Independent Drug Action in Combination Therapy: Implications for Precision Oncology

New immunotherapeutic approaches for cancer treatment

Neoplasms are a worldwide recognized non-contagious disease which has the most mortality rate after cardiovascular diseases. For decades, there has been a vast amount of study on treatment methods of cancer which has led to conventional therapies such as chemotherapy, radiation therapy, surgery and so on. Clinicians and researchers believed that there is an urgent need, considering the high rate of incidence and prevalence, for an alternative treatment option which is more efficacious and has less adverse effects than the above-mentioned treatments. Immunotherapy has emerged as a potential treatment alternative in a few years and became one of the fastest developing therapeutic approaches. Different kinds of immunotherapies are FDA approved and available for treatment of various cancer types. In this review, we have summarized the major immunotherapy methods including checkpoint inhibitors, CAR T cell therapies and cancer vaccines. Furthermore, application of combination therapy, precision medicine, biomarker discovery, overcoming resistance and reduction of adverse effects are discussed in this study.

Extraction, Characterization, and Evaluation of the Cytotoxic Activity of Piperine in Its Isolated form and in Combination with Chemotherapeutics against Gastric Cancer

Gastric cancer is one of the most frequent types of neoplasms worldwide, usually presenting as aggressive and difficult-to-manage tumors. The search for new structures with anticancer potential encompasses a vast research field in which natural products arise as promising alternatives. In this scenario, piperine, an alkaloid of the Piper species, has received attention due to its biological activity, including anticancer attributes. The present work proposes three heating-independent, reliable, low-cost, and selective methods for obtaining piperine from Piper nigrum L. (Black pepper). Electronic (SEM) and optical microscopies, X-ray diffraction, nuclear magnetic resonance spectroscopies (¹³C and ¹H NMR), and optical spectroscopies (UV-Vis, photoluminescence, and FTIR) confirm the obtention of piperine crystals. The MTT assay reveals that the piperine samples exhibit good cytotoxic activity against primary and metastasis models of gastric cancer cell lines from the Brazilian Amazon. The samples showed selective cytotoxicity on the evaluated models, revealing higher effectiveness in cells bearing a higher degree of aggressiveness. Moreover, the investigated piperine crystals demonstrated the ability to act as a good cytotoxicity enhancer when combined with traditional chemotherapeutics (5-FU and GEM), allowing the drugs to achieve the same cytotoxic effect in cells employing lower concentrations. These results establish piperine as a promising molecule for therapy investigations in aggressive gastric cancer, both in its isolated form or as a bioenhancer.

Advances and Prospects in the Treatment of Pancreatic Cancer

Pancreatic cancer is a highly malignant and incurable disease, characterized by its aggressive nature and high fatality rate. The most common type is pancreatic ductal adenocarcinoma (PDAC), which has poor prognosis and high mortality rate. Current treatments for pancreatic cancer mainly encompass surgery, chemotherapy, radiotherapy, targeted therapy, and combination regimens. However, despite efforts to improve prognosis, and the 5-year survival rate for pancreatic cancer remains very low. Therefore, it's urgent to explore novel therapeutic approaches. With the rapid development of therapeutic strategies in recent years, new ideas have been provided for treating pancreatic cancer. This review expositions the advancements in nano drug delivery system, molecular targeted drugs, and photo-thermal treatment combined with nanotechnology for pancreatic cancer. It comprehensively analyzes the prospects of combined drug delivery strategies for treating pancreatic cancer, aiming at a deeper understanding of the existing drugs and therapeutic approaches, promoting the development of new therapeutic drugs, and attempting to enhance the therapeutic effect for patients with this disease.

Novel Synergies and Isolate Specificities in the Drug Interaction Landscape of Mycobacterium abscessus

Mycobacterium abscessus infections are difficult to treat and are often considered untreatable without tissue resection. Due to the intrinsic drug-resistant nature of the bacteria, combination therapy of three or more antibiotics is recommended. A major challenge in treating M. abscessus infections is the absence of a universal combination therapy with satisfying clinical success rates, leaving clinicians to treat infections using antibiotics lacking efficacy data. We systematically measured drug combinations in M. abscessus to establish a resource of drug interaction data and identify patterns of synergy to help design optimized combination therapies. We measured 191 pairwise drug combination effects among 22 antibacterials and identified 71 synergistic pairs, 54 antagonistic pairs, and 66 potentiator-antibiotic pairs. We found that commonly used drug combinations in the clinic, such as azithromycin and amikacin, are antagonistic in the lab reference strain ATCC 19977, whereas novel combinations, such as azithromycin and rifampicin, are synergistic. Another challenge in developing universally effective multidrug therapies for M. abscessus is the significant variation in drug response between isolates. We measured drug interactions in a focused set of 36 drug pairs across a small panel of clinical isolates with rough and smooth morphotypes. We observed strain-dependent drug interactions that cannot be predicted from single-drug susceptibility profiles or known drug mechanisms of action. Our study demonstrates the immense potential to identify synergistic drug combinations in the vast drug combination space and emphasizes the importance of strain-specific combination measurements for designing improved therapeutic interventions.

