Large-Scale Gene Expression Profiling Platform for Identification of Context-Dependent Drug Responses in Multicellular Tumor Spheroids

p53 stabilisation potentiates [177Lu]Lu-DOTATATE treatment in neuroblastoma xenografts

PURPOSE

Molecular radiotherapy is a treatment modality that is highly suitable for targeting micrometastases and [177Lu]Lu-DOTATATE is currently being explored as a potential novel treatment option for high-risk neuroblastoma. p53 is a key player in the proapoptotic signalling in response to radiation-induced DNA damage and is therefore a potential target for radiosensitisation.

METHODS

This study investigated the use of the p53 stabilising peptide VIP116 and [177Lu]Lu-DOTATATE, either alone or in combination, for treatment of neuroblastoma tumour xenografts in mice. Initially, the uptake of [177Lu]Lu-DOTATATE in the tumours was confirmed, and the efficacy of VIP116 as a monotherapy was evaluated. Subsequently, mice with neuroblastoma tumour xenografts were treated with placebo, VIP116, [177Lu]Lu-DOTATATE or a combination of both agents.

RESULTS

The results demonstrated that monotherapy with either VIP116 or [177Lu]Lu-DOTATATE significantly prolonged median survival compared to the placebo group (90 and 96.5 days vs. 50.5 days, respectively). Notably, the combination treatment further improved median survival to over 120 days. Furthermore, the combination group exhibited the highest percentage of complete remission, corresponding to a twofold increase compared to the placebo group. Importantly, none of the treatments induced significant nephrotoxicity. Additionally, the therapies affected various molecular targets involved in critical processes such as apoptosis, hypoxia and angiogenesis.

CONCLUSION

In conclusion, the combination of VIP116 and [177Lu]Lu-DOTATATE presents a promising novel treatment approach for neuroblastoma. These findings hold potential to advance research efforts towards a potential cure for this vulnerable patient population.

Identification of ATF3 as a novel protective signature of quiescent colorectal tumor cells

Colorectal cancer (CRC) is the third most common cancer and the second leading cause of death in the world. In most cases, drug resistance and tumor recurrence are ultimately inevitable. One obstacle is the presence of chemotherapy-insensitive quiescent cancer cells (QCCs). Identification of unique features of QCCs may facilitate the development of new targeted therapeutic strategies to eliminate tumor cells and thereby delay tumor recurrence. Here, using single-cell RNA sequencing, we classified proliferating and quiescent cancer cell populations in the human colorectal cancer spheroid model and identified ATF3 as a novel signature of QCCs that could support cells living in a metabolically restricted microenvironment. RNA velocity further showed a shift from the QCC group to the PCC group indicating the regenerative capacity of the QCCs. Our further results of epigenetic analysis, STING analysis, and evaluation of TCGA COAD datasets build a conclusion that ATF3 can interact with DDIT4 and TRIB3 at the transcriptional level. In addition, decreasing the expression level of ATF3 could enhance the efficacy of 5-FU on CRC MCTS models. In conclusion, ATF3 was identified as a novel marker of QCCs, and combining conventional drugs targeting PCCs with an option to target QCCs by reducing ATF3 expression levels may be a promising strategy for more efficient removal of tumor cells.

A platform for efficient establishment and drug-response profiling of high-grade serous ovarian cancer organoids

The broad research use of organoids from high-grade serous ovarian cancer (HGSC) has been hampered by low culture success rates and limited availability of fresh tumor material. Here, we describe a method for generation and long-term expansion of HGSC organoids with efficacy markedly improved over previous reports (53% vs. 23%-38%). We established organoids from cryopreserved material, demonstrating the feasibility of using viably biobanked tissue for HGSC organoid derivation. Genomic, histologic, and single-cell transcriptomic analyses revealed that organoids recapitulated genetic and phenotypic features of original tumors. Organoid drug responses correlated with clinical treatment outcomes, although in a culture conditions-dependent manner and only in organoids maintained in human plasma-like medium (HPLM). Organoids from consenting patients are available to the research community through a public biobank and organoid genomic data are explorable through an interactive online tool. Taken together, this resource facilitates the application of HGSC organoids in basic and translational ovarian cancer research.

