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Computational approaches to predict drug sensitivity can promote precision anticancer therapeutics. Generalizable and explainable models are of critical importance for translation to guide personalized treatment and are often overlooked in favor of prediction performance. Here, we propose PathDSP: a pathway-based model for drug sensitivity prediction that integrates chemical structure information with enrichment of cancer signaling pathways across drug-associated genes, gene expression, mutation and copy number variation data to predict drug response on the Genomics of Drug Sensitivity in Cancer dataset. Using a deep neural network, we outperform state-of-the-art deep learning models, while demonstrating good generalizability a separate dataset of the Cancer Cell Line Encyclopedia as well as provide explainable results, demonstrated through case studies that are in line with current knowledge. Additionally, our pathway-based model achieved a good performance when predicting unseen drugs and cells, with potential utility for drug development and for guiding individualized medicine.

Computational models have been successful in predicting drug sensitivity in cancer cell line data, creating an opportunity to guide precision medicine. However, translating these models to tumors remains challenging. We propose a new transfer learning workflow that transfers drug sensitivity predicting models from large-scale cancer cell lines to both tumors and patient derived xenografts based on molecular pathways derived from genomic features. We further compute feature importance to identify pathways most important to drug response prediction. We obtained good performance on tumors (AUROC = 0.77) and patient derived xenografts from triple negative breast cancers (RMSE = 0.11). Using feature importance, we highlight the association between ER-Golgi trafficking pathway in everolimus sensitivity within breast cancer patients and the role of class II histone deacetylases and interlukine-12 in response to drugs for triple-negative breast cancer. Pathway information support transfer of drug response prediction models from cell lines to tumors and can provide biological interpretation underlying the predictions, serving as a steppingstone towards usage in clinical setting.

Background The potential benefits of drug combination synergy in cancer medicine are significant, yet the risks must be carefully managed due to the possibility of increased toxicity. Although artificial intelligence applications have demonstrated notable success in predicting drug combination synergy, several key challenges persist: (1) Existing models often predict average synergy values across a restricted range of testing dosages, neglecting crucial dose amounts and the mechanisms of action of the drugs involved. (2) Many graph-based models rely on static protein–protein interactions, failing to adapt to dynamic and higher-order relationships. These limitations constrain the applicability of current methods. Results We introduce SAFER, a Sub-hypergraph Attention-based graph model, addressing these issues by incorporating complex relationships among biological knowledge networks and considering dosing effects on subject-specific networks. SAFER outperformed previous models on the benchmark and the independent test set. The analysis of subgraph attention weight for the lung cancer cell line highlighted JAK-STAT signaling pathway, PRDM12, ZNF781, and CDC5L that have been implicated in lung fibrosis. Conclusions SAFER presents an interpretable framework designed to identify drug-responsive signals. Tailored for comprehending dose effects on subject-specific molecular contexts, our model uniquely captures dose-level drug combination responses. This capability unlocks previously inaccessible avenues of investigation compared to earlier models. Furthermore, the SAFER framework can be leveraged by future inquiries to investigate molecular networks that uniquely characterize individual patients and can be applied to prioritize personalized effective treatment based on safe dose combinations.

Antrodia cinnamomea , a polyporus mushroom of Taiwan, has long been used as a remedy for cancer, hypertension, and hangover, with an annual market of over $100 million (US) in Taiwan. We obtained a 32.15-Mb genome draft containing 9,254 genes. Genome ontology enrichment and pathway analyses shed light on sexual development and the biosynthesis of sesquiterpenoids, triterpenoids, ergostanes, antroquinonol, and antrocamphin. We identified genes differentially expressed between mycelium and fruiting body and 242 proteins in the mevalonate pathway, terpenoid pathways, cytochrome P450s, and polyketide synthases, which may contribute to the production of medicinal secondary metabolites. Genes of secondary metabolite biosynthetic pathways showed expression enrichment for tissue-specific compounds, including 14-α-demethylase (CYP51F1) in fruiting body for converting lanostane to ergostane triterpenoids, coenzymes Q (COQ) for antroquinonol biosynthesis in mycelium, and polyketide synthase for antrocamphin biosynthesis in fruiting body. Our data will be useful for developing a strategy to increase the production of useful metabolites. Significance Antrodia cinnamomea , a mushroom, has long been used as a remedy for cancer, hypertension, and hangover. However, the molecular basis of its medicinal effects is unclear and its genome has not been studied. We obtained a genome draft and conducted gene annotation. Genome ontology enrichment and pathway analyses shed light on sexual development and metabolite biosynthesis. We identified genes differentially expressed between mycelium and fruiting body and also proteins in the mevalonate pathway, terpenoid pathways, cytochrome P450s, and polyketide synthases, which may contribute to production of medicinal metabolites. Genes of metabolite biosynthesis pathways showed expression enrichment for tissue-specific compounds in mycelium and in fruiting body. Our data will be useful for developing a strategy to increase the production of valuable metabolites.

Gene expression plays a key role in health and disease. Estimating the genetic components underlying gene expression can thus help understand disease etiology. Polygenic models termed “transcriptome imputation” are used to estimate the genetic component of gene expression, but these models typically consider only the cis regions of the gene. However, these cis-based models miss large variability in expression for multiple genes. Transcription factors (TFs) that regulate gene expression are natural candidates for looking for additional sources of the missing variability. We developed a hypothesis-driven approach to identify second-tier regulation by variability in TFs. Our approach tested two models representing possible mechanisms by which variations in TFs can affect gene expression: variability in the expression of the TF and genetic variants within the TF that may affect the binding affinity of the TF to the TF-binding site. We tested our TF models in whole blood and skeletal muscle tissues and identified TF variability that can partially explain missing gene expression for 1035 genes, 76% of which explains more than the cis-based models. While the discovered regulation patterns were tissue-specific, they were both enriched for immune system functionality, elucidating complex regulation patterns. Our hypothesis-driven approach is useful for identifying tissue-specific genetic regulation patterns involving variations in TF expression or binding.

