TP53 germline mutation testing in early-onset breast cancer: findings from a nationwide cohort.

Early-onset breast cancer may be due to Li-Fraumeni Syndrome (LFS). Current national and international guidelines recommend that TP53 genetic testing should be considered for women with breast cancer diagnosed before the age of 31 years. However, large studies investigating TP53 mutation prevalence in this population are scarce. We collected nationwide laboratory records for all young breast cancer patients tested for TP53 mutations in the Netherlands. Between 2005 and 2016, 370 women diagnosed with breast cancer younger than 30 years of age were tested for TP53 germline mutations, and eight (2.2%) were found to carry a (likely) pathogenic TP53 sequence variant. Among BRCA1/BRCA2 mutation negative women without a family history suggestive of LFS or a personal history of multiple LFS-related tumours, the TP53 mutation frequency was < 1% (2/233). Taking into consideration that TP53 mutation prevalence was comparable or even higher in some studies selecting patients with breast cancer onset at older ages or HER2-positive breast cancers, raises the question of whether a very early age of onset is an appropriate single TP53 genetic testing criterion.

The AURORA initiative for metastatic breast cancer.

Metastatic breast cancer is one of the leading causes of cancer-related mortality among women in the Western world. To date most research efforts have focused on the molecular analysis of the primary tumour to dissect the genotypes of the disease. However, accumulating evidence supports a molecular evolution of breast cancer during its life cycle, with metastatic lesions acquiring new molecular aberrations. Recognising this critical gap of knowledge, the Breast International Group is launching AURORA, a large, multinational, collaborative metastatic breast cancer molecular screening programme. Approximately 1300 patients with metastatic breast cancer who have received no more than one line of systemic treatment for advanced disease will, after giving informed consent, donate archived primary tumour tissue, as well as will donate tissue collected prospectively from the biopsy of metastatic lesions and blood. Both tumour tissue types, together with a blood sample, will then be subjected to next generation sequencing for a panel of cancer-related genes. The patients will be treated at the discretion of their treating physicians per standard local practice, and they will be followed for clinical outcome for 10 years. Alternatively, depending on the molecular profiles found, patients will be directed to innovative clinical trials assessing molecularly targeted agents. Samples of outlier patients considered as 'exceptional responders' or as 'rapid progressors' based on the clinical follow-up will be subjected to deeper molecular characterisation in order to identify new prognostic and predictive biomarkers. AURORA, through its innovative design, will shed light onto some of the unknown areas of metastatic breast cancer, helping to improve the clinical outcome of breast cancer patients.

Ebstein's anomaly may be caused by mutations in the sarcomere protein gene MYH7.

Ebstein's anomaly is a rare congenital heart malformation characterised by adherence of the septal and posterior leaflets of the tricuspid valve to the underlying myocardium. Associated abnormalities of left ventricular morphology and function including left ventricular noncompaction (LVNC) have been observed. An association between Ebstein's anomaly with LVNC and mutations in the sarcomeric protein gene MYH7, encoding ß-myosin heavy chain, has been shown by recent studies. This might represent a specific subtype of Ebstein's anomaly with a Mendelian inheritance pattern. In this review we discuss the association of MYH7 mutations with Ebstein's anomaly and LVNC and its implications for the clinical care for patients and their family members.

Marfan syndrome masked by Down syndrome?

Down syndrome is the most common chromosomal abnormality. A simultaneous occurrence with Marfan syndrome is extremely rare. We present a case of a 28-year-old female with Down syndrome and a mutation in the fibrillin-1 gene. The patient showed strikingly few manifestations of Marfan syndrome. Although variable expression is known to be present in Marfan syndrome, phenotypic expression of Marfan syndrome in our patient might be masked by the co-occurrence of Down syndrome. (Neth Heart J 2009;17:345-8.).

Predicting the clinical behavior of ovarian cancer from gene expression profiles.

We investigated whether prognostic information is reflected in the expression patterns of ovarian carcinoma samples. RNA obtained from seven FIGO stage I without recurrence, seven platin-sensitive advanced-stage (III or IV), and six platin-resistant advanced-stage ovarian tumors was hybridized on a complementary DNA microarray with 21,372 spotted clones. The results revealed that a considerable number of genes exhibit nonaccidental differential expression between the different tumor classes. Principal component analysis reflected the differences between the three tumor classes and their order of transition. Using a leave-one-out approach together with least squares support vector machines, we obtained an estimated classification test accuracy of 100% for the distinction between stage I and advanced-stage disease and 76.92% for the distinction between platin-resistant versus platin-sensitive disease in FIGO stage III/IV. These results indicate that gene expression patterns could be useful in clinical management of ovarian cancer.

Balancing false positives and false negatives for the detection of differential expression in malignancies.

A basic problem of microarray data analysis is to identify genes whose expression is affected by the distinction between malignancies with different properties. These genes are said to be differentially expressed. Differential expression can be detected by selecting the genes with P-values (derived using an appropriate hypothesis test) below a certain rejection level. This selection, however, is not possible without accepting some false positives and negatives since the two sets of P-values, associated with the genes whose expression is and is not affected by the distinction between the different malignancies, overlap. We describe a procedure for the study of differential expression in microarray data based on receiver-operating characteristic curves. This approach can be useful to select a rejection level that balances the number of false positives and negatives and to assess the degree of overlap between the two sets of P-values. Since this degree of overlap characterises the balance that can be reached between the number of false positives and negatives, this quantity can be seen as a quality measure of microarray data with respect to the detection of differential expression. As an example, we apply our method to data sets studying acute leukaemia.