{"paper_id":"49363c10-00d4-4e73-a60d-d82bcf43183d","body_text":"Distinct Genomic Landscape of Colorectal Signet Ring Cell Carcinoma Reveals Frequent KMT2 Family Alterations and SMAD4 Inactivation | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Distinct Genomic Landscape of Colorectal Signet Ring Cell Carcinoma Reveals Frequent KMT2 Family Alterations and SMAD4 Inactivation Krittiya Korphaisarn, Jirapat Aiamsophon, Nuttapong Ngamphaiboon, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9128596/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Background Signet ring cell colorectal carcinoma (SRCC) is a rare and highly aggressive subtype of colorectal cancer associated with poor clinical outcomes. Despite its distinct pathology, SRCC remains underrepresented in large genomic datasets, and its molecular drivers are incompletely defined. We performed comprehensive genomic profiling to characterize the mutational landscape of SRCC and explore potential therapeutic implications. Methods Formalin-fixed, paraffin-embedded tumor specimens from 39 patients with histologically confirmed colorectal SRCC were analyzed using targeted next-generation sequencing with the Oncomine™ Comprehensive Assay Plus, covering 517 cancer-related genes. Somatic alterations were identified, and tumor mutational burden (TMB) was calculated. Results High tumor mutational burden (TMB-H; ≥10 mutations/Mb) was observed in 15.4% of cases. The most frequently altered genes were KMT2C and SMAD4 (49% each), followed by TP53 (41%), CIC (31%), and ZFHX3 (28%). Recurrent alterations were also detected in KMT2A, KMT2D, CSMD3 , and ZMYM3 (each 26%). In contrast, canonical CRC driver mutations—including APC (23%), KRAS (15%), NRAS (2.5%), BRAF (10%), and PIK3CA (10%)—were less frequent than typically reported in conventional colorectal adenocarcinoma. Conclusions Colorectal SRCC exhibits a distinct genomic landscape characterized by frequent alterations in epigenetic regulators, particularly the KMT2 gene family, and recurrent disruption of the TGF-β signaling pathway through SMAD4 inactivation. The relative paucity of canonical colorectal cancer driver mutations suggests alternative oncogenic mechanisms underlying SRCC tumorigenesis. These findings provide genomic insights into this aggressive subtype and may inform future subtype-specific therapeutic strategies, including epigenetic-targeted therapies and immunotherapy in selected patients. Signet ring cell colorectal carcinoma colorectal cancer genomics epigenetic regulators KMT2 family precision oncology Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Colorectal cancer (CRC) is a leading cause of cancer-related morbidity and mortality worldwide. However, CRC represents a heterogeneous group of diseases with distinct histologic, molecular, and clinical characteristics. Among these, signet ring cell colorectal carcinoma (SRCC) is a rare histologic subtype, accounting for approximately 1–2% of CRC cases. SRCC is characterized by abundant intracellular mucin, diffuse infiltrative growth patterns, advanced stage at diagnosis, and poor clinical outcomes. Compared with conventional colorectal adenocarcinoma (AC), SRCC demonstrates more aggressive biological behavior, frequent peritoneal dissemination, and limited responsiveness to standard systemic therapies ( 1 – 4 ). The clinical management of CRC increasingly relies on molecular profiling, with biomarkers such as KRAS, NRAS, BRAF, PIK3CA , microsatellite instability (MSI), and tumor mutational burden (TMB) guiding prognostic assessment and treatment selection. However, these biomarkers have been primarily defined in AC, and their relevance in SRCC remains uncertain. Emerging evidence suggests that SRCC may harbor a distinct molecular landscape, including lower frequencies of canonical driver mutations and increased genomic instability ( 1 , 5 ). These differences raise concerns that biomarker-driven therapeutic strategies developed for conventional CRC may not fully capture the biology of SRCC. Previous genomic studies of SRCC have been limited by small cohort sizes and restricted genomic coverage. For example, Puccini A, et al. ( 6 ) reported differences in mutation frequencies between SRCC and AC, while An Y, et al ( 7 ). highlighted unique genetic alterations and immune microenvironment features in SRCC with prognostic implications. Nevertheless, comprehensive analyses encompassing epigenetic regulators, tumor suppressors, and clinically actionable biomarkers are scarce. To address these gaps, we performed targeted next-generation sequencing of 517 cancer-related genes in formalin-fixed, paraffin-embedded tumors from 39 patients with histologically confirmed SRCC. We aimed to define the mutational landscape of SRCC, evaluate clinically relevant biomarkers including tumor mutational burden, and compare genomic features with conventional colorectal adenocarcinoma and previously reported SRCC cohorts. Through comprehensive molecular profiling of a real-world cohort, this study seeks to inform precision oncology–guided therapeutic strategies for this aggressive and underrepresented colorectal cancer subtype Materials and Methods Patient Cohort and Clinical Data Thirty-nine patients with histologically confirmed SRCC were retrospectively identified from Siriraj Hospital (2007–2023) and Ramathibodi Hospital (2007–2019), Bangkok, Thailand. All cases were independently reviewed by board-certified pathologists to confirm the presence of ≥ 50% signet ring cell components, in accordance with World Health Organization classification criteria ( 8 ). Clinical data, including patient demographics, tumor location, and disease stage, were extracted from medical records for analysis. DNA Extraction and Targeted Sequencing Targeted next-generation sequencing was performed using the Oncomine™ Comprehensive Assay Plus (Thermo Fisher Scientific), which interrogates 517 cancer-related genes for single-nucleotide variants (SNVs), insertions/deletions (indels), and copy number alterations (CNAs). Library preparation was conducted according to the manufacturer’s instructions. Sequencing was performed on an Ion Torrent S5 XL system using Ion 550 chips. Tumor mutational burden–high (TMB-H) was defined as ≥ 10 mutations per megabase. Bioinformatic Analysis Sequencing reads were processed using Ion Torrent Suite software. Variant annotation was performed using vcf2maf (v1.6.22) ( 9 ) in combination with Ensembl Variant Effect Predictor (VEP, release 112) ( 10 ). High-confidence somatic variants were retained if read depth ≥ 200, alternate allele frequency ≥ 5%, and ≥ 10 supporting reads. Variants with population frequency > 1% in gnomAD, annotated as benign in dbSNP, predicted benign by both SIFT and PolyPhen-2, or located in microsatellite/simple repeat regions were excluded. Known cancer-associated variants were further annotated using the COSMIC database. Data Analysis Filtered variants were analyzed and visualized using custom R scripts and the maftools package. Analyses included mutation frequency profiling, gene-level mutation patterns, and co-occurrence analysis. Statistical Analysis Categorical variables were summarized as counts and percentages, while continuous variables were reported as median with range. Co-occurrence and mutual exclusivity analyses were performed using Fisher’s exact test. Differences in tumor mutational burden were assessed using the Mann–Whitney U test. All statistical analyses were conducted using R software (version 4.3.0), and a two-sided P-value < 0.05 was considered statistically significant. Results Patient Characteristics A total of 39 patients with histologically confirmed SRCC were included in this study. The median age at diagnosis was 49 years (range, 14–80 years), with a male predominance (61.5% male vs. 38.5% female). Tumors were predominantly located in the left-sided colon (64.1%). Most patients presented with advanced disease, with 92.3% diagnosed at stage III–IV. Tissue for tumor sequencing was obtained primarily from the primary tumor site in 92.3% of cases. High tumor mutational burden (TMB-high) was identified in 15.4% of patients (Table 1). Table 1. Baseline characteristic (N=39) Variable N % Age, years (mean, range) 49 years (14–80) Gender Male Female 24 15 61.5 38.5 Tumor location Right sided colon Left-sided colon Synchronous 13 25 1 33.3 64.1 2.6 Stage at presentation Stage I Stage II Stage III Stage IV 0 3 16 20 0 7.7 41 51.3 Tissue used for sequencing Primary tumor tissue Metastatic tissue 36 3 92.3 7.7 MMR status dMMR pMMR Not tested 1 8 30 2.6 20.5 76.9 TMB-high 6 15.4 Comprehensive Genomic Profiling of SRCC Targeted NGS revealed a distinct mutational landscape in SRCC. The most frequently mutated genes were KMT2C and SMAD4 (49% each), followed by TP53 (41%), CIC (31%), and ZFHX3 (28%). Additional recurrent mutations were observed in KMT2A , KMT2D , CSMD3 , and ZMYM3 (26% each). In contrast, canonical CRC drivers were comparatively uncommon: including APC (23%), KRAS (15%), NRAS (2.5%), BRAF (10%), PIK3CA (10%), and FBXW7 (15%) (Table 2; Figure 1). Table 2. Somatic Mutation Frequencies in SRCC vs AC Cohorts Genes % Mutation frequency in our cohort % Mutation frequency in Liao C, et al. 2022 (12) % Mutation frequency in MSKCC 2018 (11) Type SRCC, n=39 Excluding TMB-H tumors SRCC, n=33 CRC, n=316 CRC, n=1099 NGS platform 517 cancer-related genes 517 cancer- related genes 1021 cancer-related genes 341, 410, or 468 cancer-related genes KMT2C 49 39 13 6 SMAD4 49 39 14.9 19 TP53 41 45 76.6 73 CIC 31 21 NA 5 ZFHX3 28 21 NA 8 CSMD3 26 15 NA NA ZMYM3 26 15 NA NA KMT2A 26 12 9.5 5 KMT2D 26 12 13 10 APC 23 12 75.3 77 ARID1A 23 12 14.2 9 CTNNB1 18 15 12 8 NOTCH1 18 3 NA 6 KRAS 15 9 49.7 45 FBXW7 15 6 18 13 ERBB2 13 3 NA 7 BRAF 10 3 NA 11 PIK3CA 10 3 20.9 20 RNF43 5 3 10.4 9 NRAS 2.5 0 NA 4 SRCC = Signet Ring Cell Carcinoma, CRC = Colorectal Cancer To evaluate whether hypermutated tumors influenced these findings, analyses were repeated after excluding TMB-high cases (n = 33). Although mutation frequencies were modestly reduced, KMT2C and SMAD4 remained the most frequently altered genes (39% each), while TP53 mutations were observed in 45% of tumors. Several intermediate-frequency alterations (12–21%) were also detected in CIC, ZFHX3, CSMD3, CTNNB1, KIT , and ZMYM3 (Table 2; Figure 2). Co-occurrence and Mutational Patterns Analysis of mutation co-occurrence identified significant associations between NCOR1 and ZMYM3 mutations. In addition, a network of co-occurring alterations involving RBM10, ZMYM3, ZFHX3 , and CIC was observed (P < 0.05) (Figure 3). No statistically significant mutual exclusivity was observed, although trends toward exclusivity (P<0.1) were noted for several gene pairs. Discussion This study provides one of the most comprehensive NGS–based genomic characterizations of SRCC to date. Our findings demonstrate that SRCC harbors a