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Microsatellite instability status is determined by targeted sequencing with MSIcall in 25 cancer types
Journal Pre-proofs Microsatellite Instability Status is Determined by Targeted Sequencing with MSIcall in 25 Cancer Types Yosuke Hirotsu, Yuki Nagakubo, Kenji Amemiya, Toshio Oyama, Hitoshi Mochizuki, Masao Omata PII: DOI: Reference:
S0009-8981(19)32115-1 https://doi.org/10.1016/j.cca.2019.11.002 CCA 15912
To appear in:
Clinica Chimica Acta
Received Date: Revised Date: Accepted Date:
10 September 2019 31 October 2019 2 November 2019
Please cite this article as: Y. Hirotsu, Y. Nagakubo, K. Amemiya, T. Oyama, H. Mochizuki, M. Omata, Microsatellite Instability Status is Determined by Targeted Sequencing with MSIcall in 25 Cancer Types, Clinica Chimica Acta (2019), doi: https://doi.org/10.1016/j.cca.2019.11.002
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© 2019 Published by Elsevier B.V.
Microsatellite Instability Status is Determined by Targeted Sequencing with MSIcall in 25 Cancer Types
Yosuke Hirotsu1,*, Yuki Nagakubo2, Kenji Amemiya1, Toshio Oyama3, Hitoshi Mochizuki1,4 and Masao Omata4,5 1
Genome Analysis Center, 2Division of Genetics and Clinical Laboratory, 3Department of
Pathology, 4Department of Gastroenterology, Yamanashi Central Hospital, 1-1-1 Fujimi, Kofu, Yamanashi, Japan 5
The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, Japan
*Correspondence to: Yosuke Hirotsu, Genome Analysis Center, Yamanashi Central Hospital, 1-1-1 Fujimi, Kofu, Japan Email: [email protected] Tel: +81-55-253-7111, Fax: +81-55-253-8011
Keywords: microsatellite instability; immune checkpoint; MSI; IHC; NGS Word count: 3005 words Number of figures/tables: 4 figures, 3 tables Quantity of supporting information: 2 supplemental tables Running title: Target sequencing determines MSI status Grant: This study was supported by a Grant-in-Aid for Genome Research Project from Yamanashi Prefecture, The Japan Society for the Promotion of Science (JSPS) KAKENHI Early-Career Scientists, Research Grant for Young Scholars, The YASUDA Medical Foundation, and The Uehara Memorial Foundation. Abbreviations: MSI: microsatellite instability IHC: immunohistochemistry ROC: receiver operating characteristic FFPE: formalin-fixed paraffin-embedded AUC: area under the ROC curve MMR: mismatch repair MSI-H: microsatellite instability high MSI-L: microsatellite instability low MSS: microsatellite stable 1
pMMR: proficient MMR dMMR: deficient MMR NGS: next generation sequencing QMVR: quasi-monomorphic variation range PCR: polymerase chain reaction BAM: Binary Sequence Alignment/Map MAF: Mutation Annotation Format
2
ABSTRACT Background: Microsatellite instability (MSI) occurs in solid tumors and is a predictive biomarker for remarkable response to immune checkpoint inhibitors. Detection of MSI status has been conventionally conducted by PCR–electrophoresis-based assay (MSI-PCR) and immunohistochemistry (IHC) of mismatch repair proteins. However, these approaches require visual confirmation and have some difficulties in determining MSI statuses from equivocal results. Methods: We performed amplicon-based targeted sequencing of 76 microsatellite loci (MSI-NGS) in 184 formalin-fixed paraffin-embedded (FFPE) tumor tissues and baseline control samples. A bioinformatics tool, MSIcall, calculated the quantitative values based on the aligned sequence reads and evaluated MSI status. Furthermore, we examined the concordance between the results from MSI-NGS and MSI-PCR/IHC. Diagnostic accuracy, sensitivity, and specificity were estimated by receiver operating characteristic (ROC) curve analysis. For validation cohort, we studied additional 50 tumor samples to determine the MSI status. Results: Of 184 tumor samples, MSI-PCR and IHC analysis classified 161 tumors as MSS/pMMR and 23 as MSI-H/dMMR. Using MSI-NGS combined with MSIcall, we predicted MSI status with high accuracy (98.9%), specificity (91.3%), and sensitivity (100%) in 25 types of cancers. This method achieved an area under the ROC curve (AUC) value of 0.9986. Furthermore, we achieved the 100% concordant results using additional 50 samples for validation. Conclusion: We demonstrated newly developed MSI-NGS with MSIcall accurately determines the MSI status of FFPE tumor tissues thorough sequencing of tumor samples alone without patient-matched normal controls. This approach could be applied to all types of solid tumors to determine responders to immune-oncology therapy.
INTRODUCTION In the human genome, approximately 3% is occupied by simple sequence repeats scattered throughout [1]. Microsatellites are repetitive sequences with unit lengths varying from mono-, di-, tri-, tetr-, penta-, or hexa-nucleotide repeats. Because microsatellite loci are vulnerable to replication slippage, its lengths are changed during DNA replication [2]. Replication errors are recognized and repaired by DNA mismatch repair (MMR) proteins including MLH1, MSH2, MSH6, and PMS2 to maintain cellular homeostasis [3]. When MMR functions are defective, replication errors are not repaired normally, and somatic mutations accumulate. This genomic state is called microsatellite instability (MSI). MSI is frequently observed in 10%–20% of sporadic endometrial, colorectal, and 3
gastric cancers, but less often in other solid tumor types [4-6]. The MSI phenotype is frequently observed in tumors from patients who have germline mutations in MMR genes, and is known as Lynch syndrome. MSI status is determined by PCR followed by capillary electrophoresis as the gold standard (hereafter called MSI-PCR). In general, MSI-PCR amplifies a subset of representative microsatellite markers (e.g., BAT25 and BAT26) and determines whether the length of microsatellite markers is changed between paired tumor and normal control DNA. Based on the number of markers with altered lengths, tumors are classified as microsatellite instability high (MSI-H), microsatellite instability low (MSI-L), and microsatellite stable (MSS). Alternatively, immunohistochemistry (IHC) of MMR proteins is conducted to estimate either the presence of MMR protein (proficient MMR, pMMR) or the absence of MMR expression (deficient MMR, dMMR). MSI status has become a very important biomarker for immune-oncology therapy [7, 8]. The number of non-synonymous mutations increases in MSI-H/dMMR tumors compared with MSS/pMMR [9, 10]. Clinical trials have shown MSI-H/dMMR tumors respond well to immunotherapy [8, 9]. The United States Food and Drug Administration (FDA) approved an immune checkpoint inhibitor (pembrolizumab) for treatment of MSI-H metastatic solid tumors [11]. Recently, pembrolizumab was also approved in Japan for all types of solid tumor exhibiting MSI-H. Therefore, there are growing demands to survey patients who harbor MSI-H tumors and may benefit from immune-oncology therapy. To determine MSI status, MSI-PCR and IHC have been widely conducted. However, these analyses have some problems in determining MSI status due to sample issues (e.g., cytology, biopsies, and samples with low tumor purity). In addition, tissue fixation with formalin causes degradation of DNA and decreases immunogenicity. Furthermore, visual confirmation is mandatory to check the capillary electrogram by MSI-PCR and positivity of immunostaining by IHC. These procedures are subjective and required complicated interpretations from equivocal results. Therefore, an accurate and alternative method is desired to achieve high scalability. Next generation sequencing (NGS) analysis allows us to reveal both comprehensive genomic profiles and MSI status simultaneously. Although several reported microsatellite loci were extracted from whole exome or whole genome sequencing data to estimate MSI status [12], this requires computer technology and skills to manage computer language in a character user interface. In this study, we aimed to develop and validate a user-friendly analytical process for detecting MSI status. We performed NGS of 76 microsatellite loci and determined MSI status (MSI-NGS). Furthermore, both MSI-PCR and IHC were conducted to compare the results from MSI-NGS. We demonstrated this novel approach provides a highly accurate detection system without patient-matched normal 4
controls.
MATERIALS AND METHODS Patients and sample preparation This study included 184 patients who were diagnosed with several cancer types (Table 1). For the validation cohort, we analyzed MSI status in 50 patients with ovarian cancer (n=26), colorectal cancer (n=14), gastric cancer (n=5), endometrial cancer (n=4) and gallbladder cancer (n=1). Informed consent was obtained from all patients. The study was approved by the Institutional Review Board at our hospital. Tumor samples were obtained by surgery or biopsy and fixed using 10% buffered formalin [13, 14]. Tumor diagnosis was conducted by a pathologist (T.O.). Tumor DNA was extracted from two 10-µm-thick FFPE sections and from five 10-µm-thick tumor biopsy sections using an Agencourt FormaPure DNA kit (Beckman Coulter, Brea, CA, USA) according to the manufacturer's protocol. DNA concentration was determined using a NanoDrop 2000 spectrophotometer. If the tumor purity was less than 10%, we performed laser capture microdissection to enrich tumor cells. To this end, tumor tissues were placed on PEN Membrane Frame Slides and stained with hematoxylin and eosin, and then microdissected using an ArcturusXT laser capture microdissection system (Thermo Fisher Scientific, Waltham, MA, USA) [15].
MSI-PCR MSI analysis was performed on FFPE tumor DNA using the MSI Analysis System v1.2 (Promega, Wisconsin, USA) [16] or MSI (FALCO) Kit (FALCO biosystems, Kyoto, Japan), which is approved for in vitro diagnosis in Japan [17]. Both assays examined five mononucleotide repeat markers (BAT-25, BAT-26, NR-21, NR-24, and MONO-27) that are used to determine MSI status, as described previously [18, 19]. Fluorescently labelled PCR products were separated by capillary electrophoresis using a 3500 Genetic Analyzer and sizes were analyzed by GeneMapper Software 5 (Thermo Fisher Scientific). When we used MSI (FALCO), we assessed if the lengths of PCR products were within the quasi-monomorphic variation range (QMVR) [17, 20]. Visual assessment was conducted by scattered electrogram. We determined the locus with a shift (above 3 bp) as instable, a mild shift (1–2 bp) as inconclusive, and without shift as stable. The cumulative number of instable shifts at two or more of the five microsatellite loci defines MSI-H. For the purpose of this study, tumors with less than one unstable locus were interpreted as MSS.
