Linford, Jessica

5068 Background: 177 Lu-PSMA-617 (LuPSMA) is a groundbreaking therapy for mPC, but biomarkers are urgently needed to improve patient selection. Plasma-derived EVs are emerging blood-based markers of tumor burden. Here, we analyzed levels of EVs of 123 patients (pts) from 4 cohorts: a pre- and during LuPSMA treatment for a contemporary prospective Mayo Clinic cohort, pre-treatment plasma levels of EVs for a historical Tulane cohort, localized prostate cancer (LPC) after radical prostatectomy (RP), and male healthy donors (HD). Methods: Blood samples were collected from 51 pts pre-cycles (pre-C) 1-3 of LuPSMA at Mayo Clinic from 12/2024-12/2025 and 26 pts pre-C1 at Tulane Cancer Center between 12/2018-1/2025. Small and large EVs were incubated with fluorescently conjugated antibodies (PSMA, STEAP1, CEACAM5, and TROP2) and enumerated using nanoscale flow cytometry. EVs from blood samples of 10 HD and 36 LPC post-RP from 6/2020-8/2024 at Mayo Clinic as controls. AUC-ROC and logistic regression analyses were performed to identify marker signatures associated with PSA50 response. Results: Baseline demographics are shown in the table. Median levels of PSMA, STEAP1, CEACAM5, and TROP2 EV/milliliter (mL) were all significantly higher for pts with mPC (pooled Tulane and Mayo cohorts) compared to LPC post-RP (p<0.001) and HD (p<0.001). In the Mayo cohort, 10 of 51 pts (20%) had undetectable PSA pre-C1. There were no significant differences in median pre-C1 plasma EV levels for these 4 different types of EVs comparing pts with undetectable vs detectable PSA. For 41 Mayo pts with detectable PSA pre-C1, 20 achieved PSA50 response at time of data cutoff (49%), while 7 pts (17%) had persistent PSA rise on LuPSMA. For pts who achieved PSA50, there was a significant decline in both CEACAM5 and TROP2 EVs/mL pre-C1 compared to pre-C3 (19 x 10 6 vs 5.8 x 10 6 , p=0.003 and 16 x 10 6 vs 5.5 x 10 6 , p<0.001, respectively), while no significant changes occurred for the 7 pts with no PSA response. A composite score of pre-C1 CEACAM5 and TROP2 EVs (large- and small-sized EVs) demonstrated the strongest association with PSA50 response (OR=2.96, 95% CI: 1.27-6.44, p=0.038), achieving AUC of 0.73. PSMA and STEAP1 EVs did not show any significant changes from pre-C1 to pre-C3. Conclusions: Plasma-derived EVs are a promising biomarker to identify patients who will benefit from LuPSMA. Longer follow-up and enrolling a larger cohort are underway. Clinical trial information: NCT06200103 . Median values for pre-LuPSMA (Mayo and Tulane), post-RP for LPC, and HD. Mayo mPC (n=51) Tulane mPC (n=26) LPC (n=36) HD (n=10) Age (range) 73 (57-87) 71 (54-83) 66 (56-77) 55 (50-57) Prior lines (range) 1 (1-3) 4 (1-11) - - Gleason score 9 8 7 - PSA (range) 1.0 (0-182) 75 (0.8-503) - - PSMA EV/mL 3.7 x 10 6 14 x 10 6 2.0 x 10 6 1.4 x 10 6 STEAP1 EV/mL 30 x 10 6 45 x 10 6 12 x 10 6 12 x 10 6 CEACAM5 EV/mL 17 x 10 6 5.1 x 10 6 3.7 x 10 6 3.9 x 10 6 TROP2 EV/mL 13 x 10 6 5.2 x 10 6 4.5 x 10 6 3.6 x 10 6

Abstract Purpose: Circulating tumor fraction estimate (ctFE) is a machine learning–derived composite metric of circulating tumor DNA (ctDNA) burden. We hypothesized that pre-treatment and early on-treatment ctFE could robustly risk-stratify patients with locally advanced (LA) and oligometastatic non–small cell lung cancer (NSCLC) treated with radiotherapy (RT). Experimental Design: In a prospective phase II clinical trial (NCT03916419), 26 patients with unresectable stage IIB to IIIC NSCLC received magnetic resonance (MR)-guided hypofractionated chemoradiotherapy (chemoRT) followed by immunotherapy. Plasma ctDNA was profiled at baseline and mid-treatment (days 10–14) to derive ctFE and maximum variant allele frequency (Max VAF). A burden-based ctFE threshold derived from baseline samples was applied unchanged to mid-treatment samples and validated in two external cohorts: LA NSCLC treated with chemoRT (LA-RW; n = 94) and oligometastatic NSCLC treated with RT (OM-RW; n = 309). Results: Pre- and mid-treatment ctFE burden strongly stratified overall survival (OS) and progression-free survival (PFS), outperforming Max VAF and ctFE detectability. The baseline ctFE was prognostic for OS (HR 5.93, P = 0.005) and PFS (HR 11.08, P &lt; 0.001) and remained significant at mid-treatment (OS: HR 7.08; PFS: HR 12.06; both P &lt; 0.001). Early ctFE dynamics defined three molecular response groups with OS separation (median OS 60.8 vs. 13.0 vs. 2.9 months; P &lt; 0.001). ctFE remained associated with survival in both validation cohorts. Conclusions: Early ctFE derived from a clinically available, tumor-naïve ctDNA assay enables noninvasive risk stratification in LA and oligometastatic NSCLC treated with RT, supporting its use as a practical biomarker for precision treatment adaptation.

