Cancer bioinformatic methods to infer meaningful data from small-size cohorts

Nabila Bennani, Idriss M. Bennani-Baiti

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

Whole-genome analyses have uncovered that most cancer-relevant genes cluster into 12 signaling pathways. Knowledge of the signaling pathways and associated gene signatures not only allows us to understand the mechanisms of oncogenesis inherent to specific cancers but also provides us with drug targets, molecular diagnostic and prognosis factors, as well as biomarkers for patient risk stratification and treatment. Publicly available genomic data sets constitute a wealth of gene mining opportunities for hypothesis generation and testing. However, the increasingly recognized genetic and epige-netic inter- and intratumor heterogeneity, combined with the preponderance of small-size cohorts, hamper reliable analysis and discovery. Here, we review two methods that are used to infer meaningful biological events from small-size data sets and discuss some of their applications and limitations.

Original languageEnglish (US)
Pages (from-to)131-139
Number of pages9
JournalCancer Informatics
Volume14
DOIs
StatePublished - Nov 2 2015

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Computational Biology
Molecular Pathology
Neoplasm Genes
Multigene Family
Genes
Neoplasms
Carcinogenesis
Biomarkers
Genome
Pharmaceutical Preparations
Datasets
Therapeutics

Keywords

  • Cohort size
  • Expression profiling
  • Gene data set
  • Intertumor heterogeneity
  • Intratumor heterogeneity
  • Low-incidence cancers

ASJC Scopus subject areas

  • Oncology
  • Cancer Research

Cite this

Cancer bioinformatic methods to infer meaningful data from small-size cohorts. / Bennani, Nabila; Bennani-Baiti, Idriss M.

In: Cancer Informatics, Vol. 14, 02.11.2015, p. 131-139.

Research output: Contribution to journalArticle

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