Abstract
Background
Circulating tumor DNA (ctDNA) is a valuable biomarker for assessing treatment response and molecular residual disease. In advanced esophageal and gastric cancer (gastroesophageal cancer [EGC]), ctDNA dynamics remain poorly understood. The authors investigated ctDNA in patients with advanced EGC to evaluate its clinical utility.
Methods
This was a multi‐institutional, retrospective analysis of 200 patients with recurrent/metastatic EGCs who underwent commercial ctDNA testing using a personalized, tumor‐informed assay (Signatera; Natera, Inc.). Patients were divided into cohort A (N = 36; stage I–III with recurrence) or cohort B (N = 164; metastatic at diagnosis). Longitudinal ctDNA dynamics were correlated with clinical and radiographic findings.
Results
In cohort A, 31 of 36 patients (86.1%) had ctDNA collected ≤90 days before recurrence; of these, 25 of 31 patients (80.65%) were ctDNA‐positive before recurrence. In cohort B, baseline ctDNA was available for 29 of 164 patients (17.68%), and all 29 were ctDNA‐positive. Among 52 patients who had on‐treatment ctDNA assessments, 62 treatment lines were analyzed (N = 6 in cohort A; N = 46 in cohort B). All who remained ctDNA‐negative throughout treatment (Neg‐Neg) showed treatment benefit (n = 6 of 6). Those who converted to ctDNA‐positive (Neg‐Pos) progressed (n = 2 of 2). Among ctDNA‐positive patients (Pos‐Pos), 27 of 40 (67.5%) showed treatment benefit, whereas 13 of 40 (32.5%) progressed. Patients who cleared ctDNA (Pos‐Neg) had favorable outcomes (12 of 14 patients; 85.7%). A decrease >90% in ctDNA levels among Pos‐Pos patients was linked to superior progression‐fee survival. Grouping treatment lines into favorable (Neg‐Neg, Pos‐Neg, and Pos‐Pos with >90% decrease) versus unfavorable (Neg‐Pos and Pos‐Pos with a <90% decrease or an increase) had significantly improved progression‐free survival in the favorable group (p < .0001).
Conclusions
ctDNA dynamics predicted progression and may guide treatment or imaging. ctDNA offers a minimally invasive, cost‐effective adjunct to radiographic surveillance.
Keywords: circulating tumor DNA (ctDNA), ctDNA dynamics, metastatic gastroesophageal cancer, treatment response
Short abstract
Circulating tumor DNA dynamics predicted disease progression and were correlated with treatment response in patients with advanced esophageal and gastric cancer. These findings support the integration of circulating tumor DNA as a minimally invasive tool to inform clinical decisions and optimize surveillance strategies in this population.
INTRODUCTION
Esophageal and gastric cancers (esophagogastric cancers [EGCs]) account for almost 50,000 cancer diagnoses in the United States. 1 Approximately one third of patients who are diagnosed with locally advanced EGCs receive preoperative/perioperative treatment followed by surgery. Unfortunately, about 40% of patients will develop a recurrence, primarily within the first 2 years, the majority of which are distant recurrences. 2 , 3 Despite advancements in targeted therapy and checkpoint inhibitor therapy, the median overall survival (OS) does not exceed 14–18 months. 4 , 5 , 6 , 7 , 8 , 9
Patients who have completed curative‐intent treatment or are receiving treatment for advanced/metastatic disease undergo routine monitoring with laboratory and imaging data. Carcinoembryonic antigen and cancer‐related antigen 19‐9 have been used as tumor markers to detect cancer recurrence or to serve as indicators of a lack of therapeutic response. 10 Unfortunately, their sensitivity for detecting recurrence in gastric cancer does not exceed 50%, with a negative predictive value that rarely exceeds 65%. 11 Computed tomography imaging is imperfect in detecting very small burdens of recurrent disease or gauging response in difficult‐to‐see areas, such as the peritoneum. In theory, noninvasive, sensitive, and specific strategies not only could aid in detecting recurrent disease but also could complement current monitoring tools during systemic therapies. Circulating tumor DNA (ctDNA) is a novel biomarker that can aid in the measurement of tumor burden and the assessment of treatment response across various malignancies. 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 Previously, we evaluated the role of tumor‐informed ctDNA assessment for molecular residual disease detection and surveillance in locally advanced EGC. 14 Few studies have since evaluated the utility of ctDNA analysis in patients with metastatic EGC. 21 , 22 , 23
Here, we investigated whether measuring ctDNA dynamics in patients with metastatic EGC could inform treatment decisions and improve prognostication. Accordingly, we report on pretreatment ctDNA detection rates and correlate on‐treatment ctDNA dynamics with clinical assessment and radiologic results. We demonstrate that ctDNA analysis may prove a valuable tool for monitoring treatment response in metastatic EGC.
