Highlights d Genomic data linked to health records capture demography in health systems d Genetic networks reveal recent common ancestry in diverse populations d Evidence of many founder populations in New York City d Fine-scale population structure impacts genetic risk predictions

Background: Over time, the chance of cure after the diagnosis of breast cancer has been increasing, as a consequence of earlier diagnosis, improved diagnostic procedures and more effective treatment options. However, oncologists are concerned by the risk of long term treatment side effects, including congestive heart failure (CHF). Methods: In this study, we evaluated innovative circulating cardiac biomarkers during and after anthracycline-based neoadjuvant chemotherapy (NAC) in breast cancer patients. Levels of cardiac-specific troponins T (cTnT), N-terminal natriuretic peptides (NT-proBNP), soluble ST2 (sST2) and 10 circulating microRNAs (miRNAs) were measured.

Circulating microRNAs (miRNAs) are increasingly recognized as powerful biomarkers in several pathologies, including breast cancer. Here, their plasmatic levels were measured to be used as an alternative screening procedure to mammography for breast cancer diagnosis.A plasma miRNA profile was determined by RT-qPCR in a cohort of 378 women. A diagnostic model was designed based on the expression of 8 miRNAs measured first in a profiling cohort composed of 41 primary breast cancers and 45 controls, and further validated in diverse cohorts composed of 108 primary breast cancers, 88 controls, 35 breast cancers in remission, 31 metastatic breast cancers and 30 gynecologic tumors.A receiver operating characteristic curve derived from the 8-miRNA random forest based diagnostic tool exhibited an area under the curve of 0.81. The accuracy of the diagnostic tool remained unchanged considering age and tumor stage. The miRNA signature correctly identified patients with metastatic breast cancer. The use of the classification model on cohorts of patients with breast cancers in remission and with gynecologic cancers yielded prediction distributions similar to that of the control group.Using a multivariate supervised learning method and a set of 8 circulating miRNAs, we designed an accurate, minimally invasive screening tool for breast cancer.

Whole transcriptome studies typically yield large amounts of data, with expression values for all genes or transcripts of the genome. The search for genes of interest in a particular study setting can thus be a daunting task, usually relying on automated computational methods. Moreover, most biological questions imply that such a search should be performed in a multivariate setting, to take into account the inter-genes relationships. Differential expression analysis commonly yields large lists of genes deemed significant, even after adjustment for multiple testing, making the subsequent study possibilities extensive. Here, we explore the use of supervised learning methods to rank large ensembles of genes defined by their expression values measured with RNA-Seq in a typical 2 classes sample set. First, we use one of the variable importance measures generated by the random forests classification algorithm as a metric to rank genes. Second, we define the EPS (extreme pseudo-samples) pipeline, making use of VAEs (Variational Autoencoders) and regressors to extract a ranking of genes while leveraging the feature space of both virtual and comparable samples. We show that, on 12 cancer RNA-Seq data sets ranging from 323 to 1,210 samples, using either a random forests-based gene selection method or the EPS pipeline outperforms differential expression analysis for 9 and 8 out of the 12 datasets respectively, in terms of identifying subsets of genes associated with survival. These results demonstrate the potential of supervised learning-based gene selection methods in RNA-Seq studies and highlight the need to use such multivariate gene selection methods alongside the widely used differential expression analysis.

Non-coding RNAs (ncRNA) represent 1/5 of the mammalian transcript number, and 90% of the genome length is transcribed. Many ncRNAs play a role in cancer. Among them, non-coding natural antisense transcripts (ncNAT) are RNA sequences that are complementary and overlapping to those of either protein-coding (PCT) or non-coding transcripts. Several ncNATs were described as regulating protein coding gene expression on the same loci, and they are expected to act more frequently in cis compared to other ncRNAs that commonly function in trans. In this work, 22 breast cancers expressing estrogen receptors and their paired adjacent non-malignant tissues were analyzed by strand-specific RNA sequencing. To highlight ncNATs potentially playing a role in protein coding gene regulations that occur in breast cancer, three different data analysis methods were used: differential expression analysis of ncNATs between tumor and non-malignant tissues, differential correlation analysis of paired ncNAT/PCT between tumor and non-malignant tissues, and ncNAT/PCT read count ratio variation between tumor and non-malignant tissues. Each of these methods yielded lists of ncNAT/PCT pairs that were enriched in survival-associated genes. This work highlights ncNAT lists that display potential to affect the expression of protein-coding genes involved in breast cancer pathology.