The Changing Role of the Genetic Counsellor in the Genomics Era

Precision Medicine Is Changing the Roles of Healthcare Professionals, Scientists, and Research Staff: Learnings from a Childhood Cancer Precision Medicine Trial

Precision medicine programs aim to utilize novel technologies to identify personalized treatments for children with cancer. Delivering these programs requires interdisciplinary efforts, yet the many groups involved are understudied. This study explored the experiences of a broad range of professionals delivering Australia's first precision medicine trial for children with poor-prognosis cancer: the PRecISion Medicine for Children with Cancer (PRISM) national clinical trial of the Zero Childhood Cancer Program. We conducted semi-structured interviews with 85 PRISM professionals from eight professional groups, including oncologists, surgeons, clinical research associates, scientists, genetic professionals, pathologists, animal care technicians, and nurses. We analyzed interviews thematically. Professionals shared that precision medicine can add complexity to their role and result in less certain outcomes for families. Although many participants described experiencing a greater emotional impact from their work, most expressed very positive views about the impact of precision medicine on their profession and its future potential. Most reported navigating precision medicine without formal training. Each group described unique challenges involved in adapting to precision medicine in their profession. Addressing training gaps and meeting the specific needs of many professional groups involved in precision medicine will be essential to ensure the successful implementation of standard care.

Protocol to evaluate a pilot program to upskill clinicians in providing genetic testing for familial melanoma

INTRODUCTION

Genetic testing for hereditary cancers can improve long-term health outcomes through identifying high-risk individuals and facilitating targeted prevention and screening/surveillance. The rising demand for genetic testing exceeds the clinical genetic workforce capacity. Therefore, non-genetic specialists need to be empowered to offer genetic testing. However, it is unknown whether patient outcomes differ depending on whether genetic testing is offered by a genetics specialist or a trained non-genetics clinician. This paper describes a protocol for upskilling non-genetics clinicians to provide genetic testing, randomise high-risk individuals to receive testing from a trained clinician or a genetic counsellor, and then determine whether patient outcomes differed depending on provider-type.

METHODS

An experiential training program to upskill dermatologically-trained clinicians to offer genetic testing for familial melanoma is being piloted on 10-15 clinicians, prior to wider implementation. Training involves a workshop, comprised of a didactic learning presentation, case studies, simulated sessions, and provision of supporting documentation. Clinicians later observe a genetic counsellor led consultation before being observed leading a consultation. Both sessions are followed by debriefing with a genetic counsellor. Thereafter, clinicians independently offer genetic testing in the clinical trial. Individuals with a strong personal and/or family history of melanoma are recruited to a parallel-group trial and allocated to receive pre- and post- genetic testing consultation from a genetic counsellor, or a dermatologically-trained clinician. A mixed method approach measures psychosocial and behavioural outcomes. Longitudinal online surveys are administered at five timepoints from baseline to one year post-test disclosure. Semi-structured interviews with both patients and clinicians are qualitatively analysed.

SIGNIFICANCE

This is the first program to upskill dermatologically-trained clinicians to provide genetic testing for familial melanoma. This protocol describes the first clinical trial to compare patient-reported outcomes of genetic testing based on provider type (genetic counsellors vs trained non-genetic clinicians).

Attitudes of Australian dermatologists on the use of genetic testing: A cross-sectional survey with a focus on melanoma

Background: Melanoma genetic testing reportedly increases preventative behaviour without causing psychological harm. Genetic testing for familial melanoma risk is now available, yet little is known about dermatologists’ perceptions regarding the utility of testing and genetic testing ordering behaviours.

Objectives: To survey Australasian Dermatologists on the perceived utility of genetic testing, current use in practice, as well as their confidence and preferences for the delivery of genomics education.

Methods: A 37-item survey, based on previously validated instruments, was sent to accredited members of the Australasian College of Dermatologists in March 2021. Quantitative items were analysed statistically, with one open-ended question analysed qualitatively. Results: The response rate was 56% (256/461), with 60% (153/253) of respondents between 11 and 30 years post-graduation. While 44% (112/252) of respondents agreed, or strongly agreed, that genetic testing was relevant to their practice today, relevance to future practice was reported significantly higher at 84% (212/251) (t = -9.82, p < 0.001). Ninety three percent (235/254) of respondents reported rarely or never ordering genetic testing. Dermatologists who viewed genetic testing as relevant to current practice were more likely to have discussed (p < 0.001) and/or offered testing (p < 0.001). Respondents indicated high confidence in discussing family history of melanoma, but lower confidence in ordering genetic tests and interpreting results. Eighty four percent (207/247) believed that genetic testing could negatively impact life insurance, while only 26% (63/244) were aware of the moratorium on using genetic test results in underwriting in Australia. A minority (22%, 55/254) reported prior continuing education in genetics. Face-to-face courses were the preferred learning modality for upskilling.

Conclusion: Australian Dermatologists widely recognise the relevance of genetic testing to future practice, yet few currently order genetic tests. Future educational interventions could focus on how to order appropriate genetic tests and interpret results, as well as potential implications on insurance.

