BIRC5 expression by race, age and clinical factors in breast cancer patients.

PURPOSE: Survivin/BIRC5 is a proliferation marker that is associated with poor prognosis in breast cancer and an attractive therapeutic target. However, BIRC5 has not been well studied among racially diverse populations where aggressive breast cancers are prevalent. EXPERIMENTAL DESIGN: We studied BIRC5 expression in association with clinical and demographic variables and as a predictor of recurrence in 2174 participants in the Carolina Breast Cancer Study (CBCS), a population-based study that oversampled Black (n = 1113) and younger (< 50 years; n = 1137) participants with breast cancer. For comparison, similar analyses were conducted in The Cancer Genome Atlas [TCGA N = 1094, Black (n = 183), younger (n = 295)]. BIRC5 was evaluated as a continuous and categorical variable (highest quartile vs. lower three quartiles). RESULTS: Univariate, continuous BIRC5 expression was higher in breast tumors from Black women relative to non-Black women in both estrogen receptor (ER)-positive and ER-negative tumors and in analyses stratified by stage (i.e., within Stage I, Stage II, and Stage III/IV tumors). Within CBCS and TCGA, BIRC5-high was associated with young age (< 50 years) and Black race, as well as hormone receptor-negative tumors, non-Luminal A PAM50 subtypes, advanced stage, and larger tumors (> 2 cm). Relative to BIRC5-low, BIRC5-high tumors were associated with poor 5-year recurrence-free survival (RFS) among ER-positive tumors, both in unadjusted models [HR (95% CI): 2.7 (1.6, 4.6)] and after adjustment for age and stage [Adjusted HR (95% CI): 1.87 (1.07, 3.25)]. However, this relationship was not observed among ER-negative tumors [Crude HR (95% CI): 0.7 (0.39, 1.2); Adjusted HR (95% CI): 0.67 (0.37, 1.2)]. CONCLUSION: Black and younger women with breast cancer have a higher burden of BIRC5-high tumors than older and non-Black women. Emerging anti-survivin treatment strategies may be an important future direction for equitable breast cancer outcomes.

Diffsig: Associating Risk Factors With Mutational Signatures.

BACKGROUND: Somatic mutational signatures elucidate molecular vulnerabilities to therapy and therefore detecting signatures and classifying tumors with respect to signatures has clinical value. However, identifying the etiology of the mutational signatures remains a statistical challenge, with both small sample sizes and high variability in classification algorithms posing barriers. As a result, few signatures have been strongly linked to particular risk factors. METHODS: Here we develop a statistical model, Diffsig, for estimating the association of one or more continuous or categorical risk factors with DNA mutational signatures. Diffsig takes into account the uncertainty associated with assigning signatures to samples as well as multiple risk factors' simultaneous effect on observed DNA mutations. RESULTS: We applied Diffsig to breast cancer data to assess relationships between five established breast-relevant mutational signatures and etiologic variables, confirming known mechanisms of cancer development. In simulation, our model was capable of accurately estimating expected associations in a variety of contexts. CONCLUSIONS: Diffsig allows researchers to quantify and perform inference on the associations of risk factors with mutational signatures. IMPACT: We expect Diffsig to provide more robust associations of risk factors with signatures to lead to better understanding of the tumor development process and improved models of tumorigenesis.

Disparities in OncotypeDx Testing and Subsequent Chemotherapy Receipt by Geography and Socioeconomic Status.

BACKGROUND: OncotypeDx is a prognostic and predictive genomic assay used in early-stage hormone receptor positive, human epidermal growth factor receptor 2-negative (HR+/HER2-) breast cancer. It is used to inform adjuvant chemotherapy decisions, but not all eligible women receive testing. We aimed to assess variation in testing by demographics and geography, and to determine whether testing was associated with chemotherapy. METHODS: For 1,615 women in the Carolina Breast Cancer Study with HR+/HER2-, Stage I-II tumors, we estimated prevalence differences (PDs) and 95% CIs for receipt of OncotypeDx genomic testing in association with and sociodemographic characteristics. We assessed associations between testing and chemotherapy receipt overall and by race. Finally, we calculated the proportion of eligible women receiving OncotypeDx by county-level rurality, census tract-level socioeconomic status, and Area Health Education Center (AHEC) regions. RESULTS: 38% (N=609) of potentially eligible women were tested, with lower testing prevalences in Black (31%; PD = -11%, 95% CI: -16%, 6%) and low-income women (24%; PD = -20%, 95% CI: -29%, -11%) relative to non-Black and higher income women. Urban participants were less likely to be tested than rural participants, though this association varied by region. Among women with low genomic risk tumors, tested participants were 29% less likely to receive chemotherapy than untested participants (95% CI: -40%, -17%). Racial differences in chemotherapy were restricted to untested women. CONCLUSIONS: Both individual and area-level socioeconomics predict likelihood of OncotypeDx testing. IMPACT: Variable adoption of OncotypeDx by socioeconomics and across geographic settings may contribute to excess chemotherapy among HR+/HER2- patients.

