FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2302020146> ?p ?o ?g. }

• W2302020146 endingPage "1113" @default.
• W2302020146 startingPage "1103" @default.
• W2302020146 abstract "Recently, The Cancer Genome Atlas data revealed four molecular subtypes of high-grade serous ovarian carcinoma (HGSOC) exhibiting distinct prognoses. We developed four novel HGSOC histopathological subtypes by focusing on tumor microenvironment: mesenchymal transition, defined by a remarkable desmoplastic reaction; immune reactive by lymphocytes infiltrating the tumor; solid and proliferative by a solid growth pattern; and papilloglandular by a papillary architecture. Unsupervised hierarchical clustering revealed four clusters correlated with histopathological subtypes in both Kyoto and Niigata HGSOC transcriptome data sets (P < 0.001). Gene set enrichment analysis revealed pathways enriched in our histopathological classification significantly overlapped with the four molecular subtypes: mesenchymal, immunoreactive, proliferative, and differentiated (P < 0.0001, respectively). In 132 HGSOC cases, progression-free survival and overall survival were best in the immune reactive, whereas overall survival was worst in the mesenchymal transition (P < 0.001, respectively), findings reproduced in 89 validation cases (P < 0.05, respectively). The CLOVAR_MES_UP single-sample gene set enrichment analysis scores representing the mesenchymal molecular subtype were higher in paclitaxel responders than nonresponders (P = 0.002) in the GSE15622 data set. Taxane-containing regimens improved survival of cases with high MES_UP scores compared with nontaxane regimens (P < 0.001) in the GSE9891 data set. Our novel histopathological classification of HGSOC correlates with distinct prognostic transcriptome subtypes. The mesenchymal transition subtype might be particularly sensitive to taxane. Recently, The Cancer Genome Atlas data revealed four molecular subtypes of high-grade serous ovarian carcinoma (HGSOC) exhibiting distinct prognoses. We developed four novel HGSOC histopathological subtypes by focusing on tumor microenvironment: mesenchymal transition, defined by a remarkable desmoplastic reaction; immune reactive by lymphocytes infiltrating the tumor; solid and proliferative by a solid growth pattern; and papilloglandular by a papillary architecture. Unsupervised hierarchical clustering revealed four clusters correlated with histopathological subtypes in both Kyoto and Niigata HGSOC transcriptome data sets (P < 0.001). Gene set enrichment analysis revealed pathways enriched in our histopathological classification significantly overlapped with the four molecular subtypes: mesenchymal, immunoreactive, proliferative, and differentiated (P < 0.0001, respectively). In 132 HGSOC cases, progression-free survival and overall survival were best in the immune reactive, whereas overall survival was worst in the mesenchymal transition (P < 0.001, respectively), findings reproduced in 89 validation cases (P < 0.05, respectively). The CLOVAR_MES_UP single-sample gene set enrichment analysis scores representing the mesenchymal molecular subtype were higher in paclitaxel responders than nonresponders (P = 0.002) in the GSE15622 data set. Taxane-containing regimens improved survival of cases with high MES_UP scores compared with nontaxane regimens (P < 0.001) in the GSE9891 data set. Our novel histopathological classification of HGSOC correlates with distinct prognostic transcriptome subtypes. The mesenchymal transition subtype might be particularly sensitive to taxane. Ovarian carcinoma is the fifth leading cause of death among female malignancies in the United States.1Siegel R.L. Miller K.D. Jemal A. Cancer statistics, 2015.CA Cancer J Clin. 2015; 65: 5-29Crossref PubMed Scopus (11504) Google Scholar High-grade serous ovarian carcinoma (HGSOC), accounting for 68% of ovarian carcinoma, is the histological type with the worst prognosis because it is usually diagnosed at an advanced stage.2Seidman J.D. Horkayne-Szakaly I. Haiba M. Boice C.R. Kurman R.J. Ronnett B.M. The histologic type and stage distribution of ovarian carcinomas of surface epithelial origin.Int J Gynecol Pathol. 