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.

A Degron Blocking Strategy Towards Improved CRL4CRBN Recruiting PROTAC Selectivity.

Small molecules inducing protein degradation are important pharmacological tools to interrogate complex biology and are rapidly translating into clinical agents. However, to fully realise the potential of these molecules, selectivity remains a limiting challenge. Herein, we addressed the issue of selectivity in the design of CRL4CRBN recruiting PROteolysis TArgeting Chimeras (PROTACs). Thalidomide derivatives used to generate CRL4CRBN recruiting PROTACs have well described intrinsic monovalent degradation profiles by inducing the recruitment of neo-substrates, such as GSPT1, Ikaros and Aiolos. We leveraged structural insights from known CRL4CRBN neo-substrates to attenuate and indeed remove this monovalent degradation function in well-known CRL4CRBN molecular glues degraders, namely CC-885 and Pomalidomide. We then applied these design principles on a previously published BRD9 PROTAC (dBRD9-A) and generated an analogue with improved selectivity profile. Finally, we implemented a computational modelling pipeline to show that our degron blocking design does not impact PROTAC-induced ternary complex formation. We believe that the tools and principles presented in this work will be valuable to support the development of targeted protein degradation.

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.

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.

canSAR: an updated cancer research and drug discovery knowledgebase.

canSAR (http://cansar.icr.ac.uk) is a publicly available, multidisciplinary, cancer-focused knowledgebase developed to support cancer translational research and drug discovery. canSAR integrates genomic, protein, pharmacological, drug and chemical data with structural biology, protein networks and druggability data. canSAR is widely used to rapidly access information and help interpret experimental data in a translational and drug discovery context. Here we describe major enhancements to canSAR including new data, improved search and browsing capabilities, new disease and cancer cell line summaries and new and enhanced batch analysis tools.

Distinctive Behaviors of Druggable Proteins in Cellular Networks.

The interaction environment of a protein in a cellular network is important in defining the role that the protein plays in the system as a whole, and thus its potential suitability as a drug target. Despite the importance of the network environment, it is neglected during target selection for drug discovery. Here, we present the first systematic, comprehensive computational analysis of topological, community and graphical network parameters of the human interactome and identify discriminatory network patterns that strongly distinguish drug targets from the interactome as a whole. Importantly, we identify striking differences in the network behavior of targets of cancer drugs versus targets from other therapeutic areas and explore how they may relate to successful drug combinations to overcome acquired resistance to cancer drugs. We develop, computationally validate and provide the first public domain predictive algorithm for identifying druggable neighborhoods based on network parameters. We also make available full predictions for 13,345 proteins to aid target selection for drug discovery. All target predictions are available through canSAR.icr.ac.uk. Underlying data and tools are available at https://cansar.icr.ac.uk/cansar/publications/druggable_network_neighbourhoods/.

Whole genome sequencing of matched primary and metastatic acral melanomas.

Next generation sequencing has enabled systematic discovery of mutational spectra in cancer samples. Here, we used whole genome sequencing to characterize somatic mutations and structural variation in a primary acral melanoma and its lymph node metastasis. Our data show that the somatic mutational rates in this acral melanoma sample pair were more comparable to the rates reported in cancer genomes not associated with mutagenic exposure than in the genome of a melanoma cell line or the transcriptome of melanoma short-term cultures. Despite the perception that acral skin is sun-protected, the dominant mutational signature in these samples is compatible with damage due to ultraviolet light exposure. A nonsense mutation in ERCC5 discovered in both the primary and metastatic tumors could also have contributed to the mutational signature through accumulation of unrepaired dipyrimidine lesions. However, evidence of transcription-coupled repair was suggested by the lower mutational rate in the transcribed regions and expressed genes. The primary and the metastasis are highly similar at the level of global gene copy number alterations, loss of heterozygosity and single nucleotide variation (SNV). Furthermore, the majority of the SNVs in the primary tumor were propagated in the metastasis and one nonsynonymous coding SNV and one splice site mutation appeared to arise de novo in the metastatic lesion.

