Genomic analysis of hepatoblastoma identifies distinct molecular and prognostic subgroups

HepatologyVolume 65, Issue 1 p. 104-121 Hepatobiliary MalignanciesFree Access Genomic analysis of hepatoblastoma identifies distinct molecular and prognostic subgroups Pavel Sumazin, Pavel Sumazin Texas Children's Cancer Center, Baylor College of Medicine, Houston, TX Department of Pediatrics, Baylor College of Medicine, Houston, TX Dan L. Duncan Cancer Center, Baylor College of Medicine, Houston, TXSearch for more papers by this authorYidong Chen, Yidong Chen Departments of Epidemiology and Biostatistics, School of Medicine, University of Texas Health Science Center at San Antonio, San Antonio, TXSearch for more papers by this authorLisa R. Treviño, Lisa R. Treviño Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX Human Genome Sequencing Center, Baylor College of Medicine, Houston, TXSearch for more papers by this authorStephen F. Sarabia, Stephen F. Sarabia Pathology & Immunology, Baylor College of Medicine, Houston, TXSearch for more papers by this authorOliver A. Hampton, Oliver A. Hampton Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX Human Genome Sequencing Center, Baylor College of Medicine, Houston, TXSearch for more papers by this authorKayuri Patel, Kayuri Patel Pathology & Immunology, Baylor College of Medicine, Houston, TXSearch for more papers by this authorToni-Ann Mistretta, Toni-Ann Mistretta Pathology & Immunology, Baylor College of Medicine, Houston, TXSearch for more papers by this authorBarry Zorman, Barry Zorman Texas Children's Cancer Center, Baylor College of Medicine, Houston, TX Department of Pediatrics, Baylor College of Medicine, Houston, TXSearch for more papers by this authorPatrick Thompson, Patrick Thompson Texas Children's Cancer Center, Baylor College of Medicine, Houston, TX Department of Pediatrics, Baylor College of Medicine, Houston, TXSearch for more papers by this authorAndras Heczey, Andras Heczey Texas Children's Cancer Center, Baylor College of Medicine, Houston, TX Department of Pediatrics, Baylor College of Medicine, Houston, TX Dan L. Duncan Cancer Center, Baylor College of Medicine, Houston, TXSearch for more papers by this authorSarah Comerford, Sarah Comerford Departments of Molecular Genetics and Pediatrics, University of Texas Southwestern Medical Center, Dallas, TXSearch for more papers by this authorDavid A. Wheeler, David A. Wheeler Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX Human Genome Sequencing Center, Baylor College of Medicine, Houston, TXSearch for more papers by this authorMurali Chintagumpala, Murali Chintagumpala Texas Children's Cancer Center, Baylor College of Medicine, Houston, TX Department of Pediatrics, Baylor College of Medicine, Houston, TX Dan L. Duncan Cancer Center, Baylor College of Medicine, Houston, TXSearch for more papers by this authorRebecka Meyers, Rebecka Meyers Department of Pediatric Surgery, University of Utah, Salt Lake City, UTSearch for more papers by this authorDinesh Rakheja, Dinesh Rakheja Department of Pathology, University of Texas Southwestern Medical Center, Dallas, TXSearch for more papers by this authorMilton J. Finegold, Milton J. Finegold Texas Children's Cancer Center, Baylor College of Medicine, Houston, TX Department of Pediatrics, Baylor College of Medicine, Houston, TX Dan L. Duncan Cancer Center, Baylor College of Medicine, Houston, TX Pathology & Immunology, Baylor College of Medicine, Houston, TXSearch for more papers by this authorGail Tomlinson, Gail Tomlinson Departments of Pediatric Hematology and Oncology, School of Medicine, University of Texas Health Science Center at San Antonio, San Antonio, TXSearch for more papers by this authorD. Williams Parsons, Corresponding Author D. Williams Parsons dwparson@bcm.edu Texas Children's Cancer Center, Baylor College of Medicine, Houston, TX Department of Pediatrics, Baylor College of Medicine, Houston, TX Dan L. Duncan Cancer Center, Baylor College of Medicine, Houston, TX Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX ADDRESS CORRESPONDENCE AND REPRINT REQUESTS TO: D. Williams Parsons, M.D., Ph.D. 