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Biology subjects

Allette, K.

Publications and source records attributed to Allette, K..

2 recordsLinked to original sources

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↗

Multiparameter cell characterization using nanofluidic technology facilitates real-time phenotypic and genotypic elucidation of intratumor heterogeneity

Genetic and functional complexity of bulk tumor has become evident through rapid advances in sequencing technologies. As a unique integrated approach to characterizing tumor heterogeneity, we demonstrate the multifaceted capabilities of a novel nanofluidic platform to enable single-cell phenotypic and genetic profiling of ovarian cancer patient-derived tumor cells. This approach has enabled increased resolution of tumor cell phenotypic and genetic heterogeneity, providing a better understanding of underlying biological drivers of the disease. A range of CA-125 expression levels is observed within cells from individuals, demonstrating clonal diversity consistent with other phenotypic data. Further, TP53 mutation analysis demonstrates a sub-population of cells exhibiting high mutation frequency that likely drives downstream growth kinetics and protein expression. Finally, genomic data is orthogonally used to address clonal heterogeneity across ovarian tumors when compared to bulk sequencing, illustrating the potential for single-cell sequencing data integrated with cellular functional and growth data toward future therapeutic intervention.

genomics↗