iGPSe: A visual analytic system for integrative genomic based cancer patient stratification

Ding, Hao; Wang, Chao; Huang, Kun; Machiraju, Raghu

doi:10.1186/1471-2105-15-203

Cited by 20 publications

(19 citation statements)

References 37 publications

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“…Among these subtypes, basal-like breast cancer largely overlaps with triple-negative breast cancer (TNBC). Therefore, basal-like breast cancer is usually described as a form of TNBC, and these diseases are commonly studied together as a single group [ 1 ]. TNBC is an aggressive subtype of breast cancer that is defined by the absence of ER, PR and HER2, and it is characterized by poor prognosis and rarely benefits from targeted therapy [ 2 , 3 ].…”

Section: Introductionmentioning

confidence: 99%

Identification of Personalized Chemoresistance Genes in Subtypes of Basal-Like Breast Cancer Based on Functional Differences Using Pathway Analysis

Wang

et al. 2015

PLoS ONE

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Breast cancer is a highly heterogeneous disease that is clinically classified into several subtypes. Among these subtypes, basal-like breast cancer largely overlaps with triple-negative breast cancer (TNBC), and these two groups are generally studied together as a single entity. Differences in the molecular makeup of breast cancers can result in different treatment strategies and prognoses for patients with different breast cancer subtypes. Compared with other subtypes, basal-like and other ER+ breast cancer subtypes exhibit marked differences in etiologic factors, clinical characteristics and therapeutic potential. Anthracycline drugs are typically used as the first-line clinical treatment for basal-like breast cancer subtypes. However, certain patients develop drug resistance following chemotherapy, which can lead to disease relapse and death. Even among patients with basal-like breast cancer, there can be significant molecular differences, and it is difficult to identify specific drug resistance proteins in any given patient using conventional variance testing methods. Therefore, we designed a new method for identifying drug resistance genes. Subgroups, personalized biomarkers, and therapy targets were identified using cluster analysis of differentially expressed genes. We found that basal-like breast cancer could be further divided into at least four distinct subgroups, including two groups at risk for drug resistance and two groups characterized by sensitivity to pharmacotherapy. Based on functional differences among these subgroups, we identified nine biomarkers related to drug resistance: SYK, LCK, GAB2, PAWR, PPARG, MDFI, ZAP70, CIITA and ACTA1. Finally, based on the deviation scores of the examined pathways, 16 pathways were shown to exhibit varying degrees of abnormality in the various subgroups, indicating that patients with different subtypes of basal-like breast cancer can be characterized by differences in the functional status of these pathways. Therefore, these nine differentially expressed genes and their associated functional pathways should provide the basis for novel personalized clinical treatments of basal-like breast cancer.

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Section: Introductionmentioning

confidence: 99%

Identification of Personalized Chemoresistance Genes in Subtypes of Basal-Like Breast Cancer Based on Functional Differences Using Pathway Analysis

Wang

et al. 2015

PLoS ONE

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“…Also, silhouette plots are typically static, however, we could conceivably integrate the metrics used for silhouette plots in our approach. iGPSe [29], for example, is a system similar to StratomeX that integrates silhouette plots.…”

Section: Related Workmentioning

confidence: 99%

Interactive Visual Exploration and Refinement of Cluster Assignments

Kern

Lex

Gehlenborg

et al. 2017

Preprint

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Background: With ever-increasing amounts of data produced in biology research, scientists are in need of efficient data analysis methods. Cluster analysis, combined with visualization of the results, is one such method that can be used to make sense of large data volumes. At the same time, cluster analysis is known to be imperfect and depends on the choice of algorithms, parameters, and distance measures. Most clustering algorithms don't properly account for ambiguity in the source data, as records are often assigned to discrete clusters, even if an assignment is unclear. While there are metrics and visualization techniques that allow analysts to compare clusterings or to judge cluster quality, there is no comprehensive method that allows analysts to evaluate, compare, and refine cluster assignments based on the source data, derived scores, and contextual data. Results: In this paper, we introduce a method that explicitly visualizes the quality of cluster assignments, allows comparisons of clustering results and enables analysts to manually curate and refine cluster assignments. Our methods are applicable to matrix data clustered with partitional, hierarchical, and fuzzy clustering algorithms. Furthermore, we enable analysts to explore clustering results in context of other data, for example, to observe whether a clustering of genomic data results in a meaningful differentiation in phenotypes. Conclusions: Our methods are integrated into Caleydo StratomeX, a popular, web-based, disease subtype analysis tool. We show in a usage scenario that our approach can reveal ambiguities in cluster assignments and produce improved clusterings that better differentiate genotypes and phenotypes.

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“…These techniques have now been developed into a commercial tool (MammaPrint, Agendia, Netherlands) that is used for breast cancer prognosis in the clinic. The visualisation of such complex data was addressed by Ding et al, who developed a visualisation platform using unsupervised learning methods [62]. Das et al provide a review of such informatics techniques and data types that have been used for predicting cancer prognoses [63], with the aim of improving the clinical management and treatment of such patients.…”

Section: Clinical Management Outside the Icu: Wearable Sensorsmentioning

confidence: 99%