1102 Bates Avenue, Suite 1030.15 Houston, TX 77030 E-mail: dwparson@bcm.edu Tel: +1-832-824-4643 or Dolores López-Terrada, M.D., Ph.D. 6621 Fannin St., Suite AB1195 Houston, TX 77030 E-mail: doloresl@bcm.edu Tel: +1-832-824-1288Search for more papers by this authorDolores López-Terrada, Corresponding Author Dolores López-Terrada doloresl@bcm.edu Texas Children's Cancer Center, Baylor College of Medicine, Houston, TX Department of Pediatrics, Baylor College of Medicine, Houston, TX Dan L. Duncan Cancer Center, Baylor College of Medicine, Houston, TX Pathology & Immunology, Baylor College of Medicine, Houston, TX ADDRESS CORRESPONDENCE AND REPRINT REQUESTS TO: D. Williams Parsons, M.D., Ph.D. 1102 Bates Avenue, Suite 1030.15 Houston, TX 77030 E-mail: dwparson@bcm.edu Tel: +1-832-824-4643 or Dolores López-Terrada, M.D., Ph.D. 6621 Fannin St., Suite AB1195 Houston, TX 77030 E-mail: doloresl@bcm.edu Tel: +1-832-824-1288Search for more papers by this author Pavel Sumazin, Pavel Sumazin Texas Children's Cancer Center, Baylor College of Medicine, Houston, TX Department of Pediatrics, Baylor College of Medicine, Houston, TX Dan L. Duncan Cancer Center, Baylor College of Medicine, Houston, TXSearch for more papers by this authorYidong Chen, Yidong Chen Departments of Epidemiology and Biostatistics, School of Medicine, University of Texas Health Science Center at San Antonio, San Antonio, TXSearch for more papers by this authorLisa R. Treviño, Lisa R. Treviño Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX Human Genome Sequencing Center, Baylor College of Medicine, Houston, TXSearch for more papers by this authorStephen F. Sarabia, Stephen F. Sarabia Pathology & Immunology, Baylor College of Medicine, Houston, TXSearch for more papers by this authorOliver A. Hampton, Oliver A. Hampton Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX Human Genome Sequencing Center, Baylor College of Medicine, Houston, TXSearch for more papers by this authorKayuri Patel, Kayuri Patel Pathology & Immunology, Baylor College of Medicine, Houston, TXSearch for more papers by this authorToni-Ann Mistretta, Toni-Ann Mistretta Pathology & Immunology, Baylor College of Medicine, Houston, TXSearch for more papers by this authorBarry Zorman, Barry Zorman Texas Children's Cancer Center, Baylor College of Medicine, Houston, TX Department of Pediatrics, Baylor College of Medicine, Houston, TXSearch for more papers by this authorPatrick Thompson, Patrick Thompson Texas Children's Cancer Center, Baylor College of Medicine, Houston, TX Department of Pediatrics, Baylor College of Medicine, Houston, TXSearch for more papers by this authorAndras Heczey, Andras Heczey Texas Children's Cancer Center, Baylor College of Medicine, Houston, TX Department of Pediatrics, Baylor College of Medicine, Houston, TX Dan L. Duncan Cancer Center, Baylor College of Medicine, Houston, TXSearch for more papers by this authorSarah Comerford, Sarah Comerford Departments of Molecular Genetics and Pediatrics, University of Texas Southwestern Medical Center, Dallas, TXSearch for more papers by this authorDavid A. Wheeler, David A. Wheeler Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX Human Genome Sequencing Center, Baylor College of Medicine, Houston, TXSearch for more papers by this authorMurali Chintagumpala, Murali Chintagumpala Texas Children's Cancer Center, Baylor College of Medicine, Houston, TX Department of Pediatrics, Baylor College of Medicine, Houston, TX Dan L. Duncan Cancer Center, Baylor College of Medicine, Houston, TXSearch for more papers by this authorRebecka Meyers, Rebecka Meyers Department of Pediatric Surgery, University of Utah, Salt Lake City, UTSearch for more papers by this authorDinesh Rakheja, Dinesh Rakheja Department of Pathology, University of Texas Southwestern Medical Center, Dallas, TXSearch for more papers by this authorMilton J. Finegold, Milton J. Finegold Texas Children's Cancer Center, Baylor College of Medicine, Houston, TX Department of Pediatrics, Baylor College of Medicine, Houston, TX Dan L. Duncan Cancer Center, Baylor College of Medicine, Houston, TX Pathology & Immunology, Baylor