Biology-driven cancer drug development: back to the future

The Identification of the Biomarkers of Sheng-Ji Hua-Yu Formula Treated Diabetic Wound Healing Using Modular Pharmacology

Sheng-Ji Hua-Yu (SJHY) formula has been proved to reduce the severity of diabetic wound healing without significant adverse events in our previous clinical trials. However, based on multi-target characteristics, the regulatory network among herbs, ingredients, and hub genes remains to be elucidated. The current study aims to identify the biomarkers of the SJHY formula for the treatment of diabetic wound healing. First, a network of components and targets for the SJHY formula was constructed using network pharmacology. Second, the ClusterONE algorithm was used to build a modular network and identify hub genes along with kernel pathways. Third, we verified the kernel targets by molecular docking to select hub genes. In addition, the biomarkers of the SJHY formula were validated by animal experiments in a diabetic wound healing mice model. The results revealed that the SJHY formula downregulated the mRNA expression of Cxcr4, Oprd1, and Htr2a, while upregulated Adrb2, Drd, Drd4, and Hrh1. Besides, the SJHY formula upregulated the kernel pathways, neuroactive ligand-receptor interaction, and cAMP signaling pathway in the skin tissue homogenate of the diabetic wound healing mice model. In summary, this study identified the potential targets and kernel pathways, providing additional evidence for the clinical application of the SJHY formula for the treatment of diabetic wound healing.

Dynamic Collaborations for the Development of Immune Checkpoint Blockade Agents

We studied the overview of drug discovery and development to understand the recent trends and potential success factors of interorganizational collaboration by reviewing 1204 transactions performed until 2019 for 107 anticancer drugs approved by the US Food and Drug Administration (FDA) from 1999 to 2018. Immune checkpoint blockade was found to be a significantly active area in interorganizational transactions, especially the number of alliances, compared with other mechanisms of action of small molecules and biologics for cancer treatment. Furthermore, the analysis of pembrolizumab and nivolumab showed that the number of approved indications for these two drugs has been rapidly expanding since their first approval in 2014. Examination of the acquisitions and alliances regarding pembrolizumab and nivolumab showed that many combination partners were developed by US-based biotechnology or start-up companies, the majority of which were biologics. These findings suggest that immune checkpoint blockade is a paradigm for cancer treatment, resulting in huge product sales and continuous indication expansion. Additionally, interorganizational collaboration, especially trial collaboration, is a strategic approach for the development of immune checkpoint blockade agents. The translation of these empirical practices to new drug candidates is expected for the research and development of innovative drugs in the future.

Identification of highly penetrant Rb-related synthetic lethal interactions in triple negative breast cancer

Although defects in the RB1 tumour suppressor are one of the more common driver alterations found in triple-negative breast cancer (TNBC), therapeutic approaches that exploit this have not been identified. By integrating molecular profiling data with data from multiple genetic perturbation screens, we identified candidate synthetic lethal (SL) interactions associated with RB1 defects in TNBC. We refined this analysis by identifying the highly penetrant effects, reasoning that these would be more robust in the face of molecular heterogeneity and would represent more promising therapeutic targets. A significant proportion of the highly penetrant RB1 SL effects involved proteins closely associated with RB1 function, suggesting that this might be a defining characteristic. These included nuclear pore complex components associated with the MAD2 spindle checkpoint protein, the kinase and bromodomain containing transcription factor TAF1, and multiple components of the SCFSKP Cullin F box containing complex. Small-molecule inhibition of SCFSKP elicited an increase in p27Kip levels, providing a mechanistic rationale for RB1 SL. Transcript expression of SKP2, a SCFSKP component, was elevated in RB1-defective TNBCs, suggesting that in these tumours, SKP2 activity might buffer the effects of RB1 dysfunction.

