Dataset Information

Mass Spectrometry Imaging Enables Discrimination of Renal Oncocytoma from Renal Cell Cancer Subtypes and Normal Kidney Tissues.

ABSTRACT: Precise diagnosis and subtyping of kidney tumors are imperative to optimize and personalize treatment decision for patients. Patients with the most common benign renal tumor, renal oncocytomas, may be overtreated with surgical resection because of limited preoperative diagnostic methods that can accurately identify the benign condition with certainty. In this study, desorption electrospray ionization (DESI)-mass spectrometry (MS) imaging was applied to study the metabolic and lipid profiles of various types of renal tissues, including normal kidney, renal oncocytoma, and renal cell carcinomas (RCC). A total of 73,992 mass spectra from 71 patient samples were obtained and used to build predictive models using the least absolute shrinkage and selection operator (Lasso). Overall accuracies of 99.47% per pixel and 100% per patient for prediction of the three tissue types were achieved. In particular, renal oncocytoma and chromophobe RCC, which present the most significant morphologic overlap and are sometimes indistinguishable using histology alone, were also investigated and the predictive models built yielded 100% accuracy in discriminating these tumor types. Discrimination of three subtypes of RCC was also achieved on the basis of DESI-MS imaging data. Importantly, several small metabolites and lipids species were identified as characteristic of individual tissue types and chemically characterized using tandem MS and high mass accuracy measurements. Collectively, our study shows that the metabolic data acquired by DESI-MS imaging in conjunction with statistical modeling allows discrimination of renal tumors and thus has the potential to be used in the clinical setting to improve treatment of patients with kidney tumor. SIGNIFICANCE: Metabolic data acquired by mass spectrometry imaging in conjunction with statistical modeling allows discrimination of renal tumors and has the potential to be used in the clinic to improve treatment of patients.

SUBMITTER: Zhang J

PROVIDER: S-EPMC7024663 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature

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Mass Spectrometry Imaging Enables Discrimination of Renal Oncocytoma from Renal Cell Cancer Subtypes and Normal Kidney Tissues.

Zhang Jialing J Li Shirley Q SQ Lin John Q JQ Yu Wendong W Eberlin Livia S LS

Cancer research 20191216 4

Precise diagnosis and subtyping of kidney tumors are imperative to optimize and personalize treatment decision for patients. Patients with the most common benign renal tumor, renal oncocytomas, may be overtreated with surgical resection because of limited preoperative diagnostic methods that can accurately identify the benign condition with certainty. In this study, desorption electrospray ionization (DESI)-mass spectrometry (MS) imaging was applied to study the metabolic and lipid profiles of v ...[more]

PMID: 31843980

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Project description:Background: While subtyping of the majority of malignant chromophobe renal cell carcinoma (cRCC) and benign renal oncocytoma (rO) is possible on morphology alone, additional histochemical, immunohistochemical or molecular investigations are required in a subset of cases. As currently used histochemical and immunohistological stains as well as genetic aberrations show considerable overlap in both tumors, additional techniques are required for differential diagnostics. Mass spectrometry imaging (MSI) combining the detection of multiple peptides with information about their localization in tissue may be a suitable technology to overcome this diagnostic challenge. Patients and Methods: Formalin-fixed paraffin embedded (FFPE) tissue specimens from cRCC (n=71) and rO (n=64) were analyzed by MSI. Data were classified by linear discriminant analysis (LDA), classification and regression trees (CART), k-nearest neighbors (KNN), support vector machine (SVM), and random forest (RF) algorithm with internal cross validation and visualized by t-distributed stochastic neighbor embedding (t-SNE). Most important variables for classification were identified and the classification algorithm was optimized. Results: Applying different machine learning algorithms on all m/z peaks, classification accuracy between cRCC and rO was 85%, 82%, 84%, 77% and 64% for RF, SVM, KNN, CART and LDA. Under the assumption that a reduction of m/z peaks would lead to improved classification accuracy, m/z peaks were ranked based on their variable importance. Reduction to six most important m/z peaks resulted in improved accuracy of 89%, 85%, 85% and 85% for RF, SVM, KNN, and LDA and remained at the level of 77% for CART. t-SNE showed clear separation of cRCC and rO after algorithm improvement. Conclusion: In summary, we acquired MSI data on FFPE tissue specimens of cRCC and rO, performed classification and detected most relevant biomarkers for the differential diagnosis of both diseases. MSI data might be a useful adjunct method in the differential diagnosis of cRCC and rO.

Project description:Ovarian cancer is a fatal gynaecological malignancy in adult women with a five-year overall survival rate of only 30%. Glycomic and glycoproteomic profiling studies have reported extensive protein glycosylation pattern alterations in ovarian cancer. Therefore, spatio-temporal investigation of these glycosylation changes may unearth tissue-specific changes that occur in the development and progression of ovarian cancer. A novel method for investigating tissue-specific N-linked glycans is using matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) on formalin-fixed paraffin-embedded (FFPE) tissue sections that can spatially profile N-glycan compositions released from proteins in tissue-specific regions. In this study, tissue regions of interest (e.g. tumor, stroma, adipose tissue and necrotic areas) were isolated from FFPE tissue sections of advanced serous ovarian cancers (n = 3). PGC-LC-ESI-MS/MS and MALDI-MSI were used as complementary techniques to firstly generate structural information on the tissue-specific glycans in order to then obtain high resolution images of the glycan structure distribution in ovarian cancer tissue. The N-linked glycan repertoires carried by the proteins in these tissue regions were structurally characterized for the first time in FFPE ovarian cancer tissue regions, using enzymatic peptide-N-glycosidase F (PNGase F) release of N-glycans. The released glycans were analyzed by porous graphitized carbon liquid chromatography (PGC-LC) and collision induced electrospray negative mode MS fragmentation analysis. The N-glycan profiles identified by this analysis were then used to determine the location and distribution of each N-glycan on FFPE ovarian cancer sections that were treated with PNGase F using high resolution MALDI-MSI. A tissue-specific distribution of N-glycan structures identified particular regions of the ovarian cancer sections. For example, high mannose glycans were predominantly expressed in the tumor tissue region whereas complex/hybrid N-glycans were significantly abundant in the intervening stroma. Therefore, tumor and non-tumor tissue regions were clearly demarcated solely on their N-glycan structure distributions.

Dataset Information

Mass Spectrometry Imaging Enables Discrimination of Renal Oncocytoma from Renal Cell Cancer Subtypes and Normal Kidney Tissues.

Publications

Mass Spectrometry Imaging Enables Discrimination of Renal Oncocytoma from Renal Cell Cancer Subtypes and Normal Kidney Tissues.

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