bluuebunny/crossref_metadata_2025_split huggingface dataset:
Constitutional BRCA1 methylation and risk of incident triple-negative breast cancer and high-grade serous ovarian cancer.
Authors: Lonning Per Eystein , Nikolaienko Oleksii , Pan Kathy , Kurian Allison W. , Eikesdal Hans Petter Petter , Pettinger Mary , Anderson Garnet L , Prentice Ross L , Chlebowski Rowan T. , Knappskog Stian 
Published: June 2022

10509 Background: About 25% of all triple-negative breast cancer (TNBC) and 10–20% of high-grade serous ovarian cancers (HGSOC) harbor BRCA1 promoter methylation. While constitutional BRCA1 promoter methylation has been observed in normal tissues of some individuals, the potential role of normal tissue methylation as a risk factor for incident TNBC or HGSOC risk is unknown. Methods: The objective of this study was to assess potential correlation between white blood cell (WBC) BRCA1 promoter methylation and subsequent risk of incident TNBC and HGSOC. To do so, we analyzed samples from women participating in the Women’s Health Initiative (WHI) study who had not been diagnosed with either breast or ovarian cancer prior to study entrance. A total of n = 636 women developing incident TNBC and 509 women developing incident HGSOC were matched with cancer-free controls (n = 1838 and 2979) in a nested case-control design. Cancers were confirmed after central medical record review. Blood samples, collected at entry, were analyzed for BRCA1 promoter methylation by massive parallel sequencing. Associations between BRCA1 methylation and incident TNBC and incident HGSOC were analyzed by unconditional logistic regression. Results: Age at entry was 62 (median; range 50 to 79) years, with median interval to diagnosis of 9 (TNBC) and 10 (HGSOC) years. The presence of methylated BRCA1 alleles was significantly associated with higher risk of incident TNBC (OR 2.44, 95% CI 1.79–3.33; P <.001) and incident HGSOC (OR 1.87, 95% CI 1.32–2.61; P <.001). Restricting analyses to individuals with > 5 years between sampling and cancer diagnosis yielded similar results (> 5 years; TNBC: OR 2.53, 95% CI 1.81–3.54; P <.001; HGSOC: OR 1.81, 95% CI 1.21–2.63; P =.003). Across individuals, methylation was not haplotype-specific, arguing against an underlying cis-acting factor. Within individuals, BRCA1 methylation was observed on the same allele, indicating clonal expansion from a single methylation event. Conclusions: Constitutional normal tissue BRCA1 promoter methylation is significantly associated with risk of incident TNBC and HGSOC with implications for prediction of these cancers not associated with germline mutations. These findings warrant further research to determine if constitutional methylation of tumor suppressor genes are pan-cancer risk factors.
Characterization of time to diagnosis indicates shorter interval for screenable versus symptom-driven cancers.
Authors: Gainullin Vladimir , Tong Jing , Li Yali , Gupta Mudit , Lagerman Braxton , Blank Jackie , Limburg Paul J. , Hathaway Christine 
Published: June 2022

10526 Background: Cancer screening provides a straightforward diagnostic path compared to non-screening, which often requires complex evaluation for patients with non-specific symptoms. The time to diagnosis (from clinical presentation or screening event to definitive diagnosis) captures the clinical efficiency of the journey. The study characterizes time to diagnosis for cancers with and without USPSTF Grade A/B screening recommendations. This could inform the value of screening in diagnostic management and care efficiency improvements. Methods: The Geisinger Health System Phenomics Initiative Database provides deidentified EHR and tumor registry data. Patients aged 50-74 with encounters at least biennially for six years prior to a cancer diagnosis were frequency-matched to a non-cancer control group by diagnosis year. From 18 months prior to diagnosis, equally-sized random samples were drawn (with replacement) from a time-bounded sliding window at monthly intervals. Aggregated procedure and diagnostic code counts for cancer and non-cancer samples were used to calculate the Bray-Curtis dissimilarity (BCd) statistic. The start of the diagnostic path was defined as the earliest date with a monotonically-increasing statistically-significant difference in BCd (95% CI), and was stratified into cancers with and without screening and by stage. Results: 8188 new cancer cases were identified between 2010-2019; 40% had an established screening modality (breast, lung, colorectal, cervical), 55% of which were screened prior to diagnosis. The time to diagnosis for non-screenable cancers was 6 months versus 3 months for screenable cancers. The BCd for procedures differed by 0.029 (95% CI: 0.021-0.037) between the two groups at 6 months prior to diagnosis. Stages I and IV had a time to diagnosis of 3 months across all cancers versus 7 and 6 months for stages II and III, respectively. Conclusions: We report the first known quantification of time to diagnosis across multiple cancers. Screenable cancers were associated with shorter time to diagnosis than non-screenable cancers. Stage II and III cancers took longer to diagnose than stage I and IV, likely related to early detection by screening in the former and severe symptom presentation in the latter. These novel data highlight improved time to diagnosis as another achievable benefit from expanded, multi-cancer screening.
