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Objective Big Data is widely seen as a major opportunity for progress in the practice of personalized medicine, attracting the attention from medical societies and presidential teams alike as it offers a unique opportunity to enlarge the base of evidence, especially for older patients underrepresented in clinical trials. This study prospectively assessed the real-time availability of clinical cases in the Health & Research Informatics Total Cancer Care™ (TCC) database matching community patients with cancer, and the impact of such a consultation on treatment. Materials and Methods Patients aged 70 and older seen at the Lynn Cancer Institute (LCI) with a documented malignancy were eligible. Geriatric screening information and the oncologist's pre-consultation treatment plan were sent to Moffitt. A search for similar patients was done in TCC and additional information retrieved from Electronic Medical Records. A report summarizing the data was sent and the utility of such a consultation was assessed per email after the treatment decision. Results Thirty one patients were included. The geriatric screening was positive in 87.1% (27) of them. The oncogeriatric consultation took on average 2.2 working days. It influenced treatment in 38.7% (12), and modified it in 19.4% (6). The consultation was perceived as “somewhat” to “very useful” in 83.9% (26). Conclusion This study establishes a proof of concept of the feasibility of real time use of Big Data for clinical practice. The geriatric screening and the consultation report influenced treatment in 38.7% of cases and modified it in 19.4%, which compares very well with oncogeriatric literature. Additional steps are needed to render it financially and clinically viable.

Background: The impact of treatment delays on prostate cancer–specific outcomes remains ill-defined. This study investigates the effect of time to treatment on biochemical disease control after prostatectomy. Methods: This retrospective study includes 1,807 patients who received a prostatectomy as a primary treatment at two large tertiary referral centers from 1987 to 2015. Multivariate cox model with restricted cubic spline was used to identify optimal time to receive treatment and estimate the risk of biochemical recurrence. Results: Median follow-up time of the study was 46 (interquartile range, 18–86) months. Time to treatment was subcategorized based on multivariate cubic spline cox model. In multivariate spline model, adjusted for all the pertinent pretreatment variables, inflection point in the risk of biochemical recurrence was observed around 3 months, which further increased after 6 months. Based on spline model, time to treatment was then divided into 0 to 3 months (61.5%), >3 to 6 months (31.1%), and 6 months (7.4%). In the adjusted cox model, initial delays up to 6 months did not adversely affect the outcome; however, time to treatment >6 months had significantly higher risk of biochemical recurrence (HR, 1.84; 95% confidence interval, 1.30–2.60; P < 0.01). Conclusions: The initial delays up to 6 months in prostate cancer primary treatment may be sustainable without adversely affecting the outcome. However, significant delays beyond 6 months can unfavorably affect biochemical disease control. Impact: Time to treatment can aid clinicians in the decision-making of prostate cancer treatment recommendation and educate patients against unintentional treatment delays.

The extent to which prostate cancer (PCa) pathology interacts with health insurance to predict PCa outcomes remains unclear. This study will assess the overall association of health insurance on PCa disease control and analyze its interrelationship PCa pathology. A total of 674 PCa patients, treated with prostatectomy from 1987 to 2015, were included in the study. Freedom from biochemical failure (FFbF) was used as a measure of PCa disease control. Methods of categorical and survival analysis were used to analyze the relationships between health insurance, PCa pathology, and FFbF. A total of 63.3% patients were privately insured, 27.1% were publicly insured, and 9.5% were uninsured. In a multivariable model, privately (hazard ratio [HR] = 0.64, 95% confidence interval [CI]: 0.42-0.97, P = .03) and publicly (HR = 0.65, 95% CI: 0.41-1.04, P = .07) insured patients showed improvement in FFbF compared to uninsured patients. The association of health insurance was significantly stronger for the patients with pathologically low grade PCa (pathologic Gleason Score 3+3 & preoperative prostate-specific antigen ≤10 ng/mL), likelihood ratio P = .009. Privately (HR = 0.22, 95% CI: 0.10-0.46) or publicly (HR = 0.26, 95% CI: 0.11-0.60) insured patients with low grade PCa demonstrated favorable association with FFbF. Patients with private and public insurance were more likely to experience favorable treatment. The association of health insurance on PCa disease control is significantly stronger among patients with pathologically low grade PCa. This study identifies health insurance status as pretreatment surrogate for PCa disease control.

