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Improved grading and survival prediction of human astrocytic brain tumors by artificial neural network analysis of gene expression microarray data
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| Zeitschriftentitel: | Molecular Cancer Therapeutics |
|---|---|
| Personen und Körperschaften: | , , , , , , , , , |
| In: | Molecular Cancer Therapeutics, 7, 2008, 5, S. 1013-1024 |
| Format: | E-Article |
| Sprache: | Englisch |
| veröffentlicht: |
American Association for Cancer Research (AACR)
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| Schlagwörter: |
| author_facet |
Petalidis, Lawrence P. Oulas, Anastasis Backlund, Magnus Wayland, Matthew T. Liu, Lu Plant, Karen Happerfield, Lisa Freeman, Tom C. Poirazi, Panayiota Collins, V. Peter Petalidis, Lawrence P. Oulas, Anastasis Backlund, Magnus Wayland, Matthew T. Liu, Lu Plant, Karen Happerfield, Lisa Freeman, Tom C. Poirazi, Panayiota Collins, V. Peter |
|---|---|
| author |
Petalidis, Lawrence P. Oulas, Anastasis Backlund, Magnus Wayland, Matthew T. Liu, Lu Plant, Karen Happerfield, Lisa Freeman, Tom C. Poirazi, Panayiota Collins, V. Peter |
| spellingShingle |
Petalidis, Lawrence P. Oulas, Anastasis Backlund, Magnus Wayland, Matthew T. Liu, Lu Plant, Karen Happerfield, Lisa Freeman, Tom C. Poirazi, Panayiota Collins, V. Peter Molecular Cancer Therapeutics Improved grading and survival prediction of human astrocytic brain tumors by artificial neural network analysis of gene expression microarray data Cancer Research Oncology |
| author_sort |
petalidis, lawrence p. |
| spelling |
Petalidis, Lawrence P. Oulas, Anastasis Backlund, Magnus Wayland, Matthew T. Liu, Lu Plant, Karen Happerfield, Lisa Freeman, Tom C. Poirazi, Panayiota Collins, V. Peter 1535-7163 1538-8514 American Association for Cancer Research (AACR) Cancer Research Oncology http://dx.doi.org/10.1158/1535-7163.mct-07-0177 <jats:title>Abstract</jats:title> <jats:p>Histopathologic grading of astrocytic tumors based on current WHO criteria offers a valuable but simplified representation of oncologic reality and is often insufficient to predict clinical outcome. In this study, we report a new astrocytic tumor microarray gene expression data set (n = 65). We have used a simple artificial neural network algorithm to address grading of human astrocytic tumors, derive specific transcriptional signatures from histopathologic subtypes of astrocytic tumors, and asses whether these molecular signatures define survival prognostic subclasses. Fifty-nine classifier genes were identified and found to fall within three distinct functional classes, that is, angiogenesis, cell differentiation, and lower-grade astrocytic tumor discrimination. These gene classes were found to characterize three molecular tumor subtypes denoted ANGIO, INTER, and LOWER. Grading of samples using these subtypes agreed with prior histopathologic grading for both our data set (96.15%) and an independent data set. Six tumors were particularly challenging to diagnose histopathologically. We present an artificial neural network grading for these samples and offer an evidence-based interpretation of grading results using clinical metadata to substantiate findings. The prognostic value of the three identified tumor subtypes was found to outperform histopathologic grading as well as tumor subtypes reported in other studies, indicating a high survival prognostic potential for the 59 gene classifiers. Finally, 11 gene classifiers that differentiate between primary and secondary glioblastomas were also identified. [Mol Cancer Ther 2008;7(5):1013–24]</jats:p> Improved grading and survival prediction of human astrocytic brain tumors by artificial neural network analysis of gene expression microarray data Molecular Cancer Therapeutics |
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American Association for Cancer Research (AACR), 2008 |
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Molecular Cancer Therapeutics |
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| title |
Improved grading and survival prediction of human astrocytic brain tumors by artificial neural network analysis of gene expression microarray data |
| title_unstemmed |
Improved grading and survival prediction of human astrocytic brain tumors by artificial neural network analysis of gene expression microarray data |
| title_full |
Improved grading and survival prediction of human astrocytic brain tumors by artificial neural network analysis of gene expression microarray data |
