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Angiogenic mRNA and microRNA gene expression signature predicts a novel subtype of serous ovarian cancer.

Bentink S, Haibe-Kains B, Risch T, Fan JB, Hirsch MS, Holton K, Rubio R, April C, Chen J, Wickham-Garcia E, Liu J, Culhane A, Drapkin R, Quackenbush J, Matulonis UA - PLoS ONE (2012)

Bottom Line: Although ovarian cancer is notable for its initial sensitivity to platinum-based therapies, the vast majority of patients eventually develop recurrent cancer and succumb to increasingly platinum-resistant disease.Modern, targeted cancer drugs intervene in cell signaling, and identifying key disease mechanisms and pathways would greatly advance our treatment abilities.The corresponding "angiogenesis signature" was validated in ten published independent ovarian cancer gene expression datasets and is significantly associated with overall survival.

View Article: PubMed Central - PubMed

Affiliation: Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, Massachusetts, United States of America.

ABSTRACT
Ovarian cancer is the fifth leading cause of cancer death for women in the U.S. and the seventh most fatal worldwide. Although ovarian cancer is notable for its initial sensitivity to platinum-based therapies, the vast majority of patients eventually develop recurrent cancer and succumb to increasingly platinum-resistant disease. Modern, targeted cancer drugs intervene in cell signaling, and identifying key disease mechanisms and pathways would greatly advance our treatment abilities. In order to shed light on the molecular diversity of ovarian cancer, we performed comprehensive transcriptional profiling on 129 advanced stage, high grade serous ovarian cancers. We implemented a, re-sampling based version of the ISIS class discovery algorithm (rISIS: robust ISIS) and applied it to the entire set of ovarian cancer transcriptional profiles. rISIS identified a previously undescribed patient stratification, further supported by micro-RNA expression profiles, and gene set enrichment analysis found strong biological support for the stratification by extracellular matrix, cell adhesion, and angiogenesis genes. The corresponding "angiogenesis signature" was validated in ten published independent ovarian cancer gene expression datasets and is significantly associated with overall survival. The subtypes we have defined are of potential translational interest as they may be relevant for identifying patients who may benefit from the addition of anti-angiogenic therapies that are now being tested in clinical trials.

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Validation of angiogenic ovarian cancer classification in our dataset and ten independent validation datasets.Panels A, D and G display the gene expression of the 100 genes used to classify ovarian tumors into angiogenic and non-angiogenic subtypes in our dataset (129 patients), the high grade, late stage, serous tumors (1,090 patients) and all tumors (1,606 patients) in the validation set, respectively. Panels D, E and F report the corresponding distribution of the scaled subtype scores. Panels B, D and F reports the (overall) survival curves of patients having tumors of angiogenic or non-angiogenic subtype in the corresponding datasets.
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pone-0030269-g002: Validation of angiogenic ovarian cancer classification in our dataset and ten independent validation datasets.Panels A, D and G display the gene expression of the 100 genes used to classify ovarian tumors into angiogenic and non-angiogenic subtypes in our dataset (129 patients), the high grade, late stage, serous tumors (1,090 patients) and all tumors (1,606 patients) in the validation set, respectively. Panels D, E and F report the corresponding distribution of the scaled subtype scores. Panels B, D and F reports the (overall) survival curves of patients having tumors of angiogenic or non-angiogenic subtype in the corresponding datasets.

Mentions: We tested our angiogenic subtype classification for reproducibility and association with clinical variables in our original dataset and ten previously published gene expression datasets collected on a number of diverse microarray platforms (Table 1). We normalized and scaled data from each study and assigned an, angiogenic subtype score to each of the 1,606 samples in the published gene expression datasets (see Text S1 for detailed description of the methods). The results of these assignments are shown in Figure 2 for our initial set of 129 samples (Figures 2A, 2D, 2G), for the 1,090 patients from the ten published studies having high grade (≥3), late stage (≥3), serous ovarian tumors (Figures 2B, 2E, 2H), and for all 1,606 patients in the published datasets (Figures 2C, 2F, and 2I). The top figures show heatmaps for the 100 classification genes, the middle row show the bimodal distribution of classification scores in each dataset, and the bottom row of figures shows the significantly poorer survival for the angiogenic subtype relative to the non-angiogenic subtype. An independent validation that the most robust number of subtypes in the data, estimated using the Bayesian Information Criteria (BIC), is shown in Figure S1.


