Quantitative Nuclear Grading: An Objective, Artificial Intelligence-Facilitated Foundation for Grading Noninvasive Papillary Urothelial Carcinoma.

In nonmuscle invasive bladder cancer, grade drives important treatment and management decisions. However, grading is complex and qualitative, and it has considerable interobserver and intraobserver variability. Previous literature showed that nuclear features quantitatively differ between bladder cancer grades, but these studies were limited in size and scope. In this study, we aimed to measure morphometric features relevant to grading criteria and build simplified classification models that objectively distinguish between the grades of noninvasive papillary urothelial carcinoma (NPUC). We analyzed 516 low-grade and 125 high-grade 1.0-mm diameter image samples from a cohort of 371 NPUC cases. All images underwent World Health Organization/International Society of Urological Pathology 2004 consensus pathologist grading at our institution that was subsequently validated by expert genitourinary pathologists from 2 additional institutions. Automated software segmented the tissue regions and measured the nuclear features of size, shape, and mitotic rate for millions of nuclei. Then, we analyzed differences between grades and constructed classification models, which had accuracies up to 88% and areas under the curve as high as 0.94. Variation in the nuclear area was the best univariate discriminator and was prioritized, along with the mitotic index, in the top-performing classifiers. Adding shape-related variables improved accuracy further. These findings indicate that nuclear morphometry and automated mitotic figure counts can be used to objectively differentiate between grades of NPUC. Future efforts will adapt the workflow to whole slides and tune grading thresholds to best reflect time to recurrence and progression. Defining these essential quantitative elements of grading has the potential to revolutionize pathologic assessment and provide a starting point from which to improve the prognostic utility of grade.

Molecular profiling in muscle-invasive bladder cancer: more than the sum of its parts.

Bladder cancers are biologically and clinically heterogeneous. Recent large-scale transcriptomic profiling studies focusing on life-threatening muscle-invasive cases have demonstrated a small number of molecularly distinct clusters that largely explain their heterogeneity. Similar to breast cancer, these clusters reflect intrinsic urothelial cell-type differentiation programs, including those with luminal and basal cell characteristics. Also like breast cancer, each cell-based subtype demonstrates a distinct profile with regard to its prognosis and its expression of therapeutic targets. Indeed, a number of studies suggest subtype-specific differential responses to cytotoxic chemotherapy and to therapies that inhibit a number of targets, including growth factors (EGFR, ERBB2, FGFR) and immune checkpoint (PD1, PDL1) inhibitors. Despite burgeoning evidence for important clinical implications, subtyping has yet to enter into routine clinical practice. Here we review the conceptual basis for intrinsic cell subtyping in muscle-invasive bladder cancer and discuss evidence behind proposed clinical uses for subtyping as a prognostic or predictive test. In deliberating barriers to clinical implementation, we review pitfalls associated with transcriptomic profiling and illustrate a simple immunohistochemistry (IHC)-based subtyping algorithm that may serve as a faster, less expensive alternative. Envisioned as a research tool that can easily be translated into routine pathology workflow, IHC-based profiling has the potential to more rapidly establish the utility (or lack thereof) of cell type profiling in clinical practice. Copyright © 2019 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

A three-gene DNA methylation biomarker accurately classifies early stage prostate cancer.

BACKGROUND: We identify and validate accurate diagnostic biomarkers for prostate cancer through a systematic evaluation of DNA methylation alterations. MATERIALS AND METHODS: We assembled three early prostate cancer cohorts (total patients = 699) from which we collected and processed over 1300 prostatectomy tissue samples for DNA extraction. Using real-time methylation-specific PCR, we measured normalized methylation levels at 15 frequently methylated loci. After partitioning sample sets into independent training and validation cohorts, classifiers were developed using logistic regression, analyzed, and validated. RESULTS: In the training dataset, DNA methylation levels at 7 of 15 genomic loci (glutathione S-transferase Pi 1 [GSTP1], CCDC181, hyaluronan, and proteoglycan link protein 3 [HAPLN3], GSTM2, growth arrest-specific 6 [GAS6], RASSF1, and APC) showed large differences between cancer and benign samples. The best binary classifier was the GAS6/GSTP1/HAPLN3 logistic regression model, with an area under these curves of 0.97, which showed a sensitivity of 94%, and a specificity of 93% after external validation. CONCLUSION: We created and validated a multigene model for the classification of benign and malignant prostate tissue. With false positive and negative rates below 7%, this three-gene biomarker represents a promising basis for more accurate prostate cancer diagnosis.