IHC Analysis of the IHC expression of MMR proteins was performed using a clinically 5
validated standard procedure. Sections (3-μm-thick serial sections of FFPE tissue) were deparaffinized and antigen activation was performed by heat treatment at high pH at 97°C for 20 min [21]. IHC was performed on tumor samples using the Dako EnVision FLEX kit, an automated staining instrument Autostainer Link 48 system (Dako, Copenhagen, Denmark), and primary monoclonal antibodies against anti-MLH1 (clone ES05; Dako), anti-MSH2 (clone FE11; Dako), anti-MSH6 (clone EP49; Dako), and anti-PMS2 (clone EP51; Dako). All sections were evaluated by a cytologist (K.A.) and a pathologist (T.O.). Non-neoplastic cells served as a positive control. Retained expression of each protein was defined by nuclear IHC reactivity of tumor cells, whereas loss of expression of each protein was defined by the complete absence of nuclear IHC reactivity in tumor cells. The tumors were considered pMMR when nuclear staining of all four proteins in tumor tissue was present or dMMR when the nuclear staining was absent [19, 22].
MSI-NGS Multiplex polymerase chain reaction (PCR) was performed using Ion AmpliSeq Microsatellite Instability Research Panel and Ion AmpliSeq Library Kit Plus (Thermo Fisher Scientific). This panel contains a single primer pool to amplify 76 microsatellite loci that are known to be affected by MSI based on previous reports (Supplemental Table 1) [23-26]. Primers were digested with FuPa reagent and then barcoded using Ion Xpress Barcode Adapters. Purification was performed by Agencourt AMPure XP reagents (Beckman Coulter) using the KingFisher™ Duo Prime System (Thermo Fisher Scientific) [15, 27]. The library concentration was determined using an Ion Library Quantitation Kit; each library was diluted and the same amount of library was pooled for one sequence reaction. Emulsion PCR and chip loading were performed on the Ion Chef with the Ion PI Hi-Q Chef kit. CEPH DNA (ThermoFisher Scientific: cat. # 430962) was used as a control and included on the same sequencing run. Sequencing was performed using the Ion PI chip and Ion PI Hi-Q Sequencing Kit on the Ion Proton Sequencer (Thermo Fisher Scientific), as described previously [28-30].
Evaluation of MSI score with bioinformatic algorithm ‘MSIcall’ Raw signal data were analyzed using Torrent Suite version 5.10.0. The pipeline included signal processing, base calling, quality score assignment, read alignment, and quality control of mapping. Sequencing reads were aligned, generating Binary Sequence Alignment/Map (BAM) files on the Torrent Server. A bioinformatics pipeline, MSIcall (v4.1), which was downloaded online and installed, was run to process the sequencing reads mapped to the target locations and to determine the marker and MSI scores. The marker 6
score is a numerical score that is assigned to each marker with adequate coverage. It is calculated based on the homopolymer signal of each mapped read for forward and reverse directions and the mean of homopolymer signal with normalization was used to examine the distance to that of the equivalent marker on the control. MSI score was calculated as a weighted normalized sum of the marker scores in the forward and reverse direction. We selected CEPH DNA as a control. Parameter settings for MSIcall was used as follows: (1) Minimum Marker Coverage (Minimum number of filtered reads to consider a marker strand for MSI Marker Score): 30; (2) Marker Score Threshold (Threshold below which marker scores are not considered): 0.5; (3) MSI High Threshold (MSI Score Threshold above which a sample is considered MSI High.): 40. According to the parameter settings, markers with sufficient mapped filtered reads were selected to calculate marker score. Subsequently, markers with scores less than Marker Score Threshold were filtered out. Based on the calculated MSI score, Samples with MSI score more than 40 were considered as “MSI high” and samples with MSI score less than 40 as a “MSS”.
Statistical analysis All
statistical
analysis
and
model
building
were
performed
in
R
(https://www.r-project.org/). Receiver operating characteristic (ROC) curve analysis was conducted using the ROCR package [31] to evaluated the performance of scoring classifiers and to visualize curves. Areas under the receiver operating curves, sensitivity, and specificity was calculated in R.
RESULTS Sample preparation and MSI-NGS To examine the MSI status in archival FFPE tumor tissues, a total of 184 tumor tissues were analyzed in this study. Several types of cancers such as gastric (n=53), colorectal (n=36), breast (n=23), esophagus (n=16), pancreatic (n=9), endometrial (n=7), ovarian (n=6), urothelial bladder (n=6), and other cancer types (n=28) were included (Table 1). NGS library samples were prepared using the Ion AmpliSeq Microsatellite Instability Research Panel, which targets 76 microsatellite loci. This panel targeted 76 regions including 70 mononucleotide repeats and 6 di- or tri-nucleotide repeats (Supplemental Table 1). After sequencing, MSI scores were calculated with a bioinformatics pipeline, MSI call, which is a plugin running on the Torrent Server. It takes approximately four days to yield the sequencing results. Library preparations were completed within two 7
days, sequencing was conducted in one day, and MSIcall plugin analysis was completed within one hour. This scheme could shorten the turnaround time.
MSI status determined by MSI-PCR and IHC To examine the MSI status of 184 tumors, all samples were subjected to conventional MSI-PCR and IHC analyses. MSI-PCR analysis classified tumors as MSS (87.5%: 161/184) and MSI-H (12.5%: 23/184) (Table 2). IHC determined pMMR (85.9%: 158/184) and dMMR (12.5%: 23/184) but could not determine them in three cytological samples (1.6%: 3/184) (Table 2). Except for these three samples, the results from both MSI-PCR and IHC achieved a 100% concordance in 181 samples for detecting MSI status (MSS/pMMR: MSI-H/dMMR=158:23) (Table 2). Combined with these two results, 161 tumors were classified as MSS/pMMR and 23 were MSI-H/dMMR; these data were used as the benchmark for subsequent analyses (Table 2).
MSI score across several types of cancer To estimate the accuracy of MSI-NGS analysis and the MSIcall bioinformatics algorithm, we compared the results of MSI-PCR and IHC with MSI-NGS. MSIcall calculated the MSI score of 184 samples, with a median score of 8.4 (range, 0–193.9) (Figure 1A). In all 184 samples, the MSI score of MSI-H/dMMR tumors was significantly higher than that of MSS/pMMR tumors (Figure 1A and B). We next examined MSI scores in each type of cancer and observed that MSI scores of MSI-H/dMMR were significantly higher compared with those of MSS/pMMR in gastric, colorectal, endometrial, and urothelial bladder cancer (Figure 1B).
Diagnostic accuracy of MSI-NGS with the bioinformatic algorithm MSIcall To determine the cutoff value of MSI score to distinguish MSI status, we examined the relationship between MSI score and accuracy. When the cutoff was 40.20 of the MSI score, the accuracy reached its highest level (98.9%) (Figure 2A). To facilitate interpretation, we used 40 as cutoff value for interpreting MSI scores. ROC curve analyses showed that MSIcall predicted MSI scores with a specificity of 91.3% and a sensitivity of 100% (Figure 2B). ROC analysis yielded an area under the ROC curve (AUC) value of 0.9986 when distinguishing MSI-H/dMMR from MSS/pMMR (Figure 2B). Overall, low MSI scores (<40) were found in 161 MSS/pMMR and two MSI-H/dMMR tissues, whereas, high MSI scores (≥40) were found in 21 MSI-H/dMMR, but none in MSS/pMMR (Table 3).
Analysis of two discordant cases 8
We further investigated the discordant results between MSI-NGS with MSIcall and MSI-PCR/IHC. In 184 tumors, two (one colorectal and one endometrial cancer) were determined as MSI-H and dMMR by MSI-PCR and IHC but were classified as MSS by MSI-NGS. In colorectal cancer, low tumor purity (20%) possibly led to the lower MSI score (score, 25.42). In endometrial cancer, IHC analysis determined the complete loss of MLH1/PMS2 proteins (Figure 3A). MSI-PCR analysis showed the apparent peak shift was observed in two out of five microsatellite markers (Figure 3B). MSI-NGS with MSIcall estimated the low MSI score (score, 17.04). The results of NGS-MSI showed that repeat sequence slightly shortened and the peak shift of two discordant results were very mild. Therefore, the mean of homopolymer signal with normalization was not significantly distant from that of the equivalent marker on the control. Another possible reason was that aberrant microsatellite instability did not occur in a high number of loci and total MSI score did not increase in these discordant samples.
Validation cohort To validate the results and parameter setting, we performed MSI-NGS with MSIcall to determine the MSI status of additional 50 samples, which included 44 MSS and 6 MSI-H samples. As a result, there was 100% (50/50) concordance between MSI-NGS and MSI-PCR/IHC data. Positive percent agreement was 100% (6/6) and negative percent agreement was 100% (44/44). Average MSI score was 7.8 (range: 1.2-24.5) and 81.6 (range: 43.8-100.3) in MSS and MSI-H samples, respectively. Overall, we validated the accuracy of MSIcall to classify the MSI status in tumor samples.