Characterization of the genomic features and expressed fusion genes in micropapillary carcinomas of the breast.

Micropapillary carcinoma (MPC) is a rare histological special type of breast cancer, characterized by an aggressive clinical behaviour and a pattern of copy number aberrations (CNAs) distinct from that of grade- and oestrogen receptor (ER)-matched invasive carcinomas of no special type (IC-NSTs). The aims of this study were to determine whether MPCs are underpinned by a recurrent fusion gene(s) or mutations in 273 genes recurrently mutated in breast cancer. Sixteen MPCs were subjected to microarray-based comparative genomic hybridization (aCGH) analysis and Sequenom OncoCarta mutation analysis. Eight and five MPCs were subjected to targeted capture and RNA sequencing, respectively. aCGH analysis confirmed our previous observations about the repertoire of CNAs of MPCs. Sequencing analysis revealed a spectrum of mutations similar to those of luminal B IC-NSTs, and recurrent mutations affecting mitogen-activated protein kinase family genes and NBPF10. RNA-sequencing analysis identified 17 high-confidence fusion genes, eight of which were validated and two of which were in-frame. No recurrent fusions were identified in an independent series of MPCs and IC-NSTs. Forced expression of in-frame fusion genes (SLC2A1-FAF1 and BCAS4-AURKA) resulted in increased viability of breast cancer cells. In addition, genomic disruption of CDK12 caused by out-of-frame rearrangements was found in one MPC and in 13% of HER2-positive breast cancers, identified through a re-analysis of publicly available massively parallel sequencing data. In vitro analyses revealed that CDK12 gene disruption results in sensitivity to PARP inhibition, and forced expression of wild-type CDK12 in a CDK12-null cell line model resulted in relative resistance to PARP inhibition. Our findings demonstrate that MPCs are neither defined by highly recurrent mutations in the 273 genes tested, nor underpinned by a recurrent fusion gene. Although seemingly private genetic events, some of the fusion transcripts found in MPCs may play a role in maintenance of a malignant phenotype and potentially offer therapeutic opportunities.

Genome-wide functional screen identifies a compendium of genes affecting sensitivity to tamoxifen.

Therapies that target estrogen signaling have made a very considerable contribution to reducing mortality from breast cancer. However, resistance to tamoxifen remains a major clinical problem. Here we have used a genome-wide functional profiling approach to identify multiple genes that confer resistance or sensitivity to tamoxifen. Combining whole-genome shRNA screening with massively parallel sequencing, we have profiled the impact of more than 56,670 RNA interference reagents targeting 16,487 genes on the cellular response to tamoxifen. This screen, along with subsequent validation experiments, identifies a compendium of genes whose silencing causes tamoxifen resistance (including BAP1, CLPP, GPRC5D, NAE1, NF1, NIPBL, NSD1, RAD21, RARG, SMC3, and UBA3) and also a set of genes whose silencing causes sensitivity to this endocrine agent (C10orf72, C15orf55/NUT, EDF1, ING5, KRAS, NOC3L, PPP1R15B, RRAS2, TMPRSS2, and TPM4). Multiple individual genes, including NF1, a regulator of RAS signaling, also correlate with clinical outcome after tamoxifen treatment.

Analysis of 10,478 cancer genomes identifies candidate driver genes and opportunities for precision oncology.

Tumor genomic profiling is increasingly seen as a prerequisite to guide the treatment of patients with cancer. To explore the value of whole-genome sequencing (WGS) in broadening the scope of cancers potentially amenable to a precision therapy, we analysed whole-genome sequencing data on 10,478 patients spanning 35 cancer types recruited to the UK 100,000 Genomes Project. We identified 330 candidate driver genes, including 74 that are new to any cancer. We estimate that approximately 55% of patients studied harbor at least one clinically relevant mutation, predicting either sensitivity or resistance to certain treatments or clinical trial eligibility. By performing computational chemogenomic analysis of cancer mutations we identify additional targets for compounds that represent attractive candidates for future clinical trials. This study represents one of the most comprehensive efforts thus far to identify cancer driver genes in the real world setting and assess their impact on informing precision oncology.

