Cooperativity of c-MYC with Krüppel-Like Factor 6 Splice Variant 1 induces phenotypic plasticity and promotes prostate cancer progression and metastasis.

Metastasis remains a major cause of morbidity and mortality in men with prostate cancer, and the functional impact of the genetic alterations, alone or in combination, driving metastatic disease remains incompletely understood. The proto-oncogene c-MYC, commonly deregulated in prostate cancer. Transgenic expression of c-MYC is sufficient to drive the progression to prostatic intraepithelial neoplasia and ultimately to moderately differentiated localized primary tumors, however, c-MYC-driven tumors are unable to progress through the metastatic cascade, suggesting that a "second-hit" is necessary in the milieu of aberrant c-MYC-driven signaling. Here, we identified cooperativity between c-MYC and KLF6-SV1, an oncogenic splice variant of the KLF6 gene. Transgenic mice that co-expressed KLF6-SV1 and c-MYC developed progressive and metastatic prostate cancer with a histological and molecular phenotype like human prostate cancer. Silencing c-MYC expression significantly reduced tumor burden in these mice supporting the necessity for c-MYC in tumor maintenance. Unbiased global proteomic analysis of tumors from these mice revealed significantly enriched vimentin, a dedifferentiation and pro-metastatic marker, induced by KLF6-SV1. c-MYC-positive tumors were also significantly enriched for KLF6-SV1 in human prostate cancer specimens. Our findings provide evidence that KLF6-SV1 is an enhancer of c-MYC-driven prostate cancer progression and metastasis, and a correlated genetic event in human prostate cancer with potential translational significance.

Clinical and pathological characteristics of bladder cancer in post brachytherapy patients.

The long-term risk of secondary malignancy is a potential late effect of brachytherapy. However, the time interval, anatomic site and histopathology are not well studied. We sought to characterize the bladder cancers that developed following treatment of prostate cancer with brachytherapy. Between 1998 and 2014, 4570 patients were treated with brachytherapy at the BC Cancer Agency. Out of those, 69 patients subsequently developed bladder cancer, some of which could have been radiation induced. Histology slides were reviewed for all cases, and site and pathologic features were recorded. Cases were classified as luminal and basal subtypes based on GATA3 and CK5/6 immunohistochemistry. Bladder neck and trigone were among the common sites of involvement. Pathologic review of cases showed that 68 % were high-grade, 25 % were muscle-invasive, and 20 % showed variant histology, including small cell carcinoma, sarcomatoid carcinoma, squamous cell carcinoma, and adenocarcinoma. A subgroup of cases more likely to be radiation-induced, based on site and time interval, was associated with increased pathologic stage (pT1 or higher) compared to the other cases (70 % vs 34 %, p = 0.01). In conclusion, the majority of bladder cancers following brachytherapy in this cohort were of high grade and low stage at diagnosis, most of them demonstrating luminal immunophenotype. A significant number of variant histologies are seen, each demonstrating a specific immunophenotype.

Upper tract urothelial carcinoma has a luminal-papillary T-cell depleted contexture and activated FGFR3 signaling.

Upper tract urothelial carcinoma (UTUC) is characterized by a distinctly aggressive clinical phenotype. To define the biological features driving this phenotype, we performed an integrated analysis of whole-exome and RNA sequencing of UTUC. Here we report several key insights from our molecular dissection of this disease: 1) Most UTUCs are luminal-papillary; 2) UTUC has a T-cell depleted immune contexture; 3) High FGFR3 expression is enriched in UTUC and correlates with its T-cell depleted immune microenvironment; 4) Sporadic UTUC is characterized by a lower total mutational burden than urothelial carcinoma of the bladder. Our findings lay the foundation for a deeper understanding of UTUC biology and provide a rationale for the development of UTUC-specific treatment strategies.

Comparison of Artificial Intelligence Techniques to Evaluate Performance of a Classifier for Automatic Grading of Prostate Cancer From Digitized Histopathologic Images.

