circulating tumor dna sequencing analysis of gastroesophageal adenocarcinoma · purpose:...

Translational Cancer Mechanisms and Therapy

Circulating Tumor DNA Sequencing Analysis ofGastroesophageal AdenocarcinomaSteven B. Maron1, Leah M. Chase2, Samantha Lomnicki2, Sara Kochanny2,Kelly L. Moore2, Smita S. Joshi2, Stacie Landron2, Julie Johnson2,Lesli A. Kiedrowski3, Rebecca J. Nagy3, Richard B. Lanman3, Seung Tae Kim4,Jeeyun Lee4, and Daniel V.T. Catenacci2

Abstract

Purpose: Gastroesophageal adenocarcinoma (GEA) has apoor prognosis and few therapeutic options. Utilizing a 73-gene plasma-based next-generation sequencing (NGS) cell-free circulating tumor DNA (ctDNA-NGS) test, we sought toevaluate the role of ctDNA-NGS in guiding clinical decision-making in GEA.

Experimental Design: We evaluated a large cohort (n ¼2,140 tests; 1,630 patients) of ctDNA-NGS results (including369 clinically annotated patients). Patients were assessed forgenomic alteration (GA) distribution and correlation withclinicopathologic characteristics and outcomes.

Results: Treatment history, tumor site, and disease burdendictated tumor-DNA shedding and consequent ctDNA-NGSmaximum somatic variant allele frequency. Patients withlocally advanced disease having detectable ctDNA postoper-atively experienced inferior median disease-free survival (P ¼0.03). The genomic landscape was similar but not identical

to tissue-NGS, reflecting temporospatial molecular heteroge-neity, with some targetable GAs identified at higher frequencyvia ctDNA-NGS compared with previous primary tumor-NGS cohorts. Patients with known microsatellite instability-high (MSI-High) tumors were robustly detected with ctDNA-NGS. Predictive biomarker assessment was optimized byincorporating tissue-NGS and ctDNA-NGS assessment in acomplementary manner. HER2 inhibition demonstrateda profound survival benefit in HER2-amplified patients byctDNA-NGS and/or tissue-NGS (median overall survival,26.3 vs. 7.4 months; P ¼ 0.002), as did EGFR inhibition inEGFR-amplified patients (median overall survival, 21.1 vs.14.4 months; P ¼ 0.01).

Conclusions: ctDNA-NGS characterized GEA molecularheterogeneity and rendered important prognostic and predic-tive information, complementary to tissue-NGS.

See related commentary by Frankell and Smyth, p. 6893

IntroductionGastric cancer and esophageal/esophagogastric junction (EGJ)

adenocarcinoma, together gastroesophageal adenocarcinoma(GEA), is a significant global health problem (1). Median overallsurvival (mOS) of stage IV GEA is 11 to 12 months with optimalpalliative chemotherapy (2), and 16 months for erb-b2 receptortyrosine kinase 2 (HER2 or ERBB2)–amplified tumors treatedwith trastuzumab plus chemotherapy (3). To date, ramucirumab,an anti-VEGFR2 monoclonal antibody, and pembrolizumab, ananti–PD-1 monoclonal antibody, are the only other approvedbiologic therapies in subsequent-line therapy (4–9). Develop-

ment of targeted agents has been limited by low-frequencygenomic alterations (GA) and interpatient heterogeneity, exacer-bated by immense intrapatient heterogeneity—even at baselinediagnosis (10). Routine tissue-based next-generation sequencing(tissue-NGS) identified that at least 37% of GEA patients harborgene amplification in receptor tyrosine kinases (RTK), includingHER2, MET, EGFR, and FGFR2, and also downstreamKRAS (11–14).

These GAs, although each individually relatively infrequent,may have both prognostic and, importantly, predictive signifi-cance in GEA patients. This precedent was set by targeting HER2amplification with trastuzumab. However, only 47% of HER2-positive patients achieved objective response and mOS increasedto only 13.8 months, though 16 months in the most stronglyHER2-positive patients. Subsequent studieswith other anti-HER2agents were negative for first- and second-line therapy (15–19).These observations likely reflect a combination of factors, includ-ing intrapatient heterogeneity in HER2 amplification as well asinherent and/or acquired concurrent molecular resistancemechanisms.

Previously, we identified discordance between coupled syn-chronous primary and metastatic GEA lesions in 42% of single-nucleotide variants and insertions/deletions, and 63% of geneamplifications (10). However, in a small cohort of patients with"triplet-paired" primary-metastasis-ctDNA,NGS cell-free circulat-ing tumor DNA (ctDNA-NGS) GAs were concordant with met-astatic biopsies in 87.5% of cases, as defined by a predefined

1Memorial Sloan Kettering Cancer Center, New York, New York. 2The Universityof Chicago Medical Center, Chicago, Illinois. 3Guardant Health, Inc., RedwoodCity, California. 4Division of Hematology-Oncology, Department of Medicine,Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul,South Korea.

Note: Supplementary data for this article are available at Clinical CancerResearch Online (http://clincancerres.aacrjournals.org/).

CorrespondingAuthor:Daniel V.T. Catenacci, University of Chicago, 900 E 57thStreet, Suite 7128, Chicago, IL 60637. Phone: 773-702-1000; E-mail:[email protected]

Clin Cancer Res 2019;25:7098–112

doi: 10.1158/1078-0432.CCR-19-1704

�2019 American Association for Cancer Research.

ClinicalCancerResearch

Clin Cancer Res; 25(23) December 1, 20197098

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treatment assignment algorithm, suggesting that this noninvasiveapproach may be more effective in guiding targeted therapyselection in metastatic disease. The distributions of GAs assessedby tissue-NGS from early (20, 21) and advanced (22, 23) stageGEApatients have been reported.However, these studies reliedonsingle-lesion testing at one time point, and therefore could notaccount for spatial nor temporal heterogeneity. Thus, we nowturned to ctDNA-NGS, in conjunction with tissue-NGS, to obtaina comprehensive andmore complete "snapshot" of GAs and theirheterogeneity in GEA, in order to understand their implicationsfor targeted therapy.

To accomplish this task, we analyzed the largest landscapecohort of GEA patients who have undergone ctDNA-NGS to date,which included a large clinically annotated subset comprised ofpatients from the University of Chicago (UC) and SamsungMedical Center (SMC). The goals of this study were several-fold.We first sought to evaluate the detection limit of ctDNA-NGS onclinical samples, and the clinical impact of ctDNA detection onearly stage disease recurrence. We next assessed, in advanceddisease, whether baseline ctDNAquantity and early serial changescorrelated with clinical characteristics and outcomes. We thensurveyed the landscape of GEA ctDNA-NGS GAs, including MSI-high, and compared incidences with tissue-NGS cohorts. Tocorroborate earlier observations, we further characterized hetero-geneity between paired tissue-NGS and ctDNA-NGS at baselineand over time. Finally, we assessed the role of ctDNA-NGS inpredicting response and resistance of matched inhibitors tovarious RTK amplifications, including HER2, EGFR, MET, andFGFR2. To our knowledge, this represents the largest and mostcomprehensive evaluation of the clinical utility of ctDNA-NGSin GEA.