molecular architecture that is fundamentally distinct from AC, characterized by pervasive epigenetic dysregulation, frequent disruption of the TGF-β signaling pathway, and a relative paucity of canonical CRC driver mutations. Taken together, these features establish SRCC as a biologically distinct disease entity rather than merely a histologic variant of CRC. Analysis of 39 SRCC tumors revealed a mutational profile dominated by alterations in epigenetic regulators and tumor suppressor genes rather than classical oncogenic drivers. TMB-H was observed in 15.4% of cases, consistent with prior reports indicating genomic instability in subsets of SRCC ( 5 , 6 ). Importantly, the defining genomic features persisted after exclusion of TMB-H tumors, indicating that the observed mutational landscape reflects intrinsic SRCC biology rather than a hypermutation artifact. The most frequently mutated genes were KMT2C and SMAD4 (49% each), followed by TP53 (41%), CIC (31%), and ZFHX3 (28%). Additional recurrent alterations in KMT2A, KMT2D, CSMD3 , and ZMYM3 further highlight widespread disruption of chromatin remodeling and transcriptional regulation in SRCC. The KMT2 (MLL) family, comprising KMT2A, KMT2B, KMT2C , and KMT2D , plays a central role in histone methylation and transcriptional control. In AC, mutations in KMT2 genes are observed in a subset of tumors, typically at frequencies ranging from 5–18% depending on the gene ( 6 , 11 , 12 ) (Fig. 4 ) and are often associated with MSI-H status and TMB-H, with potential implications for immunotherapy responsiveness ( 12 – 14 ). In contrast, data on KMT2 alterations in SRCC remain limited, with most prior studies reporting these mutations as rare or absent ( 6 ). The high prevalence of KMT2 mutations observed in our cohort therefore represents a notable divergence from both AC and previously published SRCC datasets, suggesting that epigenetic dysregulation may be a core oncogenic driver in SRCC. Beyond tumorigenesis, KMT2 family dysfunction may influence the tumor immune microenvironment by altering chromatin accessibility and transcriptional programs, as observed in other cancer types ( 14 ). Given the aggressive clinical behavior of SRCC and its limited responsiveness to conventional therapies, KMT2 mutation status warrants further investigation as a potential prognostic biomarker or predictor of immunotherapy response. However, the mechanistic links between KMT2 alterations, chromatin remodeling, genomic instability, and immune modulation in SRCC remain largely unexplored. SMAD4 , a key mediator of the TGF-β signaling pathway, is frequently altered in CRC and plays a key role in tumor progression and metastasis. In AC, SMAD4 mutations occur in approximately 10–20% of tumors ( 15 ), often co-occurring with TP53 and KRAS alterations, and correlating with advanced disease and poor prognosis ( 16 ). In contrast, SRCC exhibits a higher prevalence of SMAD4 alterations, with prior studies reporting mutations or loss of protein expression in 30–40% of case ( 17 , 18 ). Consistent with our cohort, SMAD4 mutations were identified in 49% of SRCC tumors, or 39% after excluding TMB-high cases. Unlike AC, in which alterations of the TGF-β pathway are distributed across multiple signaling components, SRCC appears to exhibit a more focused pattern of disruption characterized by frequent SMAD4 inactivation. This suggests that SMAD4 loss may represent a central mechanism of canonical TGF-β pathway impairment in SRCC, potentially contributing to its invasive behavior, diffuse growth pattern, and poor clinical outcome. In addition to KMT2 family and SMAD4 genes, we identified recurrent alterations in several genes not typically seen in AC, including CIC, ZFHX3, CSMD3, ZMYM3 , and NOTCH1 . Many of these genes regulate transcriptional repression, chromatin organization, and developmental signaling, supporting a model in which SRCC is driven by broad transcriptional and epigenetic deregulation. CIC , a transcriptional repressor downstream of MAPK signaling, was mutated in 31% of SRCC tumors, markedly higher than the ~ 5–10% reported in AC ( 11 ). Loss of CIC function has been associated with transcriptional derepression, lineage plasticity, and altered MAPK output, potentially contributing to the poorly differentiated and invasive phenotype of SRCC. Similarly, NOTCH1 , a key regulator of cell fate determination and epithelial homeostasis, was altered in 18% of cases, compared with < 5–10% in AC ( 11 ), suggesting a potential role in promoting stem-like characteristics and cellular plasticity. Alterations in ZFHX3 (28%) and ZMYM3 (26%) were also enriched relative to AC (3–8%), where these genes are infrequently mutated ( 11 , 15 ). Both genes are implicated in transcriptional repression, DNA damage response, and tumor suppression, and their loss may further exacerbate genomic instability and aggressive tumor behavior. CSMD3 , a large tumor suppressor gene associated with genomic instability and elevated tumor mutational burden across solid tumors, was mutated in 26% of cases, considerably higher than the low mutation frequency typically reported in AC, where CSMD3 is not considered a recurrent driver gene ( 19 ). This enrichment suggests that CSMD3 disruption may reflect a permissive genomic context characteristic of SRCC, rather than background passenger mutagenesis. Notably, these alterations frequently co-occurred and showed no evidence of mutual exclusivity, in contrast to the pathway-constrained mutational patterns commonly observed in AC. This pattern suggests that SRCC tumorigenesis may not follow the canonical stepwise adenoma–carcinoma progression model but instead arises through concurrent disruption of multiple regulatory networks governing chromatin organization, transcriptional regulation, and cellular differentiation. Co-mutation analysis further revealed significant co-occurrence among NCOR1, ZMYM3, RBM10, ZFHX3 , and CIC , highlighting interconnected networks of epigenetic and transcriptional regulators. The absence of statistically significant mutual exclusivity further distinguishes SRCC from AC, where such patterns often reflect pathway-specific selective pressures. Taken together, these findings reinforce the concept that SRCC evolves through complex, non-linear molecular trajectory, driven by coordinated deregulation of transcriptional and epigenetic programs rather than dependence on single dominant oncogenic pathways. When compare with large AC cohorts from Memorial Sloan Kettering Cancer Center (MSKCC) ( 11 ) and The Cancer Genome Atlas (TCGA) ( 15 ), SRCC demonstrated a strikingly distinct mutational profile characterized by markedly lower frequencies of canonical driver mutations. In our cohort, APC mutations were identified in only 23% of cases, compared with 70–80% in AC ( 11 ). Similarly, activating mutations in KRAS, NRAS, BRAF, PIK3CA , and FBXW7 were relatively uncommon. These findings are consistent with previously published studies ( 1 , 5 , 6 , 18 ) and with recent data reported by An Y, et al. ( 7 ), further supporting the view that SRCC represents a genetically distinct subtype relative to conventional colorectal adenocarcinoma. Comparison with previously published SRCC cohorts revealed both shared and unique features (Table 3 ). Recurrent alterations in SMAD4 and TP53 , together with low frequencies of APC, KRAS, NRAS, PIK3CA , and FBXW7 , were consistently observed across studies ( 1 , 7 , 20 , 21 ). In contrast, our cohort showed enrichment of alterations in KMT2 family genes, as well as CIC, ZFHX3, CSMD3 , and ZMYM3 , which were infrequently or absence reported in earlier studies. These differences may reflect population-specific variation and/or improved detection enabled by broader targeted sequencing, highlighting the importance of subtype- and population-aware genomic profiling in SRCC. Table 3 Comparison of Mutation Frequencies Across SRCC Cohorts Gene Our SRCC Our SRCC excl. TMB-H Wen-Wu L, 2025 ( 20 ) An Y, 2025 ( 7 ) Puccini A, 2022 ( 6 ) Korphaisarn K, 2019 ( 1 ) Nam YJ, 2018 ( 19 ) Sample size 39 33 125 37 54 35 5 NGS platform 517 cancer-related genes 517 cancer-related genes OncoKB 1,216 genes 520 or 1021-gene panel 592 whole-gene targets or 44-gene oncogenic hot-spot targets 46- or 50-cancer-related genes WES/RNA-seq KMT2C 49% 39% - – 11.4% – – KMT2D 26% 12% - – 11.9% – – KMT2A 26% 12% - – – – – SMAD4 49% 39% 20.3% 32% 10% 14.3% 20% TP53 41% 45% 48% 70% 47.7% 60% 40% CIC 31% 21% - – – – – ZFHX3 28% 21% - – – – – CSMD3 26% 15% - – – – – ZMYM3 26% 15% - – – – – APC 23% 12% 15.3% 32% 22.2% 2.9% 20% KRAS 15% 9% 17.6% 16% 20% 11.4% 40% NRAS 2.5% 0% - – 0% 0% 0% BRAF 10% 3% 8.8% – 13.3% 8.6% – PIK3CA 10% 3% - 8% 2.2% 2.9% 0% FBXW7 15% 6% - 0% 2.4% 2.9% 20% RNF43 5% 3% - 19% 15.6% – – NOTCH1 18% 3% - – – – – ERBB2 13% 3% - – – – – This study has several limitations. First, the retrospective design and relatively modest sample size reflect the rarity of SRCC and may limit statistical power and generalizability. Second, functional validation of the identified genomic alterations was not performed. Finally, the cohort was derived from two tertiary centers in Thailand, which may not fully capture the broader molecular heterogeneity of SRCC. Larger, multi-ethnic studies incorporating functional and multi-omic analyses will be necessary to validate these findings and further elucidate the molecular mechanisms underlying SRCC pathogenesis. Conclusion Comprehensive genomic profiling demonstrates that SRCC represents a molecularly distinct subtype of colorectal cancer. SRCC is characterized by enrichment of alterations in epigenetic regulators, frequent inactivation of SMAD4 , and a relative paucity of canonical colorectal cancer driver mutations. These findings highlight epigenetic dysregulation and TGF-β pathway disruption as central features of SRCC tumorigenesis. Taken together, our results provide a genomic rationale for subtype-specific precision oncology strategies, including epigenetic-targeted therapies and immunotherapy in selected patients, and underscore the need for larger prospective and mechanistic studies to further define therapeutic vulnerabilities in this aggressive colorectal cancer subtype. Declarations Ethics approval and consent to participate This study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Boards (IRBs) of Siriraj Hospital (Approval No. Si 703/2024) and Ramathibodi Hospital (Approval No. MURA 2020/834). The requirement for informed consent was waived due to the retrospective use of archival tissue samples. All patient data were anonymized to protect confidentiality Consent for publication Not applicable. Competing interests All authors declare that they do not have any personal or professional conflicts of interest and have not received financial support from the companies that produce and/or distribute the drugs, devices, or materials described in this report. Funding This work was supported by the Siriraj Foundation (Grant No. D003808), the Health Systems Research Institute – Genomics Thailand Initiative Grant, and the Siriraj Core Research Facility (SiCRF). The funders had no role in the study design, data collection, analysis, interpretation, or writing of the manuscript. Author Contribution The authors confirm contribution to the paper as follows: Conceptualization, KK; methodology, KK and MP; software, JA; validation, KK, JA, ER, and MP; formal analysis, JA, ER, and MP; investigation, KK, NN, NA, AP, ER, and CA; resources, KK, NN, CA, and MP; data curation, KK, JA, EK, and MP; writing—original draft preparation, KK and JA; writing—review and editing, KK, JA, NN, NA, AP, ER, CA, and MP; visualization, KK and JA; supervision, KK; project administration, KK; funding acquisition, KK and MP. All authors reviewed the results and approved the final version of the manuscript. Acknowledgement Not applicable Data Availability The sequencing data generated in this study have been deposited in the Genome Variation Map (GVM) repository at the National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences / China National Center for Bioinformation under accession number GVM001361. 