Population-based evaluation of disparities in stomach cancer by nativity among Asian and Hispanic populations in California, 2011-2015

BACKGROUND

Stomach cancer incidence presents significant racial/ethnic disparities among racial/ethnic minority groups in the United States, particularly among Asian and Hispanic immigrant populations. However, population-based evaluation of disparities by nativity has been scarce because of the lack of nativity-specific population denominators, especially for disaggregated Asian subgroups. Population-based stomach cancer incidence and tumor characteristics by detailed race/ethnicity and nativity were examined.

METHODS

Annual age-adjusted incidence rates were calculated by race/ethnicity, sex, and nativity and tumor characteristics, such as stage and anatomic subsite, were evaluated using the 2011-2015 California Cancer Registry data. For Hispanic and Asian populations, nativity-specific population counts were estimated using the US Census and the American Community Survey Public Use Microdata Sample data.

RESULTS

During 2011-2015 in California, 14,198 patients were diagnosed with stomach cancer. Annual age-adjusted incidence rates were higher among foreign-born individuals than their US-born counterparts. The difference was modest among Hispanics (∼1.3-fold) but larger (∼2- to 3-fold) among Chinese, Japanese, and Korean Americans. The highest incidence was observed for foreign-born Korean and Japanese Americans (33 and 33 per 100,000 for men; 15 and 12 per 100,000 for women, respectively). The proportion of localized stage disease was highest among foreign-born Korean Americans (44%); a similar proportion was observed among US-born Korean Americans, although numbers were limited. For other Asians and Hispanics, the localized stage proportion was generally lower among foreign-born than US-born individuals and lowest among foreign-born Japanese Americans (23%).

CONCLUSIONS

Nativity-specific investigation with disaggregated racial/ethnic groups identified substantial stomach cancer disparities among foreign-born immigrant populations.

Genome-Wide Gene-Environment Interaction Analyses to Understand the Relationship between Red Meat and Processed Meat Intake and Colorectal Cancer Risk

BACKGROUND

High red meat and/or processed meat consumption are established colorectal cancer risk factors. We conducted a genome-wide gene-environment (GxE) interaction analysis to identify genetic variants that may modify these associations.

METHODS

A pooled sample of 29,842 colorectal cancer cases and 39,635 controls of European ancestry from 27 studies were included. Quantiles for red meat and processed meat intake were constructed from harmonized questionnaire data. Genotyping arrays were imputed to the Haplotype Reference Consortium. Two-step EDGE and joint tests of GxE interaction were utilized in our genome-wide scan.

RESULTS

Meta-analyses confirmed positive associations between increased consumption of red meat and processed meat with colorectal cancer risk [per quartile red meat OR = 1.30; 95% confidence interval (CI) = 1.21-1.41; processed meat OR = 1.40; 95% CI = 1.20-1.63]. Two significant genome-wide GxE interactions for red meat consumption were found. Joint GxE tests revealed the rs4871179 SNP in chromosome 8 (downstream of HAS2); greater than median of consumption ORs = 1.38 (95% CI = 1.29-1.46), 1.20 (95% CI = 1.12-1.27), and 1.07 (95% CI = 0.95-1.19) for CC, CG, and GG, respectively. The two-step EDGE method identified the rs35352860 SNP in chromosome 18 (SMAD7 intron); greater than median of consumption ORs = 1.18 (95% CI = 1.11-1.24), 1.35 (95% CI = 1.26-1.44), and 1.46 (95% CI = 1.26-1.69) for CC, CT, and TT, respectively.

CONCLUSIONS

We propose two novel biomarkers that support the role of meat consumption with an increased risk of colorectal cancer.

IMPACT

The reported GxE interactions may explain the increased risk of colorectal cancer in certain population subgroups.

Genome-wide interaction analysis of folate for colorectal cancer risk

BACKGROUND

Epidemiological and experimental evidence suggests that higher folate intake is associated with decreased colorectal cancer (CRC) risk; however, the mechanisms underlying this relationship are not fully understood. Genetic variation that may have a direct or indirect impact on folate metabolism can provide insights into folate's role in CRC.

