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Article

Evaluation of Microsatellite Instability via High-Resolution Melt Analysis in Colorectal Carcinomas

1
Solid Tumor Molecular Pathology Laboratory, IRCCS Azienda Ospedaliero-Universitaria Di Bologna, 40138 Bologna, Italy
2
Department of Pathology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy
3
Department of Medical and Surgical Sciences (DIMEC), University of Bologna, 40138 Bologna, Italy
4
Pathology Unit, Maggiore Hospital, 40133 Bologna, Italy
5
Unit of Medical Genetics, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy
6
Department of Pharmacy and Biotechnology (FaBiT), University of Bologna, 40127 Bologna, Italy
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
J. Mol. Pathol. 2024, 5(4), 512-519; https://doi.org/10.3390/jmp5040034
Submission received: 10 September 2024 / Revised: 29 October 2024 / Accepted: 8 November 2024 / Published: 14 November 2024

Abstract

:
Background/Objectives: Colorectal cancer (CRC) is the third leading cause of cancer death globally, with rising incidence. The immunohistochemistry (IHC) for mismatch repair (MMR) proteins is the first technique used in routine practice to evaluate an MMR status. Microsatellite instability (MSI) may be tested in case of doubt during IHC staining. This study introduces a novel high-resolution melt (HRM) protocol for MSI detection and compares it with traditional fragment length analysis (FLA) via capillary electrophoresis. Methods: A total of 100 formalin-fixed and paraffin-embedded CRC specimens were analyzed using two distinct protocols: one based on FLA (TrueMark MSI Assay kit) and another one based on HRM (AmoyDx® Microsatellite Instability Detection Kit). Results: Overall, 68 (68.0%) of the cases were MSS, and 32 (32.0%) were MSI-H. HRM analysis was first successfully carried out in all the cases. A perfect concordance in MSI evaluation between HRM and FLA was observed. HRM showed slightly shorter hands-on time and turnaround time. Conclusions: We provided evidence of the validity of this new HRM approach in determining the MSI status of colorectal carcinomas.

1. Introduction

Colorectal cancer (CRC) is the world’s third biggest cause of cancer death, and its incidence is rising in many nations [1,2].
The etiology of colorectal carcinomas is multifactorial and can be attributed to both genetic and environmental factors. Most colorectal carcinomas are sporadic, with both genetic and epigenetic alterations (which would lead to the aberrant methylation of oncosuppressor genes), resulting in an inactivation or alteration of the genes responsible for regulating the pathways of cell proliferation and differentiation, with the resulting promotion of the neoplasm [3]. Sporadic cases share many of their genetic alterations with their hereditary counterparts, and therefore the study of hereditary syndromes, such as familial adenomatous polyposis (FAP) and Lynch syndrome (HNPCC), has helped researchers to understand the molecular pathogenesis underlying sporadic colorectal carcinomas [3].
Three distinct pathways of genomic instability have been identified [4,5]: (i) the Chromosomal Instability (CIN) Pathway (70–85% of all the cases, involving the activation of a proto-oncogene such as KRAS, and the inactivation of oncosuppressors such as APC and TP53 [6]; (ii) Microsatellite Instability (MSI), due to the loss of functionality of the genes/proteins of the mismatch repair system, the main of which are MLH1 (3p21), PMS2 (7p22), MSH2 (2p16), MSH6 (2p16) [7,8]; (iii) the CpG Island Methylator Phenotype [9,10], mainly associated with the hypermethylation of the promoter of numerous genes (including MLH1) and with BRAF pathogenic mutations. 70–85% of all colorectal cancer cases are due to this pathway.
The American College of Gastroenterology and the American Society of Clinical Oncology (2015) advocate using a screening strategy for all the CRC specimens to identify patients for germline testing to diagnose Lynch syndrome [11,12,13]. The first stage is the dMMR (mismatch repair deficiency) analysis, preferably performed using immunohistochemistry (IHC) for the MLH1, MSH2, MSH6, and PMS2 proteins [14,15]. Positive immunostaining for all the proteins is consistent with an intact MMR system; hence, no additional tests are required. Samples exhibiting the immunohistochemistry loss of MMR proteins must subsequently be tested for BRAF mutations. The ACG/ASCO screening algorithm recommends performing BRAF analysis in CRC without MLH1 protein staining, followed by MLH1 promoter methylation analysis if the BRAF V600E mutation is not discovered [12,13]. Moreover, in those cases harboring MLH1 staining loss, the MLH1 promoter methylation analysis can also be performed before the BRAF evaluation [16]. In the case of immunohistochemical doubts, such as the partial loss of staining positivity, a confirmation test via MSI analysis is strongly recommended. The MSI phenotype is usually tested using PCR-based assays [17], and the fragment length analysis (FLA) is to date the widest used, even if other methods, such as automated PCR high-resolution melt curve analysis [18] or the automated microfluidic electrophoretic run chip-based assays [19,20], have also been successfully used.
The primary aim of our study was to validate a high-resolution melt (HRM) analysis method focusing on microsatellite regions distinct from the Bethesda markers for MSI detection in colorectal carcinoma specimens, using fragment length analysis (FLA) as the reference standard. By comparing the performance of HRM to FLA, we also evaluated whether HRM could offer certain advantages, such as reduced hands-on and turnaround times. In this study, we tested a new HRM protocol not including Bethesda satellites and that allows MSI analysis in CRC (AmoyDx® Microsatellite Instability Detection Kit) and compared it with fragment length assay capillary electrophoresis.

