{"paper_id":"0b867c5e-feea-44d9-8da6-bc9d857c03e9","body_text":"Mutation profile and molecular heterogeneity in mismatch repair deficient endometrial carcinoma | 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 Mutation profile and molecular heterogeneity in mismatch repair deficient endometrial carcinoma Yumeng Cai, Jing Wang, Zijuan Zhang, Pan Li, Jiuyuan Fang, Liang Cui, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4537456/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Endometrial carcinoma (EC) with deficient DNA mismatch repair (dMMR) is a specific molecular entity with unique clinicopathological features. Herein, we depicted the mutation profile of dMMR ECs and explored the molecular heterogeneity among dMMR subgroups with different etiologies. Next-generation sequencing based on a 1021-gene panel was applied to 74 dMMR ECs and 43 proficient MMR (pMMR) ECs. In addition, methylation-specific PCR was applied for accessing MLH1 promoter hypermethylation ( MLH1 me+ ) in dMMR cases. The mutation rates of PTEN , ARID1A , KRAS , and MSH2 were significantly higher in dMMR group, while the CTNNB1 and MSH3 mutations were more commonly observed in pMMR group (p < 0.05). Compared to pMMR ECs, dMMR ECs had significantly higher alteration frequencies in RTK-RAS, NOTCH, Cell Cycle and HRR pathway (p < 0.05). Remarkably, the interaction patterns within and across pathways were different between dMMR and pMMR groups. Intriguingly, no CTNNB1 mutation were found in dMMR ECs, while half of the WNT-activated pMMR ECs were CTNNB1 mutated, which were generally mutually exclusive with other WNT pathway key genes. The median tumor mutational burden (TMB) of dMMR ECs was significantly higher than pMMR ECs. However, ultra-high TMB value was related to pathogenic POLE mutation both in dMMR and pMMR ECs. As for dMMR subgroups, KEAP1 and FBXW7 mutations, which may have potential predictive effect of immunotherapy, were more prevalent in the Lynch subgroup. The Lynch subgroup also had significantly higher median TMB than the MLH1 me+ subgroup and Lynch-like subgroup. dMMR ECs has distinctive genomic profile with molecular heterogeneity, which may have potential prognostic and therapeutic implications. Endometrial carcinoma (EC) Deficient DNA mismatch repair (dMMR) Lynch syndrome MLH1 promoter hypermethylation Immunotherapy Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Endometrial carcinoma (EC) is one of the most prevalent gynecological malignancies worldwide and represents the sixth and eighth leading cause of cancer-related death in the United States and China, respectively 1 . In addition to histopathological classification, molecular characteristics have become another key dimension used to categorize ECs, providing additional prognostic and therapeutic information 2 . Roughly 30% of ECs present with DNA mismatch repair deficiency/microsatellite instability-high (dMMR/MSI-H) disease, which constitute a molecular entity with unique clinicopathological features. In previous studies, dMMR/MSI-H status has been reported to correlate with multiple adverse clinicopathologic variables in ECs, namely higher tumor grade, presence of lymphovascular invasion, and later tumor stage 3 . However, reports regarding the association between tumor MSI/MMR status and clinical outcome in EC patients have been conflicting 3 – 5 , indicating the potential heterogeneity among dMMR/MSI-H ECs. Three main etiologically distinct subgroups have been identified in dMMR cancers: The Lynch subgroup (pathogenic/likely pathogenic germline mutations in any of MLH1 , MSH2 , MSH6 , PMS2 , EPCAM ); the MLH1 -hypermethylated group ( MLH1 promoter hypermethylation without MLH1 germline mutations) ; and the Lynch-like subgroup (neither MMR gene germline mutations nor MLH1 promoter hypermethylation) 6 . Our previous studies have revealed notable differences in gene mutation profiles and signaling pathway interaction patterns among various dMMR /MSI-H subgroups in colorectal cancers 6 – 7 . In ECs, a recent study also suggested that MLH1 hypermethylated tumors would display certain distinct molecular features compared to MMR germline mutated tumors 8 . Nonetheless, the comprehensive spectrum of cancer driver genes and canonical signaling pathways alterations in dMMR/MSI-H ECs, with a detailed depiction of the molecular heterogeneity, remains to be fully illustrated. dMMR/MSI-H status is considered as an effective biomarker for immune checkpoint blockade (ICB) therapy in all solid tumors, including ECs, and typically associated with higher tumor mutational burden (TMB), increased tumor-infiltrating lymphocytes, and upregulated compensatory PD-L1 expression 3 , 9 . However, tumor immunogenicity and response to ICB therapy might vary among dMMR ECs subgroups with different etiologies 10 . Research into the molecular heterogeneity of dMMR/MSI-H ECs might provide more information for individualized clinical decision-making. In the present study, a consecutive dMMR EC cohort were investigated using comprehensive genomic profiling in comparison with pMMR ECs. We focused on key genes and pathways involved in tumorigenesis and progression, and aimed to reveal the characteristic genetic profiles and molecular heterogeneity of dMMR/MSI-H ECs, which might facilitate future individualized therapy. Materials and methods Patients enrollment and clinicopathological characteristics One hundred and seventeen patients with EC who underwent radical hysterectomy at Peking Union Medical Collage Hospital (PUMCH) between November 2017 and February 2019 were enrolled in this retrospective study. All patients did not receive anticancer treatment before surgery and the FIGO stage was recorded. The study was approved by the Institutional Review Board of PUMCH (approval number: S-K2006). Targeting sequencing Targeting sequening was performed using hybrid capture-based NGS as our previous research 6 , 7 . In brief, DNA was obtained from formalin-fixed paraffin-embedded (FFPE) tumors and normal tissues respectively. Genomic DNA libraries were applied to a 1021-gene panel including whole exons, selected introns of 288 genes, and selected regions of 733 genes (Supplementary Table 1). Single-stranded DNA nanoball (DNB) were sequenced by using 2x100 bp paired-end reads on the Gene + Seq-2000 instrument (GenePlus-Beijing). Genetic alternations, including single nucleotide variants (SNVs), small insertions and deletions (Indel), copy number variants (CNVs), and gene fusions/rearrangements, were compared with each paired normal sample to distinguish germline and somatic mutations. Sequencing data were analyzed by BWA 11 (version 0.7.12-r1039). SNVs and small Indels were identified by Mu Tect2 12 (version 4.1.8.1). Somatic mutations were identified by a VAF ≥ 1% and at least 5 high-quality reads (Phred score ≥ 30, mapping quality ≥ 30, and without paired-end reads bias). Gene mutations were annotated using ANNOVAR software 13 . CONTRA software 14 was used to detect CNVs and BreakDancer software was used to detect cancer-associated gene fusion 15 . To calculate TMB, the number of somatic, coding, nonsynonymous single nucleotide variants, and insertions and deletions mutations per megabase (Muts/Mb) of genome examined was defined. TMB levels were divided into two categories: TMB-L (tumor mutation burden-low, 1–9 Muts/Mb) and TMB-H (tumor mutation burden-high, ≥ 10 Muts/Mb). Signal pathways, key genes, and determination of mutational significance Ten canonical signaling pathways have been identified as frequently genetically altered in cancers, including the Cell Cycle pathway, Hippo pathway, MYC pathway, NOTCH pathway, NRF2 pathway, PI3K/Akt pathway, RTK-RAS pathway, TGF-β pathway, TP53 pathway, and WNT pathway, as determined by the Cancer Genome Atlas (TCGA) analysis 16 . Within our 1021-gene panel, seventy-six genes were assigned to pathways based on a combined revision of pathway analyses in previous literature and expert opinion 17 – 26 . Forty-seven genes have been identified as DDR-related genes in previous studies 27 , 28 29 and were assigned to eight DDR pathways: MMR, homologous recombination repair (HRR), Fanconi anemia (FA), base excision repair (BER), checkpoint factor (CPF), nucleotide excision repair (NER), nonhomologous end joining (NHEJ), and translesion DNA synthesis(Supplementary Table 2). Specific recurrent missense mutations, i.e., hotspot mutations, amplifications, or fusions of oncogenes were classified as activating events. Mutations of oncogenes were filtered according to the related documentation in the Catalog of Somatic Mutations in Cancer (COSMIC) 30 and OncoKB annotation 31 . For tumor suppressor genes, all loss-of-function mutations, including nonsense mutations, frame-shifting mutations, and canonical splice sites mutations were part of an inactivation event and defined as \"predicted deleterious\" mutations. Missense mutations were considered deleterious when identified in two or more of the following in silico functional analysis algorithms: predication score 0.0–0.05 in SIFT (sorting intolerant from tolerant) 32 , “possibly damaging” or “probably damaging” in polymorphism phenotyping − 2 (Polyphen2) 33 , or “medium” or “high” in MutationAssessor 34 . MLH1 promoter hypermethylation analysis MLH1 promoter hypermethylation was detected in cases with absent MLH1 expression and lack of MMR germline mutations. The detection was performed using methylation-specific PCR as previously described 7 . Statistical Analyses Categorical variables were expressed as percentages. The Chi-square test or Fisher's exact test was used to compare the frequencies of genetic alternations and to identify the coexistence or mutually exclusive associations. Correlation analysis was conducted using binary logistic regression. Analyses were performed with SPSS 25.0 software. All tests were two-sided, and a p-value < 0.05 was considered statistically significant. Results Gene mutations in dMMR and pMMR ECs Of the key genes involved in ten canonical tumor-related pathways and DDR pathways, PTEN , ARID1A , and PIK3CA had the highest mutation frequency in both dMMR (90.5%, 74.3%, 68.9%) and pMMR (67.4%, 37.2%, 62.8%) ECs, followed by KRAS (28.4%), PIK3R1 (25.7%), FAT1 (23.0%), ATM (21.6%), JAK1 (21.6%), TP53 (20.3%), FBXW7 (17.6%) and CREBBP (17.6%) in dMMR cohort, and TP53 (34.9%), PIK3R1 (27.9%), ATM (25.6%), NF1 (23.3%), POLE (23.3%), CTNNB1 (23.3%) and FBXW7 (20.9%) in pMMR cohort. In comparison with those in the pMMR group, the mutation rates of PTEN , ARID1A , KRAS , and MSH2 were significantly higher in the dMMR group (90.5% vs. 67.4%, p < 0.05; 74.3% vs. 37.2%, p < 0.05; 28.4% vs. 11.6%, p < 0.05; 16.2% vs. 2.3%, p < 0.05), while the CTNNB1 and MSH3 mutations were significantly higher in the pMMR group (0% vs. 23.3%, p < 0.05; 1.4% vs. 16.3%, p < 0.05) (Fig. 1 ). We also noted that alterations causing functional impairment of tumor suppressor gene ARID1A were mostly frameshift events affecting homopolymer sequences in dMMR group as opposed to pMMR group (65/87, 74.7% vs. 4/26, 15.4%, p < 0.05). Signaling pathway alternations in dMMR and pMMR ECs PI3K, RTK-RAS, NOTCH, TP53, and WNT pathways were altered at high frequency in both dMMR and pMMR ECs (100% and 88.4%; 75.7% and 41.9%; 62.2% and 32.6%; 36.5% and 48.8%; 32.4% and 41.9%; respectively). However, compared with the pMMR group, dMMR ECs harbored significantly higher rates of alterations in RTK-RAS, NOTCH, and Cell Cycle pathways (75.7% vs. 41.9%, p < 0.001; 62.2% vs. 32.6%, p = 0.002; 31.1% vs. 14.0%, p < 0.05), but a lower rate of TP53 pathway defection (48.8% vs. 36.5%, P = 0.19). Both dMMR and pMMR groups showed relatively low frequencies of alternation in Hippo, NRF2, TGF-β, and MYC pathway (23.0% and 18.6%; 21.6% and 16.3%; 17.6% and 14.0%; 16.2% and 0.09%, respectively) (Fig. 2 a). All of the 74 dMMR tumors exhibited alterations in at least one DDR pathway, compared with 19 out of 43 pMMR tumors (100% vs. 44.2%, p < 0.001). Of the eight included DDR pathways, MMR, HRR, and NER were identified as the most commonly defected ones in the dMMR group, with significantly higher alteration frequency compared to that in the pMMR group (100%vs. 25.6%, p < 0.001; 85.1% vs. 51.2%, p < 0.001; 32.4% vs. 23.3%, p = 0.29, respectively). The alteration rates of FA, BER, CPF, and NHEJ pathways did not present remarkable differences between dMMR and pMMR ECs (Fig. 2 b). Mutual relationships among key genes within canonical pathways in dMMR and pMMR ECs We then depicted the co-occurring and mutually exclusive relationship among mutations affecting key genes involved in PI3K, RTK-RAS, NOTCH, TP53, and WNT. PI3K, NOTCH, and TP53 pathways often had multiple alterations per tumor sample. Within the PI3K pathway, PTEN mutations were frequently accompanied by PIK3CA and/or PIK3R1 mutations in both dMMR and pMMR groups (77.0% and 67.4%, respectively). Co-alteration of PTEN , PIK3CA , and PIK3R1 , the three key PI3K signaling genes, were found in 9 out of 74 (12.2%) dMMR tumors and 7 out of 43 (16.3%) pMMR tumors, respectively. On the contrary, the RTK-RAS pathway contained predominantly mutually exclusively altered genes. KRAS was the most commonly mutated RTK-RAS signaling gene in both dMMR and pMMR groups and exhibited an almost perfect mutually exclusive pattern with other members in the RTK-RAS pathway. The only exceptions were the co-occurrence of non-canonical KRAS mutations (A59T, A146V) and NF1 deleterious mutations observed in four dMMR tumors. FGFR2 activating point mutations were found in a similar small fraction of tumors in dMMR and pMMR groups (10/74, 13.5%; 4/43, 9.3%, respectively), and largely mutually exclusive with other RTK-RAS alterations in the dMMR group (9/10, 90%). The alteration spectrum of the WNT signaling pathway displayed noticeable differences between dMMR and pMMR groups. All WNT-activated dMMR tumors were CTNNB1 wild-type, and the majority of them (16/24, 66.6%) displayed dysfunctional mutations in only one other key WNT signaling genes, including APC , RNF43 , AXIN1 , AXIN2 , and TCF7L2 . On the other hand, half of the WNT-activated pMMR tumors (10/18, 55.6%) were CTNNB1 mutated, which were generally mutually exclusive with alterations in other WNT pathway components. The remaining eight WNT-activated pMMR tumors showed high frequency (6/8, 75%) of co-alterations among key WNT pathway genes other than CTNNB1 . Mutations in key genes of the signaling pathways were shown in Fig. 3 . Mutual relationships between signaling pathways in dMMR and pMMR ECs The mutual relationship among the canonical signaling pathways differed remarkably between dMMR and pMMR ECs (Fig. 4 ). In the dMMR group, the only significant mutually exclusive relationship was found between TGF-β and NOTCH pathway (p < 0.05). No significant co-occurrence pattern among ten canonical pathways was observed. On the contrary, in the pMMR group, we identified numerous co-existence relationships within TGF-β, NOTCH, WNT, RTK-RAS, HIPPO, and NRF2 pathways (p < 0.05). Despite ranking the most commonly altered pathway, PI3K was not significantly concurrently altered with any other pathways in pMMR ECs. Tumor mutational burden (TMB) level in dMMR and pMMR ECs As shown in Fig. 5 a, the median TMB of 74 dMMR tumors was significantly higher than that of 43 pMMR tumors (37mut/Mb vs. 5mut/Mb, p < 0.0001). Of note, the highest TMB value (405 mut/Mb) was observed in only one dMMR tumor harboring somatic inactivating mutations in the exonuclease domain of POLE (S459F). Likewise, in the pMMR group, high TMB levels (≥ 10 mut/Mb, ranging from 41 to 186 mut/Mb) were generally found in tumors with deleterious POLE exo-domain mutations (P286R, S297F, F367S, V411L). Comparison of the mutation profile and TMB levels among ECs with different dMMR etiologies The 74 dMMR ECs were categorized into three subgroups according to different underlying mechanisms: the Lynch subgroup comprised 13 tumors (13/74, 17.6%) harboring pathogenic/likely pathogenic germline mutations in any of the MMR genes ( MLH1 , MSH2 , MSH6 , PMS2 , EPCAM ); the MLH1 -hypermethylated group consisted of 46 cases (46/74, 62.2%) with loss of MLH1 / PMS2 expression exhibiting MLH1 promoter hypermethylation without MLH1 germline mutations; and the Lynch-like subgroup consisted of the remaining cases with neither MMR gene germline mutations nor MLH1 promoter hypermethylation (15/74, 20.3%). Mutation profile of key genes varied by dMMR etiologies. KEAP1 (4/13, 30.8%) and FBXW7 (7/13, 53.8%) mutations were significantly more prevalent in Lynch subgroup (p < 0.001), whereas MSH2 (8/15, 53.3%) and RECQL4 mutations (2/15, 13.3%) were significantly enriched in Lynch-like subgroup (p < 0.001 and p < 0.05, respectively). Tumors in the MLH1 me+ subgroup tended to show more frequent JAK1 alterations, mainly frame-shift events, compared to Lynch and Lynch-like subgroup (30.4% vs. 7.7% vs. 6.7%, p = 0.062). The alteration spectrum of key signaling pathways, however, did not show notable differences between the three subgroups (Fig. 6 ). Median TMB was significantly higher in the Lynch subgroup (42mt/Mb, ranging from 24 to 73mt/Mb, p < 0.05), in comparison with the MLH1 me+ subgroup (29mt/Mb, ranging from 6 to 405mt/Mb) and Lynch-like subgroup (39mt/Mb, ranging from 11 to 80mt/Mb)(Figure 5 b). Discussion In this study, we performed a comprehensive molecular study of a retrospective EC cohort with 74 dMMR and 43 pMMR patients. Using targeted panel sequencing for 1021 genes, we analyzed gene mutation frequency and genetic mutual relationship of ten canonical cancer signaling pathways, namely Cell Cycle, Hippo, MYC, NOTCH, NRF2, PI3Kinase/Akt, RTK-RAS, TGF-β, P53, and β-catenin/WNT signaling. DDR-related pathway alterations and TMB levels were also evaluated. According to MMR gene germline mutations and MLH1 promoter methylation status, “Lynch”, “Lynch-like” and “ MLH1 me+ ” subgroups of dMMR ECs were identified and compared. We profiled the landscape of gene mutations and key pathway alterations in dMMR ECs, delineated patterns of co-occurrence and mutual exclusivity, and further explored the molecular heterogeneity of dMMR ECs. To illustrate the general genetic feature of dMMR ECs, we first compare the mutation profile between dMMR and pMMR group. In consistent with previous reports, the mutation frequencies of PTEN and KRAS, the two key driver genes of EC carcinogenesis, were significantly higher in dMMR group compared to pMMR group. Also, we found that ARID1A , which encodes the subunit of switch/sucrose non-fermentable (SWI/SNF) complex involved in the chromatin remodeling process, was remarkably mutated in our dMMR group. The correlation of increased ARID1A mutation frequency and dMMR/MSI-H phenotype has been described extensively in various tumor types 35 . However, unlike previous studies, which suggested loss of ARID1A expression was more prevalent in sporadic dMMR/MSI-H tumors, we found that ARID1A mutation was not correlated with the MLH1 me+ phenotype in our cohort. Although some in vivo studies suggested that ARID1A downregulation might mediate modest rather than global DNA methylation regulation early in tumorigenesis 36 , 37 , it is not completely clear if ARID1A mutation is the result or the cause of MMR deficiency secondary to promoter hypermethylation, especially in ECs. Moreover, the ARID1A mutations in all three dMMR subgroups were predominantly frameshift events affecting tandem repeat sequences, indicating that they might represent mutational targets of MSI in ECs regardless of the mechanisms underlying MMR deficiency. On pathway-level, dMMR ECs had significantly higher alternation frequencies in RTK-RAS, NOTCH, and Cell Cycle pathway compared to pMMR ECs. The genetic alterations within PI3K and RTK-RAS pathway, the two most commonly altered signaling pathways in ECs, however, did not show remarkable differences between the dMMR and pMMR groups of ECs. Notably, defections in DDR pathways other than MMR, particularly the HRR pathway, were found to be common among our dMMR cases. In a pan-cancer analysis of co-mutations among DDR pathways, the co-existence of HRR and MMR aberrations was associated with higher TMB levels, increased tumor neoantigen load, and upregulated immune gene expression, and considered as a potential biomarker for ICB therapy in some types of non-gynecologic tumors 38 . In ECs, co-mutations in the DDR pathway warrant more thorough exploration, in the hope of developing new immunotherapy predictors. The mutual relationships of ten