Race-associated biological differences among luminal A and basal-like breast cancers in the Carolina Breast Cancer Study

Loss of function of FAM177A1, a Golgi complex localized protein, causes a novel neurodevelopmental disorder

PURPOSE

The function of FAM177A1 and its relationship to human disease is largely unknown. Recent studies have demonstrated FAM177A1 to be a critical immune-associated gene. One previous case study has linked FAM177A1 to a neurodevelopmental disorder in 4 siblings.

METHODS

We identified 5 individuals from 3 unrelated families with biallelic variants in FAM177A1. The physiological function of FAM177A1 was studied in a zebrafish model organism and human cell lines with loss-of-function variants similar to the affected cohort.

RESULTS

These individuals share a characteristic phenotype defined by macrocephaly, global developmental delay, intellectual disability, seizures, behavioral abnormalities, hypotonia, and gait disturbance. We show that FAM177A1 localizes to the Golgi complex in mammalian and zebrafish cells. Intersection of the RNA sequencing and metabolomic data sets from FAM177A1-deficient human fibroblasts and whole zebrafish larvae demonstrated dysregulation of pathways associated with apoptosis, inflammation, and negative regulation of cell proliferation.

CONCLUSION

Our data shed light on the emerging function of FAM177A1 and defines FAM177A1-related neurodevelopmental disorder as a new clinical entity.

Squalene Epoxidase: Its Regulations and Links with Cancers

Squalene epoxidase (SQLE) is a key enzyme in the mevalonate-cholesterol pathway that plays a critical role in cellular physiological processes. It converts squalene to 2,3-epoxysqualene and catalyzes the first oxygenation step in the pathway. Recently, intensive efforts have been made to extend the current knowledge of SQLE in cancers through functional and mechanistic studies. However, the underlying mechanisms and the role of SQLE in cancers have not been fully elucidated yet. In this review, we retrospected current knowledge of SQLE as a rate-limiting enzyme in the mevalonate-cholesterol pathway, while shedding light on its potential as a diagnostic and prognostic marker, and revealed its therapeutic values in cancers. We showed that SQLE is regulated at different levels and is involved in the crosstalk with iron-dependent cell death. Particularly, we systemically reviewed the research findings on the role of SQLE in different cancers. Finally, we discussed the therapeutic implications of SQLE inhibitors and summarized their potential clinical values. Overall, this review discussed the multifaceted mechanisms that involve SQLE to present a vivid panorama of SQLE in cancers.

Efficacy of Single-Agent Chemotherapy in Endocrine Therapy-Refractory Metastatic Invasive Lobular Carcinoma

BACKGROUND

Hormone receptor (HR)-positive, HER2-negative metastatic invasive lobular breast cancer (mILC) is distinct from invasive ductal cancer (IDC) in clinicopathologic and molecular characteristics, impacting its response to systemic therapy. While endocrine therapy (ET) combined with targeted therapies has shown efficacy in ET-sensitive mILC, data on chemotherapy in ET-refractory mILC remain limited. We investigated the efficacy of single-agent capecitabine (CAP) versus taxanes (TAX) in ET-refractory HR+ HER2-negative patients with mILC.

MATERIALS AND METHODS

Using data from the MD Anderson prospectively collected breast cancer database, we identified patients with HR+ HER2-negative mILC who received prior ET and first-time chemotherapy in the metastatic setting. We compared outcomes between 173 CAP-treated and 96 TAX-treated patients.

RESULTS

CAP-treated patients had significantly better median progression-free survival (PFS) than TAX-treated patients (8.8 vs 5.0 months, HR 0.63, P < .001). Overall survival (OS) did not differ significantly between the groups (42.7 vs 36.6 months for CAP vs TAX, respectively, HR 0.84, P = .241). Multivariate analyses for PFS and OS revealed better outcomes in subjects with fewer metastatic sites and those exposed to more lines of ET. Additionally, Black patients showed worse OS outcomes compared to White patients (HR 2.46; P = .001).

CONCLUSION

In ET-refractory HR+ HER2-negative mILC, single-agent CAP demonstrated superior PFS compared to TAX. Our findings highlight the potential benefit of CAP in this patient subset, warranting further investigation through prospective trials.

A visualized model for identifying optimal candidates for aggressive locoregional surgical treatment in patients with bone metastases from breast cancer

Background

The impact of surgical resection of primary (PTR) on the survival of breast cancer (BC) patients with bone metastasis (BM) has been preliminarily investigated, but it remains unclear which patients are suitable for this procedure. Finally, this study aims to develop a predictive model to screen BC patients with BM who would benefit from local surgery.

Methods

BC patients with BM were identified using the Surveillance, Epidemiology, and End Results (SEER) database (2010 and 2015), and 39 patients were obtained for external validation from an Asian medical center. According to the status of local surgery, patients were divided into Surgery and Non-surgery groups. Propensity score matching (PSM) analysis was performed to reduce selection bias. Kaplan-Meier (K-M) survival and Cox regression analyses were conducted before and after PSM to study the survival difference between the two groups. The survival outcome and treatment modality were also investigated in patients with different metastatic patterns. The logistic regression analyses were utilized to determine significant surgery-benefit-related predictors, develop a screening nomogram and its online version, and quantify the beneficial probability of local surgery for BC patients with BM. Receiver operating characteristic (ROC) curves, the area under the curves (AUC), and calibration curves were plotted to evaluate the predictive performance and calibration of this model, whereas decision curve analysis (DCA) was used to assess its clinical usefulness.

Results

This study included 5,625 eligible patients, of whom 2,133 (37.92%) received surgical resection of primary lesions. K-M survival analysis and Cox regression analysis demonstrated that local surgery was independently associated with better survival. Surgery provided significant survival benefits in most subgroups and metastatic patterns. After PSM, patients who received surgery had a longer survival time (OS: 46 months vs. 32 months, p < 0.001; CSS: 50 months vs. 34 months, p < 0.001). Logistic regression analysis determined six significant surgery-benefit-related variables: T stage, radiotherapy, race, liver metastasis, brain metastasis, and breast subtype. These factors were combined to establish the nomogram and a web probability calculator (https://sunshine1.shinyapps.io/DynNomapp/), with an AUC of 0.673 in the training cohort and an AUC of 0.640 in the validation cohort. The calibration curves exhibited excellent agreement. DCA indicated that the nomogram was clinically useful. Based on this model, surgery patients were assigned into two subsets: estimated sur-non-benefit and estimated sur-benefit. Patients in the estimated sur-benefit subset were associated with longer survival (median OS: 64 months vs. 33 months, P < 0.001). Besides, there was no difference in survival between the estimated sur-non-benefit subset and the non-surgery group.

