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Mismatch Repair System Genomic Scars in Gastroesophageal Cancers: Biology and Clinical Testing

Gianluca Lopez
Konstantinos Venetis
Elham Sajjadi
1,† and
Nicola Fusco
Division of Pathology, IEO, European Institute of Oncology IRCCS, University of Milan, Via Giuseppe Ripamonti 435, 20141 Milan, Italy
School of Pathology, University of Milan, 20122 Milan, Italy
Department of Oncology and Hemato-Oncology, University of Milan, 20122 Milan, Italy
Ph.D. Program in Translational Medicine, University of Milan, 20133 Milan, Italy
Author to whom correspondence should be addressed.
These authors contributed equally to this article.
Gastrointest. Disord. 2020, 2(4), 341-352;
Submission received: 5 August 2020 / Revised: 21 September 2020 / Accepted: 23 September 2020 / Published: 28 September 2020


Alterations in the mismatch repair (MMR) system result in genomic instability, neoantigen production, and immune response in cancer. There is evidence that gastroesophageal tumors with MMR deficiency may be susceptible to immune-checkpoint inhibitors treatment, especially in those presenting at advanced-stage disease. Although a number of biomarkers have been developed in histology-agnostic settings to assess MMR status, there is evidence that a tumor-specific testing approach would improve the selection of patients for immunotherapy. However, no testing methods have been developed specifically for gastroesophageal cancers so far. Here, we discuss the state of the art, current advances, and future perspectives of MMR-related biomarkers’ biologic and clinical role in gastroesophageal cancers.

1. Introduction

The mismatch repair (MMR) system counteracts nucleotide-pair incongruities not corrected by the DNA polymerase proofreading activity, contributing to the DNA replication fidelity and overall genome stability [1]. Defects in the MMR system are commonly due to molecular alterations involving the MutS and MutL dimeric homologs, whose interaction leads to the altered bases recognition, excision, and resynthesis [1,2]. A frequent outcome of MMR deficiency (dMMR) is represented by microsatellite instability (MSI), a condition characterized by multiple alterations in the length of microsatellite regions [3]. Genomic scars in the MMR system are now known to underlie tumorigenesis and tumor progression due to the high level of accumulating mutations in dMMR cells [4].
Gastric cancer (GC) and gastroesophageal junction cancer (GEJC), jointly referred to as gastroesophageal cancers (GECs), are among the leading causes of cancer deaths [5]. In these tumors, dMMR has been described in 7–22% of cases; it represents a driver molecular event for tumorigenesis and tumor progression [6]. Several studies support the better prognosis of dMMR/MSI high (MSI-H) GEC patients, along with the higher response rate to immune-checkpoint inhibitors (ICI) [7,8,9,10]. During recent years, significant achievements have been made in the understanding of the biology of dMMR in GEC [7,11,12]. However, the value of dMMR/MSI testing for GEC management in real-life clinical practice remains a matter of controversy.
Here, we sought to provide a portrait of the current knowledge regarding the biology and clinical testing of MMR status in GEC. Particular emphasis will be given to (i) the intrinsic traits of dMMR GEC; (ii) the frequency and types of genomic scars in the MMR system in these tumors; (iii) the rationale, current armamentarium, and challenges for MMR/MSI clinical testing; and (iv) the ongoing and recently completed biomarker-based clinical trials using ICI in GEC patients.

2. Clinicopathologic and Molecular Features of MMR-Deficient Gastroesophageal Cancers

Comprehensive molecular analyses allowed for the classification of GEC into four groups characterized by either (i) Epstein–Barr virus (EBV) infection (9%), (ii) high MSI (MSI-H) (22%), (iii) genomic stability (20%), or (iv) chromosomal instability (50%) [13]. Among these molecular subtypes, dMMR/MSI-H GECs are associated with older age (median 72 years), female gender (56%), distal third of the stomach localization, intestinal-type histology, and lower stage [14]. Unlike EBVPOS GEC, MSI-H tumors usually lack actionable amplifications, such as those involving Janus kinase 2 (JAK2) and HER2 [15,16]. On the other hand, hotspot mutations in PIK3CA, HER2, and EGFR, akin pathogenic alterations in beta-2 microglobulin (B2M) from the antigen presentation pathway, are not uncommon in these patients [17,18]. In particular, B2M loss-of-function may promote a hypermutator phenotype and resistance to ICI [19]. It is not surprising that dMMR GECs, in comparison to MMR wild-type cases, show higher tumor-infiltrating lymphocytes (TILs) and overexpression of immune checkpoint molecules, such as programmed death-ligand 1 (PD-L1) [20]. Of note, MSI-H and PD-L1POS phenotype are major prognostic factors as well as predictive biomarkers for ICI efficacy in GEC [21,22,23]. Multiregion exome sequencing revealed > 20-fold levels of intratumor heterogeneity in dMMR GEC compared to other cancer types, and also long phylogenetic trunks [24].
MSI may be related to either inherited or acquired defects in the MMR system, the latter including somatic mutations, deep deletions, or epigenetic silencing [1,25,26]. A meta-analysis of 39 studies (patients n = 4895) highlighted the role of MutL homolog 1 (MLH1) promoter methylation as a founder genetic event in GEC [27]. In this study, the frequency of MLH1 epigenetic silencing was significantly higher in GEC compared to controls (odds ratio (OR) = 7.94; 95% confidence interval (95%CI) = 4.32–14.58; p < 0.001), being closely related to MSI (OR = 15.33; 95%CI = 9.26–25.36; p < 0.001) [27]. Furthermore, a meta-analysis involving 48 studies (patients n = 18,612) confirmed that patients with MSI-H GEC (n = 1718; 9.2%) had a significantly better prognosis compared to the microsatellite stable (MSS) counterparts (HR 0.69; 95%CI = 0.56–0.86; p < 0.001) [28]. Given the consistently better long-term outcome of intestinal-type MSI-H GEC, it has been proposed to classify these tumors into a distinct clinical cluster [29].

