Citations

Abstract Introduction: We previously developed a urine liquid biopsy assay to measure urine tumor DNA (utDNA) levels in bladder cancer (BC) patients. Here, we extended this approach significantly by layering in cell-free DNA fragmentomic and cell-free RNA analysis, and extending the method to the full spectrum of genitourinary (GU) cancers, to enable both GU cancer detection and tumor tissue of origin prediction. Methods: A total of 155 GU cancer patients and 34 healthy adults were enrolled in this prospective study. Preoperative urine was collected from 94 bladder cancer (BC) patients, including 65 with muscle-invasive disease, all undergoing cystectomy. Additionally, urine samples were collected from 18 renal cell carcinoma (RCC) patients undergoing nephrectomy and 42 metastatic prostate cancer (mPC) patients. Urine cfDNA and cfRNA were isolated and sequenced, with cfRNA sequenced to 100 million reads per sample on an Illumina NovaSeq X and cfDNA sequenced at 5x genome-wide coverage on a Novoseq S4 flow cell. To identify malignant tissue of origin, we analyzed somatic copy number alterations (CNAs), genome-wide fragment length ratios, and 4-mer end motifs. Tumor fraction (TFx) was estimated using ichorCNA, while DELFI was employed for fragment length ratios in 5 megabase bins across the genome and chromosome arm-level z-scores. Individual logistic regression models were developed based on each fragmentomic feature, and validated using a training and held-out testing approach. Outputs from these models were combined into a single comprehensive model, also trained and held-out tested using the same approach. Urine cfRNA transcript isoform abundance was quantified using Salmon and normalized to Transcripts Per Million (TPM) to account for sequencing depth variations. Results: Bladder cancer (BC) showed the highest mean tumor fraction (TFx, 10.5%), followed by RCC (3.8%) and mPC (3.4%). Among 94 BC patients, 42% achieved a pCR, while 58% had residual disease and did not. Patients without pCR had significantly higher urine TFx compared to those with pCR (14.7% vs. 4.7%, p=0.0008). A machine learning model based on genome-wide fragment length ratios achieved an ROC AUC of 0.74, while models using genome-wide CNAs and end motifs achieved AUCs of 0.81 and 0.88, respectively. Combining all features into a single model yielded an AUC of 0.92, with classification accuracies of 89% for bladder cancer, 80% for RCC, and 60% for prostate cancer. Additionally, urine cfRNA analysis using the top 1, 000 coding transcripts achieved an AUC of 0.95, with classification accuracies of 100% for mPC, 87% for BC, and 78% for RCC. Conclusion: Fragmentomic features from urine cfDNA and transcriptomic features from urine cfRNA demonstrate strong potential for predicting tumor tissue-of-origin in GU cancers, underscoring the promise of non-invasive diagnosis across the spectrum of GU malignancies through urine liquid biopsy. Citation Format: Pradeep Singh Chauhan, Irfan Alahi, Jessica Linford, Arpit Panda, Nathan Colon, Ryan Mueller, Faridi Qaium, Peter K. Harris, John Sheng, Eric H. Kim, Melissa A. Reimers, Zachary L. Smith, Woodson W. Smelser, Aadel A. Chaudhuri. Predicting genitourinary cancer tissue-of-origin using urine cell-free DNA fragmentomics and cell-free RNA [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 3258.