MATERIALS AND METHODS
Study cohort
This retrospective ctDNA analysis included 200 patients with recurrent/metastatic EGCs who received treatment between November 2019 and November 2023 at 28 institutions. All patients received treatment and follow‐up in accordance with standard clinical practice guidelines. Tumor‐informed ctDNA testing was ordered commercially according to the provider's clinical practice. Blood samples were collected serially during routine clinical follow‐up. Tumor tissue samples were either those collected at the time of either surgical resection or biopsy diagnosis of metastatic disease. Cohort A consisted of patients with stage I–III disease who developed a recurrence and had a ctDNA timepoint collected up to 90 days before recurrence and treatment initiation. Cohort B included patients who presented with metastatic EGC at diagnosis and had a ctDNA timepoint at any time before treatment initiation (Figure 1A). In total, 1143 longitudinal plasma samples were collected and analyzed for ctDNA.
FIGURE 1.
Exclusion and inclusion criteria for patients and the number of treatment lines were included in the analysis. (A) CONSORT diagram of patients included in the baseline ctDNA detection rate analysis for cohorts A and B. (B) Flow diagram of treatment lines included in the final analyses. (C) An example schematic of treatment response monitoring illustrating the treatment timeline (blue line) and the relative timing of ctDNA draws (circles) and imaging (squares). *The first timepoint is defined as the closest timepoint to treatment initiation. **The second timepoint is defined as a ctDNA timepoint 27 days after the start of treatment. ctDNA indicates circulating tumor DNA; CONSORT, Consolidated Standards of Reporting Trials; EGC, esophageal and gastric cancer; Tp, timepoint.
This study was conducted in accordance with the principles of the Declaration of Helsinki and the International Council for Harmonization of Technical Requirements for Pharmaceuticals for Human Use Guidelines for Good Clinical Practice. Informed consent was obtained before sample collection as part of the ordering assay. A retrospective analysis of de‐identified data, ctDNA results, and clinicopathologic data collected for quality‐assurance purposes under the Code of Federal Regulations (45 CFR 164.501) was determined to be exempt research by an independent Institutional Review Board (Salus #20099‐04).
Personalized, tumor‐informed ctDNA analysis
The patients included in this study underwent ctDNA testing using a clinically validated, personalized, tumor‐informed, 16‐plex polymerase chain reaction, next‐generation sequencing assay (Signatera; Natera, Inc.), as previously described. 17 Briefly, whole‐exome sequencing was performed on formalin‐fixed, paraffin‐embedded tumor blocks and matched normal DNA blood samples. Multiplex polymerase chain reaction primers were designed for 16 patient‐specific, somatic, single‐nucleotide variants for each patient based on the results of whole‐exome sequencing. Plasma samples that had at least two tumor‐specific variants detected were defined as ctDNA‐positive, and the ctDNA concentration was reported in mean tumor molecules (MTM) per milliliter of plasma. Previous reports have laid the foundation for the prognostic evaluation of ctDNA measured as an absolute concentration, MTM/mL, which integrates the variant allele frequency, total cfDNA mass, and plasma volume and has been shown to accurately reflect overall changes in tumor burden in response to treatment compared with the variant allele frequency. 24 ctDNA testing was performed at the discretion of the ordering physician. For analyses, baseline ctDNA timepoints were defined separately for cohorts A and B as follows: the cohort A baseline was defined as a ctDNA test up to 3 months before relapse or stage IV diagnosis and before treatment initiation for stage IV disease, and the cohort B baseline was defined as a ctDNA test before the initiation of any systemic therapy for stage IV disease.
