Queensland Melanoma Genomic Biomarker Study
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ABSTRACT:
PROVIDER: EGAS00001004619 | EGA |
REPOSITORIES: EGA
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Project description:Melanoma incidence and mortality rates are historically higher for men than women. Although emerging studies have highlighted tumorigenic roles for the male sex hormone androgen and its receptor (AR) in melanoma, cellular and molecular mechanisms underlying these sex-associated discrepancies are poorly defined. Here, we delineate a previously undisclosed mechanism by which androgen-activated AR transcriptionally upregulates fucosyltransferase 4 (FUT4) expression, which drives melanoma invasiveness by interfering with adherens junctions (AJs). Global phosphoproteomic and fucoproteomic profiling, coupled with in vitro and in vivo functional validation, further reveals that AR-induced FUT4 fucosylates L1 cell adhesion molecule (L1CAM), which is required for FUT4-increased metastatic capacity. Tumor microarray and gene expression analyses demonstrate that AR-FUT4-L1CAM-AJs signaling correlates with pathological staging in melanoma patients. By delineating key androgen-triggered signaling that enhances metastatic aggressiveness, our findings help to explain sex-associated clinical outcome disparities and highlight AR/FUT4 and its effectors as potential prognostic biomarkers and therapeutic targets in melanoma.
2024-01-15 | PXD047337 | Pride
Project description:Melanoma incidence and mortality rates are historically higher for men than women. Although emerging studies have highlighted tumorigenic roles for the male sex hormone androgen and its receptor (AR) in melanoma, cellular and molecular mechanisms underlying these sex-associated discrepancies are poorly defined. Here, we delineate a previously undisclosed mechanism by which androgen-activated AR transcriptionally upregulates fucosyltransferase 4 (FUT4) expression, which drives melanoma invasiveness by interfering with adherens junctions (AJs). Global phosphoproteomic and fucoproteomic profiling, coupled with in vitro and in vivo functional validation, further reveals that AR-induced FUT4 fucosylates L1 cell adhesion molecule (L1CAM), which is required for FUT4-increased metastatic capacity. Tumor microarray and gene expression analyses demonstrate that AR-FUT4-L1CAM-AJs signaling correlates with pathological staging in melanoma patients. By delineating key androgen-triggered signaling that enhances metastatic aggressiveness, our findings help to explain sex-associated clinical outcome disparities and highlight AR/FUT4 and its effectors as potential prognostic biomarkers and therapeutic targets in melanoma.
2024-01-15 | PXD047335 | Pride
Project description:Anatomical staging is a critical, although imperfect, instrument to assess gastric cancer prognosis and define indication for surgery and adjuvant therapy. Despite recent advances, treatment results, as a whole, remain less than satisfactory. Thus, biomarkers are sorely needed to improve risk categorization and define new molecular targets for therapy. We used microarrays to identify genes differentially expressed according to prognosis of the patients. A cohort of 15 patients was prospectively identified with a histological diagnosis of gastric adenocarcinoma, submitted to surgery with curative intent. Two prognostic patient groups were defined, according to overall survival (short x long), with a 24 month cut-off. These groups were matched by known prognostic factors: TNM staging, histological subtype (intestinal/diffuse) and gender.
2012-09-01 | E-GEOD-38749 | biostudies-arrayexpress
Project description:Melanoma can progress rapidly from a slow-growing surgically curable lesion to aggressive metastatic disease, with high mortality and poor response to current therapies. The mechanisms underlying melanoma progression and resistance to therapeutic agents are not well understood. There are few treatment options for melanoma once it has metastasized, and new biomarkers that aid diagnosis, predict clinical outcome, and suggest new therapies are required. This aim of this study was to invesigate the molecular basis of melanoma biology and heterogeneity by defining genomic characteristics that correlated with tumour phenotype in a novel panel of 25 cell lines derived largely from New Zealand patients with metastatic melanoma. The goals of this study were to (i) stratify melanoma cell lines by using global gene expression profiling ; (ii) correlate gene expression profiles with invasive potential; and (iii) identify new prognostic markers and therapeutic targets that might aid clinical cancer diagnosis and management.
