown that defects in antigen presentation, such as loss of HLA-I expression or β2-microglobulin mutations, can lead to primary or acquired resistance to immune checkpoint blockade\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. In addition, expression of major histocompatibility complex (MHC) class II molecules on tumor cells has been associated with improved response to anti-PD1/L1 therapies in HER2-negative breast cancer\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. However, the role of SFs in regulating these pathways remains underexplored in TNBC. These observations underscore the need to efficiently identify active immune evasive mechanisms in metastatic TNBC, understand their role in modulating IT response, and how SFs drive these mechanisms.\u003c/p\u003e\u003cp\u003eIn this study, we sought to identify SFs involved in tumor-intrinsic modulation of the immune response in metastatic TNBC. Through integrative transcriptomic, spatial, and functional analyses, we identified PTBP1 as a candidate regulator associated with poor survival and features of immune evasion. \u003cem\u003ePTBP1\u003c/em\u003e expression was significantly higher in metastatic compared to primary tumors and correlated with transcriptional signatures of immune dysfunction across multiple datasets. In TNBC cell models, disruption of PTBP1 led to upregulation of antigen presentation machinery, including increased expression of HLA class I and II genes and elevated surface HLA protein levels. Transcriptomic analysis of longitudinal tumor biopsies from patients enrolled in the phase II adaptive TONIC clinical trial\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e further revealed that elevated \u003cem\u003ePTBP1\u003c/em\u003e expression was associated with reduced response to PD-1 blockade, showing a similar accuracy to other factors known to predict response to IT, such as PD-L1 and TIL density. In contrast, no associations were observed in patients with early-stage TNBC treated with IT in the I-SPY2 clinical trial. Taken together, these findings support PTBP1 as a potential regulator of immune escape and a candidate biomarker with relevance for IT stratification in metastatic TNBC.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eDysregulation of splicing factors is associated with impaired immune responses and poor survival in TNBC\u003c/h2\u003e\u003cp\u003eTo investigate the role of splicing dysregulation in TNBC, we analyzed the expression of 243 genes involved in mRNA processing, comparing normal breast tissues (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;101) and primary TNBC tumors (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;95) from the TCGA cohort. This analysis revealed widespread alterations with 104 SFs significantly upregulated and 11 downregulated in TNBC tumors (p\u003csub\u003eadj\u003c/sub\u003e\u0026lt;0.01; absolute log\u003csub\u003e2\u003c/sub\u003e fold change [FC]\u0026thinsp;\u0026gt;\u0026thinsp;0.5; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). We next examined whether these dysregulated SFs might influence antitumor immunity by correlating their expression with a T cell-mediated immune response score derived from single-sample gene set enrichment analysis (ssGSEA). Importantly, we identified 15 SFs whose expression significantly correlated with this score across TCGA (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;95) and SCAN-B (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;655) cohorts, suggesting their involvement in modulating T cell activity in TNBC (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, absolute \u003cem\u003erho\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.30; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eWe additionally explored the involvement of these dysregulated SFs in TNBC clinical outcomes by assessing their association with progression-free survival (PFS) and overall survival (OS) intervals (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). Interestingly, we identified one upregulated factor, PTBP1, as the one most significantly associated with both shorter PFS (Hazard Ratio [HR]\u0026thinsp;=\u0026thinsp;1.61, p\u0026thinsp;=\u0026thinsp;0.019) and OS (HR\u0026thinsp;=\u0026thinsp;1.81, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). These associations were further validated by analysis of survival curves, which confirmed the risk stratification using PTBP1 levels in patient with TNBC (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). Altogether, these findings suggest that PTBP1 is a potential tumor-intrinsic regulator of immune suppression, which is involved in poor outcomes and faster progression to metastatic disease.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003ePTBP1 disruption induces transcriptomic reprogramming, enhancing antigen presentation pathways in TNBC cells\u003c/h3\u003e\n\u003cp\u003eTo further investigate the role of PTBP1 in immune evasion, we initially selected TNBC cell lines based on \u003cem\u003ePTBP1\u003c/em\u003e expression from the Cancer Cell Line Encyclopedia (CCLE) project (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA)\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. MDA-MB-231 and BT-549, which express high levels of \u003cem\u003ePTBP1\u003c/em\u003e, were chosen for functional studies. Using these models, we generated PTBP1 knockdown (KD) cell lines via short hairpin RNA (shRNA)-mediated silencing and confirmed the reduction in \u003cem\u003ePTBP1\u003c/em\u003e expression by qPCR (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Using flow cytometry, we determined that PTBP1 KD significantly increased the levels of MHC class I molecules on the surface of TNBC cells in both models, suggesting an enhancement of antigen presentation capability and potential facilitation of immune recognition (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTo deepen our understanding of the relationship between \u003cem\u003ePTBP1\u003c/em\u003e expression and TNBC immune evasive phenotype, we used CRISPR-Cas9 technology to generate a PTBP1 knockout (KO) model in MDA-MB-231 cells. The selected gRNAs targeted exons 6 and 11 to produce a deletion affecting the four RNA recognition motifs of the protein (\u003cb\u003eSuppl. Figure\u0026nbsp;1A-B\u003c/b\u003e). The efficiency of the CRISPR-guided disruption was assessed by qPCR (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD) and immunofluorescence, which, in addition to confirming a depletion of PTBP1 protein levels, showed a significant increase in HLA-ABC protein expression on TNBC cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE-F). Flow cytometry analysis further confirmed our previous results, showing a notable increase in MHC class I levels on the cancer cell surface of PTBP1-disrupted TNBC cells (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eG).