Assessment of treatment response
We evaluated ctDNA dynamics in patients for each line of treatment received (defined as any systemic therapy excluding maintenance and palliative treatment). For example, if a patient received combined fluorouracil, leucovorin, and oxaliplatin (first‐line therapy) for 8 months followed by paclitaxel plus ramucirumab (second‐line therapy) for 6 months, the correlation of ctDNA dynamics with response was analyzed separately for the combined fluorouracil, leucovorin, and oxaliplatin treatment course and the paclitaxel plus ramucirumab treatment course, respectively. The following inclusion criteria were used to select the lines of treatment included in the assessment of treatment response: (1) a ctDNA timepoint tested up to 90 days before treatment initiation and at least 4 weeks after initiation, and (2) a restaging imaging scan done after the second ctDNA timepoint (Figure 1B). Additional restaging imaging scans were evaluated when available. The correlation between ctDNA dynamics and restaging imaging was evaluated. Assessment of tumor response on imaging was based on Response Evaluation Criteria in Solid Tumor guidelines and included no evidence of disease, partial response, stable disease, complete response, or progressive disease. No evidence of disease, partial response, stable disease, and complete response were considered treatment benefits, and progressive disease was considered progression.
Statistical analyses
The primary end point was progression‐free survival (PFS), which was defined as the time from treatment initiation to disease progression or death. Patient characteristics were summarized using descriptive statistics, and statistical significance was evaluated using the Fisher exact test for categoric variables. The Kaplan–Meier method was used to estimate survival distributions. The log‐rank test was used to compare two survival distributions, and p ≤ .05 was considered significant. R software (version 4.4.1; R Foundation for Statistical Computing) and the R package survival (version 3.7‐0) were used.
RESULTS
Patient characteristics
Of the 200 patients, 130 (65%) were men. The median age at diagnosis was 64.26 years (range, 20.39–86.06 years). As mentioned above, this study consisted of two cohorts: cohort A (N = 36) included patients who were diagnosed with stage I–III disease and subsequently recurred with a ctDNA timepoint collected up to 90 days before recurrence, and cohort B (N = 164) comprised patients who presented with metastatic EGC at diagnosis. Additional data regarding clinicopathologic features are provided in Table 1.
TABLE 1.
Cohort demographics, clinicopathologic features, and treatment lines.
| Variable | No. (%) |
|---|---|
| Age: Median [range] | 64.26 [20.39–86.06] |
| Sex | |
| Men | 130 (65.0) |
| Women | 70 (35.0) |
| Stage | |
| I | 5 (2.50) |
| II | 7 (3.50) |
| III | 24 (12.00) |
| IV | 164 (82.00) |
| Primary tumor location | |
| Stomach | 91 (45.50) |
| Esophageal | 61 (30.50) |
| GEJ | 48 (24.00) |
| Histology | |
| Adenocarcinoma | 189 (94.50) |
| Squamous | 11 (5.50) |
| Grade | |
| 1 | 4 (2.00) |
| 2 | 50 (25.00) |
| 3 | 116 (58.00) |
| 2–3 | 18 (9.00) |
| NA | 12 (6.00) |
| Her2 status | |
| Positive | 25 (12.50) |
| Negative | 127 (63.50) |
| NA | 48 (24.00) |
| MSI status | |
| MSS | 188 (94.00) |
| MSI‐H | 12 (6.00) |
| TMB status | |
| TMB low | 187 (93.50) |
| TMB high | 13 (6.50) |
| Treatment regimens | |
| FOLFOX + ICI | 14 (22.58) |
| ICI | 8 (12.9) |
| Paclitaxel + ramucirumab | 5 (8.06) |
| FOLFOX | 4 (6.45) |
| T‐DXd | 4 (6.45) |
| Pembrolizumab + cabozantinib | 3 (4.84) |
| FLOT + ICI | 2 (3.23) |
| 5‐FU/LV | 1 (1.61) |
| 5‐FU/LV + ICI | 1 (1.61) |
| CAPOX + ICI | 1 (1.61) |
| FLOT | 1 (1.61) |
| FOLFOX + zolbetuximab | 1 (1.61) |
| Trastuzumab | 1 (1.61) |
| Other | 16 (25.81) |
| No. of treatment lines on study | |
| 1 | 21 (33.87) |
| 2 | 18 (29.03) |
| 3 | 6 (9.68) |
| ≥4 | 17 (27.42) |
Abbreviations: 5‐FU/LV, 5‐fluorouracil plus leucovorin; CAPOX, capecitabine plus oxaliplatin; FLOT, fluorouracil, leucovorin, oxaliplatin, and docetaxel; FOLFOX, fluorouracil, leucovorin, and oxaliplatin; GEJ, gastroesophageal junction; ICI, immune checkpoint inhibitor; MSI‐H, microsatellite instability‐high; MSS, microsatellite stability; NA, not available; TMB, tumor mutational burden; T‐DXd, trastuzumab deruxtecan.
