Primary triple negative breast cancers (TNBC) represent approximately 16% of all breast cancers1 and are a tumour type defined by exclusion, for which comprehensive landscapes of somatic mutation have not been determined. Here we show in 104 early TNBC cases, that at the time of diagnosis these cancers exhibit a wide and continuous spectrum of genomic evolution, with some exhibiting only a handful of somatic aberrations in a few pathways, whereas others contain hundreds of somatic events and multiple pathways implicated. Integration with matched whole transcriptome sequence data revealed that only ~36% of mutations are expressed. By examining single nucleotide variant (SNV) allelic abundance derived from deep re-sequencing (median >20,000 fold) measurements in 2414 somatic mutations, we determine for the first time in an epithelial tumour, the relative abundance of clonal genotypes among cases in the population. We show that TNBC vary widely and continuously in their clonal frequencies at the time of diagnosis, with basal subtype TNBC2,3 exhibiting more variation than non-basal TNBC. Although p53 and PIK3CA/PTEN somatic mutations appear clonally dominant compared with other pathways, in some tumours their clonal frequencies are incompatible with founder status. Mutations in cytoskeletal and cell shape/motility proteins occurred at lower clonal frequencies, suggesting they occurred later during tumour progression. Taken together our results show that future attempts to dissect the biology and therapeutic responses of TNBC will require the determination of individual tumour clonal genotypes.

We introduce a novel statistical method, PyClone, for inference of clonal population structures in cancers. PyClone is a Bayesian clustering method for grouping sets of deeply sequenced somatic mutations into putative clonal clusters while estimating their cellular prevalences and accounting for allelic imbalances introduced by segmental copy number changes and normal cell contamination. Single cell sequencing validation demonstrates that PyClone infers accurate clustering of mutations that co-occur in individual cells.Human cancer progresses under Darwinian evolution where (epi)genetic variation alters molecular phenotypes in individual cells 1 . Consequently, tumours at diagnosis often consist of multiple, genotypically distinct cell populations ( Supplementary Fig. 1) 2 . These populations, referred to as clones, are related through a phylogeny and act as substrates for selection in tumour micro-environments or with therapeutic intervention 2, 3 . The prevalence of a particular clone measured over time or in anatomic space is a reflection of its growth and proliferative fitness. Thus, ascertaining the dynamic prevalence of clones can identify precise genetic determinants of phenotypes such as acquisition of metastatic potential or chemotherapeutic resistance.In this contribution, we provide a statistical model for analysis of deeply sequenced (coverage >1000x) mutations to identify and quantify clonal populations in tumours, which extends to modelling mutations measured in multiple samples from the same patient. Our approach uses the measurement of allelic prevalence to estimate the proportion of tumour ♣

prostate or other sites. These older men particularly are vulnerable

Zebrafish (Danio rerio) are rapidly gaining popularity in translational neuroscience and behavioral research. Physiological similarity to mammals, ease of genetic manipulations, sensitivity to pharmacological and genetic factors, robust behavior, low cost, and potential for high-throughput screening contribute to the growing utility of zebrafish models in this field. Understanding zebrafish behavioral phenotypes provides important insights into neural pathways, physiological biomarkers, and genetic underpinnings of normal and pathological brain function. Novel zebrafish paradigms continue to appear with an encouraging pace, thus necessitating a consistent terminology and improved understanding of the behavioral repertoire. What can zebrafish 'do', and how does their altered brain function translate into behavioral actions? To help address these questions, we have developed a detailed catalog of zebrafish behaviors (Zebrafish Behavior Catalog, ZBC) that covers both larval and adult models. Representing a beginning of creating a more comprehensive ethogram of zebrafish behavior, this effort will improve interpretation of published findings, foster cross-species behavioral modeling, and encourage new groups to apply zebrafish neurobehavioral paradigms in their research. In addition, this glossary creates a framework for developing a zebrafish neurobehavioral ontology, ultimately to become part of a unified animal neurobehavioral ontology, which collectively will contribute to better integration of biological data within and across species.

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