Molecularly targeted cancer therapy: some lessons from the past decade

Polyploidy of MDA-MB-231 cells drives increased extravasation with enhanced cell-matrix adhesion

Metastasis, the leading cause of cancer-related deaths, involves a complex cascade of events, including extravasation. Despite extensive research into metastasis, the mechanisms underlying extravasation remain unclear. Molecular targeted therapies have advanced cancer treatment, yet their efficacy is limited, prompting exploration into novel therapeutic targets. Here, we showed the association of polyploidy in MDA-MB-231 breast cancer cells and their extravasation, using microfluidic systems to reproduce the in vivo microvascular environment. We observed enhanced extravasation in polyploid cells alongside upregulated expression of genes involved in cell-substrate adhesion and cell mechanical dynamics. These findings offer insights into the relationship between polyploidy and extravasation, highlighting potential targets for cancer therapy.

Purinergic Ca2+ Signaling as a Novel Mechanism of Drug Tolerance in BRAF-Mutant Melanoma

Drug tolerance is a major cause of relapse after cancer treatment. Despite intensive efforts, its molecular basis remains poorly understood, hampering actionable intervention. We report a previously unrecognized signaling mechanism supporting drug tolerance in BRAF-mutant melanoma treated with BRAF inhibitors that could be of general relevance to other cancers. Its key features are cell-intrinsic intracellular Ca2+ signaling initiated by P2X7 receptors (purinergic ligand-gated cation channels) and an enhanced ability for these Ca2+ signals to reactivate ERK1/2 in the drug-tolerant state. Extracellular ATP, virtually ubiquitous in living systems, is the ligand that can initiate Ca2+ spikes via P2X7 channels. ATP is abundant in the tumor microenvironment and is released by dying cells, ironically implicating treatment-initiated cancer cell death as a source of trophic stimuli that leads to ERK reactivation and drug tolerance. Such a mechanism immediately offers an explanation of the inevitable relapse after BRAFi treatment in BRAF-mutant melanoma and points to actionable strategies to overcome it.

Harnessing molecular hybridization approach to discover novel quinoline EGFR-TK inhibitors for cancer treatment

Aim: Using molecular hybridization approach, novel 18 quinoline derivatives (6a-11) were designed and synthesized as EGFR-TK inhibitors. Materials & methods: The antiproliferative activity was assessed against breast (MCF-7), leukemia (HL-60) and lung (A549) cancer cell lines. Moreover, the most active quinoline derivatives (6d and 8b) were further investigated for their potential as EGFR-TK inhibitors. In addition, cell cycle analysis and apoptosis induction activity were conducted. Results: A considerable cytotoxic activity was attained with IC50 values spanning from 0.06 to 1.12 μM. Besides, the quinoline derivatives 6d and 8b displayed potent inhibitory activity against EFGR with IC50 values of 0.18 and 0.08 μM, respectively. Conclusion: Accordingly, the afforded quinoline derivatives can be used as promising lead anticancer candidates for future optimization.

Purinergic Ca2+ signaling as a novel mechanism of drug tolerance in BRAF mutant melanoma

Drug tolerance is a major cause of relapse after cancer treatment. In spite of intensive efforts1-9, its molecular basis remains poorly understood, hampering actionable intervention. We report a previously unrecognized signaling mechanism supporting drug tolerance in BRAF-mutant melanoma treated with BRAF inhibitors that could be of general relevance to other cancers. Its key features are cell-intrinsic intracellular Ca2+ signaling initiated by P2X7 receptors (purinergic ligand-gated cation channels), and an enhanced ability for these Ca2+ signals to reactivate ERK1/2 in the drug-tolerant state. Extracellular ATP, virtually ubiquitous in living systems, is the ligand that can initiate Ca2+ spikes via P2X7 channels. ATP is abundant in the tumor microenvironment and is released by dying cells, ironically implicating treatment-initiated cancer cell death as a source of trophic stimuli that leads to ERK reactivation and drug tolerance. Such a mechanism immediately offers an explanation of the inevitable relapse after BRAFi treatment in BRAF-mutant melanoma, and points to actionable strategies to overcome it.

Computational Design, Synthesis, and Bioevaluation of 2-(Pyrimidin-4-yl)oxazole-4-carboxamide Derivatives: Dual Inhibition of EGFRWT and EGFRT790M with ADMET Profiling

The ongoing research in cancer treatment underscores the significance of dual epidermal growth factor receptor (EGFR) kinase inhibitors targeting both mutant and wild-type variants. In this study, employing in silico fragment-based drug design (FBDD) and computational analysis, we have successfully developed a novel chemical series of 2-(pyrimidin-4-yl)oxazole-4-carboxamide (16a-j) derivatives designed as dual EGFR kinase inhibitors. A comparative in vitro anticancer profile of the newly synthesized compounds (16a-j) was tested against a panel of five human cancer cell lines like prostate cancer (PC3 & DU-145), lung cancer (A549), human liver cancer (HEPG2), and breast cancer (MDA-MB-468) by employing MTT method. In this experiment a well-known anticancer agent, Etoposide was used as positive control. Most of the derivatives demonstrated significant cytotoxicity, ranging from excellent to moderate levels. The IC50 values for the synthesized compounds observed between 0.10 ± 0.052 to 9.83 ± 5.96 µM, while the positive control exhibited a range of 1.97 ± 0.45 µM to 3.08 ± 0.135 µM. These results indicate that the synthesized compounds demonstrate higher cytotoxic potency in comparison to the reference compound. Furthermore, all these compounds underwent screening against normal Vero cell lines to assess their cytotoxicity. In each case, the observed cytotoxicity values (IC50) were higher than 22 µM, affirming the compounds selectivity for cancer cell lines. Among the compounds investigated, three compounds (16a, 16e, and 16i) exhibited notable cytotoxicity, while two compounds (16g and 16h) demonstrated exceptional cytotoxicity. The selectivity index of the tested compounds indicates a pronounced preference for targeting cancer cell lines over normal cells. Furthermore, all the compounds 16a-j underwent assessment for their EGFR kinase inhibitory activity against both EGFRWT and mutated EGFRT790M. The results unveiled the potential eligibility of this new series of compounds as effective EGFR inhibitors. Moreover, compound 16h underwent additional testing for cell cycle analysis, revealing its capability to arrest the cell cycle in the G2/M phase and induce apoptosis at the IC50 concentration.

A review on metal complexes and its anti-cancer activities: Recent updates from in vivo studies

Research into cancer therapeutics has uncovered various potential medications based on metal-containing scaffolds after the discovery and clinical applications of cisplatin as an anti-cancer agent. This has resulted in many metallodrugs that can be put into medical applications. These metallodrugs have a wider variety of functions and mechanisms of action than pure organic molecules. Although platinum-based medicines are very efficient anti-cancer agents, they are often accompanied by significant side effects and toxicity and are limited by resistance. Some of the most studied and developed alternatives to platinum-based anti-cancer medications include metallodrugs based on ruthenium, gold, copper, iridium, and osmium, which showed effectiveness against many cancer cell lines. These metal-based medicines represent an exciting new category of potential cancer treatments and sparked a renewed interest in the search for effective anti-cancer therapies. Despite the widespread development of metal complexes touted as powerful and promising in vitro anti-cancer therapeutics, only a small percentage of these compounds have shown their worth in vivo models. Metallodrugs, which are more effective and less toxic than platinum-based drugs and can treat drug-resistant cancer cells, are the focus of this review. Here, we highlighted some of the most recently developed Pt, Ru, Au, Cu, Ir, and Os complexes that have shown significant in vivo antitumor properties between 2017 and 2023.