DISCUSSION In this study, we used a newly developed panel and bioinformatics pipeline, MSIcall, for detecting MSI status with NGS. The MSIcall pipeline calculated MSI score based on targeted microsatellite loci in each sample. This workflow was developed for tumor samples and baseline normal controls without patient-matched normal control samples. The performance of the method was validated in several types of cancer and compared with the results of orthogonal assays (MSI-PCR and IHC). We demonstrated that MSI-NGS with MSIcall was highly accurate, sensitive, and specific to distinguish MSS/pMMR and MSI-H/dMMR tumors. This method would enable us to combine genomic profile tests and determine MSI status with high scalability. MSI-PCR analysis and IHC are widely used to determine MSI status as the gold standard. However, both methods require visual confirmation and have some difficulties in determining MSI status in ambiguous data. Furthermore, it was reported that the 9
inconsistent results between IHC and MSI-PCR are due to several possible explanations [32] First, IHC detects the loss of MMR protein expression when protein-truncating mutations (e.g., frameshift, nonsense, and splice site mutations) occur in MMR genes. However, in some cases, pathogenic missense mutations led to functional loss in MMR proteins, but such proteins retain normal expression. In these cases, IHC misses dMMR tumors. Second, differences in tumor purity and tumor heterogeneity of MSI status affects the interpretation of results [33, 34]. Finally, defects in other mismatch repair genes may be related to microsatellite instability with minor frequency. Consequently, accurate diagnostic assays for MSI are required. Here, we introduced MSI-NGS with MSIcall as an alternative assay. MSIcall quantitatively calculates MSI score and evaluates MSI status without subjectivity. NGS analysis can provide the mutational profiles and MSI status in a single assay simultaneously. Laboratory development tests have already reported such an assay as the FoundationOne CDx (Foundation Medicine Inc.) [35]. There are growing demands for the integration of genomic profiling and MSI analysis in panel sequencing. The primer sets of Ion AmpliSeq Microsatellite Instability Research Panel could be combined with other primer set targets of interest. When combined with genomic profiles, data analysis can be completed in a cost-effective and less laborious manner. Previous reports showed MSI status can be determined by pipelines including MSIsensor [36], mSINGS [37], MOSAIC [5],MANTIS [38], MSIseq [39], and MSIpred [40] (Supplemental Table 2). In this study, we used MSIcall to calculate MSIscore to determine MSI status. These analytical pipelines use two types of data format. One method (e.g., MSIsensor, mSINGS, MOSAIC, MANTIS, and MSIcall evaluates MSI status using microsatellite unstable loci with BAM files, while the other method (e.g., MSIseq and MSIpred) uses somatic mutational profiles with Mutation Annotation Format (MAF) files (Supplemental Table 2). Compared with these pipelines, MSIcall is easy-to-use for several reasons. First, MSIcall is developed on a graphical user interface. Other pipelines are command-line based, therefore requiring knowledge of computer languages such as C++ program, Python, Perl, and R package. Second, MSIcall requires only tumor and baseline control sample data without patient-matched control samples. Besides mSINGS, most other programs need patient matched tumor-normal data to analyze MSI status. However, Kautto et al. compared the analytical performance of MANTIS, mSINGS, and MSIsensor and showed that mSINGS is less sensitive and specific compared with the other two methods. Third, MSIcall uses BAM files to selectively target 76 microsatellite loci; in contrast, some analytical pipelines need whole exome data. MSIcall needs less computer resource to conduct analysis and a shorter turnaround time. 10
In summary, MSI-NGS with MSIcall can serve as a reliable streamline tool for MSI diagnosis beyond the conventional MSI-PCR/ICH method in several types of cancers. This bioinformatics tool is freely available for online download and runs on a graphical user interface on web browsers. This tool does not require any specialized knowledge of programing and command lines. MSIcall is easy-to-use for beginners and can be widely applied for clinical diagnosis.
Acknowledgements We thank all medical and ancillary staff of the hospital and the patients for consenting to participate. We also thank Ryoka Miki (Thermo Fisher Scientific) for technical help. This study was supported by a Grant-in-Aid for Genome Research Project from Yamanashi Prefecture, The Japan Society for the Promotion of Science (JSPS) KAKENHI Early-Career Scientists, Research Grant for Young Scholars, The YASUDA Medical Foundation, and The Uehara Memorial Foundation. We thank H. Nikki March, PhD, from Edanz Group (www.edanzediting.com/ac) for editing a draft of this manuscript.
Disclosure Statement The authors declare no potential conflicts of interest.
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REFERENCES 1.
Consortium. HGS: Initial sequencing and analysis of the human genome. Nature 2001 Feb 15; 2001, 409:860-921.
2.
Levinson G, Gutman G: High frequencies of short frameshifts in poly-CA/TG tandem repeats borne by bacteriophage M13 in Escherichia coli K-12. Nucleic Acids Res 1987, 15:5323-5338.
3.
Su S, Modrich P: Escherichia coli mutS-encoded protein binds to mismatched DNA base pairs. Proc Natl Acad Sci U S A 1986, 83:5057-5061.
4.
Kim TM, Laird PW, Park PJ: The landscape of microsatellite instability in colorectal and endometrial cancer genomes. Cell 2013, 155:858-868.
5.
Hause
RJ,
Pritchard
CC,
Shendure
J,
Salipante
SJ:
Classification
and
characterization of microsatellite instability across 18 cancer types. Nat Med 2016, 22:1342-1350. 6.
Togo G, Toda N, Kanai F, Kato N, Shiratori Y, Kishi K, Imazeki F, Makuuchi M, Omata M: A transforming growth factor beta type II receptor gene mutation common in sporadic cecum cancer with microsatellite instability. Cancer Res 1996, 56:5620-5623.
7.
Dung T. Le, Jennifer N. Durham, Kellie N. Smith, Hao Wang, Bjarne R. Bartlett, Laveet K. Aulakh, Steve Lu, Holly Kemberling, Cara Wilt, Brandon S. Luber, et al: Mismatch repair deficiency predicts response of solid tumors to PD-1 blockade. Science 2017, 357:409-413.
8.
Le DT, Uram JN, Wang H, Bartlett BR, Kemberling H, Eyring AD, Skora AD, Luber BS, Azad NS, Laheru D, et al: PD-1 blockade in tumors with mismatch-repair deficiency. N Engl J Med 2015, 372:2509-2520.
9.
Yarchoan M, Hopkins A, Jaffee EM: Tumor mutational burden and response rate to PD-1 inhibition. N Engl J Med 2017, 377:2500-2501.
10.
Chalmers ZR, Connelly CF, Fabrizio D, Gay L, Ali SM, Ennis R, Schrock A, Campbell B, Shlien A, Chmielecki J, et al: Analysis of 100,000 human cancer genomes reveals the landscape of tumor mutational burden. Genome Med 2017, 9:34.
11.
Marcus L, Lemery SJ, Keegan P, Pazdur R: FDA approval summary: pembrolizumab for the treatment of microsatellite instability-high solid tumors. Clin Cancer Res 2019, 25:3753-3758.
12.
Baudrin LG, Deleuze JF, How-Kit A: Molecular and computational methods for the detection of microsatellite instability in cancer. Front Oncol 2018, 8:621.
13.
Amemiya K, Hirotsu Y, Oyama T, Omata M: Simple and rapid method to obtain high-quality tumor DNA from clinical-pathological specimens using touch imprint
12
cytology. J Vis Exp 2018. 14.
Amemiya K, Hirotsu Y, Oyama T, Omata M: Relationship between formalin reagent and success rate of targeted sequencing analysis using formalin fixed paraffin embedded tissues. Clin Chim Acta 2019, 488:129-134.
15.
Hirotsu Y, Nakagomi H, Amemiya K, Oyama T, Inoue M, Mochizuki H, Omata M: Intrinsic HER2 V777L mutation mediates resistance to trastuzumab in a breast cancer patient. Med Oncol 2017, 34:3.
16.
Bacher J, Flanagan L, Smalley R, Nassif N, Burgart L, Halberg R, Megid W, Thibodeau S: Development of a fluorescent multiplex assay for detection of MSI-High tumors. Dis Markers 2004, 20:237-250.
17.
Bando H, Okamoto W, Fukui T, Yamanaka T, Akagi K, Yoshino T: Utility of the quasi-monomorphic variation range in unresectable metastatic colorectal cancer patients. Cancer Sci 2018, 109:3411-3415.
18.
Nakagomi T, Goto T, Hirotsu Y, Shikata D, Yokoyama Y, Higuchi R, Amemiya K, Okimoto K, Oyama T, Mochizuki H, Omata M: New therapeutic targets for pulmonary sarcomatoid carcinomas based on their genomic and phylogenetic profiles. Oncotarget 2018, 9:10635-10649.
19.
Takaoka S, Hirotsu Y, Ohyama H, Mochizuki H, Amemiya K, Oyama T, Ashizawa H, Yoshimura D, Nakagomi K, Hosoda K, et al: Molecular subtype switching in early-stage gastric cancers with multiple occurrences. J Gastroenterol 2019.
20.
Buhard O, Cattaneo F, Wong YF, Yim SF, Friedman E, Flejou JF, Duval A, Hamelin R: Multipopulation analysis of polymorphisms in five mononucleotide repeats used to determine the microsatellite instability status of human tumors. J Clin Oncol 2006, 24:241-251.
21.
Hashmi AA, Ali R, Hussain ZF, Faridi N, Khan EY, Bakar SMA, Edhi MM, Khan M: Mismatch repair deficiency screening in colorectal carcinoma by a four-antibody immunohistochemical panel in Pakistani population and its correlation with histopathological parameters. World J Surg Oncol 2017, 15:116.
22.
Hirotsu Y, Mochizuki H, Amemiya K, Ohyama H, Yoshimura D, Amano H, Miura Y, Ashizawa H, Nakagomi K, Takaoka S, et al: Deficiency of mismatch repair genes is less frequently observed in signet ring cell compared with non-signet ring cell gastric cancer. Medical Oncology 2019, 36.
23.
Bonneville R, Krook M, Kautto E, Miya J, Wing M, Chen H, Reeser J, Yu L, Roychowdhury S: Landscape of microsatellite instability across 39 cancer types. JCO Precis Oncol 2017.
24.
