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TPS1598 Background: In the US, many of the 90,000 adolescents and young adults (AYAs) (i.e., individuals ages 15-39) diagnosed with cancer each year do not receive services to address the range of needs they experience during cancer treatment. AYAs’ unmet needs are associated with higher distress, poorer health-related quality of life, and higher physical symptom burden. However, AYAs often do not use services available to them through their cancer programs, even when they face no access issues (e.g., cost, insurance status, local service capacity). AYAs report barriers to service use including lack of awareness, challenges navigating large volumes of information and complex health systems, and hesitance to broach needs with providers without prompting. To facilitate a systematic and patient-centered approach to addressing AYAs' unmet needs, we leveraged user-centered design to develop the AYA Needs Assessment & Service Bridge (NA-SB). NA-SB includes a patient-reported outcome measure assessing AYAs’ physical, psychosocial, and practical needs, and a collection of referral pathways for connecting AYAs to services based on the needs they report. In this feasibility pilot study, we are evaluating the implementation of NA-SB in the University of North Carolina Lineberger Comprehensive Cancer Center (UNC) AYA Cancer Program. Methods: Eligible participants include AYAs ages 18-39 currently undergoing cancer treatment at UNC (n = 25-50). The needs assessment portion of NA-SB is administered electronically through REDCap during routine clinical encounters with the AYA team. After an AYA completes the needs assessment, an AYA provider (i.e., social worker/nurse practitioner) reviews their responses and initiates referral pathways. Six weeks following their completion of the needs assessment, AYAs complete a survey assessing their perceptions of (1) the usability of the NA-SB needs assessment, (2) the feasibility, acceptability, and appropriateness of implementing NA-SB, and (3) the extent to which their needs have been met. We are also assessing participating providers’ perceptions of NA-SB’s implementation through periodic check-in calls. Results: We will report descriptive statistics on participant demographics, needs reported, and quantitative outcomes. We will analyze data from provider check-in calls inductively to further elaborate on implementation experiences and determinants. Conclusion: By harnessing patient-reported data to facilitate care coordination for AYAs, NA-SB has the potential to improve processes of care and subsequent outcomes for AYAs, an underserved and understudied population. This pilot study represents a critical first step towards translating NA-SB into routine cancer care for AYAs. Clinical trial information: NCT04586127.

Background Young adult cancer survivors experience early aging‐related morbidities and mortality. Biological aging biomarkers may identify at‐risk survivors and increase our understanding of mechanisms underlying this accelerated aging. Methods Using an observational study design, we cross‐sectionally measured DNA methylation‐based epigenetic age in young adult cancer survivors at a tertiary, academic state cancer hospital. Participants were a convenience sample of consecutively enrolled survivors of childhood, adolescent, and young adult cancers treated with either an anthracycline or alkylating agent, and who were at least 3 months post‐treatment. Similarly aged healthy comparators were consecutively enrolled. Cancer treatment and treatment intensity were compared to DNA methylation‐based epigenetic age and pace of aging. Results Sixty survivors (58 completing assessments, mean age 20.5 years, range 18–29) and 27 comparators (mean age 20 years, range 17–29) underwent DNA methylation measurement. Survivors were predominantly female (62%) and white (60%) and averaged nearly 6 years post‐treatment (range 0.2–25 years). Both epigenetic age (AgeAccelGrim: 1.5 vs. −2.4, p < 0.0001; AgeAccelPheno 2.3 vs. −3.8, p = 0.0013) and pace of aging (DunedinPACE 0.99 vs. 0.83, p < 0.0001) were greater in survivors versus comparators. In case–case adjusted analysis, compared to survivors with normal muscle mass, myopenic survivors had higher AgeAccelGrim (2.2 years, 95% CI 0.02–4.33, p = 0.02), AgeAccelPheno (6.2 years, 2.36–10.09, p < 0.001), and DunedinPACE (0.11, 0.05–0.17, p < 0.001). Conclusions Epigenetic age is older and pace of aging is faster in young adult cancer survivors compared to noncancer peers, which is evident in the early post‐therapy period. Survivors with physiological impairment demonstrate greater epigenetic age advancement. Measures of epigenetic age may identify young adult survivors at higher risk for poor functional and health outcomes.