Materials and MethodsGEA samples

Of 2,326 ctDNA-NGS tests performed between September 30,2014, and July 11, 2018, on 1,780 gastroesophageal patients,

2,140 tests from 1,630 patients met inclusion criteria for diag-nosis with adenocarcinoma of the esophagus, gastroesophagealjunction, or stomach (GEA) after filtering out cases with reportednon-adenocarcinoma or unknown esophageal carcinoma histol-ogies (Table 1). A large subset of these cases were linked withdeidentified patient data from the UC (N ¼ 273 patients, 601tests) and SMC (N¼ 96 patients, 97 tests) in Institutional ReviewBoard (IRB)–approved tissue banks. All patient cohorts utilizedin this study are described in Supplementary Table S1. This workwas conducted in full concordance with the principles of theDeclaration of Helsinki. All patients provided written-informedconsent, where applicable, or such informed consent was waivedby IRB-approved protocols for aggregate deidentified data anal-ysis. Somatic tumor sequencing by FoundationOne (FoundationMedicine) was also linked to the UC clinical data using 617 testsfrom 457 patients, of which 203 patients also had ctDNA-NGStesting performed.

CtDNA-NGSPlasma circulating tumor DNA sequencing (ctDNA-NGS)

results were obtained using the Guardant360 test (G360, Guar-dant Health; ref. 24). The variant allele fraction of somaticalterations in plasma cfDNA is dependent on multiple factors,including mitotic activity/cell turnover rates, vascular access,location and burden of disease, and biological tumor type. Thesevariant allele fractions can also be artificially inflated due tobroader genomic context in a sample, including amplification ofthe mutated gene or LOH at the locus in question. The assay'sbioinformatics pipeline attempts to filter out alterations of pre-sumed germline origin using a betabinomial model (25). Abso-lute plasma copy number was determined utilizing the mode ofthe normalized number of cell-free DNA (cfDNA) fragmentscovering each gene to estimate the fragment number correspond-ing to two copies to derive a baseline diploid value. All values ofunique fragments for each gene were then normalized by thisbaseline value. The baseline derivationwas informedbymoleculecounts data from a large set of normal samples from healthydonors' plasma. Note that the plasma copy number was related totwo variables—the copy number in the tissue, and the amount ofshedding of tumor DNA into the blood where the tumor DNA,and thus the copy number, was expected to be diluted by abun-dant leukocyte-derived fragments, the latter having a copy num-ber of 2.0 for each autosomal gene. Centiles of gene copy numberreported in the clinical ctDNA-NGS results were denoted by a "þ"for absolute plasma copy number greater than 2.1 (

reviewed for disease location at that time and categorized forpresence/absence of involvement of: liver, lung, peritoneum,metastatic (M1) lymph node, bone, skin, brain, bone marrow,or other. The relationship between maxVAF and number of GAswith disease sites was evaluated using a Student t test, and acrossmultiple categories using ANOVA. Survival analyses were per-formed as detailed below.

Genetic landscapeThe percentage of patients with GAs (identified) in 1,627

patients was enumerated among the entire cohort using nonsy-nonymous GAs from each patient (initial test, if serial tests wereavailable). GA distribution was also assessed within the subset ofclinically annotated samples from UC and SMC, representing"Western" and "Eastern" cohorts. All patients with their initial testavailable (1,627/1,630) were included regardless of the presenceof detectableGAs. Frequencieswere calculated at the gene level perpatient, andGA frequencies of�5%were reported. This approachcalculated a denominator on a gene-by-gene basis accounting forthe genes tested/absent in a given assay version (i.e., if only 900/1,627 assays included gene X, the denominator would be 900).Synonymous mutations were excluded from analysis, and thenumber of alterations reported was corrected for removal of thesesynonymous mutations, unless stated otherwise. Differencesbetween proportions of UC versus SMC alterations were per-formed using a proportion test. Comparison between The CancerGenomeAtlas (TCGA),MSK Impact, and ctDNA-NGS results usedTCGA and MSK-Impact data from Cbioportal (accessed on Octo-

ber 14, 2018) in combination with this cfDNA cohort (23, 28).Genes reported were filtered to those available in all three datasets, and comparisons were made using proportion testing (Sup-plementary Fig. S3; Supplementary Table S4).

ctDNA as a biomarkerThe clinically annotated subset of samples was used for most

analyses (Supplementary Table S1). Cox proportional hazardsmodels were used for survival analyses and corrected using alikelihood ratio test in the Survival package in R. For gene-by-geneassessment,multiple comparison correctionwas performed usingthe Benjamini–Hochberg method. Survival was displayed usingKaplan–Meier curves generated by the SurvMiner R package.

For presurgical and minimal residual disease (MRD) analyses(Supplementary Table S3), patients were classified based upontheir diagnosis, perioperative therapy, and surgical dates. A max-VAF detection cutoff of 0.25% was used based upon reported100% sensitivity for single-nucleotide variants at this level (24),and patients were stratified into ctDNA "detected" or "notdetected." If ctDNA was sampled on multiple dates in a giveninterval (Supplementary Table S1), the first was used.

To evaluate the utility of serial ctDNA-NGS, patients wereincluded if they had at least 2 serial tests between 20 days priorto and 150 days after stage IV diagnosis. If 2 subsequent tests wereavailable within 150 days, the first was used.

The predictive utility of ctDNA-NGS was evaluated in theuntreated "Baseline-cohort" by stratifying patients into "ampli-fied" or "non-amplified" using either unadjusted (reported) or

Table 1. Patient demographics of the Global and the Clinically annotated cohorts from the UC and SMC

Characteristic Global UChicago Samsung P valuea

Number of patients (%) 1,630 (100) 273 (17) 96 (6)Number of tests (%) 2,140 601 (28) 97 (6)Number of patients with 2þ tests (%) 243 (15) 128 (47) 1 (1)

adjusted ctDNA-NGS amplification status. Aggregated adjustedctDNA and/or tissue oncogene amplification was consideredpositive if either (i) amplified-adjusted copy number (as above)in the pretreatment ctDNA assay or (ii) tissue-NGS amplificationin any patient sample was identified. Of note, tissue-NGS wasonly available for UC patients.

The majority of immuno-oncologic (IO)-treated GEA patientsreceived IO agents (defined as any anti–PD-1/PD-L1 and/or anti–CTLA-4 antibody) in later lines of therapy. Patients were includedin this analysis if ctDNA was collected within 60 days prior to IOinitiation in stage IV UC patients.

Heterogeneity between disease sitesIntrapatient heterogeneity was determined by identifying

untreated stage IV UC patients with tissue-NGS from a primaryand metastatic site within 42 days of their initial ctDNA-NGS(n ¼ 34). Common genes to tissue-NGS and ctDNA-NGS panels(n¼72)were then compiled, andGAswere tabulated by gene andpatient according to where they were identified (primary, metas-tasis, blood). GAs identified by tissue-NGS as a "VUS" or "equiv-ocal," or by ctDNA-NGS as "uncertain significance" were onlyincluded if the alternate assay identified the alteration as a non-VUS. Filtered germline GAs not clinically reported by ctDNA-NGSwere also included if the GA was also called by tissue-NGS.Analysis was repeated excluding GAs that the ctDNA-NGS assaywould be unable to detect due to technical limitations, as man-ually annotated (Fig. 4B).

