Effective Drug Combinations Research Articles

Dual-view jointly learning improves personalized drug synergy prediction.

Accurate and robust estimation of the synergistic drug combination is important for medicine precision. Although some computational methods have been developed, some predictions are still unreliable especially for the cross-dataset predictions, due to the complex mechanism of drug combinations and heterogeneity of cancer samples. We have proposed JointSyn that utilizes dual-view jointly learning to predict sample-specific effects of drug combination from drug and cell features. JointSyn outperforms existing state-of-the-art methods in predictive accuracy and robustness across various benchmarks. Each view of JointSyn captures drug synergy-related characteristics and make complementary contributes to the final prediction of drug combination. Moreover, JointSyn with fine-tuning improves its generalization ability to predict a novel drug combination or cancer sample using a small number of experimental measurements. We also used JointSyn to generate an estimated atlas of drug synergy for pan-cancer and explored the differential pattern among cancers. These results demonstrate the potential of JointSyn to predict drug synergy, supporting the development of personalized combinatorial therapies. Source code and data are available at https://github.com/LiHongCSBLab/JointSyn. Supplementary data are available at Bioinformatics online.

MGRN: toward robust drug recommendation via multi-view gating retrieval network.

Drug recommendation aims to allocate safe and effective drug combinations based on the patient's health status from electronic health records, which is crucial to assist clinical physicians in making decisions. However, the existing drug recommendation works face two key challenges: (i) difficulty in fully representing the patient's health status leads to biased drug representation; (ii) only focusing on diagnostic representations of multiple visits, neglecting the modeling of patient drug history. To address the above limitations, we propose a multi-view gating retrieval network (MGRN) for robust drug recommendation. We design visit-, sequence-, and token-level views to provide different perspectives on the interaction between patients and drugs, obtaining a more comprehensive representation of drugs. Moreover, we develop a gating drug retrieval module to capture critical drug information from multiple visits, which can assist in recommending more reasonable drug combinations for the current visit. When evaluated on publicly real-world MIMIC-III and MIMIC-IV datasets, the proposed MGRN establishes a new benchmark performance, particularly achieving improvements of 1.36%, 1.71%, 1.21% and 2.12%, 2.36%, 1.81% in Jaccard, PRAUC, and F1-score, respectively, compared to state-of-the-art models. The code is available at: https://github.com/kyosen258/MGRN.git.

Syn-COM: A Multi-Level Predictive Synergy Framework for Innovative Drug Combinations.

Drug prediction and treatment using bioinformatics and large-scale modeling have emerged as pivotal research areas. This study proposes a novel multi-level collaboration framework named Syn-COM for feature extraction and data integration of diseases and drugs. The framework aims to explore optimal drug combinations and interactions by integrating molecular virtuality, similarity clustering, overlap area, and network distance. It uniquely combines the characteristics of Chinese herbal medicine with clinical experience and innovatively assesses drug interaction and correlation through a synergy matrix. Gouty arthritis (GA) was used as a case study to validate the framework's reliability, leading to the identification of an effective drug combination for GA treatment, comprising Tamaricis Cacumen (Si = 0.73), Cuscutae Semen (Si = 0.68), Artemisiae Annuae Herba (Si = 0.62), Schizonepetae Herba (Si = 0.73), Gleditsiae Spina (Si = 0.89), Prunellae Spica (Si = 0.75), and Achyranthis Bidentatae Radix (Si = 0.62). The efficacy of the identified drug combination was confirmed through animal experiments and traditional Chinese medicine (TCM) component analysis. Results demonstrated significant reductions in the blood inflammatory factors IL1A, IL6, and uric acid, as well as downregulation of TGFB1, PTGS2, and MMP3 expression (p < 0.05), along with improvements in ankle joint swelling in GA mice. This drug combination notably enhances therapeutic outcomes in GA by targeting key genes, underscoring the potential of integrating traditional medicine with modern bioinformatics for effective disease treatment.