Conclusion

Our study further confirmed the significance of local surgery in BC patients with BM and proposed a novel tool to identify optimal surgical candidates.

Tensor Decomposition-based Feature Extraction and Classification to Detect Natural Selection from Genomic Data

Inferences of adaptive events are important for learning about traits, such as human digestion of lactose after infancy and the rapid spread of viral variants. Early efforts toward identifying footprints of natural selection from genomic data involved development of summary statistic and likelihood methods. However, such techniques are grounded in simple patterns or theoretical models that limit the complexity of settings they can explore. Due to the renaissance in artificial intelligence, machine learning methods have taken center stage in recent efforts to detect natural selection, with strategies such as convolutional neural networks applied to images of haplotypes. Yet, limitations of such techniques include estimation of large numbers of model parameters under nonconvex settings and feature identification without regard to location within an image. An alternative approach is to use tensor decomposition to extract features from multidimensional data although preserving the latent structure of the data, and to feed these features to machine learning models. Here, we adopt this framework and present a novel approach termed T-REx, which extracts features from images of haplotypes across sampled individuals using tensor decomposition, and then makes predictions from these features using classical machine learning methods. As a proof of concept, we explore the performance of T-REx on simulated neutral and selective sweep scenarios and find that it has high power and accuracy to discriminate sweeps from neutrality, robustness to common technical hurdles, and easy visualization of feature importance. Therefore, T-REx is a powerful addition to the toolkit for detecting adaptive processes from genomic data.

Differences in Tumour Aggressiveness Based on Molecular Subtype and Race Measured by [18F]FDG PET Metabolic Metrics in Patients with Invasive Carcinoma of the Breast

Breast cancer in women of African descent tends to be more aggressive with poorer prognosis. This is irrespective of the molecular subtype. [18F]FDG PET/CT metrics correlate with breast cancer aggressiveness based on molecular subtype. This study investigated the differences in [18F]FDG PET/CT metrics of locally advanced invasive ductal carcinoma (IDC) among different racial groups and molecular subtypes. Qualitative and semiquantitative readings of [18F]FDG PET/CT acquired in women with locally advanced IDC were performed. Biodata including self-identified racial grouping and histopathological data of the primary breast cancer were retrieved. Statistical analysis for differences in SUVmax, MTV and TLG of the primary tumour and the presence of regional and distant metastases was conducted based on molecular subtype and race. The primary tumour SUVmax, MTV, TLG and the prevalence of distant metastases were significantly higher in Black patients compared with other races (p < 0.05). The primary tumour SUVmax and presence of distant metastases in the luminal subtype and the primary tumour SUVmax and TLG in the basal subtype were significantly higher in Black patients compared with other races (p < 0.05). The significantly higher PET parameters in Black patients with IDC in general and in those with luminal and basal carcinoma subtypes suggest a more aggressive disease phenotype in this race.

Tensor decomposition based feature extraction and classification to detect natural selection from genomic data

Inferences of adaptive events are important for learning about traits, such as human digestion of lactose after infancy and the rapid spread of viral variants. Early efforts toward identifying footprints of natural selection from genomic data involved development of summary statistic and likelihood methods. However, such techniques are grounded in simple patterns or theoretical models that limit the complexity of settings they can explore. Due to the renaissance in artificial intelligence, machine learning methods have taken center stage in recent efforts to detect natural selection, with strategies such as convolutional neural networks applied to images of haplotypes. Yet, limitations of such techniques include estimation of large numbers of model parameters under non-convex settings and feature identification without regard to location within an image. An alternative approach is to use tensor decomposition to extract features from multidimensional data while preserving the latent structure of the data, and to feed these features to machine learning models. Here, we adopt this framework and present a novel approach termed T-REx , which extracts features from images of haplotypes across sampled individuals using tensor decomposition, and then makes predictions from these features using classical machine learning methods. As a proof of concept, we explore the performance of T-REx on simulated neutral and selective sweep scenarios and find that it has high power and accuracy to discriminate sweeps from neutrality, robustness to common technical hurdles, and easy visualization of feature importance. Therefore, T-REx is a powerful addition to the toolkit for detecting adaptive processes from genomic data.

Fibroblasts as Turned Agents in Cancer Progression

Differentiated epithelial cells reside in the homeostatic microenvironment of the native organ stroma. The stroma supports their normal function, their G0 differentiated state, and their expansion/contraction through the various stages of the life cycle and physiologic functions of the host. When malignant transformation begins, the microenvironment tries to suppress and eliminate the transformed cells, while cancer cells, in turn, try to resist these suppressive efforts. The tumor microenvironment encompasses a large variety of cell types recruited by the tumor to perform different functions, among which fibroblasts are the most abundant. The dynamics of the mutual relationship change as the sides undertake an epic battle for control of the other. In the process, the cancer "wounds" the microenvironment through a variety of mechanisms and attracts distant mesenchymal stem cells to change their function from one attempting to suppress the cancer, to one that supports its growth, survival, and metastasis. Analogous reciprocal interactions occur as well between disseminated cancer cells and the metastatic microenvironment, where the microenvironment attempts to eliminate cancer cells or suppress their proliferation. However, the altered microenvironmental cells acquire novel characteristics that support malignant progression. Investigations have attempted to use these traits as targets of novel therapeutic approaches.

Discordance between PAM50 intrinsic subtyping and immunohistochemistry in South African women with breast cancer

PURPOSE

Breast cancer is a heterogeneous disease with different gene expression profiles, treatment options and outcomes. In South Africa, tumors are classified using immunohistochemistry. In high-income countries multiparameter genomic assays are being utilized with implications for tumor classification and treatment.

METHODS

In a cohort of 378 breast cancer patients from the SABCHO study, we investigated the concordance between tumor samples classified by IHC and the PAM50 gene assay.

RESULTS

IHC classified patients as ER-positive (77.5%), PR-positive (70.6%), and HER2-positive (32.3%). These results, together with Ki67, were used as surrogates for intrinsic subtyping, and showed 6.9% IHC-A-clinical, 72.7% IHC-B-clinical, 5.3% IHC-HER2-clinical and 15.1% triple negative cancer (TNC). Typing using the PAM50 gave 19.3% luminal-A, 32.5% luminal-B, 23.5% HER2-enriched and 24.6% basal-like. The basal-like and TNC had the highest concordance, while the luminal-A and IHC-A group had the lowest concordance. By altering the cutoff for Ki67, and realigning the HER2/ER/PR-positive patients to IHC-HER2, we improved concordance with the intrinsic subtypes.

CONCLUSION

We suggest that the Ki67 be changed to a cutoff of 20-25% in our population to better reflect the luminal subtype classifications. This change would inform treatment options for breast cancer patients in settings where genomic assays are unaffordable.

