The Virtual Child.

SUMMARY: We are building the world's first Virtual Child-a computer model of normal and cancerous human development at the level of each individual cell. The Virtual Child will "develop cancer" that we will subject to unlimited virtual clinical trials that pinpoint, predict, and prioritize potential new treatments, bringing forward the day when no child dies of cancer, giving each one the opportunity to lead a full and healthy life.

Thio-2 Inhibits Key Signaling Pathways Required for the Development and Progression of Castration-resistant Prostate Cancer.

Therapies that abrogate persistent androgen receptor (AR) signaling in castration-resistant prostate cancer (CRPC) remain an unmet clinical need. The N-terminal domain of the AR that drives transcriptional activity in CRPC remains a challenging therapeutic target. Herein we demonstrate that BCL-2-associated athanogene-1 (BAG-1) mRNA is highly expressed and associates with signaling pathways, including AR signaling, that are implicated in the development and progression of CRPC. In addition, interrogation of geometric and physiochemical properties of the BAG domain of BAG-1 isoforms identifies it to be a tractable but challenging drug target. Furthermore, through BAG-1 isoform mouse knockout studies, we confirm that BAG-1 isoforms regulate hormone physiology and that therapies targeting the BAG domain will be associated with limited "on-target" toxicity. Importantly, the postulated inhibitor of BAG-1 isoforms, Thio-2, suppressed AR signaling and other important pathways implicated in the development and progression of CRPC to reduce the growth of treatment-resistant prostate cancer cell lines and patient-derived models. However, the mechanism by which Thio-2 elicits the observed phenotype needs further elucidation as the genomic abrogation of BAG-1 isoforms was unable to recapitulate the Thio-2-mediated phenotype. Overall, these data support the interrogation of related compounds with improved drug-like properties as a novel therapeutic approach in CRPC, and further highlight the clinical potential of treatments that block persistent AR signaling which are currently undergoing clinical evaluation in CRPC.

SwarmDeepSurv: swarm intelligence advances deep survival network for prognostic radiomics signatures in four solid cancers.

Survival models exist to study relationships between biomarkers and treatment effects. Deep learning-powered survival models supersede the classical Cox proportional hazards (CoxPH) model, but substantial performance drops were observed on high-dimensional features because of irrelevant/redundant information. To fill this gap, we proposed SwarmDeepSurv by integrating swarm intelligence algorithms with the deep survival model. Furthermore, four objective functions were designed to optimize prognostic prediction while regularizing selected feature numbers. When testing on multicenter sets (n = 1,058) of four different cancer types, SwarmDeepSurv was less prone to overfitting and achieved optimal patient risk stratification compared with popular survival modeling algorithms. Strikingly, SwarmDeepSurv selected different features compared with classical feature selection algorithms, including the least absolute shrinkage and selection operator (LASSO), with nearly no feature overlapping across these models. Taken together, SwarmDeepSurv offers an alternative approach to model relationships between radiomics features and survival endpoints, which can further extend to study other input data types including genomics.

canSAR: update to the cancer translational research and drug discovery knowledgebase.

canSAR (https://cansar.ai) is the largest public cancer drug discovery and translational research knowledgebase. Now hosted in its new home at MD Anderson Cancer Center, canSAR integrates billions of experimental measurements from across molecular profiling, pharmacology, chemistry, structural and systems biology. Moreover, canSAR applies a unique suite of machine learning algorithms designed to inform drug discovery. Here, we describe the latest updates to the knowledgebase, including a focus on significant novel data. These include canSAR's ligandability assessment of AlphaFold; mapping of fragment-based screening data; and new chemical bioactivity data for novel targets. We also describe enhancements to the data and interface.

Individualized Prediction of Drug Response and Rational Combination Therapy in NSCLC Using Artificial Intelligence-Enabled Studies of Acute Phosphoproteomic Changes.

We hypothesize that the study of acute protein perturbation in signal transduction by targeted anticancer drugs can predict drug sensitivity of these agents used as single agents and rational combination therapy. We assayed dynamic changes in 52 phosphoproteins caused by an acute exposure (1 hour) to clinically relevant concentrations of seven targeted anticancer drugs in 35 non-small cell lung cancer (NSCLC) cell lines and 16 samples of NSCLC cells isolated from pleural effusions. We studied drug sensitivities across 35 cell lines and synergy of combinations of all drugs in six cell lines (252 combinations). We developed orthogonal machine-learning approaches to predict drug response and rational combination therapy. Our methods predicted the most and least sensitive quartiles of drug sensitivity with an AUC of 0.79 and 0.78, respectively, whereas predictions based on mutations in three genes commonly known to predict response to the drug studied, for example, EGFR, PIK3CA, and KRAS, did not predict sensitivity (AUC of 0.5 across all quartiles). The machine-learning predictions of combinations that were compared with experimentally generated data showed a bias to the highest quartile of Bliss synergy scores (P = 0.0243). We confirmed feasibility of running such assays on 16 patient samples of freshly isolated NSCLC cells from pleural effusions. We have provided proof of concept for novel methods of using acute ex vivo exposure of cancer cells to targeted anticancer drugs to predict response as single agents or combinations. These approaches could complement current approaches using gene mutations/amplifications/rearrangements as biomarkers and demonstrate the utility of proteomics data to inform treatment selection in the clinic.

Evolution of kinase polypharmacology across HSP90 drug discovery.

Most small molecules interact with several target proteins but this polypharmacology is seldom comprehensively investigated or explicitly exploited during drug discovery. Here, we use computational and experimental methods to identify and systematically characterize the kinase cross-pharmacology of representative HSP90 inhibitors. We demonstrate that the resorcinol clinical candidates ganetespib and, to a lesser extent, luminespib, display unique off-target kinase pharmacology as compared with other HSP90 inhibitors. We also demonstrate that polypharmacology evolved during the optimization to discover luminespib and that the hit, leads, and clinical candidate all have different polypharmacological profiles. We therefore recommend the computational and experimental characterization of polypharmacology earlier in drug discovery projects to unlock new multi-target drug design opportunities.

JMJD6 Is a Druggable Oxygenase That Regulates AR-V7 Expression in Prostate Cancer.

Endocrine resistance (EnR) in advanced prostate cancer is fatal. EnR can be mediated by androgen receptor (AR) splice variants, with AR splice variant 7 (AR-V7) arguably the most clinically important variant. In this study, we determined proteins key to generating AR-V7, validated our findings using clinical samples, and studied splicing regulatory mechanisms in prostate cancer models. Triangulation studies identified JMJD6 as a key regulator of AR-V7, as evidenced by its upregulation with in vitro EnR, its downregulation alongside AR-V7 by bromodomain inhibition, and its identification as a top hit of a targeted siRNA screen of spliceosome-related genes. JMJD6 protein levels increased (P < 0.001) with castration resistance and were associated with higher AR-V7 levels and shorter survival (P = 0.048). JMJD6 knockdown reduced prostate cancer cell growth, AR-V7 levels, and recruitment of U2AF65 to AR pre-mRNA. Mutagenesis studies suggested that JMJD6 activity is key to the generation of AR-V7, with the catalytic machinery residing within a druggable pocket. Taken together, these data highlight the relationship between JMJD6 and AR-V7 in advanced prostate cancer and support further evaluation of JMJD6 as a therapeutic target in this disease. SIGNIFICANCE: This study identifies JMJD6 as being critical for the generation of AR-V7 in prostate cancer, where it may serve as a tractable target for therapeutic intervention.

canSAR: update to the cancer translational research and drug discovery knowledgebase.

canSAR (http://cansar.icr.ac.uk) is the largest, public, freely available, integrative translational research and drug discovery knowledgebase for oncology. canSAR integrates vast multidisciplinary data from across genomic, protein, pharmacological, drug and chemical data with structural biology, protein networks and more. It also provides unique data, curation and annotation and crucially, AI-informed target assessment for drug discovery. canSAR is widely used internationally by academia and industry. Here we describe significant developments and enhancements to the data, web interface and infrastructure of canSAR in the form of the new implementation of the system: canSARblack. We demonstrate new functionality in aiding translation hypothesis generation and experimental design, and show how canSAR can be adapted and utilised outside oncology.

Tuning Local Hydration Enables a Deeper Understanding of Protein-Ligand Binding: The PP1-Src Kinase Case.

Water plays a key role in biomolecular recognition and binding. Despite the development of several computational and experimental approaches, it is still challenging to comprehensively characterize water-mediated effects on the binding process. Here, we investigate how water affects the binding of Src kinase to one of its inhibitors, PP1. Src kinase is a target for treating several diseases, including cancer. We use biased molecular dynamics simulations, where the hydration of predetermined regions is tuned at will. This computational technique efficiently accelerates the SRC-PP1 binding simulation and allows us to identify several key and yet unexplored aspects of the solvent's role. This study provides a further perspective on the binding phenomenon, which may advance the current drug design approaches for the development of new kinase inhibitors.

Solution structure of the Hop TPR2A domain and investigation of target druggability by NMR, biochemical and in silico approaches.