\u003c/p\u003e\u003cp\u003eThen, we used RNA sequencing (RNA-seq) to characterize global transcriptomic changes associated with PTBP1 disruption in PTBP1-KO and PTBP1 wild-type (WT) cells. First, we verified PTBP1 disruption by finding a significant downregulation of \u003cem\u003ePTBP1\u003c/em\u003e expression. This was validated by the deletion of the exons 6 to 11 (\u003cb\u003eSuppl. Figure\u0026nbsp;1A\u003c/b\u003e). As expected, PTBP1-KO and PTBP1-WT cells presented a significantly different transcriptomic profile (\u003cb\u003eSuppl. Figure\u0026nbsp;1C\u003c/b\u003e). Specifically, we identified 1,770 upregulated and 1,002 downregulated genes between the two models (\u003cb\u003eSuppl. Figure\u0026nbsp;1D-E\u003c/b\u003e). Among the differentially expressed genes (DEGs), we found a significant enrichment in genes related to the cancer hallmarks, including \u0026ldquo;\u003cem\u003eevading immune destruction\u0026rdquo;\u003c/em\u003e, \u0026ldquo;\u003cem\u003etumor-promoting inflammation\u0026rdquo;\u003c/em\u003e, and \u0026ldquo;\u003cem\u003esustained angiogenesis\u0026rdquo;\u003c/em\u003e, three processes with a high impact on immune evasion and IT response (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eH)\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eAnalysis of Kyoto Encyclopedia of Genes and Genomes (KEGG) signatures revealed that upregulated genes were enriched in several immune-related mechanisms, including \u0026ldquo;\u003cem\u003eantigen processing and presentation\u0026rdquo;\u003c/em\u003e among the top ten most significant pathways (\u003cb\u003eSuppl. Figure\u0026nbsp;1F\u003c/b\u003e). This finding was supported by gene set enrichment analysis (GSEA), which demonstrated a significant upregulation of genes responsible for presenting antigens to immune cells (p\u0026thinsp;=\u0026thinsp;0.002; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eI). Further transcriptomic analysis revealed a significant upregulation of key components of both MHC Class I and Class II gene families in PTBP1-KO compared to WT cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eJ). These results indicate that PTBP1 suppresses antigen presentation in TNBC, and its disruption restores the expression of multiple MHC Class I and II molecules at both transcriptional and protein levels, potentially increasing TNBC tumor immunogenicity.\u003c/p\u003e\u003cp\u003eFurthermore, we analyzed publicly available gene expression datasets where the \u003cem\u003ePTBP1\u003c/em\u003e gene had been disrupted across various cancer cell lines to validate these findings. Consistent with our results in CRISPR-Cas9-mediated KO and shRNA-mediated KD, PTBP1 silencing using a doxycycline-inducible lentiviral system led to a significant upregulation of multiple HLA genes from the MHC Class I and Class II genes in TNBC (\u003cb\u003eSuppl. Figure\u0026nbsp;2\u003c/b\u003e). Interestingly, while a partial reactivation of HLA genes was detected in a glioblastoma cell line (U251), the effect was attenuated in ER\u0026thinsp;+\u0026thinsp;breast cancer (MCF-7), ovarian carcinoma (A2780), hepatocellular carcinoma (HepG2), and erythroid cell lines (\u003cb\u003eSuppl. Figure\u0026nbsp;2\u003c/b\u003e).\u003c/p\u003e\n\u003ch3\u003ePTBP1 mediates downregulation of antigen presentation in clinical tissues\u003c/h3\u003e\n\u003cp\u003eWe investigated the tumor-intrinsic role of PTBP1 in TNBC using single-cell RNA-seq (scRNA-seq) data from tumor biopsies of 20 patients with primary, treatment-na\u0026iuml;ve TNBC enrolled in the phase Ib/II clinical trial NCT03366844, which tested preoperative pembrolizumab and stereotactic radiotherapy\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Following quality control and preprocessing of the scRNA-seq data, we used canonical lineage markers to classify all cells into nine major compartments representing immune (e.g., T and NK cells, B cells, myeloid cells) and stromal populations (e.g., fibroblasts, endothelial cells), in addition to epithelial cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). The resulting unsupervised clustering showed these cellular types and revealed epithelial subtypes, including neoplastic tumor cells and normal luminal or myoepithelial populations (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB-C).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eWe then selected the epithelial population and focused our analysis on neoplastic TNBC cells, stratifying them based on \u003cem\u003ePTBP1\u003c/em\u003e expression levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). Comparative analysis revealed that PTBP1-expressing tumor cells exhibited significant downregulation of immune-related pathways, including \u0026ldquo;\u003cem\u003eantigen processing and presentation\u003c/em\u003e\u0026rdquo; (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). Moreover, these cells had a significant decrease in the expression levels of MHC Class I and II genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE). Taken together, these findings confirm that PTBP1 modulates antigen presentation in TNBC, with evidence from \u003cem\u003ein vitro\u003c/em\u003e models and surgical specimens supporting its role as an immune suppressive factor.\u003c/p\u003e\u003cp\u003e\u003cb\u003ePTBP1 is associated with T cell dysfunction but not with altered immune cell infiltration in TNBC\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAfter establishing that PTBP1 modulates antigen presentation pathways \u003cem\u003ein vitro\u003c/em\u003e, we next evaluated its relationship with immune infiltration and T cell function in TNBC tumors. First, we performed multiplex immunohistochemistry (multi-IHC) on tissue samples from 60 breast cancer patients, including 44 TNBC cases (2% Stage I, 80% Stage II, and 18% Stage III; 0% Grade 1, 57% Grade 2, and 43% Grade 3, respectively). We quantified the density of CD4⁺, CD8⁺, CD20⁺, and PTBP1⁺ cells in both stromal and epithelial (PANCK⁺) compartments (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Tumors were stratified into low, medium, and high PTBP1 expression groups based on the percentage of PTBP1⁺ cells in the epithelial compartment. Notably, we found no significant differences in the density of infiltrating immune cells across PTBP1 expression groups in either the stroma or tumor nests (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB), suggesting that PTBP1 does not significantly impair immune cell recruitment. However, together with the pathway enrichment analysis in cell models reflecting poor antigen processing and presentation on tumor cells with high \u003cem\u003ePTBP1\u003c/em\u003e expression, this result raised the possibility that PTBP1 may instead modulate the functional state of infiltrating lymphocytes rather than their overall abundance.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTo further investigate this hypothesis, we leveraged the cell types identified in the tumor microenvironment of the biopsied profiled with scRNA-seq from the NCT03366844 trial (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB)\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. We specifically focused on T cells for further detailed analysis, resolving 11 transcriptionally distinct T cell subpopulations, as previously described by Wu et al.\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. This approach allowed us to stratify T cells according to CD4, CD8, and other markers into different subsets. CD4\u003csup\u003e+\u003c/sup\u003e subsets included regulatory (FOXP3\u003csup\u003e+\u003c/sup\u003e), follicular helper (CXCL13\u003csup\u003e+\u003c/sup\u003e), naive/central memory (CCR7\u003csup\u003e+\u003c/sup\u003e), and type 1 helper effector memory T cells (IL7R\u003csup\u003e+\u003c/sup\u003e). Among CD8\u003csup\u003e+\u003c/sup\u003e clusters, we identified exhausted (LAG3\u003csup\u003e+\u003c/sup\u003e), effector (IFNG\u003csup\u003e+\u003c/sup\u003e), and quiescent (ZFP36\u003csup\u003e+\u003c/sup\u003e) T cells. Additionally, two other T cell subpopulations, comprising both CD4\u003csup\u003e+\u003c/sup\u003e and CD8\u003csup\u003e+\u003c/sup\u003e cells, were identified: one driven by a type 1 interferon signature (IFIT1\u003csup\u003e+\u003c/sup\u003e) and another associated with proliferation (MKI67\u003csup\u003e+\u003c/sup\u003e). We also identified natural killer cells (NK, AREG\u003csup\u003e+\u003c/sup\u003e) and NK T-like cells (FCGR3A\u003csup\u003e+\u003c/sup\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC).\u003c/p\u003e\u003cp\u003eTo assess the functional state of the T cell compartment, we assigned dysfunction scores to each subset based on a validated exhaustion signature\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. We then calculated a patient-level T cell dysfunction score by considering the relative abundance of each dysfunctional subtype (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). Notably, the proportion of PTBP1-positive tumor cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC) was significantly correlated with the T cell dysfunction score (r\u0026thinsp;=\u0026thinsp;0.52, p\u0026thinsp;=\u0026thinsp;0.02; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE), suggesting that elevated \u003cem\u003ePTBP1\u003c/em\u003e expression is associated with a shift in the intratumoral T cell landscape toward more dysfunctional or exhausted states. We additionally examined the relationships between \u003cem\u003ePTBP1\u003c/em\u003e expression and the abundance of specific T cell populations. The percentage of PTBP1-positive tumor cells was not significantly correlated with the proportion of any individual cell type. However, it showed a trend towards being negatively correlated with CD4\u003csup\u003e+\u003c/sup\u003e CCR7 T cells (r=-0.42, p\u0026thinsp;=\u0026thinsp;0.06, \u003cb\u003eSuppl. Figure\u0026nbsp;3\u003c/b\u003e), the subset of immune cells with the lowest dysfunctionality score, and a trend towards a positive correlation with Ki67 T cells (r\u0026thinsp;=\u0026thinsp;0.41, p\u0026thinsp;=\u0026thinsp;0.07, \u003cb\u003eSuppl. Figure\u0026nbsp;3\u003c/b\u003e) and CD8\u003csup\u003e+\u003c/sup\u003e LAG3 T cells (r\u0026thinsp;=\u0026thinsp;0.36, p\u0026thinsp;=\u0026thinsp;0.12, \u003cb\u003eSuppl. Figure\u0026nbsp;3\u003c/b\u003e), two groups of cells with a high dysfunction score (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). In agreement with our observations in multi-IHC, we found no significant correlation between \u003cem\u003ePTBP1\u003c/em\u003e expression and the proportion of TILs (r\u0026thinsp;=\u0026thinsp;0.18, p\u0026thinsp;=\u0026thinsp;0.23, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF). These results suggest that \u003cem\u003ePTBP1\u003c/em\u003e expression is associated with a distinct immune phenotype characterized by T cell dysfunction rather than by changes in total immune cell abundance, indicating that PTBP1 may play a role in the efficient response to immune checkpoint inhibition in TNBC tumors.\u003c/p\u003e\n\u003ch3\u003ePTBP1 increases with TNBC progression and is associated with immune evasion in metastatic disease\u003c/h3\u003e\n\u003cp\u003eHaving established that \u003cem\u003ePTBP1\u003c/em\u003e expression is associated with an immune-evasive phenotype in TNBC, we next investigated whether its expression increases with clinical disease progression. Analysis of patients included in the TCGA and SCAN-B datasets revealed significantly higher \u003cem\u003ePTBP1\u003c/em\u003e expression in tumors from patients with advanced AJCC stage (TCGA: p\u0026thinsp;=\u0026thinsp;0.03; SCAN-B: p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). Specifically, expression was higher in larger tumors (\u0026gt;\u0026thinsp;2 cm, T2/T3) compared to smaller ones (\u0026lt;\u0026thinsp;2 cm, T1; TCGA: p\u0026thinsp;=\u0026thinsp;0.03; SCAN-B: p\u0026thinsp;=\u0026thinsp;0.003; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). Additionally, PTBP1 was significantly associated with nodal involvement in the SCAN-B cohort (p\u0026thinsp;=\u0026thinsp;0.002), though this relationship was not observed in TCGA (p\u0026thinsp;=\u0026thinsp;0.69; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eBuilding upon these observations, we analyzed primary (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;18) and metastatic (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;34) TNBC samples from the AURORA US Metastasis Project to evaluate whether \u003cem\u003ePTBP1\u003c/em\u003e expression further increases in distant disease. Indeed, metastatic lesions exhibited significantly higher \u003cem\u003ePTBP1\u003c/em\u003e expression than matched primary tumors (p\u0026thinsp;=\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD), reinforcing its association with advanced disease states. Since PTBP1 upregulation was linked to reduced antigen presentation in early-stage TNBC, we investigated whether it also contributes to immune dysregulation in advanced TNBC. Transcriptomic data from the AURORA cohort revealed substantial downregulation of HLA class I and II genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE), and multiple immune response pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eF), including components of the antigen processing machinery (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eG) in metastatic tumors compared to primaries. To further evaluate the association of PTBP1 with immune dysfunction in metastatic TNBC, we applied the TIDE (Tumor Immune Dysfunction and Exclusion) framework, a validated computational method for predicting immune checkpoint blockade response based on transcriptional signatures of T cell dysfunction and exclusion\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Importantly, \u003cem\u003ePTBP1\u003c/em\u003e expression levels in metastatic TNBC were significantly and positively correlated with TIDE scores (r\u0026thinsp;=\u0026thinsp;0.5, p\u0026thinsp;=\u0026thinsp;0.036; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eH), suggesting that tumors with high PTBP1 are more likely to exhibit T cell dysfunction and resistance to immune checkpoint blockade. Taken together, these findings indicate that \u003cem\u003ePTBP1\u003c/em\u003e expression increases during TNBC progression and metastasis and is likely linked to a transcriptional program of tumor immune dysfunction and exclusion in metastatic TNBC.\u003c/p\u003e\u003cp\u003e\u003cb\u003ePTBP1\u003c/b\u003e \u003cb\u003eexpression associates with immunotherapy response in patients with metastatic TNBC\u003c/b\u003e\u003c/p\u003e\u003cp\u003eGiven the variable clinical benefit of IT across TNBC stages, we hypothesized that PTBP1 may influence treatment response. To evaluate this, we analyzed gene expression profiles of pre-treatment biopsies from patients with early-stage TNBC enrolled in the phase II I-SPY2 clinical trial (NCT01042379; \u003cem\u003en\u0026thinsp;=\u003c/em\u003e\u0026thinsp;50)\u003csup\u003e30,31\u003c/sup\u003e. \u003cem\u003ePTBP1\u003c/em\u003e expression did not differ significantly between responders and non-responders to neoadjuvant IT (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). Although negative, this finding agrees with our prior observations, indicating that while PTBP1 impairs the antigen presentation machinery in different TNBC models, its expression significantly increases in locally advanced and metastatic disease (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA-D). Leveraging on our findings, we examined \u003cem\u003ePTBP1\u003c/em\u003e expression in 53 biopsies from patients with metastatic TNBC enrolled in the phase II TONIC clinical trial (NCT02499367) randomized to receive one of five short-course induction therapies (control, irradiation, cyclophosphamide, cisplatin, or doxorubicin) followed by anti\u0026ndash;PD-1 therapy (nivolumab)\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. In this setting, patients with low \u003cem\u003ePTBP1\u003c/em\u003e expression in pre-nivolumab biopsies were significantly more likely to respond to treatment, regardless of the trial arm (p\u0026thinsp;=\u0026thinsp;0.03; Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). This difference is similar to what has been reported in the original molecular study of this cohort for TILs and the percentage of PD-L1 in immune cells\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAmong the TONIC trial arms, patients who received low-dose doxorubicin as induction therapy exhibited the most promising clinical objective response rate (ORR\u0026thinsp;=\u0026thinsp;35%, \u003cem\u003en\u0026thinsp;=\u003c/em\u003e\u0026thinsp;17)\u003csup\u003e23\u003c/sup\u003e. We therefore compared the expression levels of PTBP1 in paired tumor biopsies collected before and after induction treatments. Notably, metastatic TNBC tumors exposed to low-dose doxorubicin experienced a significant reduction in \u003cem\u003ePTBP1\u003c/em\u003e expression (p\u0026thinsp;=\u0026thinsp;0.03; Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC), a phenomenon not observed in any of the other induction strategies (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05; \u003cb\u003eSuppl. Figure\u0026nbsp;4A\u003c/b\u003e). Interestingly, this PTBP1 reduction was accompanied by increased expression of genes involved in antigen processing and presentation (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; \u003cb\u003eSuppl. Figure\u0026nbsp;4B\u003c/b\u003e), a transcriptional program also enriched in tumors from patients who responded to nivolumab, regardless of the induction treatment strategy (\u003cb\u003eSuppl. Figure\u0026nbsp;4C\u003c/b\u003e). To evaluate PTBP1 modulation in a controlled setting, we treated TNBC cell lines with a low-dose doxorubicin regime. Concordant with the data from tumor biopsies from patients treated in the TONIC trial, we observed a significant reduction of \u003cem\u003ePTBP1\u003c/em\u003e expression after treatment of the cells with this regimen (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD). These findings suggest a role for PTBP1 as a modulator of immune evasion in metastatic TNBC and that its downregulation, whether endogenous or therapy-induced, may restore antigen presentation and sensitize tumors to IT, potentially offering both predictive and therapeutic value beyond conventional biomarkers such as TILs and PD-L1.