Pharmacogenomics: Driving Personalized Medicine

Personalized medicine tailors therapies, disease prevention, and health maintenance to the individual, with pharmacogenomics serving as a key tool to improve outcomes and prevent adverse effects. Advances in genomics have transformed pharmacogenetics, traditionally focused on single gene-drug pairs, into pharmacogenomics, encompassing all "-omics" fields (e.g., proteomics, transcriptomics, metabolomics, and metagenomics). This review summarizes basic genomics principles relevant to translation into therapies, assessing pharmacogenomics' central role in converging diverse elements of personalized medicine. We discuss genetic variations in pharmacogenes (drug-metabolizing enzymes, drug transporters, and receptors), their clinical relevance as biomarkers, and the legacy of decades of research in pharmacogenetics. All types of therapies, including proteins, nucleic acids, viruses, cells, genes, and irradiation, can benefit from genomics, expanding the role of pharmacogenomics across medicine. Food and Drug Administration approvals of personalized therapeutics involving biomarkers increase rapidly, demonstrating the growing impact of pharmacogenomics. A beacon for all therapeutic approaches, molecularly targeted cancer therapies highlight trends in drug discovery and clinical applications. To account for human complexity, multicomponent biomarker panels encompassing genetic, personal, and environmental factors can guide diagnosis and therapies, increasingly involving artificial intelligence to cope with extreme data complexities. However, clinical application encounters substantial hurdles, such as unknown validity across ethnic groups, underlying bias in health care, and real-world validation. This review address the underlying science and technologies germane to pharmacogenomics and personalized medicine, integrated with economic, ethical, and regulatory issues, providing insights into the current status and future direction of health care. SIGNIFICANCE STATEMENT: Personalized medicine aims to optimize health care for the individual patients with use of predictive biomarkers to improve outcomes and prevent adverse effects. Pharmacogenomics drives biomarker discovery and guides the development of targeted therapeutics. This review addresses basic principles and current trends in pharmacogenomics, with large-scale data repositories accelerating medical advances. The impact of pharmacogenomics is discussed, along with hurdles impeding broad clinical implementation, in the context of clinical care, ethics, economics, and regulatory affairs.

Saracatinib synergizes with enzalutamide to downregulate AR activity in CRPC

Prostate cancer (PCa) remains the most diagnosed non-skin cancer amongst the American male population. Treatment for localized prostate cancer consists of androgen deprivation therapies (ADTs), which typically inhibit androgen production and the androgen receptor (AR). Though initially effective, a subset of patients will develop resistance to ADTs and the tumors will transition to castration-resistant prostate cancer (CRPC). Second generation hormonal therapies such as abiraterone acetate and enzalutamide are typically given to men with CRPC. However, these treatments are not curative and typically prolong survival only by a few months. Several resistance mechanisms contribute to this lack of efficacy such as the emergence of AR mutations, AR amplification, lineage plasticity, AR splice variants (AR-Vs) and increased kinase signaling. Having identified SRC kinase as a key tyrosine kinase enriched in CRPC patient tumors from our previous work, we evaluated whether inhibition of SRC kinase synergizes with enzalutamide or chemotherapy in several prostate cancer cell lines expressing variable AR isoforms. We observed robust synergy between the SRC kinase inhibitor, saracatinib, and enzalutamide, in the AR-FL+/AR-V+ CRPC cell lines, LNCaP95 and 22Rv1. We also observed that saracatinib significantly decreases AR Y534 phosphorylation, a key SRC kinase substrate residue, on AR-FL and AR-Vs, along with the AR regulome, supporting key mechanisms of synergy with enzalutamide. Lastly, we also found that the saracatinib-enzalutamide combination reduced DNA replication compared to the saracatinib-docetaxel combination, resulting in marked increased apoptosis. By elucidating this combination strategy, we provide pre-clinical data that suggests combining SRC kinase inhibitors with enzalutamide in select patients that express both AR-FL and AR-Vs.

A landscape of response to drug combinations in non-small cell lung cancer

Combination of anti-cancer drugs is broadly seen as way to overcome the often-limited efficacy of single agents. The design and testing of combinations are however very challenging. Here we present a uniquely large dataset screening over 5000 targeted agent combinations across 81 non-small cell lung cancer cell lines. Our analysis reveals a profound heterogeneity of response across the tumor models. Notably, combinations very rarely result in a strong gain in efficacy over the range of response observable with single agents. Importantly, gain of activity over single agents is more often seen when co-targeting functionally proximal genes, offering a strategy for designing more efficient combinations. Because combinatorial effect is strongly context specific, tumor specificity should be achievable. The resource provided, together with an additional validation screen sheds light on major challenges and opportunities in building efficacious combinations against cancer and provides an opportunity for training computational models for synergy prediction.

