Chaudhuri., Aadel A.

Abstract Introduction: Urine cell-free DNA (cfDNA) is a promising ultra-noninvasive analyte for genitourinary cancers (GU) cancers. In this study, we investigated urine cfDNA fragmentomic features and evaluated their ability to predict the tumor tissue of origin. Methods: A total of 204 GU cancer patients and 34 healthy adults were enrolled in this prospective study. Preoperative urine was collected from 89 bladder cancer (BC) patients (64% muscle-invasive) undergoing cystectomy, 65 renal cell carcinoma (RCC) patients undergoing nephrectomy and 50 metastatic prostate cancer (mPC) patients. Urine cfDNA was isolated and sequenced at 5x genome-wide coverage on a NovaSeq 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. CNAs and tumor fraction (TFx) were quantified using ichorCNA in 1-Mb windows, while chromosome arm-level fragment count z-scores and fragment length ratios were calculated in 5-Mb bins across the genome. Relative frequencies of all 256 possible 4-mer end motifs were calculated for each sample. Individual logistic regression models were developed based on each fragmentomic feature using leave-one-out cross validation (LOOCV). Outputs from these models were combined into multi-feature XGBoost models. Cohort-specific end-motif enrichments were identified using Kruskal-Wallis tests with FDR correction. Motifs were then hierarchically clustered based on their cohort-wise median frequencies to define groups of motifs with shared fragmentation patterns. Results: Across urine cfDNA in the GU cohort, BC showed the highest mean TFx at 10.0%, followed by mPC at 3.6% and RCC at 2.7%. A machine learning model based on 4-mer end motifs achieved a mean ROC AUC of 0.933 with classification accuracies of 63% for healthy, 87% for BC, 72% for mPC, and 83% for RCC. This model outperformed models based on ichorCNA (AUC = 0.767), chromosome arm fragment-count z-scores (AUC = 0.827), and fragment length ratios (AUC = 0.674). Combining end motifs with other fragmentomic features did not improve prediction accuracy. Multi-feature models incorporating end motifs with ichorCNA, chromosome arm-level z-scores, or fragment-length ratios yielded mean AUCs between 0.908-0.924, lower than the end-motif only model. Kruskal-Wallis tests identified 179 of 256 end motifs (79%) as significantly different across healthy, BC, mPC, and RCC. Hierarchical clustering of cohort-level median motifs revealed distinct end motif patterns: Motifs beginning with CT were enriched in RCC; AAAA was strongly enriched in BC; and motifs beginning with CA, AC, and AG were enriched in mPC. Conclusion: Urine cfDNA 4-mer end motifs capture GU cancer-specific biological patterns that enable accurate tissue-of-origin prediction, underscoring their promise as a diagnostic tool. Citation Format: Jessica Linford, Pradeep S. Chauhan, Irfan Alahi, Yohan Kim, Arpit Panda, Nathan Colon, Ryan Mueller, Faridi Qaium, Eric H. Kim, Melissa A. Reimers, Zachary L. Smith, Woodson W. Smelser, Fabrice Lucien-Matteoni, Aadel A. Chaudhuri. Urine cfDNA 4-mer end-motif signatures outperform other fragmentomic features for tissue-of-origin classification in genitourinary cancers [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 2590.

