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• W2809277839 abstract "Glypican 3 (GPC3), a membrane-bound heparan sulfate proteoglycan, is overexpressed in approximately 70% to 80% of hepatocellular carcinomas, but is not expressed commonly in healthy tissues. This raised interest in GPC3 as a drug target and several GPC3-targeting drugs are in clinical development. We therefore predicted GPC3 protein overexpression across tumors and validated these predictions. Functional genomic mRNA profiling was applied to the expression profiles of 18,055 patient-derived tumor samples to predict GPC3 overexpression at the protein level in 60 tumor types and subtypes using healthy tissues as reference. For validation, predictions were compared with immunohistochemical (IHC) staining of a breast cancer tissue microarray and literature data reporting IHC GPC3 overexpression in various solid, hematologic, and pediatric tumors. The percentage of samples with predicted GPC3 overexpression was 77% for hepatocellular carcinomas (n = 364), 45% for squamous cell lung cancers (n = 405), and 19% for head and neck squamous cell cancers (n = 344). Breast cancer tissue microarray analysis showed GPC3 expression ranged from 12% to 17% in subgroups based on estrogen receptor and human epidermal growth factor receptor 2 status. In 28 of 34 tumor types for which functional genomic mRNA data could be compared with IHC there was a relative difference of ≤10%. This study provides a data-driven prioritization of tumor types and subtypes for future research with GPC3-targeting therapies. Glypican 3 (GPC3), a membrane-bound heparan sulfate proteoglycan, is overexpressed in approximately 70% to 80% of hepatocellular carcinomas, but is not expressed commonly in healthy tissues. This raised interest in GPC3 as a drug target and several GPC3-targeting drugs are in clinical development. We therefore predicted GPC3 protein overexpression across tumors and validated these predictions. Functional genomic mRNA profiling was applied to the expression profiles of 18,055 patient-derived tumor samples to predict GPC3 overexpression at the protein level in 60 tumor types and subtypes using healthy tissues as reference. For validation, predictions were compared with immunohistochemical (IHC) staining of a breast cancer tissue microarray and literature data reporting IHC GPC3 overexpression in various solid, hematologic, and pediatric tumors. The percentage of samples with predicted GPC3 overexpression was 77% for hepatocellular carcinomas (n = 364), 45% for squamous cell lung cancers (n = 405), and 19% for head and neck squamous cell cancers (n = 344). Breast cancer tissue microarray analysis showed GPC3 expression ranged from 12% to 17% in subgroups based on estrogen receptor and human epidermal growth factor receptor 2 status. In 28 of 34 tumor types for which functional genomic mRNA data could be compared with IHC there was a relative difference of ≤10%. This study provides a data-driven prioritization of tumor types and subtypes for future research with GPC3-targeting therapies. In personalized medicine, identification of targetable tumor-specific or tumor-associated characteristics to increase therapeutic possibilities in cancer patients is of great value. Although many treatment protocols have been enhanced with novel drugs including molecularly targeted agents that inhibit specific oncogenic driver pathways, not all patients benefit because driver targets are not available for all tumor types. Interestingly, antigen targets for novel therapeutic approaches such as bispecific antibodies, antibody-drug conjugates, antibodies, or antibody fragments fused with a toxin, radioimmunoconjugates, and chimeric antigen receptors, do not have to