Multi-level gene expression signatures, but not binary, outperform Ki67 for the long term prognostication of breast cancer patients

Abstract

Proliferation-related gene signatures have been proposed to aid breast cancer management by providing reproducible prognostic and predictive information on a patient-by-patient basis. It is unclear however, whether a less demanding assessment of cell division rate (as determined in clinical setting by expression of Ki67) can function in place of gene profiling.We investigated agreement between literature-, distribution-based, as well as signature-derived values for Ki67, relative to the genomic grade index (GGI), 70-gene signature, p53 signature, recurrence score (RS), and the molecular subtype models of Sorlie, Hu, and Parker in representative sets of 253 and 159 breast cancers with a median follow-up of 13 and 14.5 years, respectively. The relevance for breast cancer specific survival was also addressed in uni- and bivariate Cox models.Taking both cohorts into account, our broad approach identified ROC optimized Ki67 cutoffs in the range of 8–28%. With optimum signature-reproducing cutoffs, similarity in classification of individual tumors was higher for binary signatures (72–85%), than multi-level signatures (67–73%). Consistent with strong agreement, no prognostic superiority was noted for either Ki67 or the binary GGI, 70-gene and p53 signatures in the Uppsala dataset by bivariate analyses. In contrast, Ki67-independent prognostic capacity could be demonstrated for RS and molecular subtypes according to Sorlie, Hu and Parker in both datasets.Our results show that the added prognostic value of binary proliferation-related gene signatures is limited for Ki67-assessed breast cancers. More complex, multi-level descriptions have a greater potential in short- and long-term prognostication for biologically relevant breast cancer subgroups.