Abstract P2-10-03: A cross-platform comparison of genomic signatures and OncotypeDx score to discover potential prognostic/predictive genes and pathways
Authors: Kuderer NM , Barry WT , Geradts J , Ginsburg GS , Lyman GH , Datto M , Liotcheva V , Isner P , Veldman T , Agarwal P , Hwang S , Ready N , Marcom PK 
Published: August 2014

Background: Microarray assessment of breast cancer demonstrates disease subsets among the major breast cancer biologic categories (ER, PR, HER-2) with likely additional prognostic and treatment (predictive) implications. Additional prognostic and predictive biomarkers and optimization of existing genomic platforms are needed to improve personalized breast cancer care.

Methods: 76 early-stage ER+ patients with adequate RNA from fresh frozen tumor specimens and a concurrent 21-gene OncotypeDX recurrence score (RS) were identified across independent Duke studies linked to routine prospective breast biospecimen collection. Expression estimates for Affymetrix H133 Plus 2.0 microarrays were reviewed for quality control (Owzar 2008) and normalized across 4 batches with ComBat. Correlations of genomic prognostic signatures to true RS were assessed using Spearman coefficients. Discovery of new gene-level and pathway-level associations to breast cancer prognosis and prediction, based on RS, were corrected for multiple comparisons using the Bonferonni for the family-wise error rate (FWER) or Benjamini-Hochberg methods for the false discovery rate (FDR).

Results: A total of 73 samples passed Affymetrix quality control: 32 “low risk”, 32 “intermediate risk”, and 9 “high risk” using standard recurrence score thresholds of &lt;18 and &gt;30. Median patient age is 55 (range 35–86). Two published algorithms to impute the RS from the Affymetrix model by Fan and Haibe-Keins were highly concordant (94%) with each other and showed strong correlation to actual RS (rho = 0.62, p = 5e−9). However, when the RS thresholds were applied to the microarray-based scores, lower agreement was observed (misclassification rate of 57%). Strong correlation was also observed with other breast signatures and RS, including an imputed 70-gene signature of MammaPrint (rho = 0.59, p = 3e−8) and the 50-gene PAM50 risk-of-relapse score (rho = 0.54, p = 8e−7), while poor correlation was seen for the GENIUS prognostic model (rho = 0.06, p = 0.6). Discovery of gene-level associations to RS identified 28 genes, including known cancer-associated genes such as BRCA2 (FWER adj p = 0.002), Cyclin E1 (FWER adj p = 0.015), and CDCA5 (FWER adj p = 0.048). Pathway-level association in KEGG and GO Biologic Processes, identified 24 and 104 categories respectively, including Cell Cycle pathways KEGG:04110 (FDR adj p = 0.002) and GO:0000085 (FDR adj p = 0.0007). Among 22 in-vitro derived oncogenic pathway signatures, significant negative correlation is seen with p53 (rho = −0.56, adj p = 4e−6) and positive correlation with beta-catenin (rho = 0.38, adj p = 0.015). A multi-gene model of association to OncotypeDX RS classification is being developed using Prediction Analysis of Microarrays (PAM).

Conclusions: Microarray-based prognostic breast cancer signatures are generally concordant. However, their application across platforms is currently sub-optimal. In the context of OncotypeDX RS, additional key cancer-associated genes and pathways are found to be associated with breast cancer prognosis, potentially providing insight into treatment opportunities for ER+ breast cancer. Validation efforts of these findings in an independent patient cohort are underway.

Funding: NCI: RC2CA14041-01 and W81XWH-07-1-0394

Citation Information: Cancer Res 2012;72(24 Suppl):Abstract nr P2-10-03.