Hempelmann JA, Lockwood CM, Konnick EQ, Schweizer MT, Antonarakis ES, Lotan TL,
13
Montgomery B, Nelson PS, Klemfuss N, Salipante SJ, Pritchard CC: Microsatellite instability in prostate cancer by PCR or next-generation sequencing. J Immunother Cancer 2018, 6:29. 25.
Maruvka YE, Mouw KW, Karlic R, Parasuraman P, Kamburov A, Polak P, Haradhvala NJ, Hess JM, Rheinbay E, Brody Y, et al: Analysis of somatic microsatellite indels identifies driver events in human tumors. Nat Biotechnol 2017, 35:951-959.
26.
Cortes-Ciriano I, Lee S, Park WY, Kim TM, Park PJ: A molecular portrait of microsatellite instability across multiple cancers. Nat Commun 2017, 8:15180.
27.
Goto T, Hirotsu Y, Amemiya K, Nakagomi T, Shikata D, Yokoyama Y, Okimoto K, Oyama T, Mochizuki H, Omata M: Distribution of circulating tumor DNA in lung cancer: analysis of the primary lung and bone marrow along with the pulmonary venous and peripheral blood. Oncotarget 2017, 8:59268–59281.
28.
Iijima Y, Hirotsu Y, Amemiya K, Ooka Y, Mochizuki H, Oyama T, Nakagomi T, Uchida Y, Kobayashi Y, Tsutsui T, et al: Very early response of circulating tumour-derived DNA in plasma predicts efficacy of nivolumab treatment in patients with non-small cell lung cancer. Eur J Cancer 2017, 86:349-357.
29.
Hirotsu Y, Kojima Y, Okimoto K, Amemiya K, Mochizuki H, Omata M: Comparison between two amplicon-based sequencing panels of different scales in the detection of somatic mutations associated with gastric cancer. BMC Genomics 2016, 17:833.
30.
Sakamoto I, Hirotsu Y, Nakagomi H, Ouchi H, Ikegami A, Teramoto K, Amemiya K, Mochizuki H, Omata M: BRCA1 and BRCA2 mutations in Japanese patients with ovarian, fallopian tube, and primary peritoneal cancer. Cancer 2016, 122:84-90.
31.
Sing T, Sander O, Beerenwinkel N, Lengauer T: ROCR: visualizing classifier performance in R. Bioinformatics 2005, 21:3940-3941.
32.
Palmieri G, Colombino M, Cossu A, Marchetti A, Botti G, Ascierto PA: Genetic instability and increased mutational load: which diagnostic tool best direct patients with cancer to immunotherapy? J Transl Med 2017, 15:17.
33.
Chapusot C, Martin L, Bouvier AM, Bonithon-Kopp C, Ecarnot-Laubriet A, Rageot D, Ponnelle T, Laurent Puig P, Faivre J, Piard F: Microsatellite instability and intratumoural heterogeneity in 100 right-sided sporadic colon carcinomas. Br J Cancer 2002, 87:400-404.
34.
Watson N, Grieu F, Morris M, Harvey J, Stewart C, Schofield L, Goldblatt J, Iacopetta B: Heterogeneous staining for mismatch repair proteins during population-based prescreening for hereditary nonpolyposis colorectal cancer. J Mol Diagn 2007, 9:472-478.
14
35.
Frampton GM, Fichtenholtz A, Otto GA, Wang K, Downing SR, He J, Schnall-Levin M, White J, Sanford EM, An P, et al: Development and validation of a clinical cancer genomic profiling test based on massively parallel DNA sequencing. Nat Biotechnol 2013, 31:1023-1031.
36.
Niu B, Ye K, Zhang Q, Lu C, Xie M, McLellan MD, Wendl MC, Ding L: MSIsensor: microsatellite instability detection using paired tumor-normal sequence data. Bioinformatics 2014, 30:1015-1016.
37.
Salipante SJ, Scroggins SM, Hampel HL, Turner EH, Pritchard CC: Microsatellite instability detection by next generation sequencing. Clin Chem 2014, 60:1192-1199.
38.
Performance evaluation for rapid detection of pan-cancer microsatellite instability with MANTIS. Oncotarget 2017, 8:7452-7463.
39.
Huang MN, McPherson JR, Cutcutache I, Teh BT, Tan P, Rozen SG: MSIseq: software for assessing microsatellite instability from catalogs of somatic mutations. Sci Rep 2015, 5:13321.
40.
Wang C, Liang C: MSIpred: a python package for tumor microsatellite instability classification from tumor mutation annotation data using a support vector machine. Sci Rep 2018, 8:17546.
Figure Legends Figure 1. MSI scores in 184 tumor samples. (A) Pink box shows MSI-H tumor samples and gray box shows MSS tumor samples. (B) Box plot showed MSI scores in all tumor samples and tumor types (gastric, colorectal, endometrial, and urothelial bladder cancer). Figure 2. MSI score discriminates MSI-H and MSS tumors. (A) Line graph shows the relationship between MSI score and accuracy. Highest accuracy is achieved when the cutoff value of MSI score is 40.20. (B) Receiver operating characteristic (ROC) curve analysis. MSI-NGS with MSIcall achieved an area under the ROC curve (AUC) value of 0.9986. Figure 3. Discordant result from endometrial tumor. (A) Immunohistochemistry of mismatch repair proteins showing absent MLH1 and PMS2 protein expression in tumor nuclei. (B) Electrogram of microsatellite analysis of five markers. Arrows shows the shortened length of PCR products of BAT26 and MONO-27 markers. Figure 4. Validation of analytical performance. Fifty tumor samples were subjected to the MSI-NGS and bioinformatics algorithm ‘MSIcall’ to calculate the MSI score. MSI status of fifty samples was determined by the MSI-PCR and IHC in advance. MSI status was classified as MSS (n=44) and MSI-H (n=6) with 100% agreement. The blue line indicates a MSI score with a threshold of 40.
15
Table 1. Number of samples of tumor types. Table 2. MSI status was determined by MSI-PCR and IHC. Table 3. Comparison of MSI-NGS results and that of MSI-PCR and IHC. Supporting Information Supplemental Table 1. Target microsatellite loci in the panel. Supplemental Table 2. Bioinformatics pipelines to analyze microsatellite instability.
16
*Highlights
1 2 3 4 5 6 7 8
Highlights ࣭MSI occurs in several solid cancer types and is predictive of response to immunotherapy. ࣭There are some issues with detection, immunohistochemistry PCR–capillary electrophoresis microsatellite markers, especially
the current gold standard methods for MSI (IHC) of mismatch repair proteins and (MSI-PCR) of mononucleotide repeat when verifying MSI status from ambiguous
14
data resulting from poor sample quality. ࣭We performed MSI-next-generation sequencing (MSI-NGS) combined with MSIcall, a bioinformatics tool, in 76 microsatellite loci in 184 formalin-fixed paraffin-embedded tumor from patients with various solid tumors. ࣭MSIcall predicted MSI scores with a specificity of 91.3%, a sensitivity of 100% and high accuracy (98.9%). ࣭We validated the additional 50 samples to classified MSI status with 100%
15
concordance.
9 10 11 12 13
16
1
Table 1
Table 1. Number of samples of tumor types Tumor type
n
Gastric cancer
53
Colorectal cancer
36
Breast cancer
23
Esophagus cancer
16
Pancreatic cancer
9
Endometrial cancer
7
Ovarian cancer
6
Urothelial bladder cancer
6
Cervical cancer
4
Hepatocellular carcinoma
3
Sarcoma
3
Small cell lung cancer
3
Cholangiocarcinoma
2
Gallbladder carcinoma
2
Germ cell tumor
1
Large cell neuroendocrine carcinoma
1
Nasopharyngeal cancer
1
Non-small cell lung cancer
1
Oral cancer
1
Paraganglioma
1
Prostate cancer
1
Salivary Gland Cancer
1
Small intestine cancer
1
Solitary Fibrous Tumor
1
Tongue cancer
1
Total
184
Table 2
Table 2. MSI status was determined by MSI-PCR and IHC
MSS by MSI-PCR and pMMR by IHC
158
MSI-H by MSI-PCR and dMMR by IHC
23
MSS by MSI-PCR
3*
Total
184
*These three cases had no available FFPE samples
Table 3
Table 3. Comparison of MSI-NGS results and that of MSI-PCR and IHC
MSI score of MSI-NGS MSI status determined
ᨪ
<40
≥40
MSS/pMMR
161
161
0
MSI-H/dMMR
23
2
21
Total
184
163
21
by MSI-PCR/IHC
160
200
(A)
Figure 1
100
50
0
100
50
0
MSS/ MSI-H/ pMMR dMMR (n=161) (n=23)
150
MSS/ pMMR (n=40)
MSI-H/ dMMR (n=13)
p<2.2x10-16
p<2.2x10-16
150
Gastric cancer (n=53)
All samples (n=184)
200
1
MSI-H
MSS
200
(B)
0
40
80
120
MSI score
MSI score
0
50
100
150
MSS/ pMMR (n=31)
MSI-H/ dMMR (n=5)
p=6.5x10-12
Colorectal cancer (n=36)
Number of samples
Figure 1
0
10
20
30
40
50
MSS/ pMMR (n=4)
MSI-H/ dMMR (n=3)
p=0.046
Endometrial cancer (n=7)
10
20
30
40
50
60
70
MSS/ pMMR (n=4)
MSI-H/ dMMR (n=2)
p=0.0085
Urothelial bladder cancer (n=6)
184
Accuracy
(A)
0.2
0.4
0.6
0.8
1.0
0
Figure 2
Figure 2
50
MSI score
100
150
200
(B)
True positive rate 0
0.2
0.4
0.6
0.8
1.0
0
0.4
0.6
0.8
False positive rate
0.2
1.0
Figure 3
(B)
(A)
BAT26
MLH1
Figure 3
NR21
MSH2
BAT25
MONO27
MSH6
NR24
PMS2
Figure 4
0
20
40
60
80
100
120
Figure 4
MSI score
1
Number of samples
MSI-H
MSS
50
S0009-8981(19)32115-1 https://doi.org/10.1016/j.cca.2019.11.002 CCA 15912
To appear in:
Clinica Chimica Acta
Received Date: Revised Date: Accepted Date:
10 September 2019 31 October 2019 2 November 2019
Please cite this article as: Y. Hirotsu, Y. Nagakubo, K. Amemiya, T. Oyama, H. Mochizuki, M. Omata, Microsatellite Instability Status is Determined by Targeted Sequencing with MSIcall in 25 Cancer Types, Clinica Chimica Acta (2019), doi: https://doi.org/10.1016/j.cca.2019.11.002
This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
© 2019 Published by Elsevier B.V.
