Citations

This guideline has been updated. This ASCO Living Guideline is reviewed on an eight-week cycle. Please see the online dynamic version for the latest updates. Living guidelines are developed for selected topic areas with rapidly evolving evidence that drives frequent change in recommended clinical practice. Living guidelines are updated on a regular schedule by a standing expert panel that systematically reviews the health literature on a continuous basis, as described in the ASCO Guidelines Methodology Manual . ASCO Living Guidelines follow the ASCO Conflict of Interest Policy Implementation for Clinical Practice Guidelines . Living Guidelines and updates are not intended to substitute for independent professional judgment of the treating clinician and do not account for individual variation among patients. See appendix for disclaimers and other important information ( Appendix 1 and Appendix 2 , online only). Updates are published regularly and can be found on the ASCO Publications website .

Abstract Spatial transcriptomics has been widely applied to study the spatial distribution of cell types, cell states, and specific gene expression in tissue samples. However, we show that there is a prevalent transcript leakage problem in spatial transcriptomics data, where transcripts expressed by a cell diffuse to its neighborhood and are recurrently detected in the nearby cells. By analyzing published data sets, we show that this problem is general across data produced from different tissues and different species using different imaging-based and sequencing-based spatial transcriptomics platforms. It affects both upstream tasks such as expression quantification as well as downstream tasks such as cell-type annotation and detection of spatially-dependent gene expression. To tackle the transcript leakage problem, we propose a reference-free Bayesian model-based method, DeLeakage, which cleans up the data much more effectively than existing denoising methods. DeLeakage also improves cell-type annotation and avoids false detection of spatially dependent expression.

e17004 Background: Living clinical practice guidelines offer the opportunity to keep recommendations aligned with the latest evidence, but they also present a fundamental challenge: as evidence accumulates, panelists cannot sustainably query and synthesize a growing body of literature. In developing ASCO's living metastatic castration-resistant prostate cancer (mCRPC) guideline, we encountered this challenge firsthand. To address it, we developed and evaluated an interactive agentic framework that enables guideline panelists to query evidence in natural language and retrieve structured facts, tables, and figures—with the goal of extending this tool as a clinician-facing companion to enhance guideline accessibility. Methods: The mCRPC systematic review dataset supporting ASCO's clinical practice guideline was used, comprising 212 structured columns capturing trial characteristics, patient populations, and outcomes extracted from 188 references across 88 clinical trials. A large language model (LLM)-based agentic framework was developed employing a coding agent that translates natural language queries into executable scripts for structured evidence retrieval. Claude (Haiku 4.5, Sonnet 4.5, and Opus 4.5) served as the core LLM agents. We constructed a benchmark of 50 fact-based queries with gold-standard answers targeting trial identification, treatment modalities, and endpoint characteristics (e.g., "Which trials evaluated systemic monotherapy?"). Performance was evaluated using exact-match accuracy for final answers, and precision, recall, and F1 score for trial-level retrieval. Results: Across 50 queries, exact-match accuracy ranged from 82% to 86%. Haiku 4.5 and Sonnet 4.5 each achieved 82% (42/50), while Opus 4.5 achieved 86% (43/50). Precision ranged from 0.93–0.97, recall from 0.94–0.96, and F1 scores from 0.91–0.94. Conclusions: This agentic framework enables scalable, auditable, natural language interrogation of guideline evidence, addressing a critical bottleneck in living guideline maintenance. For panelists, it supports rapid, reproducible evidence surveillance during recommendation development. For clinicians, it offers a foundation for an interactive companion tool that transforms static guidelines into queryable resources—enabling personalized evidence retrieval at the point of care. LLM EM Precision Recall F1 claude-haiku-4-5 0.82(42/50) 0.95 0.94 0.91 claude-sonnet-4-5 0.82(42/50) 0.97 0.96 0.93 claude-opus-4-5 0.86(43/50) 0.93 0.96 0.94

