Concordance Analysis of Microsatellite Instability via NGS and Mismatch Repair Deficiency via IHC in Endometrial and Colorectal Cancer

Concordance Analysis of Microsatellite Instability via NGS and Mismatch Repair Deficiency via IHC in Endometrial and Colorectal Cancer | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Concordance Analysis of Microsatellite Instability via NGS and Mismatch Repair Deficiency via IHC in Endometrial and Colorectal Cancer Camilla Nero, Lisa Salvatore, Simona Duranti, Gloria Anderson, and 19 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6664521/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 Microsatellite instability and mismatch repair deficiency are important biomarkers in colorectal and endometrial cancers, helping guide diagnosis, prognosis, and treatment decisions, particularly for immunotherapy. Mismatch repair status is commonly assessed using immunohistochemistry, while microsatellite instability can be detected through sequencing-based methods. In this study, we analyzed 520 tumor samples from patients with colorectal or endometrial cancer using both approaches to compare their performance. Overall, there was high agreement between the two methods, especially in colorectal cancer. However, in endometrial cancer, a lower level of concordance was observed, with several cases showing mismatch repair deficiency without detectable microsatellite instability. These differences were often explained by specific genetic features, such as mutations in DNA polymerase genes, isolated loss of mismatch repair proteins, or epigenetic alterations. We also found that adjusting the threshold used to define microsatellite instability improved detection accuracy in endometrial tumors. These findings suggest that sequencing-based detection of microsatellite instability is a reliable method but may require tumor-specific optimization. Tailoring thresholds based on cancer type could improve identification of patients who are likely to benefit from immunotherapy and enhance precision in clinical decision-making. Biological sciences/Cancer Biological sciences/Molecular biology microsatellite instability (MSI) mismatch repair deficiency (MMR) next generation sequencing (NGS) Immunohistochemistry endometrial cancer colorectal cancer Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction The mismatch repair (MMR) system is a conserved mechanism that preserves genomic integrity, by detecting and repairing DNA errors like single base mismatches or short insertions and deletions, involving proteins MLH1, MSH2, MSH6 and PMS2 [ 1 ] . Loss of MMR function (dMMR) leads to accumulation of somatic mutations, including indels in microsatellite repeats, resulting in high microsatellite instability (MSI-H). dMMR status is mostly (60–80%) caused by MLH1 gene promoter hypermethylation or somatic bi-allelic mutation in MMR genes [ 2 – 5 ] . Less commonly (10–30%) [ 6 ] , it arises from an autosomal dominant heterozygous germline variant of MMR genes, delineating Lynch syndrome, which increases the risk of cancer, particularly colorectal (CRC) and endometrial (EC). Integrated genomics data from The Cancer Genome Atlas (TGCA) Network show that around 12% of CRC and 30% of EC samples exhibit MSI-H with a hypermutated phenotype [ 7 , 8 ] . This feature is linked to prognosis and response to immunotherapy in both cancer types [ 9 , 10 ] . In CRC, dMMR is commonly linked to right-sided colon involvement, a mucinous phenotype, tumor-infiltrating lymphocytes, a Crohn's-like inflammatory reaction, multiple tumor subclones, poor differentiation, and frequently co-occurs with BRAF mutations [ 11 ] . In non-metastatic CRCs, dMMR correlates with a good prognosis and limited benefit from adjuvant therapy, particularly fluoropyrimidine [ 12 ] . In metastatic CRC, though rare (1–2%), dMMR indicates poor prognosis, reduced response to standard chemotherapy, and significant benefit from immune checkpoint inhibitors (ICIs) [ 13 ] , such as ipilimumab with nivolumab and pembrolizumab. In EC, dMMR is associated with endometrioid histology, intermediate prognosis [ 14 ] and this subgroup benefits significantly from ICIs [ 15 – 17 ] . Approved ICIs for advanced or metastatic dMMR EC include pembrolizumab and dostarlimab as monotherapies, as well as dostarlimab combined with platinum-based chemotherapy [ 15 – 17 ] . Additionally, pembrolizumab is approved in combination with lenvatinib for advanced or recurrent EC after prior platinum-based therapy [ 18 ] . For both predictive and prognostic purposes, as well as for Lynch syndrome screening, assessing MMR function in all newly diagnosed CRC and EC patients is strongly recommended by international and national guidelines [ 19 ] . Immunohistochemistry (IHC) for MMR proteins offers adequate sensibility and specificity for detecting MMR deficiency [ 20 ] . MSI testing serves as a proxy for MMR abnormalities, measuring the consequences of its deficiency through polymerase chain reaction (PCR) or next generation sequencing (NGS) techniques [ 21 ] . Due to its availability, cost-effectiveness, and quick turnaround time, IHC assessment of MMR status is the most adopted approach. However, MMR assessment is not the only molecular feature to be considered in both CRC and EC. The increasing complexity of genomic alterations has prompted referral centres to adopt comprehensive cancer genome profiling (CGP [ 21 ] ). Although these NGS-based assays indicate MSI status, there is limited evidence assessing their accuracy when compared to IHC-MMR and PCR-MSI evaluations. NGS can evaluate thousands of microsatellite loci compared with 5–7 loci detected by PCR [ 22 , 23 ] and the MSI cut-off in different tumor types is controversial, requiring further clarification. Thus, the present study aims to evaluate the concordance rate between the NGS-MSI status compared with the gold standard IHC-MMR in an unselected prospective series of CRC and EC patients from a large referral centre. Additionally, a detailed genomic, pathological, and clinical analysis of discordant cases was conducted to investigate the underlying causes of the discrepancies. Material and methods Study population At Fondazione Policlinico Universitario Agostino Gemelli IRCCS (FPG), patients with specific solid tumors are offered a tumor-only targeted NGS panel as part of an institutional CGP program (ClinicalTrials.gov Identifier: NCT06020625, registered on 31/08/2023, Protocol ID: FPG500). This program follows the Declaration of Helsinki guidelines and received approval from the local ethical committee (Protocol U 00194/23, ID number: 3837). All patients provided informed consent before partecipation. This study included all consecutive CRC and EC patients with available IHC-MMR and NGS-MSI evaluations. IHC and NGS Details on the IHC and NGS approach have been previously published and summarized in Supplementary material [ 24 – 26 ] . Histopathologic data were obtained from standardized pathology reports ( https://www.mayocliniclabs.com/test-catalog/overview/35466 ). For NGS analysis, the TruSight Oncology 500 (TSO500) panel (Illumina, San Diego, CA) was used, targeting 523 genes for substitutions, insertions/deletions, copy number alterations, selected gene rearrangements, and tumor mutational (TMB). MSI status was assessed by analyzing 130 homopolymer repeat loci and covered by a minimum of 60 full-spanning reads, with at least 40 loci required for an MSI score. An MSI score above 20% classified patients as MSI-H, per Illumina TSO500HT guidelines [ 27 ] . MSI status was reported but not used clinically. SigProfilerAssignment tool was used to compute previously known mutational signatures (COSMIC signatures) [ 28 ] and SigProfiler was employed to identify single-base-substitution (SBS) mutational signatures. Statistical analyses Results were expressed as the median and interquartile ranges (IQR) or as number and percentages. Concordance between IHC-MMR and NGS-MSI was evaluated by sensitivity, specificity, accuracy, Positive Predictive Value (PPV) and Negative Predictive Value (NPV). Cohen’s Kappa coefficient with 95% CI was calculated to assess the agreement between IHC and NGS. The Mann-Whitney U Test was used for comparing medians. Misclassified cases (dMMR/MSS or pMMR/MSI-H) were further stratified by TMB into low (< 10 mut/MB) or high ( ≥ 10 mut/MB). No missing values were replaced nor imputed. Statistical analyses were conducted using R packages (ComplexHeatmap, Circlize, maftools, ggplot2) [ 29 – 32 ] . Results From January 2022 to May 2023, a cohort of 404 EC and 232 CRC was profiled within an institutional CGP programme. NGS-MSI and IHC-MMR evaluations were available for 520 patients (352, 86% EC and 168, 72% CRC patients) as shown in Fig. 1 . Clinical characteristics of patients enrolled are shown in Supplemetary Table 1 . The overall concordance between NGS-MSI and IHC-MMR was 90% (CI: 87%-92%), with NGS-MSI showing 62% sensitivity and 99% specificity (Fig. 2 a). PPV was 98% and NPV 88%. In the EC cohort, concordance was 85% (CI: 81%-89%), with 59% sensitivity (CI: 54%, 64%) and 99% specificity (CI: 98%, 100%), while the CRC cohort showed 99% concordance (CI: 96%-100%), with 87% sensitivity (CI: 81%-91%) and 100% specificity (CI: 90%-100%) as shown in Fig. 2 b and c . The EC cohort reported a PPV of 99% (IC: 96%, 99%) and NPV of 82% (CI: 77%, 86%), while the CRC cohort had a PPV of 100% (CI: 98%, 100%) and NPV of 99% (CI: 95%, 100%). Cohen Kappa values were 0.65 (CI: 0.56, 0.73) for EC and 0.92 (CI: 0.81, 1) for CRC (both p < .001). The median MSI score in dMMR EC patients was 24.42 (IQR: 10- 40.67) and 2.44 in pMMR EC (IQR: 1.59–3.75) as shown in Fig. 3 . dMMR CRC displayed a median MSI score of 52.85 (IQR: 28.92–64.10; Fig. 1 ) while 2.48 (IQR: 1.59–4) was found in the pMMR subgroup. Among the 130 microsatellite loci included for MSI assessment, only 127 were evaluable. The median value of usable loci was 122 (range 53–127) in CRC cases and 123 (range 51–127) in EC cases, with 127 overlapping usable loci between CRC and EC ( Supplementary Fig. 1 ). EC patients exhibited significantly more frequent instability events across the 127 loci evaluated compared to CRC patients (p < 0.01, see Supplementary Fig. 1A ). However, this trend does not hold in the MSI-H subgroup, where the opposite is observed, as expected due to differences in concordance rates (p < 0.01, Supplementary Fig. 1B ). The number of unstable loci per patient varied significantly across dMMR phenotypes (MLH1/PMS2, MSH2/MSH6, PMS2 only, MSH6 only), with MSH2/MSH6 showing the highest values and PMS2 only the lowest (p = 0.003, see Fig. 4 ). Compared to pMMR/MSS patients, dMMR/MSI-H patients had higher