The New Age of -omics in Urothelial Cancer - Re-wording Its Diagnosis and Treatment

Radiomics model for preoperative prediction of 3-year survival-based CT image biomarkers in esophageal cancer

OBJECTIVE

This work aimed to develop a radiomics nomogram to predict 3-year overall survival of esophageal cancer patients after chemoradiotherapy.

METHODS

A total of 109 esophageal cancer patients, diagnosed from November 2012 to February 2015, were enrolled in this retrospective study. They were randomly divided into training set (77 cases) and verification set (32 cases). Image standardization was performed prior to feature extraction. And then, about 1670 radiomics features were extracted from the pretreatment diagnostic computed tomography image. A radiomics signature was constructed with the lasso algorithm; then, a radiomics score was calculated to reflect survival probability using the radiomics signature for each patient. A radiomics nomogram was developed by incorporating the radiomics score and clinical factors. A clinical model was constructed using clinical factors only. The performance of the nomogram was assessed with respect to its calibration and discrimination. Kaplan-Meier survival analysis was performed.

RESULTS

Sixteen radiomics features were selected to build the radiomics signature. The radiomics nomogram showed better calibration and classification capacity than the clinical model with AUC 0.96 vs. 0.72 for the training cohort, and 0.87 vs. 0.67 for the validation cohort. The model showed good discrimination with a Harrell's Concordance Index of 0.76 in the training cohort and 0.81 in the validation cohort. Decision curve analysis demonstrated the clinical usefulness of the radiomics nomogram. A significant difference (p value < 0.05; log-rank test) was observed between the survival curves of the nomogram-predicted survival and non-survival groups.

CONCLUSIONS

The present study proposed a radiomics-based nomogram involving the radiomics signature and clinical factors. It can be potentially applied in the individual preoperative prediction of 3-year survival in esophageal cancer patients.

Role of main RNA modifications in cancer: N6-methyladenosine, 5-methylcytosine, and pseudouridine

Cancer is one of the major diseases threatening human life and health worldwide. Epigenetic modification refers to heritable changes in the genetic material without any changes in the nucleic acid sequence and results in heritable phenotypic changes. Epigenetic modifications regulate many biological processes, such as growth, aging, and various diseases, including cancer. With the advancement of next-generation sequencing technology, the role of RNA modifications in cancer progression has become increasingly prominent and is a hot spot in scientific research. This review studied several common RNA modifications, such as N⁶-methyladenosine, 5-methylcytosine, and pseudouridine. The deposition and roles of these modifications in coding and noncoding RNAs are summarized in detail. Based on the RNA modification background, this review summarized the expression, function, and underlying molecular mechanism of these modifications and their regulators in cancer and further discussed the role of some existing small-molecule inhibitors. More in-depth studies on RNA modification and cancer are needed to broaden the understanding of epigenetics and cancer diagnosis, treatment, and prognosis.

Transcriptome-Wide Map of N6-Methyladenosine Methylome Profiling in Human Bladder Cancer

N⁶-Methyladenosine (m⁶A) is the most widespread internal RNA modification in several species. In spite of latest advances in researching the biological roles of m⁶A, its function in the development and progression of bladder cancer remains unclear. In this study, we used MeRIPty -55-seq and RNA-seq methods to obtain a comprehensive transcriptome-wide m⁶A profiling and gene expression pattern in bladder cancer and paired normal adjacent tissues. Our findings showed that there were 2,331 hypomethylated and 3,819 hypermethylated mRNAs, 32 hypomethylated and 105 hypermethylated lncRNAs, and 15 hypomethylated and 238 hypermethylated circRNAs in bladder cancer tissues compared to adjacent normal tissues. Furthermore, m⁶A is most often harbored in the coding sequence (CDS), with some near the start and stop codons between two groups. Functional enrichment analysis revealed that differentially methylated mRNAs, lncRNAs, and circRNAs were mostly enriched in transcriptional misregulation in cancer and TNF signaling pathway. We also found that different m⁶A methylation levels of gene might regulate its expression. In summary, our results for the first time provide an m⁶A landscape of human bladder cancer, which expand the understanding of m⁶A modifications and uncover the regulation of mRNAs, lncRNAs, and circRNAs through m⁶A modification in bladder cancer.

