Circulating Tumor DNA Analysis for Detection of Minimal Residual Disease After Chemoradiotherapy for Localized Esophageal Cancer

Journal Pre-proof Circulating tumor DNA analysis for detection of minimal residual disease after chemoradiotherapy for localized esophageal cancer Tej D. Azad, Aadel A. Chaudhuri, Penny Fang, Yawei Qiao, Mohammad S. Esfahani, Jacob J. Chabon, Emily G. Hamilton, Yi D. Yang, Alex Lovejoy, Aaron M. Newman, David M. Kurtz, Michael Jin, Joseph Schroers-Martin, Henning Stehr, Chih Long Liu, Angela Bik-Yu Hui, Viren Patel, Dipen Maru, Steven H. Lin, Ash A. Alizadeh, Maximilian Diehn PII: DOI: Reference:

S0016-5085(19)41530-8 https://doi.org/10.1053/j.gastro.2019.10.039 YGAST 62995

To appear in: Gastroenterology Accepted Date: 30 October 2019 Please cite this article as: Azad TD, Chaudhuri AA, Fang P, Qiao Y, Esfahani MS, Chabon JJ, Hamilton EG, Yang YD, Lovejoy A, Newman AM, Kurtz DM, Jin M, Schroers-Martin J, Stehr H, Liu CL, Bik-Yu Hui A, Patel V, Maru D, Lin SH, Alizadeh AA, Diehn M, Circulating tumor DNA analysis for detection of minimal residual disease after chemoradiotherapy for localized esophageal cancer, Gastroenterology (2019), doi: https://doi.org/10.1053/j.gastro.2019.10.039. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 by the AGA Institute

Circulating tumor DNA analysis for detection of minimal residual disease after chemoradiotherapy for localized esophageal cancer Authors Tej D. Azad1,2, Aadel A. Chaudhuri3, Penny Fang4, Yawei Qiao4, Mohammad S. Esfahani1,2, Jacob J. Chabon1,2, Emily G. Hamilton1,2, Yi D. Yang1,2, Alex Lovejoy1,2, Aaron M. Newman2,5,7, , David M. Kurtz2,5, Michael Jin2,5, Joseph Schroers-Martin2,5, Henning Stehr 1,2, Chih Long Liu2, Angela Bik-Yu Hui2, Viren Patel6, Dipen Maru6, Steven H. Lin4, Ash A. Alizadeh2,5, Maximilian Diehn1,2,7 Affiliations 1 Department of Radiation Oncology, Stanford University, Stanford, California, USA 2 Stanford Cancer Institute, Stanford University, Stanford, California, USA 3 Department of Radiation Oncology, Washington University in St. Louis, St. Louis, MO, USA 4 Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA 5 Division of Oncology, Department of Medicine, Stanford Cancer Institute, Stanford University, Stanford, California, USA. 6 Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA 7 Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, California, USA

Corresponding authors: Maximilian Diehn, M.D./Ph.D. Department of Radiation Oncology, Stanford University, Stanford, USA Tel: 650-721-1550 Email: [email protected] Ash A. Alizadeh, M.D./Ph.D. Division of Oncology, Department of Medicine, Stanford University, Stanford, USA Tel: 650-725-0120 Email: [email protected] Steven H. Lin, M.D./Ph.D. Department of Radiation Oncology The University of Texas MD Anderson Cancer Center, Houston, Texas, USA Tel: 713-563-8490 Email: [email protected] Author contributions Study concept and design: TDA, SHL, AAA, MD Acquisition of data: TDA Analysis and interpretation of data: TDA, AAC, PF, SHL, AAA, MD Drafting of the manuscript: All authors Critical revision of the manuscript: All authors Technical and material support: SHL, AAA, MD Study supervision: SHL, AAA, MD Running title: ctDNA analysis following CRT predicts esophageal cancer outcomes

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Disclosure of Potential Conflicts of Interest: A.A.C. reports honoraria and travel support from Roche Sequencing Solutions and Foundation Medicine. A.A.C. has received research funding from Roche Sequencing Solutions and served as a consultant for Oscar Health. A.M.N., A.A.A., and M.D. are co-inventors on patent applications related to cancer biomarkers. A.M.N. reports consultanct with CiberMed. A.A.A., M.D., and D.M.K. are consultants for Roche Molecular Systems and A. L. is employed by Roche Molecular Systems. J.J.C. served as a consultant with Lexent Bio. A.A.A. has served as a consultant for Genentech, Roche, Chugai, Gilead, and Celgene. A.A.A. reports ownership interest in CiberMed and FortySeven. M.D. has served as a consultant for Roche, AstraZeneca, and BioNTech. M.D. and A.A.C have received research funding from Varian Medical Systems. M.D. also reports ownership interest in CiberMed. S.H.L has received research funding from Genentech, New River Labs, BeyondSpring Pharmaceuticals, Hitachi Chemical Diagnostics, and Elekta. S.H.L. serves on an advisory board of AstraZeneca.

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Abstract Background & Aims: Biomarkers are needed to identify patients at risk of tumor progression following chemoradiotherapy for localized esophageal cancer. These could improve identification of patients at risk for cancer progression and selection of therapy. Methods: We performed deep sequencing (CAPP-Seq) analyses of plasma cell-free DNA collected from 45 patients before and after chemoradiotherapy for esophageal cancer, as well as DNA from leukocytes, and fixed esophageal tumor biopsies collected during esophagogastroduodenoscopy. Patients were treated from May 2010 through October 2015; 23 patients subsequently underwent esophagectomy and 22 did not undergo surgery. We also sequenced DNA from blood samples from 40 healthy individuals (controls). We analyzed 802 regions of 607 genes for single-nucleotide variants previously associated with esophageal adenocarcinoma or squamous cell carcinoma. Patients underwent imaging analyses 6–8 weeks after chemoradiotherapy and were followed for 5 years. Our primary aim was to determine whether detection of circulating tumor DNA (ctDNA) following chemoradiotherapy is associated with risk of tumor progression (growth of local, regional, or distant tumors, detected by imaging or biopsy). Results: The median proportion of tumor-derived DNA in total cell-free DNA before treatment was 0.07%, indicating that ultrasensitive assays are needed for quantification and analysis of ctDNA from localized esophageal tumors. Detection of ctDNA following chemoradiotherapy was associated with tumor progression (hazard ratio, 18.7; P<.0001), formation of distant metastases (hazard ratio, 32.1; P<.0001), and shorter disease-specific survival times (hazard ratio, 23.1; P<.0001). A higher proportion of patients with tumor progression had new mutations detected in plasma samples collected after chemoradiotherapy than patients without progression (P=.03). Detection of ctDNA after chemoradiotherapy preceded radiographic evidence of tumor progression by an average of 2.8 months. Among patients who received chemoradiotherapy without surgery, combined ctDNA and metabolic imaging analysis predicted progression in 100% of patients with tumor progression, compared with 71% for only ctDNA detection and 57% for only metabolic imaging analysis (P<.001 for comparison of either technique to combined analysis). Conclusions: In an analysis of cell-free DNA in blood samples from patients who underwent chemoradiotherapy for esophageal cancer, detection of ctDNA was associated with tumor progression, metastasis, and disease-specific survival. Analysis of ctDNA might be used to identify patients at highest risk for tumor progression. Key words: genetics; polymorphism; chemoradiotherapy; SNP Funding We acknowledge funding support from the National Cancer Institute, National Institutes of Health, Stanford Cancer Institute, Doris Duke Charitable Foundation, Ludwig Institute for Cancer Research, CRK Faculty Scholar Fund, Radiological Society of North America, American Society of Clinical Oncology, and Mabuchi Research Fund.

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Introduction Neoadjuvant chemoradiotherapy (CRT) followed by surgery is a standard approach for treatment of localized esophageal cancers (ESCA) and has been shown to improve overall survival compared to esophagectomy alone 1. Unfortunately however, a significant fraction of patients treated with CRT and surgery will ultimately progress, with overall, distant, and locoregional progression rates of 49%, 39%, and 22%, respectively 2. Additionally, in ESCA patients who have an excellent upfront response to CRT, particularly those achieving complete responses prior to resection, surgery may not offer an additional survival benefit, potentially warranting an active surveillance approach 3-7. Such personalization of treatment would be facilitated by the identification of a biomarker that can sensitively and noninvasively detect residual disease. Unfortunately, conventional clinical assessment and imaging, such as endoscopy, endoscopic ultrasound, computed tomography (CT), and magnetic resonance imaging (MRI) are currently not sufficiently robust methods of gauging treatment response to CRT independently 8, 9. Accordingly, there is an unmet clinical need to identify biomarkers that could sensitively detect residual disease and/or early progression in patients with ESCA. Analysis of circulating tumor DNA (ctDNA) shortly after completion of curative intent treatment is able to detect minimal residual disease (MRD) in several carcinomas, including breast 10, colorectal 11, and lung cancers 12, 13. Preliminary studies in patients with localized squamous ESCA have suggested feasibility of ctDNA 14 or total cell-free DNA analysis 15. However, no study to date has applied ultrasensitive next generation sequencing methods to detect ctDNA MRD in ESCA patients receiving CRT and surgery. In this study we set out to test if ctDNA analysis can predict recurrence in patients with localized ESCA earlier than standardof-care imaging. Additionally, we sought to explore whether integration of ctDNA and metabolic imaging could further improve risk stratification. Materials and Methods Additional methodologic details can be found in the Supplemental Methods. Study design and patient cohort We prospectively enrolled patients with localized ESCA at the MD Anderson Cancer Center from 5/2010 – 10/2015 with the objective of assessing the association between postCRT ctDNA detection and freedom from progression (FFP). Inclusion criteria were: age > 18 years with previously untreated stage IA to stage IIIB EAC or ESCC being treated with definitive intent, with CRT and surgery or CRT-alone. The follow up schedule included an initial follow-up visit at 6-8 weeks from end of CRT with concurrent PET-CT or CT imaging. Patients were then monitored every three months with CT or PET-CT for the first year, every four months during the second year, and every six months in years three to five. Patient demographics and clinical characteristics are summarized in Table S1. The study statistical plan used the following assumptions: 1) 50% of patients will develop recurrence (based on 2-year progression-free survival data from the CROSS trial neoadjuvant chemoradiotherapy arm1); 2) Sensitivity of ctDNA MRD detection will be 75% (conservative estimate based on 94% sensitivity observed in our recent study of localized lung cancer); 3)

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Specificity of ctDNA MRD detection will be 90% (conservative estimate based on 100% specificity observed in our recent study of localized lung cancer). Using these assumptions, the risk of progression will be 88% versus 22% for patients with positive versus negative ctDNA MRD. A total sample size of 26 patients will yield 95% power to detect this difference with a one-sided alpha of 0.05. Eligible patients underwent a pre-treatment blood draw and genotyping with CAPP-Seq applied to micro-dissected formalin-fixed, paraffin embedded (FFPE) tumor tissue from pretreatment esophagogastroduodenoscopy (EGD) using matched germline DNA. Germline DNA was extracted from the pre-treatment blood draw. Patients then received CRT, after which a second blood draw was collected for the majority of patients (n = 32). The median time between the pre- and post-CRT draws was 86 days (IQR, 76-92). Healthy adult blood donors (n = 40) were recruited through the Stanford Blood Center (Table S5). All samples were collected with informed consent and institutional review board approval in accordance with the Declaration of Helsinki. All plasma samples were analyzed by CAPP-Seq as previously described 16, 17. A total of 218 specimens were profiled, including plasma, tumor, germline, and healthy control specimens. Healthy controls were only used to tune the detection cut-off for the ctDNA biomarker, as detailed below and were not included in survival analyses. Criteria for tumor-informed ctDNA detection and post-CRT monitoring Cell-free DNA from blood samples was analyzed for presence of mutations identified by tumor genotyping. The set of mutations identified from tumor genotyping was assessed as a group in blood samples according to a previously described Monte Carlo framework 17. Detection was tuned for a specificity of 95% in a cohort of cell-free DNA from 20 independent healthy subjects using a receiver-operator curve, resulting in a ctDNA detection index < 0.06 for detection. If ctDNA detection index was > 0.06, ctDNA was classified as not detected at that time point. We report absolute ctDNA levels as haploid genome equivalents per mL of plasma, the product of total plasma cell-free DNA concentration (fluorometry by Qubit, ThermoFisher Scientific, Waltham, MA) and the relative ctDNA concentration (mean allele fraction of somatic alterations). The ctDNA mutant allele fraction was calculated by averaging the mutant allele fractions for all mutations derived from tumor genotyping for that patient. All variant calls are listed in Table S9. Somatic copy number alteration detection Somatic copy number alterations (SCNAs) were called using method we previously described 18. In brief, SCNAs were detected using a z-score based method which involves a set of background samples to capture the region-specific variabilities across the targeted regions. For each gene, we called focal amplifications and deletions using the targeted regions as determined by the ESCA CAPP-Seq panel. Criteria for putative emergent mutation detection iDES-enhanced CAPP-Seq variant-calling was performed at the first post-CRT time point with matched leukocyte DNA as previously described 16. Mutations were defined as putative emergent mutations if they met the following criteria: 1. Non-synonymous

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2. No variant reads in matched germline, pre-CRT plasma, or tumor and maximum of 1 variant read in the plasma of 20 healthy controls 3. >1500X deduped depth in pre- and post-CRT plasma, > 500X depth in matched germline, > 100X depth in tumor 4. Recurrently mutated ESCA gene (> 5% of ESCA cBioportal samples) or > 2 duplex reads 5. Not in TP53 (known clonal hematopoiesis-associated gene) Statistics and analytic plan Our primary aim was to test the hypothesis that detection of ctDNA post-CRT is associated with high risk of recurrence. The primary outcome was freedom from progression (FFP; event defined as radiographic or clinical progression). To test the main hypothesis, we employed the Kaplan–Meier method with log-rank test to estimate P values and the Cox exp(beta) method to estimate hazard ratios. The relationship of ctDNA concentration as a continuous variable with outcome was also assessed using Cox proportional hazards regression. In exploratory analyses we considered three additional survival endpoints, distant metastasis-free survival (DMFS; event defined as distant recurrence or death), local progression-free survival (LPFS; event defined as local progression or death), and diseasespecific survival (DSS; event defined as death from ESCA). For these analyses, we performed Kaplan-Meier and Cox proportional hazards analyses as above using ctDNA as a binary endpoint. To guard against guarantee-time bias,19 we used landmark analysis with the time of the post-CRT blood draw being considered the landmark. We further constructed univariate Cox proportional hazards regressions models for baseline patient and tumor features to test if these variables were associated with the primary outcome (FFP). The Wald test was used to assess the significance of covariates, and hazard ratios were calculated by exponentiation of the model coefficients. Multiple testing adjustment was not performed for secondary analyses since they were exploratory. Results Profiling of ctDNA in localized ESCA before chemoradiotherapy We retrospectively analyzed plasma samples from patients with localized esophageal adenocarcinoma (EAC, n = 35) and squamous cell carcinoma (ESCC, n = 10) who received either CRT followed by esophagectomy (n = 23) or definitive CRT (n = 22). In total, we profiled 213 blood and tissue samples from these patients and a control cohort of 40 healthy adults. Clinical characteristics of the study cohort are depicted in Figure 1B and further detailed in Table S1. Blood draws were performed before and after CRT, near the time of computed tomography (CT) and positron emission tomography–CT (PET-CT) imaging (Fig. 1A). The median time between the pre- and post-CRT draws was 86 days (IQR, 76-92). To interrogate the genetic alterations that are commonly found in ESCA, we developed a 185-kb cancer personalized profiling by deep sequencing (CAPP-Seq) panel targeting 802 regions from 607 genes harboring recurrent single-nucleotide variants (SNVs) in both EAC and ESCC (Table S2) by applying our previously described panel design strategy 16, 17 to two published ESCA whole-exome datasets 20, 21. We then used this panel to sequence tumor DNA from micro-dissected pre-treatment esophagogastroduodenoscopy (EGD) biopsy samples and

