Dataset Information

Robust pathway sampling in phenotype prediction. Application to triple negative breast cancer.

ABSTRACT: BACKGROUND:Phenotype prediction problems are usually considered ill-posed, as the amount of samples is very limited with respect to the scrutinized genetic probes. This fact complicates the sampling of the defective genetic pathways due to the high number of possible discriminatory genetic networks involved. In this research, we outline three novel sampling algorithms utilized to identify, classify and characterize the defective pathways in phenotype prediction problems, such as the Fisher's ratio sampler, the Holdout sampler and the Random sampler, and apply each one to the analysis of genetic pathways involved in tumor behavior and outcomes of triple negative breast cancers (TNBC). Altered biological pathways are identified using the most frequently sampled genes and are compared to those obtained via Bayesian Networks (BNs). RESULTS:Random, Fisher's ratio and Holdout samplers were more accurate and robust than BNs, while providing comparable insights about disease genomics. CONCLUSIONS:The three samplers tested are good alternatives to Bayesian Networks since they are less computationally demanding algorithms. Importantly, this analysis confirms the concept of "biological invariance" since the altered pathways should be independent of the sampling methodology and the classifier used for their inference. Nevertheless, still some modifications are needed in the Bayesian networks to be able to sample correctly the uncertainty space in phenotype prediction problems, since the probabilistic parameterization of the uncertainty space is not unique and the use of the optimum network might falsify the pathways analysis.

SUBMITTER: Cernea A

PROVIDER: S-EPMC7068866 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Robust pathway sampling in phenotype prediction. Application to triple negative breast cancer.

Cernea Ana A Fernández-Martínez Juan Luis JL deAndrés-Galiana Enrique J EJ Fernández-Ovies Francisco Javier FJ Alvarez-Machancoses Oscar O Fernández-Muñiz Zulima Z Saligan Leorey N LN Sonis Stephen T ST

BMC bioinformatics 20200311 Suppl 2

<h4>Background</h4>Phenotype prediction problems are usually considered ill-posed, as the amount of samples is very limited with respect to the scrutinized genetic probes. This fact complicates the sampling of the defective genetic pathways due to the high number of possible discriminatory genetic networks involved. In this research, we outline three novel sampling algorithms utilized to identify, classify and characterize the defective pathways in phenotype prediction problems, such as the Fish ...[more]

PMID: 32164540

Similar Datasets

Project description:IntroductionTriple-negative breast cancer (TNBC) is a heterogeneous disease with no definitive prognostic markers. As a major component of tight junctions, claudins (CLDNs) presumably play an important role in carcinogenesis and progression of breast cancer. This study was aimed at determining the relationship between the expression of CLDNs and the clinical outcomes of TNBCs.Materials and methodsThe surgical specimens of primary breast tumors from a consecutive cohort of 173 TNBC patients were retrospectively collected. The membranous expression of CLDN1, CLDN2, CLDN4, and CLDN7 was measured by immunohistochemistry. Then, the associations between CLDN expression, clinicopathological features, and clinical outcomes were assessed.ResultsPositive CLDN1, CLDN2, CLDN4, and CLDN7 membrane expression was detected in 44.5%, 54.9%, 76.9%, and 73.4% of the cohort specimens, respectively. A lack of CLDN1 expression was related to only lymph node metastasis (P?=?0.014). The rate of CLDN4-positive tumors was significantly increased in tumors of a higher grade (P?=?0.003). Importantly, negative CLDN1 expression was associated with worse relapse-free survival (RFS) in both lymph node positive (LN+) and negative (LN-) cases (both P<0.001). Similarly it was also associated with shorter overall survival (OS)(P?=?0.003 in LN+ cases; P?=?0.018 in LN- cases). In the LN+ subgroup, CLDN2-negative cases had a significantly higher risk of recurrence (P?=?0.008). Multivariate analysis revealed that negative CLDN1 expression was an independent prognostic factor for high risk of both recurrence and death (HR 5.529, 95% CI 2.664-11.475, P<0.001; HR 3.459, 95% CI 1.555-7.696, P?=?0.002). However, neither CLDN4 nor CLDN7 expression was associated with survival.ConclusionIn TNBC, the CLDN1-negative phenotype predicts a high risk of recurrence and death. The absence of CLDN1 expression is strongly suggested to be an independent adverse prognostic factor in this heterogeneous subtype of breast cancer.

Dataset Information

Robust pathway sampling in phenotype prediction. Application to triple negative breast cancer.

Publications

Robust pathway sampling in phenotype prediction. Application to triple negative breast cancer.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets