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J Pathol Clin Res ; 8(2): 143-154, 2022 03.
Article in English | MEDLINE | ID: mdl-34697907

ABSTRACT

Intrinsic molecular subtypes may explain marked variation between bladder cancer patients in prognosis and response to therapy. Complex testing algorithms and little attention to more prevalent, early-stage (non-muscle invasive) bladder cancers (NMIBCs) have hindered implementation of subtyping in clinical practice. Here, using a three-antibody immunohistochemistry (IHC) algorithm, we identify the diagnostic and prognostic associations of well-validated proteomic features of basal and luminal subtypes in NMIBC. By IHC, we divided 481 NMIBCs into basal (GATA3- /KRT5+ ) and luminal (GATA3+ /KRT5 variable) subtypes. We further divided the luminal subtype into URO (p16 low), URO-KRT5+ (KRT5+ ), and genomically unstable (GU) (p16 high) subtypes. Expression thresholds were confirmed using unsupervised hierarchical clustering. Subtypes were correlated with pathology and outcomes. All NMIBC cases clustered into the basal/squamous (basal) or one of the three luminal (URO, URO-KRT5+ , and GU) subtypes. Although uncommon in this NMIBC cohort, basal tumors (3%, n = 16) had dramatically higher grade (100%, n = 16, odds ratio [OR] = 13, relative risk = 3.25) and stage, and rapid progression to muscle invasion (median progression-free survival = 35.4 months, p = 0.0001). URO, the most common subtype (46%, n = 220), showed rapid recurrence (median recurrence-free survival [RFS] = 11.5 months, p = 0.039) compared to its GU counterpart (29%, n = 137, median RFS = 16.9 months), even in patients who received intravesical immunotherapy (p = 0.049). URO-KRT5+ tumors (22%, n = 108) were typically low grade (66%, n = 71, OR = 3.7) and recurred slowly (median RFS = 38.7 months). Therefore, a simple immunohistochemical algorithm can identify clinically relevant molecular subtypes of NMIBC. In routine clinical practice, this three-antibody algorithm may help clarify diagnostic dilemmas and optimize surveillance and treatment strategies for patients.


Subject(s)
Urinary Bladder Neoplasms , Algorithms , Biomarkers, Tumor/metabolism , Humans , Prognosis , Proteomics , Urinary Bladder Neoplasms/diagnosis , Urinary Bladder Neoplasms/pathology
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