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Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 15 May, 2026 Reviewers agreed at journal 14 May, 2026 Reviewers invited by journal 08 Apr, 2026 Editor assigned by journal 07 Apr, 2026 Editor invited by journal 01 Apr, 2026 Submission checks completed at journal 30 Mar, 2026 First submitted to journal 30 Mar, 2026 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-9128596\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":623181213,\"identity\":\"e1db6e6f-9643-41e8-af1c-8cbdb3c71e6e\",\"order_by\":0,\"name\":\"Krittiya 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University,\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Jirapat\",\"middleName\":\"\",\"lastName\":\"Aiamsophon\",\"suffix\":\"\"},{\"id\":623181215,\"identity\":\"86c16d97-2c16-4f42-a3e6-bcf32ff23dd4\",\"order_by\":2,\"name\":\"Nuttapong Ngamphaiboon\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Faculty of Medicine Ramathibodi Hospital\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Nuttapong\",\"middleName\":\"\",\"lastName\":\"Ngamphaiboon\",\"suffix\":\"\"},{\"id\":623181216,\"identity\":\"3e97ae2e-f914-4087-8a06-7e8f9f90ee05\",\"order_by\":3,\"name\":\"Napat Angkathunyakul\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Faculty of Medicine Siriraj Hospital, Mahidol University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Napat\",\"middleName\":\"\",\"lastName\":\"Angkathunyakul\",\"suffix\":\"\"},{\"id\":623181217,\"identity\":\"e0262f41-5434-40c0-a5f2-2398bf936ac2\",\"order_by\":4,\"name\":\"Ananya Pongpaibul\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Faculty of Medicine Siriraj Hospital, Mahidol University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Ananya\",\"middleName\":\"\",\"lastName\":\"Pongpaibul\",\"suffix\":\"\"},{\"id\":623181218,\"identity\":\"c09a13e5-ef1e-46f7-ad8e-5f5983ff843b\",\"order_by\":5,\"name\":\"Ekkapong Roothumnong\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Faculty of Medicine Siriraj Hospital, Mahidol University,\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Ekkapong\",\"middleName\":\"\",\"lastName\":\"Roothumnong\",\"suffix\":\"\"},{\"id\":623181219,\"identity\":\"9339f932-0f47-4b5e-9d2f-d91be9da1372\",\"order_by\":6,\"name\":\"Charuwan Akewanlop\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Faculty of Medicine Siriraj Hospital, Mahidol University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Charuwan\",\"middleName\":\"\",\"lastName\":\"Akewanlop\",\"suffix\":\"\"},{\"id\":623181220,\"identity\":\"e9f04476-7972-486f-ad89-c245502f17c2\",\"order_by\":7,\"name\":\"Manop Pithukpakorn\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Faculty of Medicine Siriraj Hospital, Mahidol University,\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Manop\",\"middleName\":\"\",\"lastName\":\"Pithukpakorn\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2026-03-15 12:38:02\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-9128596/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-9128596/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":107243262,\"identity\":\"6cd1b132-393c-47b8-a21c-6a112a8f96f5\",\"added_by\":\"auto\",\"created_at\":\"2026-04-19 07:50:19\",\"extension\":\"jpg\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":108197,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eSee image above for figure legend\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"2.4Figure115.3.26.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9128596/v1/eb44ed8ecaa0ba27ecfef909.jpg\"},{\"id\":107484273,\"identity\":\"4f1d3ef8-9730-4706-97fb-e46812d694e2\",\"added_by\":\"auto\",\"created_at\":\"2026-04-22 02:31:19\",\"extension\":\"jpg\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":122300,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eSee image above for figure legend\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"2.4Figure215.3.26.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9128596/v1/05e61aef3b4e7662ca3ed5f8.jpg\"},{\"id\":107243264,\"identity\":\"57b79ecf-6303-40f2-8027-683161368e7e\",\"added_by\":\"auto\",\"created_at\":\"2026-04-19 07:50:19\",\"extension\":\"jpg\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":74976,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eSee image above for figure legend\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"2.4Figure315.3.26.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9128596/v1/151a8d30fb5c87cb55885108.jpg\"},{\"id\":107243265,\"identity\":\"2c8e2c46-05af-4644-8467-e7dcccd3c846\",\"added_by\":\"auto\",\"created_at\":\"2026-04-19 07:50:19\",\"extension\":\"jpg\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":66699,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eSee image above for figure legend\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"2.4Figure415.3.26.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9128596/v1/a3ef7e46a94de31a2fbb8231.jpg\"},{\"id\":107705489,\"identity\":\"739c7860-ace7-4454-bcd4-27f91a06ea12\",\"added_by\":\"auto\",\"created_at\":\"2026-04-24 09:13:08\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":818776,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9128596/v1/2c475a18-1a09-4483-883c-5fde860506ee.pdf\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Distinct Genomic Landscape of Colorectal Signet Ring Cell Carcinoma Reveals Frequent KMT2 Family Alterations and SMAD4 Inactivation\",\"fulltext\":[{\"header\":\"Introduction\",\"content\":\"\\u003cp\\u003eColorectal cancer (CRC) is a leading cause of cancer-related morbidity and mortality worldwide. However, CRC represents a heterogeneous group of diseases with distinct histologic, molecular, and clinical characteristics. Among these, signet ring cell colorectal carcinoma (SRCC) is a rare histologic subtype, accounting for approximately 1\\u0026ndash;2% of CRC cases. SRCC is characterized by abundant intracellular mucin, diffuse infiltrative growth patterns, advanced stage at diagnosis, and poor clinical outcomes. Compared with conventional colorectal adenocarcinoma (AC), SRCC demonstrates more aggressive biological behavior, frequent peritoneal dissemination, and limited responsiveness to standard systemic therapies (\\u003cspan additionalcitationids=\\\"CR2 CR3\\\" citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eThe clinical management of CRC increasingly relies on molecular profiling, with biomarkers such as \\u003cem\\u003eKRAS, NRAS, BRAF, PIK3CA\\u003c/em\\u003e, microsatellite instability (MSI), and tumor mutational burden (TMB) guiding prognostic assessment and treatment selection. However, these biomarkers have been primarily defined in AC, and their relevance in SRCC remains uncertain. Emerging evidence suggests that SRCC may harbor a distinct molecular landscape, including lower frequencies of canonical driver mutations and increased genomic instability (\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e). These differences raise concerns that biomarker-driven therapeutic strategies developed for conventional CRC may not fully capture the biology of SRCC. Previous genomic studies of SRCC have been limited by small cohort sizes and restricted genomic coverage. For example, Puccini A, et al. (\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e) reported differences in mutation frequencies between SRCC and AC, while An Y, et al (\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e). highlighted unique genetic alterations and immune microenvironment features in SRCC with prognostic implications. Nevertheless, comprehensive analyses encompassing epigenetic regulators, tumor suppressors, and clinically actionable biomarkers are scarce.\\u003c/p\\u003e \\u003cp\\u003eTo address these gaps, we performed targeted next-generation sequencing of 517 cancer-related genes in formalin-fixed, paraffin-embedded tumors from 39 patients with histologically confirmed SRCC. We aimed to define the mutational landscape of SRCC, evaluate clinically relevant biomarkers including tumor mutational burden, and compare genomic features with conventional colorectal adenocarcinoma and previously reported SRCC cohorts. Through comprehensive molecular profiling of a real-world cohort, this study seeks to inform precision oncology\\u0026ndash;guided therapeutic strategies for this aggressive and underrepresented colorectal cancer subtype\\u003c/p\\u003e\"},{\"header\":\"Materials and Methods\",\"content\":\"\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003ePatient Cohort and Clinical Data\\u003c/h2\\u003e \\u003cp\\u003eThirty-nine patients with histologically confirmed SRCC were retrospectively identified from Siriraj Hospital (2007\\u0026ndash;2023) and Ramathibodi Hospital (2007\\u0026ndash;2019), Bangkok, Thailand. All cases were independently reviewed by board-certified pathologists to confirm the presence of \\u0026ge;\\u0026thinsp;50% signet ring cell components, in accordance with World Health Organization classification criteria (\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e). Clinical data, including patient demographics, tumor location, and disease stage, were extracted from medical records for analysis.\\u003c/p\\u003e \\u003c/div\\u003e\\n\\u003ch3\\u003eDNA Extraction and Targeted Sequencing\\u003c/h3\\u003e\\n\\u003cp\\u003eTargeted next-generation sequencing was performed using the Oncomine\\u0026trade; Comprehensive Assay Plus (Thermo Fisher Scientific), which interrogates 517 cancer-related genes for single-nucleotide variants (SNVs), insertions/deletions (indels), and copy number alterations (CNAs). Library preparation was conducted according to the manufacturer\\u0026rsquo;s instructions. Sequencing was performed on an Ion Torrent S5 XL system using Ion 550 chips. Tumor mutational burden\\u0026ndash;high (TMB-H) was defined as \\u0026ge;\\u0026thinsp;10 mutations per megabase.