Exploiting the cancer genome: strategies for the discovery and clinical development of targeted molecular therapeutics

Synthetic routes and clinical application of Small-Molecule HER2 inhibitors for cancer therapy

This comprehensive review undertakes a meticulous scrutiny of the synthesis and clinical applications pertaining to small-molecule tyrosine kinase inhibitors (TKIs) directed towards the human epidermal growth factor receptor 2 (HER2), a pivotal protagonist in the pathogenesis of cancer. Focused on compounds like lapatinib, neratinib, and tucatinib, the review delves into the intricate synthesis strategies, emphasizing the challenges associated with their structural complexity. The clinical utilization of HER2 TKIs underscores noteworthy strides in the therapeutic landscape for HER2-positive breast and gastric malignancies. Lapatinib, a dual HER2/ epidermal growth factor receptor (EGFR) inhibitor, has demonstrated efficacy in combination therapies, addressing the need for overcoming resistance mechanisms. Neratinib, an irreversible HER2 inhibitor, presents a promising avenue for patients with refractory tumors. Tucatinib, strategically engineered to traverse the blood-brain barrier, epitomizes a groundbreaking advancement in the management of metastatic HER2-positive breast cancer manifesting cerebral involvement. Despite their success, challenges such as resistance mechanisms and off-target effects persist, urging continuous research for the development of next-generation HER2 TKIs. This comprehensive review serves as a valuable resource for pharmaceutical scientists, offering insights into the synthetic intricacies and clinical impact of small-molecule TKIs targeting HER2.

Artificial intelligence for small molecule anticancer drug discovery

INTRODUCTION

The transition from conventional cytotoxic chemotherapy to targeted cancer therapy with small-molecule anticancer drugs has enhanced treatment outcomes. This approach, which now dominates cancer treatment, has its advantages. Despite the regulatory approval of several targeted molecules for clinical use, challenges such as low response rates and drug resistance still persist. Conventional drug discovery methods are costly and time-consuming, necessitating more efficient approaches. The rise of artificial intelligence (AI) and access to large-scale datasets have revolutionized the field of small-molecule cancer drug discovery. Machine learning (ML), particularly deep learning (DL) techniques, enables the rapid identification and development of novel anticancer agents by analyzing vast amounts of genomic, proteomic, and imaging data to uncover hidden patterns and relationships.

AREA COVERED

In this review, the authors explore the important landmarks in the history of AI-driven drug discovery. They also highlight various applications in small-molecule cancer drug discovery, outline the challenges faced, and provide insights for future research.

EXPERT OPINION

The advent of big data has allowed AI to penetrate and enable innovations in almost every stage of medicine discovery, transforming the landscape of oncology research through the development of state-of-the-art algorithms and models. Despite challenges in data quality, model interpretability, and technical limitations, advancements promise breakthroughs in personalized and precision oncology, revolutionizing future cancer management.

Network based approach to identify interactions between Type 2 diabetes and cancer comorbidities

High blood sugar and insulin insensitivity causes the lifelong chronic metabolic disease called Type 2 diabetes (T2D) which has a higher chance of developing different malignancies. T2D with comorbidities like Cancers can make normal medications for those disorders more difficult. There may be a significant correlation between comorbidities and have an impact on one another's health. These associations may be due to a number of direct and indirect mechanisms. Such molecular mechanisms that underpin T2D and cancer are yet unknown. However, the large volumes of data available on these diseases allowed us to use analytical tools for uncovering their interrelated pathways. Here, we tried to present a system for investigating potential comorbidity relationships between T2D and Cancer disease by looking at the molecular processes involved, analyzing a huge number of freely accessible transcriptomic datasets of various disorders using bioinformatics. Using semantic similarity and gene set enrichment analysis, we created an informatics pipeline that evaluates and integrates Gene Ontology (GO), expression of genes, and biological process data. We discovered genes that are common in T2D and Cancer along with molecular pathways and GOs. We compared the top 200 Differentially Expressed Genes (DEGs) from each selected T2D and cancer dataset and found the most significant common genes. Among all the common genes 13 genes were found most frequent. We also found 4 common GO terms: GO:0000003, GO:0000122, GO:0000165, and GO:0000278 among all the common GO terms between T2d and different cancers. Using these genes and GO term semantic similarity, we calculated the distance between these two diseases. The semantic similarity results of our study showed a higher association of Liver Cancer (LiC), Breast Cancer (BreC), Colorectal Cancer (CC), and Bladder Cancer (BlaC) with T2D. Furthermore we found KIF4A, NUSAP1, CENPF, CCNB1, TOP2A, CCNB2, RRM2, HMMR, NDC80, NCAPG, and IGFBP5 common hub proteins among different cancers correlated to T2D. AGE-RAGE signaling pathway in diabetic complications, Osteoclast differentiation, TNF signaling pathway, IL-17 signaling pathway, p53 signaling pathway, MAPK signaling pathway, Human T-cell leukemia virus 1 infection, and Non-alcoholic fatty liver disease are the 8 most significant pathways found among 18 common pathways between T2D and selected cancers. As a result of our technique, we now know more about disease pathways that are critical between T2D and cancer.

Urinary Metabolic Distinction of Niemann-Pick Class 1 Disease through the Use of Subgroup Discovery

In this investigation, we outline the applications of a data mining technique known as Subgroup Discovery (SD) to the analysis of a sample size-limited metabolomics-based dataset. The SD technique utilized a supervised learning strategy, which lies midway between classificational and descriptive criteria, in which given the descriptive property of a dataset (i.e., the response target variable of interest), the primary objective was to discover subgroups with behaviours that are distinguishable from those of the complete set (albeit with a differential statistical distribution). These approaches have, for the first time, been successfully employed for the analysis of aromatic metabolite patterns within an NMR-based urinary dataset collected from a small cohort of patients with the lysosomal storage disorder Niemann-Pick class 1 (NPC1) disease (n = 12) and utilized to distinguish these from a larger number of heterozygous (parental) control participants. These subgroup discovery strategies discovered two different NPC1 disease-specific metabolically sequential rules which permitted the reliable identification of NPC1 patients; the first of these involved 'normal' (intermediate) urinary concentrations of xanthurenate, 4-aminobenzoate, hippurate and quinaldate, and disease-downregulated levels of nicotinate and trigonelline, whereas the second comprised 'normal' 4-aminobenzoate, indoxyl sulphate, hippurate, 3-methylhistidine and quinaldate concentrations, and again downregulated nicotinate and trigonelline levels. Correspondingly, a series of five subgroup rules were generated for the heterozygous carrier control group, and 'biomarkers' featured in these included low histidine, 1-methylnicotinamide and 4-aminobenzoate concentrations, together with 'normal' levels of hippurate, hypoxanthine, quinolinate and hypoxanthine. These significant disease group-specific rules were consistent with imbalances in the combined tryptophan-nicotinamide, tryptophan, kynurenine and tyrosine metabolic pathways, along with dysregulations in those featuring histidine, 3-methylhistidine and 4-hydroxybenzoate. In principle, the novel subgroup discovery approach employed here should also be readily applicable to solving metabolomics-type problems of this nature which feature rare disease classification groupings with only limited patient participant and sample sizes available.