Large-scale comparison of machine learning methods for profiling prediction of kinase inhibitors

Conventional machine learning (ML) and deep learning (DL) play a key role in the selectivity prediction of kinase inhibitors. A number of models based on available datasets can be used to predict the kinase profile of compounds, but there is still controversy about the advantages and disadvantages of ML and DL for such tasks. In this study, we constructed a comprehensive benchmark dataset of kinase inhibitors, involving in 141,086 unique compounds and 216,823 well-defined bioassay data points for 354 kinases. We then systematically compared the performance of 12 ML and DL methods on the kinase profiling prediction task. Extensive experimental results reveal that (1) Descriptor-based ML models generally slightly outperform fingerprint-based ML models in terms of predictive performance. RF as an ensemble learning approach displays the overall best predictive performance. (2) Single-task graph-based DL models are generally inferior to conventional descriptor- and fingerprint-based ML models, however, the corresponding multi-task models generally improves the average accuracy of kinase profile prediction. For example, the multi-task FP-GNN model outperforms the conventional descriptor- and fingerprint-based ML models with an average AUC of 0.807. (3) Fusion models based on voting and stacking methods can further improve the performance of the kinase profiling prediction task, specifically, RF::AtomPairs + FP2 + RDKitDes fusion model performs best with the highest average AUC value of 0.825 on the test sets. These findings provide useful information for guiding choices of the ML and DL methods for the kinase profiling prediction tasks. Finally, an online platform called KIPP ( https://kipp.idruglab.cn ) and python software are developed based on the best models to support the kinase profiling prediction, as well as various kinase inhibitor identification tasks including virtual screening, compound repositioning and target fishing.

Advancements and perspectives of RBX2 as a molecular hallmark in cancer

Cancer is a challenging issue for human health. One of the key methods to address this issue is by comprehending the molecular causes of tumors and creating medications that target those causes. RBX2 (RING box protein 2), also known as ROC2 (Regulator of Cullins 2), RNF7 (RING Finger Protein 7), or SAG (Sensitive to Apoptosis Gene) is a key component of the Cullin-RING-type E3 ubiquitin ligases (CRLs) and overexpressed in various human cancers. RBX2 is a potential drug target, the expression of which correlates with tumor staging, grading, and prognosis analysis. Through a synergistically biological interaction with Kras mutation in preclinical models, RBX2 accelerated the progression of skin cancer, pancreatic cancer, and lung cancer. In accordance, the aberrant expression of RBX2 will lead to dysregulation of many signaling pathways, which is crucial for tumor initiation and growth. However, the impact of RBX2 on tumors also intriguingly demonstrates a spatial reliance manner. In this review, we summarized the current understanding of RBX2 in multiple cancer types and suggested a significant potential of RBX2 as a therapeutic target.

Large-Scale Machine Learning Analysis Reveals DNA Methylation and Gene Expression Response Signatures for Gemcitabine-Treated Pancreatic Cancer

Background: Gemcitabine is a first-line chemotherapy for pancreatic adenocarcinoma (PAAD), but many PAAD patients do not respond to gemcitabine-containing treatments. Being able to predict such nonresponders would hence permit the undelayed administration of more promising treatments while sparing gemcitabine life-threatening side effects for those patients. Unfortunately, the few predictors of PAAD patient response to this drug are weak, none of them exploiting yet the power of machine learning (ML). Methods: Here, we applied ML to predict the response of PAAD patients to gemcitabine from the molecular profiles of their tumors. More concretely, we collected diverse molecular profiles of PAAD patient tumors along with the corresponding clinical data (gemcitabine responses and clinical features) from the Genomic Data Commons resource. From systematically combining 8 tumor profiles with 16 classification algorithms, each of the resulting 128 ML models was evaluated by multiple 10-fold cross-validations. Results: Only 7 of these 128 models were predictive, which underlines the importance of carrying out such a large-scale analysis to avoid missing the most predictive models. These were here random forest using 4 selected mRNAs [0.44 Matthews correlation coefficient (MCC), 0.785 receiver operating characteristic-area under the curve (ROC-AUC)] and XGBoost combining 12 DNA methylation probes (0.32 MCC, 0.697 ROC-AUC). By contrast, the hENT1 marker obtained much worse random-level performance (practically 0 MCC, 0.5 ROC-AUC). Despite not being trained to predict prognosis (overall and progression-free survival), these ML models were also able to anticipate this patient outcome. Conclusions: We release these promising ML models so that they can be evaluated prospectively on other gemcitabine-treated PAAD patients.

In silico screening of chalcone derivatives as promising EGFR-TK inhibitors for the clinical treatment of cancer

Epidermal growth factor receptor (EGFR) promotes tumorigenic characteristics and activates cancer-associated signaling pathways such as Wnt/-catenin, transforming growth factor (TGF-β), and phosphoinositide-3-kinase (PI3K). Several inhibitors have been reported to suppress the activity of EGFR and are being used in cancer treatment. However, patients in the malignant stage of cancer show resistance to those inhibitors, opening a wide space for research to discover novel inhibitors. Therefore, we carried out machine learning and virtual screening to discover novel inhibitors with high affinity against EGFR-TK. Initially, a library of 2640 chalcones were screened out using a machine-learning model developed based on the random forest algorithm, exhibiting high sensitivity and a Receiver Operating Characteristic curve (ROC area) of 0.99. Furthermore, out of the initial 2640 screened compounds, 412 compounds exhibiting potential activity are subjected to evaluation for drug-likeness properties through different filters: Blood-brain barrier penetration, Lipinski's rule, CMC-50 like rule, Veber rule, and Ghose filter, alongside Cell Line Cytotoxicity Prediction. A total of 30 compounds that successfully pass through all these filters are selected for molecular docking. Of these, 6 compounds display substantial binding affinity and closer interaction with the conserved catalytic residues of the target EGFR-TK compared to the reference molecule (erlotinib). Furthermore, molecular dynamics simulation studies were conducted on four compounds (CID-375861, CID-375862, CID-23636403, and CID-259166) to confirm the stability of the docked complexes over a 100 ns simulation trajectory. Additionally, the binding free energy calculations by MMPBSA reveal that these four chalcone compounds exhibit strong affinity towards the EGFR-TK enzyme, with binding free energies of - 65.421 kJ/mol, - 94.266 kJ/mol, - 80.044 kJ/mol, and - 79.734 kJ/mol, respectively. The findings from this investigation highlight a set of promising chalcone compounds that have the potential to be developed into effective drugs for the treatment of various cancers. Further research and development on these compounds could pave the way for novel therapeutic interventions.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13205-023-03858-8.

Synthesis and preclinical evaluation of a selective MET kinase positron emission tomography tracer

The tyrosine kinase MET (hepatocyte growth factor receptor) is activated or mutated in a wide range of cancers and is often correlated with a poor prognosis. Precision medicine with positron emission tomography (PET) can potentially aid in the assessment of tumor biochemistry and heterogeneity, which can prompt the selection of the most effective therapeutic regimes. The selective MET inhibitor PF04217903 (1) formed the basis for a bioisosteric replacement, leading to the deoxyfluorinated analog [18 F]2. [18 F]2 could be synthesized with a "hydrous fluoroethylation" protocol in 6.3 ± 2.6% radiochemical yield and a molar activity of >50 GBq/μmol. In vitro autoradiography indicated that [18 F]2 selectively binds to MET in PC3 tumor tissue, and in vivo biodistribution in mice showed predominantly a hepatobiliary excretion along with a low retention of radiotracer in other organs.

