High-fat diet promotes liver tumorigenesis via palmitoylation and activation of AKT.

OBJECTIVE: Whether and how the PI3K-AKT pathway, a central node of metabolic homeostasis, is responsible for high-fat-induced non-alcoholic steatohepatitis (NASH) and hepatocellular carcinoma (HCC) remain a mystery. Characterisation of AKT regulation in this setting will provide new strategies to combat HCC. DESIGN: Metabolite library screening disclosed that palmitic acid (PA) could activate AKT. In vivo and in vitro palmitoylation assay were employed to detect AKT palmitoylation. Diverse cell and mouse models, including generation of AKT1C77S and AKT1C224S knock-in cells, Zdhhc17 and Zdhhc24 knockout mice and Akt1C224S knock-in mice were employed. Human liver tissues from patients with NASH and HCC, hydrodynamic transfection mouse model, high-fat/high-cholesterol diet (HFHCD)-induced NASH/HCC mouse model and high-fat and methionine/choline-deficient diet (HFMCD)-induced NASH mouse model were also further explored for our mechanism studies. RESULTS: By screening a metabolite library, PA has been defined to activate AKT by promoting its palmitoyl modification, an essential step for growth factor-induced AKT activation. Biologically, a high-fat diet could promote AKT kinase activity, thereby promoting NASH and liver cancer. Mechanistically, palmitoyl binding anchors AKT to the cell membrane in a PIP3-independent manner, in part by preventing AKT from assembling into an inactive polymer. The palmitoyltransferases ZDHHC17/24 were characterised to palmitoylate AKT to exert oncogenic effects. Interestingly, the anti-obesity drug orlistat or specific penetrating peptides can effectively attenuate AKT palmitoylation and activation by restricting PA synthesis or repressing AKT modification, respectively, thereby antagonising liver tumorigenesis. CONCLUSIONS: Our findings elucidate a novel fine-tuned regulation of AKT by PA-ZDHHC17/24-mediated palmitoylation, and highlight tumour therapeutic strategies by taking PA-restricted diets, limiting PA synthesis, or directly targeting AKT palmitoylation.

Integrative multiomics enhancer activity profiling identifies therapeutic vulnerabilities in cholangiocarcinoma of different etiologies.

OBJECTIVES: Cholangiocarcinoma (CCA) is a heterogeneous malignancy with high mortality and dismal prognosis, and an urgent clinical need for new therapies. Knowledge of the CCA epigenome is largely limited to aberrant DNA methylation. Dysregulation of enhancer activities has been identified to affect carcinogenesis and leveraged for new therapies but is uninvestigated in CCA. Our aim is to identify potential therapeutic targets in different subtypes of CCA through enhancer profiling. DESIGN: Integrative multiomics enhancer activity profiling of diverse CCA was performed. A panel of diverse CCA cell lines, patient-derived and cell line-derived xenografts were used to study identified enriched pathways and vulnerabilities. NanoString, multiplex immunohistochemistry staining and single-cell spatial transcriptomics were used to explore the immunogenicity of diverse CCA. RESULTS: We identified three distinct groups, associated with different etiologies and unique pathways. Drug inhibitors of identified pathways reduced tumour growth in in vitro and in vivo models. The first group (ESTRO), with mostly fluke-positive CCAs, displayed activation in estrogen signalling and were sensitive to MTOR inhibitors. Another group (OXPHO), with mostly BAP1 and IDH-mutant CCAs, displayed activated oxidative phosphorylation pathways, and were sensitive to oxidative phosphorylation inhibitors. Immune-related pathways were activated in the final group (IMMUN), made up of an immunogenic CCA subtype and CCA with aristolochic acid (AA) mutational signatures. Intratumour differences in AA mutation load were correlated to intratumour variation of different immune cell populations. CONCLUSION: Our study elucidates the mechanisms underlying enhancer dysregulation and deepens understanding of different tumourigenesis processes in distinct CCA subtypes, with potential significant therapeutics and clinical benefits.

Molecular predictors of prevention of recurrence in HCC with sorafenib as adjuvant treatment and prognostic factors in the phase 3 STORM trial.

