Circulating tumor DNA sequencing analysis of gastroesophageal … · 2019. 8. 17. · 127 gastroesophageal patients, 2140 tests from 1630 patients met inclusion criteria for diagnosis

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Circulating tumor DNA sequencing analysis of gastroesophageal adenocarcinoma 2

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Authors: Steven B. Maron1, Leah M. Chase

2, Samantha Lomnicki

2, Sara Kochanny

2, Kelly L. 4

Moore2, Smita S. Joshi

2, Stacie Landron

2, Julie Johnson

2, Lesli A. Kiedrowski

3, Rebecca J. 5

Nagy3, Richard B. Lanman

3, Seung Tae Kim

4, Jeeyun Lee

4, Daniel V.T. Catenacci*

2 6

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¹Memorial Sloan Kettering Cancer Center, New York, NY 8 2The University of Chicago Medical Center, Chicago, IL 9

2Guardant Health, Inc., Redwood City, CA 10

3 Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, 11

Sungkyunkwan University School of Medicine, Seoul, South Korea 12

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Running Title: ctDNA in gastroesophageal adenocarcinoma 14

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Keywords: Circulating tumor DNA, ctDNA, Cell-free DNA, gastroesophageal adenocarcinoma, 16

next generation sequencing, targeted therapy, molecular heterogeneity 17

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*Corresponding Author 19

Daniel V.T. Catenacci, MD 20

University of Chicago Comprehensive Cancer Center, Section of Hematology/Oncology 21

5841 S. Maryland Avenue 22

Chicago, IL 60637 23

E-mail: [email protected] 24

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Competing interests: DVTC has received research funding from Genentech/Roche, Amgen, and 27

honoraria from Genentech/Roche, Amgen, Eli Lilly, Five Prime, Merck, BMS, Taiho, Astellas, 28

Guardant Health, Foundation Medicine, Tempus. LAK, RJN, and RBL are employees and 29

shareholders of Guardant Health. 30

31

Financial support: This work was supported by ASCO Young Investigator Award, AACR 32

Gastric Cancer Fellowship, Paul Calabrese K12 (SBM); NIH K23 award (CA178203-01A1), 33

UCCCC (University of Chicago Comprehensive Cancer Center) Award in Precision Oncology—34

CCSG (Cancer Center Support Grant) (P30CA014599), Castle Foundation, LLK (Live Like 35

Katie) Foundation Award, Ullman Scholar Award and the Sal Ferrara II Fund for PANGEA 36

(DVTC). 37

38

Word count: 5893 39

Figures: 5 40

Tables: 1 41

Supplementary Files: 2 42

Supplementary Figures: 5 43

Supplementary Tables: 15 44

Research. on January 28, 2021. © 2019 American Association for Cancerclincancerres.aacrjournals.org Downloaded from

Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on August 19, 2019; DOI: 10.1158/1078-0432.CCR-19-1704

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Statement of Translational Relevance: This is the largest and most comprehensive evaluation 45

of ctDNA-NGS for GEA, and demonstrates a comparable but not identical incidence rate of 46

common GAs as seen in recent large-scale tissue-based analyses. Using clinically-linked samples 47

from nearly 400 patients, this study initially evaluates determinants of ctDNA detection 48

including disease sites, tumor burden, and collection timing relative to treatment that can aide in 49

timing clinical collection. It also highlights the high degree of intra-patient molecular 50

heterogeneity in GEA through space and time, which is optimally characterized by ctDNA-NGS 51

in conjunction with tissue-NGS, and explains why so many targeted therapy trials fail in GEA. 52

Finally, the predictive nature of specific ctDNA GAs including MSI-High and ERBB2 (HER2) 53

and EGFR amplifications are described – including strategies with which we can better identify 54

targeted therapy populations in a heterogeneous cancer by using ctDNA-NGS. 55

Abstract: 56

Purpose: Gastroesophageal adenocarcinoma (GEA) has a poor prognosis and few therapeutic 57

options. Utilizing a 73-gene plasma-based next-generation sequencing (NGS) cell-free 58

circulating tumor DNA (ctDNA-NGS) test, we sought to evaluate the role of ctDNA-NGS in 59

guiding clinical decision-making in GEA. Experimental Design: We evaluated a large cohort 60

(n=2140 tests; 1630 patients) of ctDNA-NGS results (including 369 clinically-annotated pts). 61

Patients were assessed for genomic alteration (GA) distribution and correlation with 62

clinicopathologic characteristics and outcomes. Results: Treatment history, tumor site, and 63

disease burden dictated tumor-DNA shedding and consequent ctDNA-NGS maximum somatic 64

variant allele frequency (maxVAF). Patients with locally advanced disease having detectable 65

ctDNA post-operatively experienced inferior median disease-free survival (mDFS) (p=0.03). The 66

genomic landscape was similar but not identical to tissue-NGS, reflecting temporospatial 67




3

molecular heterogeneity, with some targetable GAs identified at higher frequency via ctDNA-68

NGS compared to previous primary tumor-NGS cohorts. Patients with known MSI-High tumors 69

were robustly detected with ctDNA-NGS. Predictive biomarker assessment was optimized by 70

incorporating tissue-NGS and ctDNA-NGS assessment in a complementary manner. HER2-71

inhibition demonstrated a profound survival benefit in HER2 amplified patients by ctDNA-NGS 72

and/or tissue-NGS (mOS 26.3 versus 7.4 months (p=0.002)), as did EGFR inhibition in EGFR 73

amplified patients (mOS 21.1 versus 14.4 months (p=0.01)). Conclusions: ctDNA-NGS 74

characterized GEA molecular heterogeneity and rendered important prognostic and predictive 75

information, complementary to tissue-NGS. 76

77

78

Gastric cancer (GC) and esophageal/esophagogastric junction (EGJ) adenocarcinoma, together 79

gastroesophageal adenocarcinoma (GEA), is a significant global health problem.1 Median overall 80

survival (mOS) of stage IV GEA is 11-12 months with optimal palliative chemotherapy,2 and 16 81

months for erb-b2 receptor tyrosine kinase 2 (HER2 or ERBB2) amplified tumors treated with 82

trastuzumab plus chemotherapy.3 To date, ramucirumab, an anti-VEGFR2 monoclonal antibody, 83

and pembrolizumab, an anti-PD-1 monoclonal antibody, are the only other approved biologic 84

therapies in subsequent-line therapy.4-9

Development of targeted agents has been limited by low 85

frequency genomic alterations (GAs) and inter-patient heterogeneity, exacerbated by immense 86

intra-patient heterogeneity - even at baseline diagnosis.10

Routine tissue-based next-generation 87

sequencing (tissue-NGS) identified that at least 37% of GEA patients harbor gene amplification 88

in receptor tyrosine kinases (RTKs), including HER2, MET, EGFR, and FGFR2, and also 89

downstream KRAS.11-14

90




4

These GAs, while each individually relatively infrequent, may have both prognostic and, 91

importantly, predictive significance in GEA patients. This precedent was set by targeting HER2 92

amplification with trastuzumab. However, only 47% of HER2-positive patients achieved 93

objective response and mOS increased to only 13.8-14.2 months,3 while subsequent studies with 94

other anti-HER2 agents were negative for first and second line therapy.15-19

These observations 95

likely reflect a combination of factors, including intra-patient heterogeneity in HER2 96

amplification as well as inherent and/or acquired concurrent molecular resistance mechanisms. 97

Previously, we identified discordance between coupled synchronous primary and 98

metastatic GEA lesions in 42% of single nucleotide variants and insertions/deletions, and 63% of 99

gene amplifications.10

However, in a small cohort of patients with ‘triplet-paired’ primary-100

metastasis-ctDNA, ctDNA-NGS GAs were concordant with metastatic biopsies in 87.5% of 101

cases, as defined by a predefined treatment assignment algorithm, suggesting that this 102

noninvasive approach may be more effective in guiding targeted therapy selection in metastatic 103

disease. The distributions of GAs assessed by tissue-NGS from early20,21

and advanced22,23

stage 104

GEA patients have been reported. However, these studies relied on single-lesion testing at one 105

time point, and therefore could not account for spatial nor temporal heterogeneity. Thus, we now 106

turned to ctDNA-NGS, in conjunction with tissue-NGS, to obtain a comprehensive and more 107

complete ‘snapshot’ of GAs and their heterogeneity in GEA, in order to understand their 108

implications for targeted therapy. 109

To accomplish this task, we analyzed the largest landscape cohort of GEA patients who 110

have undergone ctDNA-NGS to-date, which included a large clinically annotated subset 111

comprised of patients from the University of Chicago (UC) and Samsung Medical Center 112

