Multi-omic data helps improve prediction of personalised tumor suppressors and oncogenes

The progression of tumorigenesis starts with a few mutational and structural driver events in the cell. Various cohort-based computational tools exist to identify driver genes but require a large number of samples to produce reliable results. Many studies use different methods to identify driver mutations/genes from mutations that have no impact on tumour progression; however, a small fraction of patients show no mutational events in any known driver genes. Current unsupervised methods map somatic and expression data onto a network to identify the perturbation in the network. Our method is the first machine learning model to classify genes as tumour suppressor gene (TSG), oncogene (OG) or neutral, thus assigning the functional impact of the gene in the patient. In this study, we develop a multi-omic approach, PIVOT (Personalised Identification of driVer OGs and TSGs), to train on experimentally or computationally validated mutational and structural driver events. Given the lack of any gold standards for the identification of personalised driver genes, we label the data using four strategies and, based on classification metrics, show gene-based labelling strategies perform best. We build different models using SNV, RNA, and multi-omic features to be used based on the data available. Our models trained on multi-omic data improved predictions compared to mutation and expression data, achieving an accuracy >0.99 for BRCA, LUAD and COAD datasets. We show network and expression-based features contribute the most to PIVOT. Our predictions on BRCA, COAD and LUAD cancer types reveal commonly altered genes such as TP53, and PIK3CA, which are predicted drivers for multiple cancer types. Along with known driver genes, our models also identify new driver genes such as PRKCA, SOX9 and PSMD4. Our multi-omic model labels both CNV and mutations with a more considerable contribution by CNV alterations. While predicting labels for genes mutated in multiple samples, we also label rare driver events occurring in as few as one sample. We also identify genes with dual roles within the same cancer type. Overall, PIVOT labels personalised driver genes as TSGs and OGs and also identifies rare driver genes. PIVOT is available at https://github.com/RamanLab/PIVOT.

Current Understanding of Exosomal MicroRNAs in Glioma Immune Regulation and Therapeutic Responses

Exosomes, the small extracellular vesicles, are released by multiple cell types, including tumor cells, and represent a novel avenue for intercellular communication via transferring diverse biomolecules. Recently, microRNAs (miRNAs) were demonstrated to be enclosed in exosomes and therefore was protected from degradation. Such exosomal miRNAs can be transmitted to recipient cells where they could regulate multiple cancer-associated biological processes. Accumulative evidence suggests that exosomal miRNAs serve essential roles in modifying the glioma immune microenvironment and potentially affecting the malignant behaviors and therapeutic responses. As exosomal miRNAs are detectable in almost all kinds of biofluids and correlated with clinicopathological characteristics of glioma, they might be served as promising biomarkers for gliomas. We reviewed the novel findings regarding the biological functions of exosomal miRNAs during glioma pathogenesis and immune regulation. Furthermore, we elaborated on their potential clinical applications as biomarkers in glioma diagnosis, prognosis and treatment response prediction. Finally, we summarized the accessible databases that can be employed for exosome-associated miRNAs identification and functional exploration of cancers, including glioma.

Functional Stratification of Cancer Drugs through Integrated Network Similarity

Heterogeneity across tumors is the main obstacle in developing treatment strategies. Drug molecules not only perturb their immediate protein targets but also modulate multiple signaling pathways. In this study, we explored the networks modulated by several drug molecules across multiple cancer cell lines by integrating the drug targets with transcriptomic and phosphoproteomic data. As a result, we obtained 236 reconstructed networks covering five cell lines and 70 drugs. A rigorous topological and pathway analysis showed that chemically and functionally different drugs may modulate overlapping networks. Additionally, we revealed a set of tumor-specific hidden pathways with the help of drug network models that are not detectable from the initial data. The difference in the target selectivity of the drugs leads to disjoint networks despite sharing the exact mechanism of action, e.g., HDAC inhibitors. We also used the reconstructed network models to study potential drug combinations based on the topological separation, found literature evidence for a set of drug pairs. Overall, the network-level exploration of the drug perturbations may potentially help optimize treatment strategies and suggest new drug combinations.