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matched leukocyte DNA (Table S3). In silico validation of the esophageal CAPP-Seq panel in independent ESCA whole exome sequencing data sets 22, 23 predicted that > 1 mutation would be identified in 97.8% of EAC patients and 87.5% of ESCC patients (Fig. S1). Corresponding with these expectations, we identified ≥ 1 mutation in 44 of 45 (97.8%) tumors from our cohort, with a median of four SNVs (range, 0-23) and median of 0 copy number alterations (range, 0-3) per case (Fig. 1B, Fig. S1).Consistent with previous studies of EAC and ESCC mutation profiles 20-23, commonly mutated genes in our cohort included TP53, ERBB2, and CDKN2A. We next applied our CAPP-Seq ESCA panel to cell-free DNA (cfDNA) extracted from pre- and post-CRT plasma samples (Table S4). Here, we used two strategies, including one informed by tumor biopsies, and a second approach naïve to these variants and directly identifying ctDNA in the blood. For the tumor-informed strategy, which is similar to that used in prior ctDNA MRD studies in other tumor types10-13, 24, 25, we identified somatic variants in tumor biopsies after consideration of germline alleles within matched leukocytes. We then applied a tumor-informed ctDNA detection approach that employs a previously described Monte Carlobased framework to determine if a blood sample contains ctDNA 17. Using this approach, we detected ctDNA in 27 patients before CRT (Fig. 1B) for a pre-treatment sensitivity of 60%. Specificity was 95% based on analysis of cell-free DNA from 20 independent healthy subjects profiled with the same esophageal CAPP-Seq assay (Table S5). In patients with detectable ctDNA, a median of two mutations were detected at a median relative ctDNA concentration of 0.065% (range, 0-0.91%) and median absolute ctDNA concentration was 1.7 haploid genome equivalents per milliliter (hGE/mL) (range, 0-65.2). ctDNA concentrations in stage III EACs were significantly lower than those we observed in stage III lung adenocarcinomas (P = .009, Fig. 1C), even when considering only driver mutations26 (P = .004; Fig S2A), or normalizing by PETCT-based metabolic tumor volume (P = .04; Fig. S2E). Notably, we found that patients with ESCC had on average 7-fold higher ctDNA levels than patients with EAC (P = .01; Fig. 1D and Fig. S3), consistent with observations of higher ctDNA shedding in lung squamous cell carcinomas compared to lung adenocarcinomas 13. In a multivariable regression model accounting for histology, pre-CRT metabolic tumor volume (MTV), age, sex, and nodal involvement, histology (β = 11.0, P = .03) and pre-CRT MTV (β = 0.3, P = .002) were independently associated with absolute ctDNA concentration. Absolute pre-CRT ctDNA concentration (in hGE/mL) was strongly correlated with MTV at diagnosis (ρ = 0.60, P < .001; Fig. 1E), while relative ctDNA concentration (variant allele fraction, VAF) did not (ρ = 0.29, P = .13; Fig. S4). This observation likely reflects the fact that relative ctDNA concentration can be affected by changes in the amount of cfDNA contributed by non-malignant cells. Pre-CRT MTV (OR = 1.16, P = .02) and age (OR = 0.91, P = .048) were associated with detection of ctDNA pre-CRT (Table S6). To further explore factors affecting detectability of mutations in plasma, we next asked whether lower VAF tumor mutations were less likely to be detected in plasma than higher VAF tumor mutations. Among patients with detectable ctDNA, we observed that tumor VAF was significantly higher for mutations present in cfDNA compared to mutations that were not. This was true in both pre- or post-CRT plasma (P = .01; 2-way ANOVA; Fig. 2A. Prognostic utility of tumor-informed post-CRT ctDNA detection in localized ESCA

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Next, we evaluated the prognostic value of ctDNA detection following CRT in patients for whom a post-CRT blood draw was obtained (EAC, n = 23; ESCC, n = 8). We detected tumor-informed ctDNA at the post-CRT time point in five patients (16.1%). All five patients had EAC histology and were treated with definitive CRT alone. Consistent with pre-CRT ctDNA assessments, TP53 was the most common mutation detected in plasma (Fig. 2B). Median postCRT relative ctDNA concentration was 0.02% (IQR, 0.008% - 0.03%) and absolute ctDNA concentration was 0.98 hGE/mL (IQR, 0.36 - 1.16). Patients with detectable ctDNA post-CRT also had significantly increased risk of disease progression (HR 18.7, P < .0001; Fig. 2C), distant metastasis (HR 32.1, P < .0001; Fig. 2D), and disease-specific death (HR 23.1, P < .0001; Fig. 2E), compared to patients with undetectable ctDNA after CRT. Detection of ctDNA post-CRT was not associated with increased risk of local progression (P = 0.17, Fig. S5), although this analysis may have been impacted by 55% of patients undergoing surgery after CRT. We further observed that consideration of ctDNA as a continuous variable (hGE/mL) was also associated with increased risk of disease progression (HR 1.62, P = .007). Putative emergent mutations following CRT may be associated with disease progression Among ESCA patients undergoing neoadjuvant therapy, recent evidence from a study comparing tissue samples before and after chemotherapy suggests that new mutations may emerge following treatment 27. We conducted an exploratory analysis to determine if we could observe a similar phenomenon using ctDNA. For this analysis, we performed tumor-naïve genotyping on post-CRT plasma to identify nonsynonymous mutations that were not observed in pre-CRT plasma and tumor. In order to minimize the likelihood of detecting mutations due to clonal hematopoiesis 28, 29, we excluded mutations in TP53, the only gene in our panel implicated as being recurrently involved in clonal hematopoiesis as well as any mutations that were also observed in the matched leukocyte sample (see Methods). Using this approach, we observed new nonsynonymous mutations in the post-CRT plasma of 4 out of the 10 patients who progressed (Fig. 3A, B). Two new nonsynonymous mutations were detected in the post-CRT plasma of one patient (EP30) (Fig. 3A, B) who did not progress. Notably, unlike the other four patients with putative emergent mutations, EP30 underwent surgery 28 days following the post-CRT blood draw and was found to have residual tumor cells. A follow up blood draw from this patient taken after surgery did not contain these or any other new mutations. We therefore speculate that resection may have removed all residual tumor in this patient. Progressors were significantly more likely to have new mutations detected following CRT than non-progressors (P = .027, Fig. 3B). Figure 3C illustrates the clinical course of a patient with Stage II EAC who was treated with definitive CRT. Of five mutations found in the patient’s tumor, one (DCLK K207M) was detected following CRT. Emergent mutation analysis revealed a new ARID1A G696V mutation that was absent from the pre-CRT plasma, tumor, and matched leukocyte DNA. This patient developed distant metastasis 75 days following detection of this emergent variant. To further explore the potential utility of emergent mutation analysis we combined tumorinformed and tumor-naïve detection and considered plasma samples as positive if ctDNA was detected using either approach. As above, we found that ctDNA detection at the MRD timepoint was strongly prognostic for FFP (HR 13.2, P < .0001, Fig. 3D), DMFS (HR 28.0, P < .0001, Fig. S6A), and DSS (P < .0001, Fig. S6B). In univariable and multivariable Cox regressions, ctDNA

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detection at the MRD timepoint was independently associated with inferior FFP (HR 15.3, P = .0012; Table S7-8). Taken together, our findings suggest that residual ctDNA after CRT for ESCA is a strong negative prognostic marker and enables identification of patients at risk for developing recurrence. Post-CRT ctDNA detection enables earlier identification of recurrence compared to PETCT Recurrence was detected significantly earlier with ctDNA than standard-of-care PET-CT (P = .0026; Fig. 4A). Detection of ctDNA preceded recurrence detected by PET-CT in 70% of patients with an average lead time of 114.9 days (standard error of the mean, 32.9; Fig. 4B). For example, patient EP58 presented with stage IIIA EAC and had detectable pre-treatment ctDNA (Fig. 4C). The first post-CRT PET/CT at 36 days after completion of therapy revealed partial response to therapy and ctDNA was detectable in the blood draw at this time. At the subsequent follow up, 131 days after CRT, the patient was found to have distant metastasis to the liver. In contrast, patient EP29 presented with stage IIB EAC, was treated with neoadjuvant CRT, and noted to have an equivocal post-CRT PET/CT (65 days post-CRT). ctDNA from a blood draw at this same timepoint was undetectable. This patient went on to receive esophagectomy (81 days post-CRT) and histological analysis revealed a pathologic complete response (pCR) (Fig. 4D). The patient remains disease free two years post-CRT. ctDNA detection may enable earlier identification of distant metastasis compared to standard-of-care imaging. Integration of ctDNA and PET-CT in patients receiving definitive CRT Although the preferred treatment for localized ESCA is CRT followed by surgery, a significant subgroup of patients are medically unfit to undergo surgery or refuse it and for these patients, definitive CRT is an accepted treatment option 30, 31. In our cohort, we identified 12 patients receiving only CRT with both ctDNA and PET-CT evaluable following treatment (EAC, n = 10; ESCC, n = 2). Among these patients, those with detectable ctDNA at the MRD timepoint had a significantly higher risk of disease progression (HR 6.4, P = .009; Fig. 5A) and diseasespecific death (HR 9.1, P = .015; Fig. 5B). Post-CRT ctDNA detection enabled robust detection of distant metastasis (4/4 patients) but was less informative for detection of isolated locoregional recurrence (1/3 patients, Fig. S7A). Given the observation that ctDNA analysis appeared to be more sensitive for prediction of future distant metastasis than local recurrence, we hypothesized that integration of ctDNA with an imaging biomarker capable of detecting residual local disease would further improve performance. Prior studies have suggested that changes in FDG uptake on PET-CT after treatment for localized ESCA are associated with response to therapy and survival 32-34. In an exploratory analysis, we idenitifed a cutpoint for total lesion glycolysis (TLG) that optimally stratified patients who recurred from those who did not. Specifically, we employed a leave-oneout croos-validation approach to identifiy a percent TLG change (∆TLG) of -48.5% as best predicting ultimate recurrence (Supplementary Methods). ∆TLG displayed a sensitivity of 57.1% and specificity of 100% for recurrence prediction, compared to a sensitivity of 71.4% and specificity of 100% for ctDNA. Integration of ctDNA and PET-CT improved performance to a

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sensitivity of 100% and specificity of 100%, significantly higher than either ctDNA or ∆TLG alone (P < .001; Fig. 5C). Patients with detectable ctDNA post-CRT or ∆TLG > -48.5% had significantly increased risk of disease progression (P = .001; Fig. 5D), death from ESCA (P = .026; Fig. 5E), and local progression (P = .02; Fig. S7B), compared to patients with undetectable ctDNA and ∆TLG < 48.5%. Figure 5F illustrates the case of a patient with Stage IIA ESCC treated with definitive CRT who had detectable ctDNA pre-treatment and was negative by the integrated metric postCRT. This patient remains disease free nearly three years after diagnosis. These preliminary data suggest that an integrated ctDNA-imaging biomarker may allow effective risk stratification of patients treated with CRT alone. Discussion Aggressive treatment with trimodality therapy consisting of CRT and esophagectomy confers a significant survival benefit but also results in considerable morbidity35, 36. For example, in the pivotal ChemoRadiotherapy for Oesophageal cancer followed by Surgery Study (CROSS), 29% of all patients (ESCC, 49%; EAC, 23%) had a pCR following CRT 1, suggesting that these patients may be candidates for treatment de-escalation. Conversely, the CROSS trial also demonstrated an overall long-term recurrence rate of ~50% 2, indicating that a significant fraction of patients could potentially benefit from further escalation of therapy. Therefore, biomarkers that can distinguish between patients at highest risk for recurrence despite trimodality therapy from those with complete response to CRT alone are a major unmet need. Our results suggest ctDNA analysis appears to be a useful approach for risk stratification of patients with ESCA. Specifically, we found that detection of ctDNA MRD following CRT was strongly prognostic for FFP and DSS. Moreover, ctDNA detection post-CRT was more strongly predictive of distant metastasis, the most common pattern of recurrence after trimodality therapy 2 , than local progression. However, it should be noted that the results for patient EP30 suggest that post-CRT ctDNA may also be able to detect local residual disease in some cases. In this case, mutations detected following CRT were absent in post-surgery plasma and this patient did not develop recurrence, suggesting resection contributed to oncologic control. We also found that ctDNA detection post-CRT was able to detect recurrence nearly three months earlier than PET-CT imaging. In an exploratory analysis of patients receiving CRT-alone, we found that integration of ctDNA analysis and PET-CT response assessment may allow identification of patients who could avoid esophagectomy. We observed a pre-CRT median ctDNA VAF of 0.07% (IQR, 0.03 – 0.14), which after adjusting for volume is nearly 10-fold lower than in our recent study of localized lung cancer even after controlling for tumor volume 12. This suggests that ESCAs shed low amounts of ctDNA and that ultrasensitive approaches such as CAPP-Seq will be required to reliably detect ctDNA in patients with localized disease. It is important to note that our approach is unique compared to most other currently available ctDNA detection assays because it involves a tumor mutation-informed bioinformatic approach that maximizes sensitivity while maintaining high specificity. The low levels of ctDNA we observed suggest that ctDNA detection methods based only on plasma analysis (i.e. tumor-naïve) may be insufficiently sensitive to robustly detect ctDNA in localized ESCA.

