OvirusTdb: A database of oncolytic viruses for the advancement of therapeutics in cancer

Cat-E: A comprehensive web tool for exploring cancer targeting strategies

Identifying potential cancer-associated genes and drug targets from omics data is challenging due to its diverse sources and analyses, requiring advanced skills and large amounts of time. To facilitate such analysis, we developed Cat-E (Cancer Target Explorer), a novel R/Shiny web tool designed for comprehensive analysis with evaluation according to cancer-related omics data. Cat-E is accessible at https://cat-e.bioinfo-wuerz.eu/. Cat-E compiles information on oncolytic viruses, cell lines, gene markers, and clinical studies by integrating molecular datasets from key databases such as OvirusTB, TCGA, DrugBANK, and PubChem. Users can use all datasets and upload their data to perform multiple analyses, such as differential gene expression analysis, metabolic pathway exploration, metabolic flux analysis, GO and KEGG enrichment analysis, survival analysis, immune signature analysis, single nucleotide variation analysis, dynamic analysis of gene expression changes and gene regulatory network changes, and protein structure prediction. Cancer target evaluation by Cat-E is demonstrated here on lung adenocarcinoma (LUAD) datasets. By offering a user-friendly interface and detailed user manual, Cat-E eliminates the need for advanced computational expertise, making it accessible to experimental biologists, undergraduate and graduate students, and oncology clinicians. It serves as a valuable tool for investigating genetic variations across diverse cancer types, facilitating the identification of novel diagnostic markers and potential therapeutic targets.

Alliance between titans: combination strategies of CAR-T cell therapy and oncolytic virus for the treatment of hematological malignancies

There have been several clinical studies using chimeric antigen receptor (CAR)-T cell therapy for different hematological malignancies. It has transformed the therapy landscape for hematologic malignancies dramatically. Nonetheless, in acute myeloid leukemia (AML) and T cell malignancies, it still has a dismal prognosis. Even in the most promising locations, recurrence with CAR-T treatment remains a big concern. Oncolytic viruses (OVs) can directly lyse tumor cells or cause immune responses, and they can be manipulated to create therapeutic proteins, increasing anticancer efficacy. Oncolytic viruses have been proven in a rising number of studies to be beneficial in hematological malignancies. There are limitations that cannot be avoided by using either treatment alone, and the combination of CAR-T cell therapy and oncolytic virus therapy may complement the disadvantages of individual application, enhance the advantages of their respective treatment methods and improve the treatment effect. The alternatives for combining two therapies in hematological malignancies are discussed in this article.

Risk assessment of cancer patients based on HLA-I alleles, neobinders and expression of cytokines

Advancements in cancer immunotherapy have shown significant outcomes in treating cancers. To design effective immunotherapy, it's important to understand immune response of a patient based on its genomic profile. However, analyses to do that requires proficiency in the bioinformatic methods. Swiftly growing sequencing technologies and statistical methods create a blockage for the scientists who want to find the biomarkers for different cancers but don't have detailed knowledge of coding or tool. Here, we are providing a web-based resource that gives scientists with no bioinformatics expertise, the ability to obtain the prognostic biomarkers for different cancer types at different levels. We computed prognostic biomarkers from 8346 cancer patients for twenty cancer types. These biomarkers were computed based on i) presence of 352 Human leukocyte antigen class-I, ii) 660959 tumor-specific HLA1 neobinders, and iii) expression profile of 153 cytokines. It was observed that survival risk of cancer patients depends on presence of certain type of HLA-I alleles; for example, liver hepatocellular carcinoma patients with HLA-A*03:01 are at lower risk. Our analysis indicates that neobinders of HLA-I alleles have high correlation with overall survival of certain type of cancer patients. For example, HLA-B*07:02 binders have 0.49 correlation with survival of lung squamous cell carcinoma and -0.77 with kidney chromophobe patients. Additionally, we computed prognostic biomarkers based on cytokine expressions. Higher expression of few cytokines is survival favorable like IL-2 for bladder urothelial carcinoma, whereas IL-5R is survival unfavorable for kidney chromophobe patients. Freely accessible to public, CancerHLA-I maintains raw and analysed data (https://webs.iiitd.edu.in/raghava/cancerhla1/).

Artificial Intelligence and Radiomics: Clinical Applications for Patients with Advanced Melanoma Treated with Immunotherapy

Immunotherapy has greatly improved the outcomes of patients with metastatic melanoma. However, it has also led to new patterns of response and progression, creating an unmet need for better biomarkers to identify patients likely to achieve a lasting clinical benefit or experience immune-related adverse events. In this study, we performed a focused literature survey covering the application of artificial intelligence (AI; in the form of radiomics, machine learning, and deep learning) to patients diagnosed with melanoma and treated with immunotherapy, reviewing 12 studies relevant to the topic published up to early 2022. The most commonly investigated imaging modality was CT imaging in isolation (n = 9, 75.0%), while patient cohorts were most frequently recruited retrospectively and from single institutions (n = 7, 58.3%). Most studies concerned the development of AI tools to assist in prognostication (n = 5, 41.7%) or the prediction of treatment response (n = 6, 50.0%). Validation methods were disparate, with two studies (16.7%) performing no validation and equal numbers using cross-validation (n = 3, 25%), a validation set (n = 3, 25%), or a test set (n = 3, 25%). Only one study used both validation and test sets (n = 1, 8.3%). Overall, promising results have been observed for the application of AI to immunotherapy-treated melanoma. Further improvement and eventual integration into clinical practice may be achieved through the implementation of rigorous validation using heterogeneous, prospective patient cohorts.