Biological evaluation, docking studies, and in silico ADME prediction of some pyrimidine and pyridine derivatives as potential EGFRWT and EGFRT790M inhibitors

Herein, a set of pyridine and pyrimidine derivatives were assessed for their impact on the cell cycle and apoptosis. Human breast cancer (MCF7), hepatocellular carcinoma (HEPG2), larynx cancer (HEP2), lung cancer (H460), colon cancers (HCT116 and Caco2), and hypopharyngeal cancer (FADU), and normal Vero cell lines were used. Compounds 8 and 14 displayed outstanding effects on the investigated cell lines and were further tested for their antioxidant activity in MCF7, H460, FADU, HEP2, HEPG2, HCT116, Caco2, and Vero cells by measuring superoxide dismutase (SOD), malondialdehyde content (MDA), reduced glutathione (GSH), and nitric oxide (NO) content. Besides, Annexin V-FITC apoptosis detection and cell cycle DNA index using the HEPG-2 cell line were established on both compounds as well. Furthermore, compounds 8 and 14 were assessed for their EGFR kinase (Wild and T790M) inhibitory activities, revealing eligible potential. Additionally, molecular docking, ADME, and SAR studies were carried out for the investigated candidates.

Photoimmunotechnology as a powerful biological tool for molecular-based elimination of target cells and microbes, including bacteria, fungi and viruses

Microbial pathogens, including bacteria, fungi and viruses, can develop resistance to clinically used drugs; therefore, finding new therapeutic agents is an ongoing challenge. Recently, we reported the photoimmuno-antimicrobial strategy (PIAS), a type of photoimmunotechnology, that enables molecularly targeted elimination of a wide range of microbes, including the viral pathogen severe acute respiratory syndrome coronavirus 2 and the multidrug-resistant bacterial pathogen methicillin-resistant Staphylococcus aureus (MRSA). PIAS works in the same way as photoimmunotherapy (PIT), which has been used to treat recurrent head and neck cancer in Japan since 2020. Both PIAS and PIT use a monoclonal antibody conjugated to a phthalocyanine derivative dye that undergoes a shape change when photoactivated. This shape change induces a structural change in the antibody-dye conjugate, resulting in physical stress within the binding sites of the conjugate and disrupting them. Therefore, targeting accuracy and flexibility can be determined based on the specificity of the antibody used. In this protocol, we describe how to design a treatment strategy, label monoclonal antibodies with the dye and characterize the products. We provide detailed examples of how to set up and perform PIAS and PIT applications in vitro and in vivo. These examples are PIAS against microbes using MRSA as a representative subject, PIAS against viruses using severe acute respiratory syndrome coronavirus 2 in VeroE6/TMPRSS2 cells, PIAS against MRSA-infected animals, and in vitro and in vivo PIT against cancer cells. The in vitro and in vivo protocols can be completed in ~3 h and 2 weeks, respectively.

Genetic screening for anticancer genes highlights FBLN5 as a synthetic lethal partner of MYC

BACKGROUND

When ectopically overexpressed, anticancer genes, such as TRAIL, PAR4 and ORCTL3, specifically destroy tumour cells without harming untransformed cells. Anticancer genes can not only serve as powerful tumour specific therapy tools but studying their mode of action can reveal mechanisms underlying the neoplastic transformation, sustenance and spread.

METHODS

Anticancer gene discovery is normally accidental. Here we describe a systematic, gain of function, forward genetic screen in mammalian cells to isolate novel anticancer genes of human origin. Continuing with over 30,000 transcripts from our previous study, 377 cell death inducing genes were subjected to screening. FBLN5 was chosen, as a proof of principle, for mechanistic gene expression profiling, comparison pathways analyses and functional studies.

RESULTS

Sixteen novel anticancer genes were isolated; these included non-coding RNAs, protein-coding genes and novel transcripts, such as ZNF436-AS1, SMLR1, TMEFF2, LINC01529, HYAL2, NEIL2, FBLN5, YPEL4 and PHKA2-processed transcript. FBLN5 selectively caused inhibition of MYC in COS-7 (transformed) cells but not in CV-1 (normal) cells. MYC was identified as synthetic lethality partner of FBLN5 where MYC transformed CV-1 cells experienced cell death upon FBLN5 transfection, whereas FBLN5 lost cell death induction in MCF-7 cells upon MYC knockdown.

CONCLUSIONS

Sixteen novel anticancer genes are present in human genome including FBLN5. MYC is a synthetic lethality partner of FBLN5. Video Abstract.

Invivo anti-cancer activity of 10-methyl-aplog-1, a simplified analog of aplysiatoxin, and its possible signaling pathway associated with G1 arrest

Naturally occurring protein kinase C (PKC) activators such as phorbol esters, teleocidins, and aplysiatoxins, have the potential to become anti-cancer agents, since they are anti-proliferative against specific cancer cell lines in vitro. However, their potent tumor-promoting and proinflammatory activities have hampered their clinical uses. Recently, we developed 10-methyl-aplog-1 (1), a simplified analog of tumor-promoting debromoaplysiatoxin (DAT), which retained anti-proliferative activity comparable to DAT, but induced neither tumorigenesis nor inflammation on mouse skin. Our previous study suggested that PKCα and δ were involved in the cell line-selective anti-proliferative activity of 1, but the downstream signaling of PKC isozymes remained unknown. In this study, we confirmed that 1 inhibited the growth of three aplog-sensitive cancer cell lines (NCI-H460, HCC-2998, and HBC-4) without severe side effects in mice xenograft models. In addition, in vitro analysis using A549, one of the aplog-sensitive cell lines in vitro, revealed that PKCα induced PP2A-mediated attenuation of the Akt/S6 signaling axis. Since S6 inhibition in A549 was reported to result in G1 arrest, this pathway could be involved in the PKCα-dependent anti-proliferative activity of 1.

Design and synthesis of novel pyrazolopyrimidine candidates as promising EGFR-T790M inhibitors and apoptosis inducers

Aim: Our objective was to design and synthesize a new range of pyrazolopyrimidines while maintaining the key pharmacophoric features of EGFR tyrosine kinase inhibitors. Materials & methods: Percentage inhibition in 14 human cancer cell lines and IC50 values were recorded. Compounds 6c, 7e and 7f were examined against both wild and mutant (T790M) EGFR subtypes. Apoptosis markers, cell cycle arrest, apoptosis assay and molecular docking were performed. Results: Compounds 6c, 7e and 7f demonstrated superior inhibitory potentials against wild and mutant (T790M) EGFR subtypes. A molecular docking study showed that compounds 6c and 7e had the best fit. Conclusion: The designed candidates demonstrated superior inhibitory potential as promising EGFR-T790M inhibitors that agrees with the proposed rationale.

A comparison of mutation and amplification-driven resistance mechanisms and their impacts on tumor recurrence

Tumor recurrence, driven by the evolution of drug resistance is a major barrier to therapeutic success in cancer. Tumor drug resistance is often caused by genetic alterations such as point mutation, which refers to the modification of a single genomic base pair, or gene amplification, which refers to the duplication of a region of DNA that contains a gene. These mechanisms typically confer varying degrees of resistance, and they tend to occur at vastly different frequencies. Here we investigate the dependence of tumor recurrence dynamics on these mechanisms of resistance, using stochastic multi-type branching process models. We derive tumor extinction probabilities and deterministic estimates for the tumor recurrence time, defined as the time when an initially drug sensitive tumor surpasses its original size after developing resistance. For models of amplification-driven and mutation-driven resistance, we prove law of large numbers results regarding the convergence of the stochastic recurrence times to their mean. Additionally, we prove sufficient and necessary conditions for a tumor to escape extinction under the gene amplification model, discuss behavior under biologically relevant parameters, and compare the recurrence time and tumor composition in the mutation and amplification models both analytically and using simulations. In comparing these mechanisms, we find that the ratio between recurrence times driven by amplification versus mutation depends linearly on the number of amplification events required to acquire the same degree of resistance as a mutation event, and we find that the relative frequency of amplification and mutation events plays a key role in determining the mechanism under which recurrence is more rapid for any specific system. In the amplification-driven resistance model, we also observe that increasing drug concentration leads to a stronger initial reduction in tumor burden, but that the eventual recurrent tumor population is less heterogeneous, more aggressive and harbors higher levels of drug-resistance.