3. Types and Frequency of MMR Alterations in Gastroesophageal Cancers: Analysis of Publicly Available Genomic Datasets

The most common types of MMR genetic alterations occurring in GEC are represented by inactivating mutations and deep deletions of the four key genes involved in this system, i.e., MLH1, postmeiotic segregation increased 2 (PMS2), and MutS homologs 2 (MSH2) and 6 (MSH6) [24]. The analysis of high throughput genomic data from 8 different studies (patients total n = 973) [13,30,31,32,33,34,35,36,37] reveals that 75 (7.7%) GEC patients harbor mutations and/or deep deletions in MLH1 (n = 17, 1.8%), PMS2 (n = 14, 1.4%), MSH2 (n = 18, 1.9%), and/or MSH6 (n = 35, 3.8%), as depicted in Figure 1. The overall mutational count of these tumors is significantly higher in dMMR tumors (p < 0.001, Kruskal Wallis Test), with a median of 924.5 mutations/Mb (range 34–3185.5, interquartile range 338–1477) in these cases compared to 105 mutations/Mb in the pMMR samples (range 1–319, interquartile range 69–169), as shown in Supplementary Materials Figure S1. Interestingly, no somatic molecular alterations in other DNA repair genes were shared between the samples with the highest mutational count and those from the control group.
Taken together, the MutS complex was the most frequently altered MMR component, being targeted by mutations and/or deletions in 51 (68.0%) samples. Of these, 2 (2.7%) cases showed alterations in both MutS homologs, while 16 (21.3%) and 26 (34.7%) cases harbored private mutations in MSH2 and MSH6, respectively. Notably, none of the MutL-deficient tumors displayed mutations and/or deep deletions involving a protein-pair. Hence, 10 (13.3%) and 14 (18.7%) GEC were altered in PMS2 and MLH1, respectively. Finally, a subset of patients (n = 7, 9.3%) had both MutL and MutS complexes impaired, including 3 (4.0%) and 4 (5.3%) cases with MLH1/MSH6 and PMS2/MSH6 altered pairs, respectively. The relative prevalence of MMR alterations across the four components, as well as the co-occurrence of multiple mutations in different genes, are outlined in Figure 2.
Among the truncating, missense, and inframe mutations detected, the highest prevalence of likely loss-of-function alterations was observed for MLH1 and MSH2, while only the hotspot regions in positions 414 and 1070–1104 were targeted by likely oncogenic mutations in PMS2 and MSH6, respectively (Figure 3).
Statistically significant enrichments for titin (TTN), AT-rich interactive domain-containing protein 1A (ARID1A), histone-lysine N-methyltransferase 2D (KMT2D), and activin receptor type-2A (ACVR2A) were observed in dMMR tumors. In addition, dMMR GEC showed enrichments also for genes belonging to the EGFR family, PI3K family, and immune-checkpoint pathways, as shown in Table 1 and Supplementary Materials Figure S2. In this original analysis of the publicly available genomic data, we only considered individual mutations and the overall tumor mutation burden. For this reason, information on the gene signatures as well as forest plot analysis is not provided here.

4. MMR Clinical Testing in GEC: Rationale, Currently Available Strategies, Unaddressed Issues

The assessment of MMR status in GEC has proven to be challenging due to limitations of the existing methods and the substantial absence of companion diagnostic (CDx) tests and/or tumor-specific guidelines [1,6,38,39]. Analysis of the protein expression in the neoplastic cells using antibodies against the four MMR components by means of immunohistochemistry and MSI testing by polymerase chain reaction (PCR) and or next-generation sequencing (NGS) is widely employed [40]. Importantly, not all MMR proteins evenly affect either mutational load or MSI when deficient [41,42]. There are indications on the clinical value of MMR immunohistochemistry in GEC prognostication. However, several issues related to technical difficulties and/or tumor-specific characteristics (e.g. intratumor heterogeneity) have to be addressed [43,44]. In this scenario, MSI testing can be helpful [45]. Similar to colorectal cancer, the original MSI Bethesda panel which consists of five microsatellite repeats, including two mononucleotide repeats (i.e. BAT25 and BAT26) and three dinucleotide repeats (i.e. D2S123, D5S346, and D17S250) is being employed [46]. However, many Centers have recently adopted panels consisting of mononucleotide markers due to their higher sensitivity and specificity compared to dinucleotide markers [47]. Unlike in other types of tumors (e.g., breast cancer), there is a general agreement on the high correlation between MSI testing and MMR immunohistochemistry in GEC [48,49,50,51,52,53]. Epigenetic silencing of MLH1 by promoter hypermethylation is a crucial event that leads to dMMR both in the sporadic setting (more than 50% of MSI GCs) and in familial MSI GEC cases [43,54]. Methylation-specific PCR for the 5′ CpG promoter of MLH1 is primarily carried out for the assessment of this condition [55], but is not commonly used in routine diagnostic processes. NGS-based methods for the detection of microsatellite status are being already performed in GEC [56]. Despite the advantages such as the coverage of a broader range of microsatellite loci, to date, this technique is more time-consuming and not as cost-effective as the traditional PCR analysis method. Tumor mutation burden (TMB) is an emerging biomarker that could be used as a surrogate biomarker for DNA repair alterations [57]. TMB analysis requires suitable NGS panels as well as guidelines on the genes to include in the TMB count. Both the well-validated and emerging methods represent candidate tools for MMR testing in GEC, but they need to be profoundly tested in order to overcome existing limitations and make routine testing feasible.

