Preclinical biomarkers for a cyclin-dependent kinase inhibitor translate to candidate pharmacodynamic biomarkers in phase I patients

An Overview of CDK Enzyme Inhibitors in Cancer Therapy

The ability to address the cell cycle in cancer therapy brings up new medication development possibilities. Cyclin-dependent kinases are a group of proteins that control the progression of the cell cycle. The CDK/cyclin complexes are activated when specific CDK sites are phosphorylated. Because of their non-selectivity and severe toxicity, most first-generation CDK inhibitors (also known as pan-CDK inhibitors) have not been authorized for clinical usage. Despite this, significant progress has been made in allowing pan-CDK inhibitors to be employed in clinical settings. Pan-CDK inhibitors' toxicity and side effects have been lowered in recent years because of the introduction of combination therapy techniques. As a result of this, pan-CDK inhibitors have regained a lot of clinical potential as a combination therapy approach. The CDK family members have been introduced in this overview, and their important roles in cell cycle control have been discussed. Then, we have described the current state of CDK inhibitor research, with a focus on inhibitors other than CDK4/6. We have mentioned first-generation pan-CDKIs, flavopiridol and roscovitine, as well as second-generation CDKIs, dinaciclib, P276-00, AT7519, TG02, roniciclib, and RGB-286638, based on their research phases, clinical trials, and cancer targeting. CDKIs are CDK4/6, CDK7, CDK9, and CDK12 inhibitors. Finally, we have looked into the efficacy of CDK inhibitors and PD1/PDL1 antibodies when used together, which could lead to the development of a viable cancer treatment strategy.

Antiproliferative Activity of (-)-Isopulegol-based 1,3-Oxazine, 1,3-Thiazine and 2,4-Diaminopyrimidine Derivatives

A series of novel heterocyclic structures, namely 1,3‐oxazines, 1,3‐thiazines and 2,4‐diaminopyrimidines, were designed and synthesised. The bioassay tests demonstrated that, among these analogues, 2,4‐diaminopyridine derivatives showed significant antiproliferative activity against different human cancer cell lines (A2780, SiHa, HeLa, MCF‐7 and MDA‐MB‐231). Pyrimidines substituted with N²‐(p‐trifluoromethyl)aniline, in particular, displayed a potent inhibitory effect on the growth of cancer cells. Structure–activity relationships were also studied from the aspects of stereochemistry on the aminodiol moiety as well as exploring the effects of substituents on the pyrimidine scaffold.

Tribbles Gene Expression Profiles in Colorectal Cancer

Colorectal cancer (CRC) is the third most common cancer and the second leading cause of death due to cancer in the world. Therefore, the identification of novel druggable targets is urgently needed. Tribbles proteins belong to a pseudokinase family, previously recognized in CRC as oncogenes and potential therapeutic targets. Here, we analyzed the expression of TRIB1, TRIB2, and TRIB3 simultaneously in 33 data sets from CRC based on available GEO profiles. We show that all three Tribbles genes are overrepresented in CRC cell lines and primary tumors, though depending on specific features of the CRC samples. Higher expression of TRIB2 in the tumor microenvironment and TRIB3 overexpression in an early stage of CRC development, unveil a potential and unexplored role for these proteins in the context of CRC. Differential Tribbles expression was also explored in diverse cellular experimental conditions where either genetic or pharmacological approaches were used, providing novel hints for future research. This comprehensive bioinformatic analysis provides new insights into Tribbles gene expression and transcript regulation in CRC.

Tribbles Gene Expression Profiles in Colorectal Cancer

Colorectal cancer (CRC) is the third most common cancer and the second leading cause of death due to cancer in the world. Therefore, the identification of novel druggable targets is urgently needed. Tribbles proteins belong to a pseudokinase family, previously recognized in CRC as oncogenes and potential therapeutic targets. Here, we analyzed the expression of TRIB1, TRIB2, and TRIB3 simultaneously in 33 data sets from CRC based on available GEO profiles. We show that all three Tribbles genes are overrepresented in CRC cell lines and primary tumors, though depending on specific features of the CRC samples. Higher expression of TRIB2 in the tumor microenvironment and TRIB3 overexpression in an early stage of CRC development, unveil a potential and unexplored role for these proteins in the context of CRC. Differential Tribbles expression was also explored in diverse cellular experimental conditions where either genetic or pharmacological approaches were used, providing novel hints for future research. This comprehensive bioinformatic analysis provides new insights into Tribbles gene expression and transcript regulation in CRC.

