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Multivariate statistical differentiation of renal cell carcinomas based on lipidomic analysis by ambient ionization imaging mass spectrometry

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Abstract

Desorption electrospray ionization (DESI) mass spectrometry (MS) was used in an imaging mode to interrogate the lipid profiles of thin tissue sections of 11 sample pairs of human papillary renal cell carcinoma (RCC) and adjacent normal tissue and nine sample pairs of clear cell RCC and adjacent normal tissue. DESI-MS images showing the spatial distributions of particular glycerophospholipids (GPs) and free fatty acids in the negative ion mode were compared to serial tissue sections stained with hematoxylin and eosin (H&E). Increased absolute intensities as well as changes in relative abundance were seen for particular compounds in the tumor regions of the samples. Multivariate statistical analysis using orthogonal projection to latent structures treated partial least square discriminate analysis (PLS-DA) was used for visualization and classification of the tissue pairs using the full mass spectra as predictors. PLS-DA successfully distinguished tumor from normal tissue for both papillary and clear cell RCC with misclassification rates obtained from the validation set of 14.3% and 7.8%, respectively. It was also used to distinguish papillary and clear cell RCC from each other and from the combined normal tissues with a reasonable misclassification rate of 23%, as determined from the validation set. Overall DESI-MS imaging combined with multivariate statistical analysis shows promise as a molecular pathology technique for diagnosing cancerous and normal tissue on the basis of GP profiles.

Molecular disease diagnostics by DESI without sample preparation. a Good information is obtained by mapping the distribution of individual compounds in the tissue (e.g., PI(18:0/20:4). b Even better discrimination between tumor and healthy tissue is achieved using PLS-DA to consider all the data after having established through a training set of samples the features that correlate with disease as recognized by standard H&E stain pathological examination

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Acknowledgments

We thank the Purdue University Center for Cancer Research and its director, Timothy Ratliff, for assistance in obtaining the human kidney cancer samples. This work was supported by the National Institutes of Health (Grant 1 R21 EB00 9459-01).

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Authors and Affiliations

  1. Department of Chemistry and Center for Analytical Instrumentation Development, Purdue University, West Lafayette, IN, 47907, USA

    Allison L. Dill, Livia S. Eberlin, Anthony B. Costa, Demian R. Ifa & R. Graham Cooks

  2. Department of Statistics, Purdue University, West Lafayette, IN, 47907, USA

    Cheng Zheng & Olga Vitek

  3. Department of Pathology and Laboratory Medicine, Indiana University School of Medicine, Indianapolis, IN, 46202, USA

    Liang Cheng

  4. Department of Urology, Indiana University School of Medicine, Indianapolis, IN, 46202, USA

    Timothy A. Masterson & Michael O. Koch

  5. Department of Chemistry, Purdue University, West Lafayette, IN, 47907, USA

    R. Graham Cooks

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  1. Allison L. Dill
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Correspondence to R. Graham Cooks.

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Dill, A.L., Eberlin, L.S., Zheng, C. et al. Multivariate statistical differentiation of renal cell carcinomas based on lipidomic analysis by ambient ionization imaging mass spectrometry. Anal Bioanal Chem 398, 2969–2978 (2010). https://doi.org/10.1007/s00216-010-4259-6

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