scholarly journals A Review of Cancer Genetics and Genomics Studies in Africa

2021 ◽  
Vol 10 ◽  
Author(s):  
Solomon O. Rotimi ◽  
Oluwakemi A. Rotimi ◽  
Bodour Salhia
Keyword(s):  
Cancer Genomics ◽  
Cancer Genetics ◽  
Cervix Uterus ◽  
Cancer Genes ◽  
Multiple Cancer ◽  
Biological Regulation ◽  
Mesh Terms ◽  
African Populations ◽  
Cancer Types ◽  
Black Populations

Cancer is the second leading cause of death globally and is projected to overtake infectious disease as the leading cause of mortality in Africa within the next two decades. Cancer is a group of genomic diseases that presents with intra- and inter-population unique phenotypes, with Black populations having the burden of morbidity and mortality for most types. At large, the prevention and treatment of cancers have been propelled by the understanding of the genetic make-up of the disease of mostly non-African populations. By the same token, there is a wide knowledge gap in understanding the underlying genetic causes of, and genomic alterations associated with, cancer among black Africans. Accordingly, we performed a review of the literature to survey existing studies on cancer genetics/genomics and curated findings pertaining to publications across multiple cancer types conducted on African populations. We used PubMed MeSH terms to retrieve the relevant publications from 1990 to December 2019. The metadata of these publications were extracted using R text mining packages: RISmed and Pubmed.mineR. The data showed that only 0.329% of cancer publications globally were on Africa, and only 0.016% were on cancer genetics/genomics from Africa. Although the most prevalent cancers in Africa are cancers of the breast, cervix, uterus, and prostate, publications representing breast, colorectal, liver, and blood cancers were the most frequent in our review. The most frequently reported cancer genes were BRCA1, BRCA2, and TP53. Next, the genes reported in the reviewed publications’ abstracts were extracted and annotated into three gene ontology classes. Genes in the cellular component class were mostly associated with cell part and organelle part, while those in biological process and molecular function classes were mainly associated with cell process, biological regulation, and binding, and catalytic activity, respectively. Overall, this review highlights the paucity of research on cancer genomics on African populations, identified gaps, and discussed the need for concerted efforts to encourage more research on cancer genomics in Africa.

scholarly journals Discovering novel driver mutations from pan-cancer analysis of mutational and gene expression profiles

PLoS ONE ◽  
2020 ◽  
Vol 15 (11) ◽  
pp. e0242780
Author(s):  
Houriiyah Tegally ◽  
Kevin H. Kensler ◽  
Zahra Mungloo-Dilmohamud ◽  
Anisah W. Ghoorah ◽  
Timothy R. Rebbeck ◽  
...  
Keyword(s):  
Gene Expression ◽  
Large Scale ◽  
Cancer Genomics ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Driver Mutations ◽  
Literature Mining ◽  
Cancer Genes ◽  
Multiple Cancer ◽  
Driver Genes

As the genomic profile across cancers varies from person to person, patient prognosis and treatment may differ based on the mutational signature of each tumour. Thus, it is critical to understand genomic drivers of cancer and identify potential mutational commonalities across tumors originating at diverse anatomical sites. Large-scale cancer genomics initiatives, such as TCGA, ICGC and GENIE have enabled the analysis of thousands of tumour genomes. Our goal was to identify new cancer-causing mutations that may be common across tumour sites using mutational and gene expression profiles. Genomic and transcriptomic data from breast, ovarian, and prostate cancers were aggregated and analysed using differential gene expression methods to identify the effect of specific mutations on the expression of multiple genes. Mutated genes associated with the most differentially expressed genes were considered to be novel candidates for driver mutations, and were validated through literature mining, pathway analysis and clinical data investigation. Our driver selection method successfully identified 116 probable novel cancer-causing genes, with 4 discovered in patients having no alterations in any known driver genes: MXRA5, OBSCN, RYR1, and TG. The candidate genes previously not officially classified as cancer-causing showed enrichment in cancer pathways and in cancer diseases. They also matched expectations pertaining to properties of cancer genes, for instance, showing larger gene and protein lengths, and having mutation patterns suggesting oncogenic or tumor suppressor properties. Our approach allows for the identification of novel putative driver genes that are common across cancer sites using an unbiased approach without any a priori knowledge on pathways or gene interactions and is therefore an agnostic approach to the identification of putative common driver genes acting at multiple cancer sites.

