Diaz, J. E. | Search bioRxiv

bioRxiv2020

Functional Exploration of Copy Number Alterations in a Drosophila Model of Triple Negative Breast Cancer

Accounting for 10-20% of breast cancer cases, TNBC is associated with a disproportionate number of breast cancer deaths. Despite recent progress, many patients fail to respond to current targeted therapies. Responses to chemotherapy are variable, and the tumor characteristics that determine response are poorly understood. One challenge in studying TNBC is its genomic profile: outside of TP53 loss, most cases are characterized by copy number alterations (CNAs), making modeling the disease in whole animals challenging. We analyzed 186 previously identified CNA regions in breast cancer to rank genes within each region by likelihood of acting as a tumor driver. We characterized a Drosophila p53-Myc model of TNBC, demonstrating aspects of transformation. We then used this model to assess highly ranked genes, identifying 48 as functional drivers. To demonstrate the utility of this functional database, we combined six of these drivers with p53-Myc to generate six 3-hit genotypes. These 3-hit models showed increased aspects of transformation as well as resistance to the standard-of-care chemotherapeutic drug fluorouracil. Our work provides a functional database of CNA-associated TNBC drivers, and uses this database to support the model that increased genetic complexity leads to increased therapeutic resistance. Further, we provide a template for an integrated computational/whole animal approach to identify functional drivers of transformation and drug resistance within CNAs for other tumor types.

genetics↗

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↗