MAP3K1/MAP2K4 Mutations Drive Breast Cancer Progression by Compensating TP53 Loss via JNK2-p53-FOSL1 Axis Inactivation

MAP3K1/MAP2K4 Mutations Drive Breast Cancer Progression by Compensating TP53 Loss via JNK2-p53-FOSL1 Axis Inactivation | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article MAP3K1/MAP2K4 Mutations Drive Breast Cancer Progression by Compensating TP53 Loss via JNK2-p53-FOSL1 Axis Inactivation Sike Hu, Ailing Ji, Manxue Wang, Xia Li, Ying Zhang, Ruifang Gao, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7033784/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 16 Dec, 2025 Read the published version in Breast Cancer Research → Version 1 posted 10 You are reading this latest preprint version Abstract Background Breast cancer (BRCA) pathogenesis involves somatic mutations in key oncogenes and tumor suppressor genes, but the full spectrum of driver mutations and their functional impact remains incompletely understood. This study aimed to identify novel BRCA-specific driver mutations and elucidate their role in tumor progression. Methods We performed integrative analysis of The Cancer Genome Atlas (TCGA) datasets to identify recurrent mutations in BRCA. Statistical analyses included mutation frequency assessment, mutual exclusivity testing, and survival correlation. Functional validation was conducted using xenograft models to evaluate tumor proliferation and metastasis. Results We identified MAP3K1 and MAP2K4 as novel BRCA-specific driver genes, mutated in 8.77% and 4.02% of cases, respectively. These mutations showed strict mutual exclusivity with TP53 alterations, indicating functional redundancy within a shared pathway. Mechanistically, MAP3K1/MAP2K4 frameshift mutations inactivated the JNK2-p53-FOSL1 axis, relieving p53-mediated suppression of the pro-metastatic factor FRA1. While TP53 mutation rates (36.4%) appeared discordantly low given BRCA aggressiveness, inclusion of MAP3K1/MAP2K4 mutations (16.6%) revealed a compensatory inactivation rate of 53.01%, aligning BRCA with other high-malignancy cancers. Functional studies demonstrated that dominant-negative MAP2K4 (MKK4DN) promoted tumor proliferation and metastasis by suppressing JNK2-mediated p53 phosphorylation and upregulating FRA1. Clinically, patients with MAP3K1/MAP2K4 frameshift mutations exhibited significantly worse overall survival. Conclusions Our findings establish MAP3K1-MAP2K4-JNK2-p53-FOSL1 as a critical tumor-suppressive axis in BRCA, where MAP3K1/MAP2K4 mutations functionally compensate for TP53 loss to drive malignancy. This study expands the genomic framework of BRCA pathogenesis and identifies potential therapeutic targets for TP53-mutant breast cancers. breast cancer driver mutations MAP3K1 MAP2K4 JNK2 p53 FRA1/FOSL1 mutual exclusivity metastasis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1 Background Cancer is a heterogeneous disease driven by somatic mutations that promote uncontrolled cellular proliferation ( 1 ). A fundamental aspect of tumorigenesis involves "driver genes"—genes whose mutations confer selective growth advantages, enabling malignant transformation ( 2 , 3 ). These drivers are broadly classified into oncogenes (e.g., constitutively active KRAS) ( 4 ), which hyperactivate proliferative pathways, and tumor suppressor genes (e.g., TP53) ( 5 ), which lose regulatory functions upon inactivation. The interplay between these two classes of genes, whether inherited or acquired somatically, underpins cancer initiation and progression ( 2 , 3 ). Advances in cancer genomics have revolutionized our understanding of carcinogenesis by systematically identifying driver mutations across tumor types. These discoveries have not only elucidated molecular mechanisms of tumorigenesis but also facilitated the development of targeted therapies and genomic biomarkers for prognosis and treatment stratification. However, in breast cancer (BRCA), the landscape of driver mutations and their functional consequences remains incompletely defined ( 6 ). While canonical drivers such as TP53 and PIK3CA are well-characterized, the discovery of novel drivers—and their mechanistic roles in tumor progression—is critical for refining diagnostic precision and therapeutic strategies. Here, we aimed to address this gap by performing a comprehensive genomic analysis of BRCA using TCGA dataset. We identified MAP3K1 and MAP2K4 as recurrently mutated candidate driver genes with potential functional significance. Intriguingly, these mutations exhibited strict mutual exclusivity with TP53 alterations, suggesting compensatory roles within a shared pathway. Mechanistically, we demonstrate that MAP3K1 and MAP2K4 mutations converge on inactivation of the JNK2-p53-FOSL1 axis, a critical pathway for suppressing metastatic progression. This finding reveals how these mutations phenocopy TP53 loss to enhance tumor aggressiveness—a phenomenon previously attributed primarily to TP53 dysfunction alone. By uncovering these novel drivers and their interplay within the MAP3K1-MAP2K4-JNK2-p53-FOSL1 axis, our study expands the genomic framework of BRCA pathogenesis. These insights not only identify new therapeutic vulnerabilities but also highlight the combinatorial effects of driver mutations in shaping tumor behavior. Further exploration of these pathways may inform targeted strategies to counteract metastasis, particularly in TP53-mutant breast cancers. 2 Methods 2.1 Cell Lines and Culture Conditions Human breast cancer cell lines (ZR-75-1, MCF-7, BT-474, MDA-MB-231, MDA-MB-436, SUM149-PT, and T47D) were acquired from the American Type Culture Collection (ATCC, Manassas, VA, USA). All cell lines were authenticated using human short tandem repeat (STR) DNA profiling and routinely screened for mycoplasma contamination via polymerase chain reaction (PCR). 2.2 shRNA constructs, plasmids, and generation of stable cell lines ShRNA sequences targeting p53 and JNK2 were cloned into the pLV-H1-EF1α-puro lentiviral vector (Biosettia Inc., San Diego, CA). The constructed plasmids were packaged into lentiviral particles using standard protocols, and the resulting particles were used to transduce ZR-75-1 and MCF-7 cell lines. Stable transductants were selected by culturing in medium supplemented with 6µg/mL puromycin (Sigma-Aldrich) for 7–10 days. For MKK4D overexpression in ZR-75-1 cells, the cells were transfected with the PLV-MKK4DN vector. Transfected cells were then selected using 10 µg/mL blasticidin Sigma-Aldrich) until stable pools were established. 2.3 Western Blot Analysis Cells were lysed in RIPA buffer supplemented with protease and phosphatase inhibitor cocktails (Roche). Protein concentrations were determined using the BCA Protein Assay Kit (Pierce Biotechnology). Equal amounts of protein (20–50 µg) were denatured in 2× Laemmle buffer at 95°C for 5 min and separated by 10% SDS-PAGE. Proteins were transferred to PVDF membranes (Millipore, IPVH00010) using a wet transfer system at 100 V for 90 min. Membranes were blocked with 5% non-fat dry milk in TBST for 1 h at room temperature, then incubated overnight at 4°C with the following primary antibodies diluted in blocking buffer: Phospho-JNK (Thr183/Tyr185) (CST #9251, 1:1000),JNK2 (CST #9258, 1:1000), Phospho-p53 (Ser15) (CST #9284, 1:800),MKK4(CST #9152, 1:1000), FRA1 (CST #5281, 1:500), β-Actin (CST #4967, 1:2000) as loading control. After washing, membranes were incubated with HRP-conjugated anti-rabbit or anti-mouse secondary antibodies (CST #7074/7076, 1:5000) for 1 h at room temperature. Protein bands were visualized using ECL Prime Western Blotting Detection Reagent (Millipore) and imaged using a Tanon 5200 Chemiluminescent Imaging System (Tanon, China). 2.4 Correlation Analysis of MAPK9 Phosphorylation and FOSL1 Expression Proteomic and phosphoproteomic data for LIHC, GBM, HGSOC, and PAAD were obtained from the Clinical Proteomic Tumor Analysis Consortium (CPTAC) database ( https://pdc.cancer.gov/pdc/ ). To ensure data consistency, we exclusively analyzed samples processed within the same experimental batch to minimize potential batch effects. Raw data were normalized using variance-stabilizing transformation to account for technical variations. We then extracted: ( 1 ) total protein expression levels for MAPK8, MAPK9, FOSL1, and ( 2 ) corresponding phosphorylation data for MAPK8 and MAPK9 protein. Results were visualized using ggplot2-generated scatter plots. 2.5 MAP2K4/MAP3K1 Mutation Analysis in BRCA Mutation data were obtained from TCGA database ( https://portal.gdc.cancer.gov/ ), focusing on BRCA cohort. We first identified the top 20 most frequently mutated genes in BRCA based on mutation frequency analysis. Mutation spectra (including missense, frameshift, and nonsense variants) were visualized using waterfall plots generated through cBioPortal's OncoMatrix tool to evaluate co-occurrence and mutual exclusivity patterns among JNK pathway regulators. Specific mutation sites in MAP2K4 and MAP3K1 were mapped using ProteinPaint, with particular attention to functional domains affected by these mutations. To assess the clinical relevance of these mutations, we performed survival analysis comparing overall survival (OS) between mutant and wild-type groups using Kaplan-Meier curves, focusing specifically on frameshift mutations. All analyses—including mutation frequency calculations, OncoMatrix generation, cohort stratification, and survival analysis—were conducted using the TCGA Analysis Center to ensure standardized data processing and reproducibility. 