Addressing health disparities in cancer with genomics

Nursing strategies to address health disparities in genomics-informed care: a scoping review

OBJECTIVE

The objective of this review was to map the available global evidence on strategies that nurses can use to facilitate genomics-informed health care to address health disparities to inform the development of a research and action agenda.

INTRODUCTION

The integration of genomics into health care is improving patient outcomes through better prevention, diagnostics, and treatment; however, scholars have noted concerns with widening health disparities. Nurses work across the health system and can address health disparities from a clinical, research, education, policy, and leadership perspective. To do this, a comprehensive understanding of existing genomics-informed strategies is required.

INCLUSION CRITERIA

Published (qualitative, quantitative, mixed methods studies, systematic and literature reviews and text and opinion papers) and unpublished (gray) literature that focuses on genomics-informed nursing strategies to address health disparities over the last 10 years were included. No limitations were placed on language.

METHODS

The review was conducted in accordance with the JBI methodology for scoping reviews. A search was undertaken on May 25, 2023, across 5 databases: MEDLINE (Ovid), Embase, Cochrane Library (Ovid), APA PsycINFO (EBSCOhost), and CINAHL (EBSCOhost). Gray literature was searched through websites, including the International Society of Nurses in Genetics and the Global Genomics Nursing Alliance. Abstracts, titles, and full texts were screened by 2 or more independent reviewers. Data were extracted using a data extraction tool. The coded data were analyzed by 2 or more independent reviewers using conventional content analysis and the summarized results are presented using descriptive statistics and evidence tables.

RESULTS

In total, we screened 818 records and 31 were included in the review. The majority of papers were published in either 2019 (n=5, 16%), 2020 (n=5, 16%), or 2021 (n=5, 16%). Most papers came from the United States (n=25, 81%) followed by the Netherlands (n=3, 10%), United Kingdom (n=1, 3%), Tanzania (n=1, 3%) and written from a global perspective (n=1, 3%). Nearly half the papers discussed cancer-related conditions (n=14, 45%) and most of the others did not specify a disease or condition (n=12, 30%). In terms of population, nurse clinicians were mentioned the most frequently (n=16, 52%) followed by nurse researchers, scholars, or scientists (n=8, 26%). The patient population varied, with African American patients or communities (n=7, 23%) and racial or ethnic minorities (n=6, 19%) discussed most frequently. The majority of equity issues focused on inequitable access to genetic and genomics health services amongst ethnic and racial groups (n=14, 45%), individuals with lower educational attainment or health literacy (n=6, 19%), individuals with lower socioeconomic status (n=3, 10%), migrants (n=3, 10%), individuals with lack of insurance coverage (n=2, 6%), individuals living in rural or remote areas (n=1, 3%) individuals of older age (n=1, 3%). Root causes contributing to health disparity issues varied at the patient, provider, and system levels. Strategies were grouped into 2 categories: those to prepare the nursing workforce and those nurses can implement in practice. We further categorized the strategies by domains of practice, including clinical practice, education, research, policy advocacy, and leadership. Papers that mentioned strategies focused on preparing the nursing workforce were largely related to the education domain (n=16, 52%), while papers that mentioned strategies that nurses can implement were mostly related to clinical practice (n=19, 61%).

CONCLUSIONS

Nurses in all domains of practice can draw on the identified strategies to address health disparities related to genomics in health care. We found a notable lack of intervention and evaluation studies exploring the impact on health and equity outcomes. Additional research informed by implementation science and that measures health outcomes is needed to identify best practices.

SUPPLEMENTAL DIGITAL CONTENT

A French-language version of the abstract of this review is available as Supplemental Digital Content [ http://links.lww.com/SRX/A65 ].

Long-read sequencing for brain tumors

Brain tumors and genomics have a long-standing history given that glioblastoma was the first cancer studied by the cancer genome atlas. The numerous and continuous advances through the decades in sequencing technologies have aided in the advanced molecular characterization of brain tumors for diagnosis, prognosis, and treatment. Since the implementation of molecular biomarkers by the WHO CNS in 2016, the genomics of brain tumors has been integrated into diagnostic criteria. Long-read sequencing, also known as third generation sequencing, is an emerging technique that allows for the sequencing of longer DNA segments leading to improved detection of structural variants and epigenetics. These capabilities are opening a way for better characterization of brain tumors. Here, we present a comprehensive summary of the state of the art of third-generation sequencing in the application for brain tumor diagnosis, prognosis, and treatment. We discuss the advantages and potential new implementations of long-read sequencing into clinical paradigms for neuro-oncology patients.

