Abstract 2219: Urine cell-free DNA multi-omics to detect molecular residual disease and predict survival in bladder cancer patients

• Published in: Cancer research (Chicago, Ill.) Ill.), 2023-04, Vol.83 (7_Supplement), p.2219-2219
• Main Authors: Chauhan, Pradeep Singh, Shiang, Alexander L., Alahi, Irfan, Sundby, R. Taylor, Feng, Wenjia, Gungoren, Bilge, Nawaf, Cayce, Chen, Kevin, Babbra, Ramandeep K., Harris, Peter K., Qaium, Faridi, Hatscher, Casey, Antiporda, Anna, Brunt, Lindsey, Mayer, Lindsey R., Shern, Jack F., Baumann, Brian C., Kim, Eric H., Reimers, Melissa A., Smith, Zachary L., Chaudhuri, Aadel A.
• Format: Article
• Language: English
• ISSN: 1538-7445; 1538-7445
• DOI: 10.1158/1538-7445.AM2023-2219

Background: Circulating cell-free tumor DNA (ctDNA) analysis for minimal residual disease (MRD) detection is transforming cancer care. However, the sensitivity of these approaches remains subpar and there is significant risk of false negative results. We analyzed the most proximal biofluid (urine) in localized muscle-invasive bladder cancer (MIBC) patients, and performed two orthogonal ctDNA analytical techniques, one focusing on driver mutation detection, and another focusing on genome-wide copy number alterations. The primary objective of this combinatorial approach was to sensitively detect ctDNA MRD, predict pathologic complete response (pCR), and improve patient risk-stratification. Methods: Urine samples from 74 localized bladder cancer patients were collected preoperatively on the day of curative-intent radical cystectomy (RC) to assess urine tumor DNA (utDNA). We performed ultra-low pass whole genome sequencing (ULP-WGS) of urine cfDNA from all 74 patients as well as 15 healthy adults. Tumor fraction (TFx) level based on genome-wide copy number alterations was estimated using ichorCNA. Variant allele frequency (VAF) based on single-nucleotide variants (SNVs) was estimated by uCAPP-Seq. We also noninvasively inferred tumor mutation burden (iTMB). pCR was determined by surgical pathology. A random forest (RF) model with leave-one-out cross-validation (LOOCV) was utilized to predict disease status. Kaplan-Meier (KM) and Cox proportional hazards model survival analyses were performed to assess overall survival (OS) and progression-free survival (PFS). Results: Our study cohort consisted of 74 patients, of which 58 (78%) harbored localized MIBC, and 16 (22%) harbored treatment-refractory high-risk localized NMIBC. Among MIBC patients, 64% (37/58) received neo-adjuvant chemotherapy. VAF, iTMB and TFx levels significantly predicted pCR status. Our RF model incorporating these three utDNA parameters achieved a sensitivity of 87%, a negative predictive value of 77%, and a positive predictive value of 65% for predicting residual disease. AUC for the model was 0.80 (p