Considerations in Methods and Timing for Delivery of Genetic Counseling Information to Pediatric Oncology Patients and Families.

Many pediatric oncology patients and their families may benefit from genetic counseling and testing; however, identifying the best timing and delivery method for these referrals is sometimes a challenge. The goal of this study was to understand how and when caregivers prefer to receive information about genetic counseling and testing. A total of 56 surveys completed by caregivers at The Johns Hopkins Hospital Pediatric Oncology unit in Baltimore, Maryland were analyzed. A sizeable subset of respondents was interested in receiving information about the availability of genetic counseling from an oncology doctor or nurse, but not a genetic counselor (n=13/55, 24%). Most respondents preferred to be informed about genetic services at diagnosis (n=28/54, 52%) or within 1 to 2 months of diagnosis (n=14/54, 26%). In conclusion, patients and their families may benefit from prompt and early recognition of the risk of cancer predisposition syndromes, preferably within the first 2 months following diagnosis. Oncology professionals are an important source of information, and can introduce the availability of genetic counseling services and motivate families to undergo genetic testing, though alternative communication methods such as brochures may also be useful.

Characterization of Non-Monotonic Relationships between Tumor Mutational Burden and Clinical Outcomes.

Potential clinical biomarkers are often assessed with Cox regressions or their ability to differentiate two groups of patients based on a single cutoff. However, both of these approaches assume a monotonic relationship between the potential biomarker and survival. Tumor mutational burden (TMB) is currently being studied as a predictive biomarker for immunotherapy, and a single cutoff is often used to divide patients. In this study, we introduce a two-cutoff approach that allows splitting of patients when a non-monotonic relationship is present and explore the use of neural networks to model more complex relationships of TMB to outcome data. Using real-world data, we find that while in most cases the true relationship between TMB and survival appears monotonic, that is not always the case and researchers should be made aware of this possibility. SIGNIFICANCE: When a non-monotonic relationship to survival is present it is not possible to divide patients by a single value of a predictor. Neural networks allow for complex transformations and can be used to correctly split patients when a non-monotonic relationship is present.