The identification of early-stage breast cancer patients at high risk of relapse would allow tailoring of adjuvant therapy approaches. We assessed whether analysis of circulating tumor DNA (ctDNA) in plasma can be used to monitor for minimal residual disease (MRD) in breast cancer. In a prospective cohort of 55 early breast cancer patients receiving neoadjuvant chemotherapy, detection of ctDNA in plasma after completion of apparently curative treatment-either at a single postsurgical time point or with serial follow-up plasma samples-predicted metastatic relapse with high accuracy [hazard ratio, 25.1 (confidence interval, 4.08 to 130.5; log-rank P < 0.0001) or 12.0 (confidence interval, 3.36 to 43.07; log-rank P < 0.0001), respectively]. Mutation tracking in serial samples increased sensitivity for the prediction of relapse, with a median lead time of 7.9 months over clinical relapse. We further demonstrated that targeted capture sequencing analysis of ctDNA could define the genetic events of MRD, and that MRD sequencing predicted the genetic events of the subsequent metastatic relapse more accurately than sequencing of the primary cancer. Mutation tracking can therefore identify early breast cancer patients at high risk of relapse. Subsequent adjuvant therapeutic interventions could be tailored to the genetic events present in the MRD, a therapeutic approach that could in part combat the challenge posed by intratumor genetic heterogeneity.

Acquired ESR1 mutations are a major mechanism of resistance to aromatase inhibitors (AI). We developed ultra-high sensitivity multiplexed digital PCR assays for ESR1 mutations in circulating tumor DNA (ctDNA) and used these to investigate the clinical relevance and origin of ESR1 mutations in a cohort of 171 women with advanced breast cancer. ESR1 mutation status in ctDNA showed high concordance with contemporaneous tumor biopsies, and could be assessed in samples shipped at room temperature in preservative tubes without loss of accuracy. ESR1 mutations were found exclusively in patients with estrogen receptor positive breast cancer previously exposed to AI. Patients with ESR1 mutations had a substantially shorter progression-free survival on subsequent AI-based therapy (HR 3.1, 95%CI 1.9-23.1, log rank p=0.0041). ESR1 mutation prevalence differed markedly between patients that were first exposed to AI during the adjuvant and metastatic settings (5.8% (3/52) vs 36.4% (16/44) respectively, p=0.0002). In an independent cohort, ESR1 mutations were identified in 0% (0/32, 95%CI 0-10.9%) tumor biopsies taken after progression on adjuvant AI. In a patient with serial samples taken during metastatic treatment, ESR1 mutation was selected during metastatic AI therapy, to become the dominant clone in the cancer. ESR1 mutations can be robustly identified with ctDNA analysis and predict for resistance to subsequent AI therapy. ESR1 mutations are rarely acquired during adjuvant AI therapy, but are commonly selected by therapy for metastatic disease, providing evidence that the mechanisms of resistance to targeted therapy may be substantially different between the treatment of micro-metastatic and overt metastatic cancer.

CNB can be used with confidence for ER and HER2 determination. For PgR, due to a substantial discordance between CNB and EB, results from CNB should be used with caution.

IntroductionVery few studies have investigated whether the time elapsed between surgical resection and tissue fixation or the difference between core-cut and excision biopsies impact on immunohistochemically measured biomarkers, including phosphorylated proteins in primary breast cancer. The aim of this study was to characterise the differences in immunoreactivity of common biomarkers that may occur (1) as a result of tissue handling at surgery and (2) between core-cuts and resected tumours.MethodsCore-cuts taken from surgical breast cancer specimens immediately after resection (sample A) and after routine X-ray of the excised tumour (sample B) were formalin-fixed and paraffin-embedded and compared with the routinely fixed resection specimen (sample C). The variation in immunohistochemical expression of Ki67, oestrogen receptor (ER), progesterone receptor (PgR), human epidermal growth factor 2 (HER2), p-Akt and p-Erk1/2 were investigated.ResultsTwenty-one tissue sets with adequate tumour were available. Median time between collection of core-cuts A and B was 30 minutes (range, 20 to 80 minutes). None of the markers showed significant differences between samples A and B. Similarly, Ki67, ER, PgR and HER2 did not differ significantly between core-cuts and main resection specimen, although there was a trend for lower resection values for ER (P = 0.06). However, p-Akt and p-Erk1/2 were markedly lower in resections than core-cuts (median, 27 versus 101 and 69 versus 193, respectively; both P < 0.0001 [two-sided]). This difference was significantly greater in mastectomy than in lumpectomy specimens for p-Erk1/2 (P = 0.01).ConclusionsThe delay in fixation in core-cuts taken after postoperative X-ray of resection specimens has no significant impact on expression of Ki67, ER, PgR, HER2, p-Akt or p-Erk1/2. However, extreme loss of phospho-staining can occur during routine fixation of resection specimens. These differences are likely attributable to suboptimal fixation and may have major repercussions for clinical research involving these markers.

