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Intratumor heterogeneity in human glioblastoma reflects cancer evolutionary dynamics.

Al's Comment:

 This project tested multiple samples from each tumor to see if there were variations within the same tumor, and found striking differences which may have a mahor effect on treatments.  If true (and this was a small study which needs to be repeated), then the concept of personalized medicine - targetting the tumor's specific mutations - might not work, as they found different parts of the tumor had different subtypes.


Posted on: 02/16/2013

Proc Natl Acad Sci U S A. 2013 Feb 14. [Epub ahead of print]
Intratumor heterogeneity in human glioblastoma reflects cancer evolutionary dynamics.
Sottoriva A, Spiteri I, Piccirillo SG, Touloumis A, Collins VP, Marioni JC, Curtis C, Watts C, Tavaré S.
Department of Oncology, University of Cambridge, Cambridge CB2 2XZ, United Kingdom.

Abstract
Glioblastoma (GB) is the most common and aggressive primary brain malignancy, with poor prognosis and a lack of effective therapeutic options. Accumulating evidence suggests that intratumor heterogeneity likely is the key to understanding treatment failure. However, the extent of intratumor heterogeneity as a result of tumor evolution is still poorly understood. To address this, we developed a unique surgical multisampling scheme to collect spatially distinct tumor fragments from 11 GB patients. We present an integrated genomic analysis that uncovers extensive intratumor heterogeneity, with most patients displaying different GB subtypes within the same tumor. Moreover, we reconstructed the phylogeny of the fragments for each patient, identifying copy number alterations in EGFR and CDKN2A/B/p14ARF as early events, and aberrations in PDGFRA and PTEN as later events during cancer progression. We also characterized the clonal organization of each tumor fragment at the single-molecule level, detecting multiple coexisting cell lineages. Our results reveal the genome-wide architecture of intratumor variability in GB across multiple spatial scales and patient-specific patterns of cancer evolution, with consequences for treatment design.

PMID: 23412337 [PubMed - as supplied by publisher]

 


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