2004; 23: 41-44Crossref PubMed Scopus (392) Google Scholar Chemotherapy with taxane and platinum is typically provided after debulking surgery to eradicate tumor cells, and 75% of HGSOC cases respond to this initial treatment. However, many patients experience recurrence and eventually succumb to this disease.3Miller D.S. Blessing J.A. Krasner C.N. Mannel R.S. Hanjani P. Pearl M.L. Waggoner S.E. Boardman C.H. Phase II evaluation of pemetrexed in the treatment of recurrent or persistent platinum-resistant ovarian or primary peritoneal carcinoma: a study of the Gynecologic Oncology Group.J Clin Oncol. 2009; 27: 2686-2691Crossref PubMed Scopus (142) Google Scholar Silverberg4Silverberg S.G. Histopathologic grading of ovarian carcinoma: a review and proposal.Int J Gynecol Pathol. 2000; 19: 7-15Crossref PubMed Scopus (306) Google Scholar proposed a three-tier grading system (G1, G2, and G3) for invasive ovarian carcinoma, including the serous type. This system used architectural patterning, cytologic atypia, and mitotic figures. Low-grade serous ovarian carcinoma, which corresponds to G1, is different from HGSOC, corresponding to G2/G3, both clinically and biologically. Low-grade serous ovarian carcinoma has a good prognosis and carries KRAS and BRAF mutations, whereas HGSOC has a poor prognosis and carries TP53 and BRCA mutations.5Vang R. Shih Ie M. Kurman R.J. Ovarian low-grade and high-grade serous carcinoma: pathogenesis, clinicopathologic and molecular biologic features, and diagnostic problems.Adv Anat Pathol. 2009; 16: 267-282Crossref PubMed Scopus (414) Google Scholar Because G2 and G3 are not substantially different clinically and biologically,6Malpica A. Deavers M.T. Lu K. Bodurka D.C. Atkinson E.N. Gershenson D.M. Silva E.G. Grading ovarian serous carcinoma using a two-tier system.Am J Surg Pathol. 2004; 28: 496-504Crossref PubMed Scopus (521) Google Scholar, 7Singer G. Kurman R.J. Chang H.W. Cho S.K. Shih Ie M. Diverse tumorigenic pathways in ovarian serous carcinoma.Am J Pathol. 2002; 160: 1223-1228Abstract Full Text Full Text PDF PubMed Scopus (306) Google Scholar serous ovarian carcinoma is currently classified into low-grade serous ovarian carcinoma and HGSOC.8Bodurka D.C. Deavers M.T. Tian C. Sun C.C. Malpica A. Coleman R.L. Lu K.H. Sood A.K. Birrer M.J. Ozols R. Baergen R. Emerson R.E. Steinhoff M. Behmaram B. Rasty G. Gershenson D.M. Reclassification of serous ovarian carcinoma by a 2-tier system: a Gynecologic Oncology Group Study.Cancer. 2012; 118: 3087-3094Crossref PubMed Scopus (116) Google Scholar Given the diverse prognoses and chemotherapeutic responses, further histopathological classification of HGSOC is warranted for individualization of treatment.9Bowtell D.D. The genesis and evolution of high-grade serous ovarian cancer.Nat Rev Cancer. 2010; 10: 803-808Crossref PubMed Scopus (305) Google Scholar Notably, histopathological classifications proposed so far do not include features of the interaction with stromal cells. Recently, analyses of gene expression microarray data from The Cancer Genome Atlas (TCGA) project revealed that HGSOC could be classified as one of four gene expression subtypes: mesenchymal, immunoreactive, proliferative, or differentiated.10Cancer Genome Atlas Research NetworkIntegrated genomic analyses of ovarian carcinoma.Nature. 2011; 474: 609-615Crossref PubMed Scopus (5385) Google Scholar, 11Verhaak R.G. Tamayo P. Yang J.Y. Hubbard D. Zhang H. Creighton C.J. et al.Cancer Genome Atlas Research NetworkPrognostically relevant gene signatures of high-grade serous ovarian carcinoma.J Clin Invest. 2013; 123: 517-525PubMed Google Scholar These subtypes were reproducible in an Australian data set (C1, C2, C5, and C4, respectively).12Tothill R.W. Tinker A.V. George J. Brown R. Fox S.B. Lade S. Johnson D.S. Trivett M.K. Etemadmoghadam D. Locandro B. Traficante N. Fereday S. Hung J.A. Chiew Y.E. Haviv I. Gertig D. DeFazio A. Bowtell D.D. Australian Ovarian Cancer Study GroupNovel molecular subtypes of serous and endometrioid ovarian cancer linked to clinical outcome.Clin Cancer Res. 2008; 14: 5198-5208Crossref PubMed Scopus (1024) Google Scholar Both studies reported that tumors of mesenchymal phenotype (mesenchymal or C1) had poor prognoses, whereas immunoreactive or C2 tumors had favorable prognoses. These subtypes have specific histopathological features. The immunoreactive subtype shows prominent infiltration of lymphocytes into the tumor, and the mesenchymal subtype has marked tissue desmoplasia.11Verhaak R.G. Tamayo P. Yang J.Y. Hubbard D. Zhang H. Creighton C.J. et al.Cancer Genome Atlas Research NetworkPrognostically relevant gene signatures of high-grade serous ovarian carcinoma.J Clin Invest. 