OBJECTIVES

Our aim was to perform a genome-wide interaction analysis to identify genetic variants that may modify the association of folate on CRC risk.

METHODS

We applied traditional case-control logistic regression, joint 3-degree of freedom, and a 2-step weighted hypothesis approach to test the interactions of common variants (allele frequency >1%) across the genome and dietary folate, folic acid supplement use, and total folate in relation to risk of CRC in 30,550 cases and 42,336 controls from 51 studies from 3 genetic consortia (CCFR, CORECT, GECCO).

RESULTS

Inverse associations of dietary, total folate, and folic acid supplement with CRC were found (odds ratio [OR]: 0.93; 95% confidence interval [CI]: 0.90, 0.96; and 0.91; 95% CI: 0.89, 0.94 per quartile higher intake, and 0.82 (95% CI: 0.78, 0.88) for users compared with nonusers, respectively). Interactions (P-interaction < 5×10-8) of folic acid supplement and variants in the 3p25.2 locus (in the region of Synapsin II [SYN2]/tissue inhibitor of metalloproteinase 4 [TIMP4]) were found using traditional interaction analysis, with variant rs150924902 (located upstream to SYN2) showing the strongest interaction. In stratified analyses by rs150924902 genotypes, folate supplementation was associated with decreased CRC risk among those carrying the TT genotype (OR: 0.82; 95% CI: 0.79, 0.86) but increased CRC risk among those carrying the TA genotype (OR: 1.63; 95% CI: 1.29, 2.05), suggesting a qualitative interaction (P-interaction = 1.4×10-8). No interactions were observed for dietary and total folate.

CONCLUSIONS

Variation in 3p25.2 locus may modify the association of folate supplement with CRC risk. Experimental studies and studies incorporating other relevant omics data are warranted to validate this finding.

New UNOS allocation system associated with no added benefit in waitlist outcomes and worse post-transplant survival in heart-kidney patients

BACKGROUND

The 2018 United Network for Organ Sharing (UNOS) heart transplant policy change (PC) sought to improve waitlist risk stratification to decrease waitlist mortality and promote geographically broader sharing for high-acuity patients awaiting heart transplantation. Our analysis sought to determine the effect of the UNOS PC on outcomes in patients waiting for, or who have received, a heart-kidney transplantation.

METHODS

We analyzed adult (≥18 years old), first-time, heart-only and heart-kidney transplant candidates and recipients from the UNOS Registry. Patients were divided into pre-PC (PRE: October 18, 2016-May 30, 2018) and post-PC (POST: October 18, 2018-May 30, 2020) groups for comparison. Competing risks analysis (subdistribution and cause-specific hazards analyses) was performed to assess for differences in waitlist death/deterioration or heart transplantation. One-year post-transplant survival was assessed with Kaplan-Meier and Cox analyses. We included an interaction term (policy era × heart ± kidney) in our analyses to evaluate the effect of PC on outcomes in heart-kidney patients.

RESULTS

One-year post-transplant survival was similar (p = 0.83) for PRE heart-kidney and heart-only recipients, but worse (p < 0.001) for POST heart-kidney vs heart-only recipients. There was a policy-era interaction between heart-kidney and heart-only recipients (HR 1.92[1.04,3.55], p = 0.038) indicating a detrimental effect of policy on 1-year survival in POST vs PRE heart-kidney recipients. No added beneficial effect of PC on waitlist outcomes in heart-kidney vs heart-only candidates was observed.

CONCLUSIONS

There was no added policy-era benefit on waitlist outcomes for heart-kidney candidates when compared to heart-only candidates. POST heart-kidney recipients experienced worse 1-year survival compared to PRE heart-kidney recipients with no policy effect on heart-only recipients.