Analyzing the scaffold diversity of cyclin-dependent kinase inhibitors and revisiting the clinical and preclinical pipeline

Kinases have gained an important place in the list of vital therapeutic targets because of their overwhelming clinical success in the last two decades. Among various clinically validated kinases, the cyclin-dependent kinases (CDK) are one of the extensively studied drug targets for clinical development. Food and Drug Administration has approved three CDK inhibitors for therapeutic use, and at least 27 inhibitors are under active clinical development. In the last decade, research and development in this area took a rapid pace, and thus the analysis of scaffold diversity is essential for future drug design. Available reviews lack the systematic study and discussion on the scaffold diversity of CDK inhibitors. Herein we have reviewed and critically analyzed the chemical diversity present in the preclinical and clinical pipeline of CDK inhibitors. Our analysis has shown that although several scaffolds represent CDK inhibitors, only the amino-pyrimidine is a well-represented scaffold. The three-nitrogen framework of amino-pyrimidine is a fundamental hinge-binding unit. Further, we have discussed the selectivity aspects among CDKs, the clinical trial dose-limiting toxicities, and highlighted the most advanced clinical candidates. We also discuss the changing paradigm towards selective inhibitors and an overview of ATP-binding pockets of all druggable CDKs. We carefully analyzed the clinical pipeline to unravel the candidates that are currently under active clinical development. In addition to the plenty of dual CDK4/6 inhibitors, there are many selective CDK7, CDK9, and CDK8/19 inhibitors in the clinical pipeline.

A novel approach for the analysis of time-course gene expression data based on computing with words

In this paper, a novel approach is proposed for the analysis of time-course gene expression data based on the path-breaking work of Zadeh, Computing with Words. This method can automatically discover the patterns of temporal gene expression profile in terms of two distinguishing descriptions: linguistic description that is understandable and interpretable for human inference; and type-2 fuzzy description that is suitable for robust machine inference in the presence of uncertainty. In contrast to conventional static data mining methods which focus on the steady-state gene expression levels, the proposed scheme is a new time-series pattern mining technique for dynamical modeling of gene expression. To evaluate the performance of this paradigm, it is applied to a case study dataset from Gene Expression Omnibus (GEO) which includes the temporal transcriptional profile of human colon cancer cells. The goal is to investigate the pharmacodynamics of two anticancer drugs. The transient and steady-state analysis of the transcriptional response clearly demonstrates the ability of the proposed approach to reveal the pharmacodynamical effects of drug type and dosage on the expression of genes.

Strategic applications of gene expression: from drug discovery/development to bedside

Gene expression is useful for identifying the molecular signature of a disease and for correlating a pharmacodynamic marker with the dose-dependent cellular responses to exposure of a drug. Gene expression offers utility to guide drug discovery by illustrating engagement of the desired cellular pathways/networks, as well as avoidance of acting on the toxicological pathways. Successful employment of gene-expression signatures in the later stages of drug development depends on their linkage to clinically meaningful phenotypic characteristics and requires a biologically meaningful mechanism combined with a stringent statistical rigor. Much of the success in clinical drug development is hinged on predefining the signature genes for their fitness for purposes of application. Specific examples are highlighted to illustrate the breadth and depth of the potential utility of gene-expression signatures in drug discovery and clinical development to targeted therapeutics at the bedside.

Systematic study of human long intergenic non-coding RNAs and their impact on cancer

The functional impact of several long intergenic non-coding RNAs (lincRNAs) has been characterized in previous studies. However, it is difficult to identify lincRNAs on a large-scale and to ascertain their functions or predict their structures in laboratory experiments because of the diversity, lack of knowledge and specificity of expression of lincRNAs. Furthermore, although there are a few well-characterized examples of lincRNAs associated with cancers, these are just the tip of the iceberg owing to the complexity of cancer. Here, by combining RNA-Seq data from several kinds of human cell lines with chromatin-state maps and human expressed sequence tags, we successfully identified more than 3000 human lincRNAs, most of which were new ones. Subsequently, we predicted the functions of 105 lincRNAs based on a coding-non-coding gene co-expression network. Finally, we propose a genetic mediator and key regulator model to unveil the subtle relationships between lincRNAs and lung cancer. Twelve lincRNAs may be principal players in lung tumorigenesis. The present study combines large-scale identification and functional prediction of human lincRNAs, and is a pioneering work in characterizing cancer-associated lincRNAs by bioinformatics.