Survival Disparities in US Black Compared to White Women with Hormone Receptor Positive-HER2 Negative Breast Cancer

Black women in the US have significantly higher breast cancer mortality than White women. Within biomarker-defined tumor subtypes, disparate outcomes seem to be limited to women with hormone receptor positive and HER2 negative (HR+/HER2-) breast cancer, a subtype usually associated with favorable prognosis. In this review, we present data from an array of studies that demonstrate significantly higher mortality in Black compared to White women with HR+/HER2-breast cancer and contrast these data to studies from integrated healthcare systems that failed to find survival differences. Then, we describe factors, both biological and non-biological, that may contribute to disparate survival in Black women.

The role of serine metabolism in lung cancer: From oncogenesis to tumor treatment

Metabolic reprogramming is an important hallmark of malignant tumors. Serine is a non-essential amino acid involved in cell proliferation. Serine metabolism, especially the de novo serine synthesis pathway, forms a metabolic network with glycolysis, folate cycle, and one-carbon metabolism, which is essential for rapidly proliferating cells. Owing to the rapid development in metabolomics, abnormal serine metabolism may serve as a biomarker for the early diagnosis and pathological typing of tumors. Targeting serine metabolism also plays an essential role in precision and personalized cancer therapy. This article is a systematic review of de novo serine biosynthesis and the link between serine and folate metabolism in tumorigenesis, particularly in lung cancer. In addition, we discuss the potential of serine metabolism to improve tumor treatment.

Targeting the key cholesterol biosynthesis enzyme squalene monooxygenasefor cancer therapy

Cholesterol metabolism is often dysregulated in cancer. Squalene monooxygenase (SQLE) is the second rate-limiting enzyme involved in cholesterol synthesis. Since the discovery of SQLE dysregulation in cancer, compelling evidence has indicated that SQLE plays a vital role in cancer initiation and progression and is a promising therapeutic target for cancer treatment. In this review, we provide an overview of the role and regulation of SQLE in cancer and summarize the updates of antitumor therapy targeting SQLE.

Tumor Molecular Profiling in Hispanics: Moving Towards Precision Oncology and Health Equity

BACKGROUND

Tumor molecular profiling techniques, such as next-generation sequencing (NGS) to identify somatic genetic alterations, allow physicians to have a better understanding of the affected carcinogenic pathways and guide targeted therapy. The objective of our study was to characterize common somatic alterations and carcinogenic pathways among Puerto Rican Hispanics with solid tumors.

METHODS

We conducted a single-institution, retrospective study to characterize molecular tumor profiles using a 592-gene NGS platform. Actionable mutations with current or developing therapies targeting affected genes/pathways were highlighted.

RESULTS

Tumors from 50 Hispanic patients were evaluated using CARIS Life Science© NGS testing. The median age of our study population was 55 (range 21-84); 54% (n = 27) were males. The primary tumor sites were colorectal (n = 24), gastric (n = 5), breast (n = 4), and lung (n = 3). The most common genetic mutations identified were in TP53 (44%), APC (38%), and KRAS (32%); followed by alterations in EGFR (4%), HER2 (6%), and homologous recombinant deficiency genes (BRCA2, 6%). Genetic alterations were found in multiple signaling pathways particularly in the cell cycle control pathway, MAPK and Wnt/β-Catenin signaling pathways. Targetable biomarkers were identified in 27/50 (54.0%) of tumors.

DISCUSSION

Molecular profiling techniques, such as next-generation sequencing, have substantially expanded access to alterations in the cancer genome. Our findings demonstrated important actionable mutations in most of the tumors evaluated and support the integration of somatic mutation profiling in the evaluation of Hispanic cancer patients with advanced cancer to help guide therapeutic options.

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.

Oncotype DX Risk Recurrence Score and Total Mortality for Early-Stage Breast Cancer by Race/Ethnicity

BACKGROUND

Oncotype DX recurrence score (ODX RS) is a prognostic biomarker for early-stage, node-negative, estrogen receptor-positive (ER+) breast cancer. Whether test uptake, associated factors, and the test's prognostic values differ by race/ethnicity is unknown.

METHODS

From the National Cancer Database, 2010-2014, we identified 227,259 early-stage ER+, node-negative breast cancer cases. Logistic regression was used to examine ODX RS uptake and associated factors among non-Hispanic White (White), non-Hispanic Black (Black), Hispanic, and Asian American patients. Cox regression was used to estimate hazard ratios (HR) and 95% confidence intervals (CI) for overall mortality with ODX RS by race/ethnicity.

RESULTS

White patients were more likely to receive an ODX RS test compared with Black, Hispanic, and Asian American patients (36.7%, 32.8%, 31.6%, and 35.5%, respectively; P < 0.001). Disparities persisted after adjustments for demographics, clinical characteristics, and access-to-care, with rate ratios of 0.87 (95% CI, 0.85-0.88), 0.82 (95% CI, 0.80-0.85), and 0.89 (95% CI, 0.87-0.92), respectively, for Black, Hispanic, and Asian American compared with White patients. Black patients had higher proportions of high-risk scores (≥26) compared with White, Hispanic, and Asian American patients (19.1%, 14.0%, 14.2%, and 15.6%, respectively; P < 0.0001). ODX RS was predictive for total mortality across all races/ethnicities, particularly younger patients (<50). No significant race/ethnicity interactions were observed.

CONCLUSIONS

Although ODX RS uptake and risk distribution varied by race/ethnicity, ODX RS was prognostic for mortality across groups.

IMPACT

These findings emphasize the importance of developing strategies to increase ODX RS uptake among racial/ethnic minorities and call for more investigations on potential racial/ethnic differences in breast cancer biology. See related commentary by Wang et al., p. 704.

