Large inherent variability in data derived from highly standardised cell culture experiments.

Cancer drug development is hindered by high clinical attrition rates, which are blamed on weak predictive power by preclinical models and limited replicability of preclinical findings. However, the technically feasible level of replicability remains unknown. To fill this gap, we conducted an analysis of data from the NCI60 cancer cell line screen (2.8 million compound/cell line experiments), which is to our knowledge the largest depository of experiments that have been repeatedly performed over decades. The findings revealed profound intra-laboratory data variability, although all experiments were executed following highly standardised protocols that avoid all known confounders of data quality. All compound/ cell line combinations with > 100 independent biological replicates displayed maximum GI50 (50% growth inhibition) fold changes (highest/ lowest GI50) > 5% and 70.5% displayed maximum fold changes > 1000. The highest maximum fold change was 3.16 × 1010 (lowest GI50: 7.93 ×10-10 µM, highest GI50: 25.0 µM). FDA-approved drugs and experimental agents displayed similar variation. Variability remained high after outlier removal, when only considering experiments that tested drugs at the same concentration range, and when only considering NCI60-provided quality-controlled data. In conclusion, high variability is an intrinsic feature of anti-cancer drug testing, even among standardised experiments in a world-leading research environment. Awareness of this inherent variability will support realistic data interpretation and inspire research to improve data robustness. Further research will have to show whether the inclusion of a wider variety of model systems, such as animal and/ or patient-derived models, may improve data robustness.

Changes associated with Ebola virus adaptation to novel species.

MOTIVATION: Ebola viruses are not pathogenic but can be adapted to replicate and cause disease in rodents. Here, we used a structural bioinformatics approach to analyze the mutations associated with Ebola virus adaptation to rodents to elucidate the determinants of host-specific Ebola virus pathogenicity. RESULTS: We identified 33 different mutations associated with Ebola virus adaptation to rodents in the proteins GP, NP, L, VP24 and VP35. Only VP24, GP and NP were consistently found mutated in rodent-adapted Ebola virus strains. Fewer than five mutations in these genes seem to be required for the adaptation of Ebola viruses to a new species. The role of mutations in GP and NP is not clear. However, three VP24 mutations located in the protein interface with karyopherin α5 may enable VP24 to inhibit karyopherins and subsequently the host interferon response. Three further VP24 mutations change hydrogen bonding or cause conformational changes. Hence, there is evidence that few mutations including crucial mutations in VP24 enable Ebola virus adaptation to new hosts. Since Reston virus, the only non-human pathogenic Ebolavirus species circulates in pigs in Asia, this raises concerns that few mutations may result in novel human pathogenic Ebolaviruses. CONTACT: m.n.wass@kent.ac.uk , m.michaelis@kent.ac.uk or j.s.rossman@kent.ac.uk. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Epigenetic profiling of prostate cancer reveals potential prognostic signatures.

PURPOSE: While epigenetic profiling discovered biomarkers in several tumor entities, its application in prostate cancer is still limited. We explored DNA methylation-based deconvolution of benign and malignant prostate tissue for biomarker discovery and the potential of radiomics as a non-invasive surrogate. METHODS: We retrospectively included 30 patients (63 [58-79] years) with prostate cancer (PCa) who had a multiparametric MRI of the prostate before radical prostatectomy between 2014 and 2019. The control group comprised four patients with benign prostate tissue adjacent to the PCa lesions and four patients with benign prostatic hyperplasia. Tissue punches of all lesions were obtained. DNA methylation analysis and reference-free in silico deconvolution were conducted to retrieve Latent Methylation Components (LCMs). LCM-based clustering was analyzed for cellular composition and correlated with clinical disease parameters. Additionally, PCa and adjacent benign lesions were analyzed using radiomics to predict the epigenetic signatures non-invasively. RESULTS: LCMs identified two clusters with potential prognostic impact. Cluster one was associated with malignant prostate tissue (p < 0.001) and reduced immune-cell-related signatures (p = 0.004) of CD19 and CD4 cells. Cluster one comprised exclusively malignant prostate tissue enriched for significant prostate cancer and advanced tumor stages (p < 0.03 for both). No radiomics model could non-invasively predict the epigenetic clusters. CONCLUSION: Epigenetic clusters were associated with prognostically and clinically relevant metrics in prostate cancer. Further, immune cell-related signatures differed significantly between prognostically favorable and unfavorable clusters. Further research is necessary to explore potential diagnostic and therapeutic implications.