A DNA copy number alteration classifier as a prognostic tool for prostate cancer patients.

BACKGROUND: Distinguishing between true indolent and potentially life-threatening prostate cancer is challenging in tumours displaying clinicopathologic features associated with low or intermediate risk of relapse. Several somatic DNA copy number alterations (CNAs) have been identified as potential prognostic biomarkers, but the standard cytogenetic method to assess them has a limited multiplexing capability. METHODS: Multiplex ligation-dependent probe amplification (MLPA) targeting 14 genes was optimised to survey 448 tumours of patients with low or intermediate risk (Grade Group 1-3, Gleason score ≤7) who underwent radical prostatectomy. A 6-gene CNA classifier was developed using random survival forest and Cox proportional hazard modelling to predict biochemical recurrence. RESULTS: The classifier score was significantly associated with biochemical recurrence after adjusting for standard clinicopathologic variables and the known prognostic index CAPRA-S score with a hazard ratio of 2.17 and 1.80, respectively (n = 406, P < 0.01). The prognostic value of this classifier was externally validated in published CNA data from three radical prostatectomy cohorts and one radiation therapy pre-treatment biopsy cohort. CONCLUSION: The 6-gene CNA classifier generated by a single MLPA assay compatible with the small quantities of DNA extracted from formalin-fixed paraffin-embedded (FFPE) tissue specimens has the potential to improve the clinical management of patients with low or intermediate risk disease.

Splicing is an alternate oncogenic pathway activation mechanism in glioma.

High-grade diffuse glioma (HGG) is the leading cause of brain tumour death. While the genetic drivers of HGG have been well described, targeting these has thus far had little impact on survival suggesting other mechanisms are at play. Here we interrogate the alternative splicing landscape of pediatric and adult HGG through multi-omic analyses, uncovering an increased splicing burden compared with normal brain. The rate of recurrent alternative splicing in cancer drivers exceeds their mutation rate, a pattern that is recapitulated in pan-cancer analyses, and is associated with worse prognosis in HGG. We investigate potential oncogenicity by interrogating cancer pathways affected by alternative splicing in HGG; spliced cancer drivers include members of the RAS/MAPK pathway. RAS suppressor neurofibromin 1 is differentially spliced to a less active isoform in >80% of HGG downstream from REST upregulation, activating the RAS/MAPK pathway and reducing glioblastoma patient survival. Overall, our results identify non-mutagenic mechanisms by which cancers activate oncogenic pathways which need to accounted for in personalized medicine approaches.

Prognostic significance of T cells, PD-L1 immune checkpoint and tumour associated macrophages in clear cell carcinoma of the ovary.

OBJECTIVE: To define the pre-treatment tumour immune landscape of clear cell carcinoma of the ovary (CCOC). METHODS: We investigated the infiltration profiles of selected immune cell populations and immune checkpoint proteins that have been previously shown to have prognostic relevance in high grade serous carcinoma of the ovary to determine their association with clinical outcomes in CCOC patients. Using multiplex immunohistochemistry, we evaluated the density of CD3+, FoxP3+, CD8+ T cells, CD20+ B cells and expression of PD-1, PD-L1 and IDO1 immune checkpoints in a cohort of 162 CCOC tumour specimens on a tissue microarray. RESULTS: Increased infiltration of CD3+ CD8- (helper T) cells, CD8+ (cytotoxic T) cells, and CD68+ macrophages significantly associated with shorter disease-free survival, recurrence-free survival and overall survival. Importantly, higher expression of PD-L1 and IDO-1 immune checkpoints was associated with better clinical outcomes. CONCLUSION: Findings from our study are foundational towards the development of immune classifiers and biomarkers of response to immune checkpoint blockade therapy in CCOC.

Autorepressor PsrA is required for optimal Legionella pneumophila growth in Acanthamoeba castellanii protozoa.

