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Distinguishing indolent from clinically significant localized prostate cancer is a major clinical challenge and influences clinical decision-making between treatment and active surveillance. The development of novel predictive biomarkers will help with risk stratification, and clinical decision-making, leading to a decrease in over or under-treatment of patients with prostate cancer. Here, we report that Trop2 is a prognostic tissue biomarker for clinically significant prostate cancer by utilizing the Canary Prostate Cancer Tissue Microarray (CPCTA) cohort composed of over 1100 patients from a multi-institutional study. We demonstrate that elevated Trop2 expression is correlated with worse clinical features including Gleason score, age, and pre-operative PSA levels. More importantly, we demonstrate that elevated Trop2 expression at radical prostatectomy predicts worse overall survival in men undergoing radical prostatectomy. Additionally, we detect shed Trop2 in urine from men with clinically significant prostate cancer. Our study identifies Trop2 as a novel tissue prognostic biomarker and a candidate non-invasive marker for prostate cancer.

Phosphatase and tensin homolog (PTEN) loss is associated with adverse outcomes in prostate cancer and can be measured via immunohistochemistry. The purpose of the study was to establish the clinical application of an in-house developed artificial intelligence (AI) image analysis workflow for automated detection of PTEN loss on digital images for identifying patients at risk of early recurrence and metastasis. Postsurgical tissue microarray sections from the Canary Foundation (n = 1264) stained with anti-PTEN antibody were evaluated independently by pathologist conventional visual scoring (cPTEN) and an automated AI-based image analysis pipeline (AI-PTEN). The relationship of PTEN evaluation methods with cancer recurrence and metastasis was analyzed using multivariable Cox proportional hazard and decision curve models. Both cPTEN scoring by the pathologist and quantification of PTEN loss by AI (high-risk AI-qPTEN) were significantly associated with shorter metastasis-free survival (MFS) in univariable analysis (cPTEN hazard ratio [HR], 1.54; CI, 1.07-2.21; P = .019; AI-qPTEN HR, 2.55; CI, 1.83-3.56; P < .001). In multivariable analyses, AI-qPTEN showed a statistically significant association with shorter MFS (HR, 2.17; CI, 1.49-3.17; P < .001) and recurrence-free survival (HR, 1.36; CI, 1.06-1.75; P = .016) when adjusting for relevant postsurgical clinical nomogram (Cancer of the Prostate Risk Assessment [CAPRA] postsurgical score [CAPRA-S]), whereas cPTEN does not show a statistically significant association (HR, 1.33; CI, 0.89-2; P = .2 and HR, 1.26; CI, 0.99-1.62; P = .063, respectively) when adjusting for CAPRA-S risk stratification. More importantly, AI-qPTEN was associated with shorter MFS in patients with favorable pathological stage and negative surgical margins (HR, 2.72; CI, 1.46-5.06; P = .002). Workflow also demonstrated enhanced clinical utility in decision curve analysis, more accurately identifying men who might benefit from adjuvant therapy postsurgery. This study demonstrates the clinical value of an affordable and fully automated AI-powered PTEN assessment for evaluating the risk of developing metastasis or disease recurrence after radical prostatectomy. Adding the AI-qPTEN assessment workflow to clinical variables may affect postoperative surveillance or management options, particularly in low-risk patients.

The diagnostic work-up for Sjögrens syndrome is challenging and complex, including testing for serum autoantibodies to SSA/Ro and a labial salivary gland biopsy. Furthermore, the diagnosis is often delayed. In this study, we tested the hypothesis that anti-SSA/Ro autoantibodies are detectable in the saliva of patients with primary Sjögrens syndrome (pSS) because the disease affects the salivary glands, and these autoantibodies display greater discriminatory performance in saliva than in serum. SSA/Ro-52 Ags were used to develop what is, to our knowledge, a novel quantitative electrochemical-based immunoassay: the electric field-induced release and measurement (EFIRM) platform. The clinical utility was determined by measuring salivary anti-SSA/Ro-52 autoantibodies in patients with pSS and sicca (n = 34), patients without pSS with sicca (n = 35), and healthy subjects (n = 41). The statistical analysis of discrimination included the area under the receiver operating characteristic curve. Salivary anti-SSA/Ro-52 autoantibodies were measured in 94% (32 of 34) of patients with pSS with 85% (29 of 34) seropositivity. Four of the five seronegative patients with pSS had EFIRM-measurable anti-SSA/Ro-52 autoantibodies in saliva. Additionally, 60% (21 of 35) of the seronegative patients without pSS who had sicca had EFIRM-detectable SSA/Ro-52 autoantibodies in saliva, indicating the onset of autoimmune disease. Two of the 41 healthy control subjects had EFIRM-detectable SSA/Ro-52 autoantibodies in their saliva. Salivary SSA/Ro-52 autoantibodies significantly discriminated patients with pSS or patients with the initial stage of autoimmune disease from healthy subjects with an area under the receiver operating characteristic curve of 0.91. Our findings suggest that the proposed saliva SSA/Ro-52 immunoassay improves early and accurate diagnosis of seronegative patients with pSS and patients with early-onset autoimmune disease.