median TMB values p < 0.001; Supplementary Table 1 ) and greater enrichment in indels (EC: 24.68% vs 5.04%, p < 0.001; CRC: 19.62% vs 9.16%, p < 0.001) as shown in Supplementary Fig. 2 . Discordant cases Figure 5 and Supplementary Table 2 summarize the main molecular and pathological features of the 53 discordant cases: 51 EC (96%) and 2 CRC (4%). Among the discordant EC cases, 50 were dMMR/MSS and one was pMMR/MSI-H. Both discordant CRC cases were dMMR/MSS. The median number of loci used to calculate the MSI score was nearly identical between discordant and concordant cases (123 vs. 122). In the 50 dMMR/MSS discordant EC cases, the most common IHC-MMR alteration was MLH1/PMS2 loss, occurring in 56% of cases. Additionally, three EC cases involved MSH2/MSH6 loss, two showed an isolated loss of PMS2, and ten exhibited an isolated loss of MSH6 (Fig. 5 ). Atypical patterns of IHC-MMR such as dot-like or partial loss of MLH1/PMS2 or isolated MLH1 loss were found in 7 dMMR/MSS EC; moreover, 1 dMMR/MSS CRC cases exhibited a MSH2/MSH6 deficiency associated to partial loss of MLH1/PMS2. Within the MLH1/PMS2-altered EC subgroup, the evaluation of MLH1 hypermethylation was available for 86% of cases; of these, 64% were found to be hypermethylated. In 8 cases of dMRR/MSS EC, variants in MMR genes were identified, whereas none were found in dMMR/MSS CRC cases; of these, 6 were confirmed to be of germline origin. Five discordant EC cases were classified as POLE mutant ( POLE mut). Their median TMB value was 222 (IQR: 73–380), higher compared to the dMMR/MSI-H EC group (p = 0.05; Supplementary Table 1, Supplementary Table 2 ). At IHC-MMR staining, four cases ( POLE mutation p.P286R c.857C > G) had a dot-like MLH1/PMS2 or isolated MLH1 loss while one case ( POLE mutation p.S297F c.890C > T) displayed an isolated loss of MSH6. POLE mutation was considered the driver for somatic MMR mutations observed in 2 cases (Fig. 5 ). All 5 discordant POLE mut cases showed the expected COSMIC signatures (SBS10a, SBS10b), while 20 discordant cases, including 2 POLE mut, exhibited the dMMR-associated one (see Supplementary Fig. 3 ). Excluding POLE mut cases, both EC and CRC dMMR/MSS cases had significant lower TMB values that dMMR/MSI-H ones (p = 0.04; Supplementary Fig. 3) . Similarly, the rate of indels in this subgroup was lower than dMMR/MSI-H ones (p < 0.001; Supplementary Fig. 3 ). In our cohort, an MSI threshold of 6.1 was identified as optimal for predicting dMMR status in EC, achieving an AUC of 0.9 (Sensitivity = 81%, Specificity = 96.5%) ( Supplementary Fig. 4 ). This threshold accurately reclassified 26 discordant cases as dMMR/MSI-H but incorrectly classified 6 previously concordant pMMR/MSI-H patients, reducing the overall number of discordant cases to 31. Of the 26 reclassified cases, 12 exhibited dMMR-associated COSMIC signatures ( Supplementary Fig. 4 ). Finally, from a therapeutic point of view, ICI was administrated to 13 dMMR CRC patients and 2 dMMR EC patients. Among CRC cases, the two dMMR/MSS patients progressed after 3.3 and 7.1 months respectively as shown in Fig. 6 . Both dMMR/MSI-H EC patients are currently undergoing the second course of ICI. Discussion MMR/MSI status is of substantial clinical importance due to its key role in the diagnostic, prognostic, predictive, and therapeutic stratification of various cancers, particularly EC and CRC and for universal screening for Lynch syndrome. While IHC-MMR and PCR-MSI are considered the gold standard assessments, NGS-MSI is gaining increasing attention due to its ability to provide at the same time also broader molecular profiling for solid tumors. Our study shows a 90% concordance rate between IHC-MMR and NGS-MSI in a large, unselected series of prospectively clinically sequenced CRC and EC patients from a large referral centre. This rate was lower in EC cases (85%) compared to CRC cases (99%). NGS-MSI failed to identify 41% of IHC-MMR deficient EC patients. Although false-negative rates for PCR-MSI testing in dMMR non-CRC tumors are well documented, our study found a greater discordance rate between IHC-MMR and NGS-MSI compared to most previous reports adopting PCR-MSI (1–7% vs. 15%) [ 33 – 36 ] . This discrepancy in PCR-MSI is largely attributed to these tests being originally developed for CRC patients. Moreover, it is important to note that, unlike most common PCR-MSI panels, which evaluate 5 or 6 mononucleotide-repeat sequences and classify MSI based on instability in two or more loci, the assay adopted in our study includes only NR27 locus. Two studies have focused specifically on NGS-MSI rather than PCR-MSI. In a small cohort study (28 CRC, 21 EC) comparing IHC-MMR, PCR-MSI, and NGS-MSI, lower concordance in EC (94–96%) compared to CRC (99%) was found, regardless of the method [ 37 ] . In a large cohort of 1942 solid (CRC, n = 609; EC n = 3) cancer cases profiled with TSO500 HT, only 10 patients were discordant (dMMR/MSS using a cut off of 20%) [ 38 ] ; to avoid discrepancies, the authors suggested introducing a borderline MSI category (MSI score ≥ 7 and < 20%) and confirming results with PCR-MSI and IHC-MMR. Similarly, our results indicate that by lowering MSI score threshold to 6.1 the overall number of discordant cases can be reduced to 31. This is further supported by differences in the number of unstable loci among MMR phenotypes, likely explaining reduced MSI scores in cases with MLH1/PMS2 loss, which account for 56% of discordant EC cases. Plausible reasons for discordance were identified for 19 out of 53 cases. In both discordant CRC cases, poor response to ICI corresponded with low MSI scores and TMB; one also displayed an atypical IHC-MMR staining pattern. Among discordant EC cases, one-third displayed POLE hotspot mutations (10%) or atypical MMR deficiency patterns (4%) or MSH6 protein loss (10%). POLE mutations are prevalent in the prognostic characterization of EC patients. Secondary somatic MMR mutations, often G > T transversions in an AGA context, are typically linked to the ultramutated phenotype observed in these cases. This can impact MSI or MMR detection, leading to inconsistent test results [ 35 ] . Unusual dMMR phenotypes are reported as more prevalent in non-CRC cases (particularly in EC) and frequently associated with genetic syndromes (44.9% vs. 21.4% in classical dMMR) [ 39 ] . MSH6 protein loss is known to correlate with an MSS profile even in PCR-MSI, as MSH3 can interact with MSH2 to correct DNA mismatch errors [ 40 ] . In contrast, no explanation was found in nearly 60% of discordant EC cases, which revealed Lynch syndrome in 12%, somatic MMR genes variants in 4% and MLH1 hypermethylation in 37%; however, lowering the MSI threshold to 6.1 could correctly reclassify 74% of the MLH1 hypermethylated discordant cases. The main limitations of our study include the absence of a third confirmatory method like PCR-MSI, lack of long-term follow-up for prognostic correlation, and a limited number of EC patients receiving ICI-based treatment due to most cases being uterine-confined. Additionally, the MSI score algorithm is limited to the loci available with the context of a CGP panel. The strengths are the large patient cohort and the availability of MLH1 hypermethylation and germline/somatic evaluations, which corroborate the results in discordant cases. Further analyses on the impact of concurrent somatic mutations and the pattern of unstable loci in concordant and discordant cases, especially comparing EC to CRC, are underway. Conclusions These findings highlight the importance of tumor-specific interpretation for tumor-agnostic tests, often based on prevalent cancer populations. While NGS panels are promoted for precision medicine, diagnostic approaches should accounts for biological variations among cancers. Declarations Acknowledgements Ministry of Health – Ricerca Corrente 2025 MoH – Rc2025 Author contributions CN, LS,SD, GA, LM, contributed to study design, data interpretation, literature search, and writing of the manuscript; IM, GC, GT contributed to data collection; KM, GM, LG contributed to data analysis and generation of figures; AM, GM contributed in genomic analysis; AS, AP contributed in pathological analysis; AP, MAC, VI, VS, NN, FF, GS, GT contributed in revision of the manuscript. All authors read and approved the final paper. Ethics approval and consent to participate The study has been conducted following the Declaration of Helsinki and has received approval from the Fondazione Policlinico Universitario “A. Gemelli” IRCCS ethical committee (Protocol U 00194/23, ID number: 3837). Prior to participation, all patients provided informed consent. Additional Information Data Availability Statement The datasets generated and analyzed during the current study are not publicly available due to Ethical reasons, but are available from the corresponding author on reasonable request. Financial & competing interests disclosure This study was partially funded by the Italian Ministry of Health (Ricerca Corrente; no grant number provided). C.N. has received travel support from MSD, Illumina, Menarini, and AstraZeneca, and honoraria from Veeva, GSK, MSD, AstraZeneca, Altems, Illumina, and Guardant Health. L.S. has served as a consultant or advisor for Amgen, AstraZeneca, Bristol-Myers Squibb, Daiichi-Sankyo, Incyte, Lilly, Merck Serono, MSD, and Servier. She has also received research funding from Amgen, Astellas, AstraZeneca, Bayer, BMS, Daiichi-Sankyo, Hutchinson, Incyte, Merck Serono, Mirati, MSD, Pfizer, and Roche. She is part of the speakers’ bureaus of Amgen, Bristol-Myers Squibb, GlaxoSmithKline, Lilly, Merck Serono, Pierre Fabre, Roche, and Servier. M.A.C. has received travel and hospitality support from Pierre Fabre, Amgen, Merck, Servier, and Bayer. He also participates in the advisory board of Merck. V.S. has received honoraria or consultation fees from Immunogen, MSD, GSK, Menarini, and Steam Line, and participated in company-sponsored speaker’s bureaus for MSD, AstraZeneca, GSK, and EISAI. N.N. declares receiving speaker’s fees and/or participating in advisory boards for MSD, Bayer, Biocartis, Illumina, Incyte, Roche, BMS, Merck, Thermo Fisher, AstraZeneca, and Eli Lilly. He also received financial support for research projects (institutional grants) from Merck, Thermo Fisher, QIAGEN, Roche, AstraZeneca, Biocartis, and Illumina. He has non-financial interests as the President of the International Quality Network for Pathology (IQN Path) and as the Past President of the Italian Cancer Society (SIC). F.F. has received research funding from Clovis, GSK, MSD, and PharmaMar, as well as personal and financial interests with GSK, MSD, SYSMEX, and STRYKER. G.S. has received research support from MSD and honoraria from Clovis Oncology, and serves as a consultant for Tesaro and Johnson & Johnson. G.T. has participated in advisory boards and sponsored meetings organized by BMS, MSD, Merck, AstraZeneca, Pfizer, Servier, Roche, and Dompé. All other authors have declared no conflicts of interest. References Olave, M. C. & Graham, R. P. Mismatch repair deficiency: The what, how and why it is important. Genes Chromosomes Cancer . 