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Significant racial disparities in prostate cancer (PCa) outcomes have been reported, with African‐American men (AAM) more likely to endure adverse oncologic outcomes. Despite efforts to dissipate racial disparities in PCa, a survival gap persists and it remains unclear to what extent this disparity can be explained by known clinicodemographic factors. In this study, we leveraged our large institutional database, spanning over 25 years, to investigate whether AAM continued to experience poor PCa outcomes and factors that may contribute to racial disparities in PCa. A total of 7307 patients diagnosed with PCa from 1989 through 2015 were included. Associations of race and clinicodemographic characteristics were analyzed using chi‐square for categorical and Mann–Whitney U‐test for continuous variables. Racial differences in prostate cancer outcomes were analyzed using competing risk analysis methods of Fine and Gray. Median follow‐up time was 106 months. There were 2304 deaths recorded, of which 432 resulted from PCa. AAM were more likely to be diagnosed at an earlier age (median 60 vs. 65 years, P = 60 years), racial differences in PCSM were more pronounced, with AAM experiencing higher rates of PCSM (HR, 2.05, 95% CI, 1.26–3.34, P = 0.003). After adjustment of clinicodemographic and potential risk factors, AAM continue to experience an increased risk of mortality from PCa, especially older AAM. Furthermore, AAM are more likely to be diagnosed at an early age and more likely to have higher comorbidity indices.

Liquid chromatography-selected reaction monitoring (LC-SRM) mass spectrometry has developed into a versatile tool for quantification of proteins with a wide range of applications in basic science, translational research, and clinical patient assessment. This strategy uniquely complements traditional pathology approaches, like hematoxylin and eosin (H&E) staining and immunohistochemistry (IHC). The multiplexing capabilities offered by mass spectrometry are currently unmatched by other techniques. However, quantification of biomarkers in tissue specimens without the other data obtained from H&E-stained slides or IHC, including tumor cellularity or percentage of positively stained cells inter alia, may not provide as much information that is needed to fully understand tumor biology or properly assess the patient. Therefore, additional characterization of the tissue proteome is needed, which in turn requires the ability to assess protein markers across a wide range of expression levels from a single sample. This protocol provides an example of multiplexed analysis in breast tumor tissue quantifying specific biomarkers, specifically estrogen receptor, progesterone receptor, and the HER2 receptor tyrosine kinase, in combination with other proteins that can report on tissue content and other aspects of tumor biology.

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.

Objective: The aim of the current study was to examine whether there were racial differences in patterns of care between Non-Hispanic White (NHW) and African-American (AA) women diagnosed with regional and distant epithelial ovarian cancer living in Florida. Design: This epidemiologic cross-sectional study utilized data on incident cases of ovarian cancer diagnosed between 1981 and 2000 among Florida residents reported to the statewide Florida Cancer Data System (FCDS). Materials and Methods: The study population consisted of 13,858 AA and NHW women diagnosed with regional or distant epithelial ovarian cancer between 1981 and 2000 identified from using FCDS data. The variables of interest included: race/ethnicity, marital status, tobacco use, age at diagnosis, stage at diagnosis, surgery, chemotherapy, no treatment, other therapy, and a combination of surgery and chemotherapy. Adjusted odds ratios (ORs) and 95% confidence intervals (CIs) were calculated to determine the receipt of treatment by st...

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 Intraductal papillary mucinous neoplasms (IPMNs) are pancreatic ductal adenocarcinoma (PDAC) precursors. Differentiating between high-risk IPMNs that warrant surgical resection and low-risk IPMNs that can be monitored is a significant clinical problem, and we sought to discover a panel of mi(cro)RNAs that accurately classify IPMN risk status. Methodology/Principal Findings In a discovery phase, genome-wide miRNA expression profiling was performed on 28 surgically-resected, pathologically-confirmed IPMNs (19 high-risk, 9 low-risk) using Taqman MicroRNA Arrays. A validation phase was performed in 21 independent IPMNs (13 high-risk, 8 low-risk). We also explored associations between miRNA expression level and various clinical and pathological factors and examined genes and pathways regulated by the identified miRNAs by integrating data from bioinformatic analyses and microarray analysis of miRNA gene targets. Six miRNAs (miR-100, miR-99b, miR-99a, miR-342-3p, miR-126, miR-130a) were down-regulated in high-risk versus low-risk IPMNs and distinguished between groups (P

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.