(SMC). The goals of this study were several-fold. We first sought to evaluate the detection limit 113




5

of ctDNA-NGS on clinical samples, and the clinical impact of ctDNA detection on early stage 114

disease recurrence. We next assessed, in advanced disease, whether baseline ctDNA quantity and 115

early serial changes correlated with clinical characteristics and outcomes. We then surveyed the 116

landscape of GEA ctDNA-NGS GAs, including MSI-High, and compared incidences to tissue-117

NGS cohorts. To corroborate earlier observations, we further characterized heterogeneity 118

between paired tissue-NGS and ctDNA-NGS at baseline and over time. Finally, we assessed the 119

role of ctDNA-NGS in predicting response and resistance of matched inhibitors to various RTK 120

amplifications, including HER2, EGFR, MET and FGFR2. To our knowledge, this represents the 121

largest and most comprehensive evaluation of the clinical utility of ctDNA-NGS in GEA. 122

123

Online Methods 124

GEA Samples 125

Of 2326 ctDNA-NGS tests performed between 9/30/14-7/11/18 on 1780 126

gastroesophageal patients, 2140 tests from 1630 patients met inclusion criteria for diagnosis with 127

adenocarcinoma of the esophagus, gastroesophageal junction, or stomach (GEA) after filtering 128

out cases with reported non-adenocarcinoma or unknown esophageal carcinoma histologies 129

(Table 1). A large subset of these cases were linked with de-identified patient data from the 130

University of Chicago (UC) (Chicago, IL) (N=273 pts, 601 tests) and Samsung Medical Center 131

(SMC) (Seoul, Korea) (N=96 pts, 97 tests) in institutional review board approved tissue banks. 132

All patient cohorts utilized in this study are described in Table S1. This work was conducted in 133

full concordance with the principles of the Declaration of Helsinki. All patients provided written 134

informed consent, where applicable, or such informed consent was waived by IRB-approved 135

protocols for aggregate de-identified data analysis. Somatic tumor sequencing by Foundation 136




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One (Foundation Medicine, Cambridge, MA) was also linked to the UC clinical data using 617 137

tests from 457 patients, of which 203 patients also had ctDNA-NGS testing performed. 138

139

Circulating tumor DNA NGS 140

Plasma circulating tumor DNA sequencing (ctDNA-NGS) results were obtained using the 141

Guardant360 test (G360, Guardant Health; Redwood City; CA).24

The variant allele fraction of 142

somatic alterations in plasma cfDNA is dependent on multiple factors, including mitotic 143

activity/cell turnover rates, vascular access, location and burden of disease, and biological tumor 144

type. These variant allele fractions can also be artificially inflated due to broader genomic 145

context in a sample, including amplification of the mutated gene or loss of heterozygosity at the 146

locus in question. The assay’s bioinformatics pipeline attempts to filter out alterations of 147

presumed germline origin using a betabinomial model.25

Absolute plasma copy number was 148

determined utilizing the mode of the normalized number of cell-free DNA fragments covering 149

each gene to estimate the fragment number corresponding to two copies to derive a baseline 150

diploid value. All values of unique fragments for each gene were then normalized by this 151

baseline value. The baseline derivation was informed by molecule counts data from a large set of 152

normal samples from healthy donors’ plasma. Note that the plasma copy number was related to 153

two variables - the copy number in the tissue, and the amount of shedding of tumor DNA into the 154

blood where the tumor DNA - and thus the copy number, was expected to be diluted by abundant 155

leukocyte-derived fragments, the latter having a copy number of 2.0 for each autosomal gene. 156

Centiles of gene copy number reported in the clinical ctDNA-NGS results were denoted by a ‘+’ 157

for absolute plasma copy number greater than 2.1 (<50th

percentile), ‘++’ for copy number 158

greater than 2.4 but less than 4 (<90th

percentile), or ‘+++’ for copy number greater than 4 (≥90th

159




7

percentile). In this study, absolute plasma copy number or presence/absence of amplification 160

were used – not these percentiles. Adjusted copy number was calculated from the copy number / 161

(maxVAF+0.01) for each test. Values above the “Global-cohort” median adjusted copy number 162

for a given gene were considered amplified. TMB estimation by ctDNA and tissue NGS were 163

provided by Guardant Health and Foundation Medicine, respectively, according to previously 164

published methodology.26,27

165

166

Tumor Location 167

Records from UC and SMC patients who had their initial blood collection for ctDNA 168

prior to stage IV therapy initiation were chart reviewed for disease location at that time and 169

categorized for presence/absence of involvement of: liver, lung, peritoneum, metastatic (M1) 170

lymph node, bone, skin, brain, bone marrow or other. The relationship between maxVAF and 171

number of GAs with disease sites was evaluated using Student t-testing, and across multiple 172

categories using ANOVA. Survival analyses were performed as detailed below. 173

174

175

Genetic Landscape 176

The percent of patients with genomic alterations (GAs identified) in 1627 patients was 177

enumerated amongst the entire cohort using nonsynonymous GAs from each patient (initial test, 178

if serial tests were available). GA distribution was also assessed within the subset of Clinically-179

annotated samples from UC and SMC, representing ‘Western’ and ‘Eastern’ cohorts. All patients 180

with their initial test available (1627/1630) were included regardless of the presence of 181

detectable GAs. Frequencies were calculated at the gene level per patient, and GA frequencies of 182




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>5% were reported. This approach calculated a denominator on a gene-by-gene basis accounting 183

for the genes tested/absent in a given assay version (i.e. if only 900/1627 assays included gene X, 184

the denominator would be 900). Synonymous mutations were excluded from analysis, and the 185

number of alterations reported was corrected for removal of these synonymous mutations, unless 186

stated otherwise. Differences between proportion of UC versus SMC alterations was performed 187

using a proportion test. Comparison between TCGA, MSK Impact, and ctDNA-NGS results used 188

TCGA and MSK-Impact data from Cbioportal (accessed on 10/14/18) in combination with this 189

cfDNA cohort.28,29

Genes reported were filtered to those available in all 3 data sets, and 190

comparisons were made using proportion testing (Figure S3, Table S4). 191

192

ctDNA as a biomarker 193

The Clinically-annotated subset of samples were used for most analyses (Table S1). Cox 194

proportional-hazards models were used for survival analyses and corrected using a likelihood 195

ratio test in the Survival package in R. For gene-by-gene assessment, multiple comparison 196

correction was performed using the Benjamini & Hochberg method. Survival was displayed 197

using Kaplan-Meier curves generated by the SurvMiner R package. 198

For pre-surgical and minimal residual disease (MRD) analyses (Table S3), patients were 199

classified based upon their diagnosis, peri-operative therapy, and surgical dates. A maxVAF 200

detection cutoff of 0.25% was used based upon reported 100% sensitivity for single nucleotide 201

variants at this level,24

and patients were stratified into ctDNA “detected” or “not detected”. If 202

ctDNA was sampled on multiple dates in a given interval (Table S1), the first was used. 203




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To evaluate the utility of serial ctDNA-NGS, patients were included if they had at least 2 204

serial tests between 20 days prior to and 150 days after stage IV diagnosis. If 2 subsequent tests 205

were available within 150 days, the first was used. 206

The predictive utility of ctDNA NGS was evaluated in the untreated “Baseline-cohort” by 207

stratifying patients into “amplified” or “non-amplified” using either unadjusted (reported) or 208

adjusted ctDNA-NGS amplification status. Aggregated adjusted ctDNA and/or tissue oncogene 209

amplification was considered positive if either a) amplified adjusted copy number (as above) in 210

the pre-treatment ctDNA assay or b) tissue NGS amplification in any patient sample was 211

identified. Of note, tissue NGS was only available for UC patients. 212

The majority of immuno-oncologic (IO) treated GEA patients received IO agents 213

(defined as any anti-PD1/PDL1 and/or anti-CTLA4 antibody) in later lines of therapy. Patients 214

were included in this analysis if ctDNA was collected within 60 days prior to IO initiation in 215

stage IV UC patients. 216

217

Heterogeneity Between Disease Sites 218

Intra-patient heterogeneity was determined by identifying untreated stage IV UC patients 219

with tissue-NGS from a primary and metastatic site within 42 days of their initial ctDNA-NGS 220

(n=34). Common genes to tissue-NGS and ctDNA-NGS panels (n=72) were then compiled, and 221

GAs were tabulated by gene and patient according to where they were identified (primary, 222

metastasis, blood). GAs identified by tissue-NGS as a “VUS” or “equivocal”, or by ctDNA-NGS 223

as “uncertain significance” were only included if the alternate assay identified the alteration as a 224

non-VUS. Filtered germline GAs not clinically reported by ctDNA-NGS were also included if 225

the GA was also called by tissue-NGS. Analysis was repeated excluding GAs that the ctDNA-226




