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The Cancer Genome Atlas (TCGA) is one of the largest biorepositories of digital histology. Deep learning (DL) models have been trained on TCGA to predict numerous features directly from histology, including survival, gene expression patterns, and driver mutations. However, we demonstrate that these features vary substantially across tissue submitting sites in TCGA for over 3,000 patients with six cancer subtypes. Additionally, we show that histologic image differences between submitting sites can easily be identified with DL. Site detection remains possible despite commonly used color normalization and augmentation methods, and we quantify the image characteristics constituting this site-specific digital histology signature. We demonstrate that these site-specific signatures lead to biased accuracy for prediction of features including survival, genomic mutations, and tumor stage. Furthermore, ethnicity can also be inferred from site-specific signatures, which must be accounted for to ensure equitable application of DL. These site-specific signatures can lead to overoptimistic estimates of model performance, and we propose a quadratic programming method that abrogates this bias by ensuring models are not trained and validated on samples from the same site. Deep learning models have been trained on The Cancer Genome Atlas to predict numerous features directly from histology, including survival, gene expression patterns, and driver mutations. Here, the authors demonstrate that site-specific histologic signatures can lead to biased estimates of accuracy for such models, and propose a method to minimize such bias.

Among 695 women with clear-cell carcinoma of the vagina and cervix who were followed for a median of nearly 23 years, 5-year survival was 86% among women with documented diethylstilbestrol exposure and 81% among those without such exposure.

Purpose To evaluate weight change patterns over time following the diagnosis of breast cancer and to examine the association of post-diagnosis weight change and survival outcomes in Black and White patients. Methods The study included 2888 women diagnosed with non-metastatic breast cancer in 2000–2017 in Chicago. Longitudinal repeated measures of weight and height were collected, along with a questionnaire survey including questions on body size. Multilevel mixed-effects models were used to examine changes in body mass index (BMI). Delayed entry Cox proportional hazards models were used to investigate the impacts of changing slope of BMI on survival outcomes. Results At diagnosis, most patients were overweight or obese with a mean BMI of 27.5 kg/m 2 and 31.5 kg/m 2 for Blacks and Whites, respectively. Notably, about 45% of the patients had cachexia before death and substantial weight loss started about 30 months before death. In multivariable-adjusted analyses, compared to stable weight, BMI loss (> 0.5 kg/m 2 /year) showed greater than 2-fold increased risk in overall survival (hazard ratio [HR] = 2.60, 95% CI 1.88–3.59), breast cancer-specific survival (HR = 3.05, 95% CI 1.91–4.86), and disease-free survival (HR = 2.12, 95% CI 1.52–2.96). The associations were not modified by race, age at diagnosis, and pre-diagnostic weight. BMI gain (> 0.5 kg/m 2 /year) was also related to worse survival, but the effect was weak (HR = 1.60, 95% CI 1.10–2.33 for overall survival). Conclusion BMI loss is a strong predictor of worse breast cancer outcomes. Growing prevalence of obesity may hide diagnosis of cancer cachexia, which can occur in a large proportion of breast cancer patients long before death.

Prolonged survival of patients with stage IV breast cancer could change the role of radiotherapy for local control of breast primary, but its survival benefit remains unclear. Our aim is to investigate the survival benefit of radiotherapy in de novo stage IV breast cancer. Stage IV breast cancer patients who received breast surgery and have survived 12 months after diagnosis (landmark analysis) were included in the study from 2010 to 2015 of the National Cancer DataBase. Multivariable Cox models and a propensity score matching were used to control for confounding effects. Of 11,850 patients, 3629 (30.6%) underwent postoperative radiotherapy to breast or chest wall and 8221 (69.4%) did not. In multivariable analysis adjusting for multiple prognostic variables, postoperative radiotherapy was significantly associated with better survival (hazard ratio [HR] 0.74, 95% confidence interval [95%CI] 0.69–0.80; P  < 0.001). Radiotherapy was associated with improved survival in patients with bone ( P  < 0.001) or lung metastasis ( P  = 0.014), but not in patients with liver ( P  = 0.549) or brain metastasis ( P  = 0.407). Radiotherapy was also associated with improved survival in patients with one ( P  < 0.001) or two metastatic sites ( P  = 0.028), but not in patients with three or more metastatic sites ( P  = 0.916). The survival impact of radiotherapy did not differ among subtypes. The results of survival analysis in the propensity score-matched sub-cohort were precisely consistent with those of multivariable analysis. These real-world data show that postoperative radiotherapy might improve overall survival for de novo Stage IV breast cancer with bone or lung metastasis, regardless of subtypes.

