Landscape of cancer associated EpCAM mutations: molecular modeling, Predictive Insights and Impact on Patient Survival

Landscape of cancer associated EpCAM mutations: molecular modeling, Predictive Insights and Impact on Patient Survival | 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 Landscape of cancer associated EpCAM mutations: molecular modeling, Predictive Insights and Impact on Patient Survival This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6174915/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 01 Jul, 2025 Read the published version in BMC Cancer → Version 1 posted 3 You are reading this latest preprint version Abstract Background EpCAM (epithelial cell adhesion molecule) is a key regulator of epithelial cell-cell adhesion, signal transduction, tissue regeneration, and serves as a stem cell marker. It is frequently overexpressed in epithelial cancers and is linked to tumor progression, survival, and metastasis. However, the functional impact of EpCAM mutations in cancer remains poorly understood. Methods To investigate the role of EpCAM mutations, we performed a comprehensive analysis of cancer cohorts from multiple genomic datasets, identifying novel somatic EpCAM mutations across diverse epithelial cancers. Using bioinformatics tools (SIFT, PolyPhen-2, Mutation Assessor) and molecular modeling, we assessed the potential impact of these mutations. Further, homology modeling and all-atom molecular dynamics (MD) simulations were conducted to evaluate structural changes. Results Our findings revealed that cancer-associated mutations, particularly in the TY-1 and RCD regions, induce structural instability in EpCAM, leading to altered functional properties. Patient cohort analyses indicated that EpCAM mutations correlate with reduced survival rates in colon and hepatocellular carcinoma and contribute to early tumor progression in lung cancer. Moreover, introducing these mutations into lung cancer cells enhanced their sensitivity to MEK inhibitors, suggesting a potential therapeutic vulnerability. Conclusion This study provides novel insights into the structural and functional consequences of EpCAM mutations in cancer, demonstrating their association with reduced survival, tumor progression, and drug sensitivity. These findings highlight EpCAM as a promising therapeutic target in epithelial cancers. EpCAM mutation Cancer molecular modelling Figures Figure 1 Figure 2 INTRODUCTION EpCAM, a transmembrane signaling protein identified as a tumor-associated antigen that is overexpressed in various epithelial cancers [ 1 , 2 ]. Beyond its role in mediating cell-cell adhesion, EpCAM is known as a signaling molecule implicated in cancer cell proliferation, invasion, migration, differentiation, and immune evasion [ 3 ]. Its role in cancer biology and consistent expression across several cancer types make EpCAM a key biomarker and therapeutic target. In normal tissue EpCAM is found on the basolateral surfaces of epithelial cells in tissues such as the skin, gastrointestinal tract, respiratory tract, lung and reproductive organs. EpCAM is highly expressed during embryogenesis, where it facilitates cell proliferation and differentiation [ 4 , 5 ]. In well-characterized phenotype of EpCAM mutations, autosomal recessive mutations in the EpCAM gene are associated with congenital tufting enteropathy (CTE) [ 6 , 7 ]. The prevalence of germline MLH1 hyper-methylation and EpCAM deletions is notably high among genetically confirmed cases of Lynch syndrome [ 6 , 8 ]. This epigenetic silencing predisposes individuals to Lynch syndrome, an inherited condition linked to colorectal, endometrial, and other cancers. Moreover, the loss of EpCAM function has been linked to phenotypic alterations, including epithelial-to-mesenchymal transition (EMT) [ 9 ], while gain-of-function have been implicated in enhanced oncogenic signaling [ 10 ]. EpCAM plays a crucial role in regulating key cancer-associated signaling pathways, including AP-1, NF-kB, Wnt/β-catenin, PI3K/AKT, and MAPK/ERK [ 9 , 11 – 15 ]. By stabilizing β-catenin and promoting its nuclear translocation, EpCAM drives the transcription of Wnt target genes [ 16 ]. Similarly, its interactions with components of the PI3K/AKT pathway promote tumor cell survival and resistance to apoptosis. EpCAM mutations have also been shown to dysregulate ERK signaling, contributing to drug resistance, a major challenge in cancer therapy. For instance, dysregulated ERK signaling can be linked to reduced sensitivity to targeted therapies, highlighting the clinical implications of EpCAM dysfunction [ 9 ]. Its expression on the surface of cancer cells has facilitated its use as a therapeutic target in antibody-drug conjugates [ 17 ]. However, mutations in EpCAM are now being recognized as factors influencing the effectiveness of these therapeutic interventions. For example, altered EpCAM expression and function can be linked to enhanced drug resistance, primarily due to changes in cancer cell signaling or survival mechanisms. Our recent studies have demonstrated that cancer associated EpCAM mutations resulted in the loss of its function, altered localization, and promotion of epithelial-to-mesenchymal transition (EMT), thereby facilitating tumor metastasis [ 18 , 19 ]. In the present study, for the first time we investigate the landscape of EpCAM mutations across major cancers of epithelial origin. Using bioinformatics approach, we screened for damaging mutations and employed homology modeling and molecular dynamics (MD) simulations. Recent molecular dynamics studies on gene mutations have provided valuable insights into protein function, stability, and interactions, uncovering mechanisms that are challenging to observe experimentally. These findings highlight how mutations can lead to the loss or gain of gene activity, offering a deeper understanding of their impact on biological processes [ 20 – 22 ]. Through in-silico analyses and experimental validation, we demonstrate that certain EpCAM mutations result in a loss of structural stability thereby disrupting ERK signaling. Furthermore, the co-occurrence of EpCAM mutations with known oncogenes reveals potential therapeutic vulnerabilities. Detecting these mutations early in disease progression, particularly in the genomic era, could enhance patient survival predictions and guide the development of targeted therapeutic strategies. MATERIALS AND METHODS Cell culture and reagents All studied cell lines were obtained from the American Type Culture Collection (ATCC, Rockville, MD, USA). A549 (ATCC#CCL-185), H1299 (ATCC#CCRL-5803), H460 (ATCC#HTB-177), H226 (ATCC#HTB-177) and H23 (ATCC#CRL-5800). Phoenix-AMPHO (ATCC#CRL-3213) cells cultured in DMEM. NSCLC cells were cultured in RPMI-1640. Both culture media were supplemented with 10% FBS, 2 mM glutamine (Gibco #25030081), 1% penicillin/streptomycin, and incubated at 37°C in an atmosphere of 95% air and 5% CO 2 . The MAPK inhibitor Trametinib was purchased from Selleck Chemicals (Houston, TX, USA). Cell culture were monitored for mycoplasma routinely. EpCAM mutation constructs. The EpCAM nucleotide sequence was accessed with NCBI reference sequence NM_002354. EpCAM deletion mutants were generated based on the genomic data sets ( Figure S2 ). To the wild-type EpCAM single amino acids change was made by custom gene synthesis of EpCAM-D92V, EpCAM-E137A, EpCAM-Y186C, EpCAM-Q204E, EpCAM-N111K, EpCAM-P84S, and EpCAM-Y215S. EpCAM mutant constructs were generated using synthetic gene fragments from Integrated DNA Technologies (IDT, Coralville, IA) as described before [ 18 ]. For example, C66Y EpCAM was generated as a G-block fragment (197G > A; substitution position 197, G➞A). The DNA sequence was sub-cloned into both pcDNA3 (HindIII-XbaI restriction sites) or the retroviral vector pBABE-puro (BamHI-SalI restriction after mutating BamHI sites). Retroviral transduction In a six well plate, Phoenix-AMPHO packaging cells were transfected when nearly confluent with 2.5 µg of pBABE-Puro-EpCAM constructs using FuGENE-HD (Promega#E2311). Forty-eight hours post transfection, viral supernatants were collected, filtered through 0.45 µm filters, and then added to the cells in media containing 8 µg/mL protamine sulfate. After one-two successive retroviral infections, cells were grown for 48 h and selected in puromycin for 2 weeks. SRE-ERK dual Luciferase Assay SRE Reporter assay was performed as described earlier [ 14 ]. Briefly, cells were cultured in 12-well plate for 24h. DNA constructs 400ng-SRE-Luc (Promega# E1340) together with internal control plasmid 50ng-pRLSV40-Luc (Promega# E2231) was transfected using FuGENE-HD (Promega # E2311). After 12h post transfection cells were serum-starved for 12h, treated with or without 20% FBS overnight. Cells were lysed, and the dual luciferase assay was performed using the Dual-Luciferase Reporter assay system (Promega #E1910). Mean values of luciferase activity relative to untreated and were calculated from triplicate wells. The experiments were repeated three times to ensure consistency. Mean ± SD of three technical replicates. * p < 0.05, ** p < 0.001, two-tailed Student’s t -test. Trametinib treatment and Cell viability Assay EpCAM transduced or transfected cells 5.0 × 10³ were seeded into 96-well plates and cultured in a 5% CO₂ incubator for 24h. Trametinib (Selleck Chemicals #S2673) was prepared in DMSO and applied at concentrations ranging from 100 to 200 nM. Cell survival was assessed after 72 hours later using the Cell Titer-Glo (Promega#G7570) a) according to the manufacturer's instructions. Luminescence was recorded by SpectraMax-i3 (Molecular Devices). Cell death was monitored using Annexin-V staining (#V13241, ThermoFisher) as recommended by manufacturer. EpCAM mutation dataset EpCAM mutation status and sample related information were collected from the cBioPortal for Cancer Genomics ( https://www.cbioportal.org ). AACR-GENIE ( https://genie.cbioportal.org ) and the Catalogue Of Somatic Mutations In Cancer (COSMIC, https://cancer.sanger.ac.uk/cosmic ) [ 23 , 24 ]. All compiled data is included in the supplementary data Figure S2 . The mutation data was restricted to somatic mutations where protein altered in single amino acids. Overlapping Samples from GENIE v.13 and cBioPortal were removed. For TMB distributions, tumors with TMB between 5 and 600 were grouped in increment of 5 or 100 for 10 different cancer types. EpCAM mutation per group was identified as 1, 2–4, 5–9, 10–19, 20–29, 30–50 and increment of 100 till 600 (see Fig. 2 D ) . Cell line EpCAM expression was accessed through the publicly available Cancer Cell Line Encyclopedia (CCLE, https://portals.broadinstitute.org/ccle ) at the Broad Institute. EpCAM expression from NCI-60 cell line panel (GSE32474) was accessed, data was plotted using GraphPad. EpCAM expression in 70 NSCLC tumor lines GSE32989 was accessed and analyzed using GENE-E ( https://broadinstitute.org/GENE-E/ ). Molecular modeling of EpCAM The 3D structure of human EpCAM wild type (EpCAM WT ) was build using homology modelling with the help of Modeller 10.2 software [ 25 ]. The crystal structure of human was used (PDB ID: 4MZV) ( https://www.rcsb.org/ ) as a template to build full length 3D model of EpCAM WT and generated total 500 different conformations of EpCAM WT . The 3D structure of EpCAM WT was selected based on DOPE score (-26725.0 Kcal/Mol). The Structural refinement of the modelled EpCAM WT structure was done by performing all atom MD simulations in explicit solvent using GROMACS 2021.5. The force filed amberff99SBildn was used to generate topology files and TIP3P model for solvation with periodic boundary conditions. The required number of counterions were added to neutralize the system. Steric clashes or bad contacts raised during homology modelling were relaxed by performing energy minimization with Steepest-Descent method followed by Conjugate-Gradient. Canonical ensembles NVT and NPT were used to equilibrate the system for 1ns. Further, unrestrained MD simulation was performed for the period of 1µs to get detailed insights to the structural stability and evolution of diverse conformations over the potential energy surface. The long-range electrostatic interactions were treated with PME and LINCS algorithm to constraint the H-bonds. The coordinates and energies were recorded at every 2 fs and 200 ps respectively. A modified Berendsen thermostat and Parrinello-Rahman algorithm was used to maintain constant temperature (300K) and pressure (1bar) during the simulation. The structure of EpCAM WT having least energy near global state was extracted from the MD trajectory and then used further to generate homology models of mutant EpCAM structures for EpCAM D92V , EpCAM E137A , EpCAM Y186C , EpCAM Q204E , EpCAM N111K , EpCAM P84S , and EpCAM Y215S ). The MD simulations for the mutants were also performed using the similar protocol adopted for EpCAM WT simulation. The trajectories obtained were checked for quality and detailed structural analysis was performed using in build Gromacs tools and other necessary packages such as DSSP [ 26 ], CPPTRAJ [ 27 ] wherever required. The plots were generated using Grace 5.1.25 and quality images were prepared using UCSF Chimera 1.15[ 28 ]. Statistical analysis All experiments were performed at least three times in triplicate. All statistical analyses were performed using GraphPad Prism 9.4 (GraphPad, La Jolla, CA). Numerical data are presented as mean ± sd. Single comparisons were performed by unpaired Student’s t tests and multiple comparisons were performed by ANOVA. P-values < 0.05 were considered to be statistically significant. Kaplan-Meier survival analysis was performed based on EpCAM mutation and the relevant data of patient populations, GraphPad Prism was used to analyze and plot the graphs. RESULTS EpCAM expression is restricted to epithelial type cells. To assess overall expressions of EpCAM at RNA and protein levels in normal tissues, two data sets were analysed. Results highlights significant expression of EpCAM RNA and protein within the gastrointestinal tract and other tissues (Fig. 1 , A-B). Conversely, EpCAM expression is largely absent in non-epithelial tissues such as connective tissue, muscle, and hematopoietic cells under normal physiological conditions. Notably, EpCAM expression is frequently elevated in epithelial-origin malignancies, underscoring its relevance as a diagnostic and therapeutic target. For example, elevated EpCAM levels are consistently observed in adenocarcinomas of the breast [ 29 ], colon [ 30 ], oesophagus [ 31 ], pancreas [ 32 ], ovary, and prostate [ 33 ], as well as in squamous cell carcinomas of the lung, and cervix [ 34 ]. Detailed analysis of a lung adenocarcinoma cohort demonstrates upregulation of EpCAM in tumors (Fig. 1 , C), with consistent expression across different disease stages and consistently in EGFR, KRAS oncogene-driven tumors (Fig. 1 , D). In hepatocellular carcinoma (HCC), EpCAM is typically absent in mature hepatocytes but is expressed in hepatic progenitor cells and certain tumor subsets, suggesting its involvement in tumor initiation and cellular dedifferentiation [ 35 ]. Cancer Cell Line Encyclopedia (CCLE) [ 36 ] and NCI-60 [ 37 ] datasets further reveal that EpCAM expression is