Hepatocellular carcinoma (HCC) is the second-leading cause of cancer-related death worldwide and the factors influencing HCC progression are poorly understood. Here we reveal that HCC progression via induction of epithelial-mesenchymal transition (EMT) is closely associated with the expression of CD36/fatty acid translocase and elevated free fatty acid (FFA) levels. Although obesity is manifested as elevated FFA levels, the degree of EMT was not associated with the body mass index of the patients, highlighting the specific roles of CD36 and FFA uptake. Treatment of human liver cancer cell lines with FFAs exacerbated the EMT phenotype, whereas chemical inhibition of CD36 mitigated these effects. Furthermore, the Wnt and TGF-β signaling pathways were activated upon FFA treatment, potentially acting as upstream activators of the EMT program. These results provide the first direct evidence associating CD36 and elevated FFAs with HCC progression.

Reprogramming of cellular metabolism is a hallmark feature of cancer cells. While a distinct set of processes drive metastasis when compared to tumorigenesis, it is yet unclear if genetic alterations in metabolic pathways are associated with metastatic progression of human cancers. Here, we analyzed the mutation, copy number variation and gene expression patterns of a literature-derived model of metabolic genes associated with glycolysis (Warburg effect), fatty acid metabolism (lipogenesis, oxidation, lipolysis, esterification) and fatty acid uptake in >9000 primary or metastatic tumor samples from the multi-cancer TCGA datasets. Our association analysis revealed a uniform pattern of Warburg effect mutations influencing prognosis across all tumor types, while copy number alterations in the electron transport chain gene SCO2, fatty acid uptake (CAV1, CD36) and lipogenesis (PPARA, PPARD, MLXIPL) genes were enriched in metastatic tumors. Using gene expression profiles, we established a gene-signature (CAV1, CD36, MLXIPL, CPT1C, CYP2E1) that strongly associated with epithelial-mesenchymal program across multiple cancers. Moreover, stratification of samples based on the copy number or expression profiles of the genes identified in our analysis revealed a significant effect on patient survival rates, thus confirming prominent roles of fatty acid uptake and metabolism in metastatic progression and poor prognosis of human cancers.

Knowledge of the fine location of neutralizing and non-neutralizing epitopes on human pathogens affords a better understanding of the structural basis of antibody efficacy, which will expedite rational design of vaccines, prophylactics, and therapeutics. However, full utilization of the wealth of information from single cell techniques and antibody repertoire sequencing awaits the development of a high-throughput, inexpensive method to map the conformational epitopes for antibody-antigen interactions. Here we show such an approach that combines comprehensive mutagenesis, cell surface display, and DNA deep sequencing. We develop analytical equations to identify epitope positions, and show the method effectiveness by mapping the fine epitope for different antibodies targeting TNF, Pertussis Toxin, and the cancer target TROP2. In all three cases, the experimentally determined conformational epitope was consistent with previous experimental datasets, confirming the reliability of the experimental pipeline. Once the comprehensive library is generated, fine conformational epitope maps can be prepared at a rate of four per day.

Obtaining accurate drug response data in large cohorts of cancer patients is very challenging; thus, most cancer pharmacogenomics discovery is conducted in preclinical studies, typically using cell lines and mouse models. However, these platforms suffer from serious limitations, including small sample sizes. Here, we have developed a novel computational method that allows us to impute drug response in very large clinical cancer genomics data sets, such as The Cancer Genome Atlas (TCGA). The approach works by creating statistical models relating gene expression to drug response in large panels of cancer cell lines and applying these models to tumor gene expression data in the clinical data sets (e.g., TCGA). This yields an imputed drug response for every drug in each patient. These imputed drug response data are then associated with somatic genetic variants measured in the clinical cohort, such as copy number changes or mutations in protein coding genes. These analyses recapitulated drug associations for known clinically actionable somatic genetic alterations and identified new predictive biomarkers for existing drugs.

The evolution of resistance in high-grade serous ovarian cancer (HGSOC) cells following chemotherapy is only partially understood. To understand the selection of factors driving heterogeneity before and through adaptation to treatment, we profile single-cell RNA-sequencing (scRNA-seq) transcriptomes of HGSOC tumors collected longitudinally during therapy. We analyze scRNA-seq data from two independent patient cohorts to reveal that HGSOC is driven by three archetypal phenotypes, defined as oncogenic states that describe the majority of the transcriptome variation. Using a multi-task learning approach to identify the biological tasks of each archetype, we identify metabolism and proliferation, cellular defense response, and DNA repair signaling as consistent cell states found across patients. Our analysis demonstrates a shift in favor of the metabolism and proliferation archetype versus cellular defense response archetype in cancer cells that received multiple lines of treatment. While archetypes are not consistently associated with specific whole-genome driver mutations, they are closely associated with subclonal populations at the single-cell level, indicating that subclones within a tumor often specialize in unique biological tasks. Our study reveals the core archetypes found in progressive HGSOC and shows consistent enrichment of subclones with the metabolism and proliferation archetype as resistance is acquired to multiple lines of therapy.