Gene-Level Germline Contributions to Clinical Risk of Recurrence Scores in Black and White Patients with Breast Cancer

Continuous risk of recurrence scores (CRS) based on tumor gene expression are vital prognostic tools for breast cancer. Studies have shown that Black women (BW) have higher CRS than White women (WW). Although systemic injustices contribute substantially to breast cancer disparities, evidence of biological and germline contributions is emerging. In this study, we investigated germline genetic associations with CRS and CRS disparity using approaches modeled after transcriptome-wide association studies (TWAS). In the Carolina Breast Cancer Study, using race-specific predictive models of tumor expression from germline genetics, we performed race-stratified (N = 1,043 WW, 1,083 BW) linear regressions of three CRS (ROR-S: PAM50 subtype score; proliferation score; ROR-P: ROR-S plus proliferation score) on imputed tumor genetically regulated tumor expression (GReX). Bayesian multivariate regression and adaptive shrinkage tested GReX-prioritized genes for associations with tumor PAM50 expression and subtype to elucidate patterns of germline regulation underlying GReX-CRS associations. At FDR-adjusted P < 0.10, 7 and 1 GReX prioritized genes among WW and BW, respectively. Among WW, CRS were positively associated with MCM10, FAM64A, CCNB2, and MMP1 GReX and negatively associated with VAV3, PCSK6, and GNG11 GReX. Among BW, higher MMP1 GReX predicted lower proliferation score and ROR-P. GReX-prioritized gene and PAM50 tumor expression associations highlighted potential mechanisms for GReX-prioritized gene to CRS associations. Among patients with breast cancer, differential germline associations with CRS were found by race, underscoring the need for larger, diverse datasets in molecular studies of breast cancer. These findings also suggest possible germline trans-regulation of PAM50 tumor expression, with potential implications for CRS interpretation in clinical settings. SIGNIFICANCE: This study identifies race-specific genetic associations with breast cancer risk of recurrence scores and suggests mediation of these associations by PAM50 subtype and expression, with implications for clinical interpretation of these scores.

Expression profile of tumour suppressor protein p53 and its regulator MDM2 in a cohort of breast cancer patients in a Tertiary Hospital in Ghana

BACKGROUND

Inactivation or mutation of the tumour suppressor gene p53 or its regulator mouse double minute 2 (MDM2) is the commonest event in breast cancer. These altered genes usually express abnormally high levels of their proteins in many carcinomas. The phenotypic expression of p53 and MDM2 in breast cancer cases in our setting is not known. This study investigated the expression of the tumour suppressor protein p53 and its regulator MDM2, using immunohistochemistry in a Ghana breast cancer cohort.

METHOD

A 9-year retrospective cross-sectional study on archived tissue blocks-formalin fixed paraffin embedded tissue (FFPE) was carried out. Demographic data were abstracted. Based on complete clinical data and availability of FFPE archived blocks 203 cases were selected for tissue micro array (TMA) construction. The TMA sections were subjected to immunohistochemistry (IHC) (ER, PR, HER2, p53, and MDM2). Expression of p53 and MDM2 were related to grade and molecular subtypes.

RESULTS

The age ranged from 17 to 92 years (mean = 49.34 ± 13.74). Most of the cases were high grade; grade II (34.9%) and grade III (55.7%). Fifty-four percent of the cases were triple negative. Invasive ductal carcinoma no special type was the commonest histotype (87.1%). Thirty-six percent (36%) of the cases expressed p53. Significant associations were found between p53 overexpression and histological grade (p = 0.034), triple negative (p = 0.0333) and luminal B (p<0.01) tumors. Most cases (93.1%) were negative for MDM2 expression. Significant association was found between MDM2 and HER2 over-expression as well as Ki-67. There was no significant positive correlation between MDM2 and p53 co-expression (p>0.05).

CONCLUSION

The elevated level of p53 expression in the aggressive breast cancer phenotypes (high histological grade and triple negative) in our cohort suggest that P53 elevation may be a poor prognostic marker in our setting. High expression of MDM2 in our cohort with high Ki67; also in cases with Her2/neu overexpression known with predictable poor prognosis in the absence of target therapy suggest MDM2 may be associated with aggressive biological behaviour in our breast cancer cases. The non-significant association of p53 and MDM2 expression in the same cases as also documented by previous studies suggest independent genetic pathway in tumourigenesis.

VEGF-B, VEGF-A, FLT-1, KDR, ERBB2, EGFR, GRB2, RAC1, CDH1 and HYAL-1 Genes Expression Analysis in Canine Mammary Gland Tumors and the Association with Tumor ClinicoPathological Parameters and Dog Breed Assessment

Canine mammary gland tumors (CMTs) are one of the most prevalent cancers in dogs and a good model for human breast cancer (BC), however gene expression analysis of CMTs is scarce. Although divergence of genes expression has been found in BC of different human races, no such research of different dog's breeds has been done. The purpose of this study was to investigate expression of the VEGF-B, VEGF-A, FLT-1, KDR, ERBB2, EGFR, GRB2, RAC1, CDH1 and HYAL-1 genes of canine mammary carcinomas, compare the expression levels with clinicopathological parameters and analyze expression disparities between different breeds. Carcinomas and adjacent tissues were collected from female dogs to perform routine histopathology, immunohistochemistry (IHC) and quantitative real-time polymerase chain reaction (qRT-PCR). We found that VEGF-B and EGFR genes were overexpressed in the mammary gland carcinomas compared to adjacent tissue. VEGF-B gene expression had associations with different parameters (tumor size, grade, and absence of metastasis). Furthermore, differences in VEGF-B, FLT1, ERBB2, GRB2, RAC1, CDH1 and HYAL-1 genes expression have been found in different breed dogs (German Shepherd, Yorkshire Terrier) and mixed-breed dogs indicating that a dog's breed could determine a molecular difference, outcome of cancer and should be accounted as a confounding factor in the future gene expression research.

Prognostic Score for De Novo Metastatic Breast Cancer With Liver Metastasis and Its Predictive Value of Locoregional Treatment Benefit

Background

There is a significant survival difference and lack of effective treatment among breast cancer patients with liver metastasis. This present study aimed to construct a novel prognostic score for predicting the prognosis and locoregional treatment benefit of de novo metastatic breast cancer with liver metastasis (BCLM).

Methods

In total, 2,398 eligible patients between 2010 and 2016 were selected from the Surveillance, Epidemiology, and End Results (SEER) database. They were assigned to the training set including 1,662 patients (2010–2014) and validation set comprising 736 patients (2015–2016) depending on the time of diagnosis. The prognostic score was based on regression coefficients in the multivariate Cox regression analysis. And then, patients were stratified into low-, intermediate-, and high-risk groups by the prognostic score. The discrimination and calibration of prognostic score were evaluated using time-dependent receiver operating characteristic (ROC) curves analysis and calibration curves, respectively. Subgroup analysis was performed to evaluate locoregional surgery and chemotherapy benefit in different risk groups.

Results

Age, race, insurance and marital status, T stage, pathological grade, molecular subtypes, and extrahepatic metastasis were identified as independent prognostic variables in the prognostic score. The prognostic score showed high discrimination power with an area under the curve (AUC) of 0.77 and 0.72 and excellent agreement suggested by calibration plots in the training and validation sets, respectively. Intermediate-risk [hazard ratio (HR) 2.39, 95% confidence interval (CI) 2.09–2.73, P<0.001] and high-risk groups (HR 4.88; 95% CI 4.13–5.76; P<0.001) had significantly worse prognosis in comparison with the low-risk group. The median overall survival (OS) in three prognostic groups were 44, 18, and 7 months, with a 3-year survival rate of 56, 23, and 7%, respectively. Apart from the high-risk group (HR 0.79; 95% CI 0.56–1.10; P=0.157), the low-risk (HR 0.64; 95% CI 0.49–0.84; P=0.001) and intermediate-risk groups (HR 0.68; 95% CI 0.55–0.85; P=0.001) could benefit from the surgery of primary site, while chemotherapy improved prognosis in all risk groups.