2013; 123: 517-525PubMed Google Scholar, 12Tothill R.W. Tinker A.V. George J. Brown R. Fox S.B. Lade S. Johnson D.S. Trivett M.K. Etemadmoghadam D. Locandro B. Traficante N. Fereday S. Hung J.A. Chiew Y.E. Haviv I. Gertig D. DeFazio A. Bowtell D.D. Australian Ovarian Cancer Study GroupNovel molecular subtypes of serous and endometrioid ovarian cancer linked to clinical outcome.Clin Cancer Res. 2008; 14: 5198-5208Crossref PubMed Scopus (1024) Google Scholar In this way, HGSOC gene expression subtypes were histopathologically characterized by the interaction between tumor and surrounding cells rather than by the tumor cells themselves. Our aim was to establish a novel histopathological classification of HGSOC by taking into account the status of the microenvironment, including infiltration of immune cells and stromal features, thereby corresponding to gene expression subtypes relevant to prognosis and biological differences. We show that this novel classification could lead to individualization of chemotherapeutic regimens for HSGOC. A total of 221 HGSOC from the ovary, fallopian tube, and peritoneum, without any pretreatment, were analyzed. One hundred cases underwent surgery between 1997 and 2012 at Kyoto University Hospital (Kyoto, Japan), 32 underwent surgery between 1999 and 2009 at Niigata University Hospital (Niigata, Japan), 42 were registered for the JGOG3016 clinical trial13Katsumata N. Yasuda M. Takahashi F. Isonishi S. Jobo T. Aoki D. Tsuda H. Sugiyama T. Kodama S. Kimura E. Ochiai K. Noda K. Japanese Gynecologic Oncology GroupDose-dense paclitaxel once a week in combination with carboplatin every 3 weeks for advanced ovarian cancer: a phase 3, open-label, randomised controlled trial.Lancet. 2009; 374: 1331-1338Abstract Full Text Full Text PDF PubMed Scopus (626) Google Scholar from Jikei University Hospital (Tokyo, Japan), and 47 underwent surgery between 2002 and 2012 from Kindai University Hospital (Osaka, Japan). Sample size (n = 221) was determined by information based on a previous report.12Tothill R.W. Tinker A.V. George J. Brown R. Fox S.B. Lade S. Johnson D.S. Trivett M.K. Etemadmoghadam D. Locandro B. Traficante N. Fereday S. Hung J.A. Chiew Y.E. Haviv I. Gertig D. DeFazio A. Bowtell D.D. Australian Ovarian Cancer Study GroupNovel molecular subtypes of serous and endometrioid ovarian cancer linked to clinical outcome.Clin Cancer Res. 2008; 14: 5198-5208Crossref PubMed Scopus (1024) Google Scholar Samples were collected after obtaining written informed consent. The study was approved by each of the institutional ethics committees. Hematoxylin and eosin (H&E)–stained slides from the Kyoto and Niigata HGSOC cases were used to develop a histopathological classification algorithm. The Jikei and Kindai cases served as a validation set for survival analyses. Where available, primary tumors were used for the classification as well as metastatic tumors for cases where only metastatic tumors were available. Tumor-infiltrating lymphocytes were counted with respect to localization inside or outside cancer nests using a representative slide from each case at ×400 magnification. The number of tumor-infiltrating lymphocytes was defined as the median count obtained from five areas showing the most abundant infiltration. Two gynecologic pathologists (R.M. and I.K.) reviewed H&E slides independently to classify HGSOC. When their histopathological diagnoses were different, the decisions were made by discussion (R.M. and I.K.). Five of the authors, specializing in gynecologic pathology and oncology, including two board-certified pathologists (Y.M. and S.M.), evaluated microscopic features of cases for histopathological classification. The training set, generated by R.M. and I.K., consists of 20 H&E-stained virtual slides from 20 HGSOC cases containing five of each subtype (ie, one representative slide per case). The digital slides were generated using the NanoZoomer Digital Pathology System produced by Hamamatsu Photonics (Hamamatsu, Japan). After reviewing the training set, N.M., T.B., K.Y., Y.M., and S.M. independently reviewed another 28 HGSOC cases and assigned a diagnosis for the histopathological subtype. Then, an interobserver agreement was assessed by six observers (observers A, B, C, D, E, and F). The number of evaluated