Screening mammography reduces breast cancer mortality, but studies analyzing interval cancers diagnosed after negative screens have shown that many cancers are missed. Supplemental screening using magnetic resonance imaging (MRI) can reduce the number of missed cancers. However, as qualified MRI staff are lacking, the equipment is expensive to purchase and cost-effectiveness for screening may not be convincing, the utilization of MRI is currently limited. An effective method for triaging individuals to supplemental MRI screening is therefore needed. We conducted a randomized clinical trial, ScreenTrustMRI, using a recently developed artificial intelligence (AI) tool to score each mammogram. We offered trial participation to individuals with a negative screening mammogram and a high AI score (top 6.9%). Upon agreeing to participate, individuals were assigned randomly to one of two groups: those receiving supplemental MRI and those not receiving MRI. The primary endpoint of ScreenTrustMRI is advanced breast cancer defined as either interval cancer, invasive component larger than 15 mm or lymph node positive cancer, based on a 27-month follow-up time from the initial screening. Secondary endpoints, prespecified in the study protocol to be reported before the primary outcome, include cancer detected by supplemental MRI, which is the focus of the current paper. Compared with traditional breast density measures used in a previous clinical trial, the current AI method was nearly four times more efficient in terms of cancers detected per 1,000 MRI examinations (64 versus 16.5). Most additional cancers detected were invasive and several were multifocal, suggesting that their detection was timely. Altogether, our results show that using an AI-based score to select a small proportion (6.9%) of individuals for supplemental MRI after negative mammography detects many missed cancers, making the cost per cancer detected comparable with screening mammography. ClinicalTrials.gov registration: NCT04832594.

We compared estrogen receptor (ER), progesterone receptor (PR), human epidermal growth-factor receptor 2 (HER2), Ki67, and grade scores among the pathology departments in Sweden. We investigated how ER and HER2 positivity rates affect the distribution of endocrine and HER2-targeted treatments among oncology departments. All breast cancer patients diagnosed between 2013 and 2018 in Sweden were identified in the National Quality Register for Breast Cancer. Cases with data on ER, PR, HER2, Ki67, grade, and treatment were selected (43,261 cases from 29 departments following the guidelines for biomarker testing). The ER positivity rates ranged from 84.2% to 97.6% with 6/29 labs out of the overall confidence intervals (CIs), while PR rates varied between 64.8% and 86.6% with 7/29 labs out of the CIs. HER2 positivity rates ranged from 9.4% to 16.3%, with 3/29 labs out of the overall CIs. Median Ki67 varied between 15% and 30%, where 19/29 labs showed significant intra-laboratory variability. The proportion of grade-II cases varied between 42.9% and 57.1%, and 13/29 labs were outside of the CI. Adjusting for patient characteristics, the proportion of endocrine and anti-HER2 treatments followed the rate of ER and HER2 positivity, illustrating the clinical effect of inter- and intra-laboratory variability. There was limited variability among departments in ER, PR, and HER2 testing. However, even a few outlier pathology labs affected endocrine and HER2-targeted treatment rates in a clinically relevant proportion, suggesting the need for improvement. High variability was found in grading and Ki67 assessment, illustrating the need for the adoption of new technologies in practice.

Introduction: The cancer stem cell model implies a hierarchical organization within breast tumors maintained by cancer stem-like cells (CSCs). Accordingly, CSCs are a subpopulation of cancer cells with capacity for self-renewal, differentiation and tumor initiation. These cells can be isolated through the phenotypic markers CD44+/CD24-, expression of ALDH1 and an ability to form nonadherent, multicellular spheres in vitro. However, controversies to describe the stem cell model exist; it is unclear whether the tumorigenicity of CSCs in vivo is solely a proxy for a certain genotype. Moreover, in vivo evidence is lacking to fully define the reversibility of CSC differentiation. Methods: In order to answer these questions, we undertook exome sequencing of CSCs from 12 breast cancer patients, along with paired primary tumor samples. As suggested by stem classical cell biology, we assumed that the number of mutations in the CSC subpopulation should be lower and distinct compared to the differentiated tumor cells with higher proliferation. Results: Our analysis revealed that the majority of somatic mutations are shared between CSCs and bulk primary tumor, with similar frequencies in the two. Conclusions: The data presented here exclude the possibility that CSCs are only a phenotypic consequence of certain somatic mutations, that is a distinct and non-reversible population of cells. In addition, our results imply that CSCs must be a population of cells that can dynamically switch from differentiated tumor cells, and vice versa. This finding increases our understanding of CSC function in tumor heterogeneity and the importance of identifying drugs to counter de-differentiation rather than targeting CSCs.