Molecularly Imprinted Carriers for Diagnostics and Therapy-A Critical Appraisal

Simultaneous diagnostics and targeted therapy provide a theranostic approach, an instrument of personalized medicine-one of the most-promising trends in current medicine. Except for the appropriate drug used during the treatment, a strong focus is put on the development of effective drug carriers. Among the various materials applied in the production of drug carriers, molecularly imprinted polymers (MIPs) are one of the candidates with great potential for use in theranostics. MIP properties such as chemical and thermal stability, together with capability to integrate with other materials are important in the case of diagnostics and therapy. Moreover, the MIP specificity, which is important for targeted drug delivery and bioimaging of particular cells, is a result of the preparation process, conducted in the presence of the template molecule, which often is the same as the target compound. This review focused on the application of MIPs in theranostics. As a an introduction, the current trends in theranostics are described prior to the characterization of the concept of molecular imprinting technology. Next, a detailed discussion of the construction strategies of MIPs for diagnostics and therapy according to targeting and theranostic approaches is provided. Finally, frontiers and future prospects are presented, stating the direction for further development of this class of materials.

From multi-omics data to the cancer druggable gene discovery: a novel machine learning-based approach

The development of targeted drugs allows precision medicine in cancer treatment and optimal targeted therapies. Accurate identification of cancer druggable genes helps strengthen the understanding of targeted cancer therapy and promotes precise cancer treatment. However, rare cancer-druggable genes have been found due to the multi-omics data's diversity and complexity. This study proposes deep forest for cancer druggable genes discovery (DF-CAGE), a novel machine learning-based method for cancer-druggable gene discovery. DF-CAGE integrated the somatic mutations, copy number variants, DNA methylation and RNA-Seq data across ˜10 000 TCGA profiles to identify the landscape of the cancer-druggable genes. We found that DF-CAGE discovers the commonalities of currently known cancer-druggable genes from the perspective of multi-omics data and achieved excellent performance on OncoKB, Target and Drugbank data sets. Among the ˜20 000 protein-coding genes, DF-CAGE pinpointed 465 potential cancer-druggable genes. We found that the candidate cancer druggable genes (CDG) are clinically meaningful and divided the CDG into known, reliable and potential gene sets. Finally, we analyzed the omics data's contribution to identifying druggable genes. We found that DF-CAGE reports druggable genes mainly based on the copy number variations (CNVs) data, the gene rearrangements and the mutation rates in the population. These findings may enlighten the future study and development of new drugs.

Antitumor pharmacological research in the era of personalized medicine

Anticancer drug discovery has yielded unprecedented progress in recent decades, resulting in the approval of innovative treatment options for patients and the successful implementation of personalized medicine in clinical practice. This remarkable progress has also reshaped the research scope of pharmacological research. This article, as a tribute to cancer research at Shanghai Institute of Materia Medica in celebration of the institute's 90th birthday, provides an overview of the conceptual revolution occurring in anticancer therapy, and summarizes our recent progress in the development of molecularly targeted therapeutics and exploration of new strategies in personalized medicine. With this review, we hope to provide a glimpse into how antitumor pharmacological researchers have embraced the new era of personalized medicine research and to propose a future path for anticancer drug discovery and pharmacological research.

Oncogenic Role of ADAM32 in Hepatoblastoma: A Potential Molecular Target for Therapy

Outcomes of pediatric hepatoblastoma (HBL) have improved, but refractory cases still occur. More effective and safer drugs are needed that are based on molecular mechanisms. A disintegrin and metalloproteases (ADAMs) are expressed with high frequency in various human carcinomas and play an important role in cancer progression. In this study, we analyzed expression of ADAMs in HBL with a cDNA microarray dataset and found that the expression level of ADAM32 is particularly high. To investigate the role of ADAM32 in cancer, forced expression or knockdown experiments were conducted with HepG2 and HBL primary cells. Colony formation, cell migration and invasion, and cell viability were increased in HepG2 expressing ADAM32, whereas knockdown of ADAM32 induced a decrease in these cellular functions. Quantitative RT-PCR demonstrated an association between ADAM32 expression and the expression of genes related to cancer stem cells and epithelial–mesenchymal transition (EMT), suggesting a role of ADAM32 in cancer stemness and EMT. Furthermore, knockdown of ADAM32 increased cisplatin-induced apoptosis, and this effect was attenuated by a caspase-8 inhibitor, suggesting that ADAM32 plays a role in extrinsic apoptosis signaling. We conclude that ADAM32 plays a crucial role in progression of HBL, so it might be a promising molecular target in anticancer therapy.

Naturally derived indole alkaloids targeting regulated cell death (RCD) for cancer therapy: from molecular mechanisms to potential therapeutic targets

Regulated cell death (RCD) is a critical and active process that is controlled by specific signal transduction pathways and can be regulated by genetic signals or drug interventions. Meanwhile, RCD is closely related to the occurrence and therapy of multiple human cancers. Generally, RCD subroutines are the key signals of tumorigenesis, which are contributed to our better understanding of cancer pathogenesis and therapeutics. Indole alkaloids derived from natural sources are well defined for their outstanding biological and pharmacological properties, like vincristine, vinblastine, staurosporine, indirubin, and 3,3′-diindolylmethane, which are currently used in the clinic or under clinical assessment. Moreover, such compounds play a significant role in discovering novel anticancer agents. Thus, here we systemically summarized recent advances in indole alkaloids as anticancer agents by targeting different RCD subroutines, including the classical apoptosis and autophagic cell death signaling pathways as well as the crucial signaling pathways of other RCD subroutines, such as ferroptosis, mitotic catastrophe, necroptosis, and anoikis, in cancer. Moreover, we further discussed the cross talk between different RCD subroutines mediated by indole alkaloids and the combined strategies of multiple agents (e.g., 3,10-dibromofascaplysin combined with olaparib) to exhibit therapeutic potential against various cancers by regulating RCD subroutines. In short, the information provided in this review on the regulation of cell death by indole alkaloids against different targets is expected to be beneficial for the design of novel molecules with greater targeting and biological properties, thereby facilitating the development of new strategies for cancer therapy.

Interpretable Machine Learning Models to Predict the Resistance of Breast Cancer Patients to Doxorubicin from Their microRNA Profiles

Doxorubicin is a common treatment for breast cancer. However, not all patients respond to this drug, which sometimes causes life-threatening side effects. Accurately anticipating doxorubicin-resistant patients would therefore permit to spare them this risk while considering alternative treatments without delay. Stratifying patients based on molecular markers in their pretreatment tumors is a promising approach to advance toward this ambitious goal, but single-gene gene markers such as HER2 expression have not shown to be sufficiently predictive. The recent availability of matched doxorubicin-response and diverse molecular profiles across breast cancer patients permits now analysis at a much larger scale. 16 machine learning algorithms and 8 molecular profiles are systematically evaluated on the same cohort of patients. Only 2 of the 128 resulting models are substantially predictive, showing that they can be easily missed by a standard-scale analysis. The best model is classification and regression tree (CART) nonlinearly combining 4 selected miRNA isoforms to predict doxorubicin response (median Matthew correlation coefficient (MCC) and area under the curve (AUC) of 0.56 and 0.80, respectively). By contrast, HER2 expression is significantly less predictive (median MCC and AUC of 0.14 and 0.57, respectively). As the predictive accuracy of this CART model increases with larger training sets, its update with future data should result in even better accuracy.

