Published February 19, 2024 | Version v1
Dataset Open

Data from: Distinct longitudinal patterns of urine tumor DNA in patients undergoing surveillance for bladder cancer

Authors/Creators

  • 1. Institute for Cancer Research, Oslo University Hospital

Description

Abstract:

Cystoscopy is the gold standard for surveillance of non-muscle invasive bladder cancer (NMIBC), but is invasive and has suboptimal accuracy. The aim of this study was to investigate the potential of urine tumor DNA (utDNA) to replace cystoscopy for surveillance of bladder cancer recurrences. In this longitudinal, prospective and observational study, 47 patients were followed for recurrence for two years, involving droplet digital PCR (ddPCR) analysis of utDNA using the BladMetrix DNA methylation biomarker test at each cystoscopy control. utDNA was detected in 21/23 recurrences (91% sensitivity), including 5/5 T1, T2 and CIS tumors (100%) and 10/12 Ta tumors (83%), with <1% false negative test results. Importantly, utDNA analysis showed potential to reduce the number of cystoscopies by 55%, benefitting 79% of the patients. Eleven of 23 recurrences (48%) were detected earlier with utDNA than with cystoscopy, and distinct patterns of residual utDNA post-surgery indicated minimal residual disease (MRD) or field effect in 6% and 15% of the patients, respectively. In conclusion, utDNA analysis shows high sensitivity to detect tumor recurrence, potential to reduce the number of cystoscopies, and promise to guide patient-specific surveillance regimes.

Methods

Patients with a suspected bladder cancer scheduled for TURB at Oslo University Hospital, Aker, Oslo, Norway from January 2017 to September 2018 were evaluated for inclusion. Patients that qualified for inclusion (i.e. with a non-muscle invasive bladder cancer) were asked to provide two urine samples at each hospital visit, i.e. both at inclusion and at every follow-up cystoscopy control. Urine samples were processed using a standard centrifugation protocol. DNA was extracted from urine pellets using the QIAamp DNA Mini Kit (Qiagen, Hilden, Germany), and bisulfite conversion was performed using the EpiTect Bisulfite Kit (Qiagen), both procedures according to the manufacturers' protocols. Droplet digital PCR (ddPCR) was performed using the QX200™ Droplet Digital™ PCR System (BioRad, Hercules, CA, USA), following the manufacturers' specifications. Partition classification, i.e. dicthomization of positive and negative droplets, was performed in R (version 4.1.0) using the PoDCall shiny app (https://bioconductor.org/packages/PoDCall/). The 4Plex was used as an internal control for normalization (Pharo et al., Clin Epigen, 2018). The utDNA analysis was defined as positive if ≥2/8 biomarkers were methylated (i.e. a positive BladMetrix test), and negative if <2/8 biomarkers were methylated (i.e. a negative BladMetrix test).

Notes

Description of the data and file structure

This dataset consists of raw data from droplet digital PCR (ddPCR) analysis of urine tumor DNA (utDNA) from bladder cancer patients under follow-up for recurrence, using the BladMetrix test. The BladMetrix urine test consists of eight DNA methylation biomarkers, where each biomarker is run separately in a duplex ddPCR reaction with the 4Plex internal control (Pharo et al. Clin Epigenetics. 2018).

The raw ddPCR data was exported from the QuantaSoft Software 1.7.4.0917 (BioRad) in the form of amplitude csv files, and one csv file represents one ddPCR reaction/well. In the csv files, each line indicates the fluorescence amplitude intensity data from one droplet, and the number of lines corresponds to the number of droplets generated for the respective reaction. The first column ("Ch1 Amplitude") indicates the fluorescence amplitude value of the biomarker assay (Channel 1; FAM) and the second column ("Ch2 Amplitude") indicates the fluorescence amplitude value of the 4Plex internal control (Channel 2; VIC). The third column ("Cluster") indicates which cluster the droplet has originally been assigned to (i.e. double negative, single positive for either the biomarker or the 4Plex control, or double positive).

The current data set consits of 12 folders with raw ddPCR data from both patient samples and neagtive and positive controls. One folder contains data from samples run on the same 96-well plate. Below follows an explanation for the data in the 12 folders:

  • "Monitoring_Inclusion": Includes samples from the time point of inclusion of the patients. The folder contains eight subfolders, where each subfolder contains data from a ddPCR replicate plate. The eight biomarkers have been analyzed on the individual replicate plates, and the name of the biomarker assay is specified in the name of the subfolder.

  • "Monitoring1-Monitoring9": Includes samples from the follow-up/monitoring time points of the patients. Each of the "Monitoring1-9"-folders contains eight subfolders with data from a ddPCR replicate plate. The eight biomarkers have been analyzed on the individual replicate plates, and the name of the biomarker assay is specified in the name of the subfolder (similar structure as the "inclusion samples" explained above).

  • "Monitoring_reruns_2018" and "Monitoring_reruns_2022": Includes samples that for various quality control reasons have been re-run. On both of the re-run plates, several biomarker assays have been included on the same plate.

For downstream analysis, all csv files were uploaded platewise (i.e. folderwise) to the partition classification algorithm PoDCall - POsitive Droplet CALLer - which automatically dicthomizes positive and negative droplets and returns both non-normalized and normalized concentrations. PoDCall is an R package with an accompagnying shiny graphical user interface (GUI), and is freely availabel on Bioconductor (https://www.bioconductor.org/packages/release/bioc/html/PoDCall.html).

For further information or questions, the corresponding author, Guro E. Lind, can be contacted.

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