restricted to cancer cell lines derived from epithelial origin tissues such as the colon-gastrointestinal tract, ovary, prostate, breast, lung, and kidney ( Figure S1 A-B ). This tissue-specific expression pattern reinforces the utility of EpCAM as a marker for epithelial malignancies and a target for precision oncology. Comprehensive analysis of EpCAM mutations Expanding upon the insights gained from our previous studies demonstrating that cancer-associated EpCAM mutations play critical roles as tumor suppressor or tumor promoter [ 18 , 19 ]. Some EpCAM mutations results in the loss of its membrane localization, secretion, and binding to CTSL, suggesting its role as a tumor suppressor [ 18 ]. On the other hand, EpCAM mutations in the LDL domain in cancer cells driven by RAS signalling promoted invasion, migration and sensitized to drugs [ 19 ]. To further elucidate the functional implications of EpCAM mutations in the cancer, we employed advanced predictive and experimental techniques, paving the way for a deeper understanding of the role of these mutations in pathology. Four different cohorts cBioPortal, COSMIC, AACR-GENIE and FMI data (Foundation Medicine-NCI) were accessed (Fig. 2 A ) . Building on our previous reports and updated TCGA data, we undertook a systematic analysis to catalogue and understand the role of novel EpCAM mutations in cancer. By analyzing over 300 studies comprising 300,300 samples, we determined an overall EpCAM mutation frequency across all cancer types (Fig. 2 B). These cancer-associated mutations were distributed as missense (79%), nonsense (6%), splice (7%), frameshift (4%), deletion (2%), and fusion mutations. Notably, MSH2-EpCAM fusion were recorded in samples from patients with esophageal adenocarcinoma, squamous cell carcinoma, and stomach adenocarcinoma ( data Figure S2A ). This is consistent with prior reports where exon 9 deletions in EpCAM led to MSH2 promoter hyper-methylation and silencing [ 38 ]. Additionally, we identified EpCAM-ABCG8 and NUP42-EpCAM fusions in high-grade uterine and ovarian carcinomas ( Figure S2A ). Missense EpCAM mutations were also identified in commonly used CCLE cell lines ( Figure S2C ). While mutations were distributed across all nine exons of EpCAM, no specific hotspot regions were identified. Among the 160 unique protein-altering mutations identified in initial screening, the highest mutation frequencies were found in lung cancer (29 cases) and skin cancer (22 cases), followed by colorectal cancer (13 cases) and uterine cancer (13 cases). Interestingly, colorectal cancer a cancer type known to overexpress EpCAM displayed a higher prevalence of splice-related mutations. Of particular note, EpCAM mutations frequency was lowest from 1,346 pancreatic cancer samples analysed ( Figure S2A ). To examine the co-occurrence of somatic EpCAM mutations across varying tumor mutation burden (TMB) groups, we categorized the data into two major groups: low TMB (1–100) and high TMB (> 100) (Fig. 2 D, see methods ) . The total number of somatic EpCAM mutations was then tabulated from each TMB category. An initial search from the GENIE cohort [ 23 ] across various cancer types revealed mutation frequency of EpCAM at 0.5%, with 889 EpCAM mutations including recurring from 1,783,034 samples ( Figure S2 ). As depicted in Fig. 2 D, majority of EpCAM mutations in lung cancer were primarily found in samples with fewer than 100 TMB group. For breast, prostate and pancreatic cancer, they were mostly restricted to around 15 TMB group. In contrast, EpCAM mutations in colon and uterine cancers were spread across all TMB groups (5-600 TMB see methods). The restriction of EpCAM mutations to samples with relatively low TMB in lung and breast cancers suggests that these mutations may play important role in early stage of tumorigenesis. In contrast, the widespread distribution of EpCAM mutations in colon and uterine cancers indicates a more pervasive involvement in both early and late stages of tumor progression. Structural Stability Analysis of Previously Characterized EpCAM Mutations We utilized two experimentally validated damaging mutations, C66Y and L240A as a proof of concept to validate our strategy [ 18 , 19 ]. Using MD simulation to ensure the quality of the obtained trajectories, we performed a quality check by plotting the potential energy, temperature, and pressure throughout the dynamics. The values for EpCAM-L240A were − 127562 kJ/mol for potential energy, 1.53331 K for temperature, and 6.06631 bar for pressure, while for EpCAM-C66Y, the corresponding values were − 111969 kJ/mol, 0.231919 K, and 7.42474 bar. These values exhibited minimal fluctuations during the 1µs simulation, with temperature and pressure maintained at 300K and 1 bar, respectively. To gain deeper structural and functional insights, we compared the time-dependent evolution of RMSD values with EpCAM-WT, as shown in Fig. 3 A, EpCAM-L240A displayed a steady increase in RMSD throughout the 1µs simulation, indicating significant conformational changes compared to its initial structure. Specifically, the starting structure of the TY-1 loop in EpCAM-L240A, formed by residues Ala63 to Arg138, was extended, but during the MD simulation, it moved toward both the N-terminal and C-terminal regions, adopting a more compact globular shape. Additionally, the ridge on the C-terminal domain (RCD) exhibited a closing movement (Fig. 3 C ). In contrast, the TY-1 loop of the EpCAM-C66Y mutant showed a closing movement toward the TYD region (Fig. 3 D ) . The steady decrease in the radius of gyration (Rg) of EpCAM-L240A further supports its compact folding compared to EpCAM-WT, as depicted in Fig. 3 C. The RMSD values for EpCAM-WT and EpCAM-C66Y exhibit stable dynamic behavior throughout the simulation period ( Fig. 3 B ) . In contrast, the residual fluctuations for EpCAM-WT are significantly lower than those observed in the two mutants, EpCAM-L240A and EpCAM-C66Y. The TY-1 loop in both mutants shows considerable dynamics, with RMS fluctuations reaching up to 8.5 Å. EpCAM-C66Y exhibits partial unfolding and refolding during the simulation (Fig. 3 D ) . Residues from the hydrophobic core of EpCAM-C66Y become buried within the protein's interior, making them less exposed to the solvent (Fig. 3 E ) . Meanwhile, EpCAM-L240A and EpCAM-WT display similar trends in their SASA values, suggesting stable dynamics. The distribution of the backbone RMSD values indicates that EpCAM-WT and EpCAM-C66Y have a higher probability of maintaining RMSD values under 5Å, while EpCAM-L240A shows a maximum RMSD value of around 6 Å (Fig. 3 F ) . The TY-1 loop of EpCAM-C66Y also exhibits flexibility, although its RCD region (ridge on CD) undergoes significant conformational changes, leading to a closure movement at the C-terminal domain. In contrast, the TY-1 loop in EpCAM-WT does not show any such closure movement in the RCD, CTD, or TYD regions, although structural transitions within the TY-1 loop are observed during the MD simulation (Fig. 3 ). Intramolecular hydrogen bonds (H-bonds), essential for stabilizing protein structure, were analyzed for EpCAM-WT, EpCAM-L240A, and EpCAM-C66Y. The maximum number of H-bonds formed in each variant is shown in Fig. 3 G. The MD simulation indicated that EpCAM-L240A undergoes significant conformational changes during the simulation (Figs. 3 H ) . To quantify these movements, DynDom was used to identify the fixed (residues 30–73 and 95–265) and moving (residues 74–94) domains in EpCAM-L240A. The moving domain shows a rotational movement of 108.7 degrees, a translational movement of -7.7 Å, and a closure movement of 95.0%. Bending region analysis reveals that residues from the TY-1 loop, specifically Lys70-Gly75 and Gly93-Asp98, serve as hinges facilitating this large rotational and closure movement (see Figure S3) . In contrast, DynDom did not identify significant dynamic movements in EpCAM-WT or EpCAM-C66Y, as their conformational changes were too subtle for detection (data not shown). A 2D projection of the principal components, PC1 and PC2, indicates that EpCAM-L240A occupies a larger conformational space in the free energy landscape, whereas EpCAM-WT forms a compact cluster, occupying less space. EpCAM-C66Y shows fewer diverse conformations compared to EpCAM-L240A (Fig. 3 I ) . The cosine content values for EpCAM-WT, EpCAM-L240A, and EpCAM-C66Y are 0.78, 0.92, and 0.91, respectively, indicating good sampling across the conformational space. The dynamic cross-correlation matrix (DCCM) was generated using representative frames from the MD simulations to explore the concerted motion of the individual domains of EpCAM. Figure 3 J displays the DCCM plots for EpCAM-WT, EpCAM-L240A, and EpCAM-C66Y. The diagonal amber line indicates strong self-correlation of individual residues, while the amplitude of positive to negative correlations is represented by a gradient from amber to blue. In EpCAM-WT, the dynamic TY-1 loop region shows a moderately negative correlation with the N-terminal domain (NTD), while this negative correlation diminishes in both EpCAM-L240A and EpCAM-C66Y. Additionally; the amplitude of positive correlation is higher in EpCAM-C66Y than in EpCAM-L240A. Both mutants exhibit increased positive correlation between the RCD region and the TY-1 loop during the simulation. Overall, the amplitude of negative correlations observed in EpCAM-WT decreases substantially in both mutants. This suggests that both mutants experience positive cooperative motion, particularly in the RCD, NTD, and TY-1 loop regions, indicating significant local conformational changes that could influence EpCAM dimerization and protein-protein interactions. In conclusion, the molecular modeling study reveals that the EpCAM-L240A mutant exhibits highly dynamic behavior, particularly at the TY-1 loop and RCD regions, making it relatively less stable compared to EpCAM-WT. Although EpCAM-C66Y undergoes moderate conformational changes, it significantly alters the native conformation of the TY-1 loop. We propose that the TY-1 loop plays a crucial role in the dimerization of human EpCAM, and recent studies suggest that the TY-1 loop/domain of EpCAM binds and inhibit protease CTSL (Cathepsin-L) [ 18 ]. The substantial structural changes, particularly in the TY-1 loop/domain, may contribute loss of CTSL binding to promote tumor cell invasion and metastasis. Altogether, MD simulation and previously reported functional data suggests that both EpCAM-L240A and EpCAM-C66Y mutations exhibited a loss of EpCAM structure and thereby function. Bioinformatics analysis and Molecular modeling of lung cancer associated EpCAM mutations To systematically catalogue and assess large list of damaging mutations in EpCAM, first we employed three computational tools sorting intolerant from tolerant (SIFT), polymorphism phenotyping (PolyPhen) and mutation assessor [ 39 – 42 ]. The results were normalized from the three computational tools on a scale from 0 to 1, with zero representing the wild-type EpCAM sequence. By combining the outputs from all three tools, we were able to generate a comprehensive mutational impact score. After initial screening, we selected seven high frequency, high scoring mutations from lung cancer for further validation. This approach offered a valuable framework for screening and prioritizing mutations for functional validation. To assess the quality of the obtained trajectories following molecular dynamics simulations, first we plotted the potential energy, temperature, and pressure throughout the dynamics. The values for the mutants EpCAM were: EpCAMD92V: (-104671 kJ/mol), (0.12362 K), (-5.72833 bar) EpCAME137A: (-100359 kJ/mol), (0.244 K), (4.62956 bar) EpCAMY186C: (-118137 kJ/mol), (0.100088 K), (-0.119114 bar) EpCAMQ204E: (-100348 kJ/mol), (0.0841861 K), (1.89489 bar) EpCAMN111K: (-119172 kJ/mol), (0.0313864 K), (1.91565 bar) EpCAMP84S: (-99460.7 kJ/mol), (0.050235 K), (4.04775 bar) EpCAMY215S: (-101241 kJ/mol), (0.289105 K), (-2.47406 bar) These mutants showed minimal fluctuations during the 1µs simulation, with temperature and pressure maintained at 300K and 1 bar, respectively. Molecular dynamics simulations were employed to estimate the structural and conformational changes of the proteins. To gain deeper structural and functional insights into the novel point mutants of EpCAM, including those related to lung cancer, we also compared the time-dependent evolution of the RMSD values for EpCAM mutants with those of EpCAM-WT. The RMSD profile for the backbone of EpCAM-WT and its seven mutants was generated for all atoms from the initial structure to assess the impact of mutations on the stability of the protein structure (Fig. 4 B,C). The MD simulation of EpCAM-WT exhibited a steady RMSD of approximately 4.5 Å throughout the simulation. EpCAM-N111K exhibited fluctuations around 4.5 Å, with RMSD values showing similar trends to the wild type from the start to 700 ns. After 700 ns, the RMSD decreased to approximately 3 Å at 850 ns, before rising again to 5 Å at 1 ms, as seen in Fig. 4 D-E. In contrast, EpCAM-D92V showed fluctuations between 50 ns and 200 ns, with a decrease in RMSD from 220 ns to 400 ns. After that, the RMSD remained relatively stable at around 4.5 Å until 850 ns, after which some fluctuations occurred between 850 ns and 900 ns, as shown in Figure S4A . The TY-1 loop of EpCAM-D92V remained flexible during the simulation. For the mutant EpCAM-E137A, the RMSD increased to approximately 3.6 Å, indicating structural instability as observed in Fig. 4 G. EpCAMY-186C exhibited similar RMSD values to EpCAM-WT up to the last 1000 ns. However, the mutant EpCAM-Q204E displayed a notably unstable structure, with RMSD values remaining stable up to 500 ns, after which they increased to 8 Å at 1 ms, suggesting a loss of structural stability due to the mutation at position Q204E. EpCAM-P84S showed distinct deviations, ranging from approximately 2.5 to 6 Å. Initially, its RMSD was lower than that of the wild type from 50 ns to 650 ns, after which it steadily increased to 6 Å. Finally, EpCAM-Y215S required some time to achieve a relatively stable structure as observed in Fig. 4 G. Next, we analyzed how cancer-associated mutations affect the dynamic behavior of various regions in EpCAM, including the N-terminal domain (NTD), C-terminal domain (CTD), ridge on CD (RCD), stalk and C-terminal domain (SCD), and importantly, the TY-1 loop. This was done by calculating the root mean square fluctuation (RMSF) of the Cα atoms for all the mutants and comparing them to the EpCAM-WT. The residual fluctuations in EpCAM-WT were significantly lower compared to the seven mutants particularly when observing the dynamics of the TY-1 loop (residues 63–138) and its interactions with different protein sites. The TY-1 loop in all mutants EpCAM-P84S, EpCAM-Q204E, EpCAM-M92V, EpCAM-E137A, EpCAM-N111K, EpCAM-Y186C, and EpCAM-Y215S was highly dynamic, with RMS fluctuations reaching up to 9 Å, 8 Å, 7.5 Å, 7 Å, 6 Å, 5.5 Å, and 5 Å, respectively (Fig. 4 and S4 ) . EpCAM-P84S exhibited partial unfolding and folding, with the highest fluctuations occurring in the TY-1 loop residues between ~ 75–100, as illustrated in Figures S4A . Residues 25–75, which are part of the NTD, showed some motion in EpCAM-D92V, EpCAM-E137A, and