Investing in Africa's scientific future.

Africa bears a disproportionate burden of infectious diseases, accounting for a substantial percentage of global cases. Malaria, HIV/AIDS, tuberculosis, cholera, Ebola, Lassa fever, and other tropical diseases, such as dengue and chikungunya, have had a profound impact on morbidity and mortality. Various factors contribute to the higher prevalence and incidence of infectious diseases in Africa, including socioeconomic challenges, limited access to health care, inadequate sanitation and hygiene infrastructure, climate-related factors, and endemicity of certain diseases in specific regions. A skilled workforce is crucial to addressing these challenges. Unfortunately, many countries in Africa often lack the required resources, and aspiring scientists frequently seek educational and career opportunities abroad, leading to a substantial loss of talent and expertise from the continent. This talent migration, referred to as "brain drain," exacerbates the existing training gaps and hampers the sustainability of research within Africa.

Bayesian optimization of multivariate genomic prediction models based on secondary traits for improved accuracy gains and phenotyping costs.

KEY MESSAGE: We propose a novel approach to the Bayesian optimization of multivariate genomic prediction models based on secondary traits to improve accuracy gains and phenotyping costs via efficient Pareto frontier estimation. Multivariate genomic prediction based on secondary traits, such as data from various omics technologies including high-throughput phenotyping (e.g., unmanned aerial vehicle-based remote sensing), has attracted much attention because it offers improved accuracy gains compared with genomic prediction based only on marker genotypes. Although there is a trade-off between accuracy gains and phenotyping costs of secondary traits, no attempt has been made to optimize these trade-offs. In this study, we propose a novel approach to optimize multivariate genomic prediction models for secondary traits measurable at early growth stages for improved accuracy gains and phenotyping costs. The proposed approach employs Bayesian optimization for efficient Pareto frontier estimation, representing the maximum accuracy at a given cost. The proposed approach successfully estimated the optimal secondary trait combinations across a range of costs while providing genomic predictions for only about [Formula: see text] of all possible combinations. The simulation results reflecting the characteristics of each scenario of the simulated target traits showed that the obtained optimal combinations were reasonable. Analysis of real-time target trait data showed that the proposed multivariate genomic prediction model had significantly superior accuracy compared to the univariate genomic prediction model.

Returning actionable genomic results in a research biobank: Analytic validity, clinical implementation, and resource utilization.

Over 100 million research participants around the world have had research array-based genotyping (GT) or genome sequencing (GS), but only a small fraction of these have been offered return of actionable genomic findings (gRoR). Between 2017 and 2021, we analyzed genomic results from 36,417 participants in the Mass General Brigham Biobank and offered to confirm and return pathogenic and likely pathogenic variants (PLPVs) in 59 genes. Variant verification prior to participant recontact revealed that GT falsely identified PLPVs in 44.9% of samples, and GT failed to identify 72.0% of PLPVs detected in a subset of samples that were also sequenced. GT and GS detected verified PLPVs in 1% and 2.5% of the cohort, respectively. Of 256 participants who were alerted that they carried actionable PLPVs, 37.5% actively or passively declined further disclosure. 76.3% of those carrying PLPVs were unaware that they were carrying the variant, and over half of those met published professional criteria for genetic testing but had never been tested. This gRoR protocol cost approximately $129,000 USD per year in laboratory testing and research staff support, representing $14 per participant whose DNA was analyzed or $3,224 per participant in whom a PLPV was confirmed and disclosed. These data provide logistical details around gRoR that could help other investigators planning to return genomic results.

Using Online Mendelian Inheritance in Man in low- and middle-income countries.

Online Mendelian Inheritance in Man (OMIM®), an online catalog of human genes and genetic disorders, has been used in the low- and middle-income countries largely as a tool for improving clinical care, teaching genetics and genomics, and for clinical and research analysis of next-generation sequencing. By facilitating free access to curated, updated, and comprehensive information in genetics and genomics, OMIM has led to better clinical care and research advancement in countries where clinicians and researchers in private or public hospitals and universities cannot afford to pay for other resources including journal subscriptions.

Molecular Genomic Assessment Using a Blood-based mRNA Signature (NETest) is Cost-effective and Predicts Neuroendocrine Tumor Recurrence With 94% Accuracy.