The Differential Experience of COVID-19 on Asian American Subgroups: The Los Angeles Pandemic Surveillance Cohort Study

Data from Asian Americans (AsA) are commonly aggregated in research studies and reporting, obscuring the significant differences across AsA subgroups. We investigated the differential experience of AsA subgroups in COVID-19 testing, vaccination, engagement in risky and protective behaviors and mental health status against this infectious disease. We surveyed a representative sample of the Los Angeles County population (N = 5500) in April 2021 as part of the Los Angeles Pandemic Surveillance Cohort Study and focused on participants who self-identified as AsA (N = 756). There were significant differences across the AsA subgroups, with Koreans, Asian Indians, and Other Asians living in areas with higher COVID-19 mortality rates, and Asian Indians demonstrating the lowest proportion of COVID-19 vaccination. Vietnamese and Koreans had a higher proportion of becoming unemployed during the pandemic. Although the AsA sample on average demonstrated better outcomes than other racial and ethnic groups, the apparent advantages were heterogenous and due to specific subgroups of AsAs rather than AsAs as a whole. The observed differences in COVID-19 measures across AsA subgroups underscore the need to disaggregate AsA data to identify and reduce existing disparities.

Effect of the UNOS policy change on rates of rejection, infection, and hospital readmission following heart transplantation

BACKGROUND

The 2018 adult heart allocation policy sought to improve waitlist risk stratification, reduce waitlist mortality, and increase organ access. This system prioritized patients at greatest risk for waitlist mortality, especially individuals requiring temporary mechanical circulatory support (tMCS). Posttransplant complications are significantly higher in patients on tMCS before transplantation, and early posttransplant complications impact long-term mortality. We sought to determine if policy change affected early posttransplant complication rates of rejection, infection, and hospitalization.

METHODS

We included all adult, heart-only, single-organ heart transplant recipients from the UNOS registry with pre-policy (PRE) individuals transplanted between November 1, 2016, and October 31, 2017, and post-policy (POST) between November 1, 2018, and October 31, 2019. We used a multivariable logistic regression analysis to assess the effect of policy change on posttransplant rejection, infection, and hospitalization. Two COVID-19 eras (2019-2020, 2020-2021) were included in our analysis.

RESULTS

The majority of baseline characteristics were comparable between PRE and POST era recipients. The odds of treated rejection (p = 0.8), hospitalization (p = 0.69), and hospitalization due to rejection (p = 0.76) and infection (p = 0.66) were similar between PRE and POST eras; there was a trend towards reduced odds of rejection (p = 0.08). In both COVID eras, there was a clear reduction in rejection and treated rejection with no effect on hospitalization for rejection or infection. Odds of all-cause hospitalization was increased in both COVID eras.

CONCLUSIONS

The UNOS policy change improves access to heart transplantation for higher acuity patients without increasing early posttransplant rates of treated rejection or hospitalization for rejection or infection, factors which portend risk for long-term posttransplant mortality.

A Genetic Locus within the FMN1/GREM1 Gene Region Interacts with Body Mass Index in Colorectal Cancer Risk

Colorectal cancer risk can be impacted by genetic, environmental, and lifestyle factors, including diet and obesity. Gene-environment interactions (G × E) can provide biological insights into the effects of obesity on colorectal cancer risk. Here, we assessed potential genome-wide G × E interactions between body mass index (BMI) and common SNPs for colorectal cancer risk using data from 36,415 colorectal cancer cases and 48,451 controls from three international colorectal cancer consortia (CCFR, CORECT, and GECCO). The G × E tests included the conventional logistic regression using multiplicative terms (one degree of freedom, 1DF test), the two-step EDGE method, and the joint 3DF test, each of which is powerful for detecting G × E interactions under specific conditions. BMI was associated with higher colorectal cancer risk. The two-step approach revealed a statistically significant G×BMI interaction located within the Formin 1/Gremlin 1 (FMN1/GREM1) gene region (rs58349661). This SNP was also identified by the 3DF test, with a suggestive statistical significance in the 1DF test. Among participants with the CC genotype of rs58349661, overweight and obesity categories were associated with higher colorectal cancer risk, whereas null associations were observed across BMI categories in those with the TT genotype. Using data from three large international consortia, this study discovered a locus in the FMN1/GREM1 gene region that interacts with BMI on the association with colorectal cancer risk. Further studies should examine the potential mechanisms through which this locus modifies the etiologic link between obesity and colorectal cancer.