Interferon-Stimulated Exonuclease Gene 20 is A Prognosis and Predictive Biomarker and Correlated with Macrophages M2 Polarization in Glioblastoma: A Pan-Cancer Analysis

Background: Since the mutation of isocitrate dehydrogenase 1 was confirmed to be different in the tumor microenvironment of multiple cancer types, several researchers have included it in the study of tumor-infiltrating immune cells. Interferon-stimulated exonuclease gene 20 (ISG20) plays a role in the modulation of immunity and inflammation, and its abnormally high expression is conducive for the progression of tumor malignancy. However, whether ISG20 is associated with isocitrate dehydrogenase 1 mutation during tumorigenesis and cancer progression remains unknown to date. Methods: TIMER2.0, ONCOMINE, GEPIA2, TCGA and CGGA were applied to assess the clinical significance of ISG20 and its correlation with tumor-infiltrating immune cells in glioma. cBioPortal and MethSurv databases were used to observe the genetic and DNA methylation changes of ISG20, respectively. Visualization of data was mostly achieved by R language. Quantitative real-time PCR (qRT-PCR) and Immunohistochemistry (IHC) was performed to evaluate the mRNA and protein expression.Results: ISG20 expression was significantly different in most cancers. However, when we combined ISG20 with isocitrate dehydrogenase 1 mutation, we found significant differences only in glioblastoma (GBM). The clinical values of ISG20 in glioblastoma showed that the ISG20 overexpression was strongly associated with a worse overall survival (OS). Additionally, ISG20 was altered in 9% of samples of patients with GBM, and ISG20 expression was negatively correlated with its DNA methylation level. More importantly, ISG20 expression was associated with macrophage alternatively activated (M2) polarization in glioblastoma. Conclusions: ISG20 overexpression is conducive to malignant phenotype but adverse to OS, suggesting that ISG20 is a potential therapeutic target and prognosis and predictive biomarker in patients with GBM.

Screening and Comprehensive Analysis of Cancer-Associated tRNA-Derived Fragments

tRNA-derived fragments (tRFs) constitute a novel class of small non-coding RNA cleaved from tRNAs. In recent years, researches have shown the regulatory roles of a few tRFs in cancers, illuminating a new direction for tRF-centric cancer researches. Nonetheless, more specific screening of tRFs related to oncogenesis pathways, cancer progression stages and cancer prognosis is continuously demanded to reveal the landscape of the cancer-associated tRFs. In this work, by combining the clinical information recorded in The Cancer Genome Atlas (TCGA) and the tRF expression profiles curated by MINTbase v2.0, we systematically screened 1,516 cancer-associated tRFs (ca-tRFs) across seven cancer types. The ca-tRF set collectively combined the differentially expressed tRFs between cancer samples and control samples, the tRFs significantly correlated with tumor stage and the tRFs significantly correlated with patient survival. By incorporating our previous tRF-target dataset, we found the ca-tRFs tend to target cancer-associated genes and onco-pathways like ATF6-mediated unfolded protein response, angiogenesis, cell cycle process regulation, focal adhesion, PI3K-Akt signaling pathway, cellular senescence and FoxO signaling pathway across multiple cancer types. And cell composition analysis implies that the expressions of ca-tRFs are more likely to be correlated with T-cell infiltration. We also found the ca-tRF expression pattern is informative to prognosis, suggesting plausible tRF-based cancer subtypes. Together, our systematic analysis demonstrates the potentially extensive involvements of tRFs in cancers, and provides a reasonable list of cancer-associated tRFs for further investigations.

