Machine Learning Prediction of Cancer Cell Sensitivity to Drugs Based on Genomic and Chemical Properties

Predicting the response of a specific cancer to a therapy is a major goal in modern oncology that should ultimately lead to a personalised treatment. High-throughput screenings of potentially active compounds against a panel of genomically heterogeneous cancer cell lines have unveiled multiple relationships between genomic alterations and drug responses. Various computational approaches have been proposed to predict sensitivity based on genomic features, while others have used the chemical properties of the drugs to ascertain their effect. In an effort to integrate these complementary approaches, we developed machine learning models to predict the response of cancer cell lines to drug treatment, quantified through IC50 values, based on both the genomic features of the cell lines and the chemical properties of the considered drugs. Models predicted IC50 values in a 8-fold cross-validation and an independent blind test with coefficient of determination R2 of 0.72 and 0.64 respectively. Furthermore, models were able to predict with comparable accuracy (R2 of 0.61) IC50s of cell lines from a tissue not used in the training stage. Our in silico models can be used to optimise the experimental design of drug-cell screenings by estimating a large proportion of missing IC50 values rather than experimentally measuring them. The implications of our results go beyond virtual drug screening design: potentially thousands of drugs could be probed in silico to systematically test their potential efficacy as anti-tumour agents based on their structure, thus providing a computational framework to identify new drug repositioning opportunities as well as ultimately be useful for personalized medicine by linking the genomic traits of patients to drug sensitivity

Spatial transcriptomics reveals altered lipid metabolism and inflammation-related gene expression of sebaceous glands in psoriasis and atopic dermatitis

Sebaceous glands drive acne, however, their role in other inflammatory skin diseases remains unclear. To shed light on their potential contribution to disease development, we investigated the spatial transcriptome of sebaceous glands in psoriasis and atopic dermatitis patients across lesional and non-lesional human skin samples. Both atopic dermatitis and psoriasis sebaceous glands expressed genes encoding key proteins for lipid metabolism and transport such as ALOX15B, APOC1, FABP7, FADS1/2, FASN, PPARG, and RARRES1. Also, inflammation-related SAA1 was identified as a common spatially variable gene. In atopic dermatitis, genes mainly related to lipid metabolism (e.g. ACAD8, FADS6, or EBP) as well as disease-specific genes, i.e., Th2 inflammation-related lipid-regulating HSD3B1 were differentially expressed. On the contrary, in psoriasis, more inflammation-related spatially variable genes (e.g. SERPINF1, FKBP5, IFIT1/3, DDX58) were identified. Other psoriasis-specific enriched pathways included lipid metabolism (e.g. ACOT4, S1PR3), keratinization (e.g. LCE5A, KRT5/7/16), neutrophil degranulation, and antimicrobial peptides (e.g. LTF, DEFB4A, S100A7-9). In conclusion, our results show that sebaceous glands contribute to skin homeostasis with a cell type-specific lipid metabolism, which is influenced by the inflammatory microenvironment. These findings further support that sebaceous glands are not bystanders in inflammatory skin diseases, but can actively and differentially modulate inflammation in a disease-specific manner

Small phytoplankton dominate western North Atlantic biomass

The North Atlantic phytoplankton spring bloom is the pinnacle in an annual cycle that is driven by physical, chemical, and biological seasonality. Despite its important contributions to the global carbon cycle, transitions in plankton community composition between the winter and spring have been scarcely examined in the North Atlantic. Phytoplankton composition in early winter was compared with latitudinal transects that captured the subsequent spring bloom climax. Amplicon sequence variants (ASVs), imaging flow cytometry, and flow-cytometry provided a synoptic view of phytoplankton diversity. Phytoplankton communities were not uniform across the sites studied, but rather mapped with apparent fidelity onto subpolar- and subtropical-influenced water masses of the North Atlantic. At most stations, cells < 20-µm diameter were the main contributors to phytoplankton biomass. Winter phytoplankton communities were dominated by cyanobacteria and pico-phytoeukaryotes. These transitioned to more diverse and dynamic spring communities in which pico- and nano-phytoeukaryotes, including many prasinophyte algae, dominated. Diatoms, which are often assumed to be the dominant phytoplankton in blooms, were contributors but not the major component of biomass. We show that diverse, small phytoplankton taxa are unexpectedly common in the western North Atlantic and that regional influences play a large role in modulating community transitions during the seasonal progression of blooms

EXPORTS Measurements and Protocols for the NE Pacific Campaign

EXport Processes in the Ocean from Remote Sensing (EXPORTS) is a large-scale NASA-led and NSF co-funded field campaign that will provide critical information for quantifying the export and fate of upper ocean net primary production (NPP) using satellite information and state of the art technology

Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen

The effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven approaches to find optimal patient-specific treatments. Here we report AstraZeneca's large drug combination dataset, consisting of 11,576 experiments from 910 combinations across 85 molecularly characterized cancer cell lines, and results of a DREAM Challenge to evaluate computational strategies for predicting synergistic drug pairs and biomarkers. 160 teams participated to provide a comprehensive methodological development and benchmarking. Winning methods incorporate prior knowledge of drug-target interactions. Synergy is predicted with an accuracy matching biological replicates for >60% of combinations. However, 20% of drug combinations are poorly predicted by all methods. Genomic rationale for synergy predictions are identified, including ADAM17 inhibitor antagonism when combined with PIK3CB/D inhibition contrasting to synergy when combined with other PI3K-pathway inhibitors in PIK3CA mutant cells

Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen

The effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven approaches to find optimal patient-specific treatments. Here we report AstraZeneca's large drug combination dataset, consisting of 11,576 experiments from 910 combinations across 85 molecularly characterized cancer cell lines, and results of a DREAM Challenge to evaluate computational strategies for predicting synergistic drug pairs and biomarkers. 160 teams participated to provide a comprehensive methodological development and benchmarking. Winning methods incorporate prior knowledge of drug-target interactions. Synergy is predicted with an accuracy matching biological replicates for >60% of combinations. However, 20% of drug combinations are poorly predicted by all methods. Genomic rationale for synergy predictions are identified, including ADAM17 inhibitor antagonism when combined with PIK3CB/D inhibition contrasting to synergy when combined with other PI3K-pathway inhibitors in PIK3CA mutant cells.We thank the Genomics of Drug Sensitivity in Cancer and COSMIC teams at the Wellcome Trust Sanger Institute for help with the preparation of the molecular data, Denes Turei for help with Omnipath, and Katjusa Koler for help with matching drug names across combination screens. We thank AstraZeneca for funding and provision of data to the DREAM Consortium to run the challenge, and funding from the European Union Horizon 2020 research (under grant agreement No 668858 PrECISE to J.S.R.), the Joint Research Center for Computational Biomedicine (which is partially funded by Bayer AG) to J.S.R., National Institute for Health Research (NIHR) Sheffield Biomedical Research Center, Premium Postdoctoral Fellowship Program of the Hungarian Academy of Sciences. M.G lab is supported by Wellcome Trust (102696 and 206194)

Inferred Ancestral Origin of Cancer Cell Lines Associates with Differential Drug Response

Disparities between risk, treatment outcomes and survival rates in cancer patients across the world may be attributed to socioeconomic factors. In addition, the role of ancestry is frequently discussed. In preclinical studies, high-throughput drug screens in cancer cell lines have empowered the identification of clinically relevant molecular biomarkers of drug sensitivity; however, the genetic ancestry from tissue donors has been largely neglected in this setting. In order to address this, here, we show that the inferred ancestry of cancer cell lines is conserved and may impact drug response in patients as a predictive covariate in high-throughput drug screens. We found that there are differential drug responses between European and East Asian ancestries, especially when treated with PI3K/mTOR inhibitors. Our finding emphasizes a new angle in precision medicine, as cancer intervention strategies should consider the germline landscape, thereby reducing the failure rate of clinical trials

Delta-like 4 is required for pulmonary vascular arborization and alveolarization in the developing lung

The molecular mechanisms by which endothelial cells (ECs) regulate pulmonary vascularization and contribute to alveolar epithelial cell development during lung morphogenesis remain unknown. We tested the hypothesis that delta-like 4 (DLL4), an EC Notch ligand, is critical for alveolarization by combining lung mapping and functional studies in human tissue and DLL4-haploinsufficient mice (Dll4+/lacz). DLL4 expressed in a PECAM-restricted manner in capillaries, arteries, and the alveolar septum from the canalicular to alveolar stage in mice and humans. Dll4 haploinsufficiency resulted in exuberant, nondirectional vascular patterning at E17.5 and P6, followed by smaller capillaries and fewer intermediate blood vessels at P14. Vascular defects coincided with polarization of lung EC expression toward JAG1-NICD-HES1 signature and decreased tip cell-like (Car4) markers. Dll4+/lacZ mice had impaired terminal bronchiole development at the canalicular stage and impaired alveolarization upon lung maturity. We discovered that alveolar type I cell (Aqp5) markers progressively decreased in Dll4+/lacZ mice after birth. Moreover, in human lung EC, DLL4 deficiency programmed a hypersprouting angiogenic phenotype cell autonomously. In conclusion, DLL4 is expressed from the canalicular to alveolar stage in mice and humans, and Dll4 haploinsufficiency programs dysmorphic microvascularization, impairing alveolarization. Our study reveals an obligate role for DLL4-regulated angiogenesis in distal lung morphogenesis