This paper addresses patient heterogeneity associated with prediction problems in biomedical applications. We propose a systematic hypothesis testing approach to determine the existence of patient subgroup structure and the number of subgroups in patient population if subgroups exist. A mixture of generalized linear models is considered to model the relationship between the disease outcome and patient characteristics and clinical factors, including targeted biomarker profiles. We construct a test statistic based on expectation maximization (EM) algorithm and derive its asymptotic distribution under the null hypothesis. An important computational advantage of the test is that the involved parameter estimates under the complex alternative hypothesis can be obtained through a small number of EM iterations, rather than optimizing the objective function. We demonstrate the finite sample performance of the proposed test in terms of type-I error rate and power, using extensive simulation studies. The applicability of the proposed method is illustrated through an application to a multicenter prostate cancer study.

Cribriform growth pattern is well-established as an adverse pathologic feature in prostate cancer. The literature suggests "large" cribriform glands associate with aggressive behavior; however, published studies use varying definitions for "large". We aimed to identify an outcome-based quantitative cut-off for "large" vs "small" cribriform glands. We conducted an initial training phase using the tissue microarray based Canary retrospective radical prostatectomy cohort. Of 1287 patients analyzed, cribriform growth was observed in 307 (24%). Using Kaplan-Meier estimates of recurrence-free survival curves (RFS) that were stratified by cribriform gland size, we identified 0.25 mm as the optimal cutoff to identify more aggressive disease. In univariable and multivariable Cox proportional hazard analyses, size >0.25 mm was a significant predictor of worse RFS compared to patients with cribriform glands ≤0.25 mm, independent of pre-operative PSA, grade, stage and margin status (p 0.25 mm had a significantly lower RFS relative to patients with cribriform glands ≤0.25 mm (each subset p = 0.004). Furthermore, there was no significant difference in outcomes between patients with cribriform glands ≤ 0.25 mm and patients without cribriform glands. The >0.25 mm cut-off was validated as statistically significant in a separate 419 patient, completely embedded whole-section radical prostatectomy cohort by biochemical recurrence, metastasis-free survival, and disease specific death, even when cases with admixed Gleason pattern 5 carcinoma were excluded. In summary, our findings support reporting cribriform gland size and identify 0.25 mm as an optimal outcome-based quantitative measure for defining "large" cribriform glands. Moreover, cribriform glands >0.25 mm are associated with potential for metastatic disease independent of Gleason pattern 5 adenocarcinoma.

To develop and validate a new tissue-based biomarker that improves prediction of outcomes in localized prostate cancer by quantifying the host response to tumor. We use digital image analysis and machine learning to develop a biomarker of the prostate stroma called quantitative reactive stroma (qRS). qRS is a measure of percentage tumor area with a distinct, reactive stromal architecture. Kaplan Meier analysis was used to determine survival in a large retrospective cohort of radical prostatectomy samples. qRS was validated in two additional, distinct cohorts that include international cases and tissue from both radical prostatectomy and biopsy specimens. In the developmental cohort (Baylor College of Medicine, n = 482), patients whose tumor had qRS > 34% had increased risk of prostate cancer-specific death (HR 2.94; p = 0.039). This result was replicated in two validation cohorts, where patients with qRS > 34% had increased risk of prostate cancer-specific death (MEDVAMC; n = 332; HR 2.64; p = 0.02) and also biochemical recurrence (Canary; n = 988; HR 1.51; p = 0.001). By multivariate analysis, these associations were shown to hold independent predictive value when compared to currently used clinicopathologic factors including Gleason score and PSA. qRS is a new, validated biomarker that predicts prostate cancer death and biochemical recurrence across three distinct cohorts. It measures host-response rather than tumor-based characteristics, and provides information not represented by standard prognostic measurements.

