Drug sensitivity in cancer cell lines is not tissue-specific

S-allyl-cysteine triggers cytotoxic events in rat glioblastoma RG2 and C6 cells and improves the effect of temozolomide through the regulation of oxidative responses

Glioblastoma (GBM) is an aggressive form of cancer affecting the Central Nervous System (CNS) of thousands of people every year. Redox alterations have been shown to play a key role in the development and progression of these tumors as Reactive Oxygen Species (ROS) formation is involved in the modulation of several signaling pathways, transcription factors, and cytokine formation. The second-generation oral alkylating agent temozolomide (TMZ) is the first-line chemotherapeutic drug used to treat of GBM, though patients often develop primary and secondary resistance, reducing its efficacy. Antioxidants represent promising and potential coadjutant agents as they can reduce excessive ROS formation derived from chemo- and radiotherapy, while decreasing pharmacological resistance. S-allyl-cysteine (SAC) has been shown to inhibit the proliferation of several types of cancer cells, though its precise antiproliferative mechanisms remain poorly investigated. To date, SAC effects have been poorly explored in GBM cells. Here, we investigated the effects of SAC in vitro, either alone or in combination with TMZ, on several toxic and modulatory endpoints-including oxidative stress markers and transcriptional regulation-in two glioblastoma cell lines from rats, RG2 and C6, to elucidate some of the biochemical and cellular mechanisms underlying its antiproliferative properties. SAC (1-750 µM) decreased cell viability in both cell lines in a concentration-dependent manner, although C6 cells were more resistant to SAC at several of the tested concentrations. TMZ also produced a concentration-dependent effect, decreasing cell viability of both cell lines. In combination, SAC (1 µM or 100 µM) and TMZ (500 µM) enhanced the effects of each other. SAC also augmented the lipoperoxidative effect of TMZ and reduced cell antioxidant resistance in both cell lines by decreasing the TMZ-induced increase in the GSH/GSSG ratio. In RG2 and C6 cells, SAC per se had no effect on Nrf2/ARE binding activity, while in RG2 cells TMZ and the combination of SAC + TMZ decreased this activity. Our results demonstrate that SAC, alone or in combination with TMZ, exerts antitumor effects mediated by regulatory mechanisms of redox activity responses. SAC is also a safe drug for testing in other models as it produces non-toxic effects in primary astrocytes. Combined, these effects suggest that SAC affords antioxidant properties and potential antitumor efficacy against GBM.

Globospiramine from Voacanga globosa Exerts Robust Cytotoxic and Antiproliferative Activities on Cancer Cells by Inducing Caspase-Dependent Apoptosis in A549 Cells and Inhibiting MAPK14 (p38α): In Vitro and Computational Investigations

Bisindole alkaloids are a source of inspiration for the design and discovery of new-generation anticancer agents. In this study, we investigated the cytotoxic and antiproliferative activities of three spirobisindole alkaloids from the traditional anticancer Philippine medicinal plant Voacanga globosa, along with their mechanisms of action. Thus, the alkaloids globospiramine (1), deoxyvobtusine (2), and vobtusine lactone (3) showed in vitro cytotoxicity and antiproliferative activities against the tested cell lines (L929, KB3.1, A431, MCF-7, A549, PC-3, and SKOV-3) using MTT and CellTiter-Blue assays. Globospiramine (1) was also screened against a panel of breast cancer cell lines using the sulforhodamine B (SRB) assay and showed moderate cytotoxicity. It also promoted the activation of apoptotic effector caspases 3 and 7 using Caspase-Glo 3/7 and CellEvent-3/7 apoptosis assays. Increased expressions of cleaved caspase 3 and PARP in A549 cells treated with 1 were also observed. Apoptotic activity was also confirmed when globospiramine (1) failed to promote the rapid loss of membrane integrity according to the HeLa cell membrane permeability assay. Network pharmacology analysis, molecular docking, and molecular dynamics simulations identified MAPK14 (p38α), a pharmacological target leading to cancer cell apoptosis, as a putative target. Low toxicity risks and favorable drug-likeness were also predicted for 1. Overall, our study demonstrated the anticancer potentials and apoptotic mechanisms of globospiramine (1), validating the traditional medicinal use of Voacanga globosa.