5. MMR Status Assessment to Select GEC Patients for Immunotherapy

The role of MSI as a predictive biomarker is clinically relevant in GEC [13]. To inform ICI treatment, this analysis should be performed for all the histologic subtypes [26,58]. The MAGIC trial, by studying dMMR and MSI-H individuals, showed a positive prognostic effect on surgically-treated GEC and a fairly negative prognostic effect in chemotherapy-treated patients [8]. The opposite prognostic value in surgical against cytotoxic settings highlights the different significance of MMR/MSI alterations in relation to disease stage, and therefore, to its evolution. The need for alternatives to traditional regimens of chemotherapy in advanced-stage settings is evident.
Lately, the U.S. Food and Drugs Administration (FDA) granted accelerated approval of pembrolizumab for the treatment of adult and pediatric patients with unresectable or metastatic MSI-H and/or dMMR solid tumors that continued to progress after conventional treatment, irrespective of the tumors’ primary site [59,60]. Later on, the FDA approved pembrolizumab for the treatment of patients with recurrent locally advanced or metastatic GEC [59,61]. According to KEYNOTE 059—a multicohort study of pembrolizumab monotherapy in advanced GC treatment—a higher objective response rate (ORR) was seen in patients with MSI-H tumors (57.1%) in comparison to patients with non-MSI-H tumors (9%) [62]. Although ORR was higher in patients with MSI-H tumors, the prevalence of these tumors in this cohort was very low (7/174, 4%), and overall, the majority of responses were found in non-MSI-high patients [62]. Based on this study, Kim et al. reported that MSI-H and EBVPOS individuals are more probable to respond to pembrolizumab [63]. Among all MSI-H responders, only one out of seven failed to respond to this ICI therapy. Further analysis of this single case showed that a lack of response could be associated with MLH1 heterogeneity [63]. Considering the tumor heterogeneity of GC, ctDNA measurement has been suggested as an operative solution for optimal patient selection in immunotherapy [64]. Recent results from the KEYNOTE-062—a phase III clinical trial that compares pembrolizumab with or without chemotherapy versus chemotherapy in GEC patients—revealed that MSI-H tumors treated with pembrolizumab have better outcomes (Table 2) [65]. Accordingly, a combination of pembrolizumab with chemotherapy compared to chemotherapy alone is related to a greater ORR (64.7% versus 36.8%). Moreover, in the MSI-H population, the median PFS in pembrolizumab therapy, 11.2 months (95%CI, 1.5-NR), is almost double that of chemotherapy—6.6 months (95%CI, 4.4–8.3) [65].
The aforementioned studies have highlighted the clinical importance of MMR/MSI status in ICI therapy in GC. Added to these, phase II ongoing clinical trials, (NCT03959293) and (NCT04152889) are currently assessing the combination of chemotherapy and immunotherapy in GEC patients with MSS or MSI status (Table 2). The former aims to assess the rate of patients without progression at 4 months, treated with FOLFIRI plus durvalumab versus FOLFIRI plus durvalumab with tremelimumab. The latter evaluates the safety of camrelizumab in combination with docetaxel + S-1 as an adjuvant treatment therapy in PD-L1pos, MSI-H, dMMR, and EBVPOS individuals.
On the other hand, further ongoing clinical trials are considering MMR/MSI status as a biomarker for patient selection in ICI studies. Patients with unresectable or metastatic solid tumors with MSI-H/dMMR, including GC, are eligible. For example, phase IIb (NCT03228667) and phase I/II (NCT03126110) trials are currently evaluating the response profile (ORR, OS, PFS) after receiving ICI combination therapy with ALT-803 (an IL-15 superagonist [66]) and INCAGN01876 (agonist antibody leading to T cell proliferation [67]), respectively, in MSI-H/dMMR cancers. Other MSI-H/dMMR based clinical trials aim to assess the dosing levels of different agents in combination with ICIs therapy. For instance, DSP-7888 (a peptide cancer vaccine that comprises peptides derived from the Wilms tumor gene 1 (WT1) protein [68]) (NCT03311334) and Regorafenib (NCT03475953).
A phase II ongoing clinical trial (NCT02589496) has primarily reported MSI as a positive prognostic biomarker (Table 2). This study assesses the effect of pembrolizumab on advanced GEC (with a progression after first-line therapy-platinum and fluoropyrimidine). Patients are divided into four subgroups of I) epithelial MSS/TP53 inactive, II) epithelial MSS/TP53 active, III) MSI, and IV) mesenchymal. Interestingly, the MSI subgroup has shown the most promising survival of 5.6 years among other subgroups. All these findings highlight the significance of genomic scars in ICI therapy.

6. Future Prospects for MMR Testing and Precision Cancer Medicine

Based on the results of the recent clinical and translational studies, the role of MMR in the clinical setting of GEC is expected to become more and more important due to the predictive implications in terms of ICI therapies. NGS potentially harbors a tremendous impact in the study of MMR status, since it can identify mutations, assess promoter methylation, and potentially identify genetic alterations in other genes involved in the immune-checkpoint and related pathways. The standardization of methodologies remains of extreme importance, both in terms of interlaboratory methodologies, and in terms of interassays significance and interpretation. Immunohistochemistry for MMR proteins is likely to remain in effect for some time, given its low costs and widely adopted usage; the same applies to MSI analysis. The TMB and neoantigen-density assays could prove to be more comprehensive assays in terms of evaluation of eligibility to ICI therapy; further clinical trials are needed in order to fully assess their potential.

Supplementary Materials

The following are available online at, Figure S1: Mutational count (mut/Mb) in MMR mutated versus MMR wild-type GEC in The Cancer Genome Atlas Network cohorts. Figure S2: Comparison between MMR-altered vs MMR-unaltered groups of genetic alteration frequency of the 5 most frequently altered genes in our analysis (A), EGFR family genes (B), PI3K/Akt family genes (C), and PD-1/PD-L1 genes (D).

Author Contributions

Conceptualization, N.F.; methodology, G.L., N.F.; formal analysis, G.L., K.V., E.S., N.F.; data curation, K.V., E.S.; writing—original draft preparation, G.L., K.V., E.S.; writing—review and editing, K.V., E.S., N.F.; visualization and image processing, G.L., K.V., N.F.; supervision, N.F.; project administration, N.F. All authors have read and agreed to the published version of the manuscript.


This research received no external funding.

Conflicts of Interest

N.F. received honoraria for a consulting/advisory role from Merck Sharp & Dohme (MSD), Boehringer Ingelheim, and Novartis. These companies had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; and/or in the decision to publish the results. All the other authors declare no conflicts of interest.


MMRMismatch repair
dMMRMMR deficiency
MSIMicrosatellite instability
GECGastroesophageal cancers


