Druggable genome and precision medicine in cancer: current challenges

Exploring the Pharmacological Potential of Carrageenan Disaccharides as Antitumor Agents: An In Silico Approach

Carrageenans have demonstrated enhanced antitumor activity upon depolymerization into disaccharides. However, the pharmacological viability of these disaccharides and their mechanisms of antitumor action remains to be fully elucidated. This study aimed to employ computational tools to investigate the pharmacological properties and molecular targets pertinent to cancer of the disaccharides derived from the primary carrageenans. Analyses of pharmacological properties predicted by the pkCSM and SwissADME servers indicated that the disaccharides possess a favorable pharmacokinetic profile, although they encounter permeability challenges primarily due to their high polarity and low lipophilicity. Target prediction using SwissTarget and PPB2 identified five carbonic anhydrases, which are also targets of oncology drugs, as common targets for the disaccharides. Molecular docking performed with AutoDock Vina revealed that the binding energies of the disaccharides with carbonic anhydrases were comparable to or greater than those of existing drugs that target these lyases. Notably, six of the complexes formed exhibited interactions between the disaccharides and the zinc cofactor, which represents a primary mechanism of inhibition for these targets. Furthermore, molecular dynamics simulations conducted using GROMACS demonstrated a stable interaction between the disaccharides and carbonic anhydrases. These findings offer new insights into the pharmacological properties and mechanisms of action of carrageenan-derived disaccharides, highlighting their potential for further exploration in clinical trials and experimental studies.

Epigenetic silencing of KCTD8 promotes hepatocellular carcinoma growth by activating PI3K/AKT signaling

Aim: The aim of current study is to explore the epigenetic changes and function of KCTD8 in human hepatocellular carcinoma (HCC). Materials & methods: HCC cell lines and tissue samples were employed. Methylation specific PCR, flow cytometry, immunoprecipitation and xenograft mouse models were used.Results: KCTD8 was methylated in 44.83% (104/232) of HCC and its methylation may act as an independent poor prognostic marker. KCTD8 expression was regulated by DNA methylation. KCTD8 suppressed HCC cell growth both in vitro and in vivo via inhibiting PI3K/AKT pathway.Conclusion: Methylation of KCTD8 is an independent poor prognostic marker, and epigenetic silencing of KCTD8 increases the malignant tendency in HCC.

Challenges and solutions to system-wide use of precision oncology as the standard of care paradigm

The personalised oncology paradigm remains challenging to deliver despite technological advances in genomics-based identification of actionable variants combined with the increasing focus of drug development on these specific targets. To ensure we continue to build concerted momentum to improve outcomes across all cancer types, financial, technological and operational barriers need to be addressed. For example, complete integration and certification of the 'molecular tumour board' into 'standard of care' ensures a unified clinical decision pathway that both counteracts fragmentation and is the cornerstone of evidence-based delivery inside and outside of a research setting. Generally, integrated delivery has been restricted to specific (common) cancer types either within major cancer centres or small regional networks. Here, we focus on solutions in real-world integration of genomics, pathology, surgery, oncological treatments, data from clinical source systems and analysis of whole-body imaging as digital data that can facilitate cost-effectiveness analysis, clinical trial recruitment, and outcome assessment. This urgent imperative for cancer also extends across the early diagnosis and adjuvant treatment interventions, individualised cancer vaccines, immune cell therapies, personalised synthetic lethal therapeutics and cancer screening and prevention. Oncology care systems worldwide require proactive step-changes in solutions that include inter-operative digital working that can solve patient centred challenges to ensure inclusive, quality, sustainable, fair and cost-effective adoption and efficient delivery. Here we highlight workforce, technical, clinical, regulatory and economic challenges that prevent the implementation of precision oncology at scale, and offer a systematic roadmap of integrated solutions for standard of care based on minimal essential digital tools. These include unified decision support tools, quality control, data flows within an ethical and legal data framework, training and certification, monitoring and feedback. Bridging the technical, operational, regulatory and economic gaps demands the joint actions from public and industry stakeholders across national and global boundaries.