Patterns of chemotherapy receipt among patients with hormone receptor-positive, HER2-negative breast cancer.

BACKGROUND: Breast cancer chemotherapy utilization not only may differ by race and age, but also varies by genomic risk, tumor characteristics, and patient characteristics. Studies in demographically diverse populations with both clinical and genomic data are necessary to understand potential disparities by race and age. METHODS: In the Carolina Breast Cancer Study Phase 3 (2008-2013), chemotherapy receipt (yes/no) and regimen type were assessed in association with age and race among hormone receptor (HR) positive and HER2-negative tumors (n = 1862). Odds ratios were estimated for the association between demographic factors and chemotherapy receipt. RESULTS: Monotonic decreases in frequency of adjuvant chemotherapy receipt were observed over time during the study period, while neoadjuvant chemotherapy was stable. Younger age was associated with chemotherapy receipt (OR [95% CI]: 2.9 [2.4, 3.6]) and with anthracycline-based regimens (OR [95% CI]: 1.7 [1.3, 2.4]). Participants who had Medicaid (OR [95% CI]: 1.8 [1.3, 2.5]), lived in rural settings (OR [95% CI]: 1.4 [1.0, 2.0]), or were Black (OR [95% CI]: 1.5 [1.2, 1.8]) had slightly higher odds of chemotherapy, but these associations were non-significant with adjustment for stage and grade. Associations between younger age and chemotherapy receipt were strongest among women who did not receive genomic testing. CONCLUSIONS: While race was not strongly associated with chemotherapy receipt, younger age remains a strong predictor of chemotherapy receipt, even with adjustment for clinical factors and among women who receive genomic testing.

Incorporating RNA-based Risk Scores for Genomic Instability to Predict Breast Cancer Recurrence and Immunogenicity in a Diverse Population.

Markers of genomic instability, including TP53 status and homologous recombination deficiency (HRD), are candidate biomarkers of immunogenicity and immune-mediated survival, but little is known about the distribution of these markers in large, population-based cohorts of racially diverse patients with breast cancer. In prior clinical trials, DNA-based approaches have been emphasized, but recent data suggest that RNA-based assessment can capture pathway differences conveniently and may be streamlined with other RNA-based genomic risk scores. Thus, we used RNA expression to study genomic instability (HRD and TP53 pathways) in context of the breast cancer immune microenvironment in three datasets (total n = 4,892), including 1,942 samples from the Carolina Breast Cancer Study, a population-based study that oversampled Black (n = 1,026) and younger women (n = 1,032). Across all studies, 36.9% of estrogen receptor (ER)-positive and 92.6% of ER-negative breast cancer had presence of at least one genomic instability signature. TP53 and HRD status were significantly associated with immune expression in both ER-positive and ER-negative breast cancer. RNA-based genomic instability signatures were associated with higher PD-L1, CD8 T-cell marker, and global and multimarker immune cell expression. Among tumors with genomic instability signatures, adaptive immune response was associated with improved recurrence-free survival regardless of ER status, highlighting genomic instability as a candidate marker for predicting immunotherapy response. Leveraging a convenient, integrated RNA-based approach, this analysis shows that genomic instability interacts with immune response, an important target in breast cancer overall and in Black women who experience higher frequency of TP53 and HR deficiency. Significance: Despite promising advances in breast cancer immunotherapy, predictive biomarkers that are valid across diverse populations and breast cancer subtypes are needed. Genomic instability signatures can be coordinated with other RNA-based scores to define immunogenic breast cancers and may have value in stratifying immunotherapy trial participants.

Diffsig: Associating Risk Factors With Mutational Signatures.