2.6 Pan-Cancer Analysis of MAP2K4/MAP3K1/TP53 Mutations and FOSL1 Correlation To investigate the relationship between TP53 mutations and FOSL1 expression while assessing potential compensatory roles of MAP2K4/MAP3K1 mutations in TP53-deficient breast cancers, we analyzed TCGA datasets through GDC Data Transfer Tool 2.0. Mutation data for MAP2K4, MAP3K1, and TP53 were collected across cancer types. For TP53-FOSL1 correlation studies, cases were stratified into: ( 1 ) P53null group containing TP53 frameshift mutations or ATM/ATR frameshift mutations; ( 2 ) WTp53 group. Breast cancer-specific analyses employed tiered cohort definitions: Initial comparison contrasted p53/ATM/ATR frameshift cases against WTp53 controls. Enhanced analysis compared cohort B (p53/ATM/ATR frameshift + MAP2K4/MAP3K1 frameshift mutations) with cohort A (strict WTp53 controls lacking mutations in TP53, ATM/ATR, MAP2K4/MAP3K1, or upstream JNK pathway components). This stratification enabled precise evaluation of mutation patterns and their functional impacts. 2.7 Lung Metastasis Mouse Model All animal procedures were performed in compliance with protocols approved by the Institutional Animal Care and Use Committee (IACUC) of Tianjin Institute of Medical and Pharmaceutical Sciences. Female NOD/SCID mice (6–8 weeks old) were purchased from Beijing Vital River Laboratory Animal Technology and maintained under specific pathogen-free conditions. MCF-7 cells (4 *10 6 cells in 200 µL PBS) stably overexpressing MKK4DN or empty vector control were bilaterally injected into the mammary fat pads. Tumor dimensions were measured twice weekly using digital calipers, and tumor volume was calculated as: (length × width²)/2 (mm³). After 38 days, mice were euthanized and tumor tissues were excised, weighed, and photographed. For metastasis evaluation, Lungs were perfused with 4% paraformaldehyde (PFA) via tracheal instillation. Macroscopic metastases were counted under a dissecting microscope and measured using ImageJ software (NIH). 2.8 Statistical analysis Statistical analyses were conducted in Graph Pad Prism9.0 (CA, USA). Statistical significance was calculated using paired/unpaired Student’s t-tests or One-way ANOVA as appropriate. Pearson’s correlation analysis was used to assess the correlation between the two sets of data. Survival rates were calculated with the K-M method. Each experiment was performed at least three times. Statistical significance was set at P < 0.05. 3 Results 3.1 Identification of MAP3K1/MAP2K4 as Breast Cancer-Specific Driver Genes Through integrative analysis of TCGA pan-cancer genomic datasets, we systematically characterized somatic copy number alterations (SCNAs) and exonic mutations across protein-coding genes. While significant heterogeneity in mutational burden was observed across malignancies, breast cancer (BRCA) exhibited a distinctive mutation profile: MAP3K1 and MAP2K4 emerged as the 7th (8.77%) and 16th (4.02%) most frequently mutated genes, respectively (Fig. 1 A). This mutation prevalence significantly exceeded rates observed in other tumor types, suggesting BRCA-specific driver functions. Functionally positioned as critical kinase cascade regulators, MAP3K1 phosphorylates MAP2K1/MAP2K4 to activate ERK/JNK pathways( 7 ), while MAP2K4 directly mediates JNK/p38 signaling( 8 – 10 ). Since both MAP3K1/MAP2K4 are upstream of JNK, we investigate the mutations of other upstream. Strikingly, mutations in these JNK pathway regulators (MAP3K1/MAP2K4 included) demonstrated strict mutual exclusivity (Fig. 1 B) - only one altered gene per tumor-recapitulating the classical pattern observed in KRAS/BRAF mutations within the RAS-RAF-MAPK pathway( 11 ). This "single-hit" configuration aligns with the established paradigm of oncogenic pathway activation requiring mutation at only one critical node( 12 ). These findings collectively suggest that MAP3K1/MAP2K4 mutations drive BRCA tumorigenesis through JNK pathway dysfunction. The observed mutual exclusivity implies biological redundancy, where concurrent mutations in the same signaling cascade would provide no incremental selective advantage. This pattern reinforces their classification as bona fide driver alterations specific to breast cancer pathogenesis. 3.2 Functional Characterization of MAP3K1 and MAP2K4 as Tumor Suppressor Genes Driver genes are conventionally classified into two functional categories: oncogenes and tumor suppressor genes (TSGs). Our systematic analysis of mutational landscapes in breast cancer (BRCA) reveals compelling evidence for MAP3K1 and MAP2K4 functioning as TSGs through distinct inactivation mechanisms. In the TCGA BRCA cohort, MAP3K1 showed 115 genetic alterations across 85 cases, comprising splice region variants (SPLICE_REGION), silent mutations (SILENT), frameshift deletions (FRAMESHIFT_DEL), splice site mutations (SPLICE), nonsense mutations (NONSENSE), missense mutations (MISSENSE), and frameshift insertions (FRAMESHIFT_INS). Notably, frameshift mutations predominated (60/85 cases, 70.6%) (Fig. 2 A), suggesting pervasive loss-of-function alterations. Similarly, MAP2K4 exhibited 35 alterations with frameshift events in 10 cases (28.6%), while remaining missense mutations clustered within catalytic domains and conserved phosphorylation sites. This mutation spectrum implies dual inactivation mechanisms: complete functional ablation through frameshifts, and partial impairment via catalytic domain disruption - a pattern requiring further mechanistic validation. These mutational profiles align with canonical TSG inactivation patterns, characterized by truncating mutations (frameshifts/nonsense) and allelic loss (LOH). The high prevalence of frameshift mutations induces premature stop codons, producing truncated proteins that disrupt tumor-suppressive functions through either dominant-negative effects or complete loss of signaling capacity. Clinically, patients with MAP3K1/MAP2K4 frameshift mutations (Cohort S2, n = 68) demonstrated significantly reduced overall survival compared to JNK pathway-intact controls (Cohort S1, n = 915) (log-rank P < 0.001) (Fig. 2 B). This prognostic association underscores the tumor-suppressive role of intact MAP3K1/MAP2K4 signaling, where genetic inactivation accelerates disease progression through JNK pathway dysregulation. 3.3 Molecular Mechanism of MAP3K1/MAP2K4-Mediated Tumor Suppression via JNK2-FRA1 Signaling Axis While MAP3K1 and MAP2K4 (MKK4) have been reported to suppress tumor metastasis in various cancers ( 13 – 17 ), the mechanisms by which their mutations drive tumor progression remain incompletely understood. Building on our previous discovery of the "Goldilocks principle" for JNK signaling—where moderate activation promotes metastasis but hyperactivation suppresses it, primarily via FRA1/FOSL1 regulation( 18 )—we hypothesized that MAP3K1/MAP2K4, as upstream regulators of JNK, might modulate malignant transformation through selective activation of JNK. Western blot analysis revealed an inverse correlation between p-JNK1/2 and FRA1 levels in breast cancer cell lines: MCF-7, ZR-75-1, and BT-474 exhibited high p-JNK1/2 with low FRA1, whereas MDA-MB-231, MDA-MB-436, sum149-PT and T47D showed reduced p-JNK1/2 accompanied by elevated FRA1(Fig. 3 A). To dissect JNK1 versus JNK2 contributions, we analyzed CPTAC pan-cancer data, observing distinct patterns: hepatocellular carcinomas (LIHC) exhibited elevated p-JNK1/2 with suppressed FRA1; gliomas (GBM) displayed high p-JNK2 but low p-JNK1 and FRA1; pancreatic (PAAD) and ovarian (HGSOC) cancers demonstrated low p-JNK1/2 and high FRA1 (Fig. 3 B). These findings suggest no significant association between p-JNK1 and FOSL1, whereas p-JNK2 inversely correlated with FOSL1. Functional validation in MCF-7 and ZR-75-1 cells showed that JNK2 knockdown upregulated FRA1 (Fig. 3 C). Similarly, overexpression of a dominant-negative MAP2K4 mutant (MKK4DN) increased FRA1 levels (Fig. 3 D). Crucially, MKK4DN overexpression in JNK2-knockdown cells did not further enhance FRA1 (Fig. 3 E), confirming that MAP2K4 suppresses FRA1-mediated oncogenesis specifically through JNK2 activation. These findings demonstrate that MAP3K1/MAP2K4 mutations drive tumor proliferation and metastasis by relieving JNK2-mediated suppression of FRA1, establishing a mechanistic foundation for their tumor-promoting role in BRCA. 3.4 Compensatory Role of MAP3K1 & MAP2K4 Mutations for p53 Loss in BRCA Our previous studies demonstrated that phosphorylation of p53 at serine 15 leads to its binding to the FOSL1 promoter, resulting in suppression of downstream FRA1 expression and subsequent inhibition of tumor proliferation. This phosphorylation can be activated by various kinases including JNK2 and ATM/ATR. Analysis of TCGA data revealed a significant mutual exclusivity pattern among MAP3K1, MAP2K4, and TP53 mutations