Abstract Background: Liquid biopsy approaches for cancer detection often rely on tissue-specific methylation or fragmentation features to infer tumor origin. However, a pan-aerodigestive cancer detection strategy is clinically valuable given shared risk exposures, field cancerization, and frequent diagnostic ambiguity among lung, esophageal, and head & neck cancers. We hypothesized that advanced-stage disease exhibits convergent chromatin remodeling, producing a dominant malignancy-associated fragmentomic signal that supersedes subtler tissue-of-origin patterns. Methods: Whole-genome EM-seq (∼15×) was performed on plasma cfDNA from 120 patients with stage III/IV aerodigestive cancers (lung, esophageal, head & neck; squamous and adenocarcinoma) and 60 age-matched controls. Four fragmentomic modalities were assessed: (1) genome-wide fragmentation in 5-Mb bins (DELFI-like), (2) NMF spectral components, (3) 4-mer end motifs, and (4) promoter-centric nucleosome scores (Griffin). All preprocessing occurred strictly within each training fold of a repeated stratified 10-fold CV (100 repeats) to prevent data leakage. No test-sample information was used in training. Hyperparameters were tuned only within training partitions. Random Forest classifiers were trained under this fully nested scheme. Results: The multimodal classifier robustly detected advanced aerodigestive malignancies with a mean AUROC 0.86 and AUPRC 0.94. However, modality dissection revealed a clear performance hierarchy: nucleosome positioning (Griffin) alone achieved AUROC 0.85 and AUPRC 0.93, nearly matching the full integrated model, whereas end motifs performed poorly (AUROC 0.61). Biological interpretation demonstrated prominent nucleosome depletion centered at promoters of proliferation-associated genes, indicating a convergent epigenetic state of promoter opening across late-stage aerodigestive malignancies. Conclusions: Advanced aerodigestive cancers exhibit a dominant, convergent chromatin remodeling signature detectable in plasma cfDNA, driven primarily by altered nucleosome positioning. In late-stage disease, this shared malignancy-associated fragmentomic signal outweighs tissue-of-origin-dependent patterns. These findings highlight the utility of nucleosome-based fragmentomics not merely for detection, but for resolving diagnostic ambiguity in patients with indeterminate masses and for monitoring total tumor burden in high-risk aerodigestive malignancies. Citation Format: Axel Misael Hidalgo, Ayesha Hashmi, Jeff Szymanski, Pradeep Chauhan, Lilli Greiner, Faridi Qaium, Daniel J. Ma, David M. Routman, Katie M. Van Abel, Aadel A. Chaudhuri.. Convergent chromatin remodeling enables pan-aerodigestive detection of advanced malignancies in plasma cfDNA [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 3862.

Abstract Background: Personalizing chemoradiotherapy (chemoRT) for locally advanced non-small cell lung cancer (LA-NSCLC) is limited by the lack of early biomarkers that inform response during treatment. Current circulating tumor DNA (ctDNA) molecular residual disease assays predict post-treatment outcomes but lack mid-treatment utility. ctDNA burden estimation has traditionally relied on variant-centric metrics such as maximum variant allele fraction (Max VAF), which rely on mutation-specific signals and may be confounded by histology-dependent shedding variability, allelic imbalance, and clonal hematopoiesis. Here we present circulating tumor fraction estimate (ctFE), a machine-learning composite score that integrates VAF distributions, copy-number alterations, and germline B-allele frequency deviations to approximate global tumor burden using a widely available, tumor-naïve clinical platform. Methods: A burden-based ctFE threshold was derived using pre-treatment plasma from a prospective phase II clinical trial of MR-guided hypofractionated chemoRT (LA-WU, n=26), locked, and applied unchanged to early mid-treatment samples (day 10-14). To test scalability and generalizability, its prognostic performance was validated in two real-world cohorts: 94 LA-NSCLC patients receiving definitive chemoRT (LA-RW) and 309 oligometastatic NSCLC patients receiving consolidative RT (OM-RW). ctFE and Max VAF were compared as continuous predictors across cohorts. Results: ctFE consistently outperformed Max VAF. In LA-WU, pre-treatment ctFE was associated with overall survival (OS HR=1.15, p=0.04) and progression-free survival (PFS HR=1.84, p=0.009), whereas Max VAF was not. Mid-treatment ctFE remained prognostic for PFS (HR=1.14, p=0.026). Early ctFE dynamics defined three molecular response groups with marked OS separation: consistently low, responder, and nonresponder groups (median OS 60.8 vs 13.0 vs 2.9 months, respectively; p<0.001). In multivariable models including both biomarkers, higher ctFE remained independently associated with worse survival in LA-RW (OS HR=1.88, p=0.010) and OM-RW (OS HR=1.37, p=0.040; PFS HR=1.45, p=0.008), whereas Max VAF did not. The locked ctFE threshold stratified OS across all cohorts (LA-WU HR=5.93; LA-RW HR=9.08; OM-RW HR=2.26; all p<0.001). Conclusion: ctFE provides clinically meaningful pre- and mid-treatment risk stratification and consistently outperforms Max VAF across locally advanced and oligometastatic NSCLC cohorts. We show that ctFE is a biologically informative, clinically generalizable and scalable ctDNA burden metric measurable using a tumor-naïve, off-the-shelf assay, supporting its practical utility in biomarker-adapted radiotherapy strategies. Citation Format: Ayesha Hashmi, Jessica Linford, Pradeep S. Chauhan, Kaushal Parikh, Rotem Ben-Shachar, John Guittar, Malvika Pillai, Jyoti Patel, Matteo Bergsagel, Nicholas P. Semenkovich, Kenneth R. Olivier, Sean S. Park, Dawn Owen, David M. Routman, Katie N. Lee, Alex D. Sherry, Aaron S. Mansfield, Daniel Morgensztern, Ramaswamy Govindan, Clifford G. Robinson, Carmen Bergom, Saiama N. Waqar, Bruna Pellini Ferreira, Gregory R. Vlacich, Aadel A. Chaudhuri. Early ctDNA quantification by ctFE outperforms Max VAF for survival stratification across locally advanced and oligometastatic NSCLC treated with radiotherapy [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 1183.