SIGNIFICANCE

This gene-environment interaction analysis revealed a genetic locus in FMN1/GREM1 that interacts with body mass index in colorectal cancer risk, suggesting potential implications for precision prevention strategies.

A note on p-value multiple-testing adjustment for two-step genome-wide gene-environment interactions scans

Two-step testing is the state-of-the art approach for performing genome-wide interaction scans (GWIS). It is computationally efficient and yields higher power than standard single-step-based GWIS for virtually all biologically plausible scenarios. However, while two-step tests control the genome-wide type I error rate at the desired level, the lack of associated valid p-values can make it difficult for users to compare with single step-results. We show how multiple-testing adjusted p-values can be defined for two-step test based on standard multiple-testing theory, and how they can be in turn scaled to make valid comparisons with single-step tests possible.

Improved two-step testing of genome-wide gene-environment interactions

Two-step tests for gene-environment (G × E $G\times E$ ) interactions exploit marginal single-nucleotide polymorphism (SNP) effects to improve the power of a genome-wide interaction scan. They combine a screening step based on marginal effects used to "bin" SNPs for weighted hypothesis testing in the second step to deliver greater power over single-step tests while preserving the genome-wide Type I error. However, the presence of many SNPs with detectable marginal effects on the trait of interest can reduce power by "displacing" true interactions with weaker marginal effects and by adding to the number of tests that need to be corrected for multiple testing. We introduce a new significance-based allocation into bins for Step-2G × E $G\times E$ testing that overcomes the displacement issue and propose a computationally efficient approach to account for multiple testing within bins. Simulation results demonstrate that these simple improvements can provide substantially greater power than current methods under several scenarios. An application to a multistudy collaboration for understanding colorectal cancer reveals a G × Sex interaction located near the SMAD7 gene.

Quantification of Severe Acute Respiratory Syndrome Coronavirus 2 Binding Antibody Levels To Assess Infection and Vaccine-Induced Immunity Using WHO Standards

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) binding antibody (Ab) levels following vaccination or natural infection could be used as a surrogate for immune protection if results of serological assays were standardized to yield quantitative results using an international standard. Using a bead-based serological assay (Luminex xMAP), anti-receptor binding domain (anti-RBD) Ab levels were determined for 1,450 participants enrolled in the Los Angeles Pandemic Surveillance Cohort (LAPSC) study. For 123 participants, SARS-CoV-2 binding antibody unit (BAU) levels were also quantified using WHO standards and then compared to the semiquantitative results. Samples were chosen to represent the range of results and time from vaccination. Antibody levels and decay rates were then compared using unadjusted and adjusted linear regression models. The linear range of the assay used in this study was determined to be 300 to 5,000 mean fluorescence intensity units (MFI). Among the fully vaccinated groups (vaccinated only and vaccinated with past infection), 84.8% had anti-RBD MFI values above the linear range of >5,000 MFI, and 33.8% had values of >15,000 MFI. Among vaccinated participants with past infection (hybrid immunity), 97% had anti-RBD values of >5,000 MFI and 70% (120/171) had anti-RBD values of >15,000 MFI. In the subgroup quantified using the WHO control, BAU levels were significantly higher than the semiquantitative MFI results. In vaccinated participants, Ab decay levels were similar between infected and noninfected groups (P = 0.337). These results demonstrate that accurate quantitation is possible if standardized with an international standard. BAU can then be compared over time or between subjects and would be useful in clinical decision making. IMPORTANCE Accurate quantification of SARS-CoV-2-specific antibodies can be achieved using a universal standard with sample dilution within the linear range. With hybrid immunity being now common, it is critical to use protocols adapted to high Ab levels to standardize serological results. We validated this approach with the Los Angeles Pandemic Surveillance Cohort by comparing the antibody decay rates in vaccinated participants and vaccinated infected participants.