Abstract Introduction: Breast cancer genome-wide association studies (GWAS) have identified over 200 susceptibility loci, many replicated in diverse populations. However, cross-ancestry evaluation of breast cancer genetic architecture remains limited. We examined breast cancer genetic architecture using GWAS summary results from European (EUR; cases (ca) = 188,474, controls (co) = 96,201), East Asian (EAS; ca = 20,393, co = 86,329), African American (AA; ca = 9,235, co = 10,184), and US Hispanic/Latina and Latin American (H/L; ca = 2,396, co =7,468) studies. Methods: GWAS results were derived from the Breast Cancer Association Consortium, the African Ancestry Breast Cancer Genetics Consortium (AABCG), Biobank Japan, and a meta-analysis of five studies of H/L women with breast cancer. Linkage disequilibrium (LD) scores were generated for EUR, EAS, and AMR using the 1000 Genomes Project, while AA LD scores were derived from a subset of AABCG controls. Heritability was estimated for each ancestry group using LD score regression. Genetic correlations between populations were assessed via Popcorn. Polygenicity was analyzed using GENESIS; however, due to the limited sample size in the H/L studies, these estimates were restricted to AA, EAS, and EUR. Enrichment of heritability across regulatory elements was evaluated using stratified LD score regression across all populations. Results: The logit-scale heritability and corresponding standard error (SE) were 0.466 (0.066) for EAS, 0.501 (0.050) for EUR, 0.588 (0.360) for H/L, and 0.614 (0.095) for AA. The estimated number of independent susceptibility loci was 4,446 for EAS, 5,235 for EUR, and 8,308 for AA. Using a clumping and thresholding approach, an optimal set of common variants were projected to explain 38.6% (EUR), 39.4% (EAS), and 26.2% (AA) of genetic variance for samples of 100,000 cases and 100,000 controls with AUCs from corresponding estimated polygenic risk scores (PRSs) of 0.621 (EUR), 0.634 (EAS), and 0.611 (AA). Genetic correlations were strongest between EUR and EAS (ρ = 0.79, SE = 0.08) and EUR and H/L (ρ = 0.68, SE = 0.21), and weakest between AA and EAS (ρ = 0.42, SE = 0.14) and AA and H/L (ρ = 0.26, SE = 0.24). Among 73 genomic features, we found significant (P < 0.05/73) enrichment in heritability in EUR for ‘ancient promoter’ regions, transcription factor binding sites, H3K4me3, ‘super-enhancers’, H3K4me1, H3K27ac; in EAS for ‘super-enhancers’; and in AA for H3K27ac. No genomic features were significantly enriched in H/L, likely due to limited power. Conclusion: These findings suggest a shared breast cancer genetic architecture across diverse populations, as well as the potential for a similar level of breast cancer risk stratification by PRS in these populations. Expanding GWAS in underrepresented populations is essential to improve genetic risk predictions and foster equitable cancer prevention. Citation Format: James L. Li, Maria Zanti, Jacob Williams, Om Jahagirdar, Guochong Jia, Qiang Hu, Jean-Tristan Brandenburg, Li Yan, Weang-Kee Ho, Jingmei Li, José P. Miranda, Devika Godbole, Julie-Alexia Dias, Leila Dorling, Wenlong C. Chen, Nicholas Boddicker, Ying Wang, Alicia Martin, Martin J. Zhang, Yan Zhang, Joe Dennis, Esther M. John, Gabriela Torres-Mejia, Larry Kushi, Jeffrey Weitzel, Susan L. Neuhausen, Luis Carvajal-Carmona, Christopher Haiman, Elad Ziv, Laura Fejerman, Wei Zheng, Dezheng Huo, Douglas Easton, Nilanjan Chatterjee, Peter Kraft, Montserrat Garcia-Closas, Wendy Wong, Kyriaki Michailidou, Qianqian Zhu, Diptavo Dutta, Thomas U. Ahearn, Haoyu Zhang. Genetic architecture of breast cancer across diverse populations: Assessing heritability, genetic correlation, and polygenicity [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 2282.

