Clinical, technical, and environmental biases influencing equitable access to clinical genetics/genomics testing: A points to consider statement of the American College of Medical Genetics and Genomics (ACMG)

Genomic testing in neonates

Recent technological advances have led to the expansion of testing options for newborns with suspected rare genetic conditions, particularly in high-income healthcare settings. This article summarises the key genomic testing approaches, their indications and potential limitations.

Atypical Diabetes: What Have We Learned and What Does the Future Hold?

As our understanding of the pathophysiology of diabetes evolves, we increasingly recognize that many patients may have a form of diabetes that does not neatly fit with a diagnosis of either type 1 or type 2 diabetes. The discovery and description of these forms of "atypical diabetes" have led to major contributions to our collective understanding of the basic biology that drives insulin secretion, insulin resistance, and islet autoimmunity. These discoveries now pave the way to a better classification of diabetes based on distinct endotypes. In this review, we highlight the key biological and clinical insights that can be gained from studying known forms of atypical diabetes. Additionally, we provide a framework for identification of patients with atypical diabetes based on their clinical, metabolic, and molecular features. Helpful clinical and genetic resources for evaluating patients suspected of having atypical diabetes are provided. Therefore, appreciating the various endotypes associated with atypical diabetes will enhance diagnostic accuracy and facilitate targeted treatment decisions.

Defining and Reducing Variant Classification Disparities

Background

Multiplexed Assays of Variant Effects (MAVEs) can test all possible single variants in a gene of interest. The resulting saturation-style data may help resolve variant classification disparities between populations, especially for variants of uncertain significance (VUS).

Methods

We analyzed clinical significance classifications in 213,663 individuals of European-like genetic ancestry versus 206,975 individuals of non-European-like genetic ancestry from All of Us and the Genome Aggregation Database. Then, we incorporated clinically calibrated MAVE data into the Clinical Genome Resource's Variant Curation Expert Panel rules to automate VUS reclassification for BRCA1, TP53, and PTEN .

Results

Using two orthogonal statistical approaches, we show a higher prevalence ( p ≤5.95e-06) of VUS in individuals of non-European-like genetic ancestry across all medical specialties assessed in all three databases. Further, in the non-European-like genetic ancestry group, higher rates of Benign or Likely Benign and variants with no clinical designation ( p ≤2.5e-05) were found across many medical specialties, whereas Pathogenic or Likely Pathogenic assignments were higher in individuals of European-like genetic ancestry ( p ≤2.5e-05). Using MAVE data, we reclassified VUS in individuals of non-European-like genetic ancestry at a significantly higher rate in comparison to reclassified VUS from European-like genetic ancestry ( p =9.1e-03) effectively compensating for the VUS disparity. Further, essential code analysis showed equitable impact of MAVE evidence codes but inequitable impact of allele frequency ( p =7.47e-06) and computational predictor ( p =6.92e-05) evidence codes for individuals of non-European-like genetic ancestry.

Conclusions

Generation of saturation-style MAVE data should be a priority to reduce VUS disparities and produce equitable training data for future computational predictors.

Disparities in Genetic Testing for Neurologic Disorders

BACKGROUND AND OBJECTIVES

Genetic testing is now the standard of care for many neurologic conditions. Health care disparities are unfortunately widespread in the US health care system, but disparities in the utilization of genetic testing for neurologic conditions have not been studied. We tested the hypothesis that access to and results of genetic testing vary according to race, ethnicity, sex, socioeconomic status, and insurance status for adults with neurologic conditions.

METHODS

We analyzed retrospective data from patients who underwent genetic evaluation and testing through our institution's neurogenetics program. We tested for differences between demographic groups in 3 steps of a genetic evaluation pathway: (1) attending a neurogenetic evaluation, (2) completing genetic testing, and (3) receiving a diagnostic result. We compared patients on this genetic evaluation pathway with the population of all neurology outpatients at our institution, using univariate and multivariable logistic regression analyses.

RESULTS

Between 2015 and 2022, a total of 128,440 patients were seen in our outpatient neurology clinics and 2,540 patients underwent genetic evaluation. Black patients were less than half as likely as White patients to be evaluated (odds ratio [OR] 0.49, p < 0.001), and this disparity was similar after controlling for other demographic factors in multivariable analysis. Patients from the least wealthy quartile of zip codes were also less likely to be evaluated (OR 0.67, p < 0.001). Among patients who underwent evaluation, there were no disparities in the likelihood of completing genetic testing, nor in the likelihood of a diagnostic result after adjusting for age. Analyses restricted to specific indications for genetic testing supported these findings.