Abstract Renal tumors are highly heterogeneous, ranging from benign lesions and indolent renal cell carcinoma (RCC) to highly aggressive phenotypes. Accurately determining tumor aggressiveness before treatment remains a major clinical challenge. Conventional MR imaging lacks sensitivity to microarchitectural differences underlying tumor biology, and biopsy is limited by sampling error and tumor heterogeneity. Radiomics offers a noninvasive approach to extract high dimensional features that may capture biologically relevant patterns. In this IRB-approved retrospective study, we evaluated MR-based radiomics combined with machine learning (ML) to differentiate indolent from aggressive tumors. Pre-operative clinically acquired MR images obtained from eighty-two RCC patients (aggressive, n = 56; indolent, n = 26) who underwent nephrectomy were included for the analysis. Regions of interest (ROIs) of the entire tumor were manually drawn using 3D Slicer on the morphological T2-weighted images and four phases (pre-contrast [PRE], corticomedullary [CM], nephrographic [NG], and delayed [DEL]) from the contrast-enhanced MR images, normalized, and used for radiomic feature extraction (PyRadiomics, v3.1.0). Independently, tumors were grouped into aggressive and indolent phenotypes based on the established histological classification of Mayo Clinic. Statistical analysis used two-sided t-test. Features selected based on mutual information were used for ML classification (Random Under-Sampling Boosting (RUSBoost)) and performance assessed using accuracy, sensitivity, precision, specificity, F1-score, and area under the curve (AUC). Of 107 radiomics features extracted, the following numbers showed statistically significant differences between aggressive and indolent tumors (p < 0.05): CM phase (n = 19), NG phase (n = 24), DEL phase (n = 22), and T2-weighted images (n = 29). Several features consistently differed among contrast-enhanced images and included first-order (length, surface area, volume), GLCM (correlation, IDM, IMC1, IMC2), GLDM (dependence entropy, dependence non-uniformity, dependence variance), GLRLM (gray-level non-uniformity, run entropy, run-length non-uniformity), GLSZM (high gray-level emphasis, zone entropy), and NGTDM (coarseness). Using the 15 features with highest mutual information across 50 patients (aggressive, n = 31; indolent, n = 19), a five-fold cross-validation RUSBoost classifier achieved accuracy 0.81, sensitivity 0.84, precision 0.87, specificity 0.77, F1-score 0.85, and AUC 0.80 for identifying the aggressive phenotype. MRI radiomics features show potential to differentiate aggressive from indolent renal tumors. ML-based classification using these features demonstrates promising accuracy. Further optimization and validation in larger cohorts, may help guide individualized care, including consideration of active surveillance for indolent renal tumors. Citation Format: Huan Lu, Salim Rukhsar, Xueqing Yin, Garima Suman, Ashish Khandelwal, Ananth J. Madhuranthakam, Durga Udayakumar. MRI radiomic features to differentiate aggressive and indolent renal tumors [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 2142.

Abstract Background The ability of large language models (LLMs) to work collaboratively and screen studies in a systematic review (SR) is under-explored. Hence, we aimed to evaluate the effectiveness of LLMs in automating the process of screening in systematic reviews. Methods This is an observational study which included labeled data (title and abstracts) for five SRs. Originally, two reviewers screened the citations independently for eligibility. A third reviewer cross-checked each citation for quality assurance. GPT-4, Claude-3-Sonnet, and Gemini-Pro-1.0 were used using zero-shot chain-of-thought prompting. Collaborative approaches included (i): conflict resolution using benefit of the doubt, (ii) majority voting using an independent third LLM and (iii) conflict resolution using an informed third LLM. Performance was assessed using accuracy, precision for exclusion, and recall for inclusion. Work saved over samples (WSS) was computed to estimate the reduction in manual human effort. Results A total of 11300 articles were included in this study. The individual models, GPT-4, Claude-3-Sonnet, and Gemini-Pro-1.0 exhibited a high precision for exclusion, achieving 99.7%, 99.7%, and 99.2% and high recall for inclusion achieving 95.5%, 96.6% and 85.7%, respectively. However, the collaborative approach utilizing the two best-performing models (GPT-4 and Claude-3S) achieved an average precision of 99.9% and a recall of 98.5% (across all collaborative approaches). Furthermore, the proposed collaborative approach resulted in an average WSS of 63.5%, compared to the average WSS of 45.2% for individual models. Conversational LLM interactions showed a consistent pattern of results. Limitations This study was limited due to reliance on proprietary models, and evaluation on oncology datasets. Conclusion Evidence shows that collaborative LLMs enable efficient, high-performing screening in systematic reviews, supporting continuous evidence updates. Primary funding source NIH (U24CA265879-01-1) and Carolyn-Ann-Kennedy-Bacon Fund.

This guideline has been updated. This ASCO Living Guideline is reviewed on an eight-week cycle. Please see the online dynamic version for the latest updates. Living guidelines are developed for selected topic areas with rapidly evolving evidence that drives frequent change in recommended clinical practice. Living guidelines are updated on a regular schedule by a standing expert panel that systematically reviews the health literature on a continuous basis, as described in the ASCO Guidelines Methodology Manual . ASCO Living Guidelines follow the ASCO Conflict of Interest Policy Implementation for Clinical Practice Guidelines . Living Guidelines and updates are not intended to substitute for independent professional judgment of the treating clinician and do not account for individual variation among patients. See appendix for disclaimers and other important information ( Appendix 1 and Appendix 2 ). Updates are published regularly and can be found at www.asco.org/genitourinary-cancer-guidelines .