Aspiring toward equitable benefits from genomic advances to individuals of ancestrally diverse backgrounds

Advancements in genomic technologies have shown remarkable promise for improving health trajectories. The Human Genome Project has catalyzed the integration of genomic tools into clinical practice, such as disease risk assessment, prenatal testing and reproductive genomics, cancer diagnostics and prognostication, and therapeutic decision making. Despite the promise of genomic technologies, their full potential remains untapped without including individuals of diverse ancestries and integrating social determinants of health (SDOHs). The NHGRI launched the 2020 Strategic Vision with ten bold predictions by 2030, including "individuals from ancestrally diverse backgrounds will benefit equitably from advances in human genomics." Meeting this goal requires a holistic approach that brings together genomic advancements with careful consideration to healthcare access as well as SDOHs to ensure that translation of genetics research is inclusive, affordable, and accessible and ultimately narrows rather than widens health disparities. With this prediction in mind, this review delves into the two paramount applications of genetic testing-reproductive genomics and precision oncology. When discussing these applications of genomic advancements, we evaluate current accessibility limitations, highlight challenges in achieving representativeness, and propose paths forward to realize the ultimate goal of their equitable applications.

Identification of germline population variants misclassified as cancer-associated somatic variants

Introduction

Databases used for clinical interpretation in oncology rely on genetic data derived primarily from patients of European ancestry, leading to biases in cancer genetics research and clinical practice. One practical issue that arises in this context is the potential misclassification of multi-ancestral population variants as tumor-associated because they are not represented in reference genomes against which tumor sequencing data is aligned.

Methods

To systematically find misclassified variants, we compared somatic variants in census genes from the Catalogue of Somatic Mutations in Cancer (COSMIC) V99 with multi-ancestral population variants from the Genome Aggregation Databases' Linkage Disequilibrium (GnomAD). By comparing genomic coordinates, reference, and alternate alleles, we could identify misclassified variants in genes associated with cancer.

Results

We found 192 of 208 genes in COSMIC's cancer-associated census genes (92.31%) to be associated with variant misclassifications. Among the 1,906,732 variants in COSMIC, 6,957 variants (0.36%) aligned with normal population variants in GnomAD, concerning for misclassification. The African / African American ancestral population included the greatest number of misclassified variants and also had the greatest number of unique misclassified variants.

Conclusion

The direct, systematic comparison of variants from COSMIC for co-occurrence in GnomAD supports a more accurate interpretation of tumor sequencing data and reduces bias related to genomic ancestry.

Genomics-informed nursing strategies and health equity: A scoping review protocol

OBJECTIVE

The objective of this scoping review is to map the available evidence on strategies that nurses can use to facilitate genomics-informed healthcare to address health disparities.

INTRODUCTION

Advancements in genomics over the last two decades have led to an increase in the delivery of genomics-informed health care. Although the integration of genomics into health care services continues to enhance patient outcomes, access to genomic technologies is not equitable, exacerbating existing health disparities amongst certain populations. As the largest portion of the health workforce, nurses play a critical role in the delivery of equitable genomics-informed care. However, little is known about how nurses can help address health disparities within the context of genomics-informed health care. A review of the literature will provide the necessary foundation to identify promising practices, policy, and knowledge gaps for further areas of inquiry.

INCLUSION CRITERIA

We will include papers that explore strategies that nurses can undertake to facilitate genomics-informed care to address health disparities.

METHODS

This review will be conducted using JBI methodology for scoping reviews. We will search electronic databases including MEDLINE (OVID), EMBASE, Cochrane Library, PsychInfo, and CINAHL for quantitative and qualitative studies, systematic reviews and grey literature. Theses, books, and unavailable full-text papers will be excluded. The search will be limited to papers from 2013 and beyond. Two reviewers will screen titles and abstracts followed by full-text and disagreements will be resolved by a third reviewer. We will use a data extraction tool using Microsoft Excel and analyse data using descriptive statistics and conventional content analysis. Findings will be presented in the form of evidence tables and a narrative summary. We will report findings using the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR).

DISCUSSION

Genomics will continue to transform all aspects of health care across the wellness continuum from prevention, assessment, diagnosis, management, treatment, and palliative care. The identification of nursing strategies to address health disparities will build the foundation for policy and practice to ensure that the integration of genomic technologies benefits everyone.

Reducing Time to Diagnosis of Rare Genetic Diseases in a Medically Underserved Hispanic Population- Lessons Learned for Meaningful Engagement

Background

The utilization of genomic information to improve health outcomes is progressively becoming more common in clinical practice. Nonetheless, disparities persist in accessing genetic services among ethnic minorities, individuals with low socioeconomic status, and other vulnerable populations. The Rio Grande Valley at the Texas-Mexico border is predominantly Hispanic with a high poverty rate and an increased prevalence of birth defects, with very limited access to genetics services. The cost of a diagnosis is often times out of reach for these underserved families. Funded by the National Center for Advancing Translational Sciences (NCATS), Project GIVE (Genetic Inclusion by Virtual Evaluation) was launched in 2022 to shorten the time to diagnosis and alleviate healthcare inequities in this region, with the goal of improving pediatric health outcomes.