An Update on Protein Kinases as Therapeutic Targets-Part I: Protein Kinase C Activation and Its Role in Cancer and Cardiovascular Diseases

Protein kinases are one of the most significant drug targets in the human proteome, historically harnessed for the treatment of cancer, cardiovascular disease, and a growing number of other conditions, including autoimmune and inflammatory processes. Since the approval of the first kinase inhibitors in the late 1990s and early 2000s, the field has grown exponentially, comprising 98 approved therapeutics to date, 37 of which were approved between 2016 and 2021. While many of these small-molecule protein kinase inhibitors that interact orthosterically with the protein kinase ATP binding pocket have been massively successful for oncological indications, their poor selectively for protein kinase isozymes have limited them due to toxicities in their application to other disease spaces. Thus, recent attention has turned to the use of alternative allosteric binding mechanisms and improved drug platforms such as modified peptides to design protein kinase modulators with enhanced selectivity and other pharmacological properties. Herein we review the role of different protein kinase C (PKC) isoforms in cancer and cardiovascular disease, with particular attention to PKC-family inhibitors. We discuss translational examples and carefully consider the advantages and limitations of each compound (Part I). We also discuss the recent advances in the field of protein kinase modulators, leverage molecular docking to model inhibitor-kinase interactions, and propose mechanisms of action that will aid in the design of next-generation protein kinase modulators (Part II).

Exploring the promising potential of induced pluripotent stem cells in cancer research and therapy

The advent of iPSCs has brought about a significant transformation in stem cell research, opening up promising avenues for advancing cancer treatment. The formation of cancer is a multifaceted process influenced by genetic, epigenetic, and environmental factors. iPSCs offer a distinctive platform for investigating the origin of cancer, paving the way for novel approaches to cancer treatment, drug testing, and tailored medical interventions. This review article will provide an overview of the science behind iPSCs, the current limitations and challenges in iPSC-based cancer therapy, the ethical and social implications, and the comparative analysis with other stem cell types for cancer treatment. The article will also discuss the applications of iPSCs in tumorigenesis, the future of iPSCs in tumorigenesis research, and highlight successful case studies utilizing iPSCs in tumorigenesis research. The conclusion will summarize the advancements made in iPSC-based tumorigenesis research and the importance of continued investment in iPSC research to unlock the full potential of these cells.

The Future of Precision Oncology

Our understanding of the molecular mechanisms underlying cancer development and evolution have evolved rapidly over recent years, and the variation from one patient to another is now widely recognized. Consequently, one-size-fits-all approaches to the treatment of cancer have been superseded by precision medicines that target specific disease characteristics, promising maximum clinical efficacy, minimal safety concerns, and reduced economic burden. While precision oncology has been very successful in the treatment of some tumors with specific characteristics, a large number of patients do not yet have access to precision medicines for their disease. The success of next-generation precision oncology depends on the discovery of new actionable disease characteristics, rapid, accurate, and comprehensive diagnosis of complex phenotypes within each patient, novel clinical trial designs with improved response rates, and worldwide access to novel targeted anticancer therapies for all patients. This review outlines some of the current technological trends, and highlights some of the complex multidisciplinary efforts that are underway to ensure that many more patients with cancer will be able to benefit from precision oncology in the near future.

How can we deliver on the promise of precision medicine in oncology and beyond? A practical roadmap for action

Background

Precision medicine (PM) is a form of personalized medicine that recognizes that individuals with the same condition may have different underlying factors and uses molecular information to provide tailored treatments. This approach can improve treatment outcomes and transform lives through favorable risk/benefit ratios, avoidance of ineffective interventions, and possible cost savings, as evidenced in the field of lung cancer and other oncology/therapeutic settings, including cardiac disease, diabetes, and rare diseases. However, the potential benefits of PM have yet to be fully realized.

Discussion

There are many barriers to the implementation of PM in clinical practice, including fragmentation of the PM landscape, siloed approaches to address shared challenges, unwarranted variation in availability and access to PM, lack of standardization, and limited understanding of patients' experience and needs throughout the PM pathway. We believe that a diverse, intersectoral multistakeholder collaboration, with three main pillars of activity: generation of data to demonstrate the benefit of PM, education to support informed decision-making, and addressing barriers across the patient pathway, is necessary to reach the shared goal of making PM an accessible and sustainable reality. Besides healthcare providers, researchers, policymakers/regulators/payers, and industry representatives, patients in particular must be equal partners and should be central to the PM approach-from early research through to clinical trials and approval of new treatments-to ensure it represents their entire experience and identifies barriers, solutions, and opportunities at the point of delivery.