Targeting cell cycle regulation in cancer therapy

Cell proliferation is an essential mechanism for growth, development and regeneration of eukaryotic organisms; however, it is also the cause of one of the most devastating diseases of our era: cancer. Given the relevance of the processes in which cell proliferation is involved, its regulation is of paramount importance for multicellular organisms. Cell division is orchestrated by a complex network of interactions between proteins, metabolism and microenvironment including several signaling pathways and mechanisms of control aiming to enable cell proliferation only in response to specific stimuli and under adequate conditions. Three main players have been identified in the coordinated variation of the many molecules that play a role in cell cycle: i) The cell cycle protein machinery including cyclin-dependent kinases (CDK)-cyclin complexes and related kinases, ii) The metabolic enzymes and related metabolites and iii) The reactive-oxygen species (ROS) and cellular redox status. The role of these key players and the interaction between oscillatory and non-oscillatory species have proved essential for driving the cell cycle. Moreover, cancer development has been associated to defects in all of them. Here, we provide an overview on the role of CDK-cyclin complexes, metabolic adaptations and oxidative stress in regulating progression through each cell cycle phase and transitions between them. Thus, new approaches for the design of innovative cancer therapies targeting crosstalk between cell cycle simultaneous events are proposed.

Hexim-1 modulates androgen receptor and the TGF-β signaling during the progression of prostate cancer

BACKGROUND

Androgen and TGF-β signaling are important components during the progression of prostate cancer. However, whether common molecular events participate in the activation of these signaling pathways are less understood.

METHOD

Hexim 1 expression was detected by immunohistochemistry of human tissue microarrays and TRAMP mouse models. The in vivo significance of Hexim-1 was established by crossing the TRAMP mouse model of prostate cancer with Hexim-1 heterozygous mice. TRAMP C2 cell line was also modified to delete one copy of Hexim-1 gene to generate TRAMP-C2-Hexim-1+/- cell lines.

RESULTS

In this report, we observed that Hexim-1 protein expression is absent in normal prostate but highly expressed in adenocarcinoma of the prostate and a characteristic sub-cellular distribution among normal, benign hyperplasia, and adenocarcinoma of the prostate. Heterozygosity of the Hexim-1 gene in the prostate cancer mice model and the TRAMP-C2 cell line, leads to increased Cdk9-dependent serine phosphorylation on protein targets such as the androgen receptor (AR) and the TGF-β-dependent downstream transcription factors, such as the SMAD proteins.

CONCLUSION

Our results suggest that changes in the Hexim-1 protein expression and cellular distribution significantly influences the AR activation and the TGF-β signaling. Thus, Hexim-1 is likely to play a significant role in prostate cancer progression.

Assessment of network perturbation amplitudes by applying high-throughput data to causal biological networks

Background

High-throughput measurement technologies produce data sets that have the potential to elucidate the biological impact of disease, drug treatment, and environmental agents on humans. The scientific community faces an ongoing challenge in the analysis of these rich data sources to more accurately characterize biological processes that have been perturbed at the mechanistic level. Here, a new approach is built on previous methodologies in which high-throughput data was interpreted using prior biological knowledge of cause and effect relationships. These relationships are structured into network models that describe specific biological processes, such as inflammatory signaling or cell cycle progression. This enables quantitative assessment of network perturbation in response to a given stimulus.