61 (3), 314–321. 10.1002/gcc.23015 (2022). Veigl, M. L. et al. Biallelic inactivation of hMLH1 by epigenetic gene silencing, a novel mechanism causing human MSI cancers. Proc. Natl. Acad. Sci. U S A . 95 (15), 8698–8702. 10.1073/pnas.95.15.8698 (1998). Vilar, E. & Gruber, S. B. Microsatellite instability in colorectal cancer—the stable evidence. Nat. Rev. Clin. Oncol. 7 (3), 153–162. 10.1038/nrclinonc.2009.237 (2010). Hampel, H. et al. Screening for the Lynch syndrome (hereditary nonpolyposis colorectal cancer). N Engl. J. Med. 352 (18), 1851–1860. 10.1056/NEJMoa043146 (2005). Parsons, M. T. et al. Correlation of tumour BRAF mutations and MLH1 methylation with germline mismatch repair (MMR) gene mutation status: a literature review assessing utility of tumour features for MMR variant classification. J. Med. Genet. 49 (3), 151–157. 10.1136/jmedgenet-2011-100714 (2012). Ryan, N. A. J. et al. The proportion of endometrial cancers associated with Lynch syndrome: a systematic review of the literature and meta-analysis. Genet. Med. 21 (10), 2167–2180. 10.1038/s41436-019-0536-8 (2019). Cancer Genome Atlas Research Network et al. Integrated genomic characterization of endometrial carcinoma. Nature 497 (7447), 67–73. 10.1038/nature12113 (2013). Cancer Genome Atlas Network. Comprehensive molecular characterization of human colon and rectal cancer. Nature 487 (7407), 330–337. 10.1038/nature11252 (2012). Wang, Y. et al. Predicting prognosis and immunotherapy response in multiple cancers based on the association of PANoptosis-related genes with tumor heterogeneity. Genes (Basel) . 14 (11), 1994. 10.3390/genes14111994 (2023). Li, K. et al. Microsatellite instability: a review of what the oncologist should know. Cancer Cell. Int. 20 , 16. 10.1186/s12935-019-1091-8 (2020). Sepulveda, A. R. et al. Molecular biomarkers for the evaluation of colorectal cancer: guideline from the American Society for Clinical Pathology, College of American Pathologists, Association for Molecular Pathology, and the American Society of Clinical Oncology. J. Clin. Oncol. 35 (13), 1453–1486. 10.1200/JCO.2016.71.9807 (2017). Sargent, D. J. et al. Defective mismatch repair as a predictive marker for lack of efficacy of fluorouracil-based adjuvant therapy in colon cancer. J. Clin. Oncol. 28 (19), 3219–3226. 10.1200/JCO.2009.27.1825 (2010). Diaz, L. A. et al. KEYNOTE-177 Investigators. Pembrolizumab versus chemotherapy for microsatellite instability-high or mismatch repair-deficient metastatic colorectal cancer (KEYNOTE-177): final analysis of a randomised, open-label, phase 3 study. Lancet Oncol. 23 (5), 659–670. 10.1016/S1470-2045(22)00197-8 (2022). European Society of Gynaecological Oncology (ESGO). ESGO-ESTRO-ESP Cervical Cancer Guidelines. ESGO website Published May 1 , (2023). https://guidelines.esgo.org Merck, S. & Dohme, L. L. C. Study of Pembrolizumab (MK-3475) in Participants With Advanced Solid Tumors (KEYNOTE-158). ClinicalTrials.gov identifier: NCT02628067. Published December 17, 2015. Updated May 16, Accessed September 5, 2024. (2023). GlaxoSmithKline. Study to evaluate the efficacy and safety of Dostarlimab in combination with Carboplatin-Paclitaxel in participants with recurrent or primary advanced endometrial cancer (RUBY). ClinicalTrials.gov identifier: NCT03981796. Published June 11, 2019. Updated May 7. Accessed September 5, 2024. (2024). GlaxoSmithKline. Study of Dostarlimab (TSR-042) in participants with recurrent or advanced solid tumors (GARNET). ClinicalTrials.gov identifier: NCT02715284. Published March 14, 2016. Updated August 1, 2023. Accessed September 5, (2024). Merck, S. & Dohme, L. L. C. Lenvatinib in combination with Pembrolizumab in advanced endometrial cancer (Study 309/KEYNOTE-775). ClinicalTrials.gov identifier: NCT03517449. (2024). Published May 7, 2018. Updated July 25, 2023. Accessed September 5. Oaknin, A. et al. Endometrial cancer: ESMO Clinical Practice Guideline for diagnosis, treatment and follow-up. Ann. Oncol. 33 (8), 860–877. 10.1016/j.annonc.2022.05.009 (2022). Ferguson, J. et al. Immunohistochemistry (IHC) for MMR proteins: a reliable method for identifying mismatch repair deficiency in colorectal cancers. J. Clin. Oncol. 32 (1), 33–45 (2014). Luchini, C. et al. ESMO recommendations on microsatellite instability testing for immunotherapy in cancer, and its relationship with PD-1/PD-L1 expression and tumour mutational burden: a systematic review-based approach. Ann. Oncol. 30 (8), 1232–1243. 10.1093/annonc/mdz116 (2019). Hause, R. J. et al. Classification and characterization of microsatellite instability across 18 cancer types. Nat. Med. 22 (11), 1342–1350. 10.1038/nm.4191 (2016). Zhu, L. et al. A novel and reliable method to detect microsatellite instability in colorectal cancer by next-generation sequencing. J. Mol. Diagn. 20 (2), 225–231. 10.1016/j.jmoldx.2017.11.007 (2018). Addante, F. et al. Mismatch repair deficiency as a predictive and prognostic biomarker in endometrial cancer: a review on immunohistochemistry staining patterns and clinical implications. Int. J. Mol. Sci. 25 (3), 1056. 10.3390/ijms25021056 (2024). Nero, C. et al. Further refining 2020 ESGO/ESTRO/ESP molecular risk classes in patients with early-stage endometrial cancer: A propensity score-matched analysis. Cancer 128 (15), 2898–2907. 10.1002/cncr.34331 (2022). Nero, C. et al. Integrating a comprehensive cancer genome profiling into clinical practice: a blueprint in an Italian referral center. J. Pers. Med. 12 (6), 1746. 10.3390/jpm12101746 (2022). Illumina, I. TruSight Oncology 500: Tumor Mutational Burden (TMB) Analysis. Illumina website. Published. Accessed September 5, 2024. (2018). https://www.illumina.com/content/dam/illumina-marketing/documents/products/appnotes/trusight-oncology-500-tmb-analysis-1170-2018-009.pdf Alexandrov, L. B. et al. The repertoire of mutational signatures in human cancer. Nature 578 (7793), 94–101. 10.1038/s41586-020-1943-3 (2020). Gu, Z. et al. Complex heatmaps reveal patterns and correlations in multidimensional genomic data. Bioinformatics 32 (18), 2847–2849. 10.1093/bioinformatics/btw313 (2016). Gu, Z. et al. circlize implements and enhances circular visualization in R. Bioinformatics 30 (19), 2811–2812. 10.1093/bioinformatics/btu393 (2014). Mayakonda, A. et al. Maftools: efficient and comprehensive analysis of somatic variants in cancer. Genome Res. 28 (12), 1747–1756. 10.1101/gr.239244.118 (2018). Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer-, 2016). Riedinger, C. J. et al. Characterization of mismatch-repair/microsatellite instability-discordant endometrial cancers. Cancer 130 (3), 385–399. 10.1002/cncr.35030 (2024). McConechy, M. K. et al. Detection of DNA mismatch repair (MMR) deficiencies by immunohistochemistry can effectively diagnose the microsatellite instability (MSI) phenotype in endometrial carcinomas. Gynecol. Oncol. 137 (2), 306–310. 10.1016/j.ygyno.2015.01.541 (2015). Stelloo, E. et al. Practical guidance for mismatch repair-deficiency testing in endometrial cancer. Ann. Oncol. 28 (1), 96–102. 10.1093/annonc/mdw542 (2017). Goodfellow, P. J. et al. Prevalence of defective DNA mismatch repair and MSH6 mutation in an unselected series of endometrial cancers. Proc. Natl. Acad. Sci. U S A . 100 (10), 5908–5913. 10.1073/pnas.1030231100 (2003). Dedeurwaerdere, F. et al. Comparison of microsatellite instability detection by immunohistochemistry and molecular techniques in colorectal and endometrial cancer. Sci. Rep. 11 , 12880. 10.1038/s41598-021-91974-x (2021). Kang, S. Y. et al. Comparative analysis of microsatellite instability by next-generation sequencing, MSI PCR and MMR immunohistochemistry in 1942 solid cancers. Pathol. Res. Pract. 233 , 153874. 10.1016/j.prp.2022.153874 (2022). Jaffrelot, M. et al. An unusual phenotype occurs in 15% of mismatch repair-deficient tumors and is associated with non-colorectal cancers and genetic syndromes. Mod. Pathol. 35 (5), 427–437. 10.1038/s41379-021-00918-3 (2022). Pećina-Šlaus, N. et al. Mismatch repair pathway, genome stability and cancer. Front. Mol. Biosci. 7 , 122. 10.3389/fmolb.2020.00122 (2020). Additional Declarations No competing interests reported. Supplementary Files SupplementaryFiguresandTables.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6664521","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":483124904,"identity":"bb2f975e-a837-4c17-a432-ee3fc0b1c7d6","order_by":0,"name":"Camilla Nero","email":"","orcid":"","institution":"Fondazione Policlinico Universitario Agostino Gemelli IRCCS","correspondingAuthor":false,"prefix":"","firstName":"Camilla","middleName":"","lastName":"Nero","suffix":""},{"id":483124905,"identity":"9a783d1e-e375-437e-ae38-c7cb71acc7fe","order_by":1,"name":"Lisa Salvatore","email":"data:image/png;base64,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","orcid":"","institution":"Fondazione Policlinico Universitario Agostino Gemelli IRCCS","correspondingAuthor":true,"prefix":"","firstName":"Lisa","middleName":"","lastName":"Salvatore","suffix":""},{"id":483124906,"identity":"e7f161b2-bacb-4d0e-80cd-53b702b8d2eb","order_by":2,"name":"Simona Duranti","email":"","orcid":"","institution":"Fondazione Policlinico Universitario Agostino Gemelli IRCCS","correspondingAuthor":false,"prefix":"","firstName":"Simona","middleName":"","lastName":"Duranti","suffix":""},{"id":483124907,"identity":"5eb01ae3-14c1-4102-b947-399d7dd03cb5","order_by":3,"name":"Gloria Anderson","email":"","orcid":"","institution":"Università Cattolica del Sacro Cuore","correspondingAuthor":false,"prefix":"","firstName":"Gloria","middleName":"","lastName":"Anderson","suffix":""},{"id":483124908,"identity":"ed6809fc-8156-4af6-a402-9900803889de","order_by":4,"name":"Luca Mastrantoni","email":"","orcid":"","institution":"Università Cattolica del Sacro Cuore","correspondingAuthor":false,"prefix":"","firstName":"Luca","middleName":"","lastName":"Mastrantoni","suffix":""},{"id":483124909,"identity":"5c6c715b-75a0-4f68-ae69-025512bed4a8","order_by":5,"name":"Mina Karimi","email":"","orcid":"","institution":"Gemelli Science and Technology Park (G-STeP), Fondazione Policlinico Universitario Agostino Gemelli IRCCS","correspondingAuthor":false,"prefix":"","firstName":"Mina","middleName":"","lastName":"Karimi","suffix":""},{"id":483124910,"identity":"154c7ca6-94ef-4a16-96a8-c433ce7a4d6d","order_by":6,"name":"Giulia