Targeted Therapy and Personalized Medicine

Targeted therapy and personalized medicine are novel emerging disciplines of cancer research intended for treatment and prevention. One of the most significant advancements in modern oncology is the shift from an organ-centric strategy to a personalized strategy guided by deep molecular analysis. This shift in view, which focuses on the tumour's precise molecular changes, has paved the way for individualized treatment. Researchers and clinicians are using targeted therapies to select the best treatment available based on the molecular characterization of malignant cancer. In the treatment of a cancer, personalized medicine entails the use of genetic, immunological, and proteomic profiling to provide therapeutic alternatives as well as prognostic information about cancer. In this book, targeted therapies and personalized medicine have been covered for specific malignancies, including latest FDA-approved targeted therapies and it also sheds light on effective anti-cancer regimens and drug resistance. This will help to enhance our ability to conduct individualized health planning, make early diagnoses, and choose optimal medications for each cancer patient with predictable side effects and outcomes in a quickly evolving era. Various applications and tools' capacity have been improved for early diagnosis of cancer and the growing number of clinical trials that choose specific molecular targets reflects this predicament. Nevertheless, there are several limitations that must need to be addressed. Hence, in this chapter, we will discuss recent advancements, challenges, and opportunities in personalized medicine for various cancers, with a specific emphasis on target therapies in diagnostics and therapeutics.

COVID-19 Candidate Genes and Pathways Potentially Share the Association with Lung Cancer

COVID-19 is considered as the most challenging in the current situation but lung cancer is also the leading cause of death in the global population. These two malignancies are among the leading human diseases and are highly complex in terms of diagnostic and therapeutic approaches as well as the most frequent and highly complex and heterogeneous in nature. Based on the latest update, it is known that the patients suffering from lung cancer, are considered to be significantly at higher risk of COVID-19 infection in terms of survival and there are a number of evidences which support the hypothesis that these diseases may share the same functions and functional components. Multi-level unwanted alterations such as (epi-)genetic alterations, changes at the transcriptional level, and altered signaling pathways (receptor, cytoplasmic, and nuclear level) are the major sources which promote a number of complex diseases and such heterogeneous level of complexities are considered as the major barrier in the development of therapeutics. With so many challenges, it is critical to understand the relationships and the common shared aberrations between them which is difficult to unravel and understand. A simple approach has been applied for this study where differential gene expression analysis, pathway enrichment, and network level understanding are carried out. Since, gene expression changes and genomic alterations are related to the COVID-19 and lung cancer but their pattern varies significantly. Based on the recent studies, it appears that the patients suffering from lung cancer and and simultaneously infected with COVID-19, then survival chance is lessened. So, we have designed our goal to understand the genes commonly overexpressed and commonly enriched pathways in case of COVID-19 and lung cancer. For this purpose, we have presented the summarized review of the previous works where the pathogenesis of lung cancer and COVID-19 infection have been focused and we have also presented the new finding of our analysis. So, this work not only presents the review work but also the research work. This review and research study leads to the conclusion that growth promoting pathways (EGFR, Ras, and PI3K), growth inhibitory pathways (p53 and STK11), apoptotic pathways (Bcl- 2/Bax/Fas), and DDR pathways and genes are commonly and dominantly altered in both the cases COVID-19 and lung cancer.

Updates and Original Case Studies Focused on the NMR-Linked Metabolomics Analysis of Human Oral Fluids Part II: Applications to the Diagnosis and Prognostic Monitoring of Oral and Systemic Cancers

Human saliva offers many advantages over other biofluids regarding its use and value as a bioanalytical medium for the identification and prognostic monitoring of human diseases, mainly because its collection is largely non-invasive, is relatively cheap, and does not require any major clinical supervision, nor supervisory input. Indeed, participants donating this biofluid for such purposes, including the identification, validation and quantification of surrogate biomarkers, may easily self-collect such samples in their homes following the provision of full collection details to them by researchers. In this report, the authors have focused on the applications of metabolomics technologies to the diagnosis and progressive severity monitoring of human cancer conditions, firstly oral cancers (e.g., oral cavity squamous cell carcinoma), and secondly extra-oral (systemic) cancers such as lung, breast and prostate cancers. For each publication reviewed, the authors provide a detailed evaluation and critical appraisal of the experimental design, sample size, ease of sample collection (usually but not exclusively as whole mouth saliva (WMS)), their transport, length of storage and preparation for analysis. Moreover, recommended protocols for the optimisation of NMR pulse sequences for analysis, along with the application of methods and techniques for verifying and resonance assignments and validating the quantification of biomolecules responsible, are critically considered. In view of the authors’ specialisms and research interests, the majority of these investigations were conducted using NMR-based metabolomics techniques. The extension of these studies to determinations of metabolic pathways which have been pathologically disturbed in these diseases is also assessed here and reviewed. Where available, data for the monitoring of patients’ responses to chemotherapeutic treatments, and in one case, radiotherapy, are also evaluated herein. Additionally, a novel case study featured evaluates the molecular nature, levels and diagnostic potential of ¹H NMR-detectable salivary ‘acute-phase’ glycoprotein carbohydrate side chains, and/or their monomeric saccharide derivatives, as biomarkers for cancer and inflammatory conditions.

BRCA mutations lead to XIAP overexpression and sensitise ovarian cancer to inhibitor of apoptosis (IAP) family inhibitors

Background

We tested the hypothesis that inhibitor of apoptosis family (IAP) proteins may be altered in BRCA1-mutated ovarian cancers and that could affect the sensitivity to IAP inhibitors.

Methods

The levels of IAP proteins were evaluated in human cancers and cell lines. Cell lines were used to determine the effects of IAP inhibitors. The in vivo effects of treatments were evaluated in PDX mouse models.

Results

Expression of X-linked inhibitor of apoptosis (XIAP) is increased in BRCA1-mutated cancers and high levels are associated with improved patient outcomes after platinum chemotherapy. XIAP overexpression is mediated by NF-kB activation and is associated with an optimisation of PARP. BRCA1-mutated cell lines are particularly sensitive to IAP inhibitors due to an inhibitory effect on PARP. Both a BRCA1-mutated cell line with acquired resistance to PARP inhibitors and one with restored BRCA1 remain sensitive to IAP inhibitors. Treatment with IAP inhibitors restores the efficacy of PARP inhibition in these cell lines. The IAP inhibitor LCL161 alone and in combination with a PARP inhibitor, exhibited antitumour effects in PDX mouse models of resistant BRCA2 and 1-mutated ovarian cancer, respectively.

Conclusion

A clinical trial may be justified to further investigate the utility of IAP inhibitors.

