The U.S. Preventive Services Task Force recently concluded that the harms of existing prostate-specific antigen (PSA) screening strategies outweigh the benefits.
To evaluate comparative effectiveness ...of alternative PSA screening strategies.
Microsimulation model of prostate cancer incidence and mortality quantifying harms and lives saved for alternative PSA screening strategies.
National and trial data on PSA growth, screening and biopsy patterns, incidence, treatment distributions, treatment efficacy, and mortality.
A contemporary cohort of U.S. men.
Lifetime.
Societal.
35 screening strategies that vary by start and stop ages, screening intervals, and thresholds for biopsy referral.
PSA tests, false-positive test results, cancer detected, overdiagnoses, prostate cancer deaths, lives saved, and months of life saved.
Without screening, the risk for prostate cancer death is 2.86%. A reference strategy that screens men aged 50 to 74 years annually with a PSA threshold for biopsy referral of 4 µg/L reduces the risk for prostate cancer death to 2.15%, with risk for overdiagnosis of 3.3%. A strategy that uses higher PSA thresholds for biopsy referral in older men achieves a similar risk for prostate cancer death (2.23%) but reduces the risk for overdiagnosis to 2.3%. A strategy that screens biennially with longer screening intervals for men with low PSA levels achieves similar risks for prostate cancer death (2.27%) and overdiagnosis (2.4%), but reduces total tests by 59% and false-positive results by 50%.
Varying incidence inputs or reducing the survival improvement due to screening did not change conclusions.
The model is a simplification of the natural history of prostate cancer, and improvement in survival due to screening is uncertain.
Compared with standard screening, PSA screening strategies that use higher thresholds for biopsy referral for older men and that screen men with low PSA levels less frequently can reduce harms while preserving lives.
National Cancer Institute and Centers for Disease Control and Prevention.
Factors influencing differential responses of prostate tumors to androgen receptor (AR) axis-directed therapeutics are poorly understood, and predictors of treatment efficacy are needed. We ...hypothesized that the efficacy of inhibiting DHT ligand synthesis would associate with intra-tumoral androgen ratios indicative of relative dependence on DHT-mediated growth.
We characterized two androgen-sensitive prostate cancer xenograft models after androgen suppression by castration in combination with the SRD5A inhibitor, dutasteride, as well as a panel of castration resistant metastases obtained via rapid autopsy.
In LuCaP35 tumors (intra-tumoral T:DHT ratio 2:1) dutasteride suppressed DHT to 0.02 ng/gm and prolonged survival vs. castration alone (337 vs.152 days, HR 2.8, p = 0.0015). In LuCaP96 tumors (T:DHT 10:1), survival was not improved despite similar DHT reduction (0.02 ng/gm). LuCaP35 demonstrated higher expression of steroid biosynthetic enzymes maintaining DHT levels (5-fold higher SRD5A1, 41 fold higher, 99-fold higher RL-HSD, p<0.0001 for both), reconstitution of intra-tumoral DHT (to ∼30% of untreated tumors), and ∼2 fold increased expression of full length AR. In contrast, LuCaP96 demonstrated higher levels of steroid catabolizing enzymes (6.9-fold higher AKR1C2, 3000-fold higher UGT2B15, p = 0.002 and p<0.0001 respectively), persistent suppression of intra-tumoral DHT, and 6-8 fold induction of full length AR and the ligand independent V7 AR splice variant. Human metastases demonstrated bio-active androgen levels and AR full length and AR splice-variant expression consistent with the range observed in xenografts.
Intrinsic differences in basal steroidogenesis, as well as variable expression of full length and splice-variant AR, associate with response and resistance to pre-receptor AR ligand suppression. Expression of steroidogenic enzymes and AR isoforms may serve as potential biomarkers of sensitivity to potent AR-axis inhibition and should be validated in additional models.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
The ability to interrogate circulating tumor cells (CTC) and disseminated tumor cells (DTC) is restricted by the small number detected and isolated (typically <10). To determine if a commercially ...available technology could provide a transcriptomic profile of a single prostate cancer (PCa) cell, we clonally selected and cultured a single passage of cell cycle synchronized C4-2B PCa cells. Ten sets of single, 5-, or 10-cells were isolated using a micromanipulator under direct visualization with an inverted microscope. Additionally, two groups of 10 individual DTC, each isolated from bone marrow of 2 patients with metastatic PCa were obtained. RNA was amplified using the WT-Ovation™ One-Direct Amplification System. The amplified material was hybridized on a 44K Whole Human Gene Expression Microarray. A high stringency threshold, a mean Alexa Fluor® 3 signal intensity above 300, was used for gene detection. Relative expression levels were validated for select genes using real-time PCR (RT-qPCR).
Using this approach, 22,410, 20,423, and 17,009 probes were positive on the arrays from 10-cell pools, 5-cell pools, and single-cells, respectively. The sensitivity and specificity of gene detection on the single-cell analyses were 0.739 and 0.972 respectively when compared to 10-cell pools, and 0.814 and 0.979 respectively when compared to 5-cell pools, demonstrating a low false positive rate. Among 10,000 randomly selected pairs of genes, the Pearson correlation coefficient was 0.875 between the single-cell and 5-cell pools and 0.783 between the single-cell and 10-cell pools. As expected, abundant transcripts in the 5- and 10-cell samples were detected by RT-qPCR in the single-cell isolates, while lower abundance messages were not. Using the same stringency, 16,039 probes were positive on the patient single-cell arrays. Cluster analysis showed that all 10 DTC grouped together within each patient.
A transcriptomic profile can be reliably obtained from a single cell using commercially available technology. As expected, fewer amplified genes are detected from a single-cell sample than from pooled-cell samples, however this method can be used to reliably obtain a transcriptomic profile from DTC isolated from the bone marrow of patients with PCa.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Shared patient-physician decision making regarding the treatment of prostate cancer detected by prostate specific antigen screening involves a complex calculus weighing cancer risk and patient life ...expectancy. We sought to quantify these competing risks using the probability that the cancer was over diagnosed, ie would not have been clinically diagnosed (diagnosed without screening) during the remaining lifetime of the patient.
Using an established model of prostate cancer screening and clinical diagnosis we simulated screen detected cases and determined whether a modeled clinical diagnosis would occur before noncancer death. Time of noncancer death was based on comorbidity adjusted population lifetables. Logistic regression models were fitted to the simulated data and used to estimate over diagnosis probabilities given patient age, prostate specific antigen level, Gleason sum and comorbidity category. An online calculator was developed to communicate over diagnosis estimates. Face validity and ease of use were assessed by surveying 32 clinical experts.
Estimated probabilities of over diagnosis ranged from 4% to 78% across clinicopathological variables and comorbidity status. When ignoring comorbidity, the estimated probability of over diagnosis in a 70-year-old man with prostate specific antigen 9.4 ng/ml and Gleason 6 was 34%. With severe comorbidities the estimate increased to 51%. Such a personalization may help inform the choice between active surveillance and definitive treatment. Based on responses from 20 of 32 experts we modified the explanation of over diagnosis for the online calculator and the input method for comorbid conditions.
The probability of over diagnosis is strongly influenced by comorbidity status in addition to age. Personalized estimates incorporating comorbidity may contribute to shared decision making between patients and providers regarding personalized treatment selection.
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