canonical pathways displayed remarkable differences between the dMMR and pMMR ECs. We observed multiple co-existent canonical signaling pathways in the pMMR group, rather than the dMMR group, which indicates a potential for combination therapy in pMMR ECs. We further explored the interactions within pathways both in dMMR and pMMR ECs, which have not been addressed in previous studies. The most significant difference was reflected in the WNT signaling pathway. Among ECs displaying genetic alterations in WNT pathway components, we found that CTNNB1 mutations, generally considered as the hallmark of aberrant Wnt/β-catenin signaling, were completely absent in the dMMR group while presented in half of the pMMR group. Several prior studies revealed the association of CTNNB1 mutation with increased risk of recurrence in ECs but generally included a heterogeneous population comprised of both dMMR and pMMR cases 39 , 40 . Our data suggested that the prognostic value of CTNNB1 mutation in ECs should focus on pMMR cases in future studies. Moreover, the interaction relationship among other key WNT signaling pathway genes displayed noticeable differences between dMMR and pMMR groups, manifesting as mutually exclusive relationships in dMMR group, and co-occurrence relationships in pMMR group. This finding indicated the different modes of WNT signaling pathway aberrations between dMMR and pMMR ECs, suggesting that alternative mechanisms might be responsible for WNT pathway activation in EC tumorigenesis. In most cases, PI3K signaling aberrations were caused by PTEN mutations accompanied by PIK3CA and/or PIK3R1 mutations, verifying the synergistic effects of PI3K pathway mutations in EC tumorigenesis proposed by previous studies 41 , 42 . KRAS mutation was shown to be the primary mechanism of RTK-RAS signaling activation. Whilst canonical KRAS mutations do not co-exist with other alterations within the RTK-RAS pathway, non-canonical KRAS mutations generally co-occur with NF1 dysfunctional mutations, suggesting that such pairs of mutations might act cooperatively to provide a selective advantage in tumorigenesis of ECs regardless of MMR/MSI status 43 . It has been well-established that dMMR/MSI-H status is a favorable prognostic factor in certain cancer types and a predictor for anti-PD-1/PD-L1 immunotherapy efficacy in solid tumors. However, previous studies reported inconsistent findings comparing outcomes between dMMR/MSI-H ECs and ECs of NSMP 3 , 44 , 45 . It has also been reported that ECs harboring MLH1 hypermethylation showed poorer response to ICB therapy compared with Lynch syndrome-associated ECs, which implied the different immunotherapy response-associated genetic alternations may exist in etiologically distinct EC subgroups. In line with a recent EC study, we noticed a tendency of increased JAK1 mutations in the MLH1 me+ subgroup. JAK pathway alterations had been proved to correlate with primary or acquired ICB resistance 10 . Therefore, we hypothesize that molecular heterogeneity among dMMR etiological subgroups might partially account for the different immunotherapy outcomes. The Lynch-associated ECs in our cohort were enriched for FBXW7 and KEAP1 mutations. The association of FBXW7 and/or KEAP1 mutations with poor response to checkpoint blockade therapy in multiple solid tumors, including ECs, has been evidenced in clinical studies 46 , 47 , 48 . In recent published in vivo studies, FBXW7 and KEAP1 were suggested to confer immune checkpoint blockade by alternating tumor microenvironment instead of directly modifying the tumor, through the way of decreasing T-cell infiltration and downregulating IFN-γ signaling, respectively 49 , 50 . On the other hand, Lynch group had the highest median TMB level among the three dMMR subgroups, implicating a \"hotter\" tumor microenvironment favoring immunotherapy. This seemingly paradox finding strengthened the notion that any single biomarker could be inaccurate to predict immunotherapy response. It is rational to combine different biomarkers representing not only tumor intrinsic features but also the complex interplay between the tumor and its microenvironment. To summarize, our comprehensive molecular study uncovered significant differences in the mutation spectrum and interaction patterns of key genes and pathways between dMMR and pMMR ECs. We also revealed the molecular heterogeneity among dMMR subgroups with different etiologies. Our findings may have potential prognostic and therapeutic implications. Abbreviations BER, base excision repair; COSMIC, Catalog of Somatic Mutations in Cancer; CNVs, copy number variants; CPF, checkpoint factor; dMMR, deficient DNA mismatch repair; DNB, DNA nanoball; EC, endometrial carcinoma; FA, Fanconi anemia; FFPE, formalin-fixed paraffin-embedded; HRR, homologous recombination repair; ICB, immune checkpoint blockade; Indel, small insertions and deletions; MSI-H, microsatellite instability-high; MLH1 me+ , MLH1 promoter hypermethylation; NER, nucleotide excision repair; NHEJ, nonhomologous end joining; pMMR, proficient DNA mismatch repair; Polyphen2, polymorphism phenotyping-2; SWI/SNF, switch/sucrose non-fermentable. SNVs, single nucleotide variants; SIFT, sorting intolerant from tolerant; TCGA, the Cancer Genome Atlas; TMB, tumor mutational burden. Declarations Ethics approval and consent to participate The study was approved by the Institutional Review Board of PUMCH (approval number: S-K2006). Consent for publication Not applicable Availability of data and materials All data, analytic methods, and study materials will be made available to other researchers upon request via emailing to [email protected] . Competing interests The authors declare that they have no competing interests Funding This research is supported by: Supported by the Non-profit Central Research Institute Fund of Chinese Academy of Medical Sciences (2021-RW320-004) National High Level Hospital Clinical Research Funding (2022-PUMCH-B-063) Authors' contributions CYM and WJ performed the research and composed the manuscript; WJ contributed to data analysis and modification of the manuscript. ZZJ contributed to data collection and data analysis; CL and PJY conducted the exprements; LP, FJY and CL contributed to the data analysis; LP drew and revised Figures; ZYH helped to collect data; YY, WHW and LZY contributed to the conception and design of the study; and all authors have read and approved the final manuscript. Acknowledgements Not applicable References Bruggmann D, Ouassou K, Klingelhofer D, Bohlmann MK, Jaque J, Groneberg DA. Endometrial cancer: mapping the global landscape of research. J Transl Med. Oct 2020;12(1):386. Bell DW, Ellenson LH. Molecular Genetics of Endometrial Carcinoma. Annu Rev Pathol Jan. 2019;24:14:339–67. McMeekin DS, Tritchler DL, Cohn DE, et al. Clinicopathologic Significance of Mismatch Repair Defects in Endometrial Cancer: An NRG Oncology/Gynecologic Oncology Group Study. J Clin Oncol Sep. 2016;1(25):3062–8. Cosgrove CM, Cohn DE, Hampel H, et al. 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Cancer Epidemiol Biomarkers Prev. Jan 2016;25(1):193–200. Forbes SA, Beare D, Boutselakis H, et al. COSMIC: somatic cancer genetics at high-resolution. Nucleic Acids Res Jan. 2017;4(D1):D777–83. Chakravarty D, Gao J, Phillips SM et al. Jul. OncoKB: A Precision Oncology Knowledge Base. JCO Precis Oncol. 2017;2017. Sim NL, Kumar P, Hu J, Henikoff S, Schneider G, Ng PC. SIFT web server: predicting effects of amino acid substitutions on proteins. Nucleic Acids Res Jul. 2012;40:W452–457. (Web Server issue). Adzhubei IA, Schmidt S, Peshkin L, et al. A method and server for predicting damaging missense mutations. Nat Methods Apr. 2010;7(4):248–9. Reva B, Antipin Y, Sander C. Predicting the functional impact of protein mutations: application to cancer genomics. Nucleic Acids Res Sep. 2011;1(17):e118. Toumpeki C, Liberis A, Tsirkas I, et al. The Role of ARID1A in Endometrial Cancer and the Molecular Pathways Associated With Pathogenesis and Cancer Progression. Vivo May-Jun. 2019;33(3):659–67. Lakshminarasimhan R, Andreu-Vieyra C, Lawrenson K, et al. Down-regulation of ARID1A is sufficient to initiate neoplastic transformation along with epigenetic reprogramming in non-tumorigenic endometriotic cells. Cancer Lett Aug. 2017;10:401:11–9. Yamada H, Takeshima H, Fujiki R, et al. ARID1A loss-of-function induces CpG island methylator phenotype. Cancer Lett Apr. 2022;28:532:215587. Wang Z, Zhao J, Wang G, et al. Comutations in DNA Damage Response Pathways Serve as Potential Biomarkers for Immune Checkpoint Blockade. Cancer Res Nov. 2018;15(22):6486–96. Kurnit KC, Kim GN, Fellman BM, et al. CTNNB1 (beta-catenin) mutation identifies low grade, early stage endometrial cancer patients at increased risk of recurrence. Mod Pathol Jul. 2017;30(7):1032–41. Travaglino A, Raffone A, Saccone G, et al. Immunohistochemical Nuclear Expression of beta-Catenin as a Surrogate of CTNNB1 Exon 3 Mutation in Endometrial Cancer. Am J Clin Pathol Apr. 2019;2(5):529–38. Cheung LW, Hennessy BT, Li J, et al. High frequency of PIK3R1 and PIK3R2 mutations in endometrial cancer elucidates a novel mechanism for regulation of PTEN protein stability. Cancer Discov. Jul 2011;1(2):170–85. Oda K, Okada J, Timmerman L, et al. PIK3CA cooperates with other phosphatidylinositol 3'-kinase pathway mutations to effect oncogenic transformation. Cancer Res Oct. 2008;1(19):8127–36. Philpott C, Tovell H, Frayling IM, Cooper DN, Upadhyaya M. The NF1 somatic mutational landscape in sporadic human cancers. Hum Genomics Jun. 2017;21(1):13. Cosgrove CM, Tritchler DL, Cohn DE, et al. An NRG Oncology/GOG study of molecular classification for risk prediction in endometrioid endometrial cancer. Gynecol Oncol. Jan 2018;148(1):174–80. Diaz-Padilla I, Romero N, Amir E, et al. Mismatch repair status and clinical outcome in endometrial cancer: a systematic review and meta-analysis. Crit Rev Oncol Hematol. Oct 2013;88(1):154–67. Cheng W, Xu B, Zhang H, Fang S. Lung adenocarcinoma patients with KEAP1 mutation harboring low immune cell infiltration and low activity of immune environment. Thorac Cancer Sep. 2021;12(18):2458–67. Chen X, Su C, Ren S, Zhou C, Jiang T. Pan-cancer analysis of KEAP1 mutations as biomarkers for immunotherapy outcomes. Ann Transl Med. Feb 2020;8(4):141. Choi M, Kadara H, Zhang J, et al. Mutation profiles in early-stage lung squamous cell carcinoma with clinical follow-up and correlation with markers of immune function. Ann Oncol Jan. 2017;1(1):83–9. Liu J, Wei L, Hu N et al. FBW7-mediated ubiquitination and destruction of PD-1 protein primes sensitivity to anti-PD-1 immunotherapy in non-small cell lung cancer. J Immunother Cancer Sep 2022;10(9). Fox DB, Ebright RY, Hong X, et al. Downregulation of KEAP1 in melanoma promotes resistance to immune checkpoint blockade. NPJ Precis Oncol Mar. 2023;2(1):25. Additional Declarations No competing interests reported. 