OBJECTIVE: Sorafenib is the standard systemic therapy for advanced hepatocellular carcinoma (HCC). Survival benefits of resection/local ablation for early HCC are compromised by 70% 5-year recurrence rates. The phase 3 STORM trial comparing sorafenib with placebo as adjuvant treatment did not achieve its primary endpoint of improving recurrence-free survival (RFS). The biomarker companion study BIOSTORM aims to define (A) predictors of recurrence prevention with sorafenib and (B) prognostic factors with B level of evidence. DESIGN: Tumour tissue from 188 patients randomised to receive sorafenib (83) or placebo (105) in the STORM trial was collected. Analyses included gene expression profiling, targeted exome sequencing (19 known oncodrivers), immunohistochemistry (pERK, pVEGFR2, Ki67), fluorescence in situ hybridisation (VEGFA) and immunome. A gene signature capturing improved RFS in sorafenib-treated patients was generated. All 70 RFS events were recurrences, thus time to recurrence equalled RFS. Predictive and prognostic value was assessed using Cox regression models and interaction test. RESULTS: BIOSTORM recapitulates clinicopathological characteristics of STORM. None of the biomarkers tested (related to angiogenesis and proliferation) or previously proposed gene signatures, or mutations predicted sorafenib benefit or recurrence. A newly generated 146-gene signature identifying 30% of patients captured benefit to sorafenib in terms of RFS (p of interaction=0.04). These sorafenib RFS responders were significantly enriched in CD4+ T, B and cytolytic natural killer cells, and lacked activated adaptive immune components. Hepatocytic pERK (HR=2.41; p=0.012) and microvascular invasion (HR=2.09; p=0.017) were independent prognostic factors. CONCLUSION: In BIOSTORM, only hepatocytic pERK and microvascular invasion predicted poor RFS. No mutation, gene amplification or previously proposed gene signatures predicted sorafenib benefit. A newly generated multigene signature associated with improved RFS on sorafenib warrants further validation. TRIAL REGISTRATION NUMBER: NCT00692770.

Identification of major factors associated with failed clinical molecular oncology testing performed by next generation sequencing (NGS).

PURPOSE: DNA analysis by NGS has become important to direct the clinical care of cancer patients. However, NGS is not successful in all cases, and the factors responsible for test failures have not been systematically evaluated. MATERIALS AND METHODS: A series of 1528 solid and hematolymphoid tumor specimens was tested by an NGS comprehensive cancer panel during 2012-2014. DNA was extracted and 2×101 bp paired-end sequence reads were generated on cancer-related genes utilizing Illumina HiSeq and MiSeq platforms. RESULTS: Testing was unsuccessful in 343 (22.5%) specimens. The failure was due to insufficient tissue (INST) in 223/343 (65%) cases, insufficient DNA (INS-DNA) in 99/343 (28.9%) cases, and failed library (FL) in 21/343 (6.1%) cases. 87/99 (88%) of the INS-DNA cases had below 10 ng DNA available for testing. Factors associated with INST and INS-DNA failures were site of biopsy (SOB) and type of biopsy (TOB) (both p < 0.0001), and clinical setting of biopsy (CSB, initial diagnosis or recurrence) (p < 0.0001). Factors common to INST and FL were age of specimen (p ≤ 0.006) and tumor viability (p ≤ 0.05). Factors common to INS-DNA and FL were DNA purity and DNA degradation (all p ≤ 0.005). In multivariate analysis, common predictors for INST and INS-DNA included CSB (p = 0.048 and p < 0.0001) and TOB (both p ≤ 0.003), respectively. SOB (p = 0.004) and number of cores (p = 0.001) were specific for INS-DNA, whereas TOB and DNA degradation were associated with FL (p = 0.04 and 0.02, respectively). CONCLUSIONS: Pre-analytical causes (INST and INS-DNA) accounted for about 90% of all failed cases; independent of test design. Clinical setting; site and type of biopsy; and number of cores used for testing all correlated with failure. Accounting for these factors at the time of tissue biopsy acquisition could improve the analytic success rate.