Erratum: iTAG an optimized IMiD-induced degron for targeted protein degradation in human and murine cells.

[This corrects the article DOI: 10.1016/j.isci.2023.107059.].

ROCK: a resource for integrative breast cancer data analysis.

Given the steady increase in breast cancer rates in both the developed and developing world, there has been a concerted research effort undertaken worldwide to understand the molecular mechanisms underpinning the disease. The data generated from numerous clinical trials and experimental studies shed light on different aspects of the disease. We present a new version of the ROCK database (rock.icr.ac.uk), which integrates such diverse data types allowing unique analyses of published breast cancer experimental data. We have added several new data types and analysis modules to ROCK, which allow the user to interactively query and research the huge amounts of available experimental data and perform complex correlations across studies and data types such as gene expression, genomic copy number aberrations, micro RNA expression, RNA interference, survival analysis, clinical annotation and signalling protein networks. We present the recent and major functional updates and enhancements to the ROCK resource, including new analysis modules and microRNA and NGS data integration, and illustrate how ROCK can be used to confirm known experimental results as well as generate novel leads and new experimental hypotheses using the Wnt signalling cell surface receptor FZD7 and the Myc oncogene. ROCK provides a unique breast cancer analysis platform of integrated experimental datasets at the genomic, transcriptomic and proteomic level. This paper presents how ROCK has transitioned from being simply a database to an interactive resource useful to the broader breast cancer research community in our effort to facilitate research into the underlying molecular mechanisms of breast cancer.

iTAG an optimized IMiD-induced degron for targeted protein degradation in human and murine cells.

To address the limitation associated with degron based systems, we have developed iTAG, a synthetic tag based on IMiDs/CELMoDs mechanism of action that improves and addresses the limitations of both PROTAC and previous IMiDs/CeLMoDs based tags. Using structural and sequence analysis, we systematically explored native and chimeric degron containing domains (DCDs) and evaluated their ability to induce degradation. We identified the optimal chimeric iTAG(DCD23 60aa) that elicits robust degradation of targets across cell types and subcellular localizations without exhibiting the well documented "hook effect" of PROTAC-based systems. We showed that iTAG can also induce target degradation by murine CRBN and enabled the exploration of natural neo-substrates that can be degraded by murine CRBN. Hence, the iTAG system constitutes a versatile tool to degrade targets across the human and murine proteome.

Whole genome in vivo RNAi screening identifies the leukemia inhibitory factor receptor as a novel breast tumor suppressor.

Cancer is caused by mutations in oncogenes and tumor suppressor genes, resulting in the deregulation of processes fundamental to the normal behavior of cells. The identification and characterization of oncogenes and tumor suppressors has led to new treatment strategies that have significantly improved cancer outcome. The advent of next generation sequencing has allowed the elucidation of the fine structure of cancer genomes, however, the identification of pathogenic changes is complicated by the inherent genomic instability of cancer cells. Therefore, functional approaches for the identification of novel genes involved in the initiation and development of tumors are critical. Here we report the first whole human genome in vivo RNA interference screen to identify functionally important tumor suppressor genes. Using our novel approach, we identify previously validated tumor suppressor genes including TP53 and MNT, as well as several novel candidate tumor suppressor genes including leukemia inhibitory factor receptor (LIFR). We show that LIFR is a key novel tumor suppressor, whose deregulation may drive the transformation of a significant proportion of human breast cancers. These results demonstrate the power of genome wide in vivo RNAi screens as a method for identifying novel genes regulating tumorigenesis.

Transcriptome analysis of embryonic mammary cells reveals insights into mammary lineage establishment.

