{"paper_id":"3d67d577-ae52-496c-a804-391d7cf0e2e6","body_text":"AI-Driven Prediction Unveils SMAD4-Specific HLA-Restricted Epitopes: A Novel Therapeutic Target for Precision Treatment of Peritoneal Metastasis in Colorectal 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 Research Article AI-Driven Prediction Unveils SMAD4-Specific HLA-Restricted Epitopes: A Novel Therapeutic Target for Precision Treatment of Peritoneal Metastasis in Colorectal Cancer Min Chen, Tengfei Wang, Xiaorong Feng, Yuzhao Gao, Xiaolong Wei, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7066314/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Peritoneal metastasis (CRC-PM), a lethal feature of advanced colorectal cancer, lacks effective treatments. Neoantigen-based immunotherapy has emerged as a promising therapeutic strategy for metastatic CRC. Methods Multi-omics including TIMER and TISIDB tools, two computational pipelines of NetMHCpan and GenoImmune's artificial intelligence (AI)-driven Neoantigen Techonology (GIANT) were employed to characterize somatic mutations in CRC-PM, focusing on SMAD4-derived neoantigens. Results Genomic analysis of 2,424 CRC and 124 CRC-PM patients revealed KRAS, SMAD4, BRAF, and RNF43 mutations enriched in CRC-PM. KRAS/SMAD4 alterations correlated with higher tumor mutational burden (TMB) and enhanced immune infiltration. Ten novel SMAD4 mutation sites were uniquely identified in CRC-PM, that were associated with lymphocyte populations and immune checkpoint (ICP) gene expression. Neoantigen prediction of SMAD4 mutations identified 20 and 48 MHC-I candidates, using NetMHCpan and GIANT, respectively., Dominant variants (R361C, R361H, D424V, and A118V) of SMAD4 mutations showed strong affinity for HLA-B*07:02, HLA-A*24:02, and HLA-B*40:01. MHC-II epitopes D424V and W524C prioritized HLA-DRB1*15:01/13:02. Conclusions These findings highlight SMAD4 as a recurrent immunogenic driver in CRC-PM, indicating its potential as a neoantigen vaccine target for precision immunotherapy. SMAD4 colorectal cancer peritoneal metastasis neoantigen immunotherapy vaccine Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Colorectal cancer (CRC) ranks as the second-leading cause of cancer-related death worldwide 1 . Approximately 25% of CRC patients present with metastasis at the time of diagnosis 2 . Peritoneal metastasis and subsequent development of malignant ascites are the most frequent cause of death in patients with advanced CRC. 3 , 4 Due to the lack of effective therapeutic options, the median survival time for patients with peritoneal metastasis from CRC ranges from six to twelve months post-diagnosis. 5 Next-generation sequencing (NGS) has identified specific genetic alterations in CRC-PM genome, highlighting potential targets for therapies such as anti-VEGF agents or the BRAF 600E inhibitor vemurafenib. However, the clinical benefits of molecular-targeted therapies remain limited to a small patient subset. Genetic mutations in APC, TP53RAS, PIK3CA, and SMAD4 have been linked to metastatic progression in CRC. 6 – 8 SMAD4 is located on chromosome 18q21, resides in a region commonly associated with allelic loss, a hallmark of metastasis and poor prognosis in colorectal cancer. 9 , 10 SMAD4, a key driver of CRC progression, harbors inactivating mutations in 5–24.2% of cases, which are strongly associated with metastatic progression. 11 SMAD4 regulates CRC pathogenesis and progression and is further implicated in poor prognosis among metastatic colorectal cancer (mCRC) patients. 12 However, the precise functional role of these mutations in promoting peritoneal metastasis of CRC remains unclear. 13 Immune checkpoint blockade (ICB) therapies have achieved notable success in treating solid tumors. 14 however, less than 5% of deficient mismatch repair (dMMR) mCRC benefit from ICB immunotherapy. 15 The advent of next-generation sequencing (NGS) has enabled the identification of patient-specific neoantigens (neoAgs) for cancer vaccine development. Over the past decade, neoantigens have emerged as optimal targets for stimulating antitumor immunity. 16 , 17 This process depends on the ability of identified neopeptides with high affinity to bind with human leukocyte antigen (HLA) molecules, a critical factor in inducing an immune response. 18 , 19 To optimize neoAg identification, multiple computational tools leverage diverse algorithms to predict binding affinities for HLA class I/II alleles. Integrating these tools may enhance immunogenicity predictions by combining their distinct criteria and algorithmic strengths. Neoantigen-based vaccines have demonstrated promising efficacy in clinical trials across malignancies, including melanoma, glioblastoma multiforme, pancreatic adenocarcinoma, and liver cancer. 20 – 22 Recently, Yu et al. 23 demonstrated that personalized neoantigen vaccine is safe, feasible, and effective in microsatellite-stable (MSS) CRC patients with postoperative recurrence or metastasis. Tran et al. reported complete regression of all seven lung metastases following infusion of HLA-C*08:02–restricted tumor-infiltrating lymphocytes targeting KRAS G12D . 24 Given these advances in neoantigen-based immunotherapy, 25 researchers are exploring tumor neoAgs as a promising therapeutic strategy for CRC-PM. In this study, we performed integrated genomic profiling and computational neoantigen prediction using samples from 2,424 advanced CRC patients and 124 CRC-PM patients. Our analysis revealed multiple genetic alterations enriched in CRC-PM tumors. We identified a positive correlation between SMAD4 mutations and tumor-infiltrating lymphocytes (TILs) or immune checkpoint (ICP) genes, suggesting SMAD4-derived neoantigens as potential targets for CRC-PM immunotherapy. Methods Data Sources Genomic data from 2,424 CRC patients (including 124 CRC-PM) were obtained from cBioPortal and published studies, including datasets generated by Cercek et al. 43 , Lenos et al. , 44 Yaeger et al. , 26 Heuvelings et al. . 45 Neoantigen prediction Neoantigen prediction was performed using artificial intelligence (AI)-driven computational algorithms to identify peptides derived from somatic SMAD4 mutations. High-frequency HLA alleles for European and East Asian populations were retrieved from the Allele Frequency Net Database. Sixteen HLA class I and three HLA class II (HLA-DRB1) alleles were selected. For MHC class II neoAg screening, NetMHCIIpan4.1 was utilized. Two pipelines were employed to prioritize class I neoAgs as follows: 1) NetMHCpan-pipeline: NetMHCpan4.1 46 predicted MHC class I epitopes using 9–12-mer peptides, with binding affinity thresholds of IC50 < 500 nM or %Rank ≤ 2. NetMHCIIpan4.1 47 screened 15-mer peptides for MHC class II binding; 2) G IANT-pipeline: GIANT, an in-house algorithm (BGI-GenoImmune, Wuhan, China), predicted neoAg immunogenicity using MHC binding affinity, peptide presentation, expression levels, and physicochemical properties (threshold: GIANT score ≥ 0.5). A second algorithm (EPIP) evaluated epitope presentation using convolutional neural networks (CNNs) trained on 180,000 HLA ligands, with a presentation score threshold of ≥ 0.1. Immune Profiling The Tumor Immune Estimation Resource (TIMER) 48 quantified immune cell infiltration across cancer types, while TISIDB, 49 a complementary web resource, quantified tumor-immune system interactions. Both tools analyzed correlations between SMAD4 alterations and immune features. Structural modeling The wild-type SMAD4 crystal structure (PDB ID: 1DD1) was obtained from the Protein Data Bank. The Arg361 residue was mutated to R361C and R361H using PyMOL’s built-in mutagenesis wizard. Statistical analysis and software for visualization Genomic Alterations: Oncogenic variants were filtered using OncoKB 50 (oncokb.org) and ClinVar. Mutation frequencies were compared via Chi-square tests, and TMB differences were assessed using Mann-Whitney U-tests. Immune Correlation: Partial Spearman’s correlation, adjusted for tumor purity, assessed relationships between SMAD4 expression and immune infiltration. Wilcoxon rank-sum tests evaluated associations between SMAD4 alterations and immune checkpoint (ICP) gene expression (significance threshold: p ≤ 0.05). Visualization: R software visualized driver gene alterations and TMB distributions. Results Study design and patient characteristics We analyzed a total of 2548 colorectal adenocarcinomas, including 2424 primary tumors and 124 peritoneal metastases (PM) (Fig. 1 , Table S1 -10). Tumors were sequenced using MSK-IMPACT and a custom-targeted NGS panel targeting 11 canonical CRC driver genes. Clinical characteristics for the 124 CRC-PM and 694 CRC cohorts are summarized in Table S 1. The CRC-PM cohort included 100 individuals of white ethnicity and 7 of Asian ethnicity, while the CRC cohort comprised of 694 individuals of white ethnicity. Patients diagnosed with CRC-PM (n = 124) and CRC (n = 2424) who underwent next-generation sequencing via cBioPortal and published studies were included in the genetic analysis. Analyzing the CRC peritoneal metastasis- related driver genes To compare driver gene alteration in CRC (n = 694), we sought to identify elevated mutation frequencies (MFs) of driver genes in CRC-PM (n = 124) (Fig. 2 A). Consistent with a prior report 26 , the most frequently mutated genes were APC (75%), TP53 (68%), KRAS (44%), PIK3CA (22%), and SMAD4 (15%) (Fig. 2 B, Table S2 and 3). Our analysis showed that KRAS and SMAD4 exhibited significantly elevated mutation frequencies (MFs) in CRC-PM (10% and 4% increases, respectively (Fig. 2 B, Table S3 ). Additionally, the homozygous deletion (HOMDEL) of SMAD4 occurred more frequently associated with CRC peritoneal metastasis compared to CRC (7.9% vs 1.9% in CRC, p = 0.01, Fig. 2 C, Table S4). Notably, our results indicated that mutations in KRAS and SMAD4 correlated positively with tumor mutational burden (TMB) in both cohorts (Fig. 2 D and 2 E, Table S5 and Table 6). Spectrum of somatic mutations of KRAS and SMAD4 Co-occurring KRAS and SMAD4 mutations were observed in 47.2% (50/106) of CRC patients, rising to 62.5% (15/24) in CRC-PM (Table S2 , 6), KRAS G12D and KRAS G12V predominated in CRC, while KRAS G12D (17% vs. 12%) and KRAS G13D (9.7% vs. 7.8%) were elevated in CRC-PM (Table S7). To identify SMAD4 mutations specific to peritoneal metastases, we analyzed 124 CRC patients with peritoneal metastases and compared them with a large cohort of CRC patients (n = 2424). Data from 21 bowel cancer studies in the cBioPortal database were aggregate compiling SMAD4 mutation data from 2,424 CRC patients, identifying 351 mutation sites in 312 patients (Table S8). In the CRC-PM cohort, missense mutations (67%, 18/27) were the most frequent somatic alterations in SMAD4, followed by in-frame insertions/deletions (22% vs. 13% in CRC; 6/27), nonsense mutations (7%, 2/27), and splice site mutations (4%, 1/27) (Fig. 2 A, Table S2 ). Ten of twenty-seven (37%) SMAD4 somatic mutations were firstly identified unique in CRC-PM cohorts (Fig. 3 A, Table S2 and Table 8). These unique mutations comprised 50% missense single-nucleotide variants (SNVs) and 50% insertions/deletions (InDels). Pathogenic Mutations of SMAD4 in CRC-PM Pathogenic mutations in CRC-PM were distributed across the SMAD4 gene, with a significantly higher mutant frequency (8.87%, 11/124) compared to CRC (3.26%) (Fig. 3 B and 3 C, p = 0.002, Table S9). Notably, 86% (6/7) clustered within the C-terminal SMAD4 homology domain 2 (MH2) (Fig. 3 C). Consistent with previous observations, 27 , 28 two missense mutation hotspots (D351–P356/R361 and A406/K428/R515), located within the L1 loop—a structural motif critical for R-SMAD binding and phosphorylation) , 29 exhibited higher frequencies in CRC-PM (8.06% vs 4.33% in CRC, p = 0.051, Fig. 3 D and 3 E, Table S10). Particularly, the R361C mutation was the most frequent in CRC-PM at 4.03%, compared to its ranking as the second most frequent mutation in CRC at 0.91% (Fig. 3 F, p = 0.004, Table S 10). These alterations likely confer heightened pathogenicity by potentially disrupting SMAD4 homo-/hetero-oligomerization with R-SMAD proteins or a reduction of SMAD4 stability. Putative neoantigens of SMAD4 mutation in CRC-PM Using two NetMHCpan and GIANT pipelines (Fig. 4 A, Table S11 and Table 12), 20 and 48 neoAgs were identified, respectively, with 14 overlapping between both pipelines. R361H, R361C, D424V, A118V, and G419W were the frequently predicted neoAgs binding to Class I alleles (Fig. 4 A). The top Class I alleles included HLA-B*07:02, HLA-B*40:01, HLA-A*02:01 using NetMHCpan pipeline, and HLA-B*07:02, HLA-A*24:02, HLA-B*35:01, HLA-C*07:02 using GIANT tool. Furthermore, D424V and W524C were identified for Class II alleles of HLA-DRB1*15:01 and HLA-DRB1*13:02 with high affinities using NetMHCIIpan4.1 (Fig. 4 B, Table S13). Collectively, the frequently predicted neoAgs derived from SMAD4 mutations located in the functional domain MH2. Association of SMAD4 mutations with immune infiltration and immune checkpoint genes To