Google goes cancer: improving outcome prediction for cancer patients by network-based ranking of marker genes.
Predicting the clinical outcome of cancer patients based on the expression of marker genes in their tumors has received increasing interest in the past decade. Accurate predictors of outcome and response to therapy could be used to personalize and thereby improve therapy. However, state of the art m...
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Public Library of Science (PLoS)
2012
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oai:doaj.org-article:62632a3a8c0343be8f02bd4e0e2a108d2021-11-18T05:51:20ZGoogle goes cancer: improving outcome prediction for cancer patients by network-based ranking of marker genes.1553-734X1553-735810.1371/journal.pcbi.1002511https://doaj.org/article/62632a3a8c0343be8f02bd4e0e2a108d2012-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/22615549/pdf/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Predicting the clinical outcome of cancer patients based on the expression of marker genes in their tumors has received increasing interest in the past decade. Accurate predictors of outcome and response to therapy could be used to personalize and thereby improve therapy. However, state of the art methods used so far often found marker genes with limited prediction accuracy, limited reproducibility, and unclear biological relevance. To address this problem, we developed a novel computational approach to identify genes prognostic for outcome that couples gene expression measurements from primary tumor samples with a network of known relationships between the genes. Our approach ranks genes according to their prognostic relevance using both expression and network information in a manner similar to Google's PageRank. We applied this method to gene expression profiles which we obtained from 30 patients with pancreatic cancer, and identified seven candidate marker genes prognostic for outcome. Compared to genes found with state of the art methods, such as Pearson correlation of gene expression with survival time, we improve the prediction accuracy by up to 7%. Accuracies were assessed using support vector machine classifiers and Monte Carlo cross-validation. We then validated the prognostic value of our seven candidate markers using immunohistochemistry on an independent set of 412 pancreatic cancer samples. Notably, signatures derived from our candidate markers were independently predictive of outcome and superior to established clinical prognostic factors such as grade, tumor size, and nodal status. As the amount of genomic data of individual tumors grows rapidly, our algorithm meets the need for powerful computational approaches that are key to exploit these data for personalized cancer therapies in clinical practice.Christof WinterGlen KristiansenStephan KerstingJanine RoyDaniela AustThomas KnöselPetra RümmeleBeatrix JahnkeVera HentrichFelix RückertMarco NiedergethmannWilko WeichertMarcus BahraHans J SchlittUtz SettmacherHelmut FriessMarkus BüchlerHans-Detlev SaegerMichael SchroederChristian PilarskyRobert GrützmannPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 8, Iss 5, p e1002511 (2012) |
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DOAJ |
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| topic |
Biology (General) QH301-705.5 |
| spellingShingle |
Biology (General) QH301-705.5 Christof Winter Glen Kristiansen Stephan Kersting Janine Roy Daniela Aust Thomas Knösel Petra Rümmele Beatrix Jahnke Vera Hentrich Felix Rückert Marco Niedergethmann Wilko Weichert Marcus Bahra Hans J Schlitt Utz Settmacher Helmut Friess Markus Büchler Hans-Detlev Saeger Michael Schroeder Christian Pilarsky Robert Grützmann Google goes cancer: improving outcome prediction for cancer patients by network-based ranking of marker genes. |
| description |
Predicting the clinical outcome of cancer patients based on the expression of marker genes in their tumors has received increasing interest in the past decade. Accurate predictors of outcome and response to therapy could be used to personalize and thereby improve therapy. However, state of the art methods used so far often found marker genes with limited prediction accuracy, limited reproducibility, and unclear biological relevance. To address this problem, we developed a novel computational approach to identify genes prognostic for outcome that couples gene expression measurements from primary tumor samples with a network of known relationships between the genes. Our approach ranks genes according to their prognostic relevance using both expression and network information in a manner similar to Google's PageRank. We applied this method to gene expression profiles which we obtained from 30 patients with pancreatic cancer, and identified seven candidate marker genes prognostic for outcome. Compared to genes found with state of the art methods, such as Pearson correlation of gene expression with survival time, we improve the prediction accuracy by up to 7%. Accuracies were assessed using support vector machine classifiers and Monte Carlo cross-validation. We then validated the prognostic value of our seven candidate markers using immunohistochemistry on an independent set of 412 pancreatic cancer samples. Notably, signatures derived from our candidate markers were independently predictive of outcome and superior to established clinical prognostic factors such as grade, tumor size, and nodal status. As the amount of genomic data of individual tumors grows rapidly, our algorithm meets the need for powerful computational approaches that are key to exploit these data for personalized cancer therapies in clinical practice. |
| format |
article |
| author |
Christof Winter Glen Kristiansen Stephan Kersting Janine Roy Daniela Aust Thomas Knösel Petra Rümmele Beatrix Jahnke Vera Hentrich Felix Rückert Marco Niedergethmann Wilko Weichert Marcus Bahra Hans J Schlitt Utz Settmacher Helmut Friess Markus Büchler Hans-Detlev Saeger Michael Schroeder Christian Pilarsky Robert Grützmann |
| author_facet |
Christof Winter Glen Kristiansen Stephan Kersting Janine Roy Daniela Aust Thomas Knösel Petra Rümmele Beatrix Jahnke Vera Hentrich Felix Rückert Marco Niedergethmann Wilko Weichert Marcus Bahra Hans J Schlitt Utz Settmacher Helmut Friess Markus Büchler Hans-Detlev Saeger Michael Schroeder Christian Pilarsky Robert Grützmann |
| author_sort |
Christof Winter |
| title |
Google goes cancer: improving outcome prediction for cancer patients by network-based ranking of marker genes. |
| title_short |
Google goes cancer: improving outcome prediction for cancer patients by network-based ranking of marker genes. |
| title_full |
Google goes cancer: improving outcome prediction for cancer patients by network-based ranking of marker genes. |
| title_fullStr |
Google goes cancer: improving outcome prediction for cancer patients by network-based ranking of marker genes. |
| title_full_unstemmed |
Google goes cancer: improving outcome prediction for cancer patients by network-based ranking of marker genes. |
| title_sort |
google goes cancer: improving outcome prediction for cancer patients by network-based ranking of marker genes. |
| publisher |
Public Library of Science (PLoS) |
| publishDate |
2012 |
| url |
https://doaj.org/article/62632a3a8c0343be8f02bd4e0e2a108d |
| work_keys_str_mv |
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