Abstract Background: Combination immune checkpoint inhibitor (ICI) therapy represents a standard-of-care for patients with metastatic melanoma. However, >40% of patients do not respond to treatment and severe immune-related toxicity affects up to 60% of patients. Accordingly, we need more precise ways to select patients for combination ICIs. To address this, we developed LiquidTME, a liquid biopsy method based on Spatial EcoTyper (Cancer Res (2025) 85 (8_Supplement_1): 153) that leverages a deep learning approach to noninvasively assess the tumor microenvironment from cell-free DNA (cfDNA) methylation data. LiquidTME was previously trained to predict ICI response using 78 patients with advanced melanoma treated at Yale University. Here we performed a blinded validation of LiquidTME in coordination with clinicians at Washington University (WashU). Methods: The WashU cohort consisted of pre-ICI plasma from 34 patients with advanced melanoma, each treated with ipilimumab and nivolumab (n=27) or relatlimab and nivolumab (n=7). ICI response was classified as durable clinical benefit (DCB) or no durable benefit (NDB) by a board-certified oncologist. Tumor mutational burden (TMB) was determined for 27 patients using commercial CLIA assays and analyzed as nonsynonymous mutations (mt) per megabase (Mb), using the FDA-approved 10 mt/Mb cutpoint for TMB-high and -low groups. The WashU cohort was sent to a scientific team that was blinded to all clinical and TMB data. Cell-free DNA was extracted from 2 mL plasma per patient and subjected to enzymatic methyl-seq (EM-seq) at a median depth of 15x. LiquidTME was performed on the resulting profiles, yielding a binary response prediction and a continuous response score for each sample. Test results were locked down and returned. Both assays were compared by AUC and two-sided Wilcoxon rank-sum tests for response classification, and Kaplan-Meier analysis for progression-free survival (PFS). Results: Median follow-up time of the cohort was 21.6 months. LiquidTME performed on cycle 1 day 1 pre-ICI plasma significantly stratified PFS with a hazard ratio (HR) of 0.27 (P = 0.005), with LiquidTME(+) patients achieving a median PFS of 1.7 years longer than LiquidTME(-) patients. LiquidTME distinguished DCB from NDB with an AUC of 0.77 (P = 0.007). Similar performance was seen across distinct combination ICI regimens. In patients with TMB data, LiquidTME maintained whole-cohort performance with a PFS HR of 0.30 (P < 0.03; AUC = 0.77). In contrast, TMB high vs. low subsets failed to stratify PFS (HR = 0.49; P = 0.17) or response (AUC = 0.53; P = 0.8). Conclusion: LiquidTME identified durable responders to combination ICI from pretreatment plasma in this blinded validation cohort of melanoma patients. Given its favorable performance, LiquidTME shows promise as a clinical tool to guide personalized immunotherapy decision-making. Citation Format: Aadel A. Chaudhuri, David Y. Chen, Tucker Hansen, Mirna Jarosz, Vincent A. Miller, Aaron M. Newman. Blinded clinical validation of LiquidTME, a cell-free DNA assay for predicting response to immunotherapy by noninvasively profiling the tumor microenvironment [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 1040.