VGLL4 Selectively Represses YAP-Dependent Gene Induction and Tumorigenic Phenotypes in Breast Cancer

Members of the mammalian Vestigial-like (VGLL) family of transcriptional cofactors activate genes in response to a wide variety of environmental cues. Recently, VGLL proteins have been proposed to regulate key signaling networks involved in cancer development and progression. However, the biological and clinical significance of VGLL dysregulation in human breast cancer pathogenesis remains unknown. Here, we report that diminished VGLL4 expression, but not VGLL1-3, correlated with both shorter relapse-free survival and shorter disease-specific survival of cancer patients with different molecular subtypes of breast cancer. Additionally, we further demonstrate that overexpression of VGLL4 reduces breast cancer cell proliferation, migration, intravasation/extravasation potential, favors cell death, and suppresses tumor growth in vivo. Mechanistically, VGLL4 negatively regulates the TEAD1-YAP1 transcriptional complex and exerts its growth inhibitory control through its evolutionary conserved TDU2 domain at its C-terminus. The results suggest that VGLL4 is a candidate tumor suppressor gene which acts by selectively antagonizing YAP-dependent tumor growth. VGLL4 may be a promising therapeutic target in breast cancer.

Preclinical evaluation of biomarkers associated with antitumor activity of MELK inhibitor

MELK is upregulated in various types of human cancer and is known to be associated with cancer progression, maintenance of stemness, and poor prognosis. OTS167, a MELK kinase inhibitor, shows potent growth-suppressive effect on human tumors in a xenograft model, but the detailed mode of action has not been fully elucidated. In this study, we demonstrate the molecular mechanism of action of MELK inhibitor OTS167 in a preclinical model. OTS167-treated cells caused morphological transformation, induced the differentiation markers, and reduced stem-cell marker expression. Furthermore, we identified DEPDC1, known as an oncogene, as an additional downstream molecule of the MELK signaling pathway. MELK enhanced DEPDC1 phosphorylation and its stability. The expression of MELK and downstream molecules was decreased in OTS167-treated xenograft tumor tissues, which revealed central necrosis and significant growth suppression. Our data should further shed light on the mechanism of action how OTS167 suppresses tumor growth through the inhibition of the MELK signaling pathway and suggest the possibility of biomarkers for the assessment of clinical efficacy.

Antitumor and immunomodulatory activities of thiosemicarbazones and 1,3-Thiazoles in Jurkat and HT-29 cells

Cancer remains a high incidence and mortality disease, causing around 8.2 million of deaths in the last year. Current chemotherapy needs to be expanded, making research for new drugs a necessary task. Immune system modulation is an emerging concept in cancer cell proliferation control. In fact, there are a number of mechanisms underlying the role immune system plays in tumor cells. In this work, we describe the structural design, synthesis, antitumor and immunomodulatory potential of 31 new 1,3-thiazole and thiosemicarbazone compounds. Cisplatin was used as anticancer drug control. Cytotoxicity against J774A.1 macrophages and antitumor activity against HT-29 and Jurkat cells was determined. These 1,3-thiazole and thiosemicarbazone compounds not only exhibited cytotoxicity in cancer cells, but were able to cause irreversible cancer cell damage by inducing necrosis and apoptosis. In addition, these compounds, especially pyridyl-thiazoles compounds, regulated immune factors such as interleukin 10 and tumor necrosis factor, possible by directing immune system in favor of modulating cancer cell proliferation. By examining their pharmacological activity, we were able to identify new potent and selective anticancer compounds.