Abstract C024: Quantitative mass spectrometry-based multi-omic profiling to overcome drug resistance in pancreatic cancer post KRAS G12D inhibition

Abstract Recently developed inhibitors targeting the KRASG12D oncoprotein are undergoing clinical trial evaluation including in patients with pancreatic adenocarcinoma (PDAC). However, resistance to such targeted therapies is a frequent occurrence, potentially undermining the long-term success of KRAS inhibitors (KRASi). To uncover the adaptive pathways and cell surface biomarkers to KRASi and to identify effective drug combinations that can overcome resistance, we employ a mass spectrometry-based quantitative temporal proteomics workflow. This approach allows us to map the proteomic changes in response to KRASi in both in vitro and in vivo PDAC models. We identify and quantify 10,898 proteins, creating the most extensive proteomic dataset related to KRASi to date. We identify shared mechanisms of both acute and long-term adaptation to KRASi across PDAC models. Utilizing this proteomic information, we are pinpointing drug combinations that pair KRASi with inhibitors of CDK4/6, AURKA, and mTOR to increase cytotoxicity and thereby reduce development of long-term resistance. Surfaceome analyses identifies cell surface proteins upregulated upon acute and long-term KRASi, including TACSTD2 (Trop-2). Antibody-drug conjugates targeting upregulated surface proteins may be a useful orthogonal approach towards preventing or circumventing KRASi resistance with testing of Trop-2 and other candidates ongoing. In summary, through mass spectrometry-based quantitative temporal proteomics, we identify proteomic adaptations to KRASi in PDAC and propose combination treatments with promising therapeutic potential. Citation Format: Qijia Yu, Nicole Sindoni, Huan Zhang, Ziyue Li, Joao A. Paulo, Andrew J. Aguirre, Joseph D. Mancias. Quantitative mass spectrometry-based multi-omic profiling to overcome drug resistance in pancreatic cancer post KRAS G12D inhibition [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pancreatic Cancer Research; 2024 Sep 15-18; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl_2):Abstract nr C024.

Enhancing efficacy of the MEK inhibitor trametinib with paclitaxel in KRAS -mutated colorectal cancer.

KRAS is frequently mutated in the tumors of patients with metastatic colorectal cancer (mCRC) and thus represents a valid target for therapy. However, the strategies of targeting KRAS directly and targeting the downstream effector mitogen-activated protein kinase kinase (MEK) via monotherapies have shown limited efficacy. Thus, there is a strong need for novel, effective combination therapies to improve MEK-inhibitor efficacy in patients with KRAS -mutated mCRC. Our objective was to identify novel drug combinations that enhance MEK-inhibitor efficacy in patients with KRAS -mutated mCRC. In this study, we performed unbiased high-throughput screening (HTS) to identify drugs that enhance the efficacy of MEK inhibitors in vitro , and we validated the efficacy of the drugs in vivo . HTS was performed using 3-dimensional CRC spheroids. Trametinib, the anchor drug, was probed with 2 clinically ready libraries of 252 drugs to identify effective drug combinations. The effects of the drug combinations on CRC cell proliferation and apoptosis were further validated using cell growth assays, flow cytometry, and biochemical assays. Proteomic and immunostaining studies were performed to determine the effects of the drugs on molecular signaling and cell division. The effects of the drug combinations were examined in vivo using CRC patient-derived xenografts. HTS identified paclitaxel as being synergistic with trametinib. In vitro validation showed that, compared with monotherapies, this drug combination demonstrated strong inhibition of cell growth, reduced colony formation, and enhanced apoptosis in multiple KRAS -mutated CRC cell lines. Mechanistically, combining trametinib with paclitaxel led to alterations in signaling mediators that block cell cycle progression and increases in microtubule stability that resulted in significantly higher defects in the mitosis. Finally, the combination of trametinib with paclitaxel exhibited significant inhibition of tumor growth in several KRAS -mutant patient-derived xenograft mouse models. Our data provide evidence supporting clinical trials of trametinib with paclitaxel as a novel therapeutic option for patients with KRAS -mutated, metastatic CRC.

A general prediction model for compound-protein interactions based on deep learning.

