The Clinicopathological Features and Prognostic Significance of HER2-Low in Early Breast Tumors Patients Prognostic Comparison of HER-Low and HER2-Negative Breast Cancer Stratified by Hormone Receptor Status.

Introduction: There has been increased interest in HER2-low breast tumors recently, as these tumors may have distinct clinical and molecular characteristics compared to HER2-negative and HER2-positive tumors. A new nomenclature has been proposed for HER2 1+ and HER2 2+ tumors that are confirmed negative according to fluorescence in situ hybridization (FISH). These tumors are now referred to as HER2-low, and it is thought that they may represent a distinct subtype of breast cancer that warrants further investigation. In this study, we aimed to evaluate the clinicopathological characteristics and prognostic impact of this particular subtype in a North-African context where HER2-low breast cancer is a relatively understudied subtype, particularly in non-Western populations. Methods: We conducted a retrospective cohort study on 1955 breast tumors in Moroccan patients over 10 years, collected at the Pathology Department of Ibn Rochd University Hospital in Casablanca and at the pathology department of Hassan II University Hospital in Fes. We elaborated on their complete immunohistochemical profile based on the main breast cancer biomarkers: Ki-67, HER2, estrogen, and progesterone receptors. Their overall survival and disease free survival data were also retrieved from their respective records. Results: Out of 1955 BC patients, 49.3% were classified as HER2-low; of which 80.7% and 19.2% were hormone receptors positive and negative, respectively. The clinicopathologic features indicate that HER2-low subtype tumors behave much more like HER2-positive than HER2-negative tumors. The survival analysis showed that the HER2-low subtype-belonging patients present significantly the poorest prognosis in disease-free survival (p = 0.003) in comparison with HER2-negative ones. When considering the hormonal status, hormonal-dependent tumors show a significant difference according to HER2 subtypes in disease-free survival (p < 0.001). Yet no significant difference was shown among hormonal negative tumors. Moreover, patients with hormonal positive tumors and simultaneously belonging to the HER2-low subgroup present a significantly good prognosis in overall survival compared to the ones with hormonal negative tumors (p = 0.008). Conclusion: Our study has shown that the HER2-low phenotype is common among hormone-positive patients. The clinicopathological features and prognostic data indicate that the hormonal receptors effect and HER2 heterogeneity are crucial factors to consider. It is important to note that this particular subgroup is different from the HER2-negative one and should not be treated in the same way. Therefore, this study offers a new perspective in the management of HER2-low patients and can serve as a basis for future prospective analyses.

Identification of a minimum number of genes to predict triple-negative breast cancer subgroups from gene expression profiles.

BACKGROUND: Triple-negative breast cancer (TNBC) is a very heterogeneous disease. Several gene expression and mutation profiling approaches were used to classify it, and all converged to the identification of distinct molecular subtypes, with some overlapping across different approaches. However, a standardised tool to routinely classify TNBC in the clinics and guide personalised treatment is lacking. We aimed at defining a specific gene signature for each of the six TNBC subtypes proposed by Lehman et al. in 2011 (basal-like 1 (BL1); basal-like 2 (BL2); mesenchymal (M); immunomodulatory (IM); mesenchymal stem-like (MSL); and luminal androgen receptor (LAR)), to be able to accurately predict them. METHODS: Lehman's TNBCtype subtyping tool was applied to RNA-sequencing data from 482 TNBC (GSE164458), and a minimal subtype-specific gene signature was defined by combining two class comparison techniques with seven attribute selection methods. Several machine learning algorithms for subtype prediction were used, and the best classifier was applied on microarray data from 72 Italian TNBC and on the TNBC subset of the BRCA-TCGA data set. RESULTS: We identified two signatures with the 120 and 81 top up- and downregulated genes that define the six TNBC subtypes, with prediction accuracy ranging from 88.6 to 89.4%, and even improving after removal of the least important genes. Network analysis was used to identify highly interconnected genes within each subgroup. Two druggable matrix metalloproteinases were found in the BL1 and BL2 subsets, and several druggable targets were complementary to androgen receptor or aromatase in the LAR subset. Several secondary drug-target interactions were found among the upregulated genes in the M, IM and MSL subsets. CONCLUSIONS: Our study took full advantage of available TNBC data sets to stratify samples and genes into distinct subtypes, according to gene expression profiles. The development of a data mining approach to acquire a large amount of information from several data sets has allowed us to identify a well-determined minimal number of genes that may help in the recognition of TNBC subtypes. These genes, most of which have been previously found to be associated with breast cancer, have the potential to become novel diagnostic markers and/or therapeutic targets for specific TNBC subsets.

Ki-67 proliferation index to further stratify invasive breast cancer molecular subtypes: Northern African comparative cohort-study with external TCGA-BRCA and METABRIC validation.

Introduction: breast cancer (BC) is a malignancy with very high incidence and mortality in Africa, especially in Western Africa, where more than 25 thousand deaths are registered every year. Not all BC have the same prognosis, and being able to personalize treatment and predict aggressiveness is of crucial importance. The purpose of our study is to explore further subdivisions associated with prognosis, beyond breast cancer molecular classification that is routinely established in pathology departments. Methods: we conducted a 5-year retrospective cohort study on 1266 invasive BC of Moroccan patients, collected at the Pathology Department of Ibn-Rochd University Hospital in Casablanca, and followed at King Mohammed VI National Centre for the Treatment of Cancers. We elaborated an Estimation-Maximization Clustering, based on the main BC biomarkers: Ki-67, HER2, estrogen and progesterone receptors, evaluated by immunohistochemistry. Two independent datasets (TCGA-BRCA and Metabric) were also analyzed to assess the external reproducibility of the results. Results: each molecular subgroup could be partitioned into two further subdivisions: Cluster1, with average Ki-67 of 16.26% (±11.9) across all molecular subgroups and higher frequency within luminal BC, and Cluster2, with average Ki-67 of 68.8%(±18) across all molecular subgroups and higher frequency in HER2 as well as in triple-negative BC. Overall survival of the two Clusters was significantly different, with 5-year rates of 52 and 37 months for Custer1 and Cluster2, respectively (p=0.000001). Moreover, mortality rates within the same molecular subgroup, especially in luminal B HER2-, varied remarkably depending on Cluster membership (6% for C1 and 18% for C2 after 1 year of follow-up). Two different algorithms to evaluate the prognostic importance, variable selection using random forests (VSURF) and Minimal depth, ranked the subdivision proposed as one of the 4 most influential features being able to predict patient survival better than several histoprognostic features, both in the Moroccan and in the external datasets. Conclusion: our results highlight a new refinement of the BC molecular classification and provide a simple and improved way to classify tumors that could be applied in low to middle-income countries. This is the first study of its kind addressed in an African context.