The claudin-low breast cancer subtype is defined by gene expression characteristics and encompasses a remarkably diverse range of breast tumors. Here, we investigate genomic, transcriptomic, and clinical features of claudin-low breast tumors. We show that claudin-low is not simply a subtype analogous to the intrinsic subtypes (basal-like, HER2-enriched, luminal A, luminal B and normal-like) as previously portrayed, but is a complex additional phenotype which may permeate breast tumors of various intrinsic subtypes. Claudin-low tumors are distinguished by low genomic instability, mutational burden and proliferation levels, and high levels of immune and stromal cell infiltration. In other aspects, claudin-low tumors reflect characteristics of their intrinsic subtype. Finally, we explore an alternative method for identifying claudin-low tumors and thereby uncover potential weaknesses in the established claudin-low classifier. In sum, these findings elucidate the heterogeneity in claudin-low breast tumors, and substantiate a re-definition of claudin-low as a cancer phenotype.

The aim of this study was to investigate the prognostic value of the PAM50 intrinsic subtypes and risk of recurrence (ROR) score in patients with early breast cancer and long-term follow-up. A special focus was placed on hormone receptor-positive/human epidermal growth factor receptor 2-negative (HR+/HER2−) pN0 patients not treated with chemotherapy. Patients with early breast cancer (n = 653) enrolled in the observational Oslo1 study (1995–1998) were followed for distant recurrence and breast cancer death. Clinicopathological parameters were collected from hospital records. The primary tumors were analyzed using the Prosigna® PAM50 assay to determine the prognostic value of the intrinsic subtypes and ROR score in comparison with pathological characteristics. The primary endpoints were distant disease-free survival (DDFS) and breast cancer-specific survival (BCSS). Of 653 tumors, 52.2% were classified as luminal A, 26.5% as luminal B, 10.6% as HER2-enriched, and 10.7% as basal-like. Among the HR+/HER2− patients (n = 476), 37.8% were categorized as low risk by ROR score, 22.7% as intermediate risk, and 39.5% as high risk. Median follow-up durations for BCSS and DDFS were 16.6 and 7.1 years, respectively. Multivariate analysis showed that intrinsic subtypes (all patients) and ROR risk classification (HR+/HER2− patients) yielded strong prognostic information. Among the HR+/HER2− pN0 patients with no adjuvant treatment (n = 231), 53.7% of patients had a low ROR, and their prognosis at 15 years was excellent (15-year BCSS 96.3%). Patients with intermediate risk had reduced survival compared with those with low risk (p = 0.005). In contrast, no difference in survival between the low- and intermediate-risk groups was seen for HR+/HER2− pN0 patients who received tamoxifen only. Ki-67 protein, grade, and ROR score were analyzed in the unselected, untreated pT1pN0 HR+/HER2− population (n = 171). In multivariate analysis, ROR score outperformed both Ki-67 and grade. Furthermore, 55% of patients who according to the PREDICT tool ( http://www.predict.nhs.uk/ ) would be considered chemotherapy candidates were ROR low risk (33%) or luminal A ROR intermediate risk (22%). The PAM50 intrinsic subtype classification and ROR score improve classification of patients with breast cancer into prognostic groups, allowing for a more precise identification of future recurrence risk and providing an improved basis for adjuvant treatment decisions. Node-negative patients with low ROR scores had an excellent outcome at 15 years even in the absence of adjuvant therapy.

Introduction: Ductal carcinoma in situ (DCIS) is a non-invasive lesion of the breast that is frequently detected by mammography and subsequently removed by surgery. However, it is estimated that about half of the detected lesions would never have progressed into invasive cancer. Identifying DCIS and invasive cancer specific epigenetic lesions and understanding how these epigenetic changes are involved in triggering tumour progression is important for a better understanding of which lesions are at risk of becoming invasive. Methods: Quantitative DNA methylation analysis of ABCB1, CDKN2A/p16 ^INK4a , ESR1, FOXC1, GSTP1, IGF2, MGMT, MLH1, PPP2R2B, PTEN and RASSF1A was performed by pyrosequencing in a series of 27 pure DCIS, 28 small invasive ductal carcinomas (IDCs), 34 IDCs with a DCIS component and 5 normal breast tissue samples. FOXC1, ABCB1, PPP2R2B and PTEN were analyzed in 23 additional normal breast tissue samples. Real-Time PCR expression analysis was performed for FOXC1. Results: Aberrant DNA methylation was observed in all three diagnosis groups for the following genes: ABCB1, FOXC1, GSTP1, MGMT, MLH1, PPP2R2B, PTEN and RASSF1A. For most of these genes, methylation was already present at the DCIS level with the same frequency as within IDCs. For FOXC1 significant differences in methylation levels were observed between normal breast tissue and invasive tumours (P < 0.001). The average DNA methylation levels were significantly higher in the pure IDCs and IDCs with DCIS compared to pure DCIS (P = 0.007 and P = 0.001, respectively). Real-time PCR analysis of FOXC1 expression from 25 DCIS, 23 IDCs and 28 normal tissue samples showed lower gene expression levels of FOXC1 in both methylated and unmethylated tumours compared to normal tissue (P < 0.001). DNA methylation levels of FOXC1, GSTP1, ABCB1 and RASSF1A were higher in oestrogen receptor (ER) positive vs. ER negative tumours; whereas methylation levels of FOXC1, ABCB1, PPP2R2B and PTEN were lower in tumours with a TP53 mutation. Conclusions: Quantitative methylation analysis identified ABCB1, FOXC1, PPP2R2B and PTEN as novel genes to be methylated in DCIS. In particular, FOXC1 showed a significant increase in the methylation frequency in invasive tumours. Low FOXC1 gene expression in both methylated and unmethylated DCIS and IDCs indicates that the loss of its expression is an early event during breast cancer progression.

Breast cancer is a heterogenous disease with variability in tumor cells and in the surrounding tumor microenvironment (TME). Understanding the molecular diversity in breast cancer is critical for improving prediction of therapeutic response and prognostication. High-plex spatial profiling of tumors enables characterization of heterogeneity in the breast TME, which can holistically illuminate the biology of tumor growth, dissemination and, ultimately, response to therapy. The GeoMx Digital Spatial Profiler (DSP) enables researchers to spatially resolve and quantify proteins and RNA transcripts from tissue sections. The platform is compatible with both formalin-fixed paraffin-embedded and frozen tissues. RNA profiling was developed at the whole transcriptome level for human and mouse samples and protein profiling of 100-plex for human samples. Tissue can be optically segmented for analysis of regions of interest or cell populations to study biology-directed tissue characterization. The GeoMx Breast Cancer Consortium (GBCC) is composed of breast cancer researchers who are developing innovative approaches for spatial profiling to accelerate biomarker discovery. Here, the GBCC presents best practices for GeoMx profiling to promote the collection of high-quality data, optimization of data analysis and integration of datasets to advance collaboration and meta-analyses. Although the capabilities of the platform are presented in the context of breast cancer research, they can be generalized to a variety of other tumor types that are characterized by high heterogeneity.