Many studies have tried to identify the gene expression-based “intrinsic” subtypes using a variety of methods for the sake of simplicity, cost, and available technologies. Methods that can be used from formalin-fixed, paraffin-embedded tissues are optimal since this is how samples are procured and archived in most pathology departments. The two preferred technologies for gene expression profiling from FFPE tissues are RT-qPCR [17, 18] and Nanostring nCounter . The nCounter system uses color-coded probes that bind directly to the RNA transcript without reverse transcription and PCR amplification. While these methods have high agreement for gene quantification, other methodologies may lead to different conclusions and treatment decisions. For instance, in the NCIC.CTG MA.12 clinical trial that randomized pre-menopausal women with primary breast cancer to tamoxifen versus placebo it was found that a panel 6 IHC antibodies for subtyping was not prognostic but the PAM50 RT-qPCR subtypes were prognostic . In another randomized study (NCIC.CTG MA.5) that assessed PAM50 subtype sensitivity to anthracycline-based chemotherapy, it was shown that the HER2-E subtype received the most benefit, while women with Basal-like tumors had no benefit from this aggressive treatment . This study and the MA.5 trial found that only about two-thirds of clinically Her2+ tumors are classified as HER2-E and about the same percent of triple negatives are classified as Basal-like. Thus, only a subset of the IHC defined groups overlap with PAM50 subtype classification, which may have ramifications for clinical trial findings and predicting therapy benefit.
Receiver Operator Characteristic (ROC) curves are commonly used in medicine to optimize the sensitivity/specificity of an assay depending on the purpose of the test (i.e. screening, monitoring, prognosis, etc.) . In clinical pathology, ROC curves are often used to validate a new methodology against an existing “gold” standard. A major limitation to this approach is that cut-offs are then determined by comparison to an often less than perfect reference. We used an approach for selecting single (ESR1, PGR, ERBB2) and meta-gene (proliferation) cut-offs that was based on the distribution of expression of these markers across the different subtypes. This method showed to be reproducible in an independent test set.
The ROC curves showed high agreement between RT-qPCR and the standard IHC biomarkers. ESR1 had high sensitivity although the cut-off for ER + status was 10% positive staining nuclei, whereas the new recommendation for determining ER status is 1% . These borderline cases for ER positivity may be better characterized by the overall subtype biology. For ERBB2, there was high specificity, which is optimal since confirmatory CISH or FISH would only be performed when it was uncertain if the gene was truly amplified . It has been suggested that the use of single gene RT-qPCR measurement for ERBB2 is insufficient for determining HER2 positive samples that may benefit from trastuzumab/Herceptin® therapy . Dabbs et al. found that the negative predictive value for determining HER2/ERBB2 status was high between the HercepTest and the GHI Oncotype Dx qPCR assay (99%); but the concordance for positive HER2/ERBB2 samples was only 28%. In contrast, we showed that the concordance between HER2 (IHC/CISH) and ERBB2 (RT-qPCR) is greater than 90% when restricted to the HER2-E subtype.
In order to determine if there was a prognostic difference between the RT-qPCR and IHC we included both methods in a Cox proportional hazards model and showed that gene expression remained significant in the multivariate analysis and replaced IHC. Furthermore, the outcome plots for women with tumors scored positive for ER by IHC but negative for ESR1 had outcomes similar to women that were ER-/ESR1-. Conversely, women with ER- tumors by IHC but positive for ESR1 had similar outcomes to women with ER+/ESR1+ disease. Thus, despite the fact that patients were treated in favor of the IHC diagnosis (i.e. ER + disease was treated with adjuvant tamoxifen) the course of disease was in agreement with the gene expression determination. The better prognosis seen in the ESR1+ but ER- subtype is curious since these patients would not have been given adjuvant endocrine blockade therapy. However, gene expression for ESR1 may be identifying the “true” luminal origin of these tumors which have a better prognosis, regardless of therapy . In addition, the patients included in the test set were locally advanced and received chemotherapy that can cause chemotherapy induced amenorrhea and a reduction in ovarian function , which again may benefit the luminal subtype most.
The Normal subtype was developed from reduction mammoplasty “normal” breast tissue and serves as a quality control measure since these cases would be considered to have an insufficient amount of tumor tissue to make a tumor subtype call. Interference studies showed that the introduction of “normal” breast tissue RNA caused a systematic shift in subtype assignment with subtypes switching to Normal, except Luminal B which changed to Luminal A.
None of the assignment switches occurred until the introduction of 50% “normal” breast tissue RNA. The greatest risk of misclassification would come from Luminal B subtypes masquerading as Luminal A tumors because of “normal” tissue contamination ; however, these tumors maintain a high proliferation score suggesting they are still a high risk Luminal tumor.
A fifth tumor type that has often been referred to as “Normal-like” has been suggested to be an artifact of having too few tumor cells and a large background of normal breast cells in the sample. Our mixing experiments here support this hypothesis and show that when increasing amounts of “normal” tissue RNA is added to a tumor it switches into the Normal-like group. It is, however, suspected that some tumors now called Normal-like may be put into the recently described Claudin-low classification . The Claudin-low subtype is mostly triple-negative, shares biomarkers in common with normal breast epithelial cells and Basal-like tumors, and may be caused by deficiency in either BRCA1or p53, or both; however there is no clinical indication for Claudin-low, and most are typically classified as Basal-like. There are now many more groups of tumors being identified with transcriptome and copy number variance analyses [38, 39]. The overlap between these new groups, existing subtypes, and standard biomarkers already in practice should allow for more personalized treatments and better outcomes in the future.