2. Materials and Methods

The DNA samples were retrieved from anonymized electronic files from the Solid Tumor Molecular Pathology Laboratory, IRCCS Policlinico di S.Orsola Bologna (Italy) to find samples of colorectal carcinomas. A total of 100 consecutive DNA CRC specimens were analyzed at the Solid Tumor Molecular Pathology Laboratory in Bologna using two distinct protocols: a TrueMark MSI Assay kit (Thermo Fisher Scientific, Waltham, MA, USA), based on FLA, and an AmoyDx® Microsatellite Instability Detection Kit (AmoyDx, Xiamen, China), based on HRM. All the samples were extracted from formalin-fixed and paraffin-embedded (FFPE) specimens, starting from 2 to 4 10 um-thick unstained sections. The area of interest was selected by a pathologist in a final hematoxylin and eosin (H&E) section, for ensuring at least 20% of the neoplastic cells in the area used for the analysis. The extraction was performed using the QuickExtract FFPE DNA Extraction Kit (LGC Biosearch Technology, Steinach, Germany), and then quantified using the Qubit fluorometer (Thermo Fisher Scientific).

2.1. MSI by Fragment Length Analysis

Fragment length analysis (FLA) was performed using the TrueMark MSI Assay kit (Thermo Fisher Scientific); 5 ng of gDNA was used for amplification. The kit allowed the analysis of 15 satellites, 13 mononucleotidic markers (BAT-25, BAT-26, BAT-40, CAT-25, NR-21, NR-22, NR-24, NR-27, ABI-16, ABI-17, ABI-19, ABI-20A, ABI-20B) and 2 pentanucleotide repeats (PentaD, TH-01, sample ID markers). In the samples where a non-neoplastic specimen was available, the FLA was performed both on the neoplastic and control specimens. Briefly, in a total of 10 µL, a mix was made containing approximately 5 ng of gDNA, 4 µL of MSI Assay Master Mix, and 1 µL of MSI Assay Primer Mix. The 10 µL were aliquoted into one well for each sample to be analyzed. The samples were then amplified with the following program: 95 °C for 11 min, [94 °C for 20 secs, 59 °C for 2 min] for 29 cycles, and 60 °C for 25 min. At the end of PCR, 2 µL of PCR product was added to a mixture of Formamide (17 µL) and Size Standard (1 µL—GeneScan™ 600 LIZ™, Thermo Fisher Scientific), and aliquoted to a MicroAmp™ Optical 96-Well Reaction Plate. The samples were then run on a 3500/3500 × L Genetic Analyzer.
Results were analyzed using the GeneMapper ID-X Software tool v1.5 (Thermo Fisher Scientific). The samples were classified as MSS (no unstable markers or if less than 30% of the evaluable markers were unstable), MSI-Low (MSI-L, at least one unstable marker but less than 30% of the analyzed microsatellites), or MSI-High (MSI-H, equal or more than 30% of the evaluable markers were unstable).

