Chemogenomic library design strategies for precision oncology, applied to phenotypic profiling of glioblastoma patient cells.

Designing a targeted screening library of bioactive small molecules is a challenging task since most compounds modulate their effects through multiple protein targets with varying degrees of potency and selectivity. We implemented analytic procedures for designing anticancer compound libraries adjusted for library size, cellular activity, chemical diversity and availability, and target selectivity. The resulting compound collections cover a wide range of protein targets and biological pathways implicated in various cancers, making them widely applicable to precision oncology. We characterized the compound and target spaces of the virtual libraries, in comparison with a minimal screening library of 1,211 compounds for targeting 1,386 anticancer proteins. In a pilot screening study, we identified patient-specific vulnerabilities by imaging glioma stem cells from patients with glioblastoma (GBM), using a physical library of 789 compounds that cover 1,320 of the anticancer targets. The cell survival profiling revealed highly heterogeneous phenotypic responses across the patients and GBM subtypes.

Discovery of host-directed modulators of virus infection by probing the SARS-CoV-2-host protein-protein interaction network.

The ongoing coronavirus disease 2019 (COVID-19) pandemic has highlighted the need to better understand virus-host interactions. We developed a network-based method that expands the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2)-host protein interaction network and identifies host targets that modulate viral infection. To disrupt the SARS-CoV-2 interactome, we systematically probed for potent compounds that selectively target the identified host proteins with high expression in cells relevant to COVID-19. We experimentally tested seven chemical inhibitors of the identified host proteins for modulation of SARS-CoV-2 infection in human cells that express ACE2 and TMPRSS2. Inhibition of the epigenetic regulators bromodomain-containing protein 4 (BRD4) and histone deacetylase 2 (HDAC2), along with ubiquitin-specific peptidase (USP10), enhanced SARS-CoV-2 infection. Such proviral effect was observed upon treatment with compounds JQ1, vorinostat, romidepsin and spautin-1, when measured by cytopathic effect and validated by viral RNA assays, suggesting that the host proteins HDAC2, BRD4 and USP10 have antiviral functions. We observed marked differences in antiviral effects across cell lines, which may have consequences for identification of selective modulators of viral infection or potential antiviral therapeutics. While network-based approaches enable systematic identification of host targets and selective compounds that may modulate the SARS-CoV-2 interactome, further developments are warranted to increase their accuracy and cell-context specificity.

Functional Testing to Characterize and Stratify PI3K Inhibitor Responses in Chronic Lymphocytic Leukemia.

PURPOSE: PI3K inhibitors (PI3Ki) are approved for relapsed chronic lymphocytic leukemia (CLL). Although patients may show an initial response to these therapies, development of treatment intolerance or resistance remain clinical challenges. To overcome these, prediction of individual treatment responses based on actionable biomarkers is needed. Here, we characterized the activity and cellular effects of 10 PI3Ki and investigated whether functional analyses can identify treatment vulnerabilities in PI3Ki-refractory/intolerant CLL and stratify responders to PI3Ki. EXPERIMENTAL DESIGN: Peripheral blood mononuclear cell samples (n = 51 in total) from treatment-naïve and PI3Ki-treated patients with CLL were studied. Cells were profiled against 10 PI3Ki and the Bcl-2 antagonist venetoclax. Cell signaling and immune phenotypes were analyzed by flow cytometry. Cell viability was monitored by detection of cleaved caspase-3 and the CellTiter-Glo assay. RESULTS: pan-PI3Kis were most effective at inhibiting PI3K signaling and cell viability, and showed activity in CLL cells from both treatment-naïve and idelalisib-refractory/intolerant patients. CLL cells from idelalisib-refractory/intolerant patients showed overall reduced protein phosphorylation levels. The pan-PI3Ki copanlisib, but not the p110δ inhibitor idelalisib, inhibited PI3K signaling in CD4+ and CD8+ T cells in addition to CD19+ B cells, but did not significantly affect T-cell numbers. Combination treatment with a PI3Ki and venetoclax resulted in synergistic induction of apoptosis. Analysis of drug sensitivities to 73 drug combinations and profiling of 31 proteins stratified responders to idelalisib and umbralisib, respectively. CONCLUSIONS: Our findings suggest novel treatment vulnerabilities in idelalisib-refractory/intolerant CLL, and indicate that ex vivo functional profiling may stratify PI3Ki responders.

Systematic review of computational methods for drug combination prediction.

Synergistic effects between drugs are rare and highly context-dependent and patient-specific. Hence, there is a need to develop novel approaches to stratify patients for optimal therapy regimens, especially in the context of personalized design of combinatorial treatments. Computational methods enable systematic in-silico screening of combination effects, and can thereby prioritize most potent combinations for further testing, among the massive number of potential combinations. To help researchers to choose a prediction method that best fits for various real-world applications, we carried out a systematic literature review of 117 computational methods developed to date for drug combination prediction, and classified the methods in terms of their combination prediction tasks and input data requirements. Most current methods focus on prediction or classification of combination synergy, and only a few methods consider the efficacy and potential toxicity of the combinations, which are the key determinants of therapeutic success of drug treatments. Furthermore, there is a need to further develop methods that enable dose-specific predictions of combination effects across multiple doses, which is important for clinical translation of the predictions, as well as model-based identification of biomarkers predictive of heterogeneous drug combination responses. Even if most of the computational methods reviewed focus on anticancer applications, many of the modelling approaches are also applicable to antiviral and other diseases or indications.

Computational Pipeline for Rational Drug Combination Screening in Patient-Derived Cells.

In many complex diseases, such as cancers, resistance to monotherapies easily occurs, and longer-term treatment responses often require combinatorial therapies as next-line regimens. However, due to a massive number of possible drug combinations to test, there is a need for systematic and rational approaches to finding safe and effective drug combinations for each individual patient. This protocol describes an ecosystem of computational methods to guide high-throughput combinatorial screening that help experimental researchers to identify optimal drug combinations in terms of synergy, efficacy, and/or selectivity for further preclinical and clinical investigation. The methods are demonstrated in the context of combinatorial screening in primary cells of leukemia patients, where the translational aim is to identify drug combinations that show not only high synergy but also maximal cancer-selectivity. The mechanism-agnostic and cost-effective computational methods are widely applicable to various cancer types, which are amenable to drug testing, as the computational methods take as input only the phenotypic measurements of a subset of drug combinations, without requiring target information or genomic profiles of the patient samples.