Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

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A genomic algorithm for the molecular classification of renal cortical neoplasms: Development and Validation

ABSTRACT: Accurate diagnostic discrimination of benign renal oncocytoma (OC) and malignant renal cell carcinomas (RCC) is not only useful for planning appropriate treatment strategies of patients with renal masses but also for estimating prognosis. Classification of renal neoplasms solely by histopathology can often be challenging for a variety of reasons. The aim of this study was to develop and validate a genomic algorithm for molecular classification of renal cortical neoplasms that could be implemented in a routine clinical diagnostic setting. Using TCGA (The Cancer Genome Atlas) copy number profiles of over 600 RCC specimens, prior FISH studies and published literature, a classification algorithm was developed consisting of 15 genomic markers: loss of VHL, 3p21, 8p, and chromosomes 1, 2, 6, 10 and 17, and gain of 5qter, 16p, 17q, 20q, and chromosomes 3, 7, and 12. Criteria for scoring specimens for the presence of each genomic marker were established. As validation, 191 surgically resected formalin-fixed paraffin-embedded renal neoplasms were blindly submitted to targeted array-CGH and were classified according to the algorithm. Upon histologic re-review leading to exclusion of three specimens and using histology as the gold standard, the algorithm correctly classified 58 of 62 (93%) clear cell renal cell carcinoma, 51 of 56 (91%) papillary RCC, and 33 of 34 (97%) chromophobe RCC. Of the 36 OC specimens, 17 were classified as OC, two as a malignant subtype, 14 as benign, and three exhibited alterations not associated with a specific subtype. In ten of the latter two groups, CCND1-rearrangement was detected by fluorescence in situ hybridization, affording a classification as OC. Together, 33 of 36 (92%) OC were classified as OC or benign. For the entire validation cohort, an overall diagnostic sensitivity of 93% and above 97% specificity was achieved, suggesting that the implementation of genome-based molecular classification in a clinical diagnostic setting could impact the overall management and outcome of patients with renal tumors. A total of 191 RCC FFPE samples are analyzed including 63 clear cell RCC (ccRCC), 57 papillary RCC (pRCC), 35 chromophobe RCC (chrRCC) and 36 oncocytoma (OC). Two-color array-comparative genomic hybdrization on custom designed using RCC DNA as test and normal sex-matched DNA as reference.

ORGANISM(S): Homo sapiens

SUBMITTER: Jane Houldsworth

PROVIDER: E-GEOD-57162 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Project description:Accurate diagnostic discrimination of benign renal oncocytoma (OC) and malignant renal cell carcinomas (RCC) is not only useful for planning appropriate treatment strategies of patients with renal masses but also for estimating prognosis. Classification of renal neoplasms solely by histopathology can often be challenging for a variety of reasons. The aim of this study was to develop and validate a genomic algorithm for molecular classification of renal cortical neoplasms that could be implemented in a routine clinical diagnostic setting. Using TCGA (The Cancer Genome Atlas) copy number profiles of over 600 RCC specimens, prior FISH studies and published literature, a classification algorithm was developed consisting of 15 genomic markers: loss of VHL, 3p21, 8p, and chromosomes 1, 2, 6, 10 and 17, and gain of 5qter, 16p, 17q, 20q, and chromosomes 3, 7, and 12. Criteria for scoring specimens for the presence of each genomic marker were established. As validation, 191 surgically resected formalin-fixed paraffin-embedded renal neoplasms were blindly submitted to targeted array-CGH and were classified according to the algorithm. Upon histologic re-review leading to exclusion of three specimens and using histology as the gold standard, the algorithm correctly classified 58 of 62 (93%) clear cell renal cell carcinoma, 51 of 56 (91%) papillary RCC, and 33 of 34 (97%) chromophobe RCC. Of the 36 OC specimens, 17 were classified as OC, two as a malignant subtype, 14 as benign, and three exhibited alterations not associated with a specific subtype. In ten of the latter two groups, CCND1-rearrangement was detected by fluorescence in situ hybridization, affording a classification as OC. Together, 33 of 36 (92%) OC were classified as OC or benign. For the entire validation cohort, an overall diagnostic sensitivity of 93% and above 97% specificity was achieved, suggesting that the implementation of genome-based molecular classification in a clinical diagnostic setting could impact the overall management and outcome of patients with renal tumors.

