Simple combination of multiple somatic variant callers to increase accuracy

Publications comparing variant caller algorithms present discordant results with contradictory rankings. Caller performances are inconsistent and wide ranging, and dependent upon input data, application, parameter settings, and evaluation metric. With no single variant caller emerging as a superior standard, combinations or ensembles of variant callers have appeared in the literature. In this study, a whole genome somatic reference standard was used to derive principles to guide strategies for combining variant calls. Then, manually annotated variants called from the whole exome sequencing of a tumor were used to corroborate these general principles. Finally, we examined the ability of these principles to reduce noise in targeted sequencing

Predicting Clinical Response to Everolimus in ER+ Breast Cancers Using Machine-Learning

Endocrine therapy remains the primary treatment choice for ER+ breast cancers. However, most advanced ER+ breast cancers ultimately develop resistance to endocrine. This acquired resistance to endocrine therapy is often driven by the activation of the PI3K/AKT/mTOR signaling pathway. Everolimus, a drug that targets and inhibits the mTOR complex has been shown to improve clinical outcomes in metastatic ER+ breast cancers. However, there are no biomarkers currently available to guide the use of everolimus in the clinic for progressive patients, where multiple therapeutic options are available. Here, we utilized gene expression signatures from 9 ER+ breast cancer cell lines and 23 patients treated with everolimus to develop and validate an integrative machine learning biomarker of mTOR inhibitor response. Our results show that the machine learning biomarker can successfully distinguish responders from non-responders and can be applied to identify patients that will most likely benefit from everolimus treatment

Utilization of Genomic Signatures to Identify Phenotype-Specific Drugs

Genetic and genomic studies highlight the substantial complexity and heterogeneity of human cancers and emphasize the general lack of therapeutics that can match this complexity. With the goal of expanding opportunities for drug discovery, we describe an approach that makes use of a phenotype-based screen combined with the use of multiple cancer cell lines. In particular, we have used the NCI-60 cancer cell line panel that includes drug sensitivity measures for over 40,000 compounds assayed on 59 independent cells lines. Targets are cancer-relevant phenotypes represented as gene expression signatures that are used to identify cells within the NCI-60 panel reflecting the signature phenotype and then connect to compounds that are selectively active against those cells. As a proof-of-concept, we show that this strategy effectively identifies compounds with selectivity to the RAS or PI3K pathways. We have then extended this strategy to identify compounds that have activity towards cells exhibiting the basal phenotype of breast cancer, a clinically-important breast cancer characterized as ER-, PR-, and Her2- that lacks viable therapeutic options. One of these compounds, Simvastatin, has previously been shown to inhibit breast cancer cell growth in vitro and importantly, has been associated with a reduction in ER-, PR- breast cancer in a clinical study. We suggest that this approach provides a novel strategy towards identification of therapeutic agents based on clinically relevant phenotypes that can augment the conventional strategies of target-based screens

Phylogenetic Inference From Single-cell RNA-seq Data

Tumors are comprised of subpopulations of cancer cells that harbor distinct genetic profiles and phenotypes that evolve over time and during treatment. By reconstructing the course of cancer evolution, we can understand the acquisition of the malignant properties that drive tumor progression. Unfortunately, recovering the evolutionary relationships of individual cancer cells linked to their phenotypes remains a difficult challenge. To address this need, we have developed PhylinSic, a method that reconstructs the phylogenetic relationships among cells linked to their gene expression profiles from single cell RNA-sequencing (scRNA-Seq) data. This method calls nucleotide bases using a probabilistic smoothing approach and then estimates a phylogenetic tree using a Bayesian modeling algorithm. We showed that PhylinSic identified evolutionary relationships underpinning drug selection and metastasis and was sensitive enough to identify subclones from genetic drift. We found that breast cancer tumors resistant to chemotherapies harbored multiple genetic lineages that independently acquired high K-Ras and β-catenin, suggesting that therapeutic strategies may need to control multiple lineages to be durable. These results demonstrated that PhylinSic can reconstruct evolution and link the genotypes and phenotypes of cells across monophyletic tumors using scRNA-Seq

Biomedical term mapping databases

Longer words and phrases are frequently mapped onto a shorter form such as abbreviations or acronyms for efficiency of communication. These abbreviations are pervasive in all aspects of biology and medicine and as the amount of biomedical literature grows, so does the number of abbreviations and the average number of definitions per abbreviation. Even more confusing, different authors will often abbreviate the same word/phrase differently. This ambiguity impedes our ability to retrieve information, integrate databases and mine textual databases for content. Efforts to standardize nomenclature, especially those doing so retrospectively, need to be aware of different abbreviatory mappings and spelling variations. To address this problem, there have been several efforts to develop computer algorithms to identify the mapping of terms between short and long form within a large body of literature. To date, four such algorithms have been applied to create online databases that comprehensively map biomedical terms and abbreviations within MEDLINE: ARGH (http://lethargy.swmed.edu/ARGH/argh.asp), the Stanford Biomedical Abbreviation Server (http://bionlp.stanford.edu/abbreviation/), AcroMed (http://medstract.med.tufts.edu/acro1.1/index.htm) and SaRAD (http://www.hpl.hp.com/research/idl/projects/abbrev.html). In addition to serving as useful computational tools, these databases serve as valuable references that help biologists keep up with an ever-expanding vocabulary of terms

Human Nop2/nsun1 Regulates Ribosome Biogenesis Through Non-catalytic Complex Formation With Box C/d Snornps

5-Methylcytosine (m5C) is a base modification broadly found on various RNAs in the human transcriptome. In eukaryotes, m5C is catalyzed by enzymes of the NSUN family composed of seven human members (NSUN1-7). NOP2/NSUN1 has been primarily characterized in budding yeast as an essential ribosome biogenesis factor required for the deposition of m5C on the 25S ribosomal RNA (rRNA). Although human NOP2/NSUN1 has been known to be an oncogene overexpressed in several types of cancer, its functions and substrates remain poorly characterized. Here, we used a miCLIP-seq approach to identify human NOP2/NSUN1 RNA substrates. Our analysis revealed that NOP2/NSUN1 catalyzes the deposition of m5C at position 4447 on the 28S rRNA. We also find that NOP2/NSUN1 binds to the 5\u27ETS region of the pre-rRNA transcript and regulates pre-rRNA processing through non-catalytic complex formation with box C/D snoRNAs. We provide evidence that NOP2/NSUN1 facilitates the recruitment of U3 and U8 snoRNAs to pre-90S ribosomal particles and their stable assembly into snoRNP complexes. Remarkably, expression of both WT and catalytically inactive NOP2/NSUN1 in knockdown background rescues the rRNA processing defects and the stable assembly of box C/D snoRNP complexes, suggesting that NOP2/NSUN1-mediated deposition of m5C on rRNA is not required for ribosome synthesis