Computational drug repurposing by exploiting large-scale gene expression data: Strategy, methods and applications

De novo drug development is an extremely complex, time-consuming and costly task. Urgent needs for therapies of various diseases have greatly accelerated searches for more effective drug development methods. Luckily, drug repurposing provides a new and effective perspective on disease treatment. Rapidly increased large-scale transcriptome data paints a detailed prospect of gene expression during disease onset and thus has received wide attention in the field of computational drug repurposing. However, how to efficiently mine transcriptome data and identify new indications for old drugs remains a critical challenge. This review discussed the irreplaceable role of transcriptome data in computational drug repurposing and summarized some representative databases, tools and strategies. More importantly, it proposed a practical guideline through establishing the correspondence between three gene expression data types and five strategies, which would facilitate researchers to adopt appropriate strategies to deeply mine large-scale transcriptome data and discover more effective therapies.

Quiescent Cancer Cells-A Potential Therapeutic Target to Overcome Tumor Resistance and Relapse

Quiescent cancer cells (QCCs) are nonproliferating cells arrested in the G0 phase, characterized by ki67^low and p27^high. QCCs avoid most chemotherapies, and some treatments could further lead to a higher proportion of QCCs in tumors. QCCs are also associated with cancer recurrence since they can re-enter a proliferative state when conditions are favorable. As QCCs lead to drug resistance and tumor recurrence, there is a great need to understand the characteristics of QCCs, decipher the mechanisms that regulate the proliferative-quiescent transition in cancer cells, and develop new strategies to eliminate QCCs residing in solid tumors. In this review, we discussed the mechanisms of QCC-induced drug resistance and tumor recurrence. We also discussed therapeutic strategies to overcome resistance and relapse by targeting QCCs, including (i) identifying reactive quiescent cancer cells and removing them via cell-cycle-dependent anticancer reagents; (ii) modulating the quiescence-to-proliferation switch; and (iii) eliminating QCCs by targeting their unique features. It is believed that the simultaneous co-targeting of proliferating and quiescent cancer cells may ultimately lead to the development of more effective therapeutic strategies for the treatment of solid tumors.

Assessment in vitro of interactions between anti-cancer drugs and noncancer drugs commonly used by cancer patients

Cancer patients often suffer from cancer symptoms, treatment complications and concomitant diseases and are, therefore, often treated with several drugs in addition to anticancer drugs. Whether such drugs, here denoted as 'concomitant drugs', have anticancer effects or interact at the tumor cell level with the anticancer drugs is not very well known. The cytotoxic effects of nine concomitant drugs and their interactions with five anti-cancer drugs commonly used for the treatment of colorectal cancer were screened over broad ranges of drug concentrations in vitro in the human colon cancer cell line HCT116wt. Seven additional tyrosine kinase inhibitors were included to further evaluate key findings as were primary cultures of tumor cells from patients with colorectal cancer. Cytotoxic effects were evaluated using the fluorometric microculture cytotoxicity assay (FMCA) and interaction analysis was based on Bliss independent interaction analysis. Simvastatin and loperamide, included here as an opioid agonists, were found to have cytotoxic effects on their own at reasonably low concentrations whereas betamethasone, enalapril, ibuprofen, metformin, metoclopramide, metoprolol and paracetamol were inactive also at very high concentrations. Drug interactions ranged from antagonistic to synergistic over the concentrations tested with a more homogenous pattern of synergy between simvastatin and protein kinase inhibitors in HCT116wt cells. Commonly used concomitant drugs are mostly neither expected to have anticancer effects nor to interact significantly with anticancer drugs frequently used for the treatment of colorectal cancer.

Emerging Promise of Computational Techniques in Anti-Cancer Research: At a Glance

