UROSCAN and UROSCANSEQ: a large-scale multicenter effort towards translation of molecular bladder cancer subtypes into clinical practice - from biobank to RNA-sequencing in real time

Prototol for the Prospective Sample Collection for Cancer of Bladder (ProCaB) Trial by the Cancer of the Bladder Leuven (CaBLe) Consortium

Non-muscle-invasive bladder cancer (NMIBC) is a heterogeneous disease categorized as low, intermediate, high, or very high risk, for which recurrence and progression rates and thus management strategies differ. Current molecular subclassification of bladder cancer (BC) is mainly based on data for muscle-invasive disease, with very few data for NMIBC. A more accurate classification system is needed for better stratification of NMIBC using multiomics and immunohistopathological molecular data alongside clinical data collected in a prospective cohort. ProCaB (Prospective Sample Collection for Cancer of Bladder) is a single-center non-interventional, prospective study recruiting all eligible patients diagnosed with BC in a tertiary center in the Flanders region of Belgium. Clinical data have been collected in a prospective registry since August 2013. Biosamples (blood, urine, and BC tissue) are collected from each patient at diagnosis and are stored at -80°C at BioBank UZ Leuven after appropriate processing according to the protocol. Multiomics (genomics, epigenetics, transcriptomics, proteomics, lipidomics, metabolomics) and immunohistopathology analyses will be performed on appropriate samples. The target is to enroll 300 patients over a 5-yr period, and all patients will be followed for 5 yr. The objective is to create a biobank of samples from patients diagnosed with BC for use in multiomics and immunohistopathological analyses. Results from these analyses, together with long-term clinical data, can be used for comprehensive multilayered molecular characterization of disease recurrence and progression in intermediate- and (very) high-risk NMIBC, identification of multibiomarker panels for better stratification, and identification of a patient subgroup that does not respond to bacillus Calmette-Guérin treatment. This trial is registered on ClinicalTrials.gov as NCT04167332.

Improved detection of clinically relevant fusion transcripts in cancer by machine learning classification

BACKGROUND

Genomic rearrangements in cancer cells can create fusion genes that encode chimeric proteins or alter the expression of coding and non-coding RNAs. In some cancer types, fusions involving specific kinases are used as targets for therapy. Fusion genes can be detected by whole genome sequencing (WGS) and targeted fusion panels, but RNA sequencing (RNA-Seq) has the advantageous capability of broadly detecting expressed fusion transcripts.

RESULTS

We developed a pipeline for validation of fusion transcripts identified in RNA-Seq data using matched WGS data from The Cancer Genome Atlas (TCGA) and applied it to 910 tumors from 11 different cancer types. This resulted in 4237 validated gene fusions, 3049 of them with at least one identified genomic breakpoint. Utilizing validated fusions as true positive events, we trained a machine learning classifier to predict true and false positive fusion transcripts from RNA-Seq data. The final precision and recall metrics of the classifier were 0.74 and 0.71, respectively, in an independent dataset of 249 breast tumors. Application of this classifier to all samples with RNA-Seq data from these cancer types vastly extended the number of likely true positive fusion transcripts and identified many potentially targetable kinase fusions. Further analysis of the validated gene fusions suggested that many are created by intrachromosomal amplification events with microhomology-mediated non-homologous end-joining.

CONCLUSIONS

A classifier trained on validated fusion events increased the accuracy of fusion transcript identification in samples without WGS data. This allowed the analysis to be extended to all samples with RNA-Seq data, facilitating studies of tumor biology and increasing the number of detected kinase fusions. Machine learning could thus be used in identification of clinically relevant fusion events for targeted therapy. The large dataset of validated gene fusions generated here presents a useful resource for development and evaluation of fusion transcript detection algorithms.

Urodrill - a novel MRI-guided endoscopic biopsy technique to sample and molecularly classify muscle-invasive bladder cancer without fractionating the specimen during transurethral resection

The current diagnostic pathway for patients with muscle-invasive bladder cancer (MIBC), which involves with computed tomography urography, cystoscopy, and transurethral resection of the bladder (TURB) to histologically confirm MIBC, delays definitive treatment. The Vesical Imaging-Reporting and Data System (VI-RADS) has been suggested for MIBC identification using magnetic resonance imaging (MRI), but a recent randomized trial reported misclassification in one-third of patients. We investigated a new endoscopic biopsy device (Urodrill) for histological confirmation of MIBC and assessment of molecular subtype by gene expression in patients with VI-RADS 4 and 5 lesions on MRI. In ten patients, Urodrill biopsies were guided by MR images to the muscle-invasive portion of the tumor via a flexible cystoscope under general anesthesia. During the same session, conventional TURB was subsequently performed. A Urodrill sample was successfully obtained in nine of ten patients. MIBC was verified in six of nine patients, and seven of nine samples contained detrusor muscle. In seven of eight patients for whom a Urodrill biopsy sample was subjected to RNA sequencing, single-sample molecular classification according to the Lund taxonomy was feasible. No complications related to the biopsy device occurred. A randomized trial comparing this new diagnostic pathway for patients with VI-RADS 4 and 5 lesions and the current standard (TURB) is warranted.

Patient summary

We report on a novel biopsy device for patients with muscle-invasive bladder cancer that facilitates histology analysis and molecular characterization of tumor samples.