\\u003c/p\\u003e\\n\\u003ch3\\u003eBioinformatic Analysis\\u003c/h3\\u003e\\n\\u003cp\\u003eSequencing reads were processed using Ion Torrent Suite software. Variant annotation was performed using vcf2maf (v1.6.22) (\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e) in combination with Ensembl Variant Effect Predictor (VEP, release 112) (\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e). High-confidence somatic variants were retained if read depth\\u0026thinsp;\\u0026ge;\\u0026thinsp;200, alternate allele frequency\\u0026thinsp;\\u0026ge;\\u0026thinsp;5%, and \\u0026ge;\\u0026thinsp;10 supporting reads. Variants with population frequency\\u0026thinsp;\\u0026gt;\\u0026thinsp;1% in gnomAD, annotated as benign in dbSNP, predicted benign by both SIFT and PolyPhen-2, or located in microsatellite/simple repeat regions were excluded. Known cancer-associated variants were further annotated using the COSMIC database.\\u003c/p\\u003e \\u003cdiv id=\\\"Sec6\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eData Analysis\\u003c/h2\\u003e \\u003cp\\u003eFiltered variants were analyzed and visualized using custom R scripts and the maftools package. Analyses included mutation frequency profiling, gene-level mutation patterns, and co-occurrence analysis.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec7\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eStatistical Analysis\\u003c/h2\\u003e \\u003cp\\u003eCategorical variables were summarized as counts and percentages, while continuous variables were reported as median with range. Co-occurrence and mutual exclusivity analyses were performed using Fisher\\u0026rsquo;s exact test. Differences in tumor mutational burden were assessed using the Mann\\u0026ndash;Whitney U test. All statistical analyses were conducted using R software (version 4.3.0), and a two-sided P-value\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05 was considered statistically significant.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"Results\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003ePatient Characteristics\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eA total of 39 patients with histologically confirmed SRCC were included in this study. The median age at diagnosis was 49 years (range, 14\\u0026ndash;80 years), with a male predominance (61.5% male vs. 38.5% female). Tumors were predominantly located in the left-sided colon (64.1%). Most patients presented with advanced disease, with 92.3% diagnosed at stage III\\u0026ndash;IV. Tissue for tumor sequencing was obtained primarily from the primary tumor site in 92.3% of cases. High tumor mutational burden (TMB-high) was identified in 15.4% of patients (Table 1).\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eTable 1. Baseline characteristic (N=39)\\u0026nbsp;\\u003c/p\\u003e\\n\\u003ctable border=\\\"1\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\"\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 255px;\\\"\\u003e\\n \\u003cp\\u003eVariable\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 179px;\\\"\\u003e\\n \\u003cp\\u003eN\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 190px;\\\"\\u003e\\n \\u003cp\\u003e%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 255px;\\\"\\u003e\\n \\u003cp\\u003eAge, years (mean, range)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 179px;\\\"\\u003e\\n \\u003cp\\u003e49 years (14\\u0026ndash;80)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 190px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 255px;\\\"\\u003e\\n \\u003cp\\u003eGender\\u003c/p\\u003e\\n \\u003cp\\u003e\\u0026nbsp; \\u0026nbsp;Male\\u003c/p\\u003e\\n \\u003cp\\u003e\\u0026nbsp; \\u0026nbsp;Female\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 179px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003cp\\u003e24\\u003c/p\\u003e\\n \\u003cp\\u003e15\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 190px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003cp\\u003e61.5\\u003c/p\\u003e\\n \\u003cp\\u003e38.5\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 255px;\\\"\\u003e\\n \\u003cp\\u003eTumor location\\u003c/p\\u003e\\n \\u003cp\\u003e\\u0026nbsp; \\u0026nbsp;Right sided colon\\u003c/p\\u003e\\n \\u003cp\\u003e\\u0026nbsp; \\u0026nbsp;Left-sided colon\\u003c/p\\u003e\\n \\u003cp\\u003e\\u0026nbsp; \\u0026nbsp;Synchronous\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 179px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003cp\\u003e13\\u003c/p\\u003e\\n \\u003cp\\u003e25\\u003c/p\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 190px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003cp\\u003e33.3\\u003c/p\\u003e\\n \\u003cp\\u003e64.1\\u003c/p\\u003e\\n \\u003cp\\u003e2.6\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 255px;\\\"\\u003e\\n \\u003cp\\u003eStage at presentation\\u003c/p\\u003e\\n \\u003cp\\u003e\\u0026nbsp; \\u0026nbsp;Stage I\\u003c/p\\u003e\\n \\u003cp\\u003e\\u0026nbsp; \\u0026nbsp;Stage II\\u003c/p\\u003e\\n \\u003cp\\u003e\\u0026nbsp; \\u0026nbsp;Stage III\\u003c/p\\u003e\\n \\u003cp\\u003e\\u0026nbsp; \\u0026nbsp;Stage IV\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 179px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003cp\\u003e3\\u003c/p\\u003e\\n \\u003cp\\u003e16\\u003c/p\\u003e\\n \\u003cp\\u003e20\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 190px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003cp\\u003e7.7\\u003c/p\\u003e\\n \\u003cp\\u003e41\\u003c/p\\u003e\\n \\u003cp\\u003e51.3\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 255px;\\\"\\u003e\\n \\u003cp\\u003eTissue used for sequencing\\u0026nbsp;\\u003c/p\\u003e\\n \\u003cp\\u003e\\u0026nbsp; \\u0026nbsp;Primary tumor tissue\\u003c/p\\u003e\\n \\u003cp\\u003e\\u0026nbsp; \\u0026nbsp;Metastatic tissue\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 179px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003cp\\u003e36\\u003c/p\\u003e\\n \\u003cp\\u003e3\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 190px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003cp\\u003e92.3\\u003c/p\\u003e\\n \\u003cp\\u003e7.7\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 255px;\\\"\\u003e\\n \\u003cp\\u003eMMR status\\u003c/p\\u003e\\n \\u003cp\\u003e\\u0026nbsp; \\u0026nbsp;dMMR\\u003c/p\\u003e\\n \\u003cp\\u003e\\u0026nbsp; \\u0026nbsp;pMMR\\u003c/p\\u003e\\n \\u003cp\\u003e\\u0026nbsp; \\u0026nbsp;Not tested\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 179px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003cp\\u003e8\\u003c/p\\u003e\\n \\u003cp\\u003e30\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 190px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003cp\\u003e2.6\\u003c/p\\u003e\\n \\u003cp\\u003e20.5\\u003c/p\\u003e\\n \\u003cp\\u003e76.9\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 255px;\\\"\\u003e\\n \\u003cp\\u003eTMB-high\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 179px;\\\"\\u003e\\n \\u003cp\\u003e6\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 190px;\\\"\\u003e\\n \\u003cp\\u003e15.4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n\\u003c/table\\u003e\\n\\u003cp\\u003e\\u0026nbsp;\\u003cstrong\\u003eComprehensive Genomic Profiling of SRCC\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eTargeted NGS revealed a distinct mutational landscape in SRCC. The most frequently mutated genes were\\u003cem\\u003e\\u0026nbsp;KMT2C\\u003c/em\\u003e and \\u003cem\\u003eSMAD4\\u003c/em\\u003e (49% each), followed by \\u003cem\\u003eTP53\\u003c/em\\u003e (41%), \\u003cem\\u003eCIC\\u003c/em\\u003e (31%), and \\u003cem\\u003eZFHX3\\u003c/em\\u003e (28%). Additional recurrent mutations were observed in \\u003cem\\u003eKMT2A\\u003c/em\\u003e, \\u003cem\\u003eKMT2D\\u003c/em\\u003e, \\u003cem\\u003eCSMD3\\u003c/em\\u003e, and \\u003cem\\u003eZMYM3\\u003c/em\\u003e (26% each). In contrast, canonical CRC drivers were comparatively uncommon: including\\u003cem\\u003e\\u0026nbsp;APC\\u003c/em\\u003e (23%), \\u003cem\\u003eKRAS\\u003c/em\\u003e (15%), \\u003cem\\u003eNRAS\\u003c/em\\u003e (2.5%), \\u003cem\\u003eBRAF\\u003c/em\\u003e (10%), \\u003cem\\u003ePIK3CA\\u003c/em\\u003e (10%), and \\u003cem\\u003eFBXW7\\u003c/em\\u003e (15%) (Table 2; Figure 1).\\u003c/p\\u003e\\n\\u003cp\\u003eTable 2. Somatic Mutation Frequencies in SRCC vs AC Cohorts\\u0026nbsp;\\u003c/p\\u003e\\n\\u003ctable border=\\\"1\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\" width=\\\"100%\\\"\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 15px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eGenes\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd colspan=\\\"2\\\" valign=\\\"top\\\" style=\\\"width: 39px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e% Mutation frequency in our cohort\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 22px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e% Mutation frequency in Liao C, et al. 2022 (12) \\u0026nbsp; \\u0026nbsp; \\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 22px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e% Mutation frequency in \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; MSKCC 2018 (11) \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp;\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 15px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u003cem\\u003eType\\u0026nbsp;\\u003c/em\\u003e\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 18px;\\\"\\u003e\\n \\u003cp\\u003eSRCC, n=39\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 21px;\\\"\\u003e\\n \\u003cp\\u003eExcluding TMB-H tumors SRCC, n=33\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 22px;\\\"\\u003e\\n \\u003cp\\u003eCRC, n=316\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 22px;\\\"\\u003e\\n \\u003cp\\u003eCRC, n=1099\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 15px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u003cem\\u003eNGS platform\\u003c/em\\u003e\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 18px;\\\"\\u003e\\n \\u003cp\\u003e517\\u0026nbsp;cancer-related genes\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 21px;\\\"\\u003e\\n \\u003cp\\u003e517 cancer- \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; related genes\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 22px;\\\"\\u003e\\n \\u003cp\\u003e1021 cancer-related genes\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 22px;\\\"\\u003e\\n \\u003cp\\u003e341, 410, or 468 cancer-related genes\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 