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DNA methylation-based classification of common renal tumors

ABSTRACT: DNA methylation-based classification of common renal tumors

PROVIDER: PRJNA1064051 | ENA |

REPOSITORIES: ENA

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DNA methylation-based classification of common renal tumors

Project description:In present work one of our main objectives was to collect enough DNA methylation profiles of diverse tumor types in renal cancer. So, we used a collection of newly processed samples on DNA methylation data to have a variety of diverse kidney tumors from various sources. We processed a subset of samples (n=156) profiled as a part of our routine clinical methylation testing for kidney cancer at NIH. We collected one subset of samples (n =173) from our collaborators at UCSF and another subset of samples (n=82) from NYU.

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DNA methylation-based classification of sinonasal tumors [test set]

Project description:The diagnosis of sinonasal tumors is challenging due to a heterogeneous spectrum of various differential diagnoses as well as poorly defined, disputed entities such as sinonasal undifferentiated carcinomas (SNUCs). In this study, we apply machine learning algorithm based on DNA methylation patterns to classify sinonasal tumors with clinical-grade reliability. We further show that sinonasal tumors with SNUC morphology are not as undifferentiated as their current terminology suggests but rather reassigned to four distinct molecular classes defined by epigenetic, mutational and proteomic profiles. This includes two classes with neuroendocrine differentiation, characterized by IDH2 or SMARCA4/ARID1A mutations with an overall favorable clinical course, whereas tumors that are driven by SMARCB1-deficiency and tumors that represent previously misclassified adenoid cystic carcinomas are highly aggressive. Our findings have the potential to dramatically improve the diagnostic classification of sinonasal tumors and will fundamentally change the current perception of SNUCs.

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Molecular Genetic Classification of clear-cell Renal Cell Carcinoma (ccRCC) based on the Gene Expression Profiling of Tumors and Tumorgrafts deficient for BAP1 or PBRM1

Project description:Renal cell carcinoma (RCC) exhibits some unusual features and genes commonly mutated in cancer are rarely mutated in clear-cell RCC (ccRCC), the most common type. The most prevalent genetic alteration in ccRCC is the inactivation of the tumor suppressor gene VHL. Using whole-genome and exome sequencing we discovered BAP1 as a novel tumor suppressor in ccRCC that shows little overlap with mutations in PBRM1, another recent tumor suppressor. Whereas VHL was mutated in 81% of the patients (142/176), PBRM1 was lost in 58% and BAP1 in 15% of the patients analyzed. All these tumor suppressor genes are located in chromosome 3p, which is partially or completely lost in most ccRCC patients. However, BAP1 but not PBRM1 loss was associated with higher Fuhrman grade and, therefore, poorer outcome. Xenograft tumors (tumorgrafts) implanted orthotopically in mice exhibited similar gene expression profiling to corresponding primary tumors. Gene expression profiling of tumors and tumorgrafts displayed different signatures for BAP1- and PBRM1-deficient samples. Thus, after inactivation of VHL, the acquisition of a mutation in BAP1 or PBRM1 defines a different program that might alter the fate of the patient. Our results establish the foundation for an integrated pathological and molecular genetic classification of about 70% of ccRCC patients, paving the way for subtype-specific treatments exploiting genetic vulnerabilities. The RNA of clear-cell renal cell carcinoma (ccRCC) primary tumors, tumors growing in immunodeficient mice (tumorgrafts), and normal kidney cortices were labeled and hybridized to Affymetrix Human Genome U133 Plus 2.0 arrays.

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Molecular Genetic Classification of clear-cell Renal Cell Carcinoma (ccRCC) based on the Gene Expression Profiling of Tumors and Tumorgrafts deficient for BAP1 or PBRM1

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