Conclusion

We propose a practical and iterative roadmap to advance PM and call for all stakeholders across the healthcare system to employ a collaborative, cocreated, patient-centered methodology to close gaps and fully realize the potential of PM.

Clinical recommendations: The role of mechanisms in the GRADE framework

The Grading of Recommendations Assessment, Development and Evaluation (GRADE) framework has become one of the most influential frameworks for assessing quality of research and developing clinical recommendations. The GRADE framework has been presented as an evolution in the Evidence-Based Medicine (EBM) movement. Both GRADE and EBM emphasize effect estimates derived from population-level clinical trials and, as a consequence, devalue the role of mechanisms as the basis for clinical decisions. Although mechanisms do not hold the epistemic privilege of rigorous clinical trials in EBM reasoning, this paper will argue that mechanisms appear to be important in the use and application of GRADE, as described in the literature. The seemingly necessary role of mechanisms in the development of clinical recommendations has, so far, received little attention and is not explicitly featured in the literature describing GRADE. The analysis of the GRADE framework presented in this paper reveals an apparent tension between EBM's willingness to downplay mechanisms and what seems their inevitable use in GRADE. In this paper, we take the position that if mechanistic reasoning is inevitable in the use of GRADE, then the instructional literature on the framework would benefit from more explicit discussion of how to consider and integrate mechanisms.

Data-driven analysis and druggability assessment methods to accelerate the identification of novel cancer targets

Over the past few decades, drug discovery has greatly improved the outcomes for patients, but several challenges continue to hinder the rapid development of novel drugs. Addressing unmet clinical needs requires the pursuit of drug targets that have a higher likelihood to lead to the development of successful drugs. Here we describe a bioinformatic approach for identifying novel cancer drug targets by performing statistical analysis to ascertain quantitative changes in expression levels between protein-coding genes, as well as co-expression networks to classify these genes into groups. Subsequently, we provide an overview of druggability assessment methodologies to prioritize and select the best targets to pursue.

Predicting cell line-specific synergistic drug combinations through a relational graph convolutional network with attention mechanism

Identifying synergistic drug combinations (SDCs) is a great challenge due to the combinatorial complexity and the fact that SDC is cell line specific. The existing computational methods either did not consider the cell line specificity of SDC, or did not perform well by building model for each cell line independently. In this paper, we present a novel encoder-decoder network named SDCNet for predicting cell line-specific SDCs. SDCNet learns common patterns across different cell lines as well as cell line-specific features in one model for drug combinations. This is realized by considering the SDC graphs of different cell lines as a relational graph, and constructing a relational graph convolutional network (R-GCN) as the encoder to learn and fuse the deep representations of drugs for different cell lines. An attention mechanism is devised to integrate the drug features from different layers of the R-GCN according to their relative importance so that representation learning is further enhanced. The common patterns are exploited through partial parameter sharing in cell line-specific decoders, which not only reconstruct the known SDCs but also predict new ones for each cell line. Experiments on various datasets demonstrate that SDCNet is superior to state-of-the-art methods and is also robust when generalized to new cell lines that are different from the training ones. Finally, the case study again confirms the effectiveness of our method in predicting novel reliable cell line-specific SDCs.

