Dataset Information

PolyClustR: defining communities of reconciled cancer subtypes with biological and prognostic significance.

ABSTRACT: BACKGROUND:To ensure cancer patients are stratified towards treatments that are optimally beneficial, it is a priority to define robust molecular subtypes using clustering methods applied to high-dimensional biological data. If each of these methods produces different numbers of clusters for the same data, it is difficult to achieve an optimal solution. Here, we introduce "polyClustR", a tool that reconciles clusters identified by different methods into subtype "communities" using a hypergeometric test or a measure of relative proportion of common samples. RESULTS:The polyClustR pipeline was initially tested using a breast cancer dataset to demonstrate how results are compatible with and add to the understanding of this well-characterised cancer. Two uveal melanoma datasets were then utilised to identify and validate novel subtype communities with significant metastasis-free prognostic differences and associations with known chromosomal aberrations. CONCLUSION:We demonstrate the value of the polyClustR approach of applying multiple consensus clustering algorithms and systematically reconciling the results in identifying novel subtype communities of two cancer types, which nevertheless are compatible with established understanding of these diseases. An R implementation of the pipeline is available at: https://github.com/syspremed/polyClustR.

SUBMITTER: Eason K

PROVIDER: S-EPMC5970540 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature

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polyClustR: defining communities of reconciled cancer subtypes with biological and prognostic significance.

Eason Katherine K Nyamundanda Gift G Sadanandam Anguraj A

BMC bioinformatics 20180525 1

<h4>Background</h4>To ensure cancer patients are stratified towards treatments that are optimally beneficial, it is a priority to define robust molecular subtypes using clustering methods applied to high-dimensional biological data. If each of these methods produces different numbers of clusters for the same data, it is difficult to achieve an optimal solution. Here, we introduce "polyClustR", a tool that reconciles clusters identified by different methods into subtype "communities" using a hype ...[more]

PMID: 29801433

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Project description:PurposeMetabolic dysregulation has been implicated as a molecular driver of breast cancer in preclinical studies, especially with respect to metastases. We hypothesized that abnormalities in patient metabolism, such as obesity and diabetes, may drive outcomes in breast cancer patients with brain metastases.MethodsWe retrospectively identified 84 consecutive patients with brain metastases from breast cancer treated with intracranial radiation therapy. Radiation was delivered as whole-brain radiation to a median dose of 3000 cGy or stereotactic radiosurgery to a median dose of 2100 cGy. Kaplan Meier curves were generated for overall survival (OS) data and Mantel-Cox regression was performed to detect differences in groups.ResultsAt analysis, 81 survival events had occurred and the median OS for the entire cohort was 21.7 months. Despite similar modified graded prognostic assessments, resection rates, and receptor status, BMI ≥ 25 kg/m2 (n = 45) was associated with decreased median OS (13.7 vs. 30.6 months; p < 0.001) and median intracranial progression-free survival (PFS) (7.4 vs. 10.9 months; p = 0.04) compared to patients with BMI < 25 kg/m2 (n = 39). Similar trends were observed among all three types of breast cancer. Patients with diabetes (n = 17) had decreased median OS (11.8 vs. 26.2 months; p < 0.001) and median intracranial PFS (4.5 vs. 10.3 months; p = 0.001) compared to non-diabetics (n = 67). On multivariate analysis, both BMI ≥ 25 kg/m2 [HR 2.35 (1.39-3.98); p = 0.002] and diabetes [HR 2.77 (1.454-5.274); p = 0.002] were associated with increased mortality.ConclusionsElevated BMI or diabetes may negatively impact both overall survival and local control in patients with brain metastases from breast cancer, highlighting the importance of the translational development of therapeutic metabolic interventions. Given its prognostic significance, BMI should be used as a stratification in future clinical trial design in this patient population.

Dataset Information

PolyClustR: defining communities of reconciled cancer subtypes with biological and prognostic significance.

Publications

polyClustR: defining communities of reconciled cancer subtypes with biological and prognostic significance.

Similar Datasets

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