Impact of normalization methods on high-throughput screening data with high hit rates and drug testing with dose-response data

Peer reviewe

Data-analysis strategies for image-based cell profiling

Image-based cell profiling is a high-throughput strategy for the quantification of phenotypic differences among a variety of cell populations. It paves the way to studying biological systems on a large scale by using chemical and genetic perturbations. The general workflow for this technology involves image acquisition with high-throughput microscopy systems and subsequent image processing and analysis. Here, we introduce the steps required to create high-quality image-based (i.e., morphological) profiles from a collection of microscopy images. We recommend techniques that have proven useful in each stage of the data analysis process, on the basis of the experience of 20 laboratories worldwide that are refining their image-based cell-profiling methodologies in pursuit of biological discovery. The recommended techniques cover alternatives that may suit various biological goals, experimental designs, and laboratories' preferences.Peer reviewe

Rethinking drug design in the artificial intelligence era

Artificial intelligence (AI) tools are increasingly being applied in drug discovery. While some protagonists point to vast opportunities potentially offered by such tools, others remain sceptical, waiting for a clear impact to be shown in drug discovery projects. The reality is probably somewhere in-between these extremes, yet it is clear that AI is providing new challenges not only for the scientists involved but also for the biopharma industry and its established processes for discovering and developing new medicines. This article presents the views of a diverse group of international experts on the 'grand challenges' in small-molecule drug discovery with AI and the approaches to address them

Data-analysis strategies for image-based cell profiling

Image-based cell profiling is a high-throughput strategy for the quantification of phenotypic differences among a variety of cell populations. It paves the way to studying biological systems on a large scale by using chemical and genetic perturbations. The general workflow for this technology involves image acquisition with high-throughput microscopy systems and subsequent image processing and analysis. Here, we introduce the steps required to create high-quality image-based (i.e., morphological) profiles from a collection of microscopy images. We recommend techniques that have proven useful in each stage of the data analysis process, on the basis of the experience of 20 laboratories worldwide that are refining their image-based cell-profiling methodologies in pursuit of biological discovery. The recommended techniques cover alternatives that may suit various biological goals, experimental designs, and laboratories' preferences

Image informatics approaches to advance cancer drug discovery

High content image-based screening assays utilise cell based models to extract and quantify morphological phenotypes induced by small molecules. The rich datasets produced can be used to identify lead compounds in drug discovery efforts, infer compound mechanism of action, or aid biological understanding with the use of tool compounds. Here I present my work developing and applying high-content image based screens of small molecules across a panel of eight genetically and morphologically distinct breast cancer cell lines. I implemented machine learning models to predict compound mechanism of action from morphological data and assessed how well these models transfer to unseen cell lines, comparing the use of numeric morphological features extracted using computer vision techniques against more modern convolutional neural networks acting on raw image data. The application of cell line panels have been widely used in pharmacogenomics in order to compare the sensitivity between genetically distinct cell lines to drug treatments and identify molecular biomarkers that predict response. I applied dimensional reduction techniques and distance metrics to develop a measure of differential morphological response between cell lines to small molecule treatment, which controls for the inherent morphological differences between untreated cell lines. These methods were then applied to a screen of 13,000 lead-like small molecules across the eight cell lines to identify compounds which produced distinct phenotypic responses between cell lines. Putative hits from a subset of approved compounds were then validated in a three-dimensional tumour spheroid assay to determine the functional effect of these compounds in more complex models, as well as proteomics to determine the responsible pathways. Using data generated from the compound screen, I carried out work towards integrating knowledge of chemical structures with morphological data to infer mechanistic information of the unannotated compounds, and assess structure activity relationships from cell-based imaging data

Identifying and targeting LIM-domain loss in clear cell renal cell carcinoma

Sequential use of targeted therapies has significantly improved overall survival in metastatic clear cell renal cell carcinoma (ccRCC) but durable responses remain rare. Improved prognostic and predictive algorithms are required. 3p loss is near ubiquitous in ccRCC. Functions of LIMD1 include regulation of hypoxia-inducible factors and microRNA-mediated gene silencing. LIMD1 loss/deregulation contributes to lung tumour formation and is associated with poor prognosis in breast cancer. The closely related family members, WTIP and Ajuba also regulate the hypoxic response and mediate micro-RNA silencing. Ajuba regulates the Hippo signaling pathway, controlling cell cycle and proliferation. In vivo, loss of LIMD1 was observed in 49% of ccRCC samples. 76% of ccRCC tumours demonstrated reduced Ajuba staining and nuclear WTIP staining was reduced in 73% of tumours compared to matched control. Co-loss of LIMD1/Ajuba/WTIP was common. Using patient-derived tumour tissue from two prospective clinical trials, LIMD1, Ajuba and WTIP staining was correlated with clinico-pathological outcome data. With the exception of loss of Ajuba staining and tumour stage, staining and outcome data did not correlate. The effects of LIMD1 loss on tumourigenesis were investigated using a paired lentiviral transduction system in ccRCC lines. LIMD1 loss did not affect cell migration, or cell cycle, however loss of LIMD1 was associated with greater hypoxic deregulation. A CRISPR-Cas-9 gene editing system was used to successfully knockout LIMD1 and Ajuba in a renal primary cell line. Using a drug-screening platform, the topoisomerase-I inhibitor irinotecan was identified as a potentially synthetically lethal drug in association with LIMD1 loss. This was validated in a further ccRCC line and in non-renal lines. Exploiting synthetic lethal approaches in ccRCC treatment has not been widely explored. Our data suggests that loss of LIMD1/Ajuba/WTIP is common and could represent a predictive biomarker such that tumours with loss/low expression could be selectively targeted.Cancer Research U