The identification of compound-protein interactions (CPIs) is crucial for drug discovery and understanding mechanisms of action. Accurate CPI prediction can elucidate drug-target-disease interactions, aiding in the discovery of candidate compounds and effective synergistic drugs, particularly from traditional Chinese medicine (TCM). Existing in silico methods face challenges in prediction accuracy and generalization due to compound and target diversity and the lack of largescale interaction datasets and negative datasets for model learning. To address these issues, we developed a computational model for CPI prediction by integrating the constructed large-scale bioactivity benchmark dataset with a deep learning (DL) algorithm. To verify the accuracy of our CPI model, we applied it to predict the targets of compounds in TCM. An herb pair of Astragalus membranaceus and Hedyotis diffusaas was used as a model, and the active compounds in this herb pair were collected from various public databases and the literature. The complete targets of these active compounds were predicted by the CPI model, resulting in an expanded target dataset. This dataset was next used for the prediction of synergistic antitumor compound combinations. The predicted multi-compound combinations were subsequently examined through in vitro cellular experiments. Our CPI model demonstrated superior performance over other machine learning models, achieving an area under the Receiver Operating Characteristic curve (AUROC) of 0.98, an area under the precision-recall curve (AUPR) of 0.98, and an accuracy (ACC) of 93.31% on the test set. The model's generalization capability and applicability were further confirmed using external databases. Utilizing this model, we predicted the targets of compounds in the herb pair of Astragalus membranaceus and Hedyotis diffusaas, yielding an expanded target dataset. Then, we integrated this expanded target dataset to predict effective drug combinations using our drug synergy prediction model DeepMDS. Experimental assay on breast cancer cell line MDA-MB-231 proved the efficacy of the best predicted multi-compound combinations: Combination I (Epicatechin, Ursolic acid, Quercetin, Aesculetin and Astragaloside IV) exhibited a half-maximal inhibitory concentration (IC50) value of 19.41μM, and a combination index (CI) value of 0.682; and Combination II (Epicatechin, Ursolic acid, Quercetin, Vanillic acid and Astragaloside IV) displayed a IC50 value of 23.83μM and a CI value of 0.805. These results validated the ability of our model to make accurate predictions for novel CPI data outside the training dataset and evaluated the reliability of the predictions, showing good applicability potential in drug discovery and in the elucidation of the bioactive compounds in TCM. Our CPI prediction model can serve as a useful tool for accurately identifying potential CPI for a wide range of proteins, and is expected to facilitate drug research, repurposing and support the understanding of TCM.

Predicting effective drug combinations for cancer treatment using a graph-based approach

Drug combination therapy, involving the use of two or more drugs, has been widely employed to treat complex diseases such as cancer. It enhances therapeutic efficacy, reduces drug resistance, and minimizes side effects. However, traditional methods to identify effective drug combinations are time-consuming, costly, and less efficient than computational methods. Therefore, developing computational approaches to predict drug combinations has become increasingly important.In this paper, we developed the Random Walk with Restart for Drug Combination (RWRDC) model to predict effective drug combinations for cancer therapy. The RWRDC model offers a quantitative mathematical method for predicting the potential effective drug combinations. Cross-validation results indicate that the RWRDC model outperforms other predictive models, particularly in breast, colorectal, and lung cancer predictions across various performance metrics. We have theoretically proven the convergence of its algorithm and provided an explanation for the algorithm's rationality. A targeted case study on breast cancer further highlights the capability of RWRDC to identify effective drug combinations. These findings highlight our model as a novel and effective tool for discovering potential effective drug combinations, offering new possibilities in therapy. Additionally, the graph-based framework of RWRDC holds potential for predicting drug combinations in other complex diseases, expanding its utility in the medical field.

Attenuation of Torque teno viral load over time in kidney transplantation recipients treated with calcineurin inhibitors is mitigated after conversion to belatacept.