Systematical analysis of ferroptosis regulators and identification of GCLM as a tumor promotor and immunological biomarker in bladder cancer

Bladder cancer (BCa) is a life-threaten disease with an increasing incidence with age, and immunotherapy has become an important treatment for BCa, while the efficiency of the immune system declines with age. It is vital to reveal the mechanisms of tumor immune microenvironment (TIME) and identify novel immunotherapy targets for BCa. Through analyzing the RNA-seq of TCGA-BLCA cohort, we distinguished two ferroptosis-related BCa clusters, and we discovered that in comparation with cluster 2, the cluster 1 BCa patients showed higher PD-L1 expression, more unfavorable overall survival and higher tumor stage and grade. XCELL analyses showed that higher level of Th2 cell and Myeloid dendritic cell were enriched in cluster 1, while NK T cell was enriched in cluster 2, and TIDE analysis revealed that cluster 2 was more sensitive to immunotherapy than cluster 1. GSEA analysis implied that Toll-like signaling pathway and JAK_STAT signaling pathway were significantly enriched in cluster 1. Subsequently, through performing bioinformatic analysis and cell experiments, we demonstrated that GCLM is overexpressed in BCa and indicates dismal prognosis, and knockdown of GCLM can significantly suppress the colony formation ability of BCa cells. Furthermore, we also found that GCLM might be correlated with immune infiltration in BCa, and can serve as a tumor promotor and immunological biomarker in BCa, our research showed the vital roles of ferroptosis regulators in TIME of BCa, and GCLM is a latent therapeutic target for cancer immunotherapy.

Prognosis and immune infiltration analysis of endoplasmic reticulum stress-related genes in bladder urothelial carcinoma

Background: Abnormal activation of endoplasmic reticulum (ER) stress sensors and their downstream signalling pathways is a key regulator of tumour growth, tumour metastasis and the response to chemotherapy, targeted therapy and immunotherapy. However, the study of ER stress on the immune microenvironment of bladder urothelial carcinoma (BLCA) is still insufficient.

Methods: Firstly, 23 ER stress genes were selected to analyse their expression differences and prognostic value in BLCA based on the existing BLCA genome atlas data. According to the expression level of ER stress-related genes in BLCA, two independent clusters were identified using consensus cluster analysis. Subsequently, the correlation between these two clusters in terms of the immune microenvironment and their prognostic value was analysed. Finally, we analysed the prognostic value of the key ER stress gene HSP90B1 in BLCA and its corresponding mechanism that affects the immune microenvironment.

Results: Consensus clustering showed a worse prognosis and higher expression of immunoassay site-related genes (HAVCR2, PDCD1, CTLA4, CD274, LAG3, TIGIT and PDCD1LG2) in cluster 1 compared with cluster 2. Additionally, both TIMER and CIBERSORT algorithms showed that the expression of immune infiltrating cells in cluster 1 was significantly higher than that in cluster 2. Subsequently, HSP90B1 was identified as a key ER stress gene in BLCA, and its high expression indicated poor prognosis and was closely related to PD1. We also analysed the correlation between HSP90B1 expression and immune-infiltrating cell related biomarkers, which showed positive results. Finally, we verified the prognostic value of HSP90B1 in BLCA using an immunohistochemical assay in a tissue microarray of 100 patients with BLCA, validating the potential of HSP90B1 as a prognostic biomarker in patients with BLCA.

Conclusion: Our work reveals that ER stress genes play a crucial role in the BLCA immunological milieu, and HSP90B1 is a potential prognostic biomarker and therapeutic target for cancer immunotherapy.

Molecular glues: enhanced protein-protein interactions and cell proteome editing

The druggable genome is limited by structural features that can be targeted by small molecules in disease-relevant proteins. While orthosteric and allosteric protein modulators have been well studied, they are limited to antagonistic/agonistic functions. This approach to protein modulation leaves many disease-relevant proteins as undruggable targets. Recently, protein-protein interaction modulation has emerged as a promising therapeutic field for previously undruggable protein targets. Molecular glues and heterobifunctional degraders such as PROTACs can facilitate protein interactions and bring the proteasome into proximity to induce targeted protein degradation. In this review, we discuss the function and rational design of molecular glues, heterobifunctional degraders, and hydrophobic tag degraders. We also review historic and novel molecular glues and targets and discuss the challenges and opportunities in this new therapeutic field.