Mantini","email":"","orcid":"","institution":"Gemelli Science and Technology Park (G-STeP), Fondazione Policlinico Universitario Agostino Gemelli IRCCS","correspondingAuthor":false,"prefix":"","firstName":"Giulia","middleName":"","lastName":"Mantini","suffix":""},{"id":483124911,"identity":"7cc98dcc-7967-461b-b96e-40298f2b9ec6","order_by":7,"name":"Angelo Minucci","email":"","orcid":"","institution":"Gemelli Science and Technology Park (G-STeP), Fondazione Policlinico Universitario Agostino Gemelli IRCCS","correspondingAuthor":false,"prefix":"","firstName":"Angelo","middleName":"","lastName":"Minucci","suffix":""},{"id":483124912,"identity":"2b727769-13d8-403a-af0b-679ac306521a","order_by":8,"name":"Giulia Maneri","email":"","orcid":"","institution":"Gemelli Science and Technology Park (G-STeP), Fondazione Policlinico Universitario Agostino Gemelli IRCCS","correspondingAuthor":false,"prefix":"","firstName":"Giulia","middleName":"","lastName":"Maneri","suffix":""},{"id":483124913,"identity":"c9ca9f70-a79c-4ec7-ade9-f5485898351a","order_by":9,"name":"Luciano Giacò","email":"","orcid":"","institution":"Gemelli Science and Technology Park (G-STeP), Fondazione Policlinico Universitario Agostino Gemelli IRCCS","correspondingAuthor":false,"prefix":"","firstName":"Luciano","middleName":"","lastName":"Giacò","suffix":""},{"id":483124914,"identity":"a31865a9-bbd2-4e1e-b7d4-429dc733cc72","order_by":10,"name":"Angela Santoro","email":"","orcid":"","institution":"UOC Anatomia Patologica, Fondazione Policlinico Universitario Agostino Gemelli IRCCS","correspondingAuthor":false,"prefix":"","firstName":"Angela","middleName":"","lastName":"Santoro","suffix":""},{"id":483124915,"identity":"4aa089d0-7b13-4f5b-bd14-c58716a2d160","order_by":11,"name":"Arianna Panfili","email":"","orcid":"","institution":"Fondazione Policlinico Universitario Agostino Gemelli IRCCS","correspondingAuthor":false,"prefix":"","firstName":"Arianna","middleName":"","lastName":"Panfili","suffix":""},{"id":483124916,"identity":"a1f3111f-c3a8-4a95-b6c7-5f25a05aee2c","order_by":12,"name":"Alessia Piermattei","email":"","orcid":"","institution":"UOC Anatomia Patologica, Fondazione Policlinico Universitario Agostino Gemelli IRCCS","correspondingAuthor":false,"prefix":"","firstName":"Alessia","middleName":"","lastName":"Piermattei","suffix":""},{"id":483124917,"identity":"659b4027-a108-442f-9b9c-712af54a2cb0","order_by":13,"name":"Ilenia Marino","email":"","orcid":"","institution":"Fondazione Policlinico Universitario Agostino Gemelli IRCCS","correspondingAuthor":false,"prefix":"","firstName":"Ilenia","middleName":"","lastName":"Marino","suffix":""},{"id":483124918,"identity":"346ad569-434d-4336-b19e-e5d6986eb289","order_by":14,"name":"Giulia Caira","email":"","orcid":"","institution":"Fondazione Policlinico Universitario Agostino Gemelli IRCCS","correspondingAuthor":false,"prefix":"","firstName":"Giulia","middleName":"","lastName":"Caira","suffix":""},{"id":483124924,"identity":"39180056-7fb1-46d9-a07f-1fea839c61f7","order_by":15,"name":"Maria Alessandra Calegari","email":"","orcid":"","institution":"Fondazione Policlinico Universitario Agostino Gemelli IRCCS","correspondingAuthor":false,"prefix":"","firstName":"Maria","middleName":"Alessandra","lastName":"Calegari","suffix":""},{"id":483124925,"identity":"f696b1bd-87f9-41a5-bf80-e57bee6119da","order_by":16,"name":"Giovanni Trovato","email":"","orcid":"","institution":"Università Cattolica del Sacro Cuore","correspondingAuthor":false,"prefix":"","firstName":"Giovanni","middleName":"","lastName":"Trovato","suffix":""},{"id":483124927,"identity":"8a936584-12aa-4b37-86a0-ded502f691f3","order_by":17,"name":"Valentina Iacobelli","email":"","orcid":"","institution":"Fondazione Policlinico Universitario Agostino Gemelli IRCCS","correspondingAuthor":false,"prefix":"","firstName":"Valentina","middleName":"","lastName":"Iacobelli","suffix":""},{"id":483124928,"identity":"a15f9c18-dc92-4656-b22a-6dd32b6c0bb7","order_by":18,"name":"Vanda Salutari","email":"","orcid":"","institution":"Fondazione Policlinico Universitario Agostino Gemelli IRCCS","correspondingAuthor":false,"prefix":"","firstName":"Vanda","middleName":"","lastName":"Salutari","suffix":""},{"id":483124932,"identity":"ddfccb99-e889-4de4-996f-609741547a91","order_by":19,"name":"Nicola Normanno","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Nicola","middleName":"","lastName":"Normanno","suffix":""},{"id":483124933,"identity":"8adfa94b-6774-4715-9fcb-03272ca5dfe1","order_by":20,"name":"Francesco Fanfani","email":"","orcid":"","institution":"Fondazione Policlinico Universitario Agostino Gemelli IRCCS","correspondingAuthor":false,"prefix":"","firstName":"Francesco","middleName":"","lastName":"Fanfani","suffix":""},{"id":483124934,"identity":"dd17c5e6-dfa9-4f02-a9bb-06a039a95ee8","order_by":21,"name":"Giovanni Scambia","email":"","orcid":"","institution":"Fondazione Policlinico Universitario Agostino Gemelli IRCCS","correspondingAuthor":false,"prefix":"","firstName":"Giovanni","middleName":"","lastName":"Scambia","suffix":""},{"id":483124935,"identity":"a950e9c2-0c6b-4b89-bf54-5dd438317dfa","order_by":22,"name":"Giampaolo Tortora","email":"","orcid":"","institution":"Fondazione Policlinico Universitario Agostino Gemelli IRCCS","correspondingAuthor":false,"prefix":"","firstName":"Giampaolo","middleName":"","lastName":"Tortora","suffix":""}],"badges":[],"createdAt":"2025-05-14 13:08:39","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6664521/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6664521/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":86659636,"identity":"8e3556f8-1bcf-41cc-83e5-b0bc1a0c7874","added_by":"auto","created_at":"2025-07-14 10:32:13","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":32006,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eStandard for Reporting Diagnostic Accuracy (STARD) Flow chart\u003c/em\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6664521/v1/26d093dc1ee518c764870bab.png"},{"id":86661834,"identity":"65ecd761-0636-4fd4-a226-af0e63158edd","added_by":"auto","created_at":"2025-07-14 10:40:13","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":23145,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eConfusion matrixes for the Overall Cohort (a), EC (b) and CCR cancers (c).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6664521/v1/b3dc316852679a4291c6d174.png"},{"id":86659642,"identity":"a86afece-2284-499f-9382-2ceffc02e693","added_by":"auto","created_at":"2025-07-14 10:32:13","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":36269,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eRaincloud plot of the study cohort divided by EC and CRC patients\u003c/em\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6664521/v1/94d6186fe03b95c6c40a1388.png"},{"id":86659671,"identity":"0749927c-92e1-4b37-9a8f-400ebefebd8a","added_by":"auto","created_at":"2025-07-14 10:32:13","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":47778,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eBoxplots of each dMMR phenotype namely MLH1 and PMS2, MSH2 and MSH6, PMS2 only, MSH6 only, showing the percentage of unstable loci for each sample (n=516). Statistics was computed for each group combination and p-value is shown only for significant differences (p-value \u0026lt; 0.05).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6664521/v1/347935fe80066c7ea4db66c2.png"},{"id":86659659,"identity":"fb93e790-0b0c-42dd-966f-95a49d042993","added_by":"auto","created_at":"2025-07-14 10:32:13","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":159778,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eCharacteristic of discordant cases (n=53)\u003c/em\u003e\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6664521/v1/cd2e5600e795703a7ecc602c.png"},{"id":86659660,"identity":"3eb08626-f989-4fc2-8065-e5bfcdfecfbc","added_by":"auto","created_at":"2025-07-14 10:32:13","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":43078,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eSwimmer plot for CRC cases.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-6664521/v1/3dc7728dfca582e540966e37.png"},{"id":92611370,"identity":"cd439938-b15b-44d0-9681-3a0422b1c22e","added_by":"auto","created_at":"2025-10-01 16:31:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1057394,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6664521/v1/6a0e15b9-16db-4c5f-b1e5-4b97879a08a8.pdf"},{"id":86661835,"identity":"0c1b56b1-ebc1-4f8b-a883-20645df9ac1c","added_by":"auto","created_at":"2025-07-14 10:40:13","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":520983,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFiguresandTables.docx","url":"https://assets-eu.researchsquare.com/files/rs-6664521/v1/83c9d51eb770f3e3c8d8160a.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Concordance Analysis of Microsatellite Instability via NGS and Mismatch Repair Deficiency via IHC in Endometrial and Colorectal Cancer","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe mismatch repair (MMR) system is a conserved mechanism that preserves genomic integrity, by detecting and repairing DNA errors like single base mismatches or short insertions and deletions, involving proteins MLH1, MSH2, MSH6 and PMS2 \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. Loss of MMR function (dMMR) leads to accumulation of somatic mutations, including indels in microsatellite repeats, resulting in high microsatellite instability (MSI-H). dMMR status is mostly (60\u0026ndash;80%) caused by MLH1 gene promoter hypermethylation or somatic bi-allelic mutation in MMR genes \u003csup\u003e[\u003cspan additionalcitationids=\"CR3 CR4\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. Less commonly (10\u0026ndash;30%) \u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e, it arises from an autosomal dominant heterozygous germline variant of MMR genes, delineating Lynch syndrome, which increases the risk of cancer, particularly colorectal (CRC) and endometrial (EC).