Background Although several transcriptome-wide association studies (TWASs) have been performed to identify genes associated with overall breast cancer (BC) risk, only a few TWAS have explored the differences in estrogen receptor-positive (ER+) and estrogen receptor-negative (ER-) breast cancer. Additionally, these studies were based on gene expression prediction models trained primarily in breast tissue, and they did not account for alternative splicing of genes. Methods In this study, we utilized two approaches to perform multi-tissue TWASs of breast cancer by ER subtype: (1) an expression-based TWAS that combined TWAS signals for each gene across multiple tissues and (2) a splicing-based TWAS that combined TWAS signals of all excised introns for each gene across tissues. To perform this TWAS, we utilized summary statistics for ER + BC from the Breast Cancer Association Consortium (BCAC) and for ER- BC from a meta-analysis of BCAC and the Consortium of Investigators of Modifiers of BRCA1 and BRCA2 (CIMBA). Results In total, we identified 230 genes in 86 loci that were associated with ER + BC and 66 genes in 29 loci that were associated with ER- BC at a Bonferroni threshold of significance. Of these genes, 2 genes associated with ER + BC at the 1q21.1 locus were located at least 1 Mb from published GWAS hits. For several well-studied tumor suppressor genes such as TP53 and CHEK2 which have historically been thought to impact BC risk through rare, penetrant mutations, we discovered that common variants, which modulate gene expression, may additionally contribute to ER + or ER- etiology. Conclusions Our study comprehensively examined how differences in common variation contribute to molecular differences between ER + and ER- BC and introduces a novel, splicing-based framework that can be used in future TWAS studies.

Background For patients with breast cancer undergoing neoadjuvant chemotherapy (NACT), most of the existing prediction models of pathologic complete response (pCR) using clinicopathological features were based on standard statistical models like logistic regression, while models based on machine learning mostly utilized imaging data and/or gene expression data. This study aims to develop a robust and accessible machine learning model to predict pCR using clinicopathological features alone, which can be used to facilitate clinical decision-making in diverse settings. Methods The model was developed and validated within the National Cancer Data Base (NCDB, 2018–2020) and an external cohort at the University of Chicago (2010–2020). We compared logistic regression and machine learning models, and examined whether incorporating quantitative clinicopathological features improved model performance. Decision curve analysis was conducted to assess the model’s clinical utility. Results We identified 56,209 NCDB patients receiving NACT (pCR rate: 34.0%). The machine learning model incorporating quantitative clinicopathological features showed the best discrimination performance among all the fitted models [area under the receiver operating characteristic curve (AUC): 0.785, 95% confidence interval (CI): 0.778–0.792], along with outstanding calibration performance. The model performed best among patients with hormone receptor positive/human epidermal growth factor receptor 2 negative (HR+/HER2-) breast cancer (AUC: 0.817, 95% CI: 0.802–0.832); and by adopting a 7% prediction threshold, the model achieved 90.5% sensitivity and 48.8% specificity, with decision curve analysis finding a 23.1% net reduction in chemotherapy use. In the external testing set of 584 patients (pCR rate: 33.4%), the model maintained robust performance both overall (AUC: 0.711, 95% CI: 0.668–0.753) and in the HR+/HER2- subgroup (AUC: 0.810, 95% CI: 0.742–0.878). Conclusions The study developed a machine learning model ( https://huolab.cri.uchicago.edu/sample-apps/pcrmodel ) to predict pCR in breast cancer patients undergoing NACT that demonstrated robust discrimination and calibration performance. The model performed particularly well among patients with HR+/HER2- breast cancer, having the potential to identify patients who are less likely to achieve pCR and can consider alternative treatment strategies over chemotherapy. The model can also serve as a robust baseline model that can be integrated with smaller datasets containing additional granular features in future research.

PurposeDifferences in tumor biology, genomic architecture, and health care delivery patterns contribute to the breast cancer mortality gap between White and Black patients in the US. Although this gap has been well documented in previous literature, it remains uncertain how large the actual effect size of race is for different survival outcomes and the four breast cancer subtypes.MethodsWe established a breast cancer patient cohort at the University of Chicago Comprehensive Cancer Center. We chose five major survival outcomes to study: overall survival, recurrence-free survival, breast-cancer-specific survival, time-to-recurrence and post-recurrence survival. Cox proportional hazards models were used to estimate the hazard ratios between Black and White patients, adjusting for selected patient, tumor, and treatment characteristics, and also stratified by the four breast cancer subtypes.ResultsThe study included 2795 stage I–III breast cancer patients (54% White and 38% Black). After adjusting for selected patient, tumor and treatment characteristics, Black patients still did worse than White patients in all five survival outcomes. The racial difference was highest within the HR−/HER2+ subgroup, in both overall survival (hazard ratio = 4.00, 95% CI 1.47–10.86) and recurrence-free survival (hazard ratio = 3.00, 95% CI 1.36–6.60), adjusting for age at diagnosis, cancer stage, and comorbidities. There was also a significant racial disparity within the HR+/HER2− group in both overall survival and recurrence-free survival.ConclusionsOur study confirmed that racial disparity existed between White and Black breast cancer patients in terms of both survival and recurrence, and found that this disparity was largest among HR−/HER2+ and HR+/HER2− patients.