Abstract Brexucabtagene autoleucel (brexu-cel) is a chimeric antigen receptor T-cell therapy approved for relapsed/refractory mantle cell lymphoma (R/R MCL). Here, we report real-world effectiveness and safety outcomes of brexu-cel in a prospective study of patients with R/R MCL, including subgroups based on prior treatment with Bruton's tyrosine kinase inhibitor, bendamustine, or autologous hematopoietic cell transplant (auto-HCT) and number of prior therapy lines, using Center for International Blood and Marrow Transplant Research registry data. A total of 476 patients with R/R MCL who received brexu-cel between July 2020 and December 2022 were included in the analysis. With a median follow-up of 13.5 months, the overall response rate was 91% and complete response rate was 82%. One-year overall survival and progression-free survival rates were 76% and 63%, respectively. One-year cumulative incidence of nonrelapse mortality was 8%. Prior auto-HCT was associated with better duration of response within 6 months after infusion (hazard ratio [HR], 0.49; 95% confidence interval [CI], 0.28-0.85) but greater risk of immune effector cell–associated neurotoxicity syndrome (odds ratio [OR], 1.66; 95% CI, 1.06-2.60). Prior bendamustine was associated with increased risk of prolonged thrombocytopenia (OR, 1.90; 95% CI, 1.13-3.21). In patients with 1 to 2 prior therapy lines, relapse or progression was less frequent compared with those with ≥3 prior lines (HR, 0.64; 95% CI, 0.42-1.00). Collectively, our results suggest that real-world outcomes with brexu-cel were consistent with those of the ZUMA-2 trial, regardless of prior therapy type or number of prior therapy lines.

Abstract Aging increases the vulnerability of kidneys to injury and impairs their regenerative capacity. SIRT3 expression declines with aging and is associated with multiple age‐related pathologies. The expression profile and functional role of SIRT3 in renal aging remain unclear. Here, SIRT3 expression in aging kidneys is assessed and analyzed for its promoter methylation patterns using methylation‐specific PCR (MSP). It is found that aging exacerbates UUO‐induced renal fibrosis, associated with downregulated SIRT3 expression. Mechanistically, age‐related SIRT3 downregulation is mediated by hypermethylation of its promoter region. SIRT3 knockout exacerbated renal fibrosis in young mice subjected to UUO, whereas SIRT3 overexpression attenuated fibrosis in aged UUO mice. Integration of RNA‐seq and immunoprecipitation‐mass spectrometry (IP‐MS) analyses revealed that SIRT3 deficiency leads to hyperacetylation of GSK3β at lysine 15 (K15). This K15 hyperacetylation inhibited GSK3β activity, consequently stabilizing its substrate β‐catenin. Furthermore, self‐assembled PEG‐PCL‐PEG micelles are designed and synthesized to encapsulate hydrophobic honokiol (HKL). These micelles significantly enhanced the aqueous solubility and oral bioavailability of free HKL, maintained stable blood concentrations, and ultimately improved its anti‐fibrotic efficacy. These findings propose novel therapeutic strategies for managing renal fibrosis in the aging population and provide a foundation for developing new drugs and combination therapies.

Background: The sural nerve is widely used as a nerve graft for various types of peripheral nerve repair due to its ease of harvest and acceptable donor-site morbidity. Nevertheless, certain clinical scenarios, such as cable grafting or multiple cross-face nerve grafts, require grafts longer than the typical 35 cm. This study aims to investigate the anatomic feasibility and clinical safety of an extended sural nerve harvest above the popliteal crease by interfascicular dissection. Methods: An anatomic study using 5 fresh-frozen cadaver legs was conducted, focusing on identifying the anatomic relationship of the sural nerve above the popliteal crease to its surrounding structures and nerve histopathology. A retrospective review was performed of all patients who underwent an extended sural nerve harvest, and patient outcomes, including nerve graft length and postoperative complications, were reviewed to evaluate the clinical feasibility of the extended harvest technique. Results: Anatomic dissection revealed that interfascicular dissection allowed for an additional 14.3 ± 6 cm of nerve length, achieving a total graft length of 49.1 ± 5 cm. Histologic analysis showed no significant differences in fascicle number or axon count along the nerve. A retrospective review, which included 7 adults and 2 pediatric patients, demonstrated that an extended sural nerve harvest was feasible and safe. The maximum graft length was 55 cm, and no intraoperative complications or functional deficits were reported. Conclusion: The extended sural nerve harvest above the popliteal crease with a step-incision approach was both anatomically feasible and clinically safe, providing additional graft length without increasing donor-site morbidity.