H&E slides had an average of 9.2 (range, 4 to 15). Immunohistochemical staining for CD8 was performed as described14Hamanishi J. Mandai M. Iwasaki M. Okazaki T. Tanaka Y. Yamaguchi K. Higuchi T. Yagi H. Takakura K. Minato N. Honjo T. Fujii S. Programmed cell death 1 ligand 1 and tumor-infiltrating CD8+ T lymphocytes are prognostic factors of human ovarian cancer.Proc Natl Acad Sci U S A. 2007; 104: 3360-3365Crossref PubMed Scopus (1149) Google Scholar and was evaluated similarly to that previously reported.14Hamanishi J. Mandai M. Iwasaki M. Okazaki T. Tanaka Y. Yamaguchi K. Higuchi T. Yagi H. Takakura K. Minato N. Honjo T. Fujii S. Programmed cell death 1 ligand 1 and tumor-infiltrating CD8+ T lymphocytes are prognostic factors of human ovarian cancer.Proc Natl Acad Sci U S A. 2007; 104: 3360-3365Crossref PubMed Scopus (1149) Google Scholar Briefly, tumor-infiltrating CD8+ T lymphocytes were counted at ×400 magnification and categorized by their localization as intraepithelial or stromal. Five areas with the most abundant infiltration were counted; the median was defined as the number of tumor-infiltrating CD8+ T lymphocytes. Affymetrix data sets GSE9891 HG-U133 plus 2.012Tothill R.W. Tinker A.V. George J. Brown R. Fox S.B. Lade S. Johnson D.S. Trivett M.K. Etemadmoghadam D. Locandro B. Traficante N. Fereday S. Hung J.A. Chiew Y.E. Haviv I. Gertig D. DeFazio A. Bowtell D.D. Australian Ovarian Cancer Study GroupNovel molecular subtypes of serous and endometrioid ovarian cancer linked to clinical outcome.Clin Cancer Res. 2008; 14: 5198-5208Crossref PubMed Scopus (1024) Google Scholar and GSE15622 HG-U133A15Ahmed A.A. Mills A.D. Ibrahim A.E. Temple J. Blenkiron C. Vias M. Massie C.E. Iyer N.G. McGeoch A. Crawford R. Nicke B. Downward J. Swanton C. Bell S.D. Earl H.M. Laskey R.A. Caldas C. Brenton J.D. The extracellular matrix protein TGFBI induces microtubule stabilization and sensitizes ovarian cancers to paclitaxel.Cancer Cell. 2007; 12: 514-527Abstract Full Text Full Text PDF PubMed Scopus (178) Google Scholar were from the National Center for Biotechnology Information's Gene Expression Omnibus website (http://www.ncbi.nlm.nih.gov/gds, last accessed November 10, 2015). TCGA HG-U133A data set was from the TCGA Data Portal (http://cancergenome.nih.gov, last accessed November 10, 2015). The Affymetrix array data were RMA normalized using the R package affy (http://www.R-project.org, last accessed November 10, 2015). We analyzed 27 tumors from 25 Kyoto cases, including two cases with both primary and metastatic tumors, using the Affymetrix HG-U133 plus 2.0 gene chip, as previously described.16Abiko K. Mandai M. Hamanishi J. Yoshioka Y. Matsumura N. Baba T. Yamaguchi K. Murakami R. Yamamoto A. Kharma B. Kosaka K. Konishi I. PD-L1 on tumor cells is induced in ascites and promotes peritoneal dissemination of ovarian cancer through CTL dysfunction.Clin Cancer Res. 2013; 19: 1363-1374Crossref PubMed Scopus (167) Google Scholar Of the 54,675 gene probes, we selected the 20,853 with annotated gene symbols, and of these, we retained the top half with the highest average expression across the 27 samples. Unsupervised hierarchical clustering with euclidean distance and complete linkage was performed using R with 136 of the gene probes having an SD >1.9. We analyzed 32 Niigata cases with the Agilent Whole Human Genome Microarray 4 × 44K G4112F gene chip, as previously described.17Yoshihara K. Tajima A. Yahata T. Kodama S. Fujiwara H. Suzuki M. Onishi Y. Hatae M. Sueyoshi K. Fujiwara H. Kudo Y. Kotera K. Masuzaki H. Tashiro H. Katabuchi H. Inoue I. Tanaka K. Gene expression profile for predicting survival in advanced-stage serous ovarian cancer across two independent datasets.PLoS One. 2010; 5: e9615Crossref PubMed Scopus (99) Google Scholar The data were normalized with 75th percentile normalization using GeneSpring GX [11.5]. We used the 29,819 gene probes with annotated gene symbols. For unsupervised hierarchical clustering, 546 gene probes with an SD >1.9 were used. These microarray data are deposited partially in Gene Expression Omnibus (http://www.ncbi.nlm.nih.gov/geo), under accession numbers GSE39204 and GSE55512 for Kyoto and GSE17260 for Niigata microarray data. We downloaded the C2 curated gene sets version 3.0 (n = 2483) from the Molecular Signatures Database (http://www.broadinstitute.org/gsea/msigdb, last accessed November 10, 2015) and conducted gene set enrichment analysis (GSEA version 2.1.0; Broad Institute, Cambridge, MA; http://software.broadinstitute.org/gsea) being applied to gene expression microarray data sets with all C2 gene sets, as described.18Subramanian A. Tamayo P. Mootha V.K. Mukherjee S. Ebert B.L. Gillette M.A. Paulovich A. Pomeroy S.L. Golub T.R. Lander E.S. Mesirov J.P. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles.Proc Natl Acad Sci U S A. 2005; 102: 15545-15550Crossref PubMed Scopus (26549) Google Scholar The 27 Kyoto cases, the 32 Niigata cases, and the 483 TCGA HGSOC cases were analyzed independently comparing one subtype with the other subtypes. We used the gene expression subtype classifications of the TCGA samples, as previously defined.11Verhaak R.G. Tamayo P. Yang J.Y. Hubbard D. Zhang H. Creighton C.J. et al.Cancer Genome Atlas Research NetworkPrognostically relevant gene signatures of high-grade serous ovarian carcinoma.J Clin Invest. 2013; 123: 517-525PubMed Google Scholar Single-sample GSEA (ssGSEA) was performed by gene level, as described (GenePattern version 3.5.0; Broad institute; http://www.broadinstitute.org/cancer/software/genepattern, last accessed November 10, 2015).19Barbie D.A. Tamayo P. Boehm J.S. Kim S.Y. Moody S.E. Dunn I.F. Schinzel A.C. Sandy P. Meylan E. Scholl C. Frohling S. Chan E.M. Sos M.L. Michel K. Mermel C. Silver S.J. Weir B.A. Reiling J.H. Sheng Q. Gupta P.B. Wadlow R.C. Le H. Hoersch S. Wittner B.S. Ramaswamy S. Livingston D.M. Sabatini D.M. Meyerson M. Thomas R.K. Lander E.S. Mesirov J.P. Root D.E. Gilliland D.G. Jacks T. Hahn W.C. Systematic RNA interference reveals that oncogenic KRAS-driven cancers require TBK1.Nature. 2009; 462: 108-112Crossref PubMed Scopus (1855) Google Scholar The Classification of Ovarian Cancer (CLOVAR) subtype signature gene sets (CLOVAR_MES_UP, CLOVAR_IMR_UP, CLOVAR_PRO_UP, and CLOVAR_DIF_UP), representative of the gene expression subtypes from the TCGA data set, were used.11Verhaak R.G. Tamayo P. Yang J.Y. Hubbard D. Zhang H. Creighton C.J. et al.Cancer Genome Atlas Research NetworkPrognostically relevant gene signatures of high-grade serous ovarian carcinoma.J Clin Invest. 2013; 123: 517-525PubMed Google Scholar For data analyses, the ssGSEA scores were normalized from 0 to 1. GraphPad Prism version 6.0 (GraphPad Software Inc., San Diego, CA) was used for χ2 analysis, unpaired t tests, one-way analysis of variance, and survival analysis. Kaplan-Meier and the Gehan-Breslow-Wilcoxon test were used for survival analysis. R×64 version 3.0.2 was used for univariate and multivariate Cox proportional-hazards regression analyses. A P < 0.05 was considered significant. For GSEA, false discovery rate q < 0.25 was considered significant. Interobserver reproducibility of the histopathological classification was evaluated with Cohen's κ coefficient using irr package in R. We defined an algorithm for HGSOC histopathological classification. TCGA data indicated that genes relevant to immune cells were up-regulated in both the immunoreactive and mesenchymal gene expression subtypes.11Verhaak R.G. Tamayo P. Yang J.Y. Hubbard D. Zhang H. Creighton C.J. et al.Cancer Genome Atlas Research NetworkPrognostically relevant gene signatures of high-grade serous ovarian carcinoma.J Clin Invest. 2013; 123: 517-525PubMed Google Scholar Therefore, we selected tumors showing a prominent mesenchymal reaction, followed by selection of tumors with a prominent immune reaction to identify the mesenchymal tumors. A mesenchymal transition (MT) feature was assigned on the basis of the MT pattern or labyrinthine pattern with a remarkable desmoplastic reaction in >10% of the tumor area. In more detail, a feature of spindle and isolated cells with destructive stromal reaction is referred to as the MT pattern, and a feature of broadly compressed large papillae infiltration with desmoplastic stroma is referred to as the labyrinthine pattern. If the MT feature was positive, the tumor was defined as being the MT subtype. In tumors other than MT subtype, an immune reactive (IR) feature was assigned when numerous lymphocytes surrounded cancer nests (>100/×400 visual field) and infiltrating cancer nests (>50/×400 visual fields) and when this was associated with a smooth invasive front. If the IR feature was positive, the tumor was defined as being the IR subtype. The remaining tumors were classified into the