\u003c/p\u003e\n\u003ch3\u003ePTBP1 may complement current immunotherapy response markers for patients with metastatic TNBC\u003c/h3\u003e\n\u003cp\u003eConsistent with our scRNA-seq and multi-IHC findings, showing that PTBP1 does not alter overall immune cell infiltration but instead modulates antigen presentation and T cell activation, we found no significant correlation between \u003cem\u003ePTBP1\u003c/em\u003e expression and CD8\u003csup\u003e+\u003c/sup\u003e T cell density, TIL percentage, PD-L1 expression, or tumor mutational burden in metastatic biopsies from patients enrolled in the TONIC trial, either before or after induction treatment (\u003cb\u003eSuppl. Figure\u0026nbsp;5A-B\u003c/b\u003e). Given this lack of association with canonical immune biomarkers, we investigated whether PTBP1 might offer independent or complementary predictive value for IT response. We observed that \u003cem\u003ePTBP1\u003c/em\u003e expression in pre-nivolumab biopsies showed modest predictive performance for objective response of patients with metastatic TNBC (AUC\u0026thinsp;=\u0026thinsp;0.74; 95% CI: 0.53\u0026ndash;0.95; Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE), comparable to established IT markers, such as TILs (AUC\u0026thinsp;=\u0026thinsp;0.67) and PD-L1 levels (AUC\u0026thinsp;=\u0026thinsp;0.66), as reported in the original study of this cohort\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Moreover, combining PTBP1 levels with PD-L1 score modestly, but not significantly (p\u0026thinsp;=\u0026thinsp;0.55) improved the predictive accuracy (AUC\u0026thinsp;=\u0026thinsp;0.82; 95% CI: 0.66\u0026ndash;0.99; Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eF) of PTBP1 alone. These results suggest that PTBP1 may serve as a complementary biomarker to refine IT stratification in metastatic TNBC.\u003c/p\u003e\u003cp\u003eImportantly, patients with high \u003cem\u003ePTBP1\u003c/em\u003e expression in pre-IT treatment biopsies had significantly shorter PFS (HR\u0026thinsp;=\u0026thinsp;2.41, p\u0026thinsp;=\u0026thinsp;0.01; Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eG) and OS (HR\u0026thinsp;=\u0026thinsp;2.48, p\u0026thinsp;=\u0026thinsp;0.03; Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eH), supporting its potential prognostic value in the context of IT. Although based on a relatively small, unadjusted cohort, the magnitude and consistency of the effect support a potential prognostic relevance of this \u003cem\u003ePTBP1\u003c/em\u003e in patients with metastatic TNBC undergoing IT. To assess whether this association extends to other cancer types treated with IT, we analyzed gene expression and survival data from 625 patients with cutaneous melanoma (n\u0026thinsp;=\u0026thinsp;251), urothelial carcinoma (n\u0026thinsp;=\u0026thinsp;348), or glioblastoma (n\u0026thinsp;=\u0026thinsp;26). In cutaneous melanoma, high \u003cem\u003ePTBP1\u003c/em\u003e expression significantly associated with reduced PFS (HR\u0026thinsp;=\u0026thinsp;2.0, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; \u003cb\u003eSuppl. Figure\u0026nbsp;5C\u003c/b\u003e). In glioblastoma, although in a small cohort, the association was even stronger (HR\u0026thinsp;=\u0026thinsp;5.04, p\u0026thinsp;=\u0026thinsp;0.001; \u003cb\u003eSuppl. Figure\u0026nbsp;5D\u003c/b\u003e) and in urothelial carcinoma, we found a non-significant trend toward worse OS in patients with higher PTBP1 levels (HR\u0026thinsp;=\u0026thinsp;1.27, p\u0026thinsp;=\u0026thinsp;0.07; \u003cb\u003eSuppl. Figure\u0026nbsp;5E\u003c/b\u003e).\u003c/p\u003e\u003cp\u003eTogether, these findings support PTBP1 as a tumor-intrinsic modulator of immune resistance across cancer types. While it does not predict response in early-stage TNBC, PTBP1 expression consistently associates with immune evasion signatures, inferior survival, and IT response failure in metastatic TNBC and other cancers, highlighting its potential as a complementary biomarker to guide IT strategies.\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eApproximately 23\u0026ndash;33% of patients with primary TNBC will develop metastases within the first five years after diagnosis\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. The prognosis for patients with advanced and metastatic TNBC remains dismal, with median PFS and OS below six and 17 months, respectively\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e,\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. Although pembrolizumab has improved outcomes in metastatic TNBC with a PD-L1 combined positive score (CPS)\u0026thinsp;\u0026ge;\u0026thinsp;10, more than 60% of these patients still relapse within a year. Importantly, while other biomarkers such as TILs have shown promising potential in predicting response to IT\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e, PD-L1 remains the only clinically accepted biomarker to date.\u003c/p\u003e\u003cp\u003ePTBP1 is a well-characterized SF with a widespread impact on mRNA processing, being able to promote exon retention or skipping and influencing gene expression programs\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. It is involved in pro-tumorigenic processes across multiple cancer types, including the modulation of tumor immunity\u003csup\u003e\u003cspan additionalcitationids=\"CR40\" citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. By integrating bulk and single-cell transcriptomics from patient samples, spatial multi-IHC profiling, and functional data across TNBC cell models, in this study, we identified that \u003cem\u003ePTBP1\u003c/em\u003e expression correlates with features of T cell dysfunction and transcriptional repression of antigen presentation pathways. These findings support a tumor-intrinsic immune suppressive phenotype, potentially driven by ongoing antigen exposure and chronic pro-inflammatory signaling, both of which can weaken cytotoxic T cell function\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Disruption of the \u003cem\u003ePTBP1\u003c/em\u003e gene expression in TNBC cell models correlates with a significant upregulation of HLA class I and II genes, which was supported by findings in independent cell lines and clinical scRNA-seq datasets. Interestingly, \u003cem\u003ePTBP1\u003c/em\u003e expression was elevated in metastatic lesions when compared to primary tumors, suggesting a potential role in immune escape during disease progression. Mechanistically, tumors from patients receiving low-dose doxorubicin, the induction arm of the TONIC trial with the most promising overall response rate, were associated with a significant reduction in \u003cem\u003ePTBP1\u003c/em\u003e expression. This observation was corroborated in our TNBC cell models, where doxorubicin exposure led to transcriptional downregulation of \u003cem\u003ePTBP1\u003c/em\u003e.