Microsatellite Instability Status is Determined by Targeted Sequencing with MSIcall in 25 Cancer Types
Yosuke Hirotsu1,*, Yuki Nagakubo2, Kenji Amemiya1, Toshio Oyama3, Hitoshi Mochizuki1,4 and Masao Omata4,5 1
Genome Analysis Center, 2Division of Genetics and Clinical Laboratory, 3Department of
Pathology, 4Department of Gastroenterology, Yamanashi Central Hospital, 1-1-1 Fujimi, Kofu, Yamanashi, Japan 5
The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, Japan
*Correspondence to: Yosuke Hirotsu, Genome Analysis Center, Yamanashi Central Hospital, 1-1-1 Fujimi, Kofu, Japan Email: [email protected] Tel: +81-55-253-7111, Fax: +81-55-253-8011
Keywords: microsatellite instability; immune checkpoint; MSI; IHC; NGS Word count: 3005 words Number of figures/tables: 4 figures, 3 tables Quantity of supporting information: 2 supplemental tables Running title: Target sequencing determines MSI status Grant: This study was supported by a Grant-in-Aid for Genome Research Project from Yamanashi Prefecture, The Japan Society for the Promotion of Science (JSPS) KAKENHI Early-Career Scientists, Research Grant for Young Scholars, The YASUDA Medical Foundation, and The Uehara Memorial Foundation. Abbreviations: MSI: microsatellite instability IHC: immunohistochemistry ROC: receiver operating characteristic FFPE: formalin-fixed paraffin-embedded AUC: area under the ROC curve MMR: mismatch repair MSI-H: microsatellite instability high MSI-L: microsatellite instability low MSS: microsatellite stable 1
pMMR: proficient MMR dMMR: deficient MMR NGS: next generation sequencing QMVR: quasi-monomorphic variation range PCR: polymerase chain reaction BAM: Binary Sequence Alignment/Map MAF: Mutation Annotation Format
2
ABSTRACT Background: Microsatellite instability (MSI) occurs in solid tumors and is a predictive biomarker for remarkable response to immune checkpoint inhibitors. Detection of MSI status has been conventionally conducted by PCR–electrophoresis-based assay (MSI-PCR) and immunohistochemistry (IHC) of mismatch repair proteins. However, these approaches require visual confirmation and have some difficulties in determining MSI statuses from equivocal results. Methods: We performed amplicon-based targeted sequencing of 76 microsatellite loci (MSI-NGS) in 184 formalin-fixed paraffin-embedded (FFPE) tumor tissues and baseline control samples. A bioinformatics tool, MSIcall, calculated the quantitative values based on the aligned sequence reads and evaluated MSI status. Furthermore, we examined the concordance between the results from MSI-NGS and MSI-PCR/IHC. Diagnostic accuracy, sensitivity, and specificity were estimated by receiver operating characteristic (ROC) curve analysis. For validation cohort, we studied additional 50 tumor samples to determine the MSI status. Results: Of 184 tumor samples, MSI-PCR and IHC analysis classified 161 tumors as MSS/pMMR and 23 as MSI-H/dMMR. Using MSI-NGS combined with MSIcall, we predicted MSI status with high accuracy (98.9%), specificity (91.3%), and sensitivity (100%) in 25 types of cancers. This method achieved an area under the ROC curve (AUC) value of 0.9986. Furthermore, we achieved the 100% concordant results using additional 50 samples for validation. Conclusion: We demonstrated newly developed MSI-NGS with MSIcall accurately determines the MSI status of FFPE tumor tissues thorough sequencing of tumor samples alone without patient-matched normal controls. This approach could be applied to all types of solid tumors to determine responders to immune-oncology therapy.
INTRODUCTION In the human genome, approximately 3% is occupied by simple sequence repeats scattered throughout [1]. Microsatellites are repetitive sequences with unit lengths varying from mono-, di-, tri-, tetr-, penta-, or hexa-nucleotide repeats. Because microsatellite loci are vulnerable to replication slippage, its lengths are changed during DNA replication [2]. Replication errors are recognized and repaired by DNA mismatch repair (MMR) proteins including MLH1, MSH2, MSH6, and PMS2 to maintain cellular homeostasis [3]. When MMR functions are defective, replication errors are not repaired normally, and somatic mutations accumulate. This genomic state is called microsatellite instability (MSI). MSI is frequently observed in 10%–20% of sporadic endometrial, colorectal, and 3
gastric cancers, but less often in other solid tumor types [4-6]. The MSI phenotype is frequently observed in tumors from patients who have germline mutations in MMR genes, and is known as Lynch syndrome. MSI status is determined by PCR followed by capillary electrophoresis as the gold standard (hereafter called MSI-PCR). In general, MSI-PCR amplifies a subset of representative microsatellite markers (e.g., BAT25 and BAT26) and determines whether the length of microsatellite markers is changed between paired tumor and normal control DNA. Based on the number of markers with altered lengths, tumors are classified as microsatellite instability high (MSI-H), microsatellite instability low (MSI-L), and microsatellite stable (MSS). Alternatively, immunohistochemistry (IHC) of MMR proteins is conducted to estimate either the presence of MMR protein (proficient MMR, pMMR) or the absence of MMR expression (deficient MMR, dMMR). MSI status has become a very important biomarker for immune-oncology therapy [7, 8]. The number of non-synonymous mutations increases in MSI-H/dMMR tumors compared with MSS/pMMR [9, 10]. Clinical trials have shown MSI-H/dMMR tumors respond well to immunotherapy [8, 9]. The United States Food and Drug Administration (FDA) approved an immune checkpoint inhibitor (pembrolizumab) for treatment of MSI-H metastatic solid tumors [11]. Recently, pembrolizumab was also approved in Japan for all types of solid tumor exhibiting MSI-H. Therefore, there are growing demands to survey patients who harbor MSI-H tumors and may benefit from immune-oncology therapy. To determine MSI status, MSI-PCR and IHC have been widely conducted. However, these analyses have some problems in determining MSI status due to sample issues (e.g., cytology, biopsies, and samples with low tumor purity). In addition, tissue fixation with formalin causes degradation of DNA and decreases immunogenicity. Furthermore, visual confirmation is mandatory to check the capillary electrogram by MSI-PCR and positivity of immunostaining by IHC. These procedures are subjective and required complicated interpretations from equivocal results. Therefore, an accurate and alternative method is desired to achieve high scalability. Next generation sequencing (NGS) analysis allows us to reveal both comprehensive genomic profiles and MSI status simultaneously. Although several reported microsatellite loci were extracted from whole exome or whole genome sequencing data to estimate MSI status [12], this requires computer technology and skills to manage computer language in a character user interface. In this study, we aimed to develop and validate a user-friendly analytical process for detecting MSI status. We performed NGS of 76 microsatellite loci and determined MSI status (MSI-NGS). Furthermore, both MSI-PCR and IHC were conducted to compare the results from MSI-NGS. We demonstrated this novel approach provides a highly accurate detection system without patient-matched normal 4
controls.
MATERIALS AND METHODS Patients and sample preparation This study included 184 patients who were diagnosed with several cancer types (Table 1). For the validation cohort, we analyzed MSI status in 50 patients with ovarian cancer (n=26), colorectal cancer (n=14), gastric cancer (n=5), endometrial cancer (n=4) and gallbladder cancer (n=1). Informed consent was obtained from all patients. The study was approved by the Institutional Review Board at our hospital. Tumor samples were obtained by surgery or biopsy and fixed using 10% buffered formalin [13, 14]. Tumor diagnosis was conducted by a pathologist (T.O.). Tumor DNA was extracted from two 10-µm-thick FFPE sections and from five 10-µm-thick tumor biopsy sections using an Agencourt FormaPure DNA kit (Beckman Coulter, Brea, CA, USA) according to the manufacturer's protocol. DNA concentration was determined using a NanoDrop 2000 spectrophotometer. If the tumor purity was less than 10%, we performed laser capture microdissection to enrich tumor cells. To this end, tumor tissues were placed on PEN Membrane Frame Slides and stained with hematoxylin and eosin, and then microdissected using an ArcturusXT laser capture microdissection system (Thermo Fisher Scientific, Waltham, MA, USA) [15].
MSI-PCR MSI analysis was performed on FFPE tumor DNA using the MSI Analysis System v1.2 (Promega, Wisconsin, USA) [16] or MSI (FALCO) Kit (FALCO biosystems, Kyoto, Japan), which is approved for in vitro diagnosis in Japan [17]. Both assays examined five mononucleotide repeat markers (BAT-25, BAT-26, NR-21, NR-24, and MONO-27) that are used to determine MSI status, as described previously [18, 19]. Fluorescently labelled PCR products were separated by capillary electrophoresis using a 3500 Genetic Analyzer and sizes were analyzed by GeneMapper Software 5 (Thermo Fisher Scientific). When we used MSI (FALCO), we assessed if the lengths of PCR products were within the quasi-monomorphic variation range (QMVR) [17, 20]. Visual assessment was conducted by scattered electrogram. We determined the locus with a shift (above 3 bp) as instable, a mild shift (1–2 bp) as inconclusive, and without shift as stable. The cumulative number of instable shifts at two or more of the five microsatellite loci defines MSI-H. For the purpose of this study, tumors with less than one unstable locus were interpreted as MSS.
