title: Cancer Bioinformatic Methods to Infer Meaningful Data From Small-Size Cohorts.
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 epigenetic 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.
authors.affiliation: Division of Hematology, Mayo Clinic, Rochester, MN 55905, USA.
authors.forename: Nabila
authors.initials: N
authors.lastname: Bennani-Baiti
authors.affiliation: The B Scientific Group (B SG), 1010 Vienna, Austria.
authors.forename: Idriss M
authors.initials: IM
authors.lastname: Bennani-Baiti
keywords: cohort size; expression profiling; gene data set; intertumor heterogeneity; intratumor heterogeneity; low-incidence cancers