Terry Hyslop

BACKGROUND: Implementation methods of risk-stratified cancer screening guidance throughout a health care system remains understudied. OBJECTIVE: Conduct a preliminary analysis of the implementation of a risk-stratified prostate cancer screening algorithm in a single health care system. DESIGN: Comparison of men seen pre-implementation (2/1/2016-2/1/2017) vs. post-implementation (2/2/2017-2/21/2018). PARTICIPANTS: Men, aged 40-75 years, without a history of prostate cancer, who were seen by a primary care provider. INTERVENTIONS: The algorithm was integrated into two components in the electronic health record (EHR): in Health Maintenance as a personalized screening reminder and in tailored messages to providers that accompanied prostate-specific antigen (PSA) results. MAIN MEASURES: Primary outcomes: percent of men who met screening algorithm criteria; percent of men with a PSA result. Logistic repeated measures mixed models were used to test for differences in the proportion of individuals that met screening criteria in the pre- and post-implementation periods with age, race, family history, and PSA level included as covariates. KEY RESULTS: During the pre- and post-implementation periods, 49,053 and 49,980 men, respectively, were seen across 26 clinics (20.6% African American). The proportion of men who met screening algorithm criteria increased from 49.3% (pre-implementation) to 68.0% (post-implementation) (p < 0.001); this increase was observed across all races, age groups, and primary care clinics. Importantly, the percent of men who had a PSA did not change: 55.3% pre-implementation, 55.0% post-implementation. The adjusted odds of meeting algorithm-based screening was 6.5-times higher in the post-implementation period than in the pre-implementation period (95% confidence interval, 5.97 to 7.05). CONCLUSIONS: In this preliminary analysis, following implementation of an EHR-based algorithm, we observed a rapid change in practice with an increase in screening in higher-risk groups balanced with a decrease in screening in low-risk groups. Future efforts will evaluate costs and downstream outcomes of this strategy.

BACKGROUND: Work is needed to better understand how joint exposure to environmental and economic factors influence cancer. We hypothesize that environmental exposures vary with socioeconomic status (SES) and urban/rural locations, and areas with minority populations coincide with high economic disadvantage and pollution. METHODS: To model joint exposure to pollution and SES, we develop a latent class mixture model (LCMM) with three latent variables (SES Advantage, SES Disadvantage, and Air Pollution) and compare the LCMM fit with K-means clustering. We ran an ANOVA to test for high exposure levels in non-Hispanic black populations. The analysis is at the census tract level for the state of North Carolina. RESULTS: The LCMM was a better and more nuanced fit to the data than K-means clustering. Our LCMM had two sublevels (low, high) within each latent class. The worst levels of exposure (high SES disadvantage, low SES advantage, high pollution) are found in 22% of census tracts, while the best levels (low SES disadvantage, high SES advantage, low pollution) are found in 5.7%. Overall, 34.1% of the census tracts exhibit high disadvantage, 66.3% have low advantage, and 59.2% have high mixtures of toxic pollutants. Areas with higher SES disadvantage had significantly higher non-Hispanic black population density (NHBPD; P < 0.001), and NHBPD was higher in areas with higher pollution (P < 0.001). CONCLUSIONS: Joint exposure to air toxins and SES varies with rural/urban location and coincides with minority populations. IMPACT: Our model can be extended to provide a holistic modeling framework for estimating disparities in cancer survival.See all articles in this CEBP Focus section, "Environmental Carcinogenesis: Pathways to Prevention."

BACKGROUND: Prevalence of Helicobacter pylori (H. pylori) infection, the main risk factor for gastric cancer, has been decreasing in the United States; however, there remains a substantial racial disparity. Moreover, the time-trends for prevalence of CagA-positive H. pylori infection, the most virulent form, are unknown in the U.S. POPULATION: We sought to assess prevalence of CagA-positive H. pylori infection over time by race in the United States. METHODS: We utilized multiplex serology to quantify antibody responses to H. pylori antigens in 4,476 participants across five cohorts that sampled adults from 1985 to 2009. Using log-binomial regression models, we calculated prevalence ratios and 95% confidence intervals for the association between H. pylori-CagA sero-prevalence and birth year by race. RESULTS: African Americans were three times more likely to be H. pylori-CagA sero-positive than Whites. After adjustment, H. pylori-CagA sero-prevalence was lower with increasing birth year among Whites (P trend = 0.001), but remained stable for African Americans. When stratified by sex and education separately, the decline in H. pylori-CagA sero-positivity among Whites remained only for females (P trend < 0.001) and was independent of educational attainment. Among African Americans, there was no difference by sex; furthermore, sero-prevalence increased with increasing birth year among those with a high school education or less (P = 0.006). CONCLUSIONS: Among individuals in the United States born from the 1920s to 1960s, H. pylori-CagA sero-prevalence has declined among Whites, but not among African Americans. IMPACT: Our findings suggest a widening racial disparity in the prevalence of the most virulent form of H. pylori, the main cause of gastric cancer.

Cancer proteogenomics promises new insights into cancer biology and treatment efficacy by integrating genomics, transcriptomics and protein profiling including modifications by mass spectrometry (MS). A critical limitation is sample input requirements that exceed many sources of clinically important material. Here we report a proteogenomics approach for core biopsies using tissue-sparing specimen processing and microscaled proteomics. As a demonstration, we analyze core needle biopsies from ERBB2 positive breast cancers before and 48-72 h after initiating neoadjuvant trastuzumab-based chemotherapy. We show greater suppression of ERBB2 protein and both ERBB2 and mTOR target phosphosite levels in cases associated with pathological complete response, and identify potential causes of treatment resistance including the absence of ERBB2 amplification, insufficient ERBB2 activity for therapeutic sensitivity despite ERBB2 amplification, and candidate resistance mechanisms including androgen receptor signaling, mucin overexpression and an inactive immune microenvironment. The clinical utility and discovery potential of proteogenomics at biopsy-scale warrants further investigation.