Object This study was undertaken to evaluate the association between age at diagnosis, patterns of care, and outcome among elderly individuals with anaplastic astrocytoma (AA) and glioblastoma multiforme (GBM). Methods Using the Surveillance, Epidemiology and End Results database, the authors identified 1753 individuals with primary GBM and 205 individuals with primary AA (diagnosed between June 1991 and December 1999) who were 66 years and older and whose records were linked to Medicare information. To facilitate gathering of prediagnosis comorbidity and postdiagnosis treatment information, only those individuals were included who had the same Medicare coverage for 6 months before and 12 months after diagnosis. The odds of undergoing various combinations of treatments and the associations with outcome were calculated by tumor type and age and adjusted by various predictors. Results Age was not associated with treatment differences in individuals with AA. Very elderly individuals (≥ 75 years old) with GBM were more likely to have biopsy only (odds ratio [OR] 2.53, 95% confidence interval [CI] 1.78–3.59), surgery only (OR 1.47, 95% CI 1.15–1.87), or biopsy and radiation (OR 1.39, 95% CI 1.07–1.82) and were less likely to receive multimodal therapy. Regardless of patient age or lesion histological characteristics, survival was decreased in patients treated with biopsy only. Individuals with GBM who had surgery only or biopsy and radiation had worse outcomes than individuals treated with surgery and radiation. There were no differences in survival by lesion histological characteristics. Very elderly individuals with malignant astrocytomas were more likely to receive limited treatment (most pronounced in individuals with GBM). Survival variation correlated with treatment combinations. Conclusions These findings suggest that in clinical neurooncology patient age is associated with not receiving effective therapies and hence worse prognosis.

Background: Gene expression profiling (GEP) is increasingly being integrated into clinical practice for prognostication of multiple myeloma. MyPRSTM utilizes the 70-gene panel with survival data derived from cohorts of myeloma patients treated under intensive clinical trial regimens including tandem transplants. We aim to validate and expand the prognostic impact of GEP characterization of myeloma in patients treated with various anti-myeloma therapies at a single academic center. Methods: MyPRSTM GEP analysison bone marrow aspirate sampleswas performed on 417 multiple myeloma patients atMoffitt Cancer Center who had bone marrow examination at different phases in the course of their disease. Myeloma risk prognostication was conducted based on the risk classification (cutoff of score 45.2), risk score (0-100), and molecular subtyping. Trend (Jonckheere-Terpstra) test was performed to evaluate the associations of GEP scores and molecular subtypes. Overall survival (OS) was estimated using the Kaplan-Meier method from the time of GEP analysis and OS curves were compared using the log-rank test. Multivariate Cox proportional hazards regression models were built for OS. Results: Median age was 62 (range, 21-86), majority of patients were male (57.6%) and 71 patients (17%) had high-risk disease based on GEP with a median score of 30.7 (range, 8.7-99.4). Median time from myeloma diagnosis to GEP was 15.8 (range, 0.1-370.1) months. Molecular subtypes werehyperdiploidy(HY) 25.2%, cycle family (CD)-2 21.1%, low bone disease (LB) 17.5%, proliferation (PR) 13.4%, MMSET associated (MS) 9.8%, MAF associated (MF) 7.7%, and CD-1 5.1%. There was an increasing trend of higher GEP scores among various molecular subtypes from CD to PR (p Conclusions: Analysis of GEP in myeloma patients irrespective of disease status treated at a single academic institution validates the prognostic significance based on 70-gene GEP score, risk groups and molecular subtyping. Although less robust, GEP risk re-classification with a new threshold value based on the median also demonstrates prognostic significance. Interestingly, parsing of the GEP-70 score by separating patients into different GEP score groups (either in 20-point increments or quartiles) results in distinct groups with distinct survival characteristics suggesting powerful prognostic value of GEP analysis. To this end, a higher GEP score was associated with a worse prognostic score even within the same GEP risk classification. These data indicate the potential for the development of a new risk classification based on GEP score into 4 risk groups. Increased numbers of patients will be required to validate the potential of these score-based risk classifications. In conclusion, our data demonstrate that GEP score remains a powerful prognostic tool regardless of the disease setting. Figure 1 (OS by GEP risk: high vs. low) Figure 1. (OS by GEP risk: high vs. low) Figure 2 (OS by new GEP score cut-off of 30.7) Figure 2. (OS by new GEP score cut-off of 30.7) Figure 3 Figure 3. Disclosures Nishihori: Signal Genetics: Research Funding; Novartis: Research Funding. Baz:Novartis: Research Funding; Takeda/Millennium: Research Funding; Signal Genetics: Research Funding; Karyopharm: Research Funding; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Bristol-Myers Squibb: Research Funding; Merck: Research Funding. Van Laar:Signal Genetics, Inc.: Employment. Bender:Signal Genetics, Inc.: Employment. Alsina:Signal Genetics: Consultancy; Novartis: Research Funding; Takeda/Millennium: Research Funding; Amgen/Onyx: Consultancy, Speakers Bureau. Shain:Novartis: Speakers Bureau; Amgen/Onyx: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Takeda/Millennium: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Signal Genetics: Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau.