2.2. MSI by High-Resolution Melt Analysis

A high-resolution melt analysis was performed using the AmoyDx® Microsatellite Instability (MSI) Detection Kit. Briefly, a total of 5 ng was used for DNA amplification. Once the reaction mix was set up, each sample was analyzed as follows: each well was already pre-filled with a multiplex of three primers, two satellites, and an internal control (IC). The following 8 mononucleotide repeats were analyzed by the kit: EIF4E3, IFT140, PPP1CC, UBAC2, PRR5-ARHGAP8, ACVR2A, TAOK3, RBM14-RBM4. The plate was then run on a Real-Time PCR System SLAN-96S for a total of 125 min [95 °C for 5 min (1 cycle); 95 °C for 5 s, 54 °C for 15 s, 72 °C for 10 s (60 cycles); 95 °C for 1 min, 45 °C for 3 min, and 35 °C for 1 min (1 cycle); the melting steps were from 35 °C to 58 °C].
The samples were then classified as MSS (0 or <25% of evaluable satellites unstable); and MSI-H (more than 25% of evaluable satellites unstable). In all the cases, only the DNA from the neoplastic specimen was analyzed.

3. Results

Details of each one of the 100 specimens are reported in Supplementary Table S1.

3.1. Fragment Length Analysis vs. HRM

Regarding fragment length analysis, the assay was successfully carried out in all of the cases. Overall, 68 (68.0%) cases were MSS (including 3 cases evaluated as MSI-L), and 32 (32.0%) were MSI-H (Table 1) (Figure 1 and Figure 2).
An HRM analysis was first successfully carried out in 95 of the 100 cases. In the five cases that were not evaluable, 7 ng of DNA, instead of 5 ng, was then used for the re-analysis. Using 7 ng of input DNA, these five cases were successfully evaluated by HRM.
Considering that according to the ESMO recommendations [21], MSI-L and MSS should be viewed as a unique clinical group (i.e., MSS sample), we observed 100% concordance between FLA and HRM analysis in evaluating the MSI status.

3.2. Hands-On Time and Turnaround Time

We also compared both techniques regarding hands-on time (HOT) and turnaround time (TAT) (Table 2). The FLA protocol was faster in sample preparation (15 min) but needs a preparation step after the PCR reaction (PCR product preparation for the capillary run). On the contrary, the HRM protocol had a slightly longer pre-PCR protocol (about 25 min), but after loading, the Real-Time machine had no other “wet” steps. Overall, both of the techniques are rapid and not time-consuming with a similar hands-on-time—about 30 min for FLA and about 25 min for HRM—and a total turnaround time of 210 min for FLA and 150 min for HRM.

