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.

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.

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.

Luminal/basal molecular subtyping in muscle-invasive bladder cancer (MIBC) has shown important relationships to prognosis and chemosensitivity, where tumours of the basal subtype have been reported to be more aggressive, yet more sensitive to cytotoxic therapy. Despite this, molecular subtyping of MIBCs has yet to play a role in clinical treatment selection, largely limited by the complexity and artifacts associated with transcriptomic profiling methods. These shortcomings have in part been addressed by a group form Lund University who validated their subtyping using immunohistochemistry (IHC) to identify 3 key intrinsic subtypes of MIBC: Urothelial-like (URO), Genomically Unstable (GU) and Basal/Squamous cell carcinoma like (SCCL). We aimed to assess the ability of a simplified IHC-based algorithm, termed “IHC3” to identify key intrinsic molecular subtypes and explore the relationships of this classification to prognosis and treatment outcomes. 3 tissue microarrays (TMAs) were constructed from (n=133) individual samples from trans-urethral resection of bladder tumour (TURBT) and cystectomy specimens from 87 patients. TMAs were stained using clinical grade IHC assays for GATA3, KRT5 and p16. Scoring was conducted with visual and digital image analysis using percentage positive tumour cells, staining intensity and spatial localization. IHC3 identified 3 subtypes of MIBC with prognostic differences. Whether given before (NACT) or after surgery (ACT) no significant association between subtype and survival benefit from chemotherapy treatment. Nevertheless, because of their inferior prognosis, Basal/SCCL and GU subtypes might derive the most benefit from an effective systemic therapy. Using just three antibodies, IHC3 identified key molecular subtypes with prognostic significance, where GU and Basal subtypes showed poor prognosis compared to URO, which showed good prognosis. Application of IHC3 to an expanded cohort of 500+ patients will allow the extension of this work to further explore the predictive significance of IHC3 subtypes and confirm their potential clinical utility.

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.

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.

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.

Introduction: DNA methylation alterations can serve as robust diagnostic and prognostic biomarkers for cancer, particularly brain, colon, and prostate cancers. However, the lack of well-controlled and sensitive multiplexed assays and bioinformatic solutions for DNA methylation has hindered clinical implementation.

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-β 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-β activation and signalling, including Thrombospondin-1, which is responsible for the activation of latent TGF-β in the stroma. In cultured lung adenocarcinoma cells treated with recombinant TGF-β, ectopic expression of miR-206 impaired canonical signalling, and expression of TGF-β 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-β, and highlight the potential utility of TGF-β inhibitors for the treatment of lung adenocarcinomas.

We examine the importance of chaotic attractors when modelling non-metastatic tumour growth using a model in which cells come in three types: host, immune, and tumour. The relationships between these cell populations are derived from the law of mass action, assuming that a conjugate is formed in the interaction between immune and tumour cells. A nonlinearity in the production of immune cells, based on previous analyses, is introduced and explained. Using previously chosen model parameters, the maximal Lyapunov exponent is calculated numerically as 0.0218, demonstrating the existence of chaotic behaviour. Under the variation of one particular nonlinear rate constant, four distinct types of attractor are observed. Of biological importance, chaotic behaviour is shown to lead to a significantly higher maximum tumour size when compared to non-chaotic behaviour. Counterintuitively, increasing the parameter associated with the killing of tumour cells by immune cells is demonstrated to increase the maximum tumour size when this parameter is below the threshold at which the equilibrium is zero tumour cells.

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.

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.

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.

A model of telomere dynamics is proposed and examined. Our model, which extends a previously introduced model that incorporates stem cells as progenitors of new cells, imposes the Hayflick limit, the maximum number of cell divisions that are possible. This new model leads to cell populations for which the average telomere length is not necessarily a monotonically decreasing function of time, in contrast to previously published models. We provide a phase diagram indicating where such results would be expected via the introduction of scaled populations, rate constants and time. The application of this model to available leukocyte baboon data is discussed. (C) 2014 Elsevier B.V. All rights reserved.

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 and copy number profiling to delineate major deregulated pathways and biomarker networks associated with the development of intrinsic chemotherapy resistance with exposure to standard first-line therapy for ovarian cancer. The study cohort comprised 28 high grade serous ovarian cancer patients divided into two groups based on their varying sensitivity to first-line chemotherapy using progression free survival (PFS) as a surrogate of response. Twelve patient tumors demonstrating relative resistance to platinum based chemotherapy corresponding to shorter PFS (less than 6 months) were compared to 16 tumors from platinum-sensitive patients (PFS more than 18months). Molecular profiling was performed using Affymetrix high-resolution microarray platforms to permit global comparisons of gene expression levels and copy number profiles between tumors from the resistant group with the sensitive group. Microarray data analysis using statistical methods revealed a set of 204 discriminating genes of which expression levels may be influencing differential chemotherapy response between the two groups. Pathway analysis of the differentiating genes showed IGF1 network to be significantly altered between the two groups in addition to PI3K, NFkB, distinguishing the chemotherapy resistant with the sensitive group. Copy number analysis showed differences in the chromosomal regions, 4q31.22, 5q13.2, 9p24.3, 2p23.2, 16q21, 6q14.1, 7p22.3, 12p13 and Xq. Integrative copy number and gene expression profiling will delineate the drivers of chemotherapy resistance in patients undergoing standard platinum-based treatment of ovarian cancer. Future studies to validate these markers are necessary to apply this knowledge to biomarker-based clinical trials. Citation Format: Madhuri Koti, Robert J. Gooding, Paulo Nuin, Alexandria Haslehurst, Colleen Crane, Johanne Weberpals, Timothy Chids, Peter Bryson, Moyez Dharsee, Kenneth R. Evans, Harriet E. Feilotter, Paul C. Park, Jeremy A. Squire. Biomarkers of chemotherapy resistance in serous epithelial ovarian cancer identified by integrative genomic and transcriptomic analysis. [abstract]. In: Proceedings of the AACR Special Conference on Advances in Ovarian Cancer Research: From Concept to Clinic; Sep 18-21, 2013; Miami, FL. Philadelphia (PA): AACR; Clin Cancer Res 2013;19(19 Suppl):Abstract nr A53.