investigate associations between SMAD4 genetic/expression profiles and immune infiltration in CRC ( n = 458), we employed the TIMER computational tool. Elevated SMAD4 expression and mutations correlated significantly with increased infiltration of dendritic cells (partial correlation = 0.517, P < 0.001), neutrophil (partial correlation = 0.434, P < 0.001), CD8 + T cells (partial correlation = 0.365, P < 0.001), macrophage (partial correlation = 0.35, P < 0.001), CD4 + T cells (partial correlation = 0.223, P < 0.01), B cells (partial correlation = 0.221, P < 0.01) (Fig. 5 A and 5 B, Fig. S1 A). However, the expression level of SMAD4 was negatively correlated with the infiltration level of memory B cells (Fig. S1 A). Deep or arm-level SMAD4 deletions correlated with reduced infiltration of dendritic cells (P < 0.001), neutrophil (P < 0.001), CD8 + T cells (P < 0.001) and B cells (P < 0.01) (Fig. 5 C). We further analyzed associations between SMAD4 expression and CRC molecular subtypes, showing that SMAD4 expression was associated with different molecular and immune subtypes (Fig. 1 B and 1 C). Collectively, these findings indicate that SMAD4 dysregulation contributes to immune infiltration in the TME. Chemokines, chemokine receptors, and immune checkpoint (ICP) genes—including immunostimulators and immunoinhibitors—play critical roles in TME dynamics and antitumor immunity. Therefore, we analyzed the correlation between the mutated SMAD4 and these genes in CRC using TISIDB. Significant correlations were identified between SMAD4 mutations and 6 chemokines, 4 chemokine receptors, and 25 ICP genes in colorectal cancer cohort (n = 365, Fig. 6 A). SMAD4 mutations exhibited positive correlation s with most genes, including CCL2 , CXCL12 , CCR5 , and CCR8 (Fig. 6 B), as well as CXCL12, ENTPD1, IL2RA, CSF1R, PDCD1LG2 , and TIGIT (Fig. 6 C and 6 D). Negative correlations were observed with a subset of genes, including CCL23 and CCL24 (Fig. 6 A and 6 B), TNFRSF25 and PVRL2 ( Fig. 6 C and 6 D). Moreover, mutated SMAD4 was negatively correlated with HLA class I alleles, including HLA-A, C, and G, while being positively correlated with HLA class II allele HLA-DOA (Fig. 6 E). These findings suggest that mutated SMAD4 might play an important role in tumor immunity, and making it an attractive target for immunotherapy. Discussion Our study elucidates the molecular landscape of colorectal cancer peritoneal metastasis (CRC-PM), identifying KRAS and SMAD4 as key pathogenic drivers with heightened clinical significance. Through integrated multi-omics analyses, we demonstrate that SMAD4 mutations correlate with enhanced immune infiltration and elevated expression of immune checkpoint (ICP) genes, revealing its immunomodulatory role in CRC-PM tumorigenesis. Notably, our integrated approach employing two orthogonal neoantigen prediction systems - NetMHCpan and GenoImmune's AI-driven Neoantigen Technology (GIANT) - successfully identified high-confidence SMAD4-derived neoantigens. These findings establish SMAD4 mutations as prioritized actionable immunotherapeutic targets, proposing dual clinical translation potential: (1) direct targeting of SMAD4 signaling pathways, and (2) AI-optimized neoantigen vaccine development for precision immunotherapy in CRC-PM. Tumor progression in CRC-PM is driven by recurrent genetic alterations in APC, TP53, KRAS, PIK3CA, and SMAD4. Consistent with prior work by Sameer et al. , 30 we observed synergistic dysregulation of SMAD4 and KRAS, collectively accelerating metastatic progression. Izeradjene et al. 31 revealed that KRAS G12D tumors exhibit a higher propensity for developing SMAD4 haploinsufficiency, which, in turn, leads to increased invasiveness of cancer cells. Particularly, Yaeger et al. 26 showed a significant difference in survival associated with SMAD4 mutations within microsatellite stable (MSS) mCRC following a systematic univariate survival analysis. In our study, a comparison between primary CRC and peritoneal dissemination identified KRAS and SMAD4 as two major molecular abnormalities, suggesting that these two drive genes are critical for peritoneal metastasis in CRC. Currently, multiple potential factors have been identified for predicting the clinical outcome of immunotherapy. TMB 32 and MSI 33 have been applied as clinical biomarkers for predicting potential responses to immune checkpoint inhibitor (ICI) immunotherapy in solid tumors. TMB enhances antigenic response through the generation of neoantigens. 34 Notably, SMAD4 and KRAS mutations correlated with elevated tumor mutational burden (TMB), implicating these alterations as potent sources of immunogenic neoantigens. Several previous functional studies have revealed that SMAD4 is a key transcription factor in the regulation of the TGF-β pathway. 35 , 36 Consistent with these reports, 8 , 27 our findings in this study highlight that SMAD4 is commonly mutated in CRC-PM cohort, with mutations occur throughout the gene, particularly in the MH2 binding domain. The MH2 domain, critical for SMAD4 oligomerization and TGF-β signaling, exhibited mutation hotspots that disrupt interactions with SMAD2/SMAD3. Furthermore, 10 unique mutation sites were detected only in 124 CRC-PM as compared to 2424 CRC genomic profiles, suggesting that these unique mutations are involved in the regulation of peritoneal metastasis of CRC cancer cells. Tumor-infiltrating lymphocytes within the tumor microenvironment have been identified as effective predictors of prognosis and immunotherapeutic efficacy in cancer. 37 Neoantigen immunogenicity critically depends on CD8 + T cell activation, which can be amplified via dendritic cell-mediated antigen presentation. 38 In this study, we found that the infiltration levels of several immune effectors, such as activated CD8 + and CD4 + T cells, along with DCs were significantly associated with mutated SMAD4. Furthermore, our study demonstrated a strong relationship between mutated SMAD4 and some key ICP genes, including CD96, CSF1R, TIGIT, CXCL12, and IL2RA, which is approved as a biomarker for regulatory T cells (Tregs) and plays a role in the regulation of immune tolerance. 39 Notably, cancer mutations can generate neo-epitopes that are recognized by T cells on HLA molecules. Our analyses showed a high correction between mutated SMAD4 and MHC molecules. In summary, these findings confirmed an important role of SMAD4 mutations in tumor immunity, suggesting that the genetic alteration of SMAD4 might provide potential molecular targets for the development of novel tumor vaccine in CRC-PM patients. While computational tools such as NetMHCpan and NetMHCIIpan provide essential predictions of neoantigen-MHC binding affinity, their clinical translation remains constrained by suboptimal validation rates. 40 To address this critical limitation, we implemented a multi-dimensional validation strategy integrating orthogonal computational frameworks from established genomics-based prediction pipelines. This systematic integration significantly improves prediction accuracy while ensuring biological relevance of candidate neoantigens. Compare to NetMHCpan-pipeline, GIANT-pipeline demonstrated superiority in predicting SMAD4 neoAgs, achieving a 92.3% (12 out of 13) vs 61.5% (8 out of 13) missense SNVs (mSNVs) prediction rate and identifying more HLA class I selected (14 vs 8). Two SMAD4 mutations, R361H and R361C, are frequently observed in CRC patients. 28 and CRC with distant metastasis. 8 Preliminary studies have shown that these two missense mutations act as activating mutations for CTNNB1, a key gene in the WNT signaling pathway. 41 In this study, the neoantigen prediction for the R361H and R361C mutations, the selected epitopes of 6 SMAD4 R361H and 6 SMAD4 R361C neopeptides exhibited strong presentation capabilities to 7 HLA class I alleles i.e., HLA-A*01:01, HLA-A*24:02, HLA-B*35:01 and HLA-C*07:02. Notably, Mennonna et al. 42 validated our predictions, demonstrating HLA-B*35:01-restricted CD8 + T cell activation against SMAD4 R361C in CRC patients. These results underscore the robustness of our pipelines in identifying clinically relevant neoAgs. A primary limitation of this study is the relatively small sample size in the peritoneal metastasis colorectal cancer (CRC-PM) cohort subjected to sequencing. This may reduce statistical power and introduce potential bias. Secondly, although we employed two distinct AI methods to predict neoantigens from mutated genes, a subset of identified high-confidence neoantigens still lacks experimental confirmation of immunogenicity. Future research should incorporate larger datasets analyzed by AI, combined with experimental validation and stratified analyses, to identify potential candidates for cancer vaccines targeting CRC-PM. Conclusion Our study identifies SMAD4 as a key driver in colorectal cancer peritoneal metastasis (CRC-PM), with recurrent mutations correlating significantly with enhanced immune infiltration and immune-related biomarker expression. We established a robust computational framework integrating machine learning (GIANT) and convolutional neural networks (EPIP) to prioritize neoantigens based on epitope binding, presentation, and immunogenicity. Our results revealed SMAD4, appears to be immunogenic and represents an potential candidate for cancer vaccines in CRC-PM. Declarations Acknowledgements This work was supported by the National Natural Science Foundation of China (grant number: 82172009), Natural Science Foundation of Shanxi Province (grant number: 202303021221200, 202203021221219 and 202103021224357), Guangdong Basic and Applied Basic Research Foundation (grant number: 2023A1515220204). Shanxi Bethune Hospital of Shanxi Medical University (grant number: 2023GZRZ04). Author contributions M.C. and HX.A.; Methodology: TF.W., XR.F and ZG.H.; Software: TF.W. , XR.F. and YZ.G.; Validation: M.C, XR.W. and HX.A.; Formal analysis: M.C, TF.W., XR.F., YZ.G. and HX.A.; Investigation: M.C., WX.L. and HX.A.; Resources: M.C., WX.L., ZG.H. and HX.A.; Data creation: TF.W., XR.F. and YZ.G.; Writing for the original draft preparation: M.C., TF.W. and XR.F.; Writing for review and editing: M.C., TF.W., XR.F. and HX.A; Visualization: XR.F., TF.W. and YZ.G.; Supervision: M.C., ZG.H., WX.L. and HX.R.; Project administration: M.C. and HX.A.; Funding acquisition: M.C. ZG.H and HX.A. All authors have read and agreed to the published version of the manuscript. Funding This study was supported by grants from the National Natural Science Foundation Committee of China (grant number: 82172009), Natural Science Foundation of Shanxi Province (grant number: 202303021221200, 202203021221219 and 202103021224357), Guangdong Basic and Applied Basic Research Foundation (grant number: 2023A1515220204). Shanxi Bethune Hospital of Shanxi Medical University (grant number: 2023GZRZ04). Data availability Data are provided within the manuscript or supplementary information files. Ethics approval and consent to participate The study utilized publicly available cBioPortal and PubMed data. As this study did not collect new data, no additional ethical approval was required. Consent for publication Not applicable. Competing interests The authors declare no competing interests. Clinical trial number Not applicable References Bray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I, Jemal A. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2024;74:229–63. 10.3322/caac.21834 . van Gestel YR, Thomassen I, Lemmens VE, Pruijt JF, van Herk-Sukel MP, Rutten HJ, Creemers GJ, de Hingh IH. Metachronous peritoneal carcinomatosis after curative treatment of colorectal cancer. Eur J Surg Oncol. 2014;40:963–9. 10.1016/j.ejso.2013.10.001 . Koppe MJ, Boerman OC, Oyen WJ, Bleichrodt RP. Peritoneal carcinomatosis of colorectal origin: incidence and current treatment strategies. Ann Surg. 2006;243:212–22. 10.1097/01.sla.0000197702.46394.16 . Lurvink RJ, Bakkers C, Rijken A, van Erning FN, Nienhuijs SW, Burger JW, Creemers GJ, Verhoef C, Lemmens VE, De Hingh IH. Increase in the incidence of synchronous and metachronous peritoneal metastases in patients with colorectal cancer: A nationwide study. Eur J Surg Oncol. 2021;47:1026–33. 10.1016/j.ejso.2020.11.135 . Maggiori L, Elias D. Curative treatment of colorectal peritoneal carcinomatosis: current status and future trends. Eur J Surg Oncol. 2010;36:599–603. 10.1016/j.ejso.2010.05.007 . Kandoth C, McLellan MD, Vandin F, Ye K, Niu B, Lu C, Xie M, Zhang Q, McMichael JF, Wyczalkowski MA, et al. Mutational landscape and significance across 12 major cancer types. Nature. 2013;502:333–9. 10.1038/nature12634 . Huang D, Sun W, Zhou Y, Li P, Chen F, Chen H, Xia D, Xu E, Lai M, Wu Y, Zhang H. Mutations of key driver genes in colorectal cancer progression and metastasis. Cancer Metastasis Rev. 2018;37:173–87. 10.1007/s10555-017-9726-5 . Miyaki M, Iijima T, Konishi M, Sakai K, Ishii A, Yasuno M, Hishima T, Koike M, Shitara N, Iwama T, et al. Higher frequency of Smad4 gene mutation in human colorectal cancer with distant metastasis. Oncogene. 