ResultsClinicopathologic characteristics

All patient cohorts utilized in this study are described inSupplementary Table S1. The "Global-cohort" of ctDNA-NGSincluded 2,140 tests on 1,630 patients (Table 1). In the Glob-al-cohort, themedian age was 63, and 71% of patients weremale.The primary anatomical tumor location was 53% gastric cancerversus 47%EGJ. Patient race, tumor grade, clinical HER2 status byconventional tissue testing (29), and tumor stage were unknownfor the majority of patients, although disease was indicated asadvanced/metastatic at the time of testing per submitted orders.The "Clinically annotated" cohort (N ¼ 369 patients, 698 tests)included 273 patients from theUC and 96 from SMC.Comparingcharacteristics between the UC and SMC Clinically annotatedcohorts, UC patients were older (median, 62 vs. 57.5, P¼ 0.003),predominantly proximal EGJ tumors (67% vs. 0%, P < 2.2 �10�6), and included 5% stage II and 16% stage III patientscompared with entirely stage IV patients in the SMC cohort. UCpatients were also more frequently HER2-positive by clinicalcriteria (IHC 3þ or IHC2þ/FISHþ) with 22% versus 8% ofpatients positive in at least one tissue sample at any time pointin their care (P ¼ 2.3 � 10�5). These large Global and Clinicallyannotated cohorts were used for subsequent analyses.

Detection of ctDNAPlasma cfDNA assays depend on shedding of tumor DNA into

the circulation (ctDNA), which then mixes with normal plasmacfDNA that is derived from routine nonmalignant cell turnover.The maximal tumor somatic variant allelic frequency (maxVAF)in the plasma reflects the largest mutated ctDNA clone detectedamong all cfDNA present, and can be used as a proxy to estimateoverall ctDNA quantity and to distinguish degree of subclonalityof alterations at lower VAFs. However, gene amplifications must

also be taken into account (26). In early analyses, we observedthat patients who had already initiated therapy within 14 daysbefore plasma collection (n ¼ 12) had a lower mean maxVAF of5% versus 11.6% in untreated patients (n ¼ 144, P ¼ 0.07), andmore of these patients demonstrated undetectable GAs. Thoughnot statistically significant, from this finding as well as observa-tions from serial response assessments discussed below, we con-cluded that prognostic and predictive ctDNA-biomarker evalua-tions would be best derived from samples obtained in untreatedstage IV patients (n ¼ 144), referred to as the "Baseline-cohort"(Supplementary Tables S1 and S2).

Using the Baseline cohort, we then assessed maxVAF as asurrogate marker for disease volume/burden and confirmed adirect correlation between the number of involved disease sitesandmaxVAF (Fig. 1A; Supplementary Table S2). Fitting with this,patients with intact primary tumors had a higher mean maxVAFof 10.9% versus 6.5% [P ¼ 0.09; 95% confidence interval (CI),0.7–9.9; Fig. 1B]. Furthermore, patients with liver involvement(n ¼ 39/144) had a higher mean maxVAF, 19.2% versus 6.2%(P ¼ 0.001; 95% CI, 5.3–20.8), as did those with lung involve-ment (n ¼ 19/144), 23.3% versus 7.6% (P ¼ 0.01; 95% CI,3.5–28.0; Fig. 1C). Conversely, those with "peritoneal-only"disease (n¼ 35/144), an aggressive subset of GEA, demonstratedthe lowest mean maxVAF of 2.5% versus 11.9% in "non-peritoneal-only" (P ¼ 5.1e�6; 95% CI, 5.6–13.6), with many"peritoneal-only" patients having undetectable ctDNA(Fig. 1D). These findings demonstrated that both disease siteand burden strongly influence tumor DNA shedding and con-sequent ctDNA-NGS sensitivity.

Clinical utility of maxVAFClinical ctDNA-NGS is generally performed in order to identify

actionable GAs, but the amount of ctDNA being shed intocirculation could itself potentially serve as a prognostic biomarkerboth in early- and late-stage disease.We tested this hypothesisfirstin the locally advanced "Pre-Neoadjuvant" cohort of patients atfirst diagnosis prior to therapy/surgery, and found that those withdetectable ctDNA (defined as maxVAF � 0.25%, n ¼ 17/29) hadshorter disease-free survival (mDFS) of 15.2 months versusunreached, though this did not reach significance (P ¼ 0.1; HR¼ 0.2; 95% CI, 0.03–2.1; Fig. 2A; Supplementary Table S3).Importantly, patients with detectable ctDNA (n ¼ 7/22) in sam-ples drawn after curative-intent resection (median, 50 days; range,20–135 days after surgery) had significantly diminishedmDFS of12.5months versus unreached (P¼0.03;HR¼0.1; 95%CI, 0.01–1.1; Fig. 2B; Supplementary Tables S3 and S4). Resolution orpersistence of detectable ctDNA helped predict nonrecurrent andrecurrent disease, respectively, in representative cases (Fig. 2C andD). Sample size was inadequate to formally assess association ofctDNA clearance by neoadjuvant and/or adjuvant therapy.Despite these small numbers, presence and quantity of ctDNAwere clearly prognostic in locally advanced disease, and should bevalidated in future large prospective studies with ctDNA-NGSassays optimized for this purpose.

Following this, because we observed that maxVAF correlatedwith burden/volume of disease, we hypothesized that highermaxVAF would portend a worse prognosis in the advancedsetting. Within the Baseline cohort, those (n ¼ 104) havingbelow-mean ("low")maxVAF (

assessed whether serial ctDNA-NGS analysis could assist withprognostication. In the Baseline cohort, those onfirst-line therapywho underwent serial ctDNA-NGS (Supplementary Table S1)within their first 150 days from stage IV diagnosis who had a�50% decline in maxVAF (n¼ 23/35) survived a median of 13.7versus 8.6months for those that did not (P¼ 0.02;HR¼ 0.3; 95%CI, 0.1–0.8; time between serial-collections: median, 68 days;range, 28–108 days; Fig. 2F); individual representative patientcases are shown (Fig. 2G and H). Taken together, the maxVAFdynamics observed suggest that ctDNA-NGS could be used as anearly prognostic biomarker, and studies assessing whether alter-ing therapy earlier in "non-responders"may bewarranted, akin toPET-directed therapy (30), in an attempt to improve outcomes.

Finally, we assessed whether maxVAF could assist in prognos-tication of patients treated with immune checkpoint inhibitors(IO) in the IO cohort (Supplementary Table S1). Twenty-sevenpatients in this IO cohort (any line of therapy: nivolumab, n¼ 12;pembrolizumab, n ¼ 13; durvalumab þ tremelimumab, n ¼ 1;tremelimumab, n ¼ 1) underwent ctDNA-NGS within 60 daysprior to IO initiation. Patients with less than the median maxVAFof 3.5% (n ¼ 14/27) had an mOS of 8.8 versus 2.5 months forthose higher than the median, from IO initiation to death (P ¼0.04; HR ¼ 0.4; 95% CI, 0.1–0.96; Fig. 2I). This suggests thatamong IO-treated patients, thosewith higher disease burdenhaveworse outcomes; IO-specific benefit within the low/high disease

burden subsets should be confirmed with prospective controlledanalyses to account for the generally recognized improved prog-nosis with low burden disease.

