TY - GEN
T1 - Molecular bases of morphometric composition in Glioblastoma multiforme
AU - Han, Ju
AU - Chang, Hang
AU - Fontenay, Gerald V.
AU - Spellman, Paul T.
AU - Borowsky, Alexander
AU - Parvin, Bahram
PY - 2012
Y1 - 2012
N2 - Integrated analysis of tissue histology with the genome-wide array (e.g., OMIC) and clinical data have the potential for hypothesis generation and be prognostic. OMIC and clinical data are typically characterized and summarized at the patient level while whole mount histological sections are often heterogeneous in terms of nuclear morphology and organization. In this paper, we propose a multilevel framework for summarization and association of morphometric data. At the lowest level, each nucleus is segmented and then profiled with a multi-dimensional representation. At the intermediate level, cellular profiles are summarized within a local neighborhood, and further clustered into subtypes. At the highest level, each patient is represented by the composition of subtypes that are computed from the intermediate level, and then integrated with OMIC and outcome data for further analysis. The framework has been applied to Glioblastoma multiforme (GBM) data from The Cancer Genome Atlas (TCGA). Based on cellularity and nuclear size, four subtypes have been identified at the intermediate level. Subsequent multi-variate survival analysis indicates that the patient composition of one of the subtypes, with extremely low cellularity and small nucleus size, has a significantly higher hazard ratio. Further correlation of this subtype with the molecular data reveals enrichment of (i) STAT3 pathway and (ii) common regulators of PKC, TNF, AGT, and PDGF.
AB - Integrated analysis of tissue histology with the genome-wide array (e.g., OMIC) and clinical data have the potential for hypothesis generation and be prognostic. OMIC and clinical data are typically characterized and summarized at the patient level while whole mount histological sections are often heterogeneous in terms of nuclear morphology and organization. In this paper, we propose a multilevel framework for summarization and association of morphometric data. At the lowest level, each nucleus is segmented and then profiled with a multi-dimensional representation. At the intermediate level, cellular profiles are summarized within a local neighborhood, and further clustered into subtypes. At the highest level, each patient is represented by the composition of subtypes that are computed from the intermediate level, and then integrated with OMIC and outcome data for further analysis. The framework has been applied to Glioblastoma multiforme (GBM) data from The Cancer Genome Atlas (TCGA). Based on cellularity and nuclear size, four subtypes have been identified at the intermediate level. Subsequent multi-variate survival analysis indicates that the patient composition of one of the subtypes, with extremely low cellularity and small nucleus size, has a significantly higher hazard ratio. Further correlation of this subtype with the molecular data reveals enrichment of (i) STAT3 pathway and (ii) common regulators of PKC, TNF, AGT, and PDGF.
KW - Cox proportional-hazards model
KW - Tumor architecture
KW - consensus clustering
KW - molecular association
UR - http://www.scopus.com/inward/record.url?scp=84864861860&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84864861860&partnerID=8YFLogxK
U2 - 10.1109/ISBI.2012.6235889
DO - 10.1109/ISBI.2012.6235889
M3 - Conference contribution
AN - SCOPUS:84864861860
SN - 9781457718588
T3 - Proceedings - International Symposium on Biomedical Imaging
SP - 1631
EP - 1634
BT - 2012 9th IEEE International Symposium on Biomedical Imaging
T2 - 2012 9th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2012
Y2 - 2 May 2012 through 5 May 2012
ER -