Yield and Utility of Germline Testing Following Tumor Sequencing in Patients With Cancer

Lincoln, Stephen E.; Nussbaum, Robert L.; Kurian, Allison W.; Nielsen, Sarah M.; Das, Kingshuk; Michalski, Scott T.; Yang, Shan; Ngo, Nhu; Blanco, Amie; Esplin, Edward D.

doi:10.1001/jamanetworkopen.2020.19452

Cited by 88 publications

(100 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In 2020, a Harvard webinar 18 described digital health technologies as a "pillar" upon which healthcare would be built and showed how growth was accelerating in the midst of the COVID pandemic. On another common healthcare front, a recent paper from Lincoln et al 19 reported that tumor tissue genome sequencing coupled with blood tests for germline mutations reveals higher than expected impacts of inherited contributions to a variety of common cancers. Such developments coupled with the growing number of gene arrays now being marketed directly to consumers 4 exemplify the importance of familiarizing medical students with the basics behind AI and genomics in precision medicine 16,17 .…”

Section: Discussionmentioning

confidence: 99%

A Model to Introduce Medical Students to the Use of Artificial Intelligence and Genomics for Precision Medicine

2021

Preprint

View full text Add to dashboard Cite

Objective: Despite the significant medical impact of artificial intelligence (AI) in healthcare, emergence of AI-related topics in medical curricula has been slow. The authors sought to introduce pre-clinical students to the importance of AI methodologies and medical applications using modular short courses focused on active learning with precision medicine as a primary use case. Materials and Methods: A short elective course was designed to introduce first-year students to how various bioinformatic and AI-related processes work and how they help classify medical data, facilitate genomic analysis and predict clinical outcomes. The course covers gene sequencing and variants, neural networks, natural language processing, medical computer vision and the limitations and ethical concerns related to use of AI in precision medicine. Online content serves as major source material. After a faculty-led introduction, sessions focus on teams of students who present course content to one another and lead discussions with faculty guidance. A related short AI course focused on gene variants was given to the entire second-year class. Results: The elective course has been taken by 74 first- year students over 8 consecutive semesters (2017-2021). The course achieved average satisfaction scores of 4.4/5.0 (n = 13) when the active learning approach became dominant in 2018. Students were able to describe accurately how bioinformatics and AI make personalized medicine possible. Students also did well on the gene variants exercise given to the entire second year class (2018), but the full class short AI course was not continued in subsequent years. Students have created a school-approved interest group in medical AI. Conclusions: This experience shows that AI-related materials can be sustainably introduced into pre-clinical medical education with precision medicine as the primary use case. This modular course design and content could be adapted easily for educational use in medical subspecialties and other health professions.

show abstract

Section: Discussionmentioning

confidence: 99%

A Model to Introduce Medical Students to the Use of Artificial Intelligence and Genomics for Precision Medicine

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Notably, negative tumor-only testing is not an adequate surrogate for dedicated germline testing, especially when the implications for cancer surveillance, treatment, and cascade testing are so critical. As recently shown by Lincoln et al, 17 Tumor result Germline and somatic results because of the technical limitations of tumor sequencing, variant interpretation differences, or differences in the genes tested in the tumor versus the germline. Perhaps most importantly, in tumor-only testing, the germline nature of findings may be misinterpreted by providers inexperienced in cancer genetics or incorrectly relayed to patients, resulting in suboptimal care not only for the patient but also for at-risk family members.…”

Section: Tumor-only Versus Parallel Tumor-normal Testingmentioning

confidence: 99%

The Future of Parallel Tumor and Germline Genetic Testing: Is There a Role for All Patients With Cancer?

Liu

Stadler

2021

Journal of the National Comprehensive Cancer Network

View full text Add to dashboard Cite

Under the traditional paradigm of genetic testing in cancer, the role of germline testing was to assess for the inherited risk of cancer, whereas the role of tumor testing was to determine therapeutic selection. Parallel tumor-normal genetic testing uses simultaneous genetic testing of the tumor and normal tissue to identify mutations and allows their classification as either germline or somatic. The increasing adoption of parallel testing has revealed a greater number of germline findings in patients who otherwise would not have met clinical criteria for testing. This result has widespread implications for the screening and further testing of at-risk relatives and for gene discovery. It has also revealed the importance of germline testing in therapeutic actionability. Herein, we describe the pros and cons of tumor-only versus parallel tumor-normal testing and summarize the data on the prevalence of incidental actionable germline findings. Because germline testing in patients with cancer continues to expand, it is imperative that systems be in place for the proper interpretation, dissemination, and counseling for patients and at-risk relatives. We also review new therapeutic approvals with germline indications and highlight the increasing importance of germline testing in selecting therapies. Because recommendations for universal genetic testing are increasing in multiple cancer types and the number of approved therapies with germline indications is also increasing, a gradual transition toward parallel tumor-normal genetic testing in all patients with cancer is foreseeable.

show abstract

“…In many settings, genetic counseling and testing occur only after a major health event, such as sudden cardiac arrest, late‐stage ovarian cancer, or unexplained seizures in children. Long diagnostic odysseys, second primary cancers (Lincoln et al, 2020), or multiple affected family members are too often the norm before genetics is considered as part of a differential diagnosis, if at all. This lack of consistency leads to disparate use of genetics within healthcare, rather than an integrated approach.…”

Section: Genetics In Healthcare: Yesterday and Todaymentioning

confidence: 99%

Democratizing genomics: Leveraging software to make genetics an integral part of routine care

Snir

Nazareth

Simmons

et al. 2020

American J of Med Genetics Pt C

Self Cite

View full text Add to dashboard Cite

Genetic testing can provide definitive molecular diagnoses and guide clinical management decisions from preconception through adulthood. Innovative solutions for scaling clinical genomics services are necessary if they are to transition from a niche specialty to a routine part of patient care. The expertise of specialists, like genetic counselors and medical geneticists, has traditionally been relied upon to facilitate testing and follow‐up, and while ideal, this approach is limited in its ability to integrate genetics into primary care. As individuals, payors, and providers increasingly realize the value of genetics in mainstream medicine, several implementation challenges need to be overcome. These include electronic health record integration, patient and provider education, tools to stay abreast of guidelines, and simplification of the test ordering process. Currently, no single platform offers a holistic view of genetic testing that streamlines the entire process across specialties that begins with identifying at‐risk patients in mainstream care settings, providing pretest education, facilitating consent and test ordering, and following up as a “genetic companion” for ongoing management. We describe our vision for using software that includes clinical‐grade chatbots and decision support tools, with direct access to genetic counselors and pharmacists within a modular, integrated, end‐to‐end testing journey.

show abstract

Yield and Utility of Germline Testing Following Tumor Sequencing in Patients With Cancer

Cited by 88 publications

References 21 publications

A Model to Introduce Medical Students to the Use of Artificial Intelligence and Genomics for Precision Medicine

A Model to Introduce Medical Students to the Use of Artificial Intelligence and Genomics for Precision Medicine

The Future of Parallel Tumor and Germline Genetic Testing: Is There a Role for All Patients With Cancer?

Democratizing genomics: Leveraging software to make genetics an integral part of routine care

Contact Info

Product

Resources

About