Torque Teno Virus (TTV) is a non-pathogenic anellovirus, highly prevalent in healthy populations. Variations in its viral load have been associated with states of diminished immunity, as occurs after organ transplantation. It is hypothesized that TTV-load might be used as a diagnostic tool to guide prescription and dosing of immunosuppressive drugs. Not much is known about the effects of combined immunosuppressive drugs on TTV replication in renal transplantation. Belatacept was introduced to counter side-effects of calcineurin inhibitors (CNI). It was never widely adopted, mainly because its association with increased risk of rejection. To investigate the differential effects of a regimen based on calcineurin inhibitors versus belatacept on TTV-loads, we measured TTV-levels in 105 patients from two randomized controlled trials in kidney transplant recipients (KTRs). We observed that time after transplantation was inversely related to TTV-levels of patients that remained on a CNI-containing regime, whereas this decline over time was diminished after conversion to belatacept. In addition, a correlation with tacrolimus-trough levels and age were found. Our study is the first report on the impact of conversion from CNI to belatacept on TTV-levels in KTR. In conclusion, the time-related decline in TTV-levels is mitigated after conversion from CNI to belatacept.

Murepavadin Enhances the Killing Efficacy of Ciprofloxacin against Pseudomonas aeruginosa by Inhibiting Drug Efflux.

Pseudomonas aeruginosa is a multidrug-resistant Gram-negative pathogen and one of the leading causes of ventilator-associated pneumonia and infections in patients with chronic obstructive pulmonary disease and cystic fibrosis. Murepavadin is a peptidomimetic that specifically targets outer-membrane lipopolysaccharide transport protein LptD of P. aeruginosa. In this study, we find that murepavadin enhances the bactericidal efficacy of ciprofloxacin. We further demonstrate that murepavadin increases intracellular accumulation of ciprofloxacin by suppressing drug efflux. In addition, the murepavadin-ciprofloxacin combination exhibits a synergistic bactericidal effect in an acute murine pneumonia model. In conclusion, our results identify an effective drug combination for the treatment of P. aeruginosa infections.

MMFSyn: A Multimodal Deep Learning Model for Predicting Anticancer Synergistic Drug Combination Effect.

Combination therapy aims to synergistically enhance efficacy or reduce toxic side effects and has widely been used in clinical practice. However, with the rapid increase in the types of drug combinations, identifying the synergistic relationships between drugs remains a highly challenging task. This paper proposes a novel deep learning model MMFSyn based on multimodal drug data combined with cell line features. Firstly, to ensure the full expression of drug molecular features, multiple modalities of drugs, including Morgan fingerprints, atom sequences, molecular diagrams, and atomic point cloud data, are extracted using SMILES. Secondly, for different modal data, a Bi-LSTM, gMLP, multi-head attention mechanism, and multi-scale GCNs are comprehensively applied to extract the drug feature. Then, it selects appropriate omics features from gene expression and mutation omics data of cancer cell lines to construct cancer cell line features. Finally, these features are combined to predict the synergistic anti-cancer drug combination effect. The experimental results verify that MMFSyn has significant advantages in performance compared to other popular methods, with a root mean square error of 13.33 and a Pearson correlation coefficient of 0.81, which indicates that MMFSyn can better capture the complex relationship between multimodal drug combinations and omics data, thereby improving the synergistic drug combination prediction.

Comparative Efficacy of Mometasone Nasal Spray Combined with different Doses of Desloratadine, and Montelukast in Childhood Allergic Rhinitis: A Randomized Clinical Trial

Allergic rhinitis is a common childhood disease. Although various drugs have been used to treat allergic rhinitis, including nasal corticosteroids, antileukotrienes, and antihistamines, there is still controversy about the optimal dose and the best combination with the highest efficacy. Higher doses of antihistamines are recommended for better control of urticaria, but there is insufficient evidence regarding the efficacy of increased doses of antihistamines in allergic rhinitis. The aim of the study was to evaluate the effectiveness of different drug combinations in the treatment of children with allergic rhinitis. Sixty-four children with persistent moderate to severe allergic rhinitis were enrolled and randomly divided into 4 groups. All children received mometasone furoate nasal spray once daily. In addition to mometasone, each group received one of the following drugs or drug combinations: daily desloratadine, twice daily desloratadine, montelukast, or a combination of desloratadine and montelukast. The severity of symptoms before and after the intervention was evaluated based on the total nasal symptoms score, including sneezing, nasal congestion, nasal itching, and rhinorrhea. Sixty patients completed the study. The reduction of nasal congestion score and total nasal symptoms score in the groups receiving desloratadine twice a day and desloratadine plus montelukast was superior to the daily desloratadine group and daily montelukast groups According to this work, the treatment of allergic rhinitis with mometasone nasal spray with desloratadine twice a day or with the combination of desloratadine and montelukast was more effective than other treatment regimens.