Research on the immune system and cancer has led to the development of new medicines that enable the former to attack cancer cells. Drugs that specifically target and destroy cancer cells are on the horizon; there are also drugs that use specific signals to stop cancer cells multiplying. Machine learning algorithms can significantly support and increase the rate of research on complicated diseases to help find new remedies. One area of medical study that could greatly benefit from machine learning algorithms is the exploration of cancer genomes and the discovery of the best treatment protocols for different subtypes of the disease. However, developing a new drug is time-consuming, complicated, dangerous, and costly. Traditional drug production can take up to 15 years, costing over USD 1 billion. Therefore, computer-aided drug design (CADD) has emerged as a powerful and promising technology to develop quicker, cheaper, and more efficient designs. Many new technologies and methods have been introduced to enhance drug development productivity and analytical methodologies, and they have become a crucial part of many drug discovery programs; many scanning programs, for example, use ligand screening and structural virtual screening techniques from hit detection to optimization. In this review, we examined various types of computational methods focusing on anticancer drugs. Machine-based learning in basic and translational cancer research that could reach new levels of personalized medicine marked by speedy and advanced data analysis is still beyond reach. Ending cancer as we know it means ensuring that every patient has access to safe and effective therapies. Recent developments in computational drug discovery technologies have had a large and remarkable impact on the design of anticancer drugs and have also yielded useful insights into the field of cancer therapy. With an emphasis on anticancer medications, we covered the various components of computer-aided drug development in this paper. Transcriptomics, toxicogenomics, functional genomics, and biological networks are only a few examples of the bioinformatics techniques used to forecast anticancer medications and treatment combinations based on multi-omics data. We believe that a general review of the databases that are now available and the computational techniques used today will be beneficial for the creation of new cancer treatment approaches.

Sorafenib and nitazoxanide disrupt mitochondrial function and inhibit regrowth capacity in three-dimensional models of hepatocellular and colorectal carcinoma

Quiescent cancer cells in malignant tumors can withstand cell-cycle active treatment and cause cancer spread and recurrence. Three-dimensional (3D) cancer cell models have led to the identification of oxidative phosphorylation (OXPHOS) as a context-dependent vulnerability. The limited treatment options for advanced hepatocellular carcinoma (HCC) and colorectal carcinoma (CRC) metastatic to the liver include the multikinase inhibitors sorafenib and regorafenib. Off-target effects of sorafenib and regorafenib are related to OXPHOS inhibition; however the importance of this feature to the effect on tumor cells has not been investigated in 3D models. We began by assessing global transcriptional responses in monolayer cell cultures, then moved on to multicellular tumor spheroids (MCTS) and tumoroids generated from a CRC patient. Cells were treated with chemotherapeutics, kinase inhibitors, and the OXPHOS inhibitors. Cells grown in 3D cultures were sensitive to the OXPHOS inhibitor nitazoxanide, sorafenib, and regorafenib and resistant to other multikinase inhibitors and chemotherapeutic drugs. Furthermore, nitazoxanide and sorafenib reduced viability, regrowth potential and inhibited mitochondrial membrane potential in an additive manner at clinically relevant concentrations. This study demonstrates that the OXPHOS inhibition caused by sorafenib and regorafenib parallels 3D activity and can be further investigated for new combination strategies.

Unravelling Mechanisms of Doxorubicin-Induced Toxicity in 3D Human Intestinal Organoids

Doxorubicin is widely used in the treatment of different cancers, and its side effects can be severe in many tissues, including the intestines. Symptoms such as diarrhoea and abdominal pain caused by intestinal inflammation lead to the interruption of chemotherapy. Nevertheless, the molecular mechanisms associated with doxorubicin intestinal toxicity have been poorly explored. This study aims to investigate such mechanisms by exposing 3D small intestine and colon organoids to doxorubicin and to evaluate transcriptomic responses in relation to viability and apoptosis as physiological endpoints. The in vitro concentrations and dosing regimens of doxorubicin were selected based on physiologically based pharmacokinetic model simulations of treatment regimens recommended for cancer patients. Cytotoxicity and cell morphology were evaluated as well as gene expression and biological pathways affected by doxorubicin. In both types of organoids, cell cycle, the p53 signalling pathway, and oxidative stress were the most affected pathways. However, significant differences between colon and SI organoids were evident, particularly in essential metabolic pathways. Short time-series expression miner was used to further explore temporal changes in gene profiles, which identified distinct tissue responses. Finally, in silico proteomics revealed important proteins involved in doxorubicin metabolism and cellular processes that were in line with the transcriptomic responses, including cell cycle and senescence, transport of molecules, and mitochondria impairment. This study provides new insight into doxorubicin-induced effects on the gene expression levels in the intestines. Currently, we are exploring the potential use of these data in establishing quantitative systems toxicology models for the prediction of drug-induced gastrointestinal toxicity.