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Our observation of low ctDNA levels in localized ESCA has important implications for future studies. Notably, the median ctDNA VAF we observed in localized ESCA (0.07%), is below the reported detection limits of most commercially available ctDNA assays. Sensitivity for ctDNA detection is determined by 1) sequencing depth (i.e. amount of cfDNA); 2) number of mutations tracked; and 3) assay background.16 Potential approaches for increasing sensitivity include increasing sequencing panel size or designing personalized ctDNA assays based on exome or whole genome sequencing of tumor tissue to track more mutations as we and others have previously reported for other tumor types.13, 16 To our knowledge, this is the first study to systematically evaluate the prognostic utility of ctDNA in a cohort of patients with localized ESCA treated with CRT and esophagectomy. Few studies have focused on analysis of ctDNA in ESCA 37-40. Two of these focused solely on copy number variants and reported associations between CNVs and survival outcomes 40, 41. We instead focused on detection of SNVs since these have a lower limit of detection than CNVs 18 and are thus better suited for the low levels of ctDNA observed in our early stage cohort. Two other studies utilized NGS-based methods for ctDNA detection, though both focused on ESCC, included advanced stage patients, had smaller sample sizes, and did not explore utility of ctDNA for detection of MRD after CRT 14, 42. Our findings of the strong prognostic power of residual ctDNA after CRT is consistent with recent studies in other solid tumors and supports the broad utility of ctDNA for MRD detection 10-12, 25. Based on our findings, response assessment via integration of ctDNA and imaging biomarkers appears a promising strategy for risk stratification in localized ESCA treated with CRT. Our data suggest that ctDNA analysis following CRT has high sensitivity for detection of occult disseminated disease but is less sensitive for detection of residual locoregional tumor deposits. Explanations for this observation include that micro metastases shed more ctDNA than residual local disease, or that the total burden of micro metastases tends to be greater than that of residual local disease. Imaging-based response assessment, such as analysis of ∆TLG, may be complementary by allowing identification of patients with incomplete local responses. Patients with favorable ctDNA and imaging responses may therefore be ideal candidates for personalized treatment approaches attempting to avoid esophagectomy. Since our analysis was exploratory, future studies will be needed to validate our findings, including the ∆TLG cut-point. Given prior challenges in finding reproducible PET-CT based predictors of local recurrence 8, 9, the combination of MRI-based response assessment with ctDNA should also be investigated. Addtionally, it would be interesting to explore combining ctDNA analysis with other potential clinical or molecular predictors of outcome in multiparmeter models that might further improve outcome prediction. Limitations of our study include that it was retrospective and had a relatively modest cohort size. However, due to the large hazard ratio associated with ctDNA MRD in our prior study of localized lung cancer12 that served as the basis for our statistical design, the number of patients analyzed was larger than the required sample size. Furthermore, the number of patients we analyzed is consistent with those evaluated for ctDNA MRD in recent studies of other tumor types10-13, 24, 25. Additionally, we had to employ leave-one-out cross-validation in the exploratory anlaysis integrating ctDNA with PET-CT. While this is a commonly used approach for establishing sensitivity and specificity in cohorts of this size, our findings should be considered exploratory and hypothesis generating and future studies in independent cohorts will

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be required to validate our findings. Separately, we were unable to explore the potential utility of other collection time points such as early during CRT or after completion of surgery due to lack of sample availability. Future studies should explore these time points. We also were unable to compare the performance of ctDNA with protein-based biomarkers, such as CA19-9, and suggest this should be done in future studies. Lastly, our observation of putative emergent mutations in a subset of patients must be interpreted with caution given that we cannot be entirely certain that these mutations are originating from tumor cells. However, given the strong association with disease recurrence it seems likely that the majority of emergent mutations arise from clonal selection of residual tumor cells. Future studies investigating emergent mutations should profile metastatic/recurrent tumor deposits in addition to pre- and post-treatment plasma samples to conclusively establish the presence of these mutations in tumor cells. In conclusion, we found that post-CRT ctDNA analysis can identify patients with localized ESCA at markedly increased risk of recurrence and death. Moreover, we explore integration of ctDNA and imaging assessment of local treatment response which may allow identification of patients treated with CRT who could be candidates for active surveillance instead of esophagectomy. Prospective studies will be required to validate our findings and to test the clinical utility of such personalized treatment approaches. Grant support This work was supported by grants from the National Cancer Institute (M.D., A.A.A., R01CA188298), the US National Institutes of Health Director’s New Innovator Award Program (M.D., 1-DP2-CA186569), a Stanford Cancer Institute-Developmental Cancer Research Award (M.D. and A.A.A.), Doris Duke Charitable Foundation Clinical Research Mentorship Fellowship (T.D.A.), the Stanford Medical Scholars Program (T.D.A.), the Ludwig Institute for Cancer Research (M.D., A.A.A.), and the CRK Faculty Scholar Fund (M.D.) , a Stanford Society for Physician Scholars Grant (T.D.A., A.A.C.), a Radiological Society of North America Resident/Fellow Grant (A.A.C.), the American Society of Clinical Oncology Young Investigator Award (A.A.C.), the Mabuchi Research Fund (S.H.L.) and the R. Lee Clark Clinical Innovator Award (S.H.L). Figure legends Figure 1. Pre-CRT assessment of ctDNA in patients with locally advanced ESCA. (A) Study overview of forty-five patients with EGD-proven locally advanced ESCA enrolled in the study. Plasma samples were collected before and after chemoradiotherapy (CRT). Following CRT, patients either underwent esophagectomy (n=23) or no further treatment (n=22). (B) Clinical characteristics and tumor genotyping results. Pre-CRT ctDNA detection status and clinical characteristics, where each column is a patient and each row is a parameter (e.g. histology). Similarly, the associated co-mutation plot depicts patient-level mutational profiles of 45 tumors from patients with esophageal cancer genotyped by our ESCA-specific NGS panel. Genes mutated in at least 5% of the patients in our cohort are depicted. The fraction of tumors with mutations in each gene is denoted on the left. (C) Comparison of absolute ctDNA concentration between Stage III esophageal adenocarcinoma (EAC, n=16) and Stage III lung adenocarcinoma (LUAD, n=10). P value calculated by the Mann-Whitney test. (D) Pre-treatment ctDNA

12

concentration in esophageal adenocarcinoma (EAC, n= 23) and esophageal squamous cell carcinoma (ESCC, n=7) patients. Patients were included if tumor-informed mutations were detected either pre- or post-CRT. Data represent mean + SEM. P value was calculated by the Mann-Whitney test. (E) Correlation of ctDNA concentration (haploid genome equivalents per mL, hGE/mL) with pre-CRT metabolic tumor volume (MTV) measured by PET-CT, stratified by histology. P value and ρ were calculated by Pearson correlation. Figure 2. Detection of tumor-informed mutations following chemoradiotherapy is strongly prognostic. (A) Tumor variant allele fraction (VAF), stratified by whether and at which time point a patient had detectable ctDNA (x-axis categories) and further stratified by whether a given mutation was detectable in plasma (column colors). P value calculated by a 2-way ANOVA and was significant for difference based on if a mutation was detected in plasma (P = .01), but not for if a patient was detected pre- or post-CRT. (B) Tumor mutations detected in plasma pre- and post-CRT. Denominator is the total number of patients with detectable ctDNA, pre- and postCRT (n=27 and n=5, respectively). Genes depicted were mutated in more than 5% of tumors in cBioportal ESCA datasets. (C-E) Kaplan–Meier analyses comparing patients with detectable and undetectable ctDNA in the post-CRT sample using tumor-informed ctDNA detection for (C) freedom from progression (P<.0001, HR = 18.7 (95%CI, 1.1-316.5)), (D) distant metastasis-free survival (P<.0001, HR = 32.1 (95%CI, 1.8-559.2)) and (E) disease-specific survival (P < .0001, HR = 23.1 (95%CI, 2.0-273.5)). ctDNA negative (n =26) versus ctDNA positive (n = 5). Time (days) was measured from the landmark (post-CRT blood draw). One patient was excluded from this analysis due to an event that occurred prior to the post-CRT blood collection. P values and hazard ratios were calculated from the log-rank test. Figure 3. Putative emergent mutations following CRT may be associated with disease progression. (A) In five patients we detected six new nonsynonymous mutations in post-CRT plasma that were absent pre-CRT. Column dot plots indicates allele fractions of both tumorinformed (circles) and emergent (squares) mutations. These mutations were absent in pre-CRT plasma and matched germline, tumor, and in 20 healthy controls. (B) Presence of putative emergent mutations in patients who developed disease progression (progressors; n = 10) versus those who did not (non-progressors; n = 20). P value derived from Fisher’s exact test. (C) Patient (EP32) with non-FDG avid stage II EAC treated with CRT-alone who developed an emergent ARID1A G696V mutation following CRT. This patient went on to develop distant metastasis (para-aortic lymph node). (D) Kaplan–Meier analysis of freedom from progression (P < .0001, HR = 13.2 (95%CI, 2.3-75.1)) comparing patients with or without detectable ctDNA post-CRT (n=8 and 23, respectively). Detectable ctDNA is defined as detection of tumorinformed or putative emergent mutations following CRT. Time (days) was measured from the landmark (post-CRT blood draw). P values and hazard ratios (HR) were calculated from the logrank test. AF, allele fraction; CRT, chemoradiotherapy; IMRT, intensity modulated radiotherapy; EAC, esophageal adenocarcinoma. Figure 4. ctDNA allows earlier and more robust detection of recurrence compared to PET-CT imaging. (A) Kaplan–Meier analysis for event-free survival (P = .0026), comparing ctDNA detection at the post-CRT time point with standard-of-care PET-CT imaging (n=10). Time to

13

event (days) was measured from end of CRT. P values and hazard ratios (HR) were calculated from the log-rank test. (B) Column dot plot of the lead time to radiographic recognition of recurrence for ctDNA detection (n=7), with the bars representing mean (114.9) and standard error of the mean (32.9). (C) Patient (EP58) with stage IIIA EAC with partial response to CRT on surveillance imaging and a decrease in post-CRT MTV but had increased levels of ctDNA postCRT and experiences distant liver metastasis. (D) Patient (EP29) with stage IIB EAC with equivocal surveillance imaging, increased post-CRT MTV, and undetectable post-CRT ctDNA who achieves long-term survival. Figure 5. Integrating ctDNA and ∆TLG enables improves detection of recurrence among patients receiving CRT alone. Kaplan–Meier analysis for (A) freedom from progression (P=.0093, HR 6.4 (95% CI 1.2-33.1)) and (B) disease-specific survival (P=.015, HR 9.1 (95% CI 1.4-60.8)) stratified by post-CRT ctDNA detection among patients receiving CRT alone (n=12). Time to event (days) was measured from post-CRT PET imaging. P values and hazard ratios (HR) were calculated from the log-rank test. (C)Mean sensitivity and specificity of ctDNA, ∆TLG, and both, for any recurrence detection (n=12). ∆TLG is defined as the percent change in TLG in response to CRT. Mean and standard deviation calculated using leave-one-out approach. P values calculated by the Mann-Whitney test. Kaplan–Meier analysis for (D) freedom from progression (P=.0011) and (E) disease-specific survival (P=.026) stratified by integration of ctDNA detection and ∆TLG at the post-CRT time point (n=12). Patients in the red curve (n=7) either had detectable ctDNA following CRT or had ∆TLG > -48.5% while patients in the blue curve (n=5) had undetectable ctDNA after CRT and had ∆TLG < -48.5%. Time to event (days) was measured from post-CRT PET imaging. P values were calculated from the log-rank test. Hazard ratios are not reported as they are undefined given the absence of any events in the ctDNA and ∆TLG negative group. (F) Patient (EP34) with stage IIA ESCC treated with CRTalone who achieved long-term survival with no evidence of disease. Patient had detectable ctDNA pre-treatment and remains undetectable following CRT. References - Author names in bold designate shared co-first authorship 1. van Hagen P, Hulshof MC, van Lanschot JJ, et al. Preoperative chemoradiotherapy for esophageal or junctional cancer. N Engl J Med 2012;366:2074-84. 2. Shapiro J, van Lanschot JJB, Hulshof M, et al. Neoadjuvant chemoradiotherapy plus surgery versus surgery alone for oesophageal or junctional cancer (CROSS): long-term results of a randomised controlled trial. Lancet Oncol 2015;16:1090-1098. 3. Noordman BJ, Spaander MCW, Valkema R, et al. Detection of residual disease after neoadjuvant chemoradiotherapy for oesophageal cancer (preSANO): a prospective multicentre, diagnostic cohort study. Lancet Oncol 2018. 4. Donahue JM, Nichols FC, Li Z, et al. Complete pathologic response after neoadjuvant chemoradiotherapy for esophageal cancer is associated with enhanced survival. Ann Thorac Surg 2009;87:392-8; discussion 398-9. 5. Bedenne L, Michel P, Bouche O, et al. Chemoradiation followed by surgery compared with chemoradiation alone in squamous cancer of the esophagus: FFCD 9102. J Clin Oncol 2007;25:1160-8.

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6. Stahl M, Stuschke M, Lehmann N, et al. Chemoradiation with and without surgery in patients with locally advanced squamous cell carcinoma of the esophagus. J Clin Oncol 2005;23:2310-7. 7. Berger AC, Farma J, Scott WJ, et al. Complete response to neoadjuvant chemoradiotherapy in esophageal carcinoma is associated with significantly improved survival. J Clin Oncol 2005;23:4330-7. 8. Schneider PM, Metzger R, Schaefer H, et al. Response evaluation by endoscopy, rebiopsy, and endoscopic ultrasound does not accurately predict histopathologic regression after neoadjuvant chemoradiation for esophageal cancer. Ann Surg 2008;248:902-8. 9. Westerterp M, van Westreenen HL, Reitsma JB, et al. Esophageal cancer: CT, endoscopic US, and FDG PET for assessment of response to neoadjuvant therapy--systematic review. Radiology 2005;236:841-51. 10. Garcia-Murillas I, Schiavon G, Weigelt B, et al. Mutation tracking in circulating tumor DNA predicts relapse in early breast cancer. Sci Transl Med 2015;7:302ra133. 11. Tie J, Wang Y, Tomasetti C, et al. Circulating tumor DNA analysis detects minimal residual disease and predicts recurrence in patients with stage II colon cancer. Sci Transl Med 2016;8:346ra92. 12. Chaudhuri AA, Chabon JJ, Lovejoy AF, et al. Early Detection of Molecular Residual Disease in Localized Lung Cancer by Circulating Tumor DNA Profiling. Cancer Discov 2017;7:1394-1403. 13. Abbosh C, Birkbak NJ, Wilson GA, et al. Phylogenetic ctDNA analysis depicts earlystage lung cancer evolution. Nature 2017;545:446-451. 14. Luo H, Li H, Hu Z, et al. Noninvasive diagnosis and monitoring of mutations by deep sequencing of circulating tumor DNA in esophageal squamous cell carcinoma. Biochem Biophys Res Commun 2016;471:596-602. 15. Hsieh CC, Hsu HS, Chang SC, et al. Circulating Cell-Free DNA Levels Could Predict Oncological Outcomes of Patients Undergoing Esophagectomy for Esophageal Squamous Cell Carcinoma. Int J Mol Sci 2016;17. 16. Newman AM, Lovejoy AF, Klass DM, et al. Integrated digital error suppression for improved detection of circulating tumor DNA. Nat Biotechnol 2016;34:547-555. 17. Newman AM, Bratman SV, To J, et al. An ultrasensitive method for quantitating circulating tumor DNA with broad patient coverage. Nat Med 2014;20:548-54. 18. Chabon JJ, Simmons AD, Lovejoy AF, et al. Circulating tumour DNA profiling reveals heterogeneity of EGFR inhibitor resistance mechanisms in lung cancer patients. Nat Commun 2016;7:11815. 19. Giobbie-Hurder A, Gelber RD, Regan MM. Challenges of guarantee-time bias. J Clin Oncol 2013;31:2963-9. 20. Dulak AM, Stojanov P, Peng S, et al. Exome and whole-genome sequencing of esophageal adenocarcinoma identifies recurrent driver events and mutational complexity. Nat Genet 2013;45:478-86. 21. Agrawal N, Jiao Y, Bettegowda C, et al. Comparative genomic analysis of esophageal adenocarcinoma and squamous cell carcinoma. Cancer Discov 2012;2:899-905. 22. Cancer Genome Atlas Research N, Analysis Working Group: Asan U, Agency BCC, et al. Integrated genomic characterization of oesophageal carcinoma. Nature 2017;541:169-175. 23. Song Y, Li L, Ou Y, et al. Identification of genomic alterations in oesophageal squamous cell cancer. Nature 2014;509:91-5. 24. Sausen M, Phallen J, Adleff V, et al. Clinical implications of genomic alterations in the tumour and circulation of pancreatic cancer patients. Nat Commun 2015;6:7686. 25. Tie J, Cohen JD, Wang Y, et al. Serial circulating tumour DNA analysis during multimodality treatment of locally advanced rectal cancer: a prospective biomarker study. Gut 2018.

