Advancing global equity in cancer genomics - challenges and opportunities in Sub-Saharan Africa

Multi Omics Applications in Biological Systems

Traditional methodologies often fall short in addressing the complexity of biological systems. In this regard, system biology omics have brought invaluable tools for conducting comprehensive analysis. Current sequencing capabilities have revolutionized genetics and genomics studies, as well as the characterization of transcriptional profiling and dynamics of several species and sample types. Biological systems experience complex biochemical processes involving thousands of molecules. These processes occur at different levels that can be studied using mass spectrometry-based (MS-based) analysis, enabling high-throughput proteomics, glycoproteomics, glycomics, metabolomics, and lipidomics analysis. Here, we present the most up-to-date techniques utilized in the completion of omics analysis. Additionally, we include some interesting examples of the applicability of multi omics to a variety of biological systems.

Advancing oncology drug therapies for sub-Saharan Africa

Cancer incidence is rising across sub-Saharan Africa (SSA), and is often characterized by late-stage presentation, early age of onset and poor survival. While a number of oncology drugs are now improving the length and quality of life for cancer patients in high-income countries, significant disparities in access to a range of oncology therapeutics exist for SSA. A number of challenges to drug access such as drug costs, lack of infrastructure and trained personnel must be urgently addressed to advance oncology therapies for SSA. We present a review of selected oncology drug therapies that are likely to benefit cancer patients with a focus on common malignancies in SSA. We collate available data from seminal clinical trials in high-income countries to highlight the potential for these therapeutics to improve cancer outcomes. In addition, we discuss the need to ensure access to drugs within the WHO Model List of Essential Medicines and highlight therapeutics that require consideration. Available and active oncology clinical trials in the region is tabulated, demonstrating the significant gaps in access to oncology drug trials across much of the region. We issue an urgent call to action to address drug access due to the predicted rise in cancer burden in the region in coming years.

Review of prostate cancer genomic studies in Africa

Prostate cancer (PCa) is the second most commonly diagnosed in men worldwide and one of the most frequent cancers in men in Africa. The heterogeneity of this cancer fosters the need to identify potential genetic risk factors/biomarkers. Omics variations may significantly contribute to early diagnosis and personalized treatment. However, there are few genomic studies of this disease in African populations. This review sheds light on the status of genomics research on PCa in Africa and outlines the common variants identified thus far. The allele frequencies of the most significant SNPs in Afro-native, Afro-descendants, and European populations were compared. We advocate how these few but promising data will aid in understanding, better diagnosing, and precisely treating this cancer and the need for further collaborative research on the genomics of PCa in the African continent.

A New Era of Neuro-Oncology Research Pioneered by Multi-Omics Analysis and Machine Learning

Although the incidence of central nervous system (CNS) cancers is not high, it significantly reduces a patient’s quality of life and results in high mortality rates. A low incidence also means a low number of cases, which in turn means a low amount of information. To compensate, researchers have tried to increase the amount of information available from a single test using high-throughput technologies. This approach, referred to as single-omics analysis, has only been partially successful as one type of data may not be able to appropriately describe all the characteristics of a tumor. It is presently unclear what type of data can describe a particular clinical situation. One way to solve this problem is to use multi-omics data. When using many types of data, a selected data type or a combination of them may effectively resolve a clinical question. Hence, we conducted a comprehensive survey of papers in the field of neuro-oncology that used multi-omics data for analysis and found that most of the papers utilized machine learning techniques. This fact shows that it is useful to utilize machine learning techniques in multi-omics analysis. In this review, we discuss the current status of multi-omics analysis in the field of neuro-oncology and the importance of using machine learning techniques.

N-Glycomics of Cerebrospinal Fluid: Method Comparison

Cerebrospinal fluid (CSF) contains valuable biological and neurological information. However, its glycomics analysis is hampered due to the low amount of protein in the biofluid, as has been demonstrated by other glycomics studies using a substantial amount of CSF. In this work, we investigated different N-glycan sample preparation approaches to develop a more sensitive method. These methods, one with an increased amount of buffer solution during the N-glycan release step with a lower amount of sample volume and the other with Filter-Aided N-Glycan Separation (FANGS), were compared with recent work to demonstrate their effectiveness. It was demonstrated that an increased amount of buffer solution showed higher intensity in comparison to the previously published method and FANGS. This suggested that digestion efficiency during the N-glycan release step was not in an optimal condition from the previously published method, and that there is a substantial loss of sample with FANGS when preparing N-glycans from CSF.