High-Throughput Strategies for the Discovery of Anticancer Drugs by Targeting Transcriptional Reprogramming

Transcriptional reprogramming contributes to the progression and recurrence of cancer. However, the poorly elucidated mechanisms of transcriptional reprogramming in tumors make the development of effective drugs difficult, and gene expression signature is helpful for connecting genetic information and pharmacologic treatment. So far, there are two gene-expression signature-based high-throughput drug discovery approaches: L1000, which measures the mRNA transcript abundance of 978 "landmark" genes, and high-throughput sequencing-based high-throughput screening (HTS²); they are suitable for anticancer drug discovery by targeting transcriptional reprogramming. L1000 uses ligation-mediated amplification and hybridization to Luminex beads and highlights gene expression changes by detecting bead colors and fluorescence intensity of phycoerythrin signal. HTS² takes advantage of RNA-mediated oligonucleotide annealing, selection, and ligation, high throughput sequencing, to quantify gene expression changes by directly measuring gene sequences. This article summarizes technological principles and applications of L1000 and HTS², and discusses their advantages and limitations in anticancer drug discovery.

Imitating Hypoxia and Tumor Microenvironment with Immune Evasion by Employing Three Dimensional in vitro Cellular Models: Impressive Tool in Drug Discovery

The heterogeneous tumor microenvironment is exceptionally perplexing and not wholly comprehended. Different multifaceted alignments lead to the generation of oxygen destitute situations within the tumor niche that modulate numerous intrinsic tumor microenvironments. Disentangling these communications is vital for scheming practical therapeutic approaches that can successfully decrease tumor allied chemotherapy resistance by utilizing the innate capability of the immune system. Several research groups have concerned with a protruding role for oxygen metabolism along with hypoxia in the immunity of healthy tissue. Hypoxia in addition to hypoxia-inducible factors (HIFs) in the tumor microenvironment plays an important part in tumor progression and endurance. Although numerous hypoxia-focused therapies have shown promising outcomes both in vitro and in vivo these outcomes have not effectively translated into clinical preliminaries. Distinctive cell culture techniques have utilized as an in vitro model for tumor niche along with tumor microenvironment and proficient in more precisely recreating tumor genomic profiles as well as envisaging therapeutic response. To study the dynamics of tumor immune evasion, three-dimensional (3D) cell cultures are more physiologically important to the hypoxic tumor microenvironment. Recent research has revealed new information and insights into our fundamental understanding of immune systems, as well as novel results that have been established as potential therapeutic targets. There are a lot of patented 3D cell culture techniques which will be highlighted in this review. At present notable 3D cell culture procedures in the hypoxic tumor microenvironment, discourse open doors to accommodate both drug repurposing, advancement, and divulgence of new medications and will deliberate the 3D cell culture methods into standard prescription disclosure especially in the field of cancer biology which will be discussing here.

Transcending toward Advanced 3D-Cell Culture Modalities: A Review about an Emerging Paradigm in Translational Oncology

Cancer is a disorder characterized by an uncontrollable overgrowth and a fast-moving spread of cells from a localized tissue to multiple organs of the body, reaching a metastatic state. Throughout years, complexity of cancer progression and invasion, high prevalence and incidence, as well as the high rise in treatment failure cases leading to a poor patient prognosis accounted for continuous experimental investigations on animals and cellular models, mainly with 2D- and 3D-cell culture. Nowadays, these research models are considered a main asset to reflect the physiological events in many cancer types in terms of cellular characteristics and features, replication and metastatic mechanisms, metabolic pathways, biomarkers expression, and chemotherapeutic agent resistance. In practice, based on research perspective and hypothesis, scientists aim to choose the best model to approach their understanding and to prove their hypothesis. Recently, 3D-cell models are seen to be highly incorporated as a crucial tool for reflecting the true cancer cell microenvironment in pharmacokinetic and pharmacodynamics studies, in addition to the intensity of anticancer drug response in pharmacogenomics trials. Hence, in this review, we shed light on the unique characteristics of 3D cells favoring its promising usage through a comparative approach with other research models, specifically 2D-cell culture. Plus, we will discuss the importance of 3D models as a direct reflector of the intrinsic cancer cell environment with the newest multiple methods and types available for 3D-cells implementation.

