Biology subjects

Losic, B.

Publications and source records attributed to Losic, B..

6 recordsLinked to original sources

bioRxiv2020

Unannotated small RNA clusters in circulating extracellular vesicles detect early stage liver cancer

BackgroundHepatocellular carcinoma (HCC) is among the deadliest malignancies and surveillance tools for early detection are suboptimal. Extracellular vesicles (EVs) have gained increasing scientific interest due to their involvement in tumor initiation and metastasis, however, most extracellular RNA (exRNA) biomarker studies are limited to annotated genomic regions. MethodsEVs were isolated with ultracentrifugation and nanoDLD and quality assessed by electron microscopy, immunoblotting, nanoparticle tracking, and deconvolution analysis. We performed genome-wide small exRNA sequencing, including unannotated transcripts. We identified small RNA clusters (smRCs) and delineated their key genomic features across biospecimens (blood, urine, tissue) and EV isolation techniques. A 3-smRC signature for early HCC detection was trained and validated in two independent cohorts. ResultsEV-derived smRCs were dominated by uncharacterized, unannotated small RNA and uniformly tiled across the genome with a consensus sequence of 20bp. A 3-smRC signature was significantly overexpressed in circulating EVs of HCC patients compared to controls at risk or patients with non-HCC malignancies (p<0.01, n=157). An independent validation in a phase 2 biomarker study revealed 86% sensitivity and 91% specificity for the detection of early HCC from controls at risk (i.e. cirrhosis or chronic liver disease, n=209) (positive predictive value (PPV): 89%, area under the ROC curve [AUC]: 0.87). The 3-smRC signature was independent of alpha-fetoprotein (p<0.0001) and a composite model yielded an increased AUC of 0.93 (sensitivity: 85%, specificity: 94%, PPV: 95%). ConclusionAn exRNA-based 3-smRC signature from plasma detects early stage HCC, which directly leads to the prospect of a minimally-invasive, blood-only, operator-independent surveillance biomarker. One sentence summaryWe employ a novel, data-driven approach to identify and characterize small RNA clusters from unannotated loci in extracellular vesicle-derived RNA across different cancer types, isolation techniques, and biofluids, facilitating discovery of a robust biomarker for detection of early stage liver cancer.

cancer biology↗

bioRxiv2020

Landscape of oncoviral genotype and co-infection via Human papilloma andHepatitis B viral tumor in-situ profiling

Hepatitis B virus (HBV) and human papillomavirus (HPV) infection are known risk factors for developing several cancers. However, the effect of viral genotype and co-infection in actually driving oncogenesis remains unclear. We have developed and deployed a new scalable, high throughput tool (ViralMine) for sensitive and precise oncoviral genotype deconvolution using tumor RNA sequencing data from 537 virally infected liver, cervical, and head and neck tumors. We provide the first comprehensive integrative landscape of tumor-viral gene expression, viral antigen immunogenicity, patient survival, and mutational profiling organized by tumor onco-viral genotype. We find that HBV and HPV genotype, and surprisingly high rates of multi-genotype co-infection, serve as significant predictors of patient survival, tumor immune responsiveness, and APOBEC activity modulation. Finally, we demonstrate that HPV genotype strongly associates with viral onco-gene expression over cancer type, implying expression may be similar across episomal and stochastic integration-based infections. Significance StatementWe demonstrate that scalable, high-accuracy oncoviral genotyping, gene expression, and co-infection estimation is feasible from legacy tumor RNA-seq data. While HBV and HPV genotype are known risk factors for oncogenesis, viral genotype and co-infection are shown to strongly associate with disease progression, patient survival, mutational signatures, and putative tumor neoantigen immunogenicity, facilitating novel clinical associations with infections.

cancer biology↗

bioRxiv2019

The transcriptomic response of cells to a drug combination is more than the sum of the responses to the monotherapies

Our ability to predict the effects of drug combinations is limited, in part by limited understanding of how the transcriptional response of two monotherapies results in that of their combination. We performed the first analysis of matched time course RNAseq profiling of cells treated with both single drugs and their combinations. The transcriptional signature of the synergistic combination we studied had unique gene expression not seen in either constituent monotherapy. This can be explained mechanistically by the sequential activation of transcription factors in time in the gene regulatory network. The nature of this transcriptional cascade suggests that drug synergy may ensue when the transcriptional responses elicited by two unrelated individual drugs are correlated. We used these results as the basis of a simple prediction algorithm attaining an AUROC of 0.84 in the prediction of synergistic drug combinations in an independent dataset.

cell biology↗

bioRxiv2019

Tumor fitness, immune exhaustion and clinical outcomes: impact of immune checkpoint inhibitors

Recently proposed tumor fitness measures, based on profiling neoepitopes for reactive viral epitope similarity, have been proposed to predict response to immune checkpoint inhibitors in melanoma and small-cell lung cancer. Here we apply these checkpoint based fitness measures measures to the matched checkpoint treatment naive TCGA samples where cytolytic activity imparts a known survival benefit. We observed no significant survival predictive power beyond that of overall patient tumor mutation burden, and furthermore, found no association between checkpoint based fitness and tumor T-cell infiltration, cytolytic activity (CYT), and abundance (TIL burden). In addition, we investigated the key assumption of viral epitope similarity driving immune response in the hepatitis B virally infected liver cancer TCGA cohort, and uncover suggestive evidence that tumor neoepitopes actually dominate viral epitopes in putative immunogenicity and plausibly drive immune response and recruitment.

cancer biology↗

bioRxiv2018

Gut microbiota density influences host physiology and is shaped by host and microbial factors

To identify factors that regulate gut microbiota density and the impact of varied microbiota density on health, we assayed this fundamental ecosystem property in fecal samples across mammals, human disease, and therapeutic interventions. Physiologic features of the host (carrying capacity) and the fitness of the gut microbiota shape microbiota density. Therapeutic manipulation of microbiota density in mice altered host metabolic and immune homeostasis. In humans, gut microbiota density was reduced in Crohns disease, ulcerative colitis, and ileal pouch-anal anastomosis. The gut microbiota in recurrent Clostridium difficile infection had lower density and reduced fitness that were restored by fecal microbiota transplantation. Understanding the interplay between microbiota and disease in terms of microbiota density, host carrying capacity, and microbiota fitness provide new insights into microbiome structure and microbiome targeted therapeutics.

microbiology↗

bioRxiv2017

STAR Chimeric Post For Rapid Detection Of Circular RNA And Fusion Transcripts

MotivationThe biological relevance of chimeric RNA alignments is now well established. Chimera arising as chromosomal fusions are often drivers of cancer, and recently discovered circular RNA are only now being characterized. While software already exists for fusion discovery and quantitation, high false positive rates and high run-times hamper scalable fusion discovery on large datasets. Furthermore, very little software is available for circular RNA detection and quantification.\n\nResultsHere we present STAR Chimeric Post (STARChip), a novel software package that processes chimeric alignments from the STAR aligner and produces annotated circular RNA and high precision fusions in a rapid, efficient, and scalable manner that is appropriate for high dimensional medical omics datasets.\n\nAvailability and ImplementationSTARChip is available at https://github.com/LosicLab/STARChip\n\nContactsbojan.losic@mssm.edu or nicholas.kipp.akers@gmail.com\n\nSupplementary InformationSupplementary figures and tables are available online.

bioinformatics↗