Abstract Breast cancer risk assessment tools are widely used in clinical practice to guide decisions regarding screening timing and modality, lifestyle interventions, genetic testing, preventive therapy, and risk-reducing surgery. Although a number of tools designed for the general population are used in practice, they face a series of challenges including: (i) modest discriminatory ability due to lack of a unified model that flexibly incorporates a comprehensive set of risk factors; (ii) inability to produce subtype-specific risk estimates, relevant for tailoring screening and/or preventive strategies (e.g. prophylactic endocrine therapy that is effective only for hormone receptor positive tumors); (iii) lack of data from diverse populations to build models that work well for all women; and, (iv) scarcity of opportunities for comparative validation of different models. The Breast Cancer Risk Prediction Project (BCRPP), a collaboration currently including 20 prospective cohorts from the National Cancer Institute’s Cohort Consortium, is addressing these challenges by harmonizing, sharing, and analyzing data on over 1.8 million women, over 115, 000 of whom have developed breast cancer. The BCRPP is developing a comprehensive tool that will predict breast cancer risk, overall and by subtypes, across major racial and ethnic groups in the United States. This tool incorporates known and emerging risk factors (e.g. reproductive history, anthropometry, smoking, alcohol intake, medication use, mammography, family history, and measured genotypes). In parallel to model building and validation efforts, BCRPP investigators are developing privacy-preserving web and mobile interfaces to deliver the risk tools to patients and clinicians. Here we present descriptive data on participating cohorts (e.g. dates of enrollment, ages at enrollment, length of follow up, numbers of cases by subtype), summarize missing data patterns and our approach to model development and validation that accounts for heterogeneity in missingness patterns across cohorts, and describe data governance procedures and data sharing infrastructure (including pooled and federated analyses, to account for policy limitations on sharing individual data). Descriptive information and procedures for requesting and accessing data are available via the BCRPP data platform (https://epidataplatforms.cancer.gov/bcrpp/). Although originally assembled for risk model development, these data on a large and diverse sample of women with extensive harmonized risk factors and covariates can be a resource to address other outstanding questions in breast cancer epidemiology across time, the lifecourse, and diverse populations. Citation Format: Peter Kraft, Laura Beane-Freeman, Julie Palmer, Ellen O'Meara, Jeanine Genkinger, James Lacey, Thomas Rohan, Lauren Teras, Marian L. Neuhouser, Kala Visvanathan, Celine Vachon, Roger Milne, Christopher Haiman, Yu Chen, Heather Eliassen, Cari Kitahara, Katie O'Brien, Emily White, Garnet Anderson, I-Min Lee, Archie Campbell, Renee Fortner, Ylva Lagerros, Sven Sandin, Mia Gaudet, Montserrat Garcia-Closas, Nilanjan Chatterjee. The Breast Cancer Risk Prediction Project: a resource of 1.8 million women with diverse backgrounds from 20 prospective cohorts to improve breast cancer risk prediction and advance breast cancer epidemiology [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 3583.

Background: Multicellular programs in the tumor microenvironment (TME) drive cancer pathogenesis and response to therapy but remain challenging to identify and profile clinically. Here, we present Spatial EcoTyper, a multimodal machine learning framework for large-scale profiling of spatially dependent cell states and multicellular ecosystems, termed spatial ecotypes (SEs). Methods and Findings: By integrating five million single-cell and spot-level spatial transcriptomes from 10 diverse human neoplasms, including carcinomas and melanomas, we identified a rich tapestry of conserved regional plasticity across nine TME cell types. We then applied Spatial EcoTyper to deeply dissect these data, revealing nine novel SEs with broad conservation - each with unique biology, TME cell states, geospatial features, and clinical outcome associations. SEs were linked to specific biogeographic domains, including the tumor core, margin, and adjacent stroma, and were remarkably well-validated in 235 held-out tumor samples profiled by either single-cell RNA sequencing, sequencing-based spatial transcriptomics (10x Visium), or high-resolution imaging-based spatial transcriptomics (Vizgen MERSCOPE and NanoString CosMx). By deconvolving SEs from over 1,200 bulk tumor RNA-seq profiles, we discovered striking associations between SE levels and response to immune checkpoint inhibitors (ICIs), outperforming nearly 40 potential correlates of ICI response, including previously described signatures of tertiary lymphoid structures and tumor tissue PD-L1 expression. Notably, baseline levels of a proinflammatory SE - localized to the tumor core and defined by a hub of unique T cell, macrophage, fibroblast, and endothelial cell states - strongly forecasted ICI benefit, while another SE - localized to the tumor margin and defined by myofibroblast and hypoxia-associated endothelial cell states - portended ICI resistance. Conclusion: Our data reveal fundamental units of TME organization and demonstrate a platform for large-scale profiling of spatial cellular ecosystems in any tissue, with implications for improved forecasting of immunotherapy response and the development of novel therapeutic strategies. Citation Format: Wubing Zhang,Erin L. Brown,Abul Usmani,Noah Earland,Hyun Soo Jeon,Irfan Alahi,Nicholas P. Semenkovich,Janella C. Schwab,Chloé B. Steen,Chloe M. Sachs,Bilge Gungoren,Faridi Qaium,Peter K. Harris,Jacqueline Mudd,Qingyuan Cai,Antonella Bacchiocchi,Ryan C. Fields,Mario Sznol,Ruth Halaban,David Y. Chen,Aadel A. Chaudhuri,Aaron M. Newman. High-resolution profiling of the tumor microenvironment with spatial ecotypes [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 153.