be drivers of tumor growth because their task is to serve as an anchor to bind the compounds. This clearly increases the total number of available antigen targets in cancer. In this context, glypican 3 (GPC3), a membrane-bound heparan sulfate proteoglycan, is an interesting antigen target. During embryogenesis, GPC3 is expressed abundantly in multiple tissues.1Iglesias B.V. Centeno G. Pascuccelli H. Ward F. Peters M.G. Filmus J. Puricelli L. de Kier Joffe E.B. Expression pattern of glypican-3 (GPC3) during human embryonic and fetal development.Histol Histopathol. 2008; 23: 1333-1340PubMed Google Scholar After birth, GPC3 expression rarely is observed in healthy tissues, although overexpression is seen in regenerating tissues.2Morford L.A. Davis C. Jin L. Dobierzewska A. Peterson M.L. Spear B.T. The oncofetal gene glypican 3 is regulated in the postnatal liver by zinc fingers and homeoboxes 2 and in the regenerating liver by alpha-fetoprotein regulator 2.Hepatology. 2007; 46: 1541-1547Crossref PubMed Scopus (73) Google Scholar For example, GPC3 overexpression is present in up to 83% of chronic nontumor cirrhotic livers; however, expression in healthy liver tissue and benign liver lesions is rare.3Abdul-Al H.M. Makhlouf H.R. Wang G. Goodman Z.D. Glypican-3 expression in benign liver tissue with active hepatitis C: implications for the diagnosis of hepatocellular carcinoma.Hum Pathol. 2008; 39: 2009-2012Crossref Scopus (70) Google Scholar, 4Llovet J.M. Chen Y. Wurmbach E. Roayaie S. Fiel M.I. Schwartz M. Thung S.N. Khitrov G. Zhang W. Villanueva A. Battiston C. Mazzaferro V. Bruix J. Waxman S. Friedman S.L. A molecular signature to discriminate dysplastic nodules from early hepatocellular carcinoma in HCV cirrhosis.Gastroenterology. 2006; 131: 1758-1767Abstract Full Text Full Text PDF PubMed Scopus (327) Google Scholar, 5Cai M.Y. Tong Z.T. Zheng F. Liao Y.J. Wang Y. Rao H.L. Chen Y.C. Wu Q.L. Liu Y.H. Guan X.Y. Lin M.C. Zeng Y.X. Kung H.F. Xie D. EZH2 protein: a promising immunomarker for the detection of hepatocellular carcinomas in liver needle biopsies.Gut. 2011; 60: 967-976Crossref PubMed Scopus (132) Google Scholar, 6Hirabayashi K. Kurokawa S. Maruno A. Yamada M. Kawaguchi Y. Nakagohri T. Mine T. Sugiyama T. Tajiri T. Nakamura N. Sex differences in immunohistochemical expression and capillary density in pancreatic solid pseudopapillary neoplasm.Ann Diagn Pathol. 2015; 19: 45-49Crossref PubMed Scopus (15) Google Scholar, 7Honsova E. Lodererova A. Frankova S. Oliverius M. Trunecka P. Glypican-3 immunostaining significantly improves histological diagnosis of hepatocellular carcinoma.Cas Lek Cesk. 2011; 150: 37-40PubMed Google Scholar, 8Swanson B.J. Yearsley M.M. Marsh W. Frankel W.L. A triple stain of reticulin, glypican-3, and glutamine synthetase: a useful aid in the diagnosis of liver lesions.Arch Pathol Lab Med. 2015; 139: 537-542Crossref PubMed Scopus (10) Google Scholar In addition, GPC3 overexpression is found in several tumors, most notably in approximately 70% to 80% of hepatocellular carcinomas (HCCs), but also in yolk sac tumors, gastric carcinoma, colorectal carcinoma, non–small cell lung cancer, and thyroid cancer.9Nguyen T. Phillips D. Jain D. Torbenson M. Wu T.T. Yeh M.M. Kakar S. Comparison of 5 immunohistochemical markers of hepatocellular differentiation for the diagnosis of hepatocellular carcinoma.Arch Pathol Lab Med. 2015; 139: 1028-1034Crossref PubMed Scopus (44) Google Scholar, 10Liu X. Wang S.K. Zhang K. Zhang H. Pan Q. Liu Z. Pan H. Xue L. Yen Y. Chu P.G. Expression of glypican 3 enriches hepatocellular carcinoma development-related genes and associates with carcinogenesis in cirrhotic livers.Carcinogenesis. 