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26. Bailey M, Tokheim C, Porta-Pardo E, et al. Comprehensive Characterization of Cancer Driver Genes and Mutations. Cell 2018;173:371-385. 27. Findlay JM, Castro-Giner F, Makino S, et al. Differential clonal evolution in oesophageal cancers in response to neo-adjuvant chemotherapy. Nat Commun 2016;7:11111. 28. Genovese G, Kahler AK, Handsaker RE, et al. Clonal hematopoiesis and blood-cancer risk inferred from blood DNA sequence. N Engl J Med 2014;371:2477-87. 29. Xie M, Lu C, Wang J, et al. Age-related mutations associated with clonal hematopoietic expansion and malignancies. Nat Med 2014;20:1472-8. 30. Park IH, Kim JY. Surveillance or resection after chemoradiation in esophageal cancer. Ann Transl Med 2018;6:82. 31. Cronin-Fenton DP, Sharp L, Carsin AE, et al. Patterns of care and effects on mortality for cancers of the oesophagus and gastric cardia: a population-based study. Eur J Cancer 2007;43:565-75. 32. Jayachandran P, Pai RK, Quon A, et al. Postchemoradiotherapy positron emission tomography predicts pathologic response and survival in patients with esophageal cancer. Int J Radiat Oncol Biol Phys 2012;84:471-7. 33. Monjazeb AM, Riedlinger G, Aklilu M, et al. Outcomes of patients with esophageal cancer staged with [(1)(8)F]fluorodeoxyglucose positron emission tomography (FDG-PET): can postchemoradiotherapy FDG-PET predict the utility of resection? J Clin Oncol 2010;28:4714-21. 34. Roedl JB, Colen RR, Holalkere NS, et al. Adenocarcinomas of the esophagus: response to chemoradiotherapy is associated with decrease of metabolic tumor volume as measured on PET-CT. Comparison to histopathologic and clinical response evaluation. Radiother Oncol 2008;89:278-86. 35. Alghamedi A, Buduhan G, Tan L, et al. Quality of life assessment in esophagectomy patients. Ann Transl Med 2018;6:84. 36. Vellayappan BA, Soon YY, Ku GY, et al. Chemoradiotherapy versus chemoradiotherapy plus surgery for esophageal cancer. Cochrane Database Syst Rev 2017;8:CD010511. 37. Lopez A, Harada K, Mizrak Kaya D, et al. Liquid biopsies in gastrointestinal malignancies: when is the big day? Expert Rev Anticancer Ther 2018;18:19-38. 38. Creemers A, Krausz S, Strijker M, et al. Clinical value of ctDNA in upper-GI cancers: A systematic review and meta-analysis. Biochim Biophys Acta 2017;1868:394-403. 39. Openshaw MR, Richards CJ, Guttery DS, et al. The genetics of gastroesophageal adenocarcinoma and the use of circulating cell free DNA for disease detection and monitoring. Expert Rev Mol Diagn 2017;17:459-470. 40. Andolfo I, Petrosino G, Vecchione L, et al. Detection of erbB2 copy number variations in plasma of patients with esophageal carcinoma. BMC Cancer 2011;11:126. 41. Komatsu S, Ichikawa D, Hirajima S, et al. Clinical impact of predicting CCND1 amplification using plasma DNA in superficial esophageal squamous cell carcinoma. Dig Dis Sci 2014;59:1152-9. 42. Ueda M, Iguchi T, Masuda T, et al. Somatic mutations in plasma cell-free DNA are diagnostic markers for esophageal squamous cell carcinoma recurrence. Oncotarget 2016;7:62280-62291.

16

C

ctDNA (hGE/mL)

A

100

P = .009

10

1

0.1

Pre-CRT ctDNA Detected Undetected

Stage

TP53 ERBB2 KIF2B CDKN2A CSMD3 EPHA6 FNDC1 PCDH17 CNGA4 CNTNAP5 DPYS EPHA3 GABRG3 MET ACTL7B ADAMTSL1 ADCY8 ATAD2B C6orf118 CYP7B1 DCLK1 KCTD8 LRP1B NEUROD4 NOVA1 PCDH15 PPP1R3A SPTA1 STON1 TFAP2D TPTE TRPS1

82% 11% 11% 9% 9% 9% 9% 9% 7% 7% 7% 7% 7% 7% 5% 5% 5% 5% 5% 5% 5% 5% 5% 5% 5% 5% 5% 5% 5% 5% 5% 5%

EGFR CDNK2A ERBB2 MET KRAS

11% 11% 9% 9% 7%

EP79 EP80 EP74 EP40 EP54 EP75 EP30 EP34 EP27 EP72 EP38 EP58 EP59 EP62 EP69 EP28 EP46 EP57 EP29 EP32 EP47 EP25 EP35 EP31 EP39 EP41 EP42 EP45 EP53 EP56 EP60 EP65 EP66 EP71 EP78 EP82 EP81 EP37 EP33 EP36 EP50 EP44 EP49 EP43 EP77

SCNAs

SNVs

Nodal Involvement

Histology EAC ESCC Stage I II III LN Involvement Present Absent Alternations Missense Stop−gained Splice CN gain CN loss

ND 0.01

EAC (n=16)

D

E

LUAD (n=10)

P = .01

100 ctDNA (hGE/mL)

Pre-CRT ctDNA Histology

10 1 0.1

ND 0.01

1000

MTV (mL)

B

EAC (n=23)

ESCC (n=7)

EAC ESCC

100

10 ρ = .60 P < .001 1 0.01 ND

0.1

1

10

ctDNA (hGE/mL)

100

A

Mutation detected Mutation undetected

Mean Tumor VAF

B TP53

Pre-CRT Plasma Sample

P = .01

CSMD3 CDKN2A NFE2L2 LRFN5

Post-CRT Plasma Sample

P = .01

DPP10 PCLO

Pre-CRT Post-CRT

CNTNAP5

20 30 Tumor VAF (%)

C

Freedom from Progression (%)

ctDNA not detected (tumor-informed approach) ctDNA detected (tumor-informed approach)

D

100 N = 26

50

0

N=5 0

500

HR = 18.7 (1.1-316.5) P < .0001 1000

1500

Time from Landmark (days)

2000

40

0

20

40

60

80

Percentage of plasma samples with detectable ctDNA that contain a mutation

100

N = 26

50

0

N=5 0

HR = 32.1 (1.8-559.2) P < .0001

500 1000 1500 2000 Time from Landmark (days)

E

Disease-Specific Survival (%)

10

Distant Metastasis Free Survival (%)

0

N = 26

100

50 N=5

0

HR = 23.1 (1.9-273.5) P < .0001 0

500 1000 1500 2000 Time from Landmark (days)

AF (%)

1

0.1

0.01 ND

C

Scan 1

0.1

0.1

No evidence of disease

Distant metastasis

Scan 2

Scan 3

Tumor-informed DCLK1 K207M TP53 R337C TRIML2 F358V SFRP4 Q51R PDGFD E224D

50

100 Time (days)

150

Progressors

20

40

60

80

100

Cases with Emergent Mutations (%)

ARID1A G696V

0

5% (1/21)

0

Emergent

ND 0.01

P = .027

pre-CRT post-CRT

5-FU+Docetaxel + IMRT

AF (%)

1

1

0.01 ND

pre-CRT post-CRT

Stage II EAC (EP32)

pre-CRTpost-CRT EP30 mutations TP53 R248Q CDKN2A P48R PCDH17 S131Y TMEM132C G192V ADAMTSL1 R898H CDH11 T320S GRM3 V191M

pre-CRT post-CRT

EP50 mutations ZNF107 Y94C NFE2L2 D29G THSD7B V921I CSMD3 T1096N PRDM1 L346F CNGA4 V202M ATRNL1 Q1122* CYP7B1 P182S SPG20 S491T MKRN3 A220V

40% (4/10)

0.01 ND

AF (%)

AF (%)

pre-CRT post-CRT

0.1

Nonprogressors

0.01 ND

B

EP42 mutations TP53 E336* PCLO I4271L

1

200

D Freedom from Progression (%)

ND 0.01

EP32 mutations DCLK1 K207M TP53 R337C TRIML2 F358V SFRP4 Q51R PDGFD E224D ARID1A G694V

AF (%)

0.1

EP27 mutations 1 TP53 C238Y CSMD3 K.3305* AJAP1 E179A GABRG3 S136Y ERBB2 G919W 0.1

AF (%)

A1

100 N = 23 ctDNA (-) ctDNA (+) 50

N=8 0

0

HR = 13.2 (2.3 - 75.1) P < .0001

500 1000 1500 Time from Landmark (days)

2000

B

Event-Free Survival (%)

100

300

Lead time to radiologic recurrence (days)

A ctDNA PET-CT

200

50

P = .0026

100

N = 10

0

0

100

200

N = 10

300

0

400

Time from end of CRT (days)

C

Partial response

1.0 0.5 0.0 ND

Scan 2

Scan 3

0

ctDNA concentration (hGE/mL)

1.5

50

100 Time (days)

150

Stage IIB EAC

D

5-FU+Docetaxel + IMRT

ctDNA concentration (hGE/mL)

Scan 1

Liver metastases

200

1.5

Scan 1 5-FU+Docetaxel + IMRT

Stage IIIA EAC

1.0 0.5 ND 0.0

0

Cancer vs. esophagitis

No evidence of disease

Scan 2 Surgery (pCR)

100 Time (days)

Scan 3

200

400 600 800

B

50

0

N=5 0

D

HR = 6.4 (1.2-33.1) P = .009

0

Disease-Specific Survival (%)

N=5

50

P = .001

500 1000 1500 Time from Landmark (days)

500 1000 Time from Landmark (days)

0

1500

ctDNA

ΔTLG Sensitivity

ctDNA + ΔTLG ctDNA (-) and ΔTLG (-) ctDNA (+) or ΔTLG (+)

100

50

F

Stage IIA ESCC

ctDNA + ΔTLG Specificity

No evidence of disease

No evidence of disease

N=5

50

0

N=7 0

P = .026

500 1000 1500 Time from Landmark (days)

80

Scan 1

Scan 2

Scan 3

60 40

-20 -40 -60

20 ND0

0

-80 0

ΔTLG ctDNA concentration

100

200 Time (days)

400

800

-100 1200

Δ Total lesion glyolysis (%)

Freedom from Progression (%)

100

0

HR = 9.1 (1.4-60.8) N = 5 P = .015

E

ctDNA + ΔTLG

0

50

0

500 1000 1500 Time from Landmark (days)

N=7

N=7

Carbo+Pac+IMRT

N=7

100

P < .001

100

ctDNA (-) ctDNA (+)

Performance of recurrence detection (%)

100

P < .001

C

ctDNA alone

ctDNA concentration (hGE/mL)

Freedom from Progression (%)

ctDNA alone

Disease-Specific Survival (%)

A

Supplementary Methods Imaging analysis All 18FDG PET-CT scans were prospectively interpreted during routine clinical evaluation by board-certified radiologists blinded to the plasma sequencing data. Metabolic tumor volume (MTV), SUVMean, and SUVMax were independently evaluated using PET Edge (MIM Software, Cleveland, OH) in all available pre- and post-CRT PET-CT scans. Total lesion glycolysis (TLG) was computed as the product of MTV and SUVMean. Primary clinical radiographic reports from post-treatment scans were classified as (i) showing no evidence of disease/excellent response, (ii) recurrent/persistent disease, or (iii) equivocal findings due to inability to distinguish tumor from other processes (e.g. radiation-induced esophagitis). Preand post-CRT PET-CT scans were available for 36 pts, with median time between the pre-CRT blood draw and post-CRT scan of 84 days (IQR, 77-90.3). Determination of ΔTLG cutoff Total lesion glycolysis (TLG) was measured pre- and post-CRT on PET-CT scans and used to calculate the percent TLG change, ΔTLG, for each patient. To determine an appropriate cutoff for distinguishing patients who recurred from those who did not, we used a leave-oneout approach. Specifically, for each training fold we identified the ΔTLG value that maximized the sum of sensitivity and specificity for discriminating progressors from non-progressors. This cut-point was then applied to the held-out patient and the results used to establish sensitivity and specificity. Finally, we selected the median cutoff value from all of the folds (-48.5%) for the analysis integrating ΔTLG with ctDNA. Response assessment and clinical endpoints Freedom from progression (FFP) was defined as the time during which the patient was free from cancer progression, distant metastasis free survival (DMFS) was defined as the time during which the patient was free from distant metastasis, local progression free survival (LPFS) was defined as the time in which the patient was free from local progression, and disease-specific survival (DSS) was defined as the time of esophageal cancer-related survival. Survival analysis Categorical time-to-event analyses of clinical endpoints including FFP, DMFS, and DSS were conducted using the log-rank test to estimate P-values and the logrank method to estimate hazard ratios, with results expressed as Kaplan-Meier plots. The relationship of ctDNA detection with survival outcomes was also assessed using Cox proportional hazards models to incorporate multiple covariates. Covariates with P < 0.1 in univariate Cox models were in multivariable models (Table S10). In the Cox regressions, the Wald test was used to assess the significance of covariates, and hazard ratios were calculated by exponentiation of the model coefficients. To address guarantee-time bias, we used landmark analysis. The post-CRT blood draw, which was used to stratify patients in Figures 2-3, served as the landmark for these analyses. In Figure 5, where ctDNA and PET are integrated, the post-CRT PET-CT imaging date was used as the landmark.

