Agnieszka Michael

Most studies indicate no benefit of adjuvant therapy with VEGFR tyrosine kinase inhibitors in advanced renal cell carcinoma (RCC). PROTECT (NCT01235962) was a randomized, double-blind, placebo-controlled phase 3 study to evaluate adjuvant pazopanib in patients with locally advanced RCC at high risk of relapse after nephrectomy (pazopanib, n = 769; placebo, n = 769). The results of the primary analysis showed no difference in disease-free survival between pazopanib 600 mg and placebo. Here we report the final overall survival (OS) analysis (median follow-up: pazopanib, 76 mo, interquartile range [IQR] 66–84; placebo, 77 mo, IQR 69–85). There was no significant difference in OS between the pazopanib and placebo arms (hazard ratio 1.0, 95% confidence interval 0.80–1.26; nominal p > 0.9). OS was worse for patients with T4 disease compared to those with less advanced disease and was better for patients with body mass index (BMI) ≥30 kg/m2 compared to those with lower BMI. OS was significantly better for patients who remained diseasefree at 2 yr after treatment compared with those who relapsed within 2 yr. These findings are consistent with the primary outcomes from PROTECT, indicating that adjuvant pazopanib does not confer a benefit in terms of OS for patients following resection of locally advanced RCC.
In the randomized, double-blind, placebo-controlled phase 3 PROTECT study, overall survival was similar for patients with locally advanced renal cell carcinoma (RCC) at high risk of relapse after nephrectomy who received adjuvant therapy with pazopanib or placebo. Pazopanib is not recommended as adjuvant therapy following resection of locally advanced RCC.
This trial is registered at Clinicaltrials.gov as NCT01235962.
Adjuvant pazopanib does not extend overall survival compared to placebo in patients with locally advanced or metastatic renal cell carcinoma.

Prostate cancer is a highly heritable disease with large disparities in incidence rates across ancestry populations. We conducted a multiancestry meta-analysis of prostate cancer genome-wide association studies (107,247 cases and 127,006 controls) and identified 86 new genetic risk variants independently associated with prostate cancer risk, bringing the total to 269 known risk variants. The top genetic risk score (GRS) decile was associated with odds ratios that ranged from 5.06 (95% confidence interval (CI), 4.84-5.29) for men of European ancestry to 3.74 (95% CI, 3.36-4.17) for men of African ancestry. Men of African ancestry were estimated to have a mean GRS that was 2.18-times higher (95% CI, 2.14-2.22), and men of East Asian ancestry 0.73-times lower (95% CI, 0.71-0.76), than men of European ancestry. These findings support the role of germline variation contributing to population differences in prostate cancer risk, with the GRS offering an approach for personalized risk prediction.

We determined the effect of sample size on performance of polygenic hazard score (PHS) models in prostate cancer. Age and genotypes were obtained for 40,861 men from the PRACTICAL consortium. The dataset included 201,590 SNPs per subject, and was split into training and testing sets. Established-SNP models considered 65 SNPs that had been previously associated with prostate cancer. Discovery-SNP models used stepwise selection to identify new SNPs. The performance of each PHS model was calculated for random sizes of the training set. The performance of a representative Established-SNP model was estimated for random sizes of the testing set. Mean HR
98/50
(hazard ratio of top 2% to average in test set) of the Established-SNP model increased from 1.73 [95% CI: 1.69–1.77] to 2.41 [2.40–2.43] when the number of training samples was increased from 1 thousand to 30 thousand. Corresponding HR
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of the Discovery-SNP model increased from 1.05 [0.93–1.18] to 2.19 [2.16–2.23]. HR
98/50
of a representative Established-SNP model using testing set sample sizes of 0.6 thousand and 6 thousand observations were 1.78 [1.70–1.85] and 1.73 [1.71–1.76], respectively. We estimate that a study population of 20 thousand men is required to develop Discovery-SNP PHS models while 10 thousand men should be sufficient for Established-SNP models.

Objectives: Prostate-specific-antigen (PSA) screening resulted in reduced prostate cancer (PCa) mortality in a large clinical trial, but due to many false positives and overdiagnosis of indolent disease, many guidelines do not endorse universal screening and instead recommend an individualized decision based on each patient’s risk. We sought to develop and validate a genetic tool to predict age of aggressive PCa onset and to guide decisions of whom to screen and at what age. Design: Genotype, PCa status, and age were analyzed to select single-nucleotide polymorphisms (SNPs) associated with PCa diagnosis. These SNPs were incorporated into a survival analysis to estimate their effects on age at diagnosis of aggressive PCa (i.e., not eligible for surveillance per NCCN Guidelines; any of: Gleason score ≥7, stage T3-T4, PSA ≥10, nodal metastasis, distant metastasis). The resulting polygenic hazard score (PHS) is an assessment of individual genetic risk. The final model was applied to an independent dataset containing genotype and screening PSA data. PHS was calculated for these men to test prediction of PCa-free survival. Setting: Multiple, international PRACTICAL consortium member institutions. Participants: All PRACTICAL consortium participants of European ancestry with known age, PCa status, and quality-assured iCOGS array genotype data. Development dataset comprised 31,747 men. Validation dataset comprised 6,411 men. Main outcome measures: PHS prediction of age of onset of aggressive PCa in validation set. Results: In the independent validation set, PHS calculated from 54 SNPs was a highly significant predictor of age at diagnosis of aggressive PCa (z=11.2, p<10-16). When men in the validation set with high PHS (>98th percentile) were compared to those with average PHS (30th-70th percentile), the hazard ratio for aggressive PCa was 2.9. Conclusions:Polygenic hazard scores give personalized genetic risk estimates that predict for age of onset of aggressive PCa.