2015; 36: 232-242Crossref PubMed Scopus (23) Google Scholar, 11Ikeda H. Sato Y. Yoneda N. Harada K. Sasaki M. Kitamura S. Sudo Y. Ooi A. Nakanuma Y. α-Fetoprotein-producing gastric carcinoma and combined hepatocellular and cholangiocarcinoma show similar morphology but different histogenesis with respect to SALL4 expression.Hum Pathol. 2012; 43: 1955-1963Crossref PubMed Scopus (48) Google Scholar, 12Foda A.A. Mohammad M.A. Abdel-Aziz A. El-Hawary A.K. Relation of glypican-3 and E-cadherin expressions to clinicopathological features and prognosis of mucinous and non-mucinous colorectal adenocarcinoma.Tumour Biol. 2015; 36: 4671-4679Crossref PubMed Scopus (11) Google Scholar, 13Yu X. Li Y. Chen S.W. Shi Y. Xu F. Differential expression of glypican-3 (GPC3) in lung squamous cell carcinoma and lung adenocarcinoma and its clinical significance.Genet Mol Res. 2015; 14: 10185-10192Crossref PubMed Scopus (20) Google Scholar, 14Yamanaka K. Ito Y. Okuyama N. Noda K. Matsumoto H. Yoshida H. Miyauchi A. Capurro M. Filmus J. Miyoshi E. Immunohistochemical study of glypican 3 in thyroid cancer.Oncology. 2007; 73: 389-394Crossref PubMed Scopus (31) Google Scholar, 15Baumhoer D. Tornillo L. Stadlmann S. Roncalli M. Diamantis E.K. Terracciano L.M. Glypican 3 expression in human nonneoplastic, preneoplastic, and neoplastic tissues: a tissue microarray analysis of 4,387 tissue samples.Am J Clin Pathol. 2008; 129: 899-906Crossref PubMed Scopus (191) Google Scholar, 16Cao D. Li J. Guo C.C. Allan R.W. Humphrey P.A. SALL4 is a novel diagnostic marker for testicular germ cell tumors.Am J Surg Pathol. 2009; 33: 1065-1077Crossref PubMed Scopus (176) Google Scholar, 17Wang F. Liu A. Peng Y. Rakheja D. Wei L. Xue D. Allan R.W. Molberg K.H. Li J. Cao D. Diagnostic utility of SALL4 in extragonadal yolk sac tumors: an immunohistochemical study of 59 cases with comparison to placental-like alkaline phosphatase, alpha-fetoprotein, and glypican-3.Am J Surg Pathol. 2009; 33: 1529-1539Crossref PubMed Scopus (88) Google Scholar GPC3 has specific characteristics that make it a potentially attractive drug target: protein expression is absent or at low levels in healthy adult tissues, it is located at the cell surface, and it is overexpressed by several tumor types. In this respect, it is critical to have good insight into its overexpression across several tumor types. Immunohistochemical (IHC) analysis enables investigation of protein overexpression of GPC3 in various tumor types and subtypes. However, gaining insight into a broad range of tumors using IHC screening for the presence of this druggable target is time consuming and demands many resources. Functional genomic mRNA profiling (FGmRNA profiling) therefore was used to predict overexpression of GPC3 at the protein level.18Fehrmann R.S. Karjalainen J.M. Krajewska M. Westra H.J. Maloney D. Simeonov A. Pers T.H. Hirschhorn J.N. Jansen R.C. Schultes E.A. van Haagen H.H. de Vries E.G. te Meerman G.J. Wijmenga C. van Vugt M.A. Franke L. Gene expression analysis identifies global gene dosage sensitivity in cancer.Nat Genet. 2015; 47: 115-125Crossref PubMed Scopus (202) Google Scholar An advantage of this method is that it can correct a gene expression profile of an individual tumor for physiological and experimental factors that may not be relevant for the observed tumor phenotype. In this study, FGmRNA profiling was applied to a large database containing a broad spectrum of tumor types and subtypes to predict GPC3 protein overexpression for each tumor type/subtype, using healthy tissue samples as reference. The predictions from FGmRNA profiling then were validated by comparing them with IHC staining of a breast cancer tissue microarray (TMA), derived from tumors of an independent set of patients. In addition, predicted GPC3 overexpression was