DISCUSSION

We observed unequal utilization of our clinical neurogenetics program for patients from marginalized and minoritized demographic groups, especially Black patients. Among patients who do undergo evaluation, all groups benefit similarly from genetic testing when it is indicated. Understanding and removing barriers to accessing genetic testing will be essential to health care equity and optimal care for all patients with neurologic disorders.

Navigating Neurogenetics for Child and Adolescent Psychiatry Practice

Neurodevelopmental disorders (NDDs) are a group of conditions characterized by impairments of brain processes that impact cognition, communication, motor abilities, and/or behavior during development. These conditions typically have significant effects across the life span and impact personal, social, academic, or occupational functioning. The US Centers for Disease Control and report that 1 in 6 children has a developmental disability, making it highly likely for child and adolescent psychiatrists to encounter children with NDDs in daily practice.1 While the etiologies of NDDs are broad, genetic syndromes are a common cause of NDDs. The diagnostic yield of thorough genetic testing for NDDs as a group is about 40% based on meta-analysis, including 30% to 50% yield in patients with global developmental delay (GDD) or intellectual disability (ID) and 15% to 20% yield in patients with in autism spectrum disorder.1-3 The findings are extremely heterogeneous, including chromosomal copy number variants (CNVs) and more than 2,000 known monogenic disorders associated with NDDs.3 Diagnostic yields will increase over time with advances in technology and disease gene discovery.3.

Racial Disparities in Accessing Care along the Continuum of Cancer Genetic Service Delivery

BACKGROUND

Public health calls to ensure equity in genomics and precision medicine necessitate a closer examination of how these efforts might differentially affect access to genetic services across demographic subgroups. This study set out to examine racial/ethnic disparities along the cancer genetic service delivery continuum.

METHODS

Retrospective data are drawn from 15 clinical sites across 6 U.S. States. Individuals who screened at-risk for hereditary cancer were: (i) referred/scheduled to see a genetic counselor (referral workflow), or (ii) offered genetic testing at the point-of-care (POC testing workflow). Logistic regression analyses evaluated the associations between race/ethnicity and several outcomes including appointment scheduling, genetic counseling, and genetic testing, controlling for demographics, clinical factors, and county-level covariates.

RESULTS

A total of 14,527 patients were identified at-risk. Genetic testing uptake was significantly higher at POC sites than referral sites (34% POC vs. 11% referral, P < 0.001). Race/ethnicity was significantly associated with testing uptake among all sites, with non-Hispanic Blacks having lower odds of testing compared with non-Hispanic Whites [aOR = 0.84; 95% confidence interval (CI), 0.71-1.00; P = 0.049]. Moreover, this disparity was observed at referral sites, but not POC sites. Among patients scheduled, non-Hispanic Blacks had lower odds of counseling (aOR = 0.28; 95% CI, 0.17-0.47; P < 0.001).

CONCLUSIONS

Findings suggest that factors influencing genetic counseling show rates may be driving disparities in genetic testing.

IMPACT

Strategies to reduce barriers to seeing a genetic counselor, including modifications to clinical workflow, may help mitigate racial/ethnic disparities in genetic testing.

Leveraging the Academic Artificial Intelligence Silecosystem to Advance the Community Oncology Enterprise

Over the last 75 years, artificial intelligence has evolved from a theoretical concept and novel paradigm describing the role that computers might play in our society to a tool with which we daily engage. In this review, we describe AI in terms of its constituent elements, the synthesis of which we refer to as the AI Silecosystem. Herein, we provide an historical perspective of the evolution of the AI Silecosystem, conceptualized and summarized as a Kuhnian paradigm. This manuscript focuses on the role that the AI Silecosystem plays in oncology and its emerging importance in the care of the community oncology patient. We observe that this important role arises out of a unique alliance between the academic oncology enterprise and community oncology practices. We provide evidence of this alliance by illustrating the practical establishment of the AI Silecosystem at the City of Hope Comprehensive Cancer Center and its team utilization by community oncology providers.