| title_fullStr |
Improved grading and survival prediction of human astrocytic brain tumors by artificial neural network analysis of gene expression microarray data |
| title_full_unstemmed |
Improved grading and survival prediction of human astrocytic brain tumors by artificial neural network analysis of gene expression microarray data |
| title_short |
Improved grading and survival prediction of human astrocytic brain tumors by artificial neural network analysis of gene expression microarray data |
| title_sort |
improved grading and survival prediction of human astrocytic brain tumors by artificial neural network analysis of gene expression microarray data |
| topic |
Cancer Research Oncology |
| url |
http://dx.doi.org/10.1158/1535-7163.mct-07-0177 |
| publishDate |
2008 |
| physical |
1013-1024 |
| description |
<jats:title>Abstract</jats:title>
<jats:p>Histopathologic grading of astrocytic tumors based on current WHO criteria offers a valuable but simplified representation of oncologic reality and is often insufficient to predict clinical outcome. In this study, we report a new astrocytic tumor microarray gene expression data set (n = 65). We have used a simple artificial neural network algorithm to address grading of human astrocytic tumors, derive specific transcriptional signatures from histopathologic subtypes of astrocytic tumors, and asses whether these molecular signatures define survival prognostic subclasses. Fifty-nine classifier genes were identified and found to fall within three distinct functional classes, that is, angiogenesis, cell differentiation, and lower-grade astrocytic tumor discrimination. These gene classes were found to characterize three molecular tumor subtypes denoted ANGIO, INTER, and LOWER. Grading of samples using these subtypes agreed with prior histopathologic grading for both our data set (96.15%) and an independent data set. Six tumors were particularly challenging to diagnose histopathologically. We present an artificial neural network grading for these samples and offer an evidence-based interpretation of grading results using clinical metadata to substantiate findings. The prognostic value of the three identified tumor subtypes was found to outperform histopathologic grading as well as tumor subtypes reported in other studies, indicating a high survival prognostic potential for the 59 gene classifiers. Finally, 11 gene classifiers that differentiate between primary and secondary glioblastomas were also identified. [Mol Cancer Ther 2008;7(5):1013–24]</jats:p> |
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| author | Petalidis, Lawrence P., Oulas, Anastasis, Backlund, Magnus, Wayland, Matthew T., Liu, Lu, Plant, Karen, Happerfield, Lisa, Freeman, Tom C., Poirazi, Panayiota, Collins, V. Peter |
| author_facet | Petalidis, Lawrence P., Oulas, Anastasis, Backlund, Magnus, Wayland, Matthew T., Liu, Lu, Plant, Karen, Happerfield, Lisa, Freeman, Tom C., Poirazi, Panayiota, Collins, V. Peter, Petalidis, Lawrence P., Oulas, Anastasis, Backlund, Magnus, Wayland, Matthew T., Liu, Lu, Plant, Karen, Happerfield, Lisa, Freeman, Tom C., Poirazi, Panayiota, Collins, V. Peter |
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| description | <jats:title>Abstract</jats:title> <jats:p>Histopathologic grading of astrocytic tumors based on current WHO criteria offers a valuable but simplified representation of oncologic reality and is often insufficient to predict clinical outcome. In this study, we report a new astrocytic tumor microarray gene expression data set (n = 65). We have used a simple artificial neural network algorithm to address grading of human astrocytic tumors, derive specific transcriptional signatures from histopathologic subtypes of astrocytic tumors, and asses whether these molecular signatures define survival prognostic subclasses. Fifty-nine classifier genes were identified and found to fall within three distinct functional classes, that is, angiogenesis, cell differentiation, and lower-grade astrocytic tumor discrimination. These gene classes were found to characterize three molecular tumor subtypes denoted ANGIO, INTER, and LOWER. Grading of samples using these subtypes agreed with prior histopathologic grading for both our data set (96.15%) and an independent data set. Six tumors were particularly challenging to diagnose histopathologically. We present an artificial neural network grading for these samples and offer an evidence-based interpretation of grading results using clinical metadata to substantiate findings. The prognostic value of the three identified tumor subtypes was found to outperform histopathologic grading as well as tumor subtypes reported in other studies, indicating a high survival prognostic potential for the 59 gene classifiers. Finally, 11 gene classifiers that differentiate between primary and secondary glioblastomas were also identified. [Mol Cancer Ther 2008;7(5):1013–24]</jats:p> |