compared with historical GPC3 protein overexpression IHC data derived from the literature. Publicly available microarray expression data were extracted (Gene Expression Omnibus, https://www.ncbi.nlm.nih.gov/geo).19Barrett T. Wilhite S.E. Ledoux P. Evangelista C. Kim I.F. Tomashevsky M. Marshall K.A. Philippy K.H. Sherman P.M. Holko M. Yefanov A. Lee H. Zhang N. Robertson C.L. Serova N. Davis S. Soboleva A. NCBI GEO: archive for functional genomics data sets-update.Nucleic Acids Res. 2013; 41: D991-D995Crossref PubMed Scopus (5058) Google Scholar Gene Expression Omnibus accession numbers are provided in Supplemental Table S1. The analysis was restricted to the Affymetrix HG-U133 Plus 2.0 (GPL570) platform (Affymetrix, Santa Clara, CA). For each sample, metadata including patient information and experimental conditions were collected in the simple omnibus format in text file format. Relevant samples were selected using a two-step approach: automatic filtering on relevant keywords followed by manual curation. Samples were retained when raw data (CEL files) were available and when the samples were representative tumor tissue samples of patients or healthy tissue samples. Preprocessing and aggregation of raw data were performed according to the robust multiarray average algorithm with RMAExpress (version 1.1.0) using the latest CDF file from Affymetrix.20Bolstad B.M. Irizarry R.A. Astrand M. Speed T.P. A comparison of normalization methods for high density oligonucleotide array data based on variance and bias.Bioinformatics. 2003; 19: 185-193Crossref PubMed Scopus (6397) Google Scholar Quality control of the resulting expression data were performed as previously described.18Fehrmann R.S. Karjalainen J.M. Krajewska M. Westra H.J. Maloney D. Simeonov A. Pers T.H. Hirschhorn J.N. Jansen R.C. Schultes E.A. van Haagen H.H. de Vries E.G. te Meerman G.J. Wijmenga C. van Vugt M.A. Franke L. Gene expression analysis identifies global gene dosage sensitivity in cancer.Nat Genet. 2015; 47: 115-125Crossref PubMed Scopus (202) Google Scholar, 21Crijns A.P. Fehrmann R.S. de Jong S. Gerbens F. Meersma G.J. Klip H.G. Hollema H. Hofstra R.M. te Meerman G.J. de Vries E.G. van der Zee A.G. Survival-related profile, pathways, and transcription factors in ovarian cancer.PLoS Med. 2009; 6: e24Crossref PubMed Scopus (138) Google Scholar, 22Heijink D.M. Fehrmann R.S. de Vries E.G. Koornstra J.J. Oosterhuis D. van der Zee A.G. Kleibeuker J.H. de Jong S. A bioinformatical and functional approach to identify novel strategies for chemoprevention of colorectal cancer.Oncogene. 2011; 30: 2026-2036Crossref PubMed Scopus (18) Google Scholar A message-digest algorithm 5 hash for each individual CEL file was used to identify and remove duplicate CEL files. For the breast cancer cohort, receptor status was collected or inferred as described previously.23Bense R.D. Sotiriou C. Piccart-Gebhart M.J. Haanen J.B. van Vugt M.A. de Vries E.G. Schroder C.P. Fehrmann R.S. Relevance of tumor-infiltrating immune cell composition and functionality for disease outcome in breast cancer.J Natl Cancer Inst. 2016; 109: 1-9Google Scholar, 24Lamberts L.E. de Groot D.J. Bense R.D. de Vries E.G. Fehrmann R.S. Functional genomic mRNA profiling of a large cancer data base demonstrates mesothelin overexpression in a broad range of tumor types.Oncotarget. 2015; 6: 28164-28172Crossref PubMed Scopus (20) Google Scholar, 25Moek K.L. de Groot D.J.A. de Vries E.G.E. Fehrmann R.S.N. The antibody-drug conjugate target landscape across a broad range of tumour types.Ann Oncol. 