Shared MHC Class II–Dependent Melanoma Ribosomal Protein L8 Identified by Phage Display

Swoboda, Rolf; Somasundaram, Rajasekharan; Caputo, Laura; Ochoa, Elizabeth M.; Gimotty, Phyllis A.; Marincola, Francesco M.; Belle, Patricia Van; Barth, Stephen; Elder, David E.; Guerry, DuPont; Czerniecki, Brian J.; Schuchter, Lynn M.; Vonderheide, Robert H.; Herlyn, Dorothee

doi:10.1158/0008-5472.can-06-2763

Cited by 14 publications

(16 citation statements)

References 19 publications

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“…41 The RPL8 antigen may represent a relevant vaccine target for patients with melanoma, glioma and breast carcinoma whose tumors express this protein. 42 Over-expression of RPL8 has never been reported in osteosarcoma though it maps in a region frequently altered in this tumor. CGH studies have previously shown frequent gain of 8q region with controversial results as to its prognostic value.…”

Section: Discussionmentioning

confidence: 94%

Molecular characterization of the response to chemotherapy in conventional osteosarcomas: Predictive value of HSD17B10 and IFITM2

Salas

Jézéquel²,

Campion

et al. 2009

Intl Journal of Cancer

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The therapy regimen of high-grade osteosarcoma includes chemotherapy followed by surgical resection and postoperative chemotherapy. The degree of necrosis following definitive surgery remains the only reliable prognostic factor and is used to guide the choice of postoperative chemotherapy. The aim of this study was to find molecular markers able to classify patients with an osteosarcoma as good or poor responders to chemotherapy before beginning treatment. Gene expression screening of 20 nonmetastatic high-grade osteosarcoma patients was performed using cDNA microarray. Expression of selected relevant genes was validated using QRT-PCR. Immunohistochemistry on tissue microarrays sections of 73 biopsies was performed to investigate protein expression. Fluorescent in situ hybridization was performed for RPL8 gene. We have found that HSD17B10 gene expression was up-regulated in poor responders and that immunohistochemistry expression of HSD17B10 on biopsy before treatment was correlated to response to chemotherapy. Other results include correlation of IFITM2, IFITM3, and RPL8 gene expression to chemotherapy response. A statistical correlation was found between polysomy 8 or gain of RPL8 and good response to chemotherapy. These data suggest that HSD17B10, RPL8, IFITM2, and IFITM3 genes are involved in the response to the chemotherapy and that HSD17B10 may be a therapeutic target. RPL8 and IFITM2 may be useful in the assessment at diagnosis and for stratifying patients taking part in randomized trials. ' 2009 UICC

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Section: Discussionmentioning

confidence: 94%

Molecular characterization of the response to chemotherapy in conventional osteosarcomas: Predictive value of HSD17B10 and IFITM2

Salas

Jézéquel²,

Campion

et al. 2009

Intl Journal of Cancer

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“…The most critical genes affected by these amplifications are RPL8, reportedly up-regulated in different cancers (e.g. HCC, melanoma, glioma and breast carcinoma 24,25 ) and MLLT4 , described to generate a fusion oncogene together with AF6 inducing leukemic transformation 26 . In terms of focal deletions, these ones affect 31% of cases, being the most common at 19p13.2 locus (containing STK11, a TSG serine/threonine-protein kinase that regulates mTOR signaling), at 19p13.12 (covering LPHN , with a protective role in non-small cell lung cancer 27 ) and at 22q13.32, described as a candidate TSG locus in other tumors 19 .…”

