A hybrid epithelial/mesenchymal state in head and neck cancer: A biomarker for survival with differential prognosis by self-reported race.

BACKGROUND: Head and neck squamous cell carcinoma (HNSCC) is the 6th leading cause of cancer-related mortality, with racial disparities amplifying the challenges in treatment. Although the relationship between hybrid epithelial/mesenchymal (E/M) states and tumor progression is of interest, no studies have characterized the clinical relevance of hybrid E/M states in head and neck cancer outcomes among self-reported racial cohorts. METHODS: Given the overlap in gene expression between hybrid E/M malignant cells and cancer-associated fibroblasts, we utilized deconvolution of bulk RNA sequencing data from oral cavity and laryngeal squamous cell carcinoma tumors from The Cancer Genome Atlas. We utilized our previously collected single-cell profiles to generate inferred malignant profiles and then scored these for hybrid E/M. We then conducted a survival analysis on overall and disease-free survival among self-reported Black and White Americans. FINDINGS: The hybrid E/M state was differentially associated with head and neck cancer survival by self-reported race and ethnicity, with a stronger association in non-Hispanic Black patients. Black patients with a high hybrid E/M score had a higher risk of death or recurrence (hazard ratio [HR]: 4.18 [95% confidence interval (CI): 2.06, 8.49]) than White patients with a high hybrid E/M score (HR: 1.58 [95% CI: 1.11, 2.26]). CONCLUSION: Our results suggest a complex interplay of social structure, racism, and genetic diversity. We implore researchers to consider the social and biological context contributing to disparities. FUNDING: A.L.M. received support from the National Institute of Minority Health and Health Disparities (K01MD013897 [principal investigator (PI), A.L.M.]). S.V.P. received support from the National Institute of Dental and Craniofacial Research (R01DE032865 [PI, S.V.P.] and R01DE032371 [PI, S.V.P.]).

How does weight gain since the age of 18 years affect breast cancer risk in later life? A meta-analysis.

Early life factors are important risk factors for breast cancer. The association between weight gain after age 18 and breast cancer risk is inconsistent across previous epidemiologic studies. To evaluate this association, we conducted a meta-analysis according to PRISMA guidelines and the established inclusion criteria. We performed a comprehensive literature search using Medline (Ovid), Embase, Scopus, Cochrane Library, and ClinicalTrials.gov to identify relevant studies published before June 3, 2022. Two reviewers independently reviewed the articles for final inclusion. Seventeen out of 4,725 unique studies met the selection criteria. The quality of studies was assessed using the Newcastle-Ottawa Scale (NOS), and all were of moderate to high quality with NOS scores ranging from 5 to 8. We included 17 studies (11 case-control, 6 cohort) in final analysis. In case-control studies, weight gain after age 18 was associated with an increased risk of breast cancer (odds ratio [OR] = 1.25; 95% CI = 1.07-1.48), when comparing the highest versus the lowest categories of weight gain. Menopausal status was a source of heterogeneity, with weight gain after age 18 associated with an increased risk of breast cancer in postmenopausal women (OR = 1.53; 95% CI = 1.40-1.68), but not in premenopausal women (OR = 1.01; 95% CI = 0.92-1.12). Additionally, a 5 kg increase in weight was positively associated with postmenopausal breast cancer risk (OR = 1.12; 95%CI = 1.05-1.21) in case-control studies. Findings from cohort studies were identical, with a positive association between weight gain after age 18 and breast cancer incidence in postmenopausal women (relative risk [RR] = 1.30; 95% CI = 1.09-1.36), but not in premenopausal women (RR = 1.06; 95% CI = 0.92-1.22). Weight gain after age 18 is a risk factor for postmenopausal breast cancer, highlighting the importance of weight control from early adulthood to reduce the incidence of postmenopausal breast cancer.

Modeling correlated pairs of mammogram images.