A chemical-genetic interaction map of small molecules using high-throughput imaging in cancer cells

Small molecules often affect multiple targets, elicit off-target effects, and induce genotype-specific responses. Chemical genetics, the mapping of the genotype dependence of a small molecule's effects across a broad spectrum of phenotypes can identify novel mechanisms of action. It can also reveal unanticipated effects and could thereby reduce high attrition rates of small molecule development pipelines. Here, we used high-content screening and image analysis to measure effects of 1,280 pharmacologically active compounds on complex phenotypes in isogenic cancer cell lines which harbor activating or inactivating mutations in key oncogenic signaling pathways. Using multiparametric chemical-genetic interaction analysis, we observed phenotypic gene-drug interactions for more than 193 compounds, with many affecting phenotypes other than cell growth. We created a resource termed the Pharmacogenetic Phenome Compendium (PGPC), which enables exploration of drug mode of action, detection of potential off-target effects, and the generation of hypotheses on drug combinations and synergism. For example, we demonstrate that MEK inhibitors amplify the viability effect of the clinically used anti-alcoholism drug disulfiram and show that the EGFR inhibitor tyrphostin AG555 has off-target activity on the proteasome. Taken together, this study demonstrates how combining multiparametric phenotyping in different genetic backgrounds can be used to predict additional mechanisms of action and to reposition clinically used drugs.

Identifying potential indicators to measure the outcome of translational cancer research: a mixed methods approach

Background

In a context where there is an increasing demand to evaluate the outcome of bio-medical research, our work aims to develop a set of indicators to measure the impact of translational cancer research. The objective of our study was to explore the scope and issues of translational research relevant to evaluation, explore the views of researchers on the evaluation of oncological translational research, and select indicators measuring the outcomes and outputs of translational research in oncology by consensus.

Methods

Semi-structured interviews amongst 23 researchers involved in translational cancer research were conducted and analysed using thematic analysis. A two-round modified Delphi survey of 35 participants with similar characteristics was then performed followed by a physical meeting. Participants rated the feasibility and validity of 60 indicators. The physical meeting was held to discuss the methodology of the new indicators.

Results

The main themes emerging from the interviews included a common definition for translational research but disagreements about the exact scope and limits of this research, the importance of multidisciplinarity and collaboration for the success of translational research, the disadvantages that translational research faces in current evaluation systems, the relative lack of pertinence of existing indicators, and propositions to measure translational cancer research in terms of clinical applications and patient outcomes. A total of 35 participants took part in the first round survey and 12 in the second round. The two-round survey helped us select a set of 18 indicators, including four that seemed to be particularly adapted to measure translational cancer research impact on health service research (number of biomarkers identified, generation of clinical guidelines, citation of research in clinical guidelines, and citation of research in public health guidelines). The feedback from participants helped refine the methodology and definition of indicators not commonly used.

Conclusion

Indicators need to be accepted by stakeholders under evaluation. This study helped the selection and refinement of indicators considered as the most relevant by researchers in translational cancer research. The feasibility and validity of those indicators will be tested in a scientometric study.

Electronic supplementary material

The online version of this article (doi:10.1186/s12961-015-0060-5) contains supplementary material, which is available to authorized users.

Hypoxia-induced carbonic anhydrase IX as a target for cancer therapy: from biology to clinical use

The tumor microenvironment includes a complicated network of physiological gradients contributing to plasticity of tumor cells and heterogeneity of tumor tissue. Hypoxia is a key component generating intratumoral oxygen gradients, which affect the cellular expression program and lead to therapy resistance and increased metastatic propensity of weakly oxygenated cell subpopulations. One of the adaptive responses of tumor cells to hypoxia involves the increased expression and functional activation of carbonic anhydrase IX (CA IX), a cancer-related cell surface enzyme catalyzing the reversible conversion of carbon dioxide to bicarbonate ion and proton. Via its catalytic activity, CA IX participates in regulation of intracellular and extracellular pH perturbations that result from hypoxia-induced changes in cellular metabolism producing excess of acid. Through the ability to regulate pH, CA IX also facilitates cell migration and invasion. In addition, CA IX has non-catalytic function in cell adhesion and spreading. Thus, CA IX endows tumor cells with survival advantages in hypoxia/acidosis and confers an increased ability to migrate, invade and metastasize. Accordingly, CA IX is expressed in a broad range of tumors, where it is associated with prognosis and therapy outcome. Its expression pattern and functional implications in tumor biology make CA IX a promising therapeutic target, which can be hit either by immunotherapy with monoclonal antibodies or with compounds inhibiting its enzyme activity. The first strategy has already reached the clinical trials, whereas the second one is still in preclinical testing. Both strategies indicate that CA IX can become a clinically useful anticancer target, but urge further efforts toward better selection of patients for immunotherapy and deeper understanding of tumor types, clinical situations and synthetic lethality interactions with other treatment approaches.