Legionella pneumophila possesses a unique intracellular lifecycle featuring distinct morphological stages that include replicative forms and transmissive cyst forms. Expression of genes associated with virulence traits and cyst morphogenesis is concomitant, and governed by a complex stringent response based-regulatory network and the stationary phase sigma factor RpoS. In Pseudomonas spp., rpoS expression is controlled by the autorepressor PsrA, and orthologs of PsrA and RpoS are required for cyst formation in Azotobacter. Here we report that the L. pneumophila psrA ortholog, expressed as a leaderless monocistronic transcript, is also an autorepressor, but is not a regulator of rpoS expression. Further, the binding site sequence recognized by L. pneumophila PsrA is different from that of Pseudomonas PsrA, suggesting a repertoire of target genes unique to L. pneumophila. While PsrA was dispensable for growth in human U937-derived macrophages, lack of PsrA affected bacterial intracellular growth in Acanthamoeba castellanii protozoa, but also increased the quantity of poly-3-hydroxybutyrate (PHB) inclusions in matured transmissive cysts. Interestingly, overexpression of PsrA increased the size and bacterial load of the replicative vacuole in both host cell types. Taken together, we report that PsrA is a host-specific requirement for optimal temporal progression of L. pneumophila intracellular lifecycle in A. castellanii.

High throughput assessment of biomarkers in tissue microarrays using artificial intelligence: PTEN loss as a proof-of-principle in multi-center prostate cancer cohorts.

Phosphatase and tensin homolog (PTEN) loss is associated with adverse outcomes in prostate cancer and has clinical potential as a prognostic biomarker. The objective of this work was to develop an artificial intelligence (AI) system for automated detection and localization of PTEN loss on immunohistochemically (IHC) stained sections. PTEN loss was assessed using IHC in two prostate tissue microarrays (TMA) (internal cohort, n = 272 and external cohort, n = 129 patients). TMA cores were visually scored for PTEN loss by pathologists and, if present, spatially annotated. Cores from each patient within the internal TMA cohort were split into 90% cross-validation (N = 2048) and 10% hold-out testing (N = 224) sets. ResNet-101 architecture was used to train core-based classification using a multi-resolution ensemble approach (×5, ×10, and ×20). For spatial annotations, single resolution pixel-based classification was trained from patches extracted at ×20 resolution, interpolated to ×40 resolution, and applied in a sliding-window fashion. A final AI-based prediction model was created from combining multi-resolution and pixel-based models. Performance was evaluated in 428 cores of external cohort. From both cohorts, a total of 2700 cores were studied, with a frequency of PTEN loss of 14.5% in internal (180/1239) and external 13.5% (43/319) cancer cores. The final AI-based prediction of PTEN status demonstrated 98.1% accuracy (95.0% sensitivity, 98.4% specificity; median dice score = 0.811) in internal cohort cross-validation set and 99.1% accuracy (100% sensitivity, 99.0% specificity; median dice score = 0.804) in internal cohort test set. Overall core-based classification in the external cohort was significantly improved in the external cohort (area under the curve = 0.964, 90.6% sensitivity, 95.7% specificity) when further trained (fine-tuned) using 15% of cohort data (19/124 patients). These results demonstrate a robust and fully automated method for detection and localization of PTEN loss in prostate cancer tissue samples. AI-based algorithms have potential to streamline sample assessment in research and clinical laboratories.

Design and Development of a Fully Synthetic Multiplex Ligation-Dependent Probe Amplification-Based Probe Mix for Detection of Copy Number Alterations in Prostate Cancer Formalin-Fixed, Paraffin-Embedded Tissue Samples.