Importance: Proper evaluation of the performance of artificial intelligence techniques in the analysis of digitized medical images is paramount for the adoption of such techniques by the medical community and regulatory agencies. Objectives: To compare several cross-validation (CV) approaches to evaluate the performance of a classifier for automatic grading of prostate cancer in digitized histopathologic images and compare the performance of the classifier when trained using data from 1 expert and multiple experts. Design, Setting, and Participants: This quality improvement study used tissue microarray data (333 cores) from 231 patients who underwent radical prostatectomy at the Vancouver General Hospital between June 27, 1997, and June 7, 2011. Digitized images of tissue cores were annotated by 6 pathologists for 4 classes (benign and Gleason grades 3, 4, and 5) between December 12, 2016, and October 5, 2017. Patches of 192 µm2 were extracted from these images. There was no overlap between patches. A deep learning classifier based on convolutional neural networks was trained to predict a class label from among the 4 classes (benign and Gleason grades 3, 4, and 5) for each image patch. The classification performance was evaluated in leave-patches-out CV, leave-cores-out CV, and leave-patients-out 20-fold CV. The analysis was performed between November 15, 2018, and January 1, 2019. Main Outcomes and Measures: The classifier performance was evaluated by its accuracy, sensitivity, and specificity in detection of cancer (benign vs cancer) and in low-grade vs high-grade differentiation (Gleason grade 3 vs grades 4-5). The statistical significance analysis was performed using the McNemar test. The agreement level between pathologists and the classifier was quantified using a quadratic-weighted κ statistic. Results: On 333 tissue microarray cores from 231 participants with prostate cancer (mean [SD] age, 63.2 [6.3] years), 20-fold leave-patches-out CV resulted in mean (SD) accuracy of 97.8% (1.2%), sensitivity of 98.5% (1.0%), and specificity of 97.5% (1.2%) for classifying benign patches vs cancerous patches. By contrast, 20-fold leave-patients-out CV resulted in mean (SD) accuracy of 85.8% (4.3%), sensitivity of 86.3% (4.1%), and specificity of 85.5% (7.2%). Similarly, 20-fold leave-cores-out CV resulted in mean (SD) accuracy of 86.7% (3.7%), sensitivity of 87.2% (4.0%), and specificity of 87.7% (5.5%). Results of McNemar tests showed that the leave-patches-out CV accuracy, sensitivity, and specificity were significantly higher than those for both leave-patients-out CV and leave-cores-out CV. Similar results were observed for classifying low-grade cancer vs high-grade cancer. When trained on a single expert, the overall agreement in grading between pathologists and the classifier ranged from 0.38 to 0.58; when trained using the majority vote among all experts, it was 0.60. Conclusions and Relevance: Results of this study suggest that in prostate cancer classification from histopathologic images, patch-wise CV and single-expert training and evaluation may lead to a biased estimation of classifier's performance. To allow reproducibility and facilitate comparison between automatic classification methods, studies in the field should evaluate their performance using patient-based CV and multiexpert data. Some of these conclusions may be generalizable to other histopathologic applications and to other applications of machine learning in medicine.

Automatic grading of prostate cancer in digitized histopathology images: Learning from multiple experts.

Prostate cancer (PCa) is a heterogeneous disease that is manifested in a diverse range of histologic patterns and its grading is therefore associated with an inter-observer variability among pathologists, which may lead to an under- or over-treatment of patients. In this work, we develop a computer aided diagnosis system for automatic grading of PCa in digitized histopathology images using supervised learning methods. Our pipeline comprises extraction of multi-scale features that include glandular, cellular, and image-based features. A number of novel features are proposed based on intra- and inter-nuclei properties; these features are shown to be among the most important ones for classification. We train our classifiers on 333 tissue microarray (TMA) cores that were sampled from 231 radical prostatectomy patients and annotated in detail by six pathologists for different Gleason grades. We also demonstrate the TMA-trained classifier's performance on additional 230 whole-mount slides of 56 patients, independent of the training dataset, by examining the automatic grading on manually marked lesions and randomly sampled 10% of the benign tissue. For the first time, we incorporate a probabilistic approach for supervised learning by multiple experts to account for the inter-observer grading variability. Through cross-validation experiments, the overall grading agreement of the classifier with the pathologists was found to be an unweighted kappa of 0.51, while the overall agreements between each pathologist and the others ranged from 0.45 to 0.62. These results suggest that our classifier's performance is within the inter-observer grading variability levels across the pathologists in our study, which are also consistent with those reported in the literature.