Systematic illumination of druggable genes in cancer genomes

By combining 6 druggable genome resources, we identify 6,083 genes as potential druggable genes (PDGs). We characterize their expression, recurrent genomic alterations, cancer dependencies, and therapeutic potentials by integrating genome, functionome, and druggome profiles across cancers. 81.5% of PDGs are reliably expressed in major adult cancers, 46.9% show selective expression patterns, and 39.1% exhibit at least one recurrent genomic alteration. We annotate a total of 784 PDGs as dependent genes for cancer cell growth. We further quantify 16 cancer-related features and estimate a PDG cancer drug target score (PCDT score). PDGs with higher PCDT scores are significantly enriched for genes encoding kinases and histone modification enzymes. Importantly, we find that a considerable portion of high PCDT score PDGs are understudied genes, providing unexplored opportunities for drug development in oncology. By integrating the druggable genome and the cancer genome, our study thus generates a comprehensive blueprint of potential druggable genes across cancers.

Jiang et al. generate a comprehensive blueprint of potential druggable genes (PDGs) across cancers by a systematic integration of the druggable genome and the cancer genome. This resource is publicly available to the cancer research community in The Cancer Druggable Gene Atlas (TCDA) through the Functional Cancer Genome data portal.

Artificial Intelligence and Cancer Drug Development

BACKGROUND

The development of cancer drugs is among the most focused "bench to bedside activities" to improve human health. Because of the amount of data publicly available to cancer research, drug development for cancers has significantly benefited from big data and Artificial Intelligence (AI). In the meantime, challenges, like curating the data of low quality, remain to be resolved.

OBJECTIVES

This review focused on the recent advancements and challenges of AI in developing cancer drugs.

METHODS

We discussed target validation, drug repositioning, de novo design, and compounds' synthetic strategies.

RESULTS AND CONCLUSION

AI can be applied to all stages during drug development, and some excellent reviews detailing the applications of AI in specific stages are available.

New approaches and procedures for cancer treatment: Current perspectives

Cancer is a global health problem responsible for one in six deaths worldwide. Treating cancer has been a highly complex process. Conventional treatment approaches, such as surgery, chemotherapy, and radiotherapy, have been in use, while significant advances are being made in recent times, including stem cell therapy, targeted therapy, ablation therapy, nanoparticles, natural antioxidants, radionics, chemodynamic therapy, sonodynamic therapy, and ferroptosis-based therapy. Current methods in oncology focus on the development of safe and efficient cancer nanomedicines. Stem cell therapy has brought promising efficacy in regenerating and repairing diseased or damaged tissues by targeting both primary and metastatic cancer foci, and nanoparticles brought new diagnostic and therapeutic options. Targeted therapy possessed breakthrough potential inhibiting the growth and spread of specific cancer cells, causing less damage to healthy cells. Ablation therapy has emerged as a minimally invasive procedure that burns or freezes cancers without the need for open surgery. Natural antioxidants demonstrated potential tracking down free radicals and neutralizing their harmful effects thereby treating or preventing cancer. Several new technologies are currently under research in clinical trials, and some of them have already been approved. This review presented an update on recent advances and breakthroughs in cancer therapies.

Developing Novel Anticancer Drugs for Targeted Populations: An Update

BACKGROUND

Due to higher failure rates, lengthy time and high cost of the traditional de novo drug discovery and development process, the rate of opportunity to get new, safe and efficacious drugs for the targeted population, including pediatric patients with cancer, becomes sluggish.

OBJECTIVES

This paper discusses the development of novel anticancer drugs focusing on the identification and selection of targeted anticancer drug development for the targeted population.

METHODS

Information presented in this review was obtained from different databases, including PUBMED, SCOPUS, Web of Science, and EMBASE. Various keywords were used as search terms.

RESULTS

The pharmaceutical companies currently are executing drug repurposing as an alternative means to accelerate the drug development process that reduces the risk of failure, time and cost, which take 3-12 years with almost 25% overall probability of success as compared to de novo drug discovery and development process (10- 17 years) which has less than 10% probability of success. An alternative strategy to the traditional de novo drug discovery and development process, called drug repurposing, is also presented.

CONCLUSION

Therefore, to continue with the progress of developing novel anticancer drugs for the targeted population, identification and selection of target to specific disease type is important. Considering the aspects of the age of the patient and the disease stages such as each cancer types are different when we study the disease at a molecular level. Drug repurposing technique becomes an influential alternative strategy to discover and develop novel anticancer drug candidates.

α3 integrin-binding peptide-functionalized polymersomes loaded with volasertib for dually-targeted molecular therapy for ovarian cancer

Ovarian cancer (OC) is a high-mortality malignancy in women with a five-year survival rate of 30-40%. There is an urgent need to develop high-efficacy and low toxic treatments for OC. Herein, we report an appealing strategy that combines α₃ integrin targeted polymersomes (A3-Ps) and targeted molecular drug, polo-like kinase 1 (PLK1) inhibitor volasertib (Vol) for dually targeted molecular therapy of OC in vivo. A3-Ps had good Vol loading of 7.7-8.0 wt.% and small size of 25-32 nm, depending on the density of α₃ integrin binding peptide A3. Interestingly, cellular uptake studies using FITC-labeled Vol revealed that A3-Ps with 20% peptide gave 2.3 and 3.3-fold better internalization in SKOV-3 OC cells compared with non-targeted Ps and free Vol, respectively. Accordingly, Vol loaded in A3-Ps showed the best inhibitory activity to SKOV-3 cells with an IC₅₀ of 49 nM, which was 3.5 times lower than free Vol. Importantly, the in vivo experiments demonstrated that A3-Ps-Vol proficiently repressed the growth of SKOV-3 tumors in mice while continuous tumor growth was observed for Ps-Vol and free Vol-treated mice. A3-Ps-Vol besides boosting anti-OC activity also reduced the systemic toxicity of Vol. This dually targeted molecular drug nanoformulation has appeared to be an especially potent and low toxic treatment modality for human ovarian cancers. STATEMENT OF SIGNIFICANCE: Volasertib provides a potential molecular therapy for PLK1-positive advanced OC patients. The initial clinical outcomes, nevertheless, showed a suboptimal efficacy, possibly resulting from its fast clearance, deficient tumor deposition and dose-limiting toxicities. Here, we show for the first time that dually targeted molecular therapy of OC using α₃ integrin-binding peptide-modified polymersomes as a vehicle gives markedly improved potency, better toleration, and depleted adverse effects in SKOV-3 tumor models, greatly outperforming free volasertib. This dually targeted strategy has emerged as an appealing treatment for malignant PLK1-positive ovarian tumors.

Type 2 Diabetes, Obesity, and Cancer Share Some Common and Critical Pathways

Diabetes and cancer are among the most frequent and complex diseases. Epidemiological evidence showed that the patients suffering from diabetes are significantly at higher risk for a number of cancer types. There are a number of evidence that support the hypothesis that these diseases are interlinked, and obesity may aggravate the risk(s) of type 2 diabetes and cancer. Multi-level unwanted alterations such as (epi-)genetic alterations, changes at the transcriptional level, and altered signaling pathways (receptor, cytoplasmic, and nuclear level) are the major source which promotes a number of complex diseases and such heterogeneous level of complexities are considered as the major barrier in the development of therapeutic agents. With so many known challenges, it is critical to understand the relationships and the commonly shared causes between type 2 diabetes and cancer, which is difficult to unravel and understand. Furthermore, the real complexity arises from contended corroborations that specific drug(s) (individually or in combination) during the treatment of type 2 diabetes may increase or decrease the cancer risk or affect cancer prognosis. In this review article, we have presented the recent and most updated evidence from the studies where the origin, biological background, the correlation between them have been presented or proved. Furthermore, we have summarized the methodological challenges and tasks that are frequently encountered. We have also outlined the physiological links between type 2 diabetes and cancers. Finally, we have presented and summarized the outline of the hallmarks for both these diseases, diabetes and cancer.