Genomic landscape of GEAAfter determining the prognostic insight of maxVAF and its

correlations between clinicopathologic features, disease burden/volume, and outcomes, while accounting for these observations,we next assessed the ctDNA-NGS GEA GA landscape at themolecular level. Of the 2,140 assays in the Global cohort, amedian of 3 GAs was identified per test (range, 0–80 GAs), andat least 1 nonsynonymousGAwas identified in 1,756 (82%) cases(Table 1; Supplementary Table S1). GAs were more commonlyidentified with proximal primary EGJ versus distal gastric cancertumors (85% vs. 79%, P ¼ 0.0009). Fifteen patients (0.9%) had�20 GAs identified in an individual test, and 10 (0.6%) had�20GAs identified once excluding synonymous mutations. Thesecases included 4 known MSI-high patients and 1 POLD1 muta-tion. The mean number of detected GAs between EGJ andgastric cancer primary sites was significantly skewed by the pres-ence of theseMSI-high or POLD1-mutated gastric cancer patients.Excluding these few special cases, significantly more GAs werefound in EGJ than gastric cancer cases (mean, 3.7 vs. 3.3, P ¼0.005). Within the Locally advanced cohort, 81% of tests iden-tified �1 GA at diagnosis. Overall, these findings demonstrate

Figure 1.

ctDNA detection and number of detected alterations is dictated by specific disease sites and burden of disease. A, The number of disease sites involved inpatients from the Baseline cohort (n¼ 144) directly correlated with maxVAF, suggesting that maxVAF reflected overall disease burden (P¼ 4.9e-8; F¼ 9.8).B, Upon stage IV diagnosis, patients with intact primary tumors (n¼ 101/144) had a generally higher mean maxVAF of 10.9% versus 6.5% for those with priorcurative intent primary tumor resection (P¼ 0.09; 95% CI, 0.7–9.9). C, In addition to disease burden, specific disease sites were associated with increasedtumor shedding and consequently maxVAF—most notably liver and lymph nodes (P¼ 0.01; F¼ 3.1). D, Conversely, patients with solely peritoneal involvement(n¼ 35/144) had a lower mean maxVAF of 2.5% versus 11.9% in patients with additional/other disease sites (P¼ 5.1e-6; 95% CI, 5.6–13.6), and many patients withsolely peritoneal involvement had no detectable ctDNA.

Maron et al.

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that at diagnosis most GEA patients, even in earlier stages, haveidentifiable ctDNA-GAs.

In addition to providing a survey of GA frequencies per sample,one can also infer TMB from the number of identified GAs, which

may have therapeutic implications (31, 32). However, this ischallenging using ctDNA due to more limited gene coveragepotentially affecting precision, and also ctDNA quantity (directlyrelated to cancer burden and tumor shed at the time of sample

Figure 2.

Prognostic implications of maxVAF and serial changes in the perioperative and newly diagnosedmetastatic settings. A, Detection of >0.25%maxVAF prior toneoadjuvant therapy was associated with a 15.2-month mDFS (n¼ 17/29), versus not reached mDFS in patients with lower or undetectable maxVAF (P¼ 0.1; HR¼ 0.2; 95% CI, 0.03–2.1). B, Patients with maxVAF > 0.25% (n¼ 7/22) within 180 postoperative days and before adjuvant therapy, if applicable, had a 12.5-monthmDFS versus unreached mDFS in patients with lower or undetectable maxVAF (P¼ 0.03; HR¼ 0.1; 95% CI, 0.01–1.1). C, Representative "tumor-response map" ofan individual demonstrating detectable pretherapy ctDNA, with postoperative clearance of ctDNA; in a patient with no evidence of recurrence on follow-upexamination approximately 24 months from surgery. D, Representative "tumor-response map" of an individual demonstrating persistent ctDNA postoperatively(maxVAF 2.3%), with recurrence within 6 months of surgery. E,Newly diagnosed metastatic patients (104/144) with below-mean ("low") maxVAF ( 0.5%) and upon repeat testing within 150 days demonstrating a decline by�50% (n¼ 23/35) demonstrated superior mOS of 13.7 versus8.6 months (P¼ 0.02; HR¼ 0.3; 95% CI, 0.1–0.8). G, Representative "tumor-response map" revealing ctDNA decline ("response") in a patient on first-line therapywho remains alive beyond 24months with stage IV GEA. H, Representative "tumor-response map" demonstrating ctDNA nonresponding patient who died fromdisease progression approximately 3 months from diagnosis of stage IV GEA, despite receiving standard therapy. I, Patients who had ctDNA tested within60 days prior to IO initiation and were found to have a lower than median maxVAF (3.5, n¼ 14/27), had a higher mOS of 7.9 versus 2.5 months for those withabove median maxVAF, from the time of IO initiation to death (P¼ 0.04; HR¼ 0.4; 95% CI, 0.1–0.96).

ctDNA Sequencing of Gastroesophageal Adenocarcinoma

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collection) influencing the raw number of detected GAs (r2 ¼0.82; P < 2.2e-16; Supplementary Fig. S1A). Therefore, we cor-rected this by calculating TMB relative to sequencing coverage andVAF (Supplementary Fig. S1B and S1C) as previouslydescribed (26). We then compared paired tissue-NGS andctDNA-NGS TMB estimates (n ¼ 86), which correlated relativelypoorly with one another (r2 ¼ 0.15; P < 0.24), though both werenow adequately independent of maxVAF after correction (Sup-plementary Fig. S1D–S1F). Significantly, all 6 patients withknownMSI-high tumors demonstrated ctDNA-TMB scores�90thpercentile of all tested samples, suggesting that for MSI-hightumors, very high ctDNA-TMB is readily detectable. Most impor-tantly, by directly sequencing microsatellite regions, ctDNA-NGSidentified 6 of 6 (100%) patients known to be MSI-high (via IHCand tissue-NGS), at a plasma maxVAF range of 0.09% to 47.7%,with obvious clinical implications (7).

We next assessed the detailed genomic landscape of thecohorts, including mutations, amplifications, indels, and splicevariants. In the Global cohort, GAs were frequently observed inTP53 (53%), HER2 (17%), EGFR (17%), KRAS (15%), MYC(13%), PIK3CA (13%), and MET (11%; Fig. 3A; SupplementaryTable S5A). GAs were further stratified into nonsynonymousmutations (Fig. 3B) and amplifications (Fig. 3C), where eventsin TP53, ARID1A, APC, and SMAD4 were typically mutations,whereas MYC, HER2, KRAS, EGFR, MET, and FGFR2 events weremore often amplifications.

We next compared the UC and SMC cohort GA landscapes,reflecting representative Western and Eastern populations(Fig. 3A–C; Supplementary Table S5B and S5C). More frequentARID1Amutations and KRAS, EGFR, and PIK3CA amplifications

were observed in the UC cohort. Specifically comparing gastriccancer cases (excluding EGJ) among UC and SMC cohorts, ahigher incidence of mutations in ARID1A and KRAS was stillobserved in the UC cohort, whereas mutations in PIK3CA weremore common in the SMC cohort.

Finally, we evaluated whether there were significant GA ratedifferences between early- and late-stage disease, or betweentissue-NGS versus ctDNA-NGS testing. Despite having compara-tively few early-stage disease samples, within the "Clinically-Annotated" cohort, a direct correlation was observed betweendisease stage and number of alterations (Supplementary Fig. S2),and likely confounded by disease burden, as elucidated above.For further comparison, we compared tissue-NGS GA incidencesfrom the previously reported TCGA cohorts representing early-stage primary tumors (stages I–III;N¼ 265; refs. 20, 21), theMSKIMPACT cohort (N ¼ 305) representing predominantly primarytumor biopsies from newly diagnosed stage IV patients (23), andwith ctDNA-NGS from the present Global cohort (N ¼ 1,627),reflecting "whole-disease" burden and predominantly pretreatedadvanced disease (Fig. 3D; Supplementary Table S5D). TP53mutations were significantly more common in MSK and TCGApatient samples (P¼ 8.4� 10�15). Amplifications ofMYC (P¼ 2� 10�6),CDK6 (P¼ 0.003), and CCNE1 (P¼ 0.0006) weremorecommon in TCGA than in the MSK and Global cohorts (Fig. 1D).HER2 amplification was seen in only 11% of Global cohortpatient samples versus 29% in MSK and 25% in TCGA (P ¼8.6 � 10�18). Most differences across the three cohorts likelyreflected a combination of sample acquisition timing, intrapati-ent heterogeneity, and/or tumor shed limitations. Specifically,"HER2 conversion" is now well recognized after treatment with

Figure 3.

