168 research outputs found
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
16. Quel est le temps nécessaire à un chirurgien pour modifier ses pratiques en chirurgie du sein ?
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