Baseline ctDNA detection
Overall baseline ctDNA results were available for 60 of 200 patients (30%) who had a median of one test/patient (range, from one to three tests). Among the patients in cohort A, 31 of 36 of patients (86.1%) had a ctDNA timepoint collected up to 90 days before recurrence. Of these, 25 of 31 patients (80.65%) were ctDNA‐positive at that baseline timepoint before clinical recurrence, whereas only six of 31 patients (19.35%) were ctDNA‐negative. Of the six ctDNA‐negative patients, two had a subsequent ctDNA‐positive result after therapy initiation, two were persistently negative after relapse, and the remaining two did not have additional ctDNA timepoints. Median baseline ctDNA levels in this cohort were 8.67 MTM/mL (range, 0.11–1282.43 MTM/mL). In cohort B, baseline ctDNA was available for 29 of 164 patients (17.68%; median ctDNA level, 14.46 MTM/mL [range, 0.41–7080.53 MTM/mL]), with a 100% (29 of 29 patients) detection rate (Figure 1A). In cohort B, there was a median of one test per patient (range, one to two tests).
Assessment of ctDNA dynamics and PFS after treatment response
In total, the patients in our cohorts received 584 lines of treatment. When correlating with ctDNA dynamics, 522 lines of treatment were excluded because ctDNA timepoints or restaging imaging studies were not available (Figure 1B). A schematic of treatment response monitoring is included in Figure 1C, which provides an example of treatment timelines and relative timing of ctDNA draws.
For the analysis, in total, 62 lines of treatment (six in cohort A, 56 in cohort B) from 52 patients (N = 6 in cohort A; N = 46 in cohort B) were included; the regimens are listed in Table 1. There was a median of one line of treatment per patient. ctDNA dynamics from the pretreatment to on‐treatment timepoints were correlated with cancer management practices based on restaging imaging performed after on‐treatment ctDNA assessment. For treatment lines in which the patient remained serially ctDNA‐negative (Neg‐Neg; N = 6), six of six patients (100%) continued to derive benefit from ongoing treatment. Of the treatment lines in which the patient was initially ctDNA‐negative and later turned positive (Neg‐Pos; N = 2), two of two patients (100%) experienced disease progression. For treatment lines in which the patient remained persistently ctDNA‐positive (Pos‐Pos; N = 40), 27 of 40 patients (67.5%) showed benefit, whereas 13 of 450 (32.5%) had progression after treatment. Finally, for treatment lines in which the patient had ctDNA clearance (Pos‐Neg; N = 14), 12 of 14 (85.7%) showed benefit from continued treatment, whereas only two of 14 (14.3%) experienced progression.
When evaluating the association between PFS and ctDNA dynamics, we observed that patients who had Pos‐Pos or Neg‐Pos dynamics had shorter, although nonsignificant, PFS compared with those who had Neg‐Neg dynamics (Pos‐Pos: HR, 6.3; 95% CI, 0.83–47.0; p = .075; Neg‐Pos: HR, 8.5; 95% CI, 0.76–95.0; p = .083). Finally, ctDNA dynamics were similar in the Pos‐Neg and Neg‐Neg patients (p = .701; Figure 2A).
FIGURE 2.
