Project description:Whole exome sequencing and copy number analysis was performed on 231 hepatocellular carcinomas (72% with hepatitis B viral infection) that were classified as early-stage hepatocellular carcinomas, candidates for surgical resection. Recurrent mutations were validated by Sanger sequencing. Unsupervised genomic analyses identified an association between specific genetic aberrations and postoperative clinical outcomes. Recurrent somatic mutations were identified in 9 genes, including TP53, CTNNB1, AXIN1, RPS6KA3, and RB1. Recurrent homozygous deletions in FAM123A, RB1, and CDKN2A, and high-copy amplifications in MYC, RSPO2, CCND1, and FGF19 were detected. Pathway analyses of these genes revealed aberrations in the p53, Wnt, PIK3/Ras, cell cycle, and chromatin remodelling pathways. RB1 mutations were significantly associated with cancer-specific and recurrence-free survival after resection (p = 0.016 and p = 0.001, respectively). FGF19 amplifications, known to activate Wnt signalling, were mutually exclusive with CTNNB1 and AXIN1 mutations, and significantly associated with cirrhosis (p = 0.017). RB1 mutations can be used as a prognostic molecular biomarker for resectable hepatocellular carcinoma. Further study is required to investigate the potential role of FGF19 amplification in driving hepatocarcinogenesis in patients with liver cirrhosis and to investigate the potential of anti-FGF19 treatment in these patients.
Project description:Hepatic resection is the most curative treatment option for early-stage hepatocellular carcinoma, but is associated with a high recurrence rate, which exceeds 50% at 5 years after surgery. Understanding the genetic basis of hepatocellular carcinoma at surgically curable stages may enable the identification of new molecular biomarkers that accurately identify patients in need of additional early therapeutic interventions. Whole exome sequencing and copy number analysis was performed on 231 hepatocellular carcinomas (72% with hepatitis B viral infection) that were classified as early-stage hepatocellular carcinomas, candidates for surgical resection. Recurrent mutations were validated by Sanger sequencing. Unsupervised genomic analyses identified an association between specific genetic aberrations and postoperative clinical outcomes. Recurrent somatic mutations were identified in 9 genes, including TP53, CTNNB1, AXIN1, RPS6KA3, and RB1. Recurrent homozygous deletions in FAM123A, RB1, and CDKN2A, and high-copy amplifications in MYC, RSPO2, CCND1, and FGF19 were detected. Pathway analyses of these genes revealed aberrations in the p53, Wnt, PIK3/Ras, cell cycle, and chromatin remodelling pathways. RB1 mutations were significantly associated with cancer-specific and recurrence-free survival after resection (p = 0.016 and p = 0.001, respectively). FGF19 amplifications, known to activate Wnt signalling, were mutually exclusive with CTNNB1 and AXIN1 mutations, and significantly associated with cirrhosis (p = 0.017). RB1 mutations can be used as a prognostic molecular biomarker for resectable hepatocellular carcinoma. Further study is required to investigate the potential role of FGF19 amplification in driving hepatocarcinogenesis in patients with liver cirrhosis and to investigate the potential of anti-FGF19 treatment in these patients.
Project description:Background & Aims: Fibrolamellar hepatocellular carcinoma (FLC) is a rare primary hepatic cancer usually developed in non-cirrhotic livers of children and young adults with unknown etiology. Treatment is limited to surgical intervention. To date, molecular pathogenesis of FLC has been poorly characterized. Herein, we aim to provide an integrative genomic analysis from a large series of FLC patients. Methods: A clinically annotated cohort of 77 FLCs was analyzed through wholetranscriptome, SNP-array and whole-exome sequencing. Non-negative matrix factorization was performed for class discovery, and GSEA, NTP, IPA and immunohistochemistry for functional annotation. GISTIC algorithm identified chromosomal aberrations; Mutect and VarScan2, somatic mutations, and Random survival forest the prognostic signature, validated in an independent cohort. Results: Unsupervised gene expression clustering revealed 3 robust molecular classes: Proliferation-51%, enriched with liver cancer proliferation signatures and mTOR signaling activation, Inflammation-26%, with pro-inflammatory cytokines signatures, and Unannotated-23%, with non-liver-related cancer signatures. Neuroendocrine genes and cholangiocyte and hepatocyte histological markers were present in all classes. FLC showed few copy number variations, being the most frequent: focal amplification at 8q24.3(12.5%), and deletions at 19p13(28%) and 22q13.32(25%). DNAJB1-PRKACA fusion transcript was observed in 79% of cases. FLC tumors had 32 damaging mutations on average, affecting uncommon genes in liver neoplasms (BRCA2, U2AF1). An 8-gene prognostic signature predicted survival in FLC patients. Conclusions: FLC genomic analysis reveals a unique molecular portrait characterized by uncommon damaging mutations and chromosomal aberrations, and a highly prevalent fusion protein. Three molecular classes, including Proliferation and Inflammation, define the biological behavior. Prognostic signature will allow better patient stratification. Gene-expression profiles of fresh frozen human fibrolamellar hepatocellular carcinoma