Recombinant Newcastle disease virus kills liver cancer in vitro and in vivo

Aim: To construct and rescue a recombinant Newcastle disease virus that can express IP10 protein and evaluate its targeted killing effect on liver cancer in vivo and in vitro. Materials & methods: Fluorescence quantitative PCR, western blot and ELISA were used to detect the expression and secretion of IP10 in cells. The H22 mouse liver cancer cells were used to establish subcutaneous tumor-bearing mice experimental animal tumor models, and the tumor growth of mice in each group was observed while receiving treatment with rLasota. Results: The recombinant Newcastle disease virus was successfully constructed and can kill tumor cells successfully. Conclusion: The rLasota-IP10-IRES-EGFP achieves antitumor effects by killing hepatocellular carcinoma cells, enhancing T-lymphocyte infiltration in tumor tissues and inhibiting neovascularization.

The impact of oncolytic adenoviral therapy on the therapeutic efficacy of PD-1/PD-L1 blockade

Immunotherapy has revolutionized treatment of cancer during the last decades. Oncolytic virotherapy has also emerged as a strategy to fight against cancer cells both via lysis of malignant cells and activating immune responses. Accepted as a logical strategy, combination of monoclonal antibodies particularly against the programmed death-1 (PD-1) and programmed death-ligand 1 (PD-L1) is introduced to improve clinical responses to immune checkpoint inhibitors (ICIs). Accordingly, Talimogene laherparepvec (T-VEC) has received approval for clinical use, while a number of oncolytic Adenoviruses (Ads) are being investigated in clinical trials of malignancies. Combination of oncolytic Ads with PD-1/PD-L1 inhibitors have shown potentials in promoting responses to ICIs, changing the tumor microenvironment, inducing long-term protection against tumor, and promoting survival among mice models of malignancies. Regarding the increasing importance of oncolytic Ads in combination therapy of cancers, in this review we decide to outline recent studies in this field.

In silico tools and databases for designing cancer immunotherapy

Immunotherapy is a rapidly growing therapy for cancer which have numerous benefits over conventional treatments like surgery, chemotherapy, and radiation. Overall survival of cancer patients has improved significantly due to the use of immunotherapy. It acts as a novel pillar for treating different malignancies from their primary to the metastatic stage. Recent preferments in high-throughput sequencing and computational immunology leads to the development of targeted immunotherapy for precision oncology. In the last few decades, several computational methods and resources have been developed for designing immunotherapy against cancer. In this review, we have summarized cancer-associated genomic, transcriptomic, and mutation profile repositories. We have also enlisted in silico methods for the prediction of vaccine candidates, HLA binders, cytokines inducing peptides, and potential neoepitopes. Of note, we have incorporated the most important bioinformatics pipelines and resources for the designing of cancer immunotherapy. Moreover, to facilitate the scientific community, we have developed a web portal entitled ImmCancer (https://webs.iiitd.edu.in/raghava/immcancer/), comprises cancer immunotherapy tools and repositories.

B3Pdb: an archive of blood-brain barrier-penetrating peptides

The blood-brain barrier poses major hurdles in the treatment of brain-related ailments. Over the past decade, interest in peptides-based therapeutics has thrived a lot because of their higher benefit to risk ratio. However, a complete knowledgebase providing a well-annotated picture of the peptide as a therapeutic molecule to cure brain-related ailments is lacking. We have built up a knowledgebase B3Pdb on blood-brain barrier (BBB)-penetrating peptides in the present study. The B3Pdb holds clinically relevant experimental information on 1225 BBB-penetrating peptides, including mode of delivery, animal model, in vitro/in vivo experiments, chemical modifications, length. Hoping that drug delivery systems can improve central nervous system disorder-related therapeutics. In this regard, B3Pdb is an important resource to support the rational design of therapeutics peptides for CNS-related disorders. The complete ready-to-use and updated database with a user-friendly web interface is available to the scientific community at https://webs.iiitd.edu.in/raghava/b3pdb/ .

CanImmunother: a manually curated database for identification of cancer immunotherapies associating with biomarkers, targets, and clinical effects

As immunotherapy is evolving into an essential armamentarium against cancers, numerous translational studies associated with relevant biomarkers, targets, and clinical effects have been reported in recent years. However, a large amount of associated experimental data remains unexplored due to the difficulty in accessibility and utilization. Here, we established a comprehensive high-quality database for cancer immunotherapy called CanImmunother (http://www.biomedical-web.com/cancerit/) through manual curation on 4515 publications. CanImmunother contains 3267 experimentally validated associations between 218 cancer sub-types across 34 body parts and 484 immunotherapies with 642 biomarkers, 108 targets, and 121 control therapies. Each association was manually curated by professional curators, incorporated with valuable annotation and cross references, and assigned with an association score for prioritization. To help clinicians and researchers in identifying and discovering better cancer immunotherapy and their respective biomarkers and targets, CanImmunother offers user-friendly web applications including search, browse, excel table, association prioritization, and network visualization. CanImmunother presents a landscape of experimental cancer immunotherapy association data, serving as a useful resource to improve our insight and to facilitate further discovery of advanced immunotherapy options for cancer patients.