College of Medicine, Houston, TXSearch for more papers by this authorGail Tomlinson, Gail Tomlinson Departments of Pediatric Hematology and Oncology, School of Medicine, University of Texas Health Science Center at San Antonio, San Antonio, TXSearch for more papers by this authorD. Williams Parsons, Corresponding Author D. Williams Parsons dwparson@bcm.edu Texas Children's Cancer Center, Baylor College of Medicine, Houston, TX Department of Pediatrics, Baylor College of Medicine, Houston, TX Dan L. Duncan Cancer Center, Baylor College of Medicine, Houston, TX Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX ADDRESS CORRESPONDENCE AND REPRINT REQUESTS TO: D. Williams Parsons, M.D., Ph.D. 1102 Bates Avenue, Suite 1030.15 Houston, TX 77030 E-mail: dwparson@bcm.edu Tel: +1-832-824-4643 or Dolores López-Terrada, M.D., Ph.D. 6621 Fannin St., Suite AB1195 Houston, TX 77030 E-mail: doloresl@bcm.edu Tel: +1-832-824-1288Search for more papers by this authorDolores López-Terrada, Corresponding Author Dolores López-Terrada doloresl@bcm.edu Texas Children's Cancer Center, Baylor College of Medicine, Houston, TX Department of Pediatrics, Baylor College of Medicine, Houston, TX Dan L. Duncan Cancer Center, Baylor College of Medicine, Houston, TX Pathology & Immunology, Baylor College of Medicine, Houston, TX ADDRESS CORRESPONDENCE AND REPRINT REQUESTS TO: D. Williams Parsons, M.D., Ph.D. 1102 Bates Avenue, Suite 1030.15 Houston, TX 77030 E-mail: dwparson@bcm.edu Tel: +1-832-824-4643 or Dolores López-Terrada, M.D., Ph.D. 6621 Fannin St., Suite AB1195 Houston, TX 77030 E-mail: doloresl@bcm.edu Tel: +1-832-824-1288Search for more papers by this author First published: 24 October 2016 https://doi.org/10.1002/hep.28888Citations: 123 Potential conflict of interest: Nothing to report. Supported by the Cancer Prevention & Research Institute of Texas (RP101195, RP120715), the National Institutes of Health (CA098543 and DK56338 to the Baylor College of Medicine Digestive Disease Center), and the Sidney Kimmel Foundation for Cancer Research). AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Abstract Despite being the most common liver cancer in children, hepatoblastoma (HB) is a rare neoplasm. Consequently, few pretreatment tumors have been molecularly profiled, and there are no validated prognostic or therapeutic biomarkers for HB patients. We report on the first large-scale effort to profile pretreatment HBs at diagnosis. Our analysis of 88 clinically annotated HBs revealed three risk-stratifying molecular subtypes that are characterized by differential activation of hepatic progenitor cell markers and metabolic pathways: high-risk tumors were characterized by up-regulated nuclear factor, erythroid 2–like 2 activity; high lin-28 homolog B, high mobility group AT-hook 2, spalt-like transcription factor 4, and alpha-fetoprotein expression; and high coordinated expression of oncofetal proteins and stem-cell markers, while low-risk tumors had low lin-28 homolog B and lethal-7 expression and high hepatic nuclear factor 1 alpha activity. Conclusion: Analysis of immunohistochemical assays using antibodies targeting these genes in a prospective study of 35 HBs suggested that these candidate biomarkers have the potential to improve risk stratification and guide treatment decisions for HB patients at diagnosis; our results pave the way for clinical collaborative studies to validate candidate biomarkers and test their potential to improve outcome for HB patients. (Hepatology 2017;65:104-121). Abbreviations AFP alpha-fetoprotein APC adenomatous polyposis coli CTNNB1 beta-catenin DLK1 delta-like 1 homolog EpCAM epithelial cell adhesion molecule FDR false discovery rate GPC3 glypican 3 GSEA gene set enrichment analysis HB hepatoblastoma HCC hepatocellular carcinoma HMGA2 high mobility group AT-hook 2 HNF1A hepatic nuclear factor 1 alpha let-7 lethal-7 LIN28B lin-28 homolog B miRNA microRNA mRNA messenger RNA NFE2L2 nuclear factor, erythroid 2–like 2 PCR polymerase chain reaction RT-PCR reverse-transcription PCR SALL4 spalt-like transcription factor 4 SNP single-nucleotide polymorphism TERT