Background: Deciding to use oral anticoagulation (OAC) for stroke prevention in patients with atrial fibrillation (AF) is complex. For many, the decision between treatment and non-treatment requires a primary tradeoff between the risks of potential stroke and increased bleeding. Hypothesis: We hypothesized that patient values and empiric stroke risk measured by CHA 2 DS 2 -VASc score would influence OAC decisions. Methods: We conducted a secondary analysis of a randomized clinical trial where eligible clinicians and patients were independently randomized to one of two decision aids (DAs) – a patient decision aid and/or an encounter decision aid. Eligible patients were adults with AF with CHA 2 DS 2 -VASc≥1 for men or ≥2 for women. Patients newly prescribed (OAC-naive subgroup) or already taking OAC (OAC-experienced subgroup) were included. We assessed patient values using a 5-point Likert scale between stroke prevention and bleeding risk after SDM during a clinical encounter. We report preliminary results using descriptive statistics and adjusted analysis to investigate the effects of CHA 2 DS 2 -VASc and expressed values on OAC use. Results: 1005 patients were included in the analysis. 828 (82%) patients valued stroke prevention over bleeding risk. Among the OAC-naive group, 175 (63%) valued stroke prevention more than bleed avoidance, 58 (20%) valued bleed avoidance more than stroke prevention, and 47 (17%) were neutral. In the unadjusted analysis, patients were more likely to value stroke prevention if they were OAC-experienced (p<0.001), older (p<0.001), female (p< 0.001), had patient-reported bleeding (p=0.003), or if CHA 2 DS 2 -VASc ≥ 2 if male, ≥3 if female (p<0.001) Table 1 . After adjustments, there were significant associations for OAC use among patients who valued stroke prevention (OR 5.35, 95%CI 3.13-9.13, p<0.001) or had higher CHA 2 DS 2 -VASc (OR 2.03, 95%CI 1.26-3.27, p=0.004). Patients who valued bleed avoidance had an opposing association not to use OAC (OR 0.23, 95%CI 0.11-0.48, p<0.001). Conclusions: Patients with elevated risk (CHA 2 DS 2 -VASc score, age, and female), and those who reported a prior bleeding event valued stroke prevention more than bleeding avoidance. Both empiric stroke risk and patient values were associated with the decision to use OAC. While the tradeoff between stroke prevention and bleeding risk is only one aspect of the decision-making process, these results indicate variability in patient values may influence OAC use.