Functional characterization and clinical significance of super-enhancers in lung adenocarcinoma

Super-enhancers (SEs) are important transcriptional regulators in tumorigenesis; however, the functional characterization and clinical significance of SEs in lung adenocarcinoma (LUAD) remain unclear. By using H3K27ac ChIP-seq data of two LUAD cell lines and eight lung tissues, we detected 1045 cancer-specific and 5032 normal-specific SEs. Compared to normal-specific SEs, cancer-specific SEs have different regulatory mechanisms where associated target genes were enriched in critical tumor-related pathways and tended to be regulated by transcription factors of Fos Proto-Oncogene, AP-1 Transcription Factor Subunit and Jun Proto-Oncogene, AP-1 Transcription Factor Subunit families. By using expression data of 513 LUAD and 57 adjacent samples from The Cancer Genome Atlas and 80 tumor-normal paired LUAD samples from the Nanjing Lung Cancer Cohort study, we performed differential expression analysis of target genes for SEs and defined 243 crucial SEs. Unsupervised clustering of crucial SEs revealed two subtypes with different levels of genomic aberrations (i.e., mutation and copy number alteration) and clinical outcomes (progression-free interval: p = 0.030; disease-free interval: p = 0.047). In addition, patients with adverse clinical outcomes were more sensitive to three small molecule inhibitors (bortezomib, doxorubicin, and etoposide), and their targets (PSMB5 and TOP2A) also have elevated expression levels among these patients. Taken together, our findings provided a comprehensive characterization of SEs in LUAD and emphasized their clinical significance in LUAD therapy.

Bayesian two-stage sequential enrichment design for biomarker-guided phase II trials for anticancer therapies

Biomarker-guided phase II trials have become increasingly important for personalized cancer treatment. In this paper, we propose a Bayesian two-stage sequential enrichment design for such biomarker-guided trials. We assumed that all patients were dichotomized as marker positive or marker negative based on their biomarker status; the positive patients were considered more likely to respond to the targeted drug. Early stopping rules and adaptive randomization methods were embedded in the design to control the number of patients receiving inferior treatment. At the same time, a Bayesian hierarchical model was used to borrow information between the positive and negative control arms to improve efficiency. Simulation results showed that the proposed design achieved higher empirical power while controlling the type I error and assigned more patients to the superior treatment arms. The operating characteristics suggested that the design has good performance and may be useful for biomarker-guided phase II trials for evaluating anticancer therapies.

Systematic evaluation and optimization of protein extraction parameters in diagnostic FFPE specimens

Objectives

Formalin-fixed paraffin-embedded (FFPE) tissue is the standard material for diagnostic pathology but poses relevant hurdles to accurate protein extraction due to cross-linking and chemical alterations. While numerous extraction protocols and chemicals have been described, systematic comparative analyses are limited. Various parameters were thus investigated in their qualitative and quantitative effects on protein extraction (PE) efficacy. Special emphasis was put on preservation of membrane proteins (MP) as key subgroup of functionally relevant proteins.

Methods

Using the example of urothelial carcinoma, FFPE tissue sections were subjected to various deparaffinization, protein extraction and antigen retrieval protocols and buffers as well as different extraction techniques. Performance was measured by protein concentration and western blot analysis of cellular compartment markers as well as liquid chromatography-coupled mass spectrometry (LC–MS).

Results

Commercially available extraction buffers showed reduced extraction of MPs and came at considerably increased costs. On-slide extraction did not improve PE whereas several other preanalytical steps could be simplified. Systematic variation of temperature and exposure duration demonstrated a quantitatively relevant corridor of optimal antigen retrieval.

Conclusions

Preanalytical protein extraction can be optimized at various levels to improve unbiased protein extraction and to reduce time and costs.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12014-022-09346-0.

Smart PROTACs Enable Controllable Protein Degradation for Precision Cancer Therapy

Proteolysis-targeting chimeras (PROTACs) are heterobifunctional chemicals that degrade proteins at the post-translational level, which represent an emerging therapeutic modality to fight cancer and other diseases. Although several PROTACs have now entered clinical trials, potential off-tissue side effects have resulted from nonspecific accumulation at non-cancerous sites after systemic administration, and this remains a major challenge. To this end, in the past 3 years, activatable PROTACs whose activity can only be launched on demand have gained tremendous momentum. In this review, we provide an overview of these new smart activatable PROTACs, which exert protein degradation action only in response to internal or external stimuli. We categorize these activatable PROTACs according to their activation mechanism contributed by different stimuli, including reduction-activatable, hypoxia-activatable, and enzyme-activatable PROTACs and photo-caged or photo-switchable PROTACs. The use of stimuli-responsive chemical blocks in these activatable PROTACs allows local activation of the antitumor effects while reducing the incidence of off-site side effects for precision cancer therapy. The design principle and category of smart PROTACs are introduced along with an overview of their therapeutic prospects and challenges.

Current Trends in Precision Medicine and Next-Generation Sequencing in Head and Neck Cancer

OPINION STATEMENT

As the field of oncology enters the era of precision medicine and targeted therapies, we have come to realize that there may be no single "magic bullet" for patients with head and neck cancer. While immune check point inhibitors and some targeted therapeutics have shown great promise in improving oncologic outcomes, the current standard of care in most patients with head and neck squamous cell carcinoma (HNSCC) remains a combination of surgery, radiation, and/or cytotoxic chemotherapy. Nevertheless, advances in precision medicine, next-generation sequencing (NGS), and targeted therapies have a potential future in the treatment of HNSCC. These roles include increased patient treatment stratification based on predictive biomarkers or targetable mutations and novel combinatorial regimens with existing HNSCC treatments. There remain challenges to precision medicine and NGS in HNSCC, including intertumor and intratumor heterogeneity, challenging targets, and need for further trials validating the utility of NGS and precision medicine. Additionally, there is a need for evidence-based practice guidelines to assist clinicians on how to appropriately incorporate NGS in care for HNSCC. In this review, we describe the current state of precision medicine and NGS in HNSCC and opportunities for future advances in this challenging but important field.

Cost-effectiveness of precision cancer medicine-current challenges in the use of next generation sequencing for comprehensive tumour genomic profiling and the role of clinical utility frameworks (Review)

Precision cancer medicine (PCM) is an emerging paradigm in oncology, which includes tumour comprehensive genomic profiling (CGP) to enable molecularly guided therapy. However, cost-effectiveness analyses of PCM are faced with several challenges and, thus, its cost-effectiveness remains unclear. Early trials using only molecularly guided therapy were faced with the challenge of providing adequate measures of outcome, which probably explains the modest treatment benefits demonstrated. Endpoints like the progression-free survival (PFS)2/PFS1 ratio may assist in overcoming this issue. Moreover, specific tumour subtypes appear to benefit more from PCM. Costs associated with next-generation sequencing (NGS) for CGP are decreasing, but targeted therapy itself represents a major cost driver. CGP not only enables prediction of response to treatment, but also resistance, and could thus prevent administration of unnecessary (and costly) therapies. In clinical practice, the presence of clinical frameworks, such as the Recommendations for the Use of NGS for Patients with Metastatic Cancers from the ESMO Precision Medicine Working Group, and the ESMO Scale for Clinical Actionability of Molecular Targets, are essential in appropriately identifying situations where PCM is clinically meaningful, thereby improving its cost-effectiveness.

Emerging role of BAD and DAD1 as potential targets and biomarkers in cancer

As key regulators of apoptosis, BAD and defender against apoptotic cell death 1 (DAD1) are associated with cancer initiation and progression. Multiple studies have demonstrated that BAD and DAD1 serve critical roles in several types of cancer and perform various functions, such as participating in cellular apoptosis, invasion and chemosensitivity, as well as their role in diagnostic/prognostic judgement, etc. Investigating the detailed mechanisms of the cancerous effects of the two proteins will contribute to enriching the options for targeted therapy, and may improve clinical treatment of cancer. The present review summarizes research advances regarding the associations of BAD and DAD1 with cancer, and a hypothesis on the feasible relationship and interaction mechanism between the two proteins is proposed. Furthermore, the present review highlights the potential of the two proteins as therapeutic targets and valuable diagnostic and prognostic biomarkers.