The cancer precision medicine knowledge base for structured clinical-grade mutations and interpretations.

OBJECTIVE: This paper describes the Precision Medicine Knowledge Base (PMKB; https://pmkb.weill.cornell.edu ), an interactive online application for collaborative editing, maintenance, and sharing of structured clinical-grade cancer mutation interpretations. MATERIALS AND METHODS: PMKB was built using the Ruby on Rails Web application framework. Leveraging existing standards such as the Human Genome Variation Society variant description format, we implemented a data model that links variants to tumor-specific and tissue-specific interpretations. Key features of PMKB include support for all major variant types, standardized authentication, distinct user roles including high-level approvers, and detailed activity history. A REpresentational State Transfer (REST) application-programming interface (API) was implemented to query the PMKB programmatically. RESULTS: At the time of writing, PMKB contains 457 variant descriptions with 281 clinical-grade interpretations. The EGFR, BRAF, KRAS, and KIT genes are associated with the largest numbers of interpretable variants. PMKB's interpretations have been used in over 1500 AmpliSeq tests and 750 whole-exome sequencing tests. The interpretations are accessed either directly via the Web interface or programmatically via the existing API. DISCUSSION: An accurate and up-to-date knowledge base of genomic alterations of clinical significance is critical to the success of precision medicine programs. The open-access, programmatically accessible PMKB represents an important attempt at creating such a resource in the field of oncology. CONCLUSION: The PMKB was designed to help collect and maintain clinical-grade mutation interpretations and facilitate reporting for clinical cancer genomic testing. The PMKB was also designed to enable the creation of clinical cancer genomics automated reporting pipelines via an API.

Technologies for label-free separation of circulating tumor cells: from historical foundations to recent developments.

Circulating tumor cells (CTCs) are malignant cells shed into the bloodstream from a tumor that have the potential to establish metastases in different anatomical sites. The separation and subsequent characterization of these cells is emerging as an important tool for both biomarker discovery and the elucidation of mechanisms of metastasis. Established methods for separating CTCs rely on biochemical markers of epithelial cells that are known to be unreliable because of epithelial-to-mesenchymal transition, which reduces expression for epithelial markers. Emerging label-free separation methods based on the biophysical and biomechanical properties of CTCs have the potential to address this key shortcoming and present greater flexibility in the subsequent characterization of these cells. In this review we first present what is known about the biophysical and biomechanical properties of CTCs from historical studies and recent research. We then review biophysical label-free technologies that have been developed for CTC separation, including techniques based on filtration, hydrodynamic chromatography, and dielectrophoresis. Finally, we evaluate these separation methods and discuss requirements for subsequent characterization of CTCs.

Characterization of a new class of androgen receptor antagonists with potential therapeutic application in advanced prostate cancer.

The human androgen receptor plays a major role in the development and progression of prostate cancer and represents a well-established drug target. All clinically approved androgen receptor antagonists possess similar chemical structures and exhibit the same mode of action on the androgen receptor. Although initially effective, resistance to these androgen receptor antagonists usually develops and the cancer quickly progresses to castration-resistant and metastatic states. Yet even in these late-stage patients, the androgen receptor is critical for the progression of the disease. Thus, there is a continuing need for novel chemical classes of androgen receptor antagonists that could help overcome the problem of resistance. In this study, we implemented and used the synergetic combination of virtual and experimental screening to discover a number of new 10-benzylidene-10H-anthracen-9-ones that not only effectively inhibit androgen receptor transcriptional activity, but also induce almost complete degradation of the androgen receptor. Of these 10-benzylidene-10H-anthracen-9-one analogues, a lead compound (VPC-3033) was identified that showed strong androgen displacement potency, effectively inhibited androgen receptor transcriptional activity, and possesses a profound ability to cause degradation of androgen receptor. Notably, VPC-3033 exhibited significant activity against prostate cancer cells that have already developed resistance to the second-generation antiandrogen enzalutamide (formerly known as MDV3100). VPC-3033 also showed strong antiandrogen receptor activity in the LNCaP in vivo xenograft model. These results provide a foundation for the development of a new class of androgen receptor antagonists that can help address the problem of antiandrogen resistance in prostate cancer.