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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-4537456\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":312460280,\"identity\":\"b9095b1f-2ee2-45b4-839f-f7983962679d\",\"order_by\":0,\"name\":\"Yumeng Cai\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Peking Union Medical College Hospital\",\"correspondingAuthor\":false,\"submittingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Yumeng\",\"middleName\":\"\",\"lastName\":\"Cai\",\"suffix\":\"\"},{\"id\":312460281,\"identity\":\"1b97ce27-9aba-4540-bdbc-711882b3a4b8\",\"order_by\":1,\"name\":\"Jing 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liang\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAtklEQVRIiWNgGAWjYBACPmYILQcimInSwgZVZkyCFiid2EC8FnbmZw+//Dmcvl0iO/FzAYNdHhEOYzM3luE5nLtzRu5m6RkMycVEaGEwk5aQOJy74UbuNmYehgNgFxLQwv5NWsLgcLoBCVp4zCQ/JBxOIElLmTTDgXTDnT1vN0vzGCQT1sLPf3yb5I8/1vLm7LkbP/NU2BHWAgJA9zQzGICZBsSoBwLGHwx1RCseBaNgFIyCEQgAtkk0An9sA0gAAAAASUVORK5CYII=\",\"orcid\":\"\",\"institution\":\"Peking Union Medical College Hospital\",\"correspondingAuthor\":true,\"submittingAuthor\":false,\"prefix\":\"\",\"firstName\":\"zhiyong\",\"middleName\":\"\",\"lastName\":\"liang\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2024-06-06 05:29:19\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-4537456/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-4537456/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":59137345,\"identity\":\"2a9899ca-11c9-4afa-982c-f7f5b0655196\",\"added_by\":\"auto\",\"created_at\":\"2024-06-26 18:58:43\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":137259,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eMutation profile of recurrently mutated genes in the dMMR and pMMR ECs. \\u003cstrong\\u003e(a) \\u003c/strong\\u003ePrevalence of most frequently mutated genes in the dMMR ECs compared with that in the pMMR ECs. \\u003cstrong\\u003e(b)\\u003c/strong\\u003e Prevalence of the most frequently mutated genes in the pMMR ECs compared with that in the dMMR ECs. dMMR deficient mismatch repair, pMMR proficient mismatch repair, EC endometrial carcinoma, MSI microsatellite instability. Asterisk (*) significant difference in mutation prevalence (Chi-square test or Fisher’s exact test, *: p \\u0026lt; 0.05).\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4537456/v1/7f2da8e6c002f8660cca2b88.png\"},{\"id\":59137347,\"identity\":\"d82caced-dc7f-4a80-9d0b-cf5b3be2df12\",\"added_by\":\"auto\",\"created_at\":\"2024-06-26 18:58:44\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":219075,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eDistribution of WNT, RTK-RAS, PI3K, TGF-β, Cell Cycle, Hippo, NRF2, NOTCH, MYC and TP53 pathways and DNA damage repair pathways with respect to their mutation frequency in the dMMR and pMMR ECs. \\u003cstrong\\u003e(a)\\u003c/strong\\u003e Distribution of WNT, RTK-RAS, PI3K, TGF-β, Cell Cycle, Hippo, NRF2, NOTCH, MYC and TP53 pathways. \\u003cstrong\\u003e(b)\\u003c/strong\\u003e Distribution of DNA damage repair pathways (apart from mismatch repair pathway); dMMR deficient mismatch repair, pMMR proficient mismatch repair, EC endometrial carcinoma, HRR homologous recombination repair, FA Fanconi anemia, BER base excision repair,CPFs checkpoint factors, NER nucleotide excision repair, NHEJ nonhomologous end joining, and TLS translesion synthesis. Asterisk (*) significant difference in mutational prevalence (Chi-square test or Fisher’s exact test, *: p \\u0026lt; 0.05, **: p \\u0026lt; 0.01, ***: p \\u0026lt; 0.001).\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4537456/v1/8d8f8d65fd67f911e6d4f4de.png\"},{\"id\":59137343,\"identity\":\"eca82b86-c6e1-4bf3-8db9-71ac93daef81\",\"added_by\":\"auto\",\"created_at\":\"2024-06-26 18:58:43\",\"extension\":\"png\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":247280,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eMutation profile of key genes in the dMMR and pMMR ECs. Columns represent individual cases sorted by MMR status and dMMR subgroups. Tracks indicate dMMR status, MLH1 promoter hypermethylation status, MMR gene germline mutation status and WNT, RTK-RAS, PI3K, TGF-β, Cell Cycle, Hippo, NRF2, NOTCH, MYC and TP53 pathway gene mutations. Individual genes are listed in rows. dMMR deficient mismatch repair, pMMR proficient mismatch repair, MSI microsatellite stability.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4537456/v1/9fc30dfa3baa91bb36a344b6.png\"},{\"id\":59137350,\"identity\":\"152c771b-64bf-4e20-83aa-2897c86952dc\",\"added_by\":\"auto\",\"created_at\":\"2024-06-26 18:58:44\",\"extension\":\"png\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":121148,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eMutual relationships among the WNT, RTK-RAS, PI3K, TGF-β, Cell Cycle, Hippo, NRF2, NOTCH, MYC and TP53 pathways between \\u003cstrong\\u003e(a)\\u003c/strong\\u003e dMMR and \\u003cstrong\\u003e(b)\\u003c/strong\\u003e pMMR EC. Co-occurrence and mutual exclusivity were identified using the Chi square test or Fisher’s exact test. The significance level is encoded in color representing −log10 (p value).\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure4.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4537456/v1/b756dca1ac33ba44f919653b.png\"},{\"id\":59137346,\"identity\":\"1ae63c57-5ed2-4c02-b5c0-86051e471a27\",\"added_by\":\"auto\",\"created_at\":\"2024-06-26 18:58:43\",\"extension\":\"png\",\"order_by\":5,\"title\":\"Figure 5\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":90881,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eComparison of TMB in the \\u003cstrong\\u003e(a)\\u003c/strong\\u003e dMMR EC and pMMR EC and \\u003cstrong\\u003e(b)\\u003c/strong\\u003e dMMR subgroups. MLH1\\u003csup\\u003eme+\\u003c/sup\\u003e hypermethylated MLH1 promoter, Lynch Lynch syndrome-associated, Lynch-like Lynch-like syndrome-associated. Asterisk (*) significant difference in mutational prevalence (Chi-square test or Fisher’s exact test, *: p \\u0026lt; 0.05, ****: p \\u0026lt; 0.0001).\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure5.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4537456/v1/a678e70b2b08dfb5ba52bde9.png\"},{\"id\":59137348,\"identity\":\"f1035fb7-adfd-4476-b875-bf97f3854f26\",\"added_by\":\"auto\",\"created_at\":\"2024-06-26 18:58:44\",\"extension\":\"png\",\"order_by\":6,\"title\":\"Figure 6\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":83162,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eComparison of gene mutation frequencies in the dMMR EC subgroups. MLH1\\u003csup\\u003eme+\\u003c/sup\\u003e hypermethylated MLH1 promoter, Lynch Lynch syndrome-associated, Lynch-like Lynch-like syndrome-associated. Asterisk (*) significant difference in mutational prevalence (Chi-square test or Fisher’s exact test, *: p \\u0026lt; 0.05, ***: p \\u0026lt;0.001).\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure6.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4537456/v1/aea7c5261ac028dda51bce93.png\"},{\"id\":66926791,\"identity\":\"c11e32e3-0915-4529-bf6c-735edef19aba\",\"added_by\":\"auto\",\"created_at\":\"2024-10-18 06:17:36\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":1786669,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4537456/v1/ce1853b5-fdac-470f-8619-8b482d7efa5f.pdf\"},{\"id\":59137349,\"identity\":\"a76bb283-375a-4a8e-a2c9-b0741bf18a7e\",\"added_by\":\"auto\",\"created_at\":\"2024-06-26 18:58:44\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":483682,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"SupplementTable.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4537456/v1/cf52800267a4c818bc276cf7.pdf\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Mutation profile and molecular heterogeneity in mismatch repair deficient endometrial carcinoma\",\"fulltext\":[{\"header\":\"Introduction\",\"content\":\"\\u003cp\\u003eEndometrial carcinoma (EC) is one of the most prevalent gynecological malignancies worldwide and represents the sixth and eighth leading cause of cancer-related death in the United States and China, respectively\\u003csup\\u003e\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e\\u003c/sup\\u003e. In addition to histopathological classification, molecular characteristics have become another key dimension used to categorize ECs, providing additional prognostic and therapeutic information\\u003csup\\u003e\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e\\u003c/sup\\u003e.