Characteristics and risk differences of different tumor size on localized prostate cancer: A retrospective cohort study in the SEER database

Clinicopathologic and survival patterns among prostate carcinosarcoma patients in the U.S. An analysis of SEER database

INTRODUCTION

Prostatic carcinosarcoma comprises <1% of all prostate neoplasms. The literature on this disease is limited to a few case studies, primarily due to the rarity of this malignancy. We aimed to investigate the demographic, clinical, and histologic factors, prognosis, and survival of prostatic carcinosarcoma.

METHODS

The Surveillance, Epidemiology, and End Results (SEER) database was used to identify patients with prostatic carcinosarcoma from 2000-2018. Demographic and clinical data, including age, race, sex, tumor grade, stage, tumor size, lymph node status, metastasis, and treatment modalities, were recorded.

RESULTS

Patients with prostatic carcinosarcoma had a median age of 72 years at diagnosis, most cases among White individuals (93%). When reported, the histologic grade comprised moderately differentiated (3.3%), poorly differentiated (56.7%), and undifferentiated/anaplastic (40%) subtypes. In patients with reported data, tumor size varied between 2-5 cm (15.8%) and >5 cm (84.2%). Distant metastasis most commonly occurred in the liver (12.5%) and lung (12.5%), followed by the bone (8.3%). The most common treatment performed was surgery with radiation (32.4%). The five-year overall survival was 11.9%.

CONCLUSIONS

Prostatic carcinosarcoma affects men in the seventh decade of life. Regional and distant tumor stage is considered an indicator of survival. Prostate carcinosarcoma is rare; due to its aggressive nature, a deeper understanding, and an improved personalized therapeutic approach are necessary for improving patient outcomes in this challenging arena of oncology.

Quantified treatment effect at the individual level is more indicative for personalized radical prostatectomy recommendation: implications for prostate cancer treatment using deep learning

BACKGROUND

There are potential uncertainties and overtreatment existing in radical prostatectomy (RP) for prostate cancer (PCa) patients, thus identifying optimal candidates is quite important.

PURPOSE

This study aims to establish a novel causal inference deep learning (DL) model to discern whether a patient can benefit more from RP and to identify heterogeneity in treatment responses among PCa patients.

METHODS

We introduce the Self-Normalizing Balanced individual treatment effect for survival data (SNB). Six models were trained to make individualized treatment recommendations for PCa patients. Inverse probability treatment weighting (IPTW) was used to avoid treatment selection bias.

RESULTS

35,236 patients were included. Patients whose actual treatment was consistent with SNB recommendations had better survival outcomes than those who were inconsistent (multivariate hazard ratio (HR): 0.76, 95% confidence interval (CI), 0.64-0.92; IPTW-adjusted HR: 0.77, 95% CI, 0.61-0.95; risk difference (RD): 3.80, 95% CI, 2.48-5.11; IPTW-adjusted RD: 2.17, 95% CI, 0.92-3.35; the difference in restricted mean survival time (dRMST): 3.81, 95% CI, 2.66-4.85; IPTW-adjusted dRMST: 3.23, 95% CI, 2.06-4.45). Keeping other covariates unchanged, patients with 1 ng/mL increase in PSA levels received RP caused 1.77 months increase in the time to 90% mortality, and the similar results could be found in age, Gleason score, tumor size, TNM stages, and metastasis status.

CONCLUSIONS

Our highly interpretable and reliable DL model (SNB) may identify patients with PCa who could benefit from RP, outperforming other models and clinical guidelines. Additionally, the DL-based treatment guidelines obtained can provide priori evidence for subsequent studies.

