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Background: Breast cancer (BC) may recur years to decades after initial treatment, leading to incurable, metastatic disease. Prior studies have shown blood biopsy can detect minimal residual disease (MRD), but not in all patients and often with very short lead time. Most efforts thus far have focused on tracking one or a few mutations via ctDNA, which may limit sensitivity. Here we sought to maximize the detection sensitivity of blood biopsies by tracking up to hundreds of individualized tumor mutations in cell-free DNA (cfDNA). Doing so enables us to break the detection ceiling imposed by the limited copies of each gene in the cell-free DNA in a blood draw. Methods: cfDNA was extracted from archival plasma samples (n=271) from 142 patients with stage 0-III BC enrolled prospectively onto two IRB-approved studies. We applied whole-exome sequencing (WES) to define up to several hundred mutations from each patient’s tumor. To limit potential errors, we employed strict criteria to select somatic SNVs to track using duplex sequencing in cfDNA. We required detection of ≥ 2 mutations for a cfDNA sample to be MRD+ and excluded any mutations also found in a patient’s own germline DNA. Results: We identified 142 patients treated for stage 0-III BC, who had postoperative blood and plasma samples available, and were monitored for distant recurrences for up to thirteen years. All patients had biopsy-proven BC, with 86 (61%) having HR+/HER2- BC, 31 (22%) having HR-/HER2-, or “triple negative” BC (TNBC), and 25 (18%) having HER2-positive disease. Three (2%) patients had stage 0 disease, 32 (23%) had stage I, 68 (42%) had stage II, and 39 (27%) had stage III BC at diagnosis. Archived plasma samples were collected post-operatively, at year 1, and at year 4. We created individualized assays targeting a median of 57 mutations (range 2 - 346) per patient. Reasoning that MRD status after completion of all local therapy and chemotherapy might best predict for distant recurrence, we conducted a landmark analysis at one year following surgery, and found all patients (n=6/6) with detectable MRD experienced recurrence (HR=21.2 (7.43-60.35)) in a median of 6.7 (range 3.4 - 15.8) months. Median lead time including all timepoints from first positive blood sample to recurrence was 18.9 (range: 3.4 - 39.2) months. Finally, in a multivariate model, MRD remained highly statistically significant independent of stage, subtype, and age at diagnosis. Conclusions: We present a novel approach to MRD tracking based on the premise that tracking many - rather than one or a handful of - mutations inherently increases sensitivity and that assay personalization maximizes sensitivity in a disease without multiple, recurrent mutations. To our knowledge, the lead time we show here is significantly longer than that seen in prior investigations, and if confirmed, could offer an opportunity to treat MRD long before the development of clinically apparent metastatic disease. Prospective studies are needed to determine whether earlier detection of MRD is clinically meaningful for patients. Citation Format: Heather A Parsons, Justin Rhoades, Sarah C. Reed, Greg Gydush, Priyanka Ram, Pedro Exman, Kan Xiong, Christopher Lo, Tianyu Li, Mark Fleharty, Greg Kirkner, Denisse Rotem, Ofir Cohen, Melissa Hughes, Shoshanna Rosenberg, Laura Collins, Kathy Miller, Brendan Blumenstiel, Carrie Cibulskis, Donna Neuberg, Samuel S. Freeman, Niall Lennon, Nikhil Wagle, Gavin Ha, Daniel G. Stover, Atish D. Choudhury, Gad Getz, Matthew Meyerson, Nancy U. Lin, Ian E. Krop, J. Christopher Love, G. Mike Makrigiorgos, Ann Partridge, Erika Mayer, Todd R. Golub, Viktor A. Adalsteinsson. Ultrasensitive detection of minimal residual disease in patients treated for breast cancer [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P5-01-03.

The Broad/IBM Cancer Resistance Project has partnered with Broad Genomics to pilot the use of cutting edge sequencing technology for the analysis of cell free DNA in blood biopsies. Working closely with the Broad's Cancer Program, Broad Genomics has developed a suite of liquid biopsy sequencing products designed to provide optimal flexibility in conducting research studies with a broad range of applications including; biomarker discovery, treatment resistance monitoring, and detection of minimal residual disease (MRD) post-surgery. Cell-free DNA is extracted from the blood, and a dual unique-molecular-indexed library is created. From this library, low coverage whole genome (ultra-low-pass 0.1x coverage) data is generated to survey sample quality and evaluate the tumor fraction in the liquid specimen. Utilizing the same library, additional assays can be selected for processing based on the research aim (Targeted Panel Assays, MRD Detection or Whole Exomes). Since our approach utilizes the same genomic material for whole genome and targeted sequencing assays, it is possible to maximize the information learned from each valuable and limited liquid biopsy specimen. Our study design takes advantage of the discovery potential of combined tissue-based sequencing and serial liquid biopsy analysis to elucidate mechanisms of cancer resistance by tracking the evolution of clonal and subclonal populations in patients samples over time. This collaboration will utilize the ultra-low-pass sequencing and whole exome sequencing together with custom analysis pipelines to correlate the genomic events with patient clinical data. We aim to process 3,000 samples from 1,000 patients over the next 3 years. To date we have processed close to 500 samples through the ultra-low-pass pipeline and 100 samples through the whole exome sequencing pipeline (results to be provided).The ability to successfully investigate treatment resistant cancers from non-invasive liquid biopsies presents new opportunities for identifying markers, understanding dynamics and monitoring tumor dissemination and clonal evolution. Citation Format: Gad Getz, Carrie Cibulskis, Ignaty Leshchiner, Megan Hanna, Dimitri Livitz, Kara Slowik, Chaya Levovitz, Filippo Utro, Kahn Rhrissorrakrai, Denisse Rotem, Gregory Gydush, Sarah C. Reed, Justin Rhoades, Gavin Ha, Samuel S. Freeman, Christopher Lo, Mark Fleharty, Justin Abreu, Katie Larkin, Michelle Cipicchio, Brendan Blumenstiel, Matt DeFelice, Jonna Grimsby, Susanna Hamilton, Niall Lennon, Viktor A. Adalsteinsson, Laxmi Parida. Broad/IBM Project: Discovery of treatment resistance mechanisms through use of liquid biopsy genomics services [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 3001.

