Purpose

The goal of this study was to test the hypothesis that by controlling intracellular uptake, organic cation transporter 1, Oct1 is a key determinant of the disposition and toxicity of cis-diammine(pyridine)chloroplatinum(II)(CDPCP) and oxaliplatin.

Methods

Pharmacokinetics, tissue accumulation and toxicity of CDPCP and oxaliplatin were compared between Oct1-/- and wild-type mice.

Results

After intravenous administration, hepatic and intestinal accumulation of CDPCP was 2.7-fold and 3.9-fold greater in Oct1 wild-type mice (p < 0.001). Deletion of Oct1 resulted in a significantly decreased clearance (0.444 ± 0.0391 ml/min*kg versus 0.649 ± 0.0807 ml/min*kg in wild-type mice, p < 0.05) and volume distribution (1.90 ± 0.161 L/kg versus 3.37 ± 0.196 L/kg in wild-type mice, p < 0.001). Moreover, Oct1 deletion resulted in more severe off-target toxicities in CDPCP-treated mice. Histologic examination of the liver and measurements of liver function indicated that the level of hepatic toxicity was mild and reversible, but was more apparent in the wild-type mice. In contrast, the effect of Oct1 on the pharmacokinetics and toxicity of oxaliplatin in the mice was minimal.

Conclusions

Our study suggests that Oct1 plays an important role in the pharmacokinetics, tissue distribution and toxicity of CDPCP, but not oxaliplatin.

Computed tomography (CT) examinations are commonly used to predict lung nodule malignancy in patients, which are shown to improve noninvasive early diagnosis of lung cancer. It remains challenging for computational approaches to achieve performance comparable to experienced radiologists. Here we present NoduleX, a systematic approach to predict lung nodule malignancy from CT data, based on deep learning convolutional neural networks (CNN). For training and validation, we analyze >1000 lung nodules in images from the LIDC/IDRI cohort. All nodules were identified and classified by four experienced thoracic radiologists who participated in the LIDC project. NoduleX achieves high accuracy for nodule malignancy classification, with an AUC of ~0.99. This is commensurate with the analysis of the dataset by experienced radiologists. Our approach, NoduleX, provides an effective framework for highly accurate nodule malignancy prediction with the model trained on a large patient population. Our results are replicable with software available at http://bioinformatics.astate.edu/NoduleX .

Genome-wide association studies present computational challenges for missing data imputation, while the advances of genotype technologies are generating datasets of large sample sizes with sample sets genotyped on multiple SNP chips. We present a new framework SparRec (Sparse Recovery) for imputation, with the following properties: (1) The optimization models of SparRec, based on low-rank and low number of co-clusters of matrices, are different from current statistics methods. While our low-rank matrix completion (LRMC) model is similar to Mendel-Impute, our matrix co-clustering factorization (MCCF) model is completely new. (2) SparRec, as other matrix completion methods, is flexible to be applied to missing data imputation for large meta-analysis with different cohorts genotyped on different sets of SNPs, even when there is no reference panel. This kind of meta-analysis is very challenging for current statistics based methods. (3) SparRec has consistent performance and achieves high recovery accuracy even when the missing data rate is as high as 90%. Compared with Mendel-Impute, our low-rank based method achieves similar accuracy and efficiency, while the co-clustering based method has advantages in running time. The testing results show that SparRec has significant advantages and competitive performance over other state-of-the-art existing statistics methods including Beagle and fastPhase.

It is challenging to cluster cancer patients of a certain histopathological type into molecular subtypes of clinical importance and identify gene signatures directly relevant to the subtypes. Current clustering approaches have inherent limitations, which prevent them from gauging the subtle heterogeneity of the molecular subtypes. In this paper we present a new framework: SPARCoC (Sparse-CoClust), which is based on a novel Common-background and Sparse-foreground Decomposition (CSD) model and the Maximum Block Improvement (MBI) co-clustering technique. SPARCoC has clear advantages compared with widely-used alternative approaches: hierarchical clustering (Hclust) and nonnegative matrix factorization (NMF). We apply SPARCoC to the study of lung adenocarcinoma (ADCA), an extremely heterogeneous histological type, and a significant challenge for molecular subtyping. For testing and verification, we use high quality gene expression profiling data of lung ADCA patients, and identify prognostic gene signatures which could cluster patients into subgroups that are significantly different in their overall survival (with p-values < 0.05). Our results are only based on gene expression profiling data analysis, without incorporating any other feature selection or clinical information; we are able to replicate our findings with completely independent datasets. SPARCoC is broadly applicable to large-scale genomic data to empower pattern discovery and cancer gene identification.