\u003c/p\u003e\u003cp\u003eOur analysis of gene expression and clinical outcomes of patients enrolled in the TONIC clinical trial showed that metastatic TNBC patients with lower \u003cem\u003ePTBP1\u003c/em\u003e expression following induction therapy had better response rates and longer survival after PD-1 blockade. This effect was not explained by differences in CD8\u003csup\u003e+\u003c/sup\u003e T cell density or TIL levels, supporting the idea that PTBP1 modulates immune evasion through alternative pathways. Notably, the expression of \u003cem\u003ePTBP1\u003c/em\u003e alone was a good predictive feature for patients in the trial (AUC\u0026thinsp;=\u0026thinsp;0.74). This performance is similar to that of two established IT predictive markers, such as TILs (AUC\u0026thinsp;=\u0026thinsp;0.67) and PD-L1 levels (AUC\u0026thinsp;=\u0026thinsp;0.66), reported in the original study of this cohort\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Recent studies show that combining gene expression with clinicopathological features can enhance survival prediction in TNBC\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. In line with these efforts, we explored the integration of PTBP1 with PD-L1 scores and found a modest but non-significant increase in predictive accuracy (AUC\u0026thinsp;=\u0026thinsp;0.82). While these findings support PTBP1 as a potentially useful feature in predictive models, our study was not specifically designed or powered to develop biomarkers for clinical use, as we previously pursued with the TNBC-ICI classifier for early-stage disease\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eBeyond TNBC, our analysis suggests that PTBP1 may have broader relevance in immune modulation, as higher expression was associated with worse clinical outcomes in patients with melanoma, glioblastoma, and urothelial carcinoma treated with IT. These observations align with growing evidence that RNA regulatory proteins contribute to immune resistance across different cancer types\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e,\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. However, in patients with early-stage TNBC enrolled in the I-SPY2 trial, \u003cem\u003ePTBP1\u003c/em\u003e expression was not associated with response to neoadjuvant IT, suggesting that its role may be context-dependent and influenced by disease stage or treatment timing.\u003c/p\u003e\u003cp\u003eCertain limitations should be considered when interpreting these results from this study. First, although the findings were supported across multiple independent datasets (TCGA, SCAN-B, multi-IHC, AURORA US, I-SPY2, TONIC, scRNA-seq; total TNBC patients \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;969), most individual cohorts are relatively small and heterogeneous in terms of study design, treatment regimens, and data collection procedures. This limits the statistical power of some comparisons and precludes multivariate adjustment for potential confounding variables. Second, the TONIC trial includes pre-IT induction regimens (chemotherapy or radiation), which may confound the interpretation of IT-specific effects. Nevertheless, this study design resembles real-world treatment paradigms, where IT is commonly administered alongside or following chemotherapy in TNBC. Importantly, the association between \u003cem\u003ePTBP1\u003c/em\u003e expression and clinical outcomes was observed across all TONIC arms. Together, the consistency of these results across bulk, single-cell, and functional datasets supports the relevance of PTBP1 in metastatic TNBC, although validation in larger prospective cohorts will be necessary to determine its clinical utility.\u003c/p\u003e\u003cp\u003eIn summary, this study identifies PTBP1 as a tumor-intrinsic regulator of immune evasion and a potential biomarker of immune dysfunction and resistance to IT in metastatic TNBC. While directly targeting PTBP1 is not currently feasible due to its widespread biological roles, its expression may help guide patient stratification strategies in clinical trials, and the modulation of its downstream regulatory network could be explored for therapeutic intervention in future studies. Our findings also highlight the value of integrating splicing-related biomarkers with established immuno-oncology markers to enhance precision in patient selection for IT. Ultimately, these results support the consideration of PTBP1 in future predictive models aimed at optimizing IT stratification and advancing personalized treatment strategies for patients with metastatic TNBC.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003eClinical and molecular data access, collection, and curation\u003c/h2\u003e\u003cp\u003eClinical data from The Cancer Genome Atlas \u0026ndash; Breast Cancer (TCGA-BRCA; \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1,006) study cohort were downloaded from the National Cancer Institute (NCI) Genomic Data Commons portal. The data were manually curated using the available pathological reports. Inclusion criteria for this study involved: Female patients 18 years of age or older with invasive ductal carcinoma, TNBC with confirmation of estrogen and progesterone receptor, and HER2 negativity (ER\u0026thinsp;\u0026lt;\u0026thinsp;10% and HER2 0, 1, or 2 in the absence of amplification as determined by in situ hybridization). TCGA-BRCA gene expression data were downloaded using the R/Bioconductor TCGAbiolinks v2.30.4 package. TNBC specimens with a tumor purity above 60%, determined by the consensus measurement of purity estimation (CPE)\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e method (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;95), and normal breast tissue samples (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;101) from the same cohort were included in the gene expression analyses. Clinical and gene expression data from the Sweden Cancerome Analysis Network \u0026ndash; Breast (SCAN-B; \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;8,168) study cohort, including TNBC invasive ductal carcinoma cases (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;655), were downloaded from the Mendeley repository (DOI: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.17632/yzxtxn4nmd.4\u003c/span\u003e\u003cspan address=\"10.17632/yzxtxn4nmd.4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003csup\u003e47\u003c/sup\u003e. Cell line gene expression data were downloaded from the Cancer Cell Line Encyclopedia (CCLE, TPM22Q2)\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e through the R/depmap v1.16.0 and ExperimentHub v2.10.0 packages.\u003c/p\u003e\u003cp\u003eGene expression datasets following PTBP1 knockdown across various cancer types were retrieved from the NCBI Gene Expression Omnibus (GEO) database. The included datasets comprised the TNBC cell line MDA-MB-231 (GSE52493, shRNA\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e), glioblastoma cell line U251 (GSE189816, shRNA\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e), hepatocellular carcinoma cell line HepG2 (GSE232354, siRNA), primary erythroid cell cultures (GSE106566, shRNA\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e), hormone receptor-positive breast cancer cell line MCF-7 (GSE206142, siRNA\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e), and ovarian carcinoma cell line A2780 (GSE52493, shRNA\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e).