in BRCA (Fig. 4 A), supporting their functional involvement in the same pathway where inactivation of any single component is sufficient to disrupt the entire axis. Pan-cancer TCGA analysis showed a positive correlation between TP53 mutation frequency (including ATM/ATR mutations that also inactivate p53 function) and tumor aggressiveness. Low-malignancy tumors like THCA (2.04%), PRAD (14.1%), and LIHC (33.1%) contrast sharply with high-malignancy tumors such as OV (87.8%), ESCA (86.4%), LUSC (84.1%), and READ (74.8%). Notably, BRCA showed a discordantly low TP53 mutation rate (36.4%) relative to its expected malignancy level (50–60% based on molecular subtypes) (Fig. 4 B). However, when including MAP3K1/MAP2K4 mutations (16.6%), the total inactivation rate reached 53.01%, comparable to STAD (53.7%) and LUAD (56.8%) (Fig. 4 C), suggesting a compensatory mechanism. Examination of p53 loss-of-function mutations revealed three distinct patterns in the p53-FOSL1 regulatory relationship. In PRAD, LIHC, and LGG, p53-null tumors exhibited significantly higher FOSL1 expression compared to p53-WT tumors, consistent with p53’s established role in transcriptionally repressing FOSL1 (Fig. 4 D). In COAD, LUAD, ESCA, and OV, p53-null tumors showed only a modest increase in FOSL1 expression relative to p53-WT tumors. This attenuation of FOSL1 upregulation likely reflects the dominant effects of concurrent oncogenic driver mutations (e.g., KRAS/PIK3CA), which mask the regulatory impact of p53 loss (Fig. 4 E). Notably, BRCA displayed a paradoxical pattern. Initial analysis revealed lower FOSL1 expression in p53-null tumors (Fig. 4 F, BRCA-1). However, after re-stratifying cohorts into Cohort A (pure p53-WT, excluding all mutations listed in Fig. 1 ) and Cohort B (p53-null or MAP3K1/MAP2K4 frameshift), Cohort B showed a marked elevation in FOSL1 levels (Fig. 4 F, BRCA-2). This aligns with the high-grade malignancy trend observed in COAD, LUAD, ESCA, and OV, resolving the initial discrepancy. These findings demonstrate that MAP3K1/MAP2K4 mutations act as a BRCA-specific compensatory mechanism for p53 pathway inactivation. By disrupting the JNK2-p53-FRA1 axis, these mutations phenocopy the effects of p53 loss—even in the absence of direct TP53 mutations—thereby driving aggressive tumor behavior. 3.5 MKK4DN Promotes Breast Cancer Metastasis via JNK-p53 Signaling To investigate the functional role of MAP2K4 (MKK4) in tumor progression, we established xenograft models by inoculating nude mice with MCF7 cells stably expressing MKK4 dominant-negative mutant (MCF7-MKK4DN) or empty vector control (MCF7-MCS) (n = 7/group). MCF7-MKK4DN group exhibited significantly larger tumor volumes, indicating accelerated local proliferation upon MKK4 inactivation (Fig. 5 A, 5 B). Terminal dissection demonstrated that MCF7-MKK4DN group developed more pulmonary metastases compared to controls (Fig. 5 C, 5 D, 5 E). Mechanistically, immunohistochemistry showed sharp reductions in phospho-JNK1/2 and phospho-p53 (Ser15) levels, accompanied by upregulation of FRA1 protein in MCF7-MKK4DN tumors (Fig. 5 F). These findings demonstrate that MKK4 inactivation disrupts JNK-mediated p53 phosphorylation, thereby releasing transcriptional repression of FRA1 to drive tumor metastasis. 4 Discussion Driver genes serve as the molecular foundation for tumorigenesis and progression, and their identification is crucial for elucidating cancer mechanisms ( 2 , 3 ). This study systematically analyzed the mutational landscape of upstream genes in the JNK signaling pathway, revealing a striking divergence in mutation frequencies between two paralogous kinases: MKK4 and its upstream regulators exhibited high-frequency driver mutations, whereas MKK7 showed significantly lower mutation rates. This disparity likely reflects their distinct signaling roles—MKK4 primarily mediates extracellular stimuli (e.g., cytokines, growth factors, and stress signals), while MKK7 predominantly responds to intracellular signals (e.g., ROS and ER stress)( 19 , 20 ). During early tumorigenesis, persistent extracellular stimuli such as chronic inflammation drive continuous MKK4 activation ( 21 ), prompting neoplastic cells to acquire MKK4 mutations as an adaptive mechanism to escape inflammatory microenvironmental pressures. In contrast, the MKK7 pathway remains less frequently activated, explaining its lower mutational burden. Our findings further demonstrate that mutations in MAP3K1 and MAP2K4, upstream kinases of JNK2, function through the MAP3K1-MAP2K4-JNK2-p53-FRA1 axis to alleviate p53-mediated suppression of FRA1 expression, thereby promoting tumor growth and metastasis. Notably, MAP3K1/MAP2K4 mutations and TP53 mutations display mutual exclusivity in breast cancer, suggesting that MAP3K1/MAP2K4 loss-of-function can serve as an alternative mechanism for p53 pathway inactivation in BRCA tumors. This observation aligns with the "minimal necessary alteration" principle of driver mutations—in key signaling pathways (e.g., MAPK, PI3K-AKT, p53), multiple mutations within the same pathway rarely confer synergistic advantages ( 22 , 23 ). For example, the mutual exclusivity of KRAS and BRAF mutations in the RAS-RAF-MAPK pathway underscores that a single driver mutation is often sufficient to maximally activate the pathway( 24 ). FRA1, a core AP-1 transcription factor family member, acts as a gatekeeper of epithelial-mesenchymal transition (EMT) and tumor metastasis ( 25 – 27 ). Its oncogenic upregulation occurs through two complementary mechanisms: ( 1 ) the MAP3K1-MAP2K4-JNK2-p53-FRA1 axis relieves tumor-suppressive effects via loss-of-function mutations, while ( 2 ) the EGFR-RAS-BRAF-ERK1/2-FRA1 pathway enhances oncogenic signaling through gain-of-function mutations (Fig. 6 ). Together, these pathways synergistically upregulate FRA1 expression through a dual "release-the-brake and step-on-the-gas" mechanism, thereby driving EMT and metastatic progression. This mechanistic interplay also accounts for the frequent co-occurrence of RAS and TP53 mutations across pan-cancer analyses, as these pathways collectively accelerate tumor development. However, while our functional validation of MAP3K1/MAP2K4 mutations relied primarily on in vitro and bioinformatic analyses, further in vivo studies are warranted to fully elucidate their role in tumor progression. 5 Conclusions In summary, our study reveals key mechanistic insights into the mutational landscape of the JNK signaling pathway in breast cancer. We further uncover the MAP3K1-MAP2K4-JNK2-p53-FRA1 axis as an alternative pathway for p53 inactivation, highlighting the principle of "minimal necessary alteration" in driver gene selection. Additionally, the dual regulation of FRA1 through complementary mechanisms underscores the complexity of oncogenic signaling networks in promoting metastasis. These findings advance our understanding of breast cancer heterogeneity and provide a framework for mechanism-driven precision medicine, where targeting specific mutational signatures (e.g., MAP3K1/MAP2K4 loss or FRA1 upregulation) could offer novel therapeutic strategies. Future studies should explore the translational potential of these discoveries in preclinical models and clinical cohorts, ultimately improving patient stratification and treatment outcomes. Abbreviations MKK4DN (dominant-negative MAP2K4) LIHC (Liver Hepatocellular Carcinoma)， GBM (Glioblastoma Multiforme)， PAAD (Pancreatic Adenocarcinoma)， HGSOC (High-Grade Serous Ovarian Cancer) Declarations Ethics approval and consent to participate All animal procedures were approved by the Tianjin Institute of Medical & Pharmaceutical Sciences Animal Care Committee and complied with international welfare standards. The maximal permitted tumor burden approved by the ethics committee was 2 cm³ and no umors exceeded this approved limit during the study. Comprehensive welfare measures— including daily health monitoring, strictly enforced humane endpoints, postoperative analgesia, and environmental enrichment—were implemented. All procedures were documented, and source data were maintained in accordance with institutional and journal requirements. Ethical approval ensured full compliance with ARRIVE guidelines. Consent for publication Not applicable Competing interests The authors declare no conflicts of interest to the present study. Funding This work was supported Science and Technology Fund of Tianjin Municipal Health Commission (NO. TJWJ2021QN077). Author Contribution SH designed the study and wrote the manuscript. AJ and MW performed the experiments and data analysis. RG performed data analysis. XL, LS and YZ performed the animal experiments. All authors edited and revised the manuscript and were involved in the final approval of the manuscript. Acknowledgements Not applicable Data Availability The datasets generated and analyzed during this study are available in the Proteomic Data Commons (PDC) database (https://pdc.cancer.gov/pdc/). 