INTRODUCTION: Identification of residual disease after neuroendocrine tumor (NET) resection is critical for management. Post-surgery imaging is insensitive, expensive, and current biomarkers ineffective. We evaluated whether the NETest, a multigene liquid biopsy blood biomarker, correlated with surgical resection and could predict recurrence. METHODS: Multicenter evaluation of NET resections over 24 months (n = 103): 47 pancreas, 26 small bowel, 26 lung, 2 appendix, 1 duodenum, 1 stomach. Surgery: R0 (83), R1/R2 (20). One millilitre of blood was collected at D0 and posroperative day (POD) 30. Transcript quantification by polymerase chain reaction (normal: ≤20), CgA by NEOLISA (normal ≤108 ng/mL). Standard-of-care (SoC) follow-up costs were calculated and compared to POD30 NETest-stratification approach. Analyses: Wilcoxon-paired test, Chi-square test. D BIOMARKERS: NETest: 103 of 103 (100%)-positive, whereas 23 of 103 (22%) were CgA-positive (Chi-square = 78, P < 0.0001).In the R0 group, the NETest decreased 59 ± 28 to 26 ± 23 (P < 0.0001); 36% (30/83) remained elevated. No significant decrease was evident for CgA. In the R1/R2 group the NETest decreased but 100% remained elevated. CgA levels did not decrease.An elevated POD30 NETest was present in R0 and 25 (83%) developed radiological recurrences. Normal score R0 s (n = 53) did not develop recurrence (Chi-square = 56, P < 0.0001). Recurrence prediction was 94% accurate with the NETest. COST EVALUATION: Using the NETest to stratify postoperative imaging resulted in a cost-savings of 42%. CONCLUSION: NETest diagnosis is more accurate than CgA (100% vs 22%). Surgery significantly decreased NETest. An elevated POD30 NETest predicted recurrence with 94% accuracy and post-surgical POD30 NETest follow-up stratification decreased costs by 42%. CgA had no surgical utility. Further studies would define the accuracy and cost-effectiveness of the NETest in the detection of postoperative recurrent disease.

Clinical and cost outcomes following genomics-informed treatment for advanced cancers.

BACKGROUND: Single-arm trials are common in precision oncology. Owing to the lack of randomized counterfactual, resultant data are not amenable to comparative outcomes analyses. Difference-in-difference (DID) methods present an opportunity to generate causal estimates of time-varying treatment outcomes. Using DID, our study estimates within-cohort effects of genomics-informed treatment versus standard care on clinical and cost outcomes. METHODS: We focus on adults with advanced cancers enrolled in the single-arm BC Cancer Personalized OncoGenomics program between 2012 and 2017. All individuals had a minimum of 1-year follow up. Logistic regression explored baseline differences across patients who received a genomics-informed treatment versus a standard care treatment after genomic sequencing. DID estimated the incremental effects of genomics-informed treatment on time to treatment discontinuation (TTD), time to next treatment (TTNT), and costs. TTD and TTNT correlate with improved response and survival. RESULTS: Our study cohort included 346 patients, of whom 140 (40%) received genomics-informed treatment after sequencing and 206 (60%) received standard care treatment. No significant differences in baseline characteristics were detected across treatment groups. DID estimated that the incremental effect of genomics-informed versus standard care treatment was 102 days (95% CI: 35, 167) on TTD, 91 days (95% CI: -9, 175) on TTNT, and CAD$91,098 (95% CI: $46,848, $176,598) on costs. Effects were most pronounced in gastrointestinal cancer patients. CONCLUSIONS: Genomics-informed treatment had a statistically significant effect on TTD compared to standard care treatment, but at increased treatment costs. Within-cohort evidence generated through this single-arm study informs the early-stage comparative effectiveness of precision oncology.

Clinically Responsive Genomic Analysis Pipelines: Elements to Improve Detection Rate and Efficiency.

Massively parallel sequencing has markedly improved mendelian diagnostic rates. This study assessed the effects of custom alterations to a diagnostic genomic bioinformatic pipeline in response to clinical need and derived practice recommendations relative to diagnostic rates and efficiency. The Genomic Annotation and Interpretation Application (GAIA) bioinformatics pipeline was designed to detect panel, exome, and genome sample integrity and prioritize gene variants in mendelian disorders. Reanalysis of selected negative cases was performed after improvements to the pipeline. GAIA improvements and their effect on sensitivity are described, including addition of a PubMed search for gene-disease associations not in the Online Mendelian Inheritance of Man database, inclusion of a process for calling low-quality variants (known as QPatch), and gene symbol nomenclature consistency checking. The new pipeline increased the diagnostic rate and reduced staff costs, resulting in a saving of US$844.34 per additional diagnosis. Recommendations for genomic analysis pipeline requirements are summarized. Clinically responsive bioinformatics pipeline improvements increase diagnostic sensitivity and increase cost-effectiveness.