Somatic mutational signatures elucidate molecular vulnerabilities to therapy and therefore detecting signatures and classifying tumors with respect to signatures has clinical value. However, identifying the etiology of the mutational signatures remains a statistical challenge, with both small sample sizes and high variability in classification algorithms posing barriers. As a result, few signatures have been strongly linked to particular risk factors. Here we present Diffsig, a model and R package for estimating the association of risk factors with mutational signatures, suggesting etiologies for the pre-defined mutational signatures. Diffsig is a Bayesian Dirichlet-multinomial hierarchical model that allows testing of any type of risk factor while taking into account the uncertainty associated with samples with a low number of observations. In simulation, we found that our method can accurately estimate risk factor-mutational signal associations. We applied Diffsig to breast cancer data to assess relationships between five established breast-relevant mutational signatures and etiologic variables, confirming known mechanisms of cancer development. Diffsig is implemented as an R package available at: https://github.com/jennprk/diffsig.

RNA-Based Classification of Homologous Recombination Deficiency in Racially Diverse Patients with Breast Cancer.

BACKGROUND: Aberrant expression of DNA repair pathways such as homologous recombination (HR) can lead to DNA repair imbalance, genomic instability, and altered chemotherapy response. DNA repair imbalance may predict prognosis, but variation in DNA repair in diverse cohorts of breast cancer patients is understudied. METHODS: To identify RNA-based patterns of DNA repair expression, we performed unsupervised clustering on 51 DNA repair-related genes in the Cancer Genome Atlas Breast Cancer [TCGA BRCA (n = 1,094)] and Carolina Breast Cancer Study [CBCS (n = 1,461)]. Using published DNA-based HR deficiency (HRD) scores (high-HRD ≥ 42) from TCGA, we trained an RNA-based supervised classifier. Unsupervised and supervised HRD classifiers were evaluated in association with demographics, tumor characteristics, and clinical outcomes. RESULTS: : Unsupervised clustering on DNA repair genes identified four clusters of breast tumors, with one group having high expression of HR genes. Approximately 39.7% of CBCS and 29.3% of TCGA breast tumors had this unsupervised high-HRD (U-HRD) profile. A supervised HRD classifier (S-HRD) trained on TCGA had 84% sensitivity and 73% specificity to detect HRD-high samples. Both U-HRD and S-HRD tumors in CBCS had higher frequency of TP53 mutant-like status (45% and 41% enrichment) and basal-like subtype (63% and 58% enrichment). S-HRD high was more common among black patients. Among chemotherapy-treated participants, recurrence was associated with S-HRD high (HR: 2.38, 95% confidence interval = 1.50-3.78). CONCLUSIONS: HRD is associated with poor prognosis and enriched in the tumors of black women. IMPACT: RNA-level indicators of HRD are predictive of breast cancer outcomes in diverse populations.

DNA Damage Repair Classifier Defines Distinct Groups in Hepatocellular Carcinoma.

DNA repair pathways have been associated with variability in hepatocellular carcinoma (HCC) clinical outcomes, but the mechanism through which DNA repair varies as a function of liver regeneration and other HCC characteristics is poorly understood. We curated a panel of 199 genes representing 15 DNA repair pathways to identify DNA repair expression classes and evaluate their associations with liver features and clinicopathologic variables in The Cancer Genome Atlas (TCGA) HCC study. We identified two groups in HCC, defined by low or high expression across all DNA repair pathways. The low-repair group had lower grade and retained the expression of classical liver markers, whereas the high-repair group had more clinically aggressive features, increased p53 mutant-like gene expression, and high liver regenerative gene expression. These pronounced features overshadowed the variation in the low-repair subset, but when considered separately, the low-repair samples included three subgroups: L1, L2, and L3. L3 had high DNA repair expression with worse progression-free (HR 1.24, 95% CI 0.81-1.91) and overall (HR 1.63, 95% CI 0.98-2.71) survival. High-repair outcomes were also significantly worse compared with the L1 and L2 groups. HCCs vary in DNA repair expression, and a subset of tumors with high regeneration profoundly disrupts liver biology and poor prognosis.

Spatial Characterization of Tumor-Infiltrating Lymphocytes and Breast Cancer Progression.