10

NGS assay would be unable to detect due to technical limitations, as manually annotated (Figure 227

4B). 228

229

Results 230

Clinicopathologic Characteristics 231

All patient cohorts utilized in this study are described in Table S1. The ‘Global-cohort’ of 232

ctDNA-NGS included 2140 tests on 1630 patients (Table 1). In the Global-cohort, the median 233

age was 63, and 71% of patients were male. The primary anatomical tumor location was 53% 234

GC versus 47% EGJ. Patient race, tumor grade, clinical HER2 status by conventional tissue 235

testing 30

, and tumor stage was unknown for the majority of patients, although disease was 236

indicated as advanced/metastatic at the time of testing per submitted orders. The ‘Clinically-237

annotated’ cohort (N=369 patients, 698 tests) included 273 patients from the University of 238

Chicago (UC) and 96 from Samsung Medical Center (SMC). Comparing characteristics between 239

the UC and SMC Clinically-annotated cohorts, UC patients were older (median 62 versus 57.5, 240

p=0.003), predominantly proximal EGJ tumors (67% versus 0%, p<2.2 x10-6

), and included 5% 241

stage II and 16% stage III patients compared with entirely stage IV patients in the SMC cohort. 242

UC patients were also more frequently HER2-positive by clinical criteria (IHC 3+ or 243

IHC2+/FISH+) with 22% versus 8% of patients positive in at least one tissue sample at any time 244

point in their care (p=2.3 x10-5

). These large Global and Clinically-annotated cohorts were used 245

for subsequent analyses. 246

247

Detection of ctDNA 248




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Plasma cell-free DNA (cfDNA) assays depend on shedding of tumor DNA into the 249

circulation (ctDNA), which then mixes with normal plasma cfDNA that is derived from routine 250

non-malignant cell turnover. The maximal tumor somatic variant allelic frequency (maxVAF) in 251

the plasma reflects the largest mutated ctDNA clone detected among all cfDNA present, and can 252

be used as a proxy to estimate overall ctDNA quantity and to establish degree of subclonality of 253

alterations at lower VAFs. However, gene amplifications must also be taken into account.26

In 254

early analyses, we observed that patients who had already initiated therapy within 14 days before 255

plasma collection (n=12) had a lower mean maxVAF of 5% versus 11.6% in untreated patients 256

(n=144, p=0.07), and more of these patients demonstrated undetectable GAs. Though not 257

statistically significant, from this finding as well as observations from serial response 258

assessments discussed below, we concluded that prognostic and predictive ctDNA-biomarker 259

evaluations would be best derived from samples obtained in untreated stage IV patients (n=144), 260

referred to as the ‘Baseline-cohort’ (Tables S1-S2). 261

Using the Baseline-cohort, we then assessed maxVAF as a surrogate marker for disease 262

volume/burden, and confirmed a direct correlation between the number of involved disease sites 263

and maxVAF (Figure 1A, Table S2). Fitting with this, patients with intact primary tumors had a 264

higher mean maxVAF of 10.9% versus 6.5% (p=0.09, 95% CI 0.7-9.9) (Figure 1B). 265

Furthermore, patients with liver involvement (n=39/144) had a higher mean maxVAF, 19.2% 266

versus 6.2% (p=0.001, 95% CI 5.3-20.8), as did those with lung involvement (n=19/144), 23.3% 267

versus 7.6% (p=0.01, 95% CI 3.5-28.0) (Figure 1C). Conversely, those with “peritoneal-only” 268

disease (n=35/144), an aggressive subset of GEA, demonstrated the lowest mean maxVAF of 269

2.5% versus 11.9% in “non-peritoneal-only” (p=5.1e-6

, 95% CI 5.6-13.6), with many “peritoneal-270

only” patients having undetectable ctDNA (Figure 1D). These findings demonstrated that both 271




12

disease site and burden strongly influence tumor DNA shedding and consequent ctDNA-NGS 272

sensitivity. 273

274

Clinical Utility of maxVAF 275

Clinical ctDNA-NGS is generally performed in order to identify actionable GAs, but the 276

amount of ctDNA being shed into circulation could itself potentially serve as a prognostic 277

biomarker both in early and late stage disease. We tested this hypothesis first in the locally-278

advanced ‘Pre-Neoadjuvant’ cohort of patients at first diagnosis prior to therapy/surgery, and 279

found that those with detectable ctDNA (defined as maxVAF>0.25%, n=17/29) had shorter 280

disease-free survival (mDFS) of 15.2 months versus unreached, though this did not reach 281

significance (p=0.1, HR=0.2, 95% CI 0.03-2.1) (Figure 2A, Table S3). Importantly, patients 282

with detectable ctDNA (n=7/22) in samples drawn after curative-intent resection (median=50 283

days, range=20-135 days after surgery) had significantly diminished mDFS of 12.5 months 284

versus unreached (p=0.03, HR=0.1, 95% CI 0.01-1.1) (Figure 2B, Table S3-S4). Resolution or 285

persistence of detectable ctDNA helped predict non-recurrent and recurrent disease, respectively, 286

in representative cases (Figure 2C-D). Sample size was inadequate to formally assess 287

association of ctDNA clearance by neoadjuvant and/or adjuvant therapy. Despite these small 288

numbers, presence and quantity of ctDNA was clearly prognostic in locally advanced disease, 289

and should be validated in future large prospective studies with ctDNA-NGS assays optimized 290

for this purpose. 291

Following this, since we observed that maxVAF correlated with burden/volume of 292

disease, we hypothesized that higher maxVAF would portend a worse prognosis in the advanced 293

setting. Within the Baseline-cohort, those (n=104) having below-mean (‘low’) maxVAF (<9.7%) 294




13

had a mOS of 14.8 versus 9.4 months for patients (n=40) with above-mean (‘high’) maxVAF 295

(p=0.1, HR 0.7, 95% CI 0.4-1.1) (Figure 2E). We next assessed whether serial ctDNA-NGS 296

analysis could assist with prognostication. In the Baseline-cohort, those on first line therapy who 297

underwent serial ctDNA-NGS (Table S1) within their first 150 days from stage IV diagnosis 298

who had a >50% decline in maxVAF (n=23/35) survived a median of 13.7 versus 8.6 months for 299

those that did not (p=0.02, HR 0.3 95% CI 0.1-0.8) (time between serial-collections: median=68 300

days, range=28-108 days) (Figure 2F); individual representative patient cases are shown (Figure 301

2G-H). Taken together, the maxVAF dynamics observed suggest that ctDNA-NGS could be 302

used as an early prognostic biomarker, and studies assessing whether altering therapy earlier in 303

‘non-responders’ may be warranted, akin to PET-directed therapy,31

in an attempt to improve 304

outcomes. 305

Finally, we assessed whether maxVAF could assist in prognostication of patients treated 306

with immune checkpoint inhibitors (IO) in the IO cohort (Table S1). Twenty-seven patients in 307

this IO cohort (any line of therapy: nivolumab n=12, pembrolizumab n=13, 308

durvalumab+tremelimumab n=1, tremelimumab n=1) underwent ctDNA-NGS within 60 days 309

prior to IO initiation. Patients with less than the median maxVAF of 3.5% (n=14/27) had a mOS 310

of 8.8 versus 2.5 months for those higher than the median, from IO initiation to death (p=0.04, 311

HR 0.4, 95% CI 0.1-0.96), (Figure 2I). This suggests that among IO treated patients, those with 312

higher disease burden have worse outcomes; IO-specific benefit within the low/high disease 313

burden subsets should be confirmed with prospective controlled analyses to account for the 314

recognized improved prognosis with low burden disease generally. 315

316

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Genomic Landscape of GEA 318

After determining the prognostic insight of maxVAF and its correlations between 319

clinicopathologic features, disease burden/volume, and outcomes, while accounting for these 320

observations, we next assessed the ctDNA-NGS GEA GA landscape at the molecular level. Of 321

the 2140 assays in the Global-cohort, a median of 3 GAs were identified per test (range 0-80 322

GAs), and at least 1 non-synonymous GA was identified in 1756 (82%) cases (Table 1, Table 323

S1). GAs were more commonly identified with proximal primary EGJ versus distal GC tumors 324

(85% versus 79%, p=0.0009). Fifteen patients (0.9%) had 20 GAs identified in an individual 325

test, and 10 (0.6%) had 20 GAs identified once excluding synonymous mutations. These cases 326

included 4 known MSI-High patients and 1 POLD1 mutation. The mean number of detected 327