In situ modeling of acquired resistance to RTK/RAS pathway targeted therapies

Intrinsic and acquired resistance limit the window of effectiveness for oncogene-targeted cancer therapies. Preclinical studies that identify synergistic combinations enhance therapeutic efficacy to target intrinsic resistance, however, methods to study acquired resistance in cell culture are lacking. Here, we describe a novel in situ resistance assay (ISRA), performed in a 96-well culture format, that models acquired resistance to RTK/RAS pathway targeted therapies. Using osimertinib resistance in EGFR-mutated lung adenocarcinoma (LUAD) as a model system, we show acquired resistance can be reliably modeled across cell lines using objectively defined osimertinib doses. Similar to patient populations, isolated osimertinib-resistant populations showed resistance via enhanced activation of multiple parallel RTKs so that individual RTK inhibitors did not re-sensitize cells to osimertinib. In contrast, inhibition of proximal RTK signaling using the SHP2 inhibitor RMC-4550 both re-sensitized resistant populations to osimertinib and prevented the development of osimertinib resistance as a primary therapy. Similar, objectively defined drug doses were used to model resistance to additional RTK/RAS pathway targeted therapies including the KRASG12C inhibitors adagrasib and sotorasib, the MEK inhibitor trametinib, and the farnesyl transferase inhibitor tipifarnib. These studies highlight the tractability of in situ resistance assays to model acquired resistance to targeted therapies and provide a framework for assessing the extent to which synergistic drug combinations can target acquired drug resistance.

Combination Early-Phase Trials of Anticancer Agents in Children and Adolescents

PURPOSE

There is an increasing need to evaluate innovative drugs for childhood cancer using combination strategies. Strong biological rationale and clinical experience suggest that multiple agents will be more efficacious than monotherapy for most diseases and may overcome resistance mechanisms and increase synergy. The process to evaluate these combination trials needs to maximize efficiency and should be agreed by all stakeholders.

METHODS

After a review of existing combination trial methodologies, regulatory requirements, and current results, a consensus among stakeholders was achieved.

RESULTS

Combinations of anticancer therapies should be developed on the basis of mechanism of action and robust preclinical evaluation, and may include data from adult clinical trials. The general principle for combination early-phase studies is that, when possible, clinical trials should be dose- and schedule-confirmatory rather than dose-exploratory, and every effort should be made to optimize doses early. Efficient early-phase combination trials should be seamless, including dose confirmation and randomized expansion. Dose evaluation designs for combinations depend on the extent of previous knowledge. If not previously evaluated, limited evaluation of monotherapy should be included in the same clinical trial as the combination. Randomized evaluation of a new agent plus standard therapy versus standard therapy is the most effective approach to isolate the effect and toxicity of the novel agent. Platform trials may be valuable in the evaluation of combination studies. Patient advocates and regulators should be engaged with investigators early in a proposed clinical development pathway and trial design must consider regulatory requirements.

CONCLUSION

An optimized, agreed approach to the design and evaluation of early-phase pediatric combination trials will accelerate drug development and benefit all stakeholders, most importantly children and adolescents with cancer.

Subcellular Organelle-Targeted Nanostructured Lipid Carriers for the Treatment of Metastatic Breast Cancer

Background

Subcellular organelle targeted nano-formulations for cancer treatment are receiving increasing attention owing to their benefits of precise drug delivery, maximized therapeutic index, and reduced off-target side effects. The nucleus and mitochondria, as the main subcellular organelles, are the significant organelles responsible for maintaining cell operation and metabolism. They can be involved in many essential physiological and pathological processes such as cell proliferation, organism metabolism, intracellular transportation, and play a critical role in regulating cell biology. Meanwhile, breast cancer metastasis is one of the leading causes of death in breast cancer patients. With the development of nanotechnology, nanomaterials have been widely used in tumor therapy.

Methods

We designed a subcellular organelle targeted nanostructured lipid carriers (NLC) to deliver paclitaxel (PTX) and gambogic acid (GA) to tumor tissues.

Results

Due to the surface of NLC being modified by subcellular organelle targeted peptide, the PTX and GA co-loaded NLC can accurately release PTX and GA in tumor cells. This property makes NLC able to easy to enter tumor site and target the specific subcellular organelle. The modified NLC can efficiently inhibit the growth of 4T1 primary tumor and lung metastasis, which may be related to the down-regulation of matrix metalloproteinase-9 (MMP-9) and BCL-2 levels, up-regulation of E-cadherin level, and antagonized PTX-induced increase of C-C chemokine ligand 2 (CCL-2) levels by GA. Meanwhile, the synergistic anti-tumor effect of GA and PTX has also been verified in vitro and in vivo experiments.