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Supplementary Files supplementaryfile.docx Cite Share Download PDF Status: Published Journal Publication published 16 Dec, 2025 Read the published version in Breast Cancer Research → Version 1 posted Editorial decision: Revision requested 13 Oct, 2025 Reviews received at journal 24 Sep, 2025 Reviewers agreed at journal 12 Sep, 2025 Reviewers agreed at journal 11 Sep, 2025 Reviews received at journal 14 Aug, 2025 Reviewers agreed at journal 05 Aug, 2025 Reviewers invited by journal 05 Aug, 2025 Editor assigned by journal 08 Jul, 2025 Submission checks completed at journal 08 Jul, 2025 First submitted to journal 02 Jul, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-7033784","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":497317694,"identity":"02fe4c31-89bf-46d7-b620-2e212dc1a1d0","order_by":0,"name":"Sike Hu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAArUlEQVRIiWNgGAWjYBACAyA+2MBgw8PP3kCaljQZyZ4DJGhhbGA4bGNww4FILeYS6Q8Pzqg4z8Nwg4Hxw8ccIrRYzsgxOLjhzG0extkNzJIztxHjsBs5DAcftt3mYZY5wMbMS5yW9AcHH/47x8MmkUC0lgSDgxsbDvDwEK/lzBuDgzOOJfNI8BxsJtIvx9Mff+ypsbO3P9588MNHYrQgAWD0jIJRMApGwSigEgAAtOU7lwd3S9EAAAAASUVORK5CYII=","orcid":"","institution":"Tianjin Institute of Medical and Pharmaceutical Sciences","correspondingAuthor":true,"prefix":"","firstName":"Sike","middleName":"","lastName":"Hu","suffix":""},{"id":497317695,"identity":"f1dfbd51-8193-4ce2-a828-25fcec0bd264","order_by":1,"name":"Ailing Ji","email":"","orcid":"","institution":"Tianjin Institute of Medical and Pharmaceutical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Ailing","middleName":"","lastName":"Ji","suffix":""},{"id":497317696,"identity":"4c644c17-a240-47cc-888f-76510faceddb","order_by":2,"name":"Manxue Wang","email":"","orcid":"","institution":"Tianjin Institute of Medical and Pharmaceutical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Manxue","middleName":"","lastName":"Wang","suffix":""},{"id":497317697,"identity":"03effb5d-39a8-4994-abb8-c7e9eef7c3c9","order_by":3,"name":"Xia Li","email":"","orcid":"","institution":"Tianjin Institute of Medical and Pharmaceutical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Xia","middleName":"","lastName":"Li","suffix":""},{"id":497317698,"identity":"9c3716ac-b5d5-4534-a270-78af0d66d6ad","order_by":4,"name":"Ying Zhang","email":"","orcid":"","institution":"Tianjin Institute of Medical and Pharmaceutical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Ying","middleName":"","lastName":"Zhang","suffix":""},{"id":497317699,"identity":"b6344525-783e-494d-96ec-2c7b152fa0e2","order_by":5,"name":"Ruifang Gao","email":"","orcid":"","institution":"Tianjin Institute of Medical and Pharmaceutical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Ruifang","middleName":"","lastName":"Gao","suffix":""},{"id":497317700,"identity":"fe19e520-f091-4818-9892-1994746201cd","order_by":6,"name":"Lili Sun","email":"","orcid":"","institution":"Tianjin Institute of Medical and Pharmaceutical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Lili","middleName":"","lastName":"Sun","suffix":""}],"badges":[],"createdAt":"2025-07-03 03:38:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7033784/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7033784/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s13058-025-02195-3","type":"published","date":"2025-12-16T15:58:38+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":88773741,"identity":"f9782687-32b4-44d7-ba8d-a00fd2e6a34e","added_by":"auto","created_at":"2025-08-11 09:58:46","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":571618,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGenomic landscape of driver mutations in breast cancer\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Top 20 most frequently mutated genes in BRCA.\u003c/p\u003e\n\u003cp\u003e(B) Waterfall plot depicting the distribution and types of somatic mutations (e.g., missense, frameshift) and assessing co-occurrence/mutual exclusivity patterns among upstream regulators of the JNK pathway.\u003c/p\u003e","description":"","filename":"Figures101.png","url":"https://assets-eu.researchsquare.com/files/rs-7033784/v1/7d938ffd7a2ec8ac87783acd.png"},{"id":88775801,"identity":"e9f18308-d8a1-481e-8638-637f00186884","added_by":"auto","created_at":"2025-08-11 10:06:47","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1039804,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFunctional characterization of MAP3K1 and MAP2K4 as tumor suppressor genes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Mutation landscape of MAP2K4 and MAP3K1 in breast cancer. Specific mutation sites were mapped using Protein Paint, with functional domains highlighted to indicate affected regions.\u003c/p\u003e\n\u003cp\u003e(B) Clinical impact of MAP2K4/MAP3K1 frameshift mutations. Kaplan-Meier survival analysis comparing overall survival (OS) between mutant and wild-type groups.\u003c/p\u003e","description":"","filename":"Figures102.png","url":"https://assets-eu.researchsquare.com/files/rs-7033784/v1/a9e5b37838e92399c62c39b2.png"},{"id":88773750,"identity":"6130b141-c821-4b7d-8206-146e6775d37d","added_by":"auto","created_at":"2025-08-11 09:58:47","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":943391,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMolecular mechanism of MAP3K1/MAP2K4-mediated tumor suppression through the JNK2-FRA1 signaling axis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Western blot analysis of phospho-JNK1/2 (T183/Y185) and FRA1 expression across a panel of BRCA cell lines.\u003c/p\u003e\n\u003cp\u003e(B) Correlation analysis between MAPK8/9 (JNK1/2) phosphorylation levels and FOSL1 (FRA1) protein expression in CPTAC datasets of LIHC, GBM, HGSOC, and PAAD.\u003c/p\u003e\n\u003cp\u003e(C) Immunoblot analysis of FRA1 and JNK2 protein levels in ZR-75-1 and MCF-7 cells following JNK2 knockdown.\u003c/p\u003e\n\u003cp\u003e(D) Immunoblot analysis of phospho-JNK1/2 and FRA1 levels in ZR-75-1 cells with MKK4DN overexpression.\u003c/p\u003e\n\u003cp\u003e(E) Immunoblot analysis of FRA1 levels in MKK4DN overexpression cells in ZR-75-1-shJNK2.\u003c/p\u003e","description":"","filename":"Figures103.png","url":"https://assets-eu.researchsquare.com/files/rs-7033784/v1/45b3e143f8cbaeac83a5d55b.png"},{"id":88777661,"identity":"17cf4471-9b7c-42f0-adfd-c2e2a622d03d","added_by":"auto","created_at":"2025-08-11 10:14:47","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1156280,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCompensatory role of MAP3K1 and MAP2K4 mutations in mutp53-deficient breast cancer\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Mutation landscape of TP53, MAP2K4, and MAP3K1 in BRCA. Waterfall plot displays mutation patterns and evaluates co-occurrence/mutual exclusivity.\u003c/p\u003e\n\u003cp\u003e(B) Pan-cancer analysis of TP53 mutation prevalence (including ATM/ATR). Mutation frequencies were quantified and visualized using stacked bar plots.\u003c/p\u003e\n\u003cp\u003e(C) Mutation frequencies of TP53 (including ATM/ATR), MAP2K4/MAP3K1, and other JNK pathway upstream regulators in BRCA. The combined mutation rate of these pathways is shown.\u003c/p\u003e\n\u003cp\u003e(D, E) Pan-cancer correlation analysis between p53 null and FOSL1 expression.\u003c/p\u003e\n\u003cp\u003e(F) Correlation analysis between MAP2K4/MAP3K1/TP53 mutations and FOSL1 expression in BRCA.\u003c/p\u003e","description":"","filename":"Figures104.png","url":"https://assets-eu.researchsquare.com/files/rs-7033784/v1/406e82b5b191b6c4187c1689.png"},{"id":88775804,"identity":"126af25a-2592-4352-9ea0-545b2c78d52b","added_by":"auto","created_at":"2025-08-11 10:06:47","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1176592,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMKK4DN promotes lung metastasis in mice\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Tumor growth curves of MCF7-MCS and MCF7-MKK4DN xenografts in nude mice. (B) Representative images showing tumor size differences. (C) Quantitative analysis of lung metastasis foci number in nude mice. The total number of metastatic foci in each lung lobe was counted. (D) Metastasis burden analysis. The total metastatic area from five lung lobes per mouse was quantified (AU, arbitrary units). (E) Representative H\u0026amp;E staining of lung metastases (red arrows indicate metastatic foci). Data are presented as mean ± SEM (n = 7); t-test: **P \u0026lt; 0.01, *P \u0026lt; 0.05, ns = not significant. (F) Immunohistochemically analysis of FRA1, p-JNK1/2, and p53ser15 expression in tumor tissues.\u003c/p\u003e","description":"","filename":"Figures105.png","url":"https://assets-eu.researchsquare.com/files/rs-7033784/v1/5763e1090e03fa35acdd5029.png"},{"id":88773747,"identity":"bd8f59f9-2153-4f7c-8421-9e9827d7d5a8","added_by":"auto","created_at":"2025-08-11 09:58:47","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":126067,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFRA1 upregulation occurs through dual mechanisms\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figures106.png","url":"https://assets-eu.researchsquare.com/files/rs-7033784/v1/08d8d21ad6dc41b42f187be9.png"},{"id":98814060,"identity":"a82e1660-e061-4d36-b8dc-0d992fdf0285","added_by":"auto","created_at":"2025-12-22 16:10:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5347585,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7033784/v1/f1575eda-59f2-49f2-aa2f-dda4bb347fce.pdf"},{"id":88773744,"identity":"cdf84d23-5a74-4140-9949-17756f112e76","added_by":"auto","created_at":"2025-08-11 09:58:47","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":2490789,"visible":true,"origin":"","legend":"","description":"","filename":"supplementaryfile.docx","url":"https://assets-eu.researchsquare.com/files/rs-7033784/v1/8fdf0cf15fde0ac36995fd2c.