Mammography remains the primary screening strategy for breast cancer, which continues to be the most prevalent cancer diagnosis among women globally. Because screening mammograms capture both the left and right breast, there is a nonnegligible correlation between the pair of images. Previous studies have explored the concept of averaging between the pair of images after proper image registration; however, no comparison has been made in directly utilizing the paired images. In this paper, we extend the bivariate functional principal component analysis over triangulations to jointly characterize the pair of imaging data bounded in an irregular domain and then nest the extracted features within the survival model to predict the onset of breast cancer. The method is applied to our motivating data from the Joanne Knight Breast Health Cohort at Siteman Cancer Center. Our findings indicate that there was no statistically significant difference in model discrimination performance between averaging the pair of images and jointly modeling the two images. Although the breast cancer study did not reveal any significant difference, it is worth noting that the methods proposed here can be readily extended to other studies involving paired or multivariate imaging data.

Racial differences in treatment and survival among older patients with multiple myeloma.

BACKGROUND: Treatments for multiple myeloma (MM) have evolved over time and improved MM survival. While racial differences in MM treatment and prognosis between non-Hispanic African American (NHAA) and non-Hispanic White (NHW) patients are well-established, it is unclear whether they have persisted after the introduction of novel agents. METHODS: Using the Surveillance, Epidemiology, and End Results-Medicare linked database, our study investigated racial difference in the receipt of treatment within 1 year following diagnosis and assessed survival outcomes among Medicare beneficiaries (≥66 years) diagnosed with MM from 2007 to 2017. We applied multivariable Cox proportional hazards models to estimate the association between race and survival and presented hazard ratios (HRs). RESULTS: Of 2094 NHAA and 11,983 NHW older patients with MM, 59.5% and 64.8% received treatment during the first year, respectively. Discrepancy in the proportion of patients receiving treatment between the two groups increased from 2.9% in 2007 to 2009 to 6.9% in 2014-2017. After controlling for relevant factors, patients who received treatment within the first year had lower mortality than those who did not (HR = 0.90, 95% confidence interval [CI]: 0.86-0.94). NHAA patients had a lower probability to receive treatments during the first year than NHW patients (HR = 0.91, 95% CI: 0.85-0.97) but had lower mortality (HR = 0.94, 95% CI: 0.88-1.00). The lower mortality was only observed among patients who received no treatment (HR = 0.84, 95% CI: 0.77-0.93); NHAA and NHW patients who received treatment had similar survival (p = 0.63). CONCLUSIONS: The increasing racial disparity in treatment utilization over time is concerning. Efforts are needed to eliminate the barriers of receiving treatment.

Plant Foods Intake and Risk of Premature Aging in Adult Survivors of Childhood Cancer in the St Jude Lifetime Cohort (SJLIFE).

PURPOSE: To identify dietary factors that are related to premature aging in adult survivors of childhood cancer, we examined the associations between plant food intakes and age-related deficit accumulation. METHODS: A total of 3,322 childhood cancer survivors (age 18-65 years, mean = 31, standard deviation = 8.4) in the St Jude Lifetime Cohort had total fruit, total vegetables and subgroups, whole grains, refined grains, nuts/seeds, and nutrients intake assessed using a food frequency questionnaire. Premature aging at baseline was assessed by the deficit accumulation index (DAI) and categorized as low, medium, and high risk. Multinomial logistic regressions (reference: low risk) adjusting for confounders estimated odds ratios (ORs) and 95% CIs. Multivariable linear regression of a continuous intake against a continuous DAI was also performed. RESULTS: Dark green vegetable (ORhigh v low = 0.47 [95% CI, 0.28 to 0.78] per 1/2 cup/1,000 kcal increment) and nuts/seeds intakes (ORhigh v low = 0.71 [95% CI, 0.47 to 1.08] per 1 oz/1,000 kcal increment; coefficientlinear = -0.0115, P = .02) were associated with a lower risk of premature aging. Conversely, refined grain intake was related to an increased risk of premature aging (ORhigh v low = 1.33 [95% CI, 0.99 to 1.78], per 1 oz/1,000 kcal increment; coefficientlinear = 0.0093, P = .005). Fruit and whole grain intakes were not associated with premature aging risk. Among nutrients abundant in plant foods, dietary folate intake was associated with a lower risk of premature aging (ORhigh v low = 0.89 [95% CI, 0.80 to 0.99] per 50 mcg/1,000 kcal increase). Beta-carotene, lutein/zeaxanthin, and vitamin E intakes from foods were also related to a modestly lower, but not statistically significant, risk of premature aging. CONCLUSION: Specific plant foods are associated with lower risk of premature aging, providing targets for the interventions to promote healthy aging in childhood cancer survivors.

