Three-antibody classifier for muscle invasive urothelial carcinoma and its correlation with p53 expression

2021 ◽  
pp. jclinpath-2021-207573
Author(s):  
Ekaterina Olkhov-Mitsel ◽  
Anjelica Hodgson ◽  
Stanley K Liu ◽  
Danny Vesprini ◽  
Bin Xu ◽  
...  
Keyword(s):  
External Validation ◽  
Morphological Features ◽  
The Cancer Genome Atlas ◽  
P53 Expression ◽  
Basal Subtype ◽  
Divergent Differentiation ◽  
Cancer Genome Atlas ◽  
P53 Status ◽  
Tcga Dataset ◽  
Muscle Invasive

AimsTo assess the utility of a three-antibody immunohistochemistry panel to classify muscle invasive bladder cancers (MIBCs) in correlation with morphological features and p53 status.MethodsA retrospective review of 243 chemotherapy naïve MIBC cystectomy specimens was performed to assess morphological features. A tissue microarray was sequentially stained with CK5/6, GATA-3 and p16. Subgroups were assigned as basal-like (CK5/6+, GATA3−) and luminal (CK5/6−, GATA3+), with the latter subdivided into genomically unstable (GU, p16+) and urothelial like (Uro, p16−) subgroups. p53 staining was assessed as abnormal/wild type. Cases from the The Cancer Genome Atlas (TCGA) portal were assessed as external validation.ResultsWe identified 78.8% luminal, 21.2% basal cases within our cohort and 63.4% luminal, 36.6% basal in the TCGA dataset. Divergent differentiation (p<0.001) was significantly associated with basal-subtype cases in both cohorts. Within the luminal subgroup (n=186), 81 cases were classified as GU and 105 as Uro. Abnormal p53 staining was noted in 48.0% of basal, 80.2% GU and 38.1% Uro cases. Further, basal-subtype tumours significantly correlated with disease-specific death compared with Uro cases in multivariate survival analysis.ConclusionsThis retrospective study demonstrates the potential utility of a three-antibody immunohistochemistry panel to differentiate luminal and basal MIBC.

scholarly journals Integrated Dissection of lncRNA-Perturbated Triplets Reveals Novel Prognostic Signatures Across Cancer Types

2020 ◽  
Vol 21 (17) ◽  
pp. 6087
Author(s):  
Yunzhen Wei ◽  
Limeng Zhou ◽  
Yingzhang Huang ◽  
Dianjing Guo
Keyword(s):  
Cancer Biology ◽  
Noncoding Rna ◽  
The Cancer Genome Atlas ◽  
Dynamic Changes ◽  
Computational Framework ◽  
Potential Biomarker ◽  
Cancer Genome Atlas ◽  
Cancer Types ◽  
Tcga Dataset ◽  
Pan Cancer

Long noncoding RNA (lncRNA)/microRNA(miRNA)/mRNA triplets contribute to cancer biology. However, identifying significative triplets remains a major challenge for cancer research. The dynamic changes among factors of the triplets have been less understood. Here, by integrating target information and expression datasets, we proposed a novel computational framework to identify the triplets termed as “lncRNA-perturbated triplets”. We applied the framework to five cancer datasets in The Cancer Genome Atlas (TCGA) project and identified 109 triplets. We showed that the paired miRNAs and mRNAs were widely perturbated by lncRNAs in different cancer types. LncRNA perturbators and lncRNA-perturbated mRNAs showed significantly higher evolutionary conservation than other lncRNAs and mRNAs. Importantly, the lncRNA-perturbated triplets exhibited high cancer specificity. The pan-cancer perturbator OIP5-AS1 had higher expression level than that of the cancer-specific perturbators. These lncRNA perturbators were significantly enriched in known cancer-related pathways. Furthermore, among the 25 lncRNA in the 109 triplets, lncRNA SNHG7 was identified as a stable potential biomarker in lung adenocarcinoma (LUAD) by combining the TCGA dataset and two independent GEO datasets. Results from cell transfection also indicated that overexpression of lncRNA SNHG7 and TUG1 enhanced the expression of the corresponding mRNA PNMA2 and CDC7 in LUAD. Our study provides a systematic dissection of lncRNA-perturbated triplets and facilitates our understanding of the molecular roles of lncRNAs in cancers.

The impact of BMI on outcomes of patients with metastatic renal cell carcinoma treated with targeted therapy: An external validation data set and analysis of underlying biology from The Cancer Genome Atlas.

2015 ◽  
Vol 33 (7_suppl) ◽  
pp. 405-405 ◽  
Author(s):  
Laurence Albiges ◽  
A. Ari Hakimi ◽  
Xun Lin ◽  
Ronit Simantov ◽  
Emily C. Zabor ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Targeted Therapy ◽  
Prognostic Factor ◽  
Renal Cell ◽  
The Cancer Genome Atlas ◽  
Cancer Genome Atlas ◽  
Tcga Dataset ◽  
The Impact ◽  
Genome Atlas ◽  
High Bmi