PURPOSE: The combination chemotherapy of fluorouracil, leucovorin, irinotecan, and oxaliplatin (FOLFIRINOX) has provided clinically meaningful improvement for pancreatic ductal adenocarcinoma (PDAC). We previously uncovered a role for the serine hydrolase carboxylesterase 2 (CES2) in mediating intratumoral activation of the prodrug irinotecan, a constituent of FOLFIRINOX. We aimed to further test the predictive value of CES2 for response to irinotecan using patient-derived xenograft (PDX) models and to elucidate the determinants of CES2 expression and response to FOLFIRINOX treatment among patients with PDAC. METHODS: PDXs were engrafted subcutaneously into nude mice and treated for 4 weeks with either saline control or irinotecan. CES2 and hepatocyte nuclear factor 4 alpha (HNF4A) expression in PDAC tissues was evaluated by immunohistochemical and Western blot analysis. Kaplan-Meier and Cox regression analyses were applied to assess the association between overall survival and hemoglobin A1C (HbA1C) levels in patients who underwent neoadjuvant FOLFIRINOX treatment. RESULTS: High CES2 activity in PDAC PDXs was associated with increased sensitivity to irinotecan. Integrated gene expression, proteomic analyses, and in vitro genetic experiments revealed that nuclear receptor HNF4A, which is upregulated in diabetes, is the upstream transcriptional regulator of CES2 expression. Elevated CES2 protein expression in PDAC tissues was positively associated with a history of type 2 diabetes (odds ratio, 4.84; P = .02). High HbA1C levels were associated with longer overall survival in patients who received neoadjuvant FOLFIRINOX treatment (P = .04). CONCLUSION: To our knowledge, we provide, for the first time, evidence that CES2 expression is associated with a history of type 2 diabetes in PDAC and that elevated HbA1C, by predicting tumor CES2 expression, may represent a novel marker for stratifying patients most likely to respond to FOLFIRINOX therapy.

Mutations identified in the epidermal growth factor receptor (EGFR) predict sensitivity to EGFR-targeted therapy for non-small cell lung carcinoma (NSCLC). We previously reported that Electric Field-Induced Release and Measurement (EFIRM)-based liquid biopsy could detect EGFR ctDNA with >94% concordance with tissue-based genotyping. A side-by-side comparison of concordance of EFIRM and droplet digital PCR (ddPCR) for the detection of the two front-line actionable EFGR mutations was performed with paired plasma and saliva samples from 13 NSCLC patients. Deep sequencing analysis based on single-strand DNA library preparation was employed to determine the size distributions of EGFR L858R ctDNA in plasma and saliva samples. EFIRM detected both EGFR mutations with 100% sensitivity in both plasma and saliva samples, whereas ddPCR detected EGFR mutations with sensitivities of 84.6% and 15.4%, respectively. In saliva samples, the majority of EGFR L858R ctDNA fragments detected were

High-quality and well-annotated biorepositories are needed to better understand the pathophysiology and biologic mechanisms of chronic pancreatitis (CP) and its consequences. We report a methodology for the development of a robust standard operating procedure (SOP) for a biorepository based on the experience of the clinical centers within the consortium to study Chronic Pancreatitis, Diabetes and Pancreas Cancer Clinical Centers (CPDPC), supported by the National Cancer Institute and the National Institute for Diabetes and Digestive and Kidney Diseases as a unique multidisciplinary model to study CP, diabetes, and pancreatic cancer in both children and adults. Standard operating procedures from the CPDPC centers were evaluated and consolidated. The literature was reviewed for standard biorepository operating procedures that facilitated downstream molecular analysis. The existing literature on biobanking practices was harmonized with the SOPs from the clinical centers to produce a biorepository for pancreatic research. This article reports the methods and basic principles behind the creation of SOPs to develop a biorepository for the CPDPC. These will serve as a guide for investigators developing biorepositories in pancreas research. Rigorous and meticulous adherence to standardized biospecimen collection will facilitate investigations to better understand the pathophysiology and biologic mechanisms of CP, diabetes, and pancreatic cancer.