Oncological Ligand-Target Binding Systems and Developmental Approaches for Cancer Theranostics

Targeted treatment of cancer hinges on the identification of specific intracellular molecular receptors on cancer cells to stimulate apoptosis for eventually inhibiting growth; the development of novel ligands to target biomarkers expressed by the cancer cells; and the creation of novel multifunctional carrier systems for targeted delivery of anticancer drugs to specific malignant sites. There are numerous receptors, antigens, and biomarkers that have been discovered as oncological targets (oncotargets) for cancer diagnosis and treatment applications. Oncotargets are critically important to navigate active anticancer drug ingredients to specific disease sites with no/minimal effect on surrounding normal cells. In silico techniques relating to genomics, proteomics, and bioinformatics have catalyzed the discovery of oncotargets for various cancer types. Effective oncotargeting requires high-affinity probes engineered for specific binding of receptors associated with the malignancy. Computational methods such as structural modeling and molecular dynamic (MD) simulations offer opportunities to structurally design novel ligands and optimize binding affinity for specific oncotargets. This article proposes a streamlined approach for the development of ligand-oncotarget bioaffinity systems via integrated structural modeling and MD simulations, making use of proteomics, genomic, and X-ray crystallographic resources, to support targeted diagnosis and treatment of cancers and tumors.

Drug repurposing using transcriptome sequencing and virtual drug screening in a patient with glioblastoma

Background Precision medicine and drug repurposing are attractive strategies, especially for tumors with worse prognosis. Glioblastoma is a highly malignant brain tumor with limited treatment options and short survival times. We identified novel BRAF (47-438del) and PIK3R1 (G376R) mutations in a glioblastoma patient by RNA-sequencing. Methods The protein expression of BRAF and PIK3R1 as well as the lack of EGFR expression as analyzed by immunohistochemistry corroborated RNA-sequencing data. The expression of additional markers (AKT, SRC, mTOR, NF-κB, Ki-67) emphasized the aggressiveness of the tumor. Then, we screened a chemical library of > 1500 FDA-approved drugs and > 25,000 novel compounds in the ZINC database to find established drugs targeting BRAF47-438del and PIK3R1-G376R mutated proteins. Results Several compounds (including anthracyclines) bound with higher affinities than the control drugs (sorafenib and vemurafenib for BRAF and PI-103 and LY-294,002 for PIK3R1). Subsequent cytotoxicity analyses showed that anthracyclines might be suitable drug candidates. Aclarubicin revealed higher cytotoxicity than both sorafenib and vemurafenib, whereas idarubicin and daunorubicin revealed higher cytotoxicity than LY-294,002. Liposomal formulations of anthracyclines may be suitable to cross the blood brain barrier. Conclusions In conclusion, we identified novel small molecules via a drug repurposing approach that could be effectively used for personalized glioblastoma therapy especially for patients carrying BRAF47-438del and PIK3R1-G376R mutations.

Development and economic trends in anticancer drugs licensed in the UK from 2015 to 2019

Analysis of new anticancer drugs licensed in the UK found that 44 new therapies were approved from 2015 to 2019. No other 5-year period has produced as many new therapies. Most new drugs are kinase inhibitors (KIs, N=18) and monoclonal antibodies (mAbs, N=16) with only one classical cytotoxic chemotherapy (CC) licensed. The average median treatment duration has risen by 55 days to 318 days (263 days in 2010-2014). Drug costs have escalated; an average treatment course now costs £62 343, compared to £35 383 in 2010-2014. New drugs are delivering significant clinical benefits with longer treatment durations. However, the financial burden is greater, heralding economic challenges for healthcare providers.

Structure-activity relationship studies of indolin-2-one derivatives as vascular endothelial growth factor receptor inhibitors and anticancer agents

Angiogenesis is a requirement for the growth of cancer cells. The family of vascular endothelial growth factor receptors (VEGFRs) is the main target in metastasis. Indolin-2-one is proved to be an essential scaffold of antiangiogenic drugs. Sunitinib is the first oral indolin-2-one derivative marketed as a VEGFR inhibitor in the treatment of renal cell carcinoma and gastrointestinal stromal tumors. Therefore, novel compounds possessing the scaffold of sunitinib were designed and synthesized by different researchers to improve the anticancer activity, bioavailability, and solubility, and to decrease the toxicity of sunitinib. In this comprehensive review, the structure-activity relationship of different indolin-2-one analogs as VEGFR inhibitors is discussed. It has been observed that the indolin-2-one core is necessary for the inhibition of VEGFRs. It was determined that substitutions at C-3 of the oxindole ring play an important role in their antiangiogenic and anticancer activities.

From patent to patient: analysing access to innovative cancer drugs

Analysis of cancer drugs licensed through the European Medicines Agency (EMA) in 2000-2016 shows that the number of authorisations increased over that timeframe. The median number of licensed drugs each year rose from six for 2000-2008 to 13.5 for 2009-2016. Over 2000-2016, there were 64 drug authorisations for haematological, 15 for breast, and 12 for skin cancer, but none for oesophageal, brain, bladder, or uterine cancer. Only 6% of authorisations included a paediatric indication. The average time for a drug to progress from patent priority date to availability on the National Health Service (NHS) increased from 12.8 years for drugs first licensed in 2000-2008 to 14.0 years for those licensed in 2009-2016. There was evidence that the most innovative drugs were not being prioritised for EMA licensing and NICE approval.

Synergistic Inhibition of Kinase Pathways Overcomes Resistance of Colorectal Cancer Spheroids to Cyclic Targeted Therapies

Cancer cells often adapt to single-agent treatments with chemotherapeutics. Activation of alternative survival pathways is a major mechanism of drug resistance. A potential approach to block this feedback signaling is using combination treatments of a pair of drugs, although toxicity has been a limiting factor. Preclinical tumor models to identify mechanisms of drug resistance and determine low but effective combination doses are critical to effectively suppress tumor growth with reduced toxicity to patients. Using our aqueous two-phase system microtechnology, we developed colorectal tumor spheroids in high-throughput and evaluated resistance of cancer cells to three mitogen-activated protein kinase inhibitors (MAPKi) in long-term cyclic treatments. Our quantitative analysis showed that the efficacy of MAPKi significantly reduced over time, leading to an increase in proliferation of HCT116 colorectal cancer cells and growth of spheroids. We established that resistance was due to feedback activation of PI3K/AKT/mTOR pathway. Using high-throughput, dose-dependent combinations of each MAPKi and a PI3K/mTOR inhibitor, we identified low-dose, synergistic combinations that blocked resistance to MAPKi and effectively suppressed the growth of colorectal tumor spheroids in long-term treatments. Our approach to study drug resistance offers the potential to determine high priority treatments to test in animal models.