2017; 28: 3083-3091Abstract Full Text Full Text PDF PubMed Scopus (35) Google Scholar The FGmRNA profiling method is described in detail by Fehrmann et al.18Fehrmann R.S. Karjalainen J.M. Krajewska M. Westra H.J. Maloney D. Simeonov A. Pers T.H. Hirschhorn J.N. Jansen R.C. Schultes E.A. van Haagen H.H. de Vries E.G. te Meerman G.J. Wijmenga C. van Vugt M.A. Franke L. Gene expression analysis identifies global gene dosage sensitivity in cancer.Nat Genet. 2015; 47: 115-125Crossref PubMed Scopus (202) Google Scholar In short, 77,840 expression profiles of publicly available samples were analyzed with principal component analysis and it was found that a limited number of transcriptional components captured the major regulators of the mRNA transcriptome. Subsequently, a subset of transcriptional components that described nongenetic regulatory factors were identified. These nongenetic transcriptional components were used as covariates to correct microarray expression data and it was observed that the residual expression signal (ie, the FGmRNA profile) captured the downstream consequences of genomic alterations on gene expression levels. The percentage of samples per tumor type, including relevant subgroups, then was predicted based on histotype (eg, adenocarcinoma) or receptor status (eg, breast cancer) with an increased FGmRNA signal for GPC3, which was used as a proxy for protein overexpression. The threshold was defined in the set of FGmRNA profiles of healthy tissues by calculating the 97.5th percentile for the FGmRNA signal of GPC3. For each tumor sample, GPC3 was marked as overexpressed when the FGmRNA signal was above the 97.5th percentile threshold as defined in the healthy tissue samples. Because the Affymetrix HG-U133 Plus 2.0 platform contains two probes representing GPC3, the highest percentage of samples with an increased FGmRNA signal was systematically reported. Seven breast cancer TMAs containing residual tumor samples of patients treated for primary breast cancer in the University Medical Center Groningen between 1996 and 2005 were stained for GPC3. TMA construction and validation for breast cancer was described previously.26Kononen J. Bubendorf L. Kallioniemi A. Barlund M. Schraml P. Leighton S. Torhorst J. Mihatsch M.J. Sauter G. Kallioniemi O.P. Tissue microarrays for high-throughput molecular profiling of tumor specimens.Nat Med. 1998; 4: 844-847Crossref PubMed Scopus (3529) Google Scholar, 27Camp R.L. Charette L.A. Rimm D.L. Validation of tissue microarray technology in breast carcinoma.Lab Invest. 2000; 80: 1943-1949Crossref PubMed Scopus (653) Google Scholar In brief, TMAs were constructed as follows: the most representative tumor area was marked on a hematoxylin and eosin–stained section. By using the hematoxylin and eosin–stained section for orientation, three 0.6-mm cores were taken from the selected area in the donor blocks and mounted on a recipient block, using a manual TMA device (Beecher Instruments, Silver Springs, MD). After this, 3-μm sections were cut from these TMA blocks using a standard microtome. Tumor samples were stained using an anti-GPC3 antibody (clone 1G12, dilution 1:100; BioMosaics, Burlington, VT) on an automated Benchmark Ultra stainer (Ventana Medical Systems, Inc., Tucson, AZ). Normal placenta was used as a positive control and normal kidney tissue was used as a negative control. Two authors (K.L.M. and D.J.A.d.G.) independently scored three cores of each tumor sample for staining intensity. Immunostains were excluded from IHC analysis if they were unrepresentative or unscorable owing to technical issues (eg, incomplete tissue transfer to the microscopic slide). The staining intensity was scored semiquantitatively as follows: 0, negative; 1+, weak; 2+, moderate; and 3+, strong; as described by Hirabayashi et al.6Hirabayashi K. Kurokawa S. Maruno A. Yamada M. Kawaguchi Y. Nakagohri T. Mine T. Sugiyama T. Tajiri T. Nakamura N. Sex differences in immunohistochemical expression and capillary density in pancreatic solid pseudopapillary neoplasm.Ann Diagn Pathol. 