BACKGROUND: Phenotypic changes during cancer progression are associated with alterations
in gene expression, which can be exploited to build molecular signatures for tumor
stage identification and prognosis. However, it is not yet known whether the relative
abundance of transcript isoforms may be informative for clinical stage and survival.
METHODS: Using information theory and machine learning methods, we integrated RNA
sequencing and clinical data from The Cancer Genome Atlas project to perform the first
systematic analysis of the prognostic potential of transcript isoforms in 12 solid
tumors to build new signatures for stage and prognosis. This study was also performed
in breast tumors according to estrogen receptor (ER) status and melanoma tumors with
proliferative and invasive phenotypes. RESULTS: Transcript isoform signatures accurately
separate early from late-stage groups and metastatic from non-metastatic tumors, and
are predictive of the survival of patients with undetermined lymph node invasion or
metastatic status. These signatures show similar, and sometimes better, accuracies
compared with known gene expression signatures in retrospective data and are largely
independent of gene expression changes. Furthermore, we show frequent transcript isoform
changes in breast tumors according to ER status, and in melanoma tumors according
to the invasive or proliferative phenotype, and derive accurate predictive models
of stage and survival within each patient subgroup. CONCLUSIONS: Our analyses reveal
new signatures based on transcript isoform abundances that characterize tumor phenotypes
and their progression independently of gene expression. Transcript isoform signatures
appear especially relevant to determine lymph node invasion and metastasis and may
potentially contribute towards current strategies of precision cancer medicine.