Relative frequency of common (>5%) nonsynonymous ctDNA alterations betweenWestern and Eastern populations and various ctDNA-NGS and tissue-NGScohorts. A, Nonsynonymous GA frequency by Global versus UChicago versus Samsung ctDNA-NGS cohorts revealed a higher rate of TP53, KRAS, ARID1A, andCDKN2A alterations (including SNVs, copy-number alterations, fusions, splice variants, and indels) in theWestern (UChicago) than Eastern (Samsung) cohorts.B,Mutation frequencies (SNVþindelþsplice variants) by cohort highlight that mutations in KRAS and ARID1A account for the increased alteration frequencydifferences between the UC and SMC cohorts. C,Oncogene amplification frequency between the UChicago and Samsung cohorts demonstrating higheramplification frequencies in global and UC cohorts than SMC patients, potentially reflecting more proximal CIN patients inWestern cases. D, GA frequencybetween resected GEA primary tumors stages I–III (TCGA), baseline primary tumor stage IV GEA (MSK Impact), and ctDNA (ctDNA-NGS) revealed similar butnot identical incidences of GAs using tissue-NGS compared with ctDNA-NGS, a reflection of different tumor stages, treatment time points, tumor sites, andbiological compartments.

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anti-HER2 therapy (33–35), and potentially accounts for lowerincidence of HER2 amplification in the Global cohort, giventhat this cohort presumably reflects patients in later lines oftherapy after already failing anti-HER2 therapy. This was address-ed in more detail in HER2 analyses below. Moreover, acknowl-edging that some Global cohort cases would have low tumorDNA shed (e.g., Peritoneal-only gastric cancer) and others col-lected at inopportune time points (e.g., shortly after effectivetherapy), the analysis was repeated by (i) including only Globalcohort cases with GAs detected and (ii) including only patientswith a maxVAF � 0.5%, to limit underestimation of ctDNA-NGSGA frequencies relative to tissue-NGS testing (SupplementaryFig. S3A–S3D; Supplementary Table S6A–S6D). Using thisapproach, TP53 mutation frequency differences lost statisticalsignificance (therefore likely driven by the DNA shedding limi-tation), though they remained significant for HER2 amplifica-tions (potentially driven by posttreatment HER2 amplificationloss in later-line settings). Overall, the GA profiles from thesecohorts using tissue-NGS or ctDNA-NGS highlight and contrastthe incidences of GAs across tumor stages, treatment time points,tumor sites, and biologic compartments. Notably, there weregenerally higher incidences of targetable GAs, particularly RTKamplifications (e.g., MET, FGFR2, and EGFR), in the Globalcohort than seen with tissue-NGS.

Gene amplification is clinically relevant in GEA due to thepredominance of chromosomally unstable disease (CIN;refs. 20, 21). Thus, we specifically assessed the incidenceof amplifications across the Global cohort and found 4,136amplifications in 813 tests from 648 patients (39.8% of Globalcohort cases). Focusing on the most immediately therapeuticallyrelevant RTKs, both EGFR andMET demonstrated predominantlylow-level ctDNA amplifications, whereas HER2 and FGFR2included a subset of patients with extremely high-level ctDNAamplifications (Supplementary Fig. S4A). Generally, higher genecopy number in tissue samples has correlated with more clinicalbenefit from respective targeted therapies (36–38). By ctDNA-NGS, the plasma absolute gene copy-number level could reflecteither homogenous amplification throughout all disease sites (inthe context of the amount of ctDNA shed or maxVAF), or it couldrepresent heterogeneity with spatially mixed amplified and non-amplified clones, again in the context of ctDNA shedding. In fact,we recently reported the high rate of GA discordance betweentissue-NGS on primary and metastatic biopsies, which was mostpronounced in RTK amplifications (10). As noted, the absolutelevel of ctDNA gene amplification is dependent on the plasmamaxVAF (point mutations/indels). For instance, we noted that alow-level ctDNA amplification observed in the context of a verylow/nondetectable ctDNAmaxVAF usually represented very hightissue gene amplification in order for it to be observed in plasma.Reciprocally, low-level gene amplification in the context of veryhigh maxVAF (i.e., high tumor burden) typically did not reflectclinically relevant high level and homogenously distributed geneamplification. Therefore, to address the limitation of tumor shed,plasma gene copy number was normalized by dividing bymaxVAFþ0.01. This "adjusted" copy-number method increasedthe ability to discern betweenhigh- and low-level tissue-NGSgeneamplification in the settings of low or high ctDNA shed (Sup-plementary Fig. S4B). Overall, ctDNA analysis effectively detectedcases with gene amplification, and when accounting for maxVAF,identified patients with RTK amplifications most likely to benefitfrom matched targeted therapy.

HER2 amplification is the only GA routinely assessed in newlydiagnosed advanced GEA patients to date, thus we sought toinvestigate this GA as it pertained to ctDNA-NGS in more detail.As above, ctDNA-NGS identified 184 HER2-amplified (11.3%)patients within the entire Global cohort (first test result if seriallytested). The distribution of amplification level across these ctDNAsamples was 33/55/96 patients having "90th" percentile amplification (see Materials and Methods),respectively (gene plasma copy-number range, 2.1–84.1;median,4.2 copies). To further assess HER2 amplification incidence andconcordance with tissue-based analyses, while considering clin-ical characteristics like treatment timing, we focused on theClinically annotated cohort. Among the 305 stage IV UC/SMCpatients, 18.4%wereHER2 amplifiedby ctDNA-NGS (range, 2.1–68.2 copies; median, 6 copies), and of these 305 patients, 35 of158 (22.2%) with available tissue-NGS were HER2 amplified.When evaluating only clinically HER2-positive stage IV patients(Supplementary Table S1), only 36 of 58 (62%) of patients haddetectable HER2 amplification by ctDNA-NGS (SupplementaryTable S7). This was recapitulated in the Baseline cohort where 17of 28 (61%) of untreated clinically HER2-positive patients alsoharbored HER2 ctDNA amplification. The discordance betweentissue versus ctDNA-NGS HER2 status could be due to tumorshedding limitations but also intrapatient molecular heterogene-ity. Thus, we further investigated the degree that each of thesefactors contributed toward the observed HER2 discordancebetween tissue-NGS and ctDNA-NGS.

