Cancer progression modeling using static sample data

Sun, Yu; Ye, Jin; Nowak, Norma J.; Goodison, Steve

doi:10.1186/preaccept-5146409691283741

Cited by 8 publications

(17 citation statements)

References 51 publications

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“…Several methods have been proposed to address the issue (20,21), but our numerical analysis showed that existing methods did not perform well (Supplementary Section S4). In our previous work, we conducted a proof-of-concept study that used static sample data to study cancer dynamics (22). Feature selection was performed within the framework of molecular classification by using patient survival data.…”

Section: Methodsmentioning

confidence: 99%

Computational approach for deriving cancer progression roadmaps from static sample data

Sun¹,

Yao²,

Yang

et al. 2017

Nucleic Acids Res

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As with any biological process, cancer development is inherently dynamic. While major efforts continue to catalog the genomic events associated with human cancer, it remains difficult to interpret and extrapolate the accumulating data to provide insights into the dynamic aspects of the disease. Here, we present a computational strategy that enables the construction of a cancer progression model using static tumor sample data. The developed approach overcame many technical limitations of existing methods. Application of the approach to breast cancer data revealed a linear, branching model with two distinct trajectories for malignant progression. The validity of the constructed model was demonstrated in 27 independent breast cancer data sets, and through visualization of the data in the context of disease progression we were able to identify a number of potentially key molecular events in the advance of breast cancer to malignancy.

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Section: Methodsmentioning

confidence: 99%

Computational approach for deriving cancer progression roadmaps from static sample data

Sun¹,

Yao²,

Yang

et al. 2017

Nucleic Acids Res

Self Cite

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“…However, as massive molecular profile data from excised tumor tissues (static samples) accumulates, it becomes possible to design integrative computation analyses that can approximate disease progression and provide insights into the molecular mechanisms of cancer. We have previously shown that it is indeed possible to derive evolutionary trajectories from static molecular data, and that breast cancer progression can be represented by a high-dimensional manifold with multiple branches [42].…”

Section: Learning Tree Structures From Real Datasetsmentioning

confidence: 99%

“…The dataset contains the expression levels of over 25, 000 gene transcripts obtained from 144 normal breast tissue samples and 1, 989 tumor tissue samples. By using a nonlinear regression method, a total of 359 genes were identified that may play a role in cancer development [42]. In the analysis, we set λ = 5 × N and d = 80 that retains 90% of energy.…”

Section: Learning Tree Structures From Real Datasetsmentioning

confidence: 99%

Probabilistic Dimensionality Reduction via Structure Learning

Wang

Mao²

2019

IEEE Trans. Pattern Anal. Mach. Intell.

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We propose an alternative probabilistic dimensionality reduction framework that can naturally integrate the generative model and the locality information of data. Based on this framework, we present a new model, which is able to learn a set of embedding points in a low-dimensional space by retaining the inherent structure from high-dimensional data. The objective function of this new model can be equivalently interpreted as two coupled learning problem, i.e., structure learning and the learning of projection matrix. Inspired by this interesting interpretation, we propose another model, which finds a set of embedding points that can directly form an explicit graph structure. We proved that the model by learning explicit graphs generalizes the reversed graph embedding method, but leads to a natural interpretation from Bayesian perspective. This can greatly facilitate data visualization and scientific discovery in downstream analysis. Extensive experiments are performed that demonstrate that the proposed framework is able to retain the inherent structure of datasets and achieve competitive quantitative results in terms of various performance evaluation criteria.

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“…The desire for levels of accuracy that can ultimately lead to clinical utility continues to drive the field to refine breast cancer subtypes (Shen et al, 2013;Parker et al, 2009;Sun et al, 2017;Haibe-Kains et al, 2012;Sun et al, 2014) and to identify molecular subtypes in other cancers (Abeshouse et al, 2015;Cancer Genome Atlas Network, 2014). The recent establishment of international cancer genome consortia (Cancer Genome Atlas Network, 2012;Abeshouse et al, 2015;Cancer Genome Atlas Network, 2014;Curtis et al, 2012) has generally overcome the sample size issue.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Approach to Identifying Breast Cancer Subtypes Using High-Dimensional Genomic Data

Chen

Yang

Goodison

et al. 2019

Preprint

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Motivation: Cancer subtype classification has the potential to significantly improve disease prognosis and develop individualized patient management. Existing methods are limited by their ability to handle extremely high-dimensional data and by the influence of misleading, irrelevant factors, resulting in ambiguous and overlapping subtypes. Results: To address the above issues, we proposed a novel approach to disentangling and eliminating irrelevant factors by leveraging the power of deep learning. Specifically, we designed a deep-learning framework, referred to as DeepType, that performs joint supervised classification, unsupervised clustering and dimensionality reduction to learn cancer-relevant data representation with cluster structure. We applied DeepType to the METABRIC breast cancer dataset and compared its performance to alternative state-of-the-art methods. Deep-Type significantly outperformed the existing methods, identifying more robust subtypes while using fewer genes. The new approach provides a framework for the derivation of more accurate and robust molecular cancer subtypes by using increasingly complex, multi-source data. Availability and implementation: An open-source software package for the proposed method is freely available at

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Cancer progression modeling using static sample data

Cited by 8 publications

References 51 publications

Computational approach for deriving cancer progression roadmaps from static sample data

Computational approach for deriving cancer progression roadmaps from static sample data

Probabilistic Dimensionality Reduction via Structure Learning

Deep Learning Approach to Identifying Breast Cancer Subtypes Using High-Dimensional Genomic Data

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