How to be good at being bad: centrosome amplification and mitotic propensity drive intratumoral heterogeneity View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2015-12

AUTHORS

Padmashree C. G. Rida, Guilherme Cantuaria, Michelle D. Reid, Omer Kucuk, Ritu Aneja

ABSTRACT

Cancer is truly an iconic disease--a tour de force whose multiple formidable strengths can be attributed to the bewildering heterogeneity that a tumor can manifest both spatially and temporally. A Darwinian evolutionary process is believed to undergird, at least in part, the generation of this heterogeneity that contributes to poor clinical outcomes. Risk assessment in clinical oncology is currently based on a small number of clinicopathologic factors (like stage, histological grade, receptor status, and serum tumor markers) and offers limited accuracy in predicting disease course as evidenced by the prognostic heterogeneity that persists in risk segments produced by present-day models. We posit that this insufficiency stems from the exclusion of key risk contributors from such models, especially the omission of certain factors implicated in generating intratumoral heterogeneity. The extent of centrosome amplification and the mitotic propensity inherent in a tumor are two such vital factors whose contributions to poor prognosis are presently overlooked in risk prognostication. Supernumerary centrosomes occur widely in tumors and are potent drivers of chromosomal instability that fosters intratumoral heterogeneity. The mitotic propensity of a proliferating population of tumor cells reflects the cell cycling kinetics of that population. Since frequent passage through improperly regulated mitotic divisions accelerates production of diverse genotypes, the mitotic propensity inherent in a tumor serves as a powerful beacon of risk. In this review, we highlight how centrosome amplification and error-prone mitoses contribute to poor clinical outcomes and urge the need to develop these cancer-specific traits as much-needed clinically-facile prognostic biomarkers with immense potential value for individualized cancer treatment in the clinic. More... »

PAGES

703-713

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s10555-015-9590-0

DOI

http://dx.doi.org/10.1007/s10555-015-9590-0

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1041335950

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/26358854


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Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s10555-015-9590-0'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s10555-015-9590-0'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s10555-015-9590-0'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s10555-015-9590-0'


 

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