National differences in cost analysis of Afirma Genomic sequencing classifier.

CONTEXT: Thyroid nodules of indeterminate cytology can be subjected to molecular testing such as the Afirma Genomic Sequencing Classifier (GSC), thereby minimizing the number of unnecessary diagnostic surgeries. OBJECTIVE: This work aimed to evaluate and compare the cost of routine GSC testing of indeterminate thyroid nodules in different countries. DESIGN, PATIENTS AND MAIN OUTCOME MEASURES: The cost of diagnostic hemithyroidectomy of indeterminate thyroid nodules was calculated by performing a Monte Carlo simulation cost analysis on a Markov decision-analytic model and then compared to that of GSC testing in the UK, Australia, USA, and Israel. RESULTS: Assuming that patients are treated by surgical resection and routine GSC testing is performed for all nodules of indeterminate significance, we found the GSC test to be more cost effective compared with diagnostic hemithyroidectomy when malignancy rates of thyroid nodules are less than 22.6%-37.1%, depending on the country where the test is performed. Given the cost of a thyroidectomy in the UK, Australia and Israel, performing routine GSC tests on all Bethesda IV nodules is more expensive than routine diagnostic hemithyroidectomy and becomes cost effective for Bethesda III when the GSC cost is below 3,031-3,087 USD. In comparison, in the USA, higher cost of thyroidectomy makes the GSC test cost effective for Bethesda III nodules at its current cost, but not for Bethesda IV nodules where it becomes cost effective under the price of 3,031 USD. CONCLUSIONS: Different molecular testing and surgical costs in different countries should be considered when performing cost analysis. In addition, since different medical centres have different malignancy rates, personalized in-house assessment of cost-effectiveness is warranted.

The role of genomics in global cancer prevention.

Despite improvements in the understanding of cancer causation, much remains unknown regarding the mechanisms by which genomic and non-genomic factors initiate carcinogenesis, drive cell invasion and metastasis, and enable cancer to develop. Technological advances have enabled the analysis of whole genomes, comprising thousands of tumours across populations worldwide, with the aim of identifying mutation signatures associated with particular tumour types. Large collaborative efforts have resulted in the identification and improved understanding of causal factors, and have shed light on new opportunities to prevent cancer. In this new era in cancer genomics, discoveries from studies conducted on an international scale can inform evidence-based strategies in cancer control along the cancer care continuum, from prevention to treatment. In this Review, we present the relevant history and emerging frontiers of cancer genetics and genomics from the perspective of global cancer prevention. We highlight the importance of local context in the adoption of new technologies and emergent evidence, with illustrative examples from worldwide. We emphasize the challenges in implementing important genomic findings in clinical settings with disparate resource availability and present a conceptual framework for the translation of such findings into clinical practice, and evidence-based policies in order to maximize the utility for a population.

Establishment and implementation of Cancer Genomic Medicine in Japan.

Approximately 1 in 2 Japanese people are estimated to be diagnosed with cancer during their lifetime. Cancer still remains the leading cause of death in Japan, therefore the government of Japan has decided to develop a better cancer control policy and launched the Cancer Genomic Medicine (CGM) program. The Ministry of Health, Labour, and Welfare (MHLW) held a consortium at their headquarters with leading academic authorities and the representatives of related organizations to discuss ways to advance CGM in Japan. Based on the report of the consortium, the CGM system under the national health insurance system has gradually been realized. Eleven hospitals were designated in February 2018 as core hospitals for CGM; subsequently, the MHLW built the Center for Cancer Genomics and Advanced Therapeutics (C-CAT) as an institution to aggregate and manage genomic and clinical information on cancer patients, and support appropriate secondary use of the aggregated information to develop research aimed at medical innovation. As the first step in Japan's CGM in routine practice, in June 2019 the MHLW started reimbursement of 2 types of tumor profiling tests for advanced solid cancer patients using the national insurance system. Japan's CGM has swiftly been spreading nationwide with the collaboration of 167 hospitals and patients. The health and research authorities are expected to embody personalized cancer medicine and promote CGM utilizing state-of-the-art technologies.

Economic impact of medical genetic testing on clinical applications in Thailand.