Gene Expression and Drug Sensitivity Analysis of Mitochondrial Chaperones Reveals That HSPD1 and TRAP1 Expression Correlates with Sensitivity to Inhibitors of DNA Replication and Mitosis

Mitochondria-critical metabolic hubs in eukaryotic cells-are involved in a wide range of cellular functions, including differentiation, proliferation, and death. Mitochondria import most of their proteins from the cytosol in a linear form, after which they are folded by mitochondrial chaperones. However, despite extensive research, the extent to which the function of particular chaperones is essential for maintaining specific mitochondrial and cellular functions remains unknown. In particular, it is not known whether mitochondrial chaperones influence the sensitivity to drugs used in the treatment of cancers. By mining gene expression and drug sensitivity data for cancer cell lines from publicly available databases, we identified mitochondrial chaperones whose expression is associated with sensitivity to oncology drugs targeting particular cellular pathways in a cancer-type-dependent manner. Importantly, we found the expression of TRAP1 and HSPD1 to be associated with sensitivity to inhibitors of DNA replication and mitosis. We confirmed experimentally that the expression of HSPD1 is associated with an increased sensitivity of ovarian cancer cells to drugs targeting mitosis and a reduced sensitivity to drugs promoting apoptosis. Taken together, our results support a model in which particular mitochondrial pathways hinge upon specific mitochondrial chaperones and provide the basis for understanding selectivity in mitochondrial chaperone-substrate specificity.

Fluorescence intensity and lifetime imaging of lipofuscin-like autofluorescence for label-free predicting clinical drug response in cancer

Conventional techniques for in vitro cancer drug screening require labor-intensive formalin fixation, paraffin embedding, and dye staining of tumor tissues at fixed endpoints. This way of assessment discards the valuable pharmacodynamic information in live cells over time. Here, we found endogenous lipofuscin-like autofluorescence acutely accumulated in the cell death process. Its unique red autofluorescence could report the apoptosis without labeling and continuously monitor the treatment responses in 3D tumor-culture models. Lifetime imaging of lipofuscin-like red autofluorescence could further distinguish necrosis from apoptosis of cells. Moreover, this endogenous fluorescent marker could visualize the apoptosis in live zebrafish embryos during development. Overall, this study validates that lipofuscin-like autofluorophore is a generic cell death marker. Its characteristic autofluorescence could label-free predict the efficacy of anti-cancer drugs in organoids or animal models.

Liposome-Encapsulated Bioactive Guttiferone E Exhibits Anti-Inflammatory Effect in Lipopolysaccharide-Stimulated MH-S Macrophages and Cytotoxicity against Human Cancer Cells

Background

Guttiferone E is a naturally occurring polyisoprenylated benzophenone exhibiting a wide range of remarkable biological activities. But its therapeutic application is still limited due to its poor water solubility. This study is aimed at preparing guttiferone E-loaded liposomes and assessing their in vitro cytotoxicity and anti-inflammatory effect.

Methods

Liposomes containing guttiferone E were prepared by the thin film hydration method, and the physicochemical characteristics were determined using dynamic light scattering, laser Doppler velocimetry, and atomic force microscopy. The cytotoxicity was assessed by the MTT assay. The fluorometric cyclooxygenase (COX) activity assay kit was used to assess the COX activity while the nitric oxide production was evaluated by the Griess reagent method.

Results

The liposomes with a mean size of 183.33 ± 17.28 nm were obtained with an entrapment efficiency of 63.86%. Guttiferone E-loaded liposomes successfully decreased the viability of cancer cells. The overall IC₅₀ values varied between 5.46 μg/mL and 22.25 μg/mL. Compared to the untreated control, guttiferone E-loaded liposomes significantly reduced the nitric oxide production and the activity of COX in a concentration-dependent manner.

Conclusion

This study indicates that liposomes can be an alternative to overcome the water insolubility issue of the bioactive guttiferone E.