GAs between EGJ and GC primary sites was significantly skewed by the presence of these MSI-328

High or POLD1 mutated GC patients. Excluding these few special cases, significantly more GAs 329

were found in EGJ than GC cases (mean 3.7 versus 3.3, p=0.005). Within the Locally-advanced 330

cohort, 81% of tests identified >1 GA at diagnosis. Overall, these findings demonstrate that at 331

diagnosis most GEA patients, even in earlier stages, have identifiable ctDNA-GAs. 332

In addition to providing a survey of GA frequencies per sample, one can also infer tumor 333

mutational burden (TMB) from the number of identified GAs, which may have therapeutic 334

implications.32,33

However, this is challenging using ctDNA due to more limited gene coverage 335

potentially affecting precision, and also ctDNA quantity (directly related to cancer burden and 336

tumor shed at the time of sample collection) influencing the raw number of detected GAs 337

(r2=0.82, p<2.2e-16) (Figure S1A). Therefore, we corrected this by calculating TMB relative to 338

sequencing coverage and VAF (Figure S1B-C), previously described.26

We then compared 339

paired tissue-NGS and ctDNA-NGS TMB estimates (n=86), which correlated relatively poorly 340




15

with one-another (r2=0.15, p<0.24), though both were now adequately independent of maxVAF 341

after correction (Figure S1D-F). Significantly, all 6 patients with known MSI-high tumors 342

demonstrated ctDNA-TMB scores >90th

percentile of all tested samples, suggesting that for MSI-343

High tumors, very high ctDNA-TMB is readily detectable. Most importantly, by directly 344

sequencing microsatellite regions, ctDNA-NGS identified 6/6 (100%) patients known to be MSI-345

High (via IHC and tissue-NGS), at a plasma maxVAF range of 0.09% – 47.7%, with obvious 346

clinical implications.7 347

We next assessed the detailed genomic landscape of the cohorts, including mutations, 348

amplifications, indels, and splice variants. In the Global-cohort, GAs were frequently observed in 349

TP53 (53%), HER2 (17%), EGFR (17%), KRAS (15%), MYC (13%), PIK3CA (13%), and MET 350

(11%) (Figure 3A, Table S5A). GAs were further stratified into non-synonymous mutations 351

(Figure 3B) and amplifications (Figure 3C), where events in TP53, ARID1A, APC, and SMAD4 352

were typically mutations, while MYC, HER2, KRAS, EGFR, MET, and FGFR2 events were more 353

often amplifications. 354

We next compared the UC and SMC cohort GA landscapes, reflecting representative 355

Western and Eastern populations (Figure 3A-C, Tables S5B-C). More frequent ARID1A 356

mutations and KRAS, EGFR, and PIK3CA amplifications were observed in the UC cohort. 357

Specifically comparing GC cases (excluding EGJ) amongst UC and SMC cohorts, a higher 358

incidence of mutations in ARID1A and KRAS was still observed in the UC cohort, while 359

mutations in PIK3CA were more common in the SMC cohort. 360

Finally, we evaluated whether there were significant GA rate differences between early 361

and late stage disease, or between tissue-NGS versus ctDNA-NGS testing. Despite having 362

comparatively few early stage disease samples, within the “Clinically-Annotated” cohort a direct 363




16

correlation was observed between disease stage and number of alterations (Figure S2), and 364

likely confounded by disease burden, as elucidated above. For further comparison, we compared 365

tissue-NGS GA incidences from the previously reported Cancer Genome Atlas (TCGA) cohorts 366

representing early stage primary tumors (Stages I-III) (N=265),20,21

the MSK IMPACT cohort 367

(N=305) representing predominantly primary tumor biopsies from newly diagnosed stage IV 368

patients,28

and with ctDNA-NGS from the present Global-cohort (N=1627), reflecting ‘whole-369

disease’ burden and predominantly pre-treated settings of advanced disease (Figure 3D, Table 370

S5D). TP53 mutations were significantly more common in MSK and TCGA patient samples 371

(p=8.4x10-15

). Amplifications of MYC (p=2x10-6

), CDK6 (p=0.003), and CCNE1 (p=0.0006) 372

were more common in TCGA than in the MSK and Global-cohorts (Figure 1D). HER2 373

amplification was seen in only 11% of Global-cohort patient samples versus 29% in MSK and 374

25% in TCGA (p=8.6x10-18

). Most differences across the three cohorts likely reflected a 375

combination of sample acquisition timing, intra-patient heterogeneity, and/or tumor shed 376

limitations. Specifically, ‘HER2 conversion’ is now well recognized after treatment with anti-377

HER2 therapy,34-36

and potentially accounts for lower incidence of HER2 amplification in the 378

Global-cohort, given that this cohort presumably reflects patients in later lines of therapy after 379

already failing anti-HER2 therapy. This was addressed in more detail in HER2-analyses below. 380

Moreover, acknowledging that some Global-cohort cases would have low tumor DNA shed (eg. 381

Peritoneal-only GC) and others collected at inopportune time points (e.g. shortly after effective 382

therapy), the analysis was repeated by a) including only Global-cohort cases with GAs detected 383

and b) including only patients with a maxVAF > 0.5%, to limit underestimation of ctDNA-NGS 384

GA frequencies relative to tissue-NGS testing (Figure S3A-D, Table S6A-D). Using this 385

approach, TP53 mutation frequency differences lost statistical significance (therefore likely 386




17

driven by the DNA shedding limitation), though they remained significant for HER2 387

amplifications (potentially driven by post-treatment HER2 amplification loss in later-line 388

settings). Overall, the GA profiles from these cohorts using tissue-NGS or ctDNA-NGS highlight 389

and contrast the incidences of GAs across tumor stages, treatment time points, tumor sites, and 390

biologic compartments. Notably, there were generally higher incidences of targetable GAs, 391

particularly RTK amplifications (e.g. MET, FGFR2, and EGFR), in the Global-cohort than seen 392

with tissue-NGS. 393

Gene amplification is clinically relevant in GEA due to the predominance of 394

chromosomally unstable disease (CIN).20,21

Thus, we specifically assessed the incidence of 395

amplifications across the Global-cohort and found 4136 amplifications in 813 tests from 648 396

patients (39.8% of Global-cohort cases). Focusing on the most immediately therapeutically 397

relevant RTKs, both EGFR and MET demonstrated predominantly low-level ctDNA 398

amplifications, while HER2 and FGFR2 included a subset of patients with extremely high-level 399

ctDNA amplifications (Figure S4A). Generally, higher gene copy number in tissue samples has 400

correlated with more clinical benefit from respective targeted therapies.37-39

By ctDNA-NGS, the 401

plasma absolute gene copy number level could reflect either homogenous amplification 402

throughout all disease sites (in the context of the amount of ctDNA shed or maxVAF), or it could 403

represent heterogeneity with spatially mixed amplified and non-amplified clones, again in the 404

context of ctDNA shedding. In fact, we recently reported the high rate of GA discordance 405

between tissue-NGS on primary and metastatic biopsies, which was most pronounced in RTK 406

amplifications.10

As noted, the absolute level of ctDNA gene amplification is dependent on the 407

plasma maxVAF (point mutations/indels). For instance, we noted that a low level ctDNA 408

amplification observed in the context of a very low/non-detectable ctDNA maxVAF usually 409




18

represented very high tissue gene amplification in order for it to be observed in plasma. 410

Reciprocally, low level gene amplification in the context of very high maxVAF (i.e. high tumor 411

burden), typically did not reflect clinically relevant high level and homogenously distributed 412

gene amplification. Therefore, to address the limitation of tumor shed, plasma gene copy number 413

was normalized by dividing by maxVAF+0.01. This “adjusted” copy number method increased 414

the ability to discern between high- and low-level tissue-NGS gene amplification in the settings 415

of low or high ctDNA shed (Figure S4B). Overall, ctDNA analysis effectively detected cases 416

with gene amplification, and when accounting for maxVAF, identified patients with RTK 417

amplifications most likely to benefit from matched targeted therapy. 418

HER2 amplification is the only GA routinely assessed in newly diagnosed advanced GEA 419

patients to-date, thus we sought to investigate this GA as it pertained to ctDNA-NGS in more 420

detail. As above, ctDNA-NGS identified 184 HER2 amplified (11.3%) patients within the entire 421

Global-cohort (first test result if serially tested). The distribution of amplification level across 422

these ctDNA samples was 33/55/96 patients having ‘<50th

/ 50th

-90th

/ or >90th

’ percentile 423

amplification (see methods), respectively, (gene plasma copy number range 2.1-84.1, median 4.2 424

copies). To further assess HER2 amplification incidence and concordance with tissue-based 425

analyses, while considering clinical characteristics like treatment timing, we focused on the 426