Dissecting mechanisms of resistance to targeted drug combination therapy in human colorectal cancer

Genomic alterations in cancer cells result in vulnerabilities that clinicians can exploit using molecularly targeted drugs, guided by knowledge of the tumour genotype. However, the selective activity of these drugs exerts an evolutionary pressure on cancers that can result in the outgrowth of resistant clones. Use of rational drug combinations can overcome resistance to targeted drugs, but resistance may eventually develop to combinatorial therapies. We selected MAPK- and PI3K-pathway inhibition in colorectal cancer as a model system to dissect out mechanisms of resistance. We focused on these signalling pathways because they are frequently activated in colorectal tumours, have well-characterised mutations and are clinically relevant. By treating a panel of 47 human colorectal cancer cell lines with a combination of MEK- and PI3K-inhibitors, we observe a synergistic inhibition of growth in almost all cell lines. Cells with KRAS mutations are less sensitive to PI3K inhibition, but are particularly sensitive to the combined treatment. Colorectal cancer cell lines with inherent or acquired resistance to monotherapy do not show a synergistic response to the combination treatment. Cells that acquire resistance to an MEK-PI3K inhibitor combination treatment still respond to an ERK-PI3K inhibitor regimen, but subsequently also acquire resistance to this combination treatment. Importantly, the mechanisms of resistance to MEK and PI3K inhibitors observed, MEK1/2 mutation or loss of PTEN, are similar to those detected in the clinic. ERK inhibitors may have clinical utility in overcoming resistance to MEK inhibitor regimes; however, we find a recurrent active site mutation of ERK2 that drives resistance to ERK inhibitors in mono- or combined regimens, suggesting that resistance will remain a hurdle. Importantly, we find that the addition of low concentrations of the BCL2-family inhibitor navitoclax to the MEK-PI3K inhibitor regimen improves the synergistic interaction and blocks the acquisition of resistance.

Predicting synthetic lethal interactions using conserved patterns in protein interaction networks

In response to a need for improved treatments, a number of promising novel targeted cancer therapies are being developed that exploit human synthetic lethal interactions. This is facilitating personalised medicine strategies in cancers where specific tumour suppressors have become inactivated. Mainly due to the constraints of the experimental procedures, relatively few human synthetic lethal interactions have been identified. Here we describe SLant (Synthetic Lethal analysis via Network topology), a computational systems approach to predicting human synthetic lethal interactions that works by identifying and exploiting conserved patterns in protein interaction network topology both within and across species. SLant out-performs previous attempts to classify human SSL interactions and experimental validation of the models predictions suggests it may provide useful guidance for future SSL screenings and ultimately aid targeted cancer therapy development.

Moonshot Objectives: Catalyze New Scientific Breakthroughs-Proteogenomics

Breaking down the silos between disciplines to accelerate the pace of cancer research is a key paradigm for the Cancer Moonshot. Molecular analyses of cancer biology have tended to segregate between a focus on nucleic acids-DNA, RNA, and their modifications-and a focus on proteins and protein function. Proteogenomics represents a fusion of those two approaches, leveraging the strengths of each to provide a more integrated vision of the flow of information from DNA to RNA to protein and eventually function at the molecular level. Proteogenomic studies have been incorporated into multiple activities associated with the Cancer Moonshot, demonstrating substantial added value. Innovative study designs integrating genomic, transcriptomic, and proteomic data, particularly those using clinically relevant samples and involving clinical trials, are poised to provide new insights regarding cancer risk, progression, and response to therapy.

Deoxypodophyllotoxin induces cell cycle arrest and apoptosis in human cholangiocarcinoma cells

Deoxypodophyllotoxin (DPT), a naturally occurring flavolignan, has a broad range of biological effects, including anti-inflammatory, anti-viral and anticancer properties. The present study investigated the anti-proliferative effect of DPT on human cholangiocarcinoma QBC939 and RBE cell lines and its underlying mechanisms of inducing cytotoxicity. MTT assays demonstrated that DPT inhibited the viability of the QBC939 and RBE cells in a dose and time-dependent manner. In addition, DPT treatment resulted in G2/M phase cell cycle arrest associated with the downregulation of Cyclin B and cyclin dependent kinase 1 and caused an increase in apoptosis that was confirmed by characteristic morphological changes. Apoptosis was accompanied by increasing B-cell lymphoma-2 (Bcl-2)/Bcl-2 associated X protein ratios and activated expression of caspase-3, -8 and -9. These findings suggested that DPT may be a novel anticancer agent against human cholangiocarcinoma.

Oncogenic RAC1 and NRAS drive resistance to endoplasmic reticulum stress through MEK/ERK signalling

Cancer cells are able to survive under conditions that cause endoplasmic reticulum stress (ER-stress), and can adapt to this stress by upregulating cell-survival signalling pathways and down-regulating apoptotic pathways. The cellular response to ER-stress is controlled by the unfolded protein response (UPR). Small Rho family GTPases are linked to many cell responses including cell growth and apoptosis. In this study, we investigate the function of small GTPases in cell survival under ER-stress. Using siRNA screening we identify that RAC1 promotes cell survival under ER-stress in cells with an oncogenic N92I RAC1 mutation. We uncover a novel connection between the UPR and N92I RAC1, whereby RAC1 attenuates phosphorylation of EIF2S1 under ER-stress and drives over-expression of ATF4 in basal conditions. Interestingly, the UPR connection does not drive resistance to ER-stress, as knockdown of ATF4 did not affect this. We further investigate cancer-associated kinase signalling pathways and show that RAC1 knockdown reduces the activity of AKT and ERK, and using a panel of clinically important kinase inhibitors, we uncover a role for MEK/ERK, but not AKT, in cell viability under ER-stress. A known major activator of ERK phosphorylation in cancer is oncogenic NRAS and we show that knockdown of NRAS in cells, which bear a Q61 NRAS mutation, sensitises to ER-stress. These findings highlight a novel mechanism for resistance to ER-stress through oncogenic activation of MEK/ERK signalling by small GTPases.

Phase I clinical trials in patients with advanced non-small cell lung cancer treated within a Drug Development Unit: What have we learnt?

OBJECTIVES

Despite advances in novel drug development for patients with advanced non-small cell lung cancer (NSCLC), there are still only a limited number of approved treatments. We therefore evaluated the clinical outcomes of patients with advanced NSCLC referred to a dedicated phase I clinical trials unit assessed baseline clinical factors associated with successful enrollment onto phase I trials.

MATERIAL AND METHODS

We conducted a retrospective study involving patients with advanced NSCLC referred to the Drug Development Unit at the RMH between January 2005 and December 2013.

RESULTS

257 patients with advanced NSCLC were referred for consideration of phase I trials, of which only 89 (35%) patients successfully commenced phase I trials. The commonest reasons for not entering study included poor ECOG performance status and rapid disease progression. A multivariate analysis identified that ECOG performance status (0-1) and RMH prognostic score (0-1) were associated with successful enrollment onto phase I trials (p<0.001). Single agent therapies included novel agents against the phosphatidylinositol-3 kinase pathway, insulin growth factor-1 receptor and pan-HER family tyrosine kinases. These trial therapies were well tolerated and mainly associated with grade 1-2 adverse events, with a minority experiencing grade 3 toxicities. Nine (10%) patients, 4 with known EGFR or KRAS mutations, achieved RECIST partial responses. Median time to progression was 2.6 months and median overall survival was 8.1 months for patients enrolled.

