Citations

Abstract Background: It is documented that inequities in research participation by minority and underrepresented populations limits the progression of personalized medicine for cancer treatment for these groups. A lack of understanding essential information during the consent process and poor physician-patient communication have been noted as two overarching barriers to patients participating in translational, clinical and biospecimen research. To address these major patient barriers, we developed a plain language research literacy support (RLS) tool to improve the consent process and facilitate dialogue between physicians/research staff and patients. We describe the tool's iterative development process and testing of the developed culturally appropriate RLS tool. Methods: We tested the tool among: 1) patient participants ≥18 years of age, who were able to read and speak English from communities associated with five academic centers in the Midwest and Northeast United States; and 2) eligible recruiters who were employed at an academic center and actively recruiting patients into research. The development process for the RLS tool consisted of two phases: cognitive and beta testing. During cognitive testing, we solicited feedback from patient and recruiter participants about the content and format of the tool including low literacy language, pictures and recommendations for additional content. The tool consisted of concept cards to provide information to assist patients in making an informed decision about participating in research. During beta testing, a condensed version of the tool was tested to assess acceptability and change in knowledge about research participation. Results: Participants included 155 patients (52: cognitive testing phase; 103: beta testing phase) and 20 recruiter participants. Patient participants were mostly females (71%). Self-identified race of patients included: Whites (43%), African Americans (32%), Native Americans (20%), and other (6%). In addition, 23% of patient participants self-identified as Hispanic. During the cognitive testing phase, patient participants reported that the information was clearly laid out and the tool explained the different steps in the recruitment and enrollment process. Patient participants indicated they had learned new information related to randomization, standard of care, coded-information, and biobanking. Although some patient participants acknowledged the cards contained a great deal of information, most reported that the information was essential. Several participants suggested cards be tailored for the Native American population. Recruiters indicated that the cards provided useful information and would assist patients and research staff. During beta testing phase, 98% of participants agreed the information on the cards helped them understand clinical research. After reviewing the cards, 49% of patient participants reported that they would be very likely to participate in research and 32% reported being somewhat likely to participate in research. The majority (61%) of participants indicated that if invited to participate in a research study, they would prefer that the cards would be mailed to them in advance while 34% wanted to review the cards at their medical appointment. Half of the participants preferred accessing the cards on a computer if administered in a video format. Conclusion: The RLS tool was well received among patient and recruiter participants, making it a potentially valuable resource for enrollment of minority and underrepresented patients into health research. The next step will be to test the RLS tool for effectiveness in a randomized controlled trial. Citation Format: Erika E. de la Riva, Rodney Haring, Elisa M. Rodriguez, Evelyn Gonzalez, Mira Katz, Nikia Clark, Whitney Ann E. Henry, Rosa O. Ortiz, Barret J. Zimmermann, Marla L. Clayman, Deborah O. Erwin, Judith S. Kaur, Melissa A. Simon. Development and beta testing of a culturally appropriate research literacy support tool to increase research participation among minority and underrepresented populations. [abstract]. In: Proceedings of the Ninth AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2016 Sep 25-28; Fort Lauderdale, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2017;26(2 Suppl):Abstract nr A11.

90 Background: Biochemical recurrence (BCR) may be an indicator of metastatic prostate cancer; however, it isn't specific enough to differentiate patients who will experience rapid onset of metastases from those who do not. Such lack of specificity leads to overtreatment of men who will likely never experience metastatic disease in their lifetime. We hypothesize that genomic biomarkers, detectable in primary tumor tissue, may better identify rapidly progressing metastatic prostate cancer. Methods: FFPE prostatectomy specimens (n = 309) from the Mayo Clinic tumor registry were profiled using a high-density expression array. Patients were retrospectively classified into three outcome groups: NED (no evidence of disease recurrence); BCR-only (patients that experienced BCR but no metastatic progression); and MET (metastatic disease confirmed by positive CT or bone scans) and were matched on several criteria for analysis . Uni- and multi-variable analyses between outcome groups were used to analyze clinical and genomic variables (adjusting for adjuvant hormone therapy as a confounding factor). Results: Median follow-up was 16.8 years and MET patients were diagnosed a mean of 3.1 years following BCR. Univariable and multivariable analysis of clinical variables failed to show statistically significant differences between NED and BCR-only outcome groups. Differential expression analysis between NED and BCR-only groups showed no significant differences after false discovery correction. Conversely, 6,277 and 52,020 biomarkers were differentially expressed between MET and the BCR-only and NED groups, respectively. Conclusions: Considering the clinical and genomic results together, patients who experienced rapid metastatic disease were significantly different from both the NED and BCR-only groups. However, no significant differences were identified between the BCR-only and NED outcome groups. These results imply that BCR is not a good surrogate for men who are likely to experience rapid onset of metastatic disease and lethal prostate cancer.