15px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u003cem\\u003eKMT2C\\u003c/em\\u003e\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 18px;\\\"\\u003e\\n \\u003cp\\u003e49\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 21px;\\\"\\u003e\\n \\u003cp\\u003e39\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 22px;\\\"\\u003e\\n \\u003cp\\u003e13\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 22px;\\\"\\u003e\\n \\u003cp\\u003e6\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 15px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u003cem\\u003eSMAD4\\u003c/em\\u003e\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 18px;\\\"\\u003e\\n \\u003cp\\u003e49\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 21px;\\\"\\u003e\\n \\u003cp\\u003e39\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 22px;\\\"\\u003e\\n \\u003cp\\u003e14.9\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 22px;\\\"\\u003e\\n \\u003cp\\u003e19\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 15px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u003cem\\u003eTP53\\u003c/em\\u003e\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 18px;\\\"\\u003e\\n \\u003cp\\u003e41\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 21px;\\\"\\u003e\\n \\u003cp\\u003e45\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 22px;\\\"\\u003e\\n \\u003cp\\u003e76.6\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 22px;\\\"\\u003e\\n \\u003cp\\u003e73\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 15px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u003cem\\u003eCIC\\u003c/em\\u003e\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 18px;\\\"\\u003e\\n \\u003cp\\u003e31\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 21px;\\\"\\u003e\\n \\u003cp\\u003e21\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 22px;\\\"\\u003e\\n \\u003cp\\u003eNA\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 22px;\\\"\\u003e\\n \\u003cp\\u003e5\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 15px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u003cem\\u003eZFHX3\\u003c/em\\u003e\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 18px;\\\"\\u003e\\n \\u003cp\\u003e28\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 21px;\\\"\\u003e\\n \\u003cp\\u003e21\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 22px;\\\"\\u003e\\n \\u003cp\\u003eNA\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 22px;\\\"\\u003e\\n \\u003cp\\u003e8\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 15px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u003cem\\u003eCSMD3\\u003c/em\\u003e\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 18px;\\\"\\u003e\\n \\u003cp\\u003e26\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 21px;\\\"\\u003e\\n \\u003cp\\u003e15\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 22px;\\\"\\u003e\\n \\u003cp\\u003eNA\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 22px;\\\"\\u003e\\n \\u003cp\\u003eNA\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 15px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u003cem\\u003eZMYM3\\u003c/em\\u003e\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 18px;\\\"\\u003e\\n \\u003cp\\u003e26\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 21px;\\\"\\u003e\\n \\u003cp\\u003e15\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 22px;\\\"\\u003e\\n \\u003cp\\u003eNA\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 22px;\\\"\\u003e\\n \\u003cp\\u003eNA\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 15px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u003cem\\u003eKMT2A\\u003c/em\\u003e\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 18px;\\\"\\u003e\\n \\u003cp\\u003e26\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 21px;\\\"\\u003e\\n \\u003cp\\u003e12\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 22px;\\\"\\u003e\\n \\u003cp\\u003e9.5\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 22px;\\\"\\u003e\\n \\u003cp\\u003e5\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 15px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u003cem\\u003eKMT2D\\u003c/em\\u003e\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 18px;\\\"\\u003e\\n \\u003cp\\u003e26\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 21px;\\\"\\u003e\\n \\u003cp\\u003e12\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 22px;\\\"\\u003e\\n \\u003cp\\u003e13\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 22px;\\\"\\u003e\\n \\u003cp\\u003e10\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 15px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u003cem\\u003eAPC\\u003c/em\\u003e\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 18px;\\\"\\u003e\\n \\u003cp\\u003e23\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 21px;\\\"\\u003e\\n \\u003cp\\u003e12\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 22px;\\\"\\u003e\\n \\u003cp\\u003e75.3\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 22px;\\\"\\u003e\\n \\u003cp\\u003e77\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 15px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u003cem\\u003eARID1A\\u003c/em\\u003e\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 18px;\\\"\\u003e\\n \\u003cp\\u003e23\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 21px;\\\"\\u003e\\n \\u003cp\\u003e12\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 22px;\\\"\\u003e\\n \\u003cp\\u003e14.2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 22px;\\\"\\u003e\\n \\u003cp\\u003e9\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 15px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u003cem\\u003eCTNNB1\\u003c/em\\u003e\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 18px;\\\"\\u003e\\n \\u003cp\\u003e18\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 21px;\\\"\\u003e\\n \\u003cp\\u003e15\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 22px;\\\"\\u003e\\n \\u003cp\\u003e12\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 22px;\\\"\\u003e\\n \\u003cp\\u003e8\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 15px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u003cem\\u003eNOTCH1\\u003c/em\\u003e\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 18px;\\\"\\u003e\\n \\u003cp\\u003e18\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 21px;\\\"\\u003e\\n \\u003cp\\u003e3\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 22px;\\\"\\u003e\\n \\u003cp\\u003eNA\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 22px;\\\"\\u003e\\n \\u003cp\\u003e6\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 15px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u003cem\\u003eKRAS\\u003c/em\\u003e\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 18px;\\\"\\u003e\\n \\u003cp\\u003e15\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 21px;\\\"\\u003e\\n \\u003cp\\u003e9\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 22px;\\\"\\u003e\\n \\u003cp\\u003e49.7\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 22px;\\\"\\u003e\\n \\u003cp\\u003e45\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 15px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u003cem\\u003eFBXW7\\u003c/em\\u003e\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 18px;\\\"\\u003e\\n \\u003cp\\u003e15\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 21px;\\\"\\u003e\\n \\u003cp\\u003e6\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 22px;\\\"\\u003e\\n \\u003cp\\u003e18\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 22px;\\\"\\u003e\\n \\u003cp\\u003e13\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 15px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u003cem\\u003eERBB2\\u003c/em\\u003e\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 18px;\\\"\\u003e\\n \\u003cp\\u003e13\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 21px;\\\"\\u003e\\n \\u003cp\\u003e3\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 22px;\\\"\\u003e\\n \\u003cp\\u003eNA\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 22px;\\\"\\u003e\\n \\u003cp\\u003e7\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 15px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u003cem\\u003eBRAF\\u003c/em\\u003e\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 18px;\\\"\\u003e\\n \\u003cp\\u003e10\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 21px;\\\"\\u003e\\n \\u003cp\\u003e3\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 22px;\\\"\\u003e\\n \\u003cp\\u003eNA\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 22px;\\\"\\u003e\\n \\u003cp\\u003e11\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 15px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u003cem\\u003ePIK3CA\\u003c/em\\u003e\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 18px;\\\"\\u003e\\n \\u003cp\\u003e10\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 21px;\\\"\\u003e\\n \\u003cp\\u003e3\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 22px;\\\"\\u003e\\n \\u003cp\\u003e20.9\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 22px;\\\"\\u003e\\n \\u003cp\\u003e20\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 15px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u003cem\\u003eRNF43\\u003c/em\\u003e\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 18px;\\\"\\u003e\\n \\u003cp\\u003e5\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 21px;\\\"\\u003e\\n \\u003cp\\u003e3\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 22px;\\\"\\u003e\\n \\u003cp\\u003e10.4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 22px;\\\"\\u003e\\n \\u003cp\\u003e9\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 15px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u003cem\\u003eNRAS\\u003c/em\\u003e\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 18px;\\\"\\u003e\\n \\u003cp\\u003e2.5\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 21px;\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 22px;\\\"\\u003e\\n \\u003cp\\u003eNA\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 22px;\\\"\\u003e\\n \\u003cp\\u003e4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n\\u003c/table\\u003e\\n\\u003cp\\u003eSRCC = Signet Ring Cell Carcinoma, CRC = Colorectal Cancer\\u003c/p\\u003e\\n\\u003cp\\u003eTo evaluate whether hypermutated tumors influenced these findings, analyses were repeated after excluding TMB-high cases (n = 33). Although mutation frequencies were modestly reduced, \\u003cem\\u003eKMT2C\\u003c/em\\u003e and \\u003cem\\u003eSMAD4\\u003c/em\\u003e remained the most frequently altered genes (39% each), while \\u003cem\\u003eTP53\\u0026nbsp;\\u003c/em\\u003emutations were observed in 45% of tumors. Several intermediate-frequency alterations (12\\u0026ndash;21%) were also detected in \\u003cem\\u003eCIC, ZFHX3, CSMD3, CTNNB1, KIT\\u003c/em\\u003e, and \\u003cem\\u003eZMYM3\\u003c/em\\u003e (Table 2; Figure 2).