Conclusions

A prognostic score was developed to accurately predict the prognosis of de novo BCLM patients. Moreover, it may be useful for further subdividing them into different risk groups and helping guide clinicians in treatment decisions.

A systematic review of datasets that can help elucidate relationships among gene expression, race, and immunohistochemistry-defined subtypes in breast cancer

Scholarly requirements have led to a massive increase of transcriptomic data in the public domain, with millions of samples available for secondary research. We identified gene-expression datasets representing 10,214 breast-cancer patients in public databases. We focused on datasets that included patient metadata on race and/or immunohistochemistry (IHC) profiling of the ER, PR, and HER-2 proteins. This review provides a summary of these datasets and describes findings from 32 research articles associated with the datasets. These studies have helped to elucidate relationships between IHC, race, and/or treatment options, as well as relationships between IHC status and the breast-cancer intrinsic subtypes. We have also identified broad themes across the analysis methodologies used in these studies, including breast cancer subtyping, deriving predictive biomarkers, identifying differentially expressed genes, and optimizing data processing. Finally, we discuss limitations of prior work and recommend future directions for reusing these datasets in secondary analyses.

Race as a Contributor to Stromal Modulation of Tumor Progression

Stromal cells play crucial roles in tumor development and are increasingly attractive targets for therapy. There are considerable racial disparities in the incidence and progression of many tumors, reflecting both environmental exposure and genetic differences existing between races. Tumorigenesis and tumor progression are linked to both the propensity to suffer an initiating event and the host response to such an event once it occurs, contributing to incidence and outcomes. In this review, we focused on racial disparities in the tumor microenvironment (TME) of different cancers as potential modulators of growth, metastasis, and response to treatment. Several studies suggest that the TME in AA has a distinct tumor biology and may facilitate both early onset and aggressive tumor growth while inhibiting anti-tumorigenic properties. The TME of AA patients often exhibits an immunosuppressive microenvironment with a substantial enrichment of immune inflammatory pathways and genes. As a result, AA patients can potentially benefit more from treatment strategies that modulate the immune system. Focusing on TME components for diagnostic and therapeutic purposes to address racial disparities is a promising area of investigation. Future basic and clinical research studies on personalized cancer diagnosis and treatment should acknowledge the significance of TME in racial disparities.

African Americans and European Americans exhibit distinct gene expression patterns across tissues and tumors associated with immunologic functions and environmental exposures

The COVID-19 pandemic has affected African American populations disproportionately with respect to prevalence, and mortality. Expression profiles represent snapshots of combined genetic, socio-environmental (including socioeconomic and environmental factors), and physiological effects on the molecular phenotype. As such, they have potential to improve biological understanding of differences among populations, and provide therapeutic biomarkers and environmental mitigation strategies. Here, we undertook a large-scale assessment of patterns of gene expression between African Americans and European Americans, mining RNA-Seq data from 25 non-diseased and diseased (tumor) tissue-types. We observed the widespread enrichment of pathways implicated in COVID-19 and integral to inflammation and reactive oxygen stress. Chemokine CCL3L3 expression is up-regulated in African Americans. GSTM1, encoding a glutathione S-transferase that metabolizes reactive oxygen species and xenobiotics, is upregulated. The little-studied F8A2 gene is up to 40-fold more highly expressed in African Americans; F8A2 encodes HAP40 protein, which mediates endosome movement, potentially altering the cellular response to SARS-CoV-2. African American expression signatures, superimposed on single cell-RNA reference data, reveal increased number or activity of esophageal glandular cells and lung ACE2-positive basal keratinocytes. Our findings establish basal prognostic signatures that can be used to refine approaches to minimize risk of severe infection and improve precision treatment of COVID-19 for African Americans. To enable dissection of causes of divergent molecular phenotypes, we advocate routine inclusion of metadata on genomic and socio-environmental factors for human RNA-sequencing studies.

Breast cancer disparities in outcomes; unmasking biological determinants associated with racial and genetic diversity

Breast cancer (BC) remains a leading cause of death among women today, and mortality among African American women in the US remains 40% higher than that of their White counterparts, despite reporting a similar incidence of disease over recent years. Previous meta-analyses and studies of BC mortality highlight that tumor characteristics, rather than socio-economic factors, drive excess mortality among African American women with BC. This is further complicated by the heterogeneity of BC, where BC can more appropriately be defined as a collection of diseases rather than a single disease. Molecular phenotyping and gene expression profiling distinguish subtypes of BC, and these subtypes have distinct prognostic outcomes. Racial disparities transcend these subtype-specific outcomes, where African American women suffer higher mortality rates among all BC subtypes. The most striking differences are observed among the most aggressive molecular subtype, triple-negative BC (TNBC), where incidence and mortality are significantly higher among African American women compared to all other race/ethnicity groups. We and others have shown that this predisposition for triple-negative disease may be linked to shared west African ancestry, where the highest rates of TNBC are observed among west African nations, and these high frequencies follow into the African diaspora. Genetic and molecular characterization of breast tumors among subtypes and racial/ethnic groups have begun to identify targets with future therapeutic potential, but more work needs to be done to identify targeted treatment options for all women who suffer from BC.

Squalene epoxidase expression is associated with breast tumor progression and with a poor prognosis in breast cancer

Differentially expressed genes (DEGs) have been previously identified using massive parallel RNA sequencing in matched normal, breast cancer (BC) and nodal metastatic tissues. Squalene epoxidase (SQLE), one of these DEGs, is a key enzyme in cholesterol synthesis. The aim of the present study was to investigate the potential involvement of SQLE in the tumorigenic process of BC and to determine its association with the clinical outcome of BC. SQLE mRNA expression was measured using reverse transcription-quantitative PCR in 10 pairs of ductal carcinoma in situ (DCIS) and BC tissues and their adjacent normal tissues. Immunohistochemical staining of SQLE on tissue microarray was performed in 26 normal breast, 79 DCIS and 198 BC samples. The role of SQLE as a prognostic biomarker in patients with BC has been verified using BreastMark. SQLE mRNA expression was significantly increased in DCIS and BC tissues compared with that in their adjacent normal tissues. High SQLE expression was detected in 0, 48.1 and 40.4% of normal breast, DCIS and BC tissues, respectively. SQLE expression in DCIS and BC tissues was significantly higher than that in normal breast tissues. High SQLE expression was observed in DCIS with higher nuclear grade, comedo-type necrosis and HER2 positivity. High SQLE expression in BC was associated with larger tumor size, nodal metastases, higher stage, HER2 subtype and distant metastatic relapse. High SQLE expression was associated with poor disease-free and overall survival, and independently predicted poor disease-free survival in patients with BC. Following BreastMark analysis, high SQLE mRNA expression in BC was significantly associated with a poor prognosis in the ‘all’, lymph node negative, lymph node positive, luminal A subtype and luminal B subtype groups. Therefore, SQLE expression may be upregulated during the tumorigenic process of BC, and high SQLE expression may be a useful biomarker for predicting a poor prognosis in patients with BC.

