Development of a multivariable risk model integrating urinary cell DNA methylation and cell-free RNA data for the detection of significant prostate cancer.

BACKGROUND: Prostate cancer exhibits severe clinical heterogeneity and there is a critical need for clinically implementable tools able to precisely and noninvasively identify patients that can either be safely removed from treatment pathways or those requiring further follow up. Our objectives were to develop a multivariable risk prediction model through the integration of clinical, urine-derived cell-free messenger RNA (cf-RNA) and urine cell DNA methylation data capable of noninvasively detecting significant prostate cancer in biopsy naïve patients. METHODS: Post-digital rectal examination urine samples previously analyzed separately for both cellular methylation and cf-RNA expression within the Movember GAP1 urine biomarker cohort were selected for a fully integrated analysis (n = 207). A robust feature selection framework, based on bootstrap resampling and permutation, was utilized to find the optimal combination of clinical and urinary markers in a random forest model, deemed ExoMeth. Out-of-bag predictions from ExoMeth were used for diagnostic evaluation in men with a clinical suspicion of prostate cancer (PSA ≥ 4 ng/mL, adverse digital rectal examination, age, or lower urinary tract symptoms). RESULTS: As ExoMeth risk score (range, 0-1) increased, the likelihood of high-grade disease being detected on biopsy was significantly greater (odds ratio = 2.04 per 0.1 ExoMeth increase, 95% confidence interval [CI]: 1.78-2.35). On an initial TRUS biopsy, ExoMeth accurately predicted the presence of Gleason score ≥3 + 4, area under the receiver-operator characteristic curve (AUC) = 0.89 (95% CI: 0.84-0.93) and was additionally capable of detecting any cancer on biopsy, AUC = 0.91 (95% CI: 0.87-0.95). Application of ExoMeth provided a net benefit over current standards of care and has the potential to reduce unnecessary biopsies by 66% when a risk threshold of 0.25 is accepted. CONCLUSION: Integration of urinary biomarkers across multiple assay methods has greater diagnostic ability than either method in isolation, providing superior predictive ability of biopsy outcomes. ExoMeth represents a more holistic view of urinary biomarkers and has the potential to result in substantial changes to how patients suspected of harboring prostate cancer are diagnosed.

An integrative DNA methylation model for improved prognostication of postsurgery recurrence and therapy in prostate cancer patients.

PURPOSE: Patients with clinically localized, high-risk prostate cancer are often treated with surgery, but exhibit variable prognosis requiring long-term monitoring. An ongoing challenge for such patients is developing optimal strategies and biomarkers capable of differentiating between men at risk of early recurrence (<3 years) that will benefit from adjuvant therapies and men at risk of late recurrence (>5 years) who will benefit from long-term monitoring and/or salvage therapies. PATIENTS AND METHODS: DNA methylation changes for 12 genes associated with disease progression were analyzed in 453 prostate tumors. A 4-gene prognostic model (4-G model) for biochemical recurrence (BCR) was derived utilizing LASSO from Cohort 1 (n = 254) and validated in Cohort 2 (n = 199). Subsequently, the 4-G model was evaluated for its association with salvage radiotherapy (RT) and/or hormone therapy, and the additive potential to CAPRA-S to develop an integrative gene model was assessed. RESULTS: The 4-G model was significantly associated with BCR in both cohorts (chi-squared analysis P≤ 0.004) and specifically, with late recurrence at 5+ years (P < 0.001, Cohort 1; P= 0.028, Cohort 2). Multivariable Cox proportional regression analysis identified the 4-G model as significantly associated with salvage RT or hormone therapy in Cohort 1 (hazard ratio (HR) 1.64, 95% confidence interval (CI) 1.29-2.10, P< 0.001) and further validated in Cohort 2 (HR 1.63, 95% CI 1.18-2.25, P< 0.001). The integrative model outperformed prostate-specific antigen and the 4-G model alone for predicting BCR and was associated with patients who received hormone therapy 3+ years postsurgery. CONCLUSIONS: We have identified and validated a novel integrative gene model as an independent prognosticator of BCR and demonstrated its association with late BCR. These patients require more long-term postsurgical monitoring and could be spared the comorbidities of adjuvant therapies.

Association of the colorectal CpG island methylator phenotype with molecular features, risk factors, and family history.

BACKGROUND: The CpG island methylator phenotype (CIMP) represents a subset of colorectal cancers characterized by widespread aberrant DNA hypermethylation at select CpG islands. The risk factors and environmental exposures contributing to etiologic heterogeneity between CIMP and non-CIMP tumors are not known. METHODS: We measured the CIMP status of 3,119 primary population-based colorectal cancer tumors from the multinational Colon Cancer Family Registry. Etiologic heterogeneity was assessed by a case-case study comparing risk factor frequency of colorectal cancer cases with CIMP and non-CIMP tumors using logistic regression to estimate the case-case odds ratio (ccOR). RESULTS: We found associations between tumor CIMP status and MSI-H (ccOR = 7.6), BRAF V600E mutation (ccOR = 59.8), proximal tumor site (ccOR = 9; all P < 0.0001), female sex [ccOR = 1.8; 95% confidence interval (CI), 1.5-2.1], older age (ccOR = 4.0 comparing over 70 years vs. under 50; 95% CI, 3.0-5.5), and family history of CRC (ccOR = 0.6; 95% CI, 0.5-0.7). While use of NSAIDs varied by tumor CIMP status for both males and females (P = 0.0001 and P = 0.02, respectively), use of multivitamin or calcium supplements did not. Only for female colorectal cancer was CIMP status associated with increased pack-years of smoking (Ptrend < 0.001) and body mass index (BMI; Ptrend = 0.03). CONCLUSIONS: The frequency of several colorectal cancer risk factors varied by CIMP status, and the associations of smoking and obesity with tumor subtype were evident only for females. IMPACT: Differences in the associations of a unique DNA methylation-based subgroup of colorectal cancer with important lifestyle and environmental exposures increase understanding of the molecular pathologic epidemiology of this heavily methylated subset of colorectal cancer. Cancer Epidemiol Biomarkers Prev; 24(3); 512-9. ©2015 AACR.