\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eCo-occurrence and Mutational Patterns\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eAnalysis of mutation co-occurrence identified significant associations between \\u003cem\\u003eNCOR1\\u003c/em\\u003e and \\u003cem\\u003eZMYM3\\u0026nbsp;\\u003c/em\\u003emutations. In addition, a network of co-occurring alterations involving \\u003cem\\u003eRBM10, ZMYM3, ZFHX3\\u003c/em\\u003e, and \\u003cem\\u003eCIC\\u003c/em\\u003e was observed (P \\u0026lt; 0.05) (Figure 3). No statistically significant mutual exclusivity was observed, although trends toward exclusivity (P\\u0026lt;0.1) were noted for several gene pairs.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cbr\\u003e\\u003c/p\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cp\\u003eThis study provides one of the most comprehensive NGS\\u0026ndash;based genomic characterizations of SRCC to date. Our findings demonstrate that SRCC harbors a molecular architecture that is fundamentally distinct from AC, characterized by pervasive epigenetic dysregulation, frequent disruption of the TGF-β signaling pathway, and a relative paucity of canonical CRC driver mutations. Taken together, these features establish SRCC as a biologically distinct disease entity rather than merely a histologic variant of CRC.\\u003c/p\\u003e \\u003cp\\u003eAnalysis of 39 SRCC tumors revealed a mutational profile dominated by alterations in epigenetic regulators and tumor suppressor genes rather than classical oncogenic drivers. TMB-H was observed in 15.4% of cases, consistent with prior reports indicating genomic instability in subsets of SRCC (\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e). Importantly, the defining genomic features persisted after exclusion of TMB-H tumors, indicating that the observed mutational landscape reflects intrinsic SRCC biology rather than a hypermutation artifact. The most frequently mutated genes were \\u003cem\\u003eKMT2C\\u003c/em\\u003e and \\u003cem\\u003eSMAD4\\u003c/em\\u003e (49% each), followed by \\u003cem\\u003eTP53\\u003c/em\\u003e (41%), \\u003cem\\u003eCIC\\u003c/em\\u003e (31%), and \\u003cem\\u003eZFHX3\\u003c/em\\u003e (28%). Additional recurrent alterations in \\u003cem\\u003eKMT2A, KMT2D, CSMD3\\u003c/em\\u003e, and \\u003cem\\u003eZMYM3\\u003c/em\\u003e further highlight widespread disruption of chromatin remodeling and transcriptional regulation in SRCC.\\u003c/p\\u003e \\u003cp\\u003eThe \\u003cem\\u003eKMT2\\u003c/em\\u003e (MLL) family, comprising \\u003cem\\u003eKMT2A, KMT2B, KMT2C\\u003c/em\\u003e, and \\u003cem\\u003eKMT2D\\u003c/em\\u003e, plays a central role in histone methylation and transcriptional control. In AC, mutations in \\u003cem\\u003eKMT2\\u003c/em\\u003e genes are observed in a subset of tumors, typically at frequencies ranging from 5\\u0026ndash;18% depending on the gene (\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e) (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e) and are often associated with MSI-H status and TMB-H, with potential implications for immunotherapy responsiveness (\\u003cspan additionalcitationids=\\\"CR13\\\" citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e). In contrast, data on \\u003cem\\u003eKMT2\\u003c/em\\u003e alterations in SRCC remain limited, with most prior studies reporting these mutations as rare or absent (\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e). The high prevalence of \\u003cem\\u003eKMT2\\u003c/em\\u003e mutations observed in our cohort therefore represents a notable divergence from both AC and previously published SRCC datasets, suggesting that epigenetic dysregulation may be a core oncogenic driver in SRCC.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eBeyond tumorigenesis, \\u003cem\\u003eKMT2\\u003c/em\\u003e family dysfunction may influence the tumor immune microenvironment by altering chromatin accessibility and transcriptional programs, as observed in other cancer types (\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e). Given the aggressive clinical behavior of SRCC and its limited responsiveness to conventional therapies, \\u003cem\\u003eKMT2\\u003c/em\\u003e mutation status warrants further investigation as a potential prognostic biomarker or predictor of immunotherapy response. However, the mechanistic links between \\u003cem\\u003eKMT2\\u003c/em\\u003e alterations, chromatin remodeling, genomic instability, and immune modulation in SRCC remain largely unexplored.\\u003c/p\\u003e \\u003cp\\u003e \\u003cem\\u003eSMAD4\\u003c/em\\u003e, a key mediator of the TGF-β signaling pathway, is frequently altered in CRC and plays a key role in tumor progression and metastasis. In AC, \\u003cem\\u003eSMAD4\\u003c/em\\u003e mutations occur in approximately 10\\u0026ndash;20% of tumors (\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e), often co-occurring with \\u003cem\\u003eTP53\\u003c/em\\u003e and \\u003cem\\u003eKRAS\\u003c/em\\u003e alterations, and correlating with advanced disease and poor prognosis (\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e). In contrast, SRCC exhibits a higher prevalence of \\u003cem\\u003eSMAD4\\u003c/em\\u003e alterations, with prior studies reporting mutations or loss of protein expression in 30\\u0026ndash;40% of case (\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e). Consistent with our cohort, \\u003cem\\u003eSMAD4\\u003c/em\\u003e mutations were identified in 49% of SRCC tumors, or 39% after excluding TMB-high cases. Unlike AC, in which alterations of the TGF-β pathway are distributed across multiple signaling components, SRCC appears to exhibit a more focused pattern of disruption characterized by frequent \\u003cem\\u003eSMAD4\\u003c/em\\u003e inactivation. This suggests that \\u003cem\\u003eSMAD4\\u003c/em\\u003e loss may represent a central mechanism of canonical TGF-β pathway impairment in SRCC, potentially contributing to its invasive behavior, diffuse growth pattern, and poor clinical outcome.\\u003c/p\\u003e \\u003cp\\u003eIn addition to \\u003cem\\u003eKMT2\\u003c/em\\u003e family and \\u003cem\\u003eSMAD4\\u003c/em\\u003e genes, we identified recurrent alterations in several genes not typically seen in AC, including \\u003cem\\u003eCIC, ZFHX3, CSMD3, ZMYM3\\u003c/em\\u003e, and \\u003cem\\u003eNOTCH1\\u003c/em\\u003e. Many of these genes regulate transcriptional repression, chromatin organization, and developmental signaling, supporting a model in which SRCC is driven by broad transcriptional and epigenetic deregulation.\\u003c/p\\u003e \\u003cp\\u003e \\u003cem\\u003eCIC\\u003c/em\\u003e, a transcriptional repressor downstream of MAPK signaling, was mutated in 31% of SRCC tumors, markedly higher than the ~\\u0026thinsp;5\\u0026ndash;10% reported in AC (\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e). Loss of \\u003cem\\u003eCIC\\u003c/em\\u003e function has been associated with transcriptional derepression, lineage plasticity, and altered MAPK output, potentially contributing to the poorly differentiated and invasive phenotype of SRCC. Similarly, \\u003cem\\u003eNOTCH1\\u003c/em\\u003e, a key regulator of cell fate determination and epithelial homeostasis, was altered in 18% of cases, compared with \\u0026lt;\\u0026thinsp;5\\u0026ndash;10% in AC (\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e), suggesting a potential role in promoting stem-like characteristics and cellular plasticity.\\u003c/p\\u003e \\u003cp\\u003eAlterations in \\u003cem\\u003eZFHX3\\u003c/em\\u003e (28%) and \\u003cem\\u003eZMYM3\\u003c/em\\u003e (26%) were also enriched relative to AC (3\\u0026ndash;8%), where these genes are infrequently mutated (\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e). Both genes are implicated in transcriptional repression, DNA damage response, and tumor suppression, and their loss may further exacerbate genomic instability and aggressive tumor behavior. \\u003cem\\u003eCSMD3\\u003c/em\\u003e, a large tumor suppressor gene associated with genomic instability and elevated tumor mutational burden across solid tumors, was mutated in 26% of cases, considerably higher than the low mutation frequency typically reported in AC, where \\u003cem\\u003eCSMD3\\u003c/em\\u003e is not considered a recurrent driver gene (\\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e). This enrichment suggests that \\u003cem\\u003eCSMD3\\u003c/em\\u003e disruption may reflect a permissive genomic context characteristic of SRCC, rather than background passenger mutagenesis.\\u003c/p\\u003e \\u003cp\\u003eNotably, these alterations frequently co-occurred and showed no evidence of mutual exclusivity, in contrast to the pathway-constrained mutational patterns commonly observed in AC. This pattern suggests that SRCC tumorigenesis may not follow the canonical stepwise adenoma\\u0026ndash;carcinoma progression model but instead arises through concurrent disruption of multiple regulatory networks governing chromatin organization, transcriptional regulation, and cellular differentiation.\\u003c/p\\u003e \\u003cp\\u003eCo-mutation analysis further revealed significant co-occurrence among \\u003cem\\u003eNCOR1, ZMYM3, RBM10, ZFHX3\\u003c/em\\u003e, and \\u003cem\\u003eCIC\\u003c/em\\u003e, highlighting interconnected networks of epigenetic and transcriptional regulators. The absence of statistically significant mutual exclusivity further distinguishes SRCC from AC, where such patterns often reflect pathway-specific selective pressures. Taken together, these findings reinforce the concept that SRCC evolves through complex, non-linear molecular trajectory, driven by coordinated deregulation of transcriptional and epigenetic programs rather than dependence on single dominant oncogenic pathways.\\u003c/p\\u003e \\u003cp\\u003eWhen compare with large AC cohorts from Memorial Sloan Kettering Cancer Center (MSKCC) (\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e) and The Cancer Genome Atlas (TCGA) (\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e), SRCC demonstrated a strikingly distinct mutational profile characterized by markedly lower frequencies of canonical driver mutations. In our cohort, \\u003cem\\u003eAPC\\u003c/em\\u003e mutations were identified in only 23% of cases, compared with 70\\u0026ndash;80% in AC (\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e). Similarly, activating mutations in \\u003cem\\u003eKRAS, NRAS, BRAF, PIK3CA\\u003c/em\\u003e, and \\u003cem\\u003eFBXW7\\u003c/em\\u003e were relatively uncommon. These findings are consistent with previously published studies (\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e) and with recent data reported by An Y, et al. (\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e), further supporting the view that SRCC represents a genetically distinct subtype relative to conventional colorectal adenocarcinoma.