Esophageal cancer sequencing panel design The sequencing panel, or selector, was designed to maximize the number of patients, and mutations per patients, detected, while simultaneously minimizing the panel size and sequencing cost (16,17). Genomic regions with recurrent somatic alterations in esophageal cancer were thus prioritized for selector design using whole exome data two studies of esophageal cancer (20,21). A previously described bioinformatics approach was applied to identify recurrently mutated regions of the genome harboring SNVs that maximally cover both patients and mutations per patient (16,17). The final selector design covered 802 genomic regions from 607 genes, totaling 185 kb, with a median of 5 predicted variants per patient. Biotinylated oligonucleotides were designed using the NimbleDesign portal (Roche NimbleGen) and genome build hg19 (NCBI Build 37.1/GRCh37). Biological specimen collection and processing Between 10-20 mL of peripheral blood was collected in K2EDTA Vacutainer tubes (Becton Dickinson, Franklin Lakes, NJ) and processed within 3 hours of phlebotomy. Tubes were centrifuged at 1,800g for 10 minutes and plasma was frozen at -80° Celsius in 1-2 mL aliquots. A portion of the leukocyte-enriched plasma-depleted whole blood was also frozen at 80° Celsius in 1-2 mL aliquots and used for isolation of germline genomic DNA as previously described (16,17). Library preparation and targeted next-generation sequencing DNA isolation, library preparation and targeted sequencing were performed using iDES-enhanced CAPP-Seq previously described (16,17). Briefly, FFPE tumor, plasma, and germline DNA (from peripheral blood mononuclear cells (PBMCs) in plasma-depleted whole blood) were used to build sequencing libraries and subjected to targeted exome capture using the esophageal cancer selector. Sequencing was performed on Illumina HiSeq 4000 instruments (Illumina, San Diego, CA) using 2 x 150 paired-end reads with custom adapters for sample multiplexing and molecular barcoding. Sequencing reads were mapped to the human genome (build hg19) followed by removal of PCR duplicates and technical artifacts as previously described (16,17). Targeted sequencing with the ESCA CAPP-Seq panel achieved median prededuplication sequencing depths of 13,141X for plasma samples, 1,331X for germline samples, and 5,219X for tumor samples. Plasma, germline, and tumor samples were sequenced to median depths of 2610X, 1331X, and 316X after removing duplicate molecules, respectively. In keeping with our previous work using iDES-enhanced CAPP-Seq, cell-free DNA sequencing reads were de-duplicated using molecular barcodes, background-polished to reduce stereotyped base substitution errors, and filtered to limit to the selector space. Custom adapter and molecular barcodes We utilized custom DNA sequencing adapters that allow for both single- and doublestranded molecular barcoding (16). The design and validation of these adapters is discussed in detail in a prior manuscript (16) and summarized here. We employed four barcodes (three exogenous and one endogenous) for each strand of a cfDNA heteroduplex molecule. For the first exogenous barcode, we replaced a section of each sample barcode used for multiplexing

with a degenerate four-base unique identifier (UID or molecular barcode), such that each strand of a cfDNA heteroduplex molecule received a separate UID. This barcode was sequenced as part of the ‘index read.’ For the second and third barcodes, we added a 2-bp UID adjacent to the ligating end of each adapter. These UIDs were sequenced as part of the main reads of the DNA insert (i.e. ‘insert’ barcodes). Upon performing paired-end sequencing, insert barcodes were concatenated in silico into a single four-base UID for each strand of DNA. Matching of complementary insert UIDs and cfDNA fragment starts and ends allowed for reconstruction of double-stranded DNA duplexes. Tumor genotyping and reporter-list construction Pre-treatment FFPE tumor DNA was available for all patients and was used for genotyping, with variants called against matched germline DNA using our previously described CAPP-Seq adaptive variant-caller (16,17). To guarantee high specificity, potential somatic variants were removed according to the following filters: 1) presence in the ExAC database; 2) presence in > 10% of healthy subject cell-free DNA (n=20) at a level of > 1 read; 3) sequencing depth at the variant loci < 0.5x median depth across the selector; 4) variant allele fraction (VAF) < 5%. When detecting ctDNA, only missense, splice, and stop-gain variants were utilized. For tumors (n = 4) which demonstrated “noisy” variant lists (> 20 SNVs remaining) after application of these filters more restrictive VAF, normalized depth, and duplex molecule filters were applied. A complete list of variants passing these filters is provided in Table S11.

Supplementary Figure Legends Figure S1. ESCA panel design application and in silico validation. The ESCA panel was validated in silico using two independent whole exome sequencing cohorts of EAC (20) and ESCC (21). The number of single nucleotide variants per case covered by the ESCA panel was calculated and the differences are shown as cumulative distributions of mutations per case. Figure S2. Comparison of ctDNA levels between Stage III esophageal adenocarcinoma (ESCA) and Stage III lung adenocarcinoma (LUAD). Panels A-C depict pretreatment ctDNA levels computed using only driver mutations (absolute, hGE/mL; volume-controlled, ratio of absolute to MTV; and relative, VAF) calculated using all available tumor-informed mutations. Panels D-F depict the same variables computed using all tumor mutations. Figure S3. Volume-controlled pretreatment ctDNA concentration in esophageal adenocarcinoma (EAC, n= 21) and esophageal squamous cell carcinoma (ESCC, n=7) patients. Patients were included if tumor-informed mutations were detected either pre- or post-CRT and pretreatment MTV values were available for normalization. Data represent mean + SEM. Figure S4. Correlation of relative ctDNA variant allele fraction (VAF) with pre-CRT MTV, stratified by histology. P value and ρ were calculated by Pearson correlation. Figure S5. Kaplan–Meier analysis for local progression free survival stratified by post-CRT detection of tumor-informed mutations (P=0.17, HR= 3.1 (95% CI, 0.31-31.5)) in all patients with evaluable post-CRT blood draw (n=31). Time (days) was measured from post-CRT blood draw. P value and hazard ratio were calculated from the log-rank test.

Figure S6. Kaplan–Meier analysis for (A) distant metastasis free survival (P< 0.0001, HR= 28.1 (95% CI, 4.01-195.4)) and (B) disease-specific survival stratified by post-CRT ctDNA detection (P< 0.0001) among patients with ctDNA evaluable at the MRD landmark (n=31). Time to event (days) was measured from the post-CRT blood draw. P value was calculated from the log-rank test. Figure S7. Kaplan–Meier analysis for local progression free survival stratified by post-CRT ctDNA detection (A, P=0.41, HR= 2.1 (95% CI, 0.35-12.3), local progression free survival stratified the integrated metric (B, P = 0.02), distant metastasis free survival stratified by postCRT ctDNA detection (C, P=0.004), and distant metastasis free survival stratified the integrated metric (D, P = 0.05) among patients receiving CRT alone (n=12). Time to event (days) was measured from post-CRT PET imaging. P values and hazard ratios were calculated from the log-rank test. Figure S8. Kaplan–Meier analyses stratified by post-CRT TLG status for freedom from progression (A, P=0.003, HR= 6.5 (95% CI, 0.91-47.2), disease-specific survival (B, P = 0.07), distant metastasis free survival (C, P = 0.26), and local progression free survival (d, P=0.06) among patients receiving CRT alone (n=12). Time to event (days) was measured from post-CRT PET imaging. P values and hazard ratios were calculated from the log-rank test.

Figure S1

1.0 TCGA EAC (n=182) ICGC ESCC (n=88) Current cohort (n=45)

Fraction of patients

0.8

0.6

0.4

0.2

0.0

0

5

10 15 Mutations

20

25

A

B

Ratio of Absolute ctDNA concentration:MTV

ctDNA (hGE/mL)

10

1

0.1

ND 0.01

100

LUAD (N = 10)

1

Ratio of Absolute ctDNA concentration:MTV

1

0.1

ND 0.01 LUAD (N = 10)

1

0.1

ND 0.01 ESCA (N = 10)

LUAD (N = 10)

ESCA (N = 10)

F 10

P = .04

LUAD (N = 10)

P = .04

10

1

1

0.1

0.1

ND 0.01 ESCA (N = 16)

P = .0004

0.1

E

P = .009

10

10

100

10

ctDNA (hGE/mL)

P = .05

0.01 ND ESCA (N = 10)

D

C

100

P = .004

ctDNA VAF (%)

100

ctDNA VAF (%)

Figure S2

ND 0.01 ESCA (N = 16)

LUAD (N = 10)

ESCA (N = 16)

LUAD (N = 10)

Figure S3

P = 0.014

Ratio of Absolute ctDNA concentration:MTV

1 0.1 0.01 0.001

ND 0.0001

EAC

ESCC

Figure S4

MTV (mL)

1000

EAC ESCC

100

10 ρ = 0.29 P = 0.13 1 0.001 ND

0.01 0.1 VAF (%)

1

Local progression free survival (%)

Figure S5

ctDNA not detected (tumor-informed approach) ctDNA detected (tumor-informed approach) 100 N = 26 N=5 50

P = 0.17 0

0

500 1000 1500 Time from Landmark (days)

2000

Figure S6

B

N = 23

100

50 N=8

ctDNA (-) ctDNA (+)

HR = 28.0 (4.01-195.4) P < 0.0001 0

0

500

1000

1500

Time from Landmark (Days)

2000

Disease-Specific Survival (%)

Distant Metastasis Free Survival (%)

A

N = 23

100

50 N=8 ctDNA (-) ctDNA (+)

0

P < 0.0001 0

500

1000

1500

Time from Landmark (Days)

2000

Figure S7 ctDNA alone

N=7

50

N=5

P = 0.41 0

0

Distant Metastasis Free Survival (%)

C

500 1000 Time from Landmark (Days)

1500

N=7 ctDNA (-) ctDNA (+)

50

N=5 P = 0.004 0

0

500 1000 Time from Landmark (days)

1500

ctDNA and ΔTLG (-) ctDNA or ΔTLG (+)

N=5

100

50 N=7 P = 0.02 0

0

D

ctDNA alone 100

Local progression free survival (%)

ctDNA (-) ctDNA (+)

100

ctDNA + ΔTLG

B

Distant Metastasis Free Survival (%)

Local progression free survival (%)

A

500 1000 Time from Landmark (Days)

1500

ctDNA + ΔTLG N=5

100

50

ctDNA and ΔTLG (-) ctDNA or ΔTLG (+)

N=7

P = 0.05 0

0

500 1000 1500 2000 Time from Landmark (days)

A

B ΔTLG alone

100

Disease-Specific Survival (%)

ΔTLG ΔTLG +

N=8 50

0

N=4 0

C

HR = 6.5 (0.91-47.2) P = .003

500 1000 1500 Time from Landmark (days)

ΔTLG alone 100

N=8 50

0

N=4

0

P = .26 500 1000 1500 Time from Landmark (days)

100

N=8 50

N=4 0

P = .07 0

D Local progression free survival (%)

Freedom from Progression (%)

ΔTLG alone

Distant metastsis free survival (%)

Figure S8

500 1000 Time from Landmark (days)

1500

ΔTLG alone 100

N=8 50

N=4 0

0

P = .06 500 1000 1500 Time from Landmark (days)

Table of Contents Table S1. Patient, treatment, and tumor baseline characteristics stratified by histology Table S2. Genes fully or partially covered in esophageal cancer focused CAPP-Seq selector. Table S3. Germline and tissue DNA metrics for patients and healthy donors. Table S4. Plasma DNA metrics and ctDNA detection of tumor-informed mutation indices at each time point for all patients. Table S5. Plasma DNA metrics for all healthy donors. Table S6. Univariate analysis of factors predictive of tumor-informed ctDNA detection, pre-CRT Table S7. Univariate Cox regressions for FFP based on patient, treatment and tumor characteristics. Table S8. Multivariable Cox regression for FFP based on post-CRT ctDNA detection at the MRD landmark (variables p < 0.2 in univariate analysis) Table S9. Variant-level data for all tumor-informed reporters at all time points for full cohort.

dices at each time point for all patients.

on, pre-CRT

or characteristics.

n at the MRD landmark (variables p < 0.2 in univariate analysis)

Table S1. Patient, treatment, and tumor baseline characteristics stratified by histology P value EAC (N = 35) ESCC (N = 10) All (N = 45) 66.1 (7.7) 70.5 (3.8) 67.1 (7.3) 0.06 Age (years), mean (SD) 32 (91.4) 8 (80.0) 40 (88.9) 0.66 Male, N (%) Follow up (days), mean (SD) 812.3 (439.0) 714.4 (456.9) 790.5 (444.9) 0.55 Smoking, N (%) 24 (68.6) 5 (50.0) 29 (64.4) 0.41 Stage, N (%) 0.66 2 (5.7) 0 (0.0) 2 (4.4) I II 9 (25.7) 2 (20.0) 11 (24.4) III 24 (68.6) 8 (80.0) 32 (71.1) 24 (68.6) 7 (70.0) 31 (68.9) 1.00 Lymph node involvement 18.7 (17.9) 25.7 (23.3) 20.3 (19.5) 0.59 MTV (mL), mean (SD) SUVMax, mean (SD) 10.4 14.8 11.4 0.15 RT Type, n (%) 0.27 IMRT 23 (65.7) 9 (90.0) 32 (71.1) Proton beam 12 (34.3) 1 (10.0) 13 (28.9) Surgery, n (%) 18 (51.4) 5 (50.0) 23 (51.1) 1.00 Recurrence, n (%) Distant 9 (25.7) 1 (10.0) 10 (22.2) 0.43 Locoregional 4 (11.4) 1 (10.0) 5 (11.1) 1.00 P value was calculated by the Mann-Whitney test for continuous variables and Chi Square test for categorical variables PET = positron emission tomography MTV = metabolic tumor volume SUV = standard uptake value (unit measurement of PET signal)

Table S2. Genes fully or partially covered in esophageal cancer focused CAPP-Seq selector (N=607) AADACL3 ABCA8 ABCB1 ACP1 ACTL7B ACTL9 ADAMTS16 ADAMTS18 ADAMTS19 ADAMTS20 ADAMTS5 ADAMTSL1 ADCY8 ADD2 ADPRHL1 ADRA1A ADRA2B AFF3 AJAP1 AKAP3 AKAP9 ALG12 ALS2CR12 ANGEL1 ANKRD30A ANKRD52 ANKS1B AP3B2 APCDD1L AQP1 ARC ARHGAP20 ARHGAP28 ARHGEF9 ARID1A

BDNF BHLHE22 BICC1 BNC2 BOC BRSK1 BSN BTBD3 C10orf128 C16orf78 C1orf101 C6orf118 C7orf60 C8orf34 CACNA1E CACNA2D3 CAPG CBLN4 CCDC110 CCDC144NL CCDC39 CCT6A CCT8L2 CD163 CD1A CD1D CD1E CD38 CD5L CDC42EP3 CDH10 CDH11 CDH18 CDH20 CDH22

