title: Predicting neuroendocrine tumor (carcinoid) neoplasia using gene expression profiling and supervised machine learning.
abstract: BACKGROUND
  A more accurate taxonomy of small intestinal (SI) neuroendocrine tumors (NETs) is necessary to accurately predict tumor behavior and prognosis and to define therapeutic strategy. In this study, the authors identified a panel of such markers that have been implicated in tumorigenicity, metastasis, and hormone production and hypothesized that transcript levels of the genes melanoma antigen family D2 (MAGE-D2), metastasis-associated 1 (MTA1), nucleosome assembly protein 1-like (NAP1L1), Ki-67 (a marker of proliferation), survivin, frizzled homolog 7 (FZD7), the Kiss1 metastasis suppressor (Kiss1), neuropilin 2 (NRP2), and chromogranin A (CgA) could be used to define primary SI NETs and to predict the development of metastases.
  
  
  METHODS
  Seventy-three clinically and World Health Organization pathologically classified NET samples (primary tumor, n = 44 samples; liver metastases, n = 29 samples) and 30 normal human enterochromaffin (EC) cell preparations were analyzed using real-time polymerase chain reaction. Transcript levels were normalized to 3 NET housekeeping genes (asparagine-linked glycosylation 9 or ALG9, transcription factor CP2 or TFCP2, and zinc finger protein 410 or ZNF410) using geNorm analysis. A predictive gene-based model was constructed using supervised learning algorithms from the transcript expression levels.
  
  
  RESULTS
  Primary SI NETs could be differentiated from normal human EC cell preparations with 100% specificity and 92% sensitivity. Well differentiated NETs (WDNETs), well differentiated neuroendocrine carcinomas, and poorly differentiated NETs (PDNETs) were classified with a specificity of 78%, 78%, and 71%, respectively; whereas poorly differentiated neuroendocrine carcinomas were misclassified as either WDNETs or PDNETs. Metastases were predicted in all cases with 100% sensitivity and specificity.
  
  
  CONCLUSIONS
  The current results indicated that gene expression profiling and supervised machine learning can be used to classify SI NET subtypes and accurately predict metastasis. The authors believe that the application of this technique will facilitate accurate molecular pathologic delineation of NET disease, better define its extent, facilitate the assessment of prognosis, and provide a guide for the identification of appropriate strategies for individualized patient treatment.
authors.affiliation: Department of Surgery, Yale University School of Medicine, New Haven, Connecticut 06520-8062, USA.
authors.forename: Ignat
authors.initials: I
authors.lastname: Drozdov
authors.forename: Mark
authors.initials: M
authors.lastname: Kidd
authors.forename: Boaz
authors.initials: B
authors.lastname: Nadler
authors.forename: Robert L
authors.initials: RL
authors.lastname: Camp
authors.forename: Shrikant M
authors.initials: SM
authors.lastname: Mane
authors.forename: Oyvind
authors.initials: O
authors.lastname: Hauso
authors.forename: Bjorn I
authors.initials: BI
authors.lastname: Gustafsson
authors.forename: Irvin M
authors.initials: IM
authors.lastname: Modlin
mesh_terms: D000465:Algorithms; D002276:Carcinoid Tumor / Q000145:classification* / Q000235:genetics*; D001330:Electronic Data Processing; D004759:Enterochromaffin Cells / Q000145:classification; D005629:Frozen Sections; D020869:Gene Expression Profiling; D006801:Humans; D007414:Intestinal Neoplasms / Q000145:classification / Q000235:genetics; D009362:Neoplasm Metastasis; D011237:Predictive Value of Tests; D012680:Sensitivity and Specificity; D054928:Validation Studies as Topic