UNOS policy change benefits high-priority patients without harming those at low priority

The heart transplantation policy change (PC) has improved outcomes in high-acuity (Old 1A, New 1-3) patients, but the effect on low-priority (Old 1B/2, New 4-6) patients is unknown. We sought to determine if low-priority patient outcomes were compromised by benefits to high-priority patients by evaluating for interaction between PC and priority status (PS). We included adult first-time heart transplant candidates and recipients from the UNOS registry during a 19-month period before and after the PC. We compared clinical characteristics and performed competing risks and survival analyses stratified by PC and PS. There was a dependence of PC and PS on waitlist death/deterioration with an interaction sub-distribution hazard ratio (adjusted sdHR) of 0.59 (0.45-0.78), p-value < .001. There was a trend toward a benefit of PC on waitlist death/deterioration (adjusted sdHR: 0.86 [0.73-1.01]; p = .07) and an increase in heart transplantation (adjusted sdHR: 1.08 [1.02-1.14], p = .007) for low-priority patients. There was no difference in 1-year post-transplant survival (log-rank p = .22) when stratifying by PC and PS. PC did not negatively affect waitlisted or transplanted low-priority patients. High-priority, post-PC patients had a targeted reduction in waitlist death/deterioration and did not come at the expense of worse post-transplant survival.

COVID-19 booster vaccine attitudes and behaviors among university students and staff in the United States: The USC Trojan pandemic research Initiative

Although authorized mRNA COVID-19 vaccines (BNT162b2 by BioNTech/Pfizer and mRNA-1273 by Moderna) significantly reduce morbidity and mortality, recent evidence suggests that immunity wanes over time, and that a booster dose could further reduce COVID-19 transmission and severe illness. However, research examining attitudes on booster willingness in diverse populations is needed. This study examined COVID-19 booster vaccine attitudes and behaviors among university students and staff in the fall of 2021. In our sample, 96.2% of respondents indicated willingness to get a COVID-19 booster shot at least once per year. In both bivariate and multivariate analyses higher trust in science was associated with having higher odds of booster willingness. Those who identify as Black, on average, reported trusting science less than other racial/ethnic groups. Our findings demonstrate high willingness to receive a COVID-19 booster shot and highlight the importance of educational messages and initiatives that focus on building trust in science to increase willingness to get the COVID-19 booster. More research is needed to better understand the impact of cultural beliefs on booster willingness and vaccine hesitancy. This understanding will help determine what messages and populations to target to increase booster willingness in the future.

Effect of UNOS policy change and exception status request on outcomes in patients bridged to heart transplant with an intra-aortic balloon pump

BACKGROUND

Intra-aortic balloon pumps (IABP) are used to bridge select end-stage heart disease patients to heart transplant (HT). IABP use and exception requests both increased dramatically after the UNOS policy change (PC). The purpose of this study was to evaluate the effect of PC and exception status requests on waitlist and post-transplant outcomes in patients bridged to HT with IABP support.

METHODS

We analyzed adult, first-time, single-organ HT recipients from the UNOS Registry either on IABP at the time of registration for HT or at the time of HT. We compared waitlist and post-HT outcomes between patients from the PRE (October 18, 2016 to May 30, 2018) and POST (October 18, 2018 to May 30, 2020) eras using Kaplan-Meier curves and time-to-event analyses.

RESULTS

A total of 1267 patients underwent HT from IABP (261 pre-policy/1006 post-policy). On multivariate analysis, PC was associated with an increase in HT (sub-distribution hazard ratio (sdHR): 2.15, p < .001) and decrease in death/deterioration (sdHR: 0.55, p = .011) on the waitlist with no effect on 1-year post-HT survival (p = .8). The exception status of patients undergoing HT was predominantly seen in the POST era (29%, 293/1006); only four patients in the PRE era. Exception requests in the POST era did not alter patient outcomes.