EpCAM-P84S mutants. Additionally, significant fluctuations were observed in the C-terminal domain, particularly in the final amino acids, with fluctuations reaching up to 19 Å, as seen in Fig. 4 G. Notably, the most pronounced fluctuations occurred in the TY-1 domain (residues 63–138) in all mutants. The EpCAM-Q204E mutant exhibited some additional fluctuation in the RCD region (residues 227–239) compared to the wild-type, as shown in Figure S4A. The SCD region (residues 202–205) remained relatively stable during the simulation across all mutants. From Figs. 4 , we can conclude that the residual fluctuations mainly occur in the NTD (residues ~ 24–62), the TY-1 loop (~ 75–100), and the final residues (264–265), with higher fluctuations observed in EpCAM-D92V, particularly in the TY-1 loop region (63–138). EpCAM-E137A exhibited significant fluctuations mainly at positions 80–95 of the TY-1 loop. EpCAM-WT demonstrated a compact structure with an RMSD range of 18.5–19.5 Å throughout the simulation, while the mutants showed a gradual loss of compactness. The mutants EpCAM-Q204E and EpCAM-P84S showed continuous folding and unfolding, with RMSD values ranging from 19.5–20.5 Å and 19–20.0 Å, respectively, as seen in Fig. 4 . The remaining mutants EpCAM-D92V, EpCAM-E137A, EpCAM-N111K, EpCAM-Y186C, and EpCAM-Y215S showed less compactness, with RMSD values ranging from 18.5–20.5 Å, 18.5–20.0 Å, 18.5–20.0 Å, 18.5–19.5 Å, and 19.0–20.0 Å, respectively. In terms of solvent accessibility, residues from the hydrophobic core of the EpCAM-Y186C, EpCAM-E137A, and EpCAM-P84S mutants were buried inside the protein, reducing their exposure to the solvent environment, as shown in Figs. 4 . In contrast, EpCAM-D92V, EpCAM-Q204E, EpCAM-N111K, and EpCAM-Y215S exhibited stable dynamics, with similar trends in their solvent-accessible surface area (SASA) values. Next, we analyzed the intramolecular hydrogen bonding interactions in EpCAM-WT and its mutants. The maximum number of H-bonding interactions for each of these mutants is depicted in Fig. 4 H. Considering the conformational changes observed from the dynamics of all mutants, we compared the 3D structures of the mutants with the wild-type EpCAM structure. Wild-type EpCAM displayed an open conformation of the TY-1 loop, but this conformation was altered in the mutants. Specifically, EpCAM-D92V exhibited changes in the TY-1 loop structure, which slightly moved toward the C-terminal domain (CTD) binding site. Additionally, the RCD region (His227-Gln239) shifted toward the CTD compared to the wild-type EpCAM. In a study it was proposed that EpCAM has two binding sites for the TY-1 loop: the TYD and CTD [ 43 ]. Our study found that in the EpCAM-E137A mutant, the TY-1 loop residue that binds to the CTD in wild-type EpCAM actually binds to the TYD site, while the TY-1 loop that normally binds to the TYD site interacts with the CTD in this mutant ( see Fig. 4 ) . In EpCAM-Q204E, EpCAM-N111K, and EpCAM-Y215S, the TY-1 loop shifted toward the TYD site, resulting in a closed conformation of the TYD. In the other mutants, no significant movement was observed. Figure 4 B, illustrates the time evolution of the secondary structure along the protein chain, showing the stability of the N-terminal α-helix and fluctuations in the central β-sheet. Hydrogen bond analysis further indicated that the α-helical regions maintained a stable network of hydrogen bonds, while the β-sheet regions experienced notable disruption in hydrogen bonding after 150 ns, correlating with the observed structural transition. To examine the structural plasticity during MD simulation, we used DSSP to track secondary structural elements in both wild-type EpCAM and its mutants. Figure 4 -B,C shows the secondary structure changes over time. Both the wild-type and mutant proteins exhibited coils, β-sheets, β-bridges, bends, turns, and various types of helices. However, the mutants showed a considerable decrease in the formation of β-sheets and bends compared to wild-type EpCAM. We performed essential dynamics (ED) analysis to study the correlated motions of the wild-type and mutant EpCAM proteins. The eigenvalues from this analysis indicated that major fluctuations in the system were confined to the first two eigenvectors for both the wild type and the mutants. As seen in Figure S4 , the mutant proteins covered a larger space along the first and second principal components (PC1 and PC2) compared to the EpCAM-WT. The probability distribution for the EpCAM-WT remained within a 6 Å threshold, while the mutant structures EpCAM-Q204E, EpCAM-Y215S, EpCAM-E137A, EpCAM-D92V, EpCAM-P84S, EpCAM-N111K, and EpCAM-Y186C showed maximum alterations of 9 Å, 7 Å, 7 Å, 7 Å, 6 Å, 6 Å, and 6 Å, respectively, (Fig. 4 B ) . In the EpCAM-WT, both correlated and anti-correlated motions were observed, while in the mutant EpCAM-Q204E, EpCAM-E137A, and EpCAM-Y215S, mostly anti-correlated motion was observed ( Figure S4B) . In summary, molecular modeling reveals that mutants EpCAM-Q204E, EpCAM-E137A, EpCAM-Y215S, EpCAM-N111K, and EpCAM-D92V exhibit significant dynamic behavior, particularly in the TY-1 loop and RCD regions during MD simulations, which leads to a less stable structure compared to EpCAM-WT. In contrast, the mutants EpCAM-P84S and EpCAM-Y186C show more moderate conformational changes. The significant structural changes observed in the TY-1 domain across all mutants would likely disrupt the structure and function of EpCAM, influencing its role in cellular processes. EpCAM mutations predicts poor survival in colon and hepatocellular cancer EpCAM frequently overexpressed in colon cancer which promote cancer cell proliferation, migration, and resistance to apoptosis [ 1 ]. EpCAM is also considered a marker of cancer stem cells in colon cancer, where its expression potentially influences the self-renewal capability of these stem cells, contributing to tumor relapse and chemo-resistance [ 13 , 17 ]. Clinically, the overexpression of EpCAM in colon cancer is associated with poor survival outcomes, likely due to its role in promoting invasiveness and metastasis. High levels of EpCAM have been correlated with advanced disease stage and worse prognosis in colon cancer patients. In hepatocellular carcinoma (HCC), EpCAM overexpression is similarly linked to poor prognosis, with elevated levels associated with tumor progression, vascular invasion, and metastasis [ 35 , 44 ]. EpCAM expression in HCC is also correlated with shorter survival and a higher likelihood of recurrence post-surgery. To investigate the role of these newly identified EpCAM mutations, a cohort of patients with EpCAM mutations alongside other driver mutations was analyzed. As shown in Figs. 5 A-C, somatic mutations in EpCAM coexist with mutations in key oncogenes such as KRAS, BRAF, p53, and EGFR. In the group with altered EpCAM expression, the survival was significantly worse, with a median survival of 42.78 months, compared to 62.25 months in the unaltered group (Fig. 5 B). As seen in Fig. 5 D,E, in HCC, EpCAM mutations were observed in conjunction with mutations in p53, MDM2, DNMT1, and KEAP1 where the overall survival for patients with EpCAM mutations was 9.86 months, compared to the unaltered group (40.45 months). These findings highlight the critical role of EpCAM mutations in cancer progression, reinforcing their involvement in tumorigenesis and their potential utility as prognostic markers. The prevalence of EpCAM mutations in NSCLC may hold significant prognostic value Building on our previous study demonstrating that RAS can stabilize EpCAM expression [ 19 ], we hypothesized that somatic mutations in the EpCAM, frequently co-occurring with driver oncogenes such as KRAS, EGFR, TP53, and BRAF, could possess prognostic significance. To investigate, first, we analyzed two cohorts (Fig. 6 A, data S6 ), and as expected, most EpCAM mutations were found in conjunction with mutations in TP53, KRAS, EGFR, STK11, BRAF, and PTEN (Fig. 6 D). The GENEI-v16 cohort [ 23 ], which includes data from 26,851 patients, revealed 284 missense mutations ( Figure S2 ). Surprisingly, overall survival did not significantly differ between patients with EpCAM mutations and those without, across both cohorts (see Fig. 6 B-C). This analysis suggests that EpCAM mutations alone may not be a significant determinant of survival in these populations. To assess this unexpected observations, we revisited the tumor mutation burden (TMB) Fig. 2 D. Interestingly, EpCAM mutations in lung cancer were predominantly found in samples with a low TMB. This suggests that these alterations may play a role in the early stages of tumor development rather than in its progression. To align our MD simulation findings with existing cohort studies, we suggest that EpCAM mutations may provide an opportunity to sensitize cells to specific drugs. To delineate the roles of EpCAM expression vs mutation, we first assessed EpCAM expression across a panel of lung cancer cell lines, focusing on epithelial characteristics. As shown in Figure S6D , EpCAM expression was predominantly observed in cells expressing epithelial markers established in NSCLC [ 45 ]. The panel revealed that the cell lines A549, H1299, H460, H226, and H23 showed undetectable levels of EpCAM (also tested in the lab, data not shown). Building on our prior report that EpCAM can influence ERK1/2 signaling [ 9 ]. We used retroviral constructs to transduce A549 cells with EpCAM-WT and seven EpCAM mutations characterized in Fig. 4 . Serum stimulation of the cells showed a marked decrease in ERK activity in EpCAM-WT cells compared to those with EpCAM mutations (Fig. 6 E). These results suggests that EpCAM mutation increased ERK signaling. To investigate the role of EpCAM mutations and drug targeting, in drug resistance, we transfected A549, H1299, H460, H226, and H23 cells, which harbor KRAS, p53, or LKB1 mutations (Fig. 6 F). Twenty-four hours post-transfection, the cells were treated with 200–500 nM Trametinib for 72 hours, followed by a cell viability assay. The results revealed that EpCAM mutations differentially activated ERK1/2 signaling, sensitizing cells to drug-induced apoptosis (Figs. 6 F–G). Notably, when correlating ERK activation with apoptotic sensitivity, we observed that EpCAM mutants exhibiting higher sensitivity to apoptosis were associated with altered localization and stabilization [ 18 ]. These findings suggest that EpCAM mutations may enhance responsiveness to MEK inhibitors. In conclusion, EpCAM mutations promote increased ERK signaling and enhance the sensitivity of lung cancer cells to MEK inhibitors, highlighting their potential as therapeutic targets to improve treatment outcomes. Discussion Cancer-related mutations that regulate critical cellular processes, including proliferation, apoptosis, DNA repair, and cell signaling, are classified as oncogenes, tumor suppressor genes, or DNA repair genes. While a single genetic mutation is rarely sufficient to cause cancer, the accumulation of multiple genetic changes both in relation to one another and over time creates opportunities to detect cancers at much earlier stages [ 46 ]. Membrane proteins play a crucial role in facilitating intercellular communication and transmitting signals within cells by mediating protein interactions and regulating downstream cellular processes. Due to their pivotal functions, membrane proteins are often key targets for drug development and therapeutic interventions. As a tumor antigen, EpCAM overexpression is linked to several cancer cells related signaling [ 9 , 11 , 13 – 16 , 19 ]. The goal of our study was to catalog cancer-related mutations of EpCAM, characterize their features, and highlight their significance to the research community for further investigation. In this study, through the integration of multiple cohort datasets to catalog all EpCAM mutations, along with in-silico tools, advanced molecular modeling, and experimental validation, we reached several key conclusions. First , our study leveraged newly curated genomic datasets to identify several novel and recurrent EpCAM mutations across multiple cancer cohorts. Despite the relatively low frequency of somatic EpCAM mutations (ranging from 0.1–0.6%), our findings underscore the biological significance of these mutations in epithelial cancers. Second , TMB and survival analyses suggest that EpCAM mutation status could serve as an important marker for prognostic evaluations, personalized therapy strategies, and suggests role of EpCAM mutations in early or late stage of tumorigenesis. Third , for the first time, we demonstrated that mutations in the EpCAM gene destabilize its protein structure, particularly affecting the TY-1 loop and the RCD region. Homology modeling and MD simulations highlighted how these structural destabilizations compromise stability of EpCAM and dynamics, which are critical for its biological functions. Fourth , EpCAM mutations may potentially disrupt protein-protein interactions with essential cellular proteins, including CTSL [ 18 ], claudin [ 47 ], EGFR [ 48 ], PKC [ 49 ] occludins, catenins, MUC1, CD44 and CD63 [ 50 – 52 ]. EpCAM has been recognized as a novel component of tetraspanin-enriched microdomains (TEMs), forming a primary complex with the tetraspanin CD9 [ 53 , 54 ]. These interactions are critical for maintaining cellular integrity, adhesion, and signaling processes. Loss of efficient binding due to structural instability could lead to dysfunctional cellular signaling pathways and tumor-promoting mechanisms. Fifth , our data provide preliminary evidence that some EpCAM mutations, such as C66Y and L240A localized in cytosol instead of membrane can potentially interfere with proto-oncogenes EGFR or KRAS signaling [ 19 ]. Finally six , Structural impairments caused by EpCAM mutations may explain the partial success of past and potential future antibody therapies targeting EpCAM [ 55 ]. In an overall study design, we performed comprehensive analysis of integrated data from major cancer genomics resources, including cBioPortal, COSMIC, AACR-GENIE, and Foundation Medicine-NCI (FMI) (Fig. 2 A-C). We identified 160 new somatic mutations in addition to our previous work [ 18 ] The data suggests with missense mutations being the most common (79%), followed by nonsense mutations (6%), splice site mutations (7%), frameshift mutations (4%), deletions (2%), and fusion mutations ( Figure S1 C ). Notably, MSH2-EpCAM fusions were identified in esophageal adenocarcinoma, squamous cell carcinoma, and stomach adenocarcinoma, aligning with previous reports of exon 9 deletions in EpCAM leading to MSH2 promoter hyper-methylation and gene silencing [ 56 ]. We also identified EpCAM-ABCG8 and NUP42-EpCAM fusions in high-grade uterine and ovarian carcinomas. In terms of mutation prevalence, EpCAM mutations were most common in lung cancer, skin cancer, and colorectal cancer, with pancreatic cancer also showing a notable concentration of mutations. (Fig. 2 B, Figure S2 ). For molecular dynamic simulation of EpCAM mutants, the cis- dimer crystal structure PDB: 4MGV [ 33 ] guided us to focus key regions for possible for the loss of function. The N-terminal domain of EpCAM, a critical region containing functional domains such as the EGF-like domain, RCD and TY-1 domain. Our