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 ). Updates are published regularly and can be found at www.asco.org/genitourinary-cancer-guidelines .

Humility is one of the most powerful and sometimes least understood virtues we can possess. It is often misinterpreted as a sign of weakness, especially today, where self-promotion is encouraged and celebrated. Many people view humility as “thinking less of ourselves.” In truth, humility is not thinking less of oneself; rather it is appropriately thinking less about oneself. Our ability as caregivers in putting the needs of the patient at the center of all we do is enhanced through our humility. A steady focus on the needs of the patient helps us gain an honest assessment of both our strengths and limitations. Studies have shown that patient outcomes and experiences are strongly associated with provider humility. This article aims to shine a light on the power of humility and its impact on patient experience, psychological and cultural safety, and the provider-patient relationship.

Abstract Neuroendocrine prostate cancer (NEPC) is an increasingly prevalent manifestation of acquired resistance to next generation AR signaling inhibitors (ARSI), that arises via transformation from advanced prostate adenocarcinoma (PRAD) through a mechanism called lineage plasticity. Through characterization of isogenic PRAD and NEPC mouse tumoroid models generated using human-relevant driver mutations (Pten-/-;Rb1-/-;Trp53-/-), we revealed an extensive epigenetic reprogramming in NEPC versus PRAD, particular at enhancers. CRISPR screens focused on TFs and coactivators revealed a NEPC lineage TF survival network comprised of ASCL1, NFIB, SOX1/11, FOXA1/2 and MYCL with exquisite dependency on the TIP60 acetyltransferase complex. During the PRAD-to-NEPC transition, FOXA1, NFIB and TIP60 chromatin binding redistributes to NEPC specific enhancers, resulting in expression of NEPC target genes in a coordinate fashion with ASCL1 functioning as master regulator, culminating in activation of MYCL. MYCL then directly recruits the TIP60 complex to target sites, where it acts as a requisite MYCL coactivator for target gene activation for NEPC growth/survival. The acetyltransferase activity of TIP60 and acetylation of its substrate H2A.Z are both required for NEPC survival, as well as two chromatin reader subunits of the TIP60 complex, BRD8 and YEATS4. Targeted chemical degradation of TIP60 revealed >500-fold dependency in NEPC relative to normal prostate, credentialing the TIP60 complex as a potential therapeutic target in NEPC. Citation Format: Zhen Sun, Jimmy L. Zhao, Wazim M. Ismail, Zahra F. Khan, Teng Han, Subhiksha Nandakumar, Serina Young, Matthew Lange, Richard Koche, Alexandre Gaspar-Maia, Charles L. Sawyers. The TIP60 acetyltransferase complex is a critical dependency in neuroendocrine prostate cancer through its role as a critical coactivator of MYCL downstream of ASCL1 [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Innovations in Prostate Cancer Research and Treatment; 2026 Jan 20-22; Philadelphia PA. Philadelphia (PA): AACR; Cancer Res 2026;86(2_Suppl):Abstract nr PR036.

Abstract Background Prostate cancer is a heterogeneous disease comprising diverse cell types within the tumor microenvironment that support cancer cell survival. Using single cell and spatial transcriptomics, we previously identified subpopulations of treatment-persistent cancer cells exhibiting regenerative properties and a distinct molecular signature. Herein, we aimed to characterize the locked ‘Persist’ signature (PS) and its association with outcomes in a large cohort of non-metastatic prostate cancer tumors. Methods Patients with non-metastatic prostate cancer that underwent Decipher prostate genomic classifier and whole-transcriptome profiling on biopsy (BX, n=146,941) and radical prostatectomy (RP, n=55,329) specimens between October 2016 and February 2024 were included (Veracyte Inc., San Diego, CA, USA). Baseline clinical and pathological data from deidentified patients as well as signature scores were retrieved from the Decipher Genomic Resource for Intelligent Discovery (GRID) registry (Clinical Trials identifier: NCT02609269). The PS score based on the expression of 17 genes was calculated for all samples. Results PS was significantly associated with adverse clinical and pathological variables. In biopsy sample, PS was higher in cases with worse T3/4 clinical stage, Grade group (GG4-5), and high/very high NCCN risk (p<0.001 for all). In RP sample, PS was significantly higher in patients with GG4-5, extraprostatic extension, seminal vesicle invasion and lymph node involvement (p<0.001 for all). PS was also higher in patients with very high Decipher (>0.85) in both BX and RP (p<0.001 for both). When evaluating the prognosis of PS in multiple retrospective cohorts, PS in biopsy was prognostic of adverse pathology features at RP. PS was also prognostic of distance metastasis (DM) in multiple RP cohorts with long-term follow up (META855). In the ARNEO trial testing apalutamide in addition to ADT as neo-adjuvant treatment prior RP, PS was prognostic of early recurrence. In the Stampede trial, PS was prognostic of progression free survival, PCSM and OS in both non-metastatic and metastatic disease. When associating PS with other GRID signatures, PS was higher in samples with homologous recombination deficiency, TP53 mutations, Luminal B and luminal proliferating subtypes. On the other hand, PS was lower in samples with low androgen receptor-activity, RB-loss and neuroendocrine-like tumors. Finally, PS was positively correlated with radiation sensitivity signatures. Conclusions Persist is a novel molecular signature associated with more aggressive tumor characteristics. Future studies aim to further examine beyond this signature potential to identify patients at risk of aggressive prostate cancer, and understand what the optimal treatments for those with treatment-persistent prostate cancer cells are. Citation Format: Francesco Ballesio, Sinja Taavitsainen, Mohammed Alshalalfa, Antti Kiviaho, Mazlina Ismail, Reetta Nätkin, Alexander Giesen, Thomas Munro, James A. Proudfoot, Hyunnam Ryu, Elai Davicioni, Matthew C. Cooperberg, R. Jeffrey. Karnes, Ruochen Jiang, Steven Joniau, Gerhardt Attard, Frank Claessens, Matti Nykter, Alfonso Urbanucci. Persist: a molecular signature for prostate cancer patients with preexisting treatment-persistent cells associated with aggressive disease [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Innovations in Prostate Cancer Research and Treatment; 2026 Jan 20-22; Philadelphia PA. Philadelphia (PA): AACR; Cancer Res 2026;86(2_Suppl):Abstract nr B079.