Heat shock protein 90 (Hsp90) is a molecular chaperone that plays an important role in tumour biology by promoting the stabilisation and activity of oncogenic 'client' proteins. Inhibition of Hsp90 by small-molecule drugs, acting via its ATP hydrolysis site, has shown promise as a molecularly targeted cancer therapy. Owing to the importance of Hop and other tetratricopeptide repeat (TPR)-containing cochaperones in regulating Hsp90 activity, the Hsp90-TPR domain interface is an alternative site for inhibitors, which could result in effects distinct from ATP site binders. The TPR binding site of Hsp90 cochaperones includes a shallow, positively charged groove that poses a significant challenge for druggability. Herein, we report the apo, solution-state structure of Hop TPR2A which enables this target for NMR-based screening approaches. We have designed prototype TPR ligands that mimic key native 'carboxylate clamp' interactions between Hsp90 and its TPR cochaperones and show that they block binding between Hop TPR2A and the Hsp90 C-terminal MEEVD peptide. We confirm direct TPR-binding of these ligands by mapping 1H-15N HSQC chemical shift perturbations to our new NMR structure. Our work provides a novel structure, a thorough assessment of druggability and robust screening approaches that may offer a potential route, albeit difficult, to address the chemically challenging nature of the Hop TPR2A target, with relevance to other TPR domain interactors.

Correction: Signalling involving MET and FAK supports cell division independent of the activity of the cell cycle-regulating CDK4/6 kinases.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

The kinase polypharmacology landscape of clinical PARP inhibitors.

Polypharmacology plays an important role in defining response and adverse effects of drugs. For some mechanisms, experimentally mapping polypharmacology is commonplace, although this is typically done within the same protein class. Four PARP inhibitors have been approved by the FDA as cancer therapeutics, yet a precise mechanistic rationale to guide clinicians on which to choose for a particular patient is lacking. The four drugs have largely similar PARP family inhibition profiles, but several differences at the molecular and clinical level have been reported that remain poorly understood. Here, we report the first comprehensive characterization of the off-target kinase landscape of four FDA-approved PARP drugs. We demonstrate that all four PARP inhibitors have a unique polypharmacological profile across the kinome. Niraparib and rucaparib inhibit DYRK1s, CDK16 and PIM3 at clinically achievable, submicromolar concentrations. These kinases represent the most potently inhibited off-targets of PARP inhibitors identified to date and should be investigated further to clarify their potential implications for efficacy and safety in the clinic. Moreover, broad kinome profiling is recommended for the development of PARP inhibitors as PARP-kinase polypharmacology could potentially be exploited to modulate efficacy and side-effect profiles.

A tailored molecular profiling programme for children with cancer to identify clinically actionable genetic alterations.

BACKGROUND: For children with cancer, the clinical integration of precision medicine to enable predictive biomarker-based therapeutic stratification is urgently needed. METHODS: We have developed a hybrid-capture next-generation sequencing (NGS) panel, specifically designed to detect genetic alterations in paediatric solid tumours, which gives reliable results from as little as 50 ng of DNA extracted from formalin-fixed paraffin-embedded (FFPE) tissue. In this study, we offered an NGS panel, with clinical reporting via a molecular tumour board for children with solid tumours. Furthermore, for a cohort of 12 patients, we used a circulating tumour DNA (ctDNA)-specific panel to sequence ctDNA from matched plasma samples and compared plasma and tumour findings. RESULTS: A total of 255 samples were submitted from 223 patients for the NGS panel. Using FFPE tissue, 82% of all submitted samples passed quality control for clinical reporting. At least one genetic alteration was detected in 70% of sequenced samples. The overall detection rate of clinically actionable alterations, defined by modified OncoKB criteria, for all sequenced samples was 51%. A total of 8 patients were sequenced at different stages of treatment. In 6 of these, there were differences in the genetic alterations detected between time points. Sequencing of matched ctDNA in a cohort of extracranial paediatric solid tumours also identified a high detection rate of somatic alterations in plasma. CONCLUSION: We demonstrate that tailored clinical molecular profiling of both tumour DNA and plasma-derived ctDNA is feasible for children with solid tumours. Furthermore, we show that a targeted NGS panel-based approach can identify actionable genetic alterations in a high proportion of patients.

Signalling involving MET and FAK supports cell division independent of the activity of the cell cycle-regulating CDK4/6 kinases.

