Development and pilot evaluation of a personalized decision support intervention for low risk prostate cancer patients.

ObjectivesDevelopment and pilot evaluation of a personalized decision support intervention to help men with early-stage prostate cancer choose among active surveillance, surgery, and radiation.MethodsWe developed a decision aid featuring long-term survival and side effects data, based on focus group input and stakeholder endorsement. We trained premedical students to administer the intervention to newly diagnosed men with low-risk prostate cancer seen at the University of California, San Francisco. Before the intervention, and after the consultation with a urologist, we administered the Decision Quality Instrument for Prostate Cancer (DQI-PC). We hypothesized increases in two knowledge items from the DQI-PC: How many men diagnosed with early-stage prostate cancer will eventually die of prostate cancer? How much would waiting 3 months to make a treatment decision affect chances of survival? Correct answers were: "Most will die of something else" and "A little or not at all."ResultsThe development phase involved 6 patients, 1 family member, 2 physicians, and 5 other health care providers. In our pilot test, 57 men consented, and 44 received the decision support intervention and completed knowledge surveys at both timepoints. Regarding the two knowledge items of interest, before the intervention, 35/56 (63%) answered both correctly, compared to 36/44 (82%) after the medical consultation (P = .04 by chi-square test).ConclusionsThe intervention was associated with increased patient knowledge. Data from this pilot have guided the development of a larger scale randomized clinical trial to improve decision quality in men with prostate cancer being treated in community settings

Quality of life for men with metastatic castrate-resistant prostate cancer participating in an aerobic and resistance exercise pilot intervention

Background: Following a prostate cancer diagnosis, disease and treatment-related symptoms may result in diminished quality of life (QoL). Whether exercise improves QoL in men with metastatic castrate-resistant prostate cancer (mCRPC) is not fully understood. Methods: We conducted a 3-arm pilot randomized controlled trial to assess the feasibility, acceptability, safety, and efficacy of a 12-week remotely monitored exercise program among men with mCRPC. Here we report qualitative changes in QoL, consistent with the guidelines for pilot trials. Men were randomized to control, aerobic exercise, or resistance exercise. Exercise prescriptions were based on baseline cardiorespiratory and strength assessments. QoL outcomes were evaluated using self-reported questionnaires (e.g., QLQ-C30, PROMIS Fatigue, Pittsburgh Sleep Quality Index (PSQI), EPIC-26) collected at baseline and 12 weeks. Results: A total of 25 men were randomized (10 control, 8 aerobic, 7 resistance). Men were predominately white (76 %) with a median age of 71 years (range: 51 – 84) and 10.5 years (range: 0.9 – 26.3) post prostate cancer diagnosis. The men reported poor sleep quality and high levels of fatigue at enrollment. Other baseline QoL metrics were relatively high. Compared to the controls at 12 weeks, the resistance arm reported some improvements in social function and urinary irritative/obstruction symptoms while the aerobic arm reported some improvements in social function and urinary incontinence, yet worsening nausea/vomiting. Compared to the resistance arm, the aerobic arm reported worse urinary irritative/obstruction symptoms and self-rated QoL, yet some improvements in emotional function, insomnia, and diarrhea. Conclusions: The 3-month exercise intervention pilot appeared to have modest effects on QoL among mCRPC survivors on ADT. Given the feasibility, acceptability, and safety demonstrated in prior analyses, evaluation of the effect of the intervention on QoL in a larger sample and for extended duration may still be warranted