2010-01-02 | GSE16404 | GEO
Project description:Circulating tumor cells (CTCs) are critical in the development of distant organ tumor metastasis, and are associated with advanced cancer stage and poor patient outcome. Here, we present the first genome-wide nucleotide-level characterization of CTCs. Our single-nucleotide polymorphism (SNP) analysis in patients with melanoma involved: 1) global comparative genomic analysis of CTCs and matched regional metastases, 2) identification of key genomic aberrations in CTCs, 3) verification of these target genes in aggressive distant tumor metastases, and 4) evidence of selective expression and functional consequence of CTC-associated genes in melanomas. We report 131 aberrant loci in CTCs that are potentially pro-metastatic, and show that such expression of a 5-marker gene panel (CSMD2, CNTNAP5, FLJ14051, ADAM6, TRPM2) in melanomas confers prognostic utility. Successful treatment of melanoma requires understanding of the metastatic process and identification of patients with tumors most likely to develop aggressive metastatic disease. Melanomas are heterogeneous, and CTCs have long been recognized as vehicles for cancer spread, representing particularly aggressive tumor clones that can evolve into successful clinical metastases. Elucidation of genomic aberrations in CTCs will aid in the development of prognostic biomarkers and therapeutic strategies to target CTCs to prevent or control distant cancer spread. This study provides the first detailed genomic confirmation of the close relation between CTCs and tumor metastases, and illustrates how CTCs can be utilized as a novel approach and rational source for identification of pro-metastatic genes in cancer research. Three individual patient cohorts were utilized in the study. CNV and LOH loci were evaluated initially in metastatic melanoma patients (n=13) in a discovery cohort. SNP loci that harbored CNV/LOH in CTCs were then separately verified for: (a) presence in distant organ metastasis (AJCC Stage IV melanoma) (n=27), and (b) relevance to prognosis in regional melanoma metastasis (AJCC Stage III melanoma) (n=35). The first discovery patient cohort group was utilized for capture of CTCs, and consisted of peripheral blood mononuclear cell (PBMC) and tumor specimens from metastatic melanoma patients (n=13). CTC-related loci were verified in a second cohort of patients with Stage IV distant organ metastases (n=27), including 15 brain, 4 lung, and 8 gastrointestinal (bowel, liver) metastases. The third patient cohort consisted of early passage (<12) established melanoma cell lines derived from 35 regional melanoma metastases (Stage III) for evaluation of the prognostic utility of the CTC-associated aberrant loci.
2014-07-01 | E-GEOD-43934 | biostudies-arrayexpress
Project description:We hypothesised that in esophagogastric junction adenocarcinomas combining molecular predictive biomarkers with FDG-PET would optimise response prediction. Changes in FDG uptake in tumours meaured by PET scans after 14 days of chemotherapy define metabolic responders and non-responders. However while <5% of metabolic non-responders will go onto have a response to the full 9-12 week chemotherapy protocol providing a high negative predictive value for FDG-PET, the positive predictive value of metabolic response is more limited. Only 50% of those patients that have a metabolic response determined by FDG PET at day 14 will go on to subsequently have a response to the full 9-12 week chemotherapy protocol . We used global gene expression profiling to identify molecular biomarkers that when combined with FDG-PET would improve predictive accuracy, in particular we aimed to identify molecular biomarkers that could sub-classify FDG PET defined metabolic responders into those that did and did not subsequently go on to have a radiological response and hence clinical benefit from the full chemotherapy protocol. Patients with stage IB-IV esophagogastric junction adenocarcinomas(n=28) received platinum combination chemotherapy .FDG-PET CT scans were performed at baseline and day 14 and expression profiling (Affymetrix ST1.0 Exon Genechips) on baseline tumour biopsies. 14 patients were classified as FDG-PET metabolic responders and gene expression was analysed in these tumour specimens to determine differences between those that did subsequently go on to have a radiological response (n=10) or did not have a radiological response (n=4) to the full 9-12 week chemotherapy protocol. A tissue microarray(n=154 esophagogastric adenocarcinomas who underwent surgery+/-neoadjuvant chemotherapy)was used with immunohistochemistry for qualification of gene expression profiling results. Radiological response was assessed after 3or4 cycles of chemotherapy by RECISTv1.1.