CNTN3 CNTN6 CNTNAP1 CNTNAP5 COL11A1 COL24A1 COL3A1 CPEB1 CPN1 CRB2 CRISPLD1 CSF3R CSMD1 CSMD3 CST2 CTNNA2 CTNNB1 CUBN CYP7B1 DBC1 DBX2 DCAF13 DCAF4L2 DCLK1 DDI1 DDO DEAF1 DGKI DIDO1 DISC1 DKK2 DLGAP4 DLX5 DMRT2 DNAH11

EGFR EHBP1L1 ELMO1 EML1 ENTHD1 EPDR1 EPHA2 EPHA3 EPHA4 EPHA5 EPHA6 EPHB1 EPHB2 EPS8L1 ERBB2 ERBB4 ERCC4 ETV1 EYS FADS2 FAM13A FAM46B FAM49A FAM5C FANCM FAT3 FAT4 FCGBP FCRL5 FECH FERD3L FFAR1 FGF17 FJX1 FLG

GAD1 IFT172 GALR1 IL7R GBE1 INHBA GBP5 INMT GDF5 INSRR GFOD1 IRX6 GFRA1 ITIH5 GHR KCNA3 GLI3 KCNB2 GLIS3 KCNC1 GPAT2 KCNC2 GPATCH3 KCNH1 GPC5 KCNH7 GPC6 KCNJ12 GPNMB KCNK13 GPR135 KCNQ2 GPR141 KCNQ3 GPR149 KCNT2 GPR156 KCNV1 GRID1 KCTD19 GRIK2 KCTD8 GRIN2A KDM2B GRIN3A KHDRBS2 GRM1 KIAA0100 GRM3 KIAA1045 GRM6 KIF1B GRM8 KIF2B GRXCR1 KIRREL GUCY1A2 KL HCFC2 KLF12 HEPACAM KLF15 HERC2 KLF5 HEY2 KLK15 HGD KLRB1 HIST1H2BHKLRG1

LRRC3B LRRC7 LRRIQ1 MAGI1 MAGI2 MAML3 MAN2A1 MAP4K2 MCHR1 MDH1B MDM4 MDN1 MET MGAT4C MICALL2 MKRN3 MMP16 MON1B MSC MTIF3 MUSK MYH1 MYO18B MYO1F MYOM2 NAALAD2 NALCN NBEA NCAM2 NCAPD2 NDN NELL1 NEUROD4 NFE2L2 NID2

NSUN4 PLXNA1 SEC14L1 SRD5A2 UACA NTN1 PNLIPRP3 SECISBP2LSSTR4 UBQLNL NUAK1 POLG SEMA3G ST6GAL1 UBR5 NUDT7 POLR1B SEMA5A ST6GAL2 UCHL5 OGFRL1 POM121L12SEMA6D ST8SIA4 UGT2B10 UNC119 OMA1 PPP1R3A SERPINA12STAC OPRM1 PRAME SETD7 STK17A UNC13C OSGIN2 PRDM1 SFMBT2 STON1-GTF2A1L UNCX OTUD7A PRDM9 SFRP4 SULF1 UQCRHL OVCH1 PREX2 SFTPC SUSD2 USH2A OXR1 PRICKLE2 SGIP1 SYNE1 USP15 PADI4 PRIM2 SH3GL2 SYT15 VEGFC PAK7 PROS1 SH3TC2 SYT9 VIP PAQR9 PRRT3 SHANK1 TAOK3 VPS33B PARK2 PSG4 SHC4 TATDN2 VSNL1 PAX1 PTCHD2 SI TBC1D19 WBSCR17 PCDH15 PTEN SIGLEC7 TBX18 WDFY3 PCDH17 PTN SIM1 TCEB3B WDR17 PCDH9 PTPN21 SIPA1L2 TECRL WNK1 PCDHA3 PTPRD SLC12A5 TECTA WNT3 PCDHA8 PTPRN2 SLC16A14 TFAP2D WNT5A PCDHB16 PTPRS SLC22A16 TG WNT7B PCDHGA12 PTPRT SLC22A9 TGM6 XAB2 PCLO PTRF SLC26A4 THSD7B XDH PCSK5 PXDN SLC35A1 TIAM1 XKR7 PDCL2 PXDNL SLC39A12 TIMD4 YIPF7 PDE10A RALGPS1 SLC39A4 TLL1 YY1AP1 PDE6D RBM46 SLC6A15 TMCC3 ZBTB20 PDGFB RGL1 SLC6A19 TMEM119 ZBTB7B TMEM132C ZFP37 PDGFC RIMS2 SLC6A2 TMEM132D ZFP42 PDHA2 RNF111 SLC8A2 PDYN ROBO1 SLCO3A1 TMEM196 ZHX3 PDZRN3 ROBO2 SLCO4C1 TNFAIP8L3 ZIC1 PDZRN4 ROR2 SLCO6A1 TNFRSF1B ZIM2 PGBD2 RRAD SLIT2 TNN ZMAT4

ZNF676 ZNF831 ZNF99 ZPLD1 ZSCAN16 ZSCAN18 ZSCAN20

ARMC1 ASCL1 ASCL4 ASPRV1 ATAD2B ATL3 ATP10A ATP10D ATRNL1 AVPR1A B3GNT7 BAHCC1 BAI3 BAZ2B BCL11A

CDH4 CDH8 CDH9 CDK14 CDKN2A CDYL2 CECR2 CELSR3 CFH CHRM2 CHST15 CLDN17 CLEC2D CNBD1 CNGA4

DNAH7 DOCK2 DPP10 DPP6 DPYS DPYSL4 DSCAM DSCAML1 DUSP12 DUSP13 DUSP6 DYSF EDIL3 EDNRB EEF1A2

FMN2 FN1 FNDC1 FOXF1 FOXG1 FPR2 FPR3 FRY FSHR FSTL5 FZD6 GABRA2 GABRA6 GABRB1 GABRG3

HIST1H2BJ KRAS HIST1H2BL KRT5 HIST1H3J KSR2 HIST1H4D LAMA5 HIST1H4E LHFPL3 HLA-DRA LPA HLCS LPAR6 HMCN1 LPL HOXA7 LPO HOXD13 LRFN5 HOXD3 LRP1 HSPA2 LRP12 HTR1A LRP1B HTR3A LRRC16B IFI16 LRRC18

NINL NKX2-2 NKX2-5 NLRP12 NOL4 NOS1 NOVA1 NOVA2 NPBWR1 NPR3 NPY5R NRG1 NRG3 NRXN1 NRXN2

PHF12 PHF21B PHLPP1 PHYHIPL PI15 PID1 PIK3CA PIK3CG PIK3R1 PKN3 PKNOX2 PLAGL1 PLCG2 PLEKHH1 PLG

RSBN1 RSPH9 RTN1 RUNX1 RYR2 RYR3 SALL1 SAMSN1 SATB1 SCAND3 SCN10A SCN11A SCN1A SCN9A SCYL2

SMAD4 SMAD9 SMARCA4 SMOC2 SNRPN SNTG2 SOHLH2 SORCS1 SOX5 SOX6 SPG20 SPOCK1 SPRED2 SPTA1 SPTB

TP53 TPCN1 TPTE TRAF6 TRIM41 TRIML2 TRIOBP TRPC4 TRPC7 TRPS1 TUBA3C TULP2 TWIST1 TXNDC2 TYR

ZNF107 ZNF165 ZNF208 ZNF267 ZNF318 ZNF415 ZNF43 ZNF432 ZNF467 ZNF518B ZNF536 ZNF549 ZNF567 ZNF568 ZNF570

Table S3. Germline and tissue DNA metrics for patients

Sample ID EP25 EP27 EP28 EP29 EP30 EP31 EP32 EP33 EP34 EP35 EP36 EP25 EP27 EP28 EP29 EP30 EP31 EP32 EP33 EP34 EP35 EP36 EP37 EP38 EP39 EP40 EP41 EP42 EP43 EP44 EP45 EP46 EP47 EP37 EP38 EP39 EP40 EP41 EP42 EP43 EP44 EP45 EP46 EP47 EP49 EP53

a

Sample b

source Tumor Tumor Tumor Tumor Tumor Tumor Tumor Tumor Tumor Tumor Tumor PBL PBL PBL PBL PBL PBL PBL PBL PBL PBL PBL Tumor Tumor Tumor Tumor Tumor Tumor Tumor Tumor Tumor Tumor Tumor PBL PBL PBL PBL PBL PBL PBL PBL PBL PBL PBL Tumor Tumor

DNA input (ng)c 39.4 57.0 100.0 78.5 74.5 12.9 14.8 36.0 81.0 45.7 13.3 32.5 20.9 32.6 6.2 17.4 16.2 85.0 9.2 100.0 58.5 39.8 2.5 73.0 32.9 53.0 67.0 23.7 95.0 47.6 14.2 100.0 9.1 52.0 100.0 100.0 100.0 59.5 100.0 27.3 100.0 67.0 100.0 100.0 30.0 27.7

Median de-duped sequencing depthd 145 391 1755 472 461 114 901 110 460 148 76 2056 787 1861 520 1480 1403 3129 857 3578 1959 2081 102 772 115 278 469 233 866 871 129 1603 112 2214 2674 3134 3220 2661 3075 1639 3205 2700 3382 3081 614 167

Table S4. Plasma DNA metrics and ctDNA detection of tumor-informed mutation indices at each time point for all patients.

index 1 2 3 4 5 6 7 8 9 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41

Patient

Plasma time pointa

Total cell-free DNA (ng mL-1)

Cell-free DNA input (ng)b

ctDNA VAF (%)c

ctDNA (hGE mL-1)d

EP25 EP25 EP27 EP27 EP28 EP29 EP29 EP30 EP30 EP31 EP32 EP32 EP33 EP34 EP34 EP34 EP35 EP36 EP36 EP37 EP37 EP38 EP38 EP39 EP39 EP40 EP41 EP41 EP41 EP42 EP42 EP42 EP42 EP43 EP43 EP44 EP44 EP44 EP45 EP45

c1 c2 c1 c2 c1 c1 c2 c1 c2 c1 c1 c2 c1 c1 c2 c3 c1 c1 c2 c1 c2 c1 c2 c1 c2 c1 c1 c2 c3 c1 c2 c3 c4 c1 c2 c1 c2 c3 c1 c2

12.2 33.2 9.5 74.9 4.1 23.2 14.6 44.2 21.0 24.2 46.5 23.1 1497.7 129.3 96.7 11.2 7.8 15.5 18.1 14.4 33.0 21.2 26.4 20.0 12.4 23.7 23.0 21.6 12.9 11.5 9.6 6.8 6.1 12.3 38.2 18.3 19.4 4.6 14.6 13.4

59.9 32.0 32.9 55.8 60.0 60.0 32.0 51.1 32.0 60.0 60.0 36.4 60.0 55.9 36.9 35.8 56.7 60.0 40.7 54.5 36.4 60.0 33.8 38.1 40.0 57.4 32.0 36.4 42.1 43.3 44.8 27.3 24.5 38.7 40.0 58.0 45.3 20.5 58.5 35.3

0.093 ND 0.071 0.086 0.067 0.039 ND 0.266 ND 0.030 ND 0.025 0.106 0.388 ND ND ND ND 0.008 ND ND 0.144 ND 0.057 ND 0.069 0.128 ND ND 0.150 ND ND ND 0.012 ND 0.144 ND ND 0.909 ND

1.37 ND 1.49 8.39 0.33 1.02 ND 16.75 ND 0.84 ND 1.16 4.16 65.23 ND ND ND ND 0.27 ND ND 3.41 ND 2.19 ND 2.07 7.59 ND ND 2.13 ND ND ND 0.29 ND 3.30 ND ND 16.49 ND

ctDNA detection ctDNA detection indexe callf 0.0004 NS 0.0001 0.0000 0.0027 0.0030 NS 0.0000 NS 0.0023 NS 0.0030 0.0022 0.0000 NS NS NS NS 0.0400 NS NS 0.0001 NS 0.0004 NS 0.0004 0.0004 NS NS 0.0002 NS NS NS 0.0452 NS 0.0001 NS NS 0.0004 NS

Detected ND Detected Detected Detected Detected ND Detected ND Detected ND Detected Detected Detected ND ND ND ND Detected ND ND Detected ND Detected ND Detected Detected ND ND Detected ND ND ND Detected ND Detected ND ND Detected ND

Clinical interpretation of scang positive NED/Excellent response positive NED/Excellent response positive positive equivocal positive equivocal positive positive NED/Excellent response positive positive equivocal NA positive positive equivocal positive equivocal positive equivocal positive NED/Excellent response positive positive NED/Excellent response NA positive equivocal NA NA positive equivocal positive NED/Excellent response NA positive equivocal

Progressed CRT?h No Yes Yes No No No Yes Yes No No Yes No Yes No No No

Yes

No No No

Table S5. Plasma DNA metrics for all healthy donors. Healthy Donor CTR11 CTR31* CTR15 CTR14 CTR16 CTR17 CTR18 CTR19 CTR20 CTR21 CTR22 CTR23 CTR24 CTR25 CTR26 CTR27 CTR28 CTR30 CTR32 CTR33 CTR35 CTR36 CTR37 CTR39 CTR40 CTR43 CTR44 CTR45 CTR46 CTR47 CTR48 CTR49 CTR50 CTR51 CTR52 CTR53 CTR54 CTR55 CTR56 CTR58

Total plasma -1

DNA (ng mL ) 32.0 32.0 32.0 32.0 32.0 32.0 32.0 32.0 23.7 32.0 19.7 32.0 32.0 32.0 32.0 32.0 32.0 20.0 32.0 32.0 32.0 32.0 32.0 32.0 32.0 32.0 32.0 32.0 32.0 32.0 32.0 32.0 32.0 32.0 32.0 32.0 32.0 32.0 32.0 32.0

Plasma DNA a

input (ng) 1.6 5.9 4.4 4.4 8.9 2.0 2.2 1.3 7.6 2.4 1.7 2.5 3.0 3.2 6.0 3.6 13.0 7.2 11.4 14.7 6.8 9.4 5.6 9.3 6.3 5.9 4.1 6.1 3.2 3.1 3.9 5.4 6.1 6.9 4.2 3.1 6.7 4.3 3.1

Median de-duped b

sequencing depth 2445 1677 2229 1962 2273 1627 1499 1416 1308 1399 1519 1284 1310 1547 1470 1605 1440 2608 2612 2231 1789 2422 1820 2457 2457 2774 1694 2203 1566 2298 2575 1810 3439 3070 3210 2016 3789 3502 3682 2515

a

Plasma DNA input into CAPP-Seq

b

De-duplication was performed as previously described by Newman et al, Nature Medicine, 2014 *Plasma DNA concentration was not recorded before processing sample

dicine, 2014 and Newman et al, Nature Biotechnology, 2016.