\u003c/p\u003e\u003cp\u003eIntegrated genomics data from The Cancer Genome Atlas (TGCA) Network show that around 12% of CRC and 30% of EC samples exhibit MSI-H with a hypermutated phenotype \u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. This feature is linked to prognosis and response to immunotherapy in both cancer types \u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. In CRC, dMMR is commonly linked to right-sided colon involvement, a mucinous phenotype, tumor-infiltrating lymphocytes, a Crohn's-like inflammatory reaction, multiple tumor subclones, poor differentiation, and frequently co-occurs with BRAF mutations \u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. In non-metastatic CRCs, dMMR correlates with a good prognosis and limited benefit from adjuvant therapy, particularly fluoropyrimidine \u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. In metastatic CRC, though rare (1\u0026ndash;2%), dMMR indicates poor prognosis, reduced response to standard chemotherapy, and significant benefit from immune checkpoint inhibitors (ICIs) \u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e, such as ipilimumab with nivolumab and pembrolizumab. In EC, dMMR is associated with endometrioid histology, intermediate prognosis \u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e and this subgroup benefits significantly from ICIs \u003csup\u003e[\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. Approved ICIs for advanced or metastatic dMMR EC include pembrolizumab and dostarlimab as monotherapies, as well as dostarlimab combined with platinum-based chemotherapy \u003csup\u003e[\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. Additionally, pembrolizumab is approved in combination with lenvatinib for advanced or recurrent EC after prior platinum-based therapy \u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eFor both predictive and prognostic purposes, as well as for Lynch syndrome screening, assessing MMR function in all newly diagnosed CRC and EC patients is strongly recommended by international and national guidelines \u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. Immunohistochemistry (IHC) for MMR proteins offers adequate sensibility and specificity for detecting MMR deficiency \u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. MSI testing serves as a proxy for MMR abnormalities, measuring the consequences of its deficiency through polymerase chain reaction (PCR) or next generation sequencing (NGS) techniques \u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. Due to its availability, cost-effectiveness, and quick turnaround time, IHC assessment of MMR status is the most adopted approach. However, MMR assessment is not the only molecular feature to be considered in both CRC and EC. The increasing complexity of genomic alterations has prompted referral centres to adopt comprehensive cancer genome profiling (CGP\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e). Although these NGS-based assays indicate MSI status, there is limited evidence assessing their accuracy when compared to IHC-MMR and PCR-MSI evaluations. NGS can evaluate thousands of microsatellite loci compared with 5\u0026ndash;7 loci detected by PCR \u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003eand the MSI cut-off in different tumor types is controversial, requiring further clarification.\u003c/p\u003e\u003cp\u003eThus, the present study aims to evaluate the concordance rate between the NGS-MSI status compared with the gold standard IHC-MMR in an unselected prospective series of CRC and EC patients from a large referral centre. Additionally, a detailed genomic, pathological, and clinical analysis of discordant cases was conducted to investigate the underlying causes of the discrepancies.\u003c/p\u003e"},{"header":"Material and methods","content":"\u003cp\u003e\u003cb\u003eStudy population\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAt Fondazione Policlinico Universitario Agostino Gemelli IRCCS (FPG), patients with specific solid tumors are offered a tumor-only targeted NGS panel as part of an institutional CGP program (ClinicalTrials.gov Identifier: NCT06020625, registered on 31/08/2023, Protocol ID: FPG500). This program follows the Declaration of Helsinki guidelines and received approval from the local ethical committee (Protocol U 00194/23, ID number: 3837). All patients provided informed consent before partecipation. This study included all consecutive CRC and EC patients with available IHC-MMR and NGS-MSI evaluations.\u003c/p\u003e\u003cp\u003e\u003cb\u003eIHC and NGS\u003c/b\u003e\u003c/p\u003e\u003cp\u003eDetails on the IHC and NGS approach have been previously published and summarized in Supplementary material \u003csup\u003e[\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. Histopathologic data were obtained from standardized pathology reports (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.mayocliniclabs.com/test-catalog/overview/35466\u003c/span\u003e\u003cspan address=\"https://www.mayocliniclabs.com/test-catalog/overview/35466\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFor NGS analysis, the TruSight Oncology 500 (TSO500) panel (Illumina, San Diego, CA) was used, targeting 523 genes for substitutions, insertions/deletions, copy number alterations, selected gene rearrangements, and tumor mutational (TMB). MSI status was assessed by analyzing 130 homopolymer repeat loci and covered by a minimum of 60 full-spanning reads, with at least 40 loci required for an MSI score. An MSI score above 20% classified patients as MSI-H, per Illumina TSO500HT guidelines \u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e. MSI status was reported but not used clinically. SigProfilerAssignment tool was used to compute previously known mutational signatures (COSMIC signatures) \u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003eand SigProfiler was employed to identify single-base-substitution (SBS) mutational signatures.\u003c/p\u003e\u003cp\u003e\u003cb\u003eStatistical analyses\u003c/b\u003e\u003c/p\u003e\u003cp\u003eResults were expressed as the median and interquartile ranges (IQR) or as number and percentages. Concordance between IHC-MMR and NGS-MSI was evaluated by sensitivity, specificity, accuracy, Positive Predictive Value (PPV) and Negative Predictive Value (NPV). Cohen\u0026rsquo;s Kappa coefficient with 95% CI was calculated to assess the agreement between IHC and NGS. The Mann-Whitney U Test was used for comparing medians. Misclassified cases (dMMR/MSS or pMMR/MSI-H) were further stratified by TMB into low (\u0026lt;\u0026thinsp;10 mut/MB) or high (\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;10 mut/MB). No missing values were replaced nor imputed. Statistical analyses were conducted using R packages (ComplexHeatmap, Circlize, maftools, ggplot2) \u003csup\u003e[\u003cspan additionalcitationids=\"CR30 CR31\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eFrom January 2022 to May 2023, a cohort of 404 EC and 232 CRC was profiled within an institutional CGP programme. NGS-MSI and IHC-MMR evaluations were available for 520 patients (352, 86% EC and 168, 72% CRC patients) as shown in Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n\u003cp\u003eClinical characteristics of patients enrolled are shown in \u003cstrong\u003eSupplemetary Table\u0026nbsp;1\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eThe overall concordance between NGS-MSI and IHC-MMR was 90% (CI: 87%-92%), with NGS-MSI showing 62% sensitivity and 99% specificity (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ea).\u003c/p\u003e\n\u003cp\u003ePPV was 98% and NPV 88%. In the EC cohort, concordance was 85% (CI: 81%-89%), with 59% sensitivity (CI: 54%, 64%) and 99% specificity (CI: 98%, 100%), while the CRC cohort showed 99% concordance (CI: 96%-100%), with 87% sensitivity (CI: 81%-91%) and 100% specificity (CI: 90%-100%) as shown in Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eb \u003cem\u003eand c\u003c/em\u003e. The EC cohort reported a PPV of 99% (IC: 96%, 99%) and NPV of 82% (CI: 77%, 86%), while the CRC cohort had a PPV of 100% (CI: 98%, 100%) and NPV of 99% (CI: 95%, 100%). Cohen Kappa values were 0.65 (CI: 0.56, 0.73) for EC and 0.92 (CI: 0.81, 1) for CRC (both p\u0026thinsp;\u0026lt;\u0026thinsp;.001).\u003c/p\u003e\n\u003cp\u003eThe median MSI score in dMMR EC patients was 24.42 (IQR: 10- 40.67) and 2.44 in pMMR EC (IQR: 1.59\u0026ndash;3.75) as shown in Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\n\u003cp\u003edMMR CRC displayed a median MSI score of 52.85 (IQR: 28.92\u0026ndash;64.10; Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e) while 2.48 (IQR: 1.59\u0026ndash;4) was found in the pMMR subgroup. Among the 130 microsatellite loci included for MSI assessment, only 127 were evaluable. The median value of usable loci was 122 (range 53\u0026ndash;127) in CRC cases and 123 (range 51\u0026ndash;127) in EC cases, with 127 overlapping usable loci between CRC and EC (\u003cem\u003eSupplementary Fig.\u0026nbsp;1\u003c/em\u003e).\u003c/p\u003e\n\u003cp\u003eEC patients exhibited significantly more frequent instability events across the 127 loci evaluated compared to CRC patients (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, see \u003cem\u003eSupplementary Fig.\u0026nbsp;1A\u003c/em\u003e). However, this trend does not hold in the MSI-H subgroup, where the opposite is observed, as expected due to differences in concordance rates (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, \u003cem\u003eSupplementary Fig.\u0026nbsp;1B\u003c/em\u003e). The number of unstable loci per patient varied significantly across dMMR phenotypes (MLH1/PMS2, MSH2/MSH6, PMS2 only, MSH6 only), with MSH2/MSH6 showing the highest values and PMS2 only the lowest (p\u0026thinsp;=\u0026thinsp;0.003, see Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eCompared to pMMR/MSS patients, dMMR/MSI-H patients had higher median TMB values p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; \u003cem\u003eSupplementary Table\u0026nbsp;1\u003c/em\u003e) and greater enrichment in \u003cem\u003eindels\u003c/em\u003e (EC: 24.68% vs 5.04%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; CRC: 19.62% vs 9.16%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) as shown in \u003cem\u003eSupplementary Fig.\u0026nbsp;2\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eDiscordant cases\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e and \u003cem\u003eSupplementary Table\u0026nbsp;2\u003c/em\u003e summarize the main molecular and pathological features of the 53 discordant cases: 51 EC (96%) and 2 CRC (4%). Among the discordant EC cases, 50 were dMMR/MSS and one was pMMR/MSI-H. Both discordant CRC cases were dMMR/MSS. The median number of loci used to calculate the MSI score was nearly identical between discordant and concordant cases (123 vs. 122). In the 50 dMMR/MSS discordant EC cases, the most common IHC-MMR alteration was MLH1/PMS2 loss, occurring in 56% of cases. Additionally, three EC cases involved MSH2/MSH6 loss, two showed an isolated loss of PMS2, and ten exhibited an isolated loss of MSH6 (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eAtypical patterns of IHC-MMR such as dot-like or partial loss of MLH1/PMS2 or isolated MLH1 loss were found in 7 dMMR/MSS EC; moreover, 1 dMMR/MSS CRC cases exhibited a MSH2/MSH6 deficiency associated to partial loss of MLH1/PMS2.