IHC Analysis of the IHC expression of MMR proteins was performed using a clinically 5
validated standard procedure. Sections (3-μm-thick serial sections of FFPE tissue) were deparaffinized and antigen activation was performed by heat treatment at high pH at 97°C for 20 min [21]. IHC was performed on tumor samples using the Dako EnVision FLEX kit, an automated staining instrument Autostainer Link 48 system (Dako, Copenhagen, Denmark), and primary monoclonal antibodies against anti-MLH1 (clone ES05; Dako), anti-MSH2 (clone FE11; Dako), anti-MSH6 (clone EP49; Dako), and anti-PMS2 (clone EP51; Dako). All sections were evaluated by a cytologist (K.A.) and a pathologist (T.O.). Non-neoplastic cells served as a positive control. Retained expression of each protein was defined by nuclear IHC reactivity of tumor cells, whereas loss of expression of each protein was defined by the complete absence of nuclear IHC reactivity in tumor cells. The tumors were considered pMMR when nuclear staining of all four proteins in tumor tissue was present or dMMR when the nuclear staining was absent [19, 22].
MSI-NGS Multiplex polymerase chain reaction (PCR) was performed using Ion AmpliSeq Microsatellite Instability Research Panel and Ion AmpliSeq Library Kit Plus (Thermo Fisher Scientific). This panel contains a single primer pool to amplify 76 microsatellite loci that are known to be affected by MSI based on previous reports (Supplemental Table 1) [23-26]. Primers were digested with FuPa reagent and then barcoded using Ion Xpress Barcode Adapters. Purification was performed by Agencourt AMPure XP reagents (Beckman Coulter) using the KingFisher™ Duo Prime System (Thermo Fisher Scientific) [15, 27]. The library concentration was determined using an Ion Library Quantitation Kit; each library was diluted and the same amount of library was pooled for one sequence reaction. Emulsion PCR and chip loading were performed on the Ion Chef with the Ion PI Hi-Q Chef kit. CEPH DNA (ThermoFisher Scientific: cat. # 430962) was used as a control and included on the same sequencing run. Sequencing was performed using the Ion PI chip and Ion PI Hi-Q Sequencing Kit on the Ion Proton Sequencer (Thermo Fisher Scientific), as described previously [28-30].
Evaluation of MSI score with bioinformatic algorithm ‘MSIcall’ Raw signal data were analyzed using Torrent Suite version 5.10.0. The pipeline included signal processing, base calling, quality score assignment, read alignment, and quality control of mapping. Sequencing reads were aligned, generating Binary Sequence Alignment/Map (BAM) files on the Torrent Server. A bioinformatics pipeline, MSIcall (v4.1), which was downloaded online and installed, was run to process the sequencing reads mapped to the target locations and to determine the marker and MSI scores. The marker 6
score is a numerical score that is assigned to each marker with adequate coverage. It is calculated based on the homopolymer signal of each mapped read for forward and reverse directions and the mean of homopolymer signal with normalization was used to examine the distance to that of the equivalent marker on the control. MSI score was calculated as a weighted normalized sum of the marker scores in the forward and reverse direction. We selected CEPH DNA as a control. Parameter settings for MSIcall was used as follows: (1) Minimum Marker Coverage (Minimum number of filtered reads to consider a marker strand for MSI Marker Score): 30; (2) Marker Score Threshold (Threshold below which marker scores are not considered): 0.5; (3) MSI High Threshold (MSI Score Threshold above which a sample is considered MSI High.): 40. According to the parameter settings, markers with sufficient mapped filtered reads were selected to calculate marker score. Subsequently, markers with scores less than Marker Score Threshold were filtered out. Based on the calculated MSI score, Samples with MSI score more than 40 were considered as “MSI high” and samples with MSI score less than 40 as a “MSS”.
Statistical analysis All
statistical
analysis
and
model
building
were
performed
in
R
(https://www.r-project.org/). Receiver operating characteristic (ROC) curve analysis was conducted using the ROCR package [31] to evaluated the performance of scoring classifiers and to visualize curves. Areas under the receiver operating curves, sensitivity, and specificity was calculated in R.
RESULTS Sample preparation and MSI-NGS To examine the MSI status in archival FFPE tumor tissues, a total of 184 tumor tissues were analyzed in this study. Several types of cancers such as gastric (n=53), colorectal (n=36), breast (n=23), esophagus (n=16), pancreatic (n=9), endometrial (n=7), ovarian (n=6), urothelial bladder (n=6), and other cancer types (n=28) were included (Table 1). NGS library samples were prepared using the Ion AmpliSeq Microsatellite Instability Research Panel, which targets 76 microsatellite loci. This panel targeted 76 regions including 70 mononucleotide repeats and 6 di- or tri-nucleotide repeats (Supplemental Table 1). After sequencing, MSI scores were calculated with a bioinformatics pipeline, MSI call, which is a plugin running on the Torrent Server. It takes approximately four days to yield the sequencing results. Library preparations were completed within two 7
days, sequencing was conducted in one day, and MSIcall plugin analysis was completed within one hour. This scheme could shorten the turnaround time.
MSI status determined by MSI-PCR and IHC To examine the MSI status of 184 tumors, all samples were subjected to conventional MSI-PCR and IHC analyses. MSI-PCR analysis classified tumors as MSS (87.5%: 161/184) and MSI-H (12.5%: 23/184) (Table 2). IHC determined pMMR (85.9%: 158/184) and dMMR (12.5%: 23/184) but could not determine them in three cytological samples (1.6%: 3/184) (Table 2). Except for these three samples, the results from both MSI-PCR and IHC achieved a 100% concordance in 181 samples for detecting MSI status (MSS/pMMR: MSI-H/dMMR=158:23) (Table 2). Combined with these two results, 161 tumors were classified as MSS/pMMR and 23 were MSI-H/dMMR; these data were used as the benchmark for subsequent analyses (Table 2).
MSI score across several types of cancer To estimate the accuracy of MSI-NGS analysis and the MSIcall bioinformatics algorithm, we compared the results of MSI-PCR and IHC with MSI-NGS. MSIcall calculated the MSI score of 184 samples, with a median score of 8.4 (range, 0–193.9) (Figure 1A). In all 184 samples, the MSI score of MSI-H/dMMR tumors was significantly higher than that of MSS/pMMR tumors (Figure 1A and B). We next examined MSI scores in each type of cancer and observed that MSI scores of MSI-H/dMMR were significantly higher compared with those of MSS/pMMR in gastric, colorectal, endometrial, and urothelial bladder cancer (Figure 1B).
Diagnostic accuracy of MSI-NGS with the bioinformatic algorithm MSIcall To determine the cutoff value of MSI score to distinguish MSI status, we examined the relationship between MSI score and accuracy. When the cutoff was 40.20 of the MSI score, the accuracy reached its highest level (98.9%) (Figure 2A). To facilitate interpretation, we used 40 as cutoff value for interpreting MSI scores. ROC curve analyses showed that MSIcall predicted MSI scores with a specificity of 91.3% and a sensitivity of 100% (Figure 2B). ROC analysis yielded an area under the ROC curve (AUC) value of 0.9986 when distinguishing MSI-H/dMMR from MSS/pMMR (Figure 2B). Overall, low MSI scores (<40) were found in 161 MSS/pMMR and two MSI-H/dMMR tissues, whereas, high MSI scores (≥40) were found in 21 MSI-H/dMMR, but none in MSS/pMMR (Table 3).
Analysis of two discordant cases 8
We further investigated the discordant results between MSI-NGS with MSIcall and MSI-PCR/IHC. In 184 tumors, two (one colorectal and one endometrial cancer) were determined as MSI-H and dMMR by MSI-PCR and IHC but were classified as MSS by MSI-NGS. In colorectal cancer, low tumor purity (20%) possibly led to the lower MSI score (score, 25.42). In endometrial cancer, IHC analysis determined the complete loss of MLH1/PMS2 proteins (Figure 3A). MSI-PCR analysis showed the apparent peak shift was observed in two out of five microsatellite markers (Figure 3B). MSI-NGS with MSIcall estimated the low MSI score (score, 17.04). The results of NGS-MSI showed that repeat sequence slightly shortened and the peak shift of two discordant results were very mild. Therefore, the mean of homopolymer signal with normalization was not significantly distant from that of the equivalent marker on the control. Another possible reason was that aberrant microsatellite instability did not occur in a high number of loci and total MSI score did not increase in these discordant samples.
Validation cohort To validate the results and parameter setting, we performed MSI-NGS with MSIcall to determine the MSI status of additional 50 samples, which included 44 MSS and 6 MSI-H samples. As a result, there was 100% (50/50) concordance between MSI-NGS and MSI-PCR/IHC data. Positive percent agreement was 100% (6/6) and negative percent agreement was 100% (44/44). Average MSI score was 7.8 (range: 1.2-24.5) and 81.6 (range: 43.8-100.3) in MSS and MSI-H samples, respectively. Overall, we validated the accuracy of MSIcall to classify the MSI status in tumor samples.
