Bladder Cancer Sample Handling and Reporting: Pathologist's Point of View

The aim of this narrative review is to provide adequate information on handling and reporting of the bladder cancer samples to improve the closely collaboration between pathologists and urologists. The main (but not exclusive) research tool used was PubMed and 87 references were selected and quoted in the text. We have considered handling of biopsies, transurethral resection (TUR), and cystectomy specimens to summarize the different methods of sampling and the related issues. Moreover, we considered and discussed the main prognostic factors, such as histological tumor type, grade, and stage of bladder cancer, that should be described in the pathological report. In addition, critical issues encountered in the interpretation of histological samples were discussed.

Investigating Different DNA Methylation Patterns at the Resolution of Methylation Haplotypes

Different DNA methylation patterns presented on different tissues or cell types are considered as one of the main reasons accounting for the tissue-specific gene expressions. In recent years, many methods have been proposed to identify differentially methylated regions (DMRs) based on the mixture of methylation signals from homologous chromosomes. To investigate the possible influence of homologous chromosomes on methylation analysis, this paper proposed a method (MHap) to construct methylation haplotypes for homologous chromosomes in CpG dense regions. Through comparing the methylation consistency between homologous chromosomes in different cell types, it can be found that majority of paired methylation haplotypes derived from homologous chromosomes are consistent, while a lower methylation consistency was observed in the breast cancer sample. It also can be observed that the hypomethylation consistency of differentiated cells is higher than that of the corresponding undifferentiated stem cells. Furthermore, based on the methylation haplotypes constructed on homologous chromosomes, a method (MHap_DMR) is developed to identify DMRs between differentiated cells and the corresponding undifferentiated stem cells, or between the breast cancer sample and the normal breast sample. Through comparing the methylation haplotype modes of DMRs in two cell types, the DNA methylation changing directions of homologous chromosomes in cell differentiation and cancerization can be revealed. The code is available at: https://github.com/xqpeng/MHap_DMR.

Identification of Hub Genes to Regulate Breast Cancer Spinal Metastases by Bioinformatics Analyses

Breast cancer (BC) had been one of the deadliest types of cancers in women worldwide. More than 65% of advanced-stage BC patients were identified to have bone metastasis. However, the molecular mechanisms involved in the BC spinal metastases remained largely unclear. This study screened dysregulated genes in the progression of BC spinal metastases by analyzing GSE22358. Moreover, we constructed PPI networks to identify key regulators in this progression. Bioinformatics analysis showed that these key regulators were involved in regulating the metabolic process, cell proliferation, Toll-like receptor and RIG-I-like receptor signaling, and mRNA surveillance. Furthermore, our analysis revealed that key regulators, including C1QB, CEP55, HIST1H2BO, IFI6, KIAA0101, PBK, SPAG5, SPP1, DCN, FZD7, KRT5, and TGFBR3, were correlated to the OS time in BC patients. In addition, we analyzed TCGA database to further confirm the expression levels of these hub genes in breast cancer. Our results showed that these regulators were significantly differentially expressed in breast cancer, which were consistent with GSE22358 dataset analysis. Furthermore, our analysis demonstrated that CEP55 was remarkably upregulated in the advanced stage of breast cancer compared to the stage I breast cancer sample and was significantly upregulated in triple-negative breast cancers (TNBC) compared to other types of breast cancers, including luminal and HER2-positive cancers, demonstrating CEP55 may have a regulatory role in TNBC. Finally, our results showed that CEP55 was the most highly expressed in Basal-like 1 TNBC and Basal-like 2 TNBC samples but the most lowly expressed in mesenchymal stem-like TNBC samples. Although more studies are still needed to understand the functions of key regulators in BC, this study provides useful information to understand the mechanisms underlying BC spinal metastases.

CONET: Copy number event tree model of evolutionary tumor history for single-cell data

Copy number alterations constitute important phenomena in tumor evolution. Whole genome single cell sequencing gives insight into copy number profiles of individual cells, but is highly noisy. Here, we propose CONET, a probabilistic model for joint inference of the evolutionary tree on copy number events and copy number calling. CONET employs an efficient MCMC procedure to search the space of possible model structures and parameters and utilizes both per-bin and per-breakpoint data. We introduce a range of model priors and penalties for efficient regularization. CONET achieves excellent performance on simulated data and for 260 cells from xenograft breast cancer sample.

