Citations

TPS10057 Background: Ewing sarcoma (EWS) is an aggressive tumor of the bone and soft tissue primarily affecting adolescents and young adults. For patients with recurrent disease, outcomes remain poor with 5-year survival rates of 10%–15%, highlighting the need for novel approaches. STEAP1 is highly expressed in EWS cell lines, xenograft models, and patient tumor samples 1 and is transcriptionally regulated by EWSR1::FLI1 , the main oncogenic driver in EWS. Xaluritamig, a humanized bispecific XmAb 2+1 T-cell engager targeting STEAP1 and CD3, has demonstrated potent, antigen-dependent T-cell–mediated lysis of STEAP1-expressing EWS cell lines 1 and induced robust in vivo antitumor activity in EWS xenograft models with concomitant CD8+ T-cell activation. In metastatic castration-resistant prostate cancer, another STEAP1-expressing solid malignancy, xaluritamig has shown meaningful antitumor activity and a manageable safety profile 2 supporting its clinical evaluation in R/R EWS. Methods: This ongoing, phase 1b, single-arm, open-label, two-part, multicenter study (NCT07297979) is evaluating xaluritamig in pediatric, adolescent, and adult patients with a histologic diagnosis of EWS and an EWSR1 :: ETS fusion gene locally confirmed by next-generation sequencing. Patients must have R/R EWS following ≥1 line of systemic therapy and Karnofsky or Lansky performance status ≥70. Part 1 (dose confirmation) consists of cohort 1 (age ≥12 years) and cohort 2 (age ≥2 to <12 years), and part 2 (dose expansion) includes patients aged ≥2 years with no upper age limit at enrollment. This study includes a 21-day screening period, a treatment period, a safety follow-up period, and a long-term follow-up period. Patients receive xaluritamig every 2 weeks once reaching the target dose until radiographic disease progression per RECIST v1.1, clinical disease progression, unacceptable toxicity, initiation of other anticancer therapy not permitted per protocol, withdrawal of consent, death, or end of study as determined by the sponsor, whichever is earlier. Primary endpoints are safety, tolerability, and determination of recommended dose(s) for expansion. Secondary endpoints are pharmacokinetics, preliminary antitumor activity, and immunogenicity. Biomarker evaluation as an exploratory endpoint includes assessment of serum cytokines, other pro-inflammatory markers, immune-cell subsets, circulating tumor DNA, STEAP1 protein expression, and tumor genomic profile and their association with safety and efficacy. CRS mitigation plan includes step-up dosing, steroid premedication, and administration of interleukin-6 inhibitors as needed. Site activation commenced in January 2026 and enrollment is ongoing. 1. Nolan-Stevaux O. Cancer Res. 2020;80(16_Suppl):DDT02–03. 2. Kelly WK, Danila DC, Lin CC, et al. Cancer Discov . 2024;14(1):76-89. Clinical trial information: NCT07297979 .

e13661 Background: The process of data abstraction and curation represents a significant proportion of clinical trial costs, requiring up to $30,000 per patient depending on the data complexity. Research variables such as stage, performance status (PS), and cancer disease status are typically present in the electronic health record (EHR) in free text but require abstraction for research use. Structuring these real-world data (RWD) in an exportable format utilizing minimum common oncology data elements (mCODE) could reduce the need for abstraction and the potential for erroneous transcription that has been shown to be 5.51-7.72% in a meta-analysis. We assessed the feasibility, availability, and accuracy of RWD for use across Alliance for Clinical Trials in Oncology sites by comparing RWD to matched electronic case report form data (CRF data) at 2 sites where EHR-integrated tools collect structured RWD for all solid tumor patients. Methods: Stage, PS, and cancer disease status (CDS) were extracted from the EHR as RWD and from eCRFs for 100 patients enrolled in National Clinical Trials Network trials across multiple disease sites from 2022 through 2025. Data from baseline and day 1 of cycles 1-4 were compared (500 encounter timepoints). Results: Stage, PS, and CDS were available from RWD more often than CRFs (85 vs 70 / 100 patients, 436 vs 416 / 500 encounters, and 345 vs 83 / 500 encounters respectively). When stage was present in both (n = 58), there were 5 mismatches (8.6%). PS had 22 mismatches (4%) and CDS had 45 mismatches (11.4%). After correcting for differences in CRF vs RWD definitions, CDS mismatches dropped to 1.2% (n = 4). Discordance between RWD and CRF data was related to differences in the timepoint of data collection (e.g., systemic staging scans completed after the initial consult), CRF data obtained from other EHR notes (e.g., oncology research nurse notes containing PS), inherent discordance between RWD and CRF data choices (e.g., responding vs complete or partial response), and failure to update EHR data capture tool fields. RWD were available 21 days (median) before CRF data. Conclusions: Our results suggest that RWD captured directly within clinical notes match CRF data, are more often present, and are available sooner. RWD could serve as a data source for trials, decreasing the need for manual curation and cost. Although some discordance exists, overall rates are low and close to the published error rate for abstraction.