Predicting Cancer Drug Response In Vivo by Learning an Optimal Feature Selection of Tumour Molecular Profiles

(1) Background: Inter-tumour heterogeneity is one of cancer’s most fundamental features. Patient stratification based on drug response prediction is hence needed for effective anti-cancer therapy. However, single-gene markers of response are rare and/or may fail to achieve a significant impact in the clinic. Machine Learning (ML) is emerging as a particularly promising complementary approach to precision oncology. (2) Methods: Here we leverage comprehensive Patient-Derived Xenograft (PDX) pharmacogenomic data sets with dimensionality-reducing ML algorithms with this purpose. (3) Results: Combining multiple gene alterations via ML leads to better discrimination between sensitive and resistant PDXs in 19 of the 26 analysed cases. Highly predictive ML models employing concise gene lists were found for three cases: paclitaxel (breast cancer), binimetinib (breast cancer) and cetuximab (colorectal cancer). Interestingly, each of these multi-gene ML models identifies some treatment-responsive PDXs not harbouring the best actionable mutation for that case. Thus, ML multi-gene predictors generally have much fewer false negatives than the corresponding single-gene marker. (4) Conclusions: As PDXs often recapitulate clinical outcomes, these results suggest that many more patients could benefit from precision oncology if ML algorithms were also applied to existing clinical pharmacogenomics data, especially those algorithms generating classifiers combining data-selected gene alterations.

Cinobufagin-induced DNA damage response activates G2/M checkpoint and apoptosis to cause selective cytotoxicity in cancer cells

Background

Processed extracts from toad skin and parotoid gland have long been used to treat various illnesses including cancer in many Asian countries. Recent studies have uncovered a family of bufadienolides as the responsible pharmacological compounds, and the two major molecules, cinobufagin and bufalin, have been shown to possess robust antitumor activity; however, the underlying mechanisms remain poorly understood.

Methods

Intracellular reactive oxygen species (ROS) were measured by DCFH-DA staining and flow cytometry, and DNA damage was analyzed by immunofluorescent staining and the alkaline comet assay. Cytotoxicity was measured by MTT as well as colony formation assays, and cell cycle and apoptosis were analyzed by flow cytometry. In addition, apoptosis was further characterized by TUNEL and mitochondrial membrane potential assays.

Results

Here we showed that sublethal doses of cinobufagin suppressed the viability of many cancer but not noncancerous cell lines. This tumor-selective cytotoxicity was preceded by a rapid, cancer-specific increase in cellular ROS and was significantly reduced by the ROS inhibitor N-acetyl cysteine (NAC), indicating oxidative stress as the primary source of cinobufagin-induced cancer cell toxicity. Sublethal cinobufagin-induced ROS overload resulted in oxidative DNA damage and intense replication stress in cancer cells, leading to strong DNA damage response (DDR) signaling. Subsequent phosphorylation of CDC25C and stabilization of p53 downstream of DDR resulted in activation of the G₂/M checkpoint followed by induction of apoptosis. These data indicate that cinobufagin suppresses cancer cell viability via DDR-mediated G₂ arrest and apoptosis.

Conclusion

As elevated oxidative pressure is shared by most cancer cells that renders them sensitive to further oxidative insult, these studies suggest that nontoxic doses of cinobufagin can be used to exploit a cancer vulnerability for induction of cancer-specific cytotoxicity.

Supplementary Information

The online version contains supplementary materials available at 10.1186/s12935-021-02150-0.

Overexpression of circRNA circFAT1 in Endometrial Cancer Cells Increases Their Stemness by Upregulating miR-21 Through Methylation

Background: Circular (Circ)RNA circFAT1 play tumor-suppressive or oncogenic roles in different cancers. Microarray analysis observed altered expression of circFAT1 in endometrial cancer (EC) and its inverse correlation with miR-21. Materials and Methods: Expression of circFAT1 and miR-21 in EC and paired nontumor tissues collected from 62 EC patients was analyzed by quantitative reverse transcription PCR (RT-qPCR). An experiment was conducted to evaluate the expression and interaction between circFAT1 and miR-21, followed by RT-qPCR and methylation-specific PCR. The role of circFAT1 and miR-21 in regulating the stemness was assessed by cell stemness assay. Heml 1.0 software was used to show differential gene expression. ANOVA Tukey's test and Pearson's correlation coefficient was used. Results: CircFAT1 was upregulated in EC and positively correlated with miR-21 across EC tissues. In RL95-2 and HEC-1-A cells, overexpression of circFAT1 increased the expression levels of miR-21 and decreased the methylation of miR-21 gene, whereas overexpression of miR-21 did not alter the expression of circFAT1. Cell stemness analysis showed that overexpression of circFAT1 and miR-21 increased cell stemness, and miR-21 inhibition decreased cell stemness. Moreover, inhibitor of miR-21 suppressed the role of circFAT1. Conclusions: In conclusion, circFAT1 is upregulated in EC and it may increase cancer cell stemness by upregulating miR-21.

CircRNA CircRIMS is Overexpressed in Esophageal Squamous Cell Carcinoma and Downregulate miR-613 Through Methylation to Increase Cell Proliferation

Purpose

CircRNA CircRIMS has been characterized as an oncogenic circRNA in gastric cancer, while its role in other cancers is unknown. This study aimed to explore the role of CircRIMS in esophageal squamous cell carcinoma (ESCC).

Patients and Methods

Tissues collected from 60 ESCC patients were subjected to extractions of total RNA and RT-qPCRs to analyze the differential expression of CircRIMS and miR-613. The 60 ESCC patients were followed up for 5 years to analyze the prognostic value of CircRIMS for ESCC. The interaction between CircRIMS and miR-613 was showed by luciferase activity assay and fluorescence in situ hybridization. The role of CircRIMS in regulating miR-613 expression and methylation was analyzed by overexpression experiments, RT-qPCRs and Western blot assay. The role of CircRIMS and miR-613 in regulating cell proliferation was analyzed using the BrdU assay. ESCC xenograft model was used to demonstrate the role of CircRIMS and miR-613 in vivo.

Results

We found that CircRIMS was overexpressed in ESCC and predicted poor survival. In addition, miR-613 was under expressed in ESCC and inversely correlated with CircRIMS. In ESCC cells, CircRIMS overexpression decreased the expression of miR-613 and increased the methylation of miR-613 gene. Cell proliferation assay showed that CircRIMS overexpression reduced the inhibitory effects of miR-613 overexpression on cell proliferation. Animal experience finally illustrated that CircRNA CircRIMS downregulated miR-613 through methylation to promote tumor growth.

Conclusion

Therefore, CircRIMS may downregulate miR-613 through methylation to increase cell proliferation in ESCC.

Lung cancer organoids analyzed on microwell arrays predict drug responses of patients within a week

While the potential of patient-derived organoids (PDOs) to predict patients' responses to anti-cancer treatments has been well recognized, the lengthy time and the low efficiency in establishing PDOs hamper the implementation of PDO-based drug sensitivity tests in clinics. We first adapt a mechanical sample processing method to generate lung cancer organoids (LCOs) from surgically resected and biopsy tumor tissues. The LCOs recapitulate the histological and genetic features of the parental tumors and have the potential to expand indefinitely. By employing an integrated superhydrophobic microwell array chip (InSMAR-chip), we demonstrate hundreds of LCOs, a number that can be generated from most of the samples at passage 0, are sufficient to produce clinically meaningful drug responses within a week. The results prove our one-week drug tests are in good agreement with patient-derived xenografts, genetic mutations of tumors, and clinical outcomes. The LCO model coupled with the microwell device provides a technically feasible means for predicting patient-specific drug responses in clinical settings.

