Novel drug candidate for the treatment of several soft‑tissue sarcoma histologic subtypes: A computational method using survival‑associated gene signatures for drug repurposing

In silico approaches for drug repurposing in oncology: a scoping review

Introduction: Cancer refers to a group of diseases characterized by the uncontrolled growth and spread of abnormal cells in the body. Due to its complexity, it has been hard to find an ideal medicine to treat all cancer types, although there is an urgent need for it. However, the cost of developing a new drug is high and time-consuming. In this sense, drug repurposing (DR) can hasten drug discovery by giving existing drugs new disease indications. Many computational methods have been applied to achieve DR, but just a few have succeeded. Therefore, this review aims to show in silico DR approaches and the gap between these strategies and their ultimate application in oncology. Methods: The scoping review was conducted according to the Arksey and O'Malley framework and the Joanna Briggs Institute recommendations. Relevant studies were identified through electronic searching of PubMed/MEDLINE, Embase, Scopus, and Web of Science databases, as well as the grey literature. We included peer-reviewed research articles involving in silico strategies applied to drug repurposing in oncology, published between 1 January 2003, and 31 December 2021. Results: We identified 238 studies for inclusion in the review. Most studies revealed that the United States, India, China, South Korea, and Italy are top publishers. Regarding cancer types, breast cancer, lymphomas and leukemias, lung, colorectal, and prostate cancer are the top investigated. Additionally, most studies solely used computational methods, and just a few assessed more complex scientific models. Lastly, molecular modeling, which includes molecular docking and molecular dynamics simulations, was the most frequently used method, followed by signature-, Machine Learning-, and network-based strategies. Discussion: DR is a trending opportunity but still demands extensive testing to ensure its safety and efficacy for the new indications. Finally, implementing DR can be challenging due to various factors, including lack of quality data, patient populations, cost, intellectual property issues, market considerations, and regulatory requirements. Despite all the hurdles, DR remains an exciting strategy for identifying new treatments for numerous diseases, including cancer types, and giving patients faster access to new medications.

Comprehensive analysis of a novel cuproptosis-related lncRNA signature associated with prognosis and tumor matrix features to predict immunotherapy in soft tissue carcinoma

Background: A crucial part of the malignant processes of soft tissue sarcoma (STS) is played by cuproptosis and lncRNAs. However, the connection between cuproptosis-related lncRNAs (CRLs) and STS is nevertheless unclear. As a result, our objective was to look into the immunological activity, clinical significance, and predictive accuracy of CRLs in STS. Methods: The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases, respectively, provided information on the expression patterns of STS patients and the general population. Cuproptosis-related lncRNA signature (CRLncSig) construction involved the univariate, multivariate, and least absolute shrinkage and selection operator Cox regression analysis. The predictive performance of the CRLncSig was evaluated using a serial analysis. Further research was done on the connections between the CRLncSig and the tumor immune milieu, somatic mutation, immunotherapy response, and chemotherapeutic drug susceptibility. Notably, an in vitro investigation served to finally validate the expression of the hallmark CRLs. Results: A novel efficient CRLncSig composed of seven CRLs was successfully constructed. Additionally, the low-CRLncSig group's prognosis was better than that of the high-CRLncSig group's based on the new CRLncSig. The innovative CRLncSig then demonstrated outstanding, consistent, and independent prognostic and predictive usefulness for patients with STS, according to the evaluation and validation data. The low-CRLncSig group's patients also displayed improved immunoreactivity phenotype, increased immune infiltration abundance and checkpoint expression, and superior immunotherapy response, whereas those in the high-CRLncSig group with worse immune status, increased tumor stemness, and higher collagen levels in the extracellular matrix. Additionally, there is a noticeable disparity in the sensitivity of widely used anti-cancer drugs amongst various populations. What's more, the nomogram constructed based on CRLncSig and clinical characteristics of patients also showed good predictive ability. Importantly, Real-Time Quantitative Polymerase Chain Reaction (RT-qPCR) demonstrated that the signature CRLs exhibited a significantly differential expression level in STS cell lines. Conclusion: In summary, this study revealed the novel CRLncSig could be used as a promising predictor for prognosis prediction, immune activity, tumor immune microenvironment, immune response, and chemotherapeutic drug susceptibility in patients with STS. This may provide an important direction for the clinical decision-making and personalized therapy of STS.

Myogenic dedifferentiation is associated with poor outcomes in retroperitoneal dedifferentiated liposarcomas

Sarcomas are a heterogenous group of malignant tumors with origin or mesenchymal differentiation, they comprise 1–2% of all solid tumors. Retroperitoneum is the second most frequent site affected. Prognosis is worse compared to the limbs, with a 5y OS of 36–58%, and 50–60% patients will relapse. Dedifferentiated liposarcomas (ddLPS) are more aggressive, it is known that presence of a de-differentiated component increases the probability of distant recurrence and lowers OS. There is little information about the specific impact of each type of de-differentiation. To determine if the presence of myogenic differentiation markers in DDLPS is an adverse prognostic factor. A retrospective, observational, analytic cohort study was performed. Cases identified from the electronic clinical files from the National Cancer Institute in Mexico City, we included cases from January 1st 2005 to December 31st 2016. We correlated the presence of expression of myogenic markers (Smooth muscle actin, Calponin, H-caldesmon, Desmin and Myogenin) in the dedifferentiated component of DDLPS with overall survival and surgical outcomes. One hundred and forty-three cases were analyzed. Eighty-two were liposarcomas, and 38 had a dedifferentiated component. Of these 38 cases, 21(55.3%) were males and, 17(44.7%) were females. Median age was 54.1(27–79) years, median tumor size was 28 cm (13–56). Most patients had locally advanced disease: 32(84.2%) were in stage IIIB. 2.6% had metastatic disease and 5(13.2%) had stage Ib at diagnosis. Myogenic marker expression was found in 18.4% of cases; these patients had a worse median survival than cases with no myogenic expression: 18 months (95% CI 15.4–20.5) vs 32 months (95% CI 21.8–42.1) p = 0.01, we also found a relation with higher postoperative morbidity in these cases (p = 0.045). The presence of myogenic differentiation markers might be associated with a worse prognosis, in our series it corelated with worse OS, however it is not a common event. Relation with surgical morbidity is to be analyzed in further studies.