Targeting the ATF6-Mediated ER Stress Response and Autophagy Blocks Integrin-Driven Prostate Cancer Progression

Prostate cancer progression to the lethal metastatic castration-resistant phenotype (mCRPC) is driven by αv integrins and is associated with Golgi disorganization and activation of the ATF6 branch of unfolded protein response (UPR). Overexpression of integrins requires N-acetylglucosaminyltransferase-V (MGAT5)-mediated glycosylation and subsequent cluster formation with Galectin-3 (Gal-3). However, the mechanism underlying this altered glycosylation is missing. For the first time, using HALO analysis of IHC, we found a strong association of integrin αv and Gal-3 at the plasma membrane (PM) in primary prostate cancer and mCRPC samples. We discovered that MGAT5 activation is caused by Golgi fragmentation and mislocalization of its competitor, N-acetylglucosaminyltransferase-III, MGAT3, from Golgi to the endoplasmic reticulum (ER). This was validated in an ethanol-induced model of ER stress, where alcohol treatment in androgen-refractory PC-3 and DU145 cells or alcohol consumption in patient with prostate cancer samples aggravates Golgi scattering, activates MGAT5, and enhances integrin expression at PM. This explains known link between alcohol consumption and prostate cancer mortality. ATF6 depletion significantly blocks UPR and reduces the number of Golgi fragments in both PC-3 and DU145 cells. Inhibition of autophagy by hydroxychloroquine (HCQ) restores compact Golgi, rescues MGAT3 intra-Golgi localization, blocks glycan modification via MGAT5, and abrogates delivery of Gal-3 to the cell surface. Importantly, the loss of Gal-3 leads to reduced integrins at PM and their accelerated internalization. ATF6 depletion and HCQ treatment synergistically decrease integrin αv and Gal-3 expression and temper orthotopic tumor growth and metastasis.

IMPLICATIONS

Combined ablation of ATF6 and autophagy can serve as new mCRPC therapeutic.

Influencing factors for mortality in prostate cancer patients with T1 and T2 stage: a retrospective cohort study

Background

Few reports have focused on the influencing factors of localized prostate cancer (PCa)-specific mortality so far. This study aimed to develop a competitive risk model for identifying the factors influencing the localized PCa mortality rate based on 135,310 subjects in the Surveillance, Epidemiology, and End Results (SEER) database.

Methods

We included 135,310 localized PCa male patients from SEER database 2004-2016 in this cohort study, and collected the baseline information of all patients, including age of diagnosis, race, marital status, socioeconomic status (SES), American Joint Committee on Cancer (AJCC) stage, prostate-specific antigen (PSA) Gleason score, and so on. The outcome was considered as PCa-specific mortality in this study. The end time of follow-up was November 2018. Independent risk factors were examined by multivariate Fine-Gray analysis. The results are shown by hazard ratio (HR) and 95% confidence interval (CI).

Results

All patients were divided into three groups: died from localized PCa (n=1,400), died from other causes (n=16,996), and survived (n=116,914). The diagnostic age of 119,899 patients was ≥55 years. The multivariate Fine-Gray analysis indicated that age of diagnosis (55-70 years: HR =1.473, 95% CI: 1.124-1.930; >70 years: HR =2.528, 95% CI: 1.901-3.362), race (American India/Alaska Native, Asian/Pacific Islander: HR =0.653, 95% CI: 0.490-0.870), marital status (divorced: HR =1.433, 95% CI: 1.197-1.717; single: HR =1.463, 95% CI: 1.244-1.719; widowed: HR =1.485, 95% CI: 1.222-1.804), therapeutic method (radiotherapy: HR =1.500; 95% CI: 1.119-2.011), SES (4-10: HR =0.799, 95% CI: 0.664-0.961; ≥11: HR =0.670; 95% CI: 0.534-0.839), AJCC stage (HR =0.820, 95% CI: 0.715-0.940), level of PSA (HR: 1.002, 95% CI: 1.002-1.002) and Gleason score (HR: 2.226, 95% CI: 2.108-2.350) were associated with the risk of localized PCa mortality.

Conclusions

The study determined the influencing factors for mortality in patients with localized PCa through a competitive risk model. This finding may provide a reference for localized PCa patients: localized PCa patients who are older, divorced, widowed, single, have a radiotherapy, have a high PSA level, and Gleason score may be at high risk.