# A population-based virtual solution to reduce gaps in genetic risk evaluation and management in families at high risk for hereditary cancer syndromes:  The Georgia-California GeneLINK Trial

> **NIH NIH U01** · UNIVERSITY OF MICHIGAN AT ANN ARBOR · 2020 · $796,445

## Abstract

Project Abstract: There is growing evidence that targeting genetic risk evaluation (GRE) in families where a
cancer susceptibility gene pathogenic variant (PV) has been identified may be the most cost-effective
approach to reduce the population burden of cancer through prevention. However, there are enormous
challenges to implementing successful cascade genetic risk evaluation in families with hereditary cancer
syndromes. The clinical context of GRE after cancer diagnosis is increasingly complex: As MGP testing has
become the norm, guideline organizations have converged on a list of >40 cancer susceptibility genes in which
PVs are clinically actionable, with wide variability in cancer threat and a myriad of strategies for prevention and
early detection. A daunting challenge is that the cancer patient is responsible for communication and
engagement of relatives for GRE. Despite the shared health threat among at risk relatives (ARRs), the social
and contextual factors that affect family communication are complex. Furthermore, ARRs are dispersed world-
wide and receive care in disparate health care practices. Importantly, there is little incentive and limited
resources for clinicians to engage cancer patients’ relatives and genetic counseling services are increasingly
strained. Given the lack of guidance for families, it is not surprising that most ARRs of cancer patients with PVs
do not undergo GRE. We are uniquely positioned to develop and optimize a direct-to-family virtual genetic risk
evaluation and testing solution offered to all at risk relatives of a population-based sample of adults recently
diagnosed with cancer in Georgia and California who tested positive for a clinically relevant PV. We will use a
unique data infrastructure we pioneered to identify and invite a diverse cohort of cancer patients with clinically
relevant PVs and their families to participate in our study. We propose a 2 x 3 factorial randomized trial of 900
patients diagnosed in 2018-2019 in the two states who had a clinically significant PV detected by genetic
testing that will offer genetic risk evaluation and testing to all 1st and 2nd degree relatives. We will evaluate the
effects of two intervention design features on patient- and relative-centered outcomes: 1) the level of
personalized family genetic risk support (a technology assisted personally tailored patient and family member
education and communication tool called the Family Genetic Health Program, FGHP) vs. the FGHP plus direct
assistance from a human FGHP Navigator); and 2) the price offered to the relatives for the genetic test
(standard $200 vs. $100 vs. $50 per test). We will determine the independent effects of the two design
features on 1) the cancer patient’s appraisal of communication and their engagement with relatives about
hereditary cancer and GRE; 2) the invited relative’s appraisal of decision-making and receipt of genetic testing;
and 3) on the enrolled relative’s completion of formal GRE. We w...

## Key facts

- **NIH application ID:** 10086540
- **Project number:** 1U01CA254822-01
- **Recipient organization:** UNIVERSITY OF MICHIGAN AT ANN ARBOR
- **Principal Investigator:** Lawrence Chin-I An
- **Activity code:** U01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $796,445
- **Award type:** 1
- **Project period:** 2020-09-15 → 2025-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10086540

## Citation

> US National Institutes of Health, RePORTER application 10086540, A population-based virtual solution to reduce gaps in genetic risk evaluation and management in families at high risk for hereditary cancer syndromes:  The Georgia-California GeneLINK Trial (1U01CA254822-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10086540. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*