Citations

Financial toxicity is a significant but often overlooked consequence of cancer for adolescents and young adults (AYAs, 15–39 years old), who face limited savings, unstable employment, and insurance gaps that heighten financial stress. As part of a larger initiative to determine whether our cancer center can use an existing social drivers of health (SDOH) screening process to identify patients who may benefit from targeted supportive interventions, we sought to characterize financial strain, toxicity, and SDOH needs in AYAs treated in our cancer center and compare them to both younger and older cancer patients. This is a cross-sectional observational study at Mayo Clinic of patients with an oncology-related department visit between 8 September 2023–4 June 2024 (N = 81,731) who completed routine institutional electronic screenings for financial strain and SDOH. Survey items assessed financial strain, transportation barriers, and food insecurity; institutional billing data captured financial toxicity (unresolved balances, collection referrals, write-offs). AYAs (n = 5655) were more likely than non-AYAs to report medium/high financial strain, have unpaid debt, or have an unresolved balance, referral to a collection agency, or bad debt write-off, with nearly double the odds of medium/high financial strain compared to patients over 40 (OR 2.02, 95% CI 1.84–2.23, p < 0.001). Within AYAs, ages 26–32 bore the greatest burden: 24.2% in the medium/high-risk category, 9.4% with unresolved billing issues or collections, and 11.6% reporting food insecurity. These findings identify AYAs—particularly those 26–32—as a distinct high-risk group for financial toxicity and health-related social needs, underscoring the need for targeted interventions, tailored financial navigation, and policy efforts to reduce economic barriers to care. Future research should evaluate the effectiveness of these interventions in mitigating financial toxicity, addressing health-related social needs, and improving patient outcomes in this population.

Purpose: Adolescents and young adults (AYAs) with cancer have unique physical, emotional, and social challenges. We aimed to identify key concerns among AYAs with cancer, evaluate pathways for connecting patients with resources, and inform the development of an AYA oncology program. Methods: This study was conducted at Mayo Clinic in Arizona and included English-speaking patients aged 18–39 years with recently diagnosed cancer. Participants completed a modified Needs Assessment Service Bridge questionnaire, which assessed patient-perceived needs with 39 items spanning 7 domains. An AYA nurse navigator discussed each participant’s results with them and connected them to the desired resources. Results: Of 52 eligible patients, 32 (62%) completed the questionnaire. Among them, 30 (94%) reported unmet needs in at least 1 domain and 19 (59%) reported unmet needs in 5 or more domains. The domains with the most frequently reported needs were health behaviors and wellness (81%), emotional health (72%), finances and everyday needs (59%), and work- and education-related assistance (56%). Younger participants reported more needs related to school life and scholarships/loans than did older participants. No significant differences were observed between the responses of men and women. Of 133 total needs identified by all participants, 93 (70%) were addressed by connecting patients to relevant resources. Conclusion: Systematic assessment of AYA needs and targeted connection to resources are essential for individualized and developmentally appropriate care. The current study identified key service gaps at our institution and will guide the continued evolution of our comprehensive AYA oncology program.

1503 Background: CAR T-cell therapy has revolutionized treatment outcomes in hematological malignancies, but has traditionally required inpatient post-infusion monitoring, which is associated with significant cost and time toxicity. Outpatient CAR T infusion and monitoring has been the standard approach at Mayo Clinic site in Rochester, MN, with demonstrated feasibility and safety (Bansal et al., ASCO 2023). We present data comparing healthcare utilization between CAR T recipients who underwent outpatient remote monitoring and those with traditional inpatient monitoring. Methods: Electronic medical records were retrospectively analyzed for 293 patients who received CAR T-cell therapy for a hematological malignancy at any of the 3 Mayo Clinic sites (MN, AZ, FL) between 2020 and 2024 . Healthcare utilization was compared between patients who received outpatient CAR T with remote patient monitoring in Rochester, MN (N = 125) and those who received inpatient CAR T in the Arizona (n = 90) and Florida (n = 78) sites. Per protocol, patients in the inpatient group underwent mandatory inpatient monitoring for 7 days, with extension per provider discretion. Data was analyzed through Chi-Square and Wilcoxon tests. P values < 0.05 were considered statistically significant. Results: There was no significant difference in baseline sex, age, ethnicity, LDH, platelets, or neutrophils among the 293 patients. Diagnoses included lymphoma (n = 175), multiple myeloma (n = 106), and B-ALL (n = 12). The most common CAR T-cell products used were axicabtagene ciloleucel (n = 138) idecabtagene vicleucel (n = 56), and ciltacabtagene autoleucel (n = 50). Median hospital days were lower in the outpatient group vs the inpatient group at 30 days (4.4 vs 13.3, P < 0.001) and 90 days (4.6 vs 13.6, P < 0.001). ICU admission occurred in 1 outpatient patient ( < 1%) vs 18 inpatient patients (11%) in the first 30 days (P < 0.001). In the first 30 days, 22% of patients in the outpatient group did not require hospitalization, while 56%, 15%, and 6% required 1, 2, and 3 hospitalizations, respectively. Similarly, only 11 (8.8%) patients in the outpatient group had ≥1 ED visit in the first 30 days compared with 32 (20%) pts in the inpatient group (P = 0.009). At 30 days, there was no difference in patient portal use, with 58% of patients using the portal at least once and a median of 1 message in both groups. Within the first 30 days, 53% and 2% of patients in the outpatient group required 1 and 2 outpatient visits, respectively, vs 29% and 2% in the inpatient group (P < 0.001). There was no difference in 30-day mortality (1.6% outpatient vs 2.4% in inpatient) between the 2 groups (P = 1.00) Conclusions: Outpatient CAR T-cell therapy monitoring was associated with significantly fewer hospital days without increased ED visits, ICU stays, portal use, or 30-day mortality. This demonstrates lower healthcare utilization for outpatient CAR-T therapy.