Tumor-infiltrating lymphocytes (TILs) have been established as a robust prognostic biomarker in breast cancer, with emerging utility in predicting treatment response in the adjuvant and neoadjuvant settings. In this study, the role of TILs in predicting overall survival and progression-free interval was evaluated in two independent cohorts of breast cancer from the Cancer Genome Atlas (TCGA BRCA) and the Carolina Breast Cancer Study (UNC CBCS). We utilized machine learning and computer vision algorithms to characterize TIL infiltrates in digital whole-slide images (WSIs) of breast cancer stained with hematoxylin and eosin (H&E). Multiple parameters were used to characterize the global abundance and spatial features of TIL infiltrates. Univariate and multivariate analyses show that large aggregates of peritumoral and intratumoral TILs (forests) were associated with longer survival, whereas the absence of intratumoral TILs (deserts) is associated with increased risk of recurrence. Patients with two or more high-risk spatial features were associated with significantly shorter progression-free interval (PFI). This study demonstrates the practical utility of Pathomics in evaluating the clinical significance of the abundance and spatial patterns of distribution of TIL infiltrates as important biomarkers in breast cancer.

APOBEC Mutagenesis Inhibits Breast Cancer Growth through Induction of T cell-Mediated Antitumor Immune Responses.

The APOBEC family of cytidine deaminases is one of the most common endogenous sources of mutations in human cancer. Genomic studies of tumors have found that APOBEC mutational signatures are enriched in the HER2 subtype of breast cancer and are associated with immunotherapy response in diverse cancer types. However, the direct consequences of APOBEC mutagenesis on the tumor immune microenvironment have not been thoroughly investigated. To address this, we developed syngeneic murine mammary tumor models with inducible expression of APOBEC3B. We found that APOBEC activity induced antitumor adaptive immune responses and CD4+ T cell-mediated, antigen-specific tumor growth inhibition. Although polyclonal APOBEC tumors had a moderate growth defect, clonal APOBEC tumors were almost completely rejected, suggesting that APOBEC-mediated genetic heterogeneity limits antitumor adaptive immune responses. Consistent with the observed immune infiltration in APOBEC tumors, APOBEC activity sensitized HER2-driven breast tumors to anti-CTLA-4 checkpoint inhibition and led to a complete response to combination anti-CTLA-4 and anti-HER2 therapy. In human breast cancers, the relationship between APOBEC mutagenesis and immunogenicity varied by breast cancer subtype and the frequency of subclonal mutations. This work provides a mechanistic basis for the sensitivity of APOBEC tumors to checkpoint inhibitors and suggests a rationale for using APOBEC mutational signatures and clonality as biomarkers predicting immunotherapy response in HER2-positive (HER2+) breast cancers.

Association between ABO and Duffy blood types and circulating chemokines and cytokines.

Blood group antigens are inherited traits that may play a role in immune and inflammatory processes. We investigated associations between blood groups and circulating inflammation-related molecules in 3537 non-Hispanic white participants selected from the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial. Whole-genome scans were used to infer blood types for 12 common antigen systems based on well-characterized single-nucleotide polymorphisms. Serum levels of 96 biomarkers were measured on multiplex fluorescent bead-based panels. We estimated marker associations with blood type using weighted linear or logistic regression models adjusted for age, sex, smoking status, and principal components of population substructure. Bonferroni correction was used to control for multiple comparisons, with two-sided p values < 0.05 considered statistically significant. Among the 1152 associations tested, 10 were statistically significant. Duffy blood type was associated with levels of CXCL6/GCP2, CXCL5/ENA78, CCL11/EOTAXIN, CXCL1/GRO, CCL2/MCP1, CCL13/MCP4, and CCL17/TARC, whereas ABO blood type was associated with levels of sVEGFR2, sVEGFR3, and sGP130. Post hoc pairwise t-tests showed that individuals with type Fy(a+b-) had the lowest mean levels of all Duffy-associated markers, while individuals with type A blood had the lowest mean levels of all ABO-associated markers. Additional work is warranted to explore potential clinical implications of these differences.

Cost-Related Nonadherence and Mortality in Patients With Chronic Disease: A Multiyear Investigation, National Health Interview Survey, 2000-2014.