Drug Combination Nanoparticles Containing Gemcitabine and Paclitaxel Enable Orthotopic 4T1 Breast Tumor Regression.

Early diagnosis, intervention, and therapeutic advancements have extended the lives of breast cancer patients; however, even with molecularly targeted therapies, many patients eventually progress to metastatic cancer. Recent data suggest that residual breast cancer cells often reside in the lymphatic system before rapidly spreading through the bloodstream. To address this challenge, an effective drug combination composed of gemcitabine (G) and paclitaxel (T) is administered intravenously in sequence at the metastatic stage, but intravenous GT infusion may limit lymphatic GT drug accessibility and asynchronous drug exposure in cancer cells within the lymph. To determine whether co-localization of intracellular gemcitabine and paclitaxel (referred to as GT) could overcome these limitations and enhance the efficacy of GT, we have evaluated a previously reported GT drug-combination formulated in nanoparticle (referred to as GT-in-DcNP) evaluated in an orthotopic breast tumor model. Previously, with indocyanine green-labeled nanoparticles, we reported that GT-in-DcNP particles after subcutaneous dosing were taken up rapidly and preferentially into the lymph instead of blood vessels. The pharmacokinetic study showed enhanced co-localization of GT within the tumors and likely through lymphatic access, before drug apparency in the plasma leading to apparent long-acting plasma time-course. The mechanisms may be related to significantly greater inhibitions of tumor growth-by 100 to 140 times-in both sub-iliac and axillary regions compared to the equivalent dosing with free-and-soluble GT formulation. Furthermore, GT-in-DcNP exhibited dose-dependent effects with significant tumor regression. In contrast, even at the highest dose of free GT combination, only a modest tumor growth reduction was notable. Preliminary studies with MDA-231-HM human breast cancer in an orthotopic xenograft model indicated that GT-in-DcNP may be effective in suppressing human breast tumor growth. Taken together, the synchronized delivery of GT-in-DcNP to mammary tumors through the lymphatic system offers enhanced cellular retention and greater efficacy.

Insights into gemcitabine resistance in pancreatic cancer: association with metabolic reprogramming and TP53 pathogenicity in patient derived xenografts

BackgroundWith poor prognosis and high mortality, pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal malignancies. Standard of care therapies for PDAC have included gemcitabine for the past three decades, although resistance often develops within weeks of chemotherapy initiation through an array of possible mechanisms.MethodsWe reanalyzed publicly available RNA-seq gene expression profiles of 28 PDAC patient-derived xenograft (PDX) models before and after a 21-day gemcitabine treatment using our validated analysis pipeline to identify molecular markers of intrinsic and acquired resistance.ResultsUsing normalized RNA-seq quantification measurements, we first identified oxidative phosphorylation and interferon alpha pathways as the two most enriched cancer hallmark gene sets in the baseline gene expression profile associated with intrinsic gemcitabine resistance and sensitivity, respectively. Furthermore, we discovered strong correlations between drug-induced expression changes in glycolysis and oxidative phosphorylation genes and response to gemcitabine, which suggests that these pathways may be associated with acquired gemcitabine resistance mechanisms. Thus, we developed prediction models using baseline gene expression profiles in those pathways and validated them in another dataset of 12 PDAC models from Novartis. We also developed prediction models based on drug-induced expression changes in genes from the Molecular Signatures Database (MSigDB)’s curated 50 cancer hallmark gene sets. Finally, pathogenic TP53 mutations correlated with treatment resistance.ConclusionOur results demonstrate that concurrent upregulation of both glycolysis and oxidative phosphorylation pathways occurs in vivo in PDAC PDXs following gemcitabine treatment and that pathogenic TP53 status had association with gemcitabine resistance in these models. Our findings may elucidate the molecular basis for gemcitabine resistance and provide insights for effective drug combination in PDAC chemotherapy.