Bioinformatics Approaches for Anti-cancer Drug Discovery

Drug discovery is important in cancer therapy and precision medicines. Traditional approaches of drug discovery are mainly based on in vivo animal experiments and in vitro drug screening, but these methods are usually expensive and laborious. In the last decade, omics data explosion provides an opportunity for computational prediction of anti-cancer drugs, improving the efficiency of drug discovery. High-throughput transcriptome data were widely used in biomarkers' identification and drug prediction by integrating with drug-response data. Moreover, biological network theory and methodology were also successfully applied to the anti-cancer drug discovery, such as studies based on protein-protein interaction network, drug-target network and disease-gene network. In this review, we summarized and discussed the bioinformatics approaches for predicting anti-cancer drugs and drug combinations based on the multi-omic data, including transcriptomics, toxicogenomics, functional genomics and biological network. We believe that the general overview of available databases and current computational methods will be helpful for the development of novel cancer therapy strategies.

Targeting the Mevalonate Pathway in Cancer

The mevalonate synthesis inhibitors, statins, are mainstay therapeutics for cholesterol management and cardiovascular health. Thirty years of research have uncovered supportive roles for the mevalonate pathway in numerous cellular processes that support oncogenesis, most recently macropinocytosis. Central to the diverse mechanisms of statin sensitivity is an acquired dependence on one mevalonate pathway output, protein geranylgeranylation. New chemical prenylation probes and the discovery of a novel geranylgeranyl transferase hold promise to deepen our understanding of statin mechanisms of action. Further, insights into statin selection and the counterproductive role of dietary geranylgeraniol highlight how we should assess statins in the clinic. Lastly, rational combination strategies preview how statins will enter the oncology toolbox.

Descriptive Proteome Analysis to Investigate Context-Dependent Treatment Responses to OXPHOS Inhibition in Colon Carcinoma Cells Grown as Monolayer and Multicellular Tumor Spheroids

We have previously identified selective upregulation of the mevalonate pathway genes upon inhibition of oxidative phosphorylation (OXPHOS) in quiescent cancer cells. Using mass spectrometry-based proteomics, we here investigated whether these responses are corroborated on the protein level and whether proteomics could yield unique insights into context-dependent biology. HCT116 colon carcinoma cells were cultured as monolayer cultures, proliferative multicellular tumor spheroids (P-MCTS), or quiescent (Q-MCTS) multicellular tumor spheroids and exposed to OXPHOS inhibitors: nitazoxanide, FCCP, oligomycin, and salinomycin or the HMG-CoA-reductase inhibitor simvastatin at two different doses for 6 and 24 h. Samples were processed using an in-depth bottom-up proteomics workflow resulting in a total of 9286 identified protein groups. Gene set enrichment analysis showed profound differences between the three cell systems and confirmed differential enrichment of hypoxia, OXPHOS, and cell cycle progression-related protein responses in P-MCTS and Q-MCTS. Treatment experiments showed that the observed drug-induced alterations in gene expression of metabolically challenged cells are not translated directly to the protein level, but the results reaffirmed OXPHOS as a selective vulnerability of quiescent cancer cells. This work provides rationale for the use of deep proteome profiling to identify context-dependent treatment responses and encourages further studies investigating metabolic processes that could be co-targeted together with OXPHOS to eradicate quiescent cancer cells.

ReSimNet: drug response similarity prediction using Siamese neural networks

MOTIVATION

Traditional drug discovery approaches identify a target for a disease and find a compound that binds to the target. In this approach, structures of compounds are considered as the most important features because it is assumed that similar structures will bind to the same target. Therefore, structural analogs of the drugs that bind to the target are selected as drug candidates. However, even though compounds are not structural analogs, they may achieve the desired response. A new drug discovery method based on drug response, which can complement the structure-based methods, is needed.

