Estimating cancer survival and clinical outcome based on genetic tumor progression scores

Rahnenführer, Jörg; Beerenwinkel, Niko; Schulz, Wolfgang A.; Hartmann, Christian; Deimling, Andreas von; Wullich, Bernd; Lengauer, Thomas

doi:10.1093/bioinformatics/bti312

Cited by 44 publications

(44 citation statements)

References 9 publications

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“…For example, the mutagenetic tree for prostate cancer found in Rahnenführer et al (2005, Fig. 2) explains only 56% of the data at a log-likelihood of only −248.4.…”

Section: Applicationsmentioning

confidence: 99%

“…Likewise, 8p− refers to the loss (−) of the small arm (p) of chromosome 8. We consider 54 prostate cancer samples, each defined by the presence or absence of the nine alterations 3q+, 4q+, 6q+, 7q+, 8p−, 8q+, 10q−, 13q+, and Xq+ as defined in Rahnenführer et al (2005).…”

Section: Applicationsmentioning

confidence: 99%

“…A special case of the D-CBN, where the poset is a tree, is known as the oncogenetic or mutagenetic tree model (Desper et al, 1999;Beerenwinkel et al, 2005b,c). It has been applied to the somatic evolution of cancer (Radmacher et al, 2001;Rahnenführer et al, 2005) and to the evolution of drug resistance in HIV (Beerenwinkel et al, 2005a). The basic mutagenetic tree model has been extended to a mixture model (Beerenwinkel et al, 2005b) and to account for longitudinal data (Beerenwinkel and Drton, 2007).…”

Section: Introductionmentioning

confidence: 99%

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Markov models for accumulating mutations

Beerenwinkel

Sullivant

2009

Biometrika

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Abstract. We introduce and analyze a waiting time model for the accumulation of genetic changes. The continuous time conjunctive Bayesian network is defined by a partially ordered set of mutations and by the rate of fixation of each mutation. The partial order encodes constraints on the order in which mutations can fixate in the population, shedding light on the mutational pathways underlying the evolutionary process. We study a censored version of the model and derive equations for an EM algorithm to perform maximum likelihood estimation of the model parameters. We also show how to select the maximum likelihood poset. The model is applied to genetic data from different cancers and from drug resistant HIV samples, indicating implications for diagnosis and treatment.

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“…For example, the mutagenetic tree for prostate cancer found in Rahnenführer et al (2005, Fig. 2) explains only 56% of the data at a log-likelihood of only −248.4.…”

Section: Applicationsmentioning

confidence: 99%

Section: Applicationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Markov models for accumulating mutations

Beerenwinkel

Sullivant

2009

Biometrika

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“…These probabilities can be converted to average waiting times, by assuming Poisson processes for the occurrence of aberrations, see Rahnenfü hrer et al [25] for details. The GPS of a tumor then is defined as the average waiting time of its pattern of genetic aberrations, given the underlying tree mixture model.…”

Section: Genetic Progression Scorementioning

confidence: 99%

Hyperdiploidy defines a distinct cytogenetic entity of meningiomas

et al. 2007

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Background The most common chromosomal aberration found in meningiomas is monosomy 22. Progression and recurrence of meningiomas are usually associated with additional chromosome losses. Rarely, however, meningiomas have strongly hyperdiploid karyotypes with over 50 chromosomes; the objective of this study was to explore the cytogenetic and histopathologic patterns as well as the clinical significance of hyperdiploidy in meningiomas. Methods Within a series of 677 consecutive meningiomas, we identified a subgroup comprising 16 cases that display a strikingly uniform pattern of hyperdiploidy mostly without structural chromosome rearrangements, as shown by banding techniques and, in the single structurally aberrant case, spectral karyotyping. Results These meningiomas each have between 50 and 56 chromosomes, with trisomy 12 (14/16 cases), trisomy 20 (13/16 cases), trisomy 5 (12/16 cases), and trisomy 17 (10/16 cases). Histomorphologically, hyperdiploid meningiomas feature a heterogeneous phenotype. However, they are associated with a higher histological grade, and decreased expression of alkaline phosphatase as compared to meningiomas with typical karyotype. In two patients, recurrences were documented and three patients died of disease during the period of observation, indicating a worse prognosis of hyperdiploid than of cytogenetically typical meningiomas. Conclusion We conclude that hyperdiploidy constitutes a small but clinically relevant entity of biologically aggressive meningiomas, which are cytogenetically distinguishable from the majority of commontype meningiomas.

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“…A number of methods for inferring temporal progression of mutations from cross-sectional data have been introduced (Desper et al, 2000(Desper et al, , 1999Beerenwinkel et al, 2005a,b;Rahnenführer et al, 2005;Tofigh et al, 2011;Hjelm et al, 2006;Gerstung et al, 2009;Beerenwinkel and Sullivant, 2009;Beerenwinkel et al, 2006Beerenwinkel et al, , 2007Gerstung et al, 2011;Sakoparnig and Beerenwinkel, 2012;Shahrabi Farahani and Lagergren, 2013) (see section 1.1). These methods consider models of increasing complexity for cancer progression: trees, mixtures of trees, and Bayesian network models with different constraints.…”

Section: Introduction Cmentioning

confidence: 99%

Simultaneous Inference of Cancer Pathways and Tumor Progression from Cross-Sectional Mutation Data

Raphael

Vandin

2014

Lecture Notes in Computer Science

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Recent cancer sequencing studies provide a wealth of somatic mutation data from a large number of patients. One of the most intriguing and challenging questions arising from this data is to determine whether the temporal order of somatic mutations in a cancer follows any common progression. Since we usually obtain only one sample from a patient, such inferences are commonly made from cross-sectional data from different patients. This analysis is complicated by the extensive variation in the somatic mutations across different patients, variation that is reduced by examining combinations of mutations in various pathways. Thus far, methods to reconstruct tumor progression at the pathway level have restricted attention to known, a priori defined pathways.In this work we show how to simultaneously infer pathways and the temporal order of their mutations from cross-sectional data, leveraging on the exclusivity property of driver mutations within a pathway. We define the pathway linear progression model, and derive a combinatorial formulation for the problem of finding the optimal model from mutation data. We show that with enough samples the optimal solution to this problem uniquely identifies the correct model with high probability even when errors are present in the mutation data. We then formulate the problem as an integer linear program (ILP), which allows the analysis of datasets from recent studies with large numbers of samples. We use our algorithm to analyze somatic mutation data from three cancer studies, including two studies from The Cancer Genome Atlas (TCGA) on large number of samples on colorectal cancer and glioblastoma. The models reconstructed with our method capture most of the current knowledge of the progression of somatic mutations in these cancer types, while also providing new insights on the tumor progression at the pathway level.

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Estimating cancer survival and clinical outcome based on genetic tumor progression scores

Abstract: Mtreemix, a software package for estimating tree mixture models, is freely available for non-commercial users at http://mtreemix.bioinf.mpi-sb.mpg.de. The raw cancer datasets and R code for the analysis with Cox models are available upon request from the corresponding author.

Cited by 44 publications

References 9 publications

Markov models for accumulating mutations

Markov models for accumulating mutations

Hyperdiploidy defines a distinct cytogenetic entity of meningiomas

Simultaneous Inference of Cancer Pathways and Tumor Progression from Cross-Sectional Mutation Data

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