Cellular senescence is a tumor suppressive mechanism that can paradoxically contribute to aging pathologies. Despite evidence of immune clearance in mouse models, it is not known how senescent cells (SnCs) persist and accumulate with age or in tumors in individuals. Here, we identify cooperative mechanisms that orchestrate the immunoevasion and persistence of normal and cancer human SnCs through extracellular targeting of natural killer receptor signaling. Damaged SnCs avoid immune recognition through MMPs-dependent shedding of NKG2D-ligands reinforced via paracrine suppression of NKG2D receptor-mediated immunosurveillance. These coordinated immunoediting processes are evident in residual, drug-resistant tumors from cohorts of >700 prostate and breast cancer patients treated with senescence-inducing genotoxic chemotherapies. Unlike in mice, these reversible senescence-subversion mechanisms are independent of p53/p16 and exacerbated in oncogenic RAS-induced senescence. Critically, the p16INK4A tumor suppressor can disengage the senescence growth arrest from the damage-associated immune senescence program, which is manifest in benign nevi lesions where indolent SnCs accumulate over time and preserve a non-pro-inflammatory tissue microenvironment maintaining NKG2D-mediated immunosurveillance. Our study shows how subpopulations of SnCs elude immunosurveillance, and reveals secretome-targeted therapeutic strategies to selectively eliminate -and restore the clearance of- the detrimental SnCs that actively persist after chemotherapy and accumulate at sites of aging pathologies.

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 < 0.001). In addition, two different subset analyses of low-intermediate risk cases (cases with Gleason score ≤ 3 + 4 = 7; and cases with Gleason score = 3 + 4 = 7/4 + 3 = 7) likewise demonstrated patients with largest cribriform diameter >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.

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.

Purpose

Active 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 methods

We 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.

Results

At 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).

Conclusions

Most 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.

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.