HRAS is a therapeutic target in malignant chemo-resistant adenomyoepithelioma of the breast

Abstract Malignant adenomyoepithelioma (AME) of the breast is an exceptionally rare form of breast cancer, with a significant metastatic potential. Chemotherapy has been used in the management of advanced AME patients, however the majority of treatments are not effective. Recent studies report recurrent mutations in the HRAS Q61 hotspot in small series of AMEs, but there are no preclinical or clinical data showing H-Ras protein as a potential therapeutic target in malignant AMEs. We performed targeted sequencing of tumours’ samples from new series of 13 AMEs, including 9 benign and 4 malignant forms. Samples from the breast tumour and the matched axillary metastasis of one malignant HRAS mutated AME were engrafted and two patient-derived xenografts (PDX) were established that reproduced the typical AME morphology. The metastasis-derived PDX was treated in vivo by different chemotherapies and a combination of MEK and BRAF inhibitors (trametinib and dabrafenib). All malignant AMEs presented a recurrent mutation in the HRAS G13R or G12S hotspot. Mutation of PIK3CA were found in both benign and malignant AMEs, while AKT1 mutations were restricted to benign AMEs. Treatment of the PDX by the MEK inhibitor trametinib, resulted in a marked anti-tumor activity, in contrast to the BRAF inhibitor and the different chemotherapies that were ineffective. Overall, these findings further expand on the genetic features of AMEs and suggest that patients carrying advanced HRAS-mutated AMEs could potentially be treated with MEK inhibitors

A critical evaluation of network and pathway based classifiers for outcome prediction in breast cancer

Recently, several classifiers that combine primary tumor data, like gene expression data, and secondary data sources, such as protein-protein interaction networks, have been proposed for predicting outcome in breast cancer. In these approaches, new composite features are typically constructed by aggregating the expression levels of several genes. The secondary data sources are employed to guide this aggregation. Although many studies claim that these approaches improve classification performance over single gene classifiers, the gain in performance is difficult to assess. This stems mainly from the fact that different breast cancer data sets and validation procedures are employed to assess the performance. Here we address these issues by employing a large cohort of six breast cancer data sets as benchmark set and by performing an unbiased evaluation of the classification accuracies of the different approaches. Contrary to previous claims, we find that composite feature classifiers do not outperform simple single gene classifiers. We investigate the effect of (1) the number of selected features; (2) the specific gene set from which features are selected; (3) the size of the training set and (4) the heterogeneity of the data set on the performance of composite feature and single gene classifiers. Strikingly, we find that randomization of secondary data sources, which destroys all biological information in these sources, does not result in a deterioration in performance of composite feature classifiers. Finally, we show that when a proper correction for gene set size is performed, the stability of single gene sets is similar to the stability of composite feature sets. Based on these results there is currently no reason to prefer prognostic classifiers based on composite features over single gene classifiers for predicting outcome in breast cancer

The influence of feature selection methods on accuracy, stability and interpretability of molecular signatures

Motivation: Biomarker discovery from high-dimensional data is a crucial problem with enormous applications in biology and medicine. It is also extremely challenging from a statistical viewpoint, but surprisingly few studies have investigated the relative strengths and weaknesses of the plethora of existing feature selection methods. Methods: We compare 32 feature selection methods on 4 public gene expression datasets for breast cancer prognosis, in terms of predictive performance, stability and functional interpretability of the signatures they produce. Results: We observe that the feature selection method has a significant influence on the accuracy, stability and interpretability of signatures. Simple filter methods generally outperform more complex embedded or wrapper methods, and ensemble feature selection has generally no positive effect. Overall a simple Student's t-test seems to provide the best results. Availability: Code and data are publicly available at http://cbio.ensmp.fr/~ahaury/

Integrative molecular and functional profiling of ERBB2-amplified breast cancers identifies new genetic dependencies.

Overexpression of the receptor tyrosine kinase ERBB2 (also known as HER2) occurs in around 15% of breast cancers and is driven by amplification of the ERBB2 gene. ERBB2 amplification is a marker of poor prognosis, and although anti-ERBB2-targeted therapies have shown significant clinical benefit, de novo and acquired resistance remains an important problem. Genomic profiling has demonstrated that ERBB2+ve breast cancers are distinguished from ER+ve and 'triple-negative' breast cancers by harbouring not only the ERBB2 amplification on 17q12, but also a number of co-amplified genes on 17q12 and amplification events on other chromosomes. Some of these genes may have important roles in influencing clinical outcome, and could represent genetic dependencies in ERBB2+ve cancers and therefore potential therapeutic targets. Here, we describe an integrated genomic, gene expression and functional analysis to determine whether the genes present within amplicons are critical for the survival of ERBB2+ve breast tumour cells. We show that only a fraction of the ERBB2-amplified breast tumour lines are truly addicted to the ERBB2 oncogene at the mRNA level and display a heterogeneous set of additional genetic dependencies. These include an addiction to the transcription factor gene TFAP2C when it is amplified and overexpressed, suggesting that TFAP2C represents a genetic dependency in some ERBB2+ve breast cancer cell

European breast surgical oncology certification theoretical and practical knowledge curriculum 2020

The Breast Surgery theoretical and practical knowledge curriculum comprehensively describes the knowledge and skills expected of a fully trained surgeon practicing in the European Union and European Economic Area (EEA). It forms part of a range of factors that contribute to the delivery of high quality cancer care. It has been developed by a panel of experts from across Europe and has been validated by professional breast surgery societies in Europe. The curriculum maps closely to the syllabus of the Union of European Medical Specialists (UEMS) Breast Surgery Exam, the UK FRCS (breast specialist interest) curriculum and other professional standards across Europe and globally (USA Society of Surgical Oncology, SSO). It is envisioned that this will serve as the basis for breast surgery training, examination and accreditation across Europe to harmonise and raise standards as breast surgery develops as a separate discipline from its parent specialties (general surgery, gynaecology, surgical oncology and plastic surgery). The curriculum is not static but will be revised and updated by the curriculum development group of the European Breast Surgical Oncology Certification group (BRESO) every 2 years