Hodgkin lymphoma is one of the most common cancers in adolescents and young adults (AYAs), a population with distinct biological, clinical, and developmental characteristics. Yet care remains fragmented between pediatric and adult systems contributing to variability in treatment exposure and survivorship outcomes. We synthesized current evidence on AYA HL epidemiology, biology, treatment strategies, supportive care needs, long-term toxicity risk, and survivorship. AYAs have access to multiple efficacious treatment options with high cure rates, but long-term survivors face significant medical, psychosocial, and financial effects with notable risks for cardiopulmonary disease, secondary malignancies, fertility impairment, and unmet psychosocial needs. Harmonized pediatric-adult treatment frameworks, AYA-focused support, and developmentally tailored survivorship care are essential to improving outcomes for AYA HL survivors.

Abstract Large Language Models (LLM) are being widely adopted into the medical field for their impressive ability to analyze and summarize large amounts of text data. These models enable clinicians and researchers to extract meaningful insights from complex datasets and may assist with decision making. Here we present our workflows and application of LLM for the interpretation and summarization of clinical data related to CAR-T cell therapy. Using an LLM (Gemini 2.5 pro) in the Google Cloud Computing (GCP) environment, two applications were developed to analyze CAR-T cell therapy clinical data: 1) extracting and summarizing CRS and ICANS event-related data to streamline the compliance team workflow, and 2) identifying features available at time of CAR-T infusion able to classify patients into high- or low-monitoring needs 14 days post CAR-T. Patient data (vitals, labs, hematology notes, and EKGs) were extracted from the electronic medical record (EMR) using Google BigQuery into SQL tables in GCP. For each application, relevant data fields were retrieved, formatted into JSON objects, and embedded in the LLM prompt for context-aware processing. Both applications have undergone iterative prompt engineering after analyzing the LLM output against ground truth data in the EMR. For the application extracting and summarizing CRS and ICANS events, the LLM was optimized on Mayo Clinic Rochester data. When compared to the IEC compliance database, the LLM achieved 100% accuracy and F1 score for CRS events, and 96% accuracy and 82% F1 score for ICANS events. The match rate for CRS and ICANS grades were 83% and 89% respectively. We applied the same LLM to Mayo Clinic Arizona (MCA) and Mayo Clinic Florida (MCF) cases, who document clinical notes and toxicity flowsheet differently, and achieved an accuracy (MCA: 93%, MCF:93%) and F1 score (MCA: 96%, MCF: 96%) for CRS and accuracy (MCA: 81%, MCF:82%) and F1 score (MCA: 76%, MCF: 75%) for ICANS. LLM was able to capture events missed by manual reviews. For most of the discrepancies where compliance team final adjudication is needed, LLM will be updated to flag discrepancies for review by the compliance team. For the application related to the monitoring needs 14 days post CAR-T, the LLM identified 5 categories predictive of high or low monitoring needs post CAR-T infusion (disease status, inflammatory and tumor burden markers, hematologic status, renal function and performance status). Our model achieved a sensitivity of 85.7%, specificity of 23.8%, and F1 score of 65.5% for our first cohort, compared with data extracted by the LLM from the EMR. For our second cohort, with demographics statistically similar to cohort 1 and using the same 5 categories, the LLM achieved a sensitivity of 83.3%, a specificity of 28.6%, and F1 score of 65.4%. Our workflow and applications of LLM’s provide examples and guidance to others interested in applying LLM’s for clinical and research applications. Citation Format: Emmanuel Contreras Guzman, Matthew Jankowski, Andre De Menezes Silva Corraes, Malvika Gupta, Monica L. Shaw, Madiha Iqbal, Talal Hilal, Saurabh Chhabra, Ricardo Daniel Parrondo, Jody K. Mclean, Kim R. Riester, Kayla Joseph, Melinda Tan, Holly Ross, Cleyonia Barnett, Sylvia Carter, Semy Girmay, Rachel Wolan, Milana Ramsey, Christian Downhour, Kristy Morgan, Shae Sibley, Erica Rushing, Lucy Holmes, Allison Burgstahler, Stephen M. Ansell, Hassan Alkhateeb, Matthew Hathcock, Ramona Bruno, Allison C. Rosenthal, Hemant Murthy, Patrick B. Johnston, Jonas Paludo, Yi Lin. Applications of large language models to CAR-T cell therapy clinical data using Google cloud computing [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 2740.

