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.

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.

Mutually exclusive mutation profiles define functionally related genes in muscle invasive bladder cancer.

Muscle Invasive bladder cancer is known to have an abundance of mutations, particularly in DNA damage response and chromatin modification genes. The role of these mutations in the development and progression of the disease is not well understood. However, a mutually exclusive mutation pattern between gene pairs could suggest gene mutations of significance. For example, a mutually exclusive mutation pattern could suggest an epistatic relationship where the outcome of a mutation in one gene would have the same outcome as a mutation in a different gene. The significance of a mutually exclusive relationship was determined by establishing a normal distribution of the conditional probabilities for having a mutation in one gene and not the other as well as the reverse relationship for each gene pairing. Then these distributions were used to determine the sigma-magnitude of standard deviation by which the observed value differed from the expected, a value that can also be interpreted as the 'p-value'. This approach led to the identification of mutually exclusive mutation patterns in KDM6A and KMT2D as well as KDM6A and RB1 that suggested the observed mutation pattern did not happen by chance. Upon further investigation of these genes and their interactions, a potential similar outcome was identified that supports the concept of epistasis. Knowledge of these mutational interactions provides a better understanding of the mechanisms underlying muscle invasive bladder cancer development, and may direct therapeutic development exploiting genotoxic chemotherapy and synthetic lethality in these pathways.

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.

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.

Reliable identification of prostate cancer using mass spectrometry metabolomic imaging in needle core biopsies.

Metabolomic profiling can aid in understanding crucial biological processes in cancer development and progression and can also yield diagnostic biomarkers. Desorption electrospray ionization coupled to mass spectrometry imaging (DESI-MSI) has been proposed as a potential adjunct to diagnostic surgical pathology, particularly for prostate cancer. However, due to low resolution sampling, small numbers of mass spectra, and little validation, published studies have yet to test whether this method is sufficiently robust to merit clinical translation. We used over 900 spatially resolved DESI-MSI spectra to establish an accurate, high-resolution metabolic profile of prostate cancer. We identified 25 differentially abundant metabolites, with cancer tissue showing increased fatty acids (FAs) and phospholipids, along with utilization of the Krebs cycle, and benign tissue showing increased levels of lyso-phosphatidylethanolamine (PE). Additionally, we identified, for the first time, two lyso-PEs with abundance that decreased with cancer grade and two phosphatidylcholines (PChs) with increased abundance with increasing cancer grade. Importantly, we developed and internally validated a multivariate metabolomic classifier for prostate cancer using 534 spatial regions of interest (ROIs) in the training cohort and 430 ROIs in the test cohort. With excellent statistical power, the training cohort achieved a balanced accuracy of 97% and validation on testing data set demonstrated 85% balanced accuracy. Given the validated accuracy of this classifier and the correlation of differentially abundant metabolites with established patterns of prostate cancer cell metabolism, we conclude that DESI-MSI is an effective tool for characterizing prostate cancer metabolism with the potential for clinical translation.

MicroRNA-206 suppresses TGF-ß signalling to limit tumor growth and metastasis in lung adenocarcinoma.

MicroRNA-206 (miR-206) has demonstrated tumor suppressive effects in a variety of cancers. Numerous studies have identified aberrantly expressed targets of miR-206 that contribute to tumor progression and metastasis, however, the broader gene-networks and pathways regulated by miR-206 remain poorly defined. Here, we have ectopically expressed miR-206 in lung adenocarcinoma cell lines and tumors to identify differentially expressed genes, and study the effects on tumor growth and metastasis. In H1299 tumor xenograft assays, stable expression of miR-206 suppressed both tumor growth and metastasis in mice. Profiling of xenograft tumors using small RNA sequencing and a targeted panel of tumor progression and metastasis-related genes revealed a network of genes involved in TGF-ß signalling that were regulated by miR-206. Among these were the TGFB1 ligand, as well as direct transcriptional targets of Smad3. Other differentially expressed genes included components of the extracellular matrix involved in TGF-ß activation and signalling, including Thrombospondin-1, which is responsible for the activation of latent TGF-ß in the stroma. In cultured lung adenocarcinoma cells treated with recombinant TGF-ß, ectopic expression of miR-206 impaired canonical signalling, and expression of TGF-ß target genes linked to epithelial-mesenchymal transition. This was due at least in part to the suppression of Smad3 protein levels in lung adenocarcinoma cells with ectopic miR-206 expression. Together, these findings indicate that miR-206 can suppress tumor progression and metastasis by limiting autocrine production of TGF-ß, and highlight the potential utility of TGF-ß inhibitors for the treatment of lung adenocarcinomas.