  1. Corti, C.; Sajjadi, E.; Fusco, N. Determination of Mismatch Repair Status in Human Cancer and Its Clinical Significance: Does One Size Fit All? Adv. Anat. Pathol. 2019, 26, 270–279. [Google Scholar] [CrossRef] [PubMed]
  2. Bradford, K.C.; Wilkins, H.; Hao, P.; Li, Z.M.; Wang, B.; Burke, D.; Wu, D.; Smith, A.E.; Spaller, L.; Du, C.; et al. Dynamic human MutSα–MutLα complexes compact mismatched DNA. Proc. Natl. Acad. Sci. USA 2020, 117, 16302–16312. [Google Scholar] [CrossRef] [PubMed]
  3. Li, K.; Luo, H.; Huang, L.; Zhu, X. Microsatellite instability: a review of what the oncologist should know. Cancer Cell Int. 2020, 20, 16. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  4. Huang, Y.; Li, G.M. DNA mismatch repair in the context of chromatin. Cell Biosci. 2020, 10, 10. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  5. Bray, F.; Ferlay, J.; Soerjomataram, I.; Siegel, R.L.; Torre, L.A.; Jemal, A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA A Cancer J. Clin. 2018, 68, 394–424. [Google Scholar] [CrossRef] [Green Version]
  6. Yuza, K.; Nagahashi, M.; Watanabe, S.; Takabe, K.; Wakai, T. Hypermutation and microsatellite instability in gastrointestinal cancers. Oncotarget 2017, 8, 112103–112115. [Google Scholar] [CrossRef] [Green Version]
  7. Kim, S.M.; An, J.Y.; Byeon, S.J.; Lee, J.; Kim, K.M.; Choi, M.G.; Lee, J.H.; Sohn, T.S.; Bae, J.M.; Kim, S. Prognostic value of mismatch repair deficiency in patients with advanced gastric cancer, treated by surgery and adjuvant 5-fluorouracil and leucovorin chemoradiotherapy. Eur. J. Surg. Oncol. 2020, 46, 189–194. [Google Scholar] [CrossRef] [Green Version]
  8. Smyth, E.C.; Wotherspoon, A.; Peckitt, C.; Gonzalez, D.; Hulkki-Wilson, S.; Eltahir, Z.; Fassan, M.; Rugge, M.; Valeri, N.; Okines, A.; et al. Mismatch Repair Deficiency, Microsatellite Instability, and Survival: An Exploratory Analysis of the Medical Research Council Adjuvant Gastric Infusional Chemotherapy (MAGIC) Trial. JAMA Oncol. 2017, 3, 1197–1203. [Google Scholar] [CrossRef] [Green Version]
  9. Sundar, R.; Smyth, E.C.; Peng, S.; Yeong, J.P.S.; Tan, P. Predictive Biomarkers of Immune Checkpoint Inhibition in Gastroesophageal Cancers. Front. Oncol. 2020, 10, 763. [Google Scholar] [CrossRef]
  10. Brar, G.; Shah, M.A. The role of pembrolizumab in the treatment of PD-L1 expressing gastric and gastroesophageal junction adenocarcinoma. Therap. Adv. Gastroenterol. 2019, 12, 1756284819869767. [Google Scholar] [CrossRef] [Green Version]
  11. An, J.Y.; Choi, Y.Y.; Lee, J.; Hyung, W.J.; Kim, K.M.; Noh, S.H.; Choi, M.G.; Cheong, J.H. A Multi-cohort Study of the Prognostic Significance of Microsatellite Instability or Mismatch Repair Status after Recurrence of Resectable Gastric Cancer. Cancer Res. Treat. 2020. [Google Scholar] [CrossRef] [PubMed]
  12. Zhang, Q.; Wang, L.; Ni, S.; Tan, C.; Cai, X.; Huang, D.; Sheng, W. Clinicopathological features and prognostic value of mismatch repair protein deficiency in gastric cancer. Int. J. Clin. Exp. Pathol. 2018, 11, 2579–2587. [Google Scholar] [PubMed]
  13. Bass, A.J.; Thorsson, V.; Shmulevich, I.; Reynolds, S.M.; Miller, M.; Bernard, B.; Hinoue, T.; Laird, P.W.; Curtis, C.; Shen, H.; et al. Comprehensive molecular characterization of gastric adenocarcinoma. Nature 2014, 513, 202–209. [Google Scholar] [CrossRef] [Green Version]
  14. Puhr, H.C.; Ilhan-Mutlu, A. Molecular profiling in gastroesophageal cancer—Clinical routine and future perspective. Memo Mag. Eur. Med. Oncol. 2019. [Google Scholar] [CrossRef] [Green Version]
  15. Morales-Sanchez, A.; Fuentes-Panana, E.M. Epstein-Barr Virus-associated Gastric Cancer and Potential Mechanisms of Oncogenesis. Curr. Cancer Drug Targets 2017, 17, 534–554. [Google Scholar] [CrossRef]
  16. Fusco, N.; Bosari, S. HER2 aberrations and heterogeneity in cancers of the digestive system: Implications for pathologists and gastroenterologists. World J. Gastroenterol. 2016, 22, 7926–7937. [Google Scholar] [CrossRef]
  17. Patel, T.H.; Cecchini, M. Targeted Therapies in Advanced Gastric Cancer. Curr. Treat Options Oncol. 2020, 21, 70. [Google Scholar] [CrossRef]
  18. Park, Y.; Koh, J.; Kwak, Y.; Ahn, S.H.; Park, D.J.; Kim, H.H.; Kim, W.H.; Lee, H.S. Clinicopathologic significance of human leukocyte antigen class I expression in patients with stage II and III gastric cancer. Cancer Immunol. Immunother. 2019, 68, 1779–1790. [Google Scholar] [CrossRef]
  19. Torrejon, D.Y.; Abril-Rodriguez, G.; Champhekar, A.S.; Tsoi, J.; Campbell, K.M.; Kalbasi, A.; Parisi, G.; Zaretsky, J.M.; Garcia-Diaz, A.; Puig-Saus, C.; et al. Overcoming Genetically Based Resistance Mechanisms to PD-1 Blockade. Cancer Discov. 2020, 10, 1140–1157. [Google Scholar] [CrossRef]
  20. Vrána, D.; Matzenauer, M.; Neoral, Č.; Aujeský, R.; Vrba, R.; Melichar, B.; Rušarová, N.; Bartoušková, M.; Jankowski, J. From Tumor Immunology to Immunotherapy in Gastric and Esophageal Cancer. Int. J. Mol. Sci. 2018, 20, 13. [Google Scholar] [CrossRef] [Green Version]