INTRODUCTION: The mammary primordium forms during embryogenesis as a result of inductive interactions between its constitutive tissues, the mesenchyme and epithelium, and represents the earliest evidence of commitment to the mammary lineage. Previous studies of embryonic mouse mammary epithelium indicated that, by mid-gestation, these cells are determined to a mammary cell fate and that a stem cell population has been delimited. Mammary mesenchyme can induce mammary development from simple epithelium even across species and classes, and can partially restore features of differentiated tissue to mouse mammary tumours in co-culture experiments. Despite these exciting properties, the molecular identity of embryonic mammary cells remains to be fully characterised. METHODS: Here, we define the transcriptome of the mammary primordium and the two distinct cellular compartments that comprise it, the mammary primordial bud epithelium and mammary mesenchyme. Pathway and network analysis was performed and comparisons of embryonic mammary gene expression profiles to those of both postnatal mouse and human mammary epithelial cell sub-populations and stroma were made. RESULTS: Several of the genes we have detected in our embryonic mammary cell signatures were previously shown to regulate mammary cell fate and development, but we also identified a large number of novel candidates. Additionally, we determined genes that were expressed by both embryonic and postnatal mammary cells, which represent candidate regulators of mammary cell fate, differentiation and progenitor cell function that could signal from mammary lineage inception during embryogenesis through postnatal development. Comparison of embryonic mammary cell signatures with those of human breast cells identified potential regulators of mammary progenitor cell functions conserved across species. CONCLUSIONS: These results provide new insights into genetic regulatory mechanisms of mammary development, particularly identification of novel potential regulators of mammary fate and mesenchymal-epithelial cross-talk. Since cancers may represent diseases of mesenchymal-epithelial communications, we anticipate these results will provide foundations for further studies into the fundamental links between developmental, stem cell and breast cancer biology.

ROCK: a breast cancer functional genomics resource.

The clinical and pathological heterogeneity of breast cancer has instigated efforts to stratify breast cancer sub-types according to molecular profiles. These profiling efforts are now being augmented by large-scale functional screening of breast tumour cell lines, using approaches such as RNA interference. We have developed ROCK ( rock.icr.ac.uk ) to provide a unique, publicly accessible resource for the integration of breast cancer functional and molecular profiling datasets. ROCK provides a simple online interface for the navigation and cross-correlation of gene expression, aCGH and RNAi screen data. It enables the interrogation of gene lists in the context of statistically analysed functional genomic datasets, interaction networks, pathways, GO terms, mutations and drug targets. The interface also provides interactive visualisations of datasets and interaction networks. ROCK collates data from a wealth of breast cancer molecular profiling and functional screening studies into a single portal, where analysed and annotated results can be accessed at the level of a gene, sample or study. We believe that portals such as ROCK will not only afford researchers rapid access to profiling data, but also aid the integration of different data types, thus enhancing the discovery of novel targets and biomarkers for breast cancer.

Transcriptome analysis of mammary epithelial subpopulations identifies novel determinants of lineage commitment and cell fate.