Results

Four complementary methods were devised to quantify treatment-induced activity changes in processes described by network models. In addition, companion statistics were developed to qualify significance and specificity of the results. This approach is called Network Perturbation Amplitude (NPA) scoring because the amplitudes of treatment-induced perturbations are computed for biological network models. The NPA methods were tested on two transcriptomic data sets: normal human bronchial epithelial (NHBE) cells treated with the pro-inflammatory signaling mediator TNFα, and HCT116 colon cancer cells treated with the CDK cell cycle inhibitor R547. Each data set was scored against network models representing different aspects of inflammatory signaling and cell cycle progression, and these scores were compared with independent measures of pathway activity in NHBE cells to verify the approach. The NPA scoring method successfully quantified the amplitude of TNFα-induced perturbation for each network model when compared against NF-κB nuclear localization and cell number. In addition, the degree and specificity to which CDK-inhibition affected cell cycle and inflammatory signaling were meaningfully determined.

Conclusions

The NPA scoring method leverages high-throughput measurements and a priori literature-derived knowledge in the form of network models to characterize the activity change for a broad collection of biological processes at high-resolution. Applications of this framework include comparative assessment of the biological impact caused by environmental factors, toxic substances, or drug treatments.

A genomic approach to predict synergistic combinations for breast cancer treatment

We leverage genomic and biochemical data to identify synergistic drug regimens for breast cancer. In order to study the mechanism of the histone deacetylase (HDAC) inhibitors valproic acid (VPA) and suberoylanilide hydroxamic acid (SAHA) in breast cancer, we generated and validated genomic profiles of drug response using a series of breast cancer cell lines sensitive to each drug. These genomic profiles were then used to model drug response in human breast tumors and show significant correlation between VPA and SAHA response profiles in multiple breast tumor data sets, highlighting their similar mechanism of action. The genes deregulated by VPA and SAHA converge on the cell cycle pathway (Bayes factor 5.21 and 5.94, respectively; P-value 10(-8.6) and 10(-9), respectively). In particular, VPA and SAHA upregulate key cyclin-dependent kinase (CDK) inhibitors. In two independent datasets, cancer cells treated with CDK inhibitors have similar gene expression profile changes to the cellular response to HDAC inhibitors. Together, these results led us to hypothesize that VPA and SAHA may interact synergistically with CDK inhibitors such as PD-033299. Experiments show that HDAC and CDK inhibitors have statistically significant synergy in both breast cancer cell lines and primary 3-dimensional cultures of cells from pleural effusions of patients. Therefore, synergistic relationships between HDAC and CDK inhibitors may provide an effective combinatorial regimen for breast cancer. Importantly, these studies provide an example of how genomic analysis of drug-response profiles can be used to design rational drug combinations for cancer treatment.

Cyclin D as a therapeutic target in cancer

Cyclin D1, and to a lesser extent the other D-type cyclins, is frequently deregulated in cancer and is a biomarker of cancer phenotype and disease progression. The ability of these cyclins to activate the cyclin-dependent kinases (CDKs) CDK4 and CDK6 is the most extensively documented mechanism for their oncogenic actions and provides an attractive therapeutic target. Is this an effective means of targeting the cyclin D oncogenes, and how might the patient subgroups that are most likely to benefit be identified?

Transcriptional analysis of an E2F gene signature as a biomarker of activity of the cyclin-dependent kinase inhibitor PHA-793887 in tumor and skin biopsies from a phase I clinical study

A transcriptional signature of the pan-cyclin-dependent kinase (Cdk) inhibitor PHA-793887 was evaluated as a potential pharmacodynamic and/or response biomarker in tumor and skin biopsies from patients treated in a phase I clinical study. We first analyzed the expression of a number of known E2F-dependent genes that were predicted to be modulated after Cdk2 and Cdk4 inhibition in xenograft tumor and skin samples of mice treated with the compound. This panel of 58 selected genes was then analyzed in biopsies from seven patients treated with PHA-793887 in a phase I dose escalation clinical trial in solid tumors. Quantitative real-time PCR or microarray analyses were done in paired skin and tumor biopsies obtained at baseline and at cycle 1. Analysis by quantitative real-time PCR of the signature in skin biopsies of patients treated at three different doses showed significant transcriptional downregulation with a dose-response correlation. These data show that PHA-793887 modulates genes involved in cell cycle regulation and proliferation in a clinical setting. The observed changes are consistent with its mechanism of action and correlate with target modulation in skin and with clinical benefit in tumors.