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Multiomic analysis of microRNA-mediated regulation reveals a proliferative axis involving miR-10b in fibrolamellar carcinoma
Adam B. Francisco, Matt Kanke, Andrew P. Massa, Timothy A. Dinh, Ramja Sritharan, Khashayar Vakili, Nabeel Bardeesy, Praveen Sethupathy
Adam B. Francisco, Matt Kanke, Andrew P. Massa, Timothy A. Dinh, Ramja Sritharan, Khashayar Vakili, Nabeel Bardeesy, Praveen Sethupathy
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Research Article Oncology

Multiomic analysis of microRNA-mediated regulation reveals a proliferative axis involving miR-10b in fibrolamellar carcinoma

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Abstract

Fibrolamellar carcinoma (FLC) is an aggressive liver cancer primarily afflicting adolescents and young adults. Most patients with FLC harbor a heterozygous deletion on chromosome 19 that leads to the oncogenic gene fusion, DNAJB1-PRKACA. There are currently no effective therapeutics for FLC. To address that, it is critical to gain deeper mechanistic insight into FLC pathogenesis. We assembled a large sample set of FLC and nonmalignant liver tissue (n = 52) and performed integrative multiomic analysis. Specifically, we carried out small RNA sequencing to define altered microRNA expression patterns in tumor samples and then coupled this analysis with RNA sequencing and chromatin run-on sequencing data to identify candidate master microRNA regulators of gene expression in FLC. We also evaluated the relationship between DNAJB1-PRKACA and microRNAs of interest in several human and mouse cell models. Finally, we performed loss-of-function experiments for a specific microRNA in cells established from a patient-derived xenograft (PDX) model. We identified miR-10b-5p as the top candidate pro-proliferative microRNA in FLC. In multiple human cell models, overexpression of DNAJB1-PRKACA led to significant upregulation of miR-10b-5p. Inhibition of miR-10b in PDX-derived cells increased the expression of several potentially novel target genes, concomitant with a significant reduction in metabolic activity, proliferation, and anchorage-independent growth. This study highlights a potentially novel proliferative axis in FLC and provides a rich resource for further investigation of FLC etiology.

Authors

Adam B. Francisco, Matt Kanke, Andrew P. Massa, Timothy A. Dinh, Ramja Sritharan, Khashayar Vakili, Nabeel Bardeesy, Praveen Sethupathy

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Figure 1

miR-10b is among the most upregulated microRNAs in FLC.

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miR-10b is among the most upregulated microRNAs in FLC.
(A) qPCR showing...
(A) qPCR showing the relative quantitative value (RQV) of DP in a subset of FLC samples (n = 15) compared with NML samples (n = 6). Data points represent individual patient samples. Cycle threshold values can be found in Supplemental Table 1. (B) Patient age distribution across FLC samples used in this study for which age is known (n = 35). (C) Principal component analysis of VST normalized counts for the NML (n = 10) and FLC (n = 33) data sets. The percent of variation explained is indicated for component 1 (x axis) and component 2 (y axis). NML and FLC samples are colored green and red. (D) Principal component analysis plot in which the patient age and sexual phenotype information are overlaid. Female, male, and unreported patients are indicated by circles, triangles, and squares, respectively. The color intensity, from dark to light, indicates increasing patient age at the time of surgery. (E) Unsupervised hierarchical clustering of the Euclidean distances among samples was calculated based on VST normalized counts. FLC and NML samples are indicted by red and green boxes. (F) Volcano plot showing microRNAs that are significantly differentially expressed (average normalized counts > 1000 in either NML or FLC, coefficient of variance < 2 across FLC samples). Dashed lines represent the log2 FC of expression –2/+2 (vertical) and adjusted P = 0.05 (horizontal). Up- or downregulated microRNAs are colored red or blue, respectively. (G and H) Heatmaps showing the normalized expression of up- or downregulated microRNAs (in rows) in each patient sample (in columns). Expression is scaled by row with a max/min of +2/–2 shown. P values are calculated by 2-tailed Student’s t test.

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