Background: A commercially available gene expression profiling (GEP) model is an important risk stratification method in multiple myeloma. GEP is helpful in dividing myeloma into low and high risk based on a pre-determined threshold, however, the value of sequential GEP testing on patients receiving sequential anti-myeloma therapies has yet to be fully determined. Methods: Fifty four myeloma patients at Moffitt Cancer Center underwent two sequential GEPs based a dedicated 70-gene panel using an Affymetrixmicroarray platform (MyPRSTM) from bone marrow aspirate samples during the course of their disease. Myeloma was characterized based on the risk classification (cutoff of score 45.2), risk score (0-100), differences in the score/classification, and molecular subtyping. Pearson correlation analysis was performed to correlate GEP scores at two time points. Overall survival (OS) was estimated using the Kaplan-Meier method from the time of first GEP analysis and OS curves were compared using the log-rank test. Multivariate Cox proportional hazards regression models were built for OS. Results: Majority were male (67%) and hadDurie-Salmon stage 2 or 3 disease (83%).Immunophenotypeswere IgG (56%), IgA (22%) and light chain (20%). At first GEP testing (Time 1), 20% (11/54) were high risk and the median score was 28.6 (range, 10.8-66.5) with frequency of molecular subtypes being cycle family (CD) 1&2 28%,hyperdiploidy(HY) 24%, low bone disease (LB) 22%, proliferation (PR) 17%, and MMSET associated (MS) 9%. There were no MAF associated cases in this cohort. At second GEP testing (Time 2), 28% (15/54) were high risk with a median score of 33.5 (range, 11.4-96.3). Pearson correlation coefficient for the GEP scores of Times 1 & 2 was r=0.77 (p Conclusions: Serial GEP analysis on myeloma patients demonstrates longitudinal changes in risk scores, molecular subtyping and risk classification suggesting clonal evolution or modifications of overall transcribed genetic signature with systemic therapy. GEP risk score may potentially provide graded risk assessment rather than dichotomous categorization as different risk threshold may be found based on observation of a new cutoff value predictive of OS. Further analysis of gene expression patterns is ongoing to elucidate risk stratification based on molecular signatures and to elucidate potential treatment associated profiles. Figure 1 Figure 1. Disclosures Nishihori: Novartis: Research Funding; Signal Genetics: Research Funding. Baz:Novartis: Research Funding; Millennium: Research Funding; Merck: Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees; Signal Genetics: Research Funding; Karyopharm: Research Funding; Bristol-Myers Squibb: Research Funding. Van Laar:Signal Genetics, Inc.: Employment. Bender:Signal Genetics, Inc.: Employment. Alsina:Takeda/Millennium: Research Funding; Novartis: Research Funding; Signal Genetics: Consultancy; Amgen/Onyx: Consultancy, Speakers Bureau. Shain:Takeda/Millennium: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Signal Genetics: Research Funding; Amgen/Onyx: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Novartis: Speakers Bureau.

Purpose Determine differences in treatment patterns among Non Hispanic White (NHW) and African American (AA) women in Florida, diagnosed with epithelial ovarian cancer. Methods All cases diagnosed with epithelial ovarian cancer, 1981–2000, were identified using the Florida Cancer Data System. Information was obtained on race, tobacco use, marital status, age at diagnosis, and stage. Treatment variables of interest were: surgery, chemotherapy, no treatment, a combination of surgery and chemotherapy and other treatment. Adjusted odds ratio (OR) and 95% confidence intervals were used to determine treatment modalities by stage and race. Results The study population consisted of 16,805 AA and NHW women with epithelial ovarian cancer, of which, 6% were identified as AA and 94% NHW women. AA women were significantly more likely to be diagnosed at a younger age, with 26.5% of AA women diagnosed at age 50 and under compared to 16.4% of NHW women. AA women were also less likely to present with localized disease (12.9 vs. 17.8%) and more likely to present with distant disease (78.0% vs.71.8%). When treatment was considered, AA women were more likely to receive no treatment (20.1% vs. 16.4%) and less likely to receive surgery and chemotherapy (35.4% vs. 39.5%) than NHW women. When evaluated by stage, treatment differences between AA and NHW women remained for local and distant disease. After adjustment for other risk factors the differences for local disease remained. Adjustment for stage, age at diagnosis and other confounding factors, resulted in an overall adjusted odds ratio for no treatment for AA women of OR=1.18 (1.00–1.39) and an adjusted OR=0.84 (0.74–0.96) for surgery and chemotherapy. Conclusion African American women were more likely to be diagnosed with ovarian cancer at a younger age than NHW women. Despite this, AA women are more likely to receive no treatment and less likely to receive surgery and chemotherapy than NHW women. Adjustment for confounding factors, reduced but did not eliminate, all these differences.