Avoiding lead-time bias by estimating stage-specific proportions of cancer and non-cancer deaths.

PURPOSE: Understanding how stage at cancer diagnosis influences cause of death, an endpoint that is not susceptible to lead-time bias, can inform population-level outcomes of cancer screening. METHODS: Using data from 17 US Surveillance, Epidemiology, and End Results registries for 1,154,515 persons aged 50-84 years at cancer diagnosis in 2006-2010, we evaluated proportional causes of death by cancer type and uniformly classified stage, following or extrapolating all patients until death through 2020. RESULTS: Most cancer patients diagnosed at stages I-II did not go on to die from their index cancer, whereas most patients diagnosed at stage IV did. For patients diagnosed with any cancer at stages I-II, an estimated 26% of deaths were due to the index cancer, 63% due to non-cancer causes, and 12% due to a subsequent primary (non-index) cancer. In contrast, for patients diagnosed with any stage IV cancer, 85% of deaths were attributed to the index cancer, with 13% non-cancer and 2% non-index-cancer deaths. Index cancer mortality from stages I-II cancer was proportionally lowest for thyroid, melanoma, uterus, prostate, and breast, and highest for pancreas, liver, esophagus, lung, and stomach. CONCLUSION: Across all cancer types, the percentage of patients who went on to die from their cancer was over three times greater when the cancer was diagnosed at stage IV than stages I-II. As mortality patterns are not influenced by lead-time bias, these data suggest that earlier detection is likely to improve outcomes across cancer types, including those currently unscreened.

Dietary patterns and their associations with sociodemographic and lifestyle factors in adult survivors of childhood cancer: a cross-sectional study.

BACKGROUND: Little is known about the specific dietary patterns in adult survivors of childhood cancer. OBJECTIVES: We aimed to identify dietary patterns specific to childhood cancer survivors and examine their associations with sociodemographic and lifestyle factors. METHODS: Adult survivors of childhood cancer (mean:31 ± 8 y; n = 3022) and noncancer controls (n = 497) in the St. Jude Lifetime Cohort self-reported diet over the past 12 mo using a validated food frequency questionnaire. Factor analysis with 48 predefined food groups was performed to identify foods consumed together. Subsequently, cluster analysis with energy-adjusted factor scores was used to categorize survivors into a mutually exclusive dietary pattern. Dietary patterns were the primary outcomes. Multivariable multinomial logistic regressions were used to cross-sectionally examine associations between sociodemographic and lifestyle factors and dietary patterns in cancer survivors. RESULTS: Among the 4 dietary patterns identified, the fast-food pattern (36 %) was the most common, followed by the Western contemporary (30 %), the plant-based (20 %), and the animal-based (14 %) patterns in childhood cancer survivors. By contrast, the plant-based (38 %) and fast-food patterns (29 %) were prevalent in controls. In survivors, male sex, younger age, lower educational attainment, and physical inactivity were associated with the fast-food, Western contemporary, or animal-based pattern. Compared with non-Hispanic White survivors consuming the plant-based diet, non-Hispanic Black survivors were 2-5 times more likely to consume the fast-food [odds ratio (OR:= 2.76; 95 % CI: 1.82, 4.18) or the animal-based diet (OR: 5.61; 95 % CI: 3.58, 8.78)]. Moreover, survivors residing in the most deprived area were 2-3 times more likely to consume the fast-food, Western contemporary, or animal-based diet. CONCLUSIONS: Unhealthy dietary patterns are prevalent in adult survivors of childhood cancer, especially those with lower socioeconomic status and racial minorities. Interventions to improve diet and health in childhood cancer survivors need to concurrently address disparities that contribute to adherence to healthy dietary practices. This trial was registered at clinicaltrials.gov as NCT00760656 (https://classic. CLINICALTRIALS: gov/ct2/show/NCT00760656).