BACKGROUND: Although the clinical benefits of medical genetic testing have been proven, there has been limited evidence on its economic impact in Thai setting. Thus, this study aimed to evaluate the economic impact of genetic testing services provided by the Center for Medical Genomics (CMG) in Thailand. METHODS: Cost-benefit analysis was conducted from provider and societal perspectives. Cost and output data of genetic testing services provided by the CMG during 2014 to 2018 and published literature reviews were applied to estimate the costs and benefits. Monetary benefits related to genetic testing services were derived through human capital approach. RESULTS: The total operation cost was 126 million baht over five years with an average annual cost of 21 million baht per year. The net benefit, benefit-to-cost ratio, and return on investment were 5,477 million baht, 43 times, and 42 times, respectively. Productivity gain was the highest proportion (50.57%) of the total benefit. CONCLUSIONS: The provision of genetic testing services at the CMG gained much more benefits than the cost. This study highlighted a good value for money in the establishment of medical genomics settings in Thailand and other developing countries.

Strategic vision for improving human health at The Forefront of Genomics.

Starting with the launch of the Human Genome Project three decades ago, and continuing after its completion in 2003, genomics has progressively come to have a central and catalytic role in basic and translational research. In addition, studies increasingly demonstrate how genomic information can be effectively used in clinical care. In the future, the anticipated advances in technology development, biological insights, and clinical applications (among others) will lead to more widespread integration of genomics into almost all areas of biomedical research, the adoption of genomics into mainstream medical and public-health practices, and an increasing relevance of genomics for everyday life. On behalf of the research community, the National Human Genome Research Institute recently completed a multi-year process of strategic engagement to identify future research priorities and opportunities in human genomics, with an emphasis on health applications. Here we describe the highest-priority elements envisioned for the cutting-edge of human genomics going forward-that is, at 'The Forefront of Genomics'.

The Melanoma Genomics Managing Your Risk Study randomised controlled trial: statistical analysis plan.

BACKGROUND: The Melanoma Genomics Managing Your Risk Study is a randomised controlled trial that aims to evaluate the efficacy of providing information on personal genomic risk of melanoma in reducing ultraviolet radiation (UV) exposure, stratified by traditional risk group (low or high phenotypic risk) in the general population. The primary outcome is objectively measured total daily Standard Erythemal Doses at 12 months. Secondary outcomes include UV exposure at specific time periods, self-reported sun protection and skin-examination behaviours, psychosocial outcomes, and ethical considerations surrounding offering genomic testing at a population level. A within-trial and modelled economic evaluation will be undertaken from an Australian health system perspective to assess the cost-effectiveness of the intervention. OBJECTIVE: To publish the pre-determined statistical analysis plan (SAP) before database lock and the start of analysis. METHODS: This SAP describes the data synthesis, analysis principles and statistical procedures for analysing the outcomes from this trial. The SAP was approved after closure of recruitment and before completion of patient follow-up. It outlines the planned primary analyses and a range of subgroup and sensitivity analyses. Health economic outcomes are not included in this plan but will be analysed separately. The SAP will be adhered to for the final data analysis of this trial to avoid potential analysis bias that may arise from knowledge of the outcome data. RESULTS: This SAP is consistent with best practice and should enable transparent reporting. CONCLUSION: This SAP has been developed for the Melanoma Genomics Managing Your Risk Study and will be followed to ensure high-quality standards of internal validity and to minimise analysis bias. TRIAL REGISTRATION: Prospectively registered with the Australian New Zealand Clinical Trials Registry, ID: ACTR N12617000691347 . Registered on 15 May 2017.

Recent advances and future perspectives in vector-omics.

We have reviewed recent progress and the remaining challenges in vector-omics. We have highlighted several technologies and applications that facilitate novel biological insights beyond achieving a reference-quality genome assembly. Among other topics, we have discussed the applications of chromatin conformation capture, chromatin accessibility assays, optical mapping, full-length RNA sequencing, single cell RNA analysis, proteomics, and population genomics. We anticipate that we will witness a great expansion in vector-omics research not only in its application in a broad range of species, but also its ability to uncover novel genetic elements and tackle previously inaccessible regions of the genome. It is our hope that the continued innovation in device portability, cost reduction, and informatics support will in the foreseeable future bring vector-omics to every vector laboratory and field station in the world, which will have an unparalleled impact on basic research and the control of vector-borne infectious diseases.