e13660 Background: We previously showed success with collection of structured data elements within the electronic health record using the minimum common oncology data elements (henceforth, mCODE tool) supported by soft-stop close visit validation (CVV) and staff support interventions in a limited cohort. Since the additional interventions are resource-intensive and difficult to apply across an entire department, we therefore implemented a department-wide roll-out supported by the least resource-intensive interventions while monitoring uptake. Methods: The mCODE tool collects five data elements: stage, disease status, performance status, intent of treatment, and intent to change therapy. The Department of Oncology providers received education on the mCODE tool and were asked to implement use at a faculty meeting; soft-stop CVV was enabled at that time. The percentage of data captured for each data element was collected from the notes of providers who did not participate in prior pilots for the 4 months post-implementation. mCODE tool usage was defined as entry of at least one data element except stage (as stage has long been a structured data element in Epic, can be entered independently of the mCODE tool, and many providers were entering stage prior to mCODE tool introduction). Results: 86 MDs and 20 APPs for a total of 106 providers were included, resulting in 14,973 visits and 8,745 unique patients. 73.6% of providers (n =78) utilized the mCODE tool. Of those who utilized the tool, median overall data capture across the four data elements was 24.0%. 22.6% of providers (n =24) achieved > 80% overall data capture and 32.1% (n =34) had over 50% overall data capture despite minimal intervention. 75.6% of MDs (n = 65) utilized the tool with a median data capture of 16.3% compared to 65% of APPs (n = 13) with a median data capture of 90.3%. Rates of data capture for individual data elements were similar. Conclusions: Use of soft-stop CVV appears to drive utilization of the mCODE tool but is insufficient to promote > 80% data capture for most providers without more intervention. Supportive interventions such as individualized assistance with incorporation into existing templates and directed communication from leadership remain necessary to promote use of structured data elements and will be implemented in sequence to understand how we achieve the target data capture ( > 80%) at scale.