CONCLUSIONS

Phase I trial therapies were generally well tolerated with potential antitumor benefit for patients with advanced NSCLC. Early referral to drug development units at time of disease progression should be considered to enhance the odds of patient participation in these studies.

Exploitation of Gene Expression and Cancer Biomarkers in Paving the Path to Era of Personalized Medicine

Cancer therapy agents have been used extensively as cytotoxic drugs against tissue or organ of a specific type of cancer. With the better understanding of molecular mechanisms underlying carcinogenesis and cellular events during cancer progression and metastasis, it is now possible to use targeted therapy for these molecular events. Targeted therapy is able to identify cancer patients with dissimilar genetic defects at cellular level for the same cancer type and consequently requires individualized approach for treatment. Cancer therapy begins to shift steadily from the traditional approach of “one regimen for all patients” to a more individualized approach, through which each patient will be treated specifically according to their specific genetic defects. Personalized medicine accordingly requires identification of indicators or markers that guide in the decision making of such therapy to the chosen patients for more effective therapy. Cancer biomarkers are frequently used in clinical practice for diagnosis and prognosis, as well as identification of responsive patients and prediction of treatment response of cancer patient. The rapid breakthrough and development of microarray and sequencing technologies is probably the main tool for paving the way toward “individualized biomarker-driven cancer therapy” or “personalized medicine”. In this review, we aim to provide an updated knowledge and overview of the current landscape of cancer biomarkers and their role in personalized medicine, emphasizing the impact of genomics on the implementation of new potential targeted therapies and development of novel cancer biomarkers in improving the outcome of cancer therapy.

'Big data' approaches for novel anti-cancer drug discovery

INTRODUCTION

The development of improved cancer therapies is frequently cited as an urgent unmet medical need. Recent advances in platform technologies and the increasing availability of biological 'big data' are providing an unparalleled opportunity to systematically identify the key genes and pathways involved in tumorigenesis. The discoveries made using these new technologies may lead to novel therapeutic interventions. Areas covered: The authors discuss the current approaches that use 'big data' to identify cancer drivers. These approaches include the analysis of genomic sequencing data, pathway data, multi-platform data, identifying genetic interactions such as synthetic lethality and using cell line data. They review how big data is being used to identify novel drug targets. The authors then provide an overview of the available data repositories and tools being used at the forefront of cancer drug discovery. Expert opinion: Targeted therapies based on the genomic events driving the tumour will eventually inform treatment protocols. However, using a tailored approach to treat all tumour patients may require developing a large repertoire of targeted drugs.

Enhancing value of clinical pharmacodynamics in oncology drug development: An alliance between quantitative pharmacology and translational science

Clinical pharmacodynamic evaluation is a key component of the "pharmacologic audit trail" in oncology drug development. We posit that its value can and should be greatly enhanced via application of a robust quantitative pharmacology framework informed by biologically mechanistic considerations. Herein, we illustrate examples of intersectional blindspots across the disciplines of quantitative pharmacology and translational science and offer a roadmap aimed at enhancing the caliber of clinical pharmacodynamic research in the development of oncology therapeutics.

Mutational patterns in oncogenes and tumour suppressors

All cancers depend upon mutations in critical genes, which confer a selective advantage to the tumour cell. Knowledge of these mutations is crucial to understanding the biology of cancer initiation and progression, and to the development of targeted therapeutic strategies. The key to understanding the contribution of a disease-associated mutation to the development and progression of cancer, comes from an understanding of the consequences of that mutation on the function of the affected protein, and the impact on the pathways in which that protein is involved. In this paper we examine the mutation patterns observed in oncogenes and tumour suppressors, and discuss different approaches that have been developed to identify driver mutations within cancers that contribute to the disease progress. We also discuss the MOKCa database where we have developed an automatic pipeline that structurally and functionally annotates all proteins from the human proteome that are mutated in cancer.

Motif mediated protein-protein interactions as drug targets

Protein-protein interactions (PPI) are involved in virtually every cellular process and thus represent an attractive target for therapeutic interventions. A significant number of protein interactions are frequently formed between globular domains and short linear peptide motifs (DMI). Targeting these DMIs has proven challenging and classical approaches to inhibiting such interactions with small molecules have had limited success. However, recent new approaches have led to the discovery of potent inhibitors, some of them, such as Obatoclax, ABT-199, AEG-40826 and SAH-p53-8 are likely to become approved drugs. These novel inhibitors belong to a wide range of different molecule classes, ranging from small molecules to peptidomimetics and biologicals. This article reviews the main reasons for limited success in targeting PPIs, discusses how successful approaches overcome these obstacles to discovery promising inhibitors for human protein double minute 2 (HDM2), B-cell lymphoma 2 (Bcl-2), X-linked inhibitor of apoptosis protein (XIAP), and provides a summary of the promising approaches currently in development that indicate the future potential of PPI inhibitors in drug discovery.

Therapeutic Innovations: Tyrosine Kinase Inhibitors in Cancer

Conventional cytotoxic chemotherapy involving DNA-interacting agents and indiscriminate cell death is no longer the future of cancer management. While chemotherapy is not likely to completely disappear from the armamentarium; the use of targeted therapies in combination with conventional treatment is becoming the standard of care in human medicine. Tyrosine kinases are pivotal points of functional cellular pathways and have been implicated in malignancy, inflammatory, and immune-mediated diseases. Pharmaceutical interventions targeting aberrant tyrosine kinase signaling has exploded and is the second most important area of drug development. The “Valley of Death” between drug discovery and approval threatens to blunt the enormous strides in cancer management seen thus far. Kinase inhibitors, as targeted small molecules, hold promise in the treatment and diagnosis of cancer. However, there are still many unanswered questions regarding the use of kinase inhibitors in the interpretation and management of cancer. Comparative oncology has the potential to address restrictions and limitations in the advancement in kinase inhibitor therapy.

Targeting Stromal-Cancer Cell Crosstalk Networks in Ovarian Cancer Treatment

Ovarian cancer is a histologically, clinically, and molecularly diverse disease with a five-year survival rate of less than 30%. It has been estimated that approximately 21,980 new cases of epithelial ovarian cancer will be diagnosed and 14,270 deaths will occur in the United States in 2015, making it the most lethal gynecologic malignancy. Ovarian tumor tissue is composed of cancer cells and a collection of different stromal cells. There is increasing evidence that demonstrates that stromal involvement is important in ovarian cancer pathogenesis. Therefore, stroma-specific signaling pathways, stroma-derived factors, and genetic changes in the tumor stroma present unique opportunities for improving the diagnosis and treatment of ovarian cancer. Cancer-associated fibroblasts (CAFs) are one of the major components of the tumor stroma that have demonstrated supportive roles in tumor progression. In this review, we highlight various types of signaling crosstalk between ovarian cancer cells and stromal cells, particularly with CAFs. In addition to evaluating the importance of signaling crosstalk in ovarian cancer progression, we discuss approaches that can be used to target tumor-promoting signaling crosstalk and how these approaches can be translated into potential ovarian cancer treatment.