DNA repair in cancer: emerging targets for personalized therapy

Genomic deoxyribonucleic acid (DNA) is under constant threat from endogenous and exogenous DNA damaging agents. Mammalian cells have evolved highly conserved DNA repair machinery to process DNA damage and maintain genomic integrity. Impaired DNA repair is a major driver for carcinogenesis and could promote aggressive cancer biology. Interestingly, in established tumors, DNA repair activity is required to counteract oxidative DNA damage that is prevalent in the tumor microenvironment. Emerging clinical data provide compelling evidence that overexpression of DNA repair factors may have prognostic and predictive significance in patients. More recently, DNA repair inhibition has emerged as a promising target for anticancer therapy. Synthetic lethality exploits intergene relationships where the loss of function of either of two related genes is nonlethal, but loss of both causes cell death. Exploiting this approach by targeting DNA repair has emerged as a promising strategy for personalized cancer therapy. In the current review, we focus on recent advances with a particular focus on synthetic lethality targeting in cancer.

Drugs for solid cancer: the productivity crisis prompts a rethink

Despite remarkable progress in cancer-drug discovery, the delivery of novel, safe, and sustainably effective products to the clinic has stalled. Using Src as a model, we examine key steps in drug development. The preclinical evidence on the relationship between Src and solid cancer is in sharp contrast with the modest anticancer effect noted in conventional clinical trials. Here, we consider Src inhibitors as an example of a promising drug class directed to invasion and metastasis and identify roadblocks in translation. We question the assumption that a drug-induced tumor shrinkage in preclinical and clinical studies predicts a successful outcome. Our analysis indicates that the key areas requiring attention are related, and include preclinical models (in vitro and mouse models), meaningful clinical trial end points, and an appreciation of the role of metastasis in morbidity and mortality. Current regulations do not reflect the natural history of the disease, and may be unrelated to the key complications: local invasion, metastasis, and the development of resistance. Alignment of preclinical and clinical studies and regulations based on mechanistic trial end points and platforms may help in overcoming these roadblocks. Viewed kaleidoscopically, most elements necessary and sufficient for a novel translational paradigm are in place.

Tankyrase-targeted therapeutics: expanding opportunities in the PARP family

The poly(ADP-ribose) polymerase (PARP) protein superfamily has wide-ranging roles in cellular processes such as DNA repair and WNT signalling. Efforts to pharmacologically target PARP enzymes have largely focused on PARP1 and the closely related PARP2, but recent work highlighting the role of another family member, tankyrase 1 (TANK1; also known as PARP5A and ARTD5), in the control of WNT signalling has fuelled interest in the development of additional inhibitors to target this enzyme class. Tankyrase function is also implicated in other processes such as the regulation of telomere length, lung fibrogenesis and myelination, suggesting that tankyrase inhibitors could have broad clinical utility. Here, we discuss the biology of tankyrases and the discovery of tankyrase-specific inhibitors. We also consider the challenges that lie ahead for the clinical development of PARP family inhibitors in general.

Functional drug-gene interactions in lung cancer

Despite the dawn of the genomic information era, the challenges of cancer treatment remain formidable. Particularly for the most prevalent cancer types, including lung cancer, successful treatment of metastatic disease is rare and escalating costs for modern targeted drugs place an increasing strain on healthcare systems. Although powerful diagnostic tools to characterize individual tumor samples in great molecular detail are becoming rapidly available, the transformation of this information into therapy provides a major challenge. A fundamental difficulty is the molecular complexity of cancer cells that often causes drug resistance, but can also render tumors exquisitely sensitive to targeted agents. By using lung cancer as an example, we outline the principles that govern drug sensitivity and resistance from a genetic perspective and discuss how in vitro chemical-genetic screens can impact on patient stratification in the clinic.