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| imprint_str_mv | American Association for Cancer Research (AACR), 2008 |
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| spelling | Petalidis, Lawrence P. Oulas, Anastasis Backlund, Magnus Wayland, Matthew T. Liu, Lu Plant, Karen Happerfield, Lisa Freeman, Tom C. Poirazi, Panayiota Collins, V. Peter 1535-7163 1538-8514 American Association for Cancer Research (AACR) Cancer Research Oncology http://dx.doi.org/10.1158/1535-7163.mct-07-0177 <jats:title>Abstract</jats:title> <jats:p>Histopathologic grading of astrocytic tumors based on current WHO criteria offers a valuable but simplified representation of oncologic reality and is often insufficient to predict clinical outcome. In this study, we report a new astrocytic tumor microarray gene expression data set (n = 65). We have used a simple artificial neural network algorithm to address grading of human astrocytic tumors, derive specific transcriptional signatures from histopathologic subtypes of astrocytic tumors, and asses whether these molecular signatures define survival prognostic subclasses. Fifty-nine classifier genes were identified and found to fall within three distinct functional classes, that is, angiogenesis, cell differentiation, and lower-grade astrocytic tumor discrimination. These gene classes were found to characterize three molecular tumor subtypes denoted ANGIO, INTER, and LOWER. Grading of samples using these subtypes agreed with prior histopathologic grading for both our data set (96.15%) and an independent data set. Six tumors were particularly challenging to diagnose histopathologically. We present an artificial neural network grading for these samples and offer an evidence-based interpretation of grading results using clinical metadata to substantiate findings. The prognostic value of the three identified tumor subtypes was found to outperform histopathologic grading as well as tumor subtypes reported in other studies, indicating a high survival prognostic potential for the 59 gene classifiers. Finally, 11 gene classifiers that differentiate between primary and secondary glioblastomas were also identified. [Mol Cancer Ther 2008;7(5):1013–24]</jats:p> Improved grading and survival prediction of human astrocytic brain tumors by artificial neural network analysis of gene expression microarray data Molecular Cancer Therapeutics |
| spellingShingle | Petalidis, Lawrence P., Oulas, Anastasis, Backlund, Magnus, Wayland, Matthew T., Liu, Lu, Plant, Karen, Happerfield, Lisa, Freeman, Tom C., Poirazi, Panayiota, Collins, V. Peter, Molecular Cancer Therapeutics, Improved grading and survival prediction of human astrocytic brain tumors by artificial neural network analysis of gene expression microarray data, Cancer Research, Oncology |
| title | Improved grading and survival prediction of human astrocytic brain tumors by artificial neural network analysis of gene expression microarray data |
| title_full | Improved grading and survival prediction of human astrocytic brain tumors by artificial neural network analysis of gene expression microarray data |
| title_fullStr | Improved grading and survival prediction of human astrocytic brain tumors by artificial neural network analysis of gene expression microarray data |
| title_full_unstemmed | Improved grading and survival prediction of human astrocytic brain tumors by artificial neural network analysis of gene expression microarray data |
| title_short | Improved grading and survival prediction of human astrocytic brain tumors by artificial neural network analysis of gene expression microarray data |
| title_sort | improved grading and survival prediction of human astrocytic brain tumors by artificial neural network analysis of gene expression microarray data |
| title_unstemmed | Improved grading and survival prediction of human astrocytic brain tumors by artificial neural network analysis of gene expression microarray data |
| topic | Cancer Research, Oncology |
| url | http://dx.doi.org/10.1158/1535-7163.mct-07-0177 |