2012-12-01 | E-MEXP-3730 | biostudies-arrayexpress
Project description:In patients with metastatic melanoma, the identification and validation of accurate prognostic biomarkers will assist rational treatment planning. Studies based on "-omics" technologies have focussed on a single high-throughput data type such as gene or microRNA transcripts. Occasionally, these features were evaluated in conjunction with limited clinico-pathologic data. With the increased availability of multiple data types, there is a pressing need to tease apart which of these sources contain the most valuable prognostic information. We evaluated and integrated several data types derived from the same tumor specimens in AJCC stage III melanoma patients - gene, protein, and microRNA expression as well as clinical, pathologic and mutation information - to determine their relative impact on prognosis. We used classification frameworks based on pre-validation and bootstrap multiple imputation classification to compare the prognostic power of each data source, both individually as well as integratively. We found that the prognostic utility of clinico-pathologic information was not out-performed by various "-omics" platforms. Rather, a combination of clinico-pathologic variables and mRNA expression data performed best. Furthermore, a patient-based classification analysis revealed that the prognostic accuracy of various data types was not the same for different patients, providing useful insights for ongoing developments in the individualized treatment of melanomas patients. SPECIAL NOTE: In this study, survival data were re-extracted from the MIA research database for all patients and brought up to date, revealing discrepancies affecting survival class in the case of four patients compared with the previous dataset (GSE53118: http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE53118). The current survival data are considered to be more accurate although the expression information has not changed. In addition, there were 5 samples (122, 144, 195, 264, and 358) for which gene expression information were not available at the time of analysis. However, the associated clinical information for these samples is provided since it was analysed elsewhere in the accompanying publication. Samples eligible for this study (n=84) were obtained from lymph node specimens (Melanoma Institute Australia (MIA) Biospecimen Bank) in which macroscopic tumor was observed, obtained from patients believed to be without distant metastases at the time of tumor banking based on clinical examination and computerised axial tomographic scanning of the brain, chest, abdomen and pelvis. Specimens were macro-dissected at time of banking and subsequently reviewed to meet minimum criteria for tumor cell content (>80%) and amount of necrosis (<30%). Linked clinical and pathologic data were obtained from the MIA research database. We previously analyzed the distribution of survival times in these samples and identified more favorable and less favorable groups as patients having time from surgery to death from melanoma greater than 4 years with no sign of relapse (n=25) or less than 1 year (n=22), respectively (Mann et al. 2013, PMID: 22931913). Since that publication, survival data have been re-extracted from the MIA research database for all patients and brought up to date, revealing discrepancies affecting survival class in the case of four patients compared with the previous dataset. The current survival data are considered to be more accurate. MRNA expression profiling and somatic mutation profiling, were performed as previously described in Mann et al. 2013 (PMID: 22931913).
2014-08-18 | E-GEOD-54467 | biostudies-arrayexpress
Project description:Despite advances in Hodgkin lymphoma (HL) treatment, about 20% of patients still die due to progressive disease. Current prognostic models predict treatment outcome with imperfect accuracy, and clinically relevant biomarkers are yet to be established that improve upon the International Prognostic Scoring (IPS) system. We analyzed 130 frozen diagnostic lymph node biopsies from classical HL patients by gene expression profiling to describe cellular signatures correlated with treatment outcome.
2010-03-01 | GSE17920 | GEO
Project description:Current staging classifications do not accurately predict the benefit of high-dose chemotherapy (HDC) with autologous stem-cell transplantation (ASCT) for patients with diffuse large B-cell lymphoma (DLBCL) at high risk (age-adjusted International Index [aaIPI] score 2 or 3), who have achieved first complete remission after R-CHOP (rituximab, cyclophosphamide, vincristine, doxorubicin, and prednisone) treatment. We aim to construct a genetic prognostic model for improving individualized risk stratification and response prediction for HDC/ASCT therapy. We identified differentially expressed mRNAs associated with relapse of DLBCL.
2023-08-16 | GSE156309 | GEO
Project description:To rapidly identify new prognostic imaging biomarkers, we propose a bioinformatics approach that integrates gene expression and image data and leverages public gene expression data. We demonstrate our approach in non-small cell lung carcinoma patients for whom CT, PET/CT and gene expression data are available but without clinical follow-up. We extracted 180 image features and 56 high quality gene expression clusters, represented by metagenes. 115 image features were expressed in terms of metagenes, using sparse linear regression and cross-validation, with an accuracy of 65-86%. After mapping the signatures to a public gene expression dataset, 26 image features were significantly associated with recurrence-free survival and 22 with overall survival. A multivariate analysis identified multiple image features that were prognostic, independent of clinical covariates. Identifying prognostic imaging biomarkers by linking images and gene expression with outcomes in public gene expression datasets promises to accelerate the role of imaging in personalized medicine. We studied 26 cases of NSCLC with both PET/CT and microarray data under IRB approval from Stanford University and the Veterans Administration Palo Alto Health Care System. The collection of tissue samples consisted of a distribution of poorly- to well-differentiated adenocarcinomas and squamous cell cancers. The surgeon had removed necrotic debris during excision and sampled cavitary lesions to include as much solid component as practical. Then, from the excised tumor, he cut a 3 to 5 mm thick slice along its longest axis, and froze it within 30 minutes of excision. We retrieved the frozen tissue and extracted the RNA that was then processed by the Stanford Functional Genomics Facility using Illumina Whole Genome Bead Chips (Human HT-12 v3.0)
2012-05-31 | E-GEOD-28827 | biostudies-arrayexpress