  21. Coutzac, C.; Pernot, S.; Chaput, N.; Zaanan, A. Immunotherapy in advanced gastric cancer, is it the future? Crit. Rev. Oncol. Hematol. 2019, 133, 25–32. [Google Scholar] [CrossRef] [PubMed]
  22. Serra, O.; Smyth, E.C.; Lordick, F. Progress and challenges in gastroesophageal cancer. Curr. Probl. Cancer 2020, 100590. [Google Scholar] [CrossRef]
  23. Pagni, F.; Guerini-Rocco, E.; Schultheis, A.M.; Grazia, G.; Rijavec, E.; Ghidini, M.; Lopez, G.; Venetis, K.; Croci, G.A.; Malapelle, U.; et al. Targeting Immune-Related Biological Processes in Solid Tumors: We do Need Biomarkers. Int. J. Mol. Sci. 2019, 20, 5452. [Google Scholar] [CrossRef] [Green Version]
  24. von Loga, K.; Woolston, A.; Punta, M.; Barber, L.J.; Griffiths, B.; Semiannikova, M.; Spain, G.; Challoner, B.; Fenwick, K.; Simon, R.; et al. Extreme intratumour heterogeneity and driver evolution in mismatch repair deficient gastro-oesophageal cancer. Nat. Commun. 2020, 11, 139. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  25. Cohen, R.; Buhard, O.; Cervera, P.; Hain, E.; Dumont, S.; Bardier, A.; Bachet, J.B.; Gornet, J.M.; Lopez-Trabada, D.; Kaci, R.; et al. Clinical and molecular characterisation of hereditary and sporadic metastatic colorectal cancers harbouring microsatellite instability/DNA mismatch repair deficiency. Eur. J. Cancer 2017, 86, 266–274. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  26. Eso, Y.; Shimizu, T.; Takeda, H.; Takai, A.; Marusawa, H. Microsatellite instability and immune checkpoint inhibitors: toward precision medicine against gastrointestinal and hepatobiliary cancers. J. Gastroenterol. 2020, 55, 15–26. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  27. Ye, P.; Shi, Y.; Li, A. Association Between hMLH1 Promoter Methylation and Risk of Gastric Cancer: A Meta-Analysis. Front. Physiol. 2018, 9, 368. [Google Scholar] [CrossRef] [PubMed]
  28. Polom, K.; Marano, L.; Marrelli, D.; De Luca, R.; Roviello, G.; Savelli, V.; Tan, P.; Roviello, F. Meta-analysis of microsatellite instability in relation to clinicopathological characteristics and overall survival in gastric cancer. Br. J. Surg. 2018, 105, 159–167. [Google Scholar] [CrossRef] [PubMed]
  29. Kim, H.; An, J.Y.; Noh, S.H.; Shin, S.K.; Lee, Y.C. High microsatellite instability predicts good prognosis in intestinal-type gastric cancers. J. Gastroenterol. Hepatol. 2011, 26, 585–592. [Google Scholar] [CrossRef] [PubMed]
  30. Dulak, A.M.; Stojanov, P.; Peng, S.; Lawrence, M.S.; Fox, C.; Stewart, C.; Bandla, S.; Imamura, Y.; Schumacher, S.E.; Shefler, E.; et al. Exome and whole-genome sequencing of esophageal adenocarcinoma identifies recurrent driver events and mutational complexity. Nat. Genet. 2013, 45, 478–486. [Google Scholar] [CrossRef]
  31. Broad, I.; Brown, U. Integrated genomic characterization of oesophageal carcinoma. Nature 2017, 541, 169–175. [Google Scholar] [CrossRef] [Green Version]
  32. Chen, K.; Yang, D.; Li, X.; Sun, B.; Song, F.; Cao, W.; Brat, D.J.; Gao, Z.; Li, H.; Liang, H.; et al. Mutational landscape of gastric adenocarcinoma in Chinese: implications for prognosis and therapy. Proc. Natl. Acad. Sci. USA 2015, 112, 1107–1112. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  33. Wang, K.; Yuen, S.T.; Xu, J.; Lee, S.P.; Yan, H.H.; Shi, S.T.; Siu, H.C.; Deng, S.; Chu, K.M.; Law, S.; et al. Whole-genome sequencing and comprehensive molecular profiling identify new driver mutations in gastric cancer. Nat. Genet. 2014, 46, 573–582. [Google Scholar] [CrossRef] [PubMed]
  34. Kakiuchi, M.; Nishizawa, T.; Ueda, H.; Gotoh, K.; Tanaka, A.; Hayashi, A.; Yamamoto, S.; Tatsuno, K.; Katoh, H.; Watanabe, Y.; et al. Recurrent gain-of-function mutations of RHOA in diffuse-type gastric carcinoma. Nat. Genet. 2014, 46, 583–587. [Google Scholar] [CrossRef] [PubMed]
  35. Wang, K.; Kan, J.; Yuen, S.T.; Shi, S.T.; Chu, K.M.; Law, S.; Chan, T.L.; Kan, Z.; Chan, A.S.; Tsui, W.Y.; et al. Exome sequencing identifies frequent mutation of ARID1A in molecular subtypes of gastric cancer. Nat. Genet. 2011, 43, 1219–1223. [Google Scholar] [CrossRef] [PubMed]
  36. Broad Institute Firehose. Stomach Adenocarcinoma (STAD); Broad Institute Firehose: Cambridge, MA, USA, 2020. [Google Scholar]
  37. Hoadley, K.A.; Yau, C.; Hinoue, T.; Wolf, D.M.; Lazar, A.J.; Drill, E.; Shen, R.; Taylor, A.M.; Cherniack, A.D.; Thorsson, V.; et al. Cell-of-Origin Patterns Dominate the Molecular Classification of 10,000 Tumors From 33 Types of Cancer. Cell 2018, 173. [Google Scholar] [CrossRef] [Green Version]
  38. van Velzen, M.J.M.; Derks, S.; van Grieken, N.C.T.; Haj Mohammad, N.; van Laarhoven, H.W.M. MSI as a predictive factor for treatment outcome of gastroesophageal adenocarcinoma. Cancer Treat Rev. 2020, 86, 102024. [Google Scholar] [CrossRef]
  39. Tessier-Cloutier, B.; Cai, E.; Schaeffer, D.F. Off-label use of common predictive biomarkers in gastrointestinal malignancies: a critical appraisal. Diagn. Pathol. 2019, 14, 62. [Google Scholar] [CrossRef] [Green Version]
  40. Cho, J.; Kang, S.Y.; Kim, K.M. MMR protein immunohistochemistry and microsatellite instability in gastric cancers. Pathology 2019, 51, 110–113. [Google Scholar] [CrossRef]