Background Strategies to estimate risk of cancer therapy‐related cardiac dysfunction (CTRCD) before initiating cardiotoxic cancer treatment are needed. We hypothesized that baseline ECG markers could identify patients at risk for CTRCD. Methods and Results In this retrospective cohort study, 1278 female patients with stage I–III HER2 (human epidermal growth factor receptor 2)‐positive breast cancer meeting the following inclusion criteria were included: baseline ECG with QRS <120 milliseconds, baseline echocardiogram, and ≥1 follow‐up echocardiogram. Quantitative measurements of ECG waveform parameters were performed using MUSE (GE Healthcare). The primary outcome of interest was CTRCD at 1 year, defined by left ventricular ejection fraction decline (≥10% to <53% or ≥16% from baseline), or clinical heart failure (New York Heart Association class III/IV). Mean age was 51.7±11.1 years, 990 (77%) received anthracyclines, and all received HER2‐targeted therapy. CTRCD occurred in 160 (13%) patients. In a multivariable Cox proportional hazards model adjusting for our previously published CTRCD risk score (composed of patient and treatment‐specific factors), 4 ECG markers remained independently associated with CTRCD risk: QRS axis, R‐wave duration (lead II), ST segment deviation (lead II), and Sokolow–Lyon voltage (all P <0.05). Compared with a model using only clinical CTRCD risk variables, addition of ECG parameters provided incremental value for predicting CTRCD risk ( P <0.001, likelihood ratio test) with continuous net reclassification improvement of 34.9% and integrated discrimination improvement of 3.4%. Conclusions Baseline ECG variables are predictive of subsequent CTRCD and provide incremental value to established clinical risk factors for CTRCD risk classification.

Abstract Hypophosphatasia (HPP) is characterized by defective bone mineralization due to reduced function of tissue-nonspecific alkaline phosphatase (TNSALP) caused by pathogenic ALPL gene variants. Hypercalcemia is more common in the perinatal and infantile forms and may be mitigated or prevented with enzyme replacement therapy asfotase alfa (AA). Here, we report a patient who developed severe hypercalcemia while receiving AA. Hypercalcemia was initially managed with intravenous fluids, dietary calcium restriction, and maximizing AA dose. Despite these measures, she required an additional hospital admission, at which time calcitonin 4 IU/kg every 12 hours was initiated. On this regimen, her calcium normalized without recurrence of severe hypercalcemia. Over the subsequent 8 months, restrictions of calcium intake were slowly lifted, and calcitonin was tapered and discontinued with maintenance of calcium within the normal range. This case underscores the significance of vigilant monitoring of calcium levels and dietary intake in infants diagnosed with HPP. While calcitonin is typically not considered as a sustained treatment for hypercalcemia, the present case illustrates the efficacy of adjunct calcitonin therapy, in conjunction with restricted calcium intake and maximum AA dosing, in managing severe hypercalcemia in an infant with perinatal HPP.

Diffuse large B cell lymphoma (DLBCL) is the most common type of non-Hodgkin lymphoma, and the activated B cell–like subtype (ABC-DLBCL) is associated with particularly poor outcome. Many ABC-DLBCLs harbor gain-of-function mutations that cause inappropriate assembly of the CARMA1-BCL10-MALT1 (CBM) signalosome, a cytoplasmic complex that drives downstream NF-κB signaling. MALT1 is the effector protein of the CBM signalosome such that its recruitment to the signalosome via interaction with BCL10 allows it to exert both protease and scaffolding activities that together synergize in driving NF-κB. Here, we demonstrate that a molecular groove located between two adjacent immunoglobulin-like domains within MALT1 represents a binding pocket for BCL10. Leveraging this discovery, we performed an in silico screen to identify small molecules that dock within this MALT1 groove and act as BCL10-MALT1 protein-protein interaction (PPI) inhibitors. We report the identification of M1i-124 as a first-in-class compound that blocks BCL10-MALT1 interaction, abrogates MALT1 scaffolding and protease activities, promotes degradation of BCL10 and MALT1 proteins, and specifically targets ABC-DLBCLs characterized by dysregulated MALT1. Our findings demonstrate that small-molecule inhibitors of BCL10-MALT1 interaction can function as potent agents to block MALT1 signaling in selected lymphomas, and provide a road map for clinical development of a new class of precision-medicine therapeutics.