previous work demonstrating how EpCAM mutations at C66Y and L240A altered the cellular phenotype in cancer cells was validated. MD simulations revealed significant structural destabilization in these mutants. For example, EpCAM-L240A exhibited heightened dynamic instability, especially in the TY-1 loop and RCD regions, resulting in a less stable structure compared to the EpCAM-WT. On the other hand, EpCAM-C66Y, while undergoing moderate conformational changes, significantly disrupted the native conformation of the TY-1 loop (Fig. 3 ). Next, we focused to study seven lung cancer associated mutations we discovered (Fig. 4 , S4, S5). Results revealed that these mutations induced significant structural fluctuations, particularly in the TY-1 loop and the RCD region. Among the analyzed mutants, EpCAM-Q204E and EpCAM-P84S showed the most pronounced instability, characterized by higher root mean square deviation (RMSD) values, indicating substantial loss of structural integrity. These mutations also caused marked conformational changes in the TY-1 loop. Specifically, the structural shift observed in EpCAM-Q204E likely hinders its interaction network, undermining its role in tumor suppression and facilitating cell migration. Similarly, the RCD region a possible hotspot for maintaining EpCAM’s structural stability and protein-binding capacity exhibited notable disruptions in mutant EpCAM-D92V. We predict that structural changes in RCD region could alter interactions with key molecular partners, such as claudins, occludins, and catenins, which are crucial for maintaining epithelial integrity and preventing tumor dissemination [ 3 , 57 ]. Interestingly, while mutations EpCAM-Y186C and EpCAM-P84S demonstrated more moderate conformational changes, their altered dynamics were still sufficient to affect EpCAM’s function. For example, the ability of EpCAM to inhibit cathepsin-L through-TY-1 loop may be impaired even by subtler alterations in structure [ 18 ]. These findings pave the way for future investigations aimed at experimentally validating these mutations' effects on EpCAM's biochemical activities and downstream signaling. Building on our previous study, we postulate that some EpCAM mutations, in conjunction with RAS, EGFR oncogenes could have detrimental effects on cancer cell growth and contribute to drug resistance [ 19 ]. Figure 5 A data of colon cancer cohort shows that somatic mutations in EpCAM often coexist with mutations in key oncogenes such as KRAS, BRAF, TP53, and EGFR. Patients with altered/mutation EpCAM exhibited significantly worse survival outcomes in colon cancer A similar trend was observed in hepatocellular carcinoma (HCC), where EpCAM mutations co-occurred with mutations in TP53, MDM2, DNMT1, and KEAP1 ( Fig. 5 B ) , and patients with EpCAM mutations exhibited significantly worse overall survival compared to those with unaltered EpCAM expression. For lung cancer cohort, highest frequency of EpCAM mutations was observed in non-small cell lung cancer (NSCLC). Somatic mutations in the EpCAM gene often co-occured with mutations in key oncogenes, such as KRAS, EGFR, TP53, and BRAF (Fig. 6 , S6). Among over 1,000 identified mutations in the EpCAM gene, 283 were somatic mutations ( Figure S2 ). Surprisingly overall survival did not differ significantly between patients with EpCAM mutations and those without across both cohorts (Fig. 6 B-C). This finding prompted further investigation into the potential role of tumor mutation burden (TMB) and the co-occurrence of EpCAM mutations. Our results suggest that EpCAM mutations, when found in conjunction with a lower TMB, may play a more prominent role in the early stages of tumor development rather than in later-stage disease progression. To validate our in-silico and MD simulations findings, we conducted experimental approach to support the data. EpCAM is known to alter ERK signaling [ 9 ]. ERK reporter assay indicated that cells with EpCAM mutations exhibited increased ERK1/2 activity, which was sensitized by Trametinib treatment (Fig. 6 E, F). Further analysis revealed that cells transfected with EpCAM mutations showed a correlation with Trametinib-induced apoptosis and ERK activity (Fig. 6 G). In conclusion, EpCAM mutations in cancer cells may lead to context-dependent alterations in protein structure and stability. Given the variety of mutations and their impact on tumor behavior, therapies targeting EpCAM could be customized to enhance efficacy. Conclusion Our study highlights the functional and structural consequences of EpCAM mutations in epithelial cancers, revealing their role in tumor progression, reduced patient survival, and altered drug sensitivity. The findings demonstrate that mutations in TY-1 and RCD regions destabilize EpCAM, potentially disrupting its adhesive and signaling functions. EpCAM-mutant lung cancer cells exhibit increased sensitivity to MEK inhibitors, suggesting a novel therapeutic avenue. These results emphasize EpCAM mutations as potential biomarkers for cancer prognosis and treatment strategies, positioning EpCAM as a promising therapeutic target in epithelial malignancies. Data availability The datasets used and/or analyzed during the current study are available as Data Figure S2 or from the corresponding author on reasonable request. Declarations Ethics approval and consent to participate Not applicable. Patient consent for publication Not applicable Author’s disclosures The authors of this manuscript have no conflicts of interest. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. No disclosures were reported by the other authors. Conflict of Interest Authors declare no conflict of interest. Abbreviations CCLE, Cancer Cell Line Encyclopedia CTD, C-terminal domain CTSL, cathepsin-L DCCM, dynamic cross-correlation matrix DSSP, defined secondary structure protein analysis EpCAM, epithelial cell adhesion molecule H-bonds, hydrogen bonds HCC, hepatocellular carcinoma MD, molecular dynamics NSCLC, non-small cell lung cancer NTD, N-terminal domain PolyPhen, polymorphism phenotyping RCD, ridge on the C-terminal domain RMSD, root mean square deviation RMSF, root mean square fluctuation SASA, solvent accessible surface area SIFT, sorting intolerant from tolerant TMB, tumor mutation burden TYD, type-1A-like domain Funding St Joseph Hospital Foundation supported the research work. Author Contribution NVS, KS and WEG were responsible for study conception, design, and supervision manuscript writing. NVS performed the in vitro experiments. ABL generated EpCAM mutation data and prepared figure 4a. PSD and KS performed Homology modeling and MD simulation Prepared Figures 3-4 . 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J Mol Diagn. 2011;13(1):93–9. Schnell U, Cirulli V, Giepmans BN. EpCAM: structure and function in health and disease. Biochim Biophys Acta. 2013;1828(8):1989–2001. Supplementary Files SupplDataandFigures.zip Cite Share Download PDF Status: Published Journal Publication published 01 Jul, 2025 Read the published version in BMC Cancer → Version 1 posted Editorial decision: Revision requested 12 Mar, 2025 Submission checks completed at journal 11 Mar, 2025 First submitted to journal 11 Mar, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6174915","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"","associatedPublications":[],"authors":[],"badges":[],"createdAt":"2025-03-07 04:53:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6174915/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6174915/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12885-025-14455-8","type":"published","date":"2025-07-01T15:58:26+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":78322515,"identity":"06b1ae39-3853-476a-b22f-52ff108485db","added_by":"auto","created_at":"2025-03-12 05:35:15","extension":"pdf","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":31552874,"visible":true,"origin":"","legend":" EpCAM expression is predominantly observed in epithelial cells and related tissues. , RNA and protein expression levels of EpCAM, based on data from Protein Atlas (), and GTEx Portal () are shown, with expression ranked from high to low. , EpCAM expression is elevated in tumor tissues compared to normal tissues, as analyzed using two independent tumor-normal paired datasets: GSE18842 and HSE1007. , Analysis of EpCAM expression across different stages of NSCLC and in relation to EGFR, KRAS, and ALK oncogenic alterations. Microarray datasets GSE43580 and GSE31210 were normalized and visualized using GraphPad. These results highlight that EpCAM expression is largely confined to normal epithelial tissues, including the gastrointestinal tract, thyroid, kidney, pancreas, breast, and lung, and is significantly upregulated in cancer tissues.","description":"","filename":"Figures16.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6174915/v1/7093523ff908bb467cecc0b5.pdf"},{"id":78322495,"identity":"2ac7eddd-6677-4a90-a3bc-2e9a9998c28f","added_by":"auto","created_at":"2025-03-12 05:35:10","extension":"pdf","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":31552874,"visible":true,"origin":"","legend":"","description":"","filename":"Figures16.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6174915/v1/82aa7bdaa2263966ce3ee753.pdf"},{"id":86181101,"identity":"ecd77b9f-df3d-4293-84d7-f6748fdc902e","added_by":"auto","created_at":"2025-07-07 16:23:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":952821,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6174915/v1/a00ad5d6-b37b-4481-ace7-e18c0571d439.pdf"},{"id":78322492,"identity":"0c8951b2-6223-415e-9684-2cdebbc116d9","added_by":"auto","created_at":"2025-03-12 05:35:07","extension":"zip","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":24298486,"visible":true,"origin":"","legend":"","description":"","filename":"SupplDataandFigures.zip","url":"https://assets-eu.researchsquare.com/files/rs-6174915/v1/20fb63a7d132e13fd7d3a072.zip"}],"financialInterests":"","formattedTitle":"Landscape of cancer associated EpCAM mutations: molecular modeling, Predictive Insights and Impact on Patient Survival","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eEpCAM, a transmembrane signaling protein identified as a tumor-associated antigen that is overexpressed in various epithelial cancers [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Beyond its role in mediating cell-cell adhesion, EpCAM is known as a signaling molecule implicated in cancer cell proliferation, invasion, migration, differentiation, and immune evasion [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Its role in cancer biology and consistent expression across several cancer types make EpCAM a key biomarker and therapeutic target. In normal tissue EpCAM is found on the basolateral surfaces of epithelial cells in tissues such as the skin, gastrointestinal tract, respiratory tract, lung and reproductive organs. EpCAM is highly expressed during embryogenesis, where it facilitates cell proliferation and differentiation [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn well-characterized phenotype of EpCAM mutations, autosomal recessive mutations in the EpCAM gene are associated with congenital tufting enteropathy (CTE) [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. The prevalence of germline MLH1 hyper-methylation and EpCAM deletions is notably high among genetically confirmed cases of Lynch syndrome [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. This epigenetic silencing predisposes individuals to Lynch syndrome, an inherited condition linked to colorectal, endometrial, and other cancers. Moreover, the loss of EpCAM function has been linked to phenotypic alterations, including epithelial-to-mesenchymal transition (EMT) [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], while gain-of-function have been implicated in enhanced oncogenic signaling [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. EpCAM plays a crucial role in regulating key cancer-associated signaling pathways, including AP-1, NF-kB, Wnt/β-catenin, PI3K/AKT, and MAPK/ERK [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan additionalcitationids=\"CR12 CR13 CR14\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. By stabilizing β-catenin and promoting its nuclear translocation, EpCAM drives the transcription of Wnt target genes [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Similarly, its interactions with components of the PI3K/AKT pathway promote tumor cell survival and resistance to apoptosis. EpCAM mutations have also been shown to dysregulate ERK signaling, contributing to drug resistance, a major challenge in cancer therapy. For instance, dysregulated ERK signaling can be linked to reduced sensitivity to targeted therapies, highlighting the clinical implications of EpCAM dysfunction [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Its expression on the surface of cancer cells has facilitated its use as a therapeutic target in antibody-drug conjugates [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. However, mutations in EpCAM are now being recognized as factors influencing the effectiveness of these therapeutic interventions. For example, altered EpCAM expression and function can be linked to enhanced drug resistance, primarily due to changes in cancer cell signaling or survival mechanisms. Our recent studies have demonstrated that cancer associated EpCAM mutations resulted in the loss of its function, altered localization, and promotion of epithelial-to-mesenchymal transition (EMT), thereby facilitating tumor metastasis [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn the present study, for the first time we investigate the landscape of EpCAM mutations across major cancers of epithelial origin. Using bioinformatics approach, we screened for damaging mutations and employed homology modeling and molecular dynamics (MD) simulations. Recent molecular dynamics studies on gene mutations have provided valuable insights into protein function, stability, and interactions, uncovering mechanisms that are challenging to observe experimentally. These findings highlight how mutations can lead to the loss or gain of gene activity, offering a deeper understanding of their impact on biological processes [\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Through in-silico analyses and experimental validation, we demonstrate that certain EpCAM mutations result in a loss of structural stability thereby disrupting ERK signaling. Furthermore, the co-occurrence of EpCAM mutations with known oncogenes reveals potential therapeutic vulnerabilities. Detecting these mutations early in disease progression, particularly in the genomic era, could enhance patient survival predictions and guide the development of targeted therapeutic strategies.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eCell culture and reagents\u003c/h2\u003e \u003cp\u003eAll studied cell lines were obtained from the American Type Culture Collection (ATCC, Rockville, MD, USA). A549 (ATCC#CCL-185), H1299 (ATCC#CCRL-5803), H460 (ATCC#HTB-177), H226 (ATCC#HTB-177) and H23 (ATCC#CRL-5800). Phoenix-AMPHO (ATCC#CRL-3213) cells cultured in DMEM. NSCLC cells were cultured in RPMI-1640. Both culture media were supplemented with 10% FBS, 2 mM glutamine (Gibco #25030081), 1% penicillin/streptomycin, and incubated at 37\u0026deg;C in an atmosphere of 95% air and 5% CO\u003csub\u003e2\u003c/sub\u003e. The MAPK inhibitor Trametinib was purchased from Selleck Chemicals (Houston, TX, USA). Cell culture were monitored for mycoplasma routinely.