Rosenthal, Allison

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.

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.

Abstract Background Chimeric-antigen-receptor (CAR) T-cell therapy has transformed outcomes for relapsed or refractory hematologic malignancies, yet early identification of patients at highest risk for death remains elusive. A prognostic model that leverages only the information routinely available at the time of CAR-T evaluationcould guide pre-infusion counseling, intensity of follow-up, and resource allocation. Methods We analyzed a prospectively maintained cohort of CAR-T recipients treated at Mayo Clinic to develop and test our model. For each patient, data (vital signs, lab results, cardiac assessments [ECG and echocardiography], and PET/CT images) from initial CAR-T evaluation (up to 60 days prior to CAR-T infusion) was considered. Vital signs and labs were left un-imputed, while ECG, echocardiographic parameters, and PET/CT features were forward-filled from the preceding 60 days. Each patient-day was labeled as “outcome present” or “no outcome,” enabling supervised training of gradient-boosting models to predict overall survival (OS) at 6 months post CAR-T infusion. Model performance was evaluated using the area under the ROC curve (AUC) with 10,000-fold bootstrapping with data shuffling between each iteration to derive confidence intervals for stability. To enhance interpretability, we also developed a simplified model using the top 19 features (determined by Shapley value-based importance), aiming to preserve predictive accuracy with a smaller feature set. Results A total of 570 CAR-T cell treated patients were included in this study (476 patients in the training cohort and 94 patients in the validation cohort). The median age was 64 years and 61% males. The primary diagnoses were B-cell lymphoma (324 patients) and multiple myeloma (246 patients). A total of 4,851 individual vital-sign readings, 11,781 laboratory measurements were included. PET/CT images were available in 318 patients. The ML model demonstrated high discriminative ability for predicting OS at 6 months post CAR-T infusion. Vital signs alone produced a mean AUC of 0.63; radiomics features alone produced a mean AUC of 0.71, while laboratory tests alone produced a mean AUC of 0.84. The features most contributory to each single-modality model were then combined into a simplified multimodal model with a total of 19 features, which produced a mean AUC of 0.87, with a 95% CI of 0.73 - 0.97. The 19 features included in the simplified model were: LDH, ferritin, uric acid, eosinophils, fibrinogen, D-dimer, heart rate, mean arterial blood pressure, QTc, QT interval, and the radiomics features of sphericity, first-order Kurtosis, first-order mean, first-order energy, first-order total energy, shape maximum2D diameter (sagittal plane), SUV CoV, SUVp90, shape flatness. This multimodal predictive model was then applied to the validation cohort (uninvolved with model training or testing), which produced an AUC of 0.93, with a 95% CI of 0.84 - 0.98. Conclusions Our ML-driven approach successfully predicts overall survival 6 months post CAR-T infusion, highlighting those individuals at most risk of poor outcomes at the time of CAR-T evaluation. The strong performance of the multimodal version of the model suggests that each data domain contributes complementary resolving power, and by including novel data types, such as PET CT radiomics features, the most accurate predictions could be realized. By providing accurate, near-real-time risk stratification for poor outcomes within 6 months of CAR-T infusion, the model could facilitate proactive patient management. Prospective studies of the integration of this ML model into CAR-T treatment workflows will be crucial to confirm its clinical benefits and to facilitate its adoption in practice.

