Precision Medicine: From Science To Value

Recurrence of Early Hepatocellular Carcinoma after Surgery May Be Related to Intestinal Oxidative Stress and the Development of a Predictive Model

Background

Early stage hepatocellular carcinoma (HCC) has a high recurrence rate after surgery and lacks reliable predictive tools. We explored the potential of combining enhanced CT with gut microbiome to develop a predictive model for recurrence after early HCC surgery.

Methods

A total of 112 patients with early HCC who underwent hepatectomy from September 2018 to December 2020 were included in this study, and the machine learning method was divided into a training group (N = 71) and a test group (N = 41) with the observed endpoint of recurrence-free survival (RFS). Features were extracted from the arterial and portal phases of enhanced computed tomography (CT) images and gut microbiome, and features with minimum absolute contraction and selection operator regression were created, and the extracted features were scored to create a preoperative prediction model by using the multivariate Cox regression analysis with risk stratification analysis.

Results

In the study cohort, the model constructed by combining radiological and gut flora features provided good predictive performance (C index, 0.811 (0.650-0.972)). The combined radiology and gut flora-based model constructed risk strata with high, intermediate, or low risk of recurrence and different characteristics of recurrent tumor imaging and gut flora. Recurrence of early stage hepatocellular carcinoma may be associated with oxidative stress in the intestinal flora.

Conclusions

This study successfully constructs a risk model integrating enhanced CT and gut microbiome characteristics that can be used for the risk of postoperative recurrence in patients with early HCC. In addition, intestinal flora associated with HCC recurrence may be involved in oxidative stress.

Applying precision medicine to the diagnosis and management of endocrine disorders

Precision medicine employs digital tools and knowledge of a patient's genetic makeup, environment and lifestyle to improve diagnostic accuracy and to develop individualised treatment and prevention strategies. Precision medicine has improved management in a number of disease areas, most notably in oncology, and it has the potential to positively impact others, including endocrine disorders. The accuracy of diagnosis in young patients with growth disorders can be improved by using biomarkers. Insulin-like growth factor I (IGF-I) is the most widely accepted biomarker of growth hormone secretion, but its predictive value for recombinant human growth hormone treatment response is modest and various factors can affect the accuracy of IGF-I measurements. These factors need to be taken into account when considering IGF-I as a component of precision medicine in the management of growth hormone deficiency. The use of genetic analyses can assist with diagnosis by confirming the aetiology, facilitate treatment decisions, guide counselling and allow prompt intervention in children with pubertal disorders, such as central precocious puberty and testotoxicosis. Precision medicine has also proven useful during the transition of young people with endocrine disorders from paediatric to adult services when patients are at heightened risk of dropping out from medical care. An understanding of the likelihood of ongoing GH deficiency, using tools such as MRI, detailed patient history and IGF-I levels, can assist in determining the need for continued recombinant human growth hormone treatment during the process of transitional care.

Unsupervised probabilistic models for sequential Electronic Health Records

We develop an unsupervised probabilistic model for heterogeneous Electronic Health Record (EHR) data. Utilizing a mixture model formulation, our approach directly models sequences of arbitrary length, such as medications and laboratory results. This allows for subgrouping and incorporation of the dynamics underlying heterogeneous data types. The model consists of a layered set of latent variables that encode underlying structure in the data. These variables represent subject subgroups at the top layer, and unobserved states for sequences in the second layer. We train this model on episodic data from subjects receiving medical care in the Kaiser Permanente Northern California integrated healthcare delivery system. The resulting properties of the trained model generate novel insight from these complex and multifaceted data. In addition, we show how the model can be used to analyze sequences that contribute to assessment of mortality likelihood.

Phenotyping, Precision Medicine, and Asthma

The traditional one-size-fits all approach based on asthma severity is archaic. Asthma is a heterogenous syndrome rather than a single disease entity. Studies evaluating observable characteristics called phenotypes have elucidated this heterogeneity. Asthma clusters demonstrate overlapping features, are generally stable over time and are reproducible. What the identification of clusters may have failed to do, is move the needle of precision medicine meaningfully in asthma. This may be related to the lack of a straightforward and clinically meaningful way to apply what we have learned about asthma clusters. Clusters are based on both clinical factors and biomarkers. The use of biomarkers is slowly gaining popularity, but phenotyping based on biomarkers is generally greatly underutilized even in subspecialty care. Biomarkers are more often used to evaluate type 2 (T2) inflammatory signatures and eosinophils (sputum and blood), fractional exhaled nitric oxide (FeNO) and serum total and specific immunoglobulin (Ig) E reliably characterize the underlying inflammatory pathways. Biomarkers perform variably and clinicians must be familiar with their advantages and disadvantages to accurately apply them in clinical care. In addition, it is increasingly clear that clinical features are critical in understanding not only phenotypic characterization but in predicting response to therapy and future risk of poor outcomes. Strategies for asthma management will need to leverage our knowledge of biomarkers and clinical features to create composite scores and risk prediction tools that are clinically applicable. Despite significant progress, many questions remain, and more work is required to accurately identify non-T2 biomarkers. Adoption of phenotyping and more consistent use of biomarkers is needed, and we should continue to encourage this incorporation into practice.

The Voices of Stakeholders Involved in Precision Medicine: The Co-Design and Evaluation of Qualitative Indicators of Intervention Acceptability, Fidelity and Context in PRecISion Medicine for Children With Cancer in Australia

We report a novel approach of amalgamating implementation outcomes of acceptability and fidelity alongside context as a new way of qualitatively evaluating implementation outcomes and context of a precision medicine intervention. A rapid qualitative online proforma was co-designed with stakeholders and sent to a purposive sample of healthcare professionals involved in an early-phase clinical trial intervention. Data were analysed using Framework Analysis. A total of 24 out of 68 proformas were returned. Although some participants raised concerns about drug medication access issues, the main intervention was well accepted and understood across professional groups. Comprehension was enhanced through exposure to specialist multidisciplinary meeting arrangements. In conclusion, a rapid data collection tool and framework are now available to assess readily measurable, qualitative indicators of acceptability, fidelity of receipt and contextual fit within the dynamic precision medicine context.