\u003c/p\u003e\u003cp\u003eData from the AURORA US Metastasis cohort (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;129) comprising 52 TNBC tumors (18 primary tumors and 34 metastatic tumors) were retrieved from the NCBI GEO (GSE209998)\u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e. Publicly available clinical and single-cell transcriptomic data from 36 patients enrolled in the phase Ib/II trial NCT03366844, including 140,828 analyzed cells, were retrieved from the NCBI GEO (GSE246613)\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. We also obtained clinical and transcriptomic data from the I-SPY2 clinical trial, including 50 patients subjected to the IT neoadjuvant regimen (NCT01042379; GSE173839, GSE194040)\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Additionally, we included clinical and transcriptomic data from 53 patients enrolled in the TONIC trial (NCT02499367)\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e, a phase II study evaluating response to nivolumab following short-course induction treatments (two weeks of low-dose cisplatin, doxorubicin, cyclophosphamide, or irradiation) in metastatic TNBC. The list of all analyzed cohorts, including clinical specimens, can be accessed in \u003cb\u003eSupplementary Table\u0026nbsp;1\u003c/b\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eSelection of splicing-associated genes\u003c/h2\u003e\u003cp\u003eA total of 280 genes involved in RNA splicing and pre-mRNA processing were selected using the R/TIN package v1.34.0 with the dataset access code \u0026lsquo;splicingFactors\u0026rsquo;\u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e. Genes with an average expression below 100 RSEM-normalized counts in tumor tissues were excluded from downstream analyses. This cutoff was applied to remove genes with negligible expression, ensuring that only the most relevant and significantly expressed genes were included. Differentially expressed SFs between TNBC tumors and normal tissue were defined as an absolute log\u003csub\u003e2\u003c/sub\u003e FC\u0026thinsp;\u0026gt;\u0026thinsp;0.5 and an adjusted p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.01.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eCell culture conditions, CRISPR-Cas9 knockout, and shRNA knockdown experiments\u003c/h2\u003e\u003cp\u003eTNBC cell lines were cultured in RPMI 1640 medium supplemented with 10% fetal bovine serum (Fisher Scientific, Madrid, Spain) and 1% penicillin-streptomycin (ThermoFisher Scientific, Madrid, Spain). TNBC cells (MDA-MB-231 and BT-549) were obtained from ATCC and maintained in a humidified incubator at 37\u0026deg;C with 5% CO\u003csub\u003e2\u003c/sub\u003e. For low-dose doxorubicin (MedChem Express, Sollentuna, Sweden; #15142A) treatments, cells were incubated at a sublethal dose (IC10, 2 nM) for 72 h.\u003c/p\u003e\u003cp\u003eTo generate the \u003cem\u003ePTBP1\u003c/em\u003e-KO, a lentiviral vector containing the \u003cem\u003eS. pyogenes\u003c/em\u003e Cas9 gene and a blasticidin resistance gene (VCAS10125; Dharmacon, Lafayette, CO, USA) was transduced into cells using lentiviral particles. Cas9-expressing cells were clonally selected and expanded. Two independent guide RNAs (gRNA; Synthego, Menlo Park, CA, USA; \u003cb\u003eSupplementary Table\u0026nbsp;2\u003c/b\u003e), targeting \u003cem\u003ePTBP1\u003c/em\u003e exons 6 and 11, were transfected into Cas9-expressing cells during the logarithmic growth phase (50\u0026ndash;70% confluency). Single clones were isolated by serial dilutions and expanded. For PTBP1 knockdown, shRNAs targeting PTBP1 were designed (IDT Technologies, IA, US). A control shRNA against SY14 (\u003cem\u003eS. cerevisiae\u003c/em\u003e) was used as a scramble (SCR) control. The construct containing the shRNA was cloned into the pLVX-shRNA2-ZsGreen plasmid (Clontech, Mountain View, CA, USA). 10 \u0026micro;g of each plasmid were mixed with 7.5 \u0026micro;g of PPAX, 2.5 \u0026micro;g of PDM2, 64 \u0026micro;L of P3000 reagent, 40 \u0026micro;L of lipofectamine 3000 reagent (Invitrogen, Waltham, MA, USA; L3000001), and 1 mL of Opti-MEM (Gibco, 31985062) and incubated at room temperature (RT) for 15 minutes. The resulting mix was then transfected into HEK293 cells to produce lentiviral particles. After 48 and 72 hours, the medium containing high-titer lentiviral particles was collected and filtered through a 0.45 \u0026micro;m filter. 500,000 cells were seeded in six-well plates with virus-containing media and 1:1,000 polybrene solution. Then, the cells were spun down at 1,000 x g for 90 minutes at 32 \u0026ordm;C and maintained with the virus-containing media for 8 hours. After five passages, the cells were selected based on high ZsGreen expression using a BD FACSAria Fusion cell sorter.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eImmunofluorescence and confocal microscopy\u003c/h2\u003e\u003cp\u003eCells were fixed in 4% paraformaldehyde in PBS for 10 minutes at RT, followed by permeabilization with 0.25% Triton X-100 in PBS for 10 minutes. Blocking was performed for 30 minutes at RT using 1% BSA with 22.52 mg/ml glycine in PBS-T (0.1% Tween 20) and 0.25% Triton X-100. Primary antibodies were diluted in 1% BSA in PBS-T and incubated for 1 hour at RT. The following primary antibodies were used: anti-PTBP1 (mouse, 1:200, ThermoFisher Scientific, Madrid, Spain, #32-4800) and anti-HLA-ABC (rabbit, 1:200, ThermoFisher Scientific, #PA5-98355). Secondary antibodies were incubated for 1 hour in the dark using wet chambers. These included goat anti-mouse IgG Alexa Fluor 488 (1 \u0026micro;g/mL; ThermoFisher Scientific, #A28175) and goat anti-rabbit IgG Alexa Fluor 568 (2 \u0026micro;g/mL; ThermoFisher Scientific, #A-11011).\u003c/p\u003e\u003cp\u003eNuclei were counterstained with DAPI (1:10,000; ThermoFisher Scientific, #62248) in PBS-T with 0.25% Triton X-100 for 5 minutes in wet chambers. Finally, cells were mounted using Fluoromount-G (ThermoFisher Scientific, #00-4958-02) and imaged using a Leica TCS SPE confocal microscope (Leica Microsystems, Wetzlar, Germany). Quantification of fluorescence intensity was performed using ImageJ.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eFlow cytometry workflow\u003c/h2\u003e\u003cp\u003eCells were dissociated by scraping in ice-cold PBS 1X into a single-cell suspension. After two washes with PBS containing 0.1% BSA, cells were blocked in 1% BSA for 20 minutes. Cells were then incubated with 5 \u0026micro;L of anti-HLA-ABC Monoclonal Antibody (W6/32, APC conjugated, eBioscience, #17-9983-42) for 45 minutes. Following incubation, cells were washed twice with 0.1% BSA and analyzed using the FACSVerse flow cytometer (BD Biosciences, Franklin Lakes, NJ, USA). The gating strategy can be checked in \u003cb\u003eSupplementary Fig.\u0026nbsp;6\u003c/b\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eRNA-Sequencing, RT-PCR, and qPCR\u003c/h2\u003e\u003cp\u003eTotal RNA was extracted using the E.Z.N.A. Total RNA Kit I (Omega Bio-Tek, Norcross, GA, USA; R6834). RNA samples with high integrity (RIN\u0026thinsp;\u0026ge;\u0026thinsp;9.5) and high purity (OD 260/280\u0026thinsp;=\u0026thinsp;1.8-2.0) were used to generate libraries using Illumina\u0026reg; TruSeq Stranded mRNA Library Prep (Illumina Inc., San Diego, CA, USA). mRNA libraries were sequenced on the Illumina NovaSeq 6000 platform in paired-end mode with a read length of 2 x 100bp at NIMGenetics (Madrid, Spain). An average of 62M paired-end reads was processed for each sample. Fastp software (v0.21.0) was used to trim adapters from FASTQ files, and sequences were aligned to the Homo Sapiens GRCh38 reference genome using HISAT2 (v2.2.0). Resulting alignments were sorted with Samtools (v1.10). The generated BAM files were used to assemble transcripts and genes before generating the read counts table with StringTie (v2.1.4).\u003c/p\u003e\u003cp\u003eFor qPCR experiments, RNA was retrotranscribed into cDNA using the SensiFAST cDNA Synthesis Kit (Meridian Bioscience, Cincinnati, OH, USA). Quantitative amplification was performed using SensiFAST\u0026trade; SYBR No-ROX (Bioline #BIO-98005) on the CFX Opus 96 Real-Time PCR System (Bio-Rad Laboratories, Hercules, CA, USA, #12011319). Gene expression levels were normalized to SDHA using the ΔΔ-comparative quantitation method. Full list of primers can be retrieved from \u003cb\u003eSupplementary Table\u0026nbsp;2.\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eGene expression data processing and analysis\u003c/h2\u003e\u003cp\u003eDEGs from RNA-seq raw counts were calculated using the R/Bioconductor DESeq2 v1.42.1 package. Gene set data from the Kyoto Encyclopedia of Genes and Genomes (KEGG) were sourced from the Molecular Signatures Database (MSigDB) via the R/msigdbr v7.5.1 package. GSEA and pathway enrichment analysis of DEGs were calculated using the R/clusterProfiler v4.10.1 package, and hallmark enrichment analysis was conducted following the approach described by Menyhart et al.