\\u003c/p\\u003e \\u003cp\\u003eComparison with previously published SRCC cohorts revealed both shared and unique features (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e). Recurrent alterations in \\u003cem\\u003eSMAD4\\u003c/em\\u003e and \\u003cem\\u003eTP53\\u003c/em\\u003e, together with low frequencies of \\u003cem\\u003eAPC, KRAS, NRAS, PIK3CA\\u003c/em\\u003e, and \\u003cem\\u003eFBXW7\\u003c/em\\u003e, were consistently observed across studies (\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e). In contrast, our cohort showed enrichment of alterations in \\u003cem\\u003eKMT2\\u003c/em\\u003e family genes, as well as \\u003cem\\u003eCIC, ZFHX3, CSMD3\\u003c/em\\u003e, and \\u003cem\\u003eZMYM3\\u003c/em\\u003e, which were infrequently or absence reported in earlier studies. These differences may reflect population-specific variation and/or improved detection enabled by broader targeted sequencing, highlighting the importance of subtype- and population-aware genomic profiling in SRCC.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab3\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 3\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eComparison of Mutation Frequencies Across SRCC Cohorts\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"8\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c8\\\" colnum=\\\"8\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eGene\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eOur SRCC\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eOur SRCC excl. TMB-H\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eWen-Wu L, 2025 (\\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eAn Y, 2025 (\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003ePuccini A, 2022 (\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eKorphaisarn K, 2019 (\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003eNam YJ, 2018 (\\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e)\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eSample size\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e39\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e33\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e125\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e37\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e54\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e35\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e5\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eNGS platform\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e517 cancer-related genes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e517 cancer-related genes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eOncoKB\\u003c/p\\u003e \\u003cp\\u003e1,216 genes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e520 or 1021-gene panel\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e592 whole-gene targets or 44-gene oncogenic hot-spot targets\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e46- or 50-cancer-related genes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003eWES/RNA-seq\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eKMT2C\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e49%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e39%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026ndash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e11.4%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u0026ndash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e\\u0026ndash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eKMT2D\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e26%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e12%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026ndash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e11.9%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u0026ndash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e\\u0026ndash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eKMT2A\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e26%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e12%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026ndash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026ndash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u0026ndash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e\\u0026ndash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eSMAD4\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e49%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e39%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e20.3%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e32%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e10%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e14.3%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e20%\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eTP53\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e41%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e45%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e48%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e70%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e47.7%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e60%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e40%\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eCIC\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e31%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e21%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026ndash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026ndash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u0026ndash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e\\u0026ndash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eZFHX3\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e28%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e21%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026ndash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026ndash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u0026ndash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e\\u0026ndash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eCSMD3\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e26%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e15%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026ndash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026ndash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u0026ndash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e\\u0026ndash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eZMYM3\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e26%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e15%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026ndash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026ndash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u0026ndash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e\\u0026ndash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eAPC\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e23%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e12%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e15.3%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e32%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e22.2%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e2.9%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e20%\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eKRAS\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e15%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e9%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e17.6%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e16%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e20%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e11.4%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e40%\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eNRAS\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2.5%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026ndash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0%\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eBRAF\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e10%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e3%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e8.8%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026ndash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e13.3%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e8.6%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e\\u0026ndash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003ePIK3CA\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e10%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e3%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e8%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e2.2%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e2.9%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0%\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eFBXW7\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e15%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e6%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e2.4%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e2.9%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e20%\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eRNF43\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e5%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e3%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e19%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e15.6%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u0026ndash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e\\u0026ndash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eNOTCH1\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e18%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e3%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026ndash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026ndash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u0026ndash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e\\u0026ndash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eERBB2\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e13%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e3%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026ndash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026ndash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u0026ndash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e\\u0026ndash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003eThis study has several limitations. First, the retrospective design and relatively modest sample size reflect the rarity of SRCC and may limit statistical power and generalizability. Second, functional validation of the identified genomic alterations was not performed. Finally, the cohort was derived from two tertiary centers in Thailand, which may not fully capture the broader molecular heterogeneity of SRCC. Larger, multi-ethnic studies incorporating functional and multi-omic analyses will be necessary to validate these findings and further elucidate the molecular mechanisms underlying SRCC pathogenesis.