CONCLUSIONS

In patients bridged to heart transplant with an IABP, policy change is associated with decreased rates of death/deterioration and increased rates of heart transplantation on the waitlist without affecting 1-year post-transplant survival. While exception status use has markedly increased post-PC, it is not associated with patient outcomes.

Characteristics associated with COVID-19 vaccination status among staff and faculty of a large, diverse University in Los Angeles: The Trojan Pandemic Response Initiative

Objective

This study examined characteristics associated with being unvaccinated among a sample of university staff and faculty prior to university campus reopening for in-person learning in 2021.

Methods

Staff and faculty responded to an email invitation to complete an online survey. Survey questions included demographic data (race/ethnicity, age, sex), COVID-19 knowledge and behaviors, employment specific data including division and subdivision (healthcare vs. non-healthcare related division); and self-reported vaccination status. A multivariable logistic regression analysis was performed to determine significant characteristics associated with the likelihood of being unvaccinated for COVID-19.

Results

Participants identifying as Asian and Asian American (aOR = 1.44, 95% CI: 1.06, 1.96), Hispanic/Latinx (aOR = 1.73, 95% CI: 1.21, 2.49) or Multicultural/Other (aOR = 1.72, 95% CI: 1.24, 2.38) had greater odds of being unvaccinated compared to Non-Hispanic White participants. Other characteristics associated with greater likelihood of being unvaccinated included working as a university staff member (vs. faculty) (aOR = 1.69, 95% CI: 1.24. 2.30), decrease in income (aOR = 1.34, 95% CI:1.05, 1.71), inability to work remotely (aOR = 1.48, 95% CI:1.13, 1.93) and not traveling outside of the Los Angeles area (aOR = 1.46, 95% CI: 1.16, 1.83). Political affiliation as an Independent (aOR = 1.39, 95% CI:1.04, 1.85) or as something else (aOR = 3.84, 95% CI: 2.72, 5.41) were more likely to be unvaccinated compared to participants identifying as Democrat.

Conclusions

Several factors associated with racial and social disparities may delay the uptake of COVID-19 vaccination. This study highlights the need for targeted educational interventions to promote vaccination among university staff and faculty.

Two-step hypothesis testing to detect gene-environment interactions in a genome-wide scan with a survival endpoint

Defined by their genetic profile, individuals may exhibit differential clinical outcomes due to an environmental exposure. Identifying subgroups based on specific exposure-modifying genes can lead to targeted interventions and focused studies. Genome-wide interaction scans (GWIS) can be performed to identify such genes, but these scans typically suffer from low power due to the large multiple testing burden. We provide a novel framework for powerful two-step hypothesis tests for GWIS with a time-to-event endpoint under the Cox proportional hazards model. In the Cox regression setting, we develop an approach that prioritizes genes for Step-2 G × E testing based on a carefully constructed Step-1 screening procedure. Simulation results demonstrate this two-step approach can lead to substantially higher power for identifying gene-environment ( G × E ) interactions compared to the standard GWIS while preserving the family wise error rate over a range of scenarios. In a taxane-anthracycline chemotherapy study for breast cancer patients, the two-step approach identifies several gene expression by treatment interactions that would not be detected using the standard GWIS.

Hierarchical Ridge Regression for Incorporating Prior Information in Genomic Studies

There is a great deal of prior knowledge about gene function and regulation in the form of annotations or prior results that, if directly integrated into individual prognostic or diagnostic studies, could improve predictive performance. For example, in a study to develop a predictive model for cancer survival based on gene expression, effect sizes from previous studies or the grouping of genes based on pathways constitute such prior knowledge. However, this external information is typically only used post-analysis to aid in the interpretation of any findings. We propose a new hierarchical two-level ridge regression model that can integrate external information in the form of "meta features" to predict an outcome. We show that the model can be fit efficiently using cyclic coordinate descent by recasting the problem as a single-level regression model. In a simulation-based evaluation we show that the proposed method outperforms standard ridge regression and competing methods that integrate prior information, in terms of prediction performance when the meta features are informative on the mean of the features, and that there is no loss in performance when the meta features are uninformative. We demonstrate our approach with applications to the prediction of chronological age based on methylation features and breast cancer mortality based on gene expression features.

