Pattern of distant recurrence according to the molecular subtypes in Korean women with breast cancer

<p>Abstract</p> <p>Background</p> <p>Distant recurrence is one of the most important risk factors in overall survival, and distant recurrence is related to a complex biologic interaction of seed and soil factors. The aim of the study was to investigate the association between the molecular subtypes and patterns of distant recurrence in patients with breast cancer.</p> <p>Methods</p> <p>In an investigation of 313 women with breast cancer who underwent surgery from 1994 and 2000, the expressions of estrogen and progestrone receptor (ER/PR), and human epithelial receptor-2 (HER2) were evaluated. The subtypes were defined as luminal-A, luminal-HER2, HER2-enriched, and triple negative breast cancer (TNBC) according to ER, PR, and HER2 status.</p> <p>Results</p> <p>Bone was the most common site of distant recurrence. The incidence of first distant recurrence site was significantly different among the subtypes. Brain metastasis was more frequent in the luminal-HER2 and TNBC subtypes. In subgroup analysis, overall survival in patients with distant recurrence after 24 months after surgery was significantly different among the subtypes.</p> <p>Conclusions</p> <p>Organ-specific metastasis may depend on the molecular subtype of breast cancer. Tailored strategies against distant metastasis concerning the molecular subtypes in breast cancer may be considered.</p

Numbers of drugs, proteins, and drug-target interactions used in this research.

<p>(a) Drug-target interactions from ChEMBL are combined with side effects and from SIDER and drufgs.com databases. (b) Drug-target interactions from STITCH are combined with side effects and from SIDER and STITCH databases. (c) Common drugs from ChEMBL and STICH are shown with the numbers of target interactions.</p

Overview of the entire method procedure.

<p>(a) Drug similarities and drug-protein interactions are used to calculate the probabilities of unknown drug-target interactions. Three different drug similarities (chemical structure, drug side effect, and DDI similarity) are applied. Two learning models (KL1IR and SVM) are used to train and test interactions. (b) Protein similarities are integrated with drug similarities to predict unknown drug-target interactions. In this process, the bipartite local model is used.</p

Predicting Drug-Target Interactions Using Drug-Drug Interactions

<div><p>Computational methods for predicting drug-target interactions have become important in drug research because they can help to reduce the time, cost, and failure rates for developing new drugs. Recently, with the accumulation of drug-related data sets related to drug side effects and pharmacological data, it has became possible to predict potential drug-target interactions. In this study, we focus on drug-drug interactions (DDI), their adverse effects () and pharmacological information (), and investigate the relationship among chemical structures, side effects, and DDIs from several data sources. In this study, data from the STITCH database, from drugs.com, and drug-target pairs from ChEMBL and SIDER were first collected. Then, by applying two machine learning approaches, a support vector machine (SVM) and a kernel-based L1-norm regularized logistic regression (KL1LR), we showed that DDI is a promising feature in predicting drug-target interactions. Next, the accuracies of predicting drug-target interactions using DDI were compared to those obtained using the chemical structure and side effects based on the SVM and KL1LR approaches, showing that DDI was the data source contributing the most for predicting drug-target interactions.</p></div

Non-zero ratios of KL1LR coefficients.

<p>In <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0080129#pone.0080129.e083" target="_blank">Equation (4</a>), is the average of kernel values between a drug and other drugs that target the given protein; is the average between a drug and other drugs not targeting the given protein. Non-zero ratios are calculated using , where is the number of non-zero coefficients and is the number of coefficients.</p

Comparison of prediction accuracies of three drug similarities in predicting drug-target interactions.

<p>AUC values are presented when two prediction methods and two drug-target interaction (DTI) data sets are used. CH and SE indicate the drug similarity based on the chemical structure and side effect, respectively. CS and CSD indicate the drug similarity by combining CH and SE, and combining CH, SE, and DDI, respectively. The last column indicates that the kernel fusion method developed in Lanckriet <i>et al.</i><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0080129#pone.0080129-Lanckriet2" target="_blank">[20]</a> is used for combining multiple kernels in SVM.</p>*<p>indicates the highest value for each combination of method and data source. For different combinations of methods and data sets, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0080129#pone.0080129.s005" target="_blank">Table S5</a> contains ROC curves of true positive rate and false positive rate, and tables of true positive, false positive, true negative, and false negative values for each threshold.</p

Drug-related data sets used in this study.

<p>Row names represent the following data: ‘Drug’ is # of drugs with target interactions, Protein’ is # of proteins from humans, ‘Drug-Protein’ is # of drug-target pairs, ‘SE’ is # of side effects, ‘Drug-SE’ is # of drug-side effect pairs, and ‘’ or ‘’ is # of drugs having DDI. The two superscripts in the last column represent the following: is # of drugs with matched identifiers from ChEMBL drugs with target interactions and SIDER side effects, and is # of drugs having DDI in .</p

Prediction accuracies of constructed from three similarity measures.

<p>Three different measures were used to construct kernels for SVM and KL1LR. These measures were then used to predict ChEMBL and STITCH drug-target interactions. (a) In the different ranges of ranks assigned by probabilities of interactions between drugs and targets in ChEMBL, ratios of known drug-target interactions in STITCH among unknown interactions in ChEMBL are presented according to their ranks. (b) The left bar is the ratio for interactions having prediction probabilities ≥0.5; among 28 unknown interactions in ChEMBL, 6 are known in STITCH. The right bar shows interactions with prediction probabilities <0.5; among 68,334 unknown interactions in ChEMBL, only 2,543 are known in STITCH. (c) In the different ranges of ranks assigned by probabilities of interactions between drugs and targets in STITCH, ratios of known drug-target interactions in ChEMBL among unknown interactions in STITCH are presented according to their ranks. (d) The left bar is the ratio for interactions having prediction probabilities ≥0.5; among 402 unknown interactions in STITCH, 20 are known in ChEMBL. The bar on the right shows interactions with prediction probabilities <0.5; among 88,028 unknown interactions in STITCH, only 712 are known in ChEMBL.</p

Comparison of STITCH <i>DDI_Pharm</i> and drugs.com. <i>DDI_AE</i>.

<p>Two DDI data sets were used to predict ChEMBL and STITCH drug-target interactions (DTI). Each kernel was measured using the shortest path method. contains 11 unreachable drugs. Therefore, we used 313 drugs from the ChEMBL and STITCH data sets.</p

Prediction accuracies of constructed from three similarity measures.

<p>Three different measures were used to construct kernels for SVM and KL1LR. These measures were then used to predict ChEMBL and STITCH drug-target interactions.</p