Bone marrow mesenchymal stem cells increase motility of prostate cancer cells via production of stromal cell-derived factor-1α.

Prostate cancer frequently metastasizes to the bone, and the interaction between cancer cells and bone microenvironment has proven to be crucial in the establishment of new metastases. Bone marrow mesenchymal stem cells (BM-MSCs) secrete various cytokines that can regulate the behaviour of neighbouring cell. However, little is known about the role of BM-MSCs in influencing the migration and the invasion of prostate cancer cells. We hypothesize that the stromal cell-derived factor-1α released by BM-MSCs may play a pivotal role in these processes. To study the interaction between factors secreted by BM-MSCs and prostate cancer cells we established an in vitro model of transwell co-culture of BM-MSCs and prostate cancer cells DU145. Using this model, we have shown that BM-MSCs produce soluble factors which increase the motility of prostate cancer cells DU145. Neutralization of stromal cell-derived factor-1α (SDF1α) via a blocking antibody significantly limits the chemoattractive effect of bone marrow MSCs. Moreover, soluble factors produced by BM-MSCs greatly activate prosurvival kinases, namely AKT and ERK 1/2. We provide further evidence that SDF1α is involved in the interaction between prostate cancer cells and BM-MSCs. Such interaction may play an important role in the migration and the invasion of prostate cancer cells within bone.

Developing a Diagnostic Multivariable Prediction Model for Urinary Tract Cancer in Patients Referred with Haematuria: Results from the IDENTIFY Collaborative Study.

BACKGROUND: Patient factors associated with urinary tract cancer can be used to risk stratify patients referred with haematuria, prioritising those with a higher risk of cancer for prompt investigation. OBJECTIVE: To develop a prediction model for urinary tract cancer in patients referred with haematuria. DESIGN, SETTING, AND PARTICIPANTS: A prospective observational study was conducted in 10 282 patients from 110 hospitals across 26 countries, aged ≥16 yr and referred to secondary care with haematuria. Patients with a known or previous urological malignancy were excluded. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: The primary outcomes were the presence or absence of urinary tract cancer (bladder cancer, upper tract urothelial cancer [UTUC], and renal cancer). Mixed-effect multivariable logistic regression was performed with site and country as random effects and clinically important patient-level candidate predictors, chosen a priori, as fixed effects. Predictors were selected primarily using clinical reasoning, in addition to backward stepwise selection. Calibration and discrimination were calculated, and bootstrap validation was performed to calculate optimism. RESULTS AND LIMITATIONS: The unadjusted prevalence was 17.2% (n = 1763) for bladder cancer, 1.20% (n = 123) for UTUC, and 1.00% (n = 103) for renal cancer. The final model included predictors of increased risk (visible haematuria, age, smoking history, male sex, and family history) and reduced risk (previous haematuria investigations, urinary tract infection, dysuria/suprapubic pain, anticoagulation, catheter use, and previous pelvic radiotherapy). The area under the receiver operating characteristic curve of the final model was 0.86 (95% confidence interval 0.85-0.87). The model is limited to patients without previous urological malignancy. CONCLUSIONS: This cancer prediction model is the first to consider established and novel urinary tract cancer diagnostic markers. It can be used in secondary care for risk stratifying patients and aid the clinician's decision-making process in prioritising patients for investigation. PATIENT SUMMARY: We have developed a tool that uses a person's characteristics to determine the risk of cancer if that person develops blood in the urine (haematuria). This can be used to help prioritise patients for further investigation.