DNA copy number alterations (CNAs) are promising biomarkers to predict prostate cancer (PCa) outcome. However, fluorescence in situ hybridization (FISH) cannot assess complex CNA signatures because of low multiplexing capabilities. Multiplex ligation-dependent probe amplification (MLPA) can detect multiple CNAs in a single PCR assay, but PCa-specific probe mixes available commercially are lacking. Synthetic MLPA probes were designed to target 10 CNAs relevant to PCa: 5q15-21.1 (CHD1), 6q15 (MAP3K7), 8p21.2 (NKX3-1), 8q24.21 (MYC), 10q23.31 (PTEN), 12p13.1 (CDKN1B), 13q14.2 (RB1), 16p13.3 (PDPK1), 16q23.1 (GABARAPL2), and 17p13.1 (TP53), with 9 control probes. In cell lines, CNAs were detected when the cancer genome was as low as 30%. Compared with FISH in radical prostatectomy formalin-fixed, paraffin-embedded samples (n = 18: 15 cancers and 3 matched benign), the MLPA assay showed median sensitivity and specificity of 80% and 93%, respectively, across all CNAs assessed. In the validation set (n = 40: 20 tumors sampled in two areas), the respective sensitivity and specificity of MLPA compared advantageously with FISH and TaqMan droplet digital PCR (ddPCR) when assessing PTEN deletion (FISH: 85% and 100%; ddPCR: 100% and 83%) and PDPK1 gain (FISH: 100% and 92%; ddPCR: 93% and 100%). This new PCa probe mix accurately identifies CNAs by MLPA across multiple genes using low quality and quantities (50 ng) of DNA extracted from clinical formalin-fixed, paraffin-embedded samples.

Risk Stratification of Prostate Cancer Through Quantitative Assessment of PTEN Loss (qPTEN).

BACKGROUND: Phosphatase and tensin homolog (PTEN) loss has long been associated with adverse findings in early prostate cancer. Studies to date have yet to employ quantitative methods (qPTEN) for measuring of prognostically relevant amounts of PTEN loss in postsurgical settings and demonstrate its clinical application. METHODS: PTEN protein levels were measured by immunohistochemistry in radical prostatectomy samples from training (n = 410) and validation (n = 272) cohorts. PTEN loss was quantified per cancer cell and per tissue microarray core. Thresholds for identifying clinically relevant PTEN loss were determined using log-rank statistics in the training cohort. Univariate (Kaplan-Meier) and multivariate (Cox proportional hazards) analyses on various subpopulations were performed to assess biochemical recurrence-free survival (BRFS) and were independently validated. All statistical tests were two-sided. RESULTS: PTEN loss in more than 65% cancer cells was most clinically relevant and had statistically significant association with reduced BRFS in training (hazard ratio [HR] = 2.48, 95% confidence interval [CI] = 1.59 to 3.87; P < .001) and validation cohorts (HR = 4.22, 95% CI = 2.01 to 8.83; P < .001). The qPTEN scoring method identified patients who recurred within 5.4 years after surgery (P < .001). In men with favorable risk of biochemical recurrence (Cancer of the Prostate Risk Assessment - Postsurgical scores <5 and no adverse pathological features), qPTEN identified a subset of patients with shorter BRFS (HR = 5.52, 95% CI = 2.36 to 12.90; P < .001) who may be considered for intensified monitoring and/or adjuvant therapy. CONCLUSIONS: Compared with previous qualitative approaches, qPTEN improves risk stratification of postradical prostatectomy patients and may be considered as a complementary tool to guide disease management after surgery.

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.

Reliability and performance of commercial RNA and DNA extraction kits for FFPE tissue cores.

Cancer biomarker studies often require nucleic acid extraction from limited amounts of formalin-fixed, paraffin-embedded (FFPE) tissues, such as histologic sections or needle cores. A major challenge is low quantity and quality of extracted nucleic acids, which can limit our ability to perform genetic analyses, and have a significant influence on overall study design. This study was aimed at identifying the most reliable and reproducible method of obtaining sufficient high-quality nucleic acids from FFPE tissues. We compared the yield and quality of nucleic acids from 0.6-mm FFPE prostate tissue cores across 16 DNA and RNA extraction protocols, using 14 commercially available kits. Nucleic acid yield was determined by fluorometry, and quality was determined by spectrophotometry. All protocols yielded nucleic acids in quantities that are compatible with downstream molecular applications. However, the protocols varied widely in the quality of the extracted RNA and DNA. Four RNA and five DNA extraction protocols, including protocols from two kits for dual-extraction of RNA and DNA from the same tissue source, were prioritized for further quality assessment based on the yield and purity of their products. Specifically, their compatibility with downstream reactions was assessed using both NanoString nCounter gene expression assays and reverse-transcriptase real-time PCR for RNA, and methylation-specific PCR assays for DNA. The kit deemed most suitable for FFPE tissue was the AllPrep kit by Qiagen because of its yield, quality, and ability to purify both RNA and DNA from the same sample, which would be advantageous in biomarker studies.