Three-Dimensional Cell Metabolomics Deciphers the Anti-Angiogenic Properties of the Radioprotectant Amifostine

Simple Summary

Cancer and inflammation share aberrant angiogenesis as a hallmark, and, thus, anti-angiogenetic strategies remain of key interest. Amifostine, which is already a drug on the market, may be of further benefit to patients also in the context of drug repurposing. To shed light on the anti-angiogenic properties of amifostine during human adult angiogenesis and grasp the early events of angiogenesis, we employed 3D cell untargeted metabolomics by liquid chromatography–mass spectrometry and nuclear magnetic resonance spectroscopy in the presence of vascular endothelial growth factor-A or deferoxamine (pro-angiogenic factors that exhibit distinct angiogenesis induction profiles). Our findings reveal mechanism-specific inhibitory profiles of amifostine against VEGF-A- and deferoxamine-induced angiogenesis. Amifostine may serve as a dual radioprotective and anti-angiogenic agent in radiotherapy patients.

Abstract

Aberrant angiogenesis is a hallmark for cancer and inflammation, a key notion in drug repurposing efforts. To delineate the anti-angiogenic properties of amifostine in a human adult angiogenesis model via 3D cell metabolomics and upon a stimulant-specific manner, a 3D cellular angiogenesis assay that recapitulates cell physiology and drug action was coupled to untargeted metabolomics by liquid chromatography–mass spectrometry and nuclear magnetic resonance spectroscopy. The early events of angiogenesis upon its most prominent stimulants (vascular endothelial growth factor-A or deferoxamine) were addressed by cell sprouting measurements. Data analyses consisted of a series of supervised and unsupervised methods as well as univariate and multivariate approaches to shed light on mechanism-specific inhibitory profiles. The 3D untargeted cell metabolomes were found to grasp the early events of angiogenesis. Evident of an initial and sharp response, the metabolites identified primarily span amino acids, sphingolipids, and nucleotides. Profiles were pathway or stimulant specific. The amifostine inhibition profile was rather similar to that of sunitinib, yet distinct, considering that the latter is a kinase inhibitor. Amifostine inhibited both. The 3D cell metabolomics shed light on the anti-angiogenic effects of amifostine against VEGF-A- and deferoxamine-induced angiogenesis. Amifostine may serve as a dual radioprotective and anti-angiogenic agent in radiotherapy patients.

Assessment of Luminal and Basal Phenotypes in Bladder Cancer

Genomic profiling studies have demonstrated that bladder cancer can be divided into two molecular subtypes referred to as luminal and basal with distinct clinical behaviors and sensitivities to frontline chemotherapy. We analyzed the mRNA expressions of signature luminal and basal genes in bladder cancer tumor samples from publicly available and MD Anderson Cancer Center cohorts. We developed a quantitative classifier referred to as basal to luminal transition (BLT) score which identified the molecular subtypes of bladder cancer with 80–94% sensitivity and 83–93% specificity. In order to facilitate molecular subtyping of bladder cancer in primary care centers, we analyzed the protein expressions of signature luminal (GATA3) and basal (KRT5/6) markers by immunohistochemistry, which identified molecular subtypes in over 80% of the cases. In conclusion, we provide a tool for assessment of molecular subtypes of bladder cancer in routine clinical practice.

N6-methyladenosine modification of ITGA6 mRNA promotes the development and progression of bladder cancer

Background

Accumulating evidence has revealed the critical roles of N⁶-methyladenosine (m⁶A) modification of mRNA in various cancers. However, the biological function and regulation of m⁶A in bladder cancer (BC) are not yet fully understood.

Methods

We performed cell phenotype analysis and established in vivo mouse xenograft models to assess the effects of m⁶A-modified ITGA6 on BC growth and progression. Methylated RNA immunoprecipitation (MeRIP), RNA immunoprecipitation and luciferase reporter and mutagenesis assays were used to define the mechanism of m⁶A-modified ITGA6. Immunohistochemical analysis was performed to assess the correlation between METTL3 and ITGA6 expression in bladder cancer patients.

Findings

We show that the m⁶A writer METTL3 and eraser ALKBH5 altered cell adhesion by regulating ITGA6 expression in bladder cancer cells. Moreover, upregulation of ITGA6 is correlated with the increase in METTL3 expression in human BC tissues, and higher expression of ITGA6 in patients indicates a lower survival rate. Mechanistically, m⁶A is highly enriched within the ITGA6 transcripts, and increased m⁶A methylations of the ITGA6 mRNA 3’UTR promotes the translation of ITGA6 mRNA via binding of the m⁶A readers YTHDF1 and YTHDF3. Inhibition of ITGA6 results in decreased growth and progression of bladder cancer cells in vitro and in vivo. Furthermore, overexpression of ITGA6 in METTL3-depleted cells partially restores the BC adhesion, migration and invasion phenotypes.