Robust artificial intelligence-powered imaging biomarker based on mammography for risk prediction of breast cancer.
Authors: Park Eun Kyung , Lee Hyeonsoo , Kim Minjeong , Kim Ki Hwan , Nam Hyeonseob , Chang Yoosoo , Ryu Seungho 
Published: June 2022

10533 Background: Accurate risk assessment allows the precise personalized screening of breast cancer. Conventional statistical risk models have been used that estimate the probability of breast cancer incidence with patient demographics, detailed personal and family history. The purpose of this study was to investigate the feasibility of the AI-powered Imaging Biomarker in mammography (IBM) which was developed to predict the risk of future breast cancer beyond the mammographic breast density. Methods: We trained and developed the deep learning risk model, the AI-powered IBM, using a total of 36,955 examinations from 21,438 patients, who underwent at least one mammogram using Hologic or Siemens machines and pathology-confirmed breast cancer outcomes. To discover the feasibility of the AI-powered IBM, mammograms and various clinical information including pathology-confirmed breast cancer outcomes were collected, which were only used for external validation. C-indices and receiver operating characteristic (ROC) curves for 1- to 5-year outcomes were obtained. We compared 5-year ROC area under the curves (AUCs) of our AI-powered IBM and statistical risk models including the Tyrer-Cuzick model and the Gail model, which were most commonly used and widely known, using DeLong’s test. Results: A total of 16,894 mammograms were collected for external validation, of which 4,002 were followed by a cancer diagnosis within 5 years. Our AI-powered IBM obtained C-index of 0.758, and the model demonstrated the risk of breast cancer with AUC of 0.895 (95% CI: 0.880, 0.909) at 1-year, 0.839 (0.824, 0.852) at 2-year, 0.807 (0.794, 0.819) at 3-year, 0.783 (0.7947, 0.819) at 4-year. The 5-year AUC of our AI-powered IBM was 0.808 (0.792, 0.822). Our AI-powered IBM showed significantly higher 5-year AUC than the Gail model (AUC: 0.572, P< 0.001) and the Tyrer-Cuzick model (0.569, P< 0.001). Conclusions: A deep learning AI-powered IBM using mammograms has a substantial potential to advance toward the robust risk prediction of breast cancer over conventional risk models. This approach for risk stratification of breast cancer might be feasible to improve personalized screening programs.
The role of health information technology in improving awareness of HPV and HPV vaccine among U.S. adults: Insights from the health information national trends survey.
Authors: Dosunmu Gideon T , Onyeaka Henry , Kyeremeh Emmanuel , Jalwa Mohammed , Onaku Emmanuella , Ofori Jillian , Amonoo Hermioni L. , Ashfaq Muhammmad Areeb , Khushman Moh'd M. 