The National Cancer Institute and the National Institute for Diabetes and Digestive and Kidney Diseases initiated the Consortium for the Study of Chronic Pancreatitis, Diabetes, and Pancreatic Cancer (CPDPC) in 2015 (the CPDPC's origin, structure, governance, and research objectives are described in another article in this journal). One of the key objectives of CPDPC is to assemble a cohort of 10,000 subjects 50 years or older with new-onset diabetes, called the NOD cohort. Using a define, enrich, and find early detection approach, the aims of the NOD study are to (a) estimate the 3-year probability of pancreatic ductal adenocarcinoma (PDAC) in NOD (define), (b) establish a biobank of clinically annotated biospecimens from presymptomatic PDAC and control new-onset type 2 diabetes mellitus subjects, (c) conduct phase 3 validation studies of promising biomarkers for identification of incident PDAC in NOD patients (enrich), and (d) provide a platform for development of a future interventional screening protocol for early detection of PDAC in patients with NOD that incorporates imaging studies and/or clinical algorithms (find). It is expected that 85 to 100 incidences of PDAC will be diagnosed during the study period in this cohort of 10,000 patients.

Prospective Evaluation of Chronic Pancreatitis for Epidemiologic and Translational Studies (PROCEED) is the first prospective, observational cohort study of chronic pancreatitis (CP) in the United States. The primary goals of PROCEED are to define disease progression, test the predictive capability of candidate biomarkers, and develop a platform to conduct translational and mechanistic studies in CP. Using objective and consensus-driven criteria, PROCEED will enroll adults at different stages of CP-controls, suspected CP, and definite CP. In addition to collecting detailed information using structured case report forms and protocol-mandated evaluations at baseline and during follow-up, PROCEED will establish a linked biorepository of blood, urine, saliva, stool, pancreatic fluid, and pancreatic tissue. Enrollment for PROCEED began in June 2017. As of July 1, 2018, nine clinical centers of the Consortium for the Study of Chronic Pancreatitis, Diabetes, and Pancreatic Cancer are enrolling, and 350 subjects have completed baseline evaluation. In conclusion, PROCEED will provide the most accurate and reliable estimates to date on progression of CP. The established cohort and biorepository will facilitate numerous analyses, leading to new strategies for diagnosis, methods to monitor disease progression, and treatment of CP.

Research progress in diseases of the exocrine pancreas [chronic pancreatitis (CP), pancreatogenic diabetes mellitus, and pancreatic cancer] has been hampered by the disorders' heterogeneity, the limitations of previous small cross-sectional studies, the inability to safely obtain pancreatic tissue for study, and the lack of structured epidemiology tools, genetic testing, and biomarker development. Mechanism-based research of these diseases has suffered from the lack of systematically collected clinical measures in longitudinal cohort studies linked with biospecimens. Given the increasing incidence and prevalence of CP and its association to the development of pancreatic cancer, its complications, high mortality rate, and associated health care cost, the National Institute for Diabetes and Digestive and Kidney Diseases and the National Cancer Institute funded the Consortium for the Study of Chronic Pancreatitis, Diabetes and Pancreatic Cancer to identify research gaps and foster multidisciplinary collaborations to better diagnose, characterize, and manage CP and its sequelae. The CPDPC structure, governance, and research objectives are described in this article. Studies undertaken by the CPDPC are described in other articles in this journal's issue.

There is an increasing need to construct a risk-prediction scoring system for survival data and identify important risk factors (e.g., biomarkers) for patient screening and treatment recommendation. However, most existing methodologies either rely on strong model assumptions (e.g., proportional hazards) or only handle binary outcomes. In this article, we propose a flexible method that simultaneously selects important risk factors and identifies the optimal linear combination of risk factors by maximizing a pseudo-likelihood function based on the time-dependent area under the receiver operating characteristic curve. Our method is particularly useful for risk evaluation and recommendation of optimal subsequent treatments. We show that the proposed method has desirable theoretical properties, including asymptotic normality and the oracle property after variable selection. Numerical performance is evaluated on several simulation data sets and an application to hepatocellular carcinoma data.

There is an increasing need to construct a risk-prediction scoring system for survival data and identify important risk factors (e.g., biomarkers) for patient screening and treatment recommendation. However, most existing methodologies either rely on strong model assumptions (e.g., proportional hazards) or only handle binary outcomes. In this article, we propose a flexible method that simultaneously selects important risk factors and identifies the optimal linear combination of risk factors by maximizing a pseudo-likelihood function based on the time-dependent area under the receiver operating characteristic curve. Our method is particularly useful for risk evaluation and recommendation of optimal subsequent treatments. We show that the proposed method has desirable theoretical properties, including asymptotic normality and the oracle property after variable selection. Numerical performance is evaluated on several simulation data sets and an application to hepatocellular carcinoma data.