Extracellular polymeric substance from Aphanizomenon flos-aquae induces apoptosis via the mitochondrial pathway in A431 human epidermoid carcinoma cells

Extracellular polymeric substance (EPS) is a substance secreted during algal growth, which has been found to have numerous health-promoting effects. In the present study, A431 human epidermoid carcinoma cells were selected as target cells and cultivated in vitro as an experimental model to investigate the anti-cancer effect of extracellular polymeric substances from Aphanizomenon flos-aquae (EPS-A) and the possible underlying mechanism. Apoptosis- and cell cycle-associated molecules as well as the mitochondrial membrane potential of the cells were quantified using flow cytometry (FCM). FCM showed that EPS-A induced cell cycle arrest, which led to a loss of mitochondrial function of the A431 cells and an increase in necrotic and late apoptotic cells. In order to evaluate the apoptosis and cell viability, acridine orange/ethidium bromide staining was used, morphological changes were observed using fluorescence microscopy and typical apoptotic characteristics were observed. Following treatment with a high dose of EPS-A, transmission electron microscopy showed nuclear fragmentation, chromosome condensation, cell shrinkage and expansion of the endoplasmic reticulum; apoptotic bodies were also observed. In conclusion, EPS-A caused cell cycle arrest, stimulated cell apoptosis via the mitochondrial pathway and exhibited important anti-cancer activity.

An overview of targeted cancer therapy

Cancer is a multifactorial disease and is one of the leading causes of death worldwide. The contributing factors include specific genetic background, chronic exposure to various environmental stresses and improper diet. All these risk factors lead to the accumulation of molecular changes or mutations in some important proteins in cells which contributes to the initiation of carcinogenesis. Chemotherapy is an effective treatment against cancer but undesirable chemotherapy reactions and the development of resistance to drugs which results in multi-drug resistance (MDR) are the major obstacles in cancer chemotherapy. Strategies which are in practice with limited success include alternative formulations e.g., liposomes, resistance modulation e.g., PSC833, antidotes/toxicity modifiers e.g., ICRF-187 and gene therapy. Targeted therapy is gaining importance due to its specificity towards cancer cells while sparing toxicity to off-target cells. The scope of this review involves the various strategies involved in targeted therapy like-monoclonal antibodies, prodrug, small molecule inhibitors and nano-particulate antibody conjugates.

Shrapnel nanoparticles loading docetaxel inhibit metastasis and growth of breast cancer

Metastasis is one of the major obstacles for the successful therapy of breast cancer. To inhibit the metastasis and growth of breast cancer simultaneously, a new docetaxel (DTX) loaded shrapnel nano delivery system with the reduction- and enzyme-sensitive properties was designed and developed. Firstly, methoxy polyethylene glycol-peptide-vitamin E succinate (PPV), a matrix metalloproteinases (MMPs)-sensitive copolymer, was synthesized by conjugating mPEG and vitamin E succinate (VES) using an enzyme-sensitive peptide. Then, DTX loaded methoxy polyethylene glycol-s-s-vitamin E succinate (PSV) micelles (DPM) @ PPV-based liposomes (DPM@PL) were prepared by the incorporation of DPM into the PPV-based liposomes. DPM@PL showed a shrapnel structure with average particle size 113.3 ± 2.7 nm. The drug loading and encapsulation efficiency of DPM@PL were 1.93% and 99.02%, respectively. An obvious burst release (>90%) of drug was observed in the simulated tumor microenvironment with MMPs and reductive glutathione. The cellular uptake and cytotoxicity of DPM@PL in 4T1 cells were significantly enhanced after the pre-treatment of activated MMP-9. Compared with Taxotere(®), DPM@PL remarkably increased the distribution of DTX in lung and tumor of 4T1 tumor-bearing mice, and inhibited the in situ tumor growth and pulmonary metastasis formation effectively through the enhanced DTX-induced apoptosis and the reduced metastasis-promoting proteins expression. Compared with saline group, the inhibitory rates of DPM@PL against tumor volume and lung metastasis were about 81% and 92%, respectively, and it didn't produce the significant systemic toxicity. As a result, DPM@PL could be a promising nano delivery system for the successful therapy of breast cancer.

Targeting of multiple oncogenic signaling pathways by Hsp90 inhibitor alone or in combination with berberine for treatment of colorectal cancer

There is a wide range of drugs and combinations under investigation and/or approved over the last decade to treat colorectal cancer (CRC), but the 5-year survival rate remains poor at stages II-IV. Therefore, new, more-efficient drugs still need to be developed that will hopefully be included in first-line therapy or overcome resistance when it appears, as part of second- or third-line treatments in the near future. In this study, we revealed that heat shock protein 90 (Hsp90) inhibitors have high therapeutic potential in CRC according to combinative analysis of NCBI's Gene Expression Omnibus (GEO) repository and chemical genomic database of Connectivity Map (CMap). We found that second generation Hsp90 inhibitor, NVP-AUY922, significantly downregulated the activities of a broad spectrum of kinases involved in regulating cell growth arrest and death of NVP-AUY922-sensitive CRC cells. To overcome NVP-AUY922-induced upregulation of survivin expression which causes drug insensitivity, we found that combining berberine (BBR), a herbal medicine with potency in inhibiting survivin expression, with NVP-AUY922 resulted in synergistic antiproliferative effects for NVP-AUY922-sensitive and -insensitive CRC cells. Furthermore, we demonstrated that treatment of NVP-AUY922-insensitive CRC cells with the combination of NVP-AUY922 and BBR caused cell growth arrest through inhibiting CDK4 expression and induction of microRNA-296-5p (miR-296-5p)-mediated suppression of Pin1-β-catenin-cyclin D1 signaling pathway. Finally, we found that the expression level of Hsp90 in tumor tissues of CRC was positively correlated with CDK4 and Pin1 expression levels. Taken together, these results indicate that combination of NVP-AUY922 and BBR therapy can inhibit multiple oncogenic signaling pathways of CRC.

Optimizing oncology therapeutics through quantitative translational and clinical pharmacology: challenges and opportunities

Despite advances in biomedical research that have deepened our understanding of cancer hallmarks, resulting in the discovery and development of targeted therapies, the success rates of oncology drug development remain low. Opportunities remain for objective dose selection informed by exposure-response understanding to optimize the benefit-risk balance of novel therapies for cancer patients. This review article discusses the principles and applications of modeling and simulation approaches across the lifecycle of development of oncology therapeutics. Illustrative examples are used to convey the value gained from integration of quantitative clinical pharmacology strategies from the preclinical-translational phase through confirmatory clinical evaluation of efficacy and safety.

Strategies for modern biomarker and drug development in oncology

Technological advancements in the molecular characterization of cancers have enabled researchers to identify an increasing number of key molecular drivers of cancer progression. These discoveries have led to multiple novel anticancer therapeutics, and clinical benefit in selected patient populations. Despite this, the identification of clinically relevant predictive biomarkers of response continues to lag behind. In this review, we discuss strategies for the molecular characterization of cancers and the importance of biomarkers for the development of novel antitumor therapeutics. We also review critical successes and failures in oncology, and detail the lessons learnt, which may aid in the acceleration of anticancer drug development and biomarker discovery.