Identification of Ferroptosis-Related Genes as Biomarkers for Sarcoma

Sarcomas are seen as mixed-up nature with genetic and transcriptional heterogeneity and poor prognosis. Although the genes involved in ferroptosis are still unclear, iron loss is considered to be the core of glioblastoma, tumor progression, and tumor microenvironment. Here, we developed and tested the prognosis of SARC, which is a genetic marker associated with iron residues. The ferroptosis-related gene expression, one-way Cox analysis, and least-selection absolute regression algorithm (LASSO) are used to track prognostic-related genes and create risk assessment models. Finally, immune system infiltration and immune control point analysis are used to study the characteristics of the tumor microenvironment related to risk assessment. Moreover, LncRNA–miRNA–mRNA network was contributed in our studies. We determined the biomarker characteristics associated with iron degradation in gene 32 and developed a risk assessment model. ROC analysis showed that its model was accurately predicted, with 1, 2, 3, 4, and 5 years of overall survival in TCGA cohort of SARC patients. A comparative analysis of settings found that overall survival (OS) was lower in the high-risk than that in the low-risk group. The nomogram survival prediction model also helped to predict the OS of SARC patients. The nomogram survival prediction model has strong predictive power for the overall survival of SARC patients in TCGA dataset. GSEA analysis shows that high-risk groups are rich in inflammation, cancer-related symptoms, and pathological processes. High risk is related to immune cell infiltration and immune checkpoint. Our prediction model is based on SARC ferritin-related genes, which may support SARC prediction and provide potential attack points.

Histochemistry, Cytochemistry and Epigenetics

Over the past few decades, many researchers have individually identified tumor-related genes, and have accumulated information on their basic research in a database. With the development of technology that can comprehensively test the expression status within a short time, oncogene panel testing has become attainable. On the other hand, changes in gene expression that do not depend on changes in base sequences, that is, epigenetics, or more comprehensively, epigenomes, are also highly involved in the development and progression of disease. Oncogene panel tests tend to focus on DNA base mutations such as point mutations, deletions, duplications, and chimera formation. Elucidation leads to correct interpretation of diseases and treatment choices, and we are in an era where integrated understanding of the genome and epigenome is indispensable. In this review, we make every effort to cover a wide range of knowledge, including data on histone protein modification, non-coding (nc)RNA and DNA methylation, and recent application trials for demonstrating epigenetic alterations in histologic and cytologic specimens. We hope this review will help marshal the knowledge accumulated by researchers involved in genomic and epigenomic studies.

Probing altered enzyme activity in the biochemical characterization of cancer

Enzymes have evolved to catalyze their precise reactions at the necessary rates, locations, and time to facilitate our development, to respond to a variety of insults and challenges, and to maintain a healthy, balanced state. Enzymes achieve this extraordinary feat through their unique kinetic parameters, myriad regulatory strategies, and their sensitivity to their surroundings, including substrate concentration and pH. The Cancer Genome Atlas (TCGA) highlights the extraordinary number of ways in which the finely tuned activities of enzymes can be disrupted, contributing to cancer development and progression often due to somatic and/or inherited genetic alterations. Rather than being limited to the domain of enzymologists, kinetic constants such as k_cat, K_m, and k_cat/K_m are highly informative parameters that can impact a cancer patient in tangible ways—these parameters can be used to sort tumor driver mutations from passenger mutations, to establish the pathways that cancer cells rely on to drive patients’ tumors, to evaluate the selectivity and efficacy of anti-cancer drugs, to identify mechanisms of resistance to treatment, and more. In this review, we will discuss how changes in enzyme activity, primarily through somatic mutation, can lead to altered kinetic parameters, new activities, or changes in conformation and oligomerization. We will also address how changes in the tumor microenvironment can affect enzymatic activity, and briefly describe how enzymology, when combined with additional powerful tools, and can provide us with tremendous insight into the chemical and molecular mechanisms of cancer.

Can Systems Biology Advance Clinical Precision Oncology?

Precision oncology is perceived as a way forward to treat individual cancer patients. However, knowing particular cancer mutations is not enough for optimal therapeutic treatment, because cancer genotype-phenotype relationships are nonlinear and dynamic. Systems biology studies the biological processes at the systems' level, using an array of techniques, ranging from statistical methods to network reconstruction and analysis, to mathematical modeling. Its goal is to reconstruct the complex and often counterintuitive dynamic behavior of biological systems and quantitatively predict their responses to environmental perturbations. In this paper, we review the impact of systems biology on precision oncology. We show examples of how the analysis of signal transduction networks allows to dissect resistance to targeted therapies and inform the choice of combinations of targeted drugs based on tumor molecular alterations. Patient-specific biomarkers based on dynamical models of signaling networks can have a greater prognostic value than conventional biomarkers. These examples support systems biology models as valuable tools to advance clinical and translational oncological research.