telomerase reverse transcriptase TF transcription factor WES whole-exome sequencing YAP1 Yes-associated protein 1 Hepatoblastoma (HB) is the most common pediatric liver tumor. It has an annual incidence rate of approximately 1.8 diagnosed cases per million in the United States, and this rate is increasing at more than 4.3% annually.1 HBs are embryonal neoplasms that are most commonly diagnosed during the first 3 years of life. They are believed to arise from hepatic cell precursors and are characterized by heterogeneous histological patterns reminiscent of liver developmental stages.2 Therapeutic strategies combining surgical resection and chemotherapy have improved outcomes for children with HB, but the prognosis for patients with advanced or chemotherapy-refractory disease remains poor.1 In addition, the most effective platinum-based agents for treatment of HB often lead to serious long-term adverse effects, including ototoxicity and nephrotoxicity.1 We describe the results of a comprehensive genomic analysis of the largest set of clinically annotated HBs reported to date. Such efforts have previously identified prognostic groups and biomarkers for other embryonal tumors3 as well as adult hepatocellular malignancies.4-6 For example, survival-predictive and metastasis-predictive biomarkers based on both gene and microRNA (miRNA) expression profiles have been reported for hepatocellular carcinoma (HCC),7 the most common liver tumor in adults.4 Interestingly, a “stem-cell” gene-expression signature, involving several oncofetal, stem-cell markers, and pluripotent stem-cell expression profiles, has been identified in a particularly aggressive type of HCC.4, 8-10 Results from recent international HB clinical studies suggest that underlying biological differences may be responsible for the prognostic variability and wide range of responses to chemotherapy seen in HB patients.11, 12 However, there are currently no biomarkers or international consensus regarding risk stratification for HB patients. In North America, Children's Oncology Group protocols have historically stratified patients by postsurgical stage and histological type, while in Europe and Japan, HB diagnosis is often based on tumor imaging criteria prior to therapy rather than pathologic analysis.13 Consequently, few pretreatment HB specimens are available for molecular profiling outside of the United States.11, 12 Activation of the canonical Wnt-signaling pathway occurs in the vast majority of HBs through somatic mutations at beta-catenin (CTNNB1) and or other Wnt-signaling genes, but it can also be caused by germline alterations including adenomatous polyposis coli (APC) mutations and mutations associated with related genetic syndromes.14, 15 It remains unclear whether Wnt-signaling pathway dysregulation is required for HB genesis or whether it is prognostically significant. Studies have also identified HB-specific expression signatures that are related to liver development and HB histologic subtypes16, 17 as well as genes that are differentially expressed in HB relative to HCC and normal liver.18 Multiple studies have speculated about the biological and prognostic importance of specific genes and signaling pathways, but these are limited by the availability of HB tumor specimens, most of which have been postchemotherapy samples with incomplete clinical annotation.19 The goal of our study was to molecularly characterize a large cohort of pretreatment, clinically and histopathologically annotated HBs of sufficient size to identify significantly predictive diagnostic and prognostic biomarkers in this disease. Conclusions from previous efforts that focused on profiles of posttreatment tumors were limited to high-risk patients and were not able to identify prognostic biomarkers. Here, pretreatment HBs were profiled by whole-exome sequencing (WES) and targeted sequencing, copy-number single-nucleotide polymorphism (SNP) arrays, and messenger RNA (mRNA) and miRNA expression arrays to identify biomarkers that differentiate between low-risk and high-risk patients. The risk-stratification potential of candidate biomarkers