The introduction of serum Prostate Specific Antigen (PSA) testing nearly 30 years ago has been associated with a significant shift towards localized disease and decreased deaths due to prostate cancer. Recognition that PSA testing has caused over diagnosis and over treatment of prostate cancer has generated considerable controversy over its value, and has spurred efforts to identify prognostic biomarkers to distinguish patients who need treatment from those that can be observed. Recent studies show that cancer is heterogeneous and forms a hierarchy of tumor cell populations. We developed a method of identifying prostate cancer differentiation states related to androgen signaling using Boolean logic. Using gene expression data, we identified two markers, CD38 and ARG2, that group prostate cancer into three differentiation states. Cancers with CD38-, ARG2- expression patterns, corresponding to an undifferentiated state, had significantly lower 10-year recurrence-free survival compared to the most differentiated group (CD38+ARG2+). We carried out immunohistochemical (IHC) staining for these two markers in a single institution (Stanford; n = 234) and multi-institution (Canary; n = 1326) cohorts. IHC staining for CD38 and ARG2 in the Stanford cohort demonstrated that combined expression of CD38 and ARG2 was prognostic. In the Canary cohort, low CD38 protein expression by IHC was significantly associated with recurrence-free survival (RFS), seminal vesicle invasion (SVI), extra-capsular extension (ECE) in univariable analysis. In multivariable analysis, ARG2 and CD38 IHC staining results were not independently associated with RFS, overall survival, or disease-specific survival after adjusting for other factors including SVI, ECE, Gleason score, pre-operative PSA, and surgical margins.

BackgroundGiven the uncertainties inherent in clinical measures of prostate cancer aggressiveness, clinically validated tissue biomarkers are needed. We tested whether Alpha-2-Glycoprotein 1, Zinc-Binding (AZGP1) protein levels, measured by immunohistochemistry, and RNA expression, by RNA in situ hybridization (RISH), predict recurrence after radical prostatectomy independent of clinical and pathological parameters.MethodsAZGP1 IHC and RISH were performed on a large multi-institutional tissue microarray resource including 1,275 men with 5 year median follow-up. The relationship between IHC and RISH expression levels was assessed using the Kappa analysis. Associations with clinical and pathological parameters were tested by the Chi-square test and the Wilcoxon rank sum test. Relationships with outcome were assessed with univariable and multivariable Cox proportional hazards models and the Log-rank test.ResultsAbsent or weak expression of AZGP1 protein was associated with worse recurrence free survival (RFS), disease specific survival, and overall survival after radical prostatectomy in univariable analysis. AZGP1 protein expression, along with pre-operative serum PSA levels, surgical margin status, seminal vesicle invasion, extracapsular extension, and Gleason score predicted RFS on multivariable analysis. Similarly, absent or low AZGP1 RNA expression by RISH predicted worse RFS after prostatectomy in univariable and multivariable analysis.ConclusionsIn our large, rigorously designed validation cohort, loss of AZGP1 expression predicts RFS after radical prostatectomy independent of clinical and pathological variables. Prostate 76:1409-1419, 2016. © 2016 Wiley Periodicals, Inc.

