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Abstract Background Preparing tumor board patient summaries is time intensive. Large-language-model based systems may automate summarization but require real-world evaluation prior to clinical use. We performed an exploratory retrospective evaluation of the Microsoft Healthcare Agent Orchestrator (HAO), deployed in a Mayo Clinic–controlled staged environment, to generate tumor board–style patient summaries from retrospective Electronic Health Record (EHR) notes. Methods HAO generated summaries for breast, hepatobiliary, and neuro-oncology tumor board cases using up to the most recent 1,000 clinical notes. Clinician reviewers evaluated outputs via REDCap surveys across perceived factuality, completeness, clarity/conciseness, temporal cohesion, comparative performance, safety, and clinical utility (0–4 Likert scale). Reviewers were permitted to query the HAO chat interface to address missing details. Automated factuality was assessed using TBFact (bidirectional entailment), reporting precision and recall against available reference summaries. Results Among 57 survey responses from 5 different physicians, mean scores exceeded 2.8 across domains, with medians of 3 for most axes. In an exploratory comparison, oncology fellows required less time to review HAO-generated summaries than to manually generate patient summaries (mean difference 13.57 minutes per patient, p<0.001), although this difference may be influenced by prior familiarity with the same cases; 96% of survey responses indicated that HAO would save time. TBFact evaluations showed higher recall than precision across domains, consistent with broad capture of reference content alongside additional content that was not present in gold-standard summaries. Attribution was viewed favorably but showed issues with primary-source specificity and link reliability. Conclusions In a controlled Mayo environment, HAO demonstrated moderate performance and was associated with reduced review time for tumor board preparation. These findings are promising but preliminary and do not establish clinical safety, noninferiority to manual review, or readiness for routine clinical use. Limitations, including verbosity, specialty-specific content gaps, and inconsistent attribution, highlight the need for iterative refinement and further evaluation. Graphical Abstract Created using ChatGPT

Recent advances in gastroesophageal cancer highlight the continued evolution of systemic therapy across disease stages, including refinements in perioperative strategies and expansion of biomarker‐directed treatment in advanced disease. Emerging therapeutic approaches, including next‐generation antibodies and combination strategies, are expected to further shape treatment selection and improve outcomes.

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.

12007 Background: YAs with cancer, defined as those diagnosed prior to the age of 40, may experience greater symptom burden compared to those diagnosed at older ages. We evaluated the impact of an ePROM-triggered, CC-based, supportive care intervention on symptom burden in YAs with cancer. Methods: The Enhanced, Electronic Health Record-facilitated Cancer Symptom Control (E2C2) trial was a cluster-randomized, pragmatic trial (NCT03892967) that enrolled oncology patients seen at Mayo Clinic Rochester and within the Mayo Clinic Health System (sites in Minnesota and Wisconsin) between March 2019 and January 2023. Patients routinely reported the severity of six SPPADE symptoms (Sleep deficit, Pain, Physical function impairment, Anxiety, Depression, and Energy deficit/fatigue) on 11-point numerical rating scales. Symptom severity was classified as none to mild (0-3), moderate (4-6), or severe (7-10). Those with moderate or severe symptoms were offered symptom management education, while those with severe symptoms were also offered the option to work virtually with a symptom care manager. In this post hoc analysis, we examined the effects of the intervention on change in symptom scores among YAs and time to improvement to a mild/moderate level in those with at least one severe symptom. Results: Among 40,659 patients who responded to at least one survey, 2,598 were YAs and 38,061 were adults ≥ 40 years. Among YAs, 10.7%, 24.3%, and 65.1% were 18-24 years, 25-31 years, and 32-39 years, respectively. A greater proportion of YA patients were female (61.5% vs 57.0%), of a non-white race (12.4% vs 5.3%), living in an urban setting (65.7% vs 55.1%), single (36.4% vs 9.6%), on non-government insurance (78.1% vs 36.6%), employed (72.6% vs 37.4%), and had a Bachelor’s degree or greater (37.8% vs 28.3%, p < 0.001 for all). Fewer YA patients had metastatic disease compared to older patients (24.2% vs 35.2%, p < 0.001). Compared to a pre-intervention baseline, the mean change in symptom score for YA patients following the intervention demonstrated improvements in anxiety (-0.195) and depression (-0.168, p < 0.0001 for both), while physical function impairment worsened over the intervention period (0.101, p = 0.014). Compared to adults ≥ 40 years, YAs experienced greater intervention effects for management of depression ( p < 0.001), fatigue ( p = 0.01), pain ( p < 0.0001), and physical function impairment ( p < 0.0001). Among YAs with a severe symptom during the intervention period, median time to improvement to a mild/moderate level was longer for anxiety compared to those ≥ 40 years (3.5 vs 2.7 months, p = 0.009). Conclusions: Routine ePROM surveillance, coupled with CC-based symptom management, led to improvements in anxiety and depression among YAs with cancer, although, time to improvement was longer for anxiety compared to adults ≥ 40 years.