Transcriptional regulation is associated with a broad range of diseases. Methods associating genetic polymorphism with gene transcription levels offer key insights for understanding the transcriptional regulation plan. The majority of gene imputation methods focus on modeling polymorphism in the cis regions of the gene, partially owing to the large genetic search space. We hypothesize that polymorphism within transcription factors (TFs) may help explain transcription levels of their transcribed genes. Here, we test this hypothesis by developing TF-TWAS: imputation models that integrate transcription factor information with transcription-wide association study methodology. By comparing TF-TWAS models to base models that use only gene cis information, we are able to estimate possible mechanisms of the TF polymorphism effect TF expression or binding affinity within four tissues whole blood, liver, brain hippocampus and coronary artery. We identified 48 genes where the TF-TWAS models explain significantly better their expression than cis models alone in at least one of the four tissues. Sixteen of these genes are associated with various diseases, including cancer, neurological, psychiatric and rare genetic diseases. Our method is a new expansion to transcriptome-wide association studies and enables the identification of new associations between polymorphism in transcription factor and gene transcription levels.

ABSTRACT Computational approaches to predict drug sensitivity can promote precision anticancer therapeutics. Generalizable and explainable models are of critical importance for translation to guide personalized treatment and are often overlooked in favor of prediction performance. Here, we propose a pathway-based model for drug sensitivity prediction that integrates chemical structure information with enrichment of cancer signaling pathways across drug-associated genes, gene expression, mutation and copy number variation data to predict drug response on the Genomics of Drug Sensitivity in Cancer (GDSC) dataset. Using a deep neural network, we outperforming state-of-the-art deep learning models, while demonstrating good generalizability a separate dataset of the Cancer Cell Line Encyclopedia (CCLE) as well as provide explainable results, demonstrated through case studies that are in line with current knowledge. Additionally, our pathway-based model achieved a good performance when predicting unseen drugs and cells, with potential utility for drug development and for guiding individualized medicine.

Background: How intranasal mupirocin decolonisation affects the human nasal microbiota remains unknown. To characterize the temporal dynamics of the nasal microbial community in healthy staphylococcal carriers in response to intranasal mupirocin decolonisation, we serially sampled the anterior nares of four healthy carriers to determine the nasal microbial profile via sequencing of bacterial 16S ribosomal DNA. Results: Before decolonisation, the nasal microbiota differed by the initial, culture-based staphylococcal carriage status, with Firmicutes (54.1%) and Proteobacteria (75.8%) dominating the microbial community in the carriers and the noncarrier, separately. The nasal microbiota lost its diversity immediately after decolonisation (Shannon diversity: 1.33, 95% confidence interval [CI]: 1.06-1.54) as compared to before decolonisation (1.78, 95%CI: 0.58-1.93). The initial staphylococcal carriage status, expression levels of human neutrophil peptide 1, and sampling times were major contributors to the between-community dissimilarities (P for marginal permutation test: .014) though of borderline significance when considering data correlation (P for blocked permutation test: .047) in both nonmetric multidimensional scaling and constrained correspondence analysis. Results of univariable and multivariable differential abundance analysis further showed that, in addition to Staphylococci, multiple genera of Actinobacteria and Proteobacteria were differentially enriched or depleted by mupirocin use. Conclusions: Mupirocin could affect both Gram-positive and Gram-negative commensals along with altered host antimicrobial responses. How the nasal microbiome recovered after short-term antibiotic perturbation depended on the initial staphylococcal carriage status. The potential risks associated with loss of colonisation resistance need to be considered in high-risk populations receiving targeted decolonisation.

How intranasal mupirocin decolonisation affects the human nasal microbiota remains unknown. To characterize the temporal dynamics of the nasal microbial community in healthy staphylococcal carriers in response to intranasal mupirocin decolonisation, we serially sampled the anterior nares of four healthy carriers to determine the nasal microbial profile via sequencing of bacterial 16S ribosomal DNA. Before decolonisation, the nasal microbiota differed by the initial, culture-based staphylococcal carriage status, with Firmicutes (54.1%) and Proteobacteria (75.8%) dominating the microbial community in the carriers and the noncarrier, separately. The nasal microbiota lost its diversity immediately after decolonisation (Shannon diversity: 1.33, 95% confidence interval [CI]: 1.06-1.54) as compared to before decolonisation (1.78, 95%CI: 0.58-1.93). The initial staphylococcal carriage status, expression levels of human neutrophil peptide 1, and sampling times were major contributors to the between-community dissimilarities (P for marginal permutation test: .014) though of borderline significance when considering data correlation (P for blocked permutation test: .047) in both nonmetric multidimensional scaling and constrained correspondence analysis. Results of univariable and multivariable differential abundance analysis further showed that, in addition to Staphylococci, multiple genera of Actinobacteria and Proteobacteria were differentially enriched or depleted by mupirocin use. Mupirocin could affect both Gram-positive and Gram-negative commensals along with altered host antimicrobial responses. How the nasal microbiome recovered after short-term antibiotic perturbation depended on the initial staphylococcal carriage status. The potential risks associated with loss of colonisation resistance need to be considered in high-risk populations receiving targeted decolonisation.