Published: June 2022

10535 Background: Despite advances in cancer prevention and wide-spread availability of Human papilloma virus (HPV) vaccines, US adults continue to have suboptimal HPV vaccination uptake with less than 50% vaccinated. Strategies aimed at enhancing HPV-related awareness are considered one of the most effective ways to improve HPV vaccine adoption and potentially eliminate HPV-related cancers. Health information technology (HIT) may influence HPV-related awareness and subsequently drive vaccine adoption. This study assessed the impact of HIT utilization on HPV and HPV vaccine awareness. Methods: Data was obtained from Health Information National Trends Survey (HINTS 5 cycles 1 and 2). Cross-sectional sample of 6,522 individuals aged 18 years or more was analyzed. The independent variables were use of smartphone, computer, or electronic means to (i) look up health information, (ii) fill a prescription online, (iii) communicate with a doctor or doctor’s office, (iv) look up test results of and (v) track health care charges. The dependent variables were HPV and HPV vaccine awareness. Chi-square analysis was used to evaluate group differences, and a multiple logistic regression was used to analyze the association between HIT utilization and HPV-related awareness controlling for sociodemographic and health-related factors. Results: Of the total sample, awareness of HPV and HPV vaccine was 62.7% and 61.8% respectively. In adjusted multivariable logistic regression analysis, those who utilized a smartphone, computer, or electronic means to look up health information (aOR 2.23; 95% CI 1.68 – 2.97, p < 0.001), communicate with healthcare provider (aOR 1.53; 95% CI 1.23 – 1.91, p < 0.001), look up test results (aOR 1.67; 95% CI 1.35 – 2.08, p < 0.001), and track health care charges (aOR 1.61; 95% CI 1.26 – 2.05, p < 0.001), were more likely to endorse HPV awareness than those who did not. Similar positive associations were observed for HIT utilization and HPV vaccine awareness. Conclusions: Our findings showed a positive association between HIT utilization and HPV-related awareness. Amid a background of sub-optimal HPV vaccination and explosion in technology, these results emphasize the potential for the role of HIT in preventive medicine. Strategies that integrate HIT into vaccine interventions and communications should be encouraged as a medium to expand HPV awareness and vaccine coverage.
Cross-ancestry polygenic risk score for breast cancer risk assessment.
Authors: Tshiaba Placede , Sun Jiayi , Ratman Dariusz , Tunstall Tate , Levy Brynn , Shah Premal , Rabinowitz Matthew , Kumar Akash , Im Kate 
Published: June 2022

10540 Background: Breast cancer (BC) risk is influenced by many common variants with small effects. Polygenic risk scores (PRS) weight these variants based on genome-wide association studies (GWAS) and aggregate them into a single measure. PRS has primarily shown benefit in Caucasian women. We established a cross-ancestry polygenic model (caPRS) which assesses risk of breast cancer across multiple ancestries. Methods: Performance of multiple BC polygenic models, both published and developed in-house, were evaluated for each of five ancestry groups: European, African, South Asian, East Asian, and Admixed American. To account for ancestry-specific mean and variance, we computed principal components (PCs) for all women by projecting their genotypes onto PCs calculated on individuals in the 1000 Genomes Project (1KGP). We next centered each ancestry-specific PRS by subtracting the PRS predicted from a linear regression of PRS against the first four PCs in unaffected individuals. Each centered PRS was then divided by the SD of the corresponding 1KGP population. We defined a cross-ancestry polygenic model as a linear combination of the best performing PRS model for each ancestry group weighted by fractional ancestry. Association of the caPRS with breast cancer risk was tested in a validation cohort of >130,000 women consisting of multiple independent cohorts (the Women’s Health Initiative, the Multiethnic Cohort, the ROOT cohort and the UK Biobank) using a multivariate logistic regression model that included caPRS, age, self-reported ancestry, personal history of ovarian cancer (when available) and first-degree family history of BC. Discrimination was assessed by the odds ratio (OR) per SD and the area under the receiver-operator curve (AUC). Results: This study included women with African/Black, East Asian, Caucasian/White, Hispanic/Latino, South Asian and ‘Other’ self-reported ancestry. The ancestry-specific models included in the caPRS ranged in size from 173 to >800,000 variants. The caPRS was associated with BC risk for women in each self-reported ancestry (Table). The caPRS offered a modest increase in performance over a commonly implemented 313-SNP PRS in non-European ancestries, most significantly in African/Black women where the OR per SD increased from 1.24 (1.08 - 1.43), p-value 2.3x10-3. Conclusions: The caPRS performed well for women of any ancestry and allows flexibility to update ancestry-specific models. These results suggest the caPRS has the potential to improve the clinical utility of existing clinical risk predictors. [Table: see text]
Predictors of use of prevention strategies among women at high-risk for breast cancer.
