No abstract

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.

The Wellcome Trust Sanger Institute is one of the world's largest genome centers, and a substantial amount of our sequencing is performed with 'next-generation' massively parallel sequencing technologies: in June 2008 the quantity of purity-filtered sequence data generated by our Genome Analyzer (Illumina) platforms reached 1 terabase, and our average weekly Illumina production output is currently 64 gigabases. Here we describe a set of improvements we have made to the standard Illumina protocols to make the library preparation more reliable in a high-throughput environment, to reduce bias, tighten insert size distribution and reliably obtain high yields of data.

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.