Using a rhabdomyosarcoma patient-derived xenograft to examine precision medicine approaches and model acquired resistance

BACKGROUND

Precision (Personalized) medicine has the potential to revolutionize patient health care especially for many cancers where the fundamental disease etiology remains either elusive or has no available therapy. Here we outline a study in alveolar rhabdomyosarcoma, in which we use gene expression profiling and a series of drug prediction algorithms combined with a matched patient-derived xenograft (PDX) model to test bioinformatically predicted therapies.

PROCEDURE

A PDX model was developed from a patient biopsy and a number of drugs identified using gene expression analysis in combination with drug prediction algorithms. Drugs chosen from each of the predictive methodologies, along with the patient's standard-of-care therapy (ICE-T), were tested in vivo in the PDX tumor. A second study was initiated using the tumors that re-grew following the ICE-T treatment. Further expression analysis identified additional therapies with potential anti-tumor efficacy.

RESULTS

A number of the predicted therapies were found to be active against the tumors in particular BGJ398 (FGFR2) and ICE-T. Re-transplanted ICE-T treated tumorgrafts demonstrated a decreased response to ICE-T recapitulating the patient's refractory disease. Gene expression profiling of the ICE-T treated tumorgrafts identified cytarabine (SLC29A1) as a potential therapy, which was shown, along with BGJ398, to be highly active in vivo.

CONCLUSIONS

This study illustrates that PDX models are suitable surrogates for testing potential therapeutic strategies based on gene expression analysis, modeling clinical drug resistance and hold the potential to assist in guiding prospective patient care.

GBM's multifaceted landscape: highlighting regional and microenvironmental heterogeneity

Gliomas are a heterogeneous group of tumors that show variable proliferative potential, invasiveness, aggressiveness, histological grading, and clinical behavior. In this review, we focus on glioblastoma multiforme (GBM), a grade IV glioma, which is the most common and malignant of primary adult brain tumors. Research over the past several decades has revealed the existence of extensive cellular, molecular, genetic, epigenetic, and metabolic heterogeneity among tumors of the same grade and even within individual tumors. Evaluation of different tumor types has shown that tumors with advanced grade and clinical aggressiveness also display enhanced molecular, cellular, and microenvironmental heterogeneity. From a therapeutic standpoint, this heterogeneity is a major clinical hurdle for devising effective therapeutic strategies for patients and challenges personalized medicine. In this review, we will highlight key aspects of GBM heterogeneity, directing special attention to regional heterogeneity, hypoxia, genomic heterogeneity, tumor-specific metabolic reprogramming, neovascularization or angiogenesis, and stromal immune cells. We will further discuss the clinical implications of GBM heterogeneity in the context of therapy.

Polymalic acid-based nano biopolymers for targeting of multiple tumor markers: an opportunity for personalized medicine?

Tumors with similar grade and morphology often respond differently to the same treatment because of variations in molecular profiling. To account for this diversity, personalized medicine is developed for silencing malignancy associated genes. Nano drugs fit these needs by targeting tumor and delivering antisense oligonucleotides for silencing of genes. As drugs for the treatment are often administered repeatedly, absence of toxicity and negligible immune response are desirable. In the example presented here, a nano medicine is synthesized from the biodegradable, non-toxic and non-immunogenic platform polymalic acid by controlled chemical ligation of antisense oligonucleotides and tumor targeting molecules. The synthesis and treatment is exemplified for human Her2-positive breast cancer using an experimental mouse model. The case can be translated towards synthesis and treatment of other tumors.

Combining trail with PI3 kinase or HSP90 inhibitors enhances apoptosis in colorectal cancer cells via suppression of survival signaling

TRAIL has been shown to induce apoptosis in cancer cells, but in some cases they fail to respond to this ligand. We explored the ability of representative phosphatidylinositol-3-kinase (PI3 Kinase)/mTOR and HSP90 inhibitors to overcome TRAIL resistance by increasing apoptosis in colorectal cancer models. We determined the sensitivity of 27 human colorectal cancer and 2 non-transformed colon epithelial cell lines to TRAIL treatment. A subset of the cancer cell lines with a range of responses to TRAIL was selected from the panel for treatment with TRAIL combined with the PI3 Kinase/mTOR inhibitor PI-103 or the HSP90 inhibitor 17-AAG (tanespimycin). Two TRAIL-resistant cell lines were selected for in vivo combination studies with TRAIL and 17-AAG. We found that 13 colorectal cancer cell lines and the 2 non-transformed colon epithelial cell lines were resistant to TRAIL. We demonstrated that co-treatment of TRAIL and PI-103 or 17-AAG was synergistic or additive and significantly enhanced apoptosis in colorectal cancer cells. This was associated with decreased expression or activity of survival protein biomarkers such as ERBB2, AKT, IKKα and XIAP. In contrast, the effect of the combination treatments in non-transformed colon cells was minimal. We show here for the first time that co-treatment in vivo with TRAIL and 17-AAG in two TRAIL-resistant human colorectal cancer xenograft models resulted in significantly greater tumor growth inhibition compared to single treatments. We propose that combining TRAIL with PI3 Kinase/mTOR or HSP90 inhibitors has therapeutic potential in the treatment of TRAIL-resistant colorectal cancers.

Cracking the cytotoxicity code: apoptotic induction of 10-acetylirciformonin B is mediated through ROS generation and mitochondrial dysfunction

A marine furanoterpenoid derivative, 10-acetylirciformonin B (10AB), was found to inhibit the proliferation of leukemia, hepatoma, and colon cancer cell lines, with selective and significant potency against leukemia cells. It induced DNA damage and apoptosis in leukemia HL 60 cells. To fully understand the mechanism behind the 10AB apoptotic induction against HL 60 cells, we extended our previous findings and further explored the precise molecular targets of 10AB. We found that the use of 10AB increased apoptosis by 8.9%-87.6% and caused disruption of mitochondrial membrane potential (MMP) by 15.2%-95.2% in a dose-dependent manner, as demonstrated by annexin-V/PI and JC-1 staining assays, respectively. Moreover, our findings indicated that the pretreatment of HL 60 cells with N-acetyl-l-cysteine (NAC), a reactive oxygen species (ROS) scavenger, diminished MMP disruption and apoptosis induced by 10AB, suggesting that ROS overproduction plays a crucial rule in the cytotoxic activity of 10AB. The results of a cell-free system assay indicated that 10AB could act as a topoisomerase catalytic inhibitor through the inhibition of topoisomerase IIα. On the protein level, the expression of the anti-apoptotic proteins Bcl-xL and Bcl-2, caspase inhibitors XIAP and survivin, as well as hexokinase II were inhibited by the use of 10AB. On the other hand, the expression of the pro-apoptotic protein Bax was increased after 10AB treatment. Taken together, our results suggest that 10AB-induced apoptosis is mediated through the overproduction of ROS and the disruption of mitochondrial metabolism.