Precision Oncology in Canada: Converting Vision to Reality with Lessons from International Programs

Canada's healthcare system, like others worldwide, is immersed in a process of evolution, attempting to adapt conventional frameworks of health technology assessment (HTA) and funding models to a new landscape of precision medicine in oncology. In particular, the need for real-world evidence in Canada is not matched by the necessary infrastructure and technologies required to integrate genomic and clinical data. Since healthcare systems in many developed nations face similar challenges, we adopted a solutions-based approach and conducted a search of worldwide programs in personalized medicine, with an emphasis on precision oncology. This search strategy included review articles published between 1 January 2016 and 1 March 2021 and hand-searches of their reference lists for relevant publications back to 1 December 2005. Thirty-nine initiatives across 37 countries in Europe, Australasia, Africa, and the Americas had the potential to lead to real-world data (RWD) on the clinical utility of oncology biomarkers. We highlight four initiatives with helpful lessons for Canada: Genomic Medicine France 2025, UNICANCER, the German Medical Informatics Initiative, and CANCER-ID. Among the 35 other programs evaluated, the main themes included the need for collaboration and systems to support data harmonization across multiple jurisdictions. In order to generate RWD in precision oncology that will prove acceptable to HTA bodies, Canada must take a national approach to biomarker strategy and unite all stakeholders at the highest level to overcome jurisdictional and technological barriers.

The environmental impact of data-driven precision medicine initiatives

Opportunities offered by precision medicine have long been promised in the medical and health literature. However, precision medicine - and the methodologies and approaches it relies on - also has adverse environmental impacts. As research into precision medicine continues to expand, there is a compelling need to consider these environmental impacts and develop means to mitigate them. In this article, we review the adverse environmental impacts associated with precision medicine, with a particular focus on those associated with its underlying need for data-intensive approaches. We illustrate the importance of considering the environmental impacts of precision medicine and describe the adverse health outcomes that are associated with climate change. We follow this with a description of how these environmental impacts are being addressed in both the health and data-driven technology sector. We then describe the (scant) literature on environmental impacts associated with data-driven precision medicine specifically. We finish by highlighting various environmental considerations that precision medicine researchers, and the field more broadly, should take into account.

Non-Invasive Data Acquisition and IoT Solution for Human Vital Signs Monitoring: Applications, Limitations and Future Prospects

The rapid development of technology has brought about a revolution in healthcare stimulating a wide range of smart and autonomous applications in homes, clinics, surgeries and hospitals. Smart healthcare opens the opportunity for a qualitative advance in the relations between healthcare providers and end-users for the provision of healthcare such as enabling doctors to diagnose remotely while optimizing the accuracy of the diagnosis and maximizing the benefits of treatment by enabling close patient monitoring. This paper presents a comprehensive review of non-invasive vital data acquisition and the Internet of Things in healthcare informatics and thus reports the challenges in healthcare informatics and suggests future work that would lead to solutions to address the open challenges in IoT and non-invasive vital data acquisition. In particular, the conducted review has revealed that there has been a daunting challenge in the development of multi-frequency vital IoT systems, and addressing this issue will help enable the vital IoT node to be reachable by the broker in multiple area ranges. Furthermore, the utilization of multi-camera systems has proven its high potential to increase the accuracy of vital data acquisition, but the implementation of such systems has not been fully developed with unfilled gaps to be bridged. Moreover, the application of deep learning to the real-time analysis of vital data on the node/edge side will enable optimal, instant offline decision making. Finally, the synergistic integration of reliable power management and energy harvesting systems into non-invasive data acquisition has been omitted so far, and the successful implementation of such systems will lead to a smart, robust, sustainable and self-powered healthcare system.

Left Atrial Appendage Occlusion: Current Stroke Prevention Strategies and a Shift Toward Data-Driven, Patient-Specific Approaches

The left atrial appendage (LAA) is a complex structure with unknown physiologic function protruding from the main body of the left atrium. In patients with atrial fibrillation, the left atrium does not contract effectively. Insufficient atrial and LAA contractility predisposes the LAA morphology to hemostasis and thrombus formation, leading to an increased risk of cardioembolic events. Oral anticoagulation therapies are the mainstay of stroke prevention options for patients; however, not all patients are candidates for long-term oral anticoagulation. Percutaneous occlusion devices are an attractive alternative to long-term anticoagulation therapy, although they are not without limitations, such as peri-implant leakage and device-related thrombosis. Although efforts have been made to reduce these risks, significant interpatient heterogeneity inevitably yields some degree of device-anatomy mismatch that is difficult to resolve using current devices and can ultimately lead to insufficient occlusion and poor patient outcomes. In this state-of-the-art review, we evaluated the anatomy of the LAA as well as the current pathophysiologic understanding and stroke prevention strategies used in the management of the risk of stroke associated with atrial fibrillation. We highlighted recent advances in computed tomography imaging, preprocedural planning, computational modeling, and novel additive manufacturing techniques, which represent the tools needed for a paradigm shift toward patient-centric LAA occlusion. Together, we envisage that these techniques will facilitate a pipeline from the imaging of patient anatomy to patient-specific computational and bench-top models that enable customized, data-driven approaches for LAA occlusion that are engineered specifically to meet each patient's unique needs.

Artificial intelligence and machine learning in precision medicine: A paradigm shift in big data analysis

The integration of artificial intelligence in precision medicine has revolutionized healthcare delivery. Precision medicine identifies the phenotype of particular patients with less-common responses to treatment. Recent studies have demonstrated that translational research exploring the convergence between artificial intelligence and precision medicine will help solve the most difficult challenges facing precision medicine. Here, we discuss different aspects of artificial intelligence in precision medicine that improve healthcare delivery. First, we discuss how artificial intelligence changes the landscape of precision medicine and the evolution of artificial intelligence in precision medicine. Second, we highlight the synergies between artificial intelligence and precision medicine and promises of artificial intelligence and precision medicine in healthcare delivery. Third, we briefly explain the promise of big data analytics and the integration of nanomaterials in precision medicine. Last, we highlight the challenges and opportunities of artificial intelligence in precision medicine.

From molecules to genomic variations: Accelerating genome analysis via intelligent algorithms and architectures

We now need more than ever to make genome analysis more intelligent. We need to read, analyze, and interpret our genomes not only quickly, but also accurately and efficiently enough to scale the analysis to population level. There currently exist major computational bottlenecks and inefficiencies throughout the entire genome analysis pipeline, because state-of-the-art genome sequencing technologies are still not able to read a genome in its entirety. We describe the ongoing journey in significantly improving the performance, accuracy, and efficiency of genome analysis using intelligent algorithms and hardware architectures. We explain state-of-the-art algorithmic methods and hardware-based acceleration approaches for each step of the genome analysis pipeline and provide experimental evaluations. Algorithmic approaches exploit the structure of the genome as well as the structure of the underlying hardware. Hardware-based acceleration approaches exploit specialized microarchitectures or various execution paradigms (e.g., processing inside or near memory) along with algorithmic changes, leading to new hardware/software co-designed systems. We conclude with a foreshadowing of future challenges, benefits, and research directions triggered by the development of both very low cost yet highly error prone new sequencing technologies and specialized hardware chips for genomics. We hope that these efforts and the challenges we discuss provide a foundation for future work in making genome analysis more intelligent.