DISCUSSION In this study, we used a newly developed panel and bioinformatics pipeline, MSIcall, for detecting MSI status with NGS. The MSIcall pipeline calculated MSI score based on targeted microsatellite loci in each sample. This workflow was developed for tumor samples and baseline normal controls without patient-matched normal control samples. The performance of the method was validated in several types of cancer and compared with the results of orthogonal assays (MSI-PCR and IHC). We demonstrated that MSI-NGS with MSIcall was highly accurate, sensitive, and specific to distinguish MSS/pMMR and MSI-H/dMMR tumors. This method would enable us to combine genomic profile tests and determine MSI status with high scalability. MSI-PCR analysis and IHC are widely used to determine MSI status as the gold standard. However, both methods require visual confirmation and have some difficulties in determining MSI status in ambiguous data. Furthermore, it was reported that the 9
inconsistent results between IHC and MSI-PCR are due to several possible explanations [32] First, IHC detects the loss of MMR protein expression when protein-truncating mutations (e.g., frameshift, nonsense, and splice site mutations) occur in MMR genes. However, in some cases, pathogenic missense mutations led to functional loss in MMR proteins, but such proteins retain normal expression. In these cases, IHC misses dMMR tumors. Second, differences in tumor purity and tumor heterogeneity of MSI status affects the interpretation of results [33, 34]. Finally, defects in other mismatch repair genes may be related to microsatellite instability with minor frequency. Consequently, accurate diagnostic assays for MSI are required. Here, we introduced MSI-NGS with MSIcall as an alternative assay. MSIcall quantitatively calculates MSI score and evaluates MSI status without subjectivity. NGS analysis can provide the mutational profiles and MSI status in a single assay simultaneously. Laboratory development tests have already reported such an assay as the FoundationOne CDx (Foundation Medicine Inc.) [35]. There are growing demands for the integration of genomic profiling and MSI analysis in panel sequencing. The primer sets of Ion AmpliSeq Microsatellite Instability Research Panel could be combined with other primer set targets of interest. When combined with genomic profiles, data analysis can be completed in a cost-effective and less laborious manner. Previous reports showed MSI status can be determined by pipelines including MSIsensor [36], mSINGS [37], MOSAIC [5],MANTIS [38], MSIseq [39], and MSIpred [40] (Supplemental Table 2). In this study, we used MSIcall to calculate MSIscore to determine MSI status. These analytical pipelines use two types of data format. One method (e.g., MSIsensor, mSINGS, MOSAIC, MANTIS, and MSIcall evaluates MSI status using microsatellite unstable loci with BAM files, while the other method (e.g., MSIseq and MSIpred) uses somatic mutational profiles with Mutation Annotation Format (MAF) files (Supplemental Table 2). Compared with these pipelines, MSIcall is easy-to-use for several reasons. First, MSIcall is developed on a graphical user interface. Other pipelines are command-line based, therefore requiring knowledge of computer languages such as C++ program, Python, Perl, and R package. Second, MSIcall requires only tumor and baseline control sample data without patient-matched control samples. Besides mSINGS, most other programs need patient matched tumor-normal data to analyze MSI status. However, Kautto et al. compared the analytical performance of MANTIS, mSINGS, and MSIsensor and showed that mSINGS is less sensitive and specific compared with the other two methods. Third, MSIcall uses BAM files to selectively target 76 microsatellite loci; in contrast, some analytical pipelines need whole exome data. MSIcall needs less computer resource to conduct analysis and a shorter turnaround time. 10
In summary, MSI-NGS with MSIcall can serve as a reliable streamline tool for MSI diagnosis beyond the conventional MSI-PCR/ICH method in several types of cancers. This bioinformatics tool is freely available for online download and runs on a graphical user interface on web browsers. This tool does not require any specialized knowledge of programing and command lines. MSIcall is easy-to-use for beginners and can be widely applied for clinical diagnosis.
Acknowledgements We thank all medical and ancillary staff of the hospital and the patients for consenting to participate. We also thank Ryoka Miki (Thermo Fisher Scientific) for technical help. This study was supported by a Grant-in-Aid for Genome Research Project from Yamanashi Prefecture, The Japan Society for the Promotion of Science (JSPS) KAKENHI Early-Career Scientists, Research Grant for Young Scholars, The YASUDA Medical Foundation, and The Uehara Memorial Foundation. We thank H. Nikki March, PhD, from Edanz Group (www.edanzediting.com/ac) for editing a draft of this manuscript.
Disclosure Statement The authors declare no potential conflicts of interest.
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REFERENCES 1.
Consortium. HGS: Initial sequencing and analysis of the human genome. Nature 2001 Feb 15; 2001, 409:860-921.
2.
Levinson G, Gutman G: High frequencies of short frameshifts in poly-CA/TG tandem repeats borne by bacteriophage M13 in Escherichia coli K-12. Nucleic Acids Res 1987, 15:5323-5338.
3.
Su S, Modrich P: Escherichia coli mutS-encoded protein binds to mismatched DNA base pairs. Proc Natl Acad Sci U S A 1986, 83:5057-5061.
4.
Kim TM, Laird PW, Park PJ: The landscape of microsatellite instability in colorectal and endometrial cancer genomes. Cell 2013, 155:858-868.
5.
Hause
RJ,
Pritchard
CC,
Shendure
J,
Salipante
SJ:
Classification
and
characterization of microsatellite instability across 18 cancer types. Nat Med 2016, 22:1342-1350. 6.
Togo G, Toda N, Kanai F, Kato N, Shiratori Y, Kishi K, Imazeki F, Makuuchi M, Omata M: A transforming growth factor beta type II receptor gene mutation common in sporadic cecum cancer with microsatellite instability. Cancer Res 1996, 56:5620-5623.
7.
Dung T. Le, Jennifer N. Durham, Kellie N. Smith, Hao Wang, Bjarne R. Bartlett, Laveet K. Aulakh, Steve Lu, Holly Kemberling, Cara Wilt, Brandon S. Luber, et al: Mismatch repair deficiency predicts response of solid tumors to PD-1 blockade. Science 2017, 357:409-413.
8.
Le DT, Uram JN, Wang H, Bartlett BR, Kemberling H, Eyring AD, Skora AD, Luber BS, Azad NS, Laheru D, et al: PD-1 blockade in tumors with mismatch-repair deficiency. N Engl J Med 2015, 372:2509-2520.
9.
Yarchoan M, Hopkins A, Jaffee EM: Tumor mutational burden and response rate to PD-1 inhibition. N Engl J Med 2017, 377:2500-2501.
10.
Chalmers ZR, Connelly CF, Fabrizio D, Gay L, Ali SM, Ennis R, Schrock A, Campbell B, Shlien A, Chmielecki J, et al: Analysis of 100,000 human cancer genomes reveals the landscape of tumor mutational burden. Genome Med 2017, 9:34.
11.
Marcus L, Lemery SJ, Keegan P, Pazdur R: FDA approval summary: pembrolizumab for the treatment of microsatellite instability-high solid tumors. Clin Cancer Res 2019, 25:3753-3758.
12.
Baudrin LG, Deleuze JF, How-Kit A: Molecular and computational methods for the detection of microsatellite instability in cancer. Front Oncol 2018, 8:621.
13.
Amemiya K, Hirotsu Y, Oyama T, Omata M: Simple and rapid method to obtain high-quality tumor DNA from clinical-pathological specimens using touch imprint
12
cytology. J Vis Exp 2018. 14.
Amemiya K, Hirotsu Y, Oyama T, Omata M: Relationship between formalin reagent and success rate of targeted sequencing analysis using formalin fixed paraffin embedded tissues. Clin Chim Acta 2019, 488:129-134.
15.
Hirotsu Y, Nakagomi H, Amemiya K, Oyama T, Inoue M, Mochizuki H, Omata M: Intrinsic HER2 V777L mutation mediates resistance to trastuzumab in a breast cancer patient. Med Oncol 2017, 34:3.
16.
Bacher J, Flanagan L, Smalley R, Nassif N, Burgart L, Halberg R, Megid W, Thibodeau S: Development of a fluorescent multiplex assay for detection of MSI-High tumors. Dis Markers 2004, 20:237-250.
17.
Bando H, Okamoto W, Fukui T, Yamanaka T, Akagi K, Yoshino T: Utility of the quasi-monomorphic variation range in unresectable metastatic colorectal cancer patients. Cancer Sci 2018, 109:3411-3415.
18.
Nakagomi T, Goto T, Hirotsu Y, Shikata D, Yokoyama Y, Higuchi R, Amemiya K, Okimoto K, Oyama T, Mochizuki H, Omata M: New therapeutic targets for pulmonary sarcomatoid carcinomas based on their genomic and phylogenetic profiles. Oncotarget 2018, 9:10635-10649.
19.
Takaoka S, Hirotsu Y, Ohyama H, Mochizuki H, Amemiya K, Oyama T, Ashizawa H, Yoshimura D, Nakagomi K, Hosoda K, et al: Molecular subtype switching in early-stage gastric cancers with multiple occurrences. J Gastroenterol 2019.
20.
Buhard O, Cattaneo F, Wong YF, Yim SF, Friedman E, Flejou JF, Duval A, Hamelin R: Multipopulation analysis of polymorphisms in five mononucleotide repeats used to determine the microsatellite instability status of human tumors. J Clin Oncol 2006, 24:241-251.
21.
Hashmi AA, Ali R, Hussain ZF, Faridi N, Khan EY, Bakar SMA, Edhi MM, Khan M: Mismatch repair deficiency screening in colorectal carcinoma by a four-antibody immunohistochemical panel in Pakistani population and its correlation with histopathological parameters. World J Surg Oncol 2017, 15:116.
22.
Hirotsu Y, Mochizuki H, Amemiya K, Ohyama H, Yoshimura D, Amano H, Miura Y, Ashizawa H, Nakagomi K, Takaoka S, et al: Deficiency of mismatch repair genes is less frequently observed in signet ring cell compared with non-signet ring cell gastric cancer. Medical Oncology 2019, 36.
23.
Bonneville R, Krook M, Kautto E, Miya J, Wing M, Chen H, Reeser J, Yu L, Roychowdhury S: Landscape of microsatellite instability across 39 cancer types. JCO Precis Oncol 2017.
24.
Hempelmann JA, Lockwood CM, Konnick EQ, Schweizer MT, Antonarakis ES, Lotan TL,
13
Montgomery B, Nelson PS, Klemfuss N, Salipante SJ, Pritchard CC: Microsatellite instability in prostate cancer by PCR or next-generation sequencing. J Immunother Cancer 2018, 6:29. 25.
Maruvka YE, Mouw KW, Karlic R, Parasuraman P, Kamburov A, Polak P, Haradhvala NJ, Hess JM, Rheinbay E, Brody Y, et al: Analysis of somatic microsatellite indels identifies driver events in human tumors. Nat Biotechnol 2017, 35:951-959.
26.
Cortes-Ciriano I, Lee S, Park WY, Kim TM, Park PJ: A molecular portrait of microsatellite instability across multiple cancers. Nat Commun 2017, 8:15180.
27.
Goto T, Hirotsu Y, Amemiya K, Nakagomi T, Shikata D, Yokoyama Y, Okimoto K, Oyama T, Mochizuki H, Omata M: Distribution of circulating tumor DNA in lung cancer: analysis of the primary lung and bone marrow along with the pulmonary venous and peripheral blood. Oncotarget 2017, 8:59268–59281.
28.
Iijima Y, Hirotsu Y, Amemiya K, Ooka Y, Mochizuki H, Oyama T, Nakagomi T, Uchida Y, Kobayashi Y, Tsutsui T, et al: Very early response of circulating tumour-derived DNA in plasma predicts efficacy of nivolumab treatment in patients with non-small cell lung cancer. Eur J Cancer 2017, 86:349-357.
29.
Hirotsu Y, Kojima Y, Okimoto K, Amemiya K, Mochizuki H, Omata M: Comparison between two amplicon-based sequencing panels of different scales in the detection of somatic mutations associated with gastric cancer. BMC Genomics 2016, 17:833.
30.
Sakamoto I, Hirotsu Y, Nakagomi H, Ouchi H, Ikegami A, Teramoto K, Amemiya K, Mochizuki H, Omata M: BRCA1 and BRCA2 mutations in Japanese patients with ovarian, fallopian tube, and primary peritoneal cancer. Cancer 2016, 122:84-90.
31.
Sing T, Sander O, Beerenwinkel N, Lengauer T: ROCR: visualizing classifier performance in R. Bioinformatics 2005, 21:3940-3941.
32.
Palmieri G, Colombino M, Cossu A, Marchetti A, Botti G, Ascierto PA: Genetic instability and increased mutational load: which diagnostic tool best direct patients with cancer to immunotherapy? J Transl Med 2017, 15:17.
33.
Chapusot C, Martin L, Bouvier AM, Bonithon-Kopp C, Ecarnot-Laubriet A, Rageot D, Ponnelle T, Laurent Puig P, Faivre J, Piard F: Microsatellite instability and intratumoural heterogeneity in 100 right-sided sporadic colon carcinomas. Br J Cancer 2002, 87:400-404.
34.
Watson N, Grieu F, Morris M, Harvey J, Stewart C, Schofield L, Goldblatt J, Iacopetta B: Heterogeneous staining for mismatch repair proteins during population-based prescreening for hereditary nonpolyposis colorectal cancer. J Mol Diagn 2007, 9:472-478.
14
35.
Frampton GM, Fichtenholtz A, Otto GA, Wang K, Downing SR, He J, Schnall-Levin M, White J, Sanford EM, An P, et al: Development and validation of a clinical cancer genomic profiling test based on massively parallel DNA sequencing. Nat Biotechnol 2013, 31:1023-1031.
36.
Niu B, Ye K, Zhang Q, Lu C, Xie M, McLellan MD, Wendl MC, Ding L: MSIsensor: microsatellite instability detection using paired tumor-normal sequence data. Bioinformatics 2014, 30:1015-1016.
37.
Salipante SJ, Scroggins SM, Hampel HL, Turner EH, Pritchard CC: Microsatellite instability detection by next generation sequencing. Clin Chem 2014, 60:1192-1199.
38.
Performance evaluation for rapid detection of pan-cancer microsatellite instability with MANTIS. Oncotarget 2017, 8:7452-7463.
39.
Huang MN, McPherson JR, Cutcutache I, Teh BT, Tan P, Rozen SG: MSIseq: software for assessing microsatellite instability from catalogs of somatic mutations. Sci Rep 2015, 5:13321.
40.
Wang C, Liang C: MSIpred: a python package for tumor microsatellite instability classification from tumor mutation annotation data using a support vector machine. Sci Rep 2018, 8:17546.
Figure Legends Figure 1. MSI scores in 184 tumor samples. (A) Pink box shows MSI-H tumor samples and gray box shows MSS tumor samples. (B) Box plot showed MSI scores in all tumor samples and tumor types (gastric, colorectal, endometrial, and urothelial bladder cancer). Figure 2. MSI score discriminates MSI-H and MSS tumors. (A) Line graph shows the relationship between MSI score and accuracy. Highest accuracy is achieved when the cutoff value of MSI score is 40.20. (B) Receiver operating characteristic (ROC) curve analysis. MSI-NGS with MSIcall achieved an area under the ROC curve (AUC) value of 0.9986. Figure 3. Discordant result from endometrial tumor. (A) Immunohistochemistry of mismatch repair proteins showing absent MLH1 and PMS2 protein expression in tumor nuclei. (B) Electrogram of microsatellite analysis of five markers. Arrows shows the shortened length of PCR products of BAT26 and MONO-27 markers. Figure 4. Validation of analytical performance. Fifty tumor samples were subjected to the MSI-NGS and bioinformatics algorithm ‘MSIcall’ to calculate the MSI score. MSI status of fifty samples was determined by the MSI-PCR and IHC in advance. MSI status was classified as MSS (n=44) and MSI-H (n=6) with 100% agreement. The blue line indicates a MSI score with a threshold of 40.
15
Table 1. Number of samples of tumor types. Table 2. MSI status was determined by MSI-PCR and IHC. Table 3. Comparison of MSI-NGS results and that of MSI-PCR and IHC. Supporting Information Supplemental Table 1. Target microsatellite loci in the panel. Supplemental Table 2. Bioinformatics pipelines to analyze microsatellite instability.
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*Highlights
1 2 3 4 5 6 7 8
Highlights ࣭MSI occurs in several solid cancer types and is predictive of response to immunotherapy. ࣭There are some issues with detection, immunohistochemistry PCR–capillary electrophoresis microsatellite markers, especially
the current gold standard methods for MSI (IHC) of mismatch repair proteins and (MSI-PCR) of mononucleotide repeat when verifying MSI status from ambiguous
14
data resulting from poor sample quality. ࣭We performed MSI-next-generation sequencing (MSI-NGS) combined with MSIcall, a bioinformatics tool, in 76 microsatellite loci in 184 formalin-fixed paraffin-embedded tumor from patients with various solid tumors. ࣭MSIcall predicted MSI scores with a specificity of 91.3%, a sensitivity of 100% and high accuracy (98.9%). ࣭We validated the additional 50 samples to classified MSI status with 100%
15
concordance.
9 10 11 12 13
16
1
Table 1
Table 1. Number of samples of tumor types Tumor type
n
Gastric cancer
53
Colorectal cancer
36
Breast cancer
23
Esophagus cancer
16
Pancreatic cancer
9
Endometrial cancer
7
Ovarian cancer
6
Urothelial bladder cancer
6
Cervical cancer
4
Hepatocellular carcinoma
3
Sarcoma
3
Small cell lung cancer
3
Cholangiocarcinoma
2
Gallbladder carcinoma
2
Germ cell tumor
1
Large cell neuroendocrine carcinoma
1
Nasopharyngeal cancer
1
Non-small cell lung cancer
1
Oral cancer
1
Paraganglioma
1
Prostate cancer
1
Salivary Gland Cancer
1
Small intestine cancer
1
Solitary Fibrous Tumor
1
Tongue cancer
1
Total
184
Table 2
Table 2. MSI status was determined by MSI-PCR and IHC
MSS by MSI-PCR and pMMR by IHC
158
MSI-H by MSI-PCR and dMMR by IHC
23
MSS by MSI-PCR
3*
Total
184
*These three cases had no available FFPE samples
Table 3
Table 3. Comparison of MSI-NGS results and that of MSI-PCR and IHC
MSI score of MSI-NGS MSI status determined
ᨪ
<40
≥40
MSS/pMMR
161
161
0
MSI-H/dMMR
23
2
21
Total
184
163
21
by MSI-PCR/IHC
160
200
(A)
Figure 1
100
50
0
100
50
0
MSS/ MSI-H/ pMMR dMMR (n=161) (n=23)
150
MSS/ pMMR (n=40)
MSI-H/ dMMR (n=13)
p<2.2x10-16
p<2.2x10-16
150
Gastric cancer (n=53)
All samples (n=184)
200
1
MSI-H
MSS
200
(B)
0
40
80
120
MSI score
MSI score
0
50
100
150
MSS/ pMMR (n=31)
MSI-H/ dMMR (n=5)
p=6.5x10-12
Colorectal cancer (n=36)
Number of samples
Figure 1
0
10
20
30
40
50
MSS/ pMMR (n=4)
MSI-H/ dMMR (n=3)
p=0.046
Endometrial cancer (n=7)
10
20
30
40
50
60
70
MSS/ pMMR (n=4)
MSI-H/ dMMR (n=2)
p=0.0085
Urothelial bladder cancer (n=6)
184
Accuracy
(A)
0.2
0.4
0.6
0.8
1.0
0
Figure 2
Figure 2
50
MSI score
100
150
200
(B)
True positive rate 0
0.2
0.4
0.6
0.8
1.0
0
0.4
0.6
0.8
False positive rate
0.2
1.0
Figure 3
(B)
(A)
BAT26
MLH1
Figure 3
NR21
MSH2
BAT25
MONO27
MSH6
NR24
PMS2
Figure 4
0
20
40
60
80
100
120
Figure 4
MSI score
1
Number of samples
MSI-H
MSS
50