Orthogonal assay for validation of Tox21 PPARγ data and applicability to in silico prediction model

High-throughput screening data from the Tox21 database is used for prioritizing hazardous chemicals and building in silico-based toxicity prediction models. One of the Tox21 dataset, peroxisome proliferator-activated receptor-gamma (PPARγ), a nuclear receptor superfamily, identified various endpoints in HEK293 cells. PPARγ mediates various toxic effects when its receptors are activated or inhibited by ligands such as thiazolidinedione and GW9662. In this study, an orthogonal assay was constructed to verify the effectiveness of the Tox21 PPARγ data, and the effect of highly reliable data on in silico model construction was investigated. The orthogonal assay was a reporter gene assay based on the PPARγ ligand binding domain in CV-1 cells. Only 39% of agonists and 55% of antagonists had similar responses in CV-1 and HEK293 cells. Thus, the effectiveness of Tox21 data on PPARγ may vary depending on the cell line. However, in silico PLS-DA analysis with only high-reliability data (i.e., the same response in both cell lines), yielded more accurate prediction of the activity of potential chemical ligands, despite the small number of samples. Thus, obtaining reliable chemical screening data for PPARγ through orthogonal analysis, even for only limited chemicals, supports the construction of highly predictive in silico models with improved screening efficiency.

Computational Screening of Anti-Cancer Drugs Identifies a New BRCA Independent Gene Expression Signature to Predict Breast Cancer Sensitivity to Cisplatin

Simple Summary

Using a collection of publicly available drug screening resources, we identified different partners of genes associated with either sensitivity or resistance to 90 anti-cancer therapies. When subsequently applying these signatures to multiple datasets, we found that these predictive models could predict a large range of drug responses in patient samples. In particular, we discovered a new gene signature to identify breast cancer tumors that are likely to respond to cisplatin in the absence of BRCA1 mutations. This work constitutes an important advance to accelerate the application of platinum-based therapies in patient groups that are not routinely treated with these drugs. In the future, this approach may help to guide the choice of drugs based on the molecular profile of the tumors.

Abstract

The development of therapies that target specific disease subtypes has dramatically improved outcomes for patients with breast cancer. However, survival gains have not been uniform across patients, even within a given molecular subtype. Large collections of publicly available drug screening data matched with transcriptomic measurements have facilitated the development of computational models that predict response to therapy. Here, we generated a series of predictive gene signatures to estimate the sensitivity of breast cancer samples to 90 drugs, comprising FDA-approved drugs or compounds in early development. To achieve this, we used a cell line-based drug screen with matched transcriptomic data to derive in silico models that we validated in large independent datasets obtained from cell lines and patient-derived xenograft (PDX) models. Robust computational signatures were obtained for 28 drugs and used to predict drug efficacy in a set of PDX models. We found that our signature for cisplatin can be used to identify tumors that are likely to respond to this drug, even in absence of the BRCA-1 mutation routinely used to select patients for platinum-based therapies. This clinically relevant observation was confirmed in multiple PDXs. Our study foreshadows an effective delivery approach for precision medicine.

Large-Scale Transcriptional Profiling of Molecular Perturbations Reveals Cell Line Specific Responses and Implications for Environmental Screening

Cell-based bioassays represent nearly half of all high-throughput screens currently conducted for risk assessment of environmental chemicals. However, there has long been a concern about the sensitivity and heterogeneity among cell lines, which were explored only in a limited manner. Here, we address this question by conducting a large-scale transcriptome analysis of the responses of discrete cell lines to specific molecules. We report the collections of >223 300 gene expression profiles from a wide array of cell lines exposed to 2243 compounds. Our results demonstrate distinct responses among cell lines at both the gene and the pathway levels. Temporal variations for a very large proportion of compounds occur as well. High sensitivity and/or heterogeneity is either cell line-specific or universal depending on the modes of action of the compounds. Among 12 representative pathways analyzed, distinct cell-chemical interactions exist. On one hand, lung carcinoma cells are always best suited for glucocorticoid receptor agonist identification, while on the other hand, high sensitivity and heterogenic features are universal for histone deacetylase inhibitors and ATPase inhibitors. Our data provide novel insights into the understanding of cell-specific responses and interactions between cells and xenobiotics. The findings have substantial implications for the design, execution, and interpretation of high-throughput screening assays in (eco)toxicology.