\u003c/p\u003e\n\u003cp\u003eWithin the MLH1/PMS2-altered EC subgroup, the evaluation of MLH1 hypermethylation was available for 86% of cases; of these, 64% were found to be hypermethylated. In 8 cases of dMRR/MSS EC, variants in MMR genes were identified, whereas none were found in dMMR/MSS CRC cases; of these, 6 were confirmed to be of germline origin.\u003c/p\u003e\n\u003cp\u003eFive discordant EC cases were classified as \u003cem\u003ePOLE\u003c/em\u003e mutant (\u003cem\u003ePOLE\u003c/em\u003emut). Their median TMB value was 222 (IQR: 73\u0026ndash;380), higher compared to the dMMR/MSI-H EC group (p\u0026thinsp;=\u0026thinsp;0.05; \u003cem\u003eSupplementary Table\u0026nbsp;1, Supplementary Table\u0026nbsp;2\u003c/em\u003e). At IHC-MMR staining, four cases (\u003cem\u003ePOLE\u003c/em\u003e mutation p.P286R c.857C\u0026thinsp;\u0026gt;\u0026thinsp;G) had a dot-like MLH1/PMS2 or isolated MLH1 loss while one case (\u003cem\u003ePOLE\u003c/em\u003e mutation p.S297F c.890C\u0026thinsp;\u0026gt;\u0026thinsp;T) displayed an isolated loss of MSH6. \u003cem\u003ePOLE\u003c/em\u003e mutation was considered the driver for somatic MMR mutations observed in 2 cases (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). All 5 discordant \u003cem\u003ePOLE\u003c/em\u003emut cases showed the expected COSMIC signatures (SBS10a, SBS10b), while 20 discordant cases, including 2 \u003cem\u003ePOLE\u003c/em\u003emut, exhibited the dMMR-associated one (see \u003cem\u003eSupplementary Fig.\u0026nbsp;3\u003c/em\u003e).\u003c/p\u003e\n\u003cp\u003eExcluding \u003cem\u003ePOLE\u003c/em\u003emut cases, both EC and CRC dMMR/MSS cases had significant lower TMB values that dMMR/MSI-H ones (p\u0026thinsp;=\u0026thinsp;0.04; \u003cem\u003eSupplementary Fig.\u0026nbsp;3)\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003eSimilarly, the rate of \u003cem\u003eindels\u003c/em\u003e in this subgroup was lower than dMMR/MSI-H ones (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; \u003cem\u003eSupplementary Fig.\u0026nbsp;3\u003c/em\u003e).\u003c/p\u003e\n\u003cp\u003eIn our cohort, an MSI threshold of 6.1 was identified as optimal for predicting dMMR status in EC, achieving an AUC of 0.9 (Sensitivity\u0026thinsp;=\u0026thinsp;81%, Specificity\u0026thinsp;=\u0026thinsp;96.5%) (\u003cem\u003eSupplementary Fig.\u0026nbsp;4\u003c/em\u003e).\u003c/p\u003e\n\u003cp\u003eThis threshold accurately reclassified 26 discordant cases as dMMR/MSI-H but incorrectly classified 6 previously concordant pMMR/MSI-H patients, reducing the overall number of discordant cases to 31. Of the 26 reclassified cases, 12 exhibited dMMR-associated COSMIC signatures (\u003cem\u003eSupplementary Fig.\u0026nbsp;4\u003c/em\u003e).\u003c/p\u003e\n\u003cp\u003eFinally, from a therapeutic point of view, ICI was administrated to 13 dMMR CRC patients and 2 dMMR EC patients. Among CRC cases, the two dMMR/MSS patients progressed after 3.3 and 7.1 months respectively as shown in Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e. Both dMMR/MSI-H EC patients are currently undergoing the second course of ICI.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eMMR/MSI status is of substantial clinical importance due to its key role in the diagnostic, prognostic, predictive, and therapeutic stratification of various cancers, particularly EC and CRC and for universal screening for Lynch syndrome. While IHC-MMR and PCR-MSI are considered the gold standard assessments, NGS-MSI is gaining increasing attention due to its ability to provide at the same time also broader molecular profiling for solid tumors. Our study shows a 90% concordance rate between IHC-MMR and NGS-MSI in a large, unselected series of prospectively clinically sequenced CRC and EC patients from a large referral centre. This rate was lower in EC cases (85%) compared to CRC cases (99%). NGS-MSI failed to identify 41% of IHC-MMR deficient EC patients. Although false-negative rates for PCR-MSI testing in dMMR non-CRC tumors are well documented, our study found a greater discordance rate between IHC-MMR and NGS-MSI compared to most previous reports adopting PCR-MSI (1\u0026ndash;7% vs. 15%) \u003csup\u003e[\u003cspan additionalcitationids=\"CR34 CR35\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e. This discrepancy in PCR-MSI is largely attributed to these tests being originally developed for CRC patients. Moreover, it is important to note that, unlike most common PCR-MSI panels, which evaluate 5 or 6 mononucleotide-repeat sequences and classify MSI based on instability in two or more loci, the assay adopted in our study includes only NR27 locus.\u003c/p\u003e\u003cp\u003eTwo studies have focused specifically on NGS-MSI rather than PCR-MSI. In a small cohort study (28 CRC, 21 EC) comparing IHC-MMR, PCR-MSI, and NGS-MSI, lower concordance in EC (94\u0026ndash;96%) compared to CRC (99%) was found, regardless of the method \u003csup\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eIn a large cohort of 1942 solid (CRC, n\u0026thinsp;=\u0026thinsp;609; EC n\u0026thinsp;=\u0026thinsp;3) cancer cases profiled with TSO500 HT, only 10 patients were discordant (dMMR/MSS using a cut off of 20%) \u003csup\u003e[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/sup\u003e; to avoid discrepancies, the authors suggested introducing a borderline MSI category (MSI score\u0026thinsp;\u0026ge;\u0026thinsp;7 and \u0026lt;\u0026thinsp;20%) and confirming results with PCR-MSI and IHC-MMR. Similarly, our results indicate that by lowering MSI score threshold to 6.1 the overall number of discordant cases can be reduced to 31. This is further supported by differences in the number of unstable loci among MMR phenotypes, likely explaining reduced MSI scores in cases with MLH1/PMS2 loss, which account for 56% of discordant EC cases.\u003c/p\u003e\u003cp\u003ePlausible reasons for discordance were identified for 19 out of 53 cases. In both discordant CRC cases, poor response to ICI corresponded with low MSI scores and TMB; one also displayed an atypical IHC-MMR staining pattern. Among discordant EC cases, one-third displayed POLE hotspot mutations (10%) or atypical MMR deficiency patterns (4%) or MSH6 protein loss (10%). POLE mutations are prevalent in the prognostic characterization of EC patients. Secondary somatic MMR mutations, often G\u0026thinsp;\u0026gt;\u0026thinsp;T transversions in an AGA context, are typically linked to the ultramutated phenotype observed in these cases. This can impact MSI or MMR detection, leading to inconsistent test results \u003csup\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e. Unusual dMMR phenotypes are reported as more prevalent in non-CRC cases (particularly in EC) and frequently associated with genetic syndromes (44.9% vs. 21.4% in classical dMMR) \u003csup\u003e[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eMSH6 protein loss is known to correlate with an MSS profile even in PCR-MSI, as MSH3 can interact with MSH2 to correct DNA mismatch errors \u003csup\u003e[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eIn contrast, no explanation was found in nearly 60% of discordant EC cases, which revealed Lynch syndrome in 12%, somatic MMR genes variants in 4% and MLH1 hypermethylation in 37%; however, lowering the MSI threshold to 6.1 could correctly reclassify 74% of the MLH1 hypermethylated discordant cases.\u003c/p\u003e\u003cp\u003eThe main limitations of our study include the absence of a third confirmatory method like PCR-MSI, lack of long-term follow-up for prognostic correlation, and a limited number of EC patients receiving ICI-based treatment due to most cases being uterine-confined. Additionally, the MSI score algorithm is limited to the loci available with the context of a CGP panel. The strengths are the large patient cohort and the availability of MLH1 hypermethylation and germline/somatic evaluations, which corroborate the results in discordant cases.\u003c/p\u003e\u003cp\u003eFurther analyses on the impact of concurrent somatic mutations and the pattern of unstable loci in concordant and discordant cases, especially comparing EC to CRC, are underway.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThese findings highlight the importance of tumor-specific interpretation for tumor-agnostic tests, often based on prevalent cancer populations. While NGS panels are promoted for precision medicine, diagnostic approaches should accounts for biological variations among cancers.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMinistry of Health – Ricerca Corrente 2025 MoH – Rc2025\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCN, LS,SD, GA, LM, contributed to study design, data interpretation, literature search, and writing of the manuscript; IM, GC, GT contributed to data collection; KM, GM, LG contributed to data analysis and generation of figures; AM, GM contributed in genomic analysis; AS, AP contributed in pathological analysis; AP, MAC, VI, VS, \u0026nbsp;NN, FF, GS, GT contributed in revision of the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll authors read and approved the final paper. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study has been conducted following the Declaration of Helsinki and has received approval from the Fondazione Policlinico Universitario “A. Gemelli” IRCCS ethical committee (Protocol U 00194/23, ID number: 3837). Prior to participation, all patients provided informed consent.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdditional Information\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analyzed during the current study are not publicly available due to Ethical reasons, but are available from the corresponding author on reasonable request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFinancial \u0026amp; competing interests disclosure\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was partially funded by the Italian Ministry of Health (Ricerca Corrente; no grant number provided).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eC.N. has received travel support from MSD, Illumina, Menarini, and AstraZeneca, and honoraria from Veeva, GSK, MSD, AstraZeneca, Altems, Illumina, and Guardant Health.\u003c/p\u003e\n\u003cp\u003eL.S. has served as a consultant or advisor for Amgen, AstraZeneca, Bristol-Myers Squibb, Daiichi-Sankyo, Incyte, Lilly, Merck Serono, MSD, and Servier. She has also received research funding from Amgen, Astellas, AstraZeneca, Bayer, BMS, Daiichi-Sankyo, Hutchinson, Incyte, Merck Serono, Mirati, MSD, Pfizer, and Roche. She is part of the speakers’ bureaus of Amgen, Bristol-Myers Squibb, GlaxoSmithKline, Lilly, Merck Serono, Pierre Fabre, Roche, and Servier.