Clinically-annotated-cohort. Among the 305 stage IV UC/SMC patients, 18.4% were HER2 427

amplified by ctDNA-NGS (range 2.1-68.2 copies, median 6 copies), and of these 305 patients, 428

35/158 (22.2%) with available tissue-NGS were HER2 amplified. When evaluating only 429

clinically HER2-positive stage IV patients (Table S1), only 36/58 (62%) of patients had 430

detectable HER2 amplification by ctDNA-NGS (Table S7). This was recapitulated in the 431

Baseline-cohort where 17/28 (61%) of untreated clinically HER2-positive patients also harbored 432




19

HER2 ctDNA amplification. The discordance between tissue versus ctDNA-NGS HER2 status 433

could be due to tumor shedding limitations but also intrapatient molecular heterogeneity. Thus, 434

we further investigated the degree that each of these factors contributed towards the observed 435

HER2 discordance between tissue-NGS and ctDNA-NGS. 436

437

Extensive Spatial and Temporal Molecular Heterogeneity in GEA 438

At initial diagnosis, spatial heterogeneity of HER2, along with other GAs, has been 439

recently detailed.10

Here we sought to further expand on this finding with additional cases, and 440

identified 34 newly diagnosed untreated stage IV GEA patients who had undergone ctDNA-NGS 441

along with tissue-NGS of both baseline primary tumor and a metastatic site (‘triplet-pairs’) 442

(Table S1). When limiting to genes present in both ctDNA and tissue panels (n=72), any GA 443

was identified in 57%, 58%, and 62% of cases within the primary tumor, metastatic tumor, and 444

ctDNA, respectively (Figure 4A). However, of the 183 characterized GAs identified, only 48 445

(26%) GAs were universally concordant within triplet-pairs. Of these, 21 (44%) were mutations 446

in TP53, which represented 81% of the TP53 GAs and were likely ‘truncal’ in the evolutionary 447

phylogenetic tree. Only 2/7 triplet-pairs were universally concordant for HER2 amplification. 448

Notably, 14%, 11%, and 22% of GAs were uniquely found in the primary, metastasis, and 449

ctDNA, respectively. Importantly, this analysis did not account for technical limitations of 450

ctDNA-NGS due to the recognized inability to detect large-scale deletion or regions not 451

sequenced. Excluding these tissue-based GAs, 149 GAs were observed across these 34 triplet-452

paired patients. Now, any GA was identified in 54%, 57%, and 74% in the primary, metastasis, 453

and ctDNA, respectively, with 11%, 8%, and 27% of GAs uniquely detected in the primary, 454

metastasis, and ctDNA, respectively (Figure 4B). Combining tissue-NGS and ctDNA-NGS 455




20

increased sensitivity for detection of HER2, EGFR, FGFR2, and MET alterations (Figure 4C). 456

This highlights the complementary benefit of using ctDNA-NGS together with tissue-NGS to 457

overcome the inherent false negative rates of either test, either due to spatial heterogeneity 458

(tissue) or technical shedding limitations (ctDNA). 459

In addition to baseline spatial molecular heterogeneity, ctDNA-NGS may detect acquired 460

resistance over time (temporal heterogeneity). First, we focused on the incidence of persistent 461

HER2 amplification versus conversion to HER2 non-amplified status after failed first line anti-462

HER2 therapy using paired pre/post therapy tissue and plasma samples. In this ’Serial-HER2’ 463

cohort, upon disease progression, only 4/15 (27%) patients demonstrated persistent HER2 464

amplification by ctDNA-NGS (Figure 4D). Two of these ctDNA amplified patients also 465

demonstrated persistent HER2 IHC 3+ expression. However, another patient retained tissue 466

HER2 amplification, but lacked HER2 ctDNA amplification upon progression – likely a result of 467

low tumor shed in this case. Those with persistent HER2-amplification by either ctDNA-NGS 468

and/or tissue, post-therapy ctDNA-NGS identified additional acquired mutations in KRAS (G12D 469

and T35A), NF1 (N1503S), and PIK3CA (E542K and S1008T), and co-amplifications of BRAF, 470

KRAS, PIK3CA, and FGFR1 as likely mechanisms of resistance (Figure 4E, Tables S7-S8). 471

Next, we assessed resistance mechanisms to targeted therapy towards other pertinent 472

RTK amplifications, including EGFR, MET and FGFR2. Resistance mechanisms to anti-EGFR 473

therapy were previously reported, and included loss of EGFR amplified clones and/or GAs 474

rendering upregulation of various bypass pathways including RTKs and MAPK/PI3K 10,39

475

Patients harboring MET and FGFR2 amplified samples treated with matched TKIs or 476

monoclonal antibodies also revealed upregulation of similar bypass pathways in RTKs and 477

MAPK/PI3K pathway GAs, and redirecting therapy based on observed ctDNA-NGS changes 478




21

yielded promising results. Based on our findings, exemplified in five cases (File S1, Figure S5), 479

it is apparent that baseline spatial and temporal heterogeneity are inter-related, since pre-existing 480

spatially distributed resistant clones were repeatedly selected under targeted therapeutic pressure, 481

yet in some instances these were not identified at baseline, and only became apparent over time. 482

ctDNA-NGS identified resistance mechanisms to targeted therapy in evaluated patients upon 483

progression and may direct optimal next-line therapy. 484

485

Role of ctDNA-NGS as a prognostic and/or predictive biomarker 486

In the context of its role in measuring tumor burden/maxVAF and accounting for inter-487

patient and intra-patient molecular heterogeneity, ctDNA-NGS may identify prognostic and/or 488

predictive GAs. To assess this, the Baseline-cohort (n=144) was again analyzed for key genes 489

(PIK3CA, BRAF, KRAS, HER2, FGFR2, MET, and EGFR) previously reported to have 490

prognostic and/or predictive significance in GEA or other cancers. 491

Presence of PIK3CA mutation corresponded with shorter survival of 3.8 versus 13.6 492

months (p=0.006, HR 3.4, 95% CI 1.6-7.2) (Figure 5A). Similarly, BRAF GAs corresponded 493

with a mOS of 5.6 months versus 13.7 months in BRAF wildtype patients (p=0.02, HR 3.0, 95% 494

CI 1.4-6.7) (Figure 5B). However, none of these nor others evaluated remained statistically 495

significant after multiple comparison correction and multivariate analyses (Supplemental Table 496

S9). Within the 144 patient cohort, only 2/11 FGFR2 amplified patients and 2/11 MET amplified 497

patients received RTK inhibitors, therefore survival analysis could not be robustly performed. 498

These data suggest that mutations in PIK3CA and GAs in BRAF portend generally poor 499

prognoses, but should be confirmed in larger clinically-annotated homogenously-treated studies. 500

501




22

HER2 502

Given that <50% of HER2-positive patients demonstrate response to first line anti-HER2 503

therapy, we asked whether incorporating ctDNA-NGS could improve the predictive utility over 504

standard single-lesion tissue-based HER2-testing. Across all “Baseline” stage IV patients having 505

both ctDNA- and tissue-NGS at any time point, 21/86 (24.4%) harbored HER2 amplification by 506

at least one approach, but only 13/86 (15.1%) were amplified by both (6/8 of discordant patients 507

were identified by tissue-NGS only) (Figure 5C), and an additional 3/86 patients were 508

considered clinically HER2 positive, but lacked amplification by tissue- or ctDNA-NGS. Among 509

HER2-targeted patients in the Baseline-cohort, 23 patients had received first line HER2-directed 510

therapy – either lapatinib (n=7), lapatinib + trastuzumab (n=1), trastuzumab (n=14), or 511

trastuzumab and pertuzumab (n=1). An additional patient was excluded from survival analysis as 512

they had received HER2-directed peri-operative therapy before recurrence. Amongst the HER2-513

targeted patients, 19/23 were clinically HER2-positive by routine tissue analyses, and only 15 514

were ctDNA-NGS HER2 amplified. Relying solely on reported ctDNA-NGS HER2 515

amplification, in this small series there was no difference in survival compared to those with or 516

without ctDNA-NGS HER2 amplification - 12.7 versus 8.7 months (p=0.4, HR 0.6, 95% CI 0.2-517