ABSTRACT 162 patients with relapsed secondary central nervous system lymphoma (R‐SCNSL) (median age, 65 years; male, 59.9%) including central nervous system (CNS)‐only ( n = 120) and concomitant CNS/systemic relapse ( n = 42) were retrospectively analyzed. Overall, 21.9% of patients were classified as high risk according to the CNS International Prognostic Index (CNS‐IPI). Several biological and clinical features were significantly associated with leptomeningeal involvement, including double‐ or triple‐hit (DHL/THL) status, MYC rearrangement, negative BCL6 expression by IHC, bone marrow involvement, and concomitant R‐SCNSL. Multivariable analysis showed that leptomeningeal involvement independently predicted inferior OS and was associated with a 98% increase in the hazard of death compared with parenchymal relapse (HR = 1.98, 95% CI: 1.20–3.26, p = 0.008). Based on anatomical localization, R‐SCNSL was classified into four subtypes: parenchymal‐only involvement (parenchymal‐CNS [P‐CNS], 68/42%) and parenchymal involvement plus systemic relapse (parenchymal‐concomitant [P‐concomitant], 17/10.5%); and leptomeningeal with or without parenchymal involvement (leptomeningeal‐CNS [LM‐CNS], 52/32.1%) and leptomeningeal with systemic relapse (leptomeningeal‐concomitant [LM‐concomitant], 25/15.4%). This anatomical classification significantly impacted OS and PFS ( p < 0.001). Two‐year OS and PFS were 58.2% and 29.1% for P‐CNS, 32.4% and 17.7% for P‐concomitant, 22% and 13.9% for LM‐CNS, and 7.1% and 0% for LM‐concomitant, respectively. ASCT showed a trend toward improved survival among patients with a response (CR/PR) in a 4‐month landmark analysis. These findings support the clinical application of the anatomical classification in the management of R‐SCNSL.

CD19-directed chimeric antigen receptor (CAR) T-cell therapies have been instrumental in improving outcomes of refractory or relapsed B-cell malignancies. However, there have been safety concerns due to recent reports of second primary malignancies (SPMs) related to CAR T-cell therapies. We reviewed articles from Embase, PubMed, and Cochrane Library records and included SPM case reports as well as cohort studies. Across published cohorts, secondary cutaneous or peripheral T-cell lymphoma (PTCL) after diffuse large B-cell lymphomas (DLBCLs) have been reported at low incidence (generally in the low single-digit percentage range). While CAR T-cell therapy is associated with these rare secondary malignancies and lineage-switch events primarily described in acute leukemia, they are clinically significant and have resulted in increased surveillance. The currently available evidence suggests that most secondary malignancies after CAR T-cell therapy are due to background risk and prior treatment exposures rather than direct CAR T-cell therapy induced oncogenesis. However, rare CAR T-cell therapy-associated second primary T-cell malignancies have been reported. To properly define incidence, mechanisms, and risk factors for CAR T-cell therapy-associated malignancies, continued prospective registry follow-up and additional research will be needed.

Abstract Castleman disease (CD) is a heterogeneous group of lymphoproliferative disorders anatomically classified by distribution (unicentric CD [UCD], oligocentric CD [oligoCD], or multicentric CD [MCD]). Human herpes virus 8–negative MCD is called idiopathic MCD (iMCD), which includes clinical subtypes with varying phenotypes and responses: TAFRO (thrombocytopenia, anasarca, fever, renal dysfunction and/or reticulin fibrosis, and organomegaly), IPL (idiopathic plasmacytic lymphadenopathy), and not otherwise specified. OligoCD has recently emerged as an intermediate form between UCD and MCD, with unclear clinical behavior, and IPL has not been validated in a Western cohort. We retrospectively analyzed 217 patients with CD evaluated at our institution between January 2004 and August 2024. Survival probabilities were compared using log-rank tests. Overall, 57% had UCD, 20% had oligoCD, and 23% had iMCD. Patients with oligoCD and iMCD more frequently exhibited systemic symptoms than those with UCD. Patients with oligoCD and iMCD had significantly shorter event-free survival (EFS) of 8.9 and 2.3 years, respectively, than those with UCD (not reached; P&lt; .001 and P&lt; .02). Among iMCD subtypes, iMCD-IPL demonstrated a longer EFS than iMCD-TAFRO (P = .02), with no deaths during the follow-up periods. The results validate oligoCD and iMCD-IPL as new subtypes in this Western cohort. The survival outcome in oligoCD was intermediate between UCD and iMCD, with only a subset of patients with oligoCD requiring systemic therapy. iMCD-IPL had a favorable survival outcome in this US cohort, similar to what has been reported in non-Western countries. Tailoring treatment strategies to disease subtypes and vigilant monitoring of oligoCD for progression may improve survival outcomes in CD.