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.

Molecular genomic features associated with in vitro response of the NCI-60 cancer cell line panel to natural products

Natural products remain a significant source of anticancer chemotherapeutics. The search for targeted drugs for cancer treatment includes consideration of natural products, which may provide new opportunities for antitumor cytotoxicity as single agents or in combination therapy. We examined the association of molecular genomic features in the well-characterized NCI-60 cancer cell line panel with in vitro response to treatment with 1302 small molecules which included natural products, semisynthetic natural product derivatives, and synthetic compounds based on a natural product pharmacophore from the Developmental Therapeutics Program of the US National Cancer Institute's database. These compounds were obtained from a variety of plant, marine, and microbial species. Molecular information utilized for the analysis included expression measures for 23059 annotated transcripts, lncRNAs, and miRNAs, and data on protein-changing single nucleotide variants in 211 cancer-related genes. We found associations of expression of multiple genes including SLFN11, CYP2J2, EPHX1, GPC1, ELF3, and MGMT involved in DNA damage repair, NOTCH family members, ABC and SLC transporters, and both mutations in tyrosine kinases and BRAF V600E with NCI-60 responses to specific categories of natural products. Hierarchical clustering identified groups of natural products, which correlated with a specific mechanism of action. Specifically, several natural product clusters were associated with SLFN11 gene expression, suggesting that potential action of these compounds may involve DNA damage. The associations between gene expression or genome alterations of functionally relevant genes with the response of cancer cells to natural products provide new information about potential mechanisms of action of these identified clusters of compounds with potentially similar biological effects. This information will assist in future drug discovery and in design of new targeted cancer chemotherapy agents.

Anticancer drug synergy prediction in understudied tissues using transfer learning

OBJECTIVE

Drug combination screening has advantages in identifying cancer treatment options with higher efficacy without degradation in terms of safety. A key challenge is that the accumulated number of observations in in-vitro drug responses varies greatly among different cancer types, where some tissues are more understudied than the others. Thus, we aim to develop a drug synergy prediction model for understudied tissues as a way of overcoming data scarcity problems.

MATERIALS AND METHODS

We collected a comprehensive set of genetic, molecular, phenotypic features for cancer cell lines. We developed a drug synergy prediction model based on multitask deep neural networks to integrate multimodal input and multiple output. We also utilized transfer learning from data-rich tissues to data-poor tissues.

RESULTS

We showed improved accuracy in predicting synergy in both data-rich tissues and understudied tissues. In data-rich tissue, the prediction model accuracy was 0.9577 AUROC for binarized classification task and 174.3 mean squared error for regression task. We observed that an adequate transfer learning strategy significantly increases accuracy in the understudied tissues.

CONCLUSIONS

Our synergy prediction model can be used to rank synergistic drug combinations in understudied tissues and thus help to prioritize future in-vitro experiments. Code is available at https://github.com/yejinjkim/synergy-transfer.

Dual Delivery of Gemcitabine and Paclitaxel by Wet-Spun Coaxial Fibers Induces Pancreatic Ductal Adenocarcinoma Cell Death, Reduces Tumor Volume, and Sensitizes Cells to Radiation

Pancreatic ductal adenocarcinoma (PDAC) has a dismal prognosis, with surgical resection of the tumor in conjunction with systemic chemotherapy the only potential curative therapy. Up to 80% of diagnosed cases are deemed unresectable, prompting the need for alternative treatment approaches. Herein, coaxial polymeric fibers loaded with two chemotherapeutic agents, gemcitabine (Gem) and paclitaxel (Ptx), are fabricated to investigate the effect of local drug delivery on PDAC cell growth in vitro and in vivo. A wet-spinning fabrication method to form a coaxial fiber with a polycaprolactone shell and alginate core loaded with Ptx and Gem, respectively, is used. In vitro, Gem+Ptx fibers display significant cytotoxicity as well as radiosensitizing properties toward PDAC cell lines greater than the equivalent free drugs, which may be attributed to a radiosensitizing effect of the polymers. In vivo studies assessing Gem+Ptx fiber efficacy found that Gem+Ptx fibers reduce tumor volume in a xenograft mouse model of PDAC. Importantly, no difference in mouse weight, circulating cytokines, or liver function is observed in mice treated with Gem+Ptx fibers compared to the empty fiber controls confirming the safety of the implant approach. With further development, Gem+Ptx fibers can improve the treatment of unresectable PDAC in the future.

