Addressing Genomic Disparities in Cancer Survivors

The goal of this observational study is to increase genetic education and genetic testing for hereditary cancer risk among Black cancer survivors. The study will:

1. Test the effectiveness of a chatbot intervention (also called relational agent, or RA) vs. enhanced usual care (EUC) on engagement in genetic education and requests for genetic testing.
2. Evaluate the impact of the chatbot vs. EUC on the process that participants use to make decisions and evaluate effects on well-being (also called psychosocial outcomes).
3. Explore the ways (methods) that influence how participants experience the intervention.

The main questions this study aims to answer are which group - the chatbot (RA) group or the EUC group - is more likely to request genetic testing and which group is more likely to get (engage with) genetic education.

Participants will be randomly assigned to either the chatbot (RA) group or EUC group. This means each participant has an equal chance of being placed in either group, just like flipping a coin. Each group will receive genetic education and have an opportunity to request genetic testing. Researchers will compare the chatbot (RA) group and the EUC group to see which may request more GT (genetic testing) and which group engages more with genetic education.

Research Design and Methods:

This study will employ a 2-arm parallel group randomized controlled trial to evaluate the efficacy of the RA intervention compared to EUC among 428 Black cancer survivors who meet the National Comprehensive Cancer Network's guidelines for genetic referral. Primary outcomes will be engagement with genetic education and uptake of genetic testing (Aim 1). The investigators will also evaluate the impact of the RA intervention on psychosocial and decision quality outcomes (Aim 2) and evaluate mediators and moderators of intervention impact (Aim 3).

Research Procedures:

The design for this study is a 2-arm parallel group trial. Randomization is at the patient level. The investigators will recruit eligible cancer survivors who will be randomized by computer to enhanced usual care (EUC) or RA. Randomization will be balanced in a 1:1 ratio - with 214 participants randomized to each arm. Because this study is designed to address the disparity of low genetic testing utilization in Black cancer patients, all study participants will be Black and approximately 10% will be Hispanic.

The investigators will utilize the EHRs at our participating sites to identify cancer survivors who have not been tested despite meeting GT eligibility criteria. Participants will be ascertained from 1) Georgetown Lombardi Comprehensive Cancer Center (LCCC) and MedStar Washington Cancer Institute (MWCI) in DC and 2) Rutgers' Cancer Institute of NJ (Rutgers CINJ New Brunswick and Rutgers CINJ Newark) including RWJ Barnabas Health community oncology sites. Participants will be women and men who self-identify as Black or African American, have a history of breast, ovarian, uterine, colorectal, prostate, or pancreatic cancer, are at least six-months post-diagnosis, and have received treatment or follow-up oncology care at one of the participating sites in the prior two years. There is no limit on the years since diagnosis as genetic testing (GT) can guide treatment decisions and provide opportunities for cascade testing to guide screening and prevention in at-risk relatives.

Eligibility Criteria:
Inclusion Criteria:

* 18-80 years of age
* Identify as Black or African American
* At least 6-months post diagnosis with any of the following cancers: breast, ovarian, uterine, prostate, colorectal, pancreatic
* Have received treatment or follow-up oncology care at one of the participating sites in the prior two years.
* Meet National Comprehensive Cancer Network criteria for germline GT
* Able to read and speak in English.

Exclusion Criteria:

* Do not speak English
* Unable to access the Internet
* Have previously undergone germline genetic testing for hereditary cancer risk
* Are unable to provide informed consent