\u003c/p\u003e \u003cp\u003e \u003cb\u003eEpCAM mutation constructs.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe EpCAM nucleotide sequence was accessed with NCBI reference sequence NM_002354. EpCAM deletion mutants were generated based on the genomic data sets (\u003cb\u003eFigure S2\u003c/b\u003e). To the wild-type EpCAM single amino acids change was made by custom gene synthesis of EpCAM-D92V, EpCAM-E137A, EpCAM-Y186C, EpCAM-Q204E, EpCAM-N111K, EpCAM-P84S, and EpCAM-Y215S. EpCAM mutant constructs were generated using synthetic gene fragments from Integrated DNA Technologies (IDT, Coralville, IA) as described before [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. For example, C66Y EpCAM was generated as a G-block fragment (197G\u0026thinsp;\u0026gt;\u0026thinsp;A; substitution position 197, G➞A). The DNA sequence was sub-cloned into both pcDNA3 (HindIII-XbaI restriction sites) or the retroviral vector pBABE-puro (BamHI-SalI restriction after mutating BamHI sites).\u003c/p\u003e \u003c/div\u003e"},{"header":"Retroviral transduction","content":"\u003cp\u003eIn a six well plate, Phoenix-AMPHO packaging cells were transfected when nearly confluent with 2.5 \u0026micro;g of pBABE-Puro-EpCAM constructs using FuGENE-HD (Promega#E2311). Forty-eight hours post transfection, viral supernatants were collected, filtered through 0.45 \u0026micro;m filters, and then added to the cells in media containing 8 \u0026micro;g/mL protamine sulfate. After one-two successive retroviral infections, cells were grown for 48 h and selected in puromycin for 2 weeks.\u003c/p\u003e"},{"header":"SRE-ERK dual Luciferase Assay","content":"\u003cp\u003eSRE Reporter assay was performed as described earlier [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Briefly, cells were cultured in 12-well plate for 24h. DNA constructs 400ng-SRE-Luc (Promega# E1340) together with internal control plasmid 50ng-pRLSV40-Luc (Promega# E2231) was transfected using FuGENE-HD (Promega # E2311). After 12h post transfection cells were serum-starved for 12h, treated with or without 20% FBS overnight. Cells were lysed, and the dual luciferase assay was performed using the Dual-Luciferase Reporter assay system (Promega #E1910). Mean values of luciferase activity relative to untreated and were calculated from triplicate wells. The experiments were repeated three times to ensure consistency. Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD of three technical replicates. * \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, ** \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, two-tailed Student\u0026rsquo;s \u003cem\u003et\u003c/em\u003e-test.\u003c/p\u003e"},{"header":"Trametinib treatment and Cell viability Assay","content":"\u003cp\u003eEpCAM transduced or transfected cells 5.0 \u0026times; 10\u0026sup3; were seeded into 96-well plates and cultured in a 5% CO₂ incubator for 24h. Trametinib (Selleck Chemicals #S2673) was prepared in DMSO and applied at concentrations ranging from 100 to 200 nM. Cell survival was assessed after 72 hours later using the Cell Titer-Glo (Promega#G7570) a) according to the manufacturer's instructions. Luminescence was recorded by SpectraMax-i3 (Molecular Devices). Cell death was monitored using Annexin-V staining (#V13241, ThermoFisher) as recommended by manufacturer.\u003c/p\u003e"},{"header":"EpCAM mutation dataset","content":"\u003cp\u003eEpCAM mutation status and sample related information were collected from the cBioPortal for Cancer Genomics (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cbioportal.org\u003c/span\u003e\u003cspan address=\"https://www.cbioportal.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). AACR-GENIE (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://genie.cbioportal.org\u003c/span\u003e\u003cspan address=\"https://genie.cbioportal.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and the Catalogue Of Somatic Mutations In Cancer (COSMIC, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cancer.sanger.ac.uk/cosmic\u003c/span\u003e\u003cspan address=\"https://cancer.sanger.ac.uk/cosmic\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. All compiled data is included in the supplementary data \u003cb\u003eFigure S2\u003c/b\u003e. The mutation data was restricted to somatic mutations where protein altered in single amino acids. Overlapping Samples from GENIE v.13 and cBioPortal were removed. For TMB distributions, tumors with TMB between 5 and 600 were grouped in increment of 5 or 100 for 10 different cancer types. EpCAM mutation per group was identified as 1, 2\u0026ndash;4, 5\u0026ndash;9, 10\u0026ndash;19, 20\u0026ndash;29, 30\u0026ndash;50 and increment of 100 till 600 (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003eD\u003cb\u003e)\u003c/b\u003e. Cell line EpCAM expression was accessed through the publicly available Cancer Cell Line Encyclopedia (CCLE, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://portals.broadinstitute.org/ccle\u003c/span\u003e\u003cspan address=\"https://portals.broadinstitute.org/ccle\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) at the Broad Institute. EpCAM expression from NCI-60 cell line panel (GSE32474) was accessed, data was plotted using GraphPad. EpCAM expression in 70 NSCLC tumor lines GSE32989 was accessed and analyzed using GENE-E (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://broadinstitute.org/GENE-E/\u003c/span\u003e\u003cspan address=\"https://broadinstitute.org/GENE-E/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eMolecular modeling of EpCAM\u003c/h2\u003e \u003cp\u003eThe 3D structure of human EpCAM wild type (EpCAM\u003csub\u003eWT\u003c/sub\u003e) was build using homology modelling with the help of Modeller 10.2 software [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The crystal structure of human was used (PDB ID: 4MZV) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.rcsb.org/\u003c/span\u003e\u003cspan address=\"https://www.rcsb.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) as a template to build full length 3D model of EpCAM\u003csub\u003eWT\u003c/sub\u003e and generated total 500 different conformations of EpCAM\u003csub\u003eWT\u003c/sub\u003e. The 3D structure of EpCAM\u003csub\u003eWT\u003c/sub\u003e was selected based on DOPE score (-26725.0 Kcal/Mol). The Structural refinement of the modelled EpCAM\u003csub\u003eWT\u003c/sub\u003e structure was done by performing all atom MD simulations in explicit solvent using GROMACS 2021.5. The force filed amberff99SBildn was used to generate topology files and TIP3P model for solvation with periodic boundary conditions. The required number of counterions were added to neutralize the system. Steric clashes or bad contacts raised during homology modelling were relaxed by performing energy minimization with Steepest-Descent method followed by Conjugate-Gradient. Canonical ensembles NVT and NPT were used to equilibrate the system for 1ns. Further, unrestrained MD simulation was performed for the period of 1\u0026micro;s to get detailed insights to the structural stability and evolution of diverse conformations over the potential energy surface. The long-range electrostatic interactions were treated with PME and LINCS algorithm to constraint the H-bonds. The coordinates and energies were recorded at every 2 fs and 200 ps respectively. A modified Berendsen thermostat and Parrinello-Rahman algorithm was used to maintain constant temperature (300K) and pressure (1bar) during the simulation. The structure of EpCAM\u003csub\u003eWT\u003c/sub\u003e having least energy near global state was extracted from the MD trajectory and then used further to generate homology models of mutant EpCAM structures for EpCAM\u003csub\u003eD92V\u003c/sub\u003e, EpCAM\u003csub\u003eE137A\u003c/sub\u003e, EpCAM\u003csub\u003eY186C\u003c/sub\u003e, EpCAM\u003csub\u003eQ204E\u003c/sub\u003e, EpCAM\u003csub\u003eN111K\u003c/sub\u003e, EpCAM\u003csub\u003eP84S\u003c/sub\u003e, and EpCAM\u003csub\u003eY215S\u003c/sub\u003e). The MD simulations for the mutants were also performed using the similar protocol adopted for EpCAM\u003csub\u003eWT\u003c/sub\u003e simulation. The trajectories obtained were checked for quality and detailed structural analysis was performed using in build Gromacs tools and other necessary packages such as DSSP [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], CPPTRAJ [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] wherever required. The plots were generated using Grace 5.1.25 and quality images were prepared using UCSF Chimera 1.15[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eAll experiments were performed at least three times in triplicate. All statistical analyses were performed using GraphPad Prism 9.4 (GraphPad, La Jolla, CA). Numerical data are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;sd. Single comparisons were performed by unpaired Student\u0026rsquo;s t tests and multiple comparisons were performed by ANOVA. P-values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered to be statistically significant. Kaplan-Meier survival analysis was performed based on EpCAM mutation and the relevant data of patient populations, GraphPad Prism was used to analyze and plot the graphs.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cp\u003e \u003cb\u003eEpCAM expression is restricted to epithelial type cells.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTo assess overall expressions of EpCAM at RNA and protein levels in normal tissues, two data sets were analysed. Results highlights significant expression of EpCAM RNA and protein within the gastrointestinal tract and other tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e, A-B). Conversely, EpCAM expression is largely absent in non-epithelial tissues such as connective tissue, muscle, and hematopoietic cells under normal physiological conditions. Notably, EpCAM expression is frequently elevated in epithelial-origin malignancies, underscoring its relevance as a diagnostic and therapeutic target. For example, elevated EpCAM levels are consistently observed in adenocarcinomas of the breast [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], colon [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], oesophagus [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], pancreas [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], ovary, and prostate [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], as well as in squamous cell carcinomas of the lung, and cervix [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Detailed analysis of a lung adenocarcinoma cohort demonstrates upregulation of EpCAM in tumors (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e, C), with consistent expression across different disease stages and consistently in EGFR, KRAS oncogene-driven tumors (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e, D). In hepatocellular carcinoma (HCC), EpCAM is typically absent in mature hepatocytes but is expressed in hepatic progenitor cells and certain tumor subsets, suggesting its involvement in tumor initiation and cellular dedifferentiation [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Cancer Cell Line Encyclopedia (CCLE) [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] and NCI-60 [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] datasets further reveal that EpCAM expression is restricted to cancer cell lines derived from epithelial origin tissues such as the colon-gastrointestinal tract, ovary, prostate, breast, lung, and kidney (\u003cb\u003eFigure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e A-B\u003c/b\u003e). This tissue-specific expression pattern reinforces the utility of EpCAM as a marker for epithelial malignancies and a target for precision oncology.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eComprehensive analysis of EpCAM mutations\u003c/h2\u003e \u003cp\u003eExpanding upon the insights gained from our previous studies demonstrating that cancer-associated EpCAM mutations play critical roles as tumor suppressor or tumor promoter [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Some EpCAM mutations results in the loss of its membrane localization, secretion, and binding to CTSL, suggesting its role as a tumor suppressor [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. On the other hand, EpCAM mutations in the LDL domain in cancer cells driven by RAS signalling promoted invasion, migration and sensitized to drugs [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. To further elucidate the functional implications of EpCAM mutations in the cancer, we employed advanced predictive and experimental techniques, paving the way for a deeper understanding of the role of these mutations in pathology. Four different cohorts cBioPortal, COSMIC, AACR-GENIE and FMI data (Foundation Medicine-NCI) were accessed (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003eA\u003cb\u003e)\u003c/b\u003e. Building on our previous reports and updated TCGA data, we undertook a systematic analysis to catalogue and understand the role of novel EpCAM mutations in cancer. By analyzing over 300 studies comprising 300,300 samples, we determined an overall EpCAM mutation frequency across all cancer types (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). These cancer-associated mutations were distributed as missense (79%), nonsense (6%), splice (7%), frameshift (4%), deletion (2%), and fusion mutations. Notably, MSH2-EpCAM fusion were recorded in samples from patients with esophageal adenocarcinoma, squamous cell carcinoma, and stomach adenocarcinoma (\u003cb\u003edata Figure S2A\u003c/b\u003e). This is consistent with prior reports where exon 9 deletions in EpCAM led to MSH2 promoter hyper-methylation and silencing [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Additionally, we identified EpCAM-ABCG8 and NUP42-EpCAM fusions in high-grade uterine and ovarian carcinomas (\u003cb\u003eFigure S2A\u003c/b\u003e). Missense EpCAM mutations were also identified in commonly used CCLE cell lines (\u003cb\u003eFigure S2C\u003c/b\u003e). While mutations were distributed across all nine exons of EpCAM, no specific hotspot regions were identified. Among the 160 unique protein-altering mutations identified in initial screening, the highest mutation frequencies were found in lung cancer (29 cases) and skin cancer (22 cases), followed by colorectal cancer (13 cases) and uterine cancer (13 cases). Interestingly, colorectal cancer a cancer type known to overexpress EpCAM displayed a higher prevalence of splice-related mutations. Of particular note, EpCAM mutations frequency was lowest from 1,346 pancreatic cancer samples analysed (\u003cb\u003eFigure S2A\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eTo examine the co-occurrence of somatic EpCAM mutations across varying tumor mutation burden (TMB) groups, we categorized the data into two major groups: low TMB (1\u0026ndash;100) and high TMB (\u0026gt;\u0026thinsp;100) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003eD, see methods\u003cb\u003e)\u003c/b\u003e. The total number of somatic EpCAM mutations was then tabulated from each TMB category. An initial search from the GENIE cohort [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] across various cancer types revealed mutation frequency of EpCAM at 0.5%, with 889 EpCAM mutations including recurring from 1,783,034 samples (\u003cb\u003eFigure S2\u003c/b\u003e). As depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003eD, majority of EpCAM mutations in lung cancer were primarily found in samples with fewer than 100 TMB group. For breast, prostate and pancreatic cancer, they were mostly restricted to around 15 TMB group. In contrast, EpCAM mutations in colon and uterine cancers were spread across all TMB groups (5-600 TMB see methods). The restriction of EpCAM mutations to samples with relatively low TMB in lung and breast cancers suggests that these mutations may play important role in early stage of tumorigenesis. In contrast, the widespread distribution of EpCAM mutations in colon and uterine cancers indicates a more pervasive involvement in both early and late stages of tumor progression.