The traditional combined hematology and oncology fellowship model is increasingly strained by accelerating subspecialization and therapeutic complexity. This commentary focuses on structural reforms within fellowship training to better align education with contemporary clinical practice and evolving workforce demands. We propose standardized, career-aligned tracks, earlier subspecialty focus within fellowship, and redesigned continuity clinic models that preserve foundational breadth while enabling depth of expertise. We further highlight how emerging assessment reforms and educational tools can support these structural changes. By aligning fellowship training with both real-world clinical demands and established educational science, we can ensure a more sustainable and effective pathway for preparing future hematologists and oncologists.

64 Background: The incidence of eoCRC (diagnosed at age <50) is increasing. We have previously shown higher rates of severe anxiety and depression in this group compared to adults diagnosed at age ≥50 (average-onset CRC, aoCRC). We assessed the impact of ePROM collection coupled with a CC-based supportive care intervention on symptom burden in patients with eoCRC. Methods: The Enhanced, EHR-facilitated Cancer Symptom Control (E2C2) trial was a cluster-randomized, pragmatic trial conducted at Mayo Clinic Rochester and within the Mayo Clinic Health System (in Minnesota and Wisconsin) between 2019 and 2023. Enrolled patients serially reported the severity of six SPPADE symptoms (Sleep deficit, Pain, Physical function impairment, Anxiety, Depression, and Energy deficit/fatigue) on 11-point numerical rating scales. Symptom scores were interpreted as none to mild (0-3), moderate (4-6), or severe (7-10). Those with moderate symptoms were offered symptom management education and self-management materials, while those with severe symptoms were offered the option to work with a symptom care manager. In this post hoc analysis we examined the effects of the intervention on change in symptom scores among those with eoCRC, and using the Kaplan-Meier method, the median time to improvement to a mild/moderate level in those with a severe symptom, comparing eoCRC with aoCRC. Results: Among 4,422 patients with CRC, 887 had eoCRC. Forty-six percent of patients were female ( p =0.61), and a greater proportion of eoCRC patients were of a non-white race (11.6% vs 4.7%), lived in an urban setting (65.6% vs 50.5%), had a Bachelor’s degree or higher (36.8% vs 24.4%), were employed (78.5% vs 36.7%), on a non-government insurance plan (83.4% vs 35.3%), single (19.7% vs 11.2%) or married (74.2% vs 67.9%), and had metastatic disease (54.8% vs 46.1%, p <0.0001 for all). Compared to a pre-intervention baseline, the mean change in symptom scores for eoCRC patients following the intervention demonstrated improvements in anxiety (-0.238), depression (-0.134), and fatigue (-0.146, p <0.05 for all). Time to symptom improvement from severe to mild/moderate was longer for anxiety and depression in eoCRC patients compared to aoCRC patients (Table 1). Conclusions: Collection of ePROMs coupled with a CC-based intervention led to improvements in anxiety, depression, and fatigue among eoCRC patients, although time to symptom improvement was longer among eoCRC patients compared to aoCRC patients. Time to improvement of severe symptom (months), median (95% CI). eoCRC aoCRC p -value Sleep 4.2 (3.1-5.8) 3.8 (2.7-5.4) 0.4 Pain 4.1 (3.0-7.5) 3.2 (2.3-5.0) 0.5 Physical function impairment 3.1 (1.9-6.3) 2.8 (2.2-4.2) 0.5 Anxiety 4.3 (3.4-9.1) 2.6 (2.0-3.5) 0.009 Depression 4.3 (2.6-7.5) 2.6 (2.1-3.5) 0.04 Fatigue 4.3 (3.7-9.5) 3.4 (2.7-4.2) 0.06