scholarly journals Pathogenic impact of transcript isoform switching in 1209 cancer samples covering 27 cancer types using an isoform-specific interaction network

2019 ◽  
Author(s):  
Abdullah Kahraman ◽  
Tülay Karakulak ◽  
Damian Szklarczyk ◽  
Christian von Mering
Keyword(s):  
Protein Interactions ◽  
Cancer Genomics ◽  
Specific Interaction ◽  
Interaction Network ◽  
Protein Translation ◽  
Cancer Type ◽  
Large Network ◽  
Cancer Genes ◽  
Female Reproduction ◽  
Cancer Types

AbstractUnder normal conditions, cells of almost all tissue types express the same predominant canonical transcript isoform at each gene locus. In cancer, however, splicing regulation is often disturbed, leading to cancer-specific switches in the most dominant transcripts (MDT). But what is the pathogenic impact of these switches and how are they driving oncogenesis? To address these questions, we have analyzed isoform-specific protein-protein interaction disruptions in 1209 cancer samples covering 27 different cancer types from the Pan-Cancer Analysis of Whole Genomes (PCAWG) project of the International Cancer Genomics Consortium (ICGC). Our study revealed large variations in the number of cancer-specific MDT (cMDT) between cancer types. While carcinomas of the head and neck, or brain, had none or only a few cMDT, cancers of the female reproduction organs showed the highest number of cMDT. Interestingly, in contrast to the mutational load, the number of cMDT was tissue-specific, i.e. cancers arising from the same primary tissue had a similar number of cMDT. Some cMDT were found in 100% of all samples in a cancer type, making them candidates for diagnostic biomarkers. cMDT showed a tendency to fall at densely populated network regions where they disrupted protein interactions in the proximity of pathogenic cancer genes. A gene ontology enrichment analysis showed that these disruptions occurred mostly in enzyme signaling, protein translation, and RNA splicing pathways. Interestingly, no significant correlation between the number of cMDT and the number of coding or non-coding mutations could be identified. However, some transcript expressions correlated with mutations in non-coding splice-site and promoter regions of their genes. This work demonstrates for the first time the large extent of cancer-specific alterations in alternative splicing for 27 different cancer types. It highlights distinct and common patterns of cMDT and suggests novel pathogenic transcripts and markers that induce large network disruptions in cancers.

scholarly journals MEXCOWalk: Mutual Exclusion and Coverage Based Random Walk to Identify Cancer Modules

2019 ◽  
Author(s):  
Rafsan Ahmed ◽  
Ilyes Baali ◽  
Cesim Erten ◽  
Evis Hoxha ◽  
Hilal Kazan
Keyword(s):  
Random Walk ◽  
Mutual Exclusion ◽  
Risk Scores ◽  
Cancer Genes ◽  
Multiple Cancer ◽  
Driver Genes ◽  
Cancer Driver ◽  
Cancer Data ◽  
Cancer Types ◽  
Pan Cancer

AbstractMotivationGenomic analyses from large cancer cohorts have revealed the mutational heterogeneity problem which hinders the identification of driver genes based only on mutation profiles. One way to tackle this problem is to incorporate the fact that genes act together in functional modules. The connectivity knowledge present in existing protein-protein interaction networks together with mutation frequencies of genes and the mutual exclusivity of cancer mutations can be utilized to increase the accuracy of identifying cancer driver modules.ResultsWe present a novel edge-weighted random walk-based approach that incorporates connectivity information in the form of protein-protein interactions, mutual exclusion, and coverage to identify cancer driver modules. MEXCOWalk outperforms several state-of-the-art computational methods on TCGA pan-cancer data in terms of recovering known cancer genes, providing modules that are capable of classifying normal and tumor samples, and that are enriched for mutations in specific cancer types. Furthermore, the risk scores determined with output modules can stratify patients into low-risk and high-risk groups in multiple cancer types. MEXCOwalk identifies modules containing both well-known cancer genes and putative cancer genes that are rarely mutated in the pan-cancer data. The data, the source code, and useful scripts are available at:https://github.com/abu-compbio/[email protected]

scholarly journals Tracing Evolutionary Ages of Cancer-Driving Sites by Cancer Somatic Mutations

2020 ◽  
Author(s):  
Xun Gu ◽  
Zhan Zou ◽  
Jingwen Yang
Keyword(s):  
Large Scale ◽  
Somatic Mutations ◽  
Cancer Genomics ◽  
Acid Sites ◽  
Homology Search ◽  
Recent Common Ancestor ◽  
Phylogenetic Position ◽  
Cancer Genes ◽  
Most Recent Common Ancestor ◽  
Cancer Types