Oncolytic viruses as a promising therapeutic strategy for hematological malignancies

The incidence of hematological malignancies such as multiple myeloma, leukemia, and lymphoma has increased over time. Although bone marrow transplantation, immunotherapy and chemotherapy have led to significant improvements in efficacy, poor prognosis in elderly patients, recurrence and high mortality among hematological malignancies remain major challenges, and innovative therapeutic strategies should be explored. Besides directly lyse tumor cells, oncolytic viruses can activate immune responses or be engineered to express therapeutic factors to increase antitumor efficacy, and have gradually been recognized as an appealing approach for fighting cancers. An increasing number of studies have applied oncolytic viruses in hematological malignancies and made progress. In particular, strategies combining immunotherapy and oncolytic virotherapy are emerging. Various phase I clinical trials of oncolytic reovirus with lenalidomide or programmed death 1(PD-1) immune checkpoint inhibitors in multiple myeloma are ongoing. Moreover, preclinical studies of combinations with chimeric antigen receptor T (CAR-T) cells are underway. Thus, oncolytic virotherapy is expected to be a promising approach to cure hematological malignancies. This review summarizes progress in oncolytic virus research in hematological malignancies. After briefly reviewing the development and oncolytic mechanism of oncolytic viruses, we focus on delivery methods of oncolytic viruses, especially systemic delivery that is suitable for hematological tumors. We then discuss the main types of oncolytic viruses applied for hematological malignancies and related clinical trials. In addition, we present several ways to improve the antitumor efficacy of oncolytic viruses. Finally, we discuss current challenges and provide suggestions for future studies.

Computational resources for identification of cancer biomarkers from omics data

Cancer is one of the most prevailing, deadly and challenging diseases worldwide. The advancement in technology led to the generation of different types of omics data at each genome level that may potentially improve the current status of cancer patients. These data have tremendous applications in managing cancer effectively with improved outcome in patients. This review summarizes the various computational resources and tools housing several types of omics data related to cancer. Major categorization of resources includes-cancer-associated multiomics data repositories, visualization/analysis tools for omics data, machine learning-based diagnostic, prognostic, and predictive biomarker tools, and data analysis algorithms employing the multiomics data. The review primarily focuses on providing comprehensive information on the open-source multiomics tools and data repositories, owing to their broader applicability, economic-benefit and usability. Sections including the comparative analysis, tools applicability and possible future directions have also been discussed in detail. We hope that this information will significantly benefit the researchers and clinicians, especially those with no sound background in bioinformatics and who lack sufficient data analysis skills to interpret something from the plethora of cancer-specific data generated nowadays.

In-silico identification of subunit vaccine candidates against lung cancer-associated oncogenic viruses

Globally, ~20% of cancer malignancies are associated with virus infections. Lung cancer is the most prevalent cancer and has a 10% 5-year survival rate when diagnosed at stage IV. Cancer vaccines and oncolytic immunotherapy are promising treatment strategies for better clinical outcomes in advanced-stage cancer patients. Here, we used a reverse vaccinology approach to devise subunit vaccine candidates against lung cancer-causing oncogenic viruses. Protein components (945) from nine oncogenic virus species were systematically analyzed to identify epitope-based subunit vaccine candidates. Best vaccine candidates were identified based on their predicted ability to stimulate humoral and cell-mediated immunity and avoid self-tolerance. Using a rigorous integrative approach, we identified 125 best antigenic epitopes with predicted B-cell, T-cell, and/or MHC-binding capability and vaccine adjuvant potential. Thirty-two of these antigenic epitopes were predicted to have IL-4/IFN-gamma inducing potential and IL-10 non-inducing potential and were predicted to bind 15 MHC-type I and 49 MHC-type II alleles. All 32 epitopes were non-allergenic and 31 were non-toxic. The identified epitopes showed good conservancy and likely bind a broad class of human HLA alleles, indicating promiscuous potential. The majority of best antigenic epitopes were derived from Human papillomavirus and Epstein-Barr virus proteins. Of the 32 epitopes, 25 promiscuous epitopes were related to E1 and E6 envelope genes and were present in multiple viral strains/species, potentially providing heterologous immunity. Further validating our results, 38 antigenic epitopes were also present in the largest experimentally-validated epitope resource, Immune Epitope Database and Analysis Resource. We further narrowed the selection to 29 antigenic epitopes with the highest immunogenic/immune-boosting potential. These epitopes possess tremendous therapeutic potential as vaccines against lung cancer-causing viruses and should be validated in future experiments. All findings are available at https://webs.iiitd.edu.in/raghava/vlcvirus/.