Improving the odds of success in antitumoral drug development using scoring approaches towards heterocyclic scaffolds

One of the most commonly discussed topics in the field of drug discovery is the continuous search for anticancer therapies, in which small-molecule development plays an important role. Although a number of techniques have been established over the past decades, one of the main methods for drug discovery and development is still represented by rational, ligand-based drug design. However, the success rate of this method could be higher if not affected by cognitive bias, which renders many potential druggable scaffolds and structures overlooked. The present study aimed to counter this bias by presenting an objective overview of the most important heterocyclic structures in the development of anti-proliferative drugs. As such, the present study analyzed data for 91,438 compounds extracted from the Developmental Therapeutics Program (DTP) database provided by the National Cancer Institute. Growth inhibition data from these compounds tested on a panel of 60 cancer cell lines representing various tissue types (NCI-60 panel) was statistically interpreted using 6 generated scores assessing activity, selectivity, growth inhibition efficacy and potency of different structural scaffolds, Bemis-Murcko skeletons, chemical features and structures common among the analyzed compounds. Of the most commonly used rings, the most prominent anti-proliferative effects were produced by quinoline, tetrahydropyran, benzimidazole and pyrazole, while overall, the optimal results were produced by complex ring structures that originate from natural compounds. These results highlight the impact of certain ring structures on the anti-proliferative effects in drug design. In addition, considering that medicinal chemists usually focus their research on simpler scaffolds the majority of the time with no significant pay-off, the present study indicates several unused complex scaffolds that could be exploited when designing anticancer therapies for optimal results in the fight against cancer.

Niclosamide inhibits the cell proliferation and enhances the responsiveness of esophageal cancer cells to chemotherapeutic agents

Niclosamide is an FDA-approved anthelmintic drug, and may elicit antineoplastic effects through direct STAT3 inhibition, which has been revealed in numerous human cancer cells. Chemotherapy is the standard treatment for advanced esophageal cancers, but also causes severe systemic side effects. The present study represents the first study evaluating the anticancer efficacy of niclosamide in esophageal cancers. Through western blot assay, it was demonstrated that niclosamide suppressed the STAT3 signaling pathway in esophageal adenocarcinoma cells (BE3) and esophageal squamous cell carcinoma cells (CE48T and CE81T). In addition, niclosamide inhibited cell proliferation as determined by 3-(4,5-dimethylthiazol-2-yl)-5-(3-carboxymethoxyphenyl)-2-(4-sulfophenyl)-2H-tetrazolium)-5-(3-carboxymethoxyphenyl)-2-(4-sulfophenyl)-2H-tetrazolium (MTS) assay and soft agar colony forming assay, and induced cell apoptosis as determined by Annexin V and PI staining. The induction of p21 and G1 arrest of the cell cycle also was revealed in niclosamide-treated CE81T cells by qPCR and flow cytometric assays, respectively. Furthermore, in the combination analysis of niclosamide and chemotherapeutic agents by MTS assay, low IC₅₀ values were detected in cells co-treated with niclosamide, with the exception of cisplatin-treated CE81T cells. To confirm the results using an apoptosis assay, the apoptotic enhancement of niclosamide was only demonstrated in CE48T cells co-treated with 5-FU, cisplatin, or paclitaxel, and in BE3 cells co-treated with paclitaxel, but not in CE81T cells. These findings indicate a future clinical application of niclosamide in esophageal cancers.

Influence of batch effect correction methods on drug induced differential gene expression profiles

Background

Batch effects were not accounted for in most of the studies of computational drug repositioning based on gene expression signatures. It is unknown how batch effect removal methods impact the results of signature-based drug repositioning. Herein, we conducted differential analyses on the Connectivity Map (CMAP) database using several batch effect correction methods to evaluate the influence of batch effect correction methods on computational drug repositioning using microarray data and compare several batch effect correction methods.

Results

Differences in average signature size were observed with different methods applied. The gene signatures identified by the Latent Effect Adjustment after Primary Projection (LEAPP) method and the methods fitted with Linear Models for Microarray Data (limma) software demonstrated little agreement. The external validity of the gene signatures was evaluated by connectivity mapping between the CMAP database and the Library of Integrated Network-based Cellular Signatures (LINCS) database. The results of connectivity mapping indicate that the genes identified were not reliable for drugs with total sample size (drug + control samples) smaller than 40, irrespective of the batch effect correction method applied. With total sample size larger than 40, the methods correcting for batch effects produced significantly better results than the method with no batch effect correction. In a simulation study, the power was generally low for simulated data with sample size smaller than 40. We observed best performance when using the limma method correcting for two principal components.

Conclusion

Batch effect correction methods strongly impact differential gene expression analysis when the sample size is large enough to contain sufficient information and thus the downstream drug repositioning. We recommend including two or three principal components as covariates in fitting models with limma when sample size is sufficient (larger than 40 drug and controls combined).

Electronic supplementary material

The online version of this article (10.1186/s12859-019-3028-6) contains supplementary material, which is available to authorized users.