Extensive spatial and temporal molecular heterogeneityin GEA

At initial diagnosis, spatial heterogeneity of HER2, along withother GAs, has been recently detailed (10). Here, we sought tofurther expand on this finding with additional cases, and iden-tified 34 newly diagnosed untreated stage IV GEA patients whohad undergone ctDNA-NGS along with tissue-NGS of both base-line primary tumor and a metastatic site ("triplet-pairs"; Supple-mentary Table S1).When limiting to genes present in both ctDNAand tissue panels (n ¼ 72), any GA was identified in 57%, 58%,and 62% of cases within the primary tumor, metastatic tumor,and ctDNA, respectively (Fig. 4A). However, of the 183 charac-terized GAs identified, only 48 (26%) GAs were universallyconcordant within triplet-pairs. Of these, 21 (44%) were muta-tions in TP53, which represented 81% of the TP53 GAs and werelikely "truncal" in the evolutionary phylogenetic tree. Only 2 of 7triplet-pairs were universally concordant forHER2 amplification.Notably, 14%, 11%, and 22% of GAs were uniquely found in theprimary, metastasis, and ctDNA, respectively. Importantly, thisanalysis did not account for technical limitations of ctDNA-NGSdue to the recognized inability to detect large-scale deletion orregions not sequenced. Excluding these tissue-based GAs, 149GAs were observed across these 34 triplet-paired patients. Now,any GA was identified in 54%, 57%, and 74% in the primary,metastasis, and ctDNA, respectively, with 11%, 8%, and 27% ofGAs uniquely detected in the primary, metastasis, and ctDNA,respectively (Fig. 4B). Combining tissue-NGS and ctDNA-NGSincreased sensitivity for detection of HER2, EGFR, FGFR2, andMET alterations (Fig. 4C). This highlights the complementarybenefit of using ctDNA-NGS together with tissue-NGS to over-come the inherent false-negative rates of either test, either due tospatial heterogeneity (tissue) or technical shedding limitations(ctDNA).






In addition tobaseline spatialmolecular heterogeneity, ctDNA-NGS may detect acquired resistance over time (temporal hetero-geneity). First, we focused on the incidence of persistent HER2amplification versus conversion to HER2 nonamplified statusafter failed first-line anti-HER2 therapy using paired pre-/post-

therapy tissue and plasma samples. In this "Serial-HER2" cohort,upon disease progression, only 4 of 15 (27%) patients demon-strated persistent HER2 amplification by ctDNA-NGS (Fig. 4D).Two of these ctDNA-amplified patients also demonstrated per-sistent HER2 IHC 3þ expression. However, another patient

Figure 4.

Intrapatient spatial and temporal heterogeneity by multisite tissue-NGS and ctDNA-NGS. A, Among untreated stage IV/recurrent untreated patients whounderwent baseline triplet-paired sequencing (NGS) of primary tumor andmetastatic (met) tumor and plasma ctDNA (n¼ 34), only 26% of characterizedalterations were identified by all three methods. Percentages by site name indicate percentage of total GAs identified across the 34-patient cohort. B, LimitingGAs to those detectable by ctDNA (n¼ 149/183 GAs in these patients), concordance between all 3 approaches increased to 32%, and ctDNAwas able to detect74% of GAs compared with 54% and 57% by tissue testing of either the primary andmetastatic site, respectively. C, Comparison between tissue and ctDNA RTKamplification in HER2, EGFR, FGFR2, andMET in baseline untreated metastatic patients, and increased sensitivity for detection was observed when using bothtissue-NGS and ctDNA-NGS. D, ctDNA-NGS representative "tumor-response map" demonstrating persistent HER2 amplification upon progression on HER2-targeted therapy. E, Tumor-response map highlighting disappearance of HER2 amplification amidst expansion of previous CCNE amplification and TP53mutation along with de novo NF1mutation in ctDNA after progression on HER2-targeted therapy.

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retained tissue HER2 amplification, but lacked HER2 ctDNAamplification upon progression—likely a result of low tumorshed in this case. Those with persistent HER2 amplificationby either ctDNA-NGS and/or tissue, posttherapy ctDNA-NGSidentified additional acquired mutations in KRAS (G12D andT35A), NF1 (N1503S), and PIK3CA (E542K and S1008T),and coamplifications of BRAF, KRAS, PIK3CA, and FGFR1 aslikely mechanisms of resistance (Fig. 4E; SupplementaryTables S7 and S8).

Next, we assessed resistance mechanisms to targeted therapytoward other pertinent RTK amplifications, including EGFR,MET,and FGFR2. Resistance mechanisms to anti-EGFR therapy werepreviously reported, and included loss of EGFR-amplified clonesand/or GAs rendering upregulation of various bypass pathwaysincluding RTKs and MAPK/PI3K (10, 38). Patients harboringMET- and FGFR2-amplified samples treated with matched TKIsor monoclonal antibodies also revealed upregulation of similarbypass pathways in RTKs and MAPK/PI3K pathway GAs, andredirecting therapy based on observed ctDNA-NGS changesyielded promising results. Based on our findings, exemplified infive cases (Supplementary File S1 and Supplementary Fig. S5), it isapparent that baseline spatial and temporal heterogeneity areinterrelated, because preexisting spatially distributed resistantclones were repeatedly selected under targeted therapeutic pres-sure, yet in some instances these were not identified at baselineand only became apparent over time. ctDNA-NGS identifiedresistance mechanisms to targeted therapy in evaluated patientsupon progression and may direct optimal next-line therapy.

Role of ctDNA-NGS as a prognostic and/or predictivebiomarker

In the context of its role in measuring tumor burden/maxVAFand accounting for interpatient and intrapatient molecular het-erogeneity, ctDNA-NGS may identify prognostic and/or predic-tive GAs. To assess this, the Baseline cohort (n ¼ 144) was againanalyzed for key genes (PIK3CA, BRAF, KRAS, HER2, FGFR2,MET, and EGFR) previously reported to have prognostic and/orpredictive significance in GEA or other cancers.

Presence of PIK3CA mutation corresponded with shorter sur-vival of 3.8 versus 13.6 months (P ¼ 0.006; HR ¼ 3.4; 95% CI,1.6–7.2; Fig. 5A). Similarly,BRAFGAs correspondedwith anmOSof 5.6 months versus 13.7 months in BRAF–wild-type patients (P¼0.02;HR, 3.0; 95%CI, 1.4–6.7; Fig. 5B).However, noneof theseor others evaluated remained statistically significant after multi-ple comparison correction and multivariate analyses (Supple-mentary Table S9). Within the 144 patient cohort, only 2 of 11FGFR2-amplified patients and 2 of 11 MET-amplified patientsreceived RTK inhibitors; therefore, survival analysis could not berobustly performed. These data suggest that mutations in PIK3CAand GAs in BRAF portend generally poor prognoses, but shouldbe confirmed in larger clinically annotated homogenously treatedstudies.

HER2Given that

Figure 5.

Survival analysis of untreated stage IV GEA patients by specific GA. A, Presence of a PIK3CA mutation corresponded with shorter survival of 3.8versus 13.6 months (P ¼ 0.006; HR ¼ 3.4; 95% CI, 1.6–7.2). B, BRAF alterations corresponded with an mOS 5.6 months versus 13.7 months in BRAFwild-type patients (P ¼ 0.02; HR ¼ 3.0; 95% CI, 1.4–6.7). C, Among the 86 patients with both tissue-NGS and ctDNA-NGS available, 24 were eitherHER2 clinically positive or HER2 amplified by tissue-NGS or ctDNA-NGS at any time during their disease—with 54% universal concordance. D, Amongall 23 patients considered clinically HER2 positive who underwent ctDNA-NGS at the time of stage IV diagnosis and then received HER2-directedtherapy, mOS was 12.7 versus 8.7 months in ctDNA HER2-amplified patients (n ¼ 15/23) versus those without ctDNA HER2 amplification (P ¼ 0.4;HR ¼ 0.6; 95% CI, 0.2–1.7). E, Among all 23 patients considered clinically HER2 positive, using an adjusted copy number, i.e., copy number/(maxVAFþ0.01), patients with a greater than median HER2 copy number (10/23) demonstrated an mOS of 15.9 versus 9.4 months in those with lowercopy number (P ¼ 0.07; HR ¼ 0.4; 95% CI, 0.1–1.1). F, Evaluating patients with proven tissue amplification and/or greater than median ctDNAamplification (n ¼ 16/23) in complementary fashion, the mOS benefit increased to 26.3 versus 7.4 months (P ¼ 0.004; HR ¼ 0.2; 95% CI, 0.05–0.6).G, EGFR amplification was not prognostic, as the mOS of EGFR amplified, nontargeted patients (n ¼ 12/130) was similar to that of non–EGFR-amplified patients—14.4 months versus 13.3 months (P ¼ 0.6; HR ¼ 1.3; 95% CI, 0.5–3.0). H, EGFR-amplified patients by ctDNA-NGS and/ortissue-NGS in the Baseline cohort who received EGFR inhibitors (n ¼ 9/27) in any line had an mOS of 21.1 versus 14.4 months for patients who didnot (P ¼ 0.01; HR ¼ 0.2; 95% CI, 0.06–0.8). I, Adjusted EGFR copy number above median or tissue amplification (n ¼ 9/14) demonstrated a21.1- versus 6.2-month mOS (P ¼ 0.001; HR ¼ 0.05; 95% CI, 0.006–0.4).