\\u003c/p\\u003e \\u003cp\\u003eRoughly 30% of ECs present with DNA mismatch repair deficiency/microsatellite instability-high (dMMR/MSI-H) disease, which constitute a molecular entity with unique clinicopathological features. In previous studies, dMMR/MSI-H status has been reported to correlate with multiple adverse clinicopathologic variables in ECs, namely higher tumor grade, presence of lymphovascular invasion, and later tumor stage\\u003csup\\u003e\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e\\u003c/sup\\u003e. However, reports regarding the association between tumor MSI/MMR status and clinical outcome in EC patients have been conflicting\\u003csup\\u003e\\u003cspan additionalcitationids=\\\"CR4\\\" citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e\\u003c/sup\\u003e, indicating the potential heterogeneity among dMMR/MSI-H ECs. Three main etiologically distinct subgroups have been identified in dMMR cancers: The Lynch subgroup (pathogenic/likely pathogenic germline mutations in any of \\u003cem\\u003eMLH1\\u003c/em\\u003e, \\u003cem\\u003eMSH2\\u003c/em\\u003e, \\u003cem\\u003eMSH6\\u003c/em\\u003e, \\u003cem\\u003ePMS2\\u003c/em\\u003e, \\u003cem\\u003eEPCAM\\u003c/em\\u003e); the \\u003cem\\u003eMLH1\\u003c/em\\u003e-hypermethylated group (\\u003cem\\u003eMLH1\\u003c/em\\u003e promoter hypermethylation without \\u003cem\\u003eMLH1\\u003c/em\\u003e germline mutations) ; and the Lynch-like subgroup (neither MMR gene germline mutations nor \\u003cem\\u003eMLH1\\u003c/em\\u003e promoter hypermethylation)\\u003csup\\u003e\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e\\u003c/sup\\u003e. Our previous studies have revealed notable differences in gene mutation profiles and signaling pathway interaction patterns among various dMMR /MSI-H subgroups in colorectal cancers\\u003csup\\u003e\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e\\u003c/sup\\u003e. In ECs, a recent study also suggested that \\u003cem\\u003eMLH1\\u003c/em\\u003e hypermethylated tumors would display certain distinct molecular features compared to MMR germline mutated tumors\\u003csup\\u003e\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e\\u003c/sup\\u003e. Nonetheless, the comprehensive spectrum of cancer driver genes and canonical signaling pathways alterations in dMMR/MSI-H ECs, with a detailed depiction of the molecular heterogeneity, remains to be fully illustrated. dMMR/MSI-H status is considered as an effective biomarker for immune checkpoint blockade (ICB) therapy in all solid tumors, including ECs, and typically associated with higher tumor mutational burden (TMB), increased tumor-infiltrating lymphocytes, and upregulated compensatory PD-L1 expression\\u003csup\\u003e\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e\\u003c/sup\\u003e. However, tumor immunogenicity and response to ICB therapy might vary among dMMR ECs subgroups with different etiologies\\u003csup\\u003e\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e\\u003c/sup\\u003e. Research into the molecular heterogeneity of dMMR/MSI-H ECs might provide more information for individualized clinical decision-making.\\u003c/p\\u003e \\u003cp\\u003eIn the present study, a consecutive dMMR EC cohort were investigated using comprehensive genomic profiling in comparison with pMMR ECs. We focused on key genes and pathways involved in tumorigenesis and progression, and aimed to reveal the characteristic genetic profiles and molecular heterogeneity of dMMR/MSI-H ECs, which might facilitate future individualized therapy.\\u003c/p\\u003e\"},{\"header\":\"Materials and methods\",\"content\":\"\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003ePatients enrollment and clinicopathological characteristics\\u003c/h2\\u003e \\u003cp\\u003eOne hundred and seventeen patients with EC who underwent radical hysterectomy at Peking Union Medical Collage Hospital (PUMCH) between November 2017 and February 2019 were enrolled in this retrospective study. All patients did not receive anticancer treatment before surgery and the FIGO stage was recorded. The study was approved by the Institutional Review Board of PUMCH (approval number: S-K2006).\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec4\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eTargeting sequencing\\u003c/h2\\u003e \\u003cp\\u003eTargeting sequening was performed using hybrid capture-based NGS as our previous research\\u003csup\\u003e\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e\\u003c/sup\\u003e. In brief, DNA was obtained from formalin-fixed paraffin-embedded (FFPE) tumors and normal tissues respectively. Genomic DNA libraries were applied to a 1021-gene panel including whole exons, selected introns of 288 genes, and selected regions of 733 genes (Supplementary Table\\u0026nbsp;1). Single-stranded DNA nanoball (DNB) were sequenced by using 2x100 bp paired-end reads on the Gene\\u0026thinsp;+\\u0026thinsp;Seq-2000 instrument (GenePlus-Beijing). Genetic alternations, including single nucleotide variants (SNVs), small insertions and deletions (Indel), copy number variants (CNVs), and gene fusions/rearrangements, were compared with each paired normal sample to distinguish germline and somatic mutations. Sequencing data were analyzed by BWA\\u003csup\\u003e\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e\\u003c/sup\\u003e (version 0.7.12-r1039). SNVs and small Indels were identified by Mu Tect2\\u003csup\\u003e12\\u003c/sup\\u003e (version 4.1.8.1). Somatic mutations were identified by a VAF\\u0026thinsp;\\u0026ge;\\u0026thinsp;1% and at least 5 high-quality reads (Phred score\\u0026thinsp;\\u0026ge;\\u0026thinsp;30, mapping quality\\u0026thinsp;\\u0026ge;\\u0026thinsp;30, and without paired-end reads bias). Gene mutations were annotated using ANNOVAR software\\u003csup\\u003e\\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e\\u003c/sup\\u003e. CONTRA software\\u003csup\\u003e\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e\\u003c/sup\\u003e was used to detect CNVs and BreakDancer software was used to detect cancer-associated gene fusion\\u003csup\\u003e\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e\\u003c/sup\\u003e. To calculate TMB, the number of somatic, coding, nonsynonymous single nucleotide variants, and insertions and deletions mutations per megabase (Muts/Mb) of genome examined was defined. TMB levels were divided into two categories: TMB-L (tumor mutation burden-low, 1\\u0026ndash;9 Muts/Mb) and TMB-H (tumor mutation burden-high, \\u0026ge; 10 Muts/Mb).\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec5\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eSignal pathways, key genes, and determination of mutational significance\\u003c/h2\\u003e \\u003cp\\u003eTen canonical signaling pathways have been identified as frequently genetically altered in cancers, including the Cell Cycle pathway, Hippo pathway, MYC pathway, NOTCH pathway, NRF2 pathway, PI3K/Akt pathway, RTK-RAS pathway, TGF-β pathway, TP53 pathway, and WNT pathway, as determined by the Cancer Genome Atlas (TCGA) analysis\\u003csup\\u003e\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e\\u003c/sup\\u003e. Within our 1021-gene panel, seventy-six genes were assigned to pathways based on a combined revision of pathway analyses in previous literature and expert opinion \\u003csup\\u003e\\u003cspan additionalcitationids=\\\"CR18 CR19 CR20 CR21 CR22 CR23 CR24 CR25\\\" citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e\\u003c/sup\\u003e. Forty-seven genes have been identified as DDR-related genes in previous studies\\u003csup\\u003e\\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e28\\u003c/span\\u003e \\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e\\u003c/sup\\u003eand were assigned to eight DDR pathways: MMR, homologous recombination repair (HRR), Fanconi anemia (FA), base excision repair (BER), checkpoint factor (CPF), nucleotide excision repair (NER), nonhomologous end joining (NHEJ), and translesion DNA synthesis(Supplementary Table\\u0026nbsp;2).\\u003c/p\\u003e \\u003cp\\u003eSpecific recurrent missense mutations, i.e., hotspot mutations, amplifications, or fusions of oncogenes were classified as activating events. Mutations of oncogenes were filtered according to the related documentation in the Catalog of Somatic Mutations in Cancer (COSMIC)\\u003csup\\u003e\\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e30\\u003c/span\\u003e\\u003c/sup\\u003e and OncoKB annotation\\u003csup\\u003e\\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e31\\u003c/span\\u003e\\u003c/sup\\u003e. For tumor suppressor genes, all loss-of-function mutations, including nonsense mutations, frame-shifting mutations, and canonical splice sites mutations were part of an inactivation event and defined as \\\"predicted deleterious\\\" mutations. Missense mutations were considered deleterious when identified in two or more of the following in silico functional analysis algorithms: predication score 0.0\\u0026ndash;0.05 in SIFT (sorting intolerant from tolerant)\\u003csup\\u003e\\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e32\\u003c/span\\u003e\\u003c/sup\\u003e, \\u0026ldquo;possibly damaging\\u0026rdquo; or \\u0026ldquo;probably damaging\\u0026rdquo; in polymorphism phenotyping \\u0026minus;\\u0026thinsp;2 (Polyphen2)\\u003csup\\u003e\\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e33\\u003c/span\\u003e\\u003c/sup\\u003e, or \\u0026ldquo;medium\\u0026rdquo; or \\u0026ldquo;high\\u0026rdquo; in MutationAssessor\\u003csup\\u003e\\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e34\\u003c/span\\u003e\\u003c/sup\\u003e.\\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003eMLH1\\u003c/b\\u003e \\u003cb\\u003epromoter hypermethylation analysis\\u003c/b\\u003e\\u003c/p\\u003e \\u003cp\\u003e \\u003cem\\u003eMLH1\\u003c/em\\u003e promoter hypermethylation was detected in cases with absent \\u003cem\\u003eMLH1\\u003c/em\\u003e expression and lack of MMR germline mutations. The detection was performed using methylation-specific PCR as previously described\\u003csup\\u003e\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e\\u003c/sup\\u003e.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec6\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eStatistical Analyses\\u003c/h2\\u003e \\u003cp\\u003eCategorical variables were expressed as percentages. The Chi-square test or Fisher's exact test was used to compare the frequencies of genetic alternations and to identify the coexistence or mutually exclusive associations. Correlation analysis was conducted using binary logistic regression. Analyses were performed with SPSS 25.0 software. All tests were two-sided, and a p-value\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05 was considered statistically significant.