Cancer nucleus: Morphology and beyond

Dey, Pranab

doi:10.1002/dc.21234

Cited by 102 publications

(80 citation statements)

References 95 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Altered nuclear scaling is associated with many cancers, and cytopathologists monitor graded increases in nuclear size for cancer diagnosis and prognosis (Zink et al, 2004;Chow et al, 2012;. Importantly, nuclear size increases in cancer are frequently independent of gross ploidy changes (Lindberg, 1992;Miyamoto et al, 1992;Sørensen et al, 1992;Wang et al, 1992;Dangou et al, 1993;van Velthoven et al, 1995;Sørensen, 1996;Dey, 2010), implicating potential cytoplasmic contributions to altered nuclear size (Goehring and Hyman, 2012). It is unknown whether changes in nuclear size contribute to carcinogenesis, and answering this question necessitates an understanding of the mechanisms responsible for regulating nuclear size.…”

Section: Introductionmentioning

confidence: 99%

Nuclear size is sensitive to NTF2 protein levels in a manner dependent on Ran binding

Vuković

Jevtić

Zhang

et al. 2016

Journal of Cell Science

View full text Add to dashboard Cite

Altered nuclear size is associated with many cancers, and determining whether cancer-associated changes in nuclear size contribute to carcinogenesis necessitates an understanding of mechanisms of nuclear size regulation. Although nuclear import rates generally positively correlate with nuclear size, NTF2 levels negatively affect nuclear size, despite the role of NTF2 (also known as NUTF2) in nuclear recycling of the import factor Ran. We show that binding of Ran to NTF2 is required for NTF2 to inhibit nuclear expansion and import of large cargo molecules in Xenopus laevis egg and embryo extracts, consistent with our observation that NTF2 reduces the diameter of the nuclear pore complex (NPC) in a Ranbinding-dependent manner. Furthermore, we demonstrate that ectopic NTF2 expression in Xenopus embryos and mammalian tissue culture cells alters nuclear size. Finally, we show that increases in nuclear size during melanoma progression correlate with reduced NTF2 expression, and increasing NTF2 levels in melanoma cells is sufficient to reduce nuclear size. These results show a conserved capacity for NTF2 to impact on nuclear size, and we propose that NTF2 might be a new cancer biomarker.

show abstract

Section: Introductionmentioning

confidence: 99%

Nuclear size is sensitive to NTF2 protein levels in a manner dependent on Ran binding

Vuković

Jevtić

Zhang

et al. 2016

Journal of Cell Science

View full text Add to dashboard Cite

show abstract

“…This produces an a/LCI scan which yields the angular scattering distribution for specific depths beneath the tissue surface. Analysis of the angular scattering distributions produces depth resolved quantitative nuclear morphology measurements, which have been shown to be a biomarker for tissue dysplasia [2,3]. A typical a/LCI system samples the tissue with a 100 μm beam and achieves a depth penetration of around 300 μm, thus sampling approximately 100 nuclei for each a/LCI scan.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of hybrid algorithm for analysis of scattered light using ex vivo nuclear morphology measurements of cervical epithelium

Drake

Bentley

et al. 2015

Biomed. Opt. Express

View full text Add to dashboard Cite

Abstract:We evaluate a new hybrid algorithm for determining nuclear morphology using angle-resolved low coherence interferometry (a/LCI) measurements in ex vivo cervical tissue. The algorithm combines Mie theory based and continuous wavelet transform inverse light scattering analysis. The hybrid algorithm was validated and compared to traditional Mie theory based analysis using an ex vivo tissue data set. The hybrid algorithm achieved 100% agreement with pathology in distinguishing dysplastic and non-dysplastic biopsy sites in the pilot study. Significantly, the new algorithm performed over four times faster than traditional Mie theory based analysis.

show abstract

“…Since significant changes of functions of any kind are manifested with changes in the texture of the tissue accompanied with discernable changes in spatial arrangement of cells and their shapes, histology is an important method for differentiating tissue and cell phenotypes. In clinical fields, histopathology, which is a branch of histology, has been regularly used in diagnosis of diseases, especially in diagnosis of cancer [10], [11].…”

Section: Introdutionmentioning

confidence: 99%

Histology Image Classification Using Supervised Classification and Multimodal Fusion

Meng

Lin

Shyu

et al. 2010

2010 IEEE International Symposium on Multimedia

View full text Add to dashboard Cite

Abstract-The fast development of microscopy imaging techniques nowadays promotes the generation of a large amount of data. These data are very crucial not only for theoretical biomedical research but also for clinical usage. In order to decrease the inter-intra observer variability and save the human effort on labeling and classifying these images, a lot of research efforts have been devoted to the development of algorithms for biomedical images. Among such efforts, histology image classification is one of the most important areas due to its broad applications in pathological diagnosis such as cancer diagnosis. To improve classification accuracy, most of the previous work focuses on extracting more features and building algorithms for a specific task. This paper proposes a framework based on the novel and robust Collateral Representative Subspace Projection Modeling (C-RSPM) supervised classification model for general histology image classification. In the proposed framework, a cell image is first divided into 25 blocks to reduce the spatial complexity of computation, and one C-RSPM model is built on each block set which contains blocks in the same location from different images. For each testing image, our proposed framework first classifies each of its blocks using the C-RSPM classification model built for that block set, and then applies a multimodal late fusion algorithm with a weighted majority voting strategy to decide the final class label of the whole image. Experimenting using three-fold cross validation with three benchmark histology data sets shows that the proposed framework outperforms other well-known classifiers in the comparison and gives better results than the highest accuracy reported previously.

show abstract

Cancer nucleus: Morphology and beyond

Cited by 102 publications

References 95 publications

Nuclear size is sensitive to NTF2 protein levels in a manner dependent on Ran binding

Nuclear size is sensitive to NTF2 protein levels in a manner dependent on Ran binding

Evaluation of hybrid algorithm for analysis of scattered light using ex vivo nuclear morphology measurements of cervical epithelium

Histology Image Classification Using Supervised Classification and Multimodal Fusion

Contact Info

Product

Resources

About