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eStructural Stability Analysis of Previously Characterized EpCAM Mutations\u003c/h2\u003e \u003cp\u003eWe utilized two experimentally validated damaging mutations, C66Y and L240A as a proof of concept to validate our strategy [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Using MD simulation to ensure the quality of the obtained trajectories, we performed a quality check by plotting the potential energy, temperature, and pressure throughout the dynamics. The values for EpCAM-L240A were \u0026minus;\u0026thinsp;127562 kJ/mol for potential energy, 1.53331 K for temperature, and 6.06631 bar for pressure, while for EpCAM-C66Y, the corresponding values were \u0026minus;\u0026thinsp;111969 kJ/mol, 0.231919 K, and 7.42474 bar. These values exhibited minimal fluctuations during the 1\u0026micro;s simulation, with temperature and pressure maintained at 300K and 1 bar, respectively. To gain deeper structural and functional insights, we compared the time-dependent evolution of RMSD values with EpCAM-WT, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, EpCAM-L240A displayed a steady increase in RMSD throughout the 1\u0026micro;s simulation, indicating significant conformational changes compared to its initial structure. Specifically, the starting structure of the TY-1 loop in EpCAM-L240A, formed by residues Ala63 to Arg138, was extended, but during the MD simulation, it moved toward both the N-terminal and C-terminal regions, adopting a more compact globular shape. Additionally, the ridge on the C-terminal domain (RCD) exhibited a closing movement (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC\u003cb\u003e).\u003c/b\u003e In contrast, the TY-1 loop of the EpCAM-C66Y mutant showed a closing movement toward the TYD region (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD\u003cb\u003e)\u003c/b\u003e. The steady decrease in the radius of gyration (Rg) of EpCAM-L240A further supports its compact folding compared to EpCAM-WT, as depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe RMSD values for EpCAM-WT and EpCAM-C66Y exhibit stable dynamic behavior throughout the simulation period \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB\u003cb\u003e)\u003c/b\u003e. In contrast, the residual fluctuations for EpCAM-WT are significantly lower than those observed in the two mutants, EpCAM-L240A and EpCAM-C66Y. The TY-1 loop in both mutants shows considerable dynamics, with RMS fluctuations reaching up to 8.5 \u0026Aring;. EpCAM-C66Y exhibits partial unfolding and refolding during the simulation (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD\u003cb\u003e)\u003c/b\u003e. Residues from the hydrophobic core of EpCAM-C66Y become buried within the protein's interior, making them less exposed to the solvent (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE\u003cb\u003e)\u003c/b\u003e. Meanwhile, EpCAM-L240A and EpCAM-WT display similar trends in their SASA values, suggesting stable dynamics. The distribution of the backbone RMSD values indicates that EpCAM-WT and EpCAM-C66Y have a higher probability of maintaining RMSD values under 5\u0026Aring;, while EpCAM-L240A shows a maximum RMSD value of around 6 \u0026Aring; (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF\u003cb\u003e)\u003c/b\u003e. The TY-1 loop of EpCAM-C66Y also exhibits flexibility, although its RCD region (ridge on CD) undergoes significant conformational changes, leading to a closure movement at the C-terminal domain. In contrast, the TY-1 loop in EpCAM-WT does not show any such closure movement in the RCD, CTD, or TYD regions, although structural transitions within the TY-1 loop are observed during the MD simulation (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIntramolecular hydrogen bonds (H-bonds), essential for stabilizing protein structure, were analyzed for EpCAM-WT, EpCAM-L240A, and EpCAM-C66Y. The maximum number of H-bonds formed in each variant is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eG. The MD simulation indicated that EpCAM-L240A undergoes significant conformational changes during the simulation (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eH\u003cb\u003e)\u003c/b\u003e. To quantify these movements, DynDom was used to identify the fixed (residues 30\u0026ndash;73 and 95\u0026ndash;265) and moving (residues 74\u0026ndash;94) domains in EpCAM-L240A. The moving domain shows a rotational movement of 108.7 degrees, a translational movement of -7.7 \u0026Aring;, and a closure movement of 95.0%. Bending region analysis reveals that residues from the TY-1 loop, specifically Lys70-Gly75 and Gly93-Asp98, serve as hinges facilitating this large rotational and closure movement (see \u003cb\u003eFigure S3)\u003c/b\u003e. In contrast, DynDom did not identify significant dynamic movements in EpCAM-WT or EpCAM-C66Y, as their conformational changes were too subtle for detection (data not shown). A 2D projection of the principal components, PC1 and PC2, indicates that EpCAM-L240A occupies a larger conformational space in the free energy landscape, whereas EpCAM-WT forms a compact cluster, occupying less space. EpCAM-C66Y shows fewer diverse conformations compared to EpCAM-L240A (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eI\u003cb\u003e)\u003c/b\u003e. The cosine content values for EpCAM-WT, EpCAM-L240A, and EpCAM-C66Y are 0.78, 0.92, and 0.91, respectively, indicating good sampling across the conformational space.\u003c/p\u003e \u003cp\u003eThe dynamic cross-correlation matrix (DCCM) was generated using representative frames from the MD simulations to explore the concerted motion of the individual domains of EpCAM. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eJ displays the DCCM plots for EpCAM-WT, EpCAM-L240A, and EpCAM-C66Y. The diagonal amber line indicates strong self-correlation of individual residues, while the amplitude of positive to negative correlations is represented by a gradient from amber to blue. In EpCAM-WT, the dynamic TY-1 loop region shows a moderately negative correlation with the N-terminal domain (NTD), while this negative correlation diminishes in both EpCAM-L240A and EpCAM-C66Y. Additionally; the amplitude of positive correlation is higher in EpCAM-C66Y than in EpCAM-L240A. Both mutants exhibit increased positive correlation between the RCD region and the TY-1 loop during the simulation. Overall, the amplitude of negative correlations observed in EpCAM-WT decreases substantially in both mutants. This suggests that both mutants experience positive cooperative motion, particularly in the RCD, NTD, and TY-1 loop regions, indicating significant local conformational changes that could influence EpCAM dimerization and protein-protein interactions.\u003c/p\u003e \u003cp\u003eIn conclusion, the molecular modeling study reveals that the EpCAM-L240A mutant exhibits highly dynamic behavior, particularly at the TY-1 loop and RCD regions, making it relatively less stable compared to EpCAM-WT. Although EpCAM-C66Y undergoes moderate conformational changes, it significantly alters the native conformation of the TY-1 loop. We propose that the TY-1 loop plays a crucial role in the dimerization of human EpCAM, and recent studies suggest that the TY-1 loop/domain of EpCAM binds and inhibit protease CTSL (Cathepsin-L) [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The substantial structural changes, particularly in the TY-1 loop/domain, may contribute loss of CTSL binding to promote tumor cell invasion and metastasis. Altogether, MD simulation and previously reported functional data suggests that both EpCAM-L240A and EpCAM-C66Y mutations exhibited a loss of EpCAM structure and thereby function.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eBioinformatics analysis and Molecular modeling of lung cancer associated EpCAM mutations\u003c/h2\u003e \u003cp\u003eTo systematically catalogue and assess large list of damaging mutations in EpCAM, first we employed three computational tools sorting intolerant from tolerant (SIFT), polymorphism phenotyping (PolyPhen) and mutation assessor [\u003cspan additionalcitationids=\"CR40 CR41\" citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. The results were normalized from the three computational tools on a scale from 0 to 1, with zero representing the wild-type EpCAM sequence. By combining the outputs from all three tools, we were able to generate a comprehensive mutational impact score. After initial screening, we selected seven high frequency, high scoring mutations from lung cancer for further validation. This approach offered a valuable framework for screening and prioritizing mutations for functional validation. To assess the quality of the obtained trajectories following molecular dynamics simulations, first we plotted the potential energy, temperature, and pressure throughout the dynamics. The values for the mutants EpCAM were:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eEpCAMD92V: (-104671 kJ/mol), (0.12362 K), (-5.72833 bar)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eEpCAME137A: (-100359 kJ/mol), (0.244 K), (4.62956 bar)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eEpCAMY186C: (-118137 kJ/mol), (0.100088 K), (-0.119114 bar)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eEpCAMQ204E: (-100348 kJ/mol), (0.0841861 K), (1.89489 bar)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eEpCAMN111K: (-119172 kJ/mol), (0.0313864 K), (1.91565 bar)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eEpCAMP84S: (-99460.7 kJ/mol), (0.050235 K), (4.04775 bar)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eEpCAMY215S: (-101241 kJ/mol), (0.289105 K), (-2.47406 bar)\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThese mutants showed minimal fluctuations during the 1\u0026micro;s simulation, with temperature and pressure maintained at 300K and 1 bar, respectively. Molecular dynamics simulations were employed to estimate the structural and conformational changes of the proteins. To gain deeper structural and functional insights into the novel point mutants of EpCAM, including those related to lung cancer, we also compared the time-dependent evolution of the RMSD values for EpCAM mutants with those of EpCAM-WT.\u003c/p\u003e \u003cp\u003eThe RMSD profile for the backbone of EpCAM-WT and its seven mutants was generated for all atoms from the initial structure to assess the impact of mutations on the stability of the protein structure (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB,C). The MD simulation of EpCAM-WT exhibited a steady RMSD of approximately 4.5 \u0026Aring; throughout the simulation. EpCAM-N111K exhibited fluctuations around 4.5 \u0026Aring;, with RMSD values showing similar trends to the wild type from the start to 700 ns. After 700 ns, the RMSD decreased to approximately 3 \u0026Aring; at 850 ns, before rising again to 5 \u0026Aring; at 1 ms, as seen in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD-E. In contrast, EpCAM-D92V showed fluctuations between 50 ns and 200 ns, with a decrease in RMSD from 220 ns to 400 ns. After that, the RMSD remained relatively stable at around 4.5 \u0026Aring; until 850 ns, after which some fluctuations occurred between 850 ns and 900 ns, as shown in \u003cb\u003eFigure S4A\u003c/b\u003e. The TY-1 loop of EpCAM-D92V remained flexible during the simulation. For the mutant EpCAM-E137A, the RMSD increased to approximately 3.6 \u0026Aring;, indicating structural instability as observed in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eG. EpCAMY-186C exhibited similar RMSD values to EpCAM-WT up to the last 1000 ns. However, the mutant EpCAM-Q204E displayed a notably unstable structure, with RMSD values remaining stable up to 500 ns, after which they increased to 8 \u0026Aring; at 1 ms, suggesting a loss of structural stability due to the mutation at position Q204E. EpCAM-P84S showed distinct deviations, ranging from approximately 2.5 to 6 \u0026Aring;. Initially, its RMSD was lower than that of the wild type from 50 ns to 650 ns, after which it steadily increased to 6 \u0026Aring;. Finally, EpCAM-Y215S required some time to achieve a relatively stable structure as observed in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eG.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eNext, we analyzed how cancer-associated mutations affect the dynamic behavior of various regions in EpCAM, including the N-terminal domain (NTD), C-terminal domain (CTD), ridge on CD (RCD), stalk and C-terminal domain (SCD), and importantly, the TY-1 loop. This was done by calculating the root mean square fluctuation (RMSF) of the Cα atoms for all the mutants and comparing them to the EpCAM-WT. The residual fluctuations in EpCAM-WT were significantly lower compared to the seven mutants particularly when observing the dynamics of the TY-1 loop (residues 63\u0026ndash;138) and its interactions with different protein sites. The TY-1 loop in all mutants EpCAM-P84S, EpCAM-Q204E, EpCAM-M92V, EpCAM-E137A, EpCAM-N111K, EpCAM-Y186C, and EpCAM-Y215S was highly dynamic, with RMS fluctuations reaching up to 9 \u0026Aring;, 8 \u0026Aring;, 7.5 \u0026Aring;, 7 \u0026Aring;, 6 \u0026Aring;, 5.5 \u0026Aring;, and 5 \u0026Aring;, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and S4\u003cb\u003e)\u003c/b\u003e. EpCAM-P84S exhibited partial unfolding and folding, with the highest fluctuations occurring in the TY-1 loop residues between ~\u0026thinsp;75\u0026ndash;100, as illustrated in \u003cb\u003eFigures S4A\u003c/b\u003e. Residues 25\u0026ndash;75, which are part of the NTD, showed some motion in EpCAM-D92V, EpCAM-E137A, and EpCAM-P84S mutants. Additionally, significant fluctuations were observed in the C-terminal domain, particularly in the final amino acids, with fluctuations reaching up to 19 \u0026Aring;, as seen in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eG. Notably, the most pronounced fluctuations occurred in the TY-1 domain (residues 63\u0026ndash;138) in all mutants. The EpCAM-Q204E mutant exhibited some additional fluctuation in the RCD region (residues 227\u0026ndash;239) compared to the wild-type, as shown in \u003cb\u003eFigure S4A.