Fibrolamellar Hepatocellular Carcinoma (FLC) is a rare liver cancer affecting adolescents and young adults, with no gender or ethnicity predilection and without history of underlying viral hepatitis, cirrhosis, or other known risk factors. Almost all FLC patients present a somatic heterozygous deletion in chromosome 19p13.12, DNAJB1::PRKACA, which is sufficient to drive FLC in mice. A few studies comparing FLC tumors with adjacent non-transformed liver (normal) samples revealed many transcriptional differences. However, there were done in very small datasets and analyzed using different bioinformatic methods, resulting in just 18-47% agreement between them. This study aims to comprehensively characterize the transcriptome of FLC at bulk and spatial single-cell resolution. The whole transcriptome of 109 FLC frozen patient samples, the largest RNA-seq dataset of FLC to date, was sequenced using different library preparation and ribosomal depletion methods. Only paired tumor and normal tissue samples resected from the same patient were used and divided into two groups: exploration (3 datasets, 67 samples) and testing (2 datasets, 17 samples). Additionally, as external validation datasets, RNA-seq samples from previously published studies were collected, including Sorenson et al. (FLC: 26, normal: 9, paired: 8), the TCGA-LIHC study (FLC: 6, normal: 1, paired: 1), Hirsch et al. (FLC: 15, normal: 3, paired: 0) and Francisco et al. (FLC: 27, normal: 10, paired: 9). All were reanalyzed using state-of-the-art bioinformatic methods: mapped to the Human Genome GRCh38.103 and transcripts quantified using Salmon 1.6.0, unsupervised clustering exploration using PCA, tSNE and UMAP, differential expression calculated using DESeq2 1.28.1, and checking for detectability and consistency among datasets. We found 857 up- and 988 down-regulated genes presenting the same dysregulation in the exploration datasets and confirmed in the testing and external datasets. We call these genes the transcriptional FLC signature. The FLC signature was further characterized by comparing it with the genes differentially expressed in other liver cancers: hepatocellular carcinoma (41 paired samples), hepatoblastoma (22 paired samples), and cholangiocarcinoma (27 paired samples). We found 276 up- and 352 down-regulated genes altered in other liver cancers as well as FLC, but 156 up- and 68 down-regulated only in FLC. The 112 genes with the strongest dysregulation (56 up and 56 down) were used for a MERFISH screening, providing for the first time a single-cell spatial transcriptomic characterization of FLC. This showed clear differential expression patterns in tumor, normal, stromal, and infiltrating immune cells, allowing the identification of how different cell types contribute to the transcriptional FLC signature. Citation Format: David Requena, Aldhair Medico, Luis F. Soto, Mahsa Shirani, James A. Saltsman, Gadi Lalazar, Michael P. LaQuaglia, Sanford M. Simon. Bulk and spatial single-cell transcriptomic characterization of fibrolamellar hepatocellular carcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 1516.

Cooperativity and antagonism between transcription factors (TFs) can drastically modify their binding to regulatory DNA elements. While mapping these relationships between TFs is important for understanding their context-specific functions, existing approaches either rely on DNA binding motif predictions, interrogate one TF at a time, or study individual TFs in parallel. Here, we introduce paired yeast one-hybrid (pY1H) assays to detect cooperativity and antagonism across hundreds of TF-pairs at DNA regions of interest. We provide evidence that a wide variety of TFs are subject to modulation by other TFs in a DNA sequence-specific manner. We also demonstrate that TF-TF relationships are often affected by alternative isoform usage, and identify cooperativity and antagonism between human TFs and viral proteins. pY1H assays provide a broadly applicable framework to study how different functional relationships affect protein occupancy at regulatory DNA regions.