  41. McCarthy, A.J.; Capo-Chichi, J.M.; Spence, T.; Grenier, S.; Stockley, T.; Kamel-Reid, S.; Serra, S.; Sabatini, P.; Chetty, R. Heterogenous loss of mismatch repair (MMR) protein expression: a challenge for immunohistochemical interpretation and microsatellite instability (MSI) evaluation. J. Pathol. Clin. Res. 2019, 5, 115–129. [Google Scholar] [CrossRef]
  42. Venetis, K.; Sajjadi, E.; Haricharan, S.; Fusco, N. Mismatch repair testing in breast cancer: the path to tumor-specific immuno-oncology biomarkers. Transl. Cancer Res. 2020, 9, 4060–4064. [Google Scholar] [CrossRef]
  43. Ratti, M.; Lampis, A.; Hahne, J.C.; Passalacqua, R.; Valeri, N. Microsatellite instability in gastric cancer: molecular bases, clinical perspectives, and new treatment approaches. Cell Mol. Life Sci. 2018, 75, 4151–4162. [Google Scholar] [CrossRef] [PubMed]
  44. Lee, H.J.; Jang, Y.J.; Lee, E.J.; Kim, J.H.; Park, S.S.; Park, S.H.; Kim, C.S.; Mok, Y.J. The significance of mismatch repair genes in gastric cancer. J. Cancer Res. Ther. 2013, 9, 80–83. [Google Scholar] [CrossRef] [PubMed]
  45. Chen, W.; Frankel, W.L. A practical guide to biomarkers for the evaluation of colorectal cancer. Mod. Pathol. 2019, 32, 1–15. [Google Scholar] [CrossRef]
  46. Boland, C.R.; Thibodeau, S.N.; Hamilton, S.R.; Sidransky, D.; Eshleman, J.R.; Burt, R.W.; Meltzer, S.J.; Rodriguez-Bigas, M.A.; Fodde, R.; Ranzani, G.N.; et al. A National Cancer Institute Workshop on Microsatellite Instability for cancer detection and familial predisposition: development of international criteria for the determination of microsatellite instability in colorectal cancer. Cancer Res. 1998, 58, 5248–5257. [Google Scholar]
  47. Buhard, O.; Suraweera, N.; Lectard, A.; Duval, A.; Hamelin, R. Quasimonomorphic Mononucleotide Repeats for High-Level Microsatellite Instability Analysis. Dis. Markers 2004, 20, 159347. [Google Scholar] [CrossRef] [Green Version]
  48. Murphy, K.M.; Zhang, S.; Geiger, T.; Hafez, M.J.; Bacher, J.; Berg, K.D.; Eshleman, J.R. Comparison of the microsatellite instability analysis system and the Bethesda panel for the determination of microsatellite instability in colorectal cancers. J. Mol. Diagn. 2006, 8, 305–311. [Google Scholar] [CrossRef] [Green Version]
  49. Bacani, J.; Zwingerman, R.; Di Nicola, N.; Spencer, S.; Wegrynowski, T.; Mitchell, K.; Hay, K.; Redston, M.; Holowaty, E.; Huntsman, D.; et al. Tumor microsatellite instability in early onset gastric cancer. J. Mol. Diagn. 2005, 7, 465–477. [Google Scholar] [CrossRef] [Green Version]
  50. Fusco, N.; Lopez, G.; Corti, C.; Pesenti, C.; Colapietro, P.; Ercoli, G.; Gaudioso, G.; Faversani, A.; Gambini, D.; Michelotti, A.; et al. Mismatch Repair Protein Loss as a Prognostic and Predictive Biomarker in Breast Cancers Regardless of Microsatellite Instability. JNCI Cancer Spectr. 2018, 2, pky056. [Google Scholar] [CrossRef] [Green Version]
  51. Lopez, G.; Noale, M.; Corti, C.; Gaudioso, G.; Sajjadi, E.; Venetis, K.; Gambini, D.; Runza, L.; Costanza, J.; Pesenti, C.; et al. PTEN Expression as a Complementary Biomarker for Mismatch Repair Testing in Breast Cancer. Int. J. Mol. Sci. 2020, 21, 1461. [Google Scholar] [CrossRef] [Green Version]
  52. Lopez, G.; Fusco, N. RE: Mismatch repair protein loss in breast cancer: clinicopathological associations in a large British Columbia cohort. Breast Cancer Res. Treat 2020. [Google Scholar] [CrossRef] [PubMed]
  53. Chen, M.L.; Chen, J.Y.; Hu, J.; Chen, Q.; Yu, L.X.; Liu, B.R.; Qian, X.P.; Yang, M. Comparison of microsatellite status detection methods in colorectal carcinoma. Int. J. Clin. Exp. Pathol. 2018, 11, 1431–1438. [Google Scholar] [PubMed]
  54. Battaglin, F.; Naseem, M.; Puccini, A.; Lenz, H.-J. Molecular biomarkers in gastro-esophageal cancer: recent developments, current trends and future directions. Cancer Cell Int. 2018, 18, 99. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  55. Leite, M.; Corso, G.; Sousa, S.; Milanezi, F.; Afonso, L.P.; Henrique, R.; Soares, J.M.; Castedo, S.; Carneiro, F.; Roviello, F.; et al. MSI phenotype and MMR alterations in familial and sporadic gastric cancer. Int. J. Cancer 2011, 128, 1606–1613. [Google Scholar] [CrossRef]
  56. Weinberg, B.A.; Xiu, J.; Hwang, J.J.; Shields, A.F.; Salem, M.E.; Marshall, J.L. Immuno-Oncology Biomarkers for Gastric and Gastroesophageal Junction Adenocarcinoma: Why PD-L1 Testing May Not Be Enough. Oncol. 2018, 23, 1171–1177. [Google Scholar] [CrossRef] [Green Version]
  57. Lin, E.M.; Gong, J.; Klempner, S.J.; Chao, J. Advances in immuno-oncology biomarkers for gastroesophageal cancer: Programmed death ligand 1, microsatellite instability, and beyond. World J. Gastroenterol. 2018, 24, 2686–2697. [Google Scholar] [CrossRef]
  58. Haag, G.M.; Czink, E.; Ahadova, A.; Schmidt, T.; Sisic, L.; Blank, S.; Heger, U.; Apostolidis, L.; Berger, A.K.; Springfeld, C.; et al. Prognostic significance of microsatellite-instability in gastric and gastroesophageal junction cancer patients undergoing neoadjuvant chemotherapy. Int. J. Cancer 2019, 144, 1697–1703. [Google Scholar] [CrossRef]