Project description:Copy number variant (CNV) analysis was performed on renal cell carcinoma (RCC) specimens (chromophobe, clear cell, oncocytoma, papillary type 1, papillary type 2) using high resolution arrays (1.85 million probes). RCC samples exhibited diverse genomic changes within and across tumor types ranging from 106 CNV segments in a clear cell specimen to 2238 CNV segments in a papillary type 2 specimen. Despite the genomic heterogeneity, distinct CNV segments were common within each of 4 tumor classifications: chromophobe (7 segments), clear cell (3 segments), oncocytoma (9 segments), and papillary type 2 (2 segments). Shared segments ranged from a 6.1 Kb deletion among oncocytomas to a 208.3 Kb deletion common to chromophobes. Among common tumor type-specific variations, chromophobe, clear cell and oncocytomas comprised exclusively non-coding DNA. No CNV regions were common to papillary type 1 specimens although there were 12 amplifications and 12 deletions in 5 of 6 samples. Three microRNAs and 12 mRNA genes had M-bM-^IM-% 98% of their coding region contained within CNV regions including multiple gene families (chromophobe: amylase 1A, 1B, 1C; oncocytoma: general transcription factor 2H2, 2B, 2C, 2D). Gene deletions involved in histone modification and chromatin remodeling affected individual subtypes (clear cell: SFMBT, SETD2; papillary type 2: BAZ1A) as well as the collective RCC group (KDM4C). The genomic amplifications/deletions identified in each renal tumor type represent potential diagnostic and/or prognostic biomarkers. Tissue samples were obtained from the University of Pittsburgh Health Sciences Tissue Bank (HSTB) using an honest broker system and according to IRB approved protocol #970480. Samples were acquired as surgical specimens, flash-frozen in a 1.8 ml cryotube (NalgeNunc, Inc., Rochester, NY) followed by immediate storage at -80C. Each tumor sample (n=27) was classified into one of 5 renal cancer subtypes (chromophobe: n=5, clear cell: n=5, oncocytoma: n=5, papillary type 1: n=6, papillary type 2: n=6) by consensus evaluation of correlative hematoxylin and eosin stained slides performed independently by 3 anatomical pathologists. The three pathologists also confirmed the absence of pathological features in adjacent normal renal samples (n=9) and this normal reference group was expanded by inclusion of 14 normal thyroid samples and 8 normal lung specimens. DNA from each of these specimens was analyzed using genotyping microarrays (SNP 6.0, Affymetrix, Sunnyvale, CA).

Project description:In order to address the progression, metastasis, and clinical heterogeneity of renal cell cancer (RCC), transcriptional profiling with oligonucleotide microarrays (22,283 genes) was done on 49 RCC tumors, 20 non-RCC renal tumors, and 23 normal kidney samples. Samples were clustered based on gene expression profiles and specific gene sets for each renal tumor type were identified. Gene expression was correlated to disease progression and a metastasis gene signature was derived. Gene signatures were identified for each tumor type with 100% accuracy. Differentially expressed genes during early tumor formation and tumor progression to metastatic RCC were found. Subsets of these genes code for secreted proteins and membrane receptors and are both potential therapeutic or diagnostic targets. A gene pattern ("metastatic signature") derived from primary tumors was very accurate in classifying tumors with and without metastases at the time of surgery. A previously described "global" metastatic signature derived by another group from various non-RCC tumors was validated in RCC. Unlike previous studies, we describe highly accurate and externally validated gene signatures for RCC subtypes and other renal tumors. Interestingly, the gene expression of primary tumors provides us information about the metastatic status in the respective patients and has the potential, if prospectively validated, to enrich the armamentarium of diagnostic tests in RCC. We validated in RCC, for the first time, a previously described metastatic signature and further showed the feasibility of applying a gene signature across different microarray platforms. Transcriptional profiling allows a better appreciation of the molecular and clinical heterogeneity in RCC. We used the following tissue samples to obtain transcriptional profiling of kidney tumors using Affymetrix HGU-133A chips: 23 Normal, 32 clear cell RCC (cRCC), 11 papillary RCC (pRCC), 6 chromophobe RCC (chrRCC), 12 Oncocytoma (OC), and 8 transitional cell carcinoma (TCC). The supplementary file 'GSE15641_mas5_data.txt' contains MAS5 signal values for the Samples included in Series GSE15641. This dataset is part of the TransQST collection.

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A genomic algorithm for the molecular classification of renal cortical neoplasms: Development and Validation

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