Background: Circulating tumor DNA (ctDNA) analysis holds potential for minimal residual disease (MRD) detection in early stage breast cancer. However, sensitivity for MRD is limited due to low ctDNA levels in early stage patients and limited blood volumes. Loss of input DNA during library preparation, limited multiplexing or low sensitivity of current molecular methods further limit accuracy. To address this gap, we have developed TARgeted DIgital Sequencing (TARDIS), a novel method for simultaneous analysis of multiple patient-specific mutations in plasma DNA. Methods: Using tumor exome sequencing, we identify and prioritize somatic founder mutations, design nested primers and evaluate them for multiplex performance. Using 5-10 ng input plasma DNA, we perform 1) targeted linear pre-amplification to improve downstream molecular conversion, 2) single-stranded adapter ligation to incorporate unique molecular identifiers (UMIs) and 3) targeted PCR to prepare sequencing-ready libraries. The resulting sequencing reads have fixed target-specific ends and variable ligation ends. We utilize fragment size and UMIs to group sequencing reads into read families. To ensure specificity, we require targeted mutations are supported by 2 or more read families. Results: To assess analytical performance, we targeted 8 mutations in cell-free DNA reference samples with 0.25%-2% mutation allele fractions (AFs). Precision across 7-16 replicates at each AF level agreed with expectations of Poisson distribution, demonstrating effective analysis of ˜70% of input DNA. At 2%, 1%, 0.5% and 0.25% AFs, variant-level sensitivity was 96.4%, 96.4%, 91.1% and 65.8%, approaching the theoretical limit given input DNA. At 0.25% AF, 3-7 mutations were detected per sample, achieving 100% sample-level sensitivity. In 16 wild-type replicates, no targeted mutations were called (100% specificity). Averaging multiple mutations improved precision in sample-level AF estimates. Mean AFs from 8 mutations for the 2% sample were 2.34%-2.80% (5.8% CV). In 6 patients with breast cancer treated with neoadjuvant therapy (NAT), we analyzed 8-18 patient-specific mutations (mean 11.8). Before treatment, ctDNA was detected in 5/6 patients at mean AFs of 0.02%-1.19% (mean 0.40%), supported by 2-10 mutations (mean 5.6). Of these 5 patients, 4 had residual disease after NAT and ctDNA was detected pre-operatively or during NAT in 3/4 patients. 1 patient achieved pathological Complete Response and ctDNA was undetectable after NAT. Conclusions: Preliminary results suggest TARDIS enables accurate MRD detection after neoadjuvant therapy in patients with early stage breast cancer. On-going work is expanding this analysis to include additional patients and investigate the clinical validity of peri-operative ctDNA monitoring. Summary of clinical resultsPatientPre-NAT Stage (TNM)SubtypeNo. of Mutations TargetedBaseline ctDNA (AF%, No. of Mutations)ctDNA after or during NAT (AF%, No. of Mutations)Residual Tumor (TNM)1T3 N1ER+ PR+ HER2-8+ (0.02%, 2)-T2 N12T3 N0TNBC12+ (0.29%, 6)+ (0.01%, 1)T1a N03T2 N1TNBC18+ (1.19%, 10)+ (0.01%, 1)T1mi N04T3 N1TNBC10+ (0.02%, 3)+ (0.05%, 3)T3 N15T2 N0TNBC9+ (0.46%, 7)-pathCR6T1c N1TNBC14--pathCR Citation Format: McDonald BR, Contente-Cuomo T, Sammut S-J, Ernst B, Odenheimer-Bergman A, Perdigones N, Chin S-F, Farooq M, Cronin PA, Anderson KS, Kosiorek H, Northfelt D, McCullough A, Patel B, Caldas C, Pockaj B, Murtaza M. Multiplexed targeted digital sequencing of circulating tumor DNA to detect minimal residual disease in early and locally advanced breast cancer [abstract]. In: Proceedings of the 2018 San Antonio Breast Cancer Symposium; 2018 Dec 4-8; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2019;79(4 Suppl):Abstract nr P4-01-21.

Background: Recent studies have demonstrated that women with early stage ER-positive (ER+) and HER2-negative (HER2-) breast cancer have a persistent risk of recurrence and cancer related death up to 20 years post diagnosis, highlighting the chronic nature of ER+ breast cancer and critical need to identify tumor characteristics that are more predictive of risk of recurrence than standard clinical covariates. However, progress in delineating the dynamics of breast cancer relapse and biomarkers of late recurrence has been hindered by the lack of large cohorts with long-term clinical follow-up and molecular information. Methods: We report the results of a cohort of 3,240 breast cancer patients from the United Kingdom and Canada with 20 years of follow-up (median 9.75 years), including 1,980 with accompanying molecular data from the primary breast tumor. Information for each patient on loco-regional recurrence (LR), distant recurrence (DR), and site(s) of metastases was collected. We developed a non-homogenous Markov chain model that accounted for different clinical endpoints and timescales, as well as competing risks of mortality and the distinct baseline hazards that characterize different molecular subgroups. This approach enabled robust analysis of the spatio-temporal dynamics of breast cancer recurrence across the clinical subgroups, PAM50 subgroups and the integrative clusters, while also enabling individual risk of relapse predictions. Results: We employed our multistate model to compute the probability of experiencing a LR or DR, as well as the baseline transition probabilities from surgery, LR or DR at various time intervals for average individuals in each of the clinical/molecular subgroups. These analyses reveal four late-recurring ER+ (predominantly HER2-) subgroups, together accounting for 26% of all ER+ tumors, with high (median 42-55%) risk of recurrence up to 20 years post-diagnosis. Each of these four subgroups maps to one of the Integrative Clusters, defined based on genomic copy number alterations and gene expression, and is enriched for a characteristic copy number amplification events: 11q13 (CCND1, RSF1), 8p12 (FGFR1, ZNF703), 17q23 (RPS6KB1) and 8q24 (MYC). These four molecular subgroups are superior in predicting late DR than standard clinical variables. Conclusions: A detailed understanding of the rates and routes of metastasis and their variability across the distinct molecular subtypes is essential for devising personalized approaches to breast cancer care. We describe a molecularly characterized breast cancer cohort with long-term clinical follow-up and a statistical modeling framework, enabling delineation of the dynamics of breast cancer recurrence at unprecedented resolution. These analyses reveal four late recurring ER+ subgroups and accompanying biomarkers that collectively define the quarter of ER+ cases at highest risk of recurrence. Our findings highlight opportunities for improved patient stratification and biomarker-driven clinical trials directed at the subset of breast cancer patients with persistent risk of recurrence. Citation Format: Curtis C, Rueda OM, Sammut S-J, Chin S-F, Caswell-Jin JL, Seoane JA, Callari M, Batra R, Pereira B, Bruna A, Ali HR, Provenzano E, Liu B, Parisien M, Gillett C, McKinney S, Green A, Murphy L, Purushotham A, Ellis I, Pharoah P, Rueda C, Aparicio S, Caldas C. Dynamics of breast cancer relapse reveal molecularly defined late recurring ER-positive subgroups: Results from the METABRIC study [abstract]. In: Proceedings of the 2018 San Antonio Breast Cancer Symposium; 2018 Dec 4-8; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2019;79(4 Suppl):Abstract nr GS3-06.