Personalized cancer therapy prioritization based on driver alteration co-occurrence patterns

Identification of actionable genomic vulnerabilities is key to precision oncology. Utilizing a large-scale drug screening in patient-derived xenografts, we uncover driver gene alteration connections, derive driver co-occurrence (DCO) networks, and relate these to drug sensitivity. Our collection of 53 drug-response predictors attains an average balanced accuracy of 58% in a cross-validation setting, rising to 66% for a subset of high-confidence predictions. We experimentally validated 12 out of 14 predictions in mice and adapted our strategy to obtain drug-response models from patients’ progression-free survival data. Our strategy reveals links between oncogenic alterations, increasing the clinical impact of genomic profiling.

Establishment of a Human Gastric Cancer Xenograft Model in Immunocompetent Mice Using the Microcarrier-6

Gastric cancer is among the most common malignant tumors of the digestive tract. Establishing a robust and reliable animal model is the foundation for studying the pathogenesis of cancer. The present study established a mouse model of gastric carcinoma by inoculating immunocompetent mice with MKN45 cells using microcarrier. Sixty male C57BL/6 mice were randomly divided into three groups: a 2D group, an empty carrier group, and a 3D group, according to the coculture system of MKN45 and the microcarrier. The mouse models were established by hypodermic injection. Time to develop tumor, rate of tumor formation, and pathological features were observed in each group. In the 3D group, the tumorigenesis time was short, while the rate of tumor formation was high (75%). There was no detectable tumor formation in either the 2D or the empty carrier group. Both H&E and immunohistochemical staining of the tumor xenograft showed characteristic evidence of human gastric neoplasms. The present study successfully established a human gastric carcinoma model in immunocompetent mice, which provides a novel and valuable animal model for the cancer research and development of anticancer drugs.

Genetic Interactions and Tissue Specificity Modulate the Association of Mutations with Drug Response

In oncology, biomarkers are widely used to predict subgroups of patients that respond to a given drug. Although clinical decisions often rely on single gene biomarkers, machine learning approaches tend to generate complex multi-gene biomarkers that are hard to interpret. Models predicting drug response based on multiple altered genes often assume that the effects of single alterations are independent. We asked whether the association of cancer driver mutations with drug response is modulated by other driver mutations or the tissue of origin. We developed an analytic framework based on linear regression to study interactions in pharmacogenomic data from two large cancer cell line panels. Starting from a model with only covariates, we included additional variables only if they significantly improved simpler models. This allows to systematically assess interactions in small, easily interpretable models. Our results show that including mutation-mutation interactions in drug response prediction models tends to improve model performance and robustness. For example, we found that TP53 mutations decrease sensitivity to BRAF inhibitors in BRAF-mutated cell lines and patient tumors, suggesting a therapeutic benefit of combining inhibition of oncogenic BRAF with reactivation of the tumor suppressor TP53. Moreover, we identified tissue-specific mutation-drug associations and synthetic lethal triplets where the simultaneous mutation of two genes sensitizes cells to a drug. In summary, our interaction-based approach contributes to a holistic view on the determining factors of drug response.

Comparison of BCR-ABL Transcript Variants Between Patients With Chronic Myeloid Leukaemia and Leukaemia Cell Lines

BACKGROUND/AIM

Chronic myeloid leukaemia (CML) is a myeloproliferative disorder characterized by the presence of breakpoint cluster region-Abelson murine leukemia (BCR-ABL1) gene fusion as a hallmark that is expressed as two major transcripts b2a2 and b3a2. The aim of this study was to compare the BCR-ABL transcripts in the blood cells of patients with CML, and in chemoresistant and chemosensitive CML cell lines to validate their use as a good method to elucidate CML biology.

