Circulating tumor DNA sequencing of gastroesophageal adenocarcinoma.

Abstract

Purpose:

Gastroesophageal adenocarcinoma (GEA) has a poor 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 to evaluate the role of ctDNA-NGS in guiding clinical decision-making in GEA.

Experimental Design:

We evaluated a large cohort (n=2140 tests; 1630 patients) of ctDNA-NGS results (including 369 clinically-annotated pts). Patients were assessed for genomic alteration (GA) distribution and correlation with clinicopathologic characteristics and outcomes.

Results:

Treatment history, tumor site, and disease burden dictated tumor-DNA shedding and consequent ctDNA-NGS maximum somatic variant allele frequency (maxVAF). Patients with locally advanced disease having detectable ctDNA post-operatively experienced inferior median disease-free survival (mDFS) (p=0.03). The genomic landscape was similar but not identical to tissue-NGS, reflecting temporospatial molecular heterogeneity, with some targetable GAs identified at higher frequency via ctDNA-NGS compared to 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 by incorporating tissue-NGS and ctDNA-NGS assessment in a complementary manner. HER2-inhibition demonstrated a profound survival benefit in HER2 amplified patients by ctDNA-NGS and/or tissue-NGS (mOS 26.3 versus 7.4 months (p=0.002)), as did EGFR inhibition in EGFR amplified patients (mOS 21.1 versus 14.4 months (p=0.01)).

Conclusions:

ctDNA-NGS characterized GEA molecular heterogeneity and rendered important prognostic and predictive information, complementary to tissue-NGS.

Gastric cancer (GC) and esophageal/esophagogastric junction (EGJ) adenocarcinoma, together gastroesophageal adenocarcinoma (GEA), is a significant global health problem.(1) Median overall survival (mOS) of stage IV GEA is 11–12 months with optimal palliative chemotherapy,(2) and 16 months for erb-b2 receptor tyrosine kinase 2 (HER2 or ERBB2) amplified tumors treated with trastuzumab plus chemotherapy.(3) To date, ramucirumab, an anti-VEGFR2 monoclonal antibody, and pembrolizumab, an anti-PD-1 monoclonal antibody, are the only other approved biologic therapies in subsequent-line therapy.(4–9) Development of targeted agents has been limited by low frequency genomic alterations (GAs) and inter-patient heterogeneity, exacerbated by immense intra-patient heterogeneity - even at baseline diagnosis.(10) Routine tissue-based next-generation sequencing (tissue-NGS) identified that at least 37% of GEA patients harbor gene amplification in receptor tyrosine kinases (RTKs), including HER2, MET, EGFR, and FGFR2, and also downstream KRAS.(11–14)

These GAs, while each individually relatively infrequent, may have both prognostic and, importantly, predictive significance in GEA patients. This precedent was set by targeting HER2 amplification with trastuzumab. However, only 47% of HER2-positive patients achieved objective response and mOS increased to only 13.8–14.2 months,(3) while subsequent studies with other anti-HER2 agents were negative for first and second line therapy.(15–19) These observations likely reflect a combination of factors, including intra-patient heterogeneity in HER2 amplification as well as inherent and/or acquired concurrent molecular resistance mechanisms.

Previously, we identified discordance between coupled synchronous primary and metastatic GEA lesions in 42% of single nucleotide variants and insertions/deletions, and 63% of gene amplifications.(10) However, in a small cohort of patients with ‘triplet-paired’ primary-metastasis-ctDNA, ctDNA-NGS GAs were concordant with metastatic biopsies in 87.5% of cases, as defined by a predefined treatment assignment algorithm, suggesting that this noninvasive approach may be more effective in guiding targeted therapy selection in metastatic disease. The distributions of GAs assessed by tissue-NGS from early(20,21) and advanced(22,23) stage GEA patients have been reported. However, these studies relied on single-lesion testing at one time point, and therefore could not account for spatial nor temporal heterogeneity. Thus, we now turned to ctDNA-NGS, in conjunction with tissue-NGS, to obtain a comprehensive and more complete ‘snapshot’ of GAs and their heterogeneity in GEA, in order to understand their implications for targeted therapy.

To accomplish this task, we analyzed the largest landscape cohort of GEA patients who have undergone ctDNA-NGS to-date, which included a large clinically annotated subset comprised of patients from the University of Chicago (UC) and Samsung Medical Center (SMC). The goals of this study were several-fold. We first sought to evaluate the detection limit of ctDNA-NGS on clinical samples, and the clinical impact of ctDNA detection on early stage disease recurrence. We next assessed, in advanced disease, whether baseline ctDNA quantity and early serial changes correlated with clinical characteristics and outcomes. We then surveyed the landscape of GEA ctDNA-NGS GAs, including MSI-High, and compared incidences to tissue-NGS cohorts. To corroborate earlier observations, we further characterized heterogeneity between paired tissue-NGS and ctDNA-NGS at baseline and over time. Finally, we assessed the role of ctDNA-NGS in predicting response and resistance of matched inhibitors to various RTK amplifications, including HER2, EGFR, MET and FGFR2. To our knowledge, this represents the largest and most comprehensive evaluation of the clinical utility of ctDNA-NGS in GEA.

Online Methods

GEA