Engagement marketing for social good: Application to the All of Us Research Program

Engagement marketing, when applied to increasing the social good, involves making a deliberate effort to engage communities with an organization’s brand that might not have otherwise happened organically. Organizations that typically focus on increasing the social good include non-profits, community organizations, public health departments, and federal, state, and local agencies. Engagement marketing builds relationships, gives a voice to, and fosters collaboration with community members to transform their insights into impactful experiences that motivate and empower them to act to increase the social good. These actions may include making an informed decision, changing a health or prosocial behavior, or joining an effort that promotes or increases social good. In this paper, we translate the commercial engagement marketing approach, typically used, and studied widely to increase profits, to one that uses engagement marketing to increase prosocial outcomes. We propose a new definition of engagement marketing applied to the social good, a multi-level conceptual framework that integrates individual, social, community and macro-level processes and outcomes, and illustrates an example applying this translated model to co-create digital engagement experiences using a human centered design approach for the All of Us Research Program. This model can also guide research and practice related to DNA-based population screening.

Artificial Intelligence, Healthcare, Clinical Genomics, and Pharmacogenomics Approaches in Precision Medicine

Precision medicine has greatly aided in improving health outcomes using earlier diagnosis and better prognosis for chronic diseases. It makes use of clinical data associated with the patient as well as their multi-omics/genomic data to reach a conclusion regarding how a physician should proceed with a specific treatment. Compared to the symptom-driven approach in medicine, precision medicine considers the critical fact that all patients do not react to the same treatment or medication in the same way. When considering the intersection of traditionally distinct arenas of medicine, that is, artificial intelligence, healthcare, clinical genomics, and pharmacogenomics—what ties them together is their impact on the development of precision medicine as a field and how they each contribute to patient-specific, rather than symptom-specific patient outcomes. This study discusses the impact and integration of these different fields in the scope of precision medicine and how they can be used in preventing and predicting acute or chronic diseases. Additionally, this study also discusses the advantages as well as the current challenges associated with artificial intelligence, healthcare, clinical genomics, and pharmacogenomics.

Investigating acceptability of a training programme in precision medicine for frontline healthcare professionals: a mixed methods study

BACKGROUND

Precision Medicine offers tailored prevention, diagnosis, treatment and management to patients that considers genomics, lifestyle and environmental factors. If implementation of Precision Medicine is to advance, effective, focused upskilling of frontline healthcare professionals through quality continuing professional development is needed. This study reports on an evidence-based approach to needs assessment to investigate the current level of knowledge of Precision Medicine, acceptable content for training, the perceived potential of a more precision approach to patient care and motivation to participate in a training programme among pharmacists, advanced practice nurses and general practitioners. Investigating perceived needs can avoid a top-down approach and support a design that is fit for purpose to targeted professions.

METHODS

This study reports on 2 focus groups (n = 12) delivered in French and German with equal professional participation of the targeted professions. The research objectives were investigated in two phases. During the first phase, a literature review and expert consultations were conducted to develop a definition of PM, patient cases and content for training. In a second phase, these investigations were further explored using focus groups to investigate acceptable learning objectives, the potential of PM to relevant professions and motivation of participants. Quantitative investigations using rating scales and visual analogues were incorporated. The focus groups were audio recorded, transcribed by intelligent verbatim and translated to English. NVivo was used for data analysis and interpretation following a hybrid approach using the Framework Method and thematic analysis. The analytical framework, Implementation Science, was applied to organise and present research data.

RESULTS

Precision Medicine is considered a new topic area, largely unfamiliar to frontline healthcare professionals.. There was acceptance of a more precision approach to care among all participants with perceived positive implications for patients. Valuable insight was gathered on acceptable content and form for training. All participants expressed concerns on readiness within their professions which included an insufficient system infrastructure, a lack of time to attend needed training, a lack of clarity for use in practice and the time needed to build a support network.

CONCLUSIONS

A precision approach to patient care is on the horizon for health care professionals not only in hospital settings but also at the community level. Our results conclude that an adaptable and flexible training programme in PM is timely, contextually relevant and conducive to the needs of targeted health professions for successful implementation. A training programme in PM will require support across sectors and stakeholders, supporting insurance models, educated patients and integrated care supported by innovative technology. Implementation Science outcomes are a useful strategy towards design of an effective training programme that can have measurable impact in practice.

An Approach to Solving the Complex Clinicogenomic Data Landscape in Precision Oncology: Learnings From the Design of WAYFIND-R, a Global Precision Oncology Registry

Precision oncology, where patients are given therapies based on their genomic profile and disease trajectory, is rapidly evolving to become a pivotal part of cancer management, supported by regulatory approvals of biomarker-matched targeted therapies and cancer immunotherapies. However, next-generation sequencing (NGS)-based technologies have revealed an increasing number of molecular-based cancer subtypes with rare patient populations, leading to difficulties in executing/recruiting for traditional clinical trials. Therefore, approval of novel therapeutics based on traditional interventional studies may be difficult and time consuming, with delayed access to innovative therapies. Real-world data (RWD) that describe the patient journey in routine clinical practice can help elucidate the clinical utility of NGS-based genomic profiling, multidisciplinary case discussions, and targeted therapies. We describe key learnings from the setup of WAYFIND-R (NCT04529122), a first-of-its-kind global cancer registry collecting RWD from patients with solid tumors who have undergone NGS-based genomic profiling. The meaning of 'generalizability' and 'high quality' for RWD across different geographic areas was revisited, together with patient recruitment processes, and data sharing and privacy. Inspired by these learnings, WAYFIND-R's design will help physicians discuss patient treatment plans with their colleagues, improve understanding of the impact of treatment decisions/cancer care processes on patient outcomes, and provide a platform to support the design and conduct of further clinical/epidemiologic research. WAYFIND-R demonstrates user-friendly, electronic case report forms, standardized collection of molecular tumor board-based decisions, and a dashboard providing investigators with access to local cohort-level data and the ability to interact with colleagues or search the entire registry to find rare populations. Overall, WAYFIND-R will inform on best practice for NGS-based treatment decisions by clinicians, foster global collaborations between cancer centers and enable robust conclusions regarding outcome data to be drawn, improve understanding of disparities in patients' access to advanced diagnostics and therapies, and ultimately drive advances in precision oncology.