MATERIALS AND METHODS

Twelve patients with CML and CML cell lines (K562, K562-LUCENA and FEPS) were analyzed by real-time polymerase chain reaction to evaluate gene expression of BCR-ABL transcripts.

RESULTS

All patients had the same expression levels of b2a2 and b3a3 transcripts, however, CML cell lines presented only b3a2 expression. There were no significant differences in absolute b3a2 expression between patients and CML cell lines.

CONCLUSION

CML cell lines provide a good in vitro alternative in that they have the same BCR-ABL expression as patients.

Encircling the regions of the pharmacogenomic landscape that determine drug response

BACKGROUND

The integration of large-scale drug sensitivity screens and genome-wide experiments is changing the field of pharmacogenomics, revealing molecular determinants of drug response without the need for previous knowledge about drug action. In particular, transcriptional signatures of drug sensitivity may guide drug repositioning, prioritize drug combinations, and point to new therapeutic biomarkers. However, the inherent complexity of transcriptional signatures, with thousands of differentially expressed genes, makes them hard to interpret, thus giving poor mechanistic insights and hampering translation to clinics.

METHODS

To simplify drug signatures, we have developed a network-based methodology to identify functionally coherent gene modules. Our strategy starts with the calculation of drug-gene correlations and is followed by a pathway-oriented filtering and a network-diffusion analysis across the interactome.

RESULTS

We apply our approach to 189 drugs tested in 671 cancer cell lines and observe a connection between gene expression levels of the modules and mechanisms of action of the drugs. Further, we characterize multiple aspects of the modules, including their functional categories, tissue-specificity, and prevalence in clinics. Finally, we prove the predictive capability of the modules and demonstrate how they can be used as gene sets in conventional enrichment analyses.

CONCLUSIONS

Network biology strategies like module detection are able to digest the outcome of large-scale pharmacogenomic initiatives, thereby contributing to their interpretability and improving the characterization of the drugs screened.

Tissue specificity of in vitro drug sensitivity

Objectives

We sought to investigate the tissue specificity of drug sensitivities in large-scale pharmacological studies and compare these associations to those found in drug clinical indications.

Materials and Methods

We leveraged the curated cell line response data from PharmacoGx and applied an enrichment algorithm on drug sensitivity values' area under the drug dose-response curves (AUCs) with and without adjustment for general level of drug sensitivity.

Results

We observed tissue specificity in 63% of tested drugs, with 8% of total interactions deemed significant (false discovery rate <0.05). By restricting the drug-tissue interactions to those with AUC > 0.2, we found that in 52% of interactions, the tissue was predictive of drug sensitivity (concordance index > 0.65). When compared with clinical indications, the observed overlap was weak (Matthew correlation coefficient, MCC = 0.0003, P > .10).

Discussion

While drugs exhibit significant tissue specificity in vitro, there is little overlap with clinical indications. This can be attributed to factors such as underlying biological differences between in vitro models and patient tumors, or the inability of tissue-specific drugs to bring additional benefits beyond gold standard treatments during clinical trials.

Conclusion

Our meta-analysis of pan-cancer drug screening datasets indicates that most tested drugs exhibit tissue-specific sensitivities in a large panel of cancer cell lines. However, the observed preclinical results do not translate to the clinical setting. Our results suggest that additional research into showing parallels between preclinical and clinical data is required to increase the translational potential of in vitro drug screening.

Report of the 14th International Conference on Malignant Lymphoma (ICML) Closed Workshop on Future Design of Clinical Trials in Lymphomas

The 14th ICML held in Lugano in June 2017 was preceded by a closed workshop (organized in collaboration with the American Association for Cancer Research and the European School of Oncology) where experts in preclinical and clinical research in lymphomas met to discuss the current drug development landscape focusing on critical open questions that need to be addressed in the future to permit a more efficient drug development paradigm in lymphoma. Topics discussed included both preclinical models that can be used to test new drugs and drug combinations, as well as the optimal design of clinical trials and the endpoints that should be used to facilitate accelerated progress. This report represents a summary of the workshop. Clin Cancer Res; 24(13); 2993-8. ©2018 AACR.