4. Discussion

The microsatellite instability state (MSI) is a phenotype resulting from alterations in the mismatch repair (MMR) system. An MSI condition reflects a malfunction of MMR proteins (MMR deficiency—dMMR) such as, for example, MLH1, PMS2, MSH2, and MSH6. The evaluation of MMR/MSI status is considered a standard clinical practice in the work-up of several tumors, such as colorectal carcinomas, gastric cancers, and endometrial carcinomas. Alterations in the MMR system can therefore be assessed either by assessing the expression of the MLH1, PMS2, MSH2, and MSH6 proteins via immunohistochemistry, or by assessing the MSI status. In general, the most frequently recognized approach for MSI evaluation depends on the fragment length analysis of the Bethesda panel, which covers two mononucleotide (BAT-25 and BAT-26) and three dinucleotide (D5S346, D2S123, and D17S250) repetitions [22] or, alternatively, a panel encompassing five poly-A mononucleotide repeats (BAT-25, BAT-26, NR-21, NR-24, and NR-27) [23]. Several studies have demonstrated that alternative methods can be successfully used for MSI evaluation [24,25,26]. One option is the fully automated PCR Idylla MSI assay (Biocartis) which evaluates MSI using seven repeat markers within 150 min per sample, including DNA extraction [18,27,28,29,30,31]. Another possibility is the use of automated microfluidic electrophoretic run chip-based assays that ensure a simple, fast, and high throughput (up to 15 samples in 10 min) approach for MSI evaluation [20]. Also, droplet-digital PCR (ddPRC) was successfully used for MSI detection, consisting of a rapid and cost-effective multiplex assay suitable for large-scale patient testing; albeit, the data must be interpreted by a specialist laboratory [29]. More recently, MSI analysis using NGS has been introduced [32], although its use in clinical practice is not yet widespread, mainly due to the fact that there is no universal consensus on the precise definition of MSI-high (MSI-H), as different gene panels and different analysis algorithms may lead to slightly different results. For this reason, MSI via an NGS test has to be verified against MMR-IHC or MSI by PCR [32].
In this study, we compared an HRM-based strategy based on microsatellite regions distinct from the Bethesda markers with a fragment length analysis (FLA), the gold standard method for MSI analysis.
There were no inconsistent findings across the board for the approaches. The unique cases with a minimal discrepancy between the two methods were categorized as MSS by HRM but MSI-L by FLA. Nevertheless, MSI-L patients ought to be categorized alongside MSS instances in accordance with standards [21], so there would be a 100% agreement considered between the two approaches.
It was shown that the FLA method required less DNA, which could be helpful for biopsies and other samples with low cellularity. Even though HRM required more starting DNA, all the instances were successfully evaluated utilizing it.
FLA took a little more hands-on time because it comprises both a post-PCR phase (plate preparation for capillary sequencer loading) and an amplification stage (PCR setup). The HRM approach proved to be incredibly fast, requiring the operator’s assistance only during the PCR setup phase, which took about 5 min. Overall, the HRM strategy required less time (150 vs. 210 min) to produce results. Furthermore, considering that in molecular pathology labs, NGS is increasingly being used over the conventional Sanger method because of its multi-gene and high-sensitivity approaches, Sanger sequencers are becoming less common than Real-Time PCR machines.
Finally, we provided evidence of the validity of an HRM approach focusing on microsatellite regions distinct from the Bethesda markers in determining the MSI status of colorectal carcinomas. Subsequent investigations may examine the suitability of this technique for assessing MSI in other neoplasms, like endometrial carcinoma, where MSI evaluation is essential for the molecular categorization of these malignancies [33,34].

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/jmp5040034/s1, Table S1: the MSI results of the 100 analyzed samples via FLA and HRM methods.

Author Contributions

Conceptualization, T.M., A.D.L. and D.d.B.; investigation, T.M., S.C., V.S., E.G., A.A., S.Z., L.M. and S.M.; data curation: V.S., L.M. and S.Z.; formal analysis: T.M., S.C., E.G., A.A., S.M. and A.D.L.; writing—original draft preparation, T.M. and D.d.B.; writing—review and editing, A.D., M.F., D.T., G.T. and A.D.L.; supervision, A.D., M.F., D.T., G.T. and D.d.B. All authors have read and agreed to the published version of the manuscript.

Funding

The work reported in this publication was funded by the Italian Ministry of Health, RC-2024-279.

Institutional Review Board Statement

All the experimental procedures were carried out in accordance with the general authorization to process personal data for scientific research purposes from “The Italian Data Protection Authority” (https://www.garanteprivacy.it/web/guest/home/docweb/-/docweb-display/docweb/2485392), last accessed on 22 August 2024). MSI analysis in CRC is a clinical practice procedure and this study did not affect the clinical management of the involved patients’ samples. All the information regarding the human material was managed using anonymous numerical codes and this study was carried out in accordance with the ethical principles of the Declaration of Helsinki (https://www.wma.net/policies-post/wma-declaration-of-helsinki-ethical-principles-for-medical-research-involving-human-subjects/, accessed on 22 August 2024). Follow-up data or personal information were not used for this study.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