Abstract Background Neurolymphomatosis (NL) is a rare and frequently underrecognized manifestation of lymphoma, characterized by infiltration of peripheral or cranial nerves by malignant lymphocytes. Limited data exist to guide optimal management. We present the largest single-enterprise (included the 3 Mayo Clinic sites) cohort to date, evaluating disease characteristics, treatment patterns, and outcomes in patients (pts) with NL. Methods We conducted a retrospective analysis of pts ≥18 years (yrs) diagnosed with clinical, radiographic, or biopsy-confirmed NL at Mayo Clinic between January 2002 and December 2024. Baseline characteristics were compared using independent t-tests for continuous variables and Fisher's exact or Pearson's chi-square tests for categorical variables. Primary endpoints were overall survival (OS) and progression-free survival (PFS). Objective response rate (ORR) was a secondary endpoint. Kaplan–Meier methods, log-rank tests, and Cox proportional hazards regression were used to assess survival outcomes and associated factors. Results We included 149 pts. Median age at diagnosis was 64 yrs (range 22–83), and 23% were female. Most presented with stage IV disease (89%), and NL was part of the initial lymphoma presentation in 42% (primary NL). Diagnostic confirmation was based on histopathology and imaging in 55%, imaging alone in 26%, histopathology alone in 16%, and clinical features in 4%. Diffuse large B-cell lymphoma was the most common histology (61%), followed by marginal zone lymphoma (7%), and T-cell lymphoma (7%). Clinical presentation included painful neuropathy (65%), concurrent CNS involvement (37%), systemic disease (43%), elevated LDH (46.9%), and ECOG ≥2 (38%). Pattern of involvement included cranial nerves (29%), peripheral nerves (28%), and spinal nerve roots (27%). Median follow-up was 92 months (95% CI, 63–119). Among 137 treatment-evaluable pts, first-line regimens included methotrexate-based (MTX) (62%), bendamustine-based (B) (11%), cytarabine-based (Ara-C) (4%), and rituximab-based (R) (excluding MTX, B, or Ara-C) (15%) therapies. Radiation was used in 18%, and 21% underwent autologous stem cell transplant (ASCT). Among 103 response-evaluable pts, the ORR was 67% (52% complete response [CR], 16% partial response [PR]). ORRs by regimen were 78% (MTX), 92% (B), and 86% (R). On univariate analysis, primary NL (OR 4.3) and frontline ASCT (OR 8.47) were associated with higher odds of response (p&lt;0.05), while ECOG ≥2 (OR 0.24) and localized disease (OR 0.33) were associated with lower odds. On multivariable analysis (MVA), primary NL (OR 5.0 [1.5–12.1], p=0.02) and ECOG &gt;1 (OR 0.2 [0.06–0.79], p=0.018) remained significant predictors of response. Median PFS was 13 months (95% CI, 9–36), with a 2-yr PFS of 43% (95% CI, 35–53). On MVA, concurrent CNS involvement (HR 2.4, p=0.05), systemic disease (HR 2.7, p=0.02), and ECOG &gt;1 (HR 2.4, p=0.02) were associated with inferior PFS, while primary NL was associated with improved PFS (HR 0.3, p=0.002). Patients undergoing frontline ASCT had a median PFS of 108 months (95% CI, 58–not reached) with a 7-yr PFS of 62% (95% CI, 46–84). Among those receiving second line and beyond (2+) therapy, 24 received MTX-based regimens, 10 received CAR-T, and 8 received tyrosine kinase inhibitors. Post-ASCT median PFS was 30 months (95% CI, 15–NR). Among CAR-T recipients, evaluable responses (n=8) included 4 CR, 1 PR, and 3 PD. Median OS was 56 months (95% CI, 29–133), with a 5-yr OS of 50% (95% CI, 41–60). On MVA, ECOG ≥2 (HR 2.5, p=0.02) predicted worse OS, while receipt of ASCT (HR 0.4, p=0.04) and primary NL (HR 0.5, p=0.04) were associated with improved OS. Conclusion This study represents the largest single-enterprise cohort of pts with NL, providing comprehensive insight into its clinical presentation, treatment patterns, and outcomes. Primary NL and preserved PS were independently associated with improved response rates and survival, while CNS and systemic involvement at diagnosis portended poorer PFS. MTX and bendamustine-based regimens achieved high ORR, and frontline ASCT was associated with significantly prolonged remission duration and OS. These findings highlight the prognostic importance of baseline disease burden and functional status, support the early incorporation of high-intensity regimens in eligible patients, and underscore the urgent need for prospective studies to guide evidence-based management in this rare and understudied population.