Financing and Reimbursement Models for Personalised Medicine: A Systematic Review to Identify Current Models and Future Options

BACKGROUND

The number of healthcare interventions described as 'personalised medicine' (PM) is increasing rapidly. As healthcare systems struggle to decide whether to fund PM innovations, it is unclear what models for financing and reimbursement are appropriate to apply in this context.

OBJECTIVE

To review financing and reimbursement models for PM, summarise their key characteristics, and describe whether they can influence the development and uptake of PM.

METHODS

A literature review was conducted in Medline, Embase, Web of Science, and Econlit to identify studies published in English between 2009 and 2021, and reviews published before 2009. Grey literature was identified through Google Scholar, Google and subject-specific webpages. Articles that described financing and reimbursement of PM, and financing of non-PM were included. Data were extracted and synthesised narratively to report on the models, as well as facilitators, incentives, barriers and disincentives that could influence PM development and uptake.

RESULTS

One hundred and fifty-three papers were included. Research and development of PM was financed through both public and private sources and reimbursed largely through traditional models such as single fees, Diagnosis-Related Groups, and bundled payments. Financial-based reimbursement, including rebates and price-volume agreements, was mainly applied to targeted therapies. Performance-based reimbursement was identified mainly for gene and targeted therapies, and some companion diagnostics. Gene therapy manufacturers offered outcome-based rebates for treatment failure for interventions including Luxturna®, Kymriah®, Yescarta®, Zynteglo®, Zolgensma® and Strimvelis®, and coverage with evidence development for Kymriah® and Yescarta®. Targeted testing with OncotypeDX® was granted value-based reimbursement through initial coverage with evidence development. The main barriers and disincentives to PM financing and reimbursement were the lack of strong links between stakeholders and the lack of demonstrable benefit and value of PM.

CONCLUSIONS

Public-private financing agreements and performance-based reimbursement models could help facilitate the development and uptake of PM interventions with proven clinical benefit.

The pharmacogenomics of selective serotonin reuptake inhibitors

Antidepressant medications are frequently used as the first line of treatment for depression. However, their effectiveness is highly variable and influenced by genetic factors. Recently, pharmacogenetic studies, including candidate-gene, genome-wide association studies or polygenic risk scores, have attempted to uncover the genetic architecture of antidepressant response. Genetic variants in at least 27 genes are linked to antidepressant treatment response in both coding and non-coding genomic regions, but evidence is largely inconclusive due to the high polygenicity of the trait and limited cohort sizes in published studies. Future studies should increase the number and diversity of participants to yield sufficient statistical power to characterize the genetic underpinnings and biological mechanisms of treatment response, improve results generalizability and reduce racial health-related inequities.

The road to precision medicine: Eliminating the "One Size Fits All" approach in Alzheimer's disease

The expeditious advancement of Alzheimer's Disease (AD) is a threat to the global healthcare system, that is further supplemented by therapeutic failure. The prevalence of this disorder has been expected to quadrupole by 2050, thereby exerting a tremendous economic pressure on medical sector, worldwide. Thus, there is a dire need of a change in conventional approaches and adopt a novel methodology of disease prevention, treatment and diagnosis. Precision medicine offers a personalized approach to disease management, It is dependent upon genetic, environmental and lifestyle factors associated with the individual, aiding to develop tailored therapeutics. Precision Medicine Initiatives are launched, worldwide, to facilitate the integration of personalized models and clinical medicine. The review aims to provide a comprehensive understanding of the neuroinflammatory processes causing AD, giving a brief overview of the disease interventions. This is further followed by the role of precision medicine in AD, constituting the genetic perspectives, operation of personalized form of medicine and optimization of clinical trials with the 3 R's, showcasing an in-depth understanding of this novel approach in varying aspects of the healthcare industry, to provide an opportunity to the global AD researchers to elucidate suitable therapeutic regimens in clinically and pathologically complex diseases, like AD.

Patient prioritization of routine and patient-reported postoperative outcome measures: a prospective, nested cross-sectional study

PURPOSE

Understanding which outcomes matter most and improving outcomes for the growing population of older surgical patients are top priorities for Canadian anesthesia research. Nevertheless, there is little understanding of which outcomes older surgical patients prioritize most highly. We evaluated how older people prioritized six outcomes after elective noncardiac surgery. These outcomes were recommended in core outcome sets for perioperative medicine.

METHODS

Following ethical approval, we conducted a prospective, nested, cross-sectional study of people one year after they had major elective noncardiac surgery. Participants were asked to rate the importance of six commonly measured outcomes (complications, length of stay, discharge disposition, days at home, disability score, and developing a new disability) on an 11-point Likert scale. Open-ended questions elicited other preferences. Pairwise comparisons were evaluated using Bayesian multivariate regression. K-means clustering identified subgroups of patients based on overall prioritization. Thematic analysis was applied to open-ended responses.

RESULTS

One hundred and one consecutive participants responded. All outcomes scored at least 7.7/10 on average. Complications and discharge location were most highly rated, but only days at home and length of stay had substantial probability (> 99%) of being rated lower than the other four outcomes. Thematic analysis identified the need for greater procedure-specific information, support services, and physical recovery measures.

CONCLUSIONS

Commonly recorded and recommended outcomes are reassuringly relevant to older people; however, system-related measures are less highly valued than those more directly related to health and function. Outcomes may need to be personalized to properly evaluate the success of perioperative care.

Discrimination, trust, and withholding information from providers: Implications for missing data and inequity

Quality care requires collaborative communication, information exchange, and decision-making between patients and providers. Complete and accurate data about patients and from patients are especially important as high volumes of data are used to build clinical decision support tools and inform precision medicine initiatives. However, systematically missing data can bias these tools and threaten their effectiveness. Data completeness relies in many ways on patients being comfortable disclosing information to their providers without prohibitive concerns about security or privacy. Patients are likely to withhold information in the context of low trust relationships with providers, but it is unknown how experiences of discrimination in the healthcare system also relate to non-disclosure. In this study, we assess the relationship between withholding information from providers, experiences of discrimination, and multiple types of patient trust. Using a nationally representative sample of US adults (n = 2,029), weighted logistic regression modeling indicated a statistically significant relationship between experiences of discrimination and withholding information from providers (OR 3.7; CI [2.6-5.2], p < .001). Low trust in provider disclosure of conflicts of interest and low trust in providers' responsible use of health information were also positively associated with non-disclosure. We further analyzed the relationship between non-disclosure and the five most common types of discrimination (e.g., discrimination based on race, education/income, weight, gender, and age). We observed that all five types were statistically significantly associated with non-disclosure (p < .05). These results suggest that experiences of discrimination and specific types of low trust have a meaningful association with a patient's willingness to share information with their provider, with important implications for the quality of data available for medical decision-making and care. Because incomplete information can contribute to lower quality care, especially in the context of data-driven decision-making, patients experiencing discrimination may be further disadvantaged and harmed by systematic data missingness in their records.