Mathematical and statistical modeling in cancer systems biology

Cancer is a major health problem with high mortality rates. In the post-genome era, investigators have access to massive amounts of rapidly accumulating high-throughput data in publicly available databases, some of which are exclusively devoted to housing Cancer data. However, data interpretation efforts have not kept pace with data collection, and gained knowledge is not necessarily translating into better diagnoses and treatments. A fundamental problem is to integrate and interpret data to further our understanding in Cancer Systems Biology. Viewing cancer as a network provides insights into the complex mechanisms underlying the disease. Mathematical and statistical models provide an avenue for cancer network modeling. In this article, we review two widely used modeling paradigms: deterministic metabolic models and statistical graphical models. The strength of these approaches lies in their flexibility and predictive power. Once a model has been validated, it can be used to make predictions and generate hypotheses. We describe a number of diverse applications to Cancer Biology, including, the system-wide effects of drug-treatments, disease prognosis, tumor classification, forecasting treatment outcomes, and survival predictions.

Genetic interactions in cancer progression and treatment

As cancer cell genomes are unveiled at a breathtaking pace, the genetic principles at play in cancer are emerging in all their complexity, prompting the assessment of classical genetic interaction models. Here, we discuss the implications of these findings for cancer progression and heterogeneity and for the development of new therapeutic approaches.

What can molecular pathology contribute to the management of renal cell carcinoma?

The incidence of renal cell carcinoma (RCC) is increasing and outcomes remain poor. One-third of patients with localized disease will relapse, and 5-year survival for patients with metastatic disease is less than 10%. No molecular test is currently available to identify which patients who have undergone 'curative' surgery will relapse, and which patients will respond to targeted therapy. Some well characterized biochemical pathways, such as those associated with von Hippel-Lindau disease, are aberrantly regulated in RCC and are associated with histological subtype, but the understanding of these pathways contributes little to the clinical management of patients with RCC. Gene expression and sequencing studies have increased our understanding of the genetic basis of the disease but have failed to establish any unified classification to improve molecular stratification or to predict which patients are likely to relapse or respond to targeted therapy. Instead, they have served to highlight that RCC is heterogeneous at histological, morphological, and molecular levels, and that novel approaches are required to resolve the complexity of RCC prognostication and prediction of treatment response.

Translating cancer research into targeted therapeutics

The emphasis in cancer drug development has shifted from cytotoxic, non-specific chemotherapies to molecularly targeted, rationally designed drugs promising greater efficacy and less side effects. Nevertheless, despite some successes drug development remains painfully slow. Here, we highlight the issues involved and suggest ways in which this process can be improved and expedited. We envision an increasing shift to integrated cancer research and biomarker-driven adaptive and hypothesis testing clinical trials. The goal is the development of specific cancer medicines to treat the individual patient, with treatment selection being driven by a detailed understanding of the genetics and biology of the patient and their cancer.

Synthetic lethal approaches to breast cancer therapy

The promise of personalized therapy for breast cancer is that therapeutic efficacy will be increased while toxic effects are reduced to a minimum. To achieve this goal, there is now an emphasis on the design of therapies that are based not only on the clinical manifestations of the disease, but also on the underlying molecular and cellular biology of cancer. However, identifying targets for personalized therapies in breast cancer is challenging. Here, we describe how biological concepts such as synthetic lethality and oncogene addiction can be used to identify new therapeutic targets and approaches. We discuss the current clinical developments in implementing synthetic lethality therapies, and highlight new ways in which this approach could be used to target specific subsets of breast cancer.