Authors: Landry Kara , Tuo Ya , Levine Emma , Srikureja Anya , Wood Marie 
Published: June 2022

10554 Background: Uptake of chemoprevention and prophylactic surgery among women at high risk for breast cancer is low, despite proven efficacy. We evaluated breast cancer risk factors as predictors of uptake of prevention treatment (PT) to better understand potential associations with these decisions. Methods: An IRB approved registry established in 2003 at the University of Vermont of high-risk women was used to evaluate the association between both modifiable (obesity, sedentary lifestyle and alcohol use) and non-modifiable (age, history of benign breast disease [BDD], genetic predisposition) risk factors and uptake of PT. Women were eligible for inclusion in this analysis if they had one of the following risk factors (BDD, strong family history, > 20% lifetime modeled risk or genetic predisposition) and completed questionnaires regarding diet and physical activity. Alcohol use was assessed using a health questionnaire and physical activity was assessed using a 7-Day Physical Activity Recall questionnaire. We used logistic regression to estimate odds ratios (OR). Results: 504 women were included and had been followed for median of 13 years. Mean age was 44.5 years (range 19 – 75), 98% were Caucasian, and mean BMI was 26.9 (range 17-57). 78% had a family history of breast cancer, 60% had >20% lifetime modeled risk, 14% had a history of benign breast disease (BBD), and 9% of women had confirmed genetic risk in high or moderate risk genes. Women may have had more than one risk factor. 55% were physically active and 55%. 4.2% were sedentary and 12.3% consumed > 1 alcoholic beverage daily. 20.8% of the cohort participated in PT (8.1% took chemoprevention, 2.0% underwent prophylactic bilateral total mastectomy and 10.7% underwent risk reducing salpingo-oophorectomy). Among non-modifiable risk factors, age, genetic predisposition (ORadj 8.66, 95% CI 2.30–32.33, p < 0.0001), and history of benign breast disease (ORadj 4.09, 95% CI 1.89–8.86, p < 0.001) were associated with uptake of PT. Increasing age was associated with increasing uptake (ptrend was > 0.0001) and highest among women aged 60-69 years (ORadj8.19, 95% CI 2.87-23.37 relative to women aged < 40). Women with a strong family history were significantly less likely to take up PT (p = 0.04). BMI, physical activity status and alcohol use were not associated with uptake of PT. Conclusions: Like other studies, we found a low uptake of PT among women at high risk for developing breast cancer. We found that modifiable risk factors were not associated with uptake of PT, which might suggest that high-risk women with modifiable risk factors could be targeted for interventions designed to modify risk (i.e. chemoprevention, weight reduction, etc). Interestingly, women with a strong family history may be less likely to take up PT, and studies to further examine this relationship may identify opportunities for interventions to improve uptake.
An update on the overall epidemiology, clinical characteristics, and outcomes from the COVID-19 and Cancer Consortium (CCC19).
Authors: Shah Dimpy P , Shah Pankil , Warner Jeremy Lyle , Batist Gerald , Friese Christopher Ryan , Griffiths Elizabeth A. , Hwang Clara , Vieira Kendra , McKay Rana R. , Mesa Ruben A. , Puc Matthew , Robilotti Elizabeth M , Ruiz-Garcia Erika , Portuguese Andrew Jay , Schmidt Andrew Lachlan , Weissmann Lisa B. , Wise-Draper Trisha Michel , Barnholtz-Sloan Jill , Peters Solange , Grivas Petros ,   COVID-19 and Cancer Consortium
Published: June 2022

10565 Background: Despite mitigation and treatment strategies, COVID-19 continues to negatively impact patients (pts) with cancer. Identifying factors that remain consistently associated with morbidity and mortality is critical for risk identification and care delivery. Methods: Using CCC19 registry data through 12/31/2021 we report clinical outcomes (30-day case fatality rate [CFR], mechanical ventilation use (MV), intensive care unit admission (ICU), and hospitalization) in adult pts with cancer and laboratory confirmed SARS-CoV-2, stratified by patient, cancer, and treatment-related factors. Results: In this cohort of 11,417 pts (with 4% reported vaccination prior to COVID-19), 55% required hospitalization, 15% ICU, 9% MV, and 12% died. Overall outcome rates remained similar for 2020 and 2021 (Table). Hydroxychloroquine was utilized in 11% and other anti-COVID-19 drugs (remdesivir, tocilizumab, convalescent