title: Progression risk assessments of individual non-invasive gastric neoplasms by genomic copy-number profile and mucin phenotype.
abstract: BACKGROUND
  Early detection and treatment of non-invasive neoplasms can effectively reduce the incidence of advanced gastric carcinoma (GC), but only when the lineage is continuous between non-invasive and advanced tumours. Although a fraction of non-invasive neoplasms progress to invasive GC, it is difficult to identify individual progression-prone non-invasive neoplasms. To classify non-invasive gland-forming gastric neoplasms into clusters of different levels of progression risk, we applied mucin phenotyping and genomic DNA microarray analyses to intramucosal gland-forming gastric neoplasms.
  
  
  METHODS
  Formalin-fixed, paraffin-embedded tissues from 19 non-invasive and 24 invasive gland-forming neoplasms were obtained via endoscopic submucosal dissection or surgical excision. According to the Vienna classification, intramucosal neoplasms were classified as low-grade or high-grade non-invasive neoplasms (LGNs [category 3] and HGNs [category 4], respectively) or invasive carcinomas (intramucosal GCs and mucosal parts of submucosal or deeper GCs [category 5]). Neoplastic lesions were characterized by mucin phenotypes determined using monoclonal antibodies against MUC2, MUC5AC, MUC6, and CD10. Genomic DNA samples from mucosal neoplasms were subjected to array-based comparative genomic hybridization and subsequent unsupervised, hierarchical clustering with selected large-sized genes.
  
  
  RESULTS
  There was no significant difference in mucin phenotype between HGNs/LGNs and invasive carcinomas. The clustering classified samples into stable, unstable, and intermediate. The histological tumour grade or mucin phenotype of non-invasive neoplasms did not correlate with the clustering results. Each cluster may represent an independent lineage of different outcome because the size distribution of non-invasive tumours among the 3 clusters almost overlapped. In contrast, the unstable cluster alone included invasive carcinomas.
  
  
  CONCLUSIONS
  These findings suggest that the outcome of individual tumours is not stochastically determined but can be predicted from the genomic copy-number profile even at the non-invasive stage. Non-invasive neoplasms of the unstable clusters, which accounted for 21% of non-invasive neoplasms, may progress to invasive carcinomas, whereas those of stable cluster may not.
authors.affiliation: Department of Pathology, Division of Molecular and Diagnostic Pathology, Shiga University of Medical Science, Otsu, 520-2192, Japan. votngocdiem@gmail.com.
authors.forename: Diem Thi-Ngoc
authors.initials: DT
authors.lastname: Vo
authors.affiliation: Department of Pathology, Division of Molecular and Diagnostic Pathology, Shiga University of Medical Science, Otsu, 520-2192, Japan. 330601@belle.shiga-med.ac.jp.
authors.forename: Takahisa
authors.initials: T
authors.lastname: Nakayama
authors.affiliation: Department of Pathology, Division of Molecular and Diagnostic Pathology, Shiga University of Medical Science, Otsu, 520-2192, Japan. yamaherob00k@gmail.com.
authors.forename: Hiroto
authors.initials: H
authors.lastname: Yamamoto
authors.affiliation: Department of Pathology, Division of Molecular and Diagnostic Pathology, Shiga University of Medical Science, Otsu, 520-2192, Japan. mukaisho@googlemail.com.
authors.forename: Ken-ichi
authors.initials: K
authors.lastname: Mukaisho
authors.affiliation: Department of Pathology, Division of Molecular and Diagnostic Pathology, Shiga University of Medical Science, Otsu, 520-2192, Japan. hattori@belle.shiga-med.ac.jp.
authors.forename: Takanori
authors.initials: T
authors.lastname: Hattori
authors.affiliation: Department of Pathology, Division of Molecular and Diagnostic Pathology, Shiga University of Medical Science, Otsu, 520-2192, Japan. sugihara@belle.shiga-med.ac.jp.
authors.forename: Hiroyuki
authors.initials: H
authors.lastname: Sugihara
mesh_terms: D000230:Adenocarcinoma / Q000175:diagnosis / Q000235:genetics; D000465:Algorithms; D014408:Biomarkers, Tumor / Q000235:genetics; D019070:Cell Lineage; D016000:Cluster Analysis; D055028:Comparative Genomic Hybridization; D018450:Disease Progression; D018628:Gene Dosage; D020869:Gene Expression Profiling; D015972:Gene Expression Regulation, Neoplastic; D015894:Genome, Human; D023281:Genomics; D006801:Humans; D007150:Immunohistochemistry; D009077:Mucins / Q000235:genetics*; D009361:Neoplasm Invasiveness; D020411:Oligonucleotide Array Sequence Analysis; D010641:Phenotype; D016133:Polymerase Chain Reaction; D018570:Risk Assessment; D013269:Stochastic Processes; D013274:Stomach Neoplasms / Q000175:diagnosis* / Q000235:genetics*
chemical_list: D014408:Biomarkers, Tumor; D009077:Mucins