Estrogen receptor-low (ER-low) HER2-negative breast cancer has similar pathological and molecular characteristics as triple-negative breast cancer (TNBC), and it is questionable whether it should be considered a separate entity. When the international guidelines lowered the cutoff for ER positivity to ≥1% in 2010, the ≥10% threshold was kept in Sweden. ER-low breast cancer (ER 1–9%) is thus in Sweden treated as TNBC. We aimed to describe patient and tumor characteristics, treatment patterns and overall survival in a Swedish population-based cohort of patients with ER-zero and ER-low HER2-negative breast cancer treated as TNBC. All TNBC cases diagnosed in Sweden 2008–2020 were included in a population-based cohort study. Patient, tumor and treatment characteristics were analyzed by ER-status (ER 0% vs 1–9%), and associations between subgroups compared using χ² test. Survival endpoint was overall survival (OS), and Kaplan–Meier curves were estimated. Cox proportional hazards models were used to estimate adjusted hazard ratios comparing ER-low to ER-zero. Of the 5655 tumors, 90.1% had an ER expression of 0%, while 9.9% were ER-low. ER-low tumors were grade III in 69.4% (80.8% in ER-zero tumors, p-value = 0.001), with a median Ki67 of 60% (63% in ER-zero tumors, p-value = 0.005). There were no significant differences in given chemotherapy (p = 0.546). A pathological complete response (pCR) was achieved in 28.1% of ER-low tumors (25.1% in ER-zero tumors). In the unadjusted analysis of OS, women with ER-low disease had a borderline but not significantly better OS than those with ER-zero disease (HR 0.84 (95% CI 0.71–1.00), p = 0.052). ER-status 1–9% vs 0% was not associated with OS in the multivariable analysis (HR 1.11 (0.90–1.36)). Distant disease-free survival did not differ by ER-status 0% vs 1–9% (HR 0.97 for ER-zero vs ER-low (0.62–1.53), p = 0.905). After preoperative treatment, the impact of pCR for OS did not significantly differ between ER-zero or ER-low disease. ER-low HER2-negative breast cancer has characteristics and prognosis similar to TNBC, when treated in the same way. Therefore, it seems reasonable to use a ≥10% threshold for ER positivity. This would provide patients with ER-low tumors the same treatment opportunities as patients with TNBC, within studies and within clinical routine. This work was financially supported by Merck Sharp & Dohme LLC, a subsidiary of Merck & Co., Inc., Rahway, NJ, USA, in accordance with terms and conditions of a Master Collaboration Agreement between the company and Karolinska Institutet.

Prior studies regarding use of Aromatase inhibitors (AIs) and risk for cardiovascular disease (CVD) have shown conflicting results. This retrospective cohort study aimed to investigate whether AIs use affects risk for CVD events in postmenopausal breast cancer survivors. Using a retrospective cohort study design, four CVD outcomes; heart failure or cardiomyopathy, arrhythmia, acute ischemic heart disease and ischemic stroke or Transient Ischemic Attack were compared with uni- and multivariate Cox regression analyses according to exposure to endocrine therapy (use of AI, tamoxifen or AI/tamoxifen sequentially) or no endocrine therapy. In total 15815 postmenopausal women, surgically treated to early breast cancer during 2006–2012, were included. No significantly increased risk for CVD events was observed in patients with AI use in the whole cohort. However, two subgroup analyses showed increased risk for CVD events in the AI/tamoxifen sequential group; heart failure in patients older than 75 years (Hazard Ratio (HR) 2.44; 95% Confidence Interval (CI): 1.32–4.54) and arrhythmia in patients without prior CVD (HR 1.45; 95% CI: 1.01–2.10). An increased risk for arrhythmia and acute ischemic heart disease in patients with at least four years of AI treatment compared with no or short-time exposure was observed (HR 2.12; 95% CI: 1.40–3.25 for arrhythmia; HR 2.03; 95% CI: 1.15–3.58 for ischemic heart disease). Our results indicate an increased risk for ischemic heart disease and arrhythmia in patients treated for more than four years with AIs. This should be considered in the risk-benefit assessment concerning endocrine therapy.