solid and proliferative (SP) subtype or the papilloglandular (PG) subtype, according to these features (Figure 1). The number of intraepithelial and stromal lymphocytes seemed to correlate with the number of intraepithelial and stromal CD8+ lymphocytes because the IR subtype had the largest number of CD8+ cells (Supplemental Figure S1). The number of mitoses did not differ among the subtypes (data not shown). To determine whether the histopathological subtype changes after metastasis, we defined subtypes using H&E slides from matched primary and metastatic HGSOC tumors. The subtypes were unchanged between primary and metastatic sites in 49 of 56 (88%) cases (Supplemental Table S1). Notably, the MT subtype did not increase in the metastatic tumors, consistent with a previous report showing the degree of desmoplasia was unchanged in extraovarian sites.12Tothill R.W. Tinker A.V. George J. Brown R. Fox S.B. Lade S. Johnson D.S. Trivett M.K. Etemadmoghadam D. Locandro B. Traficante N. Fereday S. Hung J.A. Chiew Y.E. Haviv I. Gertig D. DeFazio A. Bowtell D.D. Australian Ovarian Cancer Study GroupNovel molecular subtypes of serous and endometrioid ovarian cancer linked to clinical outcome.Clin Cancer Res. 2008; 14: 5198-5208Crossref PubMed Scopus (1024) Google Scholar Thus, for HGSOC cases for which the primary sites were not resected at the initial surgery, we defined histopathological subtypes using metastatic tumors. An interobserver validation study of diagnoses was performed by six observers (Supplemental Table S2). The overall consistency rates of diagnosed MT, IR, SP, and PG had an average of 74% (range, 61% to 89%). The κ coefficients ranged between 0.46 and 0.85, with an average of 0.64, and P values were <0.01 for all of the pairs. The consistency rates for the diagnoses of MT and the others had an average of 85% (range, 71% to 96%). The κ coefficients ranged between 0.44 and 0.92, with an average of 0.70, and P values were <0.02 for all of the pairs. The images of problematic cases of MT and other subtypes that produced conflicting assessments can be seen in Supplemental Figures S2, S3, S4, and S5. Unsupervised hierarchical clustering using Affymetrix microarray data for the 27 Kyoto HGSOC tumors generated four tumor clusters. These clusters were correlated with the histopathological subtypes (P < 0.001) (Figure 2A). The association between gene expression profiles and histopathological subtype was reproducible in an analysis of Agilent microarray data from 32 Niigata HGSOC tumors (P < 0.001) (Figure 2B). A total of 2483 pathway gene sets were analyzed for enrichment in each subtype using GSEA. Because of the small sample size of our microarray data set, we determined commonly enriched gene sets using both the Kyoto and Niigata data for the histopathological subtype analysis. As a result, 315 gene sets were enriched in MT, 192 in IR, 222 in SP, and 34 in PG. We also conducted GSEA using the TCGA data set and found 1276 gene sets were enriched in the mesenchymal gene expression subtype, 762 in immunoreactive, 764 in proliferative, and 161 in differentiated. There was significant overlap of gene sets between MT and mesenchymal, IR and immunoreactive, SP and proliferative, and PG and differentiated (P < 0.0001 for each) (Figure 2C). Commonly enriched gene sets among Kyoto, Niigata, and TCGA are listed in Supplemental Tables S3, S4, S5, and S6. In both the MT and the mesenchymal subtype, gene sets related to transforming growth factor (TGF)-β and epithelial-mesenchymal transition (EMT) were enriched, such as KEGG_TGF_BETA_SIGNALING_PATHWAY, VERRECCHIA_RESPONSE_TO_TGFB1, and ALONSO_METASTASIS_EMT_UP. In both the IR and the immunoreactive subtypes, gene sets related to the immune response were enriched, including KEGG_T_CELL_RECEPTOR_SIGNALING_PATHWAY, KEGG_ANTIGEN_PROCESSING_AND_PRESENTATION, and REACTOME_SIGNALING_IN_IMMUNE_SYSTEM. In both the SP and the proliferative subtype, gene sets related to proliferation were enriched, such as BENPORATH_PROLIFERATION and KEGG_DNA_REPLICATION. In both the PG and the differentiated subtype, gene sets representing estrogen receptor effects were enriched, such as DOANE_BREAST_CANCER_ESR1_UP and VANTVEER_BREAST_CANCER_ESR1_UP. We next performed CLOVAR ssGSEA scoring using the previously reported gene signatures representative of gene expression subtypes, although the scores did not exclusively point to the subtypes reported in the original article.11Verhaak R.G. Tamayo P. Yang J.Y. Hubbard D. Zhang H. Creighton C.J. et al.Cancer Genome Atlas Research NetworkPrognostically relevant gene signatures of high-grade serous ovarian carcinoma.J Clin Invest. 