Abstract Introduction: Underlying tumor biology in metastatic castration-resistant prostate cancer (mCRPC) is highly complex and heterogeneous. To date, mCRPC state biomarker-enriched clinical trial designs incorporating either prognostic or predictive biomarkers are limited to alterations in homologous recombination repair pathway genes. In this study, we use an integrated clinico-molecular prognostic score in mCRPC that combines plasma prognostic proteins with a plasma-based copy number alteration (CNA) risk algorithm (PMID: 40036789; PMID: 36230636) to simulate a biomarker-enriched clinical trial design. Methods: A plasma cell-free DNA (cfDNA) prognostic risk algorithm from eight CNAs (gains in AR, MYC, PIK3CA, PIK3CB; deletions in NKX3-1, TMPRSS2, TP53, ZBTB16) is combined with clinical prognostic proteins (alkaline phosphatase, lactate dehydrogenase) to generate a prognostic risk-score (RS) in three independent treatment-naïve mCRPC cohorts. Using the RS classifier, we estimate the number of high-RS patients (pts) required to be screened from an unselected mCRPC population and the number needed for enrollment to achieve adequate statistical power in a randomized trial for a predefined effect size (Hazard Ratio- 0.70; 80% power). The trial design aims to enrich High-RS pts for targeting lethal mCRPC. Trade-offs are evaluated between number of pts screened and enrolled under varying RS enrichment thresholds and degrees of enrichment. Results: The range of RS distribution in 561 mCRPC pts from three pooled datasets was 0.362-11.737. The median overall survival (OS) (months; 95% Confidence Intervals) based on risk stratification for Low-RS (≤0.8) is 46.2 (38.8-55.4); “Intermediate-risk” (RS: 0.8-2.6) is 27.1(24.3-31.5); High-Risk (RS: >2.6) is 14.9 (11.9-17.6). For a 2-year OS mCRPC endpoint, a biomarker-enriched trial restricted to pts with RS ≥2, which corresponds to the 65th percentile of the RS distribution, would require enrollment of 398 pts, after screening 1,101 mCRPC pts to achieve 80% power to detect a 0.7 treatment vs. control hazard ratio (HR). In contrast, a traditional non-enrichment clinical trial (no molecular classifier) would require 574 pts to achieve to achieve 80% power and still not achieve adequate power to detect strong treatment by risk interactions. As a pragmatic alternative, we evaluated a hybrid enrichment strategy enrolling 50% of pts from the general mCRPC population and 50% of pts with RS ≥1 (41st percentile). This biomarker enriched design which takes 50% pts with a RS of 1.0 (or more) in a trial with 24-month follow-up will require a sample size 525 patients for which 676 pts will be screened to achieve an 80% power to detect a 0.7 treatment vs. control hazard ratio. Conclusion: A plasma-based molecular classifier can be used to design an enrichment trial in biologically defined lethal mCRPC, incorporating 50% of pts with High-RS and yet balance the required sample size and number of pts to be screened enabling an adequately powered molecular classifier-based biomarker-enriched clinical trial. Citation Format: Manish Kohli, Liang Wang, Aik C. Tan, Muhammad Zaki Fadlullah, Anushka Shankar, Sumithra Mandrekar, Benjamin Haaland. Clinico-Genomic Risk Score-Based Biomarker Enrichment Clinical Trial Designs in Poor Prognosis Metastatic Castrate Resistant Prostate Cancer [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Innovations in Prostate Cancer Research and Treatment; 2026 Jan 20-22; Philadelphia PA. Philadelphia (PA): AACR; Cancer Res 2026;86(2_Suppl):Abstract nr B039.