Pectoral muscle removal in mammogram images: A novel approach for improved accuracy and efficiency.

PURPOSE: Accurate pectoral muscle removal is critical in mammographic breast density estimation and many other computer-aided algorithms. We propose a novel approach to remove pectoral muscles form mediolateral oblique (MLO) view mammograms and compare accuracy and computational efficiency with existing method (Libra). METHODS: A pectoral muscle identification pipeline was developed. The image is first binarized to enhance contrast and then the Canny algorithm was applied for edge detection. Robust interpolation is used to smooth out the pectoral muscle region. Accuracy and computational speed of pectoral muscle identification was assessed using 951 women (1,902 MLO mammograms) from the Joanne Knight Breast Health Cohort at Washington University School of Medicine. RESULTS: Our proposed algorithm exhibits lower mean error of 12.22% in comparison to Libra's estimated error of 20.44%. This 40% gain in accuracy was statistically significant (p < 0.001). The computational time for the proposed algorithm is 5.4 times faster when compared to Libra (5.1 s for proposed vs. 27.7 s for Libra per mammogram). CONCLUSION: We present a novel approach for pectoral muscle removal in mammogram images that demonstrates significant improvement in accuracy and efficiency compared to existing method. Our findings have important implications for the development of computer-aided systems and other automated tools in this field.

Natural Language Processing-Assisted Classification Models to Confirm Monoclonal Gammopathy of Undetermined Significance and Progression in Veterans' Electronic Health Records.

PURPOSE: To develop and validate natural language processing (NLP)-assisted machine learning (ML)-based classification models to confirm diagnoses of monoclonal gammopathy of undetermined significance (MGUS) and multiple myeloma (MM) from electronic health records (EHRs) in the Veterans Health Administration (VHA). MATERIALS AND METHODS: We developed precompiled lexicons and classification rules as features for the following ML classifiers: logistic regression, random forest, and support vector machines (SVMs). These features were trained on 36,044 EHR documents from a random sample of 400 patients with at least one International Classification of Disease code for MGUS diagnosis from 1999 to 2021. The best-performing feature combination was calibrated in the validation set (17,826 documents/200 patients) and evaluated in the testing set (9,250 documents/100 patients). Model performance in diagnosis confirmation was compared with manual chart review results (gold standard) using recall, precision, accuracy, and F1 score. For patients correctly labeled as disease-positive, the difference between model-identified diagnosis dates and the gold standard was also computed. RESULTS: In the testing set, the NLP-assisted classification model using SVMs achieved best performance in both MGUS and MM confirmation with recall/precision/accuracy/F1 of 98.8%/93.3%/93.0%/96.0% for MGUS and 100.0%/92.3%/99.0%/96.0% for MM. Dates of diagnoses matched (±45 days) with those of gold standard in 73.0% of model-confirmed MGUS and 84.6% of model-confirmed MM. CONCLUSION: An NLP-assisted classification model can reliably confirm MGUS and MM diagnoses and dates and extract laboratory results using automated interpretation of EHR data. This algorithm has the potential to be adapted to other disease areas in VHA EHR system.

Differential chromatin accessibility and transcriptional dynamics define breast cancer subtypes and their lineages.