1.7) (Figure 5D). However, this failed to consider the relationship between copy number and 518

maxVAF as noted earlier. After adjustment (copy number / maxVAF+0.01), patients with 519

plasma copy numbers greater than the median (n=10/23), demonstrated improved mOS of 15.9 520

versus 9.4 months (p=0.07, HR 0.4, 95% CI 0.1-1.1) (Figure 5E). Further, we identified patients 521

with low tumor DNA shed and therefore HER2 amplification not detected by ctDNA-NGS, as 522

well as molecular heterogeneity missed by single site tumor profiling by comparing those with 523

HER2 amplification present in tissue (n=11/23) or adjusted ctDNA-NGS (n=10/23 – only 5 both 524




23

tissue and ctDNA amplified). With this approach, patients with HER2 amplification had a 26.3 525

versus 7.4 month mOS in this ‘Complementary-amplified’ group (n=16/23, p=0.004, HR 0.2, 526

95% CI 0.05-0.6) (Figure 5F). ctDNA identified actionable GAs in cases that would have been 527

missed with tissue-testing alone. These findings focused on HER2 further delineate baseline and 528

temporal molecular heterogeneity of GEA and demonstrate the importance of complementary 529

tissue/plasma-NGS testing to best identify biomarkers of therapeutic relevance. 530

EGFR 531

We recently assessed the prognostic and predictive nature of EGFR amplification in 532

GEA.39

We sought to further evaluate this biomarker, and focus on the utility of ctDNA-NGS. 533

There was no difference in survival between the ctDNA-NGS amplified (n=12/130) and non-534

amplified untreated stage IV patients who did not receive EGFR-directed therapy (14.4 versus 535

13.3 months, p=0.6, HR 1.3, 95% CI 0.5-3.0) (Figure 5G), suggesting that EGFR amplification 536

does not have specific prognostic implication in this cohort. In the Baseline cohort, 22/144 537

patients had ctDNA-NGS EGFR amplification, and an additional 5 patients by tissue-NGS. Of 538

these, 14 received EGFR-directed therapy – ABT806 (n=12) or cetuximab (n=2). Amongst 539

EGFR amplified patients, those who received EGFR inhibitors (n=9/27) in any line of therapy 540

showed a mOS of 21.1 versus 14.4 months versus patients who did not (p=0.01, HR 0.2, 0.06-541

0.8) (Figure 5H). This survival benefit was accentuated to 21.1 versus 6.2 months when 542

comparing patients with either an adjusted copy number greater than median or tissue-based 543

amplification (n=9/14, p=0.001, HR 0.05, 95% CI 0.006-0.4), despite limitations by small 544

sample size (Figure 5I). These data support that EGFR amplification, again optimally captured 545

in complementary fashion by ctDNA and/or tissue NGS, is not prognostic but potentially 546

predictive of benefit to anti-EGFR therapy, consistent with previous reports.39,40

547




24

548

Discussion 549

Herein, we present the largest comprehensive analysis evaluating the utility of ctDNA-550

NGS from a large commercial database with 2140 individual tests on 1630 GEA patients, and a 551

substantial subset (698 tests from 369 patients) having clinical annotation for detailed 552

clinicopathologic and outcomes analyses. 553

554

Using these cohorts, we first established an understanding of the detection limit of 555

ctDNA-NGS as it relates to disease burden, disease site, and treatment timing. Though the 556

median maxVAF was quite low, as seen in other studies, there was a long tail of patients with a 557

high maxVAF. Biologically, this may be due to patients with very high maxVAF upon stage IV 558

diagnosis, but technically, can reflect difficulty in filtering germline alterations in patients with 559

high tumor shed and/or genomic instability. However, finding several genes at high level 560

suggests biologic origin, rather than technical.26,41

For patients with low disease burden (few 561

organ sites involved), peritoneal-only disease, and samples obtained shortly after therapy, each 562

demonstrated lower ctDNA yield and in many cases non-detectable ctDNA. The biologic reason 563

for lower plasma ctDNA in peritoneal-only disease is uncertain, but may be attributed to less 564

shed into the peripheral vascular system, which was recently noted in patients with peritoneal 565

carcinomatosis in other cancer types,42

and/or different GAs which are not assessed by the 566

ctDNA-NGS panel used. However, patients with peritoneal-only disease often have diffuse or 567

mixed-type histology, and mutations in genes such as CDH1 and RHOA associated with this 568

subtype (the TCGA ‘genomically stable’ molecular subtype) are indeed part of the 73-gene 569

ctDNA-NGS panel.20

It is noteworthy, however, that peritoneal-only disease often has 570




25

insufficient DNA even for tissue-NGS, likely due to the low viable tumor content within dense 571

desmoplastic tumors from both primary and metastatic biopsies. Therefore, future studies 572

addressing these apparent molecular profiling limitations from both tissue and plasma of this 573

difficult-to-treat subset of GEA patients are needed, as well as peritoneal fluid or lavage as 574

potential sample types. 575

576

Regardless, from these limit-of-detection observations, we next determined that residual 577

ctDNA detection after curative-intent resection reliably heralded eventual recurrence and worse 578

prognosis in early stage disease. This is consistent with reports from other tumor types,43

and 579

suggests that post-operative ctDNA-detection in GEA could be an important stratification factor 580

within prospective adjuvant therapy studies. Moreover, via prospective studies, this biomarker 581

may help to select those patients that should and should not receive further adjuvant therapy. 582

However, we must be mindful of false positives in older patients resulting from clonal 583

hematopoiesis. Three patients (all elderly) with detectable mutations after surgery, each at 584

similar low maxVAFs prior to treatment/surgery, have not recurred to date, and none of these 585

mutations were identified by tissue sequencing, which suggests that they may not be tumor-586

derived at all. Future strategies need to be mindful of both germline and hematopoietic 587

confounding. Similarly, in advanced disease, we observed that baseline ctDNA quantity and 588

early serial changes correlated with clinical characteristics and outcomes. It is possible that 589

ctDNA-NGS may also prove useful here to assess whether patients benefit from changing 590

therapy earlier in these ‘ctDNA non-responders’, prior to initial restaging CT scans. This 591

hypothesis would be particularly interesting to investigate prospectively – especially when 592

expensive or toxic therapies are employed and could be “fast-failed” early. In addition, this 593




26

approach depends on having an effective therapeutic option on which to change, which would 594

need to be validated. Our findings are corroborated by others, who also recently noted that 595

changes in maxVAF for GAs reflected response to treatment, with an early spike in the first 1-3 596

days of effective chemo- or targeted therapy followed by order of magnitude drops in maxVAF, 597

reflecting molecular response,44

but differ from that found when trending total cfDNA.45

Finally, 598

as it pertained to levels of ctDNA in the plasma, we developed a framework to optimally identify 599

and understand gene amplifications by adjusting for maxVAF in order to take into account tumor 600

burden, spatial molecular heterogeneity, and DNA shed. 601

Focusing on the landscape of GAs in GEA as determined by ctDNA-NGS, we 602

demonstrated that at first diagnosis, the vast majority of GEA patients, even in earlier stages, had 603

identifiable ctDNA-GAs, especially after excluding those with peritoneal-only disease and recent 604

therapy. Very importantly, we showed that all known MSI-High cases in our cohort having 605

ctDNA-NGS performed were accurately identified – including a patient with a maxVAF as low 606

as 0.09%. This is the highest sensitivity for plasma-detected MSI-H reported by any method to-607

date and will be a useful tool to identify this relatively infrequent but highly targetable GA where 608

traditionally tissue-based MSI testing is less routinely performed or insufficient tissue is 609

available.7,33

When comparing the ctDNA-GA landscape between the ‘Western’ UC and 610

‘Eastern’ SMC cohorts, we noted similar GA incidences, but there were also some interesting 611

differences, even after considering only the GC UC subset with the GC SMC cohort. These 612

differences included a higher incidence of KRAS and ARID1A GAs in the UC subset, which was 613

consistent with prior literature,46

while the SMC cohort was enriched for PIK3CA mutations. The 614

latter is remarkable since it has been reported that PIK3CA mutation is associated with EBV-615




27

positive GC,20,47

which may also be more common in Asian countries,48

although the literature is 616

conflicting.46,49

617

618

Comparisons of the ctDNA-GA landscape to cohorts published during manuscript 619

preparation50,51

and previously reported large-scale tissue-based analyses of GEA patients both 620

revealed similar but not identical incidences of various GAs. When dissecting this further, we 621

noted that incidence differences from these 3 large cohorts were mostly attributable to 622

differences in disease stage, sample acquisition time points, along with differing disease sites and 623

tissue compartments assessed. There were generally higher incidences of targetable GAs, 624

particularly RTK amplifications, in the Global-cohort. In this regard, ctDNA accounted for 625

increasing intra-patient molecular heterogeneity, including at baseline and secondary to 626

treatment pressure and evolving resistance. This served to survey the metastatic burden of 627

patients best, in order to determine optimal targeted therapeutic regimens. 628

629

630

Along these lines, molecular heterogeneity both between and within patients has become 631

a formidable hurdle to successful implementation of targeted therapies in GEA.10

Herein we 632

evaluated the largest ‘triplet-pairs’ cohort reported to date for GEA, and again we uncovered 633

significant discordance, including in routine known and potentially targetable RTKs such as 634

