Whole Genome Pathway Analysis Identifies an Association of Cadmium Response Gene Loss with Copy Number Variation in Mutant p53 Bearing Uterine Endometrial Carcinomas

Characterizing HDAC Pathway Copy Number Variation in Pan-Cancer

Background: Histone deacetylase (HDAC) plays a crucial role in regulating the expression and activity of a variety of genes associated with tumor progression and immunotherapeutic processes. The aim of this study was to characterize HDAC pathway copy number variation (CNV) in pan-cancer. Methods: A total of 10,678 tumor samples involving 33 types of tumors from The Cancer Genome Atlas (TCGA) were included in the study. Results: HDAC pathway CNV and CNV gain were identified as prognostic risk factors for pan-cancer species. The differences of tumor characteristics including tumor mutational burden, tumor neoantigen burden, high-microsatellite instability, and microsatellite stable between HDAC pathway CNV altered-type group and wild-type group varied among the various cancer species. In some cancer types, HDAC pathway CNV alteration was positively correlated with loss of heterozygosity, CNV burden, ploidy, and homologous recombination defect score markers, while it was significantly negatively correlated with immune score and stroma score. There were significant differences in immune characteristics such as major histocompatibility complex class I (MHC-I), MHC-II, chemokines, cytolytic-activity, and IFN-γ between the two groups. Immune cycle characteristics varied from one cancer type to another. Conclusion: This study reveals a tumor and immune profile of HDAC pathway CNV as well as its unlimited potential in immune prognosis.

AFExNet: An Adversarial Autoencoder for Differentiating Breast Cancer Sub-Types and Extracting Biologically Relevant Genes

Technological advancements in high-throughput genomics enable the generation of complex and large data sets that can be used for classification, clustering, and bio-marker identification. Modern deep learning algorithms provide us with the opportunity of finding most significant features in such huge dataset to characterize diseases (e.g., cancer) and their sub-types. Thus, developing such deep learning method, which can successfully extract meaningful features from various breast cancer sub-types, is of current research interest. In this paper, we develop dual stage (unsupervised pre-training and supervised fine-tuning) neural network architecture termed AFExNet based on adversarial auto-encoder (AAE) to extract features from high dimensional genetic data. We evaluated the performance of our model through twelve different supervised classifiers to verify the usefulness of the new features using public RNA-Seq dataset of breast cancer. AFExNet provides consistent results in all performance metrics across twelve different classifiers which makes our model classifier independent. We also develop a method named 'TopGene' to find highly weighted genes from the latent space which could be useful for finding cancer bio-markers. Put together, AFExNet has great potential for biological data to accurately and effectively extract features. Our work is fully reproducible and source code can be downloaded from Github: https://github.com/NeuroSyd/breast-cancer-sub-types.

Single-cell analysis of copy-number alterations in serous ovarian cancer reveals substantial heterogeneity in both low- and high-grade tumors

Unusually high aneuploidy is a hallmark of epithelial serous ovarian cancer (SOC). Previous analyses have focused on aneuploidy on average across all tumor cells. With the expansion of single-cell sequencing technologies, however, an analysis of copy number heterogeneity cell-to-cell is now technically feasible. Here, we describe an analysis of single-cell RNA sequencing (scRNA-seq) data to infer arm-level aneuploidy in individual serous ovarian cancer cells. By first clustering high-quality sequenced epithelial versus non-epithelial cells, high-confidence tumor cell populations were identified. InferCNV was used to predict segmented copy-number alterations (CNAs), which were then used to determine arm-level aneuploidy at the single-cell level. Control comparisons of normal cells to normal cells showed zero arm-level aneuploidy, whereas a median of four aneuploid events were detectable in cancer cells. A heterogeneity analysis of high-grade tumor cells compared to low-grade tumor cells showed similar levels of cell-to-cell variation between cancer grades. Metastatic tumors potentially showed selection pressure with reduced cell-to-cell variation compared to cells from primary tumors. Minor cell populations with CNAs similar to metastatic cells were identified within the matched primary tumors. Taken together, these results provide a minimum estimate for single-cell aneuploidy in serous ovarian cancer and demonstrate the utility of single-cell sequencing for CNA analysis.

Cadmium Exposure as a Putative Risk Factor for the Development of Pancreatic Cancer: Three Different Lines of Evidence

Although profoundly studied, etiology of pancreatic cancer (PC) is still rather scant. Exposure to cadmium (Cd), a ubiquitous metal associated with well-established toxic and carcinogenic properties, has been hypothesized to one putative cause of PC. Hence, we analyzed recently published observational studies, meta-analyses, and experimental animal and in vitro studies with the aim of summarizing the evidence of Cd involvement in PC development and describing the possible mechanisms. Consolidation of epidemiological data on PC and exposure to Cd indicated a significant association with an elevated risk of PC among general population exposed to Cd. Cadmium exposure of laboratory animals was showed to cause PC supporting the findings suggested by human studies. The concordance with human and animal studies is buttressed by in vitro studies, although in vitro data interpretation is problematic. In most instances, only significant effects are reported, and the concentrations of Cd are excessive, which would skew interpretation. Previous reports suggest that oxidative stress, apoptotic changes, and DNA cross-linking and hypermethylation are involved in Cd-mediated carcinogenesis. Undoubtedly, a significant amount of work is still needed to achieve a better understanding of the Cd involvement in pancreatic cancer which could facilitate prevention, diagnosis, and therapy of this fatal disease.

An insight into the complex roles of metallothioneins in malignant diseases with emphasis on (sub)isoforms/isoforms and epigenetics phenomena

Metallothioneins (MTs) belong to a group of small cysteine-rich proteins that are ubiquitous throughout all kingdoms. The main function of MTs is scavenging of free radicals and detoxification and homeostating of heavy metals. In humans, 16 genes localized on chromosome 16 have been identified to encode four MT isoforms labelled by numbers (MT-1-MT-4). MT-2, MT-3 and MT-4 proteins are encoded by a single gene. MT-1 comprises many (sub)isoforms. The known active MT-1 genes are MT-1A, -1B, -1E, -1F, -1G, -1H, -1M and -1X. The rest of the MT-1 genes (MT-1C, -1D, -1I, -1J and -1L) are pseudogenes. The expression and localization of individual MT (sub)isoforms and pseudogenes vary at intra-cellular level and in individual tissues. Changes in MT expression are associated with the process of carcinogenesis of various types of human malignancies, or with a more aggressive phenotype and therapeutic resistance. Hence, MT (sub)isoform profiling status could be utilized for diagnostics and therapy of tumour diseases. This review aims on a comprehensive summary of methods for analysis of MTs at (sub)isoforms levels, their expression in single tumour diseases and strategies how this knowledge can be utilized in anticancer therapy.