e13076 Background: Progression-free survival (PFS) is frequently used as a primary endpoint in contemporary oncology trials, including antibody–drug conjugate (ADC) studies in metastatic breast cancer (MBC) settings, despite uncertainty regarding its validity as a surrogate for overall survival (OS). A meta-epidemiological analysis was performed to quantify the systematic differences between the treatment effects on PFS and OS in ADC randomized clinical trials (RCTs). Methods: Phase II–III ADC RCTs with MBC patients reporting hazard ratios (HRs) with 95% confidence intervals (CIs) for both progression-free survival (PFS) and overall survival (OS) were included. Differences in treatment effects within each trial were quantified using the ratio of hazard ratios (rHR = HR_PFS/HR_OS). Random-effects models were used to pool the rHRs across the trials. Prespecified subgroup and meta-regression analyses were used to evaluate the trial characteristics, clinical context, and regulatory status. Sensitivity analyses assessed the robustness across varying assumptions of the within-trial correlation between the endpoints. Results: Twenty-one RCTs evaluating nine ADCs in patients with MBC were included, comprising three phase II (14%) and 18 phase III (86%) studies. The pooled rHR demonstrated significantly larger treatment effects for PFS than for OS (rHR, 0.79; 95% CI, 0.73–0.86), with no between-trial heterogeneity (I² = 0%, τ² = 0). Sensitivity analyses assuming within-trial correlations between PFS and OS ranging from 0 to 0.95 yielded consistent results. Greater divergence between endpoints was observed in trials enrolling patients with triple-negative breast cancer (rHR, 0.72; 95% CI, 0.61–0.85) and HER2-low disease (rHR, 0.77; 95% CI, 0.62–0.96). Phase III trials showed larger differences than phase II trials (rHR, 0.79 vs. 0.89). When OS was a secondary endpoint, divergence was greater than when OS was primary (rHR, 0.77 vs. 0.87). Across trials, 16 of 21 studies (76%) demonstrated concordant effect directions between PFS and OS: 11 (52%) with a statistically significant benefit for both endpoints and 5 (24%) with neither endpoint significant. Discordance occurred in five trials (24%), all characterized by a statistically significant PFS benefit without a corresponding OS improvement. No trial demonstrated an OS benefit in the absence of a PFS benefit. Conclusions: Across ADC trials, PFS systematically overestimated OS treatment effects, underscoring the limitations of PFS as a standalone surrogate endpoint. These findings support the cautious interpretation of PFS-driven efficacy and reinforce the need for endpoint validation frameworks tailored to the mechanism, disease context, and trial design.

9567 Background: Resected stage IIB/C and microscopic stage IIIA/B/C/D melanoma presents a significant risk of recurrence risk of 37 to 62% at 5 years (yrs). While 1 year of adjuvant PD1 therapy reduces this risk by approximately half, the individual patient’s baseline risk without treatment and their specific benefit from adjuvant PD1 remain unknown. We aimed to generate two separate predictive models for recurrence – one for untreated and one for treated pts - to calculate the individualised benefit of adjuvant PD1. Methods: Pts with resected stage IIB/C or microscopic stage IIIA/B/C/D melanoma, either treated with adjuvant PD1 or untreated at 13 major melanoma centres, and with at least 2 years of follow-up from surgery were included. We analysed pts demographics, disease characteristics, blood parameters, pathological and imaging data at baseline, and clinical outcomes. Propensity scores were estimated using logistic regression with covariates associated with treatment in univariate screening (p<0.05). Separate penalised multivariable logistic regression models were built to predict recurrence for treated and untreated pts. A tool based on these models was created to calculate the individual patient benefit from adjuvant PD1. Results: A total of 3560 pts was included, divided into untreated (discovery n=2384 and validation n=597) and treated (discovery n=404 and validation n=175) cohorts. The model for untreated pts included age, gender, primary site, histological subtype, Breslow thickness, ulceration, mitosis, number and site of regional lymph nodes (LN), mutation status and LDH (AUC 0.70 in discovery, 0.69 in validation). The model for treated pts included age, gender, Breslow thickness, site of regional LN, and mutation status (AUC 0.69 in discovery, 0.61 in validation). ADAPT-M (ADjuvant Anti-PD-1 Tool for Melanoma) calculates the individualized benefit of adjuvant PD1. For example, patient A, a woman ≤45 years old with a primary head & neck melanoma (superficial spreading, thickness >1-2 mm, no ulceration, 1 mitosis/mm 2 ), 2 clinically occult LN metastases in the neck, NRAS mutant, and normal LDH, has a 12-month recurrence risk of 21.9% untreated vs 2.3% treated (benefit 19.6%), and a 24-month risk of 41.1% untreated vs 6.4% treated (benefit 34.7%). While, patient B, a man ≤45 years old with a primary melanoma in the lower limb (nodular, thickness >1-2 mm, no ulceration, 1 mitosis/mm 2 ), 2 clinically occult LN metastases in the groin, BRAF V600 mutant, and normal LDH, has a 12-month recurrence risk of 14.2% untreated vs 11.8% treated (benefit 2.5%), and a 24-month risk of 28.0% untreated vs 30.1% treated (no benefit). Conclusions: ADAPT-M, a clinical tool that estimate individualized recurrence risks with and without adjuvant anti-PD1 therapy, quantifies the absolute benefit of treatment for pts with high-risk resected melanoma, facilitating personalized adjuvant therapy decisions.