BACKGROUND: Understanding the molecular control of cell lineages and fate determination in complex tissues is key to not only understanding the developmental biology and cellular homeostasis of such tissues but also for our understanding and interpretation of the molecular pathology of diseases such as cancer. The prerequisite for such an understanding is detailed knowledge of the cell types that make up such tissues, including their comprehensive molecular characterisation. In the mammary epithelium, the bulk of the tissue is composed of three cell lineages, namely the basal/myoepithelial, luminal epithelial estrogen receptor positive and luminal epithelial estrogen receptor negative cells. However, a detailed molecular characterisation of the transcriptomic differences between these three populations has not been carried out. RESULTS: A whole transcriptome analysis of basal/myoepithelial cells, luminal estrogen receptor negative cells and luminal estrogen receptor positive cells isolated from the virgin mouse mammary epithelium identified 861, 326 and 488 genes as highly differentially expressed in the three cell types, respectively. Network analysis of the transcriptomic data identified a subpopulation of luminal estrogen receptor negative cells with a novel potential role as non-professional immune cells. Analysis of the data for potential paracrine interacting factors showed that the basal/myoepithelial cells, remarkably, expressed over twice as many ligands and cell surface receptors as the other two populations combined. A number of transcriptional regulators were also identified that were differentially expressed between the cell lineages. One of these, Sox6, was specifically expressed in luminal estrogen receptor negative cells and functional assays confirmed that it maintained mammary epithelial cells in a differentiated luminal cell lineage. CONCLUSION: The mouse mammary epithelium is composed of three main cell types with distinct gene expression patterns. These suggest the existence of a novel functional cell type within the gland, that the basal/myoepithelial cells are key regulators of paracrine signalling and that there is a complex network of differentially expressed transcription factors controlling mammary epithelial cell fate. These data will form the basis for understanding not only cell fate determination and cellular homeostasis in the normal mammary epithelium but also the contribution of different mammary epithelial cell types to the etiology and molecular pathology of breast disease.

SiGNet: A signaling network data simulator to enable signaling network inference.

Network models are widely used to describe complex signaling systems. Cellular wiring varies in different cellular contexts and numerous inference techniques have been developed to infer the structure of a network from experimental data of the network's behavior. To objectively identify which inference strategy is best suited to a specific network, a gold standard network and dataset are required. However, suitable datasets for benchmarking are difficult to find. Numerous tools exist that can simulate data for transcriptional networks, but these are of limited use for the study of signaling networks. Here, we describe SiGNet (Signal Generator for Networks): a Cytoscape app that simulates experimental data for a signaling network of known structure. SiGNet has been developed and tested against published experimental data, incorporating information on network architecture, and the directionality and strength of interactions to create biological data in silico. SiGNet is the first tool to simulate biological signaling data, enabling an accurate and systematic assessment of inference strategies. SiGNet can also be used to produce preliminary models of key biological pathways following perturbation.

An in vivo functional screen identifies ST6GalNAc2 sialyltransferase as a breast cancer metastasis suppressor.

To interrogate the complex mechanisms involved in the later stages of cancer metastasis, we designed a functional in vivo RNA interference (RNAi) screen combined with next-generation sequencing. Using this approach, we identified the sialyltransferase ST6GalNAc2 as a novel breast cancer metastasis suppressor. Mechanistically, ST6GalNAc2 silencing alters the profile of O-glycans on the tumor cell surface, facilitating binding of the soluble lectin galectin-3. This then enhances tumor cell retention and emboli formation at metastatic sites leading to increased metastatic burden, events that can be completely blocked by galectin-3 inhibition. Critically, elevated ST6GALNAC2, but not galectin-3, expression in estrogen receptor-negative breast cancers significantly correlates with reduced frequency of metastatic events and improved survival. These data demonstrate that the prometastatic role of galectin-3 is regulated by its ability to bind to the tumor cell surface and highlight the potential of monitoring ST6GalNAc2 expression to stratify patients with breast cancer for treatment with galectin-3 inhibitors.

A modified method for whole exome resequencing from minimal amounts of starting DNA.

Next generation DNA sequencing (NGS) technologies have revolutionized the pace at which whole genome and exome sequences can be generated. However, despite these advances, many of the methods for targeted resequencing, such as the generation of high-depth exome sequences, are somewhat limited by the relatively large amounts of starting DNA that are normally required. In the case of tumour analysis this is particularly pertinent as many tumour biopsies often return submicrogram quantities of DNA, especially when tumours are microdissected prior to analysis. Here, we present a method for exome capture and resequencing using as little as 50 ng of starting DNA. The sequencing libraries generated by this minimal starting amount (MSA-Cap) method generate datasets that are comparable to standard amount (SA) whole exome libraries that use three micrograms of starting DNA. This method, which can be performed in most laboratories using commonly available reagents, has the potential to enhance large scale profiling efforts such as the resequencing of tumour exomes.