PTEN loss is a promising prognostic and predictive biomarker in prostate cancer. Because it occurs most commonly via PTEN gene deletion, we developed a clinical-grade, automated, and inexpensive immunohistochemical assay to detect PTEN loss. We studied the sensitivity and specificity of PTEN immunohistochemistry relative to four-color fluorescence in situ hybridization (FISH) for detection of PTEN gene deletion in a multi-institutional cohort of 731 primary prostate tumors. Intact PTEN immunostaining was 91% specific for the absence of PTEN gene deletion (549/602 tumors with two copies of the PTEN gene by FISH showed intact expression of PTEN by immunohistochemistry) and 97% sensitive for the presence of homozygous PTEN gene deletion (absent PTEN protein expression by immunohistochemistry in 65/67 tumors with homozygous deletion). PTEN immunohistochemistry was 65% sensitive for the presence of hemizygous PTEN gene deletion, with protein loss in 40/62 hemizygous tumors. We reviewed the 53 cases where immunohistochemistry showed PTEN protein loss and FISH showed two intact copies of the PTEN gene. On re-review, there was ambiguous immunohistochemistry loss in 6% (3/53) and failure to analyze the same tumor area by both methods in 34% (18/53). Of the remaining discordant cases, 41% (13/32) revealed hemizygous (n=8) or homozygous (n=5) PTEN gene deletion that was focal in most cases (11/13). The remaining 19 cases had two copies of the PTEN gene detected by FISH, representing truly discordant cases. Our automated PTEN immunohistochemistry assay is a sensitive method for detection of homozygous PTEN gene deletions. Immunohistochemistry screening is particularly useful to identify cases with heterogeneous PTEN gene deletion in a subset of tumor glands. Mutations, small insertions, or deletions and/or epigenetic or microRNA-mediated mechanisms may lead to PTEN protein loss in tumors with normal or hemizygous PTEN gene copy number.

BackgroundThis article describes a pilot study evaluating a novel liquid biopsy system for non-small cell lung cancer (NSCLC) patients. The electric field-induced release and measurement (EFIRM) method utilizes an electrochemical biosensor for detecting oncogenic mutations in biofluids.MethodsSaliva and plasma of 17 patients were collected from three cancer centers prior to and after surgical resection. The EFIRM method was then applied to the collected samples to assay for exon 19 deletion and p.L858 mutations. EFIRM results were compared with cobas results of exon 19 deletion and p.L858 mutation detection in cancer tissues.ResultsThe EFIRM method was found to detect exon 19 deletion with an area under the curve (AUC) of 1.0 in both saliva and plasma samples in lung cancer patients. For L858R mutation detection, the AUC of saliva was 1.0, while the AUC of plasma was 0.98. Strong correlations were also found between presurgery and post-surgery samples for both saliva (0.86 for exon 19 and 0.98 for L858R) and plasma (0.73 for exon 19 and 0.94 for L858R).ConclusionOur study demonstrates the feasibility of utilizing EFIRM to rapidly, non-invasively, and conveniently detect epidermal growth factor receptor mutations in the saliva of patients with NSCLC, with results corresponding perfectly with the results of cobas tissue genotyping.

PurposeThis study evaluated the discriminatory power of salivary transcriptomic and proteomic biomarkers in distinguishing oral squamous cell carcinoma cases from controls and potentially malignant oral disorders (PMOD).Experimental designA total of 180 samples (60 OSCC patients, 60 controls, and 60 PMOD patients) were used in the study. Seven transcriptomic markers (IL8, IL1β, SAT1, OAZ1, DUSP1, S100P, and H3F3A) were measured using qPCR, and two proteomic markers (IL8 and IL1β) were evaluated by ELISA.ResultsAmong 7 transcriptomic markers, transcript level of DUSP1 was significantly lower in OSCC patients than in controls and PMOD patients. Between the proteomic markers, the protein concentration of IL8 and IL1β was significantly higher in OSCC patients than controls and dysplasia patients. Univariate fractional polynomial (FP) models revealed that salivary IL8 protein (IL8p) has the highest AUC value between OSCC patients and controls (0.74) and between OSCC and PMOD patients (0.72). Applying a 2-marker FP model, salivary IL8p combined with IL1β gave the best AUC value for discrimination between OSCC patients and controls, as well as the IL8p combined with H3F3A mRNA, which gave the best AUC value for discrimination between OSCC and PMOD patients. Multivariate models analysis combining salivary analytes and risk factor exposure related to oral carcinogenesis formed the best combinatory variables for differentiation between OSCC versus PMOL (AUC = 0.80), OSCC versus controls (AUC = 0.87), and PMOD versus controls (AUC = 0.78).ConclusionsThe combination of transcriptomic and proteomic salivary markers is of great value for oral cancer detection and differentiation from PMOD patients and controls. Clin Cancer Res; 22(13); 3340-7. ©2016 AACR.