COVID-19 pandemic-related drugs and microplastics from mask fibers jointly affect soil functions and processes

The COVID-19 pandemic has led to an unprecedented increase in pharmaceutical drug consumption and plastic waste disposal from personal protective equipment. Most drugs consumed during the COVID-19 pandemic were used to treat other human and animal diseases. Hence, their nearly ubiquitous presence in the soil and the sharp increase in the last 3 years led us to investigate their potential impact on the environment. Similarly, the compulsory use of face masks has led to an enormous amount of plastic waste. Our study aims to investigate the combined effects of COVID-19 drugs and microplastics from FFP2 face masks on important soil processes using soil microcosm experiments. We used three null models (additive, multiplicative, and dominative models) to indicate potential interactions among different pharmaceutical drugs and mask MP. We found that the multiple-factor treatments tend to affect soil respiration and FDA hydrolysis more strongly than the individual treatments. We also found that mask microplastics when combined with pharmaceuticals caused greater negative effects on soil. Additionally, null model predictions show that combinations of high concentrations of pharmaceuticals and mask MP have antagonistic interactions on soil enzyme activities, while the joint effects of low concentrations of pharmaceuticals (with or without MP) on soil enzyme activities are mostly explained by null model predictions. Our study underscores the need for more attention on the environmental side effects of pharmaceutical contamination and their potential interactions with other anthropogenic global change factors.

Yeast Particle Encapsulation of Azole Fungicides for Enhanced Treatment of Azole-Resistant Candida albicans.

Addressing the growing problem of antifungal resistance in medicine and agriculture requires the development of new drugs and strategies to preserve the efficacy of existing fungicides. One approach is to utilize delivery technologies. Yeast particles (YPs) are 3-5 µm porous, hollow microspheres, a byproduct of food-grade Saccharomyces cerevisiae yeast extract manufacturing processes and an efficient and flexible drug delivery platform. Here, we report the use of YPs for encapsulation of tetraconazole (TET) and prothioconazole (PRO) with high payload capacity and stability. The YP PRO samples were active against both sensitive and azole-resistant strains of Candida albicans. The higher efficacy of YP PRO versus free PRO is due to interactions between PRO and saponifiable lipids in the YPs. Encapsulation of PRO in glucan lipid particles (GLPs), a highly purified form of YPs that do not contain saponifiable lipids, did not result in enhanced PRO activity. We evaluated the co-encapsulation of PRO with a mixture of the terpenes: geraniol, eugenol, and thymol. Samples co-encapsulating PRO and terpenes in YPs or GLPs were active on both sensitive and azole-resistant C. albicans. These approaches could lead to the development of more effective drug combinations co-encapsulated in YPs for agricultural or GLPs for pharmaceutical applications.

In silico Molecular Docking of Cyclic Peptides against TEM-1 Beta-Lactamases for Effective Antimicrobial Drug Development

Targeting the class A Beta lactamases Omega loop is an ideal way to combat drug resistance because of its significant role in the catalytic activity and deacylation process inhibition. Therefore, the molecular docking approach with computerized peptide-based in silico screening has been applied for the identification of inhibitors of TEM-type βLs. Among the subjected 105 peptides, Chrombacin (-47.8 KJ/mol), Gassericin A (-35.7 KJ/mol), Duramycin (-34.1 KJ/mol), Brevinin-1DYa (-34.0 KJ/mol), Amoebapore A (-31.2 KJ/mol), Mundticin ATO6 (-29.0 KJ/mol), Lactocyclicin Q (-26.3 KJ/mol), Cinnamycin (-25.9 KJ/mol showed highest binding energy. Among the peptides that showed the highest docking score Elafin, Cinnamycin, Duramycin interacted with Lys 73 of the α domain of catalytic residues of TEM-1 Beta lactamases, whereas Taromycin A, Gassericin A interacted with Lys 234 of the β domain, depicting a strong inhibition and also exhibited desirable physicochemical properties. Hence further in vitro examination of these cyclic peptides against the resistant strains is warranted to help design further novel inhibitors based on their scaffolds and also for the development of an effective drug combination regime.

Insight into Mantle Cell Lymphoma Pathobiology, Diagnosis, and Treatment Using Network-Based and Drug-Repurposing Approaches.

