Diagnostic utility of p63/P501S double sequential immunohistochemical staining in differentiating urothelial carcinoma from prostate carcinoma

<p>Abstract</p> <p>Background</p> <p>Distinguishing urothelial carcinoma (UC) from prostate carcinoma (PC) is important due to potential therapeutic and prognostic implications. However, this can be a diagnostic challenge when there is limited tissue and in poorly differentiated tumors. We evaluated the diagnostic utility of a dual immunohistochemical stain comprising p63 and P501S (prostein), applied sequentially on a single slide and visualized by double chromogen reaction, in differentiating these two cancers. Thus far, there have been no previous studies assessing the diagnostic utility of p63 and P501S combined together as a dual immunostain in distinguishing between these two cancers.</p> <p>Methods</p> <p>p63/P501S dual-color sequential immunohistochemical staining was performed on archival material from 132 patients with high-grade UC and 23 patients with PC, and evaluated for p63 (brown nuclear) and P501S (red cytoplasmic) expression. Both the staining intensity and percentage of positive tumor cells were assessed.</p> <p>Results</p> <p>p63 was positive in 119/132 of UC and negative in PC. P501S was positive in 22/23 of PC and negative in UC. The p63+/P501S- immunoprofile had 90% sensitivity and 100% specificity for UC. The p63-/P501S+ immunoprofile had 96% sensitivity and 100% specificity for PC.</p> <p>Conclusion</p> <p>Our results indicate that double sequential immunohistochemical staining with p63 and P501S is highly specific and can be a useful tool in distinguishing UC from PC especially when there is limited diagnostic tissue as it can be performed on a single slide.</p

Validation of a contemporary prostate cancer grading system using prostate cancer death as outcome

BACKGROUND: Gleason scoring (GS) has major deficiencies and a novel system of five grade groups (GS⩽6; 3+4; 4+3; 8; ⩾9) has been recently agreed and included in the WHO 2016 classification. Although verified in radical prostatectomies using PSA relapse for outcome, it has not been validated using prostate cancer death as an outcome in biopsy series. There is debate whether an ‘overall' or ‘worst' GS in biopsies series should be used. METHODS: Nine hundred and eighty-eight prostate cancer biopsy cases were identified between 1990 and 2003, and treated conservatively. Diagnosis and grade was assigned to each core as well as an overall grade. Follow-up for prostate cancer death was until 31 December 2012. A log-rank test assessed univariable differences between the five grade groups based on overall and worst grade seen, and using univariable and multivariable Cox proportional hazards. Regression was used to quantify differences in outcome. RESULTS: Using both ‘worst' and ‘overall' GS yielded highly significant results on univariate and multivariate analysis with overall GS slightly but insignificantly outperforming worst GS. There was a strong correlation with the five grade groups and prostate cancer death. CONCLUSIONS: This is the largest conservatively treated prostate cancer cohort with long-term follow-up and contemporary assessment of grade. It validates the formation of five grade groups and suggests that the ‘worst' grade is a valid prognostic measure

Gene Copy Number Estimation from Targeted Next-Generation Sequencing of Prostate Cancer Biopsies: Analytic Validation and Clinical Qualification.