Bariatric surgery in patients with breast and endometrial cancer in California: population-based prevalence and survival

BACKGROUND

The number of bariatric surgeries performed in the United States has increased substantially since the 1990's. However, the prevalence and prognostic impact of bariatric surgery, or weight loss surgery (WLS), among patients with cancer are not known.

OBJECTIVES

We investigated the population-based prevalence of WLS in women with breast or endometrial cancer and conducted exploratory analysis to examine whether postdiagnosis WLS is associated with survival.

SETTING

Administrative statewide database.

METHODS

WLS records for women with nonmetastasized breast (n = 395,146) or endometrial (n = 69,859) cancer were identified from the 1991-2014 California Cancer Registry data linked with the California Office of Statewide Health Planning and Development database. Characteristics of the patients were examined according to history of WLS. Using body mass index data available since 2011, a retrospective cohort of patients with breast or endometrial cancer and obesity (n = 12,540) was established and followed until 2017 (5% lost to follow-up). Multivariable cause-specific Cox proportional hazards models were used to examine the associations between postdiagnostic WLS and time to death.

RESULTS

WLS records were identified for 2844 (.7%) patients with breast cancer and 1140 (1.6%) patients with endometrial cancer; about half of the surgeries were performed after cancer diagnosis. Postdiagnosis WLS was performed in ∼1% of patients with obesity and was associated with a decreased hazard for death (cause-specific hazard ratio = .37; 95% confidence interval = .014-.99; P = .049), adjusting for age, stage, co-morbidity, race/ethnicity, and socioeconomic status.

CONCLUSION

About 2000 patients with breast or endometrial cancer in California underwent post-diagnosis WLS between 1991 and 2014. Our data support survival benefits of WLS after breast and endometrial cancer diagnosis.

A New ℓ0-Regularized Log-Linear Poisson Graphical Model with Applications to RNA Sequencing Data

In this article, we develop a new ℓ 0 -based sparse Poisson graphical model with applications to gene network inference from RNA-seq gene expression count data. Assuming a pair-wise Markov property, we propose to fit a separate broken adaptive ridge-regularized log-linear Poisson regression on each node to evaluate the conditional, instead of marginal, association between two genes in the presence of all other genes. The resulting sparse gene networks are generally more accurate than those generated by the ℓ 1 -regularized Poisson graphical model as demonstrated by our empirical studies. A real data illustration is given on a kidney renal clear cell carcinoma micro-RNA-seq data from the Cancer Genome Atlas.

Sign-based Shrinkage Based on an Asymmetric LASSO Penalty

Penalized regression provides an automated approach to preform simultaneous variable selection and parameter estimation and is a popular method to analyze high-dimensional data. Since the conception of the LASSO in the mid-to-late 1990s, extensive research has been done to improve penalized regression. The LASSO, and several of its variations, performs penalization symmetrically around zero. Thus, variables with the same magnitude are shrunk the same regardless of the direction of effect. To the best of our knowledge, sign-based shrinkage, preferential shrinkage based on the sign of the coefficients, has yet to be explored under the LASSO framework. We propose a generalization to the LASSO, asymmetric LASSO, that performs sign-based shrinkage. Our method is motivated by placing an asymmetric Laplace prior on the regression coefficients, rather than a symmetric Laplace prior. This corresponds to an asymmetric ℓ ₁ penalty under the penalized regression framework. In doing so, preferential shrinkage can be performed through an auxiliary tuning parameter that controls the degree of asymmetry. Our numerical studies indicate that the asymmetric LASSO performs better than the LASSO when effect sizes are sign skewed. Furthermore, in the presence of positively-skewed effects, the asymmetric LASSO is comparable to the non-negative LASSO without the need to place an a priori constraint on the effect estimates and outperforms the non-negative LASSO when negative effects are also present in the model. A real data example using the breast cancer gene expression data from The Cancer Genome Atlas is also provided, where the asymmetric LASSO identifies two potentially novel gene expressions that are associated with BRCA1 with a minor improvement in prediction performance over the LASSO and non-negative LASSO.