Correction to: Use of multicolor fluorescence in situ hybridization to detect deletions in clinical tissue sections.

Figure 2 is incorrect in the original version of this article. The correct figure 2 is provided below.

Use of multicolor fluorescence in situ hybridization to detect deletions in clinical tissue sections.

A variety of laboratory methods are available for the detection of deletions of tumor suppressor genes and losses of their proteins. The clinical utility of fluorescence in situ hybridization (FISH) for the identification of deletions of tumor suppressor genes has previously been limited by difficulties in the interpretation of FISH signal patterns. The first deletion FISH assays using formalin-fixed paraffin-embedded tissue sections had to deal with a significant background level of signal losses affecting nuclei that are truncated by the cutting process of slide preparation. Recently, more efficient probe designs, incorporating probes adjacent to the tumor suppressor gene of interest, have increased the accuracy of FISH deletion assays so that true chromosomal deletions can be readily distinguished from the false signal losses caused by sectioning artifacts. This mini-review discusses the importance of recurrent tumor suppressor gene deletions in human cancer and reviews the common FISH methods being used to detect the genomic losses encountered in clinical specimens. The use of new probe designs to recognize truncation artifacts is illustrated with a four-color PTEN FISH set optimized for prostate cancer tissue sections. Data are presented to show that when section thickness is reduced, the frequency of signal truncation losses is increased. We also provide some general guidelines that will help pathologists and cytogeneticists run routine deletion FISH assays and recognize sectioning artifacts. Finally, we summarize how recently developed sequence-based approaches are being used to identify recurrent deletions using small DNA samples from tumors.

Critical telomerase activity for uncontrolled cell growth.

The lengths of the telomere regions of chromosomes in a population of cells are modelled using a chemical master equation formalism, from which the evolution of the average number of cells of each telomere length is extracted. In particular, the role of the telomere-elongating enzyme telomerase on these dynamics is investigated. We show that for biologically relevant rates of cell birth and death, one finds a critical rate, R crit, of telomerase activity such that the total number of cells diverges. Further, R crit is similar in magnitude to the rates of mitosis and cell death. The possible relationship of this result to replicative immortality and its associated hallmark of cancer is discussed.

Molecular characterization of Gleason patterns 3 and 4 prostate cancer using reverse Warburg effect-associated genes.

BACKGROUND: Gleason scores (GS) 3+3 and 3+4 prostate cancers (PCa) differ greatly in their clinical courses, with Gleason pattern (GP) 4 representing a major independent risk factor for cancer progression. However, Gleason grade is not reliably ascertained by diagnostic biopsy, largely due to sampling inadequacies, subjectivity in the Gleason grading procedure, and a lack of more objective biomarker assays to stratify prostate cancer aggressiveness. In most aggressive cancer types, the tumor microenvironment exhibits a reciprocal pro-tumorigenic metabolic phenotype consistent with the reverse Warburg effect (RWE). The RWE can be viewed as a physiologic response to the epithelial phenotype that is independent of both the epithelial genotype and of direct tumor sampling. We hypothesize that differential expression of RWE-associated genes can be used to classify Gleason pattern, distinguishing GP3 from GP4 PCa foci. METHODS: Gene expression profiling was conducted on RNA extracted from laser-capture microdissected stromal tissue surrounding 20 GP3 and 21 GP4 cancer foci from PCa patients with GS 3+3 and GS ≥4+3, respectively. Genes were probed using a 102-gene NanoString probe set targeted towards biological processes associated with the RWE. Differentially expressed genes were identified from normalized data by univariate analysis. A top-scoring pair (TSP) analysis was completed on raw gene expression values. Genes were analyzed for enriched Gene Ontology (GO) biological processes and protein-protein interactions using STRING and GeneMANIA. RESULTS: Univariate analysis identified nine genes (FOXO1 (AUC: 0.884), GPD2, SPARC, HK2, COL1A2, ALDOA, MCT4, NRF2, and ATG5) that were differentially expressed between GP3 and GP4 stroma (p<0.05). However, following correction for false discovery, only FOXO1 retained statistical significance at q<0.05. The TSP analysis identified a significant gene pair, namely ATG5/GLUT1. Greater expression of ATG5 relative to GLUT1 correctly classified 77.4 % of GP3/GP4 samples. Enrichment for GO-biological processes revealed that catabolic glucose processes and oxidative stress response pathways were strongly associated with GP3 foci but not GP4. FOXO1 was identified as being a primary nodal protein. CONCLUSIONS: We report that RWE-associated genes can be used to distinguish between GP3 and GP4 prostate cancers. Moreover, we find that the RWE response is downregulated in the stroma surrounding GP4, possibly via modulation of FOXO1.