Interpretation

Our results demonstrate an oncogenic role of m⁶A-modified ITGA6 and show its regulatory mechanisms in BC development and progression, thus identifying a potential therapeutic target for BC.

Fund

This work was supported by National Natural Science Foundation of China (81772699, 81472999).

Computational Approaches in Theranostics: Mining and Predicting Cancer Data

The ability to understand the complexity of cancer-related data has been prompted by the applications of (1) computer and data sciences, including data mining, predictive analytics, machine learning, and artificial intelligence, and (2) advances in imaging technology and probe development. Computational modelling and simulation are systematic and cost-effective tools able to identify important temporal/spatial patterns (and relationships), characterize distinct molecular features of cancer states, and address other relevant aspects, including tumor detection and heterogeneity, progression and metastasis, and drug resistance. These approaches have provided invaluable insights for improving the experimental design of therapeutic delivery systems and for increasing the translational value of the results obtained from early and preclinical studies. The big question is: Could cancer theranostics be determined and controlled in silico? This review describes the recent progress in the development of computational models and methods used to facilitate research on the molecular basis of cancer and on the respective diagnosis and optimized treatment, with particular emphasis on the design and optimization of theranostic systems. The current role of computational approaches is providing innovative, incremental, and complementary data-driven solutions for the prediction, simplification, and characterization of cancer and intrinsic mechanisms, and to promote new data-intensive, accurate diagnostics and therapeutics.

Evaluation of lymph node status in patients with urothelial carcinoma-still in search of the perfect imaging modality: a systematic review

While accurate lymph node status evaluation in urothelial carcinoma patients is essential for the correct disease staging and, hence, establishing the most beneficial treatment strategy, the diagnostic performance of routine imaging in regards to this issue is not satisfactory. For the purpose of this article, we systematically reviewed the contemporary literature on the sensitivity and specificity of particular imaging modalities which have been studied for detecting lymph node metastases in patients diagnosed with urothelial carcinoma. The evidence reviewed shows that computed tomography (CT), although recognized as the imaging modality of choice, is associated with marked limitations, resulting in its low sensitivity for lymph node involvement detection in urothelial carcinoma patients, with no study reporting a value higher than 46% using standard cut-off values. Markedly higher sensitivity rates may be achieved with magnetic resonance imaging (MRI), especially when using ultrasmall superparamagnetic iron oxide as the contrast agent, however, no uniform protocol has been systematically studied up to date. The vast majority of recent evidence concerns positron emission tomography (PET), which is being reported to improve the diagnostic performance of CT alone, as has been demonstrated in multiple articles, which investigated the accuracy of PET/CT at primary or post-treatment staging of urothelial carcinoma patients. However, there has been substantial heterogeneity in terms of methodology and results between those studies, making it premature to draw any definitive conclusions. The results of this review lead to a conclusion, that while CT, despite being not fully satisfactory, still remains the gold-standard method of imaging for staging purposes in urothelial carcinoma, other imaging modalities are under investigation, with promising results.

Survival Prediction in High-grade Osteosarcoma Using Radiomics of Diagnostic Computed Tomography

The poor 5-year survival rate in high-grade osteosarcoma (HOS) has not been increased significantly over the past 30 years. This work aimed to develop a radiomics nomogram for survival prediction at the time of diagnosis in HOS. In this retrospective study, an initial cohort of 102 HOS patients, diagnosed from January 2008 to March 2011, was used as the training cohort. Radiomics features were extracted from the pretreatment diagnostic computed tomography images. A radiomics signature was constructed with the lasso algorithm; then, a radiomics score was calculated to reflect survival probability by using the radiomics signature for each patient. A radiomics nomogram was developed by incorporating the radiomics score and clinical factors. A clinical model was constructed by using clinical factors only. The models were validated in an independent cohort comprising 48 patients diagnosed from April 2011 to April 2012. The performance of the nomogram was assessed with respect to its calibration, discrimination, and clinical usefulness. Kaplan-Meier survival analysis was performed. The radiomics nomogram showed better calibration and classification capacity than the clinical model with AUC 0.86 vs. 0.79 for the training cohort, and 0.84 vs. 0.73 for the validation cohort. Decision curve analysis demonstrated the clinical usefulness of the radiomics nomogram. A significant difference (p-value <.05; log-rank test) was observed between the survival curves of the nomogram-predicted survival and non-survival groups. The radiomics nomogram may assist clinicians in tailoring appropriate therapy.