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Inferring models of multiscale copy number evolution for single-tumor phylogenetics.

Chowdhury SA, Gertz EM, Wangsa D, Heselmeyer-Haddad K, Ried T, Schäffer AA, Schwartz R - Bioinformatics (2015)

Bottom Line: Application of our algorithms to real cervical cancer data identifies key genomic events in disease progression consistent with prior literature.Classification experiments on cervical and tongue cancer datasets lead to improved prediction accuracy for the metastasis of primary cervical cancers and for tongue cancer survival.Our software (FISHtrees) and two datasets are available at ftp://ftp.ncbi.nlm.nih.gov/pub/FISHtrees.

View Article: PubMed Central - PubMed

Affiliation: Joint Carnegie Mellon/University of Pittsburgh PhD Program in Computational Biology, Pittsburgh, PA, USA, Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA, USA, Computational Biology Branch, National Center for Biotechnology Information, U.S. National Institutes of Health, Bethesda, MD, USA, Section of Cancer Genomics, Genetics Branch, Center for Cancer Research, National Cancer Institute, U.S. National Institutes of Health, Bethesda, MD, USA and Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, PA, USA Joint Carnegie Mellon/University of Pittsburgh PhD Program in Computational Biology, Pittsburgh, PA, USA, Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA, USA, Computational Biology Branch, National Center for Biotechnology Information, U.S. National Institutes of Health, Bethesda, MD, USA, Section of Cancer Genomics, Genetics Branch, Center for Cancer Research, National Cancer Institute, U.S. National Institutes of Health, Bethesda, MD, USA and Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, PA, USA.

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KM curves for the test of association between overall (A) and disease-free (B) survival time and tree level cell count statistics-based subgrouping of patients
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btv233-F3: KM curves for the test of association between overall (A) and disease-free (B) survival time and tree level cell count statistics-based subgrouping of patients

Mentions: We performed Kaplan–Meier (KM) analysis (survdiff function in R) to compare either the survival time or the disease-free survival time between the two groups obtained from the two subgroups of samples (Fig. 3). The subgrouping of patients yielded a significant difference in overall (P-value = 0.0443, two-sided) and disease-free (P-value = 0.0371, two-sided) survival between the two patient groups. The good prognosis cluster was assigned 33 patients and the bad prognosis cluster was assigned 32 patients. Some insight into the differences in the two groups can be gained by examining the cluster centers. The cluster center of the good prognosis group has 33% of its weight in the first 5 tree levels and 90% of its weight in the first 10 tree levels, while the cluster center of the bad prognosis group has only 16% of its weight in the first 5 tree levels and only 51% of its weight in the first 10 tree levels. We repeated the same clustering procedure and KM analyses using trees derived from our previous unweighted SD + GD algorithm (Chowdhury et al., 2014) but did not observe statistically significant differences in overall (P-value = 0.0784) or disease-free (P-value = 0.14) survival between the two patient groups with the older methods.Fig. 3.


Inferring models of multiscale copy number evolution for single-tumor phylogenetics.

Chowdhury SA, Gertz EM, Wangsa D, Heselmeyer-Haddad K, Ried T, Schäffer AA, Schwartz R - Bioinformatics (2015)

KM curves for the test of association between overall (A) and disease-free (B) survival time and tree level cell count statistics-based subgrouping of patients
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4481700&req=5

btv233-F3: KM curves for the test of association between overall (A) and disease-free (B) survival time and tree level cell count statistics-based subgrouping of patients
Mentions: We performed Kaplan–Meier (KM) analysis (survdiff function in R) to compare either the survival time or the disease-free survival time between the two groups obtained from the two subgroups of samples (Fig. 3). The subgrouping of patients yielded a significant difference in overall (P-value = 0.0443, two-sided) and disease-free (P-value = 0.0371, two-sided) survival between the two patient groups. The good prognosis cluster was assigned 33 patients and the bad prognosis cluster was assigned 32 patients. Some insight into the differences in the two groups can be gained by examining the cluster centers. The cluster center of the good prognosis group has 33% of its weight in the first 5 tree levels and 90% of its weight in the first 10 tree levels, while the cluster center of the bad prognosis group has only 16% of its weight in the first 5 tree levels and only 51% of its weight in the first 10 tree levels. We repeated the same clustering procedure and KM analyses using trees derived from our previous unweighted SD + GD algorithm (Chowdhury et al., 2014) but did not observe statistically significant differences in overall (P-value = 0.0784) or disease-free (P-value = 0.14) survival between the two patient groups with the older methods.Fig. 3.

Bottom Line: Application of our algorithms to real cervical cancer data identifies key genomic events in disease progression consistent with prior literature.Classification experiments on cervical and tongue cancer datasets lead to improved prediction accuracy for the metastasis of primary cervical cancers and for tongue cancer survival.Our software (FISHtrees) and two datasets are available at ftp://ftp.ncbi.nlm.nih.gov/pub/FISHtrees.

View Article: PubMed Central - PubMed

Affiliation: Joint Carnegie Mellon/University of Pittsburgh PhD Program in Computational Biology, Pittsburgh, PA, USA, Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA, USA, Computational Biology Branch, National Center for Biotechnology Information, U.S. National Institutes of Health, Bethesda, MD, USA, Section of Cancer Genomics, Genetics Branch, Center for Cancer Research, National Cancer Institute, U.S. National Institutes of Health, Bethesda, MD, USA and Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, PA, USA Joint Carnegie Mellon/University of Pittsburgh PhD Program in Computational Biology, Pittsburgh, PA, USA, Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA, USA, Computational Biology Branch, National Center for Biotechnology Information, U.S. National Institutes of Health, Bethesda, MD, USA, Section of Cancer Genomics, Genetics Branch, Center for Cancer Research, National Cancer Institute, U.S. National Institutes of Health, Bethesda, MD, USA and Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, PA, USA.

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Related in: MedlinePlus