BackgroundPTEN is the most commonly deleted tumor suppressor gene in primary prostate cancer (PCa) and its loss is associated with poor clinical outcomes and ERG gene rearrangement.ObjectiveWe tested whether PTEN loss is associated with shorter recurrence-free survival (RFS) in surgically treated PCa patients with known ERG status.Design setting and participantsA genetically validated, automated PTEN immunohistochemistry (IHC) protocol was used for 1275 primary prostate tumors from the Canary Foundation retrospective PCa tissue microarray cohort to assess homogeneous (in all tumor tissue sampled) or heterogeneous (in a subset of tumor tissue sampled) PTEN loss. ERG status as determined by a genetically validated IHC assay was available for a subset of 938 tumors.Outcome measurements and statistical analysisAssociations between PTEN and ERG status were assessed using Fisher's exact test. Kaplan-Meier and multivariate weighted Cox proportional models for RFS were constructed.Results and limitationsWhen compared to intact PTEN, homogeneous (hazard ratio [HR] 1.66, p = 0.001) but not heterogeneous (HR 1.24, p = 0.14) PTEN loss was significantly associated with shorter RFS in multivariate models. Among ERG-positive tumors, homogeneous (HR 3.07, p < 0.0001) but not heterogeneous (HR 1.46, p = 0.10) PTEN loss was significantly associated with shorter RFS. Among ERG-negative tumors, PTEN did not reach significance for inclusion in the final multivariate models. The interaction term for PTEN and ERG status with respect to RFS did not reach statistical significance (p = 0.11) for the current sample size.ConclusionsThese data suggest that PTEN is a useful prognostic biomarker and that there is no statistically significant interaction between PTEN and ERG status for RFS.Patient summaryWe found that loss of the PTEN tumor suppressor gene in prostate tumors as assessed by tissue staining is correlated with shorter time to prostate cancer recurrence after radical prostatectomy.

Previous studies have suggested occurrence of altered serum glycan profiles in patients with lung cancer. Here, we aimed to determine the predictive value of serum glycans to distinguish non-small cell lung cancer (NSCLC) cases from controls in prediagnostic samples using a previously validated predictive protein marker pro-SFTPB, as anchor. Blinded prediagnostic serum samples were obtained from the Carotene and Retinol Efficacy Trial (CARET), and included a discovery set of 100 NSCLC cases and 199 healthy controls. A second test set consisted of 108 cases and 216 controls. Cases and controls were matched for age at baseline (5-year groups), sex, smoking status (current vs. former), study enrollment cohort, and date of blood draw. Serum glycan profiles were determined by mass spectrometry. Twelve glycan variables were identified to have significant discriminatory power between cases and controls in the discovery set (AUC > 0.6). Of these, four were confirmed in the independent validation set. A combination marker yielded AUCs of 0.74 and 0.64 in the discovery and test set, respectively. Four glycan variables exhibited significant incremental value when combined with pro-SFTPB compared with pro-SFTPB alone with AUCs of 0.73, 0.72, 0.72, and 0.72 in the test set, indicating that serum glycan signatures have relevance to risk assessment for NSCLC.

PurposeActive surveillance represents a strategy to address the overtreatment of prostate cancer, yet uncertainty regarding individual patient outcomes remains a concern. We evaluated outcomes in a prospective multicenter study of active surveillance.Materials and methodsWe studied 905 men in the prospective Canary PASS enrolled between 2008 and 2013. We collected clinical data at study entry and at prespecified intervals, and determined associations with adverse reclassification, defined as increased Gleason grade or greater cancer volume on followup biopsy. We also evaluated the relationships of clinical parameters with pathology findings in participants who underwent surgery after a period of active surveillance.ResultsAt a median followup of 28 months 24% of participants experienced adverse reclassification, of whom 53% underwent treatment while 31% continued on active surveillance. Overall 19% of participants received treatment, 68% with adverse reclassification, while 32% opted for treatment without disease reclassification. In multivariate Cox proportional hazards modeling the percent of biopsy cores with cancer, body mass index and prostate specific antigen density were associated with adverse reclassification (p=0.01, 0.04, 0.04, respectively). Of 103 participants subsequently treated with radical prostatectomy 34% had adverse pathology, defined as primary pattern 4-5 or nonorgan confined disease, including 2 with positive lymph nodes, with no significant relationship between risk category at diagnosis and findings at surgery (p=0.76).ConclusionsMost men remain on active surveillance at 5 years without adverse reclassification or adverse pathology at surgery. However, clinical factors had only a modest association with disease reclassification, supporting the need for approaches that improve the prediction of this outcome.