Communicating Personal Melanoma Polygenic Risk Information: Participants’ Experiences of Genetic Counseling in a Community-Based Study

Personalized polygenic risk information may be used to guide risk-based melanoma prevention and early detection at a population scale, but research on communicating this information is limited. This mixed-methods study aimed to assess the acceptability of a genetic counselor (GC) phone call in communicating polygenic risk information in the Melanoma Genomics Managing Your Risk randomized controlled trial. Participants (n = 509) received personalized melanoma polygenic risk information, an educational booklet on melanoma prevention, and a GC phone call, which was audio-recorded. Participants completed the Genetic Counseling Satisfaction Survey 1-month after receiving their risk information (n = 346). A subgroup took part in a qualitative interview post-study completion (n = 20). Survey data were analyzed descriptively using SPSS, and thematic analysis of the qualitative data was conducted using NVivo 12.0 software. The survey showed a high level of acceptability for the GC phone call (mean satisfaction score overall: 4.3 out of 5, standard deviation (SD): 0.6) with differences according to gender (mean score for women: 4.4, SD: 0.6 vs. men: 4.2, SD: 0.7; p = 0.005), health literacy (lower literacy: 4.1, SD: 0.8; average: 4.3, SD: 0.6; higher: 4.4, SD: 0.6: p = 0.02) and polygenic risk group (low risk: 4.5, SD: 0.5, SD: average: 4.3, SD: 0.7, high: 4.3, SD: 0.7; p = 0.03). During the GC phone calls, the discussion predominately related to the impact of past sun exposure on personal melanoma risk. Together our findings point to the importance of further exploring educational and support needs and preferences for communicating personalized melanoma risk among population subgroups, including diverse literacy levels.

A review of deep learning applications in human genomics using next-generation sequencing data

Genomics is advancing towards data-driven science. Through the advent of high-throughput data generating technologies in human genomics, we are overwhelmed with the heap of genomic data. To extract knowledge and pattern out of this genomic data, artificial intelligence especially deep learning methods has been instrumental. In the current review, we address development and application of deep learning methods/models in different subarea of human genomics. We assessed over- and under-charted area of genomics by deep learning techniques. Deep learning algorithms underlying the genomic tools have been discussed briefly in later part of this review. Finally, we discussed briefly about the late application of deep learning tools in genomic. Conclusively, this review is timely for biotechnology or genomic scientists in order to guide them why, when and how to use deep learning methods to analyse human genomic data.

Genetic counselor roles in the undiagnosed diseases network research study: Clinical care, collaboration, and curation

Genetic counselors (GCs) are increasingly filling important positions on research study teams, but there is limited literature describing the roles of GCs in these settings. GCs on the Undiagnosed Diseases Network (UDN) study team serve in a variety of roles across the research network and provide an opportunity to better understand genetic counselor roles in research. To quantitatively characterize the tasks regularly performed and professional fulfillment derived from these tasks, two surveys were administered to UDN GCs in a stepwise fashion. Responses from the first, free-response survey elicited the scope of tasks which informed development of a second structured, multiple-select survey. In survey 2, respondents were asked to select which roles they performed. Across 19 respondents, roles in survey 2 received a total of 947 selections averaging approximately 10 selections per role. When asked to indicate what roles they performed, respondent selected a mean of 50 roles (range 22-70). Survey 2 data were analyzed via thematic coding of responses and hierarchical cluster analysis to identify patterns in responses. From the thematic analysis, 20 non-overlapping codes emerged in seven categories: clinical interaction and care, communication, curation, leadership, participant management, research, and team management. Three themes emerged from the categories that represented the roles of GCs in the UDN: clinical care, collaboration, and curation. Cluster analyses showed that responses were more similar among individuals at the same institution than between institutions. This study highlights the ways GCs apply their unique skill set in the context of a clinical translational research network. Additionally, findings from this study reinforce the wide applicability of core skills that are part of genetic counseling training. Clinical literacy, genomics expertise and analysis, interpersonal, psychosocial and counseling skills, education, professional practice skills, and an understanding of research processes make genetic counselors well suited for such roles and poised to positively impact research experiences and outcomes for participants.

Artificial intelligence in clinical and genomic diagnostics

Artificial intelligence (AI) is the development of computer systems that are able to perform tasks that normally require human intelligence. Advances in AI software and hardware, especially deep learning algorithms and the graphics processing units (GPUs) that power their training, have led to a recent and rapidly increasing interest in medical AI applications. In clinical diagnostics, AI-based computer vision approaches are poised to revolutionize image-based diagnostics, while other AI subtypes have begun to show similar promise in various diagnostic modalities. In some areas, such as clinical genomics, a specific type of AI algorithm known as deep learning is used to process large and complex genomic datasets. In this review, we first summarize the main classes of problems that AI systems are well suited to solve and describe the clinical diagnostic tasks that benefit from these solutions. Next, we focus on emerging methods for specific tasks in clinical genomics, including variant calling, genome annotation and variant classification, and phenotype-to-genotype correspondence. Finally, we end with a discussion on the future potential of AI in individualized medicine applications, especially for risk prediction in common complex diseases, and the challenges, limitations, and biases that must be carefully addressed for the successful deployment of AI in medical applications, particularly those utilizing human genetics and genomics data.