Legionella pneumophila OxyR Is a Redundant Transcriptional Regulator That Contributes to Expression Control of the Two-Component CpxRA System.

Nominally an environmental organism, Legionella pneumophila is an intracellular parasite of protozoa but is also the causative agent of the pneumonia termed Legionnaires' disease, which results from inhalation of aerosolized bacteria by susceptible humans. Coordination of gene expression by a number of identified regulatory factors, including OxyR, assists L. pneumophila in adapting to the stresses of changing environments. L. pneumophila OxyR (OxyRLp) is an ortholog of Escherichia coli OxyR; however, OxyRLp was shown elsewhere to be functionally divergent, such that it acts as a transcription regulator independently of the oxidative stress response. In this study, the use of improved gene deletion methods has enabled us to generate an unmarked in-frame deletion of oxyR in L. pneumophila Lack of OxyRLp did not affect in vitro growth or intracellular growth in Acanthamoeba castellanii protozoa and U937-derived macrophages. The expression of OxyRLp does not appear to be regulated by CpxR, even though purified recombinant CpxR bound a DNA sequence similar to that reported for CpxR elsewhere. Surprisingly, a lack of OxyRLp resulted in elevated activity of the promoters located upstream of icmR and the lpg1441-cpxA operon, and OxyRLp directly bound to these promoter regions, suggesting that OxyRLp is a direct repressor. Interestingly, a strain overexpressing OxyRLp demonstrated reduced intracellular growth in A. castellanii but not in U937-derived macrophages, suggesting that balanced expression control of the two-component CpxRA system is necessary for survival in protozoa. Taken together, this study suggests that OxyRLp is a functionally redundant transcriptional regulator in L. pneumophila under the conditions evaluated herein.IMPORTANCELegionella pneumophila is an environmental pathogen, with its transmission to the human host dependent upon its ability to replicate in protozoa and survive within its aquatic niche. Understanding the genetic factors that contribute to L. pneumophila survival within each of these unique environments will be key to limiting future point-source outbreaks of Legionnaires' disease. The transcriptional regulator L. pneumophila OxyR (OxyRLp) has been previously identified as a potential regulator of virulence traits warranting further investigation. This study demonstrated that oxyR is nonessential for L. pneumophila survival in vitro and in vivo via mutational analysis. While the mechanisms of how OxyRLp expression is regulated remain elusive, this study shows that OxyRLp negatively regulates the expression of the cpxRA two-component system necessary for intracellular survival in protozoa.

Preparation of Formalin-fixed Paraffin-embedded Tissue Cores for both RNA and DNA Extraction.

Formalin-fixed paraffin embedded tissue (FFPET) represents a valuable, well-annotated substrate for molecular investigations. The utility of FFPET in molecular analysis is complicated both by heterogeneous tissue composition and low yields when extracting nucleic acids. A literature search revealed a paucity of protocols addressing these issues, and none that showed a validated method for simultaneous extraction of RNA and DNA from regions of interest in FFPET. This method addresses both issues. Tissue specificity was achieved by mapping cancer areas of interest on microscope slides and transferring annotations onto FFPET blocks. Tissue cores were harvested from areas of interest using 0.6 mm microarray punches. Nucleic acid extraction was performed using a commercial FFPET extraction system, with modifications to homogenization, deparaffinization, and Proteinase K digestion steps to improve tissue digestion and increase nucleic acid yields. The modified protocol yields sufficient quantity and quality of nucleic acids for use in a number of downstream analyses, including a multi-analyte gene expression platform, as well as reverse transcriptase coupled real time PCR analysis of mRNA expression, and methylation-specific PCR (MSP) analysis of DNA methylation.