plasma, and/or steroids) in 30%. Higher CFRs were observed in older age, males, Black race, smoking (14%), comorbidities (pulmonary [17%], diabetes mellitus [16%], cardiovascular [19%], renal [21%]), ECOG performance status 2+ (31%), co-infection (25%), especially fungal (35%), and initial presentation with severe COVID-19 (48%). Pts with hematologic malignancy, active/progressing cancer status, or receiving systemic anti-cancer therapy within 1-3 months prior to COVID-19 also had worse CFRs. CFRs were similar across anti-cancer modalities. Other outcomes (ICU, MV, hospitalization) followed similar distributions by pt characteristics. Conclusions: Unfavorable outcome rates continue to remain high over 2 years, despite fewer case reports in 2021 owing to multiple factors (e.g., pandemic dynamics, respondent fatigue, overwhelmed healthcare systems). Pts with specific socio-demographics, performance status, comorbidities, type and status of cancer, immunosuppressive therapies, and COVID-19 severity at presentation experienced worse COVID-19 severity; and these factors should be further examined through multivariable modeling. Understanding epidemiological features, patient and cancer-related factors, and impact of anti-COVID-19 interventions can help inform risk stratification and interpretation of results from clinical trials. [Table: see text]
Genetic ancestry correlates of the cancer somatic mutational landscape from tumor profiling data of 50,000 patients with cancer.
Authors: De La Vega Francisco , Rhead Brooke , Pouliot Yannick , Guinney Justin 
Published: June 2022

10573 Background: The incidence and mortality of cancer vary widely across race and ethnicity. This is attributed to an interplay of socioeconomic factors, environmental exposures, and genetic background. Cancer genomic studies have underrepresented minorities and individuals of non-European descent, thus limiting a comprehensive understanding of disparities in the diagnosis, prognosis, and treatment of cancer among these populations. Furthermore, the social constructs of race and ethnicity are far from precise categories to understand the biological underpinnings of such differences. In this study, we use a large real-world data (RWD) patient cohort to examine associations of genetic ancestry with somatic alterations in known cancer driver genes. Methods: We inferred genetic ancestry from approximately 50,000 de-identified records from cancer patients of diverse histology who underwent tumor genomic profiling with the Tempus xT next-generation sequencing (NGS) assay. Our cohort includes patients with brain, breast, colorectal, hematopoietic, lung, and ovarian cancers, among others. We used 654 ancestry informative markers selected to overlap the target regions of the 648-gene Tempus xT NGS assay to infer global ancestry proportions at the continental level: Africa (AFR), America (AMR), Europe (EUR), East Asia (EAS), and South Asia (SAS). We imputed race/ethnicity categories using ancestry proportions on subjects lacking such metadata. Results: Most patients were of European descent (72%), however, continental genetic ancestry inference identified 4.7 and 3.8-fold more patients with substantial (> 50%) AFR and AMR ancestry, correspondingly, compared with TCGA. We observed higher percentages of AFR ancestry patients with prostate, breast, and colorectal cancer (1.8-3.1%) and AMR ancestry patients with colorectal cancer (2.4%) compared to the overall cohort-level distributions (p < 0.05). Using imputation, we identified 60% and 121% more patients as likely Black and Hispanic/Latino, respectively. We observed several associations between genetic ancestry with tumor mutation burden (TMB), e.g., a reduction in median TMB in Asian breast cancer patients (Asian TMB mean = 2.3 m/Mb vs 4.4 for Black, 3.3 for Hispanic, and 4.2 for White; p < 0.0001), in paired tumor/normal sequencing data. Furthermore, we found associations between ancestry and nonsynonymous somatic mutations in cancer genes, e.g. in CTNNB1 with EAS ancestry (OR = 1.18) and EGFR with EAS (OR = 1.24), AMR (OR = 1.30), and EUR (OR = 0.89) ancestries (all p < 0.001) in lung cancer patients. Conclusions: Our results support the use of genetic ancestry inference on RWD to improve upon the social constructs of race and ethnicity, allowing us to better understand the impact of shared germline genetic or exposure backgrounds into cancer mutational processes that influence incidence, progression, and outcomes.