\u003c/p\u003e\n\u003cp\u003eM.A.C. has received travel and hospitality support from Pierre Fabre, Amgen, Merck, Servier, and Bayer. He also participates in the advisory board of Merck.\u003c/p\u003e\n\u003cp\u003eV.S. has received honoraria or consultation fees from Immunogen, MSD, GSK, Menarini, and Steam Line, and participated in company-sponsored speaker’s bureaus for MSD, AstraZeneca, GSK, and EISAI.\u003c/p\u003e\n\u003cp\u003eN.N. declares receiving speaker’s fees and/or participating in advisory boards for MSD, Bayer, Biocartis, Illumina, Incyte, Roche, BMS, Merck, Thermo Fisher, AstraZeneca, and Eli Lilly. He also received financial support for research projects (institutional grants) from Merck, Thermo Fisher, QIAGEN, Roche, AstraZeneca, Biocartis, and Illumina. He has non-financial interests as the President of the International Quality Network for Pathology (IQN Path) and as the Past President of the Italian Cancer Society (SIC).\u003c/p\u003e\n\u003cp\u003eF.F. has received research funding from Clovis, GSK, MSD, and PharmaMar, as well as personal and financial interests with GSK, MSD, SYSMEX, and STRYKER.\u003c/p\u003e\n\u003cp\u003eG.S. has received research support from MSD and honoraria from Clovis Oncology, and serves as a consultant for Tesaro and Johnson \u0026amp; Johnson.\u003c/p\u003e\n\u003cp\u003eG.T. has participated in advisory boards and sponsored meetings organized by BMS, MSD, Merck, AstraZeneca, Pfizer, Servier, Roche, and Dompé.\u003c/p\u003e\n\u003cp\u003eAll other authors have declared no conflicts of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eOlave, M. C. \u0026amp; Graham, R. P. Mismatch repair deficiency: The what, how and why it is important. \u003cem\u003eGenes Chromosomes Cancer\u003c/em\u003e. \u003cb\u003e61\u003c/b\u003e (3), 314\u0026ndash;321. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/gcc.23015\u003c/span\u003e\u003cspan address=\"10.1002/gcc.23015\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVeigl, M. L. et al. Biallelic inactivation of hMLH1 by epigenetic gene silencing, a novel mechanism causing human MSI cancers. \u003cem\u003eProc. Natl. Acad. Sci. U S A\u003c/em\u003e. \u003cb\u003e95\u003c/b\u003e (15), 8698\u0026ndash;8702. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1073/pnas.95.15.8698\u003c/span\u003e\u003cspan address=\"10.1073/pnas.95.15.8698\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (1998).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVilar, E. \u0026amp; Gruber, S. B. Microsatellite instability in colorectal cancer\u0026mdash;the stable evidence. \u003cem\u003eNat. Rev. Clin. Oncol.\u003c/em\u003e \u003cb\u003e7\u003c/b\u003e (3), 153\u0026ndash;162. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/nrclinonc.2009.237\u003c/span\u003e\u003cspan address=\"10.1038/nrclinonc.2009.237\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2010).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHampel, H. et al. Screening for the Lynch syndrome (hereditary nonpolyposis colorectal cancer). \u003cem\u003eN Engl. J. Med.\u003c/em\u003e \u003cb\u003e352\u003c/b\u003e (18), 1851\u0026ndash;1860. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1056/NEJMoa043146\u003c/span\u003e\u003cspan address=\"10.1056/NEJMoa043146\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2005).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eParsons, M. T. et al. Correlation of tumour BRAF mutations and MLH1 methylation with germline mismatch repair (MMR) gene mutation status: a literature review assessing utility of tumour features for MMR variant classification. \u003cem\u003eJ. Med. Genet.\u003c/em\u003e \u003cb\u003e49\u003c/b\u003e (3), 151\u0026ndash;157. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1136/jmedgenet-2011-100714\u003c/span\u003e\u003cspan address=\"10.1136/jmedgenet-2011-100714\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2012).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRyan, N. A. J. et al. The proportion of endometrial cancers associated with Lynch syndrome: a systematic review of the literature and meta-analysis. \u003cem\u003eGenet. Med.\u003c/em\u003e \u003cb\u003e21\u003c/b\u003e (10), 2167\u0026ndash;2180. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41436-019-0536-8\u003c/span\u003e\u003cspan address=\"10.1038/s41436-019-0536-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2019).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCancer Genome Atlas Research Network et al. Integrated genomic characterization of endometrial carcinoma. \u003cem\u003eNature\u003c/em\u003e \u003cb\u003e497\u003c/b\u003e (7447), 67\u0026ndash;73. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/nature12113\u003c/span\u003e\u003cspan address=\"10.1038/nature12113\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2013).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCancer Genome Atlas Network. Comprehensive molecular characterization of human colon and rectal cancer. \u003cem\u003eNature\u003c/em\u003e \u003cb\u003e487\u003c/b\u003e (7407), 330\u0026ndash;337. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/nature11252\u003c/span\u003e\u003cspan address=\"10.1038/nature11252\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2012).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang, Y. et al. Predicting prognosis and immunotherapy response in multiple cancers based on the association of PANoptosis-related genes with tumor heterogeneity. \u003cem\u003eGenes (Basel)\u003c/em\u003e. \u003cb\u003e14\u003c/b\u003e (11), 1994. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/genes14111994\u003c/span\u003e\u003cspan address=\"10.3390/genes14111994\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi, K. et al. Microsatellite instability: a review of what the oncologist should know. \u003cem\u003eCancer Cell. Int.\u003c/em\u003e \u003cb\u003e20\u003c/b\u003e, 16. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12935-019-1091-8\u003c/span\u003e\u003cspan address=\"10.1186/s12935-019-1091-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSepulveda, A. R. et al. Molecular biomarkers for the evaluation of colorectal cancer: guideline from the American Society for Clinical Pathology, College of American Pathologists, Association for Molecular Pathology, and the American Society of Clinical Oncology. \u003cem\u003eJ. Clin. Oncol.\u003c/em\u003e \u003cb\u003e35\u003c/b\u003e (13), 1453\u0026ndash;1486. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1200/JCO.2016.71.9807\u003c/span\u003e\u003cspan address=\"10.1200/JCO.2016.71.9807\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2017).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSargent, D. J. et al. Defective mismatch repair as a predictive marker for lack of efficacy of fluorouracil-based adjuvant therapy in colon cancer. \u003cem\u003eJ. Clin. Oncol.\u003c/em\u003e \u003cb\u003e28\u003c/b\u003e (19), 3219\u0026ndash;3226. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1200/JCO.2009.27.1825\u003c/span\u003e\u003cspan address=\"10.1200/JCO.2009.27.1825\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2010).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDiaz, L. A. et al. KEYNOTE-177 Investigators. Pembrolizumab versus chemotherapy for microsatellite instability-high or mismatch repair-deficient metastatic colorectal cancer (KEYNOTE-177): final analysis of a randomised, open-label, phase 3 study. \u003cem\u003eLancet Oncol.\u003c/em\u003e \u003cb\u003e23\u003c/b\u003e (5), 659\u0026ndash;670. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/S1470-2045(22)00197-8\u003c/span\u003e\u003cspan address=\"10.1016/S1470-2045(22)00197-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEuropean Society of Gynaecological Oncology (ESGO). ESGO-ESTRO-ESP Cervical Cancer Guidelines. \u003cem\u003eESGO website Published May\u003c/em\u003e \u003cb\u003e1\u003c/b\u003e, (2023). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://guidelines.esgo.org\u003c/span\u003e\u003cspan address=\"https://guidelines.esgo.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMerck, S. \u0026amp; Dohme, L. L. C. Study of Pembrolizumab (MK-3475) in Participants With Advanced Solid Tumors (KEYNOTE-158). ClinicalTrials.gov identifier: NCT02628067. Published December 17, 2015. Updated May 16, Accessed September 5, 2024. (2023).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGlaxoSmithKline. Study to evaluate the efficacy and safety of Dostarlimab in combination with Carboplatin-Paclitaxel in participants with recurrent or primary advanced endometrial cancer (RUBY). ClinicalTrials.gov identifier: NCT03981796. Published June 11, 2019. Updated May 7. Accessed September 5, 2024. (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGlaxoSmithKline. Study of Dostarlimab (TSR-042) in participants with recurrent or advanced solid tumors (GARNET). ClinicalTrials.gov identifier: NCT02715284. Published March 14, 2016. Updated August 1, 2023. Accessed September 5, (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMerck, S. \u0026amp; Dohme, L. L. C. Lenvatinib in combination with Pembrolizumab in advanced endometrial cancer (Study 309/KEYNOTE-775). ClinicalTrials.gov identifier: NCT03517449. (2024). Published May 7, 2018. Updated July 25, 2023. Accessed September 5.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOaknin, A. et al. Endometrial cancer: ESMO Clinical Practice Guideline for diagnosis, treatment and follow-up. \u003cem\u003eAnn. Oncol.\u003c/em\u003e \u003cb\u003e33\u003c/b\u003e (8), 860\u0026ndash;877. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.annonc.2022.05.009\u003c/span\u003e\u003cspan address=\"10.1016/j.annonc.2022.05.009\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFerguson, J. et al. Immunohistochemistry (IHC) for MMR proteins: a reliable method for identifying mismatch repair deficiency in colorectal cancers. \u003cem\u003eJ. Clin. Oncol.\u003c/em\u003e \u003cb\u003e32\u003c/b\u003e (1), 33\u0026ndash;45 (2014).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLuchini, C. et al. ESMO recommendations on microsatellite instability testing for immunotherapy in cancer, and its relationship with PD-1/PD-L1 expression and tumour mutational burden: a systematic review-based approach. \u003cem\u003eAnn. Oncol.\u003c/em\u003e \u003cb\u003e30\u003c/b\u003e (8), 1232\u0026ndash;1243. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/annonc/mdz116\u003c/span\u003e\u003cspan address=\"10.1093/annonc/mdz116\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2019).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHause, R. J. et al. Classification and characterization of microsatellite instability across 18 cancer types. \u003cem\u003eNat. Med.