Molecular Characterization of Upper Tract Urothelial Carcinoma in the Era of Next-generation Sequencing: A Systematic Review of the Current Literature

Hassler, Melanie R.; Bray, Freddie; Catto, James W.F.; Grollman, Arthur P.; Hartmann, Arndt; Margulis, Vitaly; Matin, Surena F.; Rouprêt, Morgan; Sfakianos, John P.; Shariat, Shahrokh F.; Faltas, Bishoy

doi:10.1016/j.eururo.2020.05.039

Cited by 73 publications

(67 citation statements)

References 63 publications

(121 reference statements)

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“…Nearly 90% of tumors originating in the urinary bladder are BUC, but urothelial carcinoma (UC) may arise at any point along the entire length of the urinary tract. In particular, 5-10% of UC cases arise in the renal pelvis and ureter, noted as upper tract UC (UTUC) [1][2][3][4].…”

Section: Introductionmentioning

confidence: 99%

Comprehensive Genomic Profiling of Upper-tract and Bladder Urothelial Carcinoma

Necchi

Madison²,

Pal

et al. 2021

European Urology Focus

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Section: Introductionmentioning

confidence: 99%

Comprehensive Genomic Profiling of Upper-tract and Bladder Urothelial Carcinoma

Necchi

Madison²,

Pal

et al. 2021

European Urology Focus

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“…The frequency of FGFR3 alterations and TP53/MDM2 status is different across articles. This difference is due to the inconstant incidence of HG or primary and metastatic tumors analyzed in each article [8]. Table 3 summarizes the most common genetic mutations in UTUC divided by oncologic characteristics.…”

Section: Scorementioning

confidence: 99%

“…Differently from sporadic UTUC, FGFR3 mutations are rare in AA-induced tumors. It is reported that approximately 74% of UTUC cases in the United States showed FGFR3 mutations as compared to only 3% of nephropathic diseases [8,34].…”

Section: Genomics Of Aristolochic Acid-associated Upper Urinary Tractmentioning

confidence: 99%

“…In bladder cancer, a subtype-stratified therapeutic approach has been hypothesized [44], as summarized in Figure 3. No conclusions can be drawn regarding the distribution of molecular subtypes in UTUC, but some studies concluded that most UTUCs were of the luminal subtype with an associated T-cell-depleted microenvironment [8,16,43].…”

Section: Implications For Systemic Therapymentioning

confidence: 99%

“…We restricted our searches to publications from 2004 onward (the introduction of applicable next-generation sequencing platforms [8]). Additional papers were collected by cross-referencing the bibliography of the selected articles.…”

Section: Introductionmentioning

confidence: 99%

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Current Knowledge on Genomic Profiling of Upper Tract Urothelial Carcinoma

Lorenzis

Albo

Longo

et al. 2021

Genes

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Recent research in next-generation sequencing characterized the genomic landscape of urothelial cancer. However, the majority of the studies focused on bladder cancer (BC). Upper urinary tract urothelial carcinomas (UTUC) and BC share some histological characteristics, but, considering the differences in terms of embryologic precursors, epidemiology, genetics, medical and surgical management and response to therapy, UTUC and BC should be considered as two distinct diseases. Our objective is to analyze through a literature search the latest updates and the current knowledge about the genomics of UTUC. We also evaluate genetic differences between BC and UTUC and the potential implications for systemic therapy. Molecular subtyping and variant histology and their correlation with response to chemotherapy were also explored. In summary, the most frequent genomic variations in UTUC included FGFR3, chromatin remodeling genes, TP53/MDM2 and other tumor suppressors/oncogenes. The genomics of UTUC, integrated with clinical data, could drive the selection of patients who could benefit from targeted therapy or off-label treatment. Routine implementation of tumor genomic characterization in UTUC patients should therefore be contemplated and evaluated prospectively.

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A deep‐learning workflow to predict upper tract urothelial carcinoma protein‐based subtypes from H&E slides supporting the prioritization of patients for molecular testing

Angeloni,

van Doeveren,

Lindner

et al. 2024

The Journal of Pathology CR

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Upper tract urothelial carcinoma (UTUC) is a rare and aggressive, yet understudied, urothelial carcinoma (UC). The more frequent UC of the bladder comprises several molecular subtypes, associated with different targeted therapies and overlapping with protein‐based subtypes. However, if and how these findings extend to UTUC remains unclear. Artificial intelligence‐based approaches could help elucidate UTUC's biology and extend access to targeted treatments to a wider patient audience. Here, UTUC protein‐based subtypes were identified, and a deep‐learning (DL) workflow was developed to predict them directly from routine histopathological H&E slides. Protein‐based subtypes in a retrospective cohort of 163 invasive tumors were assigned by hierarchical clustering of the immunohistochemical expression of three luminal (FOXA1, GATA3, and CK20) and three basal (CD44, CK5, and CK14) markers. Cluster analysis identified distinctive luminal (N = 80) and basal (N = 42) subtypes. The luminal subtype mostly included pushing, papillary tumors, whereas the basal subtype diffusely infiltrating, non‐papillary tumors. DL model building relied on a transfer‐learning approach by fine‐tuning a pre‐trained ResNet50. Classification performance was measured via three‐fold repeated cross‐validation. A mean area under the receiver operating characteristic curve of 0.83 (95% CI: 0.67–0.99), 0.8 (95% CI: 0.62–0.99), and 0.81 (95% CI: 0.65–0.96) was reached in the three repetitions. High‐confidence DL‐based predicted subtypes showed significant associations (p < 0.001) with morphological features, i.e. tumor type, histological subtypes, and infiltration type. Furthermore, a significant association was found with programmed cell death ligand 1 (PD‐L1) combined positive score (p < 0.001) and FGFR3 mutational status (p = 0.002), with high‐confidence basal predictions containing a higher proportion of PD‐L1 positive samples and high‐confidence luminal predictions a higher proportion of FGFR3‐mutated samples. Testing of the DL model on an independent cohort highlighted the importance to accommodate histological subtypes. Taken together, our DL workflow can predict protein‐based UTUC subtypes, associated with the presence of targetable alterations, directly from H&E slides.

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Molecular Characterization of Upper Tract Urothelial Carcinoma in the Era of Next-generation Sequencing: A Systematic Review of the Current Literature

Cited by 73 publications

References 63 publications

Comprehensive Genomic Profiling of Upper-tract and Bladder Urothelial Carcinoma

Comprehensive Genomic Profiling of Upper-tract and Bladder Urothelial Carcinoma

Current Knowledge on Genomic Profiling of Upper Tract Urothelial Carcinoma

A deep‐learning workflow to predict upper tract urothelial carcinoma protein‐based subtypes from H&E slides supporting the prioritization of patients for molecular testing

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