All the available data are reported in the main manuscript or in the Supplementary Materials.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Bray, F.; Laversanne, M.; Sung, H.; Ferlay, J.; Siegel, R.L.; Soerjomataram, I.; Jemal, A. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 2024, 74, 229–263. [Google Scholar] [CrossRef] [PubMed]
  2. Rawla, P.; Sunkara, T.; Barsouk, A. Epidemiology of colorectal cancer: Incidence, mortality, survival, and risk factors. Prz. Gastroenterol. 2019, 14, 89–103. [Google Scholar] [CrossRef] [PubMed]
  3. Armaghany, T.; Wilson, J.D.; Chu, Q.; Mills, G. Genetic alterations in colorectal cancer. Gastrointest. Cancer Res. 2012, 5, 19–27. [Google Scholar] [PubMed]
  4. Pino, M.S.; Chung, D.C. The chromosomal instability pathway in colon cancer. Gastroenterology 2010, 138, 2059–2072. [Google Scholar] [CrossRef]
  5. Sawicki, T.; Ruszkowska, M.; Danielewicz, A.; Niedzwiedzka, E.; Arlukowicz, T.; Przybylowicz, K.E. A Review of Colorectal Cancer in Terms of Epidemiology, Risk Factors, Development, Symptoms and Diagnosis. Cancers 2021, 13, 2025. [Google Scholar] [CrossRef]
  6. Fearon, E.R.; Vogelstein, B. A genetic model for colorectal tumorigenesis. Cell 1990, 61, 759–767. [Google Scholar] [CrossRef]
  7. Baudrin, L.G.; Deleuze, J.F.; How-Kit, A. Molecular and Computational Methods for the Detection of Microsatellite Instability in Cancer. Front. Oncol. 2018, 8, 621. [Google Scholar] [CrossRef]
  8. Lengauer, C.; Kinzler, K.W.; Vogelstein, B. Genetic instabilities in human cancers. Nature 1998, 396, 643–649. [Google Scholar] [CrossRef]
  9. Gallois, C.; Laurent-Puig, P.; Taieb, J. Methylator phenotype in colorectal cancer: A prognostic factor or not? Crit. Rev. Oncol. Hematol. 2016, 99, 74–80. [Google Scholar] [CrossRef]
  10. Toyota, M.; Ahuja, N.; Ohe-Toyota, M.; Herman, J.G.; Baylin, S.B.; Issa, J.P. CpG island methylator phenotype in colorectal cancer. Proc. Natl. Acad. Sci. USA 1999, 96, 8681–8686. [Google Scholar] [CrossRef]
  11. Dinjens, W.N.; Dubbink, H.J.; Wagner, A. Guidelines on genetic evaluation and management of Lynch syndrome. Am. J. Gastroenterol. 2015, 110, 192–193. [Google Scholar] [CrossRef] [PubMed]
  12. Giardiello, F.M.; Allen, J.I.; Axilbund, J.E.; Boland, C.R.; Burke, C.A.; Burt, R.W.; Church, J.M.; Dominitz, J.A.; Johnson, D.A.; Kaltenbach, T.; et al. Guidelines on genetic evaluation and management of Lynch syndrome: A consensus statement by the US Multi-society Task Force on colorectal cancer. Am. J. Gastroenterol. 2014, 109, 1159–1179. [Google Scholar] [CrossRef] [PubMed]
  13. Syngal, S.; Brand, R.E.; Church, J.M.; Giardiello, F.M.; Hampel, H.L.; Burt, R.W. ACG clinical guideline: Genetic testing and management of hereditary gastrointestinal cancer syndromes. Am. J. Gastroenterol. 2015, 110, 223–262. [Google Scholar] [CrossRef] [PubMed]
  14. Grillo, F.; Angerilli, V.; Parente, P.; Vanoli, A.; Luchini, C.; Sciallero, S.; Puccini, A.; Bergamo, F.; Lonardi, S.; Valeri, N.; et al. Prevalence and type of MMR expression heterogeneity in colorectal adenocarcinoma: Therapeutic implications and reporting. Virchows Arch. 2024, 485, 131–135. [Google Scholar] [CrossRef]
  15. Parente, P.; Grillo, F.; Vanoli, A.; Macciomei, M.C.; Ambrosio, M.R.; Scibetta, N.; Filippi, E.; Cataldo, I.; Baron, L.; Ingravallo, G.; et al. The Day-To-Day Practice of MMR and MSI Assessment in Colorectal Adenocarcinoma: What We Know and What We Still Need to Explore. Dig. Dis. 2023, 41, 746–756. [Google Scholar] [CrossRef]
  16. Maloberti, T.; De Leo, A.; Sanza, V.; Merlo, L.; Visani, M.; Acquaviva, G.; Coluccelli, S.; Altimari, A.; Gruppioni, E.; Zagnoni, S.; et al. BRAF and MLH1 Analysis Algorithm for the Evaluation of Lynch Syndrome Risk in Colorectal Carcinoma Patients: Evidence-Based Data from the Analysis of 100 Consecutive Cases. J. Mol. Pathol. 2022, 3, 115–124. [Google Scholar] [CrossRef]
  17. Malapelle, U.; Parente, P.; Pepe, F.; De Luca, C.; Pisapia, P.; Sgariglia, R.; Nacchio, M.; Gragnano, G.; Russo, G.; Conticelli, F.; et al. Evaluation of Micro Satellite Instability and Mismatch Repair Status in Different Solid Tumors: A Multicenter Analysis in a Real World Setting. Cells 2021, 10, 1878. [Google Scholar] [CrossRef]
  18. Malapelle, U.; Parente, P.; Pepe, F.; De Luca, C.; Cerino, P.; Covelli, C.; Balestrieri, M.; Russo, G.; Bonfitto, A.; Pisapia, P.; et al. Impact of Pre-Analytical Factors on MSI Test Accuracy in Mucinous Colorectal Adenocarcinoma: A Multi-Assay Concordance Study. Cells 2020, 9, 2019. [Google Scholar] [CrossRef]