\u003c/em\u003e \u003cb\u003e22\u003c/b\u003e (11), 1342\u0026ndash;1350. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/nm.4191\u003c/span\u003e\u003cspan address=\"10.1038/nm.4191\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2016).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhu, L. et al. A novel and reliable method to detect microsatellite instability in colorectal cancer by next-generation sequencing. \u003cem\u003eJ. Mol. Diagn.\u003c/em\u003e \u003cb\u003e20\u003c/b\u003e (2), 225\u0026ndash;231. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jmoldx.2017.11.007\u003c/span\u003e\u003cspan address=\"10.1016/j.jmoldx.2017.11.007\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2018).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAddante, F. et al. Mismatch repair deficiency as a predictive and prognostic biomarker in endometrial cancer: a review on immunohistochemistry staining patterns and clinical implications. \u003cem\u003eInt. J. Mol. Sci.\u003c/em\u003e \u003cb\u003e25\u003c/b\u003e (3), 1056. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/ijms25021056\u003c/span\u003e\u003cspan address=\"10.3390/ijms25021056\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNero, C. et al. Further refining 2020 ESGO/ESTRO/ESP molecular risk classes in patients with early-stage endometrial cancer: A propensity score-matched analysis. \u003cem\u003eCancer\u003c/em\u003e \u003cb\u003e128\u003c/b\u003e (15), 2898\u0026ndash;2907. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/cncr.34331\u003c/span\u003e\u003cspan address=\"10.1002/cncr.34331\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNero, C. et al. Integrating a comprehensive cancer genome profiling into clinical practice: a blueprint in an Italian referral center. \u003cem\u003eJ. Pers. Med.\u003c/em\u003e \u003cb\u003e12\u003c/b\u003e (6), 1746. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/jpm12101746\u003c/span\u003e\u003cspan address=\"10.3390/jpm12101746\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eIllumina, I. TruSight Oncology 500: Tumor Mutational Burden (TMB) Analysis. Illumina website. Published. Accessed September 5, 2024. (2018). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.illumina.com/content/dam/illumina-marketing/documents/products/appnotes/trusight-oncology-500-tmb-analysis-1170-2018-009.pdf\u003c/span\u003e\u003cspan address=\"https://www.illumina.com/content/dam/illumina-marketing/documents/products/appnotes/trusight-oncology-500-tmb-analysis-1170-2018-009.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAlexandrov, L. B. et al. The repertoire of mutational signatures in human cancer. \u003cem\u003eNature\u003c/em\u003e \u003cb\u003e578\u003c/b\u003e (7793), 94\u0026ndash;101. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41586-020-1943-3\u003c/span\u003e\u003cspan address=\"10.1038/s41586-020-1943-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGu, Z. et al. Complex heatmaps reveal patterns and correlations in multidimensional genomic data. \u003cem\u003eBioinformatics\u003c/em\u003e \u003cb\u003e32\u003c/b\u003e (18), 2847\u0026ndash;2849. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/bioinformatics/btw313\u003c/span\u003e\u003cspan address=\"10.1093/bioinformatics/btw313\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2016).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGu, Z. et al. circlize implements and enhances circular visualization in R. \u003cem\u003eBioinformatics\u003c/em\u003e \u003cb\u003e30\u003c/b\u003e (19), 2811\u0026ndash;2812. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/bioinformatics/btu393\u003c/span\u003e\u003cspan address=\"10.1093/bioinformatics/btu393\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2014).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMayakonda, A. et al. Maftools: efficient and comprehensive analysis of somatic variants in cancer. \u003cem\u003eGenome Res.\u003c/em\u003e \u003cb\u003e28\u003c/b\u003e (12), 1747\u0026ndash;1756. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1101/gr.239244.118\u003c/span\u003e\u003cspan address=\"10.1101/gr.239244.118\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2018).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWickham, H. \u003cem\u003eggplot2: Elegant Graphics for Data Analysis\u003c/em\u003e (Springer-, 2016).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRiedinger, C. J. et al. Characterization of mismatch-repair/microsatellite instability-discordant endometrial cancers. \u003cem\u003eCancer\u003c/em\u003e \u003cb\u003e130\u003c/b\u003e (3), 385\u0026ndash;399. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/cncr.35030\u003c/span\u003e\u003cspan address=\"10.1002/cncr.35030\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMcConechy, M. K. et al. Detection of DNA mismatch repair (MMR) deficiencies by immunohistochemistry can effectively diagnose the microsatellite instability (MSI) phenotype in endometrial carcinomas. \u003cem\u003eGynecol. Oncol.\u003c/em\u003e \u003cb\u003e137\u003c/b\u003e (2), 306\u0026ndash;310. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.ygyno.2015.01.541\u003c/span\u003e\u003cspan address=\"10.1016/j.ygyno.2015.01.541\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2015).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eStelloo, E. et al. Practical guidance for mismatch repair-deficiency testing in endometrial cancer. \u003cem\u003eAnn. Oncol.\u003c/em\u003e \u003cb\u003e28\u003c/b\u003e (1), 96\u0026ndash;102. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/annonc/mdw542\u003c/span\u003e\u003cspan address=\"10.1093/annonc/mdw542\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2017).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGoodfellow, P. J. et al. Prevalence of defective DNA mismatch repair and MSH6 mutation in an unselected series of endometrial cancers. \u003cem\u003eProc. Natl. Acad. Sci. U S A\u003c/em\u003e. \u003cb\u003e100\u003c/b\u003e (10), 5908\u0026ndash;5913. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1073/pnas.1030231100\u003c/span\u003e\u003cspan address=\"10.1073/pnas.1030231100\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2003).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDedeurwaerdere, F. et al. Comparison of microsatellite instability detection by immunohistochemistry and molecular techniques in colorectal and endometrial cancer. \u003cem\u003eSci. Rep.\u003c/em\u003e \u003cb\u003e11\u003c/b\u003e, 12880. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41598-021-91974-x\u003c/span\u003e\u003cspan address=\"10.1038/s41598-021-91974-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKang, S. Y. et al. Comparative analysis of microsatellite instability by next-generation sequencing, MSI PCR and MMR immunohistochemistry in 1942 solid cancers. \u003cem\u003ePathol. Res. Pract.\u003c/em\u003e \u003cb\u003e233\u003c/b\u003e, 153874. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.prp.2022.153874\u003c/span\u003e\u003cspan address=\"10.1016/j.prp.2022.153874\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJaffrelot, M. et al. An unusual phenotype occurs in 15% of mismatch repair-deficient tumors and is associated with non-colorectal cancers and genetic syndromes. \u003cem\u003eMod. Pathol.\u003c/em\u003e \u003cb\u003e35\u003c/b\u003e (5), 427\u0026ndash;437. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41379-021-00918-3\u003c/span\u003e\u003cspan address=\"10.1038/s41379-021-00918-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePećina-Šlaus, N. et al. Mismatch repair pathway, genome stability and cancer. \u003cem\u003eFront. Mol. Biosci.\u003c/em\u003e \u003cb\u003e7\u003c/b\u003e, 122. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fmolb.2020.00122\u003c/span\u003e\u003cspan address=\"10.3389/fmolb.2020.00122\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\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":"[email protected]","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":"microsatellite instability (MSI), mismatch repair deficiency (MMR), next generation sequencing (NGS), Immunohistochemistry, endometrial cancer, colorectal cancer","lastPublishedDoi":"10.21203/rs.3.rs-6664521/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6664521/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMicrosatellite instability and mismatch repair deficiency are important biomarkers in colorectal and endometrial cancers, helping guide diagnosis, prognosis, and treatment decisions, particularly for immunotherapy. Mismatch repair status is commonly assessed using immunohistochemistry, while microsatellite instability can be detected through sequencing-based methods. In this study, we analyzed 520 tumor samples from patients with colorectal or endometrial cancer using both approaches to compare their performance. Overall, there was high agreement between the two methods, especially in colorectal cancer. However, in endometrial cancer, a lower level of concordance was observed, with several cases showing mismatch repair deficiency without detectable microsatellite instability. These differences were often explained by specific genetic features, such as mutations in DNA polymerase genes, isolated loss of mismatch repair proteins, or epigenetic alterations. We also found that adjusting the threshold used to define microsatellite instability improved detection accuracy in endometrial tumors. These findings suggest that sequencing-based detection of microsatellite instability is a reliable method but may require tumor-specific optimization. Tailoring thresholds based on cancer type could improve identification of patients who are likely to benefit from immunotherapy and enhance precision in clinical decision-making.\u003c/p\u003e","manuscriptTitle":"Concordance Analysis of Microsatellite Instability via NGS and Mismatch Repair Deficiency via IHC in Endometrial and Colorectal Cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-14 10:32:08","doi":"10.21203/rs.3.rs-6664521/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","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":"9b9614a5-6cf9-4f3f-a86d-41f0ebcb1c38","owner":[],"postedDate":"July 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":51299192,"name":"Biological sciences/Cancer"},{"id":51299193,"name":"Biological sciences/Molecular biology"}],"tags":[],"updatedAt":"2025-10-01T16:23:40+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-14 10:32:08","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6664521","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6664521","identity":"rs-6664521","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}