[Predicting tumor drug sensitivity with multi-omics data].

The prediction of tumor drug sensitivity plays an important role in clinically guiding patients' medication. In this paper, a multi-omics data-based cancer drug sensitivity prediction model was constructed by Stacking ensemble learning method. The data including gene expression, mutation, copy number variation and drug sensitivity value (IC50) of 198 drugs were downloaded from the GDSC database. Multiple feature selection methods were applied for dimensionality reduction. Six primary learners and one secondary learner were integrated into modeling by Stacking method. The model was validated with 5-fold cross-validation. In the prediction results, 36.4% of drug models' AUCs were greater than 0.9, 49.0% of drug models' AUCs were between 0.8-0.9, and the lowest drug model's AUC was 0.682. The multi-omics model for drug sensitivity prediction based on Stacking method is better than the known single-omics or multi-omics model in terms of accuracy and stability. The model based on multi-omics data is better than the single-omics data in predicting drug sensitivity. Function annotation and enrichment analysis of feature genes revealed the potential resistance mechanism of tumors to sorafenib, providing the model interpretability from a biological perspective, and demonstrated the model's potential applicability in clinical medication guidance.

dbPepNeo: a manually curated database for human tumor neoantigen peptides.

Neoantigens can function as actual antigens to facilitate tumor rejection, which play a crucial role in cancer immunology and immunotherapy. Emerging evidence revealed that neoantigens can be used to develop personalized, cancer-specific vaccines. To date, large numbers of immunogenomic peptides have been computationally predicted to be potential neoantigens. However, experimental validation remains the gold standard for potential clinical application. Experimentally validated neoantigens are rare and mostly appear scattered among scientific papers and various databases. Here, we constructed dbPepNeo, a specific database for human leukocyte antigen class I (HLA-I) binding neoantigen peptides based on mass spectrometry (MS) validation or immunoassay in human tumors. According to the verification methods of these neoantigens, the collection of peptides was classified as 295 high confidence, 247 medium confidence and 407 794 low confidence neoantigens, respectively. This can serve as a valuable resource to aid further screening for effective neoantigens, optimize a neoantigen prediction pipeline and study T-cell receptor (TCR) recognition. Three applications of dbPepNeo are shown. In summary, this work resulted in a platform to promote the screening and confirmation of potential neoantigens in cancer immunotherapy. Database URL: www.biostatistics.online/dbPepNeo/.