identified in our 88-tumor study was evaluated prospectively using protein expression profiles of 35 clinically annotated HBs. The analysis of these data provided a more in-depth view of the landscape of HB genomes and transcriptomes and identified molecular targets for HB diagnostics and therapeutics. Materials and Methods We molecularly profiled 88 HBs with corresponding surgical pathology reports following histological review (Supporting Table S1). Because of limited DNA and RNA availability in some cases, not all tumors were profiled using the same assays. In total, 34 HBs were profiled by WES and 46 by SNP arrays to identify mutations and determine copy number; 50 and 57 HBs were profiled by microarrays for mRNA and miRNA expression, respectively. All tumors were profiled for genetic alterations at CTNNB1, nuclear factor, erythroid 2–like 2 (NFE2L2), and the telomerase reverse transcriptase (TERT) promoter using targeted sequencing. HISTOLOGICAL REVIEW Histological review of representative glass slides and/or digital images of 94 samples was performed by M.J.F. and D.L.-T., who confirmed diagnoses and histological subtypes. There were no integrase interactor 1–negative (INI1 or SMARCB1) tumors included in the study, as recommended by the International Consensus HB Classification group.11 Subtypes included epithelial, pure well-differentiated fetal, pleomorphic or anaplastic fetal, embryonal, small cell, mixed, or HB with HCC features. Six samples—three non-HB malignant tumors, two samples with low HB content, and one sample with poor tumor RNA viability after extraction—were excluded from the study due to incorrect initial diagnosis or poor tumor RNA content. A total of 88 histologically confirmed cases—50 males and 38 females—were selected for profiling. CTNNB1, NFE2L2, AND TERT PROMOTER MUTATION ANALYSIS CTNNB1 exon 3 and 4 mutation analysis was completed using SuperScript One-Step reverse-transcription polymerase chain reaction (RT-PCR) with Platinum Taq reagents (Invitrogen) and primers BCAT-F and BCATlong-R to generate a 507-bp PCR product.15 Two-directional Sanger sequencing analysis of PCR products was completed with Mutation Surveyor v.4.0.4 (Softgenetics). TOPO TA cloning (Invitrogen) of CTNNB1 PCR products was performed as needed when more than one PCR product was detected by agarose gel electrophoresis or when sequence quality was poor. GENE AND miRNA EXPRESSION mRNA expression was profiled by Affymetrix microarrays in a total of 50 tumors, six normal pediatric liver tissues, and a pool of five normal fetal liver samples; two of the profiled tumors (TLT-033 and TLT-079) had insufficient follow-up or clinical annotation and were excluded from biomarker discovery efforts. miRNA expression was profiled by Agilent microarrays in a total of 57 tumors and four normal pediatric liver samples. RNA was isolated from approximately 25 mg of frozen tissue specimens using the Qiagen RNeasy Mini extraction system, followed by deoxyribonuclease 1 treatment. miRNA was simultaneously isolated using the mirVana miRNA isolation kit (Ambion). Samples extracted by both methods were subsequently quantitated using the Nanodrop ND-1000 spectrophotometer, and integrity was monitored by the Agilent 2100 Bioanalyzer capillary electrophoresis system (RNA 6000 Nano Kit). Samples were selected for RNA integrity and profiled using Human Genome U133P2 Affymetrix arrays and Agilent miRNA Microarray System v2.2. DNA AND RNA EXTRACTION DNA from frozen tumor specimens and matched normal liver tissue or peripheral blood samples was extracted using the QIAamp DNA Mini Kit (Qiagen). Samples were treated with RNAse A and eluted in Buffer AE. DNA and RNA concentration and integrity were determined with the Nanodrop ND-1000 spectrophotometer and 0.8% agarose gel electrophoresis, using the Agilent 2100 Bioanalyzer. Paired samples demonstrating intact genomic DNA were selected for WES; these included 24 and 10 tumor samples paired with normal liver and blood DNA, respectively. RNA from frozen hepatic tumor specimens and matched normal liver tissue specimens was isolated using the RNeasy Mini Kit (Qiagen) or the mirVana miRNA Isolation Kit (Ambion). Samples were treated with deoxyribonuclease 1 and eluted in nuclease-free water. Samples with RNA integrity above 6.0 were used for genomic studies. WHOLE-EXOME SEQUENCING DNA was sequenced on an Illumina Hiseq 2000 with 80 million to 100 million successful 2 × 100-bp reads per sample. Putative mutations identified through exome sequencing were confirmed on a second sequencing platform. See Supporting Information for details on library construction, sequencing, and variant calling. Tumor exomes were analyzed using the Mercury v.3 pipeline,20 including variant calling and annotation with a minimum variant ratio cutoff of 0.05.21 Somatic mutations were annotated with information from the Catalog of Somatic Mutations in Cancer database.22 SNP ARRAY Gene copy number was estimated from Affymetrix Genome-Wide Human SNP Array 6.0 profiles of 47 paired normal and HB tumor samples. DNA was digested with NspI and StyI enzymes (New England Biolabs), ligated to the respective Affymetrix adapters using T4 DNA ligase (New England Biolabs), amplified (Clontech), purified using magnetic beads (Agencourt), labeled, fragmented, and hybridized to the arrays. Following hybridization, the arrays were washed and stained with streptavidin-phycoerythrin (Invitrogen). GISTIC-normalized segments with log2 ratios >0.3 or <−0.3 were designated as copy-number gains or losses, respectively. Analysis of genes whose expression and copy-number variation profiles were significantly correlated identified a total of 174 genes with r > 0.58 (Pearson correlation between expression and copy-number variation), a minimum that was required to achieve both P < 0.01 and a false discovery rate (FDR) <0.1 for the selection. A chromosomal view of copy-number variation changes is given in Supporting Information. STANDARDIZED EXPRESSION Gene expression profiles, as estimated by individual probe sets, were standardized by transforming expression profiles, measured as maximum signal log ratio, to standard deviations from mean. Namely, for each probe set, expression mean and standard deviation were computed across all tumors—when comparing or aggregating expression—or across all nontumor samples—when identifying differentially expressed genes. Then, expression estimates in each sample were transformed to the number of standard deviations from the mean expression of this probe set.23 RNA EXPRESSION CLUSTERING Profiles of our HBs together with profiles of 29 samples given by Cairo et al.16 were processed by robust multi-array average and quantile-normalized. To exclude genes with low expression and variability across HBs, we focused on 9,835 probe sets with a maximum signal log ratio (normalized relative expression) above 6 and standard deviation above 0.3. Hierarchical clustering based on these probe sets was performed using hcluster in R with Pearson correlation and average linkage. To select the number of tumor clusters, we evaluated results using 2-10 clusters and averaged Silhouette values, Dunn's Index, Gap statistics, and cluster homogeneity and cluster separation.24-27 Analysis results, given in the Supporting Information, suggested that the optimal number of clusters is between three and five. We chose to partition the dendrogram to four clusters; partitioning to five clusters split the low-risk group into two groups with nearly identical survival and did not improve prognostic prediction. GENE SET ENRICHMENT ANALYSIS Gene set enrichment analysis (GSEA) of MSigDB v5.1 set c2 was performed with GSEA 2.2.2,28 using the 9,835-probe sets described above and with P values estimated by 10 K gene-label permutations, signal to noise metric, and the default weighted statistic. Array probes were collapsed into genes with the HGU133P2 max_probe conversion. Normal liver sample expression was estimated using six samples, including normal liver samples from patients TLT-001, TLT-041, TLT-047, TLT-048, TLT-052, and TLT-061. Favorable-prognosis HB profiles included profiles from patients who lived event-free for over 2 years following diagnosis and were diagnosed with stage 