Previous studies have established that intrinsic biological signatures are present and have prognostic significance in breast cancer cohorts from multiple different institutions, profiled with several gene expression microarray platforms (21
). In order to identify these subtypes on standard formalin-fixed, paraffin-embedded pathology specimens, we developed a qRT-PCR test based on a panel of 50 genes (9
). The analysis reported here applied this test to a series of paraffin blocks with > 15 years detailed followup.
Whereas previously assessed cohorts consisted mainly of low risk women receiving no adjuvant systemic therapy, or were heterogeneously-treated, the cases in the current study are all women with estrogen receptor positive breast cancer who received endocrine therapy as their sole adjuvant treatment, a group of particular clinical importance and contemporary relevance. In this analysis we sought to compare different technologies for predicting long term outcomes for such patients. In this study cohort, patients were diagnosed with node positive or higher risk N0 disease. Only 8% of the N0 population had grade 1 disease and 55% exhibited lymphovascular invasion (Table S2
). Under the current standard of care in most countries the majority of these patients would now be treated with adjuvant chemotherapy (25
) and extended endocrine therapy. Using a series of fixed models trained in independent data sets, we compared a standard approach using clinico-pathological information (Adjuvant! Online), to our published Luminal B discriminator based on Ki67 and HER2 immunohistochemistry additionally weighted for T stage (IHC-T), and to PAM50 gene expression based ROR models weighted for T stage (ROR-T and ROR-PT). In node-negative patients, the ROR-PT approach was the most accurate and was able to identify patients in whom 5 years of tamoxifen may be adequate treatment based on the very low late relapse rate in the 5 to 10 year window (). In node positive disease, the PAM50 approach represents an advance in prognostication, but late relapses and deaths were seen even in the lowest risk group identified using the best ROR model. Unlike in N0 disease, proliferation signature weighting did not improve the C index in node positive disease.
On this cohort, detailed centrally-determined immunohistochemical analyses have previously been performed and published (6
). C-index, Kaplan-Meier and Cox model analyses show that immunohistochemical approaches do work and provide significant prognostic information. However, the PAM50-based models are superior in terms of adding significant additional information and in their capacity to identify a particularly low-risk group of women.
We view these PAM50 models, derived from archival formalin fixed RNA, as a potential replacement for grade, hormone receptor, Ki67 and HER2 based prognostic models, but not as a replacement for pathological stage (as tumor size and nodal status remain independent predictors in multivariable models that include PAM50 based prognostic information). One weakness of our approach is that our current accounting for pathological stage is over-simplified due to the limited stage distributions and clinical information in our training sets. We analyzed the data as either node negative or node positive, and accounted for T stage by categorizing the samples as either T1 or greater. A future aim is to integrate the PAM50 data into the Adjuvant! Online approach (27
) to more completely account for the prognostic influence of pathological stage. To achieve this we would need to construct a training set that adequately includes all the 5 categories of T size and four categories of N stage used in Adjuvant! Online, in order to gauge the prognostic weight of these pathological stage categories in the setting of PAM50 information. Additionally, incorporation of all immunohistochemical data as continuous variables in a combined model may improve its prognostic value. The current series contains sufficiently detailed clinical and immunohistochemical information to contribute to such detailed comparisons, as a training set requiring further validation.
An additional caveat to our study is that the population was strongly biased towards higher risk breast cancers and so likely underestimates of the number of patients in the broader, node negative population for whom adjuvant tamoxifen would represent adequate treatment. The current generation of adjuvant aromatase inhibitor trials would be an appropriate setting to address the value of our approach further. We accept the possibility that a better model using Ki67 at a different cut point could be developed. However since we were focused on comparing fixed models, we used our published approach. Further work on the Ki67 model and cut point optimization will require independent data sets.
In comparison with other signatures such as the recurrence score and genomic grade index (1
), the PAM50 has the potential advantage of discriminating high risk patients into Luminal B, HER2-Enriched and Basal-like subtypes, who are likely to respond differently to the main systemic therapy options (endocrine, anti-HER2, and anthracycline vs. non-anthracycline vs. taxane chemotherapy regimens). The assay requires neither frozen tissue (30
) nor manual microdissection of cut sections(1
), and can be readily applied to standard paraffin blocks including archival tissues from clinical trials. Currently available assays such as Mammaprint (31
) and OncotypeDX (32
) were optimized to recognize particularly low risk patients from among a node negative early stage population who did not receive chemotherapy. Because intrinsic subtyping is designed to identify discriminative biological features of breast cancer, rather than being derived around clinical outcome in a specific population, this approach is particularly likely to extrapolate well onto other patient cohorts (33
). The current study demonstrates the ability of PAM50 to recognize a very low risk prognostic group among women receiving tamoxifen and no chemotherapy, similar to the Oncotype Dx assay(34
). A direct comparison of different expression profile approaches may become possible in the future through a reanalysis of cohorts with the PAM50 that have already been analyzed by OncotypeDX, since both assays can be applied to the same source material.
Our inability to identify a group of patients with node positive disease in whom five years of tamoxifen is adequate is reminiscent of the recent findings from the Southwest Oncology Group, who also found that a molecular signature for good outcome in N0 disease failed in node positive disease in this regard (35
). It would be relevant to study a series of patients treated with extended adjuvant aromatase inhibitor therapy, who will have even lower residual risk, as some of the patients in the low risk N+ group may simply require longer treatment with modern endocrine therapy rather than chemotherapy. The development of new approaches for defining prognosis in N+ disease is also warranted. We have already established the preoperative endocrine prognostic index (PEPI), which demonstrated that the “on endocrine treatment” Ki67 value is more effective than baseline Ki67 for the identification of patients with clinical stage 2 and 3 disease who have excellent long term outcomes after neoadjuvant endocrine therapy (36
). A comparison between Ki67 and the PAM50 based proliferation signature in the neoadjuvant endocrine therapy setting is therefore one logical next step. The applicability of this test to formalin-fixed paraffin-embedded tissues will make possible its use on large clinical trial archives that address this issue (37
). The results of our study highlight the feasibility of measuring multi-gene expression panels on such series as a means for demonstrating clinical utility, using a method readily applicable to prospective clinical samples that provides more prognostic information than clinical or standard immunohistochemical approaches.
Description of how this work might be applied to future practice of cancer medicine
Molecular intrinsic subtyping reveals the major biological categories of breast cancer. Herein we demonstrate adaptation of a 50 gene intrinsic subtyping signature for testing standard paraffin blocks. Using a large, homogeneously treated cohort of breast cancer patients, we directly compare gene expression results to high quality clinical and central immunohistochemical data. We show the PAM50 approach to be superior as a prognostic test, specifically able to identify an ultra-low risk group who may not need chemotherapy. Based on these results, intrinsic subtyping tests are now being applied to randomized clinical trials series in Canada and the USA to assess predictive capacity (already underway for response to endocrine therapy, anthracyclines and taxanes, with further studies under consideration). Should such studies prove a predictive value for intrinsic subtyping, this test could be clinically implemented in a similar form, as it has been designed for application on standard laboratory specimens.