Abstract Background In prostate cancer care, artificial intelligence (AI) models are increasingly used to generate personalized risk predictions by analyzing diverse data inputs, including clinical variables, imaging findings, and genomic indicators such as polygenic risk scores. However, effectively communicating these complex AI-derived risk estimates to patients presents significant challenges. Patients often struggle to understand probabilistic risk information, particularly when advanced algorithms generate it. Traditional cancer risk communication methods might not consider the complexities of AI outputs or differences in patient health literacy and numeracy. We propose a conceptual framework to guide patient-centered communication of AI-driven risk predictions. Methods We reviewed the existing literature on AI-based risk modeling, health risk communication, numeracy, dual-process information processing, and related theories to develop a communication framework designed explicitly for AI-based prostate cancer risk prediction tools. Results The proposed framework emphasizes clear, patient-specific risk communication strategies to enhance understanding and foster trust. It highlights a dual-process approach: offering intuitive visual aids and narrative explanations for quick, instinctive understanding, while also providing accurate statistics and explanations of the AI model’s reasoning for more deliberate processing. By addressing various patient numeracy levels and clearly explaining the AI model’s inputs, assumptions, and uncertainty in plain language, the framework aims to improve understanding and trust in AI-generated risk estimates. Additionally, to promote equity, it includes culturally responsive and accessible communication strategies tailored to patients’ linguistic, literacy, and cultural backgrounds. Conclusion This conceptual framework offers guidance on integrating AI-based risk prediction tools into prostate cancer consultations in a manner that is patient-friendly. By enhancing patients’ understanding of personalized risk information, building greater trust in AI-driven predictions, and addressing various communication needs, the framework helps facilitate more informed decision-making. Ultimately, patient-focused communication can help reduce disparities and promote more equity in prostate cancer care. Citation Format: Tingyu Zou, Folakemi Odedina. A Conceptual Framework for Communicating AI-Based Prostate Cancer Risk Predictions [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Innovations in Prostate Cancer Research and Treatment; 2026 Jan 20-22; Philadelphia PA. Philadelphia (PA): AACR; Cancer Res 2026;86(2_Suppl):Abstract nr A083.

Abstract INTRODUCTION: Prostate cancer (PC) shows marked heterogeneity in clinical outcomes, from indolent to highly aggressive disease. Alterations in TP53, PTEN, RB1, and DNA damage repair genes are linked to poor patient outcomes, yet how the interaction and co-occurrence of these alterations impact prognosis remains unclear. We leveraged the AACR Project GENIE prostate cancer cohort to assess the prognostic impact of individual and concomitant gene alterations, focusing on key PC drivers (BRCA2, TP53, PTEN, RB1). METHODS: the study was conducted in two stages: (i) identification of candidate alterations at individual gene level through a systematic literature review, followed by analyzing the prevalence and co-occurrence of genes in the AACR Project GENIE Public Release (PR) dataset. (ii) Prognostic analysis of the pre-selected biomarkers in the AACR Project GENIE Biopharma Collaborative (BPC) cohort. Analyses were performed in three clinical settings (entire BPC cohort, n=1114; subgroup of patients with localized disease at diagnosis, n=837; and subgroup of patients with metastatic PC with a biopsy collected before or at the time of mPC, n=455) using two statistical approaches: univariate analysis (UA) and inverse probability weighting using propensity scores (PS). Cox models were applied to estimate overall survival hazard ratios (HRs). RESULTS: after integrating the findings from the systematic review with the analysis of the GENIE PR (n=5684), 107 individual genes and 122 co-occurring alterations were selected for study. In the GENIE BPC (n=1114 patients) 75% were M0 at diagnosis, and 67% presented metastatic PC at any time. Alteration prevalence was: TP53, 25%; PTEN, 15%; BRCA2, 4%; RB1, 4%; co-alterations included, PTEN-TP53 (6.4%), RB1-TP53 (2.1%), PTEN-RB1 (1.3%), and BRCA2-TP53 (1.2%). All four key genes showed a statistically significant prognostic impact in the entire cohort with UA (BRCA2, HR:1.9; TP53, HR:2.6; PTEN, HR:2.4; RB1, HR:4.1, all p<0.001). Results were consistent in the subgroup analysis and when using PS. Additional genes such as FANCA, PIK3CA, and MYC were also associated with poor outcomes. Among co-alterations, BRCA2-TP53 (HR:2.0, p=0.05), RB1-TP53 (HR:3.6, p<0.001), PTEN-TP53 (HR:2.4, p<0.001); PTEN-RB1 (HR:5.5, p<0.001), and PTEN-TP53-RB1 (HR:9.0, p<0.001) were the strongest predictors of poor survival, with consistent effects across subgroups and analytical methods. Other combinations including MYC-TP53 (HR 2.2, p<0.001) and PIK3CA-TP53 (HR 3.2, p<0.001), also showed independent prognostic value. CONCLUSIONS: Genomic alterations in TP53, PTEN, RB1, and BRCA2, alone and in combination, independently associate with poor prognosis. Co-alteration patterns such as PTEN-TP53-RB1 define aggressive molecular subsets. This evidence supports integrating genomic interaction patterns for enhancing clinical risk models and guiding precision patient management. Furthermore, this study highlights the potential of AACR Project GENIE to enable data-driven development of biomarker-informed patient care strategies. Citation Format: Pablo Cresta Morgado, Victor Navarro, Guillermo Vilacampa, Haitham Alatoom, Manuel Ramos del Rio, Irbaz Riaz, Shawn Sweeney, Nikolaus Schultz, Ken Kehl, Gregory Riely, Wasim Abida, Katherine Panageas, Deborah Schrag, Philippe Bedard, Christine Micheel, Xindi Guo, Chelsea Nayan, Rodrigo Dientsmann, Joaquin Mateo. Genomic prognostic biomarkers in prostate cancer from the AACR GENIE cohort [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Innovations in Prostate Cancer Research and Treatment; 2026 Jan 20-22; Philadelphia PA. Philadelphia (PA): AACR; Cancer Res 2026;86(2_Suppl):Abstract nr A013.