BackgroundThe uncertainties inherent in clinical measures of prostate cancer (CaP) aggressiveness endorse the investigation of clinically validated tissue biomarkers. MUC1 expression has been previously reported to independently predict aggressive localized prostate cancer. We used a large cohort to validate whether MUC1 protein levels measured by immunohistochemistry (IHC) predict aggressive cancer, recurrence and survival outcomes after radical prostatectomy independent of clinical and pathological parameters.Material and methodsMUC1 IHC was performed on a multi-institutional tissue microarray (TMA) resource including 1,326 men with a median follow-up of 5 years. Associations with clinical and pathological parameters were tested by the Chi-square test and the Wilcoxon rank sum test. Relationships with outcome were assessed with univariable and multivariable Cox proportional hazard models and the Log-rank test.ResultsThe presence of MUC1 expression was significantly associated with extracapsular extension and higher Gleason score, but not with seminal vesicle invasion, age, positive surgical margins or pre-operative serum PSA levels. In univariable analyses, positive MUC1 staining was significantly associated with a worse recurrence free survival (RFS) (HR: 1.24, CI 1.03-1.49, P = 0.02), although not with disease specific survival (DSS, P>0.5). On multivariable analyses, the presence of positive surgical margins, extracapsular extension, seminal vesicle invasion, as well as higher pre-operative PSA and increasing Gleason score were independently associated with RFS, while MUC1 expression was not. Positive MUC1 expression was not independently associated with disease specific survival (DSS), but was weakly associated with overall survival (OS).ConclusionIn our large, rigorously designed validation cohort, MUC1 protein expression was associated with adverse pathological features, although it was not an independent predictor of outcome after radical prostatectomy.

PurposeWe have investigated the potential of metabolomics to discover blood-based biomarkers relevant to lung cancer screening and early detection. An untargeted metabolomics approach was applied to identify biomarker candidates using prediagnostic sera from the Beta-Carotene and Retinol Efficacy Trial (CARET) study.Patients and methodsA liquid chromatography/mass spectrometry hydrophilic interaction method designed to profile a wide range of metabolites was applied to prediagnostic serum samples from CARET participants (current or former heavy smokers), consisting of 100 patients who subsequently developed non-small-cell lung cancer (NSCLC) and 199 matched controls. A separate aliquot was used to quantify levels of pro-surfactant protein B (pro-SFTPB), a previously established protein biomarker for NSCLC. On the basis of the results from the discovery set, blinded validation of a metabolite, identified as N(1),N(12)-diacetylspermine (DAS), and pro-SFTPB was performed using an independent set of CARET prediagnostic sera from 108 patients with NSCLC and 216 matched controls.ResultsSerum DAS was elevated by 1.9-fold, demonstrating significant specificity and sensitivity in the discovery set for samples collected up to 6 months before diagnosis of NSCLC. In addition, DAS significantly complemented performance of pro-SFTPB in both the discovery and validations sets, with a combined area under the curve in the validation set of 0.808 (P < .001 v pro-SFTPB).ConclusionDAS is a novel serum metabolite with significant performance in prediagnostic NSCLC and has additive performance with pro-SFTPB.

RationaleConstitutive activation of the epidermal growth factor receptor (EGFR) is prevalent in epithelial cancers, particularly in non-small cell lung carcinoma (NSCLC). Mutations identified in EGFR predict the sensitivity to EGFR-targeted therapy. Detection of these mutations is mainly based on tissue biopsy, which is invasive, expensive, and time consuming.ObjectivesNoninvasive, real-time, inexpensive detection and monitoring of EGFR mutations in patients with NSCLC is highly desirable.MethodsWe developed a novel core technology, electric field-induced release and measurement (EFIRM), which relies on a multiplexible electrochemical sensor that can detect EGFR mutations directly in bodily fluids.Measurements and main resultsWe established EFIRM for the detection of the EGFR mutations in vitro and correlated the results with tumor size from xenografted mice. In clinical application, we demonstrated that EFIRM could detect EGFR mutations in the saliva and plasma of 22 patients with NSCLC. Finally, a blinded test was performed on saliva samples from 40 patients with NSCLC. The receiver operating characteristic analysis indicated that EFIRM detected the exon 19 deletion with an area under the curve of 0.94 and the L858R mutation with an area under the curve of 0.96.ConclusionsOur data indicate that EFIRM is effective, accurate, rapid, user-friendly, and cost effective for the detection of EGFR mutations in the saliva of patients with NSCLC. We termed this saliva-based EGFR mutation detection (SABER).