Kaplan–Meier estimates of PFS stratified by (A) ctDNA dynamics observed during all analyzed lines of treatment (N = 62) among patients with relapsed or metastatic EGC; (B) the number of treatment lines in patients who remained positive for ctDNA after treatment, representing ctDNA levels that either decreased either >90% or <90%, and (C) the number of treatment lines in patients who had favorable versus unfavorable ctDNA dynamics, as stratified by PFS. CI indicates confidence interval; ctDNA, circulating tumor DNA; EGC, gastroesophageal cancer; HR, hazard ratio; NEG, negative; PFS, progression‐free survival; POS, positive.
To further stratify treatment lines with Pos‐Pos ctDNA dynamics (n = 40) according to the degree of change in ctDNA levels, we used a 90% decrease in ctDNA levels at the first ctDNA timepoint on treatment as a cutoff, as performed previously. 25 We observed that a ctDNA decrease >90% was associated with superior PFS compared with a <90% ctDNA decrease or any increase (HR, 3.9; 95% CI, 1.13–13.5; p = .032; Figure 2B). Next, we assessed all lines of treatment and stratified them into a favorable or unfavorable group based on ctDNA dynamics, with the favorable group consisting of those who had Neg‐Neg, Pos‐Neg, and Pos‐Pos dynamics with a >90% ctDNA decrease and the unfavorable group consisting of those who had Neg‐Pos and Pos‐Pos dynamics with a <90% ctDNA decrease or an increase. As illustrated in Figure 2C, we observed that the favorable group had significantly better PFS compared with the unfavorable group (HR, 5.5; 95% CI, 2.28–13.47; p < .0001).
DISCUSSION
Reliably monitoring for cancer recurrences after curative‐intent treatment and assessing tumor response in advanced settings remain critical clinical challenges in the management of patients with EGC. Previously, we demonstrated the feasibility and utility of tumor‐informed, postoperative ctDNA analysis for risk stratification and prognostication in patients with stage I–III EGC undergoing curative‐intent therapy. 14 In the current study, we investigated the feasibility of longitudinal ctDNA analysis for monitoring treatment response in metastatic EGC. We observed that ctDNA dynamics were correlated with PFS, because the patients who remained or became ctDNA‐negative after treatment had superior PFS compared with those who remained or became ctDNA‐positive. It is noteworthy that the patients who remained ctDNA‐positive but had a >90% decrease in ctDNA levels also had improved PFS compared with individuals who had <90% decrease or an increase in ctDNA levels. Thus these findings suggest that ctDNA analysis provides valuable information for treatment decision making and prognostication in metastatic EGC.
Our findings align with previously published data, further supporting the role of ctDNA as a biomarker for monitoring treatment response in metastatic EGC. 14 , 19 , 20 For example, Ng et al. 22 conducted a single‐center, population‐based cohort study that evaluated the prognostic role of ctDNA in patients with locoregional and metastatic esophageal squamous cell carcinoma. Their findings indicated that high ctDNA levels (HR, 2.41; 95% CI, 1.18–4.95; p = .02) and alterations in prechemotherapy ctDNA (and before cycle 3; HR, 1.99; 95% CI, 1.03–3.85; p = .04) were significant prognostic factors. Similarly, van Velzen and colleagues 23 analyzed ctDNA levels at baseline and during treatment in patients (N = 72) with metastatic EGC. They observed that patients who had residual, detectable ctDNA after 9 weeks of treatment experienced worse OS and PFS (OS: HR, 4.95; 95% CI, 1.53–16.04; p = .008; PFS: HR, 4.08; 95% CI, 1.31–12.75; p = .016). In addition, in a study of 10 patients with metastatic esophageal squamous cell carcinoma, Yang and colleagues 26 demonstrated that early ctDNA dynamics more accurately predicted treatment response to first‐line immune checkpoint inhibitor therapy than radiography. Finally, He et al. 21 conducted a prospective study evaluating second‐line tislelizumab monotherapy in 12 patients with metastatic esophageal squamous cell carcinoma. Those investigators found that ctDNA dynamics were correlated with OS (p = .017). These studies, in addition to our own, highlight the potential of ctDNA as a valuable biomarker for monitoring treatment response in the context of metastatic EGC. In addition to these cohort‐level findings, case‐level reports illustrate how ctDNA can directly influence patient management. For example, Huffman et al. 14 described a patients with stage III esophageal adenocarcinoma whose ctDNA positivity prompted earlier imaging, confirming a recurrence before radiographic evidence, and subsequent treatment guided by serial ctDNA testing was associated with durable disease control. Similarly, the PANGEA trial (ClinicalTrials.gov identifier NCT02213289) 27 demonstrated the value of ctDNA profiling for capturing spatial tumor heterogeneity and tailoring targeted therapies, underscoring the clinical actionability of ctDNA in patients with EGC. Together, these studies support the emerging role of ctDNA in guiding surveillance and informing therapeutic choices in EGCs.