RESULTS

We implemented Siamese neural networks called ReSimNet that take as input two chemical compounds and predicts the CMap score of the two compounds, which we use to measure the transcriptional response similarity of the two compounds. ReSimNet learns the embedding vector of a chemical compound in a transcriptional response space. ReSimNet is trained to minimize the difference between the cosine similarity of the embedding vectors of the two compounds and the CMap score of the two compounds. ReSimNet can find pairs of compounds that are similar in response even though they may have dissimilar structures. In our quantitative evaluation, ReSimNet outperformed the baseline machine learning models. The ReSimNet ensemble model achieves a Pearson correlation of 0.518 and a precision@1% of 0.989. In addition, in the qualitative analysis, we tested ReSimNet on the ZINC15 database and showed that ReSimNet successfully identifies chemical compounds that are relevant to a prototype drug whose mechanism of action is known.

AVAILABILITY AND IMPLEMENTATION

The source code and the pre-trained weights of ReSimNet are available at https://github.com/dmis-lab/ReSimNet.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Transcriptomic Profiling of 3D Glioblastoma Tumoroids for the Identification of Mechanisms Involved in Anticancer Drug Resistance

BACKGROUND/AIM

Among various types of brain tumors, glioblastoma is the most malignant and highly aggressive brain tumor that possesses a high resistance against anticancer drugs. To understand the underlined mechanisms of tumor drug resistance, a new and more effective research approach is required. The three dimensional (3D) in vitro cell culture models could be a potential approach to study cancer features and biology, as well as screen for anti-cancer agents due to the close mimicry of the 3D tumor microenvironments.

MATERIALS AND METHODS

With our developed 3D alginate scaffolds, Ilumina RNA-sequencing was used to transcriptomically analyze and compare the gene expression profiles between glioblastoma cells in traditional 2-dimensional (2D) monolayer and in 3D Ca-alginate scaffolds at day 14. To verify the reliability and accuracy of Illumina RNA-Sequencing data, ATP-binding cassette transporter genes were chosen for quantitative real-time polymerase chain reaction) verification.

RESULTS

The results showed that 7,411 and 3,915 genes of the 3D glioblastoma were up-regulated and down-regulated, respectively, compared with the 2D-cultured glioblastoma. Furthermore, the Kyoto Encyclopaedia of Genes and Genomes pathway analysis revealed that genes related to the cell cycle and DNA replication were enriched in the group of down-regulated gene. On the other hand, the genes involved in mitogen-activated protein kinase signaling, autophagy, drug metabolism through cytochrome P450, and ATP-binding cassette transporter were found in the up-regulated gene collection.

CONCLUSION

3D glioblastoma tumoroids might potentially serve as a powerful platform for exploring glioblastoma biology. They can also be valuable in anti-glioblastoma drug screening, as well as the identification of novel molecular targets in clinical treatment of human glioblastoma.

Three-dimensional cell culture systems as an in vitro platform for cancer and stem cell modeling

Three-dimensional (3D) culture systems are becoming increasingly popular due to their ability to mimic tissue-like structures more effectively than the monolayer cultures. In cancer and stem cell research, the natural cell characteristics and architectures are closely mimicked by the 3D cell models. Thus, the 3D cell cultures are promising and suitable systems for various proposes, ranging from disease modeling to drug target identification as well as potential therapeutic substances that may transform our lives. This review provides a comprehensive compendium of recent advancements in culturing cells, in particular cancer and stem cells, using 3D culture techniques. The major approaches highlighted here include cell spheroids, hydrogel embedding, bioreactors, scaffolds, and bioprinting. In addition, the progress of employing 3D cell culture systems as a platform for cancer and stem cell research was addressed, and the prominent studies of 3D cell culture systems were discussed.

Tunable hydrogels for controlling phenotypic cancer cell states to model breast cancer dormancy and reactivation