2015; 19: 45-49Crossref PubMed Scopus (15) Google Scholar A tumor sample was considered positive when weak, moderate, or strong GPC3 staining was seen in at least 5% of tumor cells within at least one core. When staining was present in >1 core of one tumor sample the highest staining intensity consistently was reported. Different staining patterns (cytoplasmic or nuclear) were described. In case of a discrepancy between the two observers, a breast pathologist (B.v.d.V.) independently scored the tumor sample during a consensus meeting and a final verdict was reached. To collect IHC data for GPC3 protein overexpression in cancer, PubMed was searched in April 2017 for relevant articles published in English. The following search terms were used in different combinations and spelling variants: immunohistochemistry, expression, glypican 3, GPC3, cancer, tumor. The retrieved articles were completely screened for the presence of IHC staining of patient tumor tissue. Case reports and reviews were excluded. Subsequently, the number of tumor samples analyzed and the percentages of tumor samples marked as GPC3 positive were recorded per tumor type and per article. GPC3 positivity was defined as being present when it was determined as positive in the original article. In addition, ClinicalTrials.gov was searched for ongoing studies with GPC3-directed therapies on June 26, 2017. The search terms GPC3 or glypican were used. A total of 18,055 samples representing 60 tumor types, including relevant subgroups, and 3520 samples representing 22 healthy tissue types were identified. The median number of tumor samples analyzed per tumor type or subtype was 88 (interquartile range, 33 to 343), ranging from 7 in Burkitt lymphoma to 2710 in colorectal cancer. A predicted GPC3 overexpression rate in 77% of samples was observed for HCC, 45% for squamous cell lung cancer, 19% for head and neck squamous cell cancer, and 18% for squamous cell esophageal cancer. In lung cancer and esophageal cancer, the squamous cell histologic subtype showed higher predicted GPC3 overexpression compared with adenocarcinomas. In breast cancer, the predicted GPC3 overexpression was receptor status–dependent, with 13% for estrogen receptor positive, 7% for human epidermal growth factor receptor 2 positive, 14% for estrogen receptor positive/human epidermal growth factor receptor 2 positive, and 8% for triple-negative breast cancers. In total, 22 of 60 tumor types and subtypes studied showed predicted overexpression in ≥10% of samples (Figures 1, 2, and 3). Predicted GPC3 overexpression in at least 1% of samples was found in 51 of 60 tumor types and subtypes, including 8% for prostate cancer and 7% for colorectal cancer. Predicted GPC3 protein overexpression for all tested solid, hematologic, and pediatric tumors is shown in Figures 1, 2, and 3, additional information is provided in Supplemental Table S2.Figure 2Glypican 3 (GPC3) overexpression rates in solid tumor types as determined with functional genomic mRNA (FGmRNA) profiling or immunohistochemical (IHC) analyses in literature. The x axis shows the percentage of samples with overexpression of GPC3. Tumor types including relevant subgroups are shown on the y axis. HNSCC, head and neck squamous cell cancer.View Large Image Figure ViewerDownload Hi-res image Download (PPT)Figure 3Glypican 3 (GPC3) overexpression rates in hematologic and pediatric tumors as determined with functional genomic mRNA (FGmRNA) profiling or immunohistochemical (IHC) analyses in literature. The x axis presents the percentage of samples with overexpression of GPC3. Tumor types are shown on the y axis. CNS, central