Methods

Utilizing Consultagene, an innovative electronic health record (EHR) agnostic virtual telehealth and educational platform, we designed the study to recruit 100 children with rare diseases over a period of two years from this region, through peer-to-peer consultation and referral.

Conclusions

Project GIVE study has allowed advanced genetic evaluation and delivery of genome sequencing through the virtual portal, effectively circumventing the recognized socioeconomic and other barriers within this population. This paper explores the successful community engagement process and implementation of an alternate genomics evaluation platform and testing approach, aiming to reduce the diagnostic journey for individuals with rare diseases residing in a medically underserved region.

Molecular profiling of a real-world breast cancer cohort with genetically inferred ancestries reveals actionable tumor biology differences between European ancestry and African ancestry patient populations

BACKGROUND

Endocrine-resistant HR+/HER2- breast cancer (BC) and triple-negative BC (TNBC) are of interest for molecularly informed treatment due to their aggressive natures and limited treatment profiles. Patients of African Ancestry (AA) experience higher rates of TNBC and mortality than European Ancestry (EA) patients, despite lower overall BC incidence. Here, we compare the molecular landscapes of AA and EA patients with HR+/HER2- BC and TNBC in a real-world cohort to promote equity in precision oncology by illuminating the heterogeneity of potentially druggable genomic and transcriptomic pathways.

METHODS

De-identified records from patients with TNBC or HR+/HER2- BC in the Tempus Database were randomly selected (N = 5000), with most having stage IV disease. Mutations, gene expression, and transcriptional signatures were evaluated from next-generation sequencing data. Genetic ancestry was estimated from DNA-seq. Differences in mutational prevalence, gene expression, and transcriptional signatures between AA and EA were compared. EA patients were used as the reference population for log fold-changes (logFC) in expression.

RESULTS

After applying inclusion criteria, 3433 samples were evaluated (n = 623 AA and n = 2810 EA). Observed patterns of dysregulated pathways demonstrated significant heterogeneity among the two groups. Notably, PIK3CA mutations were significantly lower in AA HR+/HER2- tumors (AA = 34% vs. EA = 42%, P < 0.05) and the overall cohort (AA = 28% vs. EA = 37%, P = 2.08e-05). Conversely, KMT2C mutation was significantly more frequent in AA than EA TNBC (23% vs. 12%, P < 0.05) and HR+/HER2- (24% vs. 15%, P = 3e-03) tumors. Across all subtypes and stages, over 8000 genes were differentially expressed between the two ancestral groups including RPL10 (logFC = 2.26, P = 1.70e-162), HSPA1A (logFC = - 2.73, P = 2.43e-49), ATRX (logFC = - 1.93, P = 5.89e-83), and NUTM2F (logFC = 2.28, P = 3.22e-196). Ten differentially expressed gene sets were identified among stage IV HR+/HER2- tumors, of which four were considered relevant to BC treatment and were significantly enriched in EA: ERBB2_UP.V1_UP (P = 3.95e-06), LTE2_UP.V1_UP (P = 2.90e-05), HALLMARK_FATTY_ACID_METABOLISM (P = 0.0073), and HALLMARK_ANDROGEN_RESPONSE (P = 0.0074).

CONCLUSIONS

We observed significant differences in mutational spectra, gene expression, and relevant transcriptional signatures between patients with genetically determined African and European ancestries, particularly within the HR+/HER2- BC and TNBC subtypes. These findings could guide future development of treatment strategies by providing opportunities for biomarker-informed research and, ultimately, clinical decisions for precision oncology care in diverse populations.

Human-Centered Design to Address Biases in Artificial Intelligence

The potential of artificial intelligence (AI) to reduce health care disparities and inequities is recognized, but it can also exacerbate these issues if not implemented in an equitable manner. This perspective identifies potential biases in each stage of the AI life cycle, including data collection, annotation, machine learning model development, evaluation, deployment, operationalization, monitoring, and feedback integration. To mitigate these biases, we suggest involving a diverse group of stakeholders, using human-centered AI principles. Human-centered AI can help ensure that AI systems are designed and used in a way that benefits patients and society, which can reduce health disparities and inequities. By recognizing and addressing biases at each stage of the AI life cycle, AI can achieve its potential in health care.