During metastasis, disseminated tumor cells (DTCs) from the primary tumor infiltrate secondary organs and reside there for varying lengths of time prior to forming new tumors. The time delay between infiltration and active proliferation, known as dormancy, mediates the length of the latency period. DTCs may undergo one of four fates post-infiltration: death, cellular dormancy, dormant micrometastasis, or invasive growth which, is in part, mediated by extracellular matrix (ECM) properties. Recapitulation of these cell states using engineered hydrogels could facilitate the systematic and controlled investigation of the mechanisms by which ECM properties influence DTC fate. Toward this goal, we implemented a set of sixteen hydrogels with systematic variations in chemical (ligand (RGDS) density and enzymatic degradability) and mechanical (elasticity, swelling, mesh size) properties to investigate their influence on the fate of encapsulated metastatic breast cancer cells, MDA-MB-231. Cell viability, apoptosis, proliferation, metabolic activity, and morphological measurements were acquired at five-day intervals over fifteen days in culture. Analysis of the phenotypic metrics indicated the presence of four different cell states that were classified as: (1) high growth, (2) moderate growth, (3) single cell, restricted survival, dormancy, or (4) balanced dormancy. Correlating hydrogel properties with the resultant cancer cell state indicated that ligand (RGDS) density and enzymatic degradability likely had the most influence on cell fate. Furthermore, we demonstrate the ability to reactivate cells from the single cell, dormant state to the high growth state through a dynamic increase in ligand (RGDS) density after forty days in culture. This tunable engineered hydrogel platform offers insight into matrix properties regulating tumor dormancy, and the dormancy-proliferation switch, and may provide future translational benefits toward development of anti-dormancy therapeutic strategies.

Connectivity Mapping: Methods and Applications

Connectivity mapping resources consist of signatures representing changes in cellular state following systematic small-molecule, disease, gene, or other form of perturbations. Such resources enable the characterization of signatures from novel perturbations based on similarity; provide a global view of the space of many themed perturbations; and allow the ability to predict cellular, tissue, and organismal phenotypes for perturbagens. A signature search engine enables hypothesis generation by finding connections between query signatures and the database of signatures. This framework has been used to identify connections between small molecules and their targets, to discover cell-specific responses to perturbations and ways to reverse disease expression states with small molecules, and to predict small-molecule mimickers for existing drugs. This review provides a historical perspective and the current state of connectivity mapping resources with a focus on both methodology and community implementations.

Cancer Cell Drug Response Transcriptomes in 3D

The relevance of different in vitro culture models of cancer cells is a hot topic, but few systematic and definitive analyses in this area exist. In this issue of Cell Chemical Biology, Senkowski et al. (2016) address this issue by studying the transcriptomic profiles of drug-treated cancer cells cultured in two-dimensional and three-dimensional cultures. They describe biological findings with potential therapeutic implications and provide a unique data resource to mine.

A novel inhibitor of tumorspheres reveals the activation of the serine biosynthetic pathway upon mitochondrial inhibition

Differences in the metabolism of cancer cells or cancer stem cells (CSCs) as compared to normal cells have provided avenues to safely target cancers. To discover metabolic inhibitors of CSCs, we performed alkaline phosphatase- and tumoursphere-based drug screening using induced cancer stem cell-like cells. From the screening of a RIKEN NPDepo chemical library, we discovered NPD2381 as a novel and selective cancer-stemness inhibitor that targets mitochondrial metabolism. Using our ChemProteoBase profiling, we found that NPD2381 increases the expression of enzymes within the serine biosynthesis pathway. We also found a role for serine in protecting cancer cells from mitochondrial inhibitors. Our results suggest the existence of a compensatory mechanism to increase the level of intracellular serine in response to mitochondrial inhibitors.

Engineered In Vitro Models of Tumor Dormancy and Reactivation

Metastatic recurrence is a major hurdle to overcome for successful control of cancer-associated death. Residual tumor cells in the primary site, or disseminated tumor cells in secondary sites, can lie in a dormant state for long time periods, years to decades, before being reactivated into a proliferative growth state. The microenvironmental signals and biological mechanisms that mediate the fate of disseminated cancer cells with respect to cell death, single cell dormancy, tumor mass dormancy and metastatic growth, as well as the factors that induce reactivation, are discussed in this review. Emphasis is placed on engineered, in vitro, biomaterial-based approaches to model tumor dormancy and subsequent reactivation, with a focus on the roles of extracellular matrix, secondary cell types, biochemical signaling and drug treatment. A brief perspective of molecular targets and treatment approaches for dormant tumors is also presented. Advances in tissue-engineered platforms to induce, model, and monitor tumor dormancy and reactivation may provide much needed insight into the regulation of these processes and serve as drug discovery and testing platforms.