Conclusion

The subcellular organelle targeted peptide modified PTX+GA multifunctional nano-drug delivery system has a good therapeutic effect on tumors, and this study provides significant insights into the role of different subcellular organelles in inhibiting tumor growth and metastasis and inspires researchers to develop highly effective cancer therapeutic strategies through subcellular organelle targeted drugs.

An End-to-end In-Silico and In-Vitro Drug Repurposing Pipeline for Glioblastoma

Our study aims to address the challenges in drug development for glioblastoma, a highly aggressive brain cancer with poor prognosis. We propose a computational framework that utilizes machine learning-based propensity score matching to estimate counterfactual treatment effects and predict synergistic effects of drug combinations. Through our in-silico analysis, we identified promising drug candidates and drug combinations that warrant further investigation. To validate these computational findings, we conducted in-vitro experiments on two GBM cell lines, U87 and T98G. The experimental results demonstrated that some of the identified drugs and drug combinations indeed exhibit strong suppressive effects on GBM cell growth. Our end-to-end pipeline showcases the feasibility of integrating computational models with biological experiments to expedite drug repurposing and discovery efforts. By bridging the gap between in-silico analysis and in-vitro validation, we demonstrate the potential of this approach to accelerate the development of novel and effective treatments for glioblastoma.

p38 MAPK inhibitor SB203580 enhances anticancer activity of PARP inhibitor olaparib in a synergistic way on non-small cell lung carcinoma A549 cells

The Poly (ADP-ribose) polymerase (PARP) inhibitor (PARPi) olaparib gives promising results against various types of cancers in clinical trials. The combination of drugs always increases therapeutic efficacy because of targeting multiple pathways of cancer progression. Our objective was to explore the potential synergistic anticancer activities of olaparib combined with p38 MAPK inhibitor (MAPKi) SB203580 on non-small cell lung carcinoma (NSCLC) A549 cells. The effects of the individual compound and their combination on cell survival, DNA damage as detected by γH2AX foci, expression of key proteins in Homologous Recombination (HR) and Non-Homologous End Joining (NHEJ) repair, caspase 3 activation, nuclear fragmentation and telomerase regulation were studied in A549 cells. The results showed that olaparib and SB203580 individually reduced cell viability in a dose-dependent manner but combined treatment synergistically reduced cell viability. Olaparib combined with SB203580 significantly reduced error-free HR repair via reducing MRE11-RAD50 and promoted error-prone NHEJ repair by increasing Ku70-Ku80 leading to increased DNA damage-induced apoptosis. Notably, the alteration of proteins in HR/NHEJ pathways, DNA damage and induction of apoptosis was significant by combined treatment but not by 1 μM olaparib treatment alone. In addition, combined treatment reduced telomerase activity more than single treatment via reducing telomerase subunits. These data implicated that the anticancer potential of olaparib was significantly increased by combining SB203580 through increasing DNA damage-induced apoptosis and inhibiting telomerase activity.

Saracatinib synergizes with enzalutamide to downregulate androgen receptor activity in castration resistant prostate cancer

Prostate cancer (PCa) remains the most diagnosed non-skin cancer amongst the American male population. Treatment for localized prostate cancer consists of androgen deprivation therapies (ADTs), which typically inhibit androgen production and the androgen receptor (AR). Though initially effective, a subset of patients will develop resistance to ADTs and the tumors will transition to castration-resistant prostate cancer (CRPC). Second generation hormonal therapies such as abiraterone acetate and enzalutamide are typically given to men with CRPC. However, these treatments are not curative and typically prolong survival only by a few months. Several resistance mechanisms contribute to this lack of efficacy such as the emergence of AR mutations, AR amplification, lineage plasticity, AR splice variants (AR-Vs) and increased kinase signaling. Having identified SRC kinase as a key tyrosine kinase enriched in CRPC patient tumors from our previous work, we evaluated whether inhibition of SRC kinase synergizes with enzalutamide or chemotherapy in several prostate cancer cell lines expressing variable AR isoforms. We observed robust synergy between the SRC kinase inhibitor, saracatinib, and enzalutamide, in the AR-FL+/AR-V+ CRPC cell lines, LNCaP95 and 22Rv1. We also observed that saracatinib significantly decreases AR Y 534 phosphorylation, a key SRC kinase substrate residue, on AR-FL and AR-Vs, along with the AR regulome, supporting key mechanisms of synergy with enzalutamide. Lastly, we also found that the saracatinib-enzalutamide combination reduced DNA replication compared to the saracatinib-docetaxel combination, resulting in marked increased apoptosis. By elucidating this combination strategy, we provide pre-clinical data that suggests combining SRC kinase inhibitors with enzalutamide in select patients that express both AR-FL and AR-Vs.