Urban-Rural and Socioeconomic Differences in Patient Knowledge and Perceptions of Genomic Tumor Testing

PURPOSE

Social determinants of health, such as rurality, income, and education, may widen health disparities by driving variation in patients' knowledge and perceptions of medical interventions. This effect may be greatest for medical technologies that are hard to understand and less accessible. This study explored whether knowledge and perceptions (expectations and attitudes) of patients with cancer toward large-panel genomic tumor testing (GTT), an emerging cancer technology, vary by patient rurality independent of other socioeconomic characteristics (education and income).

METHODS

Patients with cancer enrolled in a large precision oncology initiative completed surveys measuring rurality, sociodemographic characteristics, and knowledge and perceptions of GTT. We used multivariable linear models to examine differences in GTT knowledge, expectations, and attitudes by patient rurality, education, and income level. Models controlled for age, sex and clinical cancer stage and type.

RESULTS

Rural patients had significantly lower knowledge of GTT than urban patients using bivariate models (P = .025). However, this association disappeared when adjusting for education and income level: patients with lower educational attainment and lower income had lower knowledge and higher expectations (P ≤ .002), whereas patients with higher income had more positive attitudes (P = .005). Urban patients had higher expectations of GTT compared with patients living in large rural areas (P = .011). Rurality was not associated with attitudes.

CONCLUSION

Patients' education and income level are associated with knowledge, expectations, and attitudes toward GTT, whereas rurality is associated with patient expectations. These findings suggest that efforts to promote adoption of GTT should focus on improving knowledge and awareness among individuals with low education and income. These differences may lead to downstream disparities in GTT utilization, which should be explored in future research.

Role of Polygenic Risk Score in Cancer Precision Medicine of Non-European Populations: A Systematic Review

The development of new screening methods and diagnostic tests for traits, common diseases, and cancer is linked to the advent of precision genomic medicine, in which health care is individually adjusted based on a person’s lifestyle, environmental influences, and genetic variants. Based on genome-wide association study (GWAS) analysis, rapid and continuing progress in the discovery of relevant single nucleotide polymorphisms (SNPs) for traits or complex diseases has increased interest in the potential application of genetic risk models for routine health practice. The polygenic risk score (PRS) estimates an individual’s genetic risk of a trait or disease, calculated by employing a weighted sum of allele counts combined with non-genetic variables. However, 98.38% of PRS records held in public databases relate to the European population. Therefore, PRSs for multiethnic populations are urgently needed. We performed a systematic review to discuss the role of polygenic risk scores in advancing precision medicine for different cancer types in multiethnic non-European populations.

Health equity in the implementation of genomics and precision medicine: A public health imperative

Recent reviews have emphasized the need for a health equity agenda in genomics research. To ensure that genomic discoveries can lead to improved health outcomes for all segments of the population, a health equity agenda needs to go beyond research studies. Advances in genomics and precision medicine have led to an increasing number of evidence-based applications that can reduce morbidity and mortality for millions of people (tier 1). Studies have shown lower implementation rates for selected diseases with tier 1 applications (familial hypercholesterolemia, Lynch syndrome, hereditary breast and ovarian cancer) among racial and ethnic minority groups, rural communities, uninsured or underinsured people, and those with lower education and income. We make the case that a public health agenda is needed to address disparities in implementation of genomics and precision medicine. Public health actions can be centered on population-specific needs and outcomes assessment, policy and evidence development, and assurance of delivery of effective and ethical interventions. Crucial public health activities also include engaging communities, building coalitions, improving genetic health literacy, and building a diverse workforce. Without concerted public health action, further advances in genomics with potentially broad applications could lead to further widening of health disparities in the next decade.

Harnessing big data to characterize immune-related adverse events

Immune-checkpoint inhibitors (ICIs) have transformed patient care in oncology but are associated with a unique spectrum of organ-specific inflammatory toxicities known as immune-related adverse events (irAEs). Given the expanding use of ICIs, an increasing number of patients with cancer experience irAEs, including severe irAEs. Proper diagnosis and management of irAEs are important to optimize the quality of life and long-term outcomes of patients receiving ICIs; however, owing to the substantial heterogeneity within irAEs, and despite multicentre initiatives, performing clinical studies of these toxicities with a sufficient cohort size is challenging. Pioneering studies from the past few years have demonstrated that aggregate clinical data, real-world data (such as data on pharmacovigilance or from electronic health records) and multi-omics data are alternative tools well suited to investigating the underlying mechanisms and clinical presentations of irAEs. In this Perspective, we summarize the advantages and shortcomings of different sources of 'big data' for the study of irAEs and highlight progress made using such data to identify biomarkers of irAE risk, evaluate associations between irAEs and therapeutic efficacy, and characterize the effects of demographic and anthropometric factors on irAE risk. Harnessing big data will accelerate research on irAEs and provide key insights that will improve the clinical management of patients receiving ICIs.