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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.

Our previous data illustrated that activation of the canonical Wnt signaling pathway was enriched in triple-negative breast cancer and associated with reduced overall survival in all patients. To determine whether Wnt signaling may be a promising therapeutic target for triple-negative breast cancer, we investigated whether β-catenin was necessary for tumorigenic behaviors in vivo and in vitro. β-catenin expression level was significantly reduced in two human triple-negative breast cancer cell lines, MDA-MB-231 and HCC38, using lentiviral delivery of β-catenin-specific small hairpin RNAs (shRNAs). Upon implantation of the cells in the mammary fat pad of immunocompromised mice, we found that β-catenin shRNA HCC38 cells formed markedly smaller tumors than control cells and grew much more slowly. In in vitro assays, β-catenin silencing significantly reduced the percentage of Aldefluor-positive cells, a read-out of the stem-like cell population, as well as the expression of stem cell-related target genes including Bmi-1 and c-Myc. β-catenin-knockdown cells were also significantly impaired in their ability to migrate in wound-filling assays and form anchorage-independent colonies in soft agar. β-catenin-knockdown cells were more sensitive to chemotherapeutic agents doxorubicin and cisplatin. Collectively, these data suggest that β-catenin is required for triple-negative breast cancer development by controlling numerous tumor-associated properties, such as migration, stemness, anchorage-independent growth and chemosensitivity.

Background Hypoxic tumor microenvironment and maintenance of stemness contribute to drug resistance in breast cancer. However, whether Hypoxia-inducible factor-2α (HIF-2α) in hypoxic tumor microenvironment mediates conversion to a stem cell phenotype and chemoresistance of breast tumors has not been elucidated. Methods The mRNA and protein expressions of HIF-1α, HIF-2α, Wnt and Notch pathway were determined using qRT-PCR and western blot. Cell viability and renew ability were assessed by MTT, Flow cytometric analysis and soft agar colony formation. Results In our study, acute hypoxia (6–12 h) briefly increased HIF-1α expression, while chronic hypoxia (48 h) continuously enhanced HIF-2α expression and induced the resistance of breast cancer cells to Paclitaxel (PTX). Furthermore, HIF-2α overexpression induced a stem cell phenotype, the resistance to PTX and enhanced protein expression of stem cell markers, c-Myc, OCT4 and Nanog. Most importantly, Wnt and Notch signaling, but not including Shh, pathways were both activated by HIF-2α overexpression. Dickkopf-1 (DKK-1), a Wnt pathway inhibitor, and L685,458, an inhibitor of the Notch pathway, reversed the resistance to PTX and stem phenotype conversion induced by HIF-2α overexpression. In addition, HIF-2α overexpression enhanced tumorigenicity and resistance of xenograft tumors to PTX, increased activation of the Wnt and Notch pathways and induced a stem cell phenotype in vivo. Conclusion In conclusion, HIF-2α promoted stem phenotype conversion and induced resistance to PTX by activating Wnt and Notch pathways.

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.

Precision oncology has been successful in targeting oncogenes but therapies for cancers driven by tumor suppressor loss are much more of a challenge Yes, the classic divide between a tumor suppressor and an oncogene persists but regulatory networks are interconnected, and the more tools we have to probe these perturbations, the more therapeutic options we open. [...]immunotherapy is more successful in tumors that have abundant neoantigens [likely to arise from deficient DNA repair, such as upon loss of BRCA1], something that we were able to fully understand by combining genome sequencing – both germline and cancer – with lab research. In a recent plenary lecture, you talked about studying breast cancer risk variants in Black women, a population that the health system in the US continuously underserves. Because African Americans are still distrusting the medical establishment, for valid reasons, we need community engagement to explain how genetics can work for their benefit.

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.

It is well established that the tumor microenvironment plays a major role in the aggressive behavior of malignant solid tumors. Among cell types associated with tumor microenvironment, tumor-associated macrophages (TAMs) are the most influential for tumor progression. Breast cancer is characterized by having a large population of TAMs, and experimental models have exposed multiple mechanisms by which TAMs interact with and influence the surrounding tumor cells. The process of metastasis involves tumor cells gaining access to the tissue outside the immediate tumor environment and invading the confining extracellular matrix (ECM). Supporting this process, TAMs secrete proangiogenic factors such as VEGF to build a network of vessels that provide nutrition for tumor cells, but also function as channels of transport into the ECM. Additionally, TAMs release factors to decrease the local pro-inflammatory antitumor response, suppressing it and providing a means of escape of the tumor cells. Similarly, hypoxia in the tumor microenvironment stimulates macrophages to further produce VEGF and suppress the T-cell immune responses, thus, enhancing the evasion of tumor cells and ultimately metastasis. Given the multiple roles of TAMS in breast cancer progression and metastasis, therapies targeting these cells are in development and demonstrate promising results.

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.

Background Endocrine-resistant HR+/HER2- breast cancer (BC) and triple-negative BC (TNBC) are of interest for molecularly informed treatment due to their aggressive natures and limited treatment profiles. Patients of African Ancestry (AA) experience higher rates of TNBC and mortality than European Ancestry (EA) patients, despite lower overall BC incidence. Here, we compare the molecular landscapes of AA and EA patients with HR+/HER2- BC and TNBC in a real-world cohort to promote equity in precision oncology by illuminating the heterogeneity of potentially druggable genomic and transcriptomic pathways. Methods De-identified records from patients with TNBC or HR+/HER2- BC in the Tempus Database were randomly selected ( N  = 5000), with most having stage IV disease. Mutations, gene expression, and transcriptional signatures were evaluated from next-generation sequencing data. Genetic ancestry was estimated from DNA-seq. Differences in mutational prevalence, gene expression, and transcriptional signatures between AA and EA were compared. EA patients were used as the reference population for log fold-changes (logFC) in expression. Results After applying inclusion criteria, 3433 samples were evaluated ( n  = 623 AA and n  = 2810 EA). Observed patterns of dysregulated pathways demonstrated significant heterogeneity among the two groups. Notably, PIK3CA mutations were significantly lower in AA HR+/HER2- tumors (AA = 34% vs. EA = 42%, P  < 0.05) and the overall cohort (AA = 28% vs. EA = 37%, P  = 2.08e−05). Conversely, KMT2C mutation was significantly more frequent in AA than EA TNBC (23% vs. 12%, P  < 0.05) and HR+/HER2- (24% vs. 15%, P  = 3e−03) tumors. Across all subtypes and stages, over 8000 genes were differentially expressed between the two ancestral groups including RPL10 (logFC = 2.26 , P  = 1.70e−162), HSPA1A (logFC = − 2.73, P  = 2.43e−49) , ATRX (logFC = − 1.93, P  = 5.89e−83) , and NUTM2F (logFC = 2.28, P  = 3.22e−196). Ten differentially expressed gene sets were identified among stage IV HR+/HER2- tumors, of which four were considered relevant to BC treatment and were significantly enriched in EA: ERBB2_UP.V1_UP ( P  = 3.95e−06), LTE2_UP.V1_UP ( P  = 2.90e−05), HALLMARK_FATTY_ACID_METABOLISM ( P  = 0.0073), and HALLMARK_ANDROGEN_RESPONSE ( P  = 0.0074). Conclusions We observed significant differences in mutational spectra, gene expression, and relevant transcriptional signatures between patients with genetically determined African and European ancestries, particularly within the HR+/HER2- BC and TNBC subtypes. These findings could guide future development of treatment strategies by providing opportunities for biomarker-informed research and, ultimately, clinical decisions for precision oncology care in diverse populations.