Abstract Introduction: Anti-CD19 chimeric antigen receptor T-cell (CART) therapy is feasible with an acceptable safety profile in older patients (pts) with relapsed/refractory (RR) large B-cell lymphoma (LBCL) (Tun et al, AJH 2024). However, immune effector cell-associated neurotoxicity syndrome (ICANS) is a frequent and challenging toxicity in older pts, and its characteristics and associated risk factors are not well defined. Methods: Seven centers participated in this retrospective study. Pts aged ≥65 years (yrs) with RR LBCL treated with a commercial CART product were included. Baseline characteristics, treatment, CART toxicity, and outcomes were collected. ICANS was graded by the ASTCT criteria in most pts. Cumulative incidences of ICANS were estimated using deaths/progressions as competing risks, and associations between incidences and variables were assessed by Gray's test. A 30-day landmark was set after infusion to reduce immortal time bias. Progression-free survival (PFS; time from the landmark to disease progression or death) and overall survival (OS; time from the landmark to death) were analyzed with the Kaplan-Meier method. Statistical analyses were done in JMP v18. Results: A total of 224 pts were included. Median age at infusion was 71 yrs (range 65–89; ≥80 yrs, 12%. 142 (63%) were male and 27 (13%) had an ECOG PS scale of ≥2. The median HCT CI was 1 (range 0–6). LDH was elevated in 110 (51%) pts, 23 (19%) had bulky disease ≥7 cm, 176 (82%) had advanced-stage, and 145 (71%) had extranodal disease. Median number of prior treatments was 2 (range 1–8), and 44 (20%) had prior bendamustine. Prior stem cell therapy was done in 47 (21%) pts (autologous stem cell transplant [SCT], n = 45; allogeneic SCT, n=2). Axicabtagene ciloleucel (axi-cel) was most frequently used (58%; n=131) followed by lisocabtagene maraleucel (liso-cel) (16%, n=36) and tisagenlecleucel (tisa-cel) (25%, n=57). Median duration of ICANS was 6 days (interquartile range [IQR] 3–11; range 1–303). Median time to ICANS onset was 6 days (IQR 3.8–8.0; range 0–24). The 30-day cumulative incidence of all grade ICANS was 50.9% (95% confidence interval [CI] 44.7–57.9) and grade ≥3 ICANS was 29.8% (95% CI 24.0–37.0). There was no significant difference in incidence of all grade (P=0.39) and grade ≥3 (P=0.49) ICANS as well as duration of ICANS (≥14 vs &lt;14 days; P=0.08) according to the age groups (≥65–69 vs ≥70–74 vs ≥75–79 vs ≥80–89). In contrast, pts with an ECOG PS scale of ≥2 were associated with a higher incidence of all grade (30-day estimate, 66.7% vs 47.0%; P=0.02) and grade ≥3 ICANS (30-day estimate, 53.8% vs 25.6%; P=0.002). In addition, pts with an elevated LDH level had a higher incidence of grade ≥3 ICANS than those without (30-day estimate, 37.5% vs 18.8%;P=0.004). The incidence of ICANS varied among various CART products, with 30-day estimates of 64.9% vs 33.3% vs 29.8% (P&lt;0.0001) for all grades and 40.1% vs 13.2% vs 18.0% (P=0.004) for grade ≥3 ICANS in pts treated with axi-cel vs tisa-cel vs liso-cel, respectively. A trend toward higher grade ≥3 ICANS was seen in pts treated with ≥3 prior lines than those treated with 2 or 1 (30-day estimate, 35.7% vs 28.1% vs 10.5%; P = 0.07). ICANS was managed with steroids in 47 (41%) pts, levetiracetam +/- other agents in 46 (40%) pts, biologic agents +/- other drugs in 15 (13%) pts, and supportive care in 6 (5%) pts. Median follow-up from the 30-day landmark post-infusion was 17.2 months (95% CI 14.2–22.4). Median PFS was 10.5 months (95% CI 5.7–18.2) and OS was 18.2 months (95% CI 13.3 vs 27.5). There was no difference in PFS and OS in pts who experienced ICANS vs no ICANS (median PFS 14.7 vs 10.1 months; P=0.65 and median OS 18.2 vs 24.7 months; P=0.49). However, there was a trend toward inferior PFS and OS in pts who experienced high-grade ICANS compared to those who had grade 0-2 ICANS (median PFS 9.2 vs 21.2; P=0.14 and median OS 11.7 =vs 22.9; P=0.39). Conclusions: This large study of older pts with RR LBCL demonstrated acceptable risks of ICANS, comparable to that in pivotal registration trials, with a relatively higher rate of grade ≥3 ICANS. The risks of ICANS were similar regardless of patient age, including those aged ≥80 yrs, but different among CART products. Similar to younger pts, high tumor burden disease and poor performance status at CART infusion were associated with higher risks of ICANS. A comprehensive neurologic and frailty assessment is warranted to identify suitable CART candidates.