Harmonizing across datasets to improve the transferability of drug combination prediction

Combination treatment has multiple advantages over traditional monotherapy in clinics, thus becoming a target of interest for many high-throughput screening (HTS) studies, which enables the development of machine learning models predicting the response of new drug combinations. However, most existing models have been tested only within a single study, and these models cannot generalize across different datasets due to significantly variable experimental settings. Here, we thoroughly assessed the transferability issue of single-study-derived models on new datasets. More importantly, we propose a method to overcome the experimental variability by harmonizing dose-response curves of different studies. Our method improves the prediction performance of machine learning models by 184% and 1367% compared to the baseline models in intra-study and inter-study predictions, respectively, and shows consistent improvement in multiple cross-validation settings. Our study addresses the crucial question of the transferability in drug combination predictions, which is fundamental for such models to be extrapolated to new drug combination discovery and clinical applications that are de facto different datasets.

Novel construction of multifunctional photo-responsive and nucleic acid-triggered doxorubicin-releasing liposomes for cancer therapy

All-in-one nano theranostics integrating accurate diagnosis and combined therapy is promising for high-efficacy tumor treatment and receiving significant attention. In this study, we develop photo-controlled release liposomes with nucleic acid-triggered fluorescence and photoactivity for tumor imaging and synergistic antitumor therapy. Copper phthalocyanine as a photothermal agent is fused into lipid layers to prepare liposomes encapsulating cationic zinc phthalocyanine ZnPc(TAP)₄¹²⁺ and doxorubicin, followed by the modification of RGD peptide on the surface to obtain the final product RGD-CuPc:ZnPc(TAP)₄¹²⁺:DOX@LiPOs (RCZDL). RCZDL possesses favorable stability, significant photothermal effect, and photo-controlled release function through the characterization of physicochemical properties. It is shown that the fluorescence and ROS generation could be turned on by intracellular nucleic acid after illumination. RCZDL exhibits synergistic cytotoxicity, increased apoptosis, and significantly promoted cell uptake. Subcellular localization analysis indicates that ZnPc(TAP)₄¹²⁺ tends to be distributed in the mitochondria of HepG2 cells treated with RCZDL after exposure to light. The results of experiments in vivo on H22 tumor-bearing mice demonstrate that RCZDL had excellent tumor targeting, a prominent photothermal effect at the tumor sites, and synergistic antitumor efficiency. More importantly, little RCZDL has been found to be accumulated in the liver, and most were quickly metabolized by the liver. The results confirm that the proposed new intelligent liposomes provide a simple and cost-effective way for tumor imaging and combinatorial anticancer therapy.

Rational combinations of targeted cancer therapies: background, advances and challenges

Over the past two decades, elucidation of the genetic defects that underlie cancer has resulted in a plethora of novel targeted cancer drugs. Although these agents can initially be highly effective, resistance to single-agent therapies remains a major challenge. Combining drugs can help avoid resistance, but the number of possible drug combinations vastly exceeds what can be tested clinically, both financially and in terms of patient availability. Rational drug combinations based on a deep understanding of the underlying molecular mechanisms associated with therapy resistance are potentially powerful in the treatment of cancer. Here, we discuss the mechanisms of resistance to targeted therapies and how effective drug combinations can be identified to combat resistance. The challenges in clinically developing these combinations and future perspectives are considered.

Compressed phenotypic screens for complex multicellular models and high-content assays

High-throughput phenotypic screens leveraging biochemical perturbations, high-content readouts, and complex multicellular models could advance therapeutic discovery yet remain constrained by limitations of scale. To address this, we establish a method for compressing screens by pooling perturbations followed by computational deconvolution. Conducting controlled benchmarks with a highly bioactive small molecule library and a high-content imaging readout, we demonstrate increased efficiency for compressed experimental designs compared to conventional approaches. To prove generalizability, we apply compressed screening to examine transcriptional responses of patient-derived pancreatic cancer organoids to a library of tumor-microenvironment (TME)-nominated recombinant protein ligands. Using single-cell RNA-seq as a readout, we uncover reproducible phenotypic shifts induced by ligands that correlate with clinical features in larger datasets and are distinct from reference signatures available in public databases. In sum, our approach enables phenotypic screens that interrogate complex multicellular models with rich phenotypic readouts to advance translatable drug discovery as well as basic biology.

A multivariate metal–organic framework based pH-responsive dual-drug delivery system for chemotherapy and chemodynamic therapy

By combining two different ligands and metals, MOFs can be fine-tuned for effective encapsulation and delivery of two anticancer drugs.