AbstractEvolutionary understanding of cancer genes may provide insights on the nature and evolution of complex life and the origin of multicellularity. In this study, we focus on the evolutionary ages of cancer-driving sites, and try to explore to what extent the amino acids of cancer-driving sites can be traced back to the most recent common ancestor (MRCA) of the gene. According to gene phylostraigraphy analysis, we use the definition of gene age (tg) by the most ancient phylogenetic position that can be traced back, in most cases based on the large-scale homology search of protein sequences. Our results are shown that the site-age profile of cancer-driving sites of TP53 is correlated with the number of cancer types the somatic mutations may affect. In general, those amino acid sites mutated in most cancer types are much ancient. These sites frequently mutated in cancerous cells are possibly responsible for carcinogenesis; some may be very important for basic growth of single-cell organisms, and others may contribute to complex cell regulation of multicellular organisms. The further cancer genomics analysis also indicates that ages of cancer-driving sites are ancient but may have a broad range in early stages of metazoans.

scholarly journals Cancer-mutation network and the number and specificity of driver mutations

2018 ◽  
Vol 115 (26) ◽  
pp. E6010-E6019 ◽  
Author(s):  
Jaime Iranzo ◽  
Iñigo Martincorena ◽  
Eugene V. Koonin
Keyword(s):  
Cancer Genomics ◽  
Driver Mutations ◽  
Bipartite Network ◽  
Cancer Genes ◽  
Cancer Driver ◽  
Cancer Mutation ◽  
Cancer Types ◽  
Extensive Information ◽  
The Mean ◽  
Network Component

Cancer genomics has produced extensive information on cancer-associated genes, but the number and specificity of cancer-driver mutations remains a matter of debate. We constructed a bipartite network in which 7,665 tumors from 30 cancer types are connected via shared mutations in 198 previously identified cancer genes. We show that about 27% of the tumors can be assigned to statistically supported modules, most of which encompass one or two cancer types. The rest of the tumors belong to a diffuse network component suggesting lower gene specificity of driver mutations. Linear regression of the mutational loads in cancer genes was used to estimate the number of drivers required for the onset of different cancers. The mean number of drivers in known cancer genes is approximately two, with a range of one to five. Cancers that are associated with modules had more drivers than those from the diffuse network component, suggesting that unidentified and/or interchangeable drivers exist in the latter.

Anticancer Immunotoxins, Challenges, and Approaches

2020 ◽  
Vol 26 ◽  
Author(s):  
Maryam Dashtiahangar ◽  
Leila Rahbarnia ◽  
Safar Farajnia ◽  
Arash Salmaninejad ◽  
Arezoo Gowhari Shabgah ◽  
...  
Keyword(s):  
Therapeutic Strategy ◽  
Antibody Fragment ◽  
Clinical Use ◽  
Multiple Cancer ◽  
Main Obstacle ◽  
Cancer Types ◽  
Recombinant Immunotoxins ◽  
Cancerous Cells ◽  
High Immunogenicity ◽  
Novel Therapeutic

: The development of recombinant immunotoxins (RITs) as a novel therapeutic strategy has made a revolution in the treatment of cancer. RITs are resulting from the fusion of antibodies to toxin proteins for targeting and eliminating cancerous cells by inhibiting protein synthesis. Despite indisputable outcomes of RITs regarding inhibiting multiple cancer types, high immunogenicity has been known as the main obstacle in the clinical use of RITs. Various strategies have been proposed to overcome these limitations, including immunosuppressive therapy, humanization of the antibody fragment moiety, generation of immunotoxins originated from endogenous human cytotoxic enzymes, and modification of the toxin moiety to escape the immune system. This paper devoted to reviewing recent advances in the design of immunotoxins with lower immunogenicity.

scholarly journals Ranking cancer drivers via betweenness-based outlier detection and random walks

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Cesim Erten ◽  
Aissa Houdjedj ◽  
Hilal Kazan
Keyword(s):  
Cancer Genomics ◽  
Interaction Network ◽  
Molecular Data ◽  
Alternative Methods ◽  
Patient Specific ◽  
Cancer Genes ◽  
Driver Genes ◽  
Cancer Driver ◽  
Protein Protein Interaction ◽  
Genomic Studies