Fine synthesis of Prussian-blue analogue coated gold nanoparticles (Au@PBA NPs) for sorting specific cancer cell subtypes

Multiplex surface-enhanced Raman scattering (SERS) detection of markers without background in tumor biosystems has its superiority over other optical methods. Herein, we reported a strategy of quantitative discrimination of two breast cancer cell subtypes. Based on our previous studies, two kinds of Prussian blue analogue coated gold nanoparticles (Au@PBA NPs) were designed and synthesized by the replacement of Fe²⁺ with Pb²⁺ or Cu²⁺. Therefore, two distinct SERS emissions of C≡N bonds at 2122 cm^-1 and 2176 cm^-1 have been acquired. When modified with aptamers of epithelial cell adhesion molecule (EpCAM) and epidermal growth factor receptor (EGFR), which are both expressed in MCF-7 and MDA-MB-231 cell lines but in different levels, the SERS nanoprobes simultaneously identified the relative expression of these biomarkers on the cell surface, providing a good example for ratiometric detection in biosystems without any interference. Each surface marker of tumor cells corresponds to a single SERS emission. Thus, each subtype could be described in a molecular profiling way through duplex C≡N bonds-based SERS emission, which is more advanced than traditional flow cytometry method.

Identification of Vinyl Sulfone Derivatives as EGFR Tyrosine Kinase Inhibitor: In Vitro and In Silico Studies

Epidermal growth factor receptor (EGFR), overexpressed in many types of cancer, has been proved as a high potential target for targeted cancer therapy due to its role in regulating proliferation and survival of cancer cells. In the present study, a series of designed vinyl sulfone derivatives was screened against EGFR tyrosine kinase (EGFR-TK) using in silico and in vitro studies. The molecular docking results suggested that, among 78 vinyl sulfones, there were eight compounds that could interact well with the EGFR-TK at the ATP-binding site. Afterwards, these screened compounds were tested for the inhibitory activity towards EGFR-TK using ADP-Glo™ kinase assay, and we found that only VF16 compound exhibited promising inhibitory activity against EGFR-TK with the IC₅₀ value of 7.85 ± 0.88 nM. In addition, VF16 showed a high cytotoxicity with IC₅₀ values of 33.52 ± 2.57, 54.63 ± 0.09, and 30.38 ± 1.37 µM against the A431, A549, and H1975 cancer cell lines, respectively. From 500-ns MD simulation, the structural stability of VF16 in complex with EGFR-TK was quite stable, suggesting that this compound could be a novel small molecule inhibitor targeting EGFR-TK.

Germline and Somatic Genetic Variants in the p53 Pathway Interact to Affect Cancer Risk, Progression, and Drug Response

Insights into oncogenesis derived from cancer susceptibility loci (SNP) hold the potential to facilitate better cancer management and treatment through precision oncology. However, therapeutic insights have thus far been limited by our current lack of understanding regarding both interactions of these loci with somatic cancer driver mutations and their influence on tumorigenesis. For example, although both germline and somatic genetic variation to the p53 tumor suppressor pathway are known to promote tumorigenesis, little is known about the extent to which such variants cooperate to alter pathway activity. Here we hypothesize that cancer risk-associated germline variants interact with somatic TP53 mutational status to modify cancer risk, progression, and response to therapy. Focusing on a cancer risk SNP (rs78378222) with a well-documented ability to directly influence p53 activity as well as integration of germline datasets relating to cancer susceptibility with tumor data capturing somatically-acquired genetic variation provided supportive evidence for this hypothesis. Integration of germline and somatic genetic data enabled identification of a novel entry point for therapeutic manipulation of p53 activities. A cluster of cancer risk SNPs resulted in increased expression of prosurvival p53 target gene KITLG and attenuation of p53-mediated responses to genotoxic therapies, which were reversed by pharmacologic inhibition of the prosurvival c-KIT signal. Together, our results offer evidence of how cancer susceptibility SNPs can interact with cancer driver genes to affect cancer progression and identify novel combinatorial therapies. SIGNIFICANCE: These results offer evidence of how cancer susceptibility SNPs can interact with cancer driver genes to affect cancer progression and present novel therapeutic targets.

Identifying Novel Drug Targets by iDTPnd: A Case Study of Kinase Inhibitors

Current FDA-approved kinase inhibitors cause diverse adverse effects, some of which are due to the mechanism-independent effects of these drugs. Identifying these mechanism-independent interactions could improve drug safety and support drug repurposing. Here, we develop iDTPnd (integrated Drug Target Predictor with negative dataset), a computational approach for large-scale discovery of novel targets for known drugs. For a given drug, we construct a positive structural signature as well as a negative structural signature that captures the weakly conserved structural features of drug-binding sites. To facilitate assessment of unintended targets, iDTPnd also provides a docking-based interaction score and its statistical significance. We confirm the interactions of sorafenib, imatinib, dasatinib, sunitinib, and pazopanib with their known targets at a sensitivity of 52% and a specificity of 55%. We also validate 10 predicted novel targets by using in vitro experiments. Our results suggest that proteins other than kinases, such as nuclear receptors, cytochrome P450, and MHC class I molecules, can also be physiologically relevant targets of kinase inhibitors. Our method is general and broadly applicable for the identification of protein–small molecule interactions, when sufficient drug–target 3D data are available. The code for constructing the structural signatures is available at https://sfb.kaust.edu.sa/Documents/iDTP.zip.

Natural Killer Cells and Anti-Cancer Therapies: Reciprocal Effects on Immune Function and Therapeutic Response

Simple Summary

Natural Killer (NK) cells are innate lymphocytes that play an important role in the immune response against cancer. Their activity is controlled by a balance of inhibitory and activating receptors, which in cancer can be skewed to favor their suppression in support of immune escape. It is therefore imperative to find ways to optimize their antitumor functionality. In this review, we explore and discuss how their activity influences, or even mediates, the efficacy of various anti-cancer therapies and, vice versa, how their activity can be affected by these therapies. Knowledge of the mechanisms underlying these observations could provide rationales for combining anti-cancer treatments with strategies enhancing NK cell function in order to improve their therapeutic efficacy.

Abstract

Natural Killer (NK) cells are innate immune cells with the unique ability to recognize and kill virus-infected and cancer cells without prior immune sensitization. Due to their expression of the Fc receptor CD16, effector NK cells can kill tumor cells through antibody-dependent cytotoxicity, making them relevant players in antibody-based cancer therapies. The role of NK cells in other approved and experimental anti-cancer therapies is more elusive. Here, we review the possible role of NK cells in the efficacy of various anti-tumor therapies, including radiotherapy, chemotherapy, and immunotherapy, as well as the impact of these therapies on NK cell function.

Co-delivery Systems of Multiple Drugs Using Nanotechnology for Future Cancer Therapy

Cancer treatments have improved significantly during the last decade but are not yet satisfactory. Combination therapy is often administered to improve efficacy and safety. Drug delivery systems can also improve efficacy and safety. To control the spatiotemporal distribution of drugs, nanotechnology involving liposomes, solid lipid nanoparticles, and polymeric micelles has been developed. Co-delivery systems of multiple drugs are a promising approach to combat cancer. Synergistic effects and reduced side effects are expected from the use of co-delivery systems. In this review, we summarize various co-delivery systems for multiple drugs, including small-molecule drugs, nucleic acids, genes, and proteins. Co-delivery of drugs with different properties is relatively difficult, but some researchers have succeeded in developing such co-delivery systems. Environment-responsive carrier designs can control the release of cargos. Although their preparation is more complicated than that of mono-delivery systems, co-delivery systems can simplify clinical procedures and improve patient QOL.

Upregulation of XRN2 acts as an oncogene in oral squamous cell carcinoma and correlates with poor prognosis

BACKGROUD

The 5'-3' exoribonuclease 2 (XRN2) has been reported involved in several tumors. However, the clinical significance and molecular mechanism of XRN2 in oral squamous cell carcinoma (OSCC) have not been elucidated.