405 Background: Obesity is a risk factor for renal cell carcinoma (RCC) and a poor prognostic factor across many tumor types. However, reports have suggested that RCC developing in an obesogenic environment may be more indolent. We recently reported on the favorable impact of body mass index (BMI) on survival in the International mRCC Database Consortium (IMDC). The current work aims to externally validate this finding and characterize the underlying biology. Methods: We conducted an analysis of 4,657 metastatic RCC (mRCC) patients (pts) treated on phase II-III clinical trials sponsored by Pfizer from 2003-2013. We assessed the impact of BMI on overall survival (OS), progression-free survival (PFS) and overall response rate (ORR). Additionally, we analysed metastatic pts from the clear cell RCC (ccRCC) cohort of TCGA dataset to correlate the expression of Fatty Acid Synthase (FASN) with BMI and OS. Results: At targeted therapy (TT) initiation, 1,829 (39%) pts were normal or underweight (BMI <25 kg/m2) and 2,828 (61%) were overweight or obese (BMI ≥25 kg/m2). Overall, the high BMI group had a longer median OS (23.4 months) than the low BMI group (14.5 months) (hazard ratio (HR) = 0.830, p= 0.0008, 95% CI 0.743-0.925) after adjusting for the IMDC prognostic risk group and other risks factors. In addition, pts with high BMI had improved PFS (HR=0.821, 95% CI 0.746-0.903, p<0.0001) and ORR (odds ratio =1.527, 95% CI 1.258-1.855, p<0.001). These results remain valid when stratified by line of therapy. When stratified by histological subtype, the favorable outcome associated with high BMI was only observed in ccRCC. Toxicity patterns did not differ between BMI groups. In the the Cancer Genome Atlas (TCGA) dataset (n=61), there was a trend towards improved OS in the high BMI group (p=0.07). FASN gene expression inversely correlated with both OS (p=0.002) and BMI (p=0.034). Conclusions: In an external cohort,we validate BMI as an independent prognostic factor for improved survival in mRCC. Given that this finding was observed in ccRCC only, we hypothesize that lipid metabolism may be modulated by the fat laden tumors cells. FASN staining in the IMDC cohort is ongoing to better investigate the obesity paradox in mRCC.

scholarly journals Prognostic Biomarkers in Pancreatic Cancer: Avoiding Errata When Using the TCGA Dataset

Cancers ◽  
2019 ◽  
Vol 11 (1) ◽  
pp. 126 ◽  
Author(s):  
Remy Nicolle ◽  
Jerome Raffenne ◽  
Valerie Paradis ◽  
Anne Couvelard ◽  
Aurelien de Reynies ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Prognostic Value ◽  
Prognostic Significance ◽  
The Cancer Genome Atlas ◽  
Pancreatic Tumors ◽  
Expression Data ◽  
Web Based ◽  
Molecular Alterations ◽  
Cancer Genome Atlas ◽  
Tcga Dataset

Data from the Cancer Genome Atlas (TCGA) are now easily accessible through web-based platforms with tools to assess the prognostic value of molecular alterations. Pancreatic tumors have heterogeneous biology and aggressiveness ranging from the deadly adenocarcinoma (PDAC) to the better prognosis, neuroendocrine tumors. We assessed the availability of the pancreatic cancer TCGA data (TCGA_PAAD) from several repositories and investigated the nature of each sample and how non-PDAC samples impact prognostic biomarker studies. While the clinical and genomic data (n = 185) were fairly consistent across all repositories, RNAseq profiles varied from 176 to 185. As a result, 35 RNAseq profiles (18.9%) corresponded to a normal, inflamed pancreas or non-PDAC neoplasms. This information was difficult to obtain. By considering gene expression data as continuous values, the expression of the 5312 and 4221 genes were significantly associated with the progression-free and overall survival respectively. Considering the cohort was not curated, only 4 and 14, respectively, had prognostic value in the PDAC-only cohort. Similarly, mutations in key genes or well-described miRNA lost their prognostic significance in the PDAC-only cohort. Therefore, we propose a web-based application to assess biomarkers in the curated TCGA_PAAD dataset. In conclusion, TCGA_PAAD curation is critical to avoid important biological and clinical biases from non-PDAC samples.

scholarly journals Analysis of the TCGA Dataset Reveals that Subsites of Laryngeal Squamous Cell Carcinoma Are Molecularly Distinct

Cancers ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 105
Author(s):  
Alana Sorgini ◽  
Hugh Andrew Jinwook Kim ◽  
Peter Y. F. Zeng ◽  
Mushfiq Hassan Shaikh ◽  
Neil Mundi ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Laryngeal Squamous Cell Carcinoma ◽  
Response To Therapy ◽  
The Cancer Genome Atlas ◽  
Glottic Cancer ◽  
Neural Pathways ◽  
Cancer Genome Atlas ◽  
Tcga Dataset

Laryngeal squamous cell carcinoma (LSCC) from different subsites have distinct presentations and prognosis. In this study, we carried out a multiomic comparison of LSCC subsites. The Cancer Genome Atlas (TCGA) LSCC cohort was analyzed in the R statistical environment for differences between supraglottic and glottic cancers in single nucleotide variations (SNVs), copy number alterations (CNAs), mRNA abundance, protein abundance, pathway overrepresentation, tumor microenvironment (TME), hypoxia status, and patient outcome. Supraglottic cancers had significantly higher overall and smoking-associated SNV mutational load. Pathway analysis revealed upregulation of muscle related pathways in glottic cancer and neural pathways in supraglottic cancer. Proteins involved in cancer relevant signaling pathways including PI3K/Akt/mTOR, the cell cycle, and PDL1 were differentially abundant between subsites. Glottic and supraglottic tumors have different molecular profiles, which may partially account for differences in presentation and response to therapy.

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