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"Results\",\"content\":\"\\u003cdiv id=\\\"Sec8\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eGene mutations in dMMR and pMMR ECs\\u003c/h2\\u003e \\u003cp\\u003eOf the key genes involved in ten canonical tumor-related pathways and DDR pathways, \\u003cem\\u003ePTEN\\u003c/em\\u003e, \\u003cem\\u003eARID1A\\u003c/em\\u003e, and \\u003cem\\u003ePIK3CA\\u003c/em\\u003e had the highest mutation frequency in both dMMR (90.5%, 74.3%, 68.9%) and pMMR (67.4%, 37.2%, 62.8%) ECs, followed by \\u003cem\\u003eKRAS\\u003c/em\\u003e (28.4%), \\u003cem\\u003ePIK3R1\\u003c/em\\u003e (25.7%), \\u003cem\\u003eFAT1\\u003c/em\\u003e (23.0%), \\u003cem\\u003eATM\\u003c/em\\u003e (21.6%), JAK1 (21.6%), \\u003cem\\u003eTP53\\u003c/em\\u003e (20.3%), \\u003cem\\u003eFBXW7\\u003c/em\\u003e (17.6%) and \\u003cem\\u003eCREBBP\\u003c/em\\u003e (17.6%) in dMMR cohort, and \\u003cem\\u003eTP53\\u003c/em\\u003e (34.9%), \\u003cem\\u003ePIK3R1\\u003c/em\\u003e (27.9%), \\u003cem\\u003eATM\\u003c/em\\u003e (25.6%), \\u003cem\\u003eNF1\\u003c/em\\u003e (23.3%), \\u003cem\\u003ePOLE\\u003c/em\\u003e (23.3%), \\u003cem\\u003eCTNNB1\\u003c/em\\u003e (23.3%) and \\u003cem\\u003eFBXW7\\u003c/em\\u003e (20.9%) in pMMR cohort. In comparison with those in the pMMR group, the mutation rates of \\u003cem\\u003ePTEN\\u003c/em\\u003e, \\u003cem\\u003eARID1A\\u003c/em\\u003e, \\u003cem\\u003eKRAS\\u003c/em\\u003e, and \\u003cem\\u003eMSH2\\u003c/em\\u003e were significantly higher in the dMMR group (90.5% vs. 67.4%, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05; 74.3% vs. 37.2%, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05; 28.4% vs. 11.6%, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05; 16.2% vs. 2.3%, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05), while the \\u003cem\\u003eCTNNB1\\u003c/em\\u003e and \\u003cem\\u003eMSH3\\u003c/em\\u003e mutations were significantly higher in the pMMR group (0% vs. 23.3%, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05; 1.4% vs. 16.3%, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05) (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e). We also noted that alterations causing functional impairment of tumor suppressor gene \\u003cem\\u003eARID1A\\u003c/em\\u003e were mostly frameshift events affecting homopolymer sequences in dMMR group as opposed to pMMR group (65/87, 74.7% vs. 4/26, 15.4%, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e\\n\\u003ch3\\u003eSignaling pathway alternations in dMMR and pMMR ECs\\u003c/h3\\u003e\\n\\u003cp\\u003ePI3K, RTK-RAS, NOTCH, TP53, and WNT pathways were altered at high frequency in both dMMR and pMMR ECs (100% and 88.4%; 75.7% and 41.9%; 62.2% and 32.6%; 36.5% and 48.8%; 32.4% and 41.9%; respectively). However, compared with the pMMR group, dMMR ECs harbored significantly higher rates of alterations in RTK-RAS, NOTCH, and Cell Cycle pathways (75.7% vs. 41.9%, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001; 62.2% vs. 32.6%, p\\u0026thinsp;=\\u0026thinsp;0.002; 31.1% vs. 14.0%, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05), but a lower rate of TP53 pathway defection (48.8% vs. 36.5%, P\\u0026thinsp;=\\u0026thinsp;0.19). Both dMMR and pMMR groups showed relatively low frequencies of alternation in Hippo, NRF2, TGF-β, and MYC pathway (23.0% and 18.6%; 21.6% and 16.3%; 17.6% and 14.0%; 16.2% and 0.09%, respectively) (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003ea).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eAll of the 74 dMMR tumors exhibited alterations in at least one DDR pathway, compared with 19 out of 43 pMMR tumors (100% vs. 44.2%, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001). Of the eight included DDR pathways, MMR, HRR, and NER were identified as the most commonly defected ones in the dMMR group, with significantly higher alteration frequency compared to that in the pMMR group (100%vs. 25.6%, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001; 85.1% vs. 51.2%, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001; 32.4% vs. 23.3%, p\\u0026thinsp;=\\u0026thinsp;0.29, respectively). The alteration rates of FA, BER, CPF, and NHEJ pathways did not present remarkable differences between dMMR and pMMR ECs (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eb).\\u003c/p\\u003e \\u003cdiv id=\\\"Sec10\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eMutual relationships among key genes within canonical pathways in dMMR and pMMR ECs\\u003c/h2\\u003e \\u003cp\\u003eWe then depicted the co-occurring and mutually exclusive relationship among mutations affecting key genes involved in PI3K, RTK-RAS, NOTCH, TP53, and WNT.\\u003c/p\\u003e \\u003cp\\u003ePI3K, NOTCH, and TP53 pathways often had multiple alterations per tumor sample. Within the PI3K pathway, \\u003cem\\u003ePTEN\\u003c/em\\u003e mutations were frequently accompanied by \\u003cem\\u003ePIK3CA\\u003c/em\\u003e and/or \\u003cem\\u003ePIK3R1\\u003c/em\\u003e mutations in both dMMR and pMMR groups (77.0% and 67.4%, respectively). Co-alteration of \\u003cem\\u003ePTEN\\u003c/em\\u003e, \\u003cem\\u003ePIK3CA\\u003c/em\\u003e, and \\u003cem\\u003ePIK3R1\\u003c/em\\u003e, the three key PI3K signaling genes, were found in 9 out of 74 (12.2%) dMMR tumors and 7 out of 43 (16.3%) pMMR tumors, respectively. On the contrary, the RTK-RAS pathway contained predominantly mutually exclusively altered genes. \\u003cem\\u003eKRAS\\u003c/em\\u003e was the most commonly mutated RTK-RAS signaling gene in both dMMR and pMMR groups and exhibited an almost perfect mutually exclusive pattern with other members in the RTK-RAS pathway. The only exceptions were the co-occurrence of non-canonical \\u003cem\\u003eKRAS\\u003c/em\\u003e mutations (A59T, A146V) and \\u003cem\\u003eNF1\\u003c/em\\u003e deleterious mutations observed in four dMMR tumors. \\u003cem\\u003eFGFR2\\u003c/em\\u003e activating point mutations were found in a similar small fraction of tumors in dMMR and pMMR groups (10/74, 13.5%; 4/43, 9.3%, respectively), and largely mutually exclusive with other RTK-RAS alterations in the dMMR group (9/10, 90%).\\u003c/p\\u003e \\u003cp\\u003eThe alteration spectrum of the WNT signaling pathway displayed noticeable differences between dMMR and pMMR groups. All WNT-activated dMMR tumors were \\u003cem\\u003eCTNNB1\\u003c/em\\u003e wild-type, and the majority of them (16/24, 66.6%) displayed dysfunctional mutations in only one other key WNT signaling genes, including \\u003cem\\u003eAPC\\u003c/em\\u003e, \\u003cem\\u003eRNF43\\u003c/em\\u003e, \\u003cem\\u003eAXIN1\\u003c/em\\u003e, \\u003cem\\u003eAXIN2\\u003c/em\\u003e, and \\u003cem\\u003eTCF7L2\\u003c/em\\u003e. On the other hand, half of the WNT-activated pMMR tumors (10/18, 55.6%) were \\u003cem\\u003eCTNNB1\\u003c/em\\u003e mutated, which were generally mutually exclusive with alterations in other WNT pathway components. The remaining eight WNT-activated pMMR tumors showed high frequency (6/8, 75%) of co-alterations among key WNT pathway genes other than \\u003cem\\u003eCTNNB1\\u003c/em\\u003e. Mutations in key genes of the signaling pathways were shown in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec11\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eMutual relationships between signaling pathways in dMMR and pMMR ECs\\u003c/h2\\u003e \\u003cp\\u003eThe mutual relationship among the canonical signaling pathways differed remarkably between dMMR and pMMR ECs (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e). In the dMMR group, the only significant mutually exclusive relationship was found between TGF-β and NOTCH pathway (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05). No significant co-occurrence pattern among ten canonical pathways was observed. On the contrary, in the pMMR group, we identified numerous co-existence relationships within TGF-β, NOTCH, WNT, RTK-RAS, HIPPO, and NRF2 pathways (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05). Despite ranking the most commonly altered pathway, PI3K was not significantly concurrently altered with any other pathways in pMMR ECs.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec12\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eTumor mutational burden (TMB) level in dMMR and pMMR ECs\\u003c/h2\\u003e \\u003cp\\u003eAs shown in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003ea, the median TMB of 74 dMMR tumors was significantly higher than that of 43 pMMR tumors (37mut/Mb vs. 5mut/Mb, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.0001). Of note, the highest TMB value (405 mut/Mb) was observed in only one dMMR tumor harboring somatic inactivating mutations in the exonuclease domain of \\u003cem\\u003ePOLE\\u003c/em\\u003e (S459F). Likewise, in the pMMR group, high TMB levels (\\u0026ge;\\u0026thinsp;10 mut/Mb, ranging from 41 to 186 mut/Mb) were generally found in tumors with deleterious \\u003cem\\u003ePOLE\\u003c/em\\u003e exo-domain mutations (P286R, S297F, F367S, V411L).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec13\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eComparison of the mutation profile and TMB levels among ECs with different dMMR etiologies\\u003c/h2\\u003e \\u003cp\\u003eThe 74 dMMR ECs were categorized into three subgroups according to different underlying mechanisms: the Lynch subgroup comprised 13 tumors (13/74, 17.6%) harboring pathogenic/likely pathogenic germline mutations in any of the MMR genes (\\u003cem\\u003eMLH1\\u003c/em\\u003e, \\u003cem\\u003eMSH2\\u003c/em\\u003e, \\u003cem\\u003eMSH6\\u003c/em\\u003e, \\u003cem\\u003ePMS2\\u003c/em\\u003e, \\u003cem\\u003eEPCAM\\u003c/em\\u003e); the \\u003cem\\u003eMLH1\\u003c/em\\u003e-hypermethylated group consisted of 46 cases (46/74, 62.2%) with loss of \\u003cem\\u003eMLH1\\u003c/em\\u003e/\\u003cem\\u003ePMS2\\u003c/em\\u003e expression exhibiting \\u003cem\\u003eMLH1\\u003c/em\\u003e promoter hypermethylation without \\u003cem\\u003eMLH1\\u003c/em\\u003e germline mutations; and the Lynch-like subgroup consisted of the remaining cases with neither MMR gene germline mutations nor \\u003cem\\u003eMLH1\\u003c/em\\u003e promoter hypermethylation (15/74, 20.3%).