1999;18:3098–103. 10.1038/sj.onc.1202642 . Fukushima T, Mashiko M, Takita K, Otake T, Endo Y, Sekikawa K, Takenoshita S. Mutational analysis of TGF-beta type II receptor, Smad2, Smad3, Smad4, Smad6 and Smad7 genes in colorectal cancer. J Exp Clin Cancer Res. 2003;22:315–20. Tanaka T, Watanabe T, Kazama Y, Tanaka J, Kanazawa T, Kazama S, Nagawa H. Chromosome 18q deletion and Smad4 protein inactivation correlate with liver metastasis: A study matched for T- and N- classification. Br J Cancer. 2006;95:1562–7. 10.1038/sj.bjc.6603460 . Fang T, Liang T, Wang Y, Wu H, Liu S, Xie L, Liang J, Wang C, Tan Y. Prognostic role and clinicopathological features of SMAD4 gene mutation in colorectal cancer: a systematic review and meta-analysis. BMC Gastroenterol. 2021;21:297. 10.1186/s12876-021-01864-9 . D'Agay MG, Galland L, Tharin Z, Truntzer C, Ghiringhelli F. Utility of exome sequencing in routine care for metastatic colorectal cancer. Mol Clin Oncol. 2021;15:229. 10.3892/mco.2021.2392 . Xue L, Hyman NH, Turaga KK, Eng OS. Peritoneal Metastases in Colorectal Cancer: Biology and Barriers. J Gastrointest Surg. 2020;24:720–7. 10.1007/s11605-019-04441-4 . Lesterhuis WJ, Haanen JB, Punt CJ. Cancer immunotherapy–revisited. Nat Rev Drug Discov. 2011;10:591–600. 10.1038/nrd3500 . Llosa NJ, Cruise M, Tam A, Wicks EC, Hechenbleikner EM, Taube JM, Blosser RL, Fan H, Wang H, Luber BS, et al. The vigorous immune microenvironment of microsatellite instable colon cancer is balanced by multiple counter-inhibitory checkpoints. Cancer Discov. 2015;5:43–51. 10.1158/2159–8290.CD-14-0863 . Keskin DB, Anandappa AJ, Sun J, Tirosh I, Mathewson ND, Li S, Oliveira G, Giobbie-Hurder A, Felt K, Gjini E, et al. Neoantigen vaccine generates intratumoral T cell responses in phase Ib glioblastoma trial. Nature. 2019;565:234–9. 10.1038/s41586-018-0792-9 . Fang Y, Mo F, Shou J, Wang H, Luo K, Zhang S, Han N, Li H, Ye S, Zhou Z, et al. A Pan-cancer Clinical Study of Personalized Neoantigen Vaccine Monotherapy in Treating Patients with Various Types of Advanced Solid Tumors. Clin Cancer Res. 2020;26:4511–20. 10.1158/1078 – 0432.CCR-19-2881 . Leko V, Rosenberg SA. Identifying and Targeting Human Tumor Antigens for T Cell-Based Immunotherapy of Solid Tumors. Cancer Cell. 2020;38:454–72. 10.1016/j.ccell.2020.07.013 . Pearlman AH, Hwang MS, Konig MF, Hsiue EH, Douglass J, DiNapoli SR, Mog BJ, Bettegowda C, Pardoll DM, Gabelli SB, et al. Targeting public neoantigens for cancer immunotherapy. Nat Cancer. 2021;2:487–97. 10.1038/s43018-021-00210-y . Ott PA, Hu Z, Keskin DB, Shukla SA, Sun J, Bozym DJ, Zhang W, Luoma A, Giobbie-Hurder A, Peter L, et al. An immunogenic personal neoantigen vaccine for patients with melanoma. Nature. 2017;547:217–21. 10.1038/nature22991 . Rojas LA, Sethna Z, Soares KC, Olcese C, Pang N, Patterson E, Lihm J, Ceglia N, Guasp P, Chu A, et al. Personalized RNA neoantigen vaccines stimulate T cells in pancreatic cancer. Nature. 2023;618:144–50. 10.1038/s41586-023-06063-y . Cai Z, Su X, Qiu L, Li Z, Li X, Dong X, Wei F, Zhou Y, Luo L, Chen G, et al. Personalized neoantigen vaccine prevents postoperative recurrence in hepatocellular carcinoma patients with vascular invasion. Mol Cancer. 2021;20:164. 10.1186/s12943-021-01467-8 . Yu YJ, Shan N, Li LY, Zhu YS, Lin LM, Mao CC, Hu TT, Xue XY, Su XP, Shen X, Cai ZZ. Preliminary clinical study of personalized neoantigen vaccine therapy for microsatellite stability (MSS)-advanced colorectal cancer. Cancer Immunol Immunother. 2023;72:2045–56. 10.1007/s00262-023-03386-7 . Tran E, Robbins PF, Lu YC, Prickett TD, Gartner JJ, Jia L, Pasetto A, Zheng Z, Ray S, Groh EM, et al. T-Cell Transfer Therapy Targeting Mutant KRAS in Cancer. N Engl J Med. 2016;375:2255–62. 10.1056/NEJMoa1609279 . Zhu YJ, Li X, Chen TT, Wang JX, Zhou YX, Mu XL, Du Y, Wang JL, Tang J, Liu JY. Personalised neoantigen-based therapy in colorectal cancer. Clin Transl Med. 2023;13:e1461. 10.1002/ctm2.1461 . Yaeger R, Chatila WK, Lipsyc MD, Hechtman JF, Cercek A, Sanchez-Vega F, Jayakumaran G, Middha S, Zehir A, Donoghue MTA, et al. Clinical Sequencing Defines the Genomic Landscape of Metastatic Colorectal Cancer. Cancer Cell. 2018;33:125–e136123. 10.1016/j.ccell.2017.12.004 . Iacobuzio-Donahue CA, Song J, Parmiagiani G, Yeo CJ, Hruban RH, Kern SE. Missense mutations of MADH4: characterization of the mutational hot spot and functional consequences in human tumors. Clin Cancer Res. 2004;10:1597–604. 10.1158/1078 – 0432.ccr-1121-3 . Fleming NI, Jorissen RN, Mouradov D, Christie M, Sakthianandeswaren A, Palmieri M, Day F, Li S, Tsui C, Lipton L, et al. SMAD2, SMAD3 and SMAD4 mutations in colorectal cancer. Cancer Res. 2013;73:725–35. 10.1158/0008-5472.CAN-12-2706 . Shi Y, Hata A, Lo RS, Massague J, Pavletich NP. A structural basis for mutational inactivation of the tumour suppressor Smad4. Nature. 1997;388:87–93. 10.1038/40431 . Sameer AS, Chowdri NA, Syeed N, Banday MZ, Shah ZA, Siddiqi MA. SMAD4–molecular gladiator of the TGF-beta signaling is trampled upon by mutational insufficiency in colorectal carcinoma of Kashmiri population: an analysis with relation to KRAS proto-oncogene. BMC Cancer. 2010;10. 10.1186/1471-2407-10-300 . Izeradjene K, Combs C, Best M, Gopinathan A, Wagner A, Grady WM, Deng CX, Hruban RH, Adsay NV, Tuveson DA, Hingorani SR. Kras(G12D) and Smad4/Dpc4 haploinsufficiency cooperate to induce mucinous cystic neoplasms and invasive adenocarcinoma of the pancreas. Cancer Cell. 2007;11:229–43. 10.1016/j.ccr.2007.01.017 . Samstein RM, Lee CH, Shoushtari AN, Hellmann MD, Shen R, Janjigian YY, Barron DA, Zehir A, Jordan EJ, Omuro A, et al. Tumor mutational load predicts survival after immunotherapy across multiple cancer types. Nat Genet. 2019;51:202–6. 10.1038/s41588-018-0312-8 . Ciardiello D, Vitiello PP, Cardone C, Martini G, Troiani T, Martinelli E, Ciardiello F. Immunotherapy of colorectal cancer: Challenges for therapeutic efficacy. Cancer Treat Rev. 2019;76:22–32. 10.1016/j.ctrv.2019.04.003 . Imyanitov E, Sokolenko A. (2022). Integrative Genomic Tests in Clinical Oncology. Int J Mol Sci 23 . 10.3390/ijms232113129 Akhurst RJ, Hata A. Targeting the TGFbeta signalling pathway in disease. Nat Rev Drug Discov. 2012;11:790–811. 10.1038/nrd3810 . Fujishita T, Kojima Y, Kajino-Sakamoto R, Mishiro-Sato E, Shimizu Y, Hosoda W, Yamaguchi R, Taketo MM, Aoki M. The cAMP/PKA/CREB and TGFbeta/SMAD4 Pathways Regulate Stemness and Metastatic Potential in Colorectal Cancer Cells. Cancer Res. 2022;82:4179–90. 10.1158/0008-5472.CAN-22-1369 . Azimi F, Scolyer RA, Rumcheva P, Moncrieff M, Murali R, McCarthy SW, Saw RP, Thompson JF. Tumor-infiltrating lymphocyte grade is an independent predictor of sentinel lymph node status and survival in patients with cutaneous melanoma. J Clin Oncol. 2012;30:2678–83. 10.1200/JCO.2011.37.8539 . Saxena M, van der Burg SH, Melief CJM, Bhardwaj N. Therapeutic cancer vaccines. Nat Rev Cancer. 2021;21:360–78. 10.1038/s41568-021-00346-0 . Ohue Y, Nishikawa H. Regulatory T (Treg) cells in cancer: Can Treg cells be a new therapeutic target? Cancer Sci. 2019;110:2080–9. 10.1111/cas.14069 . Bjerregaard AM, Nielsen M, Jurtz V, Barra CM, Hadrup SR, Szallasi Z, Eklund AC. An Analysis of Natural T Cell Responses to Predicted Tumor Neoepitopes. Front Immunol. 2017;8:1566. 10.3389/fimmu.2017.01566 . Lanauze CB, Sehgal P, Hayer K, Torres-Diz M, Pippin JA, Grant SFA, Thomas-Tikhonenko A. Colorectal Cancer-Associated Smad4 R361 Hotspot Mutations Boost Wnt/beta-Catenin Signaling through Enhanced Smad4-LEF1 Binding. Mol Cancer Res. 2021;19:823–33. 10.1158/1541–7786.MCR-20-0721 . Mennonna D, Maccalli C, Romano MC, Garavaglia C, Capocefalo F, Bordoni R, Severgnini M, De Bellis G, Sidney J, Sette A, et al. T cell neoepitope discovery in colorectal cancer by high throughput profiling of somatic mutations in expressed genes. Gut. 2017;66:454–63. 10.1136/gutjnl-2015-309453 . Cercek A, Chatila WK, Yaeger R, Walch H, Fernandes GDS, Krishnan A, Palmaira L, Maio A, Kemel Y, Srinivasan P, et al. A Comprehensive Comparison of Early-Onset and Average-Onset Colorectal Cancers. J Natl Cancer Inst. 2021;113:1683–92. 10.1093/jnci/djab124 . Lenos KJ, Bach S, Ferreira Moreno L, Hoorn T, Sluiter S, Bootsma NR, Vieira Braga S, Nijman FA, van den Bosch LE, Miedema T, D.M., et al. Molecular characterization of colorectal cancer related peritoneal metastatic disease. Nat Commun. 2022;13:4443. 10.1038/s41467-022-32198-z . Heuvelings DJI, Wintjens A, Moonen L, Engelen SME, de Hingh I, Valkenburg-van Iersel LB, den Dulk M, Beckervordersandforth J, Thijssen SGM, Leunissen DJG, et al. Predictive Genetic Biomarkers for the Development of Peritoneal Metastases in Colorectal Cancer. Int J Mol Sci. 2023;24. 10.3390/ijms241612830 . Reynisson B, Alvarez B, Paul S, Peters B, Nielsen M. NetMHCpan-4.1 and NetMHCIIpan-4.0: improved predictions of MHC antigen presentation by concurrent motif deconvolution and integration of MS MHC eluted ligand data. Nucleic Acids Res. 2020;48:W449–54. 10.1093/nar/gkaa379 . Reynisson B, Barra C, Kaabinejadian S, Hildebrand WH, Peters B, Nielsen M. Improved Prediction of MHC II Antigen Presentation through Integration and Motif Deconvolution of Mass Spectrometry MHC Eluted Ligand Data. J Proteome Res. 2020;19:2304–15. 10.1021/acs.jproteome.9b00874 . Li T, Fu J, Zeng Z, Cohen D, Li J, Chen Q, Li B, Liu XS. TIMER2.0 for analysis of tumor-infiltrating immune cells. Nucleic Acids Res. 2020;48:W509–14. 10.1093/nar/gkaa407 . Ru B, Wong CN, Tong Y, Zhong JY, Zhong SSW, Wu WC, Chu KC, Wong CY, Lau CY, Chen I, et al. TISIDB: an integrated repository portal for tumor-immune system interactions. Bioinformatics. 2019;35:4200–2. 10.1093/bioinformatics/btz210 . Chakravarty D, Gao J, Phillips SM, Kundra R, Zhang H, Wang J, Rudolph JE, Yaeger R, Soumerai T, Nissan MH et al. (2017). OncoKB: A Precision Oncology Knowledge Base. JCO Precis Oncol 2017 . 10.1200/PO.17.00011 Additional Declarations No competing interests reported. Supplementary Files suppl...pdf supportinginformationTables.xlsx Cite Share Download PDF Status: Posted Version 1 posted 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-7066314\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":497450843,\"identity\":\"2791ba0d-a445-4ebe-b263-35318672e5b9\",\"order_by\":0,\"name\":\"Min Chen\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAv0lEQVRIiWNgGAWjYFACHjApB8SMBxgYEojWYmDMwMDMQJqWxAaitei2nz34uODPn/R+ifwDBz5UpDHwt3fj12d2Ji/ZeGabQe7MGckMB2ecyWGQOHN2A34tN3jMpHkbDHI33EhmOMzbVsFgIJFLUIv5b54/BukGpGgxY+ZhM0iAaskhQsuZHGNp3jZjw5k9jw2AfknjIeyX42cMP/P8kZPnZ098+OBDRbIcf3svfi0YgIc05aNgFIyCUTAKsAIAwudGFyQumGMAAAAASUVORK5CYII=\",\"orcid\":\"\",\"institution\":\"Jingge Medical Technology Co.. Ltd\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Min\",\"middleName\":\"\",\"lastName\":\"Chen\",\"suffix\":\"\"},{\"id\":497450844,\"identity\":\"e03514ca-88b5-4bce-8c10-9a811d3b1995\",\"order_by\":1,\"name\":\"Tengfei Wang\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Third Hospital of Shanxi Medical University, Shanxi Academy of Medical Sciences\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Tengfei\",\"middleName\":\"\",\"lastName\":\"Wang\",\"suffix\":\"\"},{\"id\":497450845,\"identity\":\"17e10548-6e04-45a1-b5c7-4f1aae95e511\",\"order_by\":2,\"name\":\"Xiaorong Feng\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Shantou University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Xiaorong\",\"middleName\":\"\",\"lastName\":\"Feng\",\"suffix\":\"\"},{\"id\":497450846,\"identity\":\"84efe16f-04af-47b0-8ed9-a1608f3ccc35\",\"order_by\":3,\"name\":\"Yuzhao Gao\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Shanxi University of Finance and Economics\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Yuzhao\",\"middleName\":\"\",\"lastName\":\"Gao\",\"suffix\":\"\"},{\"id\":497450847,\"identity\":\"cd1eb98e-c8e7-416c-9087-64bdeeed96e9\",\"order_by\":4,\"name\":\"Xiaolong Wei\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Cancer Hospital of Shantou University Medical College\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Xiaolong\",\"middleName\":\"\",\"lastName\":\"Wei\",\"suffix\":\"\"},{\"id\":497450848,\"identity\":\"a653c9df-6cd7-445c-8865-b3626b625b30\",\"order_by\":5,\"name\":\"Zhenguo Han\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Shanxi Bethune Hospital, Third Hospital of Shanxi Medical University, Shanxi Academy of Medical Sciences\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Zhenguo\",\"middleName\":\"\",\"lastName\":\"Han\",\"suffix\":\"\"},{\"id\":497450849,\"identity\":\"2af0cd8c-9fd4-477c-b034-b4ca8aefbdd8\",\"order_by\":6,\"name\":\"Hanxiang An\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"The Cancer Center, Shanxi Bethune Hospital, Third Hospital of Shanxi Medical University, Shanxi Academy of Medical Sciences\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Hanxiang\",\"middleName\":\"\",\"lastName\":\"An\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2025-07-07 14:08:31\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-7066314/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-7066314/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":89108762,\"identity\":\"c30ec992-874e-43c0-ad8d-3d1e1259d2a2\",\"added_by\":\"auto\",\"created_at\":\"2025-08-14 18:21:56\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":64936,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eStudy design of the patient cohort\\u003c/strong\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7066314/v1/65d92e3515d2a63eda22420f.png\"},{\"id\":89108765,\"identity\":\"be75e1a9-ae1b-44bd-8a2f-9ff13b60fb66\",\"added_by\":\"auto\",\"created_at\":\"2025-08-14 18:21:56\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":149719,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eIllustrates genetic mutation profiles and tumor mutation burden (TMB) levels in SMAD4-mutant versus SMAD4 wild-type patients with CRC and CRC-PM.