Abstract Introduction: Long intergenic noncoding RNAs (lincRNAs) and circular (circ) RNAs have been shown to play key roles in carcinogenesis and tumor progression. Urine represents a promising source of tumor-reflective signals shed directly from the urothelial microenvironment in bladder cancer (BC) patients. Here, we evaluate the urine cell-free (cf) RNA transcriptome in a cohort of 50 patients with localized BC. Methods: A total of 50 localized BC patients and 24 healthy adults were enrolled. Pre-operative urine (collected on the day of radical cystectomy) was processed to isolate cfRNA from urine supernatant. Libraries were prepared using an Illumina tagmentation-based RNA enrichment workflow and sequenced to 100M paired-end reads per sample on a NovaSeq X. FASTQ files were concatenated per sample and QC-checked with RNA-SeQC and Samtools. Reads were aligned to GRCh38/hg38 with STAR, and gene counts were generated using Feature Counts. Linear differential expression analysis (DEA) was performed with DESeq2. For discovery we used random halves of TCGA-BLCA and healthy urine cfRNA samples, alongside a second DEA with random halves of urine BC cfRNA vs. controls; overlapping upregulated genes were validated in the remaining samples. For circular (circ) RNAs, annotations from MiOncoCirc were used; junctions with ≥5 supporting reads were retained, and DEA was conducted with edgeR. Results: In this study, the median age was 68 years, and 80% (40/50) were male. Among the cohort, 66% (33/50) had muscle-invasive bladder cancer (MIBC), of whom 54% received neoadjuvant chemotherapy. Differential expression analysis of TCGA-BLCA tumors and BC urine cfRNA, identified 27 genes commonly upregulated, with 23 of the 27 shared genes strikingly being long intergenic noncoding RNAs (lincRNAs). These BC/urine-specific lincRNAs—including LINC00930, LINC01004, DLG5-AS1, APTR, PSORS1C3, and SERPINB9P1—have known roles in proliferation, EMT, metabolism, and immune signaling. Outside of lincRNAs, tumor-reflective coding transcripts in BC/urine were enriched for autophagy, driven by MAP1LC3B2.TCGA-BLCA survival analysis showed that LINC00930 and PSORS1C3 were associated with worse overall survival. Expanding beyond linear RNA sequences, analysis of urine circRNAs from BC patients revealed a strong cancer-specific dysregulation pattern, with 77 circRNAs significantly upregulated compared with healthy controls. Upregulated urine circRNAs arose from host genes involved in BC-relevant pathways, including invasion (DOCK1, ARHGAP5), autophagy/ER stress (VMP1, ERN1), and luminal differentiation (GRHL2, ESRP1). Conclusion: Cell-free RNA profiling reveals a distinct transcriptomic signature including differential lincRNA- and circRNA-driven autophagy, EMT, proliferation, metabolic, differentiation, and immune signaling programs in the urine of bladder cancer patients. Citation Format: Pradeep Chauhan, Li Lin, Irfan Alahi, Jessica Linford, Lilli J. Greiner, Ayesha Hashmi, Anushka Viswanathan, Nathan Colon, Faridi Qaium, Peter K. Harris, John Sheng, Jacob J. Orme, Eric H. Kim, Zachary L. Smith, Woodson Smelser, Christopher A. Maher, Aadel A. Chaudhuri. Urine cell-free RNA captures tumor-reflective long intergenic noncoding and circular RNA programs in bladder cancer [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 1133.

Abstract Background: Spatial cellular ecosystems in the tumor microenvironment (TME) form dynamic signaling hubs that critically influence cancer disease progression and response to therapy, including response to immune checkpoint inhibitors (ICIs). However, tumor sampling bias and the impracticality of acquiring serial tumor biopsies have made it challenging to profile the TME clinically. To address this challenge, we developed Liquid EcoTyper, a multi-analyte AI framework to quantify TME spatial cellular ecosystems—termed spatial ecotypes (SEs)—noninvasively from cell-free DNA (cfDNA) methylation data. Methods: In previous work, we discovered nine spatial ecotypes, including SEs associated with ICI response (e.g., SE7 with benefit, SE4 with resistance), and quantified them from 1,249 bulk tumor RNA-seq profiles of melanoma, non-small cell lung cancer (NSCLC), and bladder cancer (BC) patients (Cancer Res (2025) 85 (8_Supplement_1): 153). Here we trained Liquid EcoTyper, an interpretable deep learning model, to transfer SE profiling to cell-free DNA using CpG methylation profiles. Performance was quantified using simulated and real data, including methylation profiles (EM-seq) of plasma cfDNA paired with tumor biopsy-derived SE levels determined by EM-seq (n=20 pairs) or 10x Visium (n=15 pairs). Clinical outcome prediction was evaluated using pretreatment plasma from patients treated with ICI alone (n=78 melanoma pts), chemotherapy and ICI (n=25 NSCLC pts), and enfortumab vedotin and ICI (n=10 localized muscle-invasive BC (MIBC) pts). Tumor mutational burden (TMB), tumor PD-L1, or circulating tumor DNA (ctDNA) were profiled as