9535 Background: MUM carries a poor prognosis with few effective treatments, especially for HLA-A*02:01-negative pts. While a subset of MUM pts responds to immune checkpoint inhibitors, the comparative efficacy of IPI+PD1 versus PD1 monotherapy is unclear. This study compares objective response rate (ORR), progression-free survival (PFS), overall survival (OS), and safety between these regimens in MUM. Methods: MUM pts treated with PD1 or IPI+PD1 at 15 major melanoma centres (from Australia, Europe, United States and Israel) were included. Demographics, patient and disease characteristics, and clinical outcomes were examined. Univariate and multivariate (MVA) analyses were performed to identify clinical predictors of response and survival. Results: Of 412 MUM pts treated, 150 (36%) had PD1 and 262 (64%) received PD1+IPI. Compared to the PD1 group, PD1+IPI-treated pts were younger (64 vs. 70 years; p<0.001), had higher rate of elevated LDH (44% vs. 31%; p=0.037), and more frequently had prior treatment with tebentafusp (6.5% vs. 1.3%; p=0.031). Median follow-up from commencement of PD1+/-IPI was 4.9 years (95% CI 4.7 – 6.1). ORR was higher in IPI+PD1 group (18%) vs. PD1 (9%) (p=0.008), particularly in males (p=0.009), patients with liver metastases (p=0.018) and with lung metastases (p=0.011), with elevated LDH (p=0.047) and with no prior treatment with tebentafusp (p=0.007). PFS and OS at 1 and 2 years were numerically higher with IPI+PD1 (1- and 2-year PFS: 23% and 15%; 1- and 2-year OS: 63% and 39%) vs. PD1 (1- and 2-year PFS: 17% and 11%; 1- and 2-year OS: 54% and 36%), but these differences were not statistically significant (p>0.05). On MVA, adjusting for predefined variables (age, gender, ECOG PS, LDH, presence/absence of liver/lung metastases, prior treatment with tebentafusp), IPI+PD1 was associated with higher ORR (OR 2.71, 1.30 – 6.05; p=0.01) but not with PFS or OS compared to PD1. Presence of liver metastases (ORR [OR 0.28; 95% CI 0.13 - 0.64], PFS [HR 1.61; 95% CI 1.11 - 2.34], OS [HR 2.02; 95% CI 1.32 - 3.10]), ECOG PS≥2 (PFS [HR 1.82; 95% CI 1.07 - 3.10], OS [HR 2.24; 95% CI 1.25-4.01]), elevated LDH (PFS [HR 1.66; 95% CI 1.30 - 2.11], OS [HR 2.39; 95% CI 1.84-3.10]) and prior tebentafusp treatment (OS [HR 2.36; 95% CI 1.21-4.59]) were also independent predictors of response and/or survival. A higher percentage of pts experienced grade ≥3 immune-related adverse events (irAEs) in the IPI+PD1 group compared to the PD1 group (34% vs 13%, p<0.0001). Most pts ceased treatment due to progression (234, 57%), and more pts stopped due to toxicity in the IPI+PD1 vs. PD1 group (25% vs. 9%, p<0.0001). Conclusions: In pts with MUM, IPI+PD1 demonstrated a higher ORR but did not improve survival compared with PD1 alone. IPI+PD1 was more toxic, leading to early treatment discontinuation in one-quarter of pts. These findings may help guide treatment selection in MUM.