Future studies are necessary to explore the integration of ctDNA‐guided treatment strategies in the management of metastatic EGC, particularly in larger, prospective cohorts. Specifically, studies could evaluate the impact of ctDNA dynamics on personalized treatment decisions, such as the escalation of therapy for patients with persistently positive ctDNA or the de‐escalation of therapy for those who achieve and maintain ctDNA negativity. Whereas our data set was limited by a heterogeneous collection of samples, future studies may evaluate the optimal cadence of ctDNA testing and its associated cost effectiveness. A hypothetical budget impact model in colorectal cancer comparing ctDNA‐guided treatment versus standard‐of‐care practices over a 12‐month surveillance period in 1000 simulated patients suggested an overall potential cost savings of 43% with ctDNA‐guided strategies. This reduction was primarily attributed to the decreased use of chemotherapy in ctDNA‐negative patients, resulting in personalized care and lower costs associated with hospitalization, toxicity, and follow‐up. 28 Prospective studies in EGCs will be needed to determine both economic impact and optimal testing intervals. Ultimately, these efforts will be crucial in further establishing the clinical utility of ctDNA for patients with metastatic EGC. Future studies should aim to validate and refine such thresholds within specific treatment contexts to ensure broader clinical applicability.
Our study has several limitations. As a retrospective analysis of a real‐world cohort, there was heterogeneity in the timing of plasma sample collection and potential selection bias in the included patients. The diversity of treatment regimens also may have influenced outcomes, and the relatively small sample size limits the generalizability of our findings. Furthermore, OS could not be evaluated because of incomplete follow‐up. Although this study focused on quantitative ctDNA dynamics, which correlated with PFS and treatment response, future prospective studies are warranted to evaluate the qualitative impact of ctDNA monitoring on patient management and outcomes.
Despite these limitations, the current study contributes to the growing body of evidence. Because metastatic EGCs present a significant challenge because of their advanced stage at diagnosis and the associated high morbidity and mortality rates, our findings underscore the potential of ctDNA as a reliable biomarker adjunct for monitoring treatment response that could help improve outcomes in this patient population.
AUTHOR CONTRIBUTIONS
Rutika Mehta: Conceptualization; data curation; methodology; investigation; supervision; project administration; resources; and writing—review and editing. Samuel Rivero‐Hinojosa: Conceptualization; methodology; software; investigation; validation; formal analysis; visualization; writing—review and editing; and resources. Farshid Dayyani: Data curation; investigation; writing—review and editing; and resources. Jenifer Ferguson: Writing—review and editing. Bushra Shariff: Data curation; investigation; writing—review and editing; and resources. Vasily N. Aushev: Investigation; validation; formal analysis; and writing—review and editing. Griffin L. Budde: Writing—review and editing. J. Bryce Ortiz: Visualization; writing—original draft; and writing—review and editing. Giby V. George: Writing—review and editing. Shruti Sharma: Methodology; software; investigation; validation; formal analysis; and writing—review and editing. Adham A. Jurdi: Conceptualization; methodology; investigation; validation; formal analysis; writing—review and editing; supervision; and project administration. Minetta C. Liu: Conceptualization; investigation; validation; formal analysis; writing—review and editing; supervision; and project administration. Ronald L. Drengler: Data curation; investigation; writing—review and editing; and resources. Samuel J. Klempner: Conceptualization; data curation; investigation; writing—review and editing; supervision; project administration; and resources.