nervous system.View Large Image Figure ViewerDownload Hi-res image Download (PPT) A total of 391 tumor samples, with an average of 2.74 assessable cores per tumor, were studied. GPC3 overexpression ranged from 12% to 17% in subgroups based on estrogen receptor and human epidermal growth factor receptor 2 status (Table 1). Both GPC3 cytoplasm and nuclear staining patterns of tumor cells were present in various intensities. Figure 4 shows representative staining patterns of GPC3 in breast cancer. Thirty tumor samples were unrepresentative or unscorable and therefore were excluded from analyses.Table 1Immunohistochemical Analysis of GPC3 Overexpression in a Breast Cancer Tissue Microarray Containing 391 Tumor SamplesTumor-receptor statusGPC3-positive tumor samples, n/total (%)Tumor samples showing cytoplasmic staining, n∗Subdivided into staining intensity.Tumor samples showing nuclear staining, n∗Subdivided into staining intensity.ER positive28/211 (13)7 IHC1+, 6 IHC2+6 IHC1+, 9 IHC2+HER2 positive†One core showed both cytoplasmic (IHC2+) and nuclear (IHC2+) staining and therefore is represented twice in the table.5/30 (17)2 IHC1+, 1 IHC2+3 IHC1+ER positive/HER2 positive10/86 (12)2 IHC1+, 2 IHC2+, 1 IHC3+5 IHC1+TNBC7/56 (13)1 IHC1+, 1 IHC2+, 3 IHC3+2 IHC1+ER, estrogen receptor; GPC3, glypican 3; HER2, human epidermal growth factor receptor 2; IHC, immunohistochemically; TNBC, triple negative breast cancer.∗ Subdivided into staining intensity.† One core showed both cytoplasmic (IHC2+) and nuclear (IHC2+) staining and therefore is represented twice in the table. Open table in a new tab ER, estrogen receptor; GPC3, glypican 3; HER2, human epidermal growth factor receptor 2; IHC, immunohistochemically; TNBC, triple negative breast cancer. In total, 166 studies were identified that used IHC to determine GPC3 protein overexpression in 107 different tumor types and subtypes in 20,653 tumor samples. The number of samples analyzed per tumor type and subtype varied between 1 for Hürthle cell thyroid cancer to 8446 for HCCs, with a median of 49 (interquartile range, 18 to 147). In total, 19 different antibodies were used, of which the 1G12 monoclonal antibody from BioMosaics (1G12; Cell Marque, Rocklin, CA; and 1G12; Santa Cruz Biotechnology, Dallas, TX) was applied most frequently. Seventy different GPC3-positivity scoring systems were used. In Table 2, GPC3 protein overexpression rates are shown for tumor types and subtypes for which data from two or more articles were available. Data concerning GPC3 overexpression in additional tumor types and subtypes are shown in Supplemental Table S3.Table 2Published Results Regarding Immunohistochemically Measured GPC3 Overexpression in TumorsTumor types and subtypesArticles, nPatients reported, nMedian GPC3 positivity across all articles, %IQRBreast cancer Ductal21475NA Lobular29412NACNS tumors Astrocytoma3780NA Atypical teratoid rhabdoid tumor33477NA Ependymoma2244NA Glioblastoma2621NA Meningioma2593NA Neuroblastoma21712NA Oligodendroglioma2372NAThyroid cancer Anaplastic2220NA Follicular36067NA Medullary21540NA Papillary6239250–73Gastrointestinal cancer Anal SCC32520NA Cholangiocarcinoma1217300–8 Cholangiocarcinoma, intrahepatic1141720–6 Colorectal adenocarcinoma∗One article did not specify the histologic subtype of colorectal patients.734820–59 Esophageal adenocarcinoma23816NA Esophageal SCC36327NA Gallbladder cancer31607NA Gastric adenocarcinoma121713141–27 Gastric cancer AFP producing35396NA HCC9884467661–83 HCC, cholangiocarcinoma combined61706538–88 HCC, fibrolamellar91352017–60 Hepatoblastoma615010090–100 Pancreatic adenocarcinoma3890NA Pancreatic cancer475426–86 Small-bowel cancer21429NAGynecological cancer Cervical SCC24819NA Endometrioid21016NA Endometrioid