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"MAP3K1/MAP2K4 Mutations Drive Breast Cancer Progression by Compensating TP53 Loss via JNK2-p53-FOSL1 Axis Inactivation","fulltext":[{"header":"1 Background","content":"\u003cp\u003eCancer is a heterogeneous disease driven by somatic mutations that promote uncontrolled cellular proliferation (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). A fundamental aspect of tumorigenesis involves \"driver genes\"\u0026mdash;genes whose mutations confer selective growth advantages, enabling malignant transformation (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). These drivers are broadly classified into oncogenes (e.g., constitutively active KRAS) (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e), which hyperactivate proliferative pathways, and tumor suppressor genes (e.g., TP53) (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e), which lose regulatory functions upon inactivation. The interplay between these two classes of genes, whether inherited or acquired somatically, underpins cancer initiation and progression (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAdvances in cancer genomics have revolutionized our understanding of carcinogenesis by systematically identifying driver mutations across tumor types. These discoveries have not only elucidated molecular mechanisms of tumorigenesis but also facilitated the development of targeted therapies and genomic biomarkers for prognosis and treatment stratification. However, in breast cancer (BRCA), the landscape of driver mutations and their functional consequences remains incompletely defined (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). While canonical drivers such as TP53 and PIK3CA are well-characterized, the discovery of novel drivers\u0026mdash;and their mechanistic roles in tumor progression\u0026mdash;is critical for refining diagnostic precision and therapeutic strategies.\u003c/p\u003e\u003cp\u003eHere, we aimed to address this gap by performing a comprehensive genomic analysis of BRCA using TCGA dataset. We identified MAP3K1 and MAP2K4 as recurrently mutated candidate driver genes with potential functional significance. Intriguingly, these mutations exhibited strict mutual exclusivity with TP53 alterations, suggesting compensatory roles within a shared pathway. Mechanistically, we demonstrate that MAP3K1 and MAP2K4 mutations converge on inactivation of the JNK2-p53-FOSL1 axis, a critical pathway for suppressing metastatic progression. This finding reveals how these mutations phenocopy TP53 loss to enhance tumor aggressiveness\u0026mdash;a phenomenon previously attributed primarily to TP53 dysfunction alone.\u003c/p\u003e\u003cp\u003eBy uncovering these novel drivers and their interplay within the MAP3K1-MAP2K4-JNK2-p53-FOSL1 axis, our study expands the genomic framework of BRCA pathogenesis. These insights not only identify new therapeutic vulnerabilities but also highlight the combinatorial effects of driver mutations in shaping tumor behavior. Further exploration of these pathways may inform targeted strategies to counteract metastasis, particularly in TP53-mutant breast cancers.\u003c/p\u003e"},{"header":"2 Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Cell Lines and Culture Conditions\u003c/h2\u003e\u003cp\u003eHuman breast cancer cell lines (ZR-75-1, MCF-7, BT-474, MDA-MB-231, MDA-MB-436, SUM149-PT, and T47D) were acquired from the American Type Culture Collection (ATCC, Manassas, VA, USA). All cell lines were authenticated using human short tandem repeat (STR) DNA profiling and routinely screened for mycoplasma contamination via polymerase chain reaction (PCR).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 shRNA constructs, plasmids, and generation of stable cell lines\u003c/h2\u003e\u003cp\u003eShRNA sequences targeting p53 and JNK2 were cloned into the pLV-H1-EF1α-puro lentiviral vector (Biosettia Inc., San Diego, CA). The constructed plasmids were packaged into lentiviral particles using standard protocols, and the resulting particles were used to transduce ZR-75-1 and MCF-7 cell lines. Stable transductants were selected by culturing in medium supplemented with 6\u0026micro;g/mL puromycin (Sigma-Aldrich) for 7\u0026ndash;10 days. For MKK4D overexpression in ZR-75-1 cells, the cells were transfected with the PLV-MKK4DN vector. Transfected cells were then selected using 10 \u0026micro;g/mL blasticidin Sigma-Aldrich) until stable pools were established.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Western Blot Analysis\u003c/h2\u003e\u003cp\u003eCells were lysed in RIPA buffer supplemented with protease and phosphatase inhibitor cocktails (Roche). Protein concentrations were determined using the BCA Protein Assay Kit (Pierce Biotechnology). Equal amounts of protein (20\u0026ndash;50 \u0026micro;g) were denatured in 2\u0026times; Laemmle buffer at 95\u0026deg;C for 5 min and separated by 10% SDS-PAGE. Proteins were transferred to PVDF membranes (Millipore, IPVH00010) using a wet transfer system at 100 V for 90 min. Membranes were blocked with 5% non-fat dry milk in TBST for 1 h at room temperature, then incubated overnight at 4\u0026deg;C with the following primary antibodies diluted in blocking buffer: Phospho-JNK (Thr183/Tyr185) (CST #9251, 1:1000),JNK2 (CST #9258, 1:1000), Phospho-p53 (Ser15) (CST #9284, 1:800),MKK4(CST #9152, 1:1000), FRA1 (CST #5281, 1:500), β-Actin (CST #4967, 1:2000) as loading control. After washing, membranes were incubated with HRP-conjugated anti-rabbit or anti-mouse secondary antibodies (CST #7074/7076, 1:5000) for 1 h at room temperature. Protein bands were visualized using ECL Prime Western Blotting Detection Reagent (Millipore) and imaged using a Tanon 5200 Chemiluminescent Imaging System (Tanon, China).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Correlation Analysis of MAPK9 Phosphorylation and FOSL1 Expression\u003c/h2\u003e\u003cp\u003eProteomic and phosphoproteomic data for LIHC, GBM, HGSOC, and PAAD were obtained from the Clinical Proteomic Tumor Analysis Consortium (CPTAC) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pdc.cancer.gov/pdc/\u003c/span\u003e\u003cspan address=\"https://pdc.cancer.gov/pdc/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). To ensure data consistency, we exclusively analyzed samples processed within the same experimental batch to minimize potential batch effects.\u003c/p\u003e\u003cp\u003eRaw data were normalized using variance-stabilizing transformation to account for technical variations. We then extracted: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) total protein expression levels for MAPK8, MAPK9, FOSL1, and (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) corresponding phosphorylation data for MAPK8 and MAPK9 protein. Results were visualized using ggplot2-generated scatter plots.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 MAP2K4/MAP3K1 Mutation Analysis in BRCA\u003c/h2\u003e\u003cp\u003eMutation data were obtained from TCGA database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://portal.gdc.cancer.gov/\u003c/span\u003e\u003cspan address=\"https://portal.gdc.cancer.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), focusing on BRCA cohort. We first identified the top 20 most frequently mutated genes in BRCA based on mutation frequency analysis. Mutation spectra (including missense, frameshift, and nonsense variants) were visualized using waterfall plots generated through cBioPortal's OncoMatrix tool to evaluate co-occurrence and mutual exclusivity patterns among JNK pathway regulators.\u003c/p\u003e\u003cp\u003eSpecific mutation sites in MAP2K4 and MAP3K1 were mapped using ProteinPaint, with particular attention to functional domains affected by these mutations. To assess the clinical relevance of these mutations, we performed survival analysis comparing overall survival (OS) between mutant and wild-type groups using Kaplan-Meier curves, focusing specifically on frameshift mutations.\u003c/p\u003e\u003cp\u003eAll analyses\u0026mdash;including mutation frequency calculations, OncoMatrix generation, cohort stratification, and survival analysis\u0026mdash;were conducted using the TCGA Analysis Center to ensure standardized data processing and reproducibility.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.6 Pan-Cancer Analysis of MAP2K4/MAP3K1/TP53 Mutations and FOSL1 Correlation\u003c/h2\u003e\u003cp\u003eTo investigate the relationship between TP53 mutations and FOSL1 expression while assessing potential compensatory roles of MAP2K4/MAP3K1 mutations in TP53-deficient breast cancers, we analyzed TCGA datasets through GDC Data Transfer Tool 2.0. Mutation data for MAP2K4, MAP3K1, and TP53 were collected across cancer types.