Genomic medicine on the frontier of precision medicine

Genomic medicine has created a great deal of hope since the completion of the Human Genome Project (HGP). Genomic medicine promises disease prevention and early diagnosis in the context of precision medicine. Precision medicine as a scientific discipline has introduced as an evolution in medicine. The rapid growth of high-development technologies permits the assessment of biological systems. Study of the integrated profiles of omics, such as genome, transcriptome, proteome and other omics information lead to significant advances in personalized and precision medicine. In the context of precision medicine, pharmacogenomics can play an important role in order to discriminate responders and non-responders to medications and avoiding toxicity and achieving the optimum dose. So precision medicine in accordance with genomic medicine will transform medicine from conventional evidence-based medicine in the diagnosis and treatment towards precision based-medicine. In this review, we have summarized the related issues for genomic medicine and precision medicine.

From saliva to SNP: non-invasive, point-of-care genotyping for precision medicine applications using recombinase polymerase amplification and giant magnetoresistive nanosensors

Genetic testing is considered a cornerstone of the precision medicine paradigm. Genotyping of single nucleotide polymorphisms (SNPs) has been shown to provide insights into several important issues, including therapy selection and drug responsiveness. However, a scarcity of widely deployable and cost-effective genotyping tools has limited the integration of precision medicine into routine clinical practice. The objective of our work was to develop a portable, cost-effective, and automated platform that performs SNP genotyping at the point-of-care (POC). Using recombinase polymerase amplification (RPA) and giant magnetoresistive (GMR) nanosensors, we present a highly automated and multiplexed point-of-care platform that utilizes direct saliva for the qualitative genotyping of four SNPs (rs4633, rs4680, rs4818, rs6269) along the catechol-O-methyltransferase gene (COMT), which is associated with the modulation of pain sensitivity and perioperative opioid use. Using this approach, we successfully amplify, detect, and genotype all four of the SNPs, demonstrating 100% accordance between the experimental results obtained using the automated RPA and GMR genotyping assay and the results obtained using a COMT PCR genotyping assay that was formerly validated using pyrosequencing. This automated, portable, and multiplexed RPA and GMR assay shows great promise as a solution for SNP genotyping at the POC and reinforces the broad applications of magnetic nanotechnology in biomedicine.

Perceived Utility of Genomic Sequencing: Qualitative Analysis and Synthesis of a Conceptual Model to Inform Patient-Centered Instrument Development

BACKGROUND AND OBJECTIVES

Successful clinical integration of genomic sequencing (GS) requires evidence of its utility. While GS potentially has benefits (utilities) or harms (disutilities) across multiple domains of life for both patients and their families, there is as yet no empirically informed conceptual model of these effects. Our objective was to develop an empirically informed conceptual model of perceived utility of GS that captures utilities and disutilities for patients and their families across diverse backgrounds.

METHODS

We took a patient-centered approach, in which we began with a review of existing literature followed by collection of primary interview data. We conducted semi-structured interviews to explore types of utility in a clinically and sociopolitically diverse sample of 60 adults from seven Clinical Sequencing Evidence-Generating Research (CSER) consortium projects. Interviewees had either personally received, or were parents of a child who had received, GS results. Qualitative data were analyzed using thematic analysis. Findings from interviews were integrated with existing literature on clinical and personal utility to form the basis of an initial conceptual model that was refined based on expert review and feedback.

RESULTS

Five key utility types that have been previously identified in qualitative literature held up as primary domains of utility and disutility in our diverse sample. Interview data were used to specify and organize subdomains of an initial conceptual model. After expert refinement, the five primary domains included in the final model are clinical, emotional, behavioral, cognitive, and social, and several subdomains are specified within each.

CONCLUSION

We present an empirically informed conceptual model of perceived utility of GS. This model can be used to guide development of instruments for patient-centered outcome measurement that capture the range of relevant utilities and disutilities and inform clinical implementation of GS.

Metaverse and Virtual Health Care in Ophthalmology: Opportunities and Challenges

ABSTRACT

The outbreak of the coronavirus disease 2019 has further increased the urgent need for digital transformation within the health care settings, with the use of artificial intelligence/deep learning, internet of things, telecommunication network/virtual platform, and blockchain. The recent advent of metaverse, an interconnected online universe, with the synergistic combination of augmented, virtual, and mixed reality described several years ago, presents a new era of immersive and real-time experiences to enhance human-to-human social interaction and connection. In health care and ophthalmology, the creation of virtual environment with three-dimensional (3D) space and avatar, could be particularly useful in patient-fronting platforms (eg, telemedicine platforms), operational uses (eg, meeting organization), digital education (eg, simulated medical and surgical education), diagnostics, and therapeutics. On the other hand, the implementation and adoption of these emerging virtual health care technologies will require multipronged approaches to ensure interoperability with real-world virtual clinical settings, user-friendliness of the technologies and clinical efficiencies while complying to the clinical, health economics, regulatory, and cybersecurity standards. To serve the urgent need, it is important for the eye community to continue to innovate, invent, adapt, and harness the unique abilities of virtual health care technology to provide better eye care worldwide.

Kidney organoids as a promising tool in nephrology

Kidney disease has become a global public health problem affecting over 750 million people worldwide and imposing a heavy economic burden on patients. The complex architecture of the human kidney makes it very difficult to study the pathophysiology of renal diseases in vitro and to develop effective therapeutic options for patients. Even though cell lines and animal models have enriched our understanding, they fail to recapitulate key aspects of human kidney development and renal disease at cellular and functional levels. Organoids can be derived from either pluripotent stem cells or adult stem cells by strictly regulating key signalling pathways. Today, these self-differentiated organoids represent a promising technology to further understand the human kidney, one of the most complex organs, in an unprecedented way. The newly established protocols improved by organ-on-chip and coculture with immune cells will push kidney organoids towards the next generation. Herein, we focus on recent achievements in the application of kidney organoids in disease modelling, nephrotoxic testing, precision medicine, biobanking, and regenerative therapy, followed by discussions of novel strategies to improve their utility for biomedical research. The applications we discuss may help to provide new ideas in clinical fields.