Identification of the IGF1/PI3K/NF κB/ERK gene signalling networks associated with chemotherapy resistance and treatment response in high-grade serous epithelial ovarian cancer.

BACKGROUND: Resistance to platinum-based chemotherapy remains a major impediment in the treatment of serous epithelial ovarian cancer. The objective of this study was to use gene expression profiling to delineate major deregulated pathways and biomarkers associated with the development of intrinsic chemotherapy resistance upon exposure to standard first-line therapy for ovarian cancer. METHODS: The study cohort comprised 28 patients divided into two groups based on their varying sensitivity to first-line chemotherapy using progression free survival (PFS) as a surrogate of response. All 28 patients had advanced stage, high-grade serous ovarian cancer, and were treated with standard platinum-based chemotherapy. Twelve patient tumours demonstrating relative resistance to platinum chemotherapy corresponding to shorter PFS (< eight months) were compared to sixteen tumours from platinum-sensitive patients (PFS > eighteen months). Whole transcriptome profiling was performed using an Affymetrix high-resolution microarray platform to permit global comparisons of gene expression profiles between tumours from the resistant group and the sensitive group. RESULTS: Microarray data analysis revealed a set of 204 discriminating genes possessing expression levels which could influence differential chemotherapy response between the two groups. Robust statistical testing was then performed which eliminated a dependence on the normalization algorithm employed, producing a restricted list of differentially regulated genes, and which found IGF1 to be the most strongly differentially expressed gene. Pathway analysis, based on the list of 204 genes, revealed enrichment in genes primarily involved in the IGF1/PI3K/NF κB/ERK gene signalling networks. CONCLUSIONS: This study has identified pathway specific prognostic biomarkers possibly underlying a differential chemotherapy response in patients undergoing standard platinum-based treatment of serous epithelial ovarian cancer. In addition, our results provide a pathway context for further experimental validations, and the findings are a significant step towards future therapeutic interventions.

Enrollment of African Americans onto clinical treatment trials: study design barriers.

PURPOSE: African Americans have the highest cancer mortality rates and poorest survival and are more often uninsured and underinsured compared with other ethnic groups. Minority participation in clinical trials has traditionally been low, with reports ranging from 3% to 20%. The present study systematically assesses 235 consecutively diagnosed African American cancer patients regarding recruitment onto cancer treatment clinical trials at Howard University Cancer Center between January 1, 2001, and December 31, 2002. Our intent is to determine the rate-limiting factors associated with enrolling African Americans onto clinical trials at a historically black medical institution. PATIENTS AND METHODS: Two hundred thirty-five consecutively diagnosed African American cancer patients were assessed for participation in clinical trials at Howard University Hospital and Cancer Center. The study population comprised 165 women and 70 men. RESULTS: The overall eligibility rate was 8.5% (20 of 235 patients); however, among those eligible, the enrollment rate (ie, enrollment among the eligible population) was 60.0% (12 of 20 patients). Comorbidities rendered 17.1% of the patient population ineligible for the trials. Advanced disease stage, associated with poor performance status, premature death, and short life expectancy, made an additional 10% of the patient population ineligible. Respiratory failure, HIV positivity, and anemia accounted for 37.8% of the comorbidities in this population. Cardiovascular diseases and renal insufficiency represented 16.2% of the comorbidities. CONCLUSION: It was evident that study design exclusion and inclusion criteria rendered the majority of the study population ineligible. Among African Americans, comorbidity is a major issue that warrants considerable attention.