Establishment and Validation of Novel Clinical Prognosis Nomograms for Luminal A Breast Cancer Patients with Bone Metastasis

PURPOSE

Overall survival (OS) and cancer-specific survival (CSS) of luminal A breast cancer (BC) patients with bone metastasis remain poor and vary dramatically from person to person. Our goal was to build two universally applicable nomograms to accurately predict OS and CSS for luminal A patients with bone metastasis.

METHODS

The data were collected from the Surveillance, Epidemiology, and End Results (SEER) database for luminal A BC patients with bone metastasis between 2010 and 2015. Univariate and multivariate Cox regression analyses were to assess and identify independent risk factors of OS and CSS. Integrating all significant predictors, nomograms and risk group stratification model was developed. The performance of the nomogram was validated with concordance index (C-index), calibration plots, and decision curve analyses (DCA) for discriminative ability, calibration, and clinical utility, respectively.

RESULTS

3171 luminal A BC patients with bone metastasis were included. Through univariate and multivariate Cox regression analyses, 12 variables were identified as both independent OS- and CSS-related factors, including age, race, primary site, histology grade, tumor size, surgery, brain metastasis, liver metastasis, lung metastasis, estrogen receptor status, progesterone receptor status, and insurance. Our nomograms for 1-, 3-, and 5-year survival were based on those significant prognostic factors to develop. The C-indexes of OS- and CSS-nomograms in the training cohort were 0.701 and 0.704, respectively. Similar results were obtained in the validation cohort. The calibration curves and DCA presented satisfactory calibration and clinical utility.

CONCLUSION

Two nomograms have good discrimination, calibration, and clinical utility, can accurately and effectively predict the prognosis of patients, and may benefit for clinical decision-making. In high-risk patients, more aggressive therapy and closer surveillance should be considered.

Prognostic factors and survival according to tumour subtype in women presenting with breast cancer bone metastases at initial diagnosis: a SEER-based study

Background

Tumour subtype has a significant effect on bone metastasis in breast cancer, but population-based estimates of the prognosis of patients with bone metastases at breast cancer diagnosis are lacking. The aim of this study was to analyse the influence of tumour subtype and other factors on the prognosis and survival of patients with bone metastases of breast cancer.

Methods

Using the Surveillance, Epidemiology, and End Results (SEER) Program data from 2012 to 2016, a retrospective cohort study was conducted to investigate stage IV breast cancer patients with bone metastases. Stage IV patient characteristics according to subtype were compared using chi-square tests. Overall survival (OS) and prognostic factors were compared using the Kaplan-Meier method and the Cox proportional hazards model, respectively.

Results

A total of 3384 stage IV patients were included in this study; 63.42% were HR+/HER2-, 19.86% were HR+/HER2+, 9.34% were HR−/HER2-, and 7.39% were HR−/HER2+. The median OS for the whole population was 38 months, and 33.9% of the patients were alive at 5 years. The median OS and five-year survival rate were significantly different among stage IV breast cancer patients with different molecular subtypes (p < 0.05). Multivariate Cox regression analysis showed that age of 55–59 (HR = 1.270), black race (HR = 1.317), grade III or IV (HR = 1.960), HR−/HER2- (HR = 2.808), lung metastases (HR = 1.378), liver metastases (HR = 2.085), and brain metastases (HR = 1.903) were independent risk factors for prognosis; married status (HR = 0.819), HR+/HER2+ (HR = 0.631), HR−/HER2+ (HR = 0.716), insurance (HR = 0.587) and surgery (HR = 0.504) were independent protection factors of prognosis. There was an interaction between the HR+/HER2+ subtype and other metastases (except bone metastases, HR = 0.694, 95% CI: 0.485–0.992), but the interaction between race and subtype did not reach significance for prognosis.

Conclusions

There were substantial differences in OS according to tumour subtype. In addition to tumour subtype, other independent predictors of OS were age at diagnosis, race, marital status, insurance, grade, surgery and visceral metastases. There was an interaction between the HR+/HER2+ subtype and other metastases (except bone metastases) for prognosis. Tumour subtype, as a significant prognostic factor, warrants further investigation.

Differences in gene-expression profiles in breast cancer between African and European-ancestry women

African American (AA) women have an excess breast cancer mortality than European American (EA) women. To investigate the contribution of tumor biology to this survival health disparity, we compared gene expression profiles in breast tumors using RNA sequencing data derived from 260 AA and 155 EA women who were prospectively enrolled in the Southern Community Cohort Study (SCCS) and developed breast cancer during follow-up. We identified 59 genes (54 protein-coding genes and 5 long intergenic non-coding RNAs) that were expressed differently between EA and AA at a stringent false discovery rate (FDR) < 0.01. A gene signature was derived with these 59 genes and externally validated using the publicly available Cancer Genome Atlas (TCGA) data from180 AA and 838 EA breast cancer patients. Applying C-statistics, we found that this 59-gene signature has a high discriminative ability in distinguishing AA and EA breast cancer patients in the TCGA dataset (C-index = 0.81). These findings may provide new insight into tumor biological differences and the causes of the survival disparity between AA and EA breast cancer patients.

Factors Associated with Endocrine Therapy Non-Adherence in Breast Cancer Survivors

BACKGROUND

For women with hormone receptor positive breast cancer, long-term endocrine therapy (ET) can greatly reduce the risk of recurrence, yet adherence is low- particularly among traditionally underserved populations.

METHODS

The Carolina Breast Cancer Study oversampled Black and young women (<50 years of age). Participants answered an ET-specific medication adherence questionnaire assessing reasons for non-adherence. We used principal factor analysis to identify latent factors describing ET non-adherence. We then performed multivariable regression to determine clinical and demographic characteristics associated with each ET non-adherence factor.