Project description:Background & Aims: Fibrolamellar hepatocellular carcinoma (FLC) is a rare primary hepatic cancer usually developed in non-cirrhotic livers of children and young adults with unknown etiology. Treatment is limited to surgical intervention. To date, molecular pathogenesis of FLC has been poorly characterized. Herein, we aim to provide an integrative genomic analysis from a large series of FLC patients. Methods: A clinically annotated cohort of 77 FLCs was analyzed through whole transcriptome, SNP-array and whole-exome sequencing. Non-negative matrix factorization was performed for class discovery, and GSEA, NTP, IPA and immunohistochemistry for functional annotation. GISTIC algorithm identified chromosomal aberrations; Mutect and VarScan2, somatic mutations, and Random survival forest the prognostic signature, validated in an independent cohort. Results: Unsupervised gene expression clustering revealed 3 robust molecular classes: Proliferation-51%, enriched with liver cancer proliferation signatures and mTOR signaling activation, Inflammation-26%, with pro-inflammatory cytokines signatures, and Unannotated-23%, with non-liver-related cancer signatures. Neuroendocrine genes and cholangiocyte and hepatocyte histological markers were present in all classes. FLC showed few copy number variations, being the most frequent: focal amplification at 8q24.3(12.5%), and deletions at 19p13(28%) and 22q13.32(25%). DNAJB1-PRKACA fusion transcript was observed in 79% of cases. FLC tumors had 32 damaging mutations on average, affecting uncommon genes in liver neoplasms (BRCA2, U2AF1). An 8-gene prognostic signature predicted survival in FLC patients. Conclusions: FLC genomic analysis reveals a unique molecular portrait characterized by uncommon damaging mutations and chromosomal aberrations, and a highly prevalent fusion protein. Three molecular classes, including Proliferation and Inflammation, define the biological behavior. Prognostic signature will allow better patient stratification. Gene-expression profiles of formalin-fixed, paraffin-embedded human fibrolamellar hepatocellular carcinoma
Project description:Aberrations in histone post-translational modifications (PTMs), as well as in the histone modifying enzymes (HMEs) that catalyze their deposition and removal, have been reported in many tumors, and many epigenetic inhibitors are currently under investigation for cancer treatment. Therefore, profiling epigenetic features in cancer could have important implications for the discovery of both biomarkers for patient stratification and novel epigenetic targets. In this study, we employed mass spectrometry based approaches to comprehensively profile histone H3 PTMs in a panel of normal and tumoral tissues for different cancer models
Project description:Fibroblast growth factor 19 (FGF19) is a gut-derived peptide hormone that is produced following activation of Farnesoid X Receptor (FXR). FGF19 is secreted and signals to the liver, where it contributes to the homeostasis of bile acid (BA), lipid and carbohydrate metabolism. FGF19 is a promising therapeutic target in metabolic syndrome and cholestatic diseases, but enthusiasm for its use has been tempered by FGF19-mediated induction of proliferation and hepatocellular carcinoma. To inform future rational design of FGF19-variants, we have conducted temporal quantitative proteomic and gene expression analyses to identify FGF19-targets related to metabolism and proliferation. Mice were fasted for 16 hours, and injected with human FGF19 (1 mg/kg body weight) or vehicle. Liver protein extracts (containing 'light' lysine) were mixed 1:1 with a spike-in protein extract from 13C6-lysine metabolically labelled mouse liver (containing 'heavy' lysine) and analysed by LC-MS/MS. Our analyses provide a resource of FGF19 target proteins in the liver. 189 proteins were upregulated (≥ 1.5 folds) and 73 proteins were downregulated (≤ -1.5 folds) by FGF19. FGF19 treatment decreased the expression of proteins involved in fatty acid (FA) synthesis, i.e. Fabp5, Scd1, and Acsl3 and increased the expression of Acox1, involved in FA oxidation. As expected, FGF19 increased the expression of proteins known to drive proliferation (i.e. Tgfbi, Vcam1, Anxa2 and Hdlbp). Importantly, many of the FGF19 targets (i.e. Pdk4, Apoa4, Fas and Stat3) have a dual function in both metabolism and cell proliferation. Therefore, our findings challenge the development of FGF19-variants that uncouple full metabolic benefit from mitogenic potential.