\u003c/p\u003e\u003cp\u003eFor TP53-FOSL1 correlation studies, cases were stratified into: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) P53null group containing TP53 frameshift mutations or ATM/ATR frameshift mutations; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) WTp53 group.\u003c/p\u003e\u003cp\u003eBreast cancer-specific analyses employed tiered cohort definitions: Initial comparison contrasted p53/ATM/ATR frameshift cases against WTp53 controls. Enhanced analysis compared cohort B (p53/ATM/ATR frameshift\u0026thinsp;+\u0026thinsp;MAP2K4/MAP3K1 frameshift mutations) with cohort A (strict WTp53 controls lacking mutations in TP53, ATM/ATR, MAP2K4/MAP3K1, or upstream JNK pathway components). This stratification enabled precise evaluation of mutation patterns and their functional impacts.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e2.7 Lung Metastasis Mouse Model\u003c/h2\u003e\u003cp\u003e All animal procedures were performed in compliance with protocols approved by the Institutional Animal Care and Use Committee (IACUC) of Tianjin Institute of Medical and Pharmaceutical Sciences. Female NOD/SCID mice (6\u0026ndash;8 weeks old) were purchased from Beijing Vital River Laboratory Animal Technology and maintained under specific pathogen-free conditions.\u003c/p\u003e\u003cp\u003eMCF-7 cells (4 *10\u003csup\u003e6\u003c/sup\u003e cells in 200 \u0026micro;L PBS) stably overexpressing MKK4DN or empty vector control were bilaterally injected into the mammary fat pads. Tumor dimensions were measured twice weekly using digital calipers, and tumor volume was calculated as: (length \u0026times; width\u0026sup2;)/2 (mm\u0026sup3;).\u003c/p\u003e\u003cp\u003eAfter 38 days, mice were euthanized and tumor tissues were excised, weighed, and photographed. For metastasis evaluation, Lungs were perfused with 4% paraformaldehyde (PFA) via tracheal instillation. Macroscopic metastases were counted under a dissecting microscope and measured using ImageJ software (NIH).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e2.8 Statistical analysis\u003c/h2\u003e\u003cp\u003eStatistical analyses were conducted in Graph Pad Prism9.0 (CA, USA). Statistical significance was calculated using paired/unpaired Student\u0026rsquo;s t-tests or One-way ANOVA as appropriate. Pearson\u0026rsquo;s correlation analysis was used to assess the correlation between the two sets of data. Survival rates were calculated with the K-M method. Each experiment was performed at least three times. Statistical significance was set at P\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Identification of MAP3K1/MAP2K4 as Breast Cancer-Specific Driver Genes\u003c/h2\u003e\u003cp\u003eThrough integrative analysis of TCGA pan-cancer genomic datasets, we systematically characterized somatic copy number alterations (SCNAs) and exonic mutations across protein-coding genes. While significant heterogeneity in mutational burden was observed across malignancies, breast cancer (BRCA) exhibited a distinctive mutation profile: MAP3K1 and MAP2K4 emerged as the 7th (8.77%) and 16th (4.02%) most frequently mutated genes, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). This mutation prevalence significantly exceeded rates observed in other tumor types, suggesting BRCA-specific driver functions.\u003c/p\u003e\u003cp\u003eFunctionally positioned as critical kinase cascade regulators, MAP3K1 phosphorylates MAP2K1/MAP2K4 to activate ERK/JNK pathways(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e), while MAP2K4 directly mediates JNK/p38 signaling(\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Since both MAP3K1/MAP2K4 are upstream of JNK, we investigate the mutations of other upstream. Strikingly, mutations in these JNK pathway regulators (MAP3K1/MAP2K4 included) demonstrated strict mutual exclusivity (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003eB) - only one altered gene per tumor-recapitulating the classical pattern observed in KRAS/BRAF mutations within the RAS-RAF-MAPK pathway(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). This \"single-hit\" configuration aligns with the established paradigm of oncogenic pathway activation requiring mutation at only one critical node(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThese findings collectively suggest that MAP3K1/MAP2K4 mutations drive BRCA tumorigenesis through JNK pathway dysfunction. The observed mutual exclusivity implies biological redundancy, where concurrent mutations in the same signaling cascade would provide no incremental selective advantage. This pattern reinforces their classification as bona fide driver alterations specific to breast cancer pathogenesis.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Functional Characterization of MAP3K1 and MAP2K4 as Tumor Suppressor Genes\u003c/h2\u003e\u003cp\u003eDriver genes are conventionally classified into two functional categories: oncogenes and tumor suppressor genes (TSGs). Our systematic analysis of mutational landscapes in breast cancer (BRCA) reveals compelling evidence for MAP3K1 and MAP2K4 functioning as TSGs through distinct inactivation mechanisms.\u003c/p\u003e\u003cp\u003eIn the TCGA BRCA cohort, MAP3K1 showed 115 genetic alterations across 85 cases, comprising splice region variants (SPLICE_REGION), silent mutations (SILENT), frameshift deletions (FRAMESHIFT_DEL), splice site mutations (SPLICE), nonsense mutations (NONSENSE), missense mutations (MISSENSE), and frameshift insertions (FRAMESHIFT_INS). Notably, frameshift mutations predominated (60/85 cases, 70.6%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eA), suggesting pervasive loss-of-function alterations. Similarly, MAP2K4 exhibited 35 alterations with frameshift events in 10 cases (28.6%), while remaining missense mutations clustered within catalytic domains and conserved phosphorylation sites. This mutation spectrum implies dual inactivation mechanisms: complete functional ablation through frameshifts, and partial impairment via catalytic domain disruption - a pattern requiring further mechanistic validation.\u003c/p\u003e\u003cp\u003eThese mutational profiles align with canonical TSG inactivation patterns, characterized by truncating mutations (frameshifts/nonsense) and allelic loss (LOH). The high prevalence of frameshift mutations induces premature stop codons, producing truncated proteins that disrupt tumor-suppressive functions through either dominant-negative effects or complete loss of signaling capacity.\u003c/p\u003e\u003cp\u003eClinically, patients with MAP3K1/MAP2K4 frameshift mutations (Cohort S2, n\u0026thinsp;=\u0026thinsp;68) demonstrated significantly reduced overall survival compared to JNK pathway-intact controls (Cohort S1, n\u0026thinsp;=\u0026thinsp;915) (log-rank P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). This prognostic association underscores the tumor-suppressive role of intact MAP3K1/MAP2K4 signaling, where genetic inactivation accelerates disease progression through JNK pathway dysregulation.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Molecular Mechanism of MAP3K1/MAP2K4-Mediated Tumor Suppression via JNK2-FRA1 Signaling Axis\u003c/h2\u003e\u003cp\u003eWhile MAP3K1 and MAP2K4 (MKK4) have been reported to suppress tumor metastasis in various cancers (\u003cspan additionalcitationids=\"CR14 CR15 CR16\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e), the mechanisms by which their mutations drive tumor progression remain incompletely understood. Building on our previous discovery of the \"Goldilocks principle\" for JNK signaling\u0026mdash;where moderate activation promotes metastasis but hyperactivation suppresses it, primarily via FRA1/FOSL1 regulation(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e)\u0026mdash;we hypothesized that MAP3K1/MAP2K4, as upstream regulators of JNK, might modulate malignant transformation through selective activation of JNK.\u003c/p\u003e\u003cp\u003eWestern blot analysis revealed an inverse correlation between p-JNK1/2 and FRA1 levels in breast cancer cell lines: MCF-7, ZR-75-1, and BT-474 exhibited high p-JNK1/2 with low FRA1, whereas MDA-MB-231, MDA-MB-436, sum149-PT and T47D showed reduced p-JNK1/2 accompanied by elevated FRA1(Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). To dissect JNK1 versus JNK2 contributions, we analyzed CPTAC pan-cancer data, observing distinct patterns: hepatocellular carcinomas (LIHC) exhibited elevated p-JNK1/2 with suppressed FRA1; gliomas (GBM) displayed high p-JNK2 but low p-JNK1 and FRA1; pancreatic (PAAD) and ovarian (HGSOC) cancers demonstrated low p-JNK1/2 and high FRA1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). These findings suggest no significant association between p-JNK1 and FOSL1, whereas p-JNK2 inversely correlated with FOSL1.\u003c/p\u003e\u003cp\u003eFunctional validation in MCF-7 and ZR-75-1 cells showed that JNK2 knockdown upregulated FRA1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). Similarly, overexpression of a dominant-negative MAP2K4 mutant (MKK4DN) increased FRA1 levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). Crucially, MKK4DN overexpression in JNK2-knockdown cells did not further enhance FRA1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eE), confirming that MAP2K4 suppresses FRA1-mediated oncogenesis specifically through JNK2 activation.