2013; 123: 517-525PubMed Google Scholar CLOVAR_MES_UP scores were high in the MT type (P = 0.001), whereas CLOVAR_IMR_UP scores were high in the IR type (P = 0.001) in the Kyoto HGSOC tumors (Figure 2D). Regarding the CLOVAR_PRO_UP and CLOVAR_DIF_UP scores, the differences among the four histopathological subtypes were not statistically significant, although the former tended to be high in the SP type and the latter in the PG type (Figure 2D). A similar trend was observed for the MT and IR types in Niigata tumors, but was not significant (Supplemental Figure S6), probably because of the difference of the microarray probes used.11Verhaak R.G. Tamayo P. Yang J.Y. Hubbard D. Zhang H. Creighton C.J. et al.Cancer Genome Atlas Research NetworkPrognostically relevant gene signatures of high-grade serous ovarian carcinoma.J Clin Invest. 2013; 123: 517-525PubMed Google Scholar From these analyses, we concluded that the MES_UP and the IMR_UP scores predict their relevant histopathological subtypes, at least from the analysis of the Affymetrix data set. We next conducted survival analyses using the HGSOC cases from Kyoto and Niigata (n = 132). These were classified into MT (n = 48), IR (n = 34), SP (n = 32), and PG (n = 18) types. Progression-free survival (PFS) for the IR type was much better than the other three types (P = 0.0008) (Figure 3A). Similarly, overall survival (OS) for the IR type was markedly better than the other three types (P = 0.0002) (Figure 3B). In addition, OS for the MT type was worse than the other three types (P = 0.001) (Figure 3B). We next analyzed the Jikei and Kindai validation set (n = 89), for which the histopathological classification was performed without any clinical information. These were classified into MT (n = 28), IR (n = 19), SP (n = 20), and PG (n = 22) types. Consistent with the analysis of Kyoto/Niigata cases, PFS for the IR type was better than the other three types (P = 0.03) (Figure 3C) and PFS for the MT type was worse than the other three types (P = 0.01) (Figure 3C). Furthermore, OS for the IR type was better than for the other three types (P = 0.02) (Figure 3D) and OS for the MT type was worse than the other three (P = 0.02) (Figure 3D). Therefore, the association between the histopathological classification and survival was reproducible. Next, other clinical variables were analyzed in relation to the histopathological classification using the 132 Kyoto/Niigata cases. We found that the MT type was significantly associated with late stage (P = 0.03) and suboptimal debulking (P < 0.001). Conversely, the IR type was associated with younger age (P = 0.04), early stage (P = 0.003), and optimal debulking (P < 0.001) (Table 1).Table 1Clinical Characteristics of the 132 HGSOC CasesSubtypeMT typeIR typeSP typePG typeNo. (%)48 (36)34 (26)32 (24)18 (14)Age, years Mean (minimum-maximum)59 (39–81)56 (34–77)62 (35–82)61 (43–75) P value0.960.040.180.43Stage I or II41244 III or IV44222814 P value0.030.0030.340.63Residual disease Optimal27332514 Suboptimal21174 P value0.00020.00060.640.77Chemotherapy Nontaxane platinum2463 Taxane platinum46302615Survival Median, months43NA6771Statistical analyses were performed comparing one subtype with the other subtypes.HGSOC, high-grade serous ovarian cancer; IR, immune reactive; MT, mesenchymal transition; NA, not available; PG, papilloglandular; SP, solid and proliferativ" @default.
• W2302020146 created "2016-06-24" @default.
• W2302020146 creator A5005542590 @default.
• W2302020146 creator A5006506867 @default.
• W2302020146 creator A5011585763 @default.
• W2302020146 creator A5023191265 @default.
• W2302020146 creator A5028817103 @default.
• W2302020146 creator A5028927203 @default.
• W2302020146 creator A5036589972 @default.
• W2302020146 creator A5037991551 @default.
• W2302020146 creator A5051800836 @default.
• W2302020146 creator A5056238856 @default.
• W2302020146 creator A5056466210 @default.
• W2302020146 creator A5059466654 @default.
• W2302020146 creator A5063042904 @default.
• W2302020146 creator A5079451508 @default.
• W2302020146 creator A5082910812 @default.
• W2302020146 creator A5085668577 @default.