RESULTS

1,231 women were included in analysis, 59% reported at least one barrier to ET adherence. We identified three latent factors which we defined as: habit - challenges developing medication-taking behavior; tradeoffs - high perceived side effect burden and medication safety concerns; and resource barriers - challenges related to cost or accessibility. Older age (50+) was associated with less reporting of habit (Adjusted Risk Ratio (aRR) 0.54[95% CI: 0.43-0.69] and resource barriers (aRR 0.66[0.43-0.997]), but was not associated with tradeoff barriers. Medicaid-insured women were more likely than privately-insured to report tradeoff (aRR:1.53 [1.10-2.13]) or resource barriers (aRR:4.43[2.49-6.57]). Black race was associated with increased reporting of all factors (habit: aRR 1.29[1.09-1.53]; tradeoffs: 1.32[1.09-1.60], resources: 1.65[1.18-2.30]).

CONCLUSION

Barriers to ET adherence were described by three distinct factors, and strongly associated with sociodemographic characteristics. Barriers to ET adherence appear inadequately addressed for younger, Black, and publicly-insured breast cancer survivors. These findings underscore the importance of developing multi-faceted, patient-centered interventions that address a diverse range of barriers to ET adherence.

Twenty Metabolic Genes Based Signature Predicts Survival of Glioma Patients

Background: Glioma, caused by carcinogenesis of brain and spinal glial cells, is the most common primary malignant brain tumor. To find the important indicator for glioma prognosis is still a challenge and the metabolic alteration of glioma has been frequently reported recently.

Methods: In our current work, a risk score model based on the expression of twenty metabolic genes was developed using the metabolic gene expressions in The Cancer Genome Atlas (TCGA) dataset, the methods of which included the cox multivariate regression and the random forest variable hunting, a kind of machine learning algorithm, and the risk score generated from this model is used to make predictions in the survival of glioma patients in the training dataset. Subsequently, the result was further verified in other three verification sets (GSE4271, GSE4412 and GSE16011). Risk score related pathways collected in the Kyoto Encyclopedia of Genes and Genomes (KEGG) database were identified using Gene Set Enrichment Analysis (GSEA).

Results: The risk score generated from our model makes good predictions in the survival of glioma patients in the training dataset and other three verification sets. By assessing the relationships between clinical indicators and the risk score, we found that the risk score was an independent and significant indicator for the prognosis of glioma patients. Simultaneously, we conducted a survival analysis of the patients who received chemotherapy and who did not, finding that the risk score was equally valid in both cases. And signaling pathways related to the genesis and development of multiple cancers were also identified.

Conclusions: In summary, our risk score model is predictive for 967 glioma patients' survival from four independent datasets, and the risk score is a meaningful and independent parameter of the clinicopathological information.

Differences in microRNA expression in breast cancer between women of African and European ancestry

Breast cancer is a heterogeneous disease, characterized by molecularly and phenotypically distinct tumor subtypes, linked to disparate clinical outcomes. American women of African ancestry (AA) are more likely than those of European ancestry (EA) to be diagnosed with aggressive, estrogen receptor negative (ER-) or triple negative breast cancer, and to die of this disease. However, the underlying causes of AA predisposition to ER-/triple negative breast cancer are still largely unknown. In this study, we performed high-throughput whole-genome miRNA expression profiling in breast tissue samples from both AA and EA women. A number of differentially expressed miRNAs, i.e., DEmiRs defined as >2-fold change in expression and false discovery rate <0.05, were identified as up- or downregulated by tumor ER status or by ancestry. We found that among 102 ER-subtype-related DEmiRs identified in breast tumors, the majority of these DEmiRs were race specific, with only 23 DEmiRs shared in tumors from both AAs and EAs; this finding indicates that there are unique subsets of miRNAs differentially expressed between ER- and ER positive tumors within AAs versus EAs. Our overall results support the notion that miRNA expression patterns may differ not only by tumor subtype but by ancestry, indicating differences in tumor biology and heterogeneity of breast cancer between AAs and EAs. These results will provide the basis for further functional analysis to elucidate biological differences between AAs and EAs and to help develop targeted treatment strategies to reduce disparities in breast cancer.

A functional role for the cancer disparity-linked genes, CRYβB2 and CRYβB2P1, in the promotion of breast cancer

Background

In the USA, the breast cancer mortality rate is 41% higher for African-American women than non-Hispanic White women. While numerous gene expression studies have classified biological features that vary by race and may contribute to poorer outcomes, few studies have experimentally tested these associations. CRYβB2 gene expression has drawn particular interest because of its association with overall survival and African-American ethnicity in multiple cancers. Several reports indicate that overexpression of the CRYβB2 pseudogene, CRYβB2P1, and not CRYβB2 is linked with race and poor outcome. It remains unclear whether either or both genes are linked to breast cancer outcomes. This study investigates CRYβB2 and CRYβB2P1 expression in human breast cancers and breast cancer cell line models, with the goal of elucidating the mechanistic contribution of CRYβB2 and CRYβB2P1 to racial disparities.

Methods

Custom scripts for CRYβB2 or CRYβB2P1 were generated and used to identify reads that uniquely aligned to either gene. Gene expression according to race and tumor subtype were assessed using all available TCGA breast cancer RNA sequencing alignment samples (n = 1221). In addition, triple-negative breast cancer models engineered to have each gene overexpressed or knocked out were developed and evaluated by in vitro, biochemical, and in vivo assays to identify biological functions.

Results

We provide evidence that CRYβB2P1 is expressed at higher levels in breast tumors compared to CRYβB2, but only CRYβB2P1 is significantly increased in African-American tumors relative to White American tumors. We show that independent of CRYβB2, CRYβB2P1 enhances tumorigenesis in vivo via promoting cell proliferation. Our data also reveal that CRYβB2P1 may function as a non-coding RNA to regulate CRYβB2 expression. A key observation is that the combined overexpression of both genes was found to suppress cell growth. CRYβB2 overexpression in triple-negative breast cancers increases invasive cellular behaviors, tumor growth, IL6 production, immune cell chemoattraction, and the expression of metastasis-associated genes. These data underscore that both CRYβB2 and CRYβB2P1 promote tumor growth, but their mechanisms for tumor promotion are likely distinct.

Conclusions

Our findings provide novel data emphasizing the need to distinguish and study the biological effects of both CRYβB2 and CRYβB2P1 as both genes independently promote tumor progression. Our data demonstrate novel molecular mechanisms of two understudied, disparity-linked molecules.

Electronic supplementary material

The online version of this article (10.1186/s13058-019-1191-3) contains supplementary material, which is available to authorized users.

Atypical Chemokine Receptor 1 (DARC/ACKR1) in Breast Tumors Is Associated with Survival, Circulating Chemokines, Tumor-Infiltrating Immune Cells, and African Ancestry

BACKGROUND

Tumor-specific immune response is an important aspect of disease prognosis and ultimately impacts treatment decisions for innovative immunotherapies. The atypical chemokine receptor 1 (ACKR1 or DARC) gene plays a pivotal role in immune regulation and harbors several single-nucleotide variants (SNV) that are specific to sub-Saharan African ancestry.