\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e(A)Oncoplot of CRC-PM depicting the top 11 recurrently altered driver genes and their somatic mutation profiles. Rectangular tiles represent individual tissue samples, with color-coding denoting mutation subtypes and patient gender. (B) Mutant frequencies of 11 driver genes in primary CRC versus CRC-PM cohorts. ***P＜0.001, *P＜0.05. (C) Comparative analysis of copy number alterations (CNAs) between the CRC-PM cohort (n=63) and CRC cohort (n=694) (D\\u0026amp;E) Tumor mutation burden (TMB) comparison between SMAD4-mutant versus SMAD4 wild-type groups in CRC-PM\\u0026nbsp; (SMAD4 gene, n\\u003csup\\u003emut\\u003c/sup\\u003e=15, n\\u003csup\\u003ewt\\u003c/sup\\u003e=48, P=0.002; KRAS gene, n\\u003csup\\u003emut\\u003c/sup\\u003e=31, n\\u003csup\\u003ewt\\u003c/sup\\u003e=32, P=0.0022) (D) and CRC (SMAD4 gene, n\\u003csup\\u003emut\\u003c/sup\\u003e=107, n\\u003csup\\u003ewt\\u003c/sup\\u003e=587, P=0.017; KRAS gene, n\\u003csup\\u003emut\\u003c/sup\\u003e=304, n\\u003csup\\u003ewt\\u003c/sup\\u003e=390, P＜0.001) (E).\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7066314/v1/9bfc9c76b5f0cf627f28fcb8.png\"},{\"id\":89109510,\"identity\":\"91b755ce-9ced-4445-a072-9ac5a9c333bd\",\"added_by\":\"auto\",\"created_at\":\"2025-08-14 18:37:56\",\"extension\":\"png\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":131564,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eCharacterization of SMAD4 mutation hotspot regions in primary colorectal cancer (CRC) and peritoneal metastasis (CRC-PM) cohorts.\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e(A)Ten unique somatic SMAD4 mutation sites identified in CRC-PM relative to CRC. (B) \\u0026nbsp;Comparative analysis of pathogenic mutation frequencies between CRC and CRC-PM cohorts. (C) Schematic diagram of domain-specific pathogenic SMAD4 mutations in the CRC-PM cohort. (D) Mutation frequencies of R-SMAD-related SMAD4 mutations in CRC-PM versus CRC cohorts across two distinct hotspot regions: D351-P356, R361 and A406, K428, R515. (E) Molecular docking analysis of the SMAD4 protein around R361 residue, highlighting structural consequences of R361C and R361H mutations. (F) Comparative analysis of R361C mutation frequency in SMAD4 between CRC and CRC-PM cohorts. **P＜0.01.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7066314/v1/4d8b36c994064c0f9053cc06.png\"},{\"id\":89109511,\"identity\":\"53ed2e04-830e-432d-84f5-14e2d8ac52fe\",\"added_by\":\"auto\",\"created_at\":\"2025-08-14 18:37:56\",\"extension\":\"png\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":238559,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003ePredicted neoantigens associated withSMAD4-mutant in CRC-PM.\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e(A)Summary of neoantigens and HLA class I pairs identified by two computational pipelines. Colors correspond to neoantigens predicted by the NetMHCpan pipeline or the GIANT pipeline, respectively. A ‘+’ symbol denotes neoantigens identified by both pipelines. (B) Neoantigens exhibiting binding affinities to HLA class II alleles (HLA-DRB1), predicted using NetMHCIIpan 4.1.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage4.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7066314/v1/8f8122843d01598e74cd0727.png\"},{\"id\":89109299,\"identity\":\"35fbb0ad-4a95-4697-a8d7-1d364489ab0a\",\"added_by\":\"auto\",\"created_at\":\"2025-08-14 18:29:56\",\"extension\":\"png\",\"order_by\":5,\"title\":\"Figure 5\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":257934,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eAssociation of SMAD4 mutations with immune cell infiltration and immune checkpoint gene expression.\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e(A)Scatter plots demonstrating the association between SMAD4 expression (Y-axis) and immune infiltrating cells (X-axis) (n=458), analyzed using the Tumor Immune Estimation Resource (TIMER) algorithm. (B) Box plots from the mutation module illustrating comparative TIMER-estimated immune cell infiltration levels between SMAD4 mutant and wild-type groups. (C) Box plots comparing tumor infiltration levels in colorectal cancer (CRC) stratified by SMAD4 somatic copy number alteration status, assessed via TIMER. ***P＜0.001, **P＜0.01, *P＜0.05.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage5.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7066314/v1/b2bb9f2dce9d6ffe0968cb3f.png\"},{\"id\":89108775,\"identity\":\"2ede6d30-e15f-4490-b100-a6c358fd9724\",\"added_by\":\"auto\",\"created_at\":\"2025-08-14 18:21:56\",\"extension\":\"png\",\"order_by\":6,\"title\":\"Figure 6\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":222684,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eAssociation of SMAD4 mutations with immune checkpoint gene expression in colorectal cancer (CRC).\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e(A-E) Spearman’s correlation analysis of SMAD4 mutation status with expression levels of chemokines: (A), receptors (B), immunostimulators (C), immunoinhibitors (D), and MHCs (E) in CRC (n=365), assessed via TISIDB.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage6.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7066314/v1/7ec4b477174826d5ed6cbbd4.png\"},{\"id\":90967084,\"identity\":\"5caf22fc-118a-4d21-8f97-0debcab8422e\",\"added_by\":\"auto\",\"created_at\":\"2025-09-10 06:47:14\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":1959378,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7066314/v1/20cfb3fb-aea6-47fa-81d5-de410baa30cd.pdf\"},{\"id\":89108784,\"identity\":\"a63e72b8-60ae-459a-9407-f72336c08a3b\",\"added_by\":\"auto\",\"created_at\":\"2025-08-14 18:21:57\",\"extension\":\"pdf\",\"order_by\":1,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":30551278,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"suppl...pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7066314/v1/f5adbc19bd90994156b18807.pdf\"},{\"id\":89108773,\"identity\":\"70ad1a36-0090-4d19-9b69-a4473c1a4ccf\",\"added_by\":\"auto\",\"created_at\":\"2025-08-14 18:21:56\",\"extension\":\"xlsx\",\"order_by\":2,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":650685,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"supportinginformationTables.xlsx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7066314/v1/c574bb7e02f8901e5a37e107.xlsx\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"AI-Driven Prediction Unveils SMAD4-Specific HLA-Restricted Epitopes: A Novel Therapeutic Target for Precision Treatment of Peritoneal Metastasis in Colorectal Cancer\",\"fulltext\":[{\"header\":\"Introduction\",\"content\":\"\\u003cp\\u003eColorectal cancer (CRC) ranks as the second-leading cause of cancer-related death worldwide\\u003csup\\u003e\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e\\u003c/sup\\u003e. Approximately 25% of CRC patients present with metastasis at the time of diagnosis\\u003csup\\u003e\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e\\u003c/sup\\u003e. Peritoneal metastasis and subsequent development of malignant ascites are the most frequent cause of death in patients with advanced CRC.\\u003csup\\u003e\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e\\u003c/sup\\u003e Due to the lack of effective therapeutic options, the median survival time for patients with peritoneal metastasis from CRC ranges from six to twelve months post-diagnosis.\\u003csup\\u003e\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e\\u003c/sup\\u003e Next-generation sequencing (NGS) has identified specific genetic alterations in CRC-PM genome, highlighting potential targets for therapies such as anti-VEGF agents or the BRAF\\u003csup\\u003e600E\\u003c/sup\\u003e inhibitor vemurafenib. However, the clinical benefits of molecular-targeted therapies remain limited to a small patient subset.\\u003c/p\\u003e\\u003cp\\u003eGenetic mutations in APC, TP53RAS, PIK3CA, and SMAD4 have been linked to metastatic progression in CRC.\\u003csup\\u003e\\u003cspan additionalcitationids=\\\"CR7\\\" citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e\\u003c/sup\\u003e SMAD4 is located on chromosome 18q21, resides in a region commonly associated with allelic loss, a hallmark of metastasis and poor prognosis in colorectal cancer.\\u003csup\\u003e\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e\\u003c/sup\\u003e SMAD4, a key driver of CRC progression, harbors inactivating mutations in 5\\u0026ndash;24.2% of cases, which are strongly associated with metastatic progression.\\u003csup\\u003e\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e\\u003c/sup\\u003e SMAD4 regulates CRC pathogenesis and progression and is further implicated in poor prognosis among metastatic colorectal cancer (mCRC) patients.\\u003csup\\u003e\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e\\u003c/sup\\u003e However, the precise functional role of these mutations in promoting peritoneal metastasis of CRC remains unclear.\\u003csup\\u003e\\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e\\u003c/sup\\u003e\\u003c/p\\u003e\\u003cp\\u003eImmune checkpoint blockade (ICB) therapies have achieved notable success in treating solid tumors.\\u003csup\\u003e\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e\\u003c/sup\\u003e however, less than 5% of deficient mismatch repair (dMMR) mCRC benefit from ICB immunotherapy.\\u003csup\\u003e\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e\\u003c/sup\\u003e The advent of next-generation sequencing (NGS) has enabled the identification of patient-specific neoantigens (neoAgs) for cancer vaccine development. Over the past decade, neoantigens have emerged as optimal targets for stimulating antitumor immunity.\\u003csup\\u003e\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e\\u003c/sup\\u003e This process depends on the ability of identified neopeptides with high affinity to bind with human leukocyte antigen (HLA) molecules, a critical factor in inducing an immune response.\\u003csup\\u003e\\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e\\u003c/sup\\u003e To optimize neoAg identification, multiple computational tools leverage diverse algorithms to predict binding affinities for HLA class I/II alleles. Integrating these tools may enhance immunogenicity predictions by combining their distinct criteria and algorithmic strengths.\\u003c/p\\u003e\\u003cp\\u003eNeoantigen-based vaccines have demonstrated promising efficacy in clinical trials across malignancies, including melanoma, glioblastoma multiforme, pancreatic adenocarcinoma, and liver cancer.\\u003csup\\u003e\\u003cspan additionalcitationids=\\\"CR21\\\" citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e\\u003c/sup\\u003e Recently, Yu et al. \\u003csup\\u003e\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e\\u003c/sup\\u003e demonstrated that personalized neoantigen vaccine is safe, feasible, and effective in microsatellite-stable (MSS) CRC patients with postoperative recurrence or metastasis. Tran et al. reported complete regression of all seven lung metastases following infusion of HLA-C*08:02\\u0026ndash;restricted tumor-infiltrating lymphocytes targeting KRAS\\u003csup\\u003eG12D\\u003c/sup\\u003e.\\u003csup\\u003e\\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e\\u003c/sup\\u003e Given these advances in neoantigen-based immunotherapy, \\u003csup\\u003e\\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e\\u003c/sup\\u003e researchers are exploring tumor neoAgs as a promising therapeutic strategy for CRC-PM.\\u003c/p\\u003e\\u003cp\\u003eIn this study, we performed integrated genomic profiling and computational neoantigen prediction using samples from 2,424 advanced CRC patients and 124 CRC-PM patients. Our analysis revealed multiple genetic alterations enriched in CRC-PM tumors. We identified a positive correlation between SMAD4 mutations and tumor-infiltrating lymphocytes (TILs) or immune checkpoint (ICP) genes, suggesting SMAD4-derived neoantigens as potential targets for CRC-PM immunotherapy.