\u003c/p\u003e\u003cp\u003eThese findings demonstrate that MAP3K1/MAP2K4 mutations drive tumor proliferation and metastasis by relieving JNK2-mediated suppression of FRA1, establishing a mechanistic foundation for their tumor-promoting role in BRCA.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Compensatory Role of MAP3K1 \u0026amp; MAP2K4 Mutations for p53 Loss in BRCA\u003c/h2\u003e\u003cp\u003eOur previous studies demonstrated that phosphorylation of p53 at serine 15 leads to its binding to the FOSL1 promoter, resulting in suppression of downstream FRA1 expression and subsequent inhibition of tumor proliferation. This phosphorylation can be activated by various kinases including JNK2 and ATM/ATR. Analysis of TCGA data revealed a significant mutual exclusivity pattern among MAP3K1, MAP2K4, and TP53 mutations in BRCA (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003eA), supporting their functional involvement in the same pathway where inactivation of any single component is sufficient to disrupt the entire axis.\u003c/p\u003e\u003cp\u003ePan-cancer TCGA analysis showed a positive correlation between TP53 mutation frequency (including ATM/ATR mutations that also inactivate p53 function) and tumor aggressiveness. Low-malignancy tumors like THCA (2.04%), PRAD (14.1%), and LIHC (33.1%) contrast sharply with high-malignancy tumors such as OV (87.8%), ESCA (86.4%), LUSC (84.1%), and READ (74.8%). Notably, BRCA showed a discordantly low TP53 mutation rate (36.4%) relative to its expected malignancy level (50\u0026ndash;60% based on molecular subtypes) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). However, when including MAP3K1/MAP2K4 mutations (16.6%), the total inactivation rate reached 53.01%, comparable to STAD (53.7%) and LUAD (56.8%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003eC), suggesting a compensatory mechanism.\u003c/p\u003e\u003cp\u003eExamination of p53 loss-of-function mutations revealed three distinct patterns in the p53-FOSL1 regulatory relationship. In PRAD, LIHC, and LGG, p53-null tumors exhibited significantly higher FOSL1 expression compared to p53-WT tumors, consistent with p53\u0026rsquo;s established role in transcriptionally repressing FOSL1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003eD).\u003c/p\u003e\u003cp\u003eIn COAD, LUAD, ESCA, and OV, p53-null tumors showed only a modest increase in FOSL1 expression relative to p53-WT tumors. This attenuation of FOSL1 upregulation likely reflects the dominant effects of concurrent oncogenic driver mutations (e.g., KRAS/PIK3CA), which mask the regulatory impact of p53 loss (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003eE).\u003c/p\u003e\u003cp\u003eNotably, BRCA displayed a paradoxical pattern. Initial analysis revealed lower FOSL1 expression in p53-null tumors (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003eF, BRCA-1). However, after re-stratifying cohorts into Cohort A (pure p53-WT, excluding all mutations listed in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e) and Cohort B (p53-null or MAP3K1/MAP2K4 frameshift), Cohort B showed a marked elevation in FOSL1 levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003eF, BRCA-2). This aligns with the high-grade malignancy trend observed in COAD, LUAD, ESCA, and OV, resolving the initial discrepancy.\u003c/p\u003e\u003cp\u003eThese findings demonstrate that MAP3K1/MAP2K4 mutations act as a BRCA-specific compensatory mechanism for p53 pathway inactivation. By disrupting the JNK2-p53-FRA1 axis, these mutations phenocopy the effects of p53 loss\u0026mdash;even in the absence of direct TP53 mutations\u0026mdash;thereby driving aggressive tumor behavior.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e3.5 MKK4DN Promotes Breast Cancer Metastasis via JNK-p53 Signaling\u003c/h2\u003e\u003cp\u003eTo investigate the functional role of MAP2K4 (MKK4) in tumor progression, we established xenograft models by inoculating nude mice with MCF7 cells stably expressing MKK4 dominant-negative mutant (MCF7-MKK4DN) or empty vector control (MCF7-MCS) (n\u0026thinsp;=\u0026thinsp;7/group). MCF7-MKK4DN group exhibited significantly larger tumor volumes, indicating accelerated local proliferation upon MKK4 inactivation (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003eA, \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). Terminal dissection demonstrated that MCF7-MKK4DN group developed more pulmonary metastases compared to controls (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003eC, \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003eD, \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003eE).\u003c/p\u003e\u003cp\u003eMechanistically, immunohistochemistry showed sharp reductions in phospho-JNK1/2 and phospho-p53 (Ser15) levels, accompanied by upregulation of FRA1 protein in MCF7-MKK4DN tumors (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003eF). These findings demonstrate that MKK4 inactivation disrupts JNK-mediated p53 phosphorylation, thereby releasing transcriptional repression of FRA1 to drive tumor metastasis.\u003c/p\u003e\u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eDriver genes serve as the molecular foundation for tumorigenesis and progression, and their identification is crucial for elucidating cancer mechanisms (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). This study systematically analyzed the mutational landscape of upstream genes in the JNK signaling pathway, revealing a striking divergence in mutation frequencies between two paralogous kinases: MKK4 and its upstream regulators exhibited high-frequency driver mutations, whereas MKK7 showed significantly lower mutation rates. This disparity likely reflects their distinct signaling roles\u0026mdash;MKK4 primarily mediates extracellular stimuli (e.g., cytokines, growth factors, and stress signals), while MKK7 predominantly responds to intracellular signals (e.g., ROS and ER stress)(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). During early tumorigenesis, persistent extracellular stimuli such as chronic inflammation drive continuous MKK4 activation (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e), prompting neoplastic cells to acquire MKK4 mutations as an adaptive mechanism to escape inflammatory microenvironmental pressures. In contrast, the MKK7 pathway remains less frequently activated, explaining its lower mutational burden.\u003c/p\u003e\u003cp\u003eOur findings further demonstrate that mutations in MAP3K1 and MAP2K4, upstream kinases of JNK2, function through the MAP3K1-MAP2K4-JNK2-p53-FRA1 axis to alleviate p53-mediated suppression of FRA1 expression, thereby promoting tumor growth and metastasis. Notably, MAP3K1/MAP2K4 mutations and TP53 mutations display mutual exclusivity in breast cancer, suggesting that MAP3K1/MAP2K4 loss-of-function can serve as an alternative mechanism for p53 pathway inactivation in BRCA tumors. This observation aligns with the \"minimal necessary alteration\" principle of driver mutations\u0026mdash;in key signaling pathways (e.g., MAPK, PI3K-AKT, p53), multiple mutations within the same pathway rarely confer synergistic advantages (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). For example, the mutual exclusivity of KRAS and BRAF mutations in the RAS-RAF-MAPK pathway underscores that a single driver mutation is often sufficient to maximally activate the pathway(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFRA1, a core AP-1 transcription factor family member, acts as a gatekeeper of epithelial-mesenchymal transition (EMT) and tumor metastasis (\u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Its oncogenic upregulation occurs through two complementary mechanisms: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) the MAP3K1-MAP2K4-JNK2-p53-FRA1 axis relieves tumor-suppressive effects via loss-of-function mutations, while (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) the EGFR-RAS-BRAF-ERK1/2-FRA1 pathway enhances oncogenic signaling through gain-of-function mutations (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Together, these pathways synergistically upregulate FRA1 expression through a dual \"release-the-brake and step-on-the-gas\" mechanism, thereby driving EMT and metastatic progression. This mechanistic interplay also accounts for the frequent co-occurrence of RAS and TP53 mutations across pan-cancer analyses, as these pathways collectively accelerate tumor development.\u003c/p\u003e\u003cp\u003eHowever, while our functional validation of MAP3K1/MAP2K4 mutations relied primarily on in vitro and bioinformatic analyses, further in vivo studies are warranted to fully elucidate their role in tumor progression.\u003c/p\u003e"},{"header":"5 Conclusions","content":"\u003cp\u003eIn summary, our study reveals key mechanistic insights into the mutational landscape of the JNK signaling pathway in breast cancer. We further uncover the MAP3K1-MAP2K4-JNK2-p53-FRA1 axis as an alternative pathway for p53 inactivation, highlighting the principle of \"minimal necessary alteration\" in driver gene selection. Additionally, the dual regulation of FRA1 through complementary mechanisms underscores the complexity of oncogenic signaling networks in promoting metastasis.