Biology subjects

Levy, S.

Publications and source records attributed to Levy, S..

5 recordsLinked to original sources

bioRxiv2018

Detection of early stage pancreatic cancer using 5-hydroxymethylcytosine signatures in circulating cell free DNA

Pancreatic cancers are typically diagnosed at late stage where disease prognosis is poor as exemplified by a 5-year survival rate of 8.2%. Earlier diagnosis would be beneficial by enabling surgical resection or earlier application of therapeutic regimens. We investigated the detection of pancreatic ductal adenocarcinoma (PDAC) in a non-invasive manner by interrogating changes in 5-hydroxymethylation cytosine status (5hmC) of circulating cell free DNA in the plasma of a PDAC cohort (n=51) in comparison with a non-cancer cohort (n=41). We found that 5hmC sites are enriched in a disease and stage specific manner in exons, 3UTRs and transcription termination sites. Our data show that 5hmC density is reduced in promoters and histone H3K4me3-associated sites with progressive disease suggesting increased transcriptional activity. 5hmC density is differentially represented in thousands of genes, and a stringently filtered set of the most significant genes points to biology related to pancreas (GATA4, GATA6, PROX1, ONECUT1) and/or cancer development (YAP1, TEAD1, PROX1, ONECUT1, ONECUT2, IGF1 and IGF2). Regularized regression models were built using 5hmC densities in statistically filtered genes or a comprehensive set of highly variable 5hmC counts in genes and performed with an AUC = 0.94-0.96 on training data. We were able to test the ability to classify PDAC and non-cancer samples with the Elastic net and Lasso models on two external pancreatic cancer 5hmC data sets and found validation performance to be AUC = 0.74-0.97. The findings suggest that 5hmC changes enable classification of PDAC patients with high fidelity and are worthy of further investigation on larger cohorts of patient samples.

cancer biology↗

bioRxiv2018

Single-cell copy number variant detection reveals the dynamics and diversity of adaptation

Copy number variants (CNVs) are a pervasive, but understudied source of genetic variation and evolutionary potential. Long-term evolution experiments in chemostats provide an ideal system for studying the molecular processes underlying CNV formation and the temporal dynamics of de novo CNVs. Here, we developed a fluorescent reporter to monitor gene amplifications and deletions at a specific locus with single-cell resolution. Using a CNV reporter in nitrogen-limited chemostats, we find that GAP1 CNVs are repeatedly generated and selected during the early stages of adaptive evolution resulting in predictable dynamics of CNV selection. However, subsequent diversification of populations defines a second phase of evolutionary dynamics that cannot be predicted. Using whole genome sequencing, we identified a variety of GAP1 CNVs that vary in size and copy number. Despite GAP1s proximity to tandem repeats that facilitate intrachromosomal recombination, we find that non-allelic homologous recombination (NAHR) between flanking tandem repeats occurs infrequently. Rather, breakpoint characterization revealed that for at least 50% of GAP1 CNVs, origin-dependent inverted-repeat amplification (ODIRA), a DNA replication mediated process, is the likely mechanism. We also find evidence that ODIRA generates DUR3 CNVs, indicating that it may be a common mechanism of gene amplification. We combined the CNV reporter with barcode lineage tracking and found that 103-104 independent CNV-containing lineages initially compete within populations, which results in extreme clonal interference. Our study introduces a novel means of studying CNVs in heterogeneous cell populations and provides insight into the underlying dynamics of CNVs in evolution.

evolutionary biology↗

bioRxiv2018

Tumor regression mediated by oncogene withdrawal or erlotinib stimulates infiltration ofinflammatory immune cells in EGFR mutant lung tumors

Epidermal Growth Factor Receptor (EGFR) tyrosine kinase inhibitors (TKIs) like erlotinib are effective for treating patients with EGFR mutant lung cancer; however, drug resistance inevitably emerges. Approaches to combine immunotherapies and targeted therapies to overcome or delay drug resistance have been hindered by limited knowledge of the effect of erlotinib on tumor-infiltrating immune cells. Using mouse models, we studied the immunological profile of mutant EGFR-driven lung tumors before and after erlotinib treatment. We found that erlotinib triggered the recruitment of inflammatory T cells into the lungs. Interestingly, this phenotype could be recapitulated by tumor regression mediated by deprivation of the EGFR oncogene indicating that tumor regression alone was sufficient for these immunostimulatory effects. Erlotinib treatment also led to increased maturation of myeloid cells and an increase in CD40+ dendritic cells. Our findings lay the foundation for understanding the effects of TKIs on the tumor microenvironment and highlights potential avenues for investigation of targeted and immuno-therapy combination strategies to treat EGFR mutant lung cancer.

cancer biology↗

bioRxiv2017

Comprehensive Benchmarking and Ensemble Approaches for Metagenomic Classifiers

BackgroundOne of the main challenges in metagenomics is the identification of microorganisms in clinical and environmental samples. While an extensive and heterogeneous set of computational tools is available to classify microorganisms using whole genome shotgun sequencing data, comprehensive comparisons of these methods are limited. In this study, we use the largest (n=35) to date set of laboratory-generated and simulated controls across 846 species to evaluate the performance of eleven metagenomics classifiers. We also assess the effects of filtering and combining tools to reduce the number of false positives.\n\nResultsTools were characterized on the basis of their ability to (1) identify taxa at the genus, species, and strain levels, (2) quantify relative abundance measures of taxa, and (3) classify individual reads to the species level. Strikingly, the number of species identified by the eleven tools can differ by over three orders of magnitude on the same datasets. However, various strategies can ameliorate taxonomic misclassification, including abundance filtering, ensemble approaches, and tool intersection. Indeed, leveraging tools with different heuristics is beneficial for improved precision. Nevertheless, these strategies were often insufficient to completely eliminate false positives from environmental samples, which are especially important where they concern medically relevant species and where customized tools may be required.\n\nConclusionsThe results of this study provide positive controls, titrated standards, and a guide for selecting tools for metagenomic analyses by comparing ranges of precision and recall. We show that proper experimental design and analysis parameters, including depth of sequencing, choice of classifier or classifiers, database size, and filtering, can reduce false positives, provide greater resolution of species in complex metagenomic samples, and improve the interpretation of results.

genomics↗

bioRxiv2017

Extremely rare variants reveal patterns of germline mutation rate heterogeneity in humans

A detailed understanding of the genome-wide variability of single-nucleotide germline mutation rates is essential to studying human genome evolution. Here we use [~]36 million singleton variants from 3,560 whole-genome sequences to infer fine-scale patterns of mutation rate heterogeneity. Mutability is jointly affected by adjacent nucleotide context and diverse genomic features of the surrounding region, including histone modifications, replication timing, and recombination rate, sometimes suggesting specific mutagenic mechanisms. Remarkably, GC content, DNase hypersensitivity, CpG islands, and H3K36 trimethylation are associated with both increased and decreased mutation rates depending on nucleotide context. We validate these estimated effects in an independent dataset of [~]46,000 de novo mutations, and confirm our estimates are more accurate than previously published estimates based on ancestrally older variants without considering genomic features. Our results thus provide the most refined portrait to date of the factors contributing to genome-wide variability of the human germline mutation rate.

genomics↗