\\u003c/p\\u003e\"},{\"header\":\"Conclusion\",\"content\":\"\\u003cp\\u003eComprehensive genomic profiling demonstrates that SRCC represents a molecularly distinct subtype of colorectal cancer. SRCC is characterized by enrichment of alterations in epigenetic regulators, frequent inactivation of \\u003cem\\u003eSMAD4\\u003c/em\\u003e, and a relative paucity of canonical colorectal cancer driver mutations. These findings highlight epigenetic dysregulation and TGF-β pathway disruption as central features of SRCC tumorigenesis. Taken together, our results provide a genomic rationale for subtype-specific precision oncology strategies, including epigenetic-targeted therapies and immunotherapy in selected patients, and underscore the need for larger prospective and mechanistic studies to further define therapeutic vulnerabilities in this aggressive colorectal cancer subtype.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\" \\u003cp\\u003e \\u003cstrong\\u003eEthics approval and consent to participate\\u003c/strong\\u003e \\u003cp\\u003e This study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Boards (IRBs) of Siriraj Hospital (Approval No. Si 703/2024) and Ramathibodi Hospital (Approval No. MURA 2020/834). The requirement for informed consent was waived due to the retrospective use of archival tissue samples. All patient data were anonymized to protect confidentiality\\u003c/p\\u003e \\u003ch2\\u003eConsent for publication\\u003c/h2\\u003e \\u003cp\\u003eNot applicable.\\u003c/p\\u003e \\u003cp\\u003e \\u003cstrong\\u003eCompeting interests\\u003c/strong\\u003e \\u003cp\\u003eAll authors declare that they do not have any personal or professional conflicts of interest and have not received financial support from the companies that produce and/or distribute the drugs, devices, or materials described in this report.\\u003c/p\\u003e \\u003ch2\\u003eFunding\\u003c/h2\\u003e \\u003cp\\u003eThis work was supported by the Siriraj Foundation (Grant No. D003808), the Health Systems Research Institute \\u0026ndash; Genomics Thailand Initiative Grant, and the Siriraj Core Research Facility (SiCRF). The funders had no role in the study design, data collection, analysis, interpretation, or writing of the manuscript.\\u003c/p\\u003e\\u003ch2\\u003eAuthor Contribution\\u003c/h2\\u003e\\u003cp\\u003eThe authors confirm contribution to the paper as follows: Conceptualization, KK; methodology, KK and MP; software, JA; validation, KK, JA, ER, and MP; formal analysis, JA, ER, and MP; investigation, KK, NN, NA, AP, ER, and CA; resources, KK, NN, CA, and MP; data curation, KK, JA, EK, and MP; writing\\u0026mdash;original draft preparation, KK and JA; writing\\u0026mdash;review and editing, KK, JA, NN, NA, AP, ER, CA, and MP; visualization, KK and JA; supervision, KK; project administration, KK; funding acquisition, KK and MP. All authors reviewed the results and approved the final version of the manuscript.\\u003c/p\\u003e\\u003ch2\\u003eAcknowledgement\\u003c/h2\\u003e \\u003cp\\u003eNot applicable\\u003c/p\\u003e\\u003ch2\\u003eData Availability\\u003c/h2\\u003e\\u003cp\\u003eThe sequencing data generated in this study have been deposited in the Genome Variation Map (GVM) repository at the National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences / China National Center for Bioinformation under accession number GVM001361. Additional processed data supporting the findings of this study are available from the corresponding author upon reasonable request.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\u003cli\\u003e\\u003cspan\\u003eKorphaisarn K, Morris V, Davis JS, Overman MJ, Fogelman DR, Kee BK, et al. Signet ring cell colorectal cancer: genomic insights into a rare subpopulation of colorectal adenocarcinoma. 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Prognostic role and clinicopathological features of SMAD4 gene mutation in colorectal cancer: a systematic review and meta-analysis. BMC Gastroenterol. 2021;21(1):297.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eKim H, Kim BH, Lee D, Shin E. Genomic alterations in signet ring and mucinous patterned colorectal carcinoma. Pathol Res Pract. 2019;215(10):152566.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eZhang WN, Liang WJ, Zhang Y, Liang MJ, Zhang MJ, Chen Q, et al. Molecular characteristics of patients with colorectal signet-ring cell carcinoma with different ABO blood groups. Heliyon. 2024;10(13):e34220.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eWolff RK, Hoffman MD, Wolff EC, Herrick JS, Sakoda LC, Samowitz WS, et al. Mutation analysis of adenomas and carcinomas of the colon: Early and late drivers. Genes Chromosomes Cancer. 2018;57(7):366\\u0026ndash;76.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eNam JY, Oh BY, Hong HK, Bae JS, Kim TW, Ha SY, et al. Molecular Characterization of Colorectal Signet-Ring Cell Carcinoma Using Whole-Exome and RNA Sequencing. Transl Oncol. 2018;11(4):836\\u0026ndash;44.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eWen Wu L, Wallam S, Wei AZ, Moy RH. Comprehensiveclinicogenomic profiling of signet ring cell carcinoma across multiple organsites. J Clin Oncol. 2025;43:3138\\u0026ndash;3138.\\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\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"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\":\"Signet ring cell colorectal carcinoma, colorectal cancer genomics, epigenetic regulators, KMT2 family, precision oncology\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-9128596/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-9128596/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003ch2\\u003eBackground\\u003c/h2\\u003e \\u003cp\\u003eSignet ring cell colorectal carcinoma (SRCC) is a rare and highly aggressive subtype of colorectal cancer associated with poor clinical outcomes. Despite its distinct pathology, SRCC remains underrepresented in large genomic datasets, and its molecular drivers are incompletely defined. We performed comprehensive genomic profiling to characterize the mutational landscape of SRCC and explore potential therapeutic implications.\\u003c/p\\u003e\\u003ch2\\u003eMethods\\u003c/h2\\u003e \\u003cp\\u003eFormalin-fixed, paraffin-embedded tumor specimens from 39 patients with histologically confirmed colorectal SRCC were analyzed using targeted next-generation sequencing with the Oncomine\\u0026trade; Comprehensive Assay Plus, covering 517 cancer-related genes. Somatic alterations were identified, and tumor mutational burden (TMB) was calculated.\\u003c/p\\u003e\\u003ch2\\u003eResults\\u003c/h2\\u003e \\u003cp\\u003eHigh tumor mutational burden (TMB-H; \\u0026ge;10 mutations/Mb) was observed in 15.4% of cases. The most frequently altered genes were \\u003cem\\u003eKMT2C\\u003c/em\\u003e and \\u003cem\\u003eSMAD4\\u003c/em\\u003e (49% each), followed by \\u003cem\\u003eTP53\\u003c/em\\u003e (41%), \\u003cem\\u003eCIC\\u003c/em\\u003e (31%), and \\u003cem\\u003eZFHX3\\u003c/em\\u003e (28%). Recurrent alterations were also detected in \\u003cem\\u003eKMT2A, KMT2D, CSMD3\\u003c/em\\u003e, and \\u003cem\\u003eZMYM3\\u003c/em\\u003e (each 26%). In contrast, canonical CRC driver mutations\\u0026mdash;including \\u003cem\\u003eAPC\\u003c/em\\u003e (23%), \\u003cem\\u003eKRAS\\u003c/em\\u003e (15%), \\u003cem\\u003eNRAS\\u003c/em\\u003e (2.5%), \\u003cem\\u003eBRAF\\u003c/em\\u003e (10%), and \\u003cem\\u003ePIK3CA\\u003c/em\\u003e (10%)\\u0026mdash;were less frequent than typically reported in conventional colorectal adenocarcinoma.\\u003c/p\\u003e\\u003ch2\\u003eConclusions\\u003c/h2\\u003e \\u003cp\\u003eColorectal SRCC exhibits a distinct genomic landscape characterized by frequent alterations in epigenetic regulators, particularly the \\u003cem\\u003eKMT2\\u003c/em\\u003e gene family, and recurrent disruption of the TGF-β signaling pathway through \\u003cem\\u003eSMAD4\\u003c/em\\u003e inactivation. The relative paucity of canonical colorectal cancer driver mutations suggests alternative oncogenic mechanisms underlying SRCC tumorigenesis. These findings provide genomic insights into this aggressive subtype and may inform future subtype-specific therapeutic strategies, including epigenetic-targeted therapies and immunotherapy in selected patients.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Distinct Genomic Landscape of Colorectal Signet Ring Cell Carcinoma Reveals Frequent KMT2 Family Alterations and SMAD4 Inactivation\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2026-04-19 07:50:10\",\"doi\":\"10.21203/rs.3.rs-9128596/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0},{\"type\":\"reviewerAgreed\",\"content\":\"287426004434025848196974082142290629735\",\"date\":\"2026-05-15T12:47:32+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"225249281358764798492221725983203962907\",\"date\":\"2026-05-14T11:50:27+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewersInvited\",\"content\":\"\",\"date\":\"2026-04-08T07:51:32+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorAssigned\",\"content\":\"\",\"date\":\"2026-04-07T07:38:37+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorInvited\",\"content\":\"\",\"date\":\"2026-04-01T21:34:05+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"checksComplete\",\"content\":\"\",\"date\":\"2026-03-30T20:38:04+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"submitted\",\"content\":\"BMC Cancer\",\"date\":\"2026-03-30T13:35:22+00:00\",\"index\":\"\",\"fulltext\":\"\"}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"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\":\"74e3d7bc-c09a-4095-80f1-dfff4e8fe076\",\"owner\":[],\"postedDate\":\"April 19th, 2026\",\"published\":true,\"recentEditorialEvents\":[{\"type\":\"reviewerAgreed\",\"content\":\"287426004434025848196974082142290629735\",\"date\":\"2026-05-15T12:47:32+00:00\",\"index\":87,\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"225249281358764798492221725983203962907\",\"date\":\"2026-05-14T11:50:27+00:00\",\"index\":86,\"fulltext\":\"\"}],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"under-review\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2026-04-19T07:50:11+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2026-04-19 07:50:10\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-9128596\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-9128596\",\"identity\":\"rs-9128596\",\"version\":[\"v1\"]},\"buildId\":\"XKTyCvWXoU3ODBz1xrDgd\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}