Project description:Background & Aims: Fibrolamellar hepatocellular carcinoma (FLC) is a rare primary hepatic cancer usually developed in non-cirrhotic livers of children and young adults with unknown etiology. Treatment is limited to surgical intervention. To date, molecular pathogenesis of FLC has been poorly characterized. Herein, we aim to provide an integrative genomic analysis from a large series of FLC patients. Methods: A clinically annotated cohort of 77 FLCs was analyzed through wholetranscriptome, SNP-array and whole-exome sequencing. Non-negative matrix factorization was performed for class discovery, and GSEA, NTP, IPA and immunohistochemistry for functional annotation. GISTIC algorithm identified chromosomal aberrations; Mutect and VarScan2, somatic mutations, and Random survival forest the prognostic signature, validated in an independent cohort. Results: Unsupervised gene expression clustering revealed 3 robust molecular classes: Proliferation-51%, enriched with liver cancer proliferation signatures and mTOR signaling activation, Inflammation-26%, with pro-inflammatory cytokines signatures, and Unannotated-23%, with non-liver-related cancer signatures. Neuroendocrine genes and cholangiocyte and hepatocyte histological markers were present in all classes. FLC showed few copy number variations, being the most frequent: focal amplification at 8q24.3(12.5%), and deletions at 19p13(28%) and 22q13.32(25%). DNAJB1-PRKACA fusion transcript was observed in 79% of cases. FLC tumors had 32 damaging mutations on average, affecting uncommon genes in liver neoplasms (BRCA2, U2AF1). An 8-gene prognostic signature predicted survival in FLC patients. Conclusions: FLC genomic analysis reveals a unique molecular portrait characterized by uncommon damaging mutations and chromosomal aberrations, and a highly prevalent fusion protein. Three molecular classes, including Proliferation and Inflammation, define the biological behavior. Prognostic signature will allow better patient stratification.
Project description:Background & Aims: Fibrolamellar hepatocellular carcinoma (FLC) is a rare primary hepatic cancer usually developed in non-cirrhotic livers of children and young adults with unknown etiology. Treatment is limited to surgical intervention. To date, molecular pathogenesis of FLC has been poorly characterized. Herein, we aim to provide an integrative genomic analysis from a large series of FLC patients. Methods: A clinically annotated cohort of 77 FLCs was analyzed through whole transcriptome, SNP-array and whole-exome sequencing. Non-negative matrix factorization was performed for class discovery, and GSEA, NTP, IPA and immunohistochemistry for functional annotation. GISTIC algorithm identified chromosomal aberrations; Mutect and VarScan2, somatic mutations, and Random survival forest the prognostic signature, validated in an independent cohort. Results: Unsupervised gene expression clustering revealed 3 robust molecular classes: Proliferation-51%, enriched with liver cancer proliferation signatures and mTOR signaling activation, Inflammation-26%, with pro-inflammatory cytokines signatures, and Unannotated-23%, with non-liver-related cancer signatures. Neuroendocrine genes and cholangiocyte and hepatocyte histological markers were present in all classes. FLC showed few copy number variations, being the most frequent: focal amplification at 8q24.3(12.5%), and deletions at 19p13(28%) and 22q13.32(25%). DNAJB1-PRKACA fusion transcript was observed in 79% of cases. FLC tumors had 32 damaging mutations on average, affecting uncommon genes in liver neoplasms (BRCA2, U2AF1). An 8-gene prognostic signature predicted survival in FLC patients. Conclusions: FLC genomic analysis reveals a unique molecular portrait characterized by uncommon damaging mutations and chromosomal aberrations, and a highly prevalent fusion protein. Three molecular classes, including Proliferation and Inflammation, define the biological behavior. Prognostic signature will allow better patient stratification.
Project description:Metastatic prostate cancers are recognized to exhibit subtypes categorized by underlying genomic alterations and phenotypes largely partitioned by androgen receptor signaling and neuroendocrine activity. In the present study we evaluated a phenotypic classification approach originally developed for subtyping breast carcinomas using the PAM50 gene signature. PAM50 subtypes associated with specific genotypes such as RB1 loss and phenotypes such as small cell/neuroendocrine carcinoma as well as tumor histology including cribriform morphology. In the context of clinical translation, PAM50 classification segregated tumors into groups with distinct druggable targets such as cell surface proteins amenable to antibody-drug-conjugates (ADCs). Classification into Luminal A, Luminal B and Basal tumors associated with time on androgen receptor signaling inhibitors, and responses to taxane chemotherapy. These findings support further clinical investigation of PAM50-based classification for prostate cancer patient stratification in therapeutic studies.