\u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eTo evaluate the activity of the T cell-mediated immune response, we selected the GO biological process \u0026ldquo;positive regulation of T cell-mediated immune response to tumor cell\u0026rdquo;. ssGSEA was performed using the R/GSVA v1.50.5 package. The gene set was obtained from the MSigDB via the R/msigdbr v7.5.1 package. For the TCGA-BRCA dataset, gene expression values were standardized using a variance-stabilizing transformation (VST) prior to ssGSEA computation. For the SCAN-B dataset, where VST could not be applied, a log2(x\u0026thinsp;+\u0026thinsp;1) transformation was used instead. The TIDE score was calculated using the TIDEpy Python package in Spyder software (Python 3.11.5)\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eMultiplex immunohistochemistry\u003c/h2\u003e\u003cp\u003eA tissue microarray including two tissue cores per patient of 60 patients with breast cancer was obtained from TissueArray.com (Derwood, MD, USA; BR1201a). Four \u0026micro;m-thick formalin-fixed paraffin-embedded (FFPE) tissue sections were baked at 60 \u0026ordm;C for 1 hour, deparaffinized with xylene, rehydrated through decreasing ethanol gradients (100%, 95%, 70%), and fixed with 10% neutral buffer formalin for 20 minutes. Antigen retrieval was performed using 1X antigen retrieval buffer, pH 6 (Opal 7-color Automation IHC kit; NEL821001KT, PerkinElmer, Waltham, MA, USA) by microwave heating. All tissue sections were blocked with the manufacturer-supplied antibody diluent/block solution for 10 minutes.\u003c/p\u003e\u003cp\u003eSlides were first incubated with the primary antibodies, followed by incubation with a secondary antibody working solution (HRP, 1X Opal Anti-Ms\u0026thinsp;+\u0026thinsp;Rb HRP, Akoya Biosciences, Marlborough, MA, USA; #SKU ARH1001EA) for 10 minutes at RT, and subsequently with Opal fluorophores (PerkinElmer Opal 7 Immunology Discover Kit, 1:100 dilution) for 10 minutes at RT. The antibody/fluorophore cycling order was as follows: 1. Anti-CD4 (EPR6855; ab133616, 1:100, Abcam, Cambridge, UK), Opal 520, 2. Anti-PTBP1 (EPR9048(B), ab133734 1:00 Abcam, Cambridge, UK), Opal 620, 3. Anti-CD8 (SP57, #7904460, ND, Roche Diagnostics, Indianapolis, USA), Opal 540, 4. Anti-CD20 (EP459Y, ab78237, 1:100, Abcam, Cambridge), Opal 570, and 5. Anti-pan Keratin (PANCK, AE1/AE3/PCK26; #7602595, ND, Roche Diagnostics, Indianapolis, USA), Opal 690. Finally, nuclei were counterstained with DAPI for 3 minutes at RT. Slides were imaged using the Mantra Quantitative Pathology Workstation and analyzed using inForm v2.4 software (Akoya Biosciences). Cell classification and tumor/stromal tissue stratification categorization, based on proximity to PANCK expression-positive cells, were performed using QuPath v0.4.3\u003csup\u003e55\u003c/sup\u003e. After machine learning-based cell classification, hematoxylin and eosin-stained tissues were used to correct misclassified tumor versus stromal regions.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eSingle-cell RNA-seq analysis\u003c/h2\u003e\u003cp\u003eFor the Shiao database\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e, data were processed using the standard R/Seurat v5.2.1 pipeline\u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e. Briefly, quality control steps were performed to remove low-quality cells, and a total of 47,894 cells from treatment-na\u0026iuml;ve biopsies were retained for downstream analyses.\u003c/p\u003e\u003cp\u003eAfter quality control, the data were log-normalized, scaled, and the most variable genes were identified. Canonical marker genes were used to determine cell types, following the methodology outlined by Wu et al.\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Malignant and normal epithelial cells were selected based on copy number variation (CNV) profiles inferred using the inferCNV algorithm. Tumors containing fewer than 50 cancer cells were excluded from the study. The FindMarkers function was used to identify DEGs (p\u003csub\u003eadj\u003c/sub\u003e\u0026lt;0.05, absolute log\u003csub\u003e2\u003c/sub\u003eFC\u0026thinsp;\u0026gt;\u0026thinsp;0.2) between PTBP1-positive and PTBP1-negative groups. Enrichment analyses were performed using the enrichKEGG function from the R/clusterprofiler package\u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e. The T cell dysfunction score for each case was calculated by evaluating the percentage of immune cell subsets, using the original scoring methodology\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003eData visualization and management\u003c/h2\u003e\u003cp\u003eFor data representation, the R/ggplot2 v.3.5.1, the R/pheatmap v.1.0.12, and R/ggpubr v.0.6.0 packages were used. Forest plots were generated using the R/forestploter v1.1.2 package. Sashimi plots were produced using the Integrative Genomics Viewer (IGV) with the Sashimi Plot function. The PTBP1 protein structure was generated using the AF-P265299-F1 AlphaFold predicted structure and visualized using the RCSB-PDB tool (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.rcsb.org/3d-view\u003c/span\u003e\u003cspan address=\"https://www.rcsb.org/3d-view\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Principal component analysis (PCA) was performed using the R/M3C v1.24.0 package, and heatmaps were created using the R/gplots package v3.1.3.1 to visualize hierarchical clustering using the Euclidean metric distances between gene expression profiles. For single-cell transcriptomic data visualizations, the R/Seurat v5.2.1 and R/scCustomize v3.0.1 packages were used. The R/ROCR v1.0-11 package was employed to compute the Receiver Operating Curves (ROC) and the corresponding AUC values. The R/tidyverse v2.0.0 package was used for data manipulation.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003eStatistics and reproducibility\u003c/h2\u003e\u003cp\u003eThe distribution of data for each variable was assessed for normality using the Shapiro-Wilk test. Unless otherwise specified, the two-sided Wilcoxon rank sum test was applied to evaluate the statistical significance of differences in non-parametric variables, whereas parametric variables were analyzed using the two-sided Student\u0026rsquo;s t-test. The Kruskal-Wallis test was used for comparison between multiple groups for non-parametric variables. When necessary, p-values were adjusted for multiple comparisons using the Benjamini-Hochberg (BH) procedure to control the false discovery rate and reduce the likelihood of Type I errors. For paired data, the paired Student\u0026rsquo;s t-test was applied. Correlations between continuous variables were calculated using either Spearman\u0026rsquo;s rank correlation (\u003cem\u003erho\u003c/em\u003e) coefficient for non-normally distributed data or Pearson\u0026rsquo;s correlation coefficient (r) for variables following a normal distribution, as determined by the Shapiro-Wilk test.\u003c/p\u003e\u003cp\u003eKaplan-Meier curves were generated using the Kaplan-Meier plotter tool\u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e, and the log-rank test was applied to assess the statistical significance of differences in survival rates. Unless otherwise specified, all computational analyses were performed using R software (v.4.3.3). To correct \u003cem\u003ePTBP1\u003c/em\u003e using PD-L1, \u003cem\u003ePTBP1\u003c/em\u003e expression was scaled to a uniform scale from 1 to 10, and this score was doubled when the PD-L1 percentage was below 5%.