\\u003c/p\\u003e\"},{\"header\":\"Methods\",\"content\":\"\\u003cp\\u003e\\u003cb\\u003eData Sources\\u003c/b\\u003e\\u003c/p\\u003e\\u003cp\\u003eGenomic data from 2,424 CRC patients (including 124 CRC-PM) were obtained from cBioPortal and published studies, including datasets generated by Cercek et al.\\u003csup\\u003e\\u003cspan citationid=\\\"CR43\\\" class=\\\"CitationRef\\\"\\u003e43\\u003c/span\\u003e\\u003c/sup\\u003e, Lenos et al. ,\\u003csup\\u003e\\u003cspan citationid=\\\"CR44\\\" class=\\\"CitationRef\\\"\\u003e44\\u003c/span\\u003e\\u003c/sup\\u003e Yaeger et al. ,\\u003csup\\u003e\\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e\\u003c/sup\\u003e Heuvelings et al. .\\u003csup\\u003e\\u003cspan citationid=\\\"CR45\\\" class=\\\"CitationRef\\\"\\u003e45\\u003c/span\\u003e\\u003c/sup\\u003e\\u003c/p\\u003e\\u003cp\\u003e\\u003cb\\u003eNeoantigen prediction\\u003c/b\\u003e\\u003c/p\\u003e\\u003cp\\u003eNeoantigen prediction was performed using artificial intelligence (AI)-driven computational algorithms to identify peptides derived from somatic \\u003cem\\u003eSMAD4\\u003c/em\\u003e mutations. High-frequency HLA alleles for European and East Asian populations were retrieved from the Allele Frequency Net Database. Sixteen HLA class I and three HLA class II (HLA-DRB1) alleles were selected. For MHC class II neoAg screening, NetMHCIIpan4.1 was utilized. Two pipelines were employed to prioritize class I neoAgs as follows: 1) NetMHCpan-pipeline: NetMHCpan4.1\\u003csup\\u003e46\\u003c/sup\\u003e predicted MHC class I epitopes using 9\\u0026ndash;12-mer peptides, with binding affinity thresholds of IC50\\u0026thinsp;\\u0026lt;\\u0026thinsp;500 nM or %Rank\\u0026thinsp;\\u0026le;\\u0026thinsp;2. NetMHCIIpan4.1\\u003csup\\u003e47\\u003c/sup\\u003e screened 15-mer peptides for MHC class II binding; 2) G IANT-pipeline: GIANT, an in-house algorithm (BGI-GenoImmune, Wuhan, China), predicted neoAg immunogenicity using MHC binding affinity, peptide presentation, expression levels, and physicochemical properties (threshold: GIANT score\\u0026thinsp;\\u0026ge;\\u0026thinsp;0.5). A second algorithm (EPIP) evaluated epitope presentation using convolutional neural networks (CNNs) trained on 180,000 HLA ligands, with a presentation score threshold of \\u0026ge;\\u0026thinsp;0.1.\\u003c/p\\u003e\\u003cp\\u003e\\u003cb\\u003eImmune Profiling\\u003c/b\\u003e\\u003c/p\\u003e\\u003cp\\u003eThe Tumor Immune Estimation Resource (TIMER)\\u003csup\\u003e\\u003cspan citationid=\\\"CR48\\\" class=\\\"CitationRef\\\"\\u003e48\\u003c/span\\u003e\\u003c/sup\\u003e quantified immune cell infiltration across cancer types, while TISIDB,\\u003csup\\u003e\\u003cspan citationid=\\\"CR49\\\" class=\\\"CitationRef\\\"\\u003e49\\u003c/span\\u003e\\u003c/sup\\u003e a complementary web resource, quantified tumor-immune system interactions. Both tools analyzed correlations between \\u003cem\\u003eSMAD4\\u003c/em\\u003e alterations and immune features.\\u003c/p\\u003e\\u003cp\\u003e\\u003cb\\u003eStructural modeling\\u003c/b\\u003e\\u003c/p\\u003e\\u003cp\\u003eThe wild-type \\u003cem\\u003eSMAD4\\u003c/em\\u003e crystal structure (PDB ID: 1DD1) was obtained from the Protein Data Bank. The Arg361 residue was mutated to R361C and R361H using PyMOL\\u0026rsquo;s built-in mutagenesis wizard.\\u003c/p\\u003e\\u003cp\\u003e\\u003cb\\u003eStatistical analysis and software for visualization\\u003c/b\\u003e\\u003c/p\\u003e\\u003cp\\u003eGenomic Alterations: Oncogenic variants were filtered using OncoKB\\u003csup\\u003e\\u003cspan citationid=\\\"CR50\\\" class=\\\"CitationRef\\\"\\u003e50\\u003c/span\\u003e\\u003c/sup\\u003e (oncokb.org) and ClinVar. Mutation frequencies were compared via Chi-square tests, and TMB differences were assessed using Mann-Whitney U-tests.\\u003c/p\\u003e\\u003cp\\u003eImmune Correlation: Partial Spearman\\u0026rsquo;s correlation, adjusted for tumor purity, assessed relationships between SMAD4 expression and immune infiltration. Wilcoxon rank-sum tests evaluated associations between \\u003cem\\u003eSMAD4\\u003c/em\\u003e alterations and immune checkpoint (ICP) gene expression (significance threshold: \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026le;\\u0026thinsp;0.05).\\u003c/p\\u003e\\u003cp\\u003eVisualization: R software visualized driver gene alterations and TMB distributions.\\u003c/p\\u003e\"},{\"header\":\"Results\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eStudy design and patient characteristics\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eWe analyzed a total of 2548 colorectal adenocarcinomas, including 2424 primary tumors and 124 peritoneal metastases (PM) (Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e, Table \\u003cspan class=\\\"InternalRef\\\"\\u003eS1\\u003c/span\\u003e-10). Tumors were sequenced using MSK-IMPACT and a custom-targeted NGS panel targeting 11 canonical CRC driver genes. Clinical characteristics for the 124 CRC-PM and 694 CRC cohorts are summarized in Table S 1. The CRC-PM cohort included 100 individuals of white ethnicity and 7 of Asian ethnicity, while the CRC cohort comprised of 694 individuals of white ethnicity.\\u003c/p\\u003e\\n\\u003cp\\u003ePatients diagnosed with CRC-PM (n\\u0026thinsp;=\\u0026thinsp;124) and CRC (n\\u0026thinsp;=\\u0026thinsp;2424) who underwent next-generation sequencing via cBioPortal and published studies were included in the genetic analysis.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAnalyzing the CRC peritoneal metastasis- related driver genes\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eTo compare driver gene alteration in CRC (n\\u0026thinsp;=\\u0026thinsp;694), we sought to identify elevated mutation frequencies (MFs) of driver genes in CRC-PM (n\\u0026thinsp;=\\u0026thinsp;124) (Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eA). Consistent with a prior report\\u003csup\\u003e\\u003cspan class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e\\u003c/sup\\u003e, the most frequently mutated genes were APC (75%), TP53 (68%), KRAS (44%), PIK3CA (22%), and SMAD4 (15%) (Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eB, Table \\u003cspan class=\\\"InternalRef\\\"\\u003eS2\\u003c/span\\u003e and 3). Our analysis showed that KRAS and SMAD4 exhibited significantly elevated mutation frequencies (MFs) in CRC-PM (10% and 4% increases, respectively (Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eB, Table \\u003cspan class=\\\"InternalRef\\\"\\u003eS3\\u003c/span\\u003e). Additionally, the homozygous deletion (HOMDEL) of SMAD4 occurred more frequently associated with CRC peritoneal metastasis compared to CRC (7.9% vs 1.9% in CRC, p\\u0026thinsp;=\\u0026thinsp;0.01, Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eC, Table S4). Notably, our results indicated that mutations in KRAS and SMAD4 correlated positively with tumor mutational burden (TMB) in both cohorts (Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eD and \\u003cspan class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eE, Table S5 and Table 6).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eSpectrum of somatic mutations of KRAS and SMAD4\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eCo-occurring KRAS and SMAD4 mutations were observed in 47.2% (50/106) of CRC patients, rising to 62.5% (15/24) in CRC-PM (Table \\u003cspan class=\\\"InternalRef\\\"\\u003eS2\\u003c/span\\u003e, 6), KRAS\\u003csup\\u003eG12D\\u003c/sup\\u003e and KRAS\\u003csup\\u003eG12V\\u003c/sup\\u003e predominated in CRC, while KRAS\\u003csup\\u003eG12D\\u003c/sup\\u003e (17% vs. 12%) and KRAS\\u003csup\\u003eG13D\\u003c/sup\\u003e (9.7% vs. 7.8%) were elevated in CRC-PM (Table S7).\\u003c/p\\u003e\\n\\u003cp\\u003eTo identify SMAD4 mutations specific to peritoneal metastases, we analyzed 124 CRC patients with peritoneal metastases and compared them with a large cohort of CRC patients (n\\u0026thinsp;=\\u0026thinsp;2424). Data from 21 bowel cancer studies in the cBioPortal database were aggregate compiling SMAD4 mutation data from 2,424 CRC patients, identifying 351 mutation sites in 312 patients (Table S8). In the CRC-PM cohort, missense mutations (67%, 18/27) were the most frequent somatic alterations in SMAD4, followed by in-frame insertions/deletions (22% vs. 13% in CRC; 6/27), nonsense mutations (7%, 2/27), and splice site mutations (4%, 1/27) (Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eA, Table \\u003cspan class=\\\"InternalRef\\\"\\u003eS2\\u003c/span\\u003e). Ten of twenty-seven (37%) SMAD4 somatic mutations were firstly identified unique in CRC-PM cohorts (Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eA, Table \\u003cspan class=\\\"InternalRef\\\"\\u003eS2\\u003c/span\\u003e and Table 8). These unique mutations comprised 50% missense single-nucleotide variants (SNVs) and 50% insertions/deletions (InDels).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003ePathogenic Mutations of SMAD4 in CRC-PM\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003ePathogenic mutations in CRC-PM were distributed across the SMAD4 gene, with a significantly higher mutant frequency (8.87%, 11/124) compared to CRC (3.26%) (Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eB and \\u003cspan class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eC, p\\u0026thinsp;=\\u0026thinsp;0.002, Table S9). Notably, 86% (6/7) clustered within the C-terminal SMAD4 homology domain 2 (MH2) (Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eC). Consistent with previous observations,\\u003csup\\u003e\\u003cspan class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e,\\u003cspan class=\\\"CitationRef\\\"\\u003e28\\u003c/span\\u003e\\u003c/sup\\u003e two missense mutation hotspots (D351\\u0026ndash;P356/R361 and A406/K428/R515), located within the L1 loop\\u0026mdash;a structural motif critical for R-SMAD binding and phosphorylation) ,\\u003csup\\u003e\\u003cspan class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e\\u003c/sup\\u003e exhibited higher frequencies in CRC-PM\\u003c/p\\u003e\\n\\u003cp\\u003e(8.06% vs 4.33% in CRC, p\\u0026thinsp;=\\u0026thinsp;0.051, Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eD and \\u003cspan class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eE, Table S10). Particularly, the R361C mutation was the most frequent in CRC-PM at 4.03%, compared to its ranking as the second most frequent mutation in CRC at 0.91% (Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eF, p\\u0026thinsp;=\\u0026thinsp;0.004, Table S 10). These alterations likely confer heightened pathogenicity by potentially disrupting SMAD4 homo-/hetero-oligomerization with R-SMAD proteins or a reduction of SMAD4 stability.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003ePutative neoantigens of SMAD4 mutation in CRC-PM\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eUsing two NetMHCpan and GIANT pipelines (Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eA, Table S11 and Table 12), 20 and 48 neoAgs were identified, respectively, with 14 overlapping between both pipelines. R361H, R361C, D424V, A118V, and G419W were the frequently predicted neoAgs binding to Class I alleles (Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eA). The top Class I alleles included HLA-B*07:02, HLA-B*40:01, HLA-A*02:01 using NetMHCpan pipeline, and HLA-B*07:02, HLA-A*24:02, HLA-B*35:01, HLA-C*07:02 using GIANT tool. Furthermore, D424V and W524C were identified for Class II alleles of HLA-DRB1*15:01 and HLA-DRB1*13:02 with high affinities using NetMHCIIpan4.1 (Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eB, Table S13). Collectively, the frequently predicted neoAgs derived from SMAD4 mutations located in the functional domain MH2.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAssociation of SMAD4 mutations with immune infiltration and immune checkpoint genes\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eTo investigate associations between SMAD4 genetic/expression profiles and immune infiltration in CRC (\\u003cem\\u003en\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;458), we employed the TIMER computational tool. Elevated \\u003cem\\u003eSMAD4\\u003c/em\\u003e expression and mutations correlated significantly with increased infiltration of dendritic cells (partial correlation\\u0026thinsp;=\\u0026thinsp;0.517, P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), neutrophil (partial correlation\\u0026thinsp;=\\u0026thinsp;0.434, P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), CD8\\u003csup\\u003e+\\u003c/sup\\u003e T cells (partial correlation\\u0026thinsp;=\\u0026thinsp;0.365, P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), macrophage (partial correlation\\u0026thinsp;=\\u0026thinsp;0.35, P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), CD4\\u003csup\\u003e+\\u003c/sup\\u003e T cells (partial correlation\\u0026thinsp;=\\u0026thinsp;0.223, P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.01), B cells (partial correlation\\u0026thinsp;=\\u0026thinsp;0.221, P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.01) (Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003eA and \\u003cspan class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003eB, Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003eS1\\u003c/span\\u003eA). However, the expression level of SMAD4 was negatively correlated with the infiltration level of memory B cells (Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003eS1\\u003c/span\\u003eA). Deep or arm-level SMAD4 deletions correlated with reduced infiltration of dendritic cells (P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), neutrophil (P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), CD8\\u003csup\\u003e+\\u003c/sup\\u003e T cells (P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001) and B cells (P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.01) (Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003eC). We further analyzed associations between \\u003cem\\u003eSMAD4\\u003c/em\\u003e expression and CRC molecular subtypes, showing that SMAD4 expression was associated with different molecular and immune subtypes (Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eB and \\u003cspan class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eC). Collectively, these findings indicate that SMAD4 dysregulation contributes to immune infiltration in the TME.