Introduction: Precision medicine approaches to guide therapy selection require routine sampling of tumors. However, tumor biopsies are not always accessible and may be confounded by spatial heterogeneity. Liquid biopsies, including analysis of cell-free DNA (cfDNA), present a non-invasive alternative which may reflect multiple tumors in the body. Previous studies have demonstrated exome-wide concordance between single-site tumor biopsies and cfDNA, but little is known about how cfDNA reflects multiple lesions within a patient. Here we sought to determine how cfDNA reflects the body-wide tumor phylogeny, which will inform the use of cfDNA for cancer precision medicine. Methods: We identified 20 patients with pancreatic cancer who had undergone rapid autopsy. We then screened cfDNA tumor fraction and performed whole-exome sequencing of cfDNA and multiple tumor biopsies for 3 patients with cfDNA tumor fraction >10%. We inferred the tumor phylogeny and then developed a statistical approach to deconvolute the contributions to cfDNA from tumor phylogenetic nodes. Finally, we determined whether shared trunk mutations could be detected in cfDNA and tumor biopsies. Results: For each patient, we found mutations shared between all sites and cfDNA, including putative driver mutations. We found mutations which were clonal in multiple regions were detectable in cfDNA, whereas mutations private to individual sites were never clonal in cfDNA. Through our deconvolution analysis, we found that cfDNA could not be modeled as a simple linear combination of individual sites, but rather that cfDNA represented multiple nodes in the inferred phylogeny. For two pancreatic adenocarcinoma patients, the inferred ancestor of the metastases had high estimated contribution (>70%) to cfDNA, while the ancestors of the primaries had lower contributions ( Conclusions: Through analyzing cfDNA and synchronous tumor biopsies from the same patient, we find trunk mutations are enriched in cfDNA as compared to the average single-site biopsy. We also predict that cfDNA represents multiple nodes in the inferred phylogeny. In cases where tumor biopsies are inaccessible, we demonstrate that cfDNA might be a promising alternative to detect trunk SSNVs. These results suggest that cfDNA may be complementary to tumor biopsies for disease monitoring and treatment selection in personalized medicine. Citation Format: Samuel S. Freeman, Ziao Lin, Gavin Ha, Ignaty Leshchiner, Justin Rhoades, Dimitri Livitz, Daniel Rosebrock, Sarah C. Reed, Gregory Gydush, Christopher Lo, Denisse Rotem, Atish D. Choudhury, Daniel G. Stover, Heather A. Parsons, Jesse S. Boehm, J Christopher Love, Matthew Meyerson, Paul Grandgenett, Michael A. Hollingsworth, Viktor A. Adalsteinsson, Gad Getz. Liquid biopsies identify trunk mutations and reflect multiple tumors in a patient [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr LB-225.

Cell free DNA (cfDNA) has been shown to be an emerging non-invasive biomarker to monitor tumor progression in cancer patients. Elevated cfDNA has been found not only from tumors, but also from normal tissues. Thus, the identification of cfDNA’s tissue-of-origin is critical to understand the mechanism of cfDNA release and tumor progression. Recent efforts to identify cfDNA’s tissue-of-origin begin to utilize cfDNA’s epigenomic status, such as DNA methylation and nucleosome spacing. However, both of these methods have limitations: (1) For nucleosome positioning, lack of reference nucleosome maps in different tumor and normal tissues has limited its application to tissue-of-origin deconvolution; (2) For DNA methylation, large DNA degradation during whole genome bisulfite sequencing (WGBS) library preparation, even with current low-input DNA technology, is still the major hurdle for its clinical application, although extensive DNA methylation studies by WGBS in tumor and normal tissues during the last decade have provided many reference maps. Very recently, a pioneer study showed significant differences between DNA fragment lengths of methylated and unmethylated cfDNA. Taking advantage of this experimental observation, we developed a machine learning approach to infer the base pair resolution DNA methylation level from fragment size information in whole genome sequencing (WGS). The predicted DNA methylation, from not only high coverage but also dozens of ultra-low-pass WGS (ULP-WGS), showed high concordance with the ground truth DNA methylation level from WGBS in the same cancer patients. Furthermore, by using hundreds of WGBS datasets from different tumor and normal tissues/cells as the reference map, we deconvoluted cfDNA’s tissue-of-origin status by inferred DNA methylation level at ULP-WGS from thousands of breast/prostate cancer samples and healthy individuals. The cfDNA’s tissue-of-origin status in cancer patients showed high concordance with confirmed metastasis tissues from physicians. Interestingly, some clinical information, such as cancer grades/stages, seemed to be correlated with cfDNA’s tissue-of-origin status. Overall, our methods here pave the road for cfDNA’s application in clinical diagnosis and monitoring. Citation Format: Yaping Liu, Sarah Reed, Atish D. Choudhury, Heather A. Parsons, Daniel G. Stover, Gavin Ha, Gregory Gydush, Justin Rhoades, Denisse Rotem, Samuel Freeman, Viktor Adalsteinsson, Manolis Kellis. Identify tissue-of-origin in cancer cfDNA by whole genome sequencing [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 5689. doi:10.1158/1538-7445.AM2017-5689