Recent reports have shown that the breast cancer incidence rate in the US stabilized after a sharp reduction in 2002 and 2003. It is important to continue monitoring breast cancer incidence rates according to age group, race/ethnicity, estrogen receptor (ER) status, and tumor stage. Age-standardized breast cancer incidence rates were calculated using data from the surveillance, epidemiology, and end results 18 registries from 2000 to 2009, for 677,774 female breast cancer patients aged 20 and above. Jointpoint regression models were used to fit a series of joined straight lines on a log scale to annual age-standardized rates. The incidence rates of all breast cancer significantly increased for non-Hispanic blacks from 2005 to 2009 (annual percentage change, APC = 2.0 %, p  = 0.01) and Asian/Pacific Islanders from 2000 to 2009 (APC = 1.2 %, p  = 0.02). Since 2004, incidence rates in women aged 40–49 years significantly increased for most racial/ethnic groups (overall APC = 1.1 %, p  = 0.001). The incidence rate of carcinoma in situ significantly increased in all racial/ethnic groups, with an APC range from 2.3 to 3.0 % ( p  < 0.005). The localized breast cancer incidence significantly increased in non-Hispanic blacks (APC = 1.3 %, p  = 0.004) and Asians (APC = 1.2 %, p  = 0.03). ER-positive breast cancer significantly increased in almost all age/race sub-groups after 2005 (APC by race: non-Hispanic whites 1.5 %, non-Hispanic blacks 4.3 %, Asian/Pacific Islanders 1.7 %, and Hispanics 1.8 %; all p values <0.05), while ER-negative breast cancer decreased in most sub-groups (APC by race: non-Hispanic whites—3.9 %, non-Hispanic blacks—3.7 %, Asian/Pacific Islanders—1.5 %, and Hispanics—4.3 %; all p values <0.05). Recently the incidence of breast cancer appears to be increasing in certain subgroups, including ER-positive, early-stage breast cancers, in particular among non-Hispanic blacks and Asian/Pacific Islanders. Further studies are warranted to examine possible reasons for these changes, such as changes in mammography screening methods and risk factors prevalence.

Black women across the African diaspora experience more aggressive breast cancer with higher mortality rates than white women of European ancestry. Although inter-ethnic germline variation is known, differential somatic evolution has not been investigated in detail. Analysis of deep whole genomes of 97 breast cancers, with RNA-seq in a subset, from women in Nigeria in comparison with The Cancer Genome Atlas (n = 76) reveal a higher rate of genomic instability and increased intra-tumoral heterogeneity as well as a unique genomic subtype defined by early clonal GATA3 mutations with a 10.5-year younger age at diagnosis. We also find non-coding mutations in bona fide drivers ( ZNF217 and SYPL1 ) and a previously unreported INDEL signature strongly associated with African ancestry proportion, underscoring the need to expand inclusion of diverse populations in biomedical research. Finally, we demonstrate that characterizing tumors for homologous recombination deficiency has significant clinical relevance in stratifying patients for potentially life-saving therapies. Breast cancer heterogeneity and tumour evolutionary trajectories remain largely unknown among women of African ancestry. Here, the authors perform whole genome and transcriptome sequencing of Nigerian breast cancer patients and identify unique evolutionary phenomena.

Background Previous work in European ancestry populations has shown that adding a polygenic risk score (PRS) to breast cancer risk prediction models based on epidemiologic factors results in better discriminatory performance as measured by the AUC (area under the curve). Following publication of the first PRS to perform well in women of African ancestry (AA-PRS), we conducted an external validation of the AA-PRS and then evaluated the addition of the AA-PRS to a risk calculator for incident breast cancer in Black women based on epidemiologic factors (BWHS model). Methods Data from the Black Women’s Health Study, an ongoing prospective cohort study of 59,000 US Black women followed by biennial questionnaire since 1995, were used to calculate AUCs and 95% confidence intervals (CIs) for discriminatory accuracy of the BWHS model, the AA-PRS alone, and a new model that combined them. Analyses were based on data from 922 women with invasive breast cancer and 1844 age-matched controls. Results AUCs were 0.577 (95% CI 0.556–0.598) for the BWHS model and 0.584 (95% CI 0.563–0.605) for the AA-PRS. For a model that combined estimates from the questionnaire-based BWHS model with the PRS, the AUC increased to 0.623 (95% CI 0.603–0.644). Conclusions This combined model represents a step forward for personalized breast cancer preventive care for US Black women, as its performance metrics are similar to those from models in other populations. Use of this new model may mitigate exacerbation of breast cancer disparities if and when it becomes feasible to include a PRS in routine health care decision-making.