9021 Background: An escape room is a team-based activity in which participants collaborate to solve puzzles within a fixed time limit and escape. Prior studies demonstrate high learner satisfaction and improved teamwork and communication with medical education escape rooms. To date, no published reports describe an escape room designed specifically for hematology/oncology (hem/onc) fellows. As part of Hem/Onc Curriculum, we designed and implemented a hem/onc-focused escape room and evaluated learner satisfaction and perceived learning impact. Methods: The escape room was designed using evidence-based learning science principles (Table 1). Teams worked to solve sequential puzzles, centered on a patient case, addressing key hem/onc concepts, including transfusion medicine, peripheral blood smears, lymphoma classification, and coagulation disorders. Each solved puzzle revealed additional patient laboratory and pathology data. Successful completion required teams to diagnose the patient and select appropriate treatment. Following the activity, teams participated in a facilitator-led debrief focused on team dynamics, communication, and problem solving. Teams observed each other and shared feedback. A larger group debrief co-led by chief fellows and faculty reviewed learning objectives and key takeaways. Learner outcomes were assessed using a post-activity survey evaluating satisfaction, engagement, and impact on learning. Results: Eighteen learners participated, including 14 hem/onc fellows, 1 APP hem/onc fellow, and 3 internal medicine residents. Three of four teams escaped within 1 hour (mean completion time, 52 minutes). All participants provided positive verbal feedback. Fifteen learners completed the post-activity survey, and all respondents reported satisfaction with the activity and would recommend it for future fellows. One-word descriptors included “engaging,” “fun,” and “challenging.” Most respondents (93.3%) agreed that the activity reinforced key hem/onc concepts and improved confidence in clinical problem-solving. All learners felt the activity supported learning, and 93.3% reported that collaboration enhanced the learning experience. Participants identified team-based problem solving, clinical application, and interactive puzzles as the most effective components. Conclusions: A hem/onc escape room resulted in high learner satisfaction and reinforced hem/onc knowledge and clinical problem-solving. This feasible, fun, and innovative activity complements traditional fellowship education by leveraging adult learning principles through gamified, team-based learning and should be incorporated into training programs’ curriculum. Learning science principles applied in a hem/onc escape room. Active Learning Belonging Contrasting Cases Debriefing Deliberate Practice Elaboration Generation Listening and Sharing Reward Worked Examples

8519 Background: Non-classical mutations (NCM) of epidermal growth factor receptor (EGFR) represent a broad group of oncogenic mutations in non-small cell lung cancer (NSCLC), often associated with resistance to osimertinib (e.g., P-loop and αC-helix compressing [PACC] mutations). Patients (pts) with NCM have inferior clinical outcomes with a high incidence of CNS metastases (CNSm). Silevertinib (BDTX-1535) is a fourth-generation, covalent EGFR TKI with high CNS penetrance targeting both EGFR classical mutations and NCM. The antitumor activity and safety of silevertinib in pts with advanced NSCLC were evaluated in a Phase 2 trial (NCT05256290). Methods: Pts with advanced EGFR NCM NSCLC who received no prior systemic therapy were enrolled (Cohort 3) based on a local molecular test. Silevertinib 200 mg was administered orally once daily. The primary endpoint of objective response rate (ORR) was assessed using RECIST v1.1. CNS ORR was assessed using Response Assessment in Neuro-Oncology for Brain Metastases (RANO-BM) in pts with no prior CNS therapy. Secondary endpoints include progression-free survival (PFS), duration of response (DOR), disease control rate (DCR), and safety. Results: From February 2024 to July 2025, 43 pts with 35 unique NCMs (including 25 pts with PACC mutations) were enrolled in Cohort 3: median age 70.0 years, 72% female, 74% white, and 42% never smoked cigarettes. As of November 3, 2025, median follow-up was 7.2 months. ORR by RECIST v1.1 is