CONFLICT OF INTEREST STATEMENT
Rutika Mehta reports personal/consulting or advisory board fees from Arcus Biosciences, Astellas, AstraZeneca, Amgen, BeiGene USA Inc., Gilead Sciences Inc., Jazz Pharmaceuticals, and Legend Biotech; and service on the Debbie's Dream Foundation Scientific Advisory Board outside the submitted work. Samuel Rivero‐Hinojosa is an employee of Natera Inc. and may own stock and/or stock options in the company. Farshid Dayyani reports institutional grants from Takeda Oncology, Taiho, Natera Inc., Ipsen, Roche, Exelixis, AstraZeneca, Astellas Pharma, and Amgen; personal/consulting or advisory fees from AstraZeneca, Daiichi‐Sankyo Company, Eisai, Jazz Pharmaceuticals, Sirtex Medical Inc., and Taiho; and honoraria or speakers' bureau fees from Takeda Oncology, Sirtex Medical Inc., Ipsen, BeiGene Ltd., and Astellas Pharma outside the submitted work. Jenifer Ferguson, Vasily N. Aushev, Griffin L. Budde, J. Bryce Ortiz, and Shruti Sharma are employees of Natera, Inc., and may own stock and/or stock options in the company. Adham A. Jurdi owns stock in Agenus, Nuvalent, and Verastem Inc.; in addition, he is an employee of Natera, Inc., and owns stock and/or stock options in the company. Minetta C. Liu reports institutional grants/contracts from Eisai, Exact Sciences, Genentech, Genomic Health, GRAIL, Menarini Silicon Biosystems, Merck, Novartis, Seattle Genetics, and Tesaro; and travel support/reimbursement from AstraZeneca, Genomic Health, and Ionis outside the submitted work; in addition, she is an employee of Natera, Inc., and owns stock in the company. Ronald L. Drengler owns stock in Natera Inc. Samuel J. Klempner reports institutional funding from the National Cancer Institute/National Institutes of Health, the American Gastroenterological Association, the Degregorio Foundation, the Gastric Cancer Foundation, Debbie's Dream Foundation, the Dana‐Farber/Harvard Cancer Center Gastrointestinal Malignancies Specialized Program of Research Excellence, the American Cancer Society, StandUp2Cancer–American Association for Cancer Research, and the Torrey Coast Foundation; personal/consulting or advisory fees from Amgen, Astellas Pharma, AstraZeneca, BeiGene USA Inc., Boehringer Ingelheim, Bristol Myers Squibb Company, Daiichi‐Sankyo Company, EISAI INC., Elevation Oncology, Gilead Sciences Inc., I‐MAB, Merck and Company Inc., Mersana Therapeutics, Novartis, SANOFI‐AVENTIS U.S. LLC, Servier Pharmaceuticals LLC, and Taiho Oncology Inc.; uncompensated service on a medical advisory board for Debbie's Dream Foundation, Hope for Stomach Cancer, and MBrace; and support for other professional activities from the National Cancer Institute, the National Comprehensive Cancer Network, and Research to Practice outside the submitted work; in addition, he owns stock or stock options in MBrace and Nuvalent. The remaining authors disclosed no conflicts of interest.
ACKNOWLEDGMENTS
Rutika Mehta received medical writing support from Natera, Inc.
Mehta R, Rivero‐Hinojosa S, Dayyani F, et al. Circulating tumor DNA informs clinical practice in patients with recurrent/metastatic gastroesophageal cancers. Cancer. 2026;e70242. doi: 10.1002/cncr.70242
DATA AVAILABILITY STATEMENT
The authors declare that all relevant, nonproprietary data used to conduct the analyses are available within the article. To protect the privacy and confidentiality of patients in this study, clinical data are not made publicly available in a repository but can be requested at any time from the corresponding author. All data shared will be de‐identified.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The authors declare that all relevant, nonproprietary data used to conduct the analyses are available within the article. To protect the privacy and confidentiality of patients in this study, clinical data are not made publicly available in a repository but can be requested at any time from the corresponding author. All data shared will be de‐identified.