Less is More? First Impressions From COSMIC-313

The COSMIC-313 phase 3 randomized controlled trial tested the triplet combination of cabozantinib with nivolumab and ipilimumab in comparison with nivolumab plus ipilimumab control as fist-line systemic therapy in metastatic clear cell renal cell carcinoma. The first results presented at the 2022 European Society of Medical Oncology Congress are a milestone for the renal cell carcinoma field because they signal the advent of triplet combinations as potential treatment options for our patients. The present commentary highlights some considerations and potential next steps based on these first impressions.

screenwerk: a modular tool for the design and analysis of drug combination screens

MOTIVATION

There is a rapidly growing interest in high-throughput drug combination screening to identify synergizing drug interactions for treatment of various maladies, such as cancer and infectious disease. This creates the need for pipelines that can be used to design such screens, perform quality control on the data and generate data files that can be analyzed by synergy-finding bioinformatics applications.

RESULTS

screenwerk is an open-source, end-to-end modular tool available as an R-package for the design and analysis of drug combination screens. The tool allows for a customized build of pipelines through its modularity and provides a flexible approach to quality control and data analysis. screenwerk is adaptable to various experimental requirements with an emphasis on precision medicine. It can be coupled to other R packages, such as bayesynergy, to identify synergistic and antagonistic drug interactions in cell lines or patient samples. screenwerk is scalable and provides a complete solution for setting up drug sensitivity screens, read raw measurements and consolidate different datasets, perform various types of quality control and analyze, report and visualize the results of drug sensitivity screens.

AVAILABILITY AND IMPLEMENTATION

The R-package and technical documentation is available at https://github.com/Enserink-lab/screenwerk; the R source code is publicly available at https://github.com/Enserink-lab/screenwerk under GNU General Public License v3.0; bayesynergy is accessible at https://github.com/ocbe-uio/bayesynergy. Selected modules are available through Galaxy, an open-source platform for FAIR data analysis at https://oncotools.elixir.no.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Alpha Ketoglutarate Downregulates the Neutral Endopeptidase and Enhances the Growth Inhibitory Activity of Thiorphan in Highly Aggressive Osteosarcoma Cells

Since natural substances are widely explored as epigenetic modulators of gene expression and epigenetic abnormalities are important causes of cancerogenesis, factors with pro-tumor activities subjected to epigenetic control, e.g., neutral endopeptidase (NEP, neprilysin), are promising anticancer targets for potential therapies acting via epigenetic regulation of gene expression. Alpha-ketoglutarate (AKG) is a naturally occurring co-substrate for enzymes involved in histone and DNA demethylation with suggested anti-cancer activity. Hence, we investigated a potential effect of AKG on the NEP expression in cells derived from various cancers (cervical, colon, osteosarcoma) and normal epithelial cells and osteoblasts. Moreover, the overall methylation status of histone H3 was explored to establish the molecular target of AKG activity. Additionally, it was investigated whether AKG in combination with thiorphan (NEP specific inhibitor) exhibited enhanced anticancer activity. The results revealed that AKG downregulated the expression of NEP at the protein level only in highly aggressive osteosarcoma HOS cells (flow cytometry and fluorometric assays), and this protease was found to be involved in AKG-induced growth inhibition in osteosarcoma cells (siRNA NEP silencing, BrdU assay, flow cytometry). Unexpectedly, AKG-induced hypermethylation of H3K27 in HOS cells, which was partially dependent on EZH2 activity. However, this effect was not implicated in the AKG-induced NEP downregulation (flow cytometry). Finally, the combined treatment with AKG and thiorphan was shown to significantly enhance the growth inhibitory potential of each one towards HOS cells (BrdU assay). These preliminary studies have shown for the first time that the downregulation of NEP expression is a promising target in therapies of NEP-implicating HOS cells. Moreover, this therapeutic goal can be achieved via AKG-induced downregulation of NEP and synergistic activity of AKG with thiorphan, i.e., a NEP specific inhibitor. Furthermore, this study has reported for the first time that exogenous AKG can influence the activity of histone methyltransferase, EZH2. However, this issue needs further investigation to elucidate the mechanisms of this phenomenon.

Synergistic Effects of the Combinational Use of Escitalopram Oxalate and 5-Fluorouracil on the Inhibition of Gastric Cancer SNU-1 Cells