shown in the table. Sixteen pts (37%) had CNSm, including 7 pts (16%) with measurable CNS disease. RANO-BM CNS response was 86% (n=6/7). Complete clearance of EGFR VAF ctDNA was observed in 21/26 (81%) evaluable pts. Serious treatment-related adverse events (TRAEs) were reported in 5 pts (12%), and 4 pts (9%) discontinued treatment due to TRAEs. The most common TRAEs included rash (19%, grade 3), diarrhea (19%, grade 3), stomatitis (9%, grade 3), and paronychia (5%, grade 3). While 77% of pts had a dose reduction, 19/22 (86%) patients with a radiographic response maintained or deepened their response after dose reduction, including all patients with CNS response. Conclusions: Silevertinib demonstrated robust antitumor activity in treatment-naïve pts with a broad spectrum of NCM EGFR driver mutations with high intracranial activity in pts with brain metastases. The safety profile was consistent with the known AEs of the EGFR TKI class. Clinical trial information: NCT05256290 . Data as of November 3, 2025 N ORR n (%) 95% CI DCR n (%) 95% CI ITT Population * 43 26 (60) (44.4, 75.0) 39 (91) (77.9, 97.4) PACC mutations 25 14 (56) (34.9, 75.6) 22 (88) (68.8, 97.5) Compound mutations † 16 11 (69) (41.3, 89.0) 15 (94) (69.8, 99.8) *No pts with classical (E746_A750del or L858R) mutations were enrolled. † Presence of ≥ 2 NCMs. As of December 27, 2025, the 6-month PFS rate was 86% overall and 80% in pts with CNSm; available PFS and DOR data will be presented.

8057 Background: The NCI Adjuvant Lung Cancer Enrichment Marker Identification and Sequencing Trials (ALCHEMIST) study tested resected stage II-IIIB(N2) NSCLC for targetable EGFR and ALK alterations and PD-L1 expression to screen patients for biomarker-driven adjuvant therapy trials (NCT02194738). Local lab results were requested when available for comparison to central lab results. Methods: We identified patients with both central and local PD-L1 results in the ALCHEMIST adjuvant immunotherapy study screening population. Discrepancies in tumor proportion score (TPS) between central and local PD-L1 were defined using prespecified cut-points (granular: <1%, 1-<5%, 5-<10%, 10-<20%, 20-<30%, 30-<40%, 40-<50%, 50-60%, 60-<70%, 70-<80%, 80-<90%, 90-<100%; clinical: <1%, 1-<50%, 50-100%). Large discrepancies were defined as ≥ 50 percentage point differences; for these cases, local PD-L1 and hematoxylin & eosin-stained slides were requested. These and all central slides were digitally scanned at 40X. Three expert pathologists reviewed the scanned slides in a blinded fashion. Differences between patient or tumor characteristics were assessed by Chi-square and Wilcoxon rank sum tests. Percent agreement was computed within and between the reviewers. Results: Central and local PD-L1 results were available for 497 patients. 177 cases (36%) had discrepancies that placed patients in different clinical TPS categories (<1%, 1-<50%, 50-100%). Among these, 43 cases had large TPS discrepancies; 14 had available local testing slides. 46 of the 320 cases without discrepancy were randomly selected as controls. No patient or tumor characteristics were associated with discrepancy, including tumor location, histology, grade or cancer stage (p > 0.05). Pairwise comparisons of TPS according to three pathologists re-reading the same scanned PD-L1 slides showed similar levels of disagreement in clinical TPS category whether there was initial discrepancy between central and local results (24%, 34%, and 23%; mean 27%) or not (33%, 30%, and 20%; mean 28%). Among the 14 cases with large initial discrepancies and local slides available, there was agreement in clinical TPS category when the anonymized central and local scanned slides were re-read by the pathologists in 54%, 38%, and 43% of cases, despite 100% non-agreement in initial reads. There were higher rates of agreement when the tested slides both originated from the same (100%) versus different (35%) blocks, when the same (48%) versus different (40%) PD-L1 antibodies were used, and when the cases were of non-squamous (52%) versus squamous (14%) cell carcinoma. Conclusions: There are substantial differences in PD-L1 TPS read by pathologists that can result in placement of patients in different categories commonly used for clinical management. These differences have clear implications in clinical trials and routine patient care.