a comparator where possible. Results: We observed striking concordance between plasma-derived SE levels and tumor biopsy-confirmed SE levels from the same patients, demonstrating that liquid biopsy analysis of cfDNA methylomes can recapitulate TME spatial biology. Underscoring specificity, no reliable SE signal was detectable from peripheral blood mononuclear cells. In pre-ICI plasma, liquid SE levels were significantly associated with ICI-based response (SE7) versus resistance (SE4) in patients with melanoma, NSCLC, and MIBC, with a mean cross-cancer area under the curve (AUC) of 0.87 for SE7 (range 0.8-0.97) and 0.81 for SE4 (range 0.76-0.85)—far surpassing TMB, tumor PD-L1, and ctDNA in evaluable patients, both in binary analyses of response and multivariable models of survival. Our results also showed potential to predict pathologic complete response in the neoadjuvant setting. Conclusion: Liquid EcoTyper is a scalable noninvasive AI-based framework for spatiotemporal assessment of the TME. By enabling TME-informed risk stratification without surgical biopsies, Liquid EcoTyper has potential to enable more precise and actionable decision making in patients with solid tumors. Citation Format: Erin L. Brown, Wubing Zhang, Abul Usmani, Noah Earland, Ayesha Hashmi, Chibuzor Olelewe, Anushka Viswanathan, Pradeep S. Chauhan, Minji Kang, Chloé B. Steen, Hyun Soo Jeon, Susanna Avagyan, Irfan Alahi, Nicholas P. Semenkovich, Matteo Bergsagel, Janella C. Schwab, Chloe M. Sachs, Faridi Qaium, Peter K. Harris, Antonella Bacchiocchi, Qingyuan Cai, Andrew J. Gentles, Rondell P. Graham, Peter C. Lucas, Ryan C. Fields, Jacob J. Orme, Aaron S. Mansfield, Mario Sznol, Ruth Halaban, David Y. Chen, Aaron M. Newman, Aadel A. Chaudhuri. Liquid biopsy profiling of the tumor microenvironment to determine response to immunotherapy regimens across solid 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 94.

Abstract Purpose: Despite advances in molecular diagnostics, glioblastoma (GBM) remains incurable, with recurrence driven by residual invasive tumor cells that escape surgical resection and therapy. Current genomic profiling to guide therapy for recurrent disease typically relies on a single-site clinical biopsy (SSCB) obtained from the contrasting-enhancing (CE) tumor core - the region targeted for maximal resection and most visible on Magnetic Resonance Imaging (MRI). However, this region poorly represents the non-enhancing (NE) invasive tumor margins, which harbor treatment-resistant and genetically distinct cell populations responsible for disease recurrence. GBM exhibits extensive spatial heterogeneity, raising concerns that a single tissue may underrepresent the true actionable landscape of the tumor. Experimental Procedures: We analyzed 302 biopsies from 66 GBM patients who underwent multiregional whole exome and RNA sequencing across CE and NE tumor regions. Of these, 17 patients also had standard-of-care SSCB molecular profiling utilizing clinically available genomic sequencing panels outside of the multiregional protocol. Variants that have been targeted in ongoing or successful human clinical trials were annotated as actionable. We compared actionability profiles between SSCB and multiregional sampling, to determine the gain of actionable variant detection across the two sampling strategies. We performed an in-silico biopsy simulation to model the coverage of these alterations across the tumor landscape with increasing sampling frequency in the multiregional protocol, versus the coverage from clinical sequencing panels on SSCBs. Results: Clinical sampling identified a median of one actionable mutation per patient (range 0-3). In contrast, multiregional profiling revealed additional potentially actionable variants in over 70% of patients, particularly within NE infiltrative regions that are not captured by conventional resection. Notably, nine potentially actionable copy-number variants, including PDGFRA amplification, were consistently absent from SSCB results and underrepresented in most clinical panels. In silico biopsy simulations demonstrated that actionable variant detection continues to rise even after sampling five additional regions, reflecting spatial segregation of therapeutic targets. Conclusions: These findings demonstrate that a single sample from the CE tumor core fails to capture the biologically diverse invasive tumor populations responsible for GBM recurrence. Multiregional sequencing uncovers clinically meaningful alterations that would otherwise remain undetected with conventional sampling, highlighting the risk of incomplete molecular characterization in current clinical biopsy workflows. Citation Format: Enmar Harfoush, Taylor Weiskittel, Mylan Blomquist, Dustin Grief, Aadel A. Chaudhuri, Leland S. Hu, Nhan L. Tran, Shannon Fortin-Ensign. Beyond a single core biopsy: Spatial genomic profiling improves therapeutic target detection in glioblastoma [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 706.