\\u003c/p\\u003e \\u003cp\\u003eMutation profile of key genes varied by dMMR etiologies. \\u003cem\\u003eKEAP1\\u003c/em\\u003e (4/13, 30.8%) and \\u003cem\\u003eFBXW7\\u003c/em\\u003e (7/13, 53.8%) mutations were significantly more prevalent in Lynch subgroup (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), whereas \\u003cem\\u003eMSH2\\u003c/em\\u003e (8/15, 53.3%) and \\u003cem\\u003eRECQL4\\u003c/em\\u003e mutations (2/15, 13.3%) were significantly enriched in Lynch-like subgroup (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001 and p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05, respectively). Tumors in the \\u003cem\\u003eMLH1\\u003c/em\\u003e\\u003csup\\u003eme+\\u003c/sup\\u003e subgroup tended to show more frequent \\u003cem\\u003eJAK1\\u003c/em\\u003e alterations, mainly frame-shift events, compared to Lynch and Lynch-like subgroup (30.4% vs. 7.7% vs. 6.7%, p\\u0026thinsp;=\\u0026thinsp;0.062). The alteration spectrum of key signaling pathways, however, did not show notable differences between the three subgroups (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig6\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eMedian TMB was significantly higher in the Lynch subgroup (42mt/Mb, ranging from 24 to 73mt/Mb, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05), in comparison with the \\u003cem\\u003eMLH1\\u003c/em\\u003e\\u003csup\\u003eme+\\u003c/sup\\u003e subgroup (29mt/Mb, ranging from 6 to 405mt/Mb) and Lynch-like subgroup (39mt/Mb, ranging from 11 to 80mt/Mb)(Figure \\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003eb).\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cp\\u003eIn this study, we performed a comprehensive molecular study of a retrospective EC cohort with 74 dMMR and 43 pMMR patients. Using targeted panel sequencing for 1021 genes, we analyzed gene mutation frequency and genetic mutual relationship of ten canonical cancer signaling pathways, namely Cell Cycle, Hippo, MYC, NOTCH, NRF2, PI3Kinase/Akt, RTK-RAS, TGF-β, P53, and β-catenin/WNT signaling. DDR-related pathway alterations and TMB levels were also evaluated. According to MMR gene germline mutations and \\u003cem\\u003eMLH1\\u003c/em\\u003e promoter methylation status, \\u0026ldquo;Lynch\\u0026rdquo;, \\u0026ldquo;Lynch-like\\u0026rdquo; and \\u0026ldquo;\\u003cem\\u003eMLH1\\u003c/em\\u003e\\u003csup\\u003eme+\\u003c/sup\\u003e\\u0026rdquo; subgroups of dMMR ECs were identified and compared. We profiled the landscape of gene mutations and key pathway alterations in dMMR ECs, delineated patterns of co-occurrence and mutual exclusivity, and further explored the molecular heterogeneity of dMMR ECs.\\u003c/p\\u003e \\u003cp\\u003eTo illustrate the general genetic feature of dMMR ECs, we first compare the mutation profile between dMMR and pMMR group. In consistent with previous reports, the mutation frequencies of PTEN and KRAS, the two key driver genes of EC carcinogenesis, were significantly higher in dMMR group compared to pMMR group. Also, we found that \\u003cem\\u003eARID1A\\u003c/em\\u003e, which encodes the subunit of switch/sucrose non-fermentable (SWI/SNF) complex involved in the chromatin remodeling process, was remarkably mutated in our dMMR group. The correlation of increased \\u003cem\\u003eARID1A\\u003c/em\\u003e mutation frequency and dMMR/MSI-H phenotype has been described extensively in various tumor types\\u003csup\\u003e\\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e35\\u003c/span\\u003e\\u003c/sup\\u003e. However, unlike previous studies, which suggested loss of \\u003cem\\u003eARID1A\\u003c/em\\u003e expression was more prevalent in sporadic dMMR/MSI-H tumors, we found that \\u003cem\\u003eARID1A\\u003c/em\\u003e mutation was not correlated with the \\u003cem\\u003eMLH1\\u003c/em\\u003e\\u003csup\\u003eme+\\u003c/sup\\u003e phenotype in our cohort. Although some \\u003cem\\u003ein vivo\\u003c/em\\u003e studies suggested that \\u003cem\\u003eARID1A\\u003c/em\\u003e downregulation might mediate modest rather than global DNA methylation regulation early in tumorigenesis\\u003csup\\u003e\\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e36\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR37\\\" class=\\\"CitationRef\\\"\\u003e37\\u003c/span\\u003e\\u003c/sup\\u003e, it is not completely clear if \\u003cem\\u003eARID1A\\u003c/em\\u003e mutation is the result or the cause of MMR deficiency secondary to promoter hypermethylation, especially in ECs. Moreover, the \\u003cem\\u003eARID1A\\u003c/em\\u003e mutations in all three dMMR subgroups were predominantly frameshift events affecting tandem repeat sequences, indicating that they might represent mutational targets of MSI in ECs regardless of the mechanisms underlying MMR deficiency.\\u003c/p\\u003e \\u003cp\\u003eOn pathway-level, dMMR ECs had significantly higher alternation frequencies in RTK-RAS, NOTCH, and Cell Cycle pathway compared to pMMR ECs. The genetic alterations within PI3K and RTK-RAS pathway, the two most commonly altered signaling pathways in ECs, however, did not show remarkable differences between the dMMR and pMMR groups of ECs. Notably, defections in DDR pathways other than MMR, particularly the HRR pathway, were found to be common among our dMMR cases. In a pan-cancer analysis of co-mutations among DDR pathways, the co-existence of HRR and MMR aberrations was associated with higher TMB levels, increased tumor neoantigen load, and upregulated immune gene expression, and considered as a potential biomarker for ICB therapy in some types of non-gynecologic tumors\\u003csup\\u003e\\u003cspan citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e38\\u003c/span\\u003e\\u003c/sup\\u003e. In ECs, co-mutations in the DDR pathway warrant more thorough exploration, in the hope of developing new immunotherapy predictors. The mutual relationships of ten canonical pathways displayed remarkable differences between the dMMR and pMMR ECs. We observed multiple co-existent canonical signaling pathways in the pMMR group, rather than the dMMR group, which indicates a potential for combination therapy in pMMR ECs.\\u003c/p\\u003e \\u003cp\\u003eWe further explored the interactions within pathways both in dMMR and pMMR ECs, which have not been addressed in previous studies. The most significant difference was reflected in the WNT signaling pathway. Among ECs displaying genetic alterations in WNT pathway components, we found that \\u003cem\\u003eCTNNB1\\u003c/em\\u003e mutations, generally considered as the hallmark of aberrant Wnt/β-catenin signaling, were completely absent in the dMMR group while presented in half of the pMMR group. Several prior studies revealed the association of \\u003cem\\u003eCTNNB1\\u003c/em\\u003e mutation with increased risk of recurrence in ECs but generally included a heterogeneous population comprised of both dMMR and pMMR cases\\u003csup\\u003e\\u003cspan citationid=\\\"CR39\\\" class=\\\"CitationRef\\\"\\u003e39\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR40\\\" class=\\\"CitationRef\\\"\\u003e40\\u003c/span\\u003e\\u003c/sup\\u003e. Our data suggested that the prognostic value of CTNNB1 mutation in ECs should focus on pMMR cases in future studies. Moreover, the interaction relationship among other key WNT signaling pathway genes displayed noticeable differences between dMMR and pMMR groups, manifesting as mutually exclusive relationships in dMMR group, and co-occurrence relationships in pMMR group. This finding indicated the different modes of WNT signaling pathway aberrations between dMMR and pMMR ECs, suggesting that alternative mechanisms might be responsible for WNT pathway activation in EC tumorigenesis. In most cases, PI3K signaling aberrations were caused by \\u003cem\\u003ePTEN\\u003c/em\\u003e mutations accompanied by \\u003cem\\u003ePIK3CA\\u003c/em\\u003e and/or \\u003cem\\u003ePIK3R1\\u003c/em\\u003e mutations, verifying the synergistic effects of PI3K pathway mutations in EC tumorigenesis proposed by previous studies\\u003csup\\u003e\\u003cspan citationid=\\\"CR41\\\" class=\\\"CitationRef\\\"\\u003e41\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR42\\\" class=\\\"CitationRef\\\"\\u003e42\\u003c/span\\u003e\\u003c/sup\\u003e. \\u003cem\\u003eKRAS\\u003c/em\\u003e mutation was shown to be the primary mechanism of RTK-RAS signaling activation. Whilst canonical \\u003cem\\u003eKRAS\\u003c/em\\u003e mutations do not co-exist with other alterations within the RTK-RAS pathway, non-canonical \\u003cem\\u003eKRAS\\u003c/em\\u003e mutations generally co-occur with \\u003cem\\u003eNF1\\u003c/em\\u003e dysfunctional mutations, suggesting that such pairs of mutations might act cooperatively to provide a selective advantage in tumorigenesis of ECs regardless of MMR/MSI status\\u003csup\\u003e\\u003cspan citationid=\\\"CR43\\\" class=\\\"CitationRef\\\"\\u003e43\\u003c/span\\u003e\\u003c/sup\\u003e.\\u003c/p\\u003e \\u003cp\\u003eIt has been well-established that dMMR/MSI-H status is a favorable prognostic factor in certain cancer types and a predictor for anti-PD-1/PD-L1 immunotherapy efficacy in solid tumors. However, previous studies reported inconsistent findings comparing outcomes between dMMR/MSI-H ECs and ECs of NSMP\\u003csup\\u003e\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR44\\\" class=\\\"CitationRef\\\"\\u003e44\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR45\\\" class=\\\"CitationRef\\\"\\u003e45\\u003c/span\\u003e\\u003c/sup\\u003e. It has also been reported that ECs harboring \\u003cem\\u003eMLH1\\u003c/em\\u003e hypermethylation showed poorer response to ICB therapy compared with Lynch syndrome-associated ECs, which implied the different immunotherapy response-associated genetic alternations may exist in etiologically distinct EC subgroups. In line with a recent EC study, we noticed a tendency of increased \\u003cem\\u003eJAK1\\u003c/em\\u003e mutations in the \\u003cem\\u003eMLH1\\u003c/em\\u003e\\u003csup\\u003eme+\\u003c/sup\\u003e subgroup. JAK pathway alterations had been proved to correlate with primary or acquired ICB resistance\\u003csup\\u003e\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e\\u003c/sup\\u003e. Therefore, we hypothesize that molecular heterogeneity among dMMR etiological subgroups might partially account for the different immunotherapy outcomes.