A 17-gene Assay to Predict Prostate Cancer Aggressiveness in the Context of Gleason Grade Heterogeneity, Tumor Multifocality, and Biopsy Undersampling

avai lable at www.sciencedirect.com journal homepage: www.europeanurology.com Genomic Prostate Score Outcome measures and statistical analysis: The main outcome measures defining aggressive PCa were clinical recurrence, PCa death, and adverse pathology at prostatec-predictive of aggressClinical validation Clinical utility tomy. Cox proportional hazards regressionmodels were used to evaluate the association between gene expression and time to event end points. Results from the prostatectomy and biopsy studies were used to develop and lock a multigene-expression-based signature, called the Genomic Prostate Score (GPS); in the validation study, logistic regression was used to test the association between the GPS and pathologic stage and grade at prostatectomy. Decision-curve analysis and risk profileswere used together with clinical and pathologic characteristics to evaluate clinical utility. Results and limitations: Of the 732 candidate genes analyzed, 288 (39%) were found to predict clinical recurrence despite heterogeneity and multifocality, and 198 (27%) were ive disease after adjustment for prostate-specific antigen, GleasonArticle inf

Tissue Sources for Accurate Measurement of Germline DNA Genotypes in Prostate Cancer Patients Treated With Radical Prostatectomy

BACKGROUND:Benign tissue from a tumor-containing organ is commonly the only available source for obtaining a patient's unmutated genome for use in cancer research. While it is critical to identify histologically normal tissue that is independent of the tumor lineage, few additional considerations are applied to the choice of this material for such measurements.METHODS:Normal formalin-fixed, paraffin-embedded seminal vesicle and urethral tissues, in addition to whole blood, were collected from 31 prostate cancer patients having undergone radical prostatectomy. Genotype concordance was evaluated for DNA from each tissue source in relation to whole blood.RESULTS:Overall, there was a greater genotype call rate for DNA derived from urethral tissue (97.0%) in comparison with patient-matched seminal vesicle tissues (95.9%, P = 0.0015). Furthermore, with reference to patient-matched whole blood, urethral samples exhibited higher genotype concordance (94.1%) than that of seminal vesicle samples (92.5%, P = 0.035).CONCLUSIONS:These findings highlight the heterogeneity between diverse sources of DNA in genotype measurement and motivate the consideration of normal tissue biases in tumor-normal analyses

Tissue Sources for Accurate Measurement of Germline DNA Genotypes in Prostate Cancer Patients Treated With Radical Prostatectomy

BACKGROUND:Benign tissue from a tumor-containing organ is commonly the only available source for obtaining a patient's unmutated genome for use in cancer research. While it is critical to identify histologically normal tissue that is independent of the tumor lineage, few additional considerations are applied to the choice of this material for such measurements.METHODS:Normal formalin-fixed, paraffin-embedded seminal vesicle and urethral tissues, in addition to whole blood, were collected from 31 prostate cancer patients having undergone radical prostatectomy. Genotype concordance was evaluated for DNA from each tissue source in relation to whole blood.RESULTS:Overall, there was a greater genotype call rate for DNA derived from urethral tissue (97.0%) in comparison with patient-matched seminal vesicle tissues (95.9%, P = 0.0015). Furthermore, with reference to patient-matched whole blood, urethral samples exhibited higher genotype concordance (94.1%) than that of seminal vesicle samples (92.5%, P = 0.035).CONCLUSIONS:These findings highlight the heterogeneity between diverse sources of DNA in genotype measurement and motivate the consideration of normal tissue biases in tumor-normal analyses

A machine learning approach to optimizing cell-free DNA sequencing panels: with an application to prostate cancer.

BackgroundCell-free DNA's (cfDNA) use as a biomarker in cancer is challenging due to genetic heterogeneity of malignancies and rarity of tumor-derived molecules. Here we describe and demonstrate a novel machine-learning guided panel design strategy for improving the detection of tumor variants in cfDNA. Using this approach, we first generated a model to classify and score candidate variants for inclusion on a prostate cancer targeted sequencing panel. We then used this panel to screen tumor variants from prostate cancer patients with localized disease in both in silico and hybrid capture settings.MethodsWhole Genome Sequence (WGS) data from 550 prostate tumors was analyzed to build a targeted sequencing panel of single point and small (< 200 bp) indel mutations, which was subsequently screened in silico against prostate tumor sequences from 5 patients to assess performance against commonly used alternative panel designs. The panel's ability to detect tumor-derived cfDNA variants was then assessed using prospectively collected cfDNA and tumor foci from a test set 18 prostate cancer patients with localized disease undergoing radical proctectomy.ResultsThe panel generated from this approach identified as top candidates mutations in known driver genes (e.g. HRAS) and prostate cancer related transcription factor binding sites (e.g. MYC, AR). It outperformed two commonly used designs in detecting somatic mutations found in the cfDNA of 5 prostate cancer patients when analyzed in an in silico setting. Additionally, hybrid capture and 2500X sequencing of cfDNA molecules using the panel resulted in detection of tumor variants in all 18 patients of a test set, where 15 of the 18 patients had detected variants found in multiple foci.ConclusionMachine learning-prioritized targeted sequencing panels may prove useful for broad and sensitive variant detection in the cfDNA of heterogeneous diseases. This strategy has implications for disease detection and monitoring when applied to the cfDNA isolated from prostate cancer patients