  19. Odenthal, M.; Barta, N.; Lohfink, D.; Drebber, U.; Schulze, F.; Dienes, H.P.; Baldus, S.E. Analysis of microsatellite instability in colorectal carcinoma by microfluidic-based chip electrophoresis. J. Clin. Pathol. 2009, 62, 850–852. [Google Scholar] [CrossRef]
  20. Pepe, F.; Smeraglio, R.; Vacirca, D.; Malapelle, U.; Barberis, M.; Troncone, G. Microsatellite instability evaluation by automated microfluidic electrophoresis: An update. J. Clin. Pathol. 2017, 70, 90–91. [Google Scholar] [CrossRef]
  21. Luchini, C.; Bibeau, F.; Ligtenberg, M.J.L.; Singh, N.; Nottegar, A.; Bosse, T.; Miller, R.; Riaz, N.; Douillard, J.Y.; Andre, F.; 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. 2019, 30, 1232–1243. [Google Scholar] [CrossRef] [PubMed]
  22. Umar, A.; Boland, C.R.; Terdiman, J.P.; Syngal, S.; de la Chapelle, A.; Ruschoff, J.; Fishel, R.; Lindor, N.M.; Burgart, L.J.; Hamelin, R.; et al. Revised Bethesda Guidelines for hereditary nonpolyposis colorectal cancer (Lynch syndrome) and microsatellite instability. J. Natl. Cancer Inst. 2004, 96, 261–268. [Google Scholar] [CrossRef] [PubMed]
  23. Goel, A.; Nagasaka, T.; Hamelin, R.; Boland, C.R. An optimized pentaplex PCR for detecting DNA mismatch repair-deficient colorectal cancers. PLoS ONE 2010, 5, e9393. [Google Scholar] [CrossRef]
  24. Libera, L.; Sahnane, N.; Pepe, F.; Pisapia, P.; De Luca, C.; Russo, G.; Parente, P.; Covelli, C.; Chiaravalli, A.M.; Sessa, F.; et al. Critical aspects of microsatellite instability testing in endometrial cancer: A comparison study. Hum. Pathol. 2022, 128, 134–140. [Google Scholar] [CrossRef] [PubMed]
  25. Russo, G.; Pepe, F.; Pisapia, P.; Palumbo, L.; Nacchio, M.; Vigliar, E.; Pallante, P.; Parente, P.; Fassan, M.; Graziano, P.; et al. Microsatellite instability evaluation of patients with solid tumour: Routine practice insight from a large series of Italian referral centre. J. Clin. Pathol. 2023, 76, 133–136. [Google Scholar] [CrossRef]
  26. Dedeurwaerdere, F.; Claes, K.B.; Van Dorpe, J.; Rottiers, I.; Van der Meulen, J.; Breyne, J.; Swaerts, K.; Martens, G. Comparison of microsatellite instability detection by immunohistochemistry and molecular techniques in colorectal and endometrial cancer. Sci. Rep. 2021, 11, 12880. [Google Scholar] [CrossRef]
  27. Ukkola, I.; Nummela, P.; Pasanen, A.; Kero, M.; Lepisto, A.; Kytola, S.; Butzow, R.; Ristimaki, A. Detection of microsatellite instability with Idylla MSI assay in colorectal and endometrial cancer. Virchows Arch. 2021, 479, 471–479. [Google Scholar] [CrossRef]
  28. Velasco, A.; Tokat, F.; Bonde, J.; Trim, N.; Bauer, E.; Meeney, A.; de Leng, W.; Chong, G.; Dalstein, V.; Kis, L.L.; et al. Multi-center real-world comparison of the fully automated Idylla microsatellite instability assay with routine molecular methods and immunohistochemistry on formalin-fixed paraffin-embedded tissue of colorectal cancer. Virchows Arch. 2021, 478, 851–863. [Google Scholar] [CrossRef]
  29. Gilson, P.; Levy, J.; Rouyer, M.; Demange, J.; Husson, M.; Bonnet, C.; Salleron, J.; Leroux, A.; Merlin, J.L.; Harle, A. Evaluation of 3 molecular-based assays for microsatellite instability detection in formalin-fixed tissues of patients with endometrial and colorectal cancers. Sci. Rep. 2020, 10, 16386. [Google Scholar] [CrossRef]
  30. Mindiola-Romero, M.A.; Green, B.D.; Al-TurkmaniPh, D.M.; Godwin, B.K.; Mackay, B.A.; Tafe, M.L.; Ren, M.B.; TsongalisPh, D.G. Novel Biocartis Idylla cartridge-based assay for detection of microsatellite instability in colorectal cancer tissues. Exp. Mol. Pathol. 2020, 116, 104519. [Google Scholar] [CrossRef]
  31. Pecriaux, A.; Favre, L.; Calderaro, J.; Charpy, C.; Derman, J.; Pujals, A. Detection of microsatellite instability in a panel of solid tumours with the Idylla MSI Test using extracted DNA. J. Clin. Pathol. 2021, 74, 36–42. [Google Scholar] [CrossRef] [PubMed]
  32. Bartley, A.N.; Mills, A.M.; Konnick, E.; Overman, M.; Ventura, C.B.; Souter, L.; Colasacco, C.; Stadler, Z.K.; Kerr, S.; Howitt, B.E.; et al. Mismatch Repair and Microsatellite Instability Testing for Immune Checkpoint Inhibitor Therapy: Guideline from the College of American Pathologists in Collaboration with the Association for Molecular Pathology and Fight Colorectal Cancer. Arch. Pathol. Lab. Med. 2022, 146, 1194–1210. [Google Scholar] [CrossRef] [PubMed]