EWS-FLI1 reprograms the metabolism of Ewing sarcoma cells via positive regulation of glutamine import and serine-glycine biosynthesis

Ewing sarcoma (EWS) is a soft tissue and bone tumor that occurs primarily in adolescents and young adults. In most cases of EWS, the chimeric transcription factor, EWS-FLI1 is the primary oncogenic driver. The epigenome of EWS cells reflects EWS-FLI1 binding and activation or repression of transcription. Here, we demonstrate that EWS-FLI1 positively regulates the expression of proteins required for serine-glycine biosynthesis and uptake of the alternative nutrient source glutamine. Specifically, we show that EWS-FLI1 activates expression of PHGDH, PSAT1, PSPH, and SHMT2. Using cell-based studies, we also establish that EWS cells are dependent on glutamine for cell survival and that EWS-FLI1 positively regulates expression of the glutamine transporter, SLC1A5 and two enzymes involved in the one-carbon cycle, MTHFD2 and MTHFD1L. Inhibition of serine-glycine biosynthesis in EWS cells impacts their redox state leading to an accumulation of reactive oxygen species, DNA damage, and apoptosis. Importantly, analysis of EWS primary tumor transcriptome data confirmed that the aforementioned genes we identified as regulated by EWS-FLI1 exhibit increased expression compared with normal tissues. Furthermore, retrospective analysis of an independent data set generated a significant stratification of the overall survival of EWS patients into low- and high-risk groups based on the expression of PHGDH, PSAT1, PSPH, SHMT2, SLC1A5, MTHFD2, and MTHFD1L. In summary, our study demonstrates that EWS-FLI1 reprograms the metabolism of EWS cells and that serine-glycine metabolism or glutamine uptake are potential targetable vulnerabilities in this tumor type.

Nitazoxanide inhibits paramyxovirus replication by targeting the Fusion protein folding: role of glycoprotein-specific thiol oxidoreductase ERp57

Paramyxoviridae, a large family of enveloped viruses harboring a nonsegmented negative-sense RNA genome, include important human pathogens as measles, mumps, respiratory syncytial virus (RSV), parainfluenza viruses, and henipaviruses, which cause some of the deadliest emerging zoonoses. There is no effective antiviral chemotherapy for most of these pathogens. Paramyxoviruses evolved a sophisticated membrane-fusion machine consisting of receptor-binding proteins and the fusion F-protein, critical for virus infectivity. Herein we identify the antiprotozoal/antimicrobial nitazoxanide as a potential anti-paramyxovirus drug targeting the F-protein. We show that nitazoxanide and its circulating-metabolite tizoxanide act at post-entry level by provoking Sendai virus and RSV F-protein aggregate formation, halting F-trafficking to the host plasma membrane. F-protein folding depends on ER-resident glycoprotein-specific thiol-oxidoreductase ERp57 for correct disulfide-bond architecture. We found that tizoxanide behaves as an ERp57 non-competitive inhibitor; the putative drug binding-site was located at the ERp57-b/b′ non-catalytic domains interface. ERp57-silencing mimicked thiazolide-induced F-protein alterations, suggesting an important role of this foldase in thiazolides anti-paramyxovirus activity. Nitazoxanide is used in the clinic as a safe and effective antiprotozoal/antimicrobial drug; its antiviral activity was shown in patients infected with hepatitis-C virus, rotavirus and influenza viruses. Our results now suggest that nitazoxanide may be effective also against paramyxovirus infection.

Institutional profile: the national Swedish academic drug discovery & development platform at SciLifeLab

The Science for Life Laboratory Drug Discovery and Development Platform (SciLifeLab DDD) was established in Stockholm and Uppsala, Sweden, in 2014. It is one of ten platforms of the Swedish national SciLifeLab which support projects run by Swedish academic researchers with large-scale technologies for molecular biosciences with a focus on health and environment. SciLifeLab was created by the coordinated effort of four universities in Stockholm and Uppsala: Stockholm University, Karolinska Institutet, KTH Royal Institute of Technology and Uppsala University, and has recently expanded to other Swedish university locations. The primary goal of the SciLifeLab DDD is to support selected academic discovery and development research projects with tools and resources to discover novel lead therapeutics, either molecules or human antibodies. Intellectual property developed with the help of SciLifeLab DDD is wholly owned by the academic research group. The bulk of SciLifeLab DDD's research and service activities are funded from the Swedish state, with only consumables paid by the academic research group through individual grants.