Mantle cell lymphoma (MCL) is a rare, incurable, and aggressive B-cell non-Hodgkin lymphoma (NHL). Early MCL diagnosis and treatment is critical and puzzling due to inter/intra-tumoral heterogeneity and limited understanding of the underlying molecular mechanisms. We developed and applied a multifaceted analysis of selected publicly available transcriptomic data of well-defined MCL stages, integrating network-based methods for pathway enrichment analysis, co-expression module alignment, drug repurposing, and prediction of effective drug combinations. We demonstrate the "butterfly effect" emerging from a small set of initially differentially expressed genes, rapidly expanding into numerous deregulated cellular processes, signaling pathways, and core machineries as MCL becomes aggressive. We explore pathogenicity-related signaling circuits by detecting common co-expression modules in MCL stages, pointing out, among others, the role of VEGFA and SPARC proteins in MCL progression and recommend further study of precise drug combinations. Our findings highlight the benefit that can be leveraged by such an approach for better understanding pathobiology and identifying high-priority novel diagnostic and prognostic biomarkers, drug targets, and efficacious combination therapies against MCL that should be further validated for their clinical impact.

A comprehensive overview of the molecular features and therapeutic targets in BRAFV600E-mutant colorectal cancer.

As one of the most prevalent digestive system tumours, colorectal cancer (CRC) poses a significant threat to global human health. With the emergence of immunotherapy and target therapy, the prognosis for the majority of CRC patients has notably improved. However, the subset of patients with BRAF exon 15 p.V600E mutation (BRAFV600E) has not experienced remarkable benefits from these therapeutic advancements. Hence, researchers have undertaken foundational investigations into the molecular pathology of this specific subtype and clinical effectiveness of diverse therapeutic drug combinations. This review comprehensively summarised the distinctive molecular features and recent clinical research advancements in BRAF-mutant CRC. To explore potential therapeutic targets, this article conducted a systematic review of ongoing clinical trials involving patients with BRAFV600E-mutant CRC.

In vivo organoid growth monitoring by stimulated Raman histology

Patient-derived tumor organoids have emerged as a crucial tool for assessing the efficacy of chemotherapy and conducting preclinical drug screenings. However, the conventional histological investigation of these organoids necessitates their devitalization through fixation and slicing, limiting their utility to a single-time analysis. Here, we use stimulated Raman histology (SRH) to demonstrate non-destructive, label-free virtual staining of 3D organoids, while preserving their viability and growth. This novel approach provides contrast similar to conventional staining methods, allowing for the continuous monitoring of organoids over time. Our results demonstrate that SRH transforms organoids from one-time use products into repeatable models, facilitating the efficient selection of effective drug combinations. This advancement holds promise for personalized cancer treatment, allowing for the dynamic assessment and optimization of chemotherapy treatments in patient-specific contexts.

Prediction of cancer drug combinations based on multidrug learning and cancer expression information injection

Compared with patients with common diseases, cancer patients usually have a more fragile cellular microenvironment and more complex or varied complications. Therefore, to meet treatment needs while also keeping the body in a state of balance between resistance and protection, there is an urgent need for the large-scale accurate identification of effective cancer drug combinations. Inspired by many approaches to drug molecule representation learning and cancer-related gene recognition, we propose a feature fusion model in this paper. The model can combine the molecular representation of drugs with the representation of target genes containing cancer information. It is a means to infuse specific disease information into drugs to guide drug combination prediction for specific cancers. Experimental results show that our method has achieved outstanding results in terms of predictive performance across seven common cancers, such as colorectal adenocarcinoma. For instance, in the case of osteosarcoma, the model achieved an AUROC of 0.9246. Additionally, excellent results were obtained in terms of running time and model complexity. New pairs of effective drug combinations can be mined for each of them, and out-of-sample predictions can be verified against relevant databases. This demonstrates that the feature fusion model can make full and convenient use of the existing advanced feature extraction technology and effectively promote the realization of precision therapy. In addition, we integrated a cancer drug combination dataset constructed from anticancer drugs. All entries in this dataset are standardized and uniformly identified, containing a wealth of information for the search of experts in related fields.