title: Association of Protein Translation and Extracellular Matrix Gene Sets with Breast Cancer Metastasis: Findings Uncovered on Analysis of Multiple Publicly Available Datasets Using Individual Patient Data Approach.
abstract: BACKGROUND
  Microarray analysis has revolutionized the role of genomic prognostication in breast cancer. However, most studies are single series studies, and suffer from methodological problems. We sought to use a meta-analytic approach in combining multiple publicly available datasets, while correcting for batch effects, to reach a more robust oncogenomic analysis.
  
  
  OBJECTIVE
  The aim of the present study was to find gene sets associated with distant metastasis free survival (DMFS) in systemically untreated, node-negative breast cancer patients, from publicly available genomic microarray datasets.
  
  
  METHODS
  Four microarray series (having 742 patients) were selected after a systematic search and combined. Cox regression for each gene was done for the combined dataset (univariate, as well as multivariate - adjusted for expression of Cell cycle related genes) and for the 4 major molecular subtypes. The centre and microarray batch effects were adjusted by including them as random effects variables. The Cox regression coefficients for each analysis were then ranked and subjected to a Gene Set Enrichment Analysis (GSEA).
  
  
  RESULTS
  Gene sets representing protein translation were independently negatively associated with metastasis in the Luminal A and Luminal B subtypes, but positively associated with metastasis in Basal tumors. Proteinaceous extracellular matrix (ECM) gene set expression was positively associated with metastasis, after adjustment for expression of cell cycle related genes on the combined dataset. Finally, the positive association of the proliferation-related genes with metastases was confirmed.
  
  
  CONCLUSIONS
  To the best of our knowledge, the results depicting mixed prognostic significance of protein translation in breast cancer subtypes are being reported for the first time. We attribute this to our study combining multiple series and performing a more robust meta-analytic Cox regression modeling on the combined dataset, thus discovering 'hidden' associations. This methodology seems to yield new and interesting results and may be used as a tool to guide new research.
authors.affiliation: Department of Pathology & Laboratory Medicine, All India Institute of Medical Sciences, Rishikesh, Uttarakhand, India.
authors.forename: Nilotpal
authors.initials: N
authors.lastname: Chowdhury
authors.affiliation: Department of Radiotherapy, All India Institute of Medical Sciences, Rishikesh, Uttarakhand, India.
authors.forename: Shantanu
authors.initials: S
authors.lastname: Sapru
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D001943:Breast Neoplasms / Q000145:classification / Q000235:genetics* / Q000473:pathology; D030541:Databases, Genetic / Q000706:statistics & numerical data; D005109:Extracellular Matrix / Q000378:metabolism*; D016326:Extracellular Matrix Proteins / Q000235:genetics*; D005260:Female; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D008875:Middle Aged; D015999:Multivariate Analysis; D009362:Neoplasm Metastasis; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods; D011379:Prognosis; D016016:Proportional Hazards Models; D014176:Protein Biosynthesis / Q000235:genetics*; D055815:Young Adult
chemical_list: D016326:Extracellular Matrix Proteins