Background. Acute cellular rejection (ACR) remains a major cause of morbidity after heart transplantation despite advances in immunosuppression. Whole genome transcriptomic profiling offers a systems-based, unbiased approach to elucidate the molecular mechanisms underlying ACR. However, noninvasive, longitudinal biomarker assessments capable of capturing the temporal dynamics of rejection biology remain scarce. Methods. RNA sequencing of peripheral blood from heart transplant recipients before, during, and after ACR was compared with nonrejection controls. Pathway analysis was conducted using differentially expressed genes (DEGs), and a machine learning approach was applied to assess gene-based prediction of ACR. Results. A total of 235 rejection-specific significant DEGs and 863 postrejection DEGs (false discovery rate < 0.05) were identified. During ACR, DEGs were enriched for T-cell activation/differentiation, apoptosis, and B-cell receptor signaling pathways. By combining the 2 sets of DEGs, a panel of 71 common genes was identified that reflected the significant, longitudinal transcriptomic dynamics of ACR. In an elastic net machine learning–based classifier, DYNLL1 and SERF2 were identified as ACR predictive genes, and achieved a cross-validated area under the receiver operating characteristic curve of 0.63. Conclusions. Peripheral blood transcriptomics identify dynamic temporal responses in ACR including T- and B-cell pathways with potential ACR predictive genes that warrant further investigation.

OBJECTIVE: Volatile anesthetics (VAs) are gaining renewed interest as a sedation strategy in the intensive care, offering an alternative to traditional IV agents. VAs provide several pharmacologic advantages, including rapid onset and offset, minimal systemic metabolism, and favorable recovery profiles. Advances in delivery systems enabled the safe and practical administration of volatile agents in the ICU. Thus, we aimed to describe the pharmacology and safety aspects of inhaled agents as well as the systems designed to deliver VAs in the ICU. DATA SOURCES: Relevant literature was identified through PubMed and MEDLINE databases. STUDY SELECTION: Original research, review articles, commentaries, and guidelines addressing safety, efficacy, and use of VAs in adult ICU patients were included. DATA EXTRACTION: Studies were reviewed by the authors, with key findings summarized and organized by pharmacologic properties, delivery systems, and safety domains. DATA SYNTHESIS: VAs are halogenated hydrocarbons whose mechanism of action is not fully understood. Although the CNS is the primary site of action, the end-tidal concentration of exhaled anesthetic is used to monitor clinical effects such as immobility to a noxious stimulus. Inhaled agents have unique pharmacokinetics, minimal metabolisms, and distinct recovery. The side effect profile is also unique, with malignant hyperthermia being the most feared, yet rare complication. Two systems for inhalational sedation delivery are available internationally, with one currently under evaluation in the United States. The systems are composed of a miniature vaporizer, delivery controller, and a monitor. The systems have distinct safety considerations, such as tidal volume limits. CONCLUSIONS: VAs can be used as sedative agents in the ICU. This article comprehensively reviews the pharmacology of VAs along with their safety profile and describes the structure and function of miniature vaporizers currently available on the world market.