451 Background: In first-line E/GEJ/G adenocarcinoma, anti-PD-1 + chemotherapy improves outcomes, yet many patients do not respond. Beyond PD-L1 CPS, tumor-intrinsic biomarkers of primary resistance remain limited. We applied a validated IFN-γ–linked T cell-inflamed (Tinfl) framework to define exclusionary programs and nominate targets. Methods: Bulk RNAseq was analyzed from TCGA ESCA/STAD adenocarcinoma (n=79, discovery) and a de novo Mayo cohort of pretreatment samples (n=74, validation) with harmonized survival and response data. Tinfl scores (160-gene signature) defined quartiles (Q1 cold, Q4 hot). Cold versus hot tumors were compared by edgeR + limma-voom to identify differentially expressed genes (DEGs) (Bioconductor v3.20). Pathway and upstream-regulator/causal-network analyses used Ingenuity Pathway Analysis seeded by top DEGs (|log2FC|>1.5, p<0.05). Survival was assessed by Cox proportional hazards models (two-sided α=0.05) and KM log-rank testing (α=0.05). A 14-gene Tinfl-weighted cold signature produced a per-sample composite score. Results: In the Mayo cohort, median age was 62 years; 72% were stage IV at diagnosis; 24% were HER2-positive and 3% MSI-H; primary sites were GEJ/esophagus/gastric 50%/34%/16%; PD-L1 CPS <1/1–5/6–49/≥50 occurred in 18%/46%/27%/4%; patients received a mean of 2.4 lines of therapy. Across discovery and validation cohorts, Tinfl-cold tumors showed significantly higher expression of developmental and lineage transcription factors (HOXC12, HOXC13, ZIC1, DLX3, EVX1), an oncofetal post-transcriptional hub (IGF2BP1), cancer-testis and piRNA components (MAGEA4, TDRD12, THEG), and neuronal or transport genes (GABRQ, KCNF1, LDLRAD1, SLC6A10P) (logFC >2, p<0.05). Pathway analysis highlighted chromatin and lineage regulators (KAT6A, SIRT1, LEF1), TGF-β signaling (TGFBR2), and repressors of innate DNA/IFN sensing (TREX1, IRF2BP2) (Z-score >2, p<0.05). The 14-gene Tinfl-weighted cold signature was higher in cold versus hot tumors (p<0.05) and showed a directional association with inferior overall survival in the Mayo validation cohort. Among patients who received first-line anti-PD-1–containing regimens, several signature members and the composite score were higher in non-responders (p<0.05). Conclusions: Using a discovery–validation design, we define tumor-intrinsic expression pathways that characterize Tinfl-cold E/GEJ/G adenocarcinoma. We also derive a concise 14-gene Tinfl-weighted cold signature that is enriched in non-responders and associated with inferior overall survival. Targetable nodes such as IGF2BP1 and MAGEA4, together with chromatin and Wnt/TGF-β regulators, support rational combinations with PD-1 blockade. Planned validation in a third cohort, single-cell confirmation of tumor-cell expression, and in vivo cytokine profiling in knockdown models will refine mechanisms and nominate clinical candidates.

THE 2025 American Society of Clinical Oncology (ASCO) Annual Meeting underscored a key principle: in today’s complex and rapidly evolving cancer landscape, the application of knowledge, not just its acquisition, is our most powerful tool for progress. Robin Zon’s presidential theme, “Driving Knowledge to Action: Building a Better Future,” was brought to life across the plenary sessions and data presentations that spanned tumor types, geographies, and therapeutic modalities. Two dominant motifs emerged from the most practice-changing studies: the use of large-scale, well-designed clinical trials to validate transformative interventions, and a growing effort to repurpose effective therapies earlier in the disease course, challenging historical treatment sequences. These principles reflect the essence of oncology’s future: evidence-powered, patient-centered, and accessible to all.

Cancer immunotherapy—including immune checkpoint inhibition (ICI) and adoptive cell therapy (ACT)—has become a standard, potentially curative treatment for a subset of advanced solid and liquid tumors. However, most patients with cancer do not benefit from the rapidly evolving improvements in the understanding of principal mechanisms determining cancer immune responsiveness (CIR); including patient-specific genetically determined and acquired factors, as well as intrinsic cancer cell biology. Though CIR is multifactorial, fundamental concepts are emerging that should be considered for the design of novel therapeutic strategies and related clinical studies. Recent advancements as well as novel approaches to address the limitations of current treatments are discussed here, with a specific focus on ICI and ACT.

Abstract The field of oncology has been transformed by immune checkpoint inhibitors (ICI) and other immune-based agents; however, many patients do not receive a durable benefit. While biomarker assessments from pivotal ICI trials have uncovered certain mechanisms of resistance, results thus far have only scraped the surface. Mechanisms of resistance are as complex as the tumor microenvironment (TME) itself, and the development of effective therapeutic strategies will only be possible by building accurate models of the tumor-immune interface. With advancement of multi-omic technologies, high-resolution characterization of the TME is now possible. In addition to sequencing of bulk tumor, single-cell transcriptomic, proteomic, and epigenomic data as well as T-cell receptor profiling can now be simultaneously measured and compared between responders and nonresponders to ICI. Spatial sequencing and imaging platforms have further expanded the dimensionality of existing technologies. Rapid advancements in computation and data sharing strategies enable development of biologically interpretable machine learning models to integrate data from high-resolution, multi-omic platforms. These models catalyze the identification of resistance mechanisms and predictors of benefit in ICI-treated patients, providing scientific foundation for novel clinical trials. Moving forward, we propose a framework by which in silico screening, functional validation, and clinical trial biomarker assessment can be used for the advancement of combined immunotherapy strategies.