Drug-induced hepatotoxicity may cause acute and chronic liver disease, leading to great concern for patient safety. It is also one of the main reasons for drug withdrawal from the market. Toxicogenomics data has been widely used in hepatotoxicity prediction. In our study, we proposed a multi-dose computational model to predict the drug-induced hepatotoxicity based on gene expression and toxicity data. The dose/concentration information after drug treatment is fully utilized in our study based on the dose-response curve, thus a more informative representative of the dose-response relationship is considered. We also proposed a new feature selection method, named MEMO, which is also one important aspect of our multi-dose model in our study, to deal with the high-dimensional toxicogenomics data. We validated the proposed model using the TG-GATEs, which is a large database recording toxicogenomics data from multiple views. The experimental results show that the drug-induced hepatotoxicity can be predicted with high accuracy and efficiency using the proposed predictive model.

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Purpose: HER2-positive breast cancer patients benefit from HER2 targeted therapies, among which the most commonly used is trastuzumab. However, acquired resistance typically happens within one year. The cellular heterogeneity of it is less clear. Methods: Here we generated trastuzumab-resistant cells in two HER2-positive breast cancer cell lines, SK-BR-3 and BT-474. Cells at different time points during the resistance induction were examined by exome sequencing to study changes of genomic alterations over time. Single cell targeted sequencing was also used to identify resistance associated concurrent mutations.Results: We found a rapid increase of copy number variation (CNV) regions and gradual accumulation of single nucleotide variations (SNVs). On the pathway level, nonsynonymous SNVs for SK-BR-3 cells were enriched in the MAPK signaling pathway, while for BT-474 cells were enriched in mTOR and PI3K-Akt signaling pathways. However, all of the three signaling pathways were in the downstream of the HER2 kinase. Putative trastuzumab-resistance associated SNVs included AIFM1 P548L and ERBB2 M833R in SK-BR-3 cells, and OR5M9 D230N, COL9A1 R627T, ITGA7 H911Q and ADAMTS19 V451L in BT-474 cells. Single cell targeted sequencing identified several concurrent mutations. By validation, we found that concurrent mutations (AIFM1 P548L and IL1RAPL2 S546C) led to a decrease of trastuzumab sensitivity. Conclusion: Taken together, our study revealed a common pathway level trastuzumab-resistance mechanism for HER2-positive breast cancer cells. In addition, our identification of concurrent SNVs associated with trastuzumab-resistance may be indicative of potential targets for the treatment of trastuzumab-resistant breast cancer patients.