Feasibility and Impact of Immunohistochemistry-based Molecular Subtyping for Muscle-invasive Bladder Cancer in Patients Treated with Radiation-based Therapy.

Background: Distinct molecular subtypes of muscle-invasive bladder cancer (MIBC) have been identified via gene expression profiling. Objective: We investigated the feasibility of a simple immunohistochemistry (IHC)-based Lund subtyping method and the association of MIBC subtypes with oncological outcomes for patients after bladder-preserving radiation-based therapy. Design setting and participants: Transurethral resected tumor tissues from 104 patients treated with radiation-based therapy were sampled on tissue microarray blocks. Outcome measurements and statistical analysis: The expression of KRT5, GATA3, and p16 proteins was scored via digital image analysis. Hierarchical clustering was used to classify tumors as the basal subtype or one of two luminal subtypes: genomically unstable (GU) or urothelial-like (URO). Subtypes were evaluated for association with complete response (CR), recurrence-free survival (RFS), and overall survival (OS). Results and limitations: The median OS was 43 mo (95% confidence interval 19-77) and median follow-up was 55 mo (interquartile range 39-75). Age and clinical stage had a significant impact on OS (p < 0.05). IHC-based subtype classification was feasible in most patients (89%). The subtype was basal in 23.6%, GU in 14.0%, URO in 31.2%, and unclassified in 31.2% of patients. No significant differences in CR, RFS, or OS were observed between the molecular subtypes. Limitations include the retrospective design and relatively small sample size. Conclusions: IHC-based molecular MIBC subtyping using a three-antibody algorithm is feasible in most patients treated with radiation-based therapy. MIBC subtype was not associated with response or survival. Further prospective studies are warranted to confirm the lack of association between molecular subtype and survival in patients treated with trimodal therapy. Patient summary: For patients with invasive bladder cancer treated with radiation-based therapy, we classified tumors into different subtypes using just three molecular stains. This method is cheaper and more widely available than the usual approach. However, we did not find an association between different cancer subtypes and survival.

Immunohistochemical Assays for Bladder Cancer Molecular Subtyping: Optimizing Parsimony and Performance of Lund Taxonomy Classifiers.

Transcriptomic and proteomic profiling classify bladder cancers into luminal and basal molecular subtypes, with controversial prognostic and predictive associations. The complexity of published subtyping algorithms is a major impediment to understanding their biology and validating or refuting their clinical use. Here, we optimize and validate compact algorithms based on the Lund taxonomy, which separates luminal subtypes into urothelial-like (Uro) and genomically unstable (GU). We characterized immunohistochemical expression data from two muscle-invasive bladder cancer cohorts (n=193, n=76) and developed efficient decision tree subtyping models using 4-fold cross-validation. We demonstrated that a published algorithm using routine assays (GATA3, KRT5, p16) classified basal/luminal subtypes and basal/Uro/GU subtypes with 86%-95% and 67%-86% accuracies, respectively. KRT14 and RB1 are less frequently used in pathology practice but achieved the simplest, most accurate models for basal/luminal and basal/Uro/GU discrimination, with 93%-96% and 85%-86% accuracies, respectively. More complex models with up to eight antibodies performed no better than simpler two- or three-antibody models. We conclude that simple immunohistochemistry classifiers can accurately identify luminal (Uro, GU) and basal subtypes and are appealing options for clinical implementation.

Diagnostic and prognostic implications of a three-antibody molecular subtyping algorithm for non-muscle invasive bladder cancer.

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.