Angiogenic mRNA and microRNA gene expression signature predicts a novel subtype of serous ovarian cancer.

Bentink S, Haibe-Kains B, Risch T, Fan JB, Hirsch MS, Holton K, Rubio R, April C, Chen J, Wickham-Garcia E, Liu J, Culhane A, Drapkin R, Quackenbush J, Matulonis UA - PLoS ONE (2012)

Validation of angiogenic ovarian cancer classification in our dataset and ten independent validation datasets.Panels A, D and G display the gene expression of the 100 genes used to classify ovarian tumors into angiogenic and non-angiogenic subtypes in our dataset (129 patients), the high grade, late stage, serous tumors (1,090 patients) and all tumors (1,606 patients) in the validation set, respectively. Panels D, E and F report the corresponding distribution of the scaled subtype scores. Panels B, D and F reports the (overall) survival curves of patients having tumors of angiogenic or non-angiogenic subtype in the corresponding datasets.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3278409&req=5

pone-0030269-g002: Validation of angiogenic ovarian cancer classification in our dataset and ten independent validation datasets.Panels A, D and G display the gene expression of the 100 genes used to classify ovarian tumors into angiogenic and non-angiogenic subtypes in our dataset (129 patients), the high grade, late stage, serous tumors (1,090 patients) and all tumors (1,606 patients) in the validation set, respectively. Panels D, E and F report the corresponding distribution of the scaled subtype scores. Panels B, D and F reports the (overall) survival curves of patients having tumors of angiogenic or non-angiogenic subtype in the corresponding datasets.
Mentions: We tested our angiogenic subtype classification for reproducibility and association with clinical variables in our original dataset and ten previously published gene expression datasets collected on a number of diverse microarray platforms (Table 1). We normalized and scaled data from each study and assigned an, angiogenic subtype score to each of the 1,606 samples in the published gene expression datasets (see Text S1 for detailed description of the methods). The results of these assignments are shown in Figure 2 for our initial set of 129 samples (Figures 2A, 2D, 2G), for the 1,090 patients from the ten published studies having high grade (≥3), late stage (≥3), serous ovarian tumors (Figures 2B, 2E, 2H), and for all 1,606 patients in the published datasets (Figures 2C, 2F, and 2I). The top figures show heatmaps for the 100 classification genes, the middle row show the bimodal distribution of classification scores in each dataset, and the bottom row of figures shows the significantly poorer survival for the angiogenic subtype relative to the non-angiogenic subtype. An independent validation that the most robust number of subtypes in the data, estimated using the Bayesian Information Criteria (BIC), is shown in Figure S1.

Bottom Line: Although ovarian cancer is notable for its initial sensitivity to platinum-based therapies, the vast majority of patients eventually develop recurrent cancer and succumb to increasingly platinum-resistant disease.Modern, targeted cancer drugs intervene in cell signaling, and identifying key disease mechanisms and pathways would greatly advance our treatment abilities.The corresponding "angiogenesis signature" was validated in ten published independent ovarian cancer gene expression datasets and is significantly associated with overall survival.

View Article: PubMed Central - PubMed

Affiliation: Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, Massachusetts, United States of America.

ABSTRACT
Ovarian cancer is the fifth leading cause of cancer death for women in the U.S. and the seventh most fatal worldwide. Although ovarian cancer is notable for its initial sensitivity to platinum-based therapies, the vast majority of patients eventually develop recurrent cancer and succumb to increasingly platinum-resistant disease. Modern, targeted cancer drugs intervene in cell signaling, and identifying key disease mechanisms and pathways would greatly advance our treatment abilities. In order to shed light on the molecular diversity of ovarian cancer, we performed comprehensive transcriptional profiling on 129 advanced stage, high grade serous ovarian cancers. We implemented a, re-sampling based version of the ISIS class discovery algorithm (rISIS: robust ISIS) and applied it to the entire set of ovarian cancer transcriptional profiles. rISIS identified a previously undescribed patient stratification, further supported by micro-RNA expression profiles, and gene set enrichment analysis found strong biological support for the stratification by extracellular matrix, cell adhesion, and angiogenesis genes. The corresponding "angiogenesis signature" was validated in ten published independent ovarian cancer gene expression datasets and is significantly associated with overall survival. The subtypes we have defined are of potential translational interest as they may be relevant for identifying patients who may benefit from the addition of anti-angiogenic therapies that are now being tested in clinical trials.

Show MeSH
Related in: MedlinePlus