\u003c/b\u003e The SCD region (residues 202\u0026ndash;205) remained relatively stable during the simulation across all mutants. From Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, we can conclude that the residual fluctuations mainly occur in the NTD (residues\u0026thinsp;~\u0026thinsp;24\u0026ndash;62), the TY-1 loop (~\u0026thinsp;75\u0026ndash;100), and the final residues (264\u0026ndash;265), with higher fluctuations observed in EpCAM-D92V, particularly in the TY-1 loop region (63\u0026ndash;138). EpCAM-E137A exhibited significant fluctuations mainly at positions 80\u0026ndash;95 of the TY-1 loop. EpCAM-WT demonstrated a compact structure with an RMSD range of 18.5\u0026ndash;19.5 \u0026Aring; throughout the simulation, while the mutants showed a gradual loss of compactness. The mutants EpCAM-Q204E and EpCAM-P84S showed continuous folding and unfolding, with RMSD values ranging from 19.5\u0026ndash;20.5 \u0026Aring; and 19\u0026ndash;20.0 \u0026Aring;, respectively, as seen in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The remaining mutants EpCAM-D92V, EpCAM-E137A, EpCAM-N111K, EpCAM-Y186C, and EpCAM-Y215S showed less compactness, with RMSD values ranging from 18.5\u0026ndash;20.5 \u0026Aring;, 18.5\u0026ndash;20.0 \u0026Aring;, 18.5\u0026ndash;20.0 \u0026Aring;, 18.5\u0026ndash;19.5 \u0026Aring;, and 19.0\u0026ndash;20.0 \u0026Aring;, respectively. In terms of solvent accessibility, residues from the hydrophobic core of the EpCAM-Y186C, EpCAM-E137A, and EpCAM-P84S mutants were buried inside the protein, reducing their exposure to the solvent environment, as shown in Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. In contrast, EpCAM-D92V, EpCAM-Q204E, EpCAM-N111K, and EpCAM-Y215S exhibited stable dynamics, with similar trends in their solvent-accessible surface area (SASA) values.\u003c/p\u003e \u003cp\u003eNext, we analyzed the intramolecular hydrogen bonding interactions in EpCAM-WT and its mutants. The maximum number of H-bonding interactions for each of these mutants is depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eH. Considering the conformational changes observed from the dynamics of all mutants, we compared the 3D structures of the mutants with the wild-type EpCAM structure. Wild-type EpCAM displayed an open conformation of the TY-1 loop, but this conformation was altered in the mutants. Specifically, EpCAM-D92V exhibited changes in the TY-1 loop structure, which slightly moved toward the C-terminal domain (CTD) binding site. Additionally, the RCD region (His227-Gln239) shifted toward the CTD compared to the wild-type EpCAM. In a study it was proposed that EpCAM has two binding sites for the TY-1 loop: the TYD and CTD [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Our study found that in the EpCAM-E137A mutant, the TY-1 loop residue that binds to the CTD in wild-type EpCAM actually binds to the TYD site, while the TY-1 loop that normally binds to the TYD site interacts with the CTD in this mutant (\u003cb\u003esee\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. In EpCAM-Q204E, EpCAM-N111K, and EpCAM-Y215S, the TY-1 loop shifted toward the TYD site, resulting in a closed conformation of the TYD. In the other mutants, no significant movement was observed.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB, illustrates the time evolution of the secondary structure along the protein chain, showing the stability of the N-terminal α-helix and fluctuations in the central β-sheet. Hydrogen bond analysis further indicated that the α-helical regions maintained a stable network of hydrogen bonds, while the β-sheet regions experienced notable disruption in hydrogen bonding after 150 ns, correlating with the observed structural transition. To examine the structural plasticity during MD simulation, we used DSSP to track secondary structural elements in both wild-type EpCAM and its mutants. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e-B,C shows the secondary structure changes over time. Both the wild-type and mutant proteins exhibited coils, β-sheets, β-bridges, bends, turns, and various types of helices. However, the mutants showed a considerable decrease in the formation of β-sheets and bends compared to wild-type EpCAM. We performed essential dynamics (ED) analysis to study the correlated motions of the wild-type and mutant EpCAM proteins. The eigenvalues from this analysis indicated that major fluctuations in the system were confined to the first two eigenvectors for both the wild type and the mutants. As seen in \u003cb\u003eFigure S4\u003c/b\u003e, the mutant proteins covered a larger space along the first and second principal components (PC1 and PC2) compared to the EpCAM-WT. The probability distribution for the EpCAM-WT remained within a 6 \u0026Aring; threshold, while the mutant structures EpCAM-Q204E, EpCAM-Y215S, EpCAM-E137A, EpCAM-D92V, EpCAM-P84S, EpCAM-N111K, and EpCAM-Y186C showed maximum alterations of 9 \u0026Aring;, 7 \u0026Aring;, 7 \u0026Aring;, 7 \u0026Aring;, 6 \u0026Aring;, 6 \u0026Aring;, and 6 \u0026Aring;, respectively, (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB\u003cb\u003e)\u003c/b\u003e. In the EpCAM-WT, both correlated and anti-correlated motions were observed, while in the mutant EpCAM-Q204E, EpCAM-E137A, and EpCAM-Y215S, mostly anti-correlated motion was observed (\u003cb\u003eFigure S4B)\u003c/b\u003e. In summary, molecular modeling reveals that mutants EpCAM-Q204E, EpCAM-E137A, EpCAM-Y215S, EpCAM-N111K, and EpCAM-D92V exhibit significant dynamic behavior, particularly in the TY-1 loop and RCD regions during MD simulations, which leads to a less stable structure compared to EpCAM-WT. In contrast, the mutants EpCAM-P84S and EpCAM-Y186C show more moderate conformational changes. The significant structural changes observed in the TY-1 domain across all mutants would likely disrupt the structure and function of EpCAM, influencing its role in cellular processes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eEpCAM mutations predicts poor survival in colon and hepatocellular cancer\u003c/h2\u003e \u003cp\u003eEpCAM frequently overexpressed in colon cancer which promote cancer cell proliferation, migration, and resistance to apoptosis [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. EpCAM is also considered a marker of cancer stem cells in colon cancer, where its expression potentially influences the self-renewal capability of these stem cells, contributing to tumor relapse and chemo-resistance [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Clinically, the overexpression of EpCAM in colon cancer is associated with poor survival outcomes, likely due to its role in promoting invasiveness and metastasis. High levels of EpCAM have been correlated with advanced disease stage and worse prognosis in colon cancer patients. In hepatocellular carcinoma (HCC), EpCAM overexpression is similarly linked to poor prognosis, with elevated levels associated with tumor progression, vascular invasion, and metastasis [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. EpCAM expression in HCC is also correlated with shorter survival and a higher likelihood of recurrence post-surgery. To investigate the role of these newly identified EpCAM mutations, a cohort of patients with EpCAM mutations alongside other driver mutations was analyzed. As shown in Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA-C, somatic mutations in EpCAM coexist with mutations in key oncogenes such as KRAS, BRAF, p53, and EGFR. In the group with altered EpCAM expression, the survival was significantly worse, with a median survival of 42.78 months, compared to 62.25 months in the unaltered group (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). As seen in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD,E, in HCC, EpCAM mutations were observed in conjunction with mutations in p53, MDM2, DNMT1, and KEAP1 where the overall survival for patients with EpCAM mutations was 9.86 months, compared to the unaltered group (40.45 months). These findings highlight the critical role of EpCAM mutations in cancer progression, reinforcing their involvement in tumorigenesis and their potential utility as prognostic markers.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eThe prevalence of EpCAM mutations in NSCLC may hold significant prognostic value\u003c/h2\u003e \u003cp\u003eBuilding on our previous study demonstrating that RAS can stabilize EpCAM expression [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], we hypothesized that somatic mutations in the EpCAM, frequently co-occurring with driver oncogenes such as KRAS, EGFR, TP53, and BRAF, could possess prognostic significance. To investigate, first, we analyzed two cohorts (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA, \u003cb\u003edata S6\u003c/b\u003e), and as expected, most EpCAM mutations were found in conjunction with mutations in TP53, KRAS, EGFR, STK11, BRAF, and PTEN (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD). The GENEI-v16 cohort [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], which includes data from 26,851 patients, revealed 284 missense mutations (\u003cb\u003eFigure S2\u003c/b\u003e). Surprisingly, overall survival did not significantly differ between patients with EpCAM mutations and those without, across both cohorts (see Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB-C). This analysis suggests that EpCAM mutations alone may not be a significant determinant of survival in these populations. To assess this unexpected observations, we revisited the tumor mutation burden (TMB) Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003eD. Interestingly, EpCAM mutations in lung cancer were predominantly found in samples with a low TMB. This suggests that these alterations may play a role in the early stages of tumor development rather than in its progression. To align our MD simulation findings with existing cohort studies, we suggest that EpCAM mutations may provide an opportunity to sensitize cells to specific drugs.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo delineate the roles of EpCAM expression vs mutation, we first assessed EpCAM expression across a panel of lung cancer cell lines, focusing on epithelial characteristics. As shown in \u003cb\u003eFigure S6D\u003c/b\u003e, EpCAM expression was predominantly observed in cells expressing epithelial markers established in NSCLC [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. The panel revealed that the cell lines A549, H1299, H460, H226, and H23 showed undetectable levels of EpCAM (also tested in the lab, data not shown). Building on our prior report that EpCAM can influence ERK1/2 signaling [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. We used retroviral constructs to transduce A549 cells with EpCAM-WT and seven EpCAM mutations characterized in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Serum stimulation of the cells showed a marked decrease in ERK activity in EpCAM-WT cells compared to those with EpCAM mutations (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE). These results suggests that EpCAM mutation increased ERK signaling. To investigate the role of EpCAM mutations and drug targeting, in drug resistance, we transfected A549, H1299, H460, H226, and H23 cells, which harbor KRAS, p53, or LKB1 mutations (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eF). Twenty-four hours post-transfection, the cells were treated with 200\u0026ndash;500 nM Trametinib for 72 hours, followed by a cell viability assay. The results revealed that EpCAM mutations differentially activated ERK1/2 signaling, sensitizing cells to drug-induced apoptosis (Figs.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eF\u0026ndash;G). Notably, when correlating ERK activation with apoptotic sensitivity, we observed that EpCAM mutants exhibiting higher sensitivity to apoptosis were associated with altered localization and stabilization [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. These findings suggest that EpCAM mutations may enhance responsiveness to MEK inhibitors. In conclusion, EpCAM mutations promote increased ERK signaling and enhance the sensitivity of lung cancer cells to MEK inhibitors, highlighting their potential as therapeutic targets to improve treatment outcomes.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eCancer-related mutations that regulate critical cellular processes, including proliferation, apoptosis, DNA repair, and cell signaling, are classified as oncogenes, tumor suppressor genes, or DNA repair genes. While a single genetic mutation is rarely sufficient to cause cancer, the accumulation of multiple genetic changes both in relation to one another and over time creates opportunities to detect cancers at much earlier stages [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Membrane proteins play a crucial role in facilitating intercellular communication and transmitting signals within cells by mediating protein interactions and regulating downstream cellular processes. Due to their pivotal functions, membrane proteins are often key targets for drug development and therapeutic interventions. As a tumor antigen, EpCAM overexpression is linked to several cancer cells related signaling [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan additionalcitationids=\"CR14 CR15\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The goal of our study was to catalog cancer-related mutations of EpCAM, characterize their features, and highlight their significance to the research community for further investigation.