How to identify patients with high-risk HR-positive/HER2- negative breast cancer in the absence of gene expression platforms

Abstract

[eng] HR-positive/HER2-negative (HR+/HER2-) is the most common breast cancer type, and it is a molecular heterogenic disease. This heterogeneity has direct prognostic and predictive implications in both early and advanced settings. Thus, identifying high-risk HR+/HER2- breast cancer patients in the clinical practice has become a necessity, even when genomic platforms are not available. In this project, we compared the intrinsic subtype classification defined by the PAM50/Prosigna® test with 4 immunohistochemistry-based biomarkers (estrogen receptor [ER], progesterone receptor [PR], Human epidermal growth factor receptor 2 [HER2], and Ki67) in two different cohorts of 517 and 1,417 patients with HR+/HER2- breast tumors, respectively. In a first study, we evaluated the performance of Ki67 as a continuous biomarker to identify Luminal A and Risk of Relapse (ROR)-low tumors. Moreover, we explored the optimal KI67 cutoff for selecting low-risk patients in the clinic. In a second study, we built and tested an IHC- based predictor to identify PAM50 non-luminal subtypes in HR+/HER2- breast cancer. Both projects should allow a more comprehensive understanding of the biological heterogeneity within HR+/HER2- early breast cancer and provide tools to identify patients with different relapsing risks. In the first study, we evaluated a cohort of 517 patients with ER+/HER2- and node-negative breast cancer. Although most patients had Luminal A (65.6%) and ROR-low tumors (70.9%), a substantial proportion (34-43%) of tumors with Ki67 0-10% had either ROR- medium or ROR-high disease; conversely, a substantial proportion (24-29%) of tumors with Ki67 10-20% had ROR-low disease. Also, we found that the optimal Ki67 cutoff for identifying Luminal A or ROR-low tumors was 14%, concordant with previous findings reported in the literature. In the second study, we created an IHC-based predictive biomarker using ER, PR, and Ki67 data, the NOLUS score, to identify PAM50 non-luminal disease, using a training dataset of 903 patients with HR+/HER2- breast tumors. When applied to the test set, the NOLUS score was statistically significantly associated with non-luminal disease (p<0.01) with an AUC of 0.902. The proportion of non-luminal tumors in NOLUS-positive and NOLUS- negative groups was 76.9% (56.4–91.0%) and 2.6% (1.4–4.5%), and the sensitivity and specificity of the pre-specified cutoffs were 59.3% and 98.7%, respectively. Based on these results, we conclude that Ki67 as a continuous variable is an unreliable biomarker to identify patients with Luminal A and/or ROR-low HR+/HER2- breast cancer. However, in the absence of gene expression platforms, the best Ki67 cutoff for determining ROR-low or Luminal A disease is 14%. The NOLUS score can help identify patients with non-luminal disease within HR+/HER2- breast cancer.