The ever-expanding landscape of cancer therapeutic approaches

Cancer is a leading cause of death and a major health problem worldwide, particularly in more developed countries. There is, therefore, an urgent clinical need to develop more effective therapies to treat cancer and metastatic disease. In this Editorial, the content of The FEBS Journal's Special Issue on Cancer Therapeutics is outlined. The interesting collection of recent articles in this issue covers a wide repertoire of cancer therapeutic approaches. While some of the articles discuss broad-spectrum applications such as immunotherapy and oncolytic virus therapy, others focus on a particular type of cancer or a signalling pathway that has gone awry such as aberrant Ca²⁺ signalling, glycosylation or pre-mRNA processing. Finally, an article featured in this issue reviews our current understanding of how cancer cells can become dormant, often for decades, and which pathways reactivate these cells to cause relapse. I am sure there is something for everyone in this issue.

ERK5 modulates IL-6 secretion and contributes to tumor-induced immune suppression

Tumors exhibit a variety of strategies to dampen antitumor immune responses. With an aim to identify factors that are secreted from tumor cells, we performed an unbiased mass spectrometry-based secretome analysis in lung cancer cells. Interleukin-6 (IL-6) has been identified as a prominent factor secreted by tumor cells and cancer-associated fibroblasts isolated from cancer patients. Incubation of dendritic cell (DC) cultures with tumor cell supernatants inhibited the production of IL-12p70 in DCs but not the surface expression of other activation markers which is reversed by treatment with IL-6 antibody. Defects in IL-12p70 production in the DCs inhibited the differentiation of Th1 but not Th2 and Th17 cells from naïve CD4⁺ T cells. We also demonstrate that the classical mitogen-activated protein kinase, ERK5/MAPK7, is required for IL-6 production in tumor cells. Inhibition of ERK5 activity or depletion of ERK5 prevented IL-6 production in tumor cells, which could be exploited for enhancing antitumor immune responses.

Comprehensive Analysis of Ferroptosis Regulators With Regard to PD-L1 and Immune Infiltration in Clear Cell Renal Cell Carcinoma

Clear cell renal cell carcinoma (ccRCC) is one of the tumor types with sensitivity to ferroptosis, and immunotherapy has emerged as a standard pillar for metastatic ccRCC treatment, while it remains largely obscure whether ferroptosis influences the tumor immune microenvironment in ccRCC. Based on available data in The Cancer Genome Atlas, divergent expression profiles of ferroptosis regulators were noted in ccRCC and normal tissues, and we also found that the ferroptosis regulators correlated with the PD-L1 expression. Two independent subtypes were determined by consensus clustering analysis according to the expression level of ferroptosis regulators in ccRCC. Cluster 1 showed lower histological tumor stage and grade, more favorable prognosis, and higher PD-L1 expression compared to cluster 2. CIBERSORT analysis revealed that cluster 2 harbored higher infiltrated levels of CD8+ T cell, Tregs, and T follicular helper cell, while cluster 1 more correlated with the monocyte, M1 macrophage, and M2 macrophage. Gene set enrichment analysis indicated that the ERBB signaling and JAK_STAT signaling pathways were significantly enriched in cluster 1. We subsequently identified CARS as the potentially key immune infiltration-related ferroptosis regulator, whose high expression showed dismal prognosis and was positively correlated with PD-L1 expression in ccRCC. We also verified the upregulation of CARS in ccRCC tissues and cell lines via qRT-PCR method. Additionally, a pan-cancer analysis demonstrated that CARS closely related to the expression of immune checkpoint-related genes (especially PD-L1) and an unfavorable prognosis in diverse cancer types. In summary, our study suggested the crucial role of ferroptosis in immune infiltration of ccRCC, and CARS is a potentially novel prognostic biomarker and potential target for cancer immunotherapy.