Breast cancer is a heterogeneous disease, and treatment is guided by biomarker profiles representing distinct molecular subtypes. Breast cancer arises from the breast ductal epithelium, and experimental data suggests breast cancer subtypes have different cells of origin within that lineage. The precise cells of origin for each subtype and the transcriptional networks that characterize these tumor-normal lineages are not established. In this work, we applied bulk, single-cell (sc), and single-nucleus (sn) multi-omic techniques as well as spatial transcriptomics and multiplex imaging on 61 samples from 37 breast cancer patients to show characteristic links in gene expression and chromatin accessibility between breast cancer subtypes and their putative cells of origin. We applied the PAM50 subtyping algorithm in tandem with bulk RNA-seq and snRNA-seq to reliably subtype even low-purity tumor samples and confirm promoter accessibility using snATAC. Trajectory analysis of chromatin accessibility and differentially accessible motifs clearly connected progenitor populations with breast cancer subtypes supporting the cell of origin for basal-like and luminal A and B tumors. Regulatory network analysis of transcription factors underscored the importance of BHLHE40 in luminal breast cancer and luminal mature cells, and KLF5 in basal-like tumors and luminal progenitor cells. Furthermore, we identify key genes defining the basal-like ( PRKCA , SOX6 , RGS6 , KCNQ3 ) and luminal A/B ( FAM155A , LRP1B ) lineages, with expression in both precursor and cancer cells and further upregulation in tumors. Exhausted CTLA4-expressing CD8+ T cells were enriched in basal-like breast cancer, suggesting altered means of immune dysfunction among breast cancer subtypes. We used spatial transcriptomics and multiplex imaging to provide spatial detail for key markers of benign and malignant cell types and immune cell colocation. These findings demonstrate analysis of paired transcription and chromatin accessibility at the single cell level is a powerful tool for investigating breast cancer lineage development and highlight transcriptional networks that define basal and luminal breast cancer lineages.

Approaches to developing de novo cancer population models to examine questions about cancer and race in bladder, gastric, and endometrial cancer and multiple myeloma: the Cancer Intervention and Surveillance Modeling Network incubator program.

BACKGROUND: We are developing 10 de novo population-level mathematical models in 4 malignancies (multiple myeloma and bladder, gastric, and uterine cancers). Each of these sites has documented disparities in outcome that are believed to be downstream effects of systemic racism. METHODS: Ten models are being independently developed as part of the Cancer Intervention and Surveillance Modeling Network incubator program. These models simulate trends in cancer incidence, early diagnosis, treatment, and mortality for the general population and are stratified by racial subgroup. Model inputs are based on large population datasets, clinical trials, and observational studies. Some core parameters are shared, and other parameters are model specific. All models are microsimulation models that use self-reported race to stratify model inputs. They can simulate the distribution of relevant risk factors (eg, smoking, obesity) and insurance status (for multiple myeloma and uterine cancer) in US birth cohorts and population. DISCUSSION: The models aim to refine approaches in prevention, detection, and management of 4 cancers given uncertainties and constraints. They will help explore whether the observed racial disparities are explainable by inequities, assess the effects of existing and potential cancer prevention and control policies on health equity and disparities, and identify policies that balance efficiency and fairness in decreasing cancer mortality.

Alvin J. Siteman Cancer Center: Cancer Prevention Perspective.

We summarize Siteman Cancer Center catchment that covers 82 counties in southern Illinois and eastern Missouri. We note both the high poverty and cancer rates in many rural counties. Siteman Community Outreach and Engagement has developed a number of strategies to move towards achieving health equity. These include NCI-funded research projects in rural clinics and outreach to improve access to cancer prevention services. To increase capacity for community-engaged research, we have developed and refined a Community Research Fellows Training Program.

Disparities in Multiple Myeloma Treatment Patterns in the United States: A Systematic Review.

We performed a systematic review of the literature investigating the demographic and insurance-related factors linked to disparities in multiple myeloma (MM) care patterns in the United States from 2003 to 2021. Forty-six observational studies were included. Disparities in MM care patterns were reported based on patient race in 76% of studies (34 out of 45 that captured race as a study variable), ethnicity in 60% (12 out of 20), insurance in 77% (17 out of 22), and distance from treating facility, urbanicity, or geographic region in 62% (13 out of 21). A smaller proportion of studies identified disparities in MM care patterns based on other socioeconomic characteristics, with 36% (9 out of 25) identifying disparities based on income estimate or employment status and 43% (6 out of 14) based on language barrier or education-related factors. Sociodemographic characteristics are frequently associated with disparities in care for individuals diagnosed with MM. There is a need for further research regarding modifiable determinants to accessing care such as insurance plan design, patient out-of-pocket costs, preauthorization criteria, as well as social determinants of health. This information can be used to develop actionable strategies for reducing MM health disparities and enhancing timely and high-quality MM care.

Disentangling age, gender, and racial/ethnic disparities in multiple myeloma burden: a modeling study.

