Biology subjects

Oh, J. H.

Publications and source records attributed to Oh, J. H..

22 records · Page 2Linked to original sources

bioRxiv2019

Serum Alpha-2-Macroglobulin as an intrinsic radioprotective factor in patients undergoing thoracic radiation therapy

ObjectiveTo investigate the impact of alpha-2-macroglobulin (A2M), a suspected intrinsic radioprotectant, on radiation pneumonitis and esophagitis. Additionally, we establish multifactorial predictive models for pneumonitis and esophagitis.\n\nMaterials/MethodsBaseline A2M levels were obtained for 258 patients prior to thoracic radiotherapy (RT). Dose-volume characteristics were extracted from treatment plans. Spearmans correlation (Rs) test was used to correlate A2M levels, smoking status and dosimetric variables with toxicities. Esophagitis and pneumonitis prediction models were built using least absolute shrinkage and selection operator (LASSO) logistic regression on 1000 bootstrapped datasets. Models were built using 2/3 of the data for training and 1/3 for validation.\n\nResultsThere were 36 (14.0%) patients with grade [≥]2 pneumonitis and 61 (23.6%) with grade [≥]2 esophagitis. The median A2M level was 191 mg/dL (range: 94-511). Never/former/current smoker status was 47 (18.2%)/179 (69.4%)/32 (12.4%). We found a significant correlation between baseline A2M levels and esophagitis (Rs=-0.18/p=0.003) and between A2M and smoking status (former or current) (Rs=0.13/p=0.04) but not between A2M and pneumonitis. On univariate analysis, significant parameters for grade [≥]2 esophagitis included number of fractions (Rs=0.47/p<0.0001), treatment days (Rs=0.44/p<0.0001), chemotherapy use (Rs=0.40/p<0.0001), dose per fraction (Rs=-0.34/p<0.0001), total dose (Rs=0.29/p<0.0001), age (Rs=-0.22/p=0.0003), and several dosimetric variables in esophagus with Rs>0.5 (p<0.0001). For pneumonitis, significant clinical parameters were treatment days (Rs=0.24/p=0.0001), chemotherapy use (Rs=0.22/p=0.0004), number of fractions (Rs=0.21/p=0.0007), dose per fraction (Rs=-0.18/p=0.0035), and total dose (Rs=0.15/p=0.013). The most significant dosimetric variable in lung and heart was D70 (Rs=0.28/p<0.0001) and max dose (Rs=0.27/p<0.0001), respectively. LASSO bootstrap logistic regression models on the validation data resulted in the area under the receiver operating characteristic curve of 0.84 and 0.75 for esophagitis and pneumonitis, respectively.\n\nConclusionOur findings show an association of higher A2M values with lower risk of radiation esophagitis and smoking status. Multivariate predictive models also confirmed a role of heart dose in the risk of pneumonitis.

cancer biology↗

bioRxiv2019

A Novel Integrative Multiomics Method Reveals a Hypoxia-Related Subgroup of Breast Cancer with Significantly Decreased Survival

The remarkable growth of multi-platform genomic profiles has led to the multiomics data integration challenge. The effective integration of such data provides a comprehensive view of the molecular complexity of cancer tumors and can significantly improve clinical out-come predictions. In this study, we present a novel network-based integration method of multiomics data as well as a clustering technique involving the Wasserstein (Earth Movers) distance from the theory of optimal mass transport. We applied our proposed method of integrative Wasserstein-based clustering (iWCluster) to invasive breast carcinoma from The Cancer Genome Atlas (TCGA) project. The subtypes were characterized by the concordant effect of mRNA expression, DNA copy number alteration, and DNA methylation as well as the interaction network connectivity of the gene products. iW-Cluster is substantially more effective in distinguishing clusters with different survival rates as compared to isolated one-dimensional conventional omics analysis. Applying iWCluster to breast cancer TCGA data successfully recovered the known PAM50 molecular subtypes. In addition, iWCluster preserves the gene-specific data, which enables us to interpret the results and perform further analysis of significant genes for a specific cluster. The gene ontology enrichment analysis of significant genes in our substantially low survival sub-group leads to the well-known phenomenon of tumor hypoxia and the transcription factor ETS1 whose expression is induced by hypoxia. Increased expression of ETS1 is associated with an increased risk of recurrence and worse prognosis in breast cancer. Consequently, we believe iWCluster has the potential to discover novel subtypes by accentuating the genes that have concordant multiomics measurements in their interaction network, which are challenging to find without the network inference or with single omics analysis.

genomics↗

bioRxiv2019

Machine Learning Methods to Identify Genetic Correlates of Radiation-Associated Contralateral Breast Cancer in the WECARE Study

The purpose of this study is to identify germline single nucleotide polymorphisms (SNPs) that optimally predict radiation-associated contralateral breast cancer (RCBC) and to provide new biological insights into the carcinogenic process. Fifty-two women with contralateral breast cancer and 153 women with unilateral breast cancer were identified within the Womens Environmental Cancer and Radiation Epidemiology (WECARE) Study who were at increased risk of RCBC because they were [≤] 40 years of age at first diagnosis of breast cancer and received a scatter radiation dose > 1 Gy to the contralateral breast. A previously reported algorithm, preconditioned random forest regression, was applied to predict the risk of developing RCBC. The resulting model produced an area under the curve of 0.62 (p=0.04) on hold-out validation data. The biological analysis identified the cyclic AMP-mediated signaling and Ephrin-A as significant biological correlates, which were previously shown to influence cell survival after radiation in an ATM-dependent manner. The key connected genes and proteins that are identified in this analysis were previously identified as relevant to breast cancer, radiation response, or both. In summary, machine learning/bioinformatics methods applied to genome-wide genotyping data have great potential to reveal plausible biological correlates associated with the risk of RCBC.

bioinformatics↗

bioRxiv2017

Characterizing Cancer Drug Response andBiological Correlates: A Geometric NetworkApproach

In the present work, we consider a geometric network approach to study common biological features of anticancer drug response. We use for this purpose the panel of 60 human cell lines (NCI-60) provided by the National Cancer Institute. Our study suggests that utilization of mathematical tools for network-based analysis can provide novel insights into drug response and cancer biology. We adopted a discrete notion of Ricci curvature to measure the robustness of biological networks constructed with a pre-treatment gene expression dataset and coupled the results with the GI50 response of the cell lines to the drugs. The link between network robustness and Ricci curvature was implemented using the theory of optimal mass transport. Our hypothesis behind this idea is that robustness in the biological network contributes to tumor drug resistance, thereby enabling us to predict the effectiveness and sensitivity of drugs in the cell lines. Based on the resulting drug response ranking, we assessed the impact of genes that are likely associated with individual drug response. For important genes identified, we performed a gene ontology enrichment analysis using a curated bioinformatics database which resulted in very plausible biological processes associated with drug response across cell lines and cell types from the biological and literature viewpoint. These results demonstrate the potential of using the mathematical network analysis in assessing drug response and in identifying relevant genomic biomarkers and biological processes for precision medicine.

cancer biology↗