  59. Prasad, V.; Kaestner, V.; Mailankody, S. Cancer Drugs Approved Based on Biomarkers and Not Tumor Type-FDA Approval of Pembrolizumab for Mismatch Repair-Deficient Solid Cancers. JAMA Oncol. 2018, 4, 157–158. [Google Scholar] [CrossRef]
  60. du Rusquec, P.; de Calbiac, O.; Robert, M.; Campone, M.; Frenel, J.S. Clinical utility of pembrolizumab in the management of advanced solid tumors: an evidence-based review on the emerging new data. Cancer Manag. Res. 2019, 11, 4297–4312. [Google Scholar] [CrossRef] [Green Version]
  61. U.S. Food and Drug Administration. FDA Grants Accelerated Approval to Pembrolizumab for Advanced Gastric Cancer; FDA: Washington, DC, USA, 2017. [Google Scholar]
  62. Fuchs, C.S.; Doi, T.; Jang, R.W.; Muro, K.; Satoh, T.; Machado, M.; Sun, W.; Jalal, S.I.; Shah, M.A.; Metges, J.P.; et al. Safety and Efficacy of Pembrolizumab Monotherapy in Patients with Previously Treated Advanced Gastric and Gastroesophageal Junction Cancer: Phase 2 Clinical KEYNOTE-059 Trial. JAMA Oncol. 2018, 4, e180013. [Google Scholar] [CrossRef]
  63. Kim, S.T.; Cristescu, R.; Bass, A.J.; Kim, K.M.; Odegaard, J.I.; Kim, K.; Liu, X.Q.; Sher, X.; Jung, H.; Lee, M.; et al. Comprehensive molecular characterization of clinical responses to PD-1 inhibition in metastatic gastric cancer. Nat. Med. 2018, 24, 1449–1458. [Google Scholar] [CrossRef] [PubMed]
  64. Zaanan, A.; Taieb, J. How to better select patients with advanced gastric cancer for immunotherapy. Transl. Gastroenterol. Hepatol. 2019, 4, 6. [Google Scholar] [PubMed]
  65. Shitara, K.; Van Cutsem, E.; Bang, Y.; Fuchs, C.S.; Wyrwicz, L.; Lee, K.W.; Kudaba, I.; Garrido, M.; Cheol Chung, H.; Castro, H.R.; et al. Pembrolizumab With or Without Chemotherapy vs Chemotherapy in Patients With Advanced G/GEJ Cancer (GC) Including Outcomes According to Microsatellite Instability-High (MSI-H) Status in KEYNOTE-062. Ann Oncol. 2019, 30 (Suppl. 5), v-878–v-879. [Google Scholar] [CrossRef]
  66. Wrangle, J.M.; Velcheti, V.; Patel, M.R.; Garrett-Mayer, E.; Hill, E.G.; Ravenel, J.G.; Miller, J.S.; Farhad, M.; Anderton, K.; Lindsey, K.; et al. ALT-803, an IL-15 superagonist, in combination with nivolumab in patients with metastatic non-small cell lung cancer: a non-randomised, open-label, phase 1b trial. Lancet Oncol. 2018, 19, 694–704. [Google Scholar] [CrossRef] [PubMed]
  67. Santuray, R.T.; Johnson, D.E.; Grandis, J.R. New Therapies in Head and Neck Cancer. Trends Cancer 2018, 4, 385–396. [Google Scholar] [CrossRef] [PubMed]
  68. Cuoco, J.A.; Benko, M.J.; Busch, C.M.; Rogers, C.M.; Prickett, J.T.; Marvin, E.A. Vaccine-Based Immunotherapeutics for the Treatment of Glioblastoma: Advances, Challenges, and Future Perspectives. World Neurosurg. 2018, 120, 302–315. [Google Scholar] [CrossRef]
Figure 1. Oncoprint visualization of missense and truncating mutations, fusions, and deep deletions of the four main mismatch repair (MMR) genes in gastroesophageal adenocarcinomas. In this analysis involving 8 different datasets available at (patients n = 973; samples n = 973), MSH6 was the most frequently altered gene with more than a two-fold frequency compared to the other MMR genes (i.e., MSH1, MLH1, and PMS2). Taken together, 75 (7.7%) samples were found to be altered in the queried genes. These tumors showed a higher mutation count. Each column represents a patient/sample and was sorted for the magnitude of alteration types in the queried genes. The types of alterations and mutation spectrum are color-coded as shown in the legend; the green bar plot refers to the mutation count, whose minimum and maximum values are reported in the green scale.
Figure 1. Oncoprint visualization of missense and truncating mutations, fusions, and deep deletions of the four main mismatch repair (MMR) genes in gastroesophageal adenocarcinomas. In this analysis involving 8 different datasets available at (patients n = 973; samples n = 973), MSH6 was the most frequently altered gene with more than a two-fold frequency compared to the other MMR genes (i.e., MSH1, MLH1, and PMS2). Taken together, 75 (7.7%) samples were found to be altered in the queried genes. These tumors showed a higher mutation count. Each column represents a patient/sample and was sorted for the magnitude of alteration types in the queried genes. The types of alterations and mutation spectrum are color-coded as shown in the legend; the green bar plot refers to the mutation count, whose minimum and maximum values are reported in the green scale.
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Figure 2. Frequency and patterns of MMR genes alterations across 75 MMR deficient gastroesophageal adenocarcinomas from The Cancer Genome Atlas Network.
Figure 2. Frequency and patterns of MMR genes alterations across 75 MMR deficient gastroesophageal adenocarcinomas from The Cancer Genome Atlas Network.
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Figure 3. Type of mutations, frequency, and affected MMR proteins domains in gastroesophageal cancers (GEC) included in the analysis from No inframe mutations were observed. All truncating mutations are labeled as likely oncogenic by OncoKB.