40 Background: Gleason score (GS) is the most widely used grading system of cell differentiation in prostate cancer and one of the best pathological predictors of disease progression. Patients with high GS (> 7) are the most likely to experience metastasis, whereas patients with low GS (< 7) are expected to have favorable long-term outcomes. In addition to the subjective nature of GS assessment, patients with GS 7 represent a heterogeneous group in terms of patient outcomes. In this study a biomarker signature is developed and validated which could improve the prediction of high risk disease among GS 7 radical prostatectomy (RP) patients. Methods: Patient specimens from the Mayo Clinic RP Registry (n = 764) were used for feature selection and training of a 392-feature K-nearest neighbor (KNN, k = 11) classifier. These 392 genomic features were identified by assessing differential expression between patients with GS < 7 and those with GS > 7, using a Bonferroni adjusted T-Test with a p-value threshold of 0.05. The classifier was subsequently validated in two independent patient cohorts (Memorial Sloan Kettering [MSKCC] and German Cancer Research Center [DKFZ]) and compared to a state of the art biomarker signature (Penney et al. 2011). Results: In the MSKCC dataset (GSE21034) our model segregated GS < 7 from GS > 7 patients (n = 56) with an area under the receiver operating characteristic curve (AUC) of 0.97, comparable to the AUC of 0.94 obtained by Penney et al. in their independent validation set (n = 45). Strong performance was observed by our model when discriminating between primary Gleason grade (pGG) 3 and pGG 4 & 5 patients in the MSKCC (n = 130) and DKFZ prostate cancer (GSE29079, n = 47) datasets, achieving AUCs of 0.75 and 0.87, respectively. In the challenging GS 7 subset, our model segregated GS 3 + 4 and 4 + 3 patients in the DKFZ dataset (n = 64) with an AUC of 0.81 outperforming the Penney et al. signature (AUC = 0.60). Conclusions: A biomarker signature was developed which discriminates between low and high GS patients, outperforming a previously reported signature. Further validation of this biomarker signature in additional post RP patients, as well as, pretreatment biopsy specimens is warranted.

36 Background: Following radical prostatectomy (RP), 30-40% of patients have adverse pathology and are deemed at high risk for metastatic progression. The objective of this study was to validate the ability of Decipher, a genomic classifier (GC), to improve prediction of metastatic disease progression compared with clinical variables in order to better identify candidates for therapy intensification. Methods: A previously developed 22-feature GC model was validated in a prospectively designed case-cohort study of a clinically high-risk population (i.e., with one or more adverse pathological features) of 1,010 RP patients treated at Mayo Clinic between 2000-2006. A random sample of 20% of the cohort was subjected to microarray analysis and GC scores were generated for 219 patients. The primary endpoint, the c-index for predicting metastatic disease progression (i.e., positive bone or CT scans) was evaluated in a blinded analysis. Cox modeling and decision curve analyses were used to compare the performance of GC to individual clinical variables and prediction models. Results: GC had a c-index 0.79 (95% CI 0.71-0.86) that was significantly better than any single clinical variable. Cumulative incidence curves in the cohort showed that 72% of patients had low GC scores with only 3% and 6% incidence of metastatic disease at 5 and 10 years post RP. In contrast, for the 28% of patients with high GC scores, the cumulative incidence was 17% and 25% at 5 and 10 years post RP. Decision curve analysis showed that the GC model had higher overall net benefit compared to clinical variables over a wide range of ‘decision-to-treat’ thresholds for risk of metastasis. In multivariable modeling with clinicopathologic variables, GC remained the only significant independent predictor of metastasis (HR=1.51, for each 0.1 unit increment, p<0.001). Conclusions: GC can better predict metastatic disease progression compared with clinical variables and may select among patients with adverse pathology a majority that is in fact at low risk for metastasis.