Purpose: Up to 30% of patients with breast cancer relapse after primary treatment. There are no sensitive and reliable tests to monitor these patients and detect distant metastases before overt recurrence. Here, we demonstrate the use of personalized circulating tumor DNA (ctDNA) profiling for detection of recurrence in breast cancer.Experimental Design: Forty-nine primary patients with breast cancer were recruited following surgery and adjuvant therapy. Plasma samples (n ¼ 208) were collected every 6 months for up to 4 years. Personalized assays targeting 16 variants selected from primary tumor whole-exome data were tested in serial plasma for the presence of ctDNA by ultradeep sequencing (average >100,000X).Results: Plasma ctDNA was detected ahead of clinical or radiologic relapse in 16 of the 18 relapsed patients (sensitivity of 89%); metastatic relapse was predicted with a lead time of up to 2 years (median, 8.9 months; range, 0.5-24.0 months). None of the 31 nonrelapsing patients were ctDNA-positive at any time point across 156 plasma samples (specificity of 100%). Of the two relapsed patients who were not detected in the study, the first had only a local recurrence, whereas the second patient had bone recurrence and had completed chemotherapy just 13 days prior to blood sampling.Conclusions: This study demonstrates that patientspecific ctDNA analysis can be a sensitive and specific approach for disease surveillance for patients with breast cancer. More importantly, earlier detection of up to 2 years provides a possible window for therapeutic intervention. Personalized profiling detects rising ctDNA ahead of clinical relapse. A-E, Plasma levels of ctDNA across serial plasma time points for five patients with breast cancer (one per panel). Mean VAFs are denoted by a dark blue circle, and solid lines represent the average VAF profile over time. The lead time is calculated as the time interval between clinical relapse (red triangle) and molecular relapse (blue triangle). CA 15-3 levels are graphed over time (teal circle), and the baseline levels (32 U/mL) are marked in light blue. F, Summary of percent VAF and number of targets detected at molecular and clinical relapse for all ctDNA-positive samples. Data are from 13 relapsed patients, excluding three patients with only one plasma time point. Coombes et al.

Automation bias (AB)--the tendency to over-rely on automation--has been studied in various academic fields. Clinical decision support systems (CDSS) aim to benefit the clinical decision-making process. Although most research shows overall improved performance with use, there is often a failure to recognize the new errors that CDSS can introduce. With a focus on healthcare, a systematic review of the literature from a variety of research fields has been carried out, assessing the frequency and severity of AB, the effect mediators, and interventions potentially mitigating this effect. This is discussed alongside automation-induced complacency, or insufficient monitoring of automation output. A mix of subject specific and freetext terms around the themes of automation, human-automation interaction, and task performance and error were used to search article databases. Of 13 821 retrieved papers, 74 met the inclusion criteria. User factors such as cognitive style, decision support systems (DSS), and task specific experience mediated AB, as did attitudinal driving factors such as trust and confidence. Environmental mediators included workload, task complexity, and time constraint, which pressurized cognitive resources. Mitigators of AB included implementation factors such as training and emphasizing user accountability, and DSS design factors such as the position of advice on the screen, updated confidence levels attached to DSS output, and the provision of information versus recommendation. By uncovering the mechanisms by which AB operates, this review aims to help optimize the clinical decision-making process for CDSS developers and healthcare practitioners.

Purpose: The purpose of this study was to directly compare mutation profiles in multiple single circulating tumor cells (CTC) and cell-free DNA (cfDNA) isolated from the same blood samples taken from patients with metastatic breast cancer (MBC). We aimed to determine whether cfDNA would reflect the heterogeneity observed in 40 single CTCs.Experimental Design: CTCs were enumerated by CELL-SEARCH. CTC count was compared with the quantity of matched cfDNA and serum CA15-3 and alkaline phosphatase (ALP) in 112 patients with MBC. In 5 patients with !100 CTCs, multiple individual EpCAM-positive CTCs were isolated by DEPArray and compared with matched cfDNA and primary tumor tissue by targeted next-generation sequencing (NGS) of about 2,200 mutations in 50 cancer genes.Results: In the whole cohort, total cfDNA levels and cell counts (!5 CTCs) were both significantly associated with overall survival, unlike CA15-3 and ALP. NGS analysis of 40 individual EpCAMpositive CTCs from 5 patients with MBC revealed mutational heterogeneity in PIK3CA, TP53, ESR1, and KRAS genes between individual CTCs. In all 5 patients, cfDNA profiles provided an accurate reflection of mutations seen in individual CTCs. ESR1 and KRAS gene mutations were absent from primary tumor tissue and therefore likely either reflect a minor subclonal mutation or were acquired with disease progression.Conclusions: Our results demonstrate that cfDNA reflects persisting EpCAM-positive CTCs in patients with high CTC counts and therefore may enable monitoring of the metastatic burden for clinical decision-making.