A machine learning approach to optimizing cell-free DNA sequencing panels: with an application to prostate cancer.

BackgroundCell-free DNA's (cfDNA) use as a biomarker in cancer is challenging due to genetic heterogeneity of malignancies and rarity of tumor-derived molecules. Here we describe and demonstrate a novel machine-learning guided panel design strategy for improving the detection of tumor variants in cfDNA. Using this approach, we first generated a model to classify and score candidate variants for inclusion on a prostate cancer targeted sequencing panel. We then used this panel to screen tumor variants from prostate cancer patients with localized disease in both in silico and hybrid capture settings.MethodsWhole Genome Sequence (WGS) data from 550 prostate tumors was analyzed to build a targeted sequencing panel of single point and small (< 200 bp) indel mutations, which was subsequently screened in silico against prostate tumor sequences from 5 patients to assess performance against commonly used alternative panel designs. The panel's ability to detect tumor-derived cfDNA variants was then assessed using prospectively collected cfDNA and tumor foci from a test set 18 prostate cancer patients with localized disease undergoing radical proctectomy.ResultsThe panel generated from this approach identified as top candidates mutations in known driver genes (e.g. HRAS) and prostate cancer related transcription factor binding sites (e.g. MYC, AR). It outperformed two commonly used designs in detecting somatic mutations found in the cfDNA of 5 prostate cancer patients when analyzed in an in silico setting. Additionally, hybrid capture and 2500X sequencing of cfDNA molecules using the panel resulted in detection of tumor variants in all 18 patients of a test set, where 15 of the 18 patients had detected variants found in multiple foci.ConclusionMachine learning-prioritized targeted sequencing panels may prove useful for broad and sensitive variant detection in the cfDNA of heterogeneous diseases. This strategy has implications for disease detection and monitoring when applied to the cfDNA isolated from prostate cancer patients

Platelet factors attenuate inflammation and rescue cognition in ageing

Identifying therapeutics to delay, and potentially reverse, age-related cognitive decline is critical in light of the increased incidence of dementia-related disorders forecasted in the growing older population1. Here we show that platelet factors transfer the benefits of young blood to the ageing brain. Systemic exposure of aged male mice to a fraction of blood plasma from young mice containing platelets decreased neuroinflammation in the hippocampus at the transcriptional and cellular level and ameliorated hippocampal-dependent cognitive impairments. Circulating levels of the platelet-derived chemokine platelet factor 4 (PF4) (also known as CXCL4) were elevated in blood plasma preparations of young mice and humans relative to older individuals. Systemic administration of exogenous PF4 attenuated age-related hippocampal neuroinflammation, elicited synaptic-plasticity-related molecular changes and improved cognition in aged mice. We implicate decreased levels of circulating pro-ageing immune factors and restoration of the ageing peripheral immune system in the beneficial effects of systemic PF4 on the aged brain. Mechanistically, we identified CXCR3 as a chemokine receptor that, in part, mediates the cellular, molecular and cognitive benefits of systemic PF4 on the aged brain. Together, our data identify platelet-derived factors as potential therapeutic targets to abate inflammation and rescue cognition in old age