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: 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&lt;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&lt;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 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.

214 Background: 177 Lu-PSMA-617 (LuPSMA) has revolutionized care for patients (pts) with metastatic castrate-resistant prostate cancer (mCRPC), but only half achieves PSA decline ≥50%. Biomarkers are urgently needed to improve pt selection. EVs and ctDNA released by cancer cells into the blood are emerging biomarkers to estimate overall tumor burden and capture tumor heterogeneity in mCRPC. Here, we analyzed pre-treatment plasma levels of tumor EVs and ctDNA in a pilot cohort of 35 pts with mCRPC treated with LuPSMA. Methods: Blood samples were collected pre-cycle 1 of LuPSMA at Tulane Cancer Center between 12/2018-1/2025. Plasma-derived small and large EVs were incubated with fluorescently conjugated antibodies and enumerated using nanoscale flow cytometry. ctDNA fraction was estimated from plasma cfDNA methylation WGS (∼10×) with ichorCNA. We also analyzed EVs from blood samples of male healthy donors (HD) and pts with localized prostate cancer (LPC) collected 1 day to 8 months after radical prostatectomy (post-RP) from 6/2020-8/2024 at Mayo Clinic as controls. Results: Median age at collection for 10 HD was 55 yrs and for 36 pts with LPC post-RP was 65 yrs. Median PSMA+ EV level was 2.0 x 10 6 /mL for post-RP and 1.4 x 10 6 /mL for HD. For 35 pts with mCRPC, 25 pts had complete data for analysis. Median PSMA+ EV level was 1.4 x 10 7 /mL, significantly higher than post-RP (p<0.001) and HD (p<0.001). At LuPSMA start, median age was 69 yrs (range 54-83), PSA was 82 ng/mL (range 0.8-503), median time from CRPC diagnosis to start was 50 months (range 6.9-285), median time from plasma collection to treatment initiation was 0.5 months (range 0-7.3). Median number of prior lines of life-extending therapy was 4 (range 1-11), including 100% androgen receptor pathway inhibitor and 75% docetaxel. Pts received median 4 cycles of LuPSMA. Median ctDNA fraction was 14.0% (range 0-51.1). Median time to reach nadir PSA for pts with any decline was 2.6 months. 15 pts (60%) achieved PSA40 (defined ≥40% decline in PSA from start to nadir) (range -99% to -42%) compared to 10 pts (40%) who did not (range -16% to rise), which predicted better median overall survival (OS) after LuPSMA start (40.7 v 6.0 months; HR 0.23, 95% CI, 0.07-0.71; p=0.0001). Pts were classified as LuPSMA responders using PSA40. Responders had a lower ratio of large to small PSMA+ EVs (0.30 v 0.53, p=0.0042) with an area under the receiver operating characteristic curve (AUC) of 0.83 (95% CI, 0.68-0.99), and a lower median ctDNA fraction (6.1% v 26.2%, p=0.0044) with an AUC of 0.83 (95% CI, 0.68-1.00). Incorporating both PSMA+ EV and ctDNA fraction together predicted PSA40 response with an AUC of 0.93 (95% CI, 0.84-1.00, p=0.0003). Conclusions: Using pre-LuPSMA plasma samples from a heavily pre-treated historical cohort, we integrated non-invasive liquid biopsy methods to successfully predict response to LuPSMA using PSA decline. Prospective validation is underway.

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 Purpose: Metastatic castration-resistant prostate cancer (mCRPC) resistant to androgen receptor signaling inhibitors (ARSI) is often lethal. Liquid biopsy biomarkers for this deadly form of disease remain under investigation, and underpinning mechanisms remain ill-understood. Experimental Design: We applied targeted cell-free DNA (cfDNA) sequencing to 126 patients with mCRPC from three academic cancer centers and separately performed genome-wide cfDNA methylation sequencing on 43 plasma samples collected prior to the initiation of first-line ARSI treatment. To analyze the genome-wide sequencing data, we performed nucleosome positioning and differential methylated region analysis. We additionally analyzed single-cell and bulk RNA sequencing data from 14 and 80 patients with mCRPC, respectively, to develop and validate a stem-like signature, which we inferred from cfDNA. Results: Targeted cfDNA sequencing detected AR/enhancer alterations prior to first-line ARSIs that correlated with significantly worse progression-free survival (P = 0.01; HR = 2.12) and overall survival (P = 0.02; HR = 2.48). Plasma methylome analysis revealed that AR/enhancer lethal mCRPC patients have significantly higher promoter-level hypomethylation than AR/enhancer wild-type mCRPC patients (P &lt; 0.0001). Moreover, gene ontology and CytoTRACE analysis of nucleosomally more accessible transcription factors in cfDNA revealed enrichment for stemness-associated transcription factors in patients with lethal mCRPC. The resulting stemness signature was then validated in a completely held-out cohort of 80 patients with mCRPC profiled by tumor RNA sequencing. Conclusions: We analyzed a total of 220 patients with mCRPC, validated the importance of cell-free AR/enhancer alterations as a prognostic biomarker in lethal mCRPC, and showed that the underlying mechanism for lethality involves reprogramming developmental states toward increased stemness. See related commentary by Nawfal et al., p. 7