Expanding Personalized, Data-Driven Dermatology: Leveraging Digital Health Technology and Machine Learning to Improve Patient Outcomes

The current revolution of digital health technology and machine learning offers enormous potential to improve patient care. Nevertheless, it is essential to recognize that dermatology requires an approach different from those of other specialties. For many dermatological conditions, there is a lack of standardized methodology for quantitatively tracking disease progression and treatment response (clinimetrics). Furthermore, dermatological diseases impact patients in complex ways, some of which can be measured only through patient reports (psychometrics). New tools using digital health technology (e.g., smartphone applications, wearable devices) can aid in capturing both clinimetric and psychometric variables over time. With these data, machine learning can inform efforts to improve health care by, for example, the identification of high-risk patient groups, optimization of treatment strategies, and prediction of disease outcomes. We use the term personalized, data-driven dermatology to refer to the use of comprehensive data to inform individual patient care and improve patient outcomes. In this paper, we provide a framework that includes data from multiple sources, leverages digital health technology, and uses machine learning. Although this framework is applicable broadly to dermatological conditions, we use the example of a serious inflammatory skin condition, chronic cutaneous graft-versus-host disease, to illustrate personalized, data-driven dermatology.

Precision Medicine Testing and Disparities in Health Care for Individuals With Non-Small Cell Lung Cancer: A Narrative Review

PROBLEM IDENTIFICATION

Precision medicine initiatives provide opportunities for optimal targeted therapy in individuals with non-small cell lung cancer. However, there are barriers to these initiatives that reflect social determinants of health.

LITERATURE SEARCH

MEDLINE®, CINAHL®, PsycINFO®, Embase®, and Google ScholarTM databases were searched for articles published in English in the United States from 2016 to 2020.

DATA EVALUATION

Data that were collected included individual demographic information, specific diagnosis, status of targeted genomic testing, and receipt of targeted therapy. All studies were retrospective and involved database review of insurance claims or medical records.

SYNTHESIS

Individuals with non-small cell lung cancer received less genetic testing and targeted therapy if they were of a lower socioeconomic status, had public health insurance or no health insurance, were Black, or lived in rural communities.

IMPLICATIONS FOR NURSING

Social determinants of health affect health equity, including in precision medicine initiatives for individuals with lung cancer. Gaining an understanding of this impact is the first step in mitigating inequities.

Assessing the cost-effectiveness of precision medicine: protocol for a systematic review and meta-analysis

INTRODUCTION

Precision medicine (PM) involves gene testing to identify disease risk, enable early diagnosis or guide therapeutic choice, and targeted gene therapy. We aim to perform a systematic review and meta-analysis to quantify the cost-effectiveness profile of PM stratified by intervention type, identify sources of heterogeneity in the value-for-money of PM.

METHODS AND ANALYSIS

We will perform a systematic search in Embase, MEDLINE, EconLit and CRD databases for studies published in English language or with translation in English between 1 January 2011 and 8 July 2021 on the topic of cost-effectiveness analysis of PM interventions. The focus will be on studies that reported health and economic outcomes. Study quality will be assessed using the Biases in Economic Studies checklist. The incremental net benefit of PM screening, diagnostic, treatment-targeting and therapeutic interventions over conventional strategies will be respectively pooled across studies using a random-effect model if heterogeneity is present, otherwise a fixed-effect model. Subgroup analyses will be performed based on disease area, WHO region and World Bank country-income level. Additionally, we will identify the potential sources of heterogeneity with random-effect meta-regressions. Finally, biases will be detected using jackknife sensitivity analysis, funnel plot assessment and Egger's tests.

ETHICS AND DISSEMINATION

For this type of study ethics approval or formal consent is not required. The results will be disseminated at various presentations and feedback sessions, in conference abstracts and manuscripts that will be submitted to peer-reviewed journals.

PROSPERO REGISTRATION NUMBER

CRD42021272956.

Utilizing Artificial Intelligence to Enhance Health Equity Among Patients with Heart Failure

Patients with heart failure (HF) are heterogeneous with various intrapersonal and interpersonal characteristics contributing to clinical outcomes. Bias, structural racism, and social determinants of health have been implicated in unequal treatment of patients with HF. Through several methodologies, artificial intelligence (AI) can provide models in HF prediction, prognostication, and provision of care, which may help prevent unequal outcomes. This review highlights AI as a strategy to address racial inequalities in HF; discusses key AI definitions within a health equity context; describes the current uses of AI in HF, strengths and harms in using AI; and offers recommendations for future directions.

Application of machine learning methods in clinical trials for precision medicine

OBJECTIVE

A key component for precision medicine is a good prediction algorithm for patients' response to treatments. We aim to implement machine learning (ML) algorithms into the response-adaptive randomization (RAR) design and improve the treatment outcomes.

MATERIALS AND METHODS

We incorporated 9 ML algorithms to model the relationship of patient responses and biomarkers in clinical trial design. Such a model predicted the response rate of each treatment for each new patient and provide guidance for treatment assignment. Realizing that no single method may fit all trials well, we also built an ensemble of these 9 methods. We evaluated their performance through quantifying the benefits for trial participants, such as the overall response rate and the percentage of patients who receive their optimal treatments.

RESULTS

Simulation studies showed that the adoption of ML methods resulted in more personalized optimal treatment assignments and higher overall response rates among trial participants. Compared with each individual ML method, the ensemble approach achieved the highest response rate and assigned the largest percentage of patients to their optimal treatments. For the real-world study, we successfully showed the potential improvements if the proposed design had been implemented in the study.

CONCLUSION

In summary, the ML-based RAR design is a promising approach for assigning more patients to their personalized effective treatments, which makes the clinical trial more ethical and appealing. These features are especially desirable for late-stage cancer patients who have failed all the Food and Drug Administration (FDA)-approved treatment options and only can get new treatments through clinical trials.

A new horizon of precision medicine: combination of the microbiome and extracellular vesicles

Over several decades, the disease pattern of intractable disease has changed from acute infection to chronic disease accompanied by immune and metabolic dysfunction. In addition, scientific evidence has shown that humans are holobionts; of the DNA in humans, 1% is derived from the human genome, and 99% is derived from microbial genomes (the microbiome). Extracellular vesicles (EVs) are lipid bilayer-delimited nanoparticles and key messengers in cell-to-cell communication. Many publications indicate that microbial EVs are both positively and negatively involved in the pathogenesis of various intractable diseases, including inflammatory diseases, metabolic disorders, and cancers. Microbial EVs in feces, blood, and urine show significant differences in their profiles between patients with a particular disease and healthy subjects, demonstrating the potential of microbial EVs as biomarkers for disease diagnosis, especially for assessing disease risk. Furthermore, microbial EV therapy offers a variety of advantages over live biotherapeutics and human cell EV (or exosome) therapy for the treatment of intractable diseases. In summary, microbial EVs are a new tool in medicine, and microbial EV technology might provide us with innovative diagnostic and therapeutic solutions in precision medicine.