METHODS

Using computational The Cancer Genome Atlas (TCGA) analysis, case-control clinical cohort Luminex assays, and CIBERSORT deconvolution, we identified distinct immune cell profile-associated DARC/ACKR1 tumor expression and race with increased macrophage subtypes and regulatory T cells in DARC/ACKR1-high tumors.

RESULTS

In this study, we report the clinical relevance of DARC/ACKR1 tumor expression in breast cancer, in the context of a tumor immune response that may be associated with sub-Saharan African ancestry. Briefly, we found that for infiltrating carcinomas, African Americans have a higher proportion of DARC/ACKR1-negative tumors compared with white Americans, and DARC/ACKR1 tumor expression is correlated with proinflammatory chemokines, CCL2/MCP-1 (P <0.0001) and anticorrelated with CXCL8/IL8 (P <0.0001). Sub-Saharan African-specific DARC/ACKR1 alleles likely drive these correlations. Relapse-free survival (RFS) and overall survival (OS) were significantly longer in individuals with DARC/ACKR1-high tumors (P <1.0 × 10-16 and P <2.2 × 10-6, respectively) across all molecular tumor subtypes.

CONCLUSIONS

DARC/AKCR1 regulates immune responses in tumors, and its expression is associated with sub-Saharan African-specific alleles. DARC/ACKR1-positive tumors will have a distinct immune response compared with DARC/AKCR1-negative tumors.

IMPACT

This study has high relevance in cancer management, as we introduce a functional regulator of inflammatory chemokines that can determine an infiltrating tumor immune cell landscape that is distinct among patients of African ancestry.

JAK3 Variant, Immune Signatures, DNA Methylation, and Social Determinants Linked to Survival Racial Disparities in Head and Neck Cancer Patients

To inform novel personalized medicine approaches for race and socioeconomic disparities in head and neck cancer, we examined germline and somatic mutations, immune signatures, and epigenetic alterations linked to neighborhood determinants of health in Black and non-Latino White (NLW) patients with head and neck cancer. Cox proportional hazards revealed that Black patients with squamous cell carcinoma of head and neck (HNSCC) with PAX5 (P = 0.06) and PAX1 (P = 0.017) promoter methylation had worse survival than NLW patients, after controlling for education, zipcode, and tumor-node-metastasis stage (n = 118). We also found that promoter methylation of PAX1 and PAX5 (n = 78), was correlated with neighborhood characteristics at the zip-code level (P < 0.05). Analyses also showed differences in the frequency of TP53 mutations (n = 32) and tumor-infiltrating lymphocyte (TIL) counts (n = 24), and the presence of a specific C → A germline mutation in JAK3, chr19:17954215 (protein P132T), in Black patients with HNSCC (n = 73; P < 0.05), when compared with NLW (n = 37) patients. TIL counts are associated (P = 0.035) with long-term (>5 years), when compared with short-term survival (<2 years). We show bio-social determinants of health associated with survival in Black patients with HNSCC, which together with racial differences shown in germline mutations, somatic mutations, and TIL counts, suggests that contextual factors may significantly inform precision oncology services for diverse populations.

Racial/ethnic differences in the outcomes of patients with metastatic breast cancer: contributions of demographic, socioeconomic, tumor and metastatic characteristics

PURPOSE

Population-based estimates of racial disparities in metastatic breast cancer are lacking. We quantified the contributions of demographic, socioeconomic, tumor, and metastatic characteristics to racial differences in metastatic breast cancer and characterized the most disproportional subgroup.

METHODS

Patients diagnosed with metastatic breast cancer between 2010 and 2014 were identified using the Surveillance, Epidemiology, and End Results database. A multivariable Cox proportional hazards model was used to adjust each set of variables. The excess relative risk of cancer-specific and all-cause death in non-Hispanic black (NHB) versus non-Hispanic white women diagnosed with metastatic breast cancer was expressed as a percentage and was stratified by the age at diagnosis.

RESULTS

We identified 13,066 female patients. NHB women exhibited substantially higher morbidity and mortality than women of other races/ethnicities. The greatest excess mortality risk for NHB women was observed in the young-onset group (18-49 years; hazard ratio: 1.57), followed by the middle-age group (50-64 years; hazard ratio: 1.42); the trend was not significant among the elderly group. Socioeconomic factors stably explained one-half of the excess risk, whereas the contribution of tumor characteristics obviously decreased with age (18-49 years, 40.7%; 50-64 years, 33.9%), and the metastatic pattern accounted for approximately one-tenth of the excess risk. Additionally, the disproportional death burden of NHB women persisted in less aggressive subgroups.

CONCLUSIONS

By providing a comprehensive assessment of racial differences in the incidence and outcomes of patients with metastatic breast cancer, we urge the implementation of targeted preventive efforts in both the public health and clinical arenas.

Novel application of normalized pointwise mutual information (NPMI) to mine biomedical literature for gene sets associated with disease: use case in breast carcinogenesis

Advances in technology within biomedical sciences have led to an inundation of data across many fields, raising new challenges in how best to integrate and analyze these resources. For example, rapid chemical screening programs like the US Environmental Protection Agency's ToxCast and the collaborative effort, Tox21, have produced massive amounts of information on putative chemical mechanisms where assay targets are identified as genes; however, systematically linking these hypothesized mechanisms with in vivo toxicity endpoints like disease outcomes remains problematic. Herein we present a novel use of normalized pointwise mutual information (NPMI) to mine biomedical literature for gene associations with biological concepts as represented by Medical Subject Headings (MeSH terms) in PubMed. Resources that tag genes to articles were integrated, then cross-species orthologs were identified using UniRef50 clusters. MeSH term frequency was normalized to reflect the MeSH tree structure, and then the resulting GeneID-MeSH associations were ranked using NPMI. The resulting network, called Entity MeSH Co-occurrence Network (EMCON), is a scalable resource for the identification and ranking of genes for a given topic of interest. The utility of EMCON was evaluated with the use case of breast carcinogenesis. Topics relevant to breast carcinogenesis were used to query EMCON and retrieve genes important to each topic. A breast cancer gene set was compiled through expert literature review (ELR) to assess performance of the search results. We found that the results from EMCON ranked the breast cancer genes from ELR higher than randomly selected genes with a recall of 0.98. Precision of the top five genes for selected topics was calculated as 0.87. This work demonstrates that EMCON can be used to link in vitro results to possible biological outcomes, thus aiding in generation of testable hypotheses for furthering understanding of biological function and the contribution of chemical exposures to disease.