Multiple myeloma (MM) is a hematological malignancy that is consistently preceded by an asymptomatic condition, monoclonal gammopathy of undetermined significance (MGUS). Disparities by age, gender, and race/ethnicity in both MGUS and MM are well-established. However, it remains unclear whether these disparities can be explained by increased incidence of MGUS and/or accelerated progression from MGUS to MM. Here, we fit a mathematical model to nationally representative data from the United States and showed that the difference in MM incidence can be explained by an increased incidence of MGUS among male and non-Hispanic Black populations. We did not find evidence showing differences in the rate of progression from MGUS to MM by either gender or race/ethnicity. Our results suggest that screening for MGUS among high-risk groups (e.g., non-Hispanic Black men) may hold promise as a strategy to reduce the burden and MM health disparities.

Adherence to healthy diet and risk of cardiovascular disease in adult survivors of childhood cancer in the St. Jude Lifetime Cohort: a cross-sectional study.

BACKGROUND: Whether diet has beneficial effects on cardiovascular disease (CVD) in childhood cancer survivors as in the general population is unknown. Therefore, we examined associations between dietary patterns and risk of CVD in adult survivors of childhood cancer. METHODS: Childhood cancer survivors, 18-65 years old in the St Jude Lifetime Cohort (1882 men and 1634 women) were included in the analysis. Dietary patterns were defined by the adherence to the Healthy Eating Index (HEI)-2015, Dietary Approaches to Stop Hypertension (DASH), and alternate Mediterranean diet (aMED) based on a food frequency questionnaire at study entry. CVD cases (323 in men and 213 in women) were defined as participants with at least one grade 2 or higher CVD-related diagnosis at baseline. Multivariable logistic regression adjusted for confounders was used to estimate odds ratios (ORs) and 95% confidence intervals (CIs) of CVD. RESULTS: Greater adherence to HEI-2015 (OR=0.88, 95% CI: 0.75-1.03, per 10 score increment), DASH (OR=0.85, 95% CI: 0.71-1.01, per 10 score increment), and aMED (OR=0.92, 95% CI: 0.84-1.00, each score increment) were, albeit trending towards significance, associated with a lower risk of CVD in women. HEI-2015 was associated with a non-significantly lower risk of CVD in men (ORQ5 vs. Q1=0.80, 95% CI: 0.50-1.28). These dietary patterns were also associated with a lower risk of CVD in survivors with high underlying CVD risk. CONCLUSIONS: As recommended to the general population, a diet rich in plant foods and moderate in animal foods needs to be a part of CVD management and prevention in childhood cancer survivors.

Association and Prediction Utilizing Craniocaudal and Mediolateral Oblique View Digital Mammography and Long-Term Breast Cancer Risk.

Mammographic percentage of volumetric density is an important risk factor for breast cancer. Epidemiology studies historically used film images often limited to craniocaudal (CC) views to estimate area-based breast density. More recent studies using digital mammography images typically use the averaged density between craniocaudal (CC) and mediolateral oblique (MLO) view mammography for 5- and 10-year risk prediction. The performance in using either and both mammogram views has not been well-investigated. We use 3,804 full-field digital mammograms from the Joanne Knight Breast Health Cohort (294 incident cases and 657 controls), to quantity the association between volumetric percentage of density extracted from either and both mammography views and to assess the 5 and 10-year breast cancer risk prediction performance. Our results show that the association between percent volumetric density from CC, MLO, and the average between the two, retain essentially the same association with breast cancer risk. The 5- and 10-year risk prediction also shows similar prediction accuracy. Thus, one view is sufficient to assess association and predict future risk of breast cancer over a 5 or 10-year interval. PREVENTION RELEVANCE: Expanding use of digital mammography and repeated screening provides opportunities for risk assessment. To use these images for risk estimates and guide risk management in real time requires efficient processing. Evaluating the contribution of different views to prediction performance can guide future applications for risk management in routine care.

Mortality in the US Populations With Monoclonal Gammopathy of Undetermined Significance.

This cohort study analyzes a nationally representative sample with a screening test for monoclonal gammopathy of undetermined significance (MGUS) to evaluate overall survival of populations with MGUS compared with those without MGUS among the general population in the US.