\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Instituto de la Salud Carlos III (ISCIII) AES2022 (#PI22/01496), co-funded by the European Union, and the Sara Borrell project (#CD22/00026), the Fundaci\u0026oacute;n CONTIGO Contra el C\u0026aacute;ncer de la Mujer (#MERIT project), the Institut d\u0026rsquo;Investigaci\u0026oacute; Sanit\u0026agrave;ria Illes Balears Financiaci\u0026oacute;n Grupos Emergentes (INSE) program, the Servei d\u0026apos;Ocupaci\u0026oacute; de les Illes Balears (SOIB) Jove Qualificats program, and the Scientific Foundation of the Spanish Association Against Cancer \u0026ndash; Illes Balears. Flow cytometry, cell sorting, and cell culture experiments were performed with the support of the Cytometry and Cell Culture Core Facility at the Institut d\u0026rsquo;Investigaci\u0026oacute; Sanit\u0026agrave;ria Illes Balears (IdISBa).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eAuthor Contributions (CRediT taxonomy)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConceptualization:\u003c/strong\u003e D.M.M., J.I.J.O., \u003cstrong\u003eMethodology:\u003c/strong\u003e M.E.M., J.I.J.O., P.L.A., D.M.M., \u003cstrong\u003eInvestigation:\u003c/strong\u003e P.L.A., A.M.P., S.I.M. (CRISPR and shRNA experiments); B.V., J.I.J.O. (multiplex IHC); M.E.M., J.I.J.O. (multiplex IHC image analysis), \u003cstrong\u003eFormal analysis:\u003c/strong\u003e M.E.M., A.F.B.L., M.P.S. (bioinformatics, RNA-seq processing, public dataset interrogation); M.E.M., A.F.B.L. (statistical analysis, data visualization), \u003cstrong\u003eData curation:\u003c/strong\u003e M.E.M., A.F.B.L., M.P.S., \u003cstrong\u003eResources:\u003c/strong\u003e M.K., D.M.M., J.I.J.O. (TONIC trial compliance, data transfer agreements), \u003cstrong\u003eVisualization:\u003c/strong\u003e M.E.M., A.F.B.L. (figures and tables), \u003cstrong\u003eValidation:\u003c/strong\u003e P.G.E. (immune biomarker interpretation); S.G.M., M.G., J.C., M.K., M.L.D. (clinical interpretation and translational contextualization), \u003cstrong\u003eSupervision:\u003c/strong\u003e D.M.M., J.I.J.O., \u003cstrong\u003eProject administration:\u003c/strong\u003e D.M.M., \u003cstrong\u003eWriting the original draft:\u003c/strong\u003e M.E.M., J.I.J.O., P.L.A., A.F.B.L., S.I.M., D.M.M., \u003cstrong\u003eWriting, reviewing \u0026amp; editing:\u003c/strong\u003e All authors.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Competing interests\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eEthics Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll publicly available cohort data analyzed in this study were collected under the respective institutional review board (IRB) approvals, in accordance with human subjects protection and data access policies, and with written informed consent from all participants. All samples were de-identified and coded according to the Health Insurance Portability and Accountability Act (HIPAA) guidelines.\u003c/p\u003e\n\u003cp\u003eAll datasets were obtained from retrospective databases, and patients were recruited at their host institutions. Data from patients enrolled in the TONIC clinical trial were included in a de-identified manner under Data Transfer Agreement 10680 DTA 15112019, linked to IRBd19-233. Based on the documents registered on 18 September 2019 in IRB ART (IRBd19-233), the Netherlands Cancer Institute \u0026ndash; Antoni van Leeuwenhoek (NKI-AVL) IRB determined that the project entitled \u003cem\u003eRNA splicing factors and the association with response to PD-1 blockade in patients with metastatic triple-negative breast cancer treated in the TONIC trial\u003c/em\u003e does not meet the criteria of the Medical Research Involving Human Subjects Act (WMO) and issued a non-WMO statement.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Materials \u0026amp; Correspondence\u003c/p\u003e\n\u003cp\u003eCorrespondence and requests for materials should be addressed to D.M.M.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eData availability\u003c/p\u003e\n\u003cp\u003eRNA-seq data of CRISPR\u0026ndash;Cas9 models generated in this study are available at the European Bioinformatics Institute (ArrayExpress) under accession number \u003cstrong\u003eE-MTAB-15284\u003c/strong\u003e. All other data sets used are publicly available and referenced in the Methods section (\u003cstrong\u003eSupplementary Table 1\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Code availability\u003c/p\u003e\n\u003cp\u003eNo custom code was generated for this project. All R scripts were adapted from publicly available packages, which are specified in the Methods section.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eHoward, F. M. \u0026amp; Olopade, O. I. Epidemiology of Triple-Negative Breast Cancer: A Review. \u003cem\u003eCancer J\u003c/em\u003e \u003cstrong\u003e27\u003c/strong\u003e, 8-16, doi:10.1097/PPO.0000000000000500 (2021).\u003c/li\u003e\n\u003cli\u003eHudis, C. A. \u0026amp; Gianni, L. Triple-negative breast cancer: an unmet medical need. \u003cem\u003eOncologist\u003c/em\u003e \u003cstrong\u003e16 Suppl 1\u003c/strong\u003e, 1-11, doi:10.1634/theoncologist.2011-S1-01 (2011).\u003c/li\u003e\n\u003cli\u003eSkinner, K. 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Web-Based Survival Analysis Tool Tailored for Medical Research (KMplot): Development and Implementation. \u003cem\u003eJ Med Internet Res\u003c/em\u003e \u003cstrong\u003e23\u003c/strong\u003e, e27633, doi:10.2196/27633 (2021).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Immunotherapy, RNA splicing, metastatic breast cancer, Triple-negative breast cancer, immune evasion, antigen presentation, immune checkpoint blockade, predictive biomarkers","lastPublishedDoi":"10.21203/rs.3.rs-7355872/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7355872/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTriple-negative breast cancer (TNBC) is a subtype with limited treatment options and poor outcomes, particularly in the metastatic setting. Although immunotherapy has shown efficacy in early-stage disease, its benefit remains suboptimal in women with locally advanced and metastatic TNBC. Here, we identify the splicing factor PTBP1 as a tumor-intrinsic regulator of immune evasion in metastatic TNBC. By integrating clinical, single-cell, and bulk transcriptomic data with multiplex immunohistochemistry, CRISPR-Cas9 genome editing, and functional assays, we show that PTBP1 impairs antigen presentation, promotes T cell dysfunction, and is associated with worse outcomes, independent of tumor-infiltrating lymphocyte levels. Furthermore, CRISPR-mediated silencing of PTBP1 restores HLA expression and reactivates antigen presentation pathways in TNBC. PTBP1 expression is elevated in metastatic compared to primary TNBC tumors and correlates with immune dysfunction signatures. Consistently, in the phase II TONIC clinical trial, metastatic TNBC patients with PTBP1-high tumors had poor response and shorter survival following PD-1 blockade, and PTBP1 expression showed a predictive performance comparable to PD-L1 and TILs in this cohort. These findings position PTBP1 as a tumor-intrinsic regulator of immune evasion and a potential biomarker to inform immunotherapy strategies in metastatic TNBC.\u003c/p\u003e","manuscriptTitle":"PTBP1 drives immune dysfunction and predicts immunotherapy response in metastatic triple-negative breast cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-12 15:19:09","doi":"10.21203/rs.3.rs-7355872/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-communications","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"NCOMMS","sideBox":"Learn more about [Nature Communications](http://www.nature.com/ncomms/)","snPcode":"","submissionUrl":"https://mts-ncomms.nature.com/","title":"Nature Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Communications","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"d4c54e8a-8569-4bb1-8190-66f36bbcb326","owner":[],"postedDate":"September 12th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":53511234,"name":"Biological sciences/Cancer/Breast cancer"},{"id":53511235,"name":"Health sciences/Oncology/Cancer/Breast cancer"}],"tags":[],"updatedAt":"2025-09-12T15:19:09+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-12 15:19:09","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7355872","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7355872","identity":"rs-7355872","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}