\\u003c/p\\u003e\\n\\u003cp\\u003eChemokines, chemokine receptors, and immune checkpoint (ICP) genes\\u0026mdash;including immunostimulators and immunoinhibitors\\u0026mdash;play critical roles in TME dynamics and antitumor immunity. Therefore, we analyzed the correlation between the mutated SMAD4 and these genes in CRC using TISIDB. Significant correlations were identified between \\u003cem\\u003eSMAD4\\u003c/em\\u003e mutations and 6 chemokines, 4 chemokine receptors, and 25 ICP genes in colorectal cancer cohort (n\\u0026thinsp;=\\u0026thinsp;365, Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003eA). \\u003cem\\u003eSMAD4\\u003c/em\\u003e mutations exhibited positive correlation\\u003cstrong\\u003es\\u003c/strong\\u003e with most genes, including \\u003cem\\u003eCCL2\\u003c/em\\u003e, \\u003cem\\u003eCXCL12\\u003c/em\\u003e, \\u003cem\\u003eCCR5\\u003c/em\\u003e, and \\u003cem\\u003eCCR8\\u003c/em\\u003e (Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003eB), as well as \\u003cem\\u003eCXCL12, ENTPD1, IL2RA, CSF1R, PDCD1LG2\\u003c/em\\u003e, and \\u003cem\\u003eTIGIT\\u003c/em\\u003e (Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003eC and \\u003cspan class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003eD). Negative correlations were observed with a subset of genes, including \\u003cem\\u003eCCL23\\u003c/em\\u003e and \\u003cem\\u003eCCL24\\u003c/em\\u003e (Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003eA and \\u003cspan class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003eB), \\u003cem\\u003eTNFRSF25\\u003c/em\\u003e and \\u003cem\\u003ePVRL2 (\\u003c/em\\u003eFig. \\u003cspan class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003eC and \\u003cspan class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003eD). Moreover, mutated \\u003cem\\u003eSMAD4\\u003c/em\\u003e was negatively correlated with HLA class I alleles, including HLA-A, C, and G, while being positively correlated with HLA class II allele HLA-DOA (Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003eE). These findings suggest that mutated \\u003cem\\u003eSMAD4\\u003c/em\\u003e might play an important role in tumor immunity, and making it an attractive target for immunotherapy.\\u003c/p\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cp\\u003eOur study elucidates the molecular landscape of colorectal cancer peritoneal metastasis (CRC-PM), identifying KRAS and SMAD4 as key pathogenic drivers with heightened clinical significance. Through integrated multi-omics analyses, we demonstrate that SMAD4 mutations correlate with enhanced immune infiltration and elevated expression of immune checkpoint (ICP) genes, revealing its immunomodulatory role in CRC-PM tumorigenesis. Notably, our integrated approach employing two orthogonal neoantigen prediction systems - NetMHCpan and GenoImmune's AI-driven Neoantigen Technology (GIANT) - successfully identified high-confidence SMAD4-derived neoantigens. These findings establish SMAD4 mutations as prioritized actionable immunotherapeutic targets, proposing dual clinical translation potential: (1) direct targeting of SMAD4 signaling pathways, and (2) AI-optimized neoantigen vaccine development for precision immunotherapy in CRC-PM.\\u003c/p\\u003e\\u003cp\\u003eTumor progression in CRC-PM is driven by recurrent genetic alterations in APC, TP53, KRAS, PIK3CA, and SMAD4. Consistent with prior work by Sameer et al. ,\\u003csup\\u003e\\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e30\\u003c/span\\u003e\\u003c/sup\\u003e we observed synergistic dysregulation of SMAD4 and KRAS, collectively accelerating metastatic progression. Izeradjene et al.\\u003csup\\u003e\\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e31\\u003c/span\\u003e\\u003c/sup\\u003e revealed that KRAS\\u003csup\\u003eG12D\\u003c/sup\\u003e tumors exhibit a higher propensity for developing SMAD4 haploinsufficiency, which, in turn, leads to increased invasiveness of cancer cells. Particularly, Yaeger et al.\\u003csup\\u003e\\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e\\u003c/sup\\u003e showed a significant difference in survival associated with SMAD4 mutations within microsatellite stable (MSS) mCRC following a systematic univariate survival analysis. In our study, a comparison between primary CRC and peritoneal dissemination identified KRAS and SMAD4 as two major molecular abnormalities, suggesting that these two drive genes are critical for peritoneal metastasis in CRC.\\u003c/p\\u003e\\u003cp\\u003eCurrently, multiple potential factors have been identified for predicting the clinical outcome of immunotherapy. TMB\\u003csup\\u003e\\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e32\\u003c/span\\u003e\\u003c/sup\\u003eand MSI\\u003csup\\u003e\\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e33\\u003c/span\\u003e\\u003c/sup\\u003ehave been applied as clinical biomarkers for predicting potential responses to immune checkpoint inhibitor (ICI) immunotherapy in solid tumors. TMB enhances antigenic response through the generation of neoantigens.\\u003csup\\u003e\\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e34\\u003c/span\\u003e\\u003c/sup\\u003e Notably, SMAD4 and KRAS mutations correlated with elevated tumor mutational burden (TMB), implicating these alterations as potent sources of immunogenic neoantigens. Several previous functional studies have revealed that SMAD4 is a key transcription factor in the regulation of the TGF-β pathway.\\u003csup\\u003e\\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e35\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e36\\u003c/span\\u003e\\u003c/sup\\u003e Consistent with these reports, \\u003csup\\u003e\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e\\u003c/sup\\u003e our findings in this study highlight that SMAD4 is commonly mutated in CRC-PM cohort, with mutations occur throughout the gene, particularly in the MH2 binding domain. The MH2 domain, critical for SMAD4 oligomerization and TGF-β signaling, exhibited mutation hotspots that disrupt interactions with SMAD2/SMAD3. Furthermore, 10 unique mutation sites were detected only in 124 CRC-PM as compared to 2424 CRC genomic profiles, suggesting that these unique mutations are involved in the regulation of peritoneal metastasis of CRC cancer cells.\\u003c/p\\u003e\\u003cp\\u003eTumor-infiltrating lymphocytes within the tumor microenvironment have been identified as effective predictors of prognosis and immunotherapeutic efficacy in cancer.\\u003csup\\u003e\\u003cspan citationid=\\\"CR37\\\" class=\\\"CitationRef\\\"\\u003e37\\u003c/span\\u003e\\u003c/sup\\u003e Neoantigen immunogenicity critically depends on CD8\\u003csup\\u003e+\\u003c/sup\\u003e T cell activation, which can be amplified via dendritic cell-mediated antigen presentation. \\u003csup\\u003e\\u003cspan citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e38\\u003c/span\\u003e\\u003c/sup\\u003e In this study, we found that the infiltration levels of several immune effectors, such as activated CD8\\u003csup\\u003e+\\u003c/sup\\u003e and CD4\\u003csup\\u003e+\\u003c/sup\\u003e T cells, along with DCs were significantly associated with mutated SMAD4. Furthermore, our study demonstrated a strong relationship between mutated SMAD4 and some key ICP genes, including CD96, CSF1R, TIGIT, CXCL12, and IL2RA, which is approved as a biomarker for regulatory T cells (Tregs) and plays a role in the regulation of immune tolerance.\\u003csup\\u003e\\u003cspan citationid=\\\"CR39\\\" class=\\\"CitationRef\\\"\\u003e39\\u003c/span\\u003e\\u003c/sup\\u003e Notably, cancer mutations can generate neo-epitopes that are recognized by T cells on HLA molecules. Our analyses showed a high correction between mutated SMAD4 and MHC molecules. In summary, these findings confirmed an important role of SMAD4 mutations in tumor immunity, suggesting that the genetic alteration of SMAD4 might provide potential molecular targets for the development of novel tumor vaccine in CRC-PM patients.\\u003c/p\\u003e\\u003cp\\u003eWhile computational tools such as NetMHCpan and NetMHCIIpan provide essential predictions of neoantigen-MHC binding affinity, their clinical translation remains constrained by suboptimal validation rates.\\u003csup\\u003e\\u003cspan citationid=\\\"CR40\\\" class=\\\"CitationRef\\\"\\u003e40\\u003c/span\\u003e\\u003c/sup\\u003e To address this critical limitation, we implemented a multi-dimensional validation strategy integrating orthogonal computational frameworks from established genomics-based prediction pipelines. This systematic integration significantly improves prediction accuracy while ensuring biological relevance of candidate neoantigens. Compare to NetMHCpan-pipeline, GIANT-pipeline demonstrated superiority in predicting SMAD4 neoAgs, achieving a 92.3% (12 out of 13) vs 61.5% (8 out of 13) missense SNVs (mSNVs) prediction rate and identifying more HLA class I selected (14 vs 8).\\u003c/p\\u003e\\u003cp\\u003eTwo SMAD4 mutations, R361H and R361C, are frequently observed in CRC patients.\\u003csup\\u003e\\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e28\\u003c/span\\u003e\\u003c/sup\\u003e and CRC with distant metastasis.\\u003csup\\u003e\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e\\u003c/sup\\u003e Preliminary studies have shown that these two missense mutations act as activating mutations for CTNNB1, a key gene in the WNT signaling pathway.\\u003csup\\u003e\\u003cspan citationid=\\\"CR41\\\" class=\\\"CitationRef\\\"\\u003e41\\u003c/span\\u003e\\u003c/sup\\u003e In this study, the neoantigen prediction for the R361H and R361C mutations, the selected epitopes of 6 SMAD4\\u003csup\\u003eR361H\\u003c/sup\\u003e and 6 SMAD4\\u003csup\\u003eR361C\\u003c/sup\\u003e neopeptides exhibited strong presentation capabilities to 7 HLA class I alleles i.e., HLA-A*01:01, HLA-A*24:02, HLA-B*35:01 and HLA-C*07:02. Notably, Mennonna et al.\\u003csup\\u003e\\u003cspan citationid=\\\"CR42\\\" class=\\\"CitationRef\\\"\\u003e42\\u003c/span\\u003e\\u003c/sup\\u003e validated our predictions, demonstrating HLA-B*35:01-restricted CD8\\u003csup\\u003e+\\u003c/sup\\u003e T cell activation against SMAD4\\u003csup\\u003eR361C\\u003c/sup\\u003e in CRC patients. These results underscore the robustness of our pipelines in identifying clinically relevant neoAgs.\\u003c/p\\u003e\\u003cp\\u003eA primary limitation of this study is the relatively small sample size in the peritoneal metastasis colorectal cancer (CRC-PM) cohort subjected to sequencing. This may reduce statistical power and introduce potential bias. Secondly, although we employed two distinct AI methods to predict neoantigens from mutated genes, a subset of identified high-confidence neoantigens still lacks experimental confirmation of immunogenicity. Future research should incorporate larger datasets analyzed by AI, combined with experimental validation and stratified analyses, to identify potential candidates for cancer vaccines targeting CRC-PM.\\u003c/p\\u003e\"},{\"header\":\"Conclusion\",\"content\":\"\\u003cp\\u003eOur study identifies SMAD4 as a key driver in colorectal cancer peritoneal metastasis (CRC-PM), with recurrent mutations correlating significantly with enhanced immune infiltration and immune-related biomarker expression. We established a robust computational framework integrating machine learning (GIANT) and convolutional neural networks (EPIP) to prioritize neoantigens based on epitope binding, presentation, and immunogenicity. Our results revealed SMAD4, appears to be immunogenic and represents an potential candidate for cancer vaccines in CRC-PM.