Section: Discussionmentioning

confidence: 99%

Unique Genomic Profile of Fibrolamellar Hepatocellular Carcinoma

et al. 2015

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Background & Aims Fibrolamellar hepatocellular carcinoma (FLC) is a rare primary hepatic cancer that develops in children and young adults without cirrhosis. Little is known about its pathogenesis, and it can only be treated with surgery. We performed an integrative genomic analysis of a large series of patients with FLC to identify associated genetic factors. Methods Using 78 clinically annotated FLC samples, we performed whole-transcriptome (n=58), single-nucleotide polymorphism array (n=41), and next-generation sequencing (n=48) analyses; we also assessed the prevalence of the DNAJB1–PRKACA fusion transcript associated with this cancer (n=73). We performed class discovery using non-negative matrix factorization, and functional annotation using gene set enrichment analyses, nearest template prediction, ingenuity pathway analyses, and immunohistochemistry. The genomic identification of significant targets in cancer algorithm was used to identify chromosomal aberrations, MuTect and VarScan2 were used to identify somatic mutations, and the random survival forest was used to determine patient prognoses. Findings were validated in an independent cohort. Results Unsupervised gene expression clustering revealed 3 robust molecular classes of tumors: the proliferation class (51% of samples) had altered expression of genes that regulate proliferation and mTOR signaling activation; the inflammation class (26% of samples) had altered expression of genes that regulate inflammation and cytokine production; and the unannotated class (23% of samples) had a gene expression signature not previously associated with liver tumors. Expression of genes that regulate neuroendocrine function, as well has histologic markers of cholangiocytes and hepatocytes, were detected in all 3 classes. FLCs had few copy number variations; the most frequent were focal amplification at 8q24.3 (in 12.5% of samples) and deletions at 19p13 (in 28% of samples) and 22q13.32 (in 25% of samples). The DNAJB1–PRKACA fusion transcript was detected in 79% of samples. FLC samples also contained mutations in cancer-related genes such as BRCA2 (in 4.2% of samples), which are uncommon in liver neoplasms. However, FLCs did not contain mutations most commonly detected in liver cancers. We identified an 8-gene signature that predicted survival of patients with FLC. Conclusions In a genomic analysis of 78 FLC samples, we identified 3 classes based on gene expression profiles. FLCs contain mutations and chromosomal aberrations not previously associated with liver cancer, and almost 80% contain the DNAJB1–PRKACA fusion transcript. Using this information, we identified a gene signature that is associated with patient survival time.

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“…In our study, RPL3 and RPL8 were identified as dysregulated ribosomal associated proteins of U87 cells. RPL8, a component of the 60S subunit of ribosome, is involved in protein synthesis and RPL8 antigen may represent a relevant vaccine target for patients with glioma [ 21 ]. Besides, the restoration of RPL3 protein may resensitize the resistant cells to the drug by regulating the reactive oxygen species (ROS) levels in lung cancer cells [ 22 ].…”

Section: Discussionmentioning

confidence: 99%

Identification of Key Candidate Proteins and Pathways Associated with Temozolomide Resistance in Glioblastoma Based on Subcellular Proteomics and Bioinformatical Analysis

Xiang

Feng

et al. 2018

BioMed Research International

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TMZ resistance remains one of the main reasons why treatment of glioblastoma (GBM) fails. In order to investigate the underlying proteins and pathways associated with TMZ resistance, we conducted a cytoplasmic proteome research of U87 cells treated with TMZ for 1 week, followed by differentially expressed proteins (DEPs) screening, KEGG pathway analysis, protein–protein interaction (PPI) network construction, and validation of key candidate proteins in TCGA dataset. A total of 161 DEPs including 65 upregulated proteins and 96 downregulated proteins were identified. Upregulated DEPs were mainly related to regulation in actin cytoskeleton, focal adhesion, and phagosome and PI3K-AKT signaling pathways which were consistent with our previous studies. Further, the most significant module consisted of 28 downregulated proteins that were filtered from the PPI network, and 9 proteins (DHX9, HNRNPR, RPL3, HNRNPA3, SF1, DDX5, EIF5B, BTF3, and RPL8) among them were identified as the key candidate proteins, which were significantly associated with prognosis of GBM patients and mainly involved in ribosome and spliceosome pathway. Taking the above into consideration, we firstly identified candidate proteins and pathways associated with TMZ resistance in GBM using proteomics and bioinformatic analysis, and these proteins could be potential biomarkers for prevention or prediction of TMZ resistance in the future.

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Shared MHC Class II–Dependent Melanoma Ribosomal Protein L8 Identified by Phage Display

Cited by 14 publications

References 19 publications

Molecular characterization of the response to chemotherapy in conventional osteosarcomas: Predictive value of HSD17B10 and IFITM2

Molecular characterization of the response to chemotherapy in conventional osteosarcomas: Predictive value of HSD17B10 and IFITM2

Unique Genomic Profile of Fibrolamellar Hepatocellular Carcinoma

Identification of Key Candidate Proteins and Pathways Associated with Temozolomide Resistance in Glioblastoma Based on Subcellular Proteomics and Bioinformatical Analysis

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