INTRODUCTION: Prescription costs are rising, and many patients with chronic illnesses have difficulty paying for prescriptions. Missing or delaying medication because of financial concerns is common; however, the effects of cost-related nonadherence (CRN) on patient outcomes have not been described. Our objective was to determine if CRN is associated with higher all-cause and disease-specific mortality among patients living with diabetes and cardiovascular disease in a representative sample of US adults. METHODS: We ascertained CRN, vital status, and cause of death for 39,571 patients with diabetes, 61,968 patients with cardiovascular disease, and 124,899 patients with hypertension in the 2000 through 2014 releases of the National Health Interview Survey. We used adjusted Cox proportional hazards models to estimate associations between CRN and all-cause mortality and CRN and disease-specific mortality. RESULTS: On average, 15% of the sample reported CRN in the year before interview. After adjusting for confounders, CRN was associated with 15% to 22% higher all-cause mortality rates for all conditions (diabetes hazard ratio [HR] = 1.18; 95% CI, 1.1-1.3; cardiovascular disease [CVD] HR = 1.15; 95% CI, 1.1-1.2; hypertension HR = 1.22; 95% CI, 1.2-1.3). Relative to no CRN, CRN was associated with 8% to 18% higher disease-specific mortality rates (diabetes HR = 1.18; 95% CI, 1.0-1.4; CVD HR = 1.09; 95% CI, 1.0-1.2; hypertension HR = 1.08; 95% CI, 0.9-1.3). CONCLUSION: Relative to full adherence, CRN is associated with higher mortality rates for patients with diabetes, cardiovascular disease, and hypertension, although associations may have weakened since 2011. Policies that increase prescription affordability may decrease mortality for patients experiencing CRN.

Lifetime patterns of comorbidity in eating disorders: An approach using sequence analysis.

OBJECTIVE: Eating disorders (EDs) have high rates of psychiatric comorbidity. This study aimed to characterize longitudinal patterns of comorbidities in adults with EDs. METHODS: Sequence analysis and hierarchical clustering were applied to ages of onset and recency for select eating, substance, mood, and anxiety disorders from the 479 participants in the Collaborative Psychiatric Epidemiology Surveys with lifetime DSM-IV bulimia nervosa, binge eating disorder, or anorexia nervosa. External validators were compared across clusters using chi-square tests. RESULTS: Five clusters were identified among individuals with any lifetime ED based on longitudinal sequence of psychiatric disorder onset and remission, characterized as: (1) multi-comorbid with early onset of comorbid disorder (46%); (2) moderate preeminent anxiety with moderate comorbidity and low ED persistence (20%); (3) late ED onset with low comorbidity (15%); (4) early onset, persistent ED with low comorbidity (14%); and (5) chronic, early onset depression (5%). Clusters were well differentiated by significant differences in age, body mass index, race, and psychiatric indicators. CONCLUSIONS: This study demonstrates a new method to assess clustering of comorbidity among individuals with lifetime EDs. Having a psychiatric diagnosis prior to an ED was associated with greater psychopathology and illness duration. Information on timing of diagnoses may allow for more refined comorbidity classification.

Metabolic Syndrome, Physical Activity, and Inflammation: A Cross-Sectional Analysis of 110 Circulating Biomarkers in Japanese Adults.

BACKGROUND: Metabolic syndrome (MetS) is a systemic inflammatory state. Low physical activity (PA) could modify this patho-physiology or act as an independent contributor to inflammation. Previous studies of both conditions have identified altered levels of inflammation- and immune-related proteins based on limited sets of candidate markers. METHODS: We investigated associations of MetS and low PA with circulating inflammation markers in a stratified random sample of Japanese adults (N = 774, mean age 60.7 years) within the Japan Public Health Center-based Prospective Study (JPHC) Cohort II. AHA/NHLBI criteria were used to define MetS (19%) and the bottom quartile of PA was considered low. 110 circulating biomarkers, including cytokines, chemokines, and soluble receptors were measured by multiplex bead-based and proximity-extension assays. Associations of MetS and low PA with marker quantiles were adjusted for each other and for age, sex, study site, cigarette smoking, alcohol consumption, and blood sample fasting state by ordinal logistic regression. P values were corrected for FDR. RESULTS: MetS was significantly associated with levels of six markers: IL18R1 [odds ratio 2.37; 95% confidence interval (CI), 1.45-3.87], CRP (2.07; 95% CI, 1.48-2.90), SAP (2.08; 95% CI, 1.47-2.95), CCL19/MIP3ß (2.06; 95% CI, 1.48-2.88), CXCL12/SDF1α+ß (0.48; 95% CI, 0.32-0.65), and CCL28 (0.44; 95% CI, 0.27-0.71). Low PA had no significant marker associations. CONCLUSIONS: Positively associated markers with MetS are mostly Th1 immune response-related and acute phase proteins, whereas negatively associated markers are generally Th2-related. IMPACT: MetS is associated with a broad range of alterations in immune and inflammatory biomarkers that may contribute to risks of various chronic diseases, independent of low PA.