Purpose: Precise detection of copy number aberrations (CNA) from tumor biopsies is critically important to the treatment of metastatic prostate cancer. The use of targeted panel next-generation sequencing (NGS) is inexpensive, high throughput, and easily feasible, allowing single-nucleotide variant calls, but CNA estimation from this remains challenging.Experimental Design: We evaluated CNVkit for CNA identification from amplicon-based targeted NGS in a cohort of 110 fresh castration-resistant prostate cancer biopsies and used capture-based whole-exome sequencing (WES), array comparative genomic hybridization (aCGH), and FISH to explore the viability of this approach.Results: We showed that this method produced highly reproducible CNA results (r = 0.92), with the use of pooled germline DNA as a coverage reference supporting precise CNA estimation. CNA estimates from targeted NGS were comparable with WES (r = 0.86) and aCGH (r = 0.7); for key selected genes (BRCA2, MYC, PIK3CA, PTEN, and RB1), CNA estimation correlated well with WES (r = 0.91) and aCGH (r = 0.84) results. The frequency of CNAs in our population was comparable with that previously described (i.e., deep deletions: BRCA2 4.5%; RB1 8.2%; PTEN 15.5%; amplification: AR 45.5%; gain: MYC 31.8%). We also showed, utilizing FISH, that CNA estimation can be impacted by intratumor heterogeneity and demonstrated that tumor microdissection allows NGS to provide more precise CNA estimates.Conclusions: Targeted NGS and CNVkit-based analyses provide a robust, precise, high-throughput, and cost-effective method for CNA estimation for the delivery of more precise patient care. Clin Cancer Res; 23(20); 6070-7. ©2017 AACR

Genomic correlates of clinical outcome in advanced prostate cancer.

Heterogeneity in the genomic landscape of metastatic prostate cancer has become apparent through several comprehensive profiling efforts, but little is known about the impact of this heterogeneity on clinical outcome. Here, we report comprehensive genomic and transcriptomic analysis of 429 patients with metastatic castration-resistant prostate cancer (mCRPC) linked with longitudinal clinical outcomes, integrating findings from whole-exome, transcriptome, and histologic analysis. For 128 patients treated with a first-line next-generation androgen receptor signaling inhibitor (ARSI; abiraterone or enzalutamide), we examined the association of 18 recurrent DNA- and RNA-based genomic alterations, including androgen receptor (AR) variant expression, AR transcriptional output, and neuroendocrine expression signatures, with clinical outcomes. Of these, only RB1 alteration was significantly associated with poor survival, whereas alterations in RB1, AR, and TP53 were associated with shorter time on treatment with an ARSI. This large analysis integrating mCRPC genomics with histology and clinical outcomes identifies RB1 genomic alteration as a potent predictor of poor outcome, and is a community resource for further interrogation of clinical and molecular associations

Cancer Biomarker Discovery: The Entropic Hallmark

Background: It is a commonly accepted belief that cancer cells modify their transcriptional state during the progression of the disease. We propose that the progression of cancer cells towards malignant phenotypes can be efficiently tracked using high-throughput technologies that follow the gradual changes observed in the gene expression profiles by employing Shannon's mathematical theory of communication. Methods based on Information Theory can then quantify the divergence of cancer cells' transcriptional profiles from those of normally appearing cells of the originating tissues. The relevance of the proposed methods can be evaluated using microarray datasets available in the public domain but the method is in principle applicable to other high-throughput methods. Methodology/Principal Findings: Using melanoma and prostate cancer datasets we illustrate how it is possible to employ Shannon Entropy and the Jensen-Shannon divergence to trace the transcriptional changes progression of the disease. We establish how the variations of these two measures correlate with established biomarkers of cancer progression. The Information Theory measures allow us to identify novel biomarkers for both progressive and relatively more sudden transcriptional changes leading to malignant phenotypes. At the same time, the methodology was able to validate a large number of genes and processes that seem to be implicated in the progression of melanoma and prostate cancer. Conclusions/Significance: We thus present a quantitative guiding rule, a new unifying hallmark of cancer: the cancer cell's transcriptome changes lead to measurable observed transitions of Normalized Shannon Entropy values (as measured by high-throughput technologies). At the same time, tumor cells increment their divergence from the normal tissue profile increasing their disorder via creation of states that we might not directly measure. This unifying hallmark allows, via the the Jensen-Shannon divergence, to identify the arrow of time of the processes from the gene expression profiles, and helps to map the phenotypical and molecular hallmarks of specific cancer subtypes. The deep mathematical basis of the approach allows us to suggest that this principle is, hopefully, of general applicability for other diseases