\u003c/p\u003e\u003cp\u003eThese findings advance our understanding of breast cancer heterogeneity and provide a framework for mechanism-driven precision medicine, where targeting specific mutational signatures (e.g., MAP3K1/MAP2K4 loss or FRA1 upregulation) could offer novel therapeutic strategies. Future studies should explore the translational potential of these discoveries in preclinical models and clinical cohorts, ultimately improving patient stratification and treatment outcomes.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eMKK4DN (dominant-negative MAP2K4)\u003c/p\u003e\n\u003cp\u003eLIHC (Liver Hepatocellular Carcinoma)，\u003c/p\u003e\n\u003cp\u003eGBM (Glioblastoma Multiforme)，\u003c/p\u003e\n\u003cp\u003ePAAD (Pancreatic Adenocarcinoma)，\u003c/p\u003e\n\u003cp\u003eHGSOC (High-Grade Serous Ovarian Cancer)\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e\n\u003cp\u003eAll animal procedures were approved by the Tianjin Institute of Medical \u0026amp; Pharmaceutical Sciences Animal Care Committee and complied with international welfare standards. The maximal permitted tumor burden approved by the ethics committee was 2 cm\u0026sup3; and no umors exceeded this approved limit during the study. Comprehensive welfare measures\u0026mdash; including daily health monitoring, strictly enforced humane endpoints, postoperative analgesia, and environmental enrichment\u0026mdash;were implemented. All procedures were documented, and source data were maintained in accordance with institutional and journal requirements. Ethical approval ensured full compliance with ARRIVE guidelines.\u003c/p\u003e\n\u003ch2\u003eConsent for publication\u003c/h2\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare no conflicts of interest to the present study.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis work was supported Science and Technology Fund of Tianjin Municipal Health Commission (NO. TJWJ2021QN077).\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eSH designed the study and wrote the manuscript. AJ and MW performed the experiments and data analysis. RG performed data analysis. XL, LS and YZ performed the animal experiments. All authors edited and revised the manuscript and were involved in the final approval of the manuscript.\u003c/p\u003e\n\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eThe datasets generated and analyzed during this study are available in the Proteomic Data Commons (PDC) database (https://pdc.cancer.gov/pdc/). The relevant PDC study IDs are as follows: LIHC: Phosphoproteome (PDC000199), Proteome (PDC000198)GBM: Phosphoproteome (PDC000448), Proteome (PDC000446)PAAD: Phosphoproteome (PDC000271), Proteome (PDC000270)HGSOC: Phosphoproteome (PDC000248), Proteome (PDC000249)\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMartinez-Jimenez F, Muinos F, Sentis I, Deu-Pons J, Reyes-Salazar I, Arnedo-Pac C, et al. A compendium of mutational cancer driver genes. Nat Rev Cancer. 2020;20(10):555\u0026ndash;72.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOstroverkhova D, Przytycka TM, Panchenko AR. Cancer driver mutations: predictions and reality. Trends Mol Med. 2023;29(7):554\u0026ndash;66.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZahir N, Sun R, Gallahan D, Gatenby RA, Curtis C. Characterizing the ecological and evolutionary dynamics of cancer. 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Int J Mol Sci. 2023;24(9).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDhillon AS, Tulchinsky E. FRA-1 as a driver of tumour heterogeneity: a nexus between oncogenes and embryonic signalling pathways in cancer. Oncogene. 2015;34(34):4421\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYoung MR, Colburn NH. Fra-1 a target for cancer prevention or intervention. Gene. 2006;379:1\u0026ndash;11.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"breast-cancer-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"brcr","sideBox":"Learn more about [Breast Cancer Research](http://breast-cancer-research.biomedcentral.com)","snPcode":"13058","submissionUrl":"https://submission.nature.com/new-submission/13058/3","title":"Breast Cancer Research","twitterHandle":"@BCRJournal","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"breast cancer, driver mutations, MAP3K1, MAP2K4, JNK2, p53, FRA1/FOSL1, mutual exclusivity, metastasis","lastPublishedDoi":"10.21203/rs.3.rs-7033784/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7033784/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eBreast cancer (BRCA) pathogenesis involves somatic mutations in key oncogenes and tumor suppressor genes, but the full spectrum of driver mutations and their functional impact remains incompletely understood. This study aimed to identify novel BRCA-specific driver mutations and elucidate their role in tumor progression.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eWe performed integrative analysis of The Cancer Genome Atlas (TCGA) datasets to identify recurrent mutations in BRCA. Statistical analyses included mutation frequency assessment, mutual exclusivity testing, and survival correlation. Functional validation was conducted using xenograft models to evaluate tumor proliferation and metastasis.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eWe identified MAP3K1 and MAP2K4 as novel BRCA-specific driver genes, mutated in 8.77% and 4.02% of cases, respectively. These mutations showed strict mutual exclusivity with TP53 alterations, indicating functional redundancy within a shared pathway. Mechanistically, MAP3K1/MAP2K4 frameshift mutations inactivated the JNK2-p53-FOSL1 axis, relieving p53-mediated suppression of the pro-metastatic factor FRA1. While TP53 mutation rates (36.4%) appeared discordantly low given BRCA aggressiveness, inclusion of MAP3K1/MAP2K4 mutations (16.6%) revealed a compensatory inactivation rate of 53.01%, aligning BRCA with other high-malignancy cancers. Functional studies demonstrated that dominant-negative MAP2K4 (MKK4DN) promoted tumor proliferation and metastasis by suppressing JNK2-mediated p53 phosphorylation and upregulating FRA1. Clinically, patients with MAP3K1/MAP2K4 frameshift mutations exhibited significantly worse overall survival.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eOur findings establish MAP3K1-MAP2K4-JNK2-p53-FOSL1 as a critical tumor-suppressive axis in BRCA, where MAP3K1/MAP2K4 mutations functionally compensate for TP53 loss to drive malignancy. This study expands the genomic framework of BRCA pathogenesis and identifies potential therapeutic targets for TP53-mutant breast cancers.\u003c/p\u003e","manuscriptTitle":"MAP3K1/MAP2K4 Mutations Drive Breast Cancer Progression by Compensating TP53 Loss via JNK2-p53-FOSL1 Axis Inactivation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-11 09:58:42","doi":"10.21203/rs.3.rs-7033784/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-13T16:08:37+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-24T16:15:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"214485628201006504827659116067669089059","date":"2025-09-12T12:43:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"333892013128396891909513151851833694316","date":"2025-09-12T02:58:54+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-14T17:30:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"128333512315990829608794089052596388993","date":"2025-08-05T16:22:02+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-05T16:15:43+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-08T05:43:13+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-08T05:39:37+00:00","index":"","fulltext":""},{"type":"submitted","content":"Breast Cancer Research","date":"2025-07-03T03:23:23+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"breast-cancer-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"brcr","sideBox":"Learn more about [Breast Cancer Research](http://breast-cancer-research.biomedcentral.com)","snPcode":"13058","submissionUrl":"https://submission.nature.com/new-submission/13058/3","title":"Breast Cancer Research","twitterHandle":"@BCRJournal","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f4b530a4-3589-486c-9f6e-c5563ae60957","owner":[],"postedDate":"August 11th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-12-22T16:03:34+00:00","versionOfRecord":{"articleIdentity":"rs-7033784","link":"https://doi.org/10.1186/s13058-025-02195-3","journal":{"identity":"breast-cancer-research","isVorOnly":false,"title":"Breast Cancer Research"},"publishedOn":"2025-12-16 15:58:38","publishedOnDateReadable":"December 16th, 2025"},"versionCreatedAt":"2025-08-11 09:58:42","video":"","vorDoi":"10.1186/s13058-025-02195-3","vorDoiUrl":"https://doi.org/10.1186/s13058-025-02195-3","workflowStages":[]},"version":"v1","identity":"rs-7033784","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7033784","identity":"rs-7033784","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}