Actinobaculum schaalii: An Emerging Uropathogen?

A. schaalii is a rare uropathogen. We report urosepsis with Actinobaculum schaalii detected serendipitously in blood and urine culture in a 79-year-old with urinary tract obstruction. This paper illuminates the flaws in our current system in detecting A. schaalii and raises awareness among clinicians and laboratory teams.

Targeting the binding function 3 (BF3) site of the human androgen receptor through virtual screening.

The androgen receptor (AR) is the best studied drug target for the treatment of prostate cancer. While there are a number of drugs that target the AR, they all work through the same mechanism of action and are prone to the development of drug resistance. There is a large unmet need for novel AR inhibitors which work through alternative mechanism(s). Recent studies have identified a novel site on the AR called binding function 3 (BF3) that is involved into AR transcriptional activity. In order to identify inhibitors that target the BF3 site, we have conducted a large-scale in silico screen followed by experimental evaluation. A number of compounds were identified that effectively inhibited the AR transcriptional activity with no obvious cytotoxicity. The mechanism of action of these compounds was validated by biochemical assays and X-ray crystallography. These findings lay a foundation for the development of alternative or supplementary therapies capable of combating prostate cancer even in its antiandrogen resistant forms.

TAF1 differentially enhances androgen receptor transcriptional activity via its N-terminal kinase and ubiquitin-activating and -conjugating domains.

Aberrant expression of androgen receptor (AR) coregulators has been linked to progression of prostate cancers to castration resistance. Using the repressed transactivator yeast two-hybrid system, we found that TATA binding protein-associated factor 1 (TAF1) interacted with the AR. In tissue microarrays, TAF1 was shown to steadily increase with duration of neoadjuvant androgen withdrawal and with progression to castration resistance. Glutathione S-transferase pulldown assays established that TAF1 bound through its acetylation and ubiquitin-activating/conjugating domains (E1/E2) directly to the AR N terminus. Coimmunoprecipitation and ChIP assays revealed colocalization of TAF1 and AR on the prostate-specific antigen promoter/enhancer in prostate cancer cells. With respect to modulation of AR activity, overexpression of TAF1 enhanced AR activity severalfold, whereas small interfering RNA knockdown of TAF1 significantly decreased AR transactivation. Although full-length TAF1 showed enhancement of both AR and some generic gene transcriptional activity, selective AR coactivator activity by TAF1 was demonstrated in transactivation experiments using cloned N-terminal kinase and E1/E2 functional domains. In keeping with AR coactivation by the ubiquitin-activating and -conjugating domain, TAF1 was found to greatly increase the cellular amount of polyubiquitinated AR. In conclusion, our results indicate that increased TAF1 expression is associated with progression of human prostate cancers to the lethal castration-resistant state. Because TAF1 is a coactivator of AR that binds and enhances AR transcriptional activity, its overexpression could be part of a compensatory mechanism adapted by cancer cells to overcome reduced levels of circulating androgens.

Rapid, non-destructive, cell-based screening assays for agents that modulate growth, death, and androgen receptor activation in prostate cancer cells.

BACKGROUND: We developed non-invasive, cell-based screening assays to rapidly and biologically assess factors that modulate prostate cancer growth and affect androgen receptor (AR) activity. METHODS: LNCaP cells, which stably express enhanced green fluorescent protein (EGFP) either constitutively or upon AR activation, were treated with a variety of agents, and then monitored by fluorescence and MTS assays for dose-dependent changes in cell number and AR activity. RESULTS: The assays were validated for rapid, fluorescence-based, quantitative measurement for the presence of growth and AR modulators. Using these assays, we found that osteoblast conditioned media (CM) enhanced prostate cancer cell growth, but not AR activity. After priming with androgen (<1 nM R1881), forskolin or the pesticide dichlorvos enhanced AR activation, whereas interleukin-6 (IL-6) inhibited it. CONCLUSION: These non-destructive, cell-based assays enable rapid systematic monitoring of the effects of drugs or complex mixtures on prostate cancer cell growth and/or AR activity.