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DiscussionHerein, we present the largest comprehensive analysis evalu-

ating the utility of ctDNA-NGS from a large commercial databasewith 2,140 individual tests on 1,630 GEA patients, and a sub-stantial subset (698 tests from 369 patients) having clinicalannotation for detailed clinicopathologic and outcomes analyses.

Using these cohorts, we first established an understanding ofthe detection limit of ctDNA-NGS as it relates to disease burden,disease site, and treatment timing. Though the median maxVAFwas quite low, as seen in other studies, there was a long tail ofpatients with a high maxVAF. Biologically, this may be due topatients with very high maxVAF upon stage IV diagnosis, buttechnically, can reflect difficulty in filtering germline alterations inpatients with high tumor shed and/or genomic instability. How-ever, finding several genes at high level suggests biological origin,rather than technical (26, 40). For patients with low diseaseburden (few organ sites involved), peritoneal-only disease, andsamples obtained shortly after therapy, each demonstrated lowerctDNA yield and in many cases nondetectable ctDNA. The bio-logical reason for lower plasma ctDNA in peritoneal-only diseaseis uncertain, butmay be attributed to less shed into the peripheralvascular system, which was recently noted in patients with peri-toneal carcinomatosis in other cancer types (41), and/or differentGAs which are not assessed by the ctDNA-NGS panel used.However, patients with peritoneal-only disease often have diffuseor mixed-type histology, and mutations in genes such as CDH1and RHOA associated with this subtype (the TCGA "genomicallystable"molecular subtype) are indeed part of the 73-gene ctDNA-NGS panel (20). It is noteworthy, however, that peritoneal-onlydisease often has insufficient DNA even for tissue-NGS, likely dueto the low viable tumor content within dense desmoplastictumors from both primary and metastatic biopsies. Therefore,future studies addressing these apparent molecular profilinglimitations from both tissue and plasma of this difficult-to-treat subset of GEA patients are needed, as well as peritonealfluid or lavage as potential sample types.

Regardless, from these limit-of-detection observations, we nextdetermined that residual ctDNA detection after curative-intentresection reliably heralded eventual recurrence and worse prog-nosis in early-stage disease. This is consistent with reports fromother tumor types (42) and suggests that postoperative ctDNAdetection in GEA could be an important stratification factorwithin prospective adjuvant therapy studies. Moreover, via pro-spective studies, this biomarker may help to select those patientsthat should and should not receive further adjuvant therapy.However, we must be mindful of false positives in older patientsresulting from clonal hematopoiesis. Three patients (all elderly)with detectable mutations after surgery, each at similar lowmaxVAFs prior to treatment/surgery, have not recurred to date,and none of thesemutationswere identified by tissue sequencing,which suggests that they may not be tumor-derived at all. Futurestrategies need to bemindful of both germline and hematopoieticconfounding. Similarly, in advanced disease, we observed thatbaseline ctDNA quantity and early serial changes correlated withclinical characteristics and outcomes. It is possible that ctDNA-NGSmay also prove useful here to assess whether patients benefitfrom changing therapy earlier in these "ctDNA non-responders,"prior to initial restaging CT scans. This hypothesis would beparticularly interesting to investigate prospectively—especiallywhen expensive or toxic therapies are employed and could be"fast-failed" early. In addition, this approach depends on having

an effective therapeutic option on which to change, which wouldneed to be validated. Our findings are corroborated by others,who also recently noted that changes inmaxVAF for GAs reflectedresponse to treatment, with an early spike in the first 1 to 3 days ofeffective chemo- or targeted therapy followed by order of mag-nitude drops in maxVAF, reflecting molecular response (43), butdiffer from that foundwhen trending total cfDNA (44). Finally, asit pertained to levels of ctDNA in the plasma, we developed aframework to optimally identify and understand gene amplifica-tions by adjusting formaxVAF in order to take into account tumorburden, spatial molecular heterogeneity, and DNA shed.

Focusing on the landscape of GAs in GEA as determined byctDNA-NGS, we demonstrated that at first diagnosis, the vastmajority of GEA patients, even in earlier stages, had identifiablectDNA-GAs, especially after excluding those with peritoneal-onlydisease and recent therapy. Very importantly, we showed that allknown MSI-high cases in our cohort having ctDNA-NGS per-formed were accurately identified—including a patient with amaxVAF as lowas 0.09%. This is the highest sensitivity for plasma-detected MSI-H reported by any method to date and will be auseful tool to identify this relatively infrequent but highly target-able GA where traditionally tissue-based MSI testing is less rou-tinely performed or insufficient tissue is available (7, 32). Whencomparing the ctDNA-GA landscape between the "Western" UCand "Eastern" SMC cohorts, we noted similar GA incidences, butthere were also some interesting differences, even after consider-ing only the gastric cancer UC subset with the gastric cancer SMCcohort. These differences included a higher incidence ofKRAS andARID1A GAs in the UC subset, which was consistent with priorliterature (45), whereas the SMC cohort was enriched for PIK3CAmutations. The latter is remarkable because it has been reportedthat PIK3CA mutation is associated with EBV-positive gastriccancer (20, 46), which may also be more common in Asiancountries (47), although the literature is conflicting (45, 48).

Comparisons of the ctDNA-GA landscape with cohorts pub-lished during article preparation (49, 50) and previously reportedlarge-scale tissue-based analyses of GEA patients both revealedsimilar but not identical incidences of various GAs. When dis-secting this further, we noted that incidence differences from thesethree large cohorts were mostly attributable to differences indisease stage, sample acquisition time points, alongwith differingdisease sites and tissue compartments assessed. There were gen-erally higher incidences of targetable GAs, particularly RTK ampli-fications, in the Global cohort. In this regard, ctDNA accountedfor increasing intrapatient molecular heterogeneity, including atbaseline and secondary to treatment pressure and evolving resis-tance. This served to survey themetastatic burden of patients best,in order to determine optimal targeted therapeutic regimens.