Rationalizing Drug Response in Cancer Cell Lines

Cancer cell lines (CCLs) play an important role in the initial stages of drug discovery allowing, among others, for the screening of drug candidates. As CCL panels continue to grow in size and diversity, many polymorphisms in genes encoding drug-metabolizing enzymes, transporters and drug targets, as well as disease-related genes have been linked to altered drug sensitivity. However, identifying the correlation between this variability and pharmacological responses remains challenging due to the heterogeneity of cancer biology and the intricate interplay between cell lines and drug molecules. Here, we propose a network-based strategy that exploits information on gene expression and somatic mutations of CCLs to group cells according to their molecular similarity. We then identify genes that are characteristic of each cluster and correlate their status with drug response. We find that CCLs with similar characteristic active network regions present specific responses to certain drugs, and identify a limited set of genes that might be directly involved in drug sensitivity or resistance.

Computational models for predicting drug responses in cancer research

The computational prediction of drug responses based on the analysis of multiple types of genome-wide molecular data is vital for accomplishing the promise of precision medicine in oncology. This will benefit cancer patients by matching their tumor characteristics to the most effective therapy available. As larger and more diverse layers of patient-related data become available, further demands for new bioinformatics approaches and expertise will arise. This article reviews key strategies, resources and techniques for the prediction of drug sensitivity in cell lines and patient-derived samples. It discusses major advances and challenges associated with the different model development steps. This review highlights major trends in this area, and will assist researchers in the assessment of recent progress and in the selection of approaches to emerging applications in oncology.

Discovering novel pharmacogenomic biomarkers by imputing drug response in cancer patients from large genomics studies

Obtaining accurate drug response data in large cohorts of cancer patients is very challenging; thus, most cancer pharmacogenomics discovery is conducted in preclinical studies, typically using cell lines and mouse models. However, these platforms suffer from serious limitations, including small sample sizes. Here, we have developed a novel computational method that allows us to impute drug response in very large clinical cancer genomics data sets, such as The Cancer Genome Atlas (TCGA). The approach works by creating statistical models relating gene expression to drug response in large panels of cancer cell lines and applying these models to tumor gene expression data in the clinical data sets (e.g., TCGA). This yields an imputed drug response for every drug in each patient. These imputed drug response data are then associated with somatic genetic variants measured in the clinical cohort, such as copy number changes or mutations in protein coding genes. These analyses recapitulated drug associations for known clinically actionable somatic genetic alterations and identified new predictive biomarkers for existing drugs.

An efficient synthesis of indolo[2,3-b]quinoline guanidine derivatives with their in vitro and in vivo study

An optimization of the guanidylation process by verifying the efficacy of common guanylation reagents in order to obtain the guanidine derivatives of indolo[2,3-b]quinoline has been performed. As a result, a high-yield procedure using N,N′-di-Boc-N′′-triflylguanidine was applied to synthesize the guanidine derivative of indolo[2,3-b]quinoline 1 in a gram scale for specific in vitro and in vivo biological research. Extensive studies on the antiproliferative activity against eight human tumor cell lines were completed. Compound 1 revealed the highest activity against A549 lung adenocarcinoma and MCF7 breast cancer cell lines. Thus, 1 was evaluated for the in vivo anticancer activity against 4T1 mammary gland carcinoma and KLN205 murine lung carcinoma in mouse models. The anticancer effect was observed in the KLN205 model with a 37% tumor growth inhibition at the 20 mg/kg dose. This anticancer activity of 1 was comparable to that of cyclophosphamide which inhibited murine lung tumor growth in the range of 27–43% at the dose of 100 mg/kg. The biochemistry research after 1 admission, including measurements of blood parameters like alanine aminotransferase, aspartate aminotransferase, lactate dehydrogenase, and urea and creatinine, were also performed.