A Cross-Sectional Study Examining the Seroprevalence of Severe Acute Respiratory Syndrome Coronavirus 2 Antibodies in a University Student Population

PURPOSE

The aim of the study was to determine the prevalence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antibodies in a university student population.

METHODS

This was a cross-sectional survey study based on the World Health Organization population-based seroepidemiological investigational protocol for SARS-CoV-2 conducted between April 29, 2020, and May 8, 2020, examining SARS-CoV-2 antibody prevalence among 790 university students in Los Angeles, CA. Participants completed a questionnaire on potential risk factors before blood sampling. Samples were analyzed using the EUROIMMUN Anti-SARS-CoV-2 ELISA (IgG) for the qualitative detection of IgG class antibodies to SARS-CoV-2 in human serum or plasma.

RESULTS

The estimated prevalence of SARS-CoV-2 antibody was 4.0% (3.0%, 5.1%). Factors associated with having a positive test included history of anosmia and/or loss of taste (95% CI: 1.4-9.6). A history of respiratory symptoms, with or without fever, was not associated with a positive antibody test.

CONCLUSIONS

Prevalence of SARS-CoV-2 antibodies in the undergraduate and graduate student university population was similar to community prevalence.

A surrogate ℓ0 sparse Cox's regression with applications to sparse high-dimensional massive sample size time-to-event data

Sparse high-dimensional massive sample size (sHDMSS) time-to-event data present multiple challenges to quantitative researchers as most current sparse survival regression methods and software will grind to a halt and become practically inoperable. This paper develops a scalable ℓ₀ -based sparse Cox regression tool for right-censored time-to-event data that easily takes advantage of existing high performance implementation of ℓ₂ -penalized regression method for sHDMSS time-to-event data. Specifically, we extend the ℓ₀ -based broken adaptive ridge (BAR) methodology to the Cox model, which involves repeatedly performing reweighted ℓ₂ -penalized regression. We rigorously show that the resulting estimator for the Cox model is selection consistent, oracle for parameter estimation, and has a grouping property for highly correlated covariates. Furthermore, we implement our BAR method in an R package for sHDMSS time-to-event data by leveraging existing efficient algorithms for massive ℓ₂ -penalized Cox regression. We evaluate the BAR Cox regression method by extensive simulations and illustrate its application on an sHDMSS time-to-event data from the National Trauma Data Bank with hundreds of thousands of observations and tens of thousands sparsely represented covariates.

Neoadjuvant anti-PD-1 immunotherapy promotes a survival benefit with intratumoral and systemic immune responses in recurrent glioblastoma

Glioblastoma is the most common primary malignant brain tumor in adults and is associated with poor survival. The Ivy Foundation Early Phase Clinical Trials Consortium conducted a randomized, multi-institution clinical trial to evaluate immune responses and survival following neoadjuvant and/or adjuvant therapy with pembrolizumab in 35 patients with recurrent, surgically resectable glioblastoma. Patients who were randomized to receive neoadjuvant pembrolizumab, with continued adjuvant therapy following surgery, had significantly extended overall survival compared to patients that were randomized to receive adjuvant, post-surgical programmed cell death protein 1 (PD-1) blockade alone. Neoadjuvant PD-1 blockade was associated with upregulation of T cell- and interferon-γ-related gene expression, but downregulation of cell-cycle-related gene expression within the tumor, which was not seen in patients that received adjuvant therapy alone. Focal induction of programmed death-ligand 1 in the tumor microenvironment, enhanced clonal expansion of T cells, decreased PD-1 expression on peripheral blood T cells and a decreasing monocytic population was observed more frequently in the neoadjuvant group than in patients treated only in the adjuvant setting. These findings suggest that the neoadjuvant administration of PD-1 blockade enhances both the local and systemic antitumor immune response and may represent a more efficacious approach to the treatment of this uniformly lethal brain tumor.