Background

Diet may substantially alter prostate cancer initiation and progression. However, large-scale clinical trials of diet modification have yet to be performed for prostate cancer. The Men's Eating and Living (MEAL) Study (CALGB 70807 [Alliance]) is investigating the effect of increased vegetable consumption on clinical progression in men with localized prostate cancer.

Study design

MEAL is a randomized, phase III clinical trial designed to test whether an intervention that increases vegetable intake will decrease the incidence of clinical progression in men with clinically localized prostate cancer on active surveillance. We are randomizing 464 patients to either a validated telephone-based diet counseling intervention or a control condition in which patients receive a published diet guideline. The intervention will continue for two years. The primary outcome variable is clinical progression defined by serum prostate-specific antigen (PSA) and pathological findings on follow-up prostate biopsy. Secondary outcome variables include incidence of surgical and non-surgical treatments for prostate cancer, prostate-cancer related patient anxiety and health-related quality of life.

Conclusion

The MEAL Study is assessing the effectiveness of a high-vegetable diet intervention for preventing clinical progression in men with localized prostate cancer on active surveillance.

Objective

To assess the feasibility of performing national, randomized trials of dietary interventions for localized prostate cancer.

Methods

The Men's Eating and Living (MEAL) study (CALGB 70807 [Alliance]) is a phase III clinical trial testing the efficacy of a high-vegetable diet to prevent progression in patients with prostate cancer on active surveillance (AS). Participants were randomized to a validated diet counselling intervention or to a control condition. Chi-squared and Kruskal-Wallis analyses were used to assess between-group differences at baseline.

Results

Between 2011 and 2015, 478 (103%) of a targeted 464 patients were randomized at 91 study sites. At baseline, the mean (sd) age was 64 (6) years and mean (sd) PSA concentration was 4.9 (2.1) ng/mL. Fifty-six (12%) participants were African-American, 17 (4%) were Hispanic/Latino, and 16 (3%) were Asian-American. There were no significant between-group differences for age (P = 0.98), race/ethnicity (P = 0.52), geographic region (P = 0.60), time since prostate cancer diagnosis (P = 0.85), PSA concentration (P = 0.96), clinical stage (T1c or T2a; P = 0.27), or Gleason sum (Gleason 6 or 3+4 = 7; P = 0.76). In a pre-planned analysis, the baseline prostate biopsy samples of the first 50 participants underwent central pathology review to confirm eligibility, with an expectation that <10% would become ineligible. One of 50 participants (2%) became ineligible.

Conclusion

The MEAL study shows the feasibility of implementing national, multi-institutional phase III clinical trials of diet for prostate cancer and of testing interventions to prevent disease progression in AS.

Renal oncocytoma (RO) accounts for 5% of renal cancers and generally behaves as a benign tumor with favorable long-term prognosis. It is difficult to confidently distinguish between benign RO and other renal malignancies, particularly chromophobe renal cell carcinoma (chRCC). Therefore, RO is often managed aggressively with surgery. We sought to identify molecular biomarkers to distinguish RO from chRCC and other malignant renal cancer mimics. In a 44-patient discovery cohort, we identified a significant differential abundance of nine genes in RO relative to chRCC. These genes were used to train a classifier to distinguish RO from chRCC in an independent 57-patient cohort. The trained classifier was then validated in five independent cohorts comprising 89 total patients. This nine-gene classifier trained on the basis of differential gene expression showed 93% sensitivity and 98% specificity for distinguishing RO from chRCC across the pooled validation cohorts, with a c-statistic of 0.978. This tool may be a useful adjunct to other diagnostic modalities to decrease the diagnostic and management uncertainty associated with small renal masses and to enable clinicians to recommend more confidently less aggressive management for some tumors. PATIENT SUMMARY: Renal oncocytoma is generally a benign form of kidney cancer that does not necessarily require surgical removal. However, it is difficult to distinguish renal oncocytoma from other more aggressive forms of kidney cancer, so it is treated most commonly with surgery. We built a classification tool based on the RNA levels of nine genes that may help avoid these surgeries by reliably distinguishing renal oncocytoma from other forms of kidney cancer.