Table S6. Univariate analysis of factors predictive of tumor-informed ctDNA detection, pre-CRT pre-CRT ctDNA detection Parameter OR 95% CI P value Age 0.91 0.82 - 0.99 0.048 Male sex 0.34 0.02 - 2.54 0.351 ESCC histology 3.37 0.72 - 24.5 0.158 Stage (I/II vs. III) 2.23 0.60 - 8.58 0.232 Pre-CRT Metabolic tumor volume 1.16 1.04 - 1.34 0.022

Table S7. Univariate Cox regressions for FFP based on patient, treatment and tumor characteristics. FFP Parameter HR 95% CI P value Age 1.04 0.94-1.15 0.47 Male sex 1.45 0.18-11.5 0.72 ESCC histology 0.70 0.15-3.29 0.65 Stage (I/II vs. III) 1.18 0.30-4.56 0.81 Lymph node status 2.19 0.47-10.3 0.32 Metabolic tumor volume 0.93 0.83-1.04 0.18 cell-free DNA concentration 0.99 0.94-1.03 0.55 Pre-CRT ctDNA concentration 0.84 0.62-1.13 0.25 Pre-CRT ctDNA status, (+/-) 0.87 0.25- 3.09 0.83 Post-CRT ctDNA status (tumor-informed or emergent mutation), (+/-) 15.43 3.82-62.2 0.0001 ESCC = Esophageal squamous cell carcinoma SUV = standard uptake value (unit measurement of positron emission tomography signal) CRT = neoadjuvant chemoradiotherapy

Table S8. Multivariable Cox regression for FFP based on post-CRT ctDNA detection at the MRD landmark (variables p < 0.2 in univariate analysis)

Parameter Metabolic tumor volume Post-CRT ctDNA status (tumor-informed or emergent mutation), (+/-)

HR 0.89 15.26

FFP 95% CI 0.77-1.02 2.94-79.3

P value 0.10 0.0012

Table S9. Variant-level data for all tumor-informed reporters at all time points for full cohort.

a

a

Patient

Chromosome

Position

EP25 EP25 EP25 EP27 EP27 EP27 EP27 EP28 EP28 EP28 EP28 EP29 EP29 EP29 EP29 EP30 EP30 EP30 EP30 EP30 EP31 EP32 EP32 EP32 EP32 EP32 EP33 EP33 EP33 EP33 EP33 EP33 EP33 EP33 EP33 EP33 EP33 EP33 EP33 EP33 EP34 EP34 EP34 EP35 EP35 EP35 EP36 EP36 EP36 EP36 EP36 EP36 EP36 EP36 EP36 EP36 EP36 EP36 EP36 EP36 EP36 EP36 EP36 EP36 EP36 EP36 EP36 EP36 EP36 EP37 EP37 EP37 EP37 EP37 EP37 EP37 EP37 EP37 EP38 EP38 EP38 EP38 EP39 EP39 EP40 EP40 EP40 EP41 EP42 EP43 EP43 EP43 EP44 EP44 EP44 EP44 EP45 EP46 EP46 EP46 EP47 EP47 EP47 EP49 EP50 EP50 EP50 EP50 EP50 EP50 EP50 EP50 EP50 EP53 EP53 EP53 EP53 EP53 EP54 EP54 EP54 EP54 EP54 EP54 EP54 EP54 EP54 EP54 EP54 EP54 EP54 EP56 EP56 EP56 EP56 EP56 EP57 EP57 EP57 EP57 EP57 EP57 EP58 EP58 EP58 EP58 EP58 EP58 EP58 EP58 EP58 EP58 EP58 EP58 EP58 EP58 EP58 EP58 EP58 EP59 EP59 EP59 EP59 EP59 EP60 EP60 EP62 EP62 EP62 EP62 EP62 EP65 EP66 EP66 EP69 EP69 EP69 EP71 EP71 EP72 EP72 EP72 EP72 EP72 EP72 EP74 EP74 EP74 EP74 EP74 EP74 EP74 EP75 EP75 EP75 EP75 EP75 EP75 EP75 EP75 EP75 EP75 EP75 EP75 EP78 EP78 EP78 EP79 EP79 EP79 EP79 EP79 EP79 EP80 EP80 EP80 EP80 EP80 EP81 EP81 EP81 EP81 EP81 EP81 EP81 EP81 EP81 EP81 EP81 EP81 EP82

chr17 chr8 chr10 chr17 chr8 chr15 chr1 chr17 chr3 chr1 chr18 chr17 chr13 chr5 chr12 chr9 chr17 chr12 chr13 chr9 chr17 chr17 chr4 chr13 chr7 chr11 chr17 chr3 chr19 chr20 chr8 chr1 chr7 chr3 chr6 chr12 chr10 chr4 chr6 chr7 chr17 chr9 chr13 chr17 chr1 chr3 chr17 chr2 chr6 chr7 chr1 chr3 chr8 chr7 chr1 chr2 chr6 chr2 chr17 chr7 chr2 chr16 chr11 chr7 chr3 chr13 chr7 chr3 chr2 chr17 chr2 chr13 chr15 chr3 chr8 chr21 chr2 chr19 chr17 chr5 chr19 chr8 chr17 chr5 chr17 chr17 chr22 chr17 chr17 chr14 chr9 chr21 chr8 chr13 chr9 chr9 chr17 chr17 chr21 chr8 chr8 chr17 chr14 chr9 chr7 chr2 chr2 chr6 chr11 chr10 chr8 chr8 chr13 chr17 chr15 chr6 chr7 chr10 chr17 chr17 chr3 chr17 chr9 chr6 chr12 chr6 chr8 chr7 chr6 chr1 chr6 chr3 chr17 chr12 chr3 chr21 chr3 chr17 chr6 chr6 chr6 chr4 chr14 chr3 chr11 chr4 chr7 chr8 chr2 chr8 chr2 chr1 chr6 chr8 chr2 chr8 chr7 chr17 chr15 chr8 chr17 chr3 chr2 chr2 chr17 chr10 chr6 chr17 chr8 chr8 chr8 chr17 chr22 chr17 chr17 chr8 chr12 chr17 chr2 chr17 chr10 chr11 chr4 chr8 chr20 chr2 chr12 chr17 chr17 chr8 chr17 chr12 chr17 chr9 chr17 chr3 chr5 chr6 chr4 chr6 chr5 chr4 chr1 chr19 chr17 chr7 chr1 chr17 chr20 chr14 chr17 chr2 chr19 chr17 chr17 chr15 chr9 chr7 chr19 chr6 chr6 chr12 chr5 chr7 chr3 chr4 chr3 chr5 chr17 chr7 chr17

7577559 88885853 55912983 7577568 113267606 27572092 4772466 7577121 89468391 158592963 48591904 7577121 36686031 135692604 25380276 21974684 7577538 128899766 58207072 18776920 7578280 7574018 189012619 36686109 37955988 103908222 51902115 41266097 52272353 1961011 88365958 152286572 113558626 96706429 28543361 45410271 89717678 47514593 28543347 116436021 7578406 21968242 58207677 7578406 158637704 119886645 51902115 141680590 50682865 116435741 193028352 114069867 105463503 113558508 211093177 48808758 159654384 23985168 66938129 113558745 48808343 31926490 6265319 90356014 89259282 58207020 113558826 112991499 125555842 51902115 23985168 38237704 27572077 96706579 131916281 28296649 48808370 58048890 7578406 101593707 31039891 113933896 7578271 136324216 7574003 37880261 17073044 7577018 7574021 42356534 121929807 10906967 133187784 58298853 16419219 111617550 7578478 7577120 10906987 132052107 32617831 7578196 26917472 18777143 64166963 178098959 138169337 106553074 6261628 117221492 65528554 113657361 36888375 7577120 54586130 27775639 55242438 50121858 7577559 51902145 96706409 7578209 21970927 66115152 55420887 165715148 116616652 82544746 159653495 196642275 70071044 147127994 7577508 130184831 147128671 38302708 74315654 7578442 146720312 165715680 165715169 96761642 26917797 96706378 6261637 44177108 41739747 105503100 196729219 116616377 60687891 114340251 50683121 116616373 163291983 104427528 116435769 7577120 27572024 65494146 7577127 1424723 141093331 125232316 7578263 7325917 100841630 7577548 65528646 105436515 133899104 7576851 41077619 7578406 7574003 105436568 86373749 7576851 1241782 7577538 55912954 92087101 44177079 113657397 23016493 116497369 23893878 7578406 7578185 110984778 37881323 55420760 7578176 21974561 51901711 36484966 24537657 152476094 188924295 159654320 63256793 10446059 159021869 49385324 7574003 136700568 33957224 7578406 44845516 68052798 37882085 125521709 7687284 37881323 7578552 48062700 111617807 19184907 11134252 159653932 159653634 118199014 180661362 136938211 178936082 162307247 89259253 1221997 37881114 19765270 7577120

Mutant allele

Ref. allele

A A T T A A C A G G T A T T G C T T A A C A C A C C C C C G G T C T T A A A T T T T A T T A C T T C C T C A T T G T G G T G T T T A G A T C T C T T A T A A T C A G A A A T A A A C A A A C G A C T A T G C C C G C A T A T A T G A A A T G A C G C T T T T T A C G G C C A C C A C G G A T T C A C T T G C T T A T A A A T A A T A A T A A T T A C C G A T A G G G G T T G A T A C T T C C A C A T A A G A A G C T A A A C A T T A T T A A T A T A A A T T A T A T A C A A T

G G C C T C A G T A C G C C T G C G C G G G A T T A T G G C C C G C C G G G G C C C G C C G T C C G G G G C C G C C A C G C G C G G A G G T C G C G G G C G C A T T T C G G G C C A G C C T A G G C C C T A G G A T G C G C G G C C G T G C G T C G C G C G G G G C C A T C G G C T C A C C C T G G G G C G C G C C G G G C G G C C C C G G C C T A A A G C G T A A T C G T G G G T C C G A G G C G G C C G G C T C G G G A G C C G G C G G G G C G G C C C G C G A G T G G C

Gene

Mutation type

TP53 missense DCAF4L2 missense PCDH15 missense TP53 missense CSMD3 stop-gained GABRG3 missense AJAP1 missense TP53 missense EPHA3 missense SPTA1 missense SMAD4 missense TP53 missense DCLK1 missense TRPC7 missense KRAS missense CDKN2A missense TP53 missense TMEM132C missense PCDH17 missense ADAMTSL1 missense TP53 missense TP53 missense TRIML2 missense DCLK1 missense SFRP4 missense PDGFD,DDI1 missense KIF2B missense CTNNB1 missense FPR2 missense PDYN missense CNBD1 missense FLG missense PPP1R3A missense EPHA6 missense SCAND3 missense DBX2 missense PTEN missense ATP10D stop-gained SCAND3 missense MET missense TP53 missense CDKN2A splice-3 PCDH17 missense TP53 missense SPTA1 missense GPR156 missense KIF2B missense LRP1B missense TFAP2D stop-gained MET missense UCHL5 missense ZBTB20 missense DPYS missense PPP1R3A missense KCNH1 missense STON1,STON1-GTF2A1L missense FNDC1 missense ATAD2B missense ABCA8 missense PPP1R3A missense STON1,STON1-GTF2A1L missense ZNF267 stop-gained CNGA4 stop-gained CDK14 missense EPHA3 missense PCDH17 missense PPP1R3A missense BOC missense CNTNAP5 missense KIF2B missense ATAD2B missense TRPC4 missense GABRG3 missense EPHA6 missense ADCY8 missense ADAMTS5 missense STON1,STON1-GTF2A1L missense ZNF549 missense TP53 missense SLCO4C1 missense ZNF536 missense CSMD3 missense TP53 missense SPOCK1 missense TP53 stop-gained ERBB2 missense CCT8L2 missense TP53 splice-5 TP53 stop-gained LRFN5 missense DBC1 missense TPTE missense KCNQ3 missense PCDH17 missense BNC2 missense ACTL7B missense TP53 missense TP53 missense TPTE missense ADCY8 missense NRG1 missense TP53 missense NOVA1 missense ADAMTSL1 missense ZNF107 missense NFE2L2 missense THSD7B missense PRDM1 missense CNGA4 missense ATRNL1 stop-gained CYP7B1 missense CSMD3 missense SPG20 missense TP53 missense UNC13C missense HIST1H2BL stop-gained EGFR missense WDFY4,LRRC18 missense TP53 missense KIF2B missense EPHA6 missense TP53 missense CDKN2A missense EYS missense NEUROD4 missense C6orf118 missense TRPS1 stop-gained PCLO stop-gained FNDC1 missense CFH missense BAI3 missense ZIC1 missense TP53 missense TMEM132D missense ZIC1 missense HLCS missense CNTN3 missense TP53 missense GRM1 missense C6orf118 missense C6orf118 missense PDHA2 missense NOVA1 missense EPHA6 missense CNGA4 missense KCTD8 missense INHBA missense LRP12 missense DNAH7 missense TRPS1 missense BCL11A missense RSBN1 missense TFAP2D missense TRPS1 missense KCNH7 missense DCAF13 missense MET missense TP53 missense GABRG3 missense BHLHE22 missense TP53 missense CNTN6 missense LRP1B missense CNTNAP5 missense-near-splice TP53 stop-gained SFMBT2 stop-gained SIM1 missense TP53 missense CYP7B1 missense DPYS missense TG missense TP53 splice-5 MCHR1 missense TP53 missense TP53 stop-gained DPYS missense MGAT4C missense TP53 splice-5 SNTG2 missense TP53 missense PCDH15 missense FAT3 missense KCTD8 missense CSMD3 missense SSTR4 missense DPP10 missense SOX5 missense TP53 missense TP53 missense KCNV1 missense ERBB2 missense NEUROD4 missense TP53 splice-5 CDKN2A missense KIF2B missense STAC missense CDH10 missense SYNE1 missense ZFP42 missense FNDC1 missense HTR1A missense ZNF518B missense IFI16 missense TULP2 missense TP53 stop-gained CHRM2 missense ZSCAN20 missense TP53 missense CDH22 missense PLEKHH1 missense ERBB2 missense CNTNAP5 stop-gained XAB2 missense ERBB2 missense TP53 stop-gained SEMA6D missense ACTL7B missense FERD3L missense SMARCA4 missense FNDC1 missense FNDC1 missense KSR2 missense TRIM41 missense PTN missense-near-splice PIK3CA missense FSTL5 missense EPHA3 missense SLC6A19 missense ERBB2 missense TMEM196 missense TP53 missense