Along these lines, molecular heterogeneity both between andwithin patients has become a formidable hurdle to successfulimplementation of targeted therapies in GEA (10). Herein, weevaluated the largest "triplet-pairs" cohort reported to date forGEA, and again we uncovered significant discordance, includingin routine known and potentially targetable RTKs such as HER2,EGFR, MET, and FGFR2. Together, these 4 RTKs account forapproximately 30% to 40% of GEA patients, which make up alarge subset of CIN tumors, and therefore a very significantconsideration for ensuing targeted therapeutic decisions. Of note,the high frequency of EGFR amplifications in our cohort likelyreflects aWestern predominance of EGJ CIN tumors at our center.We also demonstrated numerous temporal resistance






mechanisms, particularly after specific targeted therapy towardRTK amplifications, which included loss of the RTK amplificationitself and/or GAs rendering upregulation of various known path-ways to circumvent this inhibition strategy. Serial molecularprofiling led to changes in treatment decision for these cases atdisease progression points. Although EGFR amplification is notrecommended to be routinely assessed by current guidelines, ourwork builds upon our previous and others' work suggestingbenefit for these patients (38, 39, 51). It should be also notedthatmany patients in our annotated cohorts could not be assessedas "triplet pairs" due to insufficient tissue in either the primarytumor and/or the metastatic site at baseline and also at diseaseprogression. This points toward the practicality of ctDNA-NGS tobest assess baseline and temporal heterogeneity due to conve-nience, expediency, and less-invasive nature of a "liquid-biopsy"in the clinic. Our observations of vast interpatient and intrapa-tient molecular heterogeneity, spatially at baseline and tempo-rally after therapy, are very much connected. A personalizedtreatment strategy that incorporates molecular profiling fromboth the tissue and the plasma at baseline and subsequently overtime will likely be necessary in order to successfully improveoutcomes of this disease with targeted therapeutics. In fact, weobserved that incorporating tissue-NGS and ctDNA-NGS profil-ing in aggregate identified patients most likely to benefit fromanti-HER2 and other targeted therapies. These findings mirrorthose seen in lung cancer with concurrent tissue- and ctDNA-NGS (52), and a recent report suggested that "first-pass" ctDNA-NGS for lung cancer patients may spare unnecessary redundanttesting, with reflex tissue testing only if ctDNA-NGS is unreveal-ing (53). This may also be applicable for GEA and warrantsattention. Ultimately, incorporating ctDNA-NGS may be a strat-egy to overcome recognized molecular heterogeneity, both atbaseline and over time, and prospective innovative trials designsare ongoing to test this hypothesis (54, 55).

This study has some limitations. The Global cohort, albeitlarge, was relatively limited in clinical utility without the granularclinicopathologic characteristics to contextualize the GA distri-bution landscape. To address this, we combined two clinicallyannotated cohorts which provided robust understanding of GAevents with clinicopathologic perspective, and subsequent anal-yseswere restricted to samples drawnprior to any therapy to avoidunderestimating ctDNA-NGS GAs and to perform tissue-plasmaconcordance studies more precisely. Another inherent limitationwhen comparing the 73-gene cfDNA-NGS versus 315-gene tissue-NGS panel is the expected discordance resulting from technicaland biological differences between these different tests of distinctbiological compartments. Technical limitations leading to dis-cordance between tissue and plasma obviously included non-overlapping genes, but also some regions of overlapping genesnot sequenced on the ctDNA-NGS panel. Another technicallimitation is the recognized inability of ctDNA-NGS to discernlarge-scale deletions among the vast sea of wild-type cfDNA. Toaccount for these limitations and to focus on only those GAs thatoverlapped, we compared only those regions covered by bothpanels and excluded large deletions identified by tissue-NGS. Thisadmittedly underestimates the level of "real-life" discordance thatthe clinical oncologist will observe. However, by doing so, wewere able to focus on and identify specific biologic reasons fordiscordance, including disease burden and tumor site, which was

directly related to tumor shed, as well as intrapatient spatialmolecular heterogeneity. Finally, despite the relatively large sizeof the Clinically annotated cohort, inherent to low-frequencyGAs, we were unable to definitively evaluate the prognosticimportance of individual GAs nor the predictive impact of target-ing these infrequent events.

In summary, clinical ctDNA-NGS testing holds promise forGEA—both in the detection of MRD in early-stage disease andas a serial tumor marker. ctDNA-NGS used in conjunctionwith tissue-NGS may be an approach to best identify action-able GAs and resistance mechanisms in order to overcomeintrapatient heterogeneity. However, prospective validation ofthese findings in future studies is necessary for integration intoclinical care.

Disclosure of Potential Conflicts of InterestL.A. Kiedrowski holds ownership interest (including patents) in

Guardant Health, Inc. R.B. Lanman is an employee of Biolase, Inc., andholds ownership interest (including patents) in Guardant Health, Inc.,Biolase, Inc., and Forward Medical, Inc. D.V.T. Catenacci reports receivinghonoraria from Merck, Bristol-Myers Squibb, Astellas, Lilly, Taiho, FivePrime, and Gritstone, and reports receiving speakers bureau honoraria fromGuardant Health, Inc., Foundation Medicine, and Tempus. No potentialconflicts of interest were disclosed by the other authors.

Authors' ContributionsConception and design: S.B. Maron, L.A. Kiedrowski, R.B. Lanman,D.V.T. CatenacciDevelopment of methodology: S.B. Maron, L.A. Kiedrowski, R.B. Lanman,D.V.T. CatenacciAcquisition of data (provided animals, acquired and managed patients,provided facilities, etc.): S.B. Maron, L.M. Chase, S. Lomnicki, S.S. Joshi,J. Johnson, L.A. Kiedrowski, R.B. Lanman, S.T. Kim, J. Lee, D.V.T. CatenacciAnalysis and interpretation of data (e.g., statistical analysis, biostatistics,computational analysis): S.B. Maron, S.S. Joshi, J. Johnson, L.A. Kiedrowski,R.J. Nagy, R.B. Lanman, D.V.T. CatenacciWriting, review, and/or revision of the manuscript: S.B. Maron, L.M. Chase,S.S. Joshi, L.A. Kiedrowski, R.J. Nagy, R.B. Lanman, D.V.T. CatenacciAdministrative, technical, or material support (i.e., reporting or organizingdata, constructing databases): S.B. Maron, S. Lomnicki, S. Kochanny,K.L. Moore, S. Landron, L.A. Kiedrowski, S.T. Kim, D.V.T. CatenacciStudy supervision: S.B. Maron, D.V.T. Catenacci

AcknowledgmentsThe authors wish to thank all patients for generously participating in all

clinical and tissue banking studies. The authors would like to thank RajeshAcharya for his analytic tool.

Thisworkwas supported byConquer Cancer FoundationYoung InvestigatorAward, AACR Gastric Cancer Fellowship, Paul Calabrese K12 (S.B. Maron,5K12CA139160), NIH K23 award (CA178203-01A1), UCCCC (University ofChicago Comprehensive Cancer Center) Award in Precision Oncology—CCSG(Cancer Center Support Grant; P30CA014599), Castle Foundation, LLK (LiveLike Katie) Foundation Award, Ullman Scholar Award, and the Sal Ferrara IIFund for PANGEA (D.V.T. Catenacci).

The costs of publication of this article were defrayed in part by thepayment of page charges. This article must therefore be hereby markedadvertisement in accordance with 18 U.S.C. Section 1734 solely to indicatethis fact.

Received May 23, 2019; revised July 1, 2019; accepted August 14, 2019;published first August 19, 2019.

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Maron et al.




2019;25:7098-7112. Published OnlineFirst August 19, 2019.Clin Cancer Res Steven B. Maron, Leah M. Chase, Samantha Lomnicki, et al. AdenocarcinomaCirculating Tumor DNA Sequencing Analysis of Gastroesophageal

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