DrugComb update: a more comprehensive drug sensitivity data repository and analysis portal

Combinatorial therapies that target multiple pathways have shown great promises for treating complex diseases. DrugComb (https://drugcomb.org/) is a web-based portal for the deposition and analysis of drug combination screening datasets. Since its first release, DrugComb has received continuous updates on the coverage of data resources, as well as on the functionality of the web server to improve the analysis, visualization and interpretation of drug combination screens. Here we report significant updates of DrugComb, including: 1) manual curation and harmonization of more comprehensive drug combination and monotherapy screening data, not only for cancers but also for other diseases such as malaria and COVID-19; 2) enhanced algorithms for assessing the sensitivity and synergy of drug combinations; 3) network modelling tools to visualize the mechanisms of action of drugs or drug combinations for a given cancer sample; and 4) state-of-the-art machine learning models to predict drug combination sensitivity and synergy. These improvements have been provided with more user-friendly graphical interface and faster database infrastructure, which make DrugComb the most comprehensive web-based resources for the study of drug sensitivities for multiple diseases.

The Molecular Analysis of rs11614913 Polymorphism from miRNA196a Gene and Its Relationship with TNF-α Gene Expression in Cervical Cancer

Background: Cervical cancer (CC) is one of the most common malignant tumors in women, which has been diagnosed as fourth cancer in females worldwide. In addition to human papillomavirus (HPV), genetic factors, including altered expression of some microRNAs and mutations in tumor necrosis factor α (TNF-α) gene, are involved in this cancer. Objectives: This study aimed to investigate the rs11614913 polymorphism from the miRNA196a gene and its association with the expression of the TNF-α gene in cervical cancer for early diagnosis and treatment. Methods: In this study, 52 samples of pre-cancerous and cancerous lesions, and 50 tissue samples were collected from healthy subjects in an Iranian population. DNA was extracted from the samples, and rs11614913 polymorphism of the miRNA196a gene was investigated by PCR. RNA was extracted from the samples, and the expression of the miRNA196a and TNF-α genes were evaluated. Finally, for data analysis, Epi Info software version 7.1.3.10 and MedCalc Version 19.2.0 were used. Results: The frequency of CC, TC, and TT genotypes from rs11614913 polymorphism of miRNA196a gene was 0.58, 0.34, and 0.08, respectively, but in the healthy group it was 0.36, 0.46, and 0.18, respectively. The results also showed that the expression of miRNA196a and TNF-α genes in the patient group was higher than the control group. Conclusions: Based on the results of this study, a significant correlation was found between CC genotype and rs11614913 polymorphism of miRNA196a gene and TNF-α gene expression in the cervical cancer sample. Therefore, investigating these factors in patients with cervical cancer may be helpful.

Electronic Nose as a Novel Method for Diagnosing Cancer: A Systematic Review

Cancer is fast becoming the most important cause of death worldwide, its mortality being mostly caused by late or wrong diagnosis. Novel strategies have been developed to identify early signs of cancer in a minimally obtrusive way, including the Electronic Nose (E-Nose) technology, user-friendly, cost- and time-saving alternative to classical approaches. This systematic review, conducted under the PRISMA guidelines, identified 60 articles directly dealing with the E-Nose application in cancer research published up to 31 January 2020. Among these works, the vast majority reported successful E-Nose use for diagnosing Lung Cancer, showing promising results especially when employing the Aeonose tool, discriminating subjects with Lung Cancer from controls in more than 80% of individuals, in most studies. In order to tailor the main limitations of the proposed approach, including the application of the protocol to advanced stage of cancer, sample heterogeneity and massive confounders, future studies should be conducted on early stage patients, and on larger cohorts, as to better characterize the specific breathprint associated with the various subtypes of cancer. This would ultimately lead to a better and faster diagnosis and to earlier treatment, possibly reducing the burden associated to such conditions.

Highly multiplexed molecular and cellular mapping of breast cancer tissue in three dimensions using mass tomography

ABSTRACTA holistic understanding of tissue and organ structures and their functions requires the detection of molecular constituents in their original three-dimensional (3D) context. Imaging mass cytometry (IMC) makes possible the detection of up to 40 antigens and specific nucleic acids simultaneously using metal-tagged antibodies or nucleic acid probes, respectively, but has so far been restricted to two-dimensional imaging. To enable use of IMC for 3D tissue analyses, we developed mass tomography, which combines quasi deformation-free serial sectioning with novel computational methods. We utilized mass tomography to analyze a breast cancer sample. The resulting 3D representation reveals spatial and cellular heterogeneity, preferential cell-to-cell interactions, detailed tissue-architecture motifs, and the unique microenvironment of a micro-invasion, where micro-metastases clonality is examined, showing that cells arising from the same invasive area, displaying very distinct phenotypes, are all able to produce initial invasive lesions. Mass tomography will provide invaluable insights into the tissue microenvironment, cellular neighborhoods, and tissue organization.