Owing to its high recurrence rate, gastric cancer (GC) is the leading cause of tumor-related deaths worldwide. Besides surgical treatment, chemotherapy is the most commonly used treatment against GC. However, the adverse events associated with chemotherapy use limit its effectiveness in GC treatment. In this study, we investigated the effects of using combinations of low-dose 5-fluorouracil (5-FU; 0.001 and 0.01 mM) with different concentrations of escitalopram oxalate (0.01, 0.02, 0.06, and 0.2 mM) to evaluate whether the assessed combination would have synergistic effects on SNU-1 cell survival. 5-FU (0.01 mM) + escitalopram oxalate (0.02 mM) and 5-FU (0.01 mM) + escitalopram oxalate (0.06 mM) administered over 24 h showed synergistic effects on the inhibition of SNU-1 cell proliferation. Moreover, 5-FU (0.001 mM) + escitalopram oxalate (0.02 or 0.06 mM) and 5-FU (0.01 mM) + escitalopram oxalate (0.02, 0.06, or 0.2 mM) administered over 48 h showed synergistic effects on the inhibition of SNU-1 cell proliferation. Compared with controls, SNU-1 cells treated with 5-FU (0.01 mM) + escitalopram oxalate (0.02 mM) exhibited significantly increased levels of annexin V staining, reactive oxygen species, cleaved poly (ADP-ribose) polymerase, and caspase-3 proteins. Furthermore, 5-FU (12 mg/kg) + escitalopram oxalate (12.5 mg/kg) significantly attenuated xenograft SNU-1 cell proliferation in nude mice. Our study is the first to report the synergistic effects of the combinational use of low-dose 5-FU and escitalopram oxalate on inhibiting SNU-1 cell proliferation. These findings may be indicative of an alternative option for GC treatment.

Synergistic Effect of the Combined Action of Targeted and Photodynamic Therapy on HER2-Positive Breast Cancer

Development of combined schemes for the treatment of oncological diseases is a promising strategy to improve the effectiveness of antitumor therapy. This paper shows the fundamental possibility of multiplying the antitumor effect by combining targeted and photodynamic therapy. It was demonstrated that sequential treatment of HER-2 positive breast cancer cells with the targeted toxin DARPin-LoPE and the photoactive compound photodithazine leads to a synergistic enhancement of their effect. In the future, this approach is intended to achieve the maximum therapeutic effect while minimizing the risks of negative side effects.

Assessment of added activity of an antitumor agent

An unprecedented number of novel oncology drugs are under preclinical and clinical development, and nearly all are developed in combinations. With an over-reliance on biological hypotheses, there is less effort to establish single agent activity before initiating late clinical development. This may be contributing to a decreased success rate going from phase 1 to approval in the immunotherapy era. Growing evidence in clinical trial data shows that the treatment benefit from most approved combination therapies can be explained by the independent drug action model. Using this working model, we develop a simple index to measure the added antitumor activity of a new drug based on mean response duration, an endpoint that naturally combines both response status and duration information for all patients, which is shown to be highly predictive of clinical benefit of FDA-approved anti-PD-(L)1 immunotherapies. This index sheds light on challenges and opportunities in contemporary oncology drug development and provides a practical tool to assist with decision-making in early clinical trials.

WW domain binding protein 2 (WBP2) as an oncogene in breast cancer: mechanisms and therapeutic prospects-a narrative review

Background and Objective

WW domain binding protein 2 (WBP2), considered an emerging breast cancer gene, functions as a binding partner for WW domain proteins. The WBP2 gene is involved in mediating the malignant development and clinical drug resistance of breast cancer, but its potential mechanism remains unclear. Therefore, it is necessary to elucidate the mechanism of WBP2 in breast cancer, which will help to provide new methods for clinical diagnosis and treatment of breast cancer.

Methods

The PubMed database was searched using the terms "WW Domain Binding Protein 2" or "WBP2", "breast cancer" or "breast neoplasms" or "human cancer" from January 1997 through August 2022. Through the screening and evaluation of titles and abstracts, about 120 English articles were included in this study.

Key Content and Findings

By describing the multiple regulatory functions of WBP2 at the transcriptional, post-transcriptional, and post-translational levels, and summarizing how WBP2 as a key node crosstalks multiple signaling pathways, we reveal the ability of WBP2 to promote breast cancer malignant progression. In different subtypes of breast cancer, the mechanism of WBP2-mediated drug resistance is related to estrogen receptor and epidermal growth factor receptor (EGFR) 2 status, and hormones may be an essential factor in WBP2-mediated drug resistance. In addition, we discuss the application prospects of WBP2 in targeted therapy and immunotherapy and propose therapeutic strategies to overcome drug resistance in breast cancer by jointly targeting WBP2 and its related molecules. This provides a theoretical basis for the innovation of breast cancer targeted drugs.

Conclusions

WBP2 is a promising target for breast cancer therapy. Nuclear WBP2, as the main functional form of WBP2 after its activation, is a meaningful indicator for the diagnosis and prediction of breast cancer progression.