The tiny membrane-bound vesicles containing various biomolecules that the organisms comprising our microbiome release could offer a powerful tool for precision medicine. Our bodies are home to trillions of microbes, which interact closely with our tissues to maintain a healthy physiological environment. Yoon-Keun Kim of the Institute of MD Healthcare, Seoul, South Korea, and colleagues have reviewed current research into the extracellular vesicles that these microbes use to communicate with other microbes and their human hosts. The authors note that these vesicles affect tissues throughout the body, and their activities have been linked to various disorders including asthma, Crohn’s disease and cancer. A deeper understanding of how these vesicles prevent or accelerate various conditions in different individuals could yield useful new diagnostic biomarkers and provide the foundation for interventions that are optimized for each patient.

Transformed Health Ecosystems—Challenges for Security, Privacy, and Trust

A transformed health ecosystem is a multi-stakeholder coalition that collects, stores, and shares personal health information (PHI) for different purposes, such as for personalized care, prevention, health prediction, precise medicine, personal health management, and public health purposes. Those services are data driven, and a lot of PHI is needed not only from received care and treatments, but also from a person’s normal life. Collecting, processing, storing, and sharing of the huge amount of sensitive PHI in the ecosystem cause many security, privacy, and trust challenges to be solved. The authors have studied those challenges from different perspectives using existing literature and found that current security and privacy solutions are insufficient, and for the user it is difficult to know whom to trust, and how much. Furthermore, in today’s widely used privacy approaches, such as privacy as choice or control and belief or perception based trust does not work in digital health ecosystems. The authors state that it is necessary to redefine the way privacy and trust are understood in health, to develop new legislation to support new privacy and approaches, and to force the stakeholders of the health ecosystem to make their privacy and trust practices and features of their information systems available. The authors have also studied some candidate solutions for security, privacy, and trust to be used in future health ecosystems.

Transformation of Health and Social Care Systems-An Interdisciplinary Approach Toward a Foundational Architecture

Objective

For realizing pervasive and ubiquitous health and social care services in a safe and high quality as well as efficient and effective way, health and social care systems have to meet new organizational, methodological, and technological paradigms. The resulting ecosystems are highly complex, highly distributed, and highly dynamic, following inter-organizational and even international approaches. Even though based on international, but domain-specific models and standards, achieving interoperability between such systems integrating multiple domains managed by multiple disciplines and their individually skilled actors is cumbersome.

Methods

Using the abstract presentation of any system by the universal type theory as well as universal logics and combining the resulting Barendregt Cube with parameters and the engineering approach of cognitive theories, systems theory, and good modeling best practices, this study argues for a generic reference architecture model moderating between the different perspectives and disciplines involved provide on that system. To represent architectural elements consistently, an aligned system of ontologies is used.

Results

The system-oriented, architecture-centric, and ontology-based generic reference model allows for re-engineering the existing and emerging knowledge representations, models, and standards, also considering the real-world business processes and the related development process of supporting IT systems for the sake of comprehensive systems integration and interoperability. The solution enables the analysis, design, and implementation of dynamic, interoperable multi-domain systems without requesting continuous revision of existing specifications.

Multi-omics strategies for personalized and predictive medicine: past, current, and future translational opportunities

Precision medicine is driven by the paradigm shift of empowering clinicians to predict the most appropriate course of action for patients with complex diseases and improve routine medical and public health practice. It promotes integrating collective and individualized clinical data with patient specific multi-omics data to develop therapeutic strategies, and knowledgebase for predictive and personalized medicine in diverse populations. This study is based on the hypothesis that understanding patient's metabolomics and genetic make-up in conjunction with clinical data will significantly lead to determining predisposition, diagnostic, prognostic and predictive biomarkers and optimal paths providing personalized care for diverse and targeted chronic, acute, and infectious diseases. This study briefs emerging significant, and recently reported multi-omics and translational approaches aimed to facilitate implementation of precision medicine. Furthermore, it discusses current grand challenges, and the future need of Findable, Accessible, Intelligent, and Reproducible (FAIR) approach to accelerate diagnostic and preventive care delivery strategies beyond traditional symptom-driven, disease-causal medical practice.

Precision Medicine in Lung Cancer Theranostics: Paving the Way from Traditional Technology to Advance Era

Precision medicine for lung cancer theranostics is an advanced model combining prevention, diagnosis, and treatment for individual or specific population diseases to match individual patient differences. It involves collection and integration of genome, transcriptome, proteome, and metabolome features of lung cancer patients, combined with clinical characteristics. Subsequently, large data and artificial intelligence (AI) analysis have emerged to identify the most suitable therapeutic targets and personal treatment strategies for treatment of patients with lung cancer. We review the development and challenges associated with diagnosis and therapy of lung cancer from traditional technology, including immunotherapy prediction markers, liquid biopsy, surgery, and tumor immune microenvironment and patient-derived xenograft models, to AI in the era of precision medicine. AI has improved precision medicine and the predictive ability and accuracy of patient outcomes. Finally, we discuss some opportunities and challenges for lung cancer theranostics. Precision medicine in lung cancer can help us find the optimum treatment dose and time for a specific patient, which can advance the development of lung cancer therapeutics.

Precision medicine in secondary prevention of ischemic stroke: how may blood-based biomarkers help in clinical routine? An expert opinion

PURPOSE OF REVIEW

One in eight patients unfortunately suffers a new stroke within 5 years of their first stroke, even today. Research in precision medicine could lead to a more individualized treatment allocation, possibly achieving lower recurrence rates of ischemic stroke. In this narrative review, we aim to discuss potential clinical implementation of several promising candidate blood biomarkers.

RECENT FINDINGS

We discuss specifically some promising blood-based biomarkers, which may improve the identification of underlying causes as well as risk stratification of patients according to their specific cerebrovascular risk factor pattern.

SUMMARY

Multimodal profiling of ischemic stroke patients by means of blood biomarkers, in addition to established clinical and neuroradiological data, may allow in the future a refinement of decision algorithms for treatment allocation in secondary ischemic stroke prevention.