1 tumors, including TLT-023, TLT-068, TLT-031, TLT-086, TLT-043, TLT-011, TLT-028, TLT-040, TLT-042, TLT-003, TLT-049, and TLT-078. Poor-prognosis profiles were taken from patients who died of disease or relapsed with stage 3-4 tumors and included TLT-048, TLT-074, TLT-036, TLT-070, TLT-038, TLT-060, TLT-058, TLT-091, TLT-004, and TLT-009. TRANSCRIPTION FACTOR ACTIVITY INFERENCE Transcription factor (TF) activity was estimated using average standardized expression of validated targets according to TRANSFAC.29 We inferred activity for TFs with at least two targets, based on the average of their standardized profiles. TFs whose target sets overlapped by 50% or more were clustered, and a representative with the largest number of targets was selected.30 Hepatic nuclear factor 1 alpha (HNF1A) activity was inferred based on expression profiles of RIPPLY1, CLDN2, CD41B, SLC22A9, and IATIL; NFE2L2 based on profiles of NQO1, CYP2A6, PREPL, BSEP, NAT2, ASL, and PRIP; and Yes-associated protein 1 (YAP1) based on profiles of CYR61, BIRC5, CTGF, and JAG1. TESTING PREDICTIVE ACCURACY IN A VALIDATION COHORT We quantified protein expression of alpha-fetoprotein (AFP), CTNNB1, lin-28 homolog B (LIN28B), high mobility group AT-hook 2 (HMGA2), HNF1A, and NFE2L2 by immunohistochemistry in 35 clinically annotated HB tumor specimens and matched normal liver specimens using formalin-fixed, paraffin-embedded tissue blocks available in the archives of the Department of Pathology of Texas Children's Hospital, after institutional review board approval. Immunohistochemistry was performed using formalin-fixed, paraffin-embedded tissue sections and the automated Leica Bond system. Epitope retrieval was carried out on the automated Bond system using either ER1 (Leica; AR99641) (pH 6) or ER2 (Leica; AR9640) (pH 9). Sections were incubated for 15 minutes with the primary antibody. We used Bond Polymer Refine Detection (Leica; DS9800), incubation with postprimary for 8 minutes, polymer for 8 minutes, 3,3′-diaminobenzidine for 10 minutes, and hematoxylin for 5 minutes. Lists of antibodies, antigen retrieval methods, working concentrations, and interpretation (scoring) guidelines for each antibody are given in Supporting Table S5. MULTIPLE TESTING CORRECTIONS All P values were corrected for multiple testing using Bonferroni correction. See Supporting Information for the number of tests used for each statistic. Results We begin by describing results from our profiling effort. Molecular profiles were used to identify prognostic biomarkers and to construct a predictive function, which was then tested prospectively using protein expression from 35 additional HB patients. WHOLE-EXOME SEQUENCING WES revealed that HB tumors contain few coding mutations: a total of 131 somatic mutations occurring in 22 of 34 profiled tumors (3.9 mutations per tumor, range 0-24 mutations), resulting in a somatic mutation rate of <0.2 mutations/Mb of sequenced DNA (Fig. 1A). Supporting Table S2 lists all mutations identified by WES. Interestingly, younger patients were likely to have tumors with fewer somatic mutations (P < 1E-04; r = 0.62, Pearson correlation). Somatic alterations at CTNNB1 were detected in 13 tumors, and somatic mutations were discovered in genes related to regulation of oxidative stress, including recurrent point mutations at NFE2L2 (NRF2) in 3/34 HBs. NFE2L2 mutations (p.R34G × 2; p.D29N × 1) occurred in hot spots that have been described in a variety of cancer types, including HCC and cancers of the lung, endometrium, and urinary tract. Somatic and germline mutations were identified in other genes that have been implicated in both pediatric and adult cancers, including APC and the chromatin-remodeling genes MLL2 and ARID1A. WES profiles are available at European Nucleotide Archive under project PRJEB11805. Figure 1Open in figure viewerPowerPoint Genetic variability of HB tumors. (A) WES revealed few somatic mutations in 22 of the 34 profiled HBs, with significant correlation between age and mutation burden (P < 1E-4, r = 0.62). (B) Point mutations and deletions of CTNNB1 exons 3 and 4 were identified by RT-PC