\u003c/p\u003e \u003cp\u003eIn this study, through the integration of multiple cohort datasets to catalog all EpCAM mutations, along with in-silico tools, advanced molecular modeling, and experimental validation, we reached several key conclusions. \u003cem\u003eFirst\u003c/em\u003e, our study leveraged newly curated genomic datasets to identify several novel and recurrent EpCAM mutations across multiple cancer cohorts. Despite the relatively low frequency of somatic EpCAM mutations (ranging from 0.1\u0026ndash;0.6%), our findings underscore the biological significance of these mutations in epithelial cancers. \u003cem\u003eSecond\u003c/em\u003e, TMB and survival analyses suggest that EpCAM mutation status could serve as an important marker for prognostic evaluations, personalized therapy strategies, and suggests role of EpCAM mutations in early or late stage of tumorigenesis. \u003cem\u003eThird\u003c/em\u003e, for the first time, we demonstrated that mutations in the EpCAM gene destabilize its protein structure, particularly affecting the TY-1 loop and the RCD region. Homology modeling and MD simulations highlighted how these structural destabilizations compromise stability of EpCAM and dynamics, which are critical for its biological functions. \u003cem\u003eFourth\u003c/em\u003e, EpCAM mutations may potentially disrupt protein-protein interactions with essential cellular proteins, including CTSL [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], claudin [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e], EGFR [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e], PKC [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e] occludins, catenins, MUC1, CD44 and CD63 [\u003cspan additionalcitationids=\"CR51\" citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. EpCAM has been recognized as a novel component of tetraspanin-enriched microdomains (TEMs), forming a primary complex with the tetraspanin CD9 [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. These interactions are critical for maintaining cellular integrity, adhesion, and signaling processes. Loss of efficient binding due to structural instability could lead to dysfunctional cellular signaling pathways and tumor-promoting mechanisms. \u003cem\u003eFifth\u003c/em\u003e, our data provide preliminary evidence that some EpCAM mutations, such as C66Y and L240A localized in cytosol instead of membrane can potentially interfere with proto-oncogenes EGFR or KRAS signaling [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Finally \u003cem\u003esix\u003c/em\u003e, Structural impairments caused by EpCAM mutations may explain the partial success of past and potential future antibody therapies targeting EpCAM [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn an overall study design, we performed comprehensive analysis of integrated data from major cancer genomics resources, including cBioPortal, COSMIC, AACR-GENIE, and Foundation Medicine-NCI (FMI) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003eA-C). We identified 160 new somatic mutations in addition to our previous work [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] The data suggests with missense mutations being the most common (79%), followed by nonsense mutations (6%), splice site mutations (7%), frameshift mutations (4%), deletions (2%), and fusion mutations (\u003cb\u003eFigure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eC\u003c/b\u003e). Notably, MSH2-EpCAM fusions were identified in esophageal adenocarcinoma, squamous cell carcinoma, and stomach adenocarcinoma, aligning with previous reports of exon 9 deletions in EpCAM leading to MSH2 promoter hyper-methylation and gene silencing [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. We also identified EpCAM-ABCG8 and NUP42-EpCAM fusions in high-grade uterine and ovarian carcinomas. In terms of mutation prevalence, EpCAM mutations were most common in lung cancer, skin cancer, and colorectal cancer, with pancreatic cancer also showing a notable concentration of mutations. (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003eB, \u003cb\u003eFigure S2\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eFor molecular dynamic simulation of EpCAM mutants, the \u003cem\u003ecis-\u003c/em\u003edimer crystal structure PDB: 4MGV [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] guided us to focus key regions for possible for the loss of function. The N-terminal domain of EpCAM, a critical region containing functional domains such as the EGF-like domain, RCD and TY-1 domain. Our previous work demonstrating how EpCAM mutations at C66Y and L240A altered the cellular phenotype in cancer cells was validated. MD simulations revealed significant structural destabilization in these mutants. For example, EpCAM-L240A exhibited heightened dynamic instability, especially in the TY-1 loop and RCD regions, resulting in a less stable structure compared to the EpCAM-WT. On the other hand, EpCAM-C66Y, while undergoing moderate conformational changes, significantly disrupted the native conformation of the TY-1 loop (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Next, we focused to study seven lung cancer associated mutations we discovered (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, S4, S5). Results revealed that these mutations induced significant structural fluctuations, particularly in the TY-1 loop and the RCD region. Among the analyzed mutants, EpCAM-Q204E and EpCAM-P84S showed the most pronounced instability, characterized by higher root mean square deviation (RMSD) values, indicating substantial loss of structural integrity. These mutations also caused marked conformational changes in the TY-1 loop. Specifically, the structural shift observed in EpCAM-Q204E likely hinders its interaction network, undermining its role in tumor suppression and facilitating cell migration. Similarly, the RCD region a possible hotspot for maintaining EpCAM\u0026rsquo;s structural stability and protein-binding capacity exhibited notable disruptions in mutant EpCAM-D92V. We predict that structural changes in RCD region could alter interactions with key molecular partners, such as claudins, occludins, and catenins, which are crucial for maintaining epithelial integrity and preventing tumor dissemination [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. Interestingly, while mutations EpCAM-Y186C and EpCAM-P84S demonstrated more moderate conformational changes, their altered dynamics were still sufficient to affect EpCAM\u0026rsquo;s function. For example, the ability of EpCAM to inhibit cathepsin-L through-TY-1 loop may be impaired even by subtler alterations in structure [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. These findings pave the way for future investigations aimed at experimentally validating these mutations' effects on EpCAM's biochemical activities and downstream signaling.\u003c/p\u003e \u003cp\u003eBuilding on our previous study, we postulate that some EpCAM mutations, in conjunction with RAS, EGFR oncogenes could have detrimental effects on cancer cell growth and contribute to drug resistance [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA data of colon cancer cohort shows that somatic mutations in EpCAM often coexist with mutations in key oncogenes such as KRAS, BRAF, TP53, and EGFR. Patients with altered/mutation EpCAM exhibited significantly worse survival outcomes in colon cancer A similar trend was observed in hepatocellular carcinoma (HCC), where EpCAM mutations co-occurred with mutations in TP53, MDM2, DNMT1, and KEAP1 \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB\u003cb\u003e)\u003c/b\u003e, and patients with EpCAM mutations exhibited significantly worse overall survival compared to those with unaltered EpCAM expression. For lung cancer cohort, highest frequency of EpCAM mutations was observed in non-small cell lung cancer (NSCLC). Somatic mutations in the EpCAM gene often co-occured with mutations in key oncogenes, such as KRAS, EGFR, TP53, and BRAF (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, S6). Among over 1,000 identified mutations in the EpCAM gene, 283 were somatic mutations (\u003cb\u003eFigure S2\u003c/b\u003e). Surprisingly overall survival did not differ significantly between patients with EpCAM mutations and those without across both cohorts (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB-C). This finding prompted further investigation into the potential role of tumor mutation burden (TMB) and the co-occurrence of EpCAM mutations. Our results suggest that EpCAM mutations, when found in conjunction with a lower TMB, may play a more prominent role in the early stages of tumor development rather than in later-stage disease progression.\u003c/p\u003e \u003cp\u003eTo validate our in-silico and MD simulations findings, we conducted experimental approach to support the data. EpCAM is known to alter ERK signaling [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. ERK reporter assay indicated that cells with EpCAM mutations exhibited increased ERK1/2 activity, which was sensitized by Trametinib treatment (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE, F). Further analysis revealed that cells transfected with EpCAM mutations showed a correlation with Trametinib-induced apoptosis and ERK activity (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eG). In conclusion, EpCAM mutations in cancer cells may lead to context-dependent alterations in protein structure and stability. Given the variety of mutations and their impact on tumor behavior, therapies targeting EpCAM could be customized to enhance efficacy.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur study highlights the functional and structural consequences of EpCAM mutations in epithelial cancers, revealing their role in tumor progression, reduced patient survival, and altered drug sensitivity. The findings demonstrate that mutations in TY-1 and RCD regions destabilize EpCAM, potentially disrupting its adhesive and signaling functions. EpCAM-mutant lung cancer cells exhibit increased sensitivity to MEK inhibitors, suggesting a novel therapeutic avenue. These results emphasize EpCAM mutations as potential biomarkers for cancer prognosis and treatment strategies, positioning EpCAM as a promising therapeutic target in epithelial malignancies.\u003c/p\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eData availability\u003c/h2\u003e \u003cp\u003eThe datasets used and/or analyzed during the current study are available as Data Figure S2 or from the corresponding author on reasonable request.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003ePatient consent for publication\u003c/h2\u003e \u003cp\u003eNot applicable\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eAuthor\u0026rsquo;s disclosures\u003c/h2\u003e \u003cp\u003eThe authors of this manuscript have no conflicts of interest. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. No disclosures were reported by the other authors.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eConflict of Interest\u003c/h2\u003e \u003cp\u003eAuthors declare no conflict of interest.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eAbbreviations\u003c/h2\u003e \u003cp\u003eCCLE, Cancer Cell Line Encyclopedia\u003c/p\u003e \u003cp\u003eCTD, C-terminal domain\u003c/p\u003e \u003cp\u003eCTSL, cathepsin-L\u003c/p\u003e \u003cp\u003eDCCM, dynamic cross-correlation matrix\u003c/p\u003e \u003cp\u003eDSSP, defined secondary structure protein analysis\u003c/p\u003e \u003cp\u003eEpCAM, epithelial cell adhesion molecule\u003c/p\u003e \u003cp\u003eH-bonds, hydrogen bonds\u003c/p\u003e \u003cp\u003eHCC, hepatocellular carcinoma\u003c/p\u003e \u003cp\u003eMD, molecular dynamics\u003c/p\u003e \u003cp\u003eNSCLC, non-small cell lung cancer\u003c/p\u003e \u003cp\u003eNTD, N-terminal domain\u003c/p\u003e \u003cp\u003ePolyPhen, polymorphism phenotyping\u003c/p\u003e \u003cp\u003eRCD, ridge on the C-terminal domain\u003c/p\u003e \u003cp\u003eRMSD, root mean square deviation\u003c/p\u003e \u003cp\u003eRMSF, root mean square fluctuation\u003c/p\u003e \u003cp\u003eSASA, solvent accessible surface area\u003c/p\u003e \u003cp\u003eSIFT, sorting intolerant from tolerant\u003c/p\u003e \u003cp\u003eTMB, tumor mutation burden\u003c/p\u003e \u003cp\u003eTYD, type-1A-like domain\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eSt Joseph Hospital Foundation supported the research work.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eNVS, KS and WEG were responsible for study conception, design, and supervision manuscript writing. NVS performed the in vitro experiments. ABL generated EpCAM mutation data and prepared figure 4a. PSD and KS performed Homology modeling and MD simulation Prepared Figures 3-4 . TPB, RMB supervised and write manuscript. All authors have read and approved the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe thank to Kristine Nally for assistance with editing and manuscript preparation.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGastl G, Spizzo G, Obrist P, Dunser M, Mikuz G. Ep-CAM overexpression in breast cancer as a predictor of survival. Lancet. 2000;356(9246):1981\u0026ndash;2.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOsta WA, Chen Y, Mikhitarian K, Mitas M, Salem M, Hannun YA, Cole DJ, Gillanders WE. EpCAM is overexpressed in breast cancer and is a potential target for breast cancer gene therapy. 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EpCAM: structure and function in health and disease. Biochim Biophys Acta. 2013;1828(8):1989\u0026ndash;2001.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"EpCAM, mutation, Cancer, molecular modelling","lastPublishedDoi":"10.21203/rs.3.rs-6174915/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6174915/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eEpCAM (epithelial cell adhesion molecule) is a key regulator of epithelial cell-cell adhesion, signal transduction, tissue regeneration, and serves as a stem cell marker. It is frequently overexpressed in epithelial cancers and is linked to tumor progression, survival, and metastasis. However, the functional impact of EpCAM mutations in cancer remains poorly understood.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eTo investigate the role of EpCAM mutations, we performed a comprehensive analysis of cancer cohorts from multiple genomic datasets, identifying novel somatic EpCAM mutations across diverse epithelial cancers. Using bioinformatics tools (SIFT, PolyPhen-2, Mutation Assessor) and molecular modeling, we assessed the potential impact of these mutations. Further, homology modeling and all-atom molecular dynamics (MD) simulations were conducted to evaluate structural changes.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eOur findings revealed that cancer-associated mutations, particularly in the TY-1 and RCD regions, induce structural instability in EpCAM, leading to altered functional properties. Patient cohort analyses indicated that EpCAM mutations correlate with reduced survival rates in colon and hepatocellular carcinoma and contribute to early tumor progression in lung cancer. Moreover, introducing these mutations into lung cancer cells enhanced their sensitivity to MEK inhibitors, suggesting a potential therapeutic vulnerability.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThis study provides novel insights into the structural and functional consequences of EpCAM mutations in cancer, demonstrating their association with reduced survival, tumor progression, and drug sensitivity. These findings highlight EpCAM as a promising therapeutic target in epithelial cancers.\u003c/p\u003e","manuscriptTitle":"Landscape of cancer associated EpCAM mutations: molecular modeling, Predictive Insights and Impact on Patient Survival","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-12 05:35:01","doi":"10.21203/rs.3.rs-6174915/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-03-12T04:04:16+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-03-12T03:45:58+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cancer","date":"2025-03-12T03:44:48+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1e5f8690-92fe-4799-a219-85a432aee37d","owner":[],"postedDate":"March 12th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-07-07T16:22:00+00:00","versionOfRecord":{"articleIdentity":"rs-6174915","link":"https://doi.org/10.1186/s12885-025-14455-8","journal":{"identity":"bmc-cancer","isVorOnly":false,"title":"BMC Cancer"},"publishedOn":"2025-07-01 15:58:26","publishedOnDateReadable":"July 1st, 2025"},"versionCreatedAt":"2025-03-12 05:35:01","video":"","vorDoi":"10.1186/s12885-025-14455-8","vorDoiUrl":"https://doi.org/10.1186/s12885-025-14455-8","workflowStages":[]},"version":"v1","identity":"rs-6174915","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6174915","identity":"rs-6174915","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}