Design, synthesis, and evaluation of the anticancer activity of 2-amino-aryl-7-aryl-benzoxazole compounds

A series of 2-amino-aryl-7-aryl-benzoxazole derivatives have been designed, synthesized, and evaluated as anticancer agents. Fourteen of the compounds exhibited cytotoxic effects toward human A549 lung cancer cells. We found 12l was the most potent with an EC₅₀ of 0.4 μm, equivalent to the anticancer drug doxorubicin, but had low selectivity following cross screening in monkey kidney Vero cells. Eight of the most potent or most selective compounds were further profiled in additional cell lines (MCF7, NCI-H187, and KB) to better understand their cytotoxic activity. Only compound 12l had a measurable EC₅₀ in a single cell line (3.3 μm in the KB cell line). Taken together, this data suggest the series as a whole display specific cytotoxicity toward A549 cells. Cheminformatics searches pointed to JAK2 as a possible target. A subset of compounds assayed at this target showed IC₅₀ s ranging from 10 to 0.08 μm; however, no clear correlation between JAK2 potency and A549 cytotoxicity was observed.

Drug screening in 3D in vitro tumor models: overcoming current pitfalls of efficacy read-outs

There is cumulating evidence that in vitro 3D tumor models with increased physiological relevance can improve the predictive value of pre-clinical research and ultimately contribute to achieve decisions earlier during the development of cancer-targeted therapies. Due to the role of tumor microenvironment in the response of tumor cells to therapeutics, the incorporation of different elements of the tumor niche on cell model design is expected to contribute to the establishment of more predictive in vitro tumor models. This review is focused on the several challenges and adjustments that the field of oncology research is facing to translate these advanced tumor cells models to drug discovery, taking advantage of the progress on culture technologies, imaging platforms, high throughput and automated systems. The choice of 3D cell model, the experimental design, choice of read-outs and interpretation of data obtained from 3D cell models are critical aspects when considering their implementation in drug discovery. In this review, we foresee some of these aspects and depict the potential directions of pre-clinical oncology drug discovery towards improved prediction of drug efficacy.

Expression Profiles of the Individual Genes Corresponding to the Genes Generated by Cytotoxicity Experiments with Bortezomib in Multiple Myeloma

OBJECTIVE

Multiple myeloma (MM) is currently incurable due to refractory disease relapse even under novel anti-myeloma treatment. In silico studies are effective for key decision making during clinicopathological battles against the chronic course of MM. The aim of this present in silico study was to identify individual genes whose expression profiles match that of the one generated by cytotoxicity experiments for bortezomib.

MATERIALS AND METHODS

We used an in silico literature mining approach to identify potential biomarkers by creating a summarized set of metadata derived from relevant information. The E-MTAB-783 dataset containing expression data from 789 cancer cell lines including 8 myeloma cell lines with drug screening data from the Wellcome Trust Sanger Institute database obtained from ArrayExpress was "Robust Multi-array analysis" normalized using GeneSpring v.12.5. Drug toxicity data were obtained from the Genomics of Drug Sensitivity in Cancer project. In order to identify individual genes whose expression profiles matched that of the one generated by cytotoxicity experiments for bortezomib, we used a linear regression-based approach, where we searched for statistically significant correlations between gene expression values and IC50 data. The intersections of the genes were identified in 8 cell lines and used for further analysis.

RESULTS

Our linear regression model identified 73 genes and some genes expression levels were found to very closely correlated with bortezomib IC50 values. When all 73 genes were used in a hierarchical cluster analysis, two major clusters of cells representing relatively sensitive and resistant cells could be identified. Pathway and molecular function analysis of all the significant genes was also investigated, as well as the genes involved in pathways.

CONCLUSION

The findings of our present in silico study could be important not only for the understanding of the genomics of MM but also for the better arrangement of the targeted anti-myeloma therapies, such as bortezomib.

Identification of a novel human DNA ligase I inhibitor that promotes cellular apoptosis in DLD-1 cells: an in silico and in vitro mechanistic study

The compound S-097/98 is a specific inhibitor of hLig1. As shown in the figure, the compound inhibits only hLig1 while other human and non-human DNA ligases are not inhibited.