MATERIALS AND METHODS

Immunohistochemistry (IHC) was used to investigate the expression of XRN2 in OSCC and adjacent noncancerous tissues, which was further identified by western blot and GEPIA2 database analysis. Moreover, the relationship between XRN2 expression and the clinicopathological characteristics and prognosis of OSCC patients was evaluated. In addition, in vitro, the effects of XRN2 on OSCC cells were evaluated by Cell Counting Kit-8 (CCK8) assay, colony formation assay, apoptosis assay, wound healing assay, and transwell assays.

RESULTS

XRN2 was overexpressed in 44 of 77 (57.1 %) OSCC tissues. High expression of XRN2 was significantly associated with tumor differentiation (P=0.003), pathological clinical stage (P=0.045), lymph node metastasis (P=0.041), and poor overall survival (P=0.0013). Furthermore, the multivariate analysis suggested that XRN2 expression(P=0.002) was determined as an independent prognostic factor for patients with OSCC. Additionally, with functional assays in vitro, we found that downregulation of XRN2 inhibited cell proliferation, migration, and invasion, while promoted apoptosis of OSCC cells. Furthermore, knockdown of XRN2 in OSCC cells could increase the expression of E-cadherin but reduce the expression of Vimentin, which changes the characteristic of epithelial-mesenchymal transition (EMT).

CONCLUSION

XRN2 is significantly overexpressed in OSCC tissues and its upregulation was closely associated with poor prognosis of OSCC patients. XRN2 could be a useful prognostic biomarker and a potential therapeutic target for OSCC.

An innovative kinome platform to accelerate small-molecule inhibitor discovery and optimization from hits to leads

Kinases, accounting for 20% of the human genome, have been the focus of pharmaceutical drug discovery efforts for over three decades. Despite concerns surrounding the tractability of kinases as drug targets, it is evident that kinase drug discovery offers great potential, underscored by the US Food and Drug Administration (FDA) approval of 48 small-molecule kinase inhibitors. Despite these successes, it is challenging to identify novel kinome selective inhibitors with good pharmacokinetic/pharmacodynamic (PK/PD) properties, and resistance to kinase inhibitor treatment frequently arises. A new era of kinase drug discovery predicates the need for diverse and powerful tools to discover the next generation of kinase inhibitors. Here, we outline key tenets of the Bristol Meyers Squibb (BMS) kinase platform, to enable efficient generation of highly optimized kinase inhibitors.

Cutaneous reactions to pediatric cancer treatment part II: Targeted therapy

Cancer remains a leading cause of morbidity and mortality among children. Targeted therapies may improve survivorship; however, unique side-effect profiles have also emerged with these novel therapies. Changes in hair, skin, and nails-termed dermatologic adverse events (AEs)-are among the most common sequelae and may result in interruption or discontinuation of therapy. Though dermatologic AEs have been detailed in adults, these findings are not well described in the pediatric population. We reviewed the literature to characterize dermatologic AEs to anticancer targeted therapies available as of July 2020 and summarized the spectrum of clinical findings as well as treatment recommendations for children. Dermatologic AEs are among the most common AEs reported in pediatric patients receiving targeted therapy, but morphologic and histologic descriptions are often lacking in current publications. Pediatric dermatologists are uniquely poised to recognize specific morphology of dermatologic AEs and make recommendations for prevention and treatment that may improve quality of life and enable ongoing cancer therapy.

The Interactions of Nintedanib and Oral Anticoagulants-Molecular Mechanisms and Clinical Implications

Nintedanib is a synthetic orally active tyrosine kinase inhibitor, whose main action is to inhibit the receptors of the platelet-derived growth factor, fibroblast growth factor and vascular endothelial growth factor families. The drug also affects other kinases, including Src, Flt-3, LCK, LYN. Nintedanib is used in the treatment of idiopathic pulmonary fibrosis, chronic fibrosing interstitial lung diseases and lung cancer. The mechanism of action suggests that nintedanib should be considered one of the potential agents for inhibiting and revising the fibrosis process related to COVID-19 infections. Due to the known induction of coagulation pathways during COVID-19 infections, possible interaction between nintedanib and anticoagulant seems to be an extremely important issue. In theory, nintedanib could increase the bleeding risk, thrombosis and lead to thrombocytopenia. The data from clinical trials on the concomitant use of nintedanib and antithrombotic agents is very limited as this patient group was within the standard exclusion criteria. Nintedanib is an important therapeutic option, despite its interaction with anticoagulants. If anticoagulant therapy is necessary, the more effective and safer option is the concomitant administration of DOACs and nintedanib, especially when drug-monitored therapy will be used in patients at high risk of bleeding complications.

Growth of pancreatic cancers with hemizygous chromosomal 17p loss of MYBBP1A can be preferentially targeted by PARP inhibitors

Here, we selectively target pancreatic ductal adenocarcinoma (PDAC) cells harboring a hemizygous gene essential for cell growth. MYB binding protein 1A (MYBBP1A), encoding a chromatin-bound protein, is hemizygous in most of the PDAC due to a chromosome 17p deletion that also spans TP53 We find that hemizygous MYBBP1A loss in isogenic PDAC cells promotes tumorigenesis but, paradoxically, homozygous MYBBP1A loss is associated with impaired cell growth and decreased tumorigenesis. Poly-adenosine 5'-diphosphate-ribose polymerase 1 (PARP1) interacts with MYBBP1A and displaces it from chromatin. Small molecules, such as olaparib, that trap PARP1 to chromatin are able to evict the minimal pool of chromatin-bound MYBBP1A protein in MYBBP1A hemizygous cells and impair cell growth, greater than its impact on wild-type cells. Our findings reveal how a cell essential gene with one allele lost in cancer cells can be preferentially susceptible to a specific molecular therapy, when compared to wild-type cells.

Aromatic Linkers Unleash the Antiproliferative Potential of 3-Chloropiperidines Against Pancreatic Cancer Cells

In this study, we describe the synthesis and biological evaluation of a set of bis-3-chloropiperidines (B-CePs) containing rigid aromatic linker structures. A modification of the synthetic strategy also enabled the synthesis of a pilot tris-3-chloropiperidine (Tri-CeP) bearing three reactive meta-chloropiperidine moieties on the aromatic scaffold. A structure-reactivity relationship analysis of B-CePs suggests that the arrangement of the reactive units affects the DNA alkylating activity, while also revealing correlations between the electron density of the aromatic system and the reactivity with biologically relevant nucleophiles, both on isolated DNA and in cancer cells. Interestingly, all aromatic 3-chloropiperidines exhibited a marked cytotoxicity and tropism for 2D and 3D cultures of pancreatic cancer cells. Therefore, the new aromatic 3-chloropiperidines appear to be promising contenders for further development of mustard-based anticancer agents aimed at pancreatic cancers.

[Genetic instability, a factor limiting the efficiency of targeted therapies in solid oncology]

Genomic instability is one of the main properties of tumour development, promoting first the acquisition of genetic alterations and thus carcinogenesis. Then, the chronic and anarchic proliferation of cancer cells also supports and contributes to this instability allowing a continuous evolution of the tumour. The accumulation of mutations resulting from that instability contributes to tumour heterogeneity that occurs in a specific environment. The resulting diversity of oncogenic drivers further complicates the characterization of the origin of cancer cells dysfunction and consequently therapeutic decision. However, the consideration of the molecular context in oncology has initiated the development of targeted therapies. Based on the concept of oncogene addiction and synthetic lethality, these new drugs require the characterization and identification of specific tumour biomarkers. Targeted therapies have thus considerably optimized patient management, improving efficiency and quality of life while limiting the side effects observed with conventional chemotherapies. However, despite significant clinical benefits, some major limitations to their administration remain. The study of the current issues related to these new therapeutic molecules is becoming crucial for patient management towards an improvement of personalized medicine.