Surveying the Genomic Landscape Supporting the Development of Precision Military Aerospace Medicine

INTRODUCTION: Precision medicine is an approach to healthcare that is modifying clinical management by leveraging technological advances in genomics that assess a patient's genetic information to identify unique predispositions. While the civilian sector is integrating genomics widely to personalize diagnosis and treatment, the military medical environment has reacted more slowly. The operational requirements of military service encourage a tailored approach for focusing military precision medicine on occupation-specific conditions. Here, we present a survey of the genomic landscape related to military aerospace medicine.METHODS: We collated observations from genome-wide association studies (GWAS) relating genetic markers to conditions that may negatively influence flight operations and for which the U.S. Air Force School of Aerospace Medicine's Aeromedical Consult Service (ACS) provides aeromedical waiver guidance. Our sources for identifying relevant literature were the GWAS Catalog, the Atlas of GWAS Summary Statistics, and PubMed/Google Scholar searches.RESULTS: Using the ACS guidance as a starting point, we found 1572 papers describing 84 clinical conditions with genetic associations. The earliest aeromedical GWAS publication was in 2006, increasing to 225 publications in 2019. We identified 42,020 polymorphisms from more than 84 million participants across the studies.CONCLUSION: Our study revealed areas where deeper investigations into how genetic markers manifest in clinical diagnosis, prevention, or risk management could lead to increased medical readiness. Additionally, our results show those clinical areas for which guidance could include genetic risk considerations.Chapleau RR, Regn DD, de Castro MJ. Surveying the genomic landscape supporting the development of precision military aerospace medicine. Aerosp Med Hum Perform. 2022; 93(2):89-93.

Applications and Biocompatibility of Mesoporous Silica Nanocarriers in the Field of Medicine

Mesoporous silica nanocarrier (MSN) preparations have a wide range of medical applications. Studying the biocompatibility of MSN is an important part of clinical transformation. Scientists have developed different types of mesoporous silica nanocarriers (MSNs) for different applications to realize the great potential of MSNs in the field of biomedicine, especially in tumor treatment. MSNs have achieved good results in diagnostic bioimaging, tissue engineering, cancer treatment, vaccine development, biomaterial application and diagnostics. MSNs can improve the therapeutic efficiency of drugs, introduce new drug delivery strategies, and provide advantages that traditional drugs lack. It is necessary not only to innovate MSNs but also to comprehensively understand their biological distribution. In this review, we summarize the various medical uses of MSN preparations and explore the factors that affect their distribution and biocompatibility in the body based on metabolism. Designing more reasonable therapeutic nanomedicine is an important task for the further development of the potential clinical applications of MSNs.

Data Integration Challenges for Machine Learning in Precision Medicine

A main goal of Precision Medicine is that of incorporating and integrating the vast corpora on different databases about the molecular and environmental origins of disease, into analytic frameworks, allowing the development of individualized, context-dependent diagnostics, and therapeutic approaches. In this regard, artificial intelligence and machine learning approaches can be used to build analytical models of complex disease aimed at prediction of personalized health conditions and outcomes. Such models must handle the wide heterogeneity of individuals in both their genetic predisposition and their social and environmental determinants. Computational approaches to medicine need to be able to efficiently manage, visualize and integrate, large datasets combining structure, and unstructured formats. This needs to be done while constrained by different levels of confidentiality, ideally doing so within a unified analytical architecture. Efficient data integration and management is key to the successful application of computational intelligence approaches to medicine. A number of challenges arise in the design of successful designs to medical data analytics under currently demanding conditions of performance in personalized medicine, while also subject to time, computational power, and bioethical constraints. Here, we will review some of these constraints and discuss possible avenues to overcome current challenges.

A Ductal-Cell-Related Risk Model Integrating Single-Cell and Bulk Sequencing Data Predicts the Prognosis of Patients With Pancreatic Adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is a highly heterogeneous malignancy. Single-cell sequencing (scRNA-seq) technology enables quantitative gene expression measurements that underlie the phenotypic diversity of cells within a tumor. By integrating PDAC scRNA-seq and bulk sequencing data, we aim to extract relevant biological insights into the ductal cell features that lead to different prognoses. Firstly, differentially expressed genes (DEGs) of ductal cells between normal and tumor tissues were identified through scRNA-seq data analysis. The effect of DEGs on PDAC survival was then assessed in the bulk sequencing data. Based on these DEGs (LY6D, EPS8, DDIT4, TNFSF10, RBP4, NPY1R, MYADM, SLC12A2, SPCS3, NBPF15) affecting PDAC survival, a risk score model was developed to classify patients into high-risk and low-risk groups. The results showed that the overall survival was significantly longer in the low-risk group (p < 0.05). The model also revealed reliable predictive power in different subgroups of patients. The high-risk group had a higher tumor mutational burden (TMB) (p < 0.05), with significantly higher mutation frequencies in KRAS and ADAMTS12 (p < 0.05). Meanwhile, the high-risk group had a higher tumor stemness score (p < 0.05). However, there was no significant difference in the immune cell infiltration scores between the two groups. Lastly, drug candidates targeting risk model genes were identified, and seven compounds might act against PDAC through different mechanisms. In conclusion, we have developed a validated survival assessment model, which acted as an independent risk factor for PDAC.

Role of Precision Oncology in Type II Endometrial and Prostate Cancers in the African Population: Global Cancer Genomics Disparities

Precision oncology can be defined as molecular profiling of tumors to identify targetable alterations. Emerging research reports the high mortality rates associated with type II endometrial cancer in black women and with prostate cancer in men of African ancestry. The lack of adequate genetic reference information from the African genome is one of the major obstacles in exploring the benefits of precision oncology in the African context. Whilst external factors such as the geography, environment, health-care access and socio-economic status may contribute greatly towards the disparities observed in type II endometrial and prostate cancers in black populations compared to Caucasians, the contribution of African ancestry to the contribution of genetics to the etiology of these cancers cannot be ignored. Non-coding RNAs (ncRNAs) continue to emerge as important regulators of gene expression and the key molecular pathways involved in tumorigenesis. Particular attention is focused on activated/repressed genes and associated pathways, while the redundant pathways (pathways that have the same outcome or activate the same downstream effectors) are often ignored. However, comprehensive evidence to understand the relationship between type II endometrial cancer, prostate cancer and African ancestry remains poorly understood. The sub-Saharan African (SSA) region has both the highest incidence and mortality of both type II endometrial and prostate cancers. Understanding how the entire transcriptomic landscape of these two reproductive cancers is regulated by ncRNAs in an African cohort may help elucidate the relationship between race and pathological disparities of these two diseases. This review focuses on global disparities in medicine, PCa and ECa. The role of precision oncology in PCa and ECa in the African population will also be discussed.