Noncoding RNAs and Deep Learning Neural Network Discriminate Multi-Cancer Types

Detecting cancers at early stages can dramatically reduce mortality rates. Therefore, practical cancer screening at the population level is needed. To develop a comprehensive detection system to classify multiple cancer types. We integrated an artificial intelligence deep learning neural network and noncoding RNA biomarkers selected from massive data. Our system can accurately detect cancer vs. healthy objects with 96.3% of AUC of ROC (Area Under Curve of a Receiver Operating Characteristic curve), and it surprisingly reaches 78.77% of AUC when validated by real-world raw data from a completely independent data set. Even validating with raw exosome data from blood, our system can reach 72% of AUC. Moreover, our system significantly outperforms conventional machine learning models, such as random forest. Intriguingly, with no more than six biomarkers, our approach can easily discriminate any individual cancer type vs. normal with 99% to 100% AUC. Furthermore, a comprehensive marker panel can simultaneously multi-classify common cancers with a stable 82.15% accuracy rate for heterogeneous cancerous tissues and conditions.: This detection system provides a promising practical framework for automatic cancer screening at population level. Key points: (1) We developed a practical cancer screening system, which is simple, accurate, affordable, and easy to operate. (2) Our system binarily classify cancers vs. normal with >96% AUC. (3) In total, 26 individual cancer types can be easily detected by our system with 99 to 100% AUC. (4) The system can detect multiple cancer types simultaneously with >82% accuracy.

Combinatorial Therapeutic Approaches with Nanomaterial-Based Photodynamic Cancer Therapy

Photodynamic therapy (PDT), in which a light source is used in combination with a photosensitizer to induce local cell death, has shown great promise in therapeutically targeting primary tumors with negligible toxicity and minimal invasiveness. However, numerous studies have shown that noninvasive PDT alone is not sufficient to completely ablate tumors in deep tissues, due to its inherent shortcomings. Therefore, depending on the characteristics and type of tumor, PDT can be combined with surgery, radiotherapy, immunomodulators, chemotherapy, and/or targeted therapy, preferably in a patient-tailored manner. Nanoparticles are attractive delivery vehicles that can overcome the shortcomings of traditional photosensitizers, as well as enable the codelivery of multiple therapeutic drugs in a spatiotemporally controlled manner. Nanotechnology-based combination strategies have provided inspiration to improve the anticancer effects of PDT. Here, we briefly introduce the mechanism of PDT and summarize the photosensitizers that have been tested preclinically for various cancer types and clinically approved for cancer treatment. Moreover, we discuss the current challenges facing the combination of PDT and multiple cancer treatment options, and we highlight the opportunities of nanoparticle-based PDT in cancer therapies.

Metachronous multiple primary cancer of the pancreatic head and liver: a case of hepatocellular carcinoma 10 years after pancreatoduodenal resection

The Aim. Study of a clinical case of metachronous primary multiple cancer of the head of the pancreas and liver.Materials and methods. The work was carried out with modern domestic and foreign literature sources devoted to the problem of primary multiple malignant neoplasms. A retrospective analysis of the patient’s clinical and anamnestic data was performed, the necessary medical documentation was studied.Results. In 2011, a pancreatoduodenal resection was performed on a patient for ductal adenocarcinoma of the head of the pancreas. In 2021, an MRI scan revealed a formation in S5-S6 with dimensions up to 34x35x29 mm. According to the histological examination of the biopsy material, hepatocellular carcinoma was confirmed. Resection of the 5th segment of the liver was performed in the conditions of the NMIC Oncology in Rostov-on-Don.Conclusion. The presented case of primary multiple cancer of the head of the pancreas and hepatocellular carcinoma of the liver is of direct interest both from the point of view of oncological surgery and chemotherapy.

RNA Exosome Component EXOSC4 Amplified in Multiple Cancer Types Is Required for the Cancer Cell Survival

The RNA exosome is a multi-subunit ribonuclease complex that is evolutionally conserved and the major cellular machinery for the surveillance, processing, degradation, and turnover of diverse RNAs essential for cell viability. Here we performed integrated genomic and clinicopathological analyses of 27 RNA exosome components across 32 tumor types using The Cancer Genome Atlas PanCancer Atlas Studies’ datasets. We discovered that the EXOSC4 gene, which encodes a barrel component of the RNA exosome, was amplified across multiple cancer types. We further found that EXOSC4 alteration is associated with a poor prognosis of pancreatic cancer patients. Moreover, we demonstrated that EXOSC4 is required for the survival of pancreatic cancer cells. EXOSC4 also repressed BIK expression and destabilized SESN2 mRNA by promoting its degradation. Furthermore, knockdown of BIK and SESN2 could partially rescue pancreatic cells from the reduction in cell viability caused by EXOSC4 knockdown. Our study provides evidence for EXOSC4-mediated regulation of BIK and SESN2 mRNA in the survival of pancreatic tumor cells.