HER2, EGFR, MET, and FGFR2. Together, these 4 RTKs account for approximately 30-40% of 635

GEA patients, which make up a large subset of CIN tumors, and therefore a very significant 636

consideration for ensuing targeted therapeutic decisions. Of note, the high frequency of EGFR 637

amplifications in our cohort likely reflects a Western predominance of EGJ CIN tumors at our 638

center. We also demonstrated numerous temporal resistance mechanisms, particularly after 639




28

specific targeted therapy towards RTK amplifications, which included loss of the RTK 640

amplification itself and/or GAs rendering upregulation of various known pathways to circumvent 641

this inhibition strategy. Serial molecular profiling led to changes in treatment decision for these 642

cases at disease progression points. Although EGFR amplification is not recommended to be 643

routinely assessed by current guidelines, our work builds upon our previous and others’ work 644

suggesting benefit for these patients.39,40,52

It should be also noted that many patients in our 645

annotated cohorts could not be assessed as ‘triplet pairs’ due to insufficient tissue in either the 646

primary tumor and/or the metastatic site at baseline and also at disease progression. This points 647

towards the practicality of ctDNA-NGS to best assess baseline and temporal heterogeneity due to 648

convenience, expediency, and less-invasive nature of a ‘liquid-biopsy’ in the clinic. Our 649

observations of vast interpatient and intrapatient molecular heterogeneity, spatially at baseline 650

and temporally after therapy, are very much connected. A personalized treatment strategy that 651

incorporates molecular profiling from both the tissue and the plasma at baseline and 652

subsequently over time will likely be necessary in order to successfully improve outcomes of this 653

disease with targeted therapeutics. In fact, we observed that incorporating tissue-NGS and 654

ctDNA-NGS profiling in aggregate identified patients most likely to benefit from anti-HER2 and 655

other targeted therapies. These findings mirror those seen in lung cancer with concurrent tissue- 656

and ctDNA-NGS,53

and a recent report suggested that ‘first-pass’ ctDNA-NGS for lung cancer 657

patients may spare unnecessary redundant testing, with reflex tissue testing only if ctDNA-NGS 658

is unrevealing.54

This may also be applicable for GEA and warrants attention. Ultimately, 659

incorporating ctDNA-NGS may be a strategy to overcome recognized molecular heterogeneity, 660

both at baseline and over time, and prospective innovative trials designs are ongoing to test this 661

hypothesis.55

662




29

663

This study has some limitations. The Global-cohort, albeit large, was relatively limited in 664

clinical utility without the granular clinicopathologic characteristics to contextualize the GA 665

distribution landscape. To address this, we combined two clinically-annotated cohorts which 666

provided robust understanding of GA events with clinicopathologic perspective, and subsequent 667

analyses were restricted to samples drawn prior to any therapy to avoid underestimating ctDNA-668

NGS GAs and to perform tissue-plasma concordance studies more precisely. Another inherent 669

limitation when comparing the 73-gene cfDNA-NGS versus 315-gene tissue-NGS panel is the 670

expected discordance resulting from technical and biological differences between these different 671

tests of distinct biological compartments. Technical limitations leading to discordance between 672

tissue and plasma obviously included non-overlapping genes, but also some regions of 673

overlapping genes not sequenced on the ctDNA-NGS panel. Another technical limitation is the 674

recognized inability of ctDNA-NGS to discern large-scale deletions amongst the vast sea of 675

wildtype cfDNA. To account for these limitations and to focus on only those GAs that 676

overlapped, we compared only those regions covered by both panels and excluded large 677

deletions identified by tissue-NGS. This admittedly underestimates the level of ‘real-life’ 678

discordance that the clinical oncologist will observe. However, by doing so, we were able to 679

focus on and identify specific biologic reasons for discordance, including disease burden and 680

tumor site, which was directly related to tumor shed, as well as intrapatient spatial molecular 681

heterogeneity. Finally, despite the relatively large size of the Clinically-annotated cohort, 682

inherent to low-frequency GAs, was our inability to definitively evaluate the prognostic 683

importance of individual GAs nor the predictive impact of targeting these infrequent events. 684

685

686

Conclusions 687




30

688

In summary, clinical ctDNA-NGS testing holds promise for GEA – both in the detection 689

of minimal residual disease in early stage disease and as a serial tumor marker. ctDNA-NGS 690

used in conjunction with tissue-NGS may be an approach to best identify actionable GAs and 691

resistance mechanisms in order to overcome intrapatient heterogeneity. However, prospective 692

validation of these findings in future studies is necessary for integration into clinical care. 693

694

695

696

Acknowledgements 697

The authors wish to thank all patients for generously participating in all clinical and tissue 698

banking studies. 699

700

Author Contributions 701

Study conception and design: SBM, DVTC 702

Data processing: SBM, LC, SL, SK, SSJ, KM, SL, JJ, LAK, STK, JL, DVTC 703

Analysis: SBM, LAK, DVTC 704

Manuscript preparation: SBM, LAK, RJN, RBL, DVTC 705

706

707

708

709

710

711

712

713

714

715

716

717

References and Notes: 718

719

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31

3. Bang, Y.J., et al. Trastuzumab in combination with chemotherapy versus chemotherapy alone for treatment 724

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5. Wilke, H., et al. Ramucirumab plus paclitaxel versus placebo plus paclitaxel in patients with previously 730

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Lung Cancers. J Natl Cancer Inst (2018). 844

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849

850

851

852

853

854

855

856

857

858

859

860

861

862

863




34

864

Characteristic Global UChicago Samsung p-value*

Number of patients (%) 1630 (100) 273 (17) 96 (6)

Number of tests (%) 2140 601 (28) 97 (6)

Number of patients with 2+ tests (%) 243 (15) 128 (47) 1 (1) <2.2e-16

Number of tests with 1+ Alterations (%) 1756 (82) 314 (84) 71 (73) 0.0003

Median number of alterations (range) 3 (0-80) 3 (0-80) 2 (0-39) 0.3

Median age yrs. (range) 63 (19-98) 62 (19-87) 57.5 (23-82) 0.001

Male Sex – no. (%) 1164 (71) 208 (76) 60 (63) 0.01

Disease Site no. (%)

Esophagus/GEJ 773 (47) 183 (67) 0 (0) 1

Gastric 857 (53) 90 (33) 96 (100)

Race

Caucasian 204 (13) 204 (75) 0 (0)

<2.2e-16

African American 37 (2) 37 (14) 0 (0)

Asian 108 (7) 12 (4) 96 (100)

Hispanic 10 (1) 10 (4) 0 (0)

Pacific Islander 1 (0) 1 (0) 0 (0)

Other/Unknown 1270 (78) 9 (3) 0 (0)

Tumor Grade no. (%)

Well Differentiated 15 (1) 12 (4) 3 (3)

0.001

Moderately Differentiated 90 (6) 69 (25) 21 (22)

Poorly Differentiated 202 (12) 159 (58) 43 (45)

Unknown 1323 (81) 33 (12) 29 (30)

Stage upon testing no. (%)

I 6 (0) 6 (0) 0 (0)

1.9e-15

II 13 (1) 13 (5) 0 (0)

III 45 (3) 45 (16) 0 (0)

IV 305 (19) 209 (77) 96 (100)

Unknown 1261 (77) 0 (0) 0 (0)

Tissue-based Clinical HER2 – no. (%)

Positive 68 (4) 60 (22) 8 (8)

7.5e-5

Negative 253 (16) 184 (67) 69 (72)

Equivocal 10 (1) 2 (1) 8 (8)

Unknown 1299 (79) 27 (10) 11 (11)

P-values shown reflect comparison of UChicago and Samsung cohorts. 865

866

Table 1: Patient demographics of the Global and the Clinically-annotated cohorts from the 867

University of Chicago and Samsung Medical Center. 868

869

870

871




35

Figure Legends: 872

873

Figure 1. ctDNA detection and number of detected alterations is dictated by specific disease sites and burden 874

of disease. A) The number of disease sites involved in patients from the Baseline cohort (n=144) directly correlated 875

with maxVAF, suggesting that maxVAF reflected overall disease burden (p=4.9e-8, F=9.8). B) Upon stage IV 876

diagnosis, patients with intact primary tumors (n=101/144) had a generally higher mean maxVAF of 10.9% versus 877