Platinum-based chemotherapy achieves increased response rates for TN tumours, with a trend towards worse survival in early breast cancer through an improved survival in advanced disease. Prospective randomised trials are warranted.

Purpose: Estrogen withdrawal by treatment with aromatase inhibitors is the most effective form of endocrine therapy for postmenopausal estrogen receptor-positive (ERþ) breast cancer. However, response to therapy varies markedly and understanding of the precise molecular effects of aromatase inhibitors and causes of resistance is limited. We aimed to identify in clinical breast cancer those genes and pathways most associated with resistance to aromatase inhibitors by examining the global transcriptional effects of AI treatment.Experimental Design: Baseline and 2-week posttreatment biopsies were obtained from 112 postmenopausal women with ERþ breast cancer receiving neoadjuvant anastrozole. Gene expression data were obtained from 81 baseline and 2-week paired samples. Pathway analysis identified (i) the most prevalent changes in expression and (ii) the pretreatment genes/pathways most related to poor antiproliferative response.Results: A total of 1,327 genes were differentially expressed after 2-week treatment (false discovery rate < 0.01). Proliferation-associated genes and classical estrogen-dependent genes were strongly downregulated whereas collagens and chemokines were upregulated. Pretreatment expression of an inflammatory signature correlated with antiproliferative response to anastrozole and this observation was validated in an independent study. Higher expression of immune-related genes such as SLAMF8 and TNF as well as lymphocytic infiltration were associated with poorer response (P < 0.001) and validated in an independent cohort.Conclusions: The molecular response to aromatase inhibitor treatment varies greatly between patients consistent with the variable clinical benefit from aromatase inhibitor treatment. Higher baseline expression of an inflammatory signature is associated with poor antiproliferative response and should be assessed further as a novel biomarker and potential target for aromatase inhibitor-treated patients.

This study provides proof of principle that the addition of post-treatment Ki67 to RCB improves the prediction of long-term outcome. Prediction may be further improved by addition of post-treatment grade and ER and warrants further investigation for estimating post-neoadjuvant risk of recurrence. These indices may have utility in stratifying patients for novel therapeutic interventions after neoadjuvant chemotherapy.

Purpose: In a previous screen using a signal-trap library, we identified a number of secreted proteins up-regulated in primary tumor cells isolated from invasive breast cancers.The purpose of this study was to assess the expression of these genes in human invasive breast tumors and to determine the significance of their expression for prognosis in breast cancer. Experimental Design: A tissue microarray containing 245 invasive breast tumors from women treated with curative surgery followed by anthracycline-based chemotherapy and hormone therapy for the estrogen receptor (ER)^positive tumors was screened by in situ hybridization with probes against thrombospondin 3 (TSP3), insulin-like growth factor binding protein 7 (IGFBP7), tumor rejection antigen 1 (TRA1), stanniocalcin 2 (STC2), and netrin 4 (NTN4). Correlations between categorical variables were done using the m 2 test and Fisher's exact test. Cumulative survival probabilities were calculated using the Kaplan-Meier method and multivariate survival analysis was done with Cox hazard model. A series of breast cancers were also stained with NTN4 antibodies. Results: All five genes examined were expressed in invasive breast tumor cells. NTN4 protein expression was also confirmed by immunohistochemistry.Together, these data validate the design and screening of the signal-trap library. Univariate survival analysis revealed that expressions of TRA1, STC2, and NTN4 are correlated with longer disease-free survival and thatTRA1 and NTN4 are associated with longer overall survival. Multivariate analysis showed that NTN4 is an independent prognostic factor of overall survival. Conclusions:This article describes the identification of three secreted proteins, NTN4,TRA1, and STC2, as potential novel prognostic markers in breast cancer.Paracrine signaling from tumor cell to stromal cells plays a key role in regulating leukocyte recruitment, angiogenesis, and activation of stromal fibroblasts. This, in turn, changes the tumor environment, thus regulating tumor cell proliferation, survival, migration, and invasion (1 -4). As a consequence, identifying secreted proteins that are up-regulated in tumor cells is critical for understanding these complex signaling interactions and identifying key targets for therapeutic intervention. We have previously described the generation and screening of a library to preferentially identify genes overexpressed in invasive breast tumor cells (5). The screen was designed to (a) specifically enrich for transcripts that are expressed by primary tumor cells in vivo rather than by cancer cell lines or total tumor tissue in vivo, and (b) to select for transcripts bearing a functional signal sequence and thereby specifically identify overexpressed transmembrane and secreted proteins. The purpose of the current study was to verify that the overexpressed genes that encode for secreted proteins are specifically expressed by breast tumor cells and to determine their prognostic significance in a large cohort of breast cancer patients. The advent o...