Abstract Background Recent cancer genomic studies have generated detailed molecular data on a large number of cancer patients. A key remaining problem in cancer genomics is the identification of driver genes. Results We propose BetweenNet, a computational approach that integrates genomic data with a protein-protein interaction network to identify cancer driver genes. BetweenNet utilizes a measure based on betweenness centrality on patient specific networks to identify the so-called outlier genes that correspond to dysregulated genes for each patient. Setting up the relationship between the mutated genes and the outliers through a bipartite graph, it employs a random-walk process on the graph, which provides the final prioritization of the mutated genes. We compare BetweenNet against state-of-the art cancer gene prioritization methods on lung, breast, and pan-cancer datasets. Conclusions Our evaluations show that BetweenNet is better at recovering known cancer genes based on multiple reference databases. Additionally, we show that the GO terms and the reference pathways enriched in BetweenNet ranked genes and those that are enriched in known cancer genes overlap significantly when compared to the overlaps achieved by the rankings of the alternative methods.

scholarly journals Landscape of Chimeric RNAs in Non-Cancerous Cells

Genes ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 466
Author(s):  
Chen Chen ◽  
Samuel Haddox ◽  
Yue Tang ◽  
Fujun Qin ◽  
Hui Li
Keyword(s):  
Cancer Cells ◽  
Cell Lines ◽  
Drug Targets ◽  
Neoplastic Cell ◽  
Multiple Cancer ◽  
Chimeric Rnas ◽  
Healthy Donors ◽  
Cancer Types ◽  
Chimeric Rna ◽  
Cancerous Cells

Gene fusions and their products (RNA and protein) have been traditionally recognized as unique features of cancer cells and are used as ideal biomarkers and drug targets for multiple cancer types. However, recent studies have demonstrated that chimeric RNAs generated by intergenic alternative splicing can also be found in normal cells and tissues. In this study, we aim to identify chimeric RNAs in different non-neoplastic cell lines and investigate the landscape and expression of these novel candidate chimeric RNAs. To do so, we used HEK-293T, HUVEC, and LO2 cell lines as models, performed paired-end RNA sequencing, and conducted analyses for chimeric RNA profiles. Several filtering criteria were applied, and the landscape of chimeric RNAs was characterized at multiple levels and from various angles. Further, we experimentally validated 17 chimeric RNAs from different classifications. Finally, we examined a number of validated chimeric RNAs in different cancer and non-cancer cells, including blood from healthy donors, and demonstrated their ubiquitous expression pattern.

Characterizing the Heterogeneity of Small Extracellular Vesicle Populations in Multiple Cancer Types via an Ultrasensitive Chip

ACS Sensors ◽  
2021 ◽  
Author(s):  
Jing Wang ◽  
Alain Wuethrich ◽  
Richard J. Lobb ◽  
Fiach Antaw ◽  
Abu Ali Ibn Sina ◽  
...  
Keyword(s):  
Extracellular Vesicle ◽  
Multiple Cancer ◽  
Cancer Types

scholarly journals Development of Group B Coxsackievirus as an Oncolytic Virus: Opportunities and Challenges

Viruses ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 1082
Author(s):  
Huitao Liu ◽  
Honglin Luo
Keyword(s):  
Tumor Cells ◽  
Current Knowledge ◽  
Oncolytic Viruses ◽  
Human Enterovirus ◽  
Multiple Cancer ◽  
The Family ◽  
Cancer Types ◽  
Group B ◽  
Type 3 ◽  
Genus Enterovirus

Oncolytic viruses have emerged as a promising strategy for cancer therapy due to their dual ability to selectively infect and lyse tumor cells and to induce systemic anti-tumor immunity. Among various candidate viruses, coxsackievirus group B (CVBs) have attracted increasing attention in recent years. CVBs are a group of small, non-enveloped, single-stranded, positive-sense RNA viruses, belonging to species human Enterovirus B in the genus Enterovirus of the family Picornaviridae. Preclinical studies have demonstrated potent anti-tumor activities for CVBs, particularly type 3, against multiple cancer types, including lung, breast, and colorectal cancer. Various approaches have been proposed or applied to enhance the safety and specificity of CVBs towards tumor cells and to further increase their anti-tumor efficacy. This review summarizes current knowledge and strategies for developing CVBs as oncolytic viruses for cancer virotherapy. The challenges arising from these studies and future prospects are also discussed in this review.

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