Current challenges in glioblastoma: Intratumour heterogeneity, residual disease, and models to predict disease recurrence

Frontiers in Oncology

Volumn 5, Issue NOV, 2015, Pages

  Ellis, Hayley P ^a   Greenslade, Mark ^b   Powell, Ben ^a   Spiteri, Inmaculada ^c   Sottoriva, Andrea ^c   Kurian, Kathreena M ^a  

^a UNIVERSITY OF BRISTOL   (United Kingdom)

^b NORTH BRISTOL NHS TRUST   (United Kingdom)

^c INSTITUTE OF CANCER RESEARCH   (United Kingdom)

Author keywords

Bayesian models; GBM; Intratumor heterogeneity; Neural networks; Residual disease

Indexed keywords

GLIAL FIBRILLARY ACIDIC PROTEIN;

ANGIOGENESIS; BAYESIAN LEARNING; CANCER CHEMOTHERAPY; CANCER SURVIVAL; CARCINOGENESIS; CHROMOSOME; CHROMOSOME 4P; CHROMOSOME 9; CHROMOSOME ABERRATION; CLINICAL EFFECTIVENESS; COMPARATIVE STUDY; COPY NUMBER VARIATION; GENE EXPRESSION; GENE MAPPING; GENE MUTATION; GENETIC ANALYSIS; GENETIC ASSOCIATION; GENETIC VARIABILITY; GLIOBLASTOMA; GLUCOSE METABOLISM; HUMAN; MATHEMATICAL MODEL; MOLECULAR INTERACTION; NERVE CELL NETWORK; NEXT GENERATION SEQUENCING; NONHUMAN; PHENOTYPE; PHYLOGENY; REVIEW; TUMOR RECURRENCE; TUMOR RESISTANCE;

EID: 84949761583     PISSN: None     EISSN: 2234943X     Source Type: Journal    
DOI: 10.3389/fonc.2015.00251     Document Type: Review

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