Figure 3. Type of mutations, frequency, and affected MMR proteins domains in gastroesophageal cancers (GEC) included in the analysis from No inframe mutations were observed. All truncating mutations are labeled as likely oncogenic by OncoKB.
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Table 1. Association between highly recurrent mutations and mutations targeting relevant cancer genes with the MMR status in patients with gastroesophageal cancers. p-value was calculated via the Fisher Exact test; q-value was derived from the Benjamini–Hochberg procedure ( dMMR, mismatch repair-deficient; pMMR, mismatch repair proficient; IC, immune-checkpoints.
Table 1. Association between highly recurrent mutations and mutations targeting relevant cancer genes with the MMR status in patients with gastroesophageal cancers. p-value was calculated via the Fisher Exact test; q-value was derived from the Benjamini–Hochberg procedure ( dMMR, mismatch repair-deficient; pMMR, mismatch repair proficient; IC, immune-checkpoints.
dMMRpMMRLog Ratiop-Valueq-Value
Highly recurrentTTN84 (82.35%)581 (48.86%) 0.75 1.54 × 10−11 6.78 × 10−10
ARID1A 71 (69.61%) 204 (17.16%) 2.02 3.46 × 10−28 2.11 × 10−24
TP53 55 (53.92%) 647 (54.42%) −0.01 0.502 0.59
KMT2D 54 (52.94%) 108 (9.08%) 2.54 1.22 × 10−25 3.12 × 10−22
ACVR2A 53 (51.96%) 80 (6.73%) 2.95 1.13 × 10−29 2.08 × 10−25
EGFR familyEGFR 17 (16.67%) 33 (2.78%) 2.59 5.57 × 10−8 6.81 × 10−7
ERBB2 18 (17.65%) 59 (4.96%) 1.83 1.12 × 10−5 5.96 × 10−5
ERBB3 18 (17.65%) 65 (5.47%) 1.69 3.44 × 10−5 1.54 × 10−4
ERBB4 20 (19.61%) 127 (10.68%) 0.88 7.92 × 10−3 0.01
PI3K familyPIK3CA 30 (29.41%) 129 (10.85%) 1.44 1.06 × 10−6 8.00 × 10−6
PIK3CB 8 (7.84%) 20 (1.68%) 2.22 9.50 × 10−4 2.65 × 10−3
PIK3CG 17 (16.67%) 36 (3.03%) 2.46 1.50 × 10−7 1.55 × 10−6
PTEN 17 (16.67%) 58 (4.88%) 1.77 3.18 × 10−5 1.48 × 10−4
AKT1 1 (0.98%) 8 (0.67%) 0.54 0.52 0.61
AKT2 12 (11.76%) 7 (0.59%) 4.32 1.00 × 10−9 2.31 × 10−8
AKT3 2 (1.96%) 17 (1.43%) 0.46 0.45 0.54
MTOR 26 (25.49%) 40 (3.36%) 2.92 1.27 × 10−13 1.03 × 10−11
ICCD274 (PD-L1) 3 (2.94%) 5 (0.42%) 2.81 0.02 0.04
PDCD1 (PD-1)8 (7.84%)5 (0.42%) 4.22 1.08 × 10−6 8.00 × 10−6
Table 2. Ongoing and recently completed clinical trials using immune checkpoint inhibitors (ICI) in GEC patients.
Table 2. Ongoing and recently completed clinical trials using immune checkpoint inhibitors (ICI) in GEC patients.
DrugPhaseSettingStatusPatientsBasket TrialPrimary OutcomeSecondary OutcomeNCT Number
PembroIA, RAc(nr)297YesAEs, ORR-NCT01848834
PembroIIEC113YesirPFS, iORR, PFSOS, irPFS, ORR, PFSNCT01876511
PembroIAAc(nr)477YesBRRPFS, OS, DORNCT02054806
Pembro + CTIIA, RAc(nr)315NoAEs, ORR-NCT02335411
Pembro + CTIIIAAc(nr)763NoPFS, OSORR, DOR, PFS, AEsNCT02494583
Durva/Treme + CTIIAAc105NoPFSOS, AEs, QoL, TTP, PFS, BRR, DCRNCT03959293
Camre + CTIIStage IIIAc20NoDFS-NCT04152889
ALT-803 + Pembro/Nivo/Atezo/AveIIAAc611YesORRDFS, OS, AEs, QoL, PFSNCT03228667
anti-GITR + Nivo/IpiliI/IIAAc285YesAEs, ORRPFS, OSNCT03126110
DSP-7888/Nivo/PembroI/IIAAc84YesAEs, DLTs, ORRORR, DCR, DOR, PFS, OSNCT03311334
Regorafenib + AveI/IIAAc362YesRP2DMTD, DLT, AEs, BRR, ORR, PFS, OSNCT03475953
Abbreviations: A, advanced/metastatic; R, recurrent; E, early; Ac, active; nr, not recruiting; C, completed; pembro, pembrolizumab; CT, chemotherapy; Durva, durvalumab; Treme, tremelimumab; Camre, camrelizumab; Nivo, nivolumab; Atezo, atezolizumab; Ave, avelumab; GITR, glucocorticoid-induced tumor necrosis factor receptor; Ipili, ipilimumab; AEs, adverse events; ORR, objective response rate; iORR, immune-related objective response rate; irPFS, Immune-related Progression Free Survival; PFS, Progression-free survival; 5-FU, 5-fluorouracil; MSI, microsatellite instability; BRR, Best Overall Response; DOR, Duration of response; N/A, Not available; QoL, Quality of Life; TTP, Time to progress; OS, Overall survival; DCR, disease control rate; SCLC, Small cell lung cancer; RCC, Renal cell carcinoma; HCC, Hepatocellular carcinoma; PROC, Platinum-resistant ovarian cancer; MTD, Maximum tolerated cell dose; RR, Response rate; OTR, Objective tumor regression; DLT, Dose-limiting toxicity; RP2D, Recommended Phase II dose; DFS, Disease free survival. Information has been obtained from

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Lopez, G.; Venetis, K.; Sajjadi, E.; Fusco, N. Mismatch Repair System Genomic Scars in Gastroesophageal Cancers: Biology and Clinical Testing. Gastrointest. Disord. 2020, 2, 341-352.

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Lopez G, Venetis K, Sajjadi E, Fusco N. Mismatch Repair System Genomic Scars in Gastroesophageal Cancers: Biology and Clinical Testing. Gastrointestinal Disorders. 2020; 2(4):341-352.

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Lopez, Gianluca, Konstantinos Venetis, Elham Sajjadi, and Nicola Fusco. 2020. "Mismatch Repair System Genomic Scars in Gastroesophageal Cancers: Biology and Clinical Testing" Gastrointestinal Disorders 2, no. 4: 341-352.

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