Targeting the DNA damage response and repair in cancer through nucleotide metabolism

The exploitation of the DNA damage response and DNA repair proficiency of cancer cells is an important anticancer strategy. The replication and repair of DNA are dependent upon the supply of deoxynucleoside triphosphate (dNTP) building blocks, which are produced and maintained by nucleotide metabolic pathways. Enzymes within these pathways can be promising targets to selectively induce toxic DNA lesions in cancer cells. These same pathways also activate antimetabolites, an important group of chemotherapies that disrupt both nucleotide and DNA metabolism to induce DNA damage in cancer cells. Thus, dNTP metabolic enzymes can also be targeted to refine the use of these chemotherapeutics, many of which remain standard of care in common cancers. In this review article, we will discuss both these approaches exemplified by the enzymes MTH1, MTHFD2 and SAMHD1. © 2022 The Authors. Molecular Oncology published by John Wiley & Sons Ltd on behalf of Federation of European Biochemical Societies.

Drug independence and the curability of cancer by combination chemotherapy

Combination chemotherapy can cure certain leukemias and lymphomas, but most solid cancers are only curable at early stages. We review quantitative principles that explain the benefits of combining independently active cancer therapies in both settings. Understanding the mechanistic principles underlying curative treatments, including those developed many decades ago, is valuable for improving future combination therapies. We discuss contemporary evidence for long-established but currently neglected ideas of how combination therapy overcomes tumor heterogeneity. We show that a unified model of interpatient and intratumor heterogeneity describes historical progress in the treatment of pediatric acute lymphocytic leukemia (ALL), in which increasingly intensive combination regimens ultimately achieved high cure rates. We also describe three distinct aspects of drug independence that apply at different biological scales. The ability of these principles to quantitatively explain curative regimens suggests that supra-additive (synergistic) drug interactions are not required for successful combination therapy.

Combinatorial approaches for novel cardiovascular drug discovery: a review of the literature

INTRODUCTION

In this article, authors report an inclusive discussion about the combinatorial approach for the treatment of cardiovascular diseases (CVDs) and for counteracting the cardiovascular risk factors. The mentioned strategy was demonstrated to be useful for improving the efficacy of pharmacological treatments and in CVDs showed superior efficacy with respect to the classical monotherapeutic approach.

AREAS COVERED

According to this topic, authors analyzed the combinatorial treatments that are available on the market, highlighting clinical studies that demonstrated the efficacy of combinatorial drug strategies to cure CVDs and related risk factors. Furthermore, the review gives an outlook on the future perspective of this therapeutic option, highlighting novel drug targets and disease models that could help the future cardiovascular drug discovery.

EXPERT OPINION

The use of specifically designed and increasingly rational and effective drug combination therapies can therefore be considered the evolution of polypharmacy in cardiometabolic and CVDs. This approach can allow to intervene on multiple etiopathogenetic mechanisms of the disease or to act simultaneously on different pathologies/risk factors, using the combinations most suitable from a pharmacodynamic, pharmacokinetic, and toxicological perspective, thus finding the most appropriate therapeutic option.

SynergyFinder 3.0: an interactive analysis and consensus interpretation of multi-drug synergies across multiple samples

SynergyFinder (https://synergyfinder.fimm.fi) is a free web-application for interactive analysis and visualization of multi-drug combination response data. Since its first release in 2017, SynergyFinder has become a popular tool for multi-dose combination data analytics, partly because the development of its functionality and graphical interface has been driven by a diverse user community, including both chemical biologists and computational scientists. Here, we describe the latest upgrade of this community-effort, SynergyFinder release 3.0, introducing a number of novel features that support interactive multi-sample analysis of combination synergy, a novel consensus synergy score that combines multiple synergy scoring models, and an improved outlier detection functionality that eliminates false positive results, along with many other post-analysis options such as weighting of synergy by drug concentrations and distinguishing between different modes of synergy (potency and efficacy). Based on user requests, several additional improvements were also implemented, including new data visualizations and export options for multi-drug combinations. With these improvements, SynergyFinder 3.0 supports robust identification of consistent combinatorial synergies for multi-drug combinatorial discovery and clinical translation.

Systematic review of computational methods for drug combination prediction

Synergistic effects between drugs are rare and highly context-dependent and patient-specific. Hence, there is a need to develop novel approaches to stratify patients for optimal therapy regimens, especially in the context of personalized design of combinatorial treatments. Computational methods enable systematic in-silico screening of combination effects, and can thereby prioritize most potent combinations for further testing, among the massive number of potential combinations. To help researchers to choose a prediction method that best fits for various real-world applications, we carried out a systematic literature review of 117 computational methods developed to date for drug combination prediction, and classified the methods in terms of their combination prediction tasks and input data requirements. Most current methods focus on prediction or classification of combination synergy, and only a few methods consider the efficacy and potential toxicity of the combinations, which are the key determinants of therapeutic success of drug treatments. Furthermore, there is a need to further develop methods that enable dose-specific predictions of combination effects across multiple doses, which is important for clinical translation of the predictions, as well as model-based identification of biomarkers predictive of heterogeneous drug combination responses. Even if most of the computational methods reviewed focus on anticancer applications, many of the modelling approaches are also applicable to antiviral and other diseases or indications.