adenocarcinoma2494NA Endometrioid serous carcinoma24220NA Ovarian clear cell cancer62614126–49 Ovarian endometrioid cancer415285–9 Ovarian serous cancer5523111–15Lung cancer Adenocarcinoma947684–15 Large-cell carcinoma25927NA Mesothelioma2354NA SCC83625242–63 Small-cell carcinoma25913NASkin cancer Basal cell carcinoma2597NA Melanoma523300–55Sarcoma Epithelioid sarcoma2581NA Ewing sarcoma2150NA Fibrosarcoma2419NA Leiomyosarcoma32373NA Rhabdomyosarcoma53622022–33 Synovial sarcoma2763NA Undifferentiated314050NAUrogenital cancer Bladder cancer4971510–29 Prostate cancer32513NA Urothelial cancer656230–18Renal cancer Chromophobe493142–65 Clear cell465410–4 Oncocytoma34011NA Papillary414951–21 Wilms tumor38738NAGerm cell tumors Dysgerminomas22910NA Extragonadal YST612510085–100 Germ cell tumors NOS†Testicular/ovarian origin not clearly specified.35444NA Nondysgerminomas915310061–100 Nonseminomas114305243–100 Seminomas724300–8Other Malignant rhabdoid tumor33412NA NET/NEC532100–2 Salivary gland tumor2713NAAFP, α-fetoprotein producing; CNS, central nervous system; GPC3, glypican 3; HCC, hepatocellular carcinoma; IQR, interquartile range; NA, not applicable; NEC, neuroendocrine carcinoma; NET, neuroendocrine tumor; NOS, not otherwise specified; SCC, squamous cell cancer; YST, yolk sac tumor.∗ One article did not specify the histologic subtype of colorectal patients.† Testicular/ovarian origin not clearly specified. Open table in a new tab AFP, α-fetoprotein producing; CNS, central nervous system; GPC3, glypican 3; HCC, hepatocellular carcinoma; IQR, interquartile range; NA, not applicable; NEC, neuroendocrine carcinoma; NET, neuroendocrine tumor; NOS, not otherwise specified; SCC, squamous cell cancer; YST, yolk sac tumor. For 34 tumor types and subtypes, both FGmRNA profiling and IHC protein data were available (Figures 1, 2, and 3). For 19 of these tumor types and subtypes, the GPC3 protein expression predicted by FGmRNA profiling was higher than indicated by IHC data. For 16 of these, the relative difference was less than 10%. The largest discrepancy was seen for leiomyosarcoma. For this tumor, a GPC3 protein overexpression of 35% was predicted by FGmRNA profiling (n = 60), compared with 3% indicated by IHC analysis in three studies (n = 237). For 13 tumor types and subtypes a higher rate of GPC3 expression in tumors was reported with IHC compared with FGmRNA profiling. For 10 of this group, the relative difference was ≤10%. In liposarcoma, GPC3 protein overexpression was present in 52% of the 29 cases with IHC analysis in one study compared with 11% with FGmRNA profiling (n = 76). For estrogen receptor–positive breast cancer (13%) and neuroblastoma (2%), FGmRNA profiling and IHC showed the exact same results. This study shows that FGmRNA profiling can be used as a screening tool to predict GPC3 overexpression across 60 tumor types and subtypes as validated by comparison with IHC staining of a breast cancer TMA and literature data reporting IHC GPC3 overexpression in tumors. In HCC, squamous cell lung cancer and head and neck squamous cell cancer the percentages of samples with predicted GPC3 overexpression were 77%, 45%, and 19%, respectively, and these tumor types and subtypes are theref" @default.
• W2809277839 created "2018-06-29" @default.
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• W2809277839 creator A5041918765 @default.
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• W2809277839 date "2018-09-01" @default.
• W2809277839 modified "2023-10-16" @default.
• W2809277839 title "Glypican 3 Overexpression across a Broad Spectrum of Tumor Types Discovered with Functional Genomic mRNA Profiling of a Large Cancer Database" @default.
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