Deregulation of cyclin-dependent kinases 4 and 6 (CDK4/6) is highly prevalent in cancer; yet, inhibitors against these kinases are currently used only in restricted tumour contexts. The extent to which cancers depend on CDK4/6 and the mechanisms that may undermine such dependency are poorly understood. Here, we report that signalling engaging the MET proto-oncogene receptor tyrosine kinase/focal adhesion kinase (FAK) axis leads to CDK4/6-independent CDK2 activation, involving as critical mechanistic events loss of the CDKI p21CIP1 and gain of its regulator, the ubiquitin ligase subunit SKP2. Combined inhibition of MET/FAK and CDK4/6 eliminates the proliferation capacity of cancer cells in culture, and enhances tumour growth inhibition in vivo. Activation of the MET/FAK axis is known to arise through cancer extrinsic and intrinsic cues. Our work predicts that such cues support cell division independent of the activity of the cell cycle-regulating CDK4/6 kinases and identifies MET/FAK as a tractable route to broaden the utility of CDK4/6 inhibitor-based therapies in the clinic.

Differences in Signaling Patterns on PI3K Inhibition Reveal Context Specificity in KRAS-Mutant Cancers.

It is increasingly appreciated that drug response to different cancers driven by the same oncogene is different and may relate to differences in rewiring of signal transduction. We aimed to study differences in dynamic signaling changes within mutant KRAS (KRAS MT), non-small cell lung cancer (NSCLC), colorectal cancer, and pancreatic ductal adenocarcinoma (PDAC) cells. We used an antibody-based phosphoproteomic platform to study changes in 50 phosphoproteins caused by seven targeted anticancer drugs in a panel of 30 KRAS MT cell lines and cancer cells isolated from 10 patients with KRAS MT cancers. We report for the first time significant differences in dynamic signaling between colorectal cancer and NSCLC cell lines exposed to clinically relevant equimolar concentrations of the pan-PI3K inhibitor pictilisib including a lack of reduction of p-AKTser473 in colorectal cancer cell lines (P = 0.037) and lack of compensatory increase in p-MEK in NSCLC cell lines (P = 0.036). Differences in rewiring of signal transduction between tumor types driven by KRAS MT cancers exist and influence response to combination therapy using targeted agents.

canSAR: update to the cancer translational research and drug discovery knowledgebase.

canSAR (http://cansar.icr.ac.uk) is a public, freely available, integrative translational research and drug discovery knowlegebase. canSAR informs researchers to help solve key bottlenecks in cancer translation and drug discovery. It integrates genomic, protein, pharmacological, drug and chemical data with structural biology, protein networks and unique, comprehensive and orthogonal 'druggability' assessments. canSAR is widely used internationally by academia and industry. Here we describe major enhancements to canSAR including new and expanded data. We also describe the first components of canSARblack-an advanced, responsive, multi-device compatible redesign of canSAR with a question-led interface.

Leveraging Human Genetics to Guide Cancer Drug Development.

PURPOSE: The high attrition rate of cancer drug development programs is a barrier to realizing the promise of precision oncology. We have examined whether the genetic insights from genome-wide association studies of cancer can guide drug development and repurposing in oncology. MATERIALS AND METHODS: Across 37 cancers, we identified 955 genetic risk variants from the National Human Genome Research Institute-European Bioinformatics Institute genome-wide association study catalog. We linked these variants to target genes using strategies that were based on linkage disequilibrium, DNA three-dimensional structure, and integration of predicted gene function and expression. With the use of the Informa Pharmaprojects database, we identified genes that are targets of unique drugs and assessed the level of enrichment that would be afforded by incorporation of genetic information in preclinical and phase II studies. For targets not under development, we implemented machine learning approaches to assess druggability. RESULTS: For all preclinical targets incorporating genetic information, a 2.00-fold enrichment of a drug being successfully approved could be achieved (95% CI, 1.14- to 3.48-fold; P = .02). For phase II targets, a 2.75-fold enrichment could be achieved (95% CI, 1.42- to 5.35-fold; P < .001). Application of genetic information suggests potential repurposing of 15 approved nononcology drugs. CONCLUSION: The findings illustrate the value of using insights from the genetics of inherited cancer susceptibility discovery projects as part of a data-driven strategy to inform drug discovery. Support for cancer germline genetic information for prospective targets is available online from the Institute of Cancer Research.

Development of Bag-1L as a therapeutic target in androgen receptor-dependent prostate cancer.

Targeting the activation function-1 (AF-1) domain located in the N-terminus of the androgen receptor (AR) is an attractive therapeutic alternative to the current approaches to inhibit AR action in prostate cancer (PCa). Here we show that the AR AF-1 is bound by the cochaperone Bag-1L. Mutations in the AR interaction domain or loss of Bag-1L abrogate AR signaling and reduce PCa growth. Clinically, Bag-1L protein levels increase with progression to castration-resistant PCa (CRPC) and high levels of Bag-1L in primary PCa associate with a reduced clinical benefit from abiraterone when these tumors progress. Intriguingly, residues in Bag-1L important for its interaction with the AR AF-1 are within a potentially druggable pocket, implicating Bag-1L as a potential therapeutic target in PCa.