\\u003c/p\\u003e \\u003cp\\u003eThe Lynch-associated ECs in our cohort were enriched for \\u003cem\\u003eFBXW7\\u003c/em\\u003e and \\u003cem\\u003eKEAP1\\u003c/em\\u003e mutations. The association of \\u003cem\\u003eFBXW7\\u003c/em\\u003e and/or \\u003cem\\u003eKEAP1\\u003c/em\\u003e mutations with poor response to checkpoint blockade therapy in multiple solid tumors, including ECs, has been evidenced in clinical studies\\u003csup\\u003e\\u003cspan citationid=\\\"CR46\\\" class=\\\"CitationRef\\\"\\u003e46\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR47\\\" class=\\\"CitationRef\\\"\\u003e47\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR48\\\" class=\\\"CitationRef\\\"\\u003e48\\u003c/span\\u003e\\u003c/sup\\u003e. In recent published in vivo studies, \\u003cem\\u003eFBXW7\\u003c/em\\u003e and \\u003cem\\u003eKEAP1\\u003c/em\\u003e were suggested to confer immune checkpoint blockade by alternating tumor microenvironment instead of directly modifying the tumor, through the way of decreasing T-cell infiltration and downregulating IFN-γ signaling, respectively\\u003csup\\u003e\\u003cspan citationid=\\\"CR49\\\" class=\\\"CitationRef\\\"\\u003e49\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR50\\\" class=\\\"CitationRef\\\"\\u003e50\\u003c/span\\u003e\\u003c/sup\\u003e. On the other hand, Lynch group had the highest median TMB level among the three dMMR subgroups, implicating a \\\"hotter\\\" tumor microenvironment favoring immunotherapy. This seemingly paradox finding strengthened the notion that any single biomarker could be inaccurate to predict immunotherapy response. It is rational to combine different biomarkers representing not only tumor intrinsic features but also the complex interplay between the tumor and its microenvironment.\\u003c/p\\u003e \\u003cp\\u003eTo summarize, our comprehensive molecular study uncovered significant differences in the mutation spectrum and interaction patterns of key genes and pathways between dMMR and pMMR ECs. We also revealed the molecular heterogeneity among dMMR subgroups with different etiologies. Our findings may have potential prognostic and therapeutic implications.\\u003c/p\\u003e\"},{\"header\":\"Abbreviations\",\"content\":\" \\u003cp\\u003eBER, base excision repair; COSMIC, Catalog of Somatic Mutations in Cancer; CNVs, copy number variants; CPF, checkpoint factor; dMMR, deficient DNA mismatch repair; DNB, DNA nanoball; EC, endometrial carcinoma; FA, Fanconi anemia; FFPE, formalin-fixed paraffin-embedded; HRR, homologous recombination repair; ICB, immune checkpoint blockade; Indel, small insertions and deletions; MSI-H, microsatellite instability-high; \\u003cem\\u003eMLH1\\u003c/em\\u003e\\u003csup\\u003eme+\\u003c/sup\\u003e, \\u003cem\\u003eMLH1\\u003c/em\\u003e promoter hypermethylation; NER, nucleotide excision repair; NHEJ, nonhomologous end joining; pMMR, proficient DNA mismatch repair; Polyphen2, polymorphism phenotyping-2; SWI/SNF, switch/sucrose non-fermentable. SNVs, single nucleotide variants; SIFT, sorting intolerant from tolerant; TCGA, the Cancer Genome Atlas; TMB, tumor mutational burden.\\u003c/p\\u003e \\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eEthics approval and consent to participate\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe study was approved by the Institutional Review Board of PUMCH (approval number:\\u0026nbsp;S-K2006).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConsent for publication\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNot applicable\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAvailability of data and materials\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eAll data, analytic methods, and study materials will be made available to other researchers upon request via emailing to liangzy@pumch.cn.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eCompeting interests\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors declare that they have no competing interests\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eFunding\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis research is supported by:\\u003c/p\\u003e\\n\\u003col\\u003e\\n \\u003cli\\u003eSupported by the Non-profit Central Research Institute Fund of Chinese Academy of Medical Sciences (2021-RW320-004)\\u003c/li\\u003e\\n \\u003cli\\u003eNational High Level Hospital Clinical Research Funding (2022-PUMCH-B-063)\\u003c/li\\u003e\\n\\u003c/ol\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAuthors\\u0026apos; contributions\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eCYM and WJ performed the research and composed the manuscript; WJ contributed to data analysis and modification of the manuscript. ZZJ contributed to data collection and data analysis; CL and PJY conducted the exprements; LP, FJY and CL contributed to the data analysis; LP drew and revised Figures; ZYH helped to collect data; YY, WHW and LZY contributed to the conception and design of the study; and all authors have read and approved the final manuscript.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAcknowledgements\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNot applicable\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\u003cli\\u003e\\u003cspan\\u003eBruggmann D, Ouassou K, Klingelhofer D, Bohlmann MK, Jaque J, Groneberg DA. Endometrial cancer: mapping the global landscape of research. J Transl Med. Oct 2020;12(1):386.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eBell DW, Ellenson LH. Molecular Genetics of Endometrial Carcinoma. Annu Rev Pathol Jan. 2019;24:14:339\\u0026ndash;67.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eMcMeekin DS, Tritchler DL, Cohn DE, et al. Clinicopathologic Significance of Mismatch Repair Defects in Endometrial Cancer: An NRG Oncology/Gynecologic Oncology Group Study. J Clin Oncol Sep. 2016;1(25):3062\\u0026ndash;8.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eCosgrove CM, Cohn DE, Hampel H, et al. Epigenetic silencing of MLH1 in endometrial cancers is associated with larger tumor volume, increased rate of lymph node positivity and reduced recurrence-free survival. Gynecol Oncol Sep. 2017;146(3):588\\u0026ndash;95.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eCancer Genome Atlas Research N, Kandoth C, Schultz N, et al. Integrated genomic characterization of endometrial carcinoma. Nat May. 2013;2(7447):67\\u0026ndash;73.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eWang J, Li R, He Y, Yi Y, Wu H, Liang Z. Next-generation sequencing reveals heterogeneous genetic alterations in key signaling pathways of mismatch repair deficient colorectal carcinomas. Mod Pathol Dec. 2020;33(12):2591\\u0026ndash;601.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eWang J, Yi Y, Xiao Y, et al. Prevalence of recurrent oncogenic fusion in mismatch repair-deficient colorectal carcinoma with hypermethylated MLH1 and wild-type BRAF and KRAS. Mod Pathol Jul. 2019;32(7):1053\\u0026ndash;64.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eManning-Geist BL, Liu YL, Devereaux KA, et al. Microsatellite Instability-High Endometrial Cancers with MLH1 Promoter Hypermethylation Have Distinct Molecular and Clinical Profiles. 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NPJ Precis Oncol Mar. 2023;2(1):25.\\u003c/span\\u003e\\u003c/li\\u003e\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":true,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true},\"keywords\":\"Endometrial carcinoma (EC), Deficient DNA mismatch repair (dMMR), Lynch syndrome, MLH1 promoter hypermethylation, Immunotherapy\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-4537456/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-4537456/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eEndometrial carcinoma (EC) with deficient DNA mismatch repair (dMMR) is a specific molecular entity with unique clinicopathological features. Herein, we depicted the mutation profile of dMMR ECs and explored the molecular heterogeneity among dMMR subgroups with different etiologies. Next-generation sequencing based on a 1021-gene panel was applied to 74 dMMR ECs and 43 proficient MMR (pMMR) ECs. In addition, methylation-specific PCR was applied for accessing \\u003cem\\u003eMLH1\\u003c/em\\u003e promoter hypermethylation (\\u003cem\\u003eMLH1\\u003c/em\\u003e\\u003csup\\u003eme+\\u003c/sup\\u003e) in dMMR cases. The mutation rates of \\u003cem\\u003ePTEN\\u003c/em\\u003e, \\u003cem\\u003eARID1A\\u003c/em\\u003e, \\u003cem\\u003eKRAS\\u003c/em\\u003e, and \\u003cem\\u003eMSH2\\u003c/em\\u003e were significantly higher in dMMR group, while the \\u003cem\\u003eCTNNB1\\u003c/em\\u003e and \\u003cem\\u003eMSH3\\u003c/em\\u003e mutations were more commonly observed in pMMR group (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05). Compared to pMMR ECs, dMMR ECs had significantly higher alteration frequencies in RTK-RAS, NOTCH, Cell Cycle and HRR pathway (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05). Remarkably, the interaction patterns within and across pathways were different between dMMR and pMMR groups. Intriguingly, no \\u003cem\\u003eCTNNB1\\u003c/em\\u003e mutation were found in dMMR ECs, while half of the WNT-activated pMMR ECs were \\u003cem\\u003eCTNNB1\\u003c/em\\u003e mutated, which were generally mutually exclusive with other WNT pathway key genes. The median tumor mutational burden (TMB) of dMMR ECs was significantly higher than pMMR ECs. However, ultra-high TMB value was related to pathogenic \\u003cem\\u003ePOLE\\u003c/em\\u003e mutation both in dMMR and pMMR ECs. As for dMMR subgroups, \\u003cem\\u003eKEAP1\\u003c/em\\u003e and \\u003cem\\u003eFBXW7\\u003c/em\\u003e mutations, which may have potential predictive effect of immunotherapy, were more prevalent in the Lynch subgroup. The Lynch subgroup also had significantly higher median TMB than the \\u003cem\\u003eMLH1\\u003c/em\\u003e\\u003csup\\u003eme+\\u003c/sup\\u003e subgroup and Lynch-like subgroup. dMMR ECs has distinctive genomic profile with molecular heterogeneity, which may have potential prognostic and therapeutic implications.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Mutation profile and molecular heterogeneity in mismatch repair deficient endometrial carcinoma\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2024-06-26 18:58:38\",\"doi\":\"10.21203/rs.3.rs-4537456/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"8f0b8c81-1b01-4936-b3c0-d9a113edb999\",\"owner\":[],\"postedDate\":\"June 26th, 2024\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2024-10-18T06:09:26+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2024-06-26 18:58:38\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-4537456\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-4537456\",\"identity\":\"rs-4537456\",\"version\":[\"v1\"]},\"buildId\":\"WrCJVZZCHTDjtuVLN7oU0\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}