Combined gene expression and genomic profiling define two intrinsic molecular subtypes of urothelial carcinoma and gene signatures for molecular grading and outcome.
In the present investigation, we sought to refine the classification of urothelial carcinoma by combining information on gene expression, genomic, and gene mutation levels. For these purposes, we performed gene expression analysis of 144 carcinomas, and whole genome array-CGH analysis and mutation analyses of FGFR3, PIK3CA, KRAS, HRAS, NRAS, TP53, CDKN2A, and TSC1 in 103 of these cases. Hierarchical cluster analysis identified two intrinsic molecular subtypes, MS1 and MS2, which were validated and defined by the same set of genes in three independent bladder cancer data sets. The two subtypes differed with respect to gene expression and mutation profiles, as well as with the level of genomic instability. The data show that genomic instability was the most distinguishing genomic feature of MS2 tumors, and that this trait was not dependent on TP53/MDM2 alterations. By combining molecular and pathologic data, it was possible to distinguish two molecular subtypes of T(a) and T(1) tumors, respectively. In addition, we define gene signatures validated in two independent data sets that classify urothelial carcinoma into low-grade (G(1)/G(2)) and high-grade (G(3)) tumors as well as non-muscle and muscle-invasive tumors with high precisions and sensitivities, suggesting molecular grading as a relevant complement to standard pathologic grading. We also present a gene expression signature with independent prognostic effect on metastasis and disease-specific survival. We conclude that the combination of molecular and histopathologic classification systems might provide a strong improvement for bladder cancer classification and produce new insights into the development of this tumor type.
Lindgren
David
D
Department of Clinical Sciences, Oncology, Lund University, Lund, Sweden.
Frigyesi
Attila
A
Gudjonsson
Sigurdur
S
Sjödahl
Gottfrid
G
Hallden
Christer
C
Chebil
Gunilla
G
Veerla
Srinivas
S
Ryden
Tobias
T
Månsson
Wiking
W
Liedberg
Fredrik
F
Höglund
Mattias
M
D000465:Algorithms; D014408:Biomarkers, Tumor / Q000096:biosynthesis / Q000235:genetics*; D058534:Class I Phosphatidylinositol 3-Kinases; D055028:Comparative Genomic Hybridization; D020869:Gene Expression Profiling; D019942:Genes, p16; D016158:Genes, p53; D011905:Genes, ras; D023281:Genomics / Q000379:methods; D006801:Humans; D009367:Neoplasm Staging; D019869:Phosphatidylinositol 3-Kinases / Q000096:biosynthesis / Q000235:genetics; D051498:Receptor, Fibroblast Growth Factor, Type 3 / Q000096:biosynthesis / Q000235:genetics; D000077004:Tuberous Sclerosis Complex 1 Protein; D025521:Tumor Suppressor Proteins / Q000096:biosynthesis / Q000235:genetics; D001749:Urinary Bladder Neoplasms / Q000235:genetics* / Q000378:metabolism / Q000473:pathology
D014408:Biomarkers, Tumor; C000624650:TSC1 protein, human; D000077004:Tuberous Sclerosis Complex 1 Protein; D025521:Tumor Suppressor Proteins; D019869:Phosphatidylinositol 3-Kinases; D058534:Class I Phosphatidylinositol 3-Kinases; C484760:PIK3CA protein, human; C496365:FGFR3 protein, human; D051498:Receptor, Fibroblast Growth Factor, Type 3