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eAcknowledgements\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis work was supported by the National Natural Science Foundation of China (grant number: 82172009), Natural Science Foundation of Shanxi Province (grant number: 202303021221200, 202203021221219 and 202103021224357), Guangdong Basic and Applied Basic Research Foundation (grant number: 2023A1515220204). Shanxi Bethune Hospital of Shanxi Medical University (grant number: 2023GZRZ04).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAuthor contributions\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eM.C. and HX.A.; Methodology: TF.W., XR.F and ZG.H.; Software: TF.W. , XR.F. and YZ.G.; Validation: M.C, XR.W. and HX.A.; Formal analysis: M.C, TF.W., XR.F., YZ.G. and HX.A.; Investigation: M.C., WX.L. and HX.A.; Resources: M.C., WX.L., ZG.H. and HX.A.; Data creation: TF.W., XR.F. and YZ.G.; Writing for the original draft preparation: M.C., TF.W. and XR.F.; Writing for review and editing: M.C., TF.W., XR.F. and HX.A; Visualization: XR.F., TF.W. and YZ.G.; Supervision: M.C., ZG.H., WX.L. and HX.R.; Project administration: M.C. and HX.A.; Funding acquisition: M.C. ZG.H and HX.A. All authors have read and agreed to the published version of the manuscript.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eFunding\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis study was supported by grants from the National Natural Science Foundation Committee of China (grant number: 82172009), Natural Science Foundation of Shanxi Province (grant number: 202303021221200, 202203021221219 and 202103021224357), Guangdong Basic and Applied Basic Research Foundation (grant number: 2023A1515220204). Shanxi Bethune Hospital of Shanxi Medical University (grant number: 2023GZRZ04).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eData availability\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eData are provided within the manuscript or supplementary information files.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eEthics approval and consent to participate\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe study utilized publicly available cBioPortal and PubMed data. As this study did not collect new data, no additional ethical approval was required.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConsent for publication\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNot applicable.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eCompeting interests\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors declare no competing interests.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eClinical trial number\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNot applicable\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\u003cli\\u003e\\u003cspan\\u003eBray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I, Jemal A. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2024;74:229\\u0026ndash;63. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.3322/caac.21834\\u003c/span\\u003e\\u003cspan address=\\\"10.3322/caac.21834\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003evan Gestel YR, Thomassen I, Lemmens VE, Pruijt JF, van Herk-Sukel MP, Rutten HJ, Creemers GJ, de Hingh IH. Metachronous peritoneal carcinomatosis after curative treatment of colorectal cancer. Eur J Surg Oncol. 2014;40:963\\u0026ndash;9. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1016/j.ejso.2013.10.001\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/j.ejso.2013.10.001\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eKoppe MJ, Boerman OC, Oyen WJ, Bleichrodt RP. Peritoneal carcinomatosis of colorectal origin: incidence and current treatment strategies. Ann Surg. 2006;243:212\\u0026ndash;22. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1097/01.sla.0000197702.46394.16\\u003c/span\\u003e\\u003cspan address=\\\"10.1097/01.sla.0000197702.46394.16\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eLurvink RJ, Bakkers C, Rijken A, van Erning FN, Nienhuijs SW, Burger JW, Creemers GJ, Verhoef C, Lemmens VE, De Hingh IH. Increase in the incidence of synchronous and metachronous peritoneal metastases in patients with colorectal cancer: A nationwide study. Eur J Surg Oncol. 2021;47:1026\\u0026ndash;33. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1016/j.ejso.2020.11.135\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/j.ejso.2020.11.135\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eMaggiori L, Elias D. Curative treatment of colorectal peritoneal carcinomatosis: current status and future trends. Eur J Surg Oncol. 2010;36:599\\u0026ndash;603. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1016/j.ejso.2010.05.007\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/j.ejso.2010.05.007\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eKandoth C, McLellan MD, Vandin F, Ye K, Niu B, Lu C, Xie M, Zhang Q, McMichael JF, Wyczalkowski MA, et al. Mutational landscape and significance across 12 major cancer types. Nature. 2013;502:333\\u0026ndash;9. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1038/nature12634\\u003c/span\\u003e\\u003cspan address=\\\"10.1038/nature12634\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eHuang D, Sun W, Zhou Y, Li P, Chen F, Chen H, Xia D, Xu E, Lai M, Wu Y, Zhang H. Mutations of key driver genes in colorectal cancer progression and metastasis. Cancer Metastasis Rev. 2018;37:173\\u0026ndash;87. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1007/s10555-017-9726-5\\u003c/span\\u003e\\u003cspan address=\\\"10.1007/s10555-017-9726-5\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eMiyaki M, Iijima T, Konishi M, Sakai K, Ishii A, Yasuno M, Hishima T, Koike M, Shitara N, Iwama T, et al. Higher frequency of Smad4 gene mutation in human colorectal cancer with distant metastasis. Oncogene. 1999;18:3098\\u0026ndash;103. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1038/sj.onc.1202642\\u003c/span\\u003e\\u003cspan address=\\\"10.1038/sj.onc.1202642\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eFukushima T, Mashiko M, Takita K, Otake T, Endo Y, Sekikawa K, Takenoshita S. Mutational analysis of TGF-beta type II receptor, Smad2, Smad3, Smad4, Smad6 and Smad7 genes in colorectal cancer. J Exp Clin Cancer Res. 2003;22:315\\u0026ndash;20.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eTanaka T, Watanabe T, Kazama Y, Tanaka J, Kanazawa T, Kazama S, Nagawa H. Chromosome 18q deletion and Smad4 protein inactivation correlate with liver metastasis: A study matched for T- and N- classification. Br J Cancer. 2006;95:1562\\u0026ndash;7. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1038/sj.bjc.6603460\\u003c/span\\u003e\\u003cspan address=\\\"10.1038/sj.bjc.6603460\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eFang T, Liang T, Wang Y, Wu H, Liu S, Xie L, Liang J, Wang C, Tan Y. Prognostic role and clinicopathological features of SMAD4 gene mutation in colorectal cancer: a systematic review and meta-analysis. BMC Gastroenterol. 2021;21:297. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1186/s12876-021-01864-9\\u003c/span\\u003e\\u003cspan address=\\\"10.1186/s12876-021-01864-9\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eD'Agay MG, Galland L, Tharin Z, Truntzer C, Ghiringhelli F. Utility of exome sequencing in routine care for metastatic colorectal cancer. Mol Clin Oncol. 2021;15:229. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.3892/mco.2021.2392\\u003c/span\\u003e\\u003cspan address=\\\"10.3892/mco.2021.2392\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eXue L, Hyman NH, Turaga KK, Eng OS. Peritoneal Metastases in Colorectal Cancer: Biology and Barriers. J Gastrointest Surg. 2020;24:720\\u0026ndash;7. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1007/s11605-019-04441-4\\u003c/span\\u003e\\u003cspan address=\\\"10.1007/s11605-019-04441-4\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eLesterhuis WJ, Haanen JB, Punt CJ. Cancer immunotherapy\\u0026ndash;revisited. Nat Rev Drug Discov. 2011;10:591\\u0026ndash;600. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1038/nrd3500\\u003c/span\\u003e\\u003cspan address=\\\"10.1038/nrd3500\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eLlosa NJ, Cruise M, Tam A, Wicks EC, Hechenbleikner EM, Taube JM, Blosser RL, Fan H, Wang H, Luber BS, et al. The vigorous immune microenvironment of microsatellite instable colon cancer is balanced by multiple counter-inhibitory checkpoints. Cancer Discov. 2015;5:43\\u0026ndash;51. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1158/2159\\u0026ndash;8290.CD-14-0863\\u003c/span\\u003e\\u003cspan address=\\\"10.1158/2159\\u0026ndash;8290.CD-14-0863\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eKeskin DB, Anandappa AJ, Sun J, Tirosh I, Mathewson ND, Li S, Oliveira G, Giobbie-Hurder A, Felt K, Gjini E, et al. Neoantigen vaccine generates intratumoral T cell responses in phase Ib glioblastoma trial. Nature. 2019;565:234\\u0026ndash;9. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1038/s41586-018-0792-9\\u003c/span\\u003e\\u003cspan address=\\\"10.1038/s41586-018-0792-9\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eFang Y, Mo F, Shou J, Wang H, Luo K, Zhang S, Han N, Li H, Ye S, Zhou Z, et al. A Pan-cancer Clinical Study of Personalized Neoantigen Vaccine Monotherapy in Treating Patients with Various Types of Advanced Solid Tumors. Clin Cancer Res. 2020;26:4511\\u0026ndash;20. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1158/1078\\u0026thinsp;\\u0026ndash;\\u0026thinsp;0432.CCR-19-2881\\u003c/span\\u003e\\u003cspan address=\\\"10.1158/1078\\u0026thinsp;\\u0026ndash;\\u0026thinsp;0432.CCR-19-2881\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eLeko V, Rosenberg SA. Identifying and Targeting Human Tumor Antigens for T Cell-Based Immunotherapy of Solid Tumors. Cancer Cell. 2020;38:454\\u0026ndash;72. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1016/j.ccell.2020.07.013\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/j.ccell.2020.07.013\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003ePearlman AH, Hwang MS, Konig MF, Hsiue EH, Douglass J, DiNapoli SR, Mog BJ, Bettegowda C, Pardoll DM, Gabelli SB, et al. Targeting public neoantigens for cancer immunotherapy. Nat Cancer. 2021;2:487\\u0026ndash;97. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1038/s43018-021-00210-y\\u003c/span\\u003e\\u003cspan address=\\\"10.1038/s43018-021-00210-y\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eOtt PA, Hu Z, Keskin DB, Shukla SA, Sun J, Bozym DJ, Zhang W, Luoma A, Giobbie-Hurder A, Peter L, et al. An immunogenic personal neoantigen vaccine for patients with melanoma. Nature. 2017;547:217\\u0026ndash;21. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1038/nature22991\\u003c/span\\u003e\\u003cspan address=\\\"10.1038/nature22991\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eRojas LA, Sethna Z, Soares KC, Olcese C, Pang N, Patterson E, Lihm J, Ceglia N, Guasp P, Chu A, et al. Personalized RNA neoantigen vaccines stimulate T cells in pancreatic cancer. Nature. 2023;618:144\\u0026ndash;50. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1038/s41586-023-06063-y\\u003c/span\\u003e\\u003cspan address=\\\"10.1038/s41586-023-06063-y\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eCai Z, Su X, Qiu L, Li Z, Li X, Dong X, Wei F, Zhou Y, Luo L, Chen G, et al. Personalized neoantigen vaccine prevents postoperative recurrence in hepatocellular carcinoma patients with vascular invasion. Mol Cancer. 2021;20:164. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1186/s12943-021-01467-8\\u003c/span\\u003e\\u003cspan address=\\\"10.1186/s12943-021-01467-8\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eYu YJ, Shan N, Li LY, Zhu YS, Lin LM, Mao CC, Hu TT, Xue XY, Su XP, Shen X, Cai ZZ. Preliminary clinical study of personalized neoantigen vaccine therapy for microsatellite stability (MSS)-advanced colorectal cancer. Cancer Immunol Immunother. 2023;72:2045\\u0026ndash;56. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1007/s00262-023-03386-7\\u003c/span\\u003e\\u003cspan address=\\\"10.1007/s00262-023-03386-7\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eTran E, Robbins PF, Lu YC, Prickett TD, Gartner JJ, Jia L, Pasetto A, Zheng Z, Ray S, Groh EM, et al. T-Cell Transfer Therapy Targeting Mutant KRAS in Cancer. N Engl J Med. 2016;375:2255\\u0026ndash;62. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1056/NEJMoa1609279\\u003c/span\\u003e\\u003cspan address=\\\"10.1056/NEJMoa1609279\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eZhu YJ, Li X, Chen TT, Wang JX, Zhou YX, Mu XL, Du Y, Wang JL, Tang J, Liu JY. Personalised neoantigen-based therapy in colorectal cancer. Clin Transl Med. 2023;13:e1461. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1002/ctm2.1461\\u003c/span\\u003e\\u003cspan address=\\\"10.1002/ctm2.1461\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eYaeger R, Chatila WK, Lipsyc MD, Hechtman JF, Cercek A, Sanchez-Vega F, Jayakumaran G, Middha S, Zehir A, Donoghue MTA, et al. Clinical Sequencing Defines the Genomic Landscape of Metastatic Colorectal Cancer. Cancer Cell. 2018;33:125\\u0026ndash;e136123. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1016/j.ccell.2017.12.004\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/j.ccell.2017.12.004\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eIacobuzio-Donahue CA, Song J, Parmiagiani G, Yeo CJ, Hruban RH, Kern SE. Missense mutations of MADH4: characterization of the mutational hot spot and functional consequences in human tumors. Clin Cancer Res. 2004;10:1597\\u0026ndash;604. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1158/1078\\u0026thinsp;\\u0026ndash;\\u0026thinsp;0432.ccr-1121-3\\u003c/span\\u003e\\u003cspan address=\\\"10.1158/1078\\u0026thinsp;\\u0026ndash;\\u0026thinsp;0432.ccr-1121-3\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eFleming NI, Jorissen RN, Mouradov D, Christie M, Sakthianandeswaren A, Palmieri M, Day F, Li S, Tsui C, Lipton L, et al. SMAD2, SMAD3 and SMAD4 mutations in colorectal cancer. Cancer Res. 2013;73:725\\u0026ndash;35. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1158/0008-5472.CAN-12-2706\\u003c/span\\u003e\\u003cspan address=\\\"10.1158/0008-5472.CAN-12-2706\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eShi Y, Hata A, Lo RS, Massague J, Pavletich NP. A structural basis for mutational inactivation of the tumour suppressor Smad4. Nature. 1997;388:87\\u0026ndash;93. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1038/40431\\u003c/span\\u003e\\u003cspan address=\\\"10.1038/40431\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eSameer AS, Chowdri NA, Syeed N, Banday MZ, Shah ZA, Siddiqi MA. SMAD4\\u0026ndash;molecular gladiator of the TGF-beta signaling is trampled upon by mutational insufficiency in colorectal carcinoma of Kashmiri population: an analysis with relation to KRAS proto-oncogene. BMC Cancer. 2010;10. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1186/1471-2407-10-300\\u003c/span\\u003e\\u003cspan address=\\\"10.1186/1471-2407-10-300\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eIzeradjene K, Combs C, Best M, Gopinathan A, Wagner A, Grady WM, Deng CX, Hruban RH, Adsay NV, Tuveson DA, Hingorani SR. Kras(G12D) and Smad4/Dpc4 haploinsufficiency cooperate to induce mucinous cystic neoplasms and invasive adenocarcinoma of the pancreas. Cancer Cell. 2007;11:229\\u0026ndash;43. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1016/j.ccr.2007.01.017\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/j.ccr.2007.01.017\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eSamstein RM, Lee CH, Shoushtari AN, Hellmann MD, Shen R, Janjigian YY, Barron DA, Zehir A, Jordan EJ, Omuro A, et al. Tumor mutational load predicts survival after immunotherapy across multiple cancer types. Nat Genet. 2019;51:202\\u0026ndash;6. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1038/s41588-018-0312-8\\u003c/span\\u003e\\u003cspan address=\\\"10.1038/s41588-018-0312-8\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eCiardiello D, Vitiello PP, Cardone C, Martini G, Troiani T, Martinelli E, Ciardiello F. Immunotherapy of colorectal cancer: Challenges for therapeutic efficacy. Cancer Treat Rev. 2019;76:22\\u0026ndash;32. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1016/j.ctrv.2019.04.003\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/j.ctrv.2019.04.003\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eImyanitov E, Sokolenko A. (2022). Integrative Genomic Tests in Clinical Oncology. Int J Mol Sci \\u003cem\\u003e23\\u003c/em\\u003e. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.3390/ijms232113129\\u003c/span\\u003e\\u003cspan address=\\\"10.3390/ijms232113129\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eAkhurst RJ, Hata A. Targeting the TGFbeta signalling pathway in disease. Nat Rev Drug Discov. 2012;11:790\\u0026ndash;811. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1038/nrd3810\\u003c/span\\u003e\\u003cspan address=\\\"10.1038/nrd3810\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eFujishita T, Kojima Y, Kajino-Sakamoto R, Mishiro-Sato E, Shimizu Y, Hosoda W, Yamaguchi R, Taketo MM, Aoki M. The cAMP/PKA/CREB and TGFbeta/SMAD4 Pathways Regulate Stemness and Metastatic Potential in Colorectal Cancer Cells. Cancer Res. 2022;82:4179\\u0026ndash;90. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1158/0008-5472.CAN-22-1369\\u003c/span\\u003e\\u003cspan address=\\\"10.1158/0008-5472.CAN-22-1369\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eAzimi F, Scolyer RA, Rumcheva P, Moncrieff M, Murali R, McCarthy SW, Saw RP, Thompson JF. Tumor-infiltrating lymphocyte grade is an independent predictor of sentinel lymph node status and survival in patients with cutaneous melanoma. J Clin Oncol. 2012;30:2678\\u0026ndash;83. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1200/JCO.2011.37.8539\\u003c/span\\u003e\\u003cspan address=\\\"10.1200/JCO.2011.37.8539\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eSaxena M, van der Burg SH, Melief CJM, Bhardwaj N. Therapeutic cancer vaccines. Nat Rev Cancer. 2021;21:360\\u0026ndash;78. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1038/s41568-021-00346-0\\u003c/span\\u003e\\u003cspan address=\\\"10.1038/s41568-021-00346-0\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eOhue Y, Nishikawa H. Regulatory T (Treg) cells in cancer: Can Treg cells be a new therapeutic target? Cancer Sci. 2019;110:2080\\u0026ndash;9. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1111/cas.14069\\u003c/span\\u003e\\u003cspan address=\\\"10.1111/cas.14069\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eBjerregaard AM, Nielsen M, Jurtz V, Barra CM, Hadrup SR, Szallasi Z, Eklund AC. An Analysis of Natural T Cell Responses to Predicted Tumor Neoepitopes. Front Immunol. 2017;8:1566. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.3389/fimmu.2017.01566\\u003c/span\\u003e\\u003cspan address=\\\"10.3389/fimmu.2017.01566\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eLanauze CB, Sehgal P, Hayer K, Torres-Diz M, Pippin JA, Grant SFA, Thomas-Tikhonenko A. Colorectal Cancer-Associated Smad4 R361 Hotspot Mutations Boost Wnt/beta-Catenin Signaling through Enhanced Smad4-LEF1 Binding. Mol Cancer Res. 2021;19:823\\u0026ndash;33. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1158/1541\\u0026ndash;7786.MCR-20-0721\\u003c/span\\u003e\\u003cspan address=\\\"10.1158/1541\\u0026ndash;7786.MCR-20-0721\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eMennonna D, Maccalli C, Romano MC, Garavaglia C, Capocefalo F, Bordoni R, Severgnini M, De Bellis G, Sidney J, Sette A, et al. T cell neoepitope discovery in colorectal cancer by high throughput profiling of somatic mutations in expressed genes. Gut. 2017;66:454\\u0026ndash;63. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1136/gutjnl-2015-309453\\u003c/span\\u003e\\u003cspan address=\\\"10.1136/gutjnl-2015-309453\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eCercek A, Chatila WK, Yaeger R, Walch H, Fernandes GDS, Krishnan A, Palmaira L, Maio A, Kemel Y, Srinivasan P, et al. A Comprehensive Comparison of Early-Onset and Average-Onset Colorectal Cancers. J Natl Cancer Inst. 2021;113:1683\\u0026ndash;92. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1093/jnci/djab124\\u003c/span\\u003e\\u003cspan address=\\\"10.1093/jnci/djab124\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eLenos KJ, Bach S, Ferreira Moreno L, Hoorn T, Sluiter S, Bootsma NR, Vieira Braga S, Nijman FA, van den Bosch LE, Miedema T, D.M., et al. Molecular characterization of colorectal cancer related peritoneal metastatic disease. Nat Commun. 2022;13:4443. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1038/s41467-022-32198-z\\u003c/span\\u003e\\u003cspan address=\\\"10.1038/s41467-022-32198-z\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eHeuvelings DJI, Wintjens A, Moonen L, Engelen SME, de Hingh I, Valkenburg-van Iersel LB, den Dulk M, Beckervordersandforth J, Thijssen SGM, Leunissen DJG, et al. Predictive Genetic Biomarkers for the Development of Peritoneal Metastases in Colorectal Cancer. Int J Mol Sci. 2023;24. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.3390/ijms241612830\\u003c/span\\u003e\\u003cspan address=\\\"10.3390/ijms241612830\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eReynisson B, Alvarez B, Paul S, Peters B, Nielsen M. NetMHCpan-4.1 and NetMHCIIpan-4.0: improved predictions of MHC antigen presentation by concurrent motif deconvolution and integration of MS MHC eluted ligand data. Nucleic Acids Res. 2020;48:W449\\u0026ndash;54. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1093/nar/gkaa379\\u003c/span\\u003e\\u003cspan address=\\\"10.1093/nar/gkaa379\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eReynisson B, Barra C, Kaabinejadian S, Hildebrand WH, Peters B, Nielsen M. Improved Prediction of MHC II Antigen Presentation through Integration and Motif Deconvolution of Mass Spectrometry MHC Eluted Ligand Data. J Proteome Res. 2020;19:2304\\u0026ndash;15. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1021/acs.jproteome.9b00874\\u003c/span\\u003e\\u003cspan address=\\\"10.1021/acs.jproteome.9b00874\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eLi T, Fu J, Zeng Z, Cohen D, Li J, Chen Q, Li B, Liu XS. TIMER2.0 for analysis of tumor-infiltrating immune cells. Nucleic Acids Res. 2020;48:W509\\u0026ndash;14. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1093/nar/gkaa407\\u003c/span\\u003e\\u003cspan address=\\\"10.1093/nar/gkaa407\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eRu B, Wong CN, Tong Y, Zhong JY, Zhong SSW, Wu WC, Chu KC, Wong CY, Lau CY, Chen I, et al. TISIDB: an integrated repository portal for tumor-immune system interactions. Bioinformatics. 2019;35:4200\\u0026ndash;2. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1093/bioinformatics/btz210\\u003c/span\\u003e\\u003cspan address=\\\"10.1093/bioinformatics/btz210\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eChakravarty D, Gao J, Phillips SM, Kundra R, Zhang H, Wang J, Rudolph JE, Yaeger R, Soumerai T, Nissan MH et al. (2017). OncoKB: A Precision Oncology Knowledge Base. JCO Precis Oncol \\u003cem\\u003e2017\\u003c/em\\u003e. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1200/PO.17.00011\\u003c/span\\u003e\\u003cspan address=\\\"10.1200/PO.17.00011\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":true,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true},\"keywords\":\"SMAD4, colorectal cancer, peritoneal metastasis, neoantigen, immunotherapy, vaccine\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-7066314/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-7066314/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003ch2\\u003eBackground\\u003c/h2\\u003e\\u003cp\\u003ePeritoneal metastasis (CRC-PM), a lethal feature of advanced colorectal cancer, lacks effective treatments. Neoantigen-based immunotherapy has emerged as a promising therapeutic strategy for metastatic CRC.\\u003c/p\\u003e\\u003ch2\\u003eMethods\\u003c/h2\\u003e\\u003cp\\u003eMulti-omics including TIMER and TISIDB tools, two computational pipelines of NetMHCpan and GenoImmune's artificial intelligence (AI)-driven Neoantigen Techonology (GIANT) were employed to characterize somatic mutations in CRC-PM, focusing on SMAD4-derived neoantigens.\\u003c/p\\u003e\\u003ch2\\u003eResults\\u003c/h2\\u003e\\u003cp\\u003eGenomic analysis of 2,424 CRC and 124 CRC-PM patients revealed KRAS, SMAD4, BRAF, and RNF43 mutations enriched in CRC-PM. KRAS/SMAD4 alterations correlated with higher tumor mutational burden (TMB) and enhanced immune infiltration. Ten novel SMAD4 mutation sites were uniquely identified in CRC-PM, that were associated with lymphocyte populations and immune checkpoint (ICP) gene expression. Neoantigen prediction of SMAD4 mutations identified 20 and 48 MHC-I candidates, using NetMHCpan and GIANT, respectively., Dominant variants (R361C, R361H, D424V, and A118V) of SMAD4 mutations showed strong affinity for HLA-B*07:02, HLA-A*24:02, and HLA-B*40:01. MHC-II epitopes D424V and W524C prioritized HLA-DRB1*15:01/13:02.\\u003c/p\\u003e\\u003ch2\\u003eConclusions\\u003c/h2\\u003e\\u003cp\\u003eThese findings highlight SMAD4 as a recurrent immunogenic driver in CRC-PM, indicating its potential as a neoantigen vaccine target for precision immunotherapy.\\u003c/p\\u003e\",\"manuscriptTitle\":\"AI-Driven Prediction Unveils SMAD4-Specific HLA-Restricted Epitopes: A Novel Therapeutic Target for Precision Treatment of Peritoneal Metastasis in Colorectal Cancer\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2025-08-14 18:21:51\",\"doi\":\"10.21203/rs.3.rs-7066314/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"0c7c8b1e-de9f-45b9-b447-f2521352227f\",\"owner\":[],\"postedDate\":\"August 14th, 2025\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2025-09-10T06:39:03+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2025-08-14 18:21:51\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-7066314\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-7066314\",\"identity\":\"rs-7066314\",\"version\":[\"v1\"]},\"buildId\":\"8U1c8b4HqxoKbykW_rLl7\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}