Amino acid b

f

Amino acid c

change

position

SER>PHE PRO>LEU GLY>GLU CYS>TYR LYS>stop SER>TYR GLU>ALA ARG>CYS LEU>ARG LEU>PRO PRO>LEU ARG>CYS ARG>HIS GLY>SER GLN>PRO PRO>ARG ARG>GLN GLY>VAL SER>TYR ARG>HIS PRO>ARG ARG>CYS PHE>VAL LYS>MET GLN>ARG GLU>ASP MET>THR ASP>HIS GLY>ARG GLN>HIS THR>ARG ASP>ASN ILE>MET PRO>SER ARG>LYS PRO>LEU GLU>LYS TRP>stop LEU>ILE ALA>VAL ARG>HIS none ALA>THR ARG>HIS ARG>HIS PRO>LEU MET>THR CYS>TYR GLN>stop MET>ILE SER>ARG SER>TYR HIS>ASP ASP>TYR ALA>THR ARG>MET SER>CYS ARG>GLN LEU>SER GLU>GLN GLY>CYS SER>stop GLU>stop PRO>LEU LYS>ASN GLU>LYS SER>PRO GLY>ARG LEU>PHE MET>THR ARG>GLN LEU>VAL THR>ILE GLY>TRP HIS>TYR PRO>HIS PRO>THR CYS>TYR ARG>HIS LEU>VAL VAL>ASP GLU>ASP HIS>LEU ASP>TYR ARG>stop ASP>TYR ARG>TRP none GLU>stop SER>ARG THR>MET ALA>SER GLU>ASP TYR>HIS LEU>PRO ARG>CYS PRO>ARG ARG>HIS ARG>ILE ARG>HIS LEU>ARG VAL>GLY ALA>GLY LEU>PHE TYR>CYS ASP>GLY VAL>ILE LEU>PHE VAL>MET GLN>stop PRO>SER THR>ASN SER>THR ARG>LEU VAL>ILE LYS>stop GLU>ASP VAL>LEU SER>PHE ILE>THR SER>CYS HIS>ASP ARG>HIS SER>TYR LEU>PHE PHE>LEU SER>stop GLN>stop GLU>GLN PRO>ALA ASP>GLU ASP>ALA GLU>GLY LYS>ASN VAL>LEU SER>CYS GLN>HIS TYR>CYS LEU>VAL LEU>PRO ARG>SER THR>ILE ALA>THR PHE>LEU ASP>ASN SER>CYS GLN>LYS PRO>HIS CYS>SER HIS>ASP GLY>GLU LEU>ILE THR>ASN CYS>TYR ALA>VAL ASP>ASN VAL>ILE ARG>HIS MET>ILE ALA>THR GLU>LYS SER>TYR ARG>ILE LEU>PHE ARG>stop ARG>stop ALA>THR GLY>SER ASP>VAL SER>ALA ASN>THR none ARG>GLN ARG>HIS ARG>stop ASN>THR LEU>PRO none LEU>ARG ARG>GLN LEU>ILE LEU>ARG PRO>SER PRO>GLN VAL>ILE LEU>PRO GLU>LYS ARG>HIS PRO>ALA LEU>VAL VAL>MET GLN>HIS none PRO>HIS MET>ILE HIS>GLN GLU>GLN THR>ILE GLU>LYS PRO>ALA LYS>GLU VAL>ILE GLY>GLU SER>LEU ARG>stop ASN>THR GLY>SER ARG>HIS VAL>ILE ARG>GLN VAL>PHE ARG>stop THR>ILE VAL>MET TYR>stop ARG>GLN ARG>HIS ARG>CYS ARG>GLN ASP>GLU ALA>VAL ARG>HIS GLY>ARG ALA>THR GLU>LYS ASP>GLU VAL>MET MET>THR GLY>ARG SER>PHE ARG>HIS

241/394 116/396 559/1963 238/394 3305/3708 136/468 179/412 273/394 642/984 1977/2420 356/553 273/394 233/730 158/863 61/190 48/168 248/394 192/1109 131/1160 898/1763 190/394 337/394 358/388 207/730 51/347 224/397 574/674 32/782 148/352 241/255 416/437 264/4062 142/1123 236/1131 374/1326 273/340 235/404 12/1427 379/1326 1357/1409 175/394 333/1160 175/394 661/2420 560/815 574/674 1088/4600 26/453 1295/1409 13/330 353/742 132/520 182/1123 423/990 329/1183 947/1895 1102/1459 16/1582 103/1123 191/1183 307/744 470/576 68/452 142/984 114/1160 76/1123 304/1115 1053/1307 574/674 1102/1459 513/983 131/468 286/1131 550/1252 839/931 200/1183 173/641 175/394 405/725 1122/1301 531/3708 193/394 275/440 342/394 769/1256 133/558 336/394 236/720 614/762 532/552 283/873 969/1160 1023/1100 221/416 151/394 273/394 525/552 158/1252 397/646 218/394 406/508 972/1763 94/784 29/606 921/1578 347/826 202/576 1122/1380 182/507 1096/3708 491/667 273/394 1286/2215 16/127 736/1211 115/262 241/394 584/674 229/1131 214/394 144/168 324/3145 222/332 221/470 515/1295 4186/5143 651/1895 76/1232 1293/1523 32/448 258/394 164/1100 258/448 341/727 988/1029 163/394 713/1195 44/470 214/470 114/389 298/508 219/1131 205/576 374/474 76/427 794/860 2387/4025 607/1295 719/836 371/803 111/453 608/1295 560/1197 104/598 1305/1409 273/394 113/468 267/382 271/394 755/1029 3990/4600 307/1307 196/394 241/895 435/767 245/394 151/507 399/520 496/2769 319/423 175/394 342/394 381/520 252/479 281/540 248/394 569/1963 608/4558 384/474 1084/3708 125/389 255/801 222/764 175/394 222/394 234/501 839/1256 179/332 89/117 439/674 74/403 120/789 8021/8798 112/311 926/1895 252/423 632/1075 633/730 471/521 342/394 319/467 456/1044 175/394 263/829 1306/1365 951/1256 839/1307 517/856 839/1256 126/394 647/1074 135/416 27/167 973/1680 796/1895 697/1895 234/922 494/631 97/169 542/1069 732/848 133/984 628/635 815/1256 109/173 273/394

g

Tumor Total depth 380 351 139 913 451 278 491 2735 1140 1315 1104 491 336 335 464 758 1055 419 831 415 169 868 698 763 1464 1461 160 96 107 87 112 152 122 92 87 110 105 92 86 150 1050 714 918 256 155 228 117 97 103 103 117 90 92 98 94 154 90 92 103 143 120 128 90 118 94 158 106 108 90 244 167 116 120 114 141 135 128 129 1046 502 1317 1362 150 116 190 335 267 580 220 544 1190 1076 1210 1212 530 824 556 1966 846 3183 386 331 95 669 306 251 185 181 198 291 286 208 176 344 262 241 159 156 295 122 120 154 126 172 118 100 194 186 114 176 129 402 1210 514 913 739 593 508 573 409 400 382 46 52 56 52 55 34 60 44 56 56 47 47 34 43 70 53 58 121 261 109 120 119 2882 691 1337 1384 915 804 860 518 636 271 678 755 604 500 410 577 491 555 474 493 298 37 28 36 29 30 74 46 162 108 162 160 218 211 211 170 140 264 284 254 461 613 647 315 363 354 323 359 375 3097 791 363 499 376 326 340 343 189 174 197 300 212 234 256 287 223 1004

d

Total depth

32% 18% 8% 64% 30% 28% 22% 64% 29% 10% 6% 47% 20% 9% 6% 29% 18% 10% 8% 7% 36% 41% 18% 16% 11% 9% 29% 21% 19% 17% 17% 15% 15% 14% 14% 14% 13% 13% 13% 13% 55% 24% 8% 37% 36% 5% 27% 20% 19% 17% 17% 17% 16% 15% 15% 14% 13% 13% 13% 13% 13% 13% 12% 12% 12% 11% 11% 11% 11% 59% 52% 15% 12% 11% 11% 10% 10% 10% 58% 56% 32% 27% 23% 8% 23% 7% 5% 77% 21% 19% 11% 6% 23% 18% 17% 5% 32% 46% 15% 11% 90% 79% 9% 21% 23% 21% 9% 7% 7% 6% 6% 6% 6% 65% 25% 16% 11% 10% 56% 29% 18% 16% 13% 13% 12% 11% 11% 11% 11% 10% 10% 30% 24% 23% 5% 5% 35% 32% 14% 14% 9% 5% 65% 46% 43% 38% 36% 35% 35% 34% 32% 30% 30% 30% 29% 28% 27% 26% 26% 23% 16% 12% 11% 10% 52% 23% 53% 50% 43% 23% 21% 24% 15% 12% 57% 29% 28% 29% 21% 38% 26% 23% 23% 20% 9% 32% 32% 31% 28% 23% 16% 15% 39% 17% 8% 8% 7% 7% 6% 6% 6% 5% 5% 5% 32% 14% 8% 34% 9% 9% 6% 6% 6% 84% 28% 20% 13% 9% 33% 31% 27% 25% 25% 24% 23% 7% 7% 7% 6% 5% 9%

2483 2775 1905 3269 2284 3735 2911 1614 1119 1348 1340 1804 1389 1168 1547 1795 2836 1205 2795 1496 3311 2491 3217 3139 3068 3487 1267 1015 1164 512 978 1152 1244 1352 667 1239 872 1167 656 1155 1708 1278 1597 3610 4008 5131 2356 3004 3824 2216 1300 2888 3100 2393 4380 2836 3658 2392 1096 2777 3120 1813 4148 1734 4008 3500 3325 3347 2815 5535 4297 4894 5867 5688 4745 5199 5158 4715 3068 2230 3897 2779 3482 3544 3287 2669 3434 3122 2669 2694 2865 3845 2536 2081 1946 2989 3411 2412 2444 2494 2907 2898 3061 1430 1180 1365 2510 3159 1751 1905 2628 2198 1639 1373 1716 745 1969 1444 2445 3625 3616 3157 1346 2015 4074 2504 3301 3252 2783 2189 3489 524 1646 1030 1528 823 4027 2067 3643 1695 2508 2436 2753 3647 2394 3621 3164 3451 3421 3473 2688 2729 2798 3545 3405 720 3397 2386 3410 708 1867 2382 2003 1355 2519 2425 1700 2003 1037 1142 1171 1086 2210 1572 973 1631 1287 819 872 4715 4091 3566 4830 3714 3354 852 1009 1720 1560 1478 1836 1386 2943 3006 3077 3011 1851 2264 2376 4193 3139 2286 1732 2759 534 994 894 2991 4371 1677 3575 998 3294 651 543 578 732 419 1518 1725 1523 949 499 1145 1103 1372 1264 1529 1515 1605 4702

d

0.00 0.00 0.00 0.05 0.29 0.00 0.00

3301 2866 2317 2970 4012 4942 3162 3908 2485 2011 2893 3749 3993 3392 3403

0.00 0.00 0.04 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.13 0.00 0.00

4496 4560 4688

0.00 0.00 0.00

2941 2854 3598 2864 1917 3038 2804 2310 3698 2896 3535 2236 1759 2625 3188 1947 3357 2070 3404 3140 3427 3111 3082 2670 2074 2465 3160 2960 2424 2650 2642 2411 3478 2262 4736 2497 3825 3945

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.06 0.00 0.00 0.00 0.00 0.00 0.13 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

3459 2547 3167 3839 3858 5032 3913 3965 5946 6193 2271 2186 2581

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

1597 1212 1797 2997 4306 2662 1806 2822 2261 1853 1135 1214 582 1387 1121 2831 3241 2960 2833 1561 1994 3487 2496 2969 2750 2690 2160 3137 1152 3441 1830 2741 1757 1207 1907 1276 1125 1917 1441 2602 2831 2939 3080 3260 2470 2390 3088 3372 2096 2794 3177 3098 935 2870 2662 2918 1381 1583 1156 812 429 2380 1812 1520 1528 328 406 553

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.04 0.00 0.00 0.00 0.00 0.00 0.00 0.04 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.04 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.03 0.00 0.00 0.00 0.00 0.06 0.00 0.00 0.04 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Genomic coordinates are per hg19

Predicted amino acid change resulting from mutation

f

De-duplicated sequencing depth in plasma DNA at specified genomic position. De-duplication was performed as previously described by Newman et al, Nature Medicine, 2014 and Newman et al, Nature Biotechnology, 2016. Percentage of mutant reads in tumor or plasma (de-duplicated mutant reads / de-duplicated sequencing depth at that position)

Tumor sequencing by CAPP-Seq when biopsy tissue available

gPlasma time point 1 is pre-CRT and time points ≥2 are post-CRT

e

4602 4371 3650 3650 2424 2948 3722

Position of predicted amino acid change / Length of full protein

d

d

Total depth

0.12 0 0.16 0.09 0.09 0 0.1 0 0.27 0 0 0 0.07 0.09 0 0.61 0.63 0.08 0 0 0.03 0 0 0 0 0 0 1.18 0 0 0 0 0 0 0 0 0 0 0.3 0 0.82 0.16 0.19 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.05 0 0.06 0 0 0 0.05 0 0 0 0 0.02 0 0 0 0.36 0 0.22 0.11 0 0.06 0 0.15 0.13 0.15 0.04 0 0 0.47 0 0.1 0 0.91 0 0 0.04 0.17 0 0 0 0 0.07 0 0 0 0.05 0 0.05 0 0.29 0 0 0 0 0.16 0 0 0 0 0.1 0 0 0 0 0 0 0 0.38 0 0.19 0 0 0 0 0 0 0 0 0 0 0 0 0 0.03 0 0 0 0 0 0.03 0 0 0 0.75 0 0 0 0 0 0 0 0 0.35 0.35 0 0 0.17 0 0 0 0 0 0.08 0 0 0 0 0 0 0 0 0.12 0 0 0 0.14 0 0 0 0 0 0 0 0 0.04 0 0 0 0 0 0 0 0 0 0.07 0 0 0 0 0.77 0 0.35 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.06

b

g

Mutant AF (%)

2471 2182

0.00 0.00

2177 1531 2653 2763 2251 3370 2201 2088 2407 1781 1578 2290 2835 2057 3521

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

1950 3202 2843 2748 2154 2643

0.00 0.12 0.00 0.00 0.00 0.00

Plasma time point 4g

Plasma time point 3

Plasma time point 2 e

Mutant AF (%)

c

e

g

Plasma time point 1 e

Mutant AF (%)

Total depth

d

e

Mutant AF (%)

4050 4700 4374

0.00 0.00 0.00

3716 1418

0.00 0.00

4631 3551 2741 2492

0.00 0.00 0.00 0.00

1735 1812 2013

0.00 0.00 0.00

Total depth

1689

d

e

Mutant AF (%)

0.00

What You Need to Know BACKGROUND AND CONTEXT: Biomarkers are needed to identify patients at risk of tumor progression following chemoradiotherapy for localized esophageal cancer. These might help identify at risk of tumor progression and in selection of therapy. NEW FINDINGS: In an analysis of tumor DNA, germline DNA, and plasma cell-free DNA from patients who underwent chemoradiotherapy for esophageal cancer, detection of tumor DNA in blood (circulating tumor DNA, ctDNA) was associated with tumor progression, metastasis, and shorter survival times. LIMITATIONS: This was a retrospective analysis; prospective studies and validation in independent cohorts are required. IMPACT: Assays to measure ctDNA in blood samples of patients who underwent chemoradiotherapy for esophageal cancer might be used to identify those at highest risk for tumor progression. Lay Summary Azad et al. find that detection of tumor DNA in the blood of esophageal cancer patients is associated with survival and may enable personalized treatment decisions.