The International Society of Urological Pathology (ISUP) hosts a reference image database supervised by experts with the purpose of establishing an international standard in prostate cancer grading. Here, we aimed to identify areas of grading difficulties and compare the results with those obtained from an artificial intelligence system trained in grading. In a series of 87 needle biopsies of cancers selected to include problematic cases, experts failed to reach a 2/3 consensus in 41.4% (36/87). Among consensus and non-consensus cases, the weighted kappa was 0.77 (range 0.68-0.84) and 0.50 (range 0.40-0.57), respectively. Among the non-consensus cases, four main causes of disagreement were identified: the distinction between Gleason score 3 + 3 with tangential cutting artifacts vs. Gleason score 3 + 4 with poorly formed or fused glands (13 cases), Gleason score 3 + 4 vs. 4 + 3 (7 cases), Gleason score 4 + 3 vs. 4 + 4 (8 cases) and the identification of a small component of Gleason pattern 5 (6 cases). The AI system obtained a weighted kappa value of 0.53 among the non-consensus cases, placing it as the observer with the sixth best reproducibility out of a total of 24. AI may serve as a decision support and decrease inter-observer variability by its ability to make consistent decisions. The grading of these cancer patterns that best predicts outcome and guides treatment warrants further clinical and genetic studies. Results of such investigations should be used to improve calibration of AI systems.

AIMS: Despite efforts to standardise grading of prostate cancer, even among experts there is still a considerable variation in grading practices. In this study we describe the use of Pathology Imagebase, a novel reference image library, for setting an international standard in prostate cancer grading. METHODS AND RESULTS: The International Society of Urological Pathology (ISUP) recently launched a reference image database supervised by experts. A panel of 24 international experts in prostate pathology reviewed independently microphotographs of 90 cases of prostate needle biopsies with cancer. A linear weighted kappa of 0.67 (95% confidence interval = 0.62-0.72) and consensus was reached in 50 cases. The interobserver weighted kappa varied from 0.48 to 0.89. The highest level of agreement was seen for Gleason score (GS) 3 + 3 = 6 (ISUP grade 1), while higher grades and particularly GS 4 + 3 = 7 (ISUP grade 3) showed considerable disagreement. Once a two-thirds majority was reached, images were moved automatically into a public database available for all ISUP members at www.isupweb.org. Non-members are able to access a limited number of cases. CONCLUSIONS: It is anticipated that the database will assist pathologists to calibrate their grading and, hence, decrease interobserver variability. It will also help to identify instances where definitions of grades need to be clarified.

Clinical trials frequently include multiple end points that mature at different times. The initial report, typically based on the primary end point, may be published when key planned co-primary or secondary analyses are not yet available. Clinical Trial Updates provide an opportunity to disseminate additional results from studies, published in JCO or elsewhere, for which the primary end point has already been reported.Mesenchymal-epithelial transition (MET) signaling pathway plays a role in the pathogenesis of selected patients with papillary renal cell carcinoma (PRCC). In the phase II PAPMET trial (ClinicalTrials.gov identifier: NCT02761057), cabozantinib significantly prolonged progression-free survival and improved objective response rate compared with sunitinib in patients with advanced PRCC. Here, we present the final overall survival (OS) analysis. In this multicenter, randomized phase II, open-label trial, 147 patients with advanced PRCC who have received up to one previous therapy (excluding vascular endothelial growth factor-directed agents) were assigned to sunitinib, cabozantinib, crizotinib, or savolitinib. Ultimately, savolitinib and crizotinib arms were closed because of futility. With a median follow-up of 17.5 months, the median OS was 21.5 months (95% CI, 12.0 to 28.1) with cabozantinib and 17.3 months (95% CI, 12.8 to 21.8) with sunitinib (hazard ratio, 0.83; 95% CI, 0.51 to 1.36; P = .46). The OS landmark estimates for cabozantinib and sunitinib were 50% versus 39% at 24 months and 32% versus 28% at 36 months. In conclusion, we observed no significant difference in OS across treatment arms. Although cabozantinib represents a well-supported option for advanced PRCC, the lack of survival benefit underscores the need to develop novel therapies for this disease.

Artificial intelligence (AI) has shown promise for diagnosing prostate cancer in biopsies. However, results have been limited to individual studies, lacking validation in multinational settings. Competitions have been shown to be accelerators for medical imaging innovations, but their impact is hindered by lack of reproducibility and independent validation. With this in mind, we organized the PANDA challenge-the largest histopathology competition to date, joined by 1,290 developers-to catalyze development of reproducible AI algorithms for Gleason grading using 10,616 digitized prostate biopsies. We validated that a diverse set of submitted algorithms reached pathologist-level performance on independent cross-continental cohorts, fully blinded to the algorithm developers. On United States and European external validation sets, the algorithms achieved agreements of 0.862 (quadratically weighted κ, 95% confidence interval (CI), 0.840-0.884) and 0.868 (95% CI, 0.835-0.900) with expert uropathologists. Successful generalization across different patient populations, laboratories and reference standards, achieved by a variety of algorithmic approaches, warrants evaluating AI-based Gleason grading in prospective clinical trials.