6.5% for those with prior curative intent primary tumor resection (p=0.09, 95% CI 0.7-9.9). C) In addition to disease 878

burden, specific disease sites were associated with increased tumor shedding and consequently maxVAF – most 879

notably liver and lymph nodes (p=0.01,F=3.1). D) Conversely, patients with solely peritoneal involvement 880

(n=35/144), had a lower mean maxVAF of 2.5% versus 11.9% in patients with additional/other disease sites 881

(p=5.1e-6, 95% CI 5.6=13.6), and many patients with solely peritoneal involvement had no detectable ctDNA. 882

883

Figure 2. Prognostic implications of maxVAF and serial changes in the perioperative and newly diagnosed 884

metastatic settings 885

A) Detection of >0.25% maxVAF prior to neoadjuvant therapy was associated with a 15.2 month mDFS (n=17/29), 886

versus not reached mDFS in patients with lower or undetectable maxVAF (p=0.1, HR=0.2, 95% CI 0.03-2.1). B) 887

Patients with maxVAF >0.25% (n=7/22) within 180 post-operative days and before adjuvant therapy, if applicable, 888

had a 12.5 month mDFS versus unreached mDFS in patients with lower or undetectable maxVAF (p=0.03, HR=0.1, 889

95% CI 0.01-1.1). C) Representative ‘tumor-response map’ of an individual demonstrating detectable pre-therapy 890

ctDNA, with post-operative clearance of ctDNA; in a patient with no evidence of recurrence on follow up 891

examination ~24 months from surgery. D) Representative ‘tumor-response map’ of an individual demonstrating 892

persistent ctDNA post-operatively (maxVAF 2.3%), with recurrence within 6 months of surgery. E) Newly 893

diagnosed metastatic patients (104/144) with below-mean (‘low’) maxVAF (<9.7%) had a mOS of 14.8 versus 9.4 894

months for above-mean (‘high’) maxVAF (p=0.1, HR 0.7, 95% CI 0.4-1.1). F) Patients with detectable ctDNA upon 895

stage IV diagnosis (maxVAF>0.5%) and upon repeat testing within 150 days demonstrating a decline by >50% 896

(n=23/35) demonstrated superior mOS of 13.7 versus 8.6 months (p=0.02, HR 0.3 95% CI 0.1-0.8). G) 897

Representative ‘tumor-response map’ revealing ctDNA decline (“response”) in a patient on first line therapy who 898

remains alive beyond 24 months with stage IV GEA. H) Representative ‘tumor-response map’ demonstrating 899

ctDNA non-responding patient who died of from disease progression ~3 months from diagnosis of stage IV GEA, 900

despite receiving standard therapy. I) Patients who had ctDNA tested within 60 days prior to IO initiation and were 901

found to have a lower than median maxVAF (3.5, n=14/27), had a higher mOS of 7.9 versus 2.5 months for those 902

with above median maxVAF, from the time of IO initiation to death (p=0.04, HR 0.4, 95% CI 0.1-0.96). 903

904

Figure 3. Relative frequency of common (>5%) non-synonymous ctDNA alterations between Western and 905

Eastern populations and various ctDNA-NGS and tissue-NGS cohorts. 906

A) Non-synonymous GA frequency by Global versus UChicago versus Samsung ctDNA-NGS cohorts revealed a 907

higher rate of TP53, KRAS, ARID1A, and CDKN2A alterations (including SNVs, copy number alterations, fusions, 908

splice variants, and indels) in the Western (UChicago) than Eastern (Samsung) cohorts. B) Mutation frequencies 909

(SNV+indel+splice variants) by cohort highlight that mutations in KRAS and ARID1A account for the increased 910

alteration frequency differences between the UC and SMC cohorts. C) Oncogene amplification frequency between 911

the UChicago and Samsung cohorts demonstrating higher amplification frequencies in global and UC cohorts than 912

SMC patients, potentially reflecting more proximal CIN patients in Western cases. D) GA frequency between 913

resected GEA primary tumors stages I-III (TCGA), baseline primary tumor stage IV GEA (MSK Impact), and 914

ctDNA (ctDNA-NGS) revealed similar but not identical incidences of GAs using tissue-NGS compared with 915

ctDNA-NGS, a reflection of different tumor stages, treatment time points, tumor sites and biologic compartments. 916

917

918

Figure 4. Intra-patient spatial and temporal heterogeneity by multi-site tissue-NGS and ctDNA-NGS 919

A) Amongst untreated stage IV/recurrent untreated patients who underwent baseline Triplet-paired sequencing 920

(NGS) of primary tumor and metastatic (met) tumor and plasma ctDNA (n=34), only 26% of characterized 921

alterations were identified by all 3 methods. Percentages by site name indicate % of total GAs identified across the 922

34 patient cohort. B) Limiting GAs to those detectable by ctDNA (n=149/183 GAs in these patients), concordance 923

between all 3 approaches increased to 32%, and ctDNA was able to detect 74% of GAs compared with 54% and 924

57% by tissue testing of either the primary and metastatic site, respectively. C) Comparison between tissue and 925

ctDNA RTK amplification in HER2, EGFR, FGFR2, and MET in baseline untreated metastatic patients, increased 926

sensitivity for detection was observed when using both tissue-NGS and ctDNA-NGS D) ctDNA-NGS representative 927




36

‘tumor-response map’ demonstrating persistent HER2 amplification upon progression on HER2-targeted therapy. E) 928

Tumor-response map highlighting disappearance of HER2 amplification amidst expansion of previous CCNE 929

amplification and TP53 mutation along with de novo NF1 mutation in ctDNA after progression on HER2-targeted 930

therapy. 931

932

Figure 5. Survival analysis of untreated stage IV GEA patients by specific genomic alteration. 933

A) Presence of a PIK3CA mutation corresponded with shorter survival of 3.8 versus 13.6 months (p=0.006, HR 3.4, 934

95% CI 1.6-7.2). B) BRAF alterations corresponded with a mOS 5.6 months versus 13.7 months in BRAF wildtype 935

patients (p=0.02, HR 3.0, 95% CI 1.4-6.7). C) Amongst the 86 patients with both tissue-NGS and ctDNA-NGS 936

available, 24 were either HER2 clinically positive or HER2 amplified by tissue-NGS or ctDNA-NGS at any time 937

during their disease – with 54% universal concordance. D) Amongst all 23 patients considered clinically HER2 938

positive who underwent ctDNA-NGS at the time of stage IV diagnosis and then received HER2-directed therapy, 939

mOS was 12.7 versus 8.7 months in ctDNA HER2-amplified patients (n=15/23) versus those without ctDNA HER2 940

amplification (p=0.4, HR 0.6, 95% CI 0.2-1.7). E) Amongst all 23 patients considered clinically HER2 positive, 941

using an adjusted copy number, i.e. copy number/ (maxVAF+0.01), patients with a greater than median HER2 copy 942

number (10/23) demonstrated a mOS of 15.9 versus 9.4 months in those with lower copy number (p=0.07, HR 0.4, 943

95% CI 0.1-1.1). F) evaluating patients with proven tissue amplification and/or greater than median ctDNA 944

amplification (n=16/23) in complementary fashion, the mOS benefit increased to 26.3 versus 7.4 months (p=0.004, 945

HR 0.2, 95% CI 0.05-0.6). G) EGFR amplification was not prognostic, as the median overall survival of EGFR 946

amplified, non-targeted patients (n=12/130) was similar to that of non-EGFR amplified patients – 14.4 months 947

versus 13.3 months (p=0.6, HR 1.3, 95% CI 0.5-3.0). H) EGFR amplified patients by ctDNA-NGS and/or tissue-948

NGS in the Baseline cohort who received EGFR inhibitors (n=9/27) in any line had a mOS of 21.1 versus 14.4 949

months for patients who did not (p=0.01, HR 0.2, 0.06-0.8). I) Adjusted EGFR copy number above median or tissue 950

amplification (n=9/14) demonstrated a 21.1 versus 6.2 month mOS (p=0.001, HR 0.05, 95% CI 0.006-0.4). 951

952



















Published OnlineFirst August 19, 2019.Clin Cancer Res Steven B. Maron, Leah M Chase, Samantha Lomnicki, et al. gastroesophageal adenocarcinoma.Circulating tumor DNA sequencing analysis of

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Circulating tumor DNA sequencing analysis of gastroesophageal … · 2019. 8. 17. · 127 gastroesophageal patients, 2140 tests from 1630 patients met inclusion criteria for diagnosis