Improving accuracy in the grading of renal cell carcinoma by combining the quantitative description of chromatin pattern with the quantitative determination of cell kinetic parameters

François, Christine; Moreno, Christophe; Teitelbaum, Joel B.; Bigras, Gilbert; Salmon, Isabelle; Danguy, André; Brugal, G.; Velthoven, Roland Van; Kiss, Róbert; Decaestecker, Christine

doi:10.1002/(sici)1097-0320(20000215)42:1<18::aid-cyto4>3.0.co;2-s

Cited by 8 publications

(7 citation statements)

References 41 publications

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“…There is much data indicating that a high level of cell division, measured by rapid incorporation of labeled DNA precursors (33) or the activation of cell cycle proteins (34), is correlated within a particular tumor type with a poor survival. In addition, studies of the expression of genes involved in protein synthesis suggest that a high level of protein synthesis is also associated with poor prognosis (35,36).…”

Section: Discussionmentioning

confidence: 99%

A Serial Analysis of Gene Expression (SAGE) Database Analysis of Chemosensitivity

et al. 2004

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Drug sensitivity and resistance has been most extensively studied in cell lines carried in tissue culture. Furthermore, cell lines have been widely used in testing new anticancer agents, despite the widely recognized observation that cell lines are more sensitive to cytotoxic drugs than are their corresponding solid tumors. We used the Serial Analysis of Gene Expression (SAGE) database to identify differences between solid tumors and cell lines, hoping to detect genes that could potentially explain differences in drug sensitivity. SAGE libraries were available for both solid tumors and cell lines from breast, colon, ovarian, pancreatic, and prostate carcinomas and from gliomas and medulloblastomas. Sixty-two genes were identified as overexpressed in tumors. The immune response and complement pathways were the significant common themes, with extracellular matrix (ECM) proteins third. For the 61 genes overexpressed in cell lines, protein synthesis was the dominant theme. We next used the SAGE database to identify genetic differences between tumor types that convey a broad range of survival to the patients that bear them as distant metastases. SAGE gene expression data were correlated with 5-year survivals documented in the SEER (Surveillance, Epidemiology and EndResults) database for patients diagnosed with "distant" or metastatic cancers. These are unlikely to be amenable to surgical resection; therefore, survival here reflects, to some extent, sensitivity to systemic therapy, i.e., chemotherapy. Using survival data as a surrogate of chemotherapy sensitivity, a spectrum can be generated, with testicular cancer at one end and pancreatic cancer at the other. Favorable 5-year survival, despite a distant presentation, correlates with expression of protein synthesis genes. Poor 5-year survival correlates with expression of cell adhesion, cytoskeletal, and ECM genes, a pattern similar to that found to distinguish solid tumors from the more cytotoxin-sensitive cancer cell lines. One interpretation is that resistance to chemotherapy may arise, in part, from the adherent, relatively inert condition (i.e., low in protein synthesis potential) of refractory cancers. Thus, attachment or ECM genes could be targets for anticancer therapy.

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Section: Discussionmentioning

confidence: 99%

A Serial Analysis of Gene Expression (SAGE) Database Analysis of Chemosensitivity

et al. 2004

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“…Yasunaga et al [42] showed the AgNOR score to be an independent prognostic factor associated with survival; however, other studies did not find this to be so [43]. Francois et al [44] demonstrated how it may also be useful in improving conventional renal cell grading systems.…”

Section: Nuclear Parametersmentioning

confidence: 99%

Classical pathology versus molecular pathology in renal cell carcinoma

Sullivan

Rao²,

Liu³

et al. 2007

Curr Urol Rep

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Renal cell carcinoma (RCC) has been characterized based on histology, stage, and grading to predict behavior and guide therapy; however, RCC is still unpredictable, with poor prognosis in metastatic disease. The classification of RCC has been revised to account for molecular characteristics, and there has been an increasing understanding of the hereditary forms of RCC. This has led to further elucidation of pathways in the development of RCC including the hypoxia-inducible pathway and angiogenesis. Many other promising molecular modalities are in development, including gene expression profiling, nuclear parameters, and proliferation/apoptotic markers. This article discusses the current understanding of the classical pathologic features of RCC and highlights recent developments in the cellular and molecular characterization of RCC, which aim to improve the classification, prognostication, and treatment of RCC.

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“…Choi et al extended the morphological analysis to three-dimensional nuclei and applied their system to RCC grading [9]. In addition to morphological features, Francois et al used cell kinetic features in their RCC grading system [10]. Finally, Raza et al used a scale invariant feature transform (SIFT) method to classify RCC subtypes [11].…”

Section: Introductionmentioning

confidence: 99%

Histological image classification using biologically interpretable shape-based features

et al. 2013

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BackgroundAutomatic cancer diagnostic systems based on histological image classification are important for improving therapeutic decisions. Previous studies propose textural and morphological features for such systems. These features capture patterns in histological images that are useful for both cancer grading and subtyping. However, because many of these features lack a clear biological interpretation, pathologists may be reluctant to adopt these features for clinical diagnosis.MethodsWe examine the utility of biologically interpretable shape-based features for classification of histological renal tumor images. Using Fourier shape descriptors, we extract shape-based features that capture the distribution of stain-enhanced cellular and tissue structures in each image and evaluate these features using a multi-class prediction model. We compare the predictive performance of the shape-based diagnostic model to that of traditional models, i.e., using textural, morphological and topological features.ResultsThe shape-based model, with an average accuracy of 77%, outperforms or complements traditional models. We identify the most informative shapes for each renal tumor subtype from the top-selected features. Results suggest that these shapes are not only accurate diagnostic features, but also correlate with known biological characteristics of renal tumors.ConclusionsShape-based analysis of histological renal tumor images accurately classifies disease subtypes and reveals biologically insightful discriminatory features. This method for shape-based analysis can be extended to other histological datasets to aid pathologists in diagnostic and therapeutic decisions.

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Improving accuracy in the grading of renal cell carcinoma by combining the quantitative description of chromatin pattern with the quantitative determination of cell kinetic parameters

Cited by 8 publications

References 41 publications

A Serial Analysis of Gene Expression (SAGE) Database Analysis of Chemosensitivity

A Serial Analysis of Gene Expression (SAGE) Database Analysis of Chemosensitivity

Classical pathology versus molecular pathology in renal cell carcinoma

Histological image classification using biologically interpretable shape-based features

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