• W2302020146 creator A5087677239 @default.
• W2302020146 creator A5088883992 @default.
• W2302020146 creator A5089466331 @default.
• W2302020146 creator A5091086591 @default.
• W2302020146 date "2016-05-01" @default.
• W2302020146 modified "2023-10-16" @default.
• W2302020146 title "Establishment of a Novel Histopathological Classification of High-Grade Serous Ovarian Carcinoma Correlated with Prognostically Distinct Gene Expression Subtypes" @default.
• W2302020146 cites W1571075157 @default.
• W2302020146 cites W1996989652 @default.
• W2302020146 cites W2004144597 @default.
• W2302020146 cites W2005852730 @default.
• W2302020146 cites W2009213090 @default.
• W2302020146 cites W2017554332 @default.
• W2302020146 cites W2027726380 @default.
• W2302020146 cites W2036015352 @default.
• W2302020146 cites W2048572064 @default.
• W2302020146 cites W2053263835 @default.
• W2302020146 cites W2055344345 @default.
• W2302020146 cites W2059806955 @default.
• W2302020146 cites W2066414300 @default.
• W2302020146 cites W2074191719 @default.
• W2302020146 cites W2088899370 @default.
• W2302020146 cites W2097265582 @default.
• W2302020146 cites W2100578370 @default.
• W2302020146 cites W2100751052 @default.
• W2302020146 cites W2103383530 @default.
• W2302020146 cites W2104765303 @default.
• W2302020146 cites W2115852285 @default.
• W2302020146 cites W2122634638 @default.
• W2302020146 cites W2123696077 @default.
• W2302020146 cites W2123879591 @default.
• W2302020146 cites W2130410032 @default.
• W2302020146 cites W2131593373 @default.
• W2302020146 cites W2136313650 @default.
• W2302020146 cites W2146242744 @default.
• W2302020146 cites W2170552969 @default.
• W2302020146 doi "https://doi.org/10.1016/j.ajpath.2015.12.029" @default.
• W2302020146 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/26993207" @default.
• W2302020146 hasPublicationYear "2016" @default.
• W2302020146 type Work @default.
• W2302020146 sameAs 2302020146 @default.
• W2302020146 citedByCount "63" @default.
• W2302020146 countsByYear W23020201462016 @default.
• W2302020146 countsByYear W23020201462017 @default.
• W2302020146 countsByYear W23020201462018 @default.
• W2302020146 countsByYear W23020201462019 @default.
• W2302020146 countsByYear W23020201462020 @default.
• W2302020146 countsByYear W23020201462021 @default.
• W2302020146 countsByYear W23020201462022 @default.
• W2302020146 countsByYear W23020201462023 @default.
• W2302020146 crossrefType "journal-article" @default.
• W2302020146 hasAuthorship W2302020146A5005542590 @default.
• W2302020146 hasAuthorship W2302020146A5006506867 @default.
• W2302020146 hasAuthorship W2302020146A5011585763 @default.
• W2302020146 hasAuthorship W2302020146A5023191265 @default.
• W2302020146 hasAuthorship W2302020146A5028817103 @default.
• W2302020146 hasAuthorship W2302020146A5028927203 @default.
• W2302020146 hasAuthorship W2302020146A5036589972 @default.
• W2302020146 hasAuthorship W2302020146A5037991551 @default.
• W2302020146 hasAuthorship W2302020146A5051800836 @default.
• W2302020146 hasAuthorship W2302020146A5056238856 @default.
• W2302020146 hasAuthorship W2302020146A5056466210 @default.
• W2302020146 hasAuthorship W2302020146A5059466654 @default.
• W2302020146 hasAuthorship W2302020146A5063042904 @default.
• W2302020146 hasAuthorship W2302020146A5079451508 @default.
• W2302020146 hasAuthorship W2302020146A5082910812 @default.
• W2302020146 hasAuthorship W2302020146A5085668577 @default.
• W2302020146 hasAuthorship W2302020146A5087677239 @default.
• W2302020146 hasAuthorship W2302020146A5088883992 @default.
• W2302020146 hasAuthorship W2302020146A5089466331 @default.
• W2302020146 hasAuthorship W2302020146A5091086591 @default.
• W2302020146 hasBestOaLocation W23020201461 @default.
• W2302020146 hasConcept C104317684 @default.
• W2302020146 hasConcept C121608353 @default.
• W2302020146 hasConcept C142724271 @default.
• W2302020146 hasConcept C150173356 @default.
• W2302020146 hasConcept C150194340 @default.
• W2302020146 hasConcept C2777546739 @default.
• W2302020146 hasConcept C2777632260 @default.
• W2302020146 hasConcept C2780427987 @default.

• next