  33. Concin, N.; Matias-Guiu, X.; Vergote, I.; Cibula, D.; Mirza, M.R.; Marnitz, S.; Ledermann, J.; Bosse, T.; Chargari, C.; Fagotti, A.; et al. ESGO/ESTRO/ESP guidelines for the management of patients with endometrial carcinoma. Int. J. Gynecol. Cancer 2021, 31, 12–39. [Google Scholar] [CrossRef] [PubMed]
  34. de Biase, D.; Maloberti, T.; Corradini, A.G.; Rosini, F.; Grillini, M.; Ruscelli, M.; Coluccelli, S.; Altimari, A.; Gruppioni, E.; Sanza, V.; et al. Integrated clinicopathologic and molecular analysis of endometrial carcinoma: Prognostic impact of the new ESGO-ESTRO-ESP endometrial cancer risk classification and proposal of histopathologic algorithm for its implementation in clinical practice. Front. Med. 2023, 10, 1146499. [Google Scholar] [CrossRef]
Figure 1. MSS sample by FLA and HRM. (A) FLA showing three stable mononucleotide satellites (BAT25, NR24, NR21); upper box: neoplastic specimen; lower box: non-neoplastic specimen. (B) HRM results showing two stable satellites (PPP1CC, UBAC2); green curve: internal control; red curve: target 1 (PPP1CC); blue curve: target 2 (UBAC2). Target refers to the specific mononucleotide repeat markers being analyzed. Each curve represents a single MNR (either internal control, PPP1CC, or UBAC2) within the HRM analysis.
Figure 1. MSS sample by FLA and HRM. (A) FLA showing three stable mononucleotide satellites (BAT25, NR24, NR21); upper box: neoplastic specimen; lower box: non-neoplastic specimen. (B) HRM results showing two stable satellites (PPP1CC, UBAC2); green curve: internal control; red curve: target 1 (PPP1CC); blue curve: target 2 (UBAC2). Target refers to the specific mononucleotide repeat markers being analyzed. Each curve represents a single MNR (either internal control, PPP1CC, or UBAC2) within the HRM analysis.
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Figure 2. MSI sample by FLA and HRM. (A) FLA showing three unstable mononucleotide satellites (BAT25, NR24, NR21); upper box: neoplastic specimen; lower box: non-neoplastic specimen. (B) HRM results showing two unstable satellites (PPP1CC/UBAC2); green curve: internal control, red curve: target 1 (PPP1CC), blue curve: target 2 (UBAC2). Target refers to the specific mononucleotide repeat markers being analyzed. Each curve represents a single MNR (either internal control, PPP1CC, or UBAC2) within HRM analysis.
Figure 2. MSI sample by FLA and HRM. (A) FLA showing three unstable mononucleotide satellites (BAT25, NR24, NR21); upper box: neoplastic specimen; lower box: non-neoplastic specimen. (B) HRM results showing two unstable satellites (PPP1CC/UBAC2); green curve: internal control, red curve: target 1 (PPP1CC), blue curve: target 2 (UBAC2). Target refers to the specific mononucleotide repeat markers being analyzed. Each curve represents a single MNR (either internal control, PPP1CC, or UBAC2) within HRM analysis.
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Table 1. MSI results obtained using FLA and HRM techniques.
Table 1. MSI results obtained using FLA and HRM techniques.
Sample Size (N = 100)FLA
MSI-HMSI-LMSSTotal
HRMMSI-H32032
MSI-L/MSS *036568
68
Total3268100
MSI-H: high microsatellite instability; MSI-L: low microsatellite instability; MSS: microsatellite stability; HRM: high-resolution melt; FLA: fragment length analysis. * AmoyDx HRM MSI Detection Kit does not distinguish between MSS and MSI-L.
Table 2. Technical protocols comparison between FLA and HRM.
Table 2. Technical protocols comparison between FLA and HRM.
FLA MSIHRM * MSI
DNA5 ng/µL5 ng/µL (7 ng/µL in 5 samples)
Sample preparation 15 min25 min
Time for PCR (thermal cycler or Real-Time machine)120 min125 min
Sample preparation for the capillary run15 min/
Fragment analysis55 min/
Data analysis (per sample)2–5 min1–3 min
TOTAL
TAT~210 min~150 min
HOT~30 min~25 min
FLA: fragment length analysis; HRM: high-resolution melt analysis; TAT: turnaround time; HOT: hands-on time. * Performed by the AmoyDx ® Microsatellite Instability kit.
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Maloberti, T.; Coluccelli, S.; Sanza, V.; Gruppioni, E.; Altimari, A.; Zagnoni, S.; Merlo, L.; D’Errico, A.; Fiorentino, M.; Turchetti, D.; et al. Evaluation of Microsatellite Instability via High-Resolution Melt Analysis in Colorectal Carcinomas. J. Mol. Pathol. 2024, 5, 512-519. https://doi.org/10.3390/jmp5040034

AMA Style

Maloberti T, Coluccelli S, Sanza V, Gruppioni E, Altimari A, Zagnoni S, Merlo L, D’Errico A, Fiorentino M, Turchetti D, et al. Evaluation of Microsatellite Instability via High-Resolution Melt Analysis in Colorectal Carcinomas. Journal of Molecular Pathology. 2024; 5(4):512-519. https://doi.org/10.3390/jmp5040034

Chicago/Turabian Style

Maloberti, Thais, Sara Coluccelli, Viviana Sanza, Elisa Gruppioni, Annalisa Altimari, Stefano Zagnoni, Lidia Merlo, Antonietta D’Errico, Michelangelo Fiorentino, Daniela Turchetti, and et al. 2024. "Evaluation of Microsatellite Instability via High-Resolution Melt Analysis in Colorectal Carcinomas" Journal of Molecular Pathology 5, no. 4: 512-519. https://doi.org/10.3390/jmp5040034

APA Style

Maloberti, T., Coluccelli, S., Sanza, V., Gruppioni, E., Altimari, A., Zagnoni, S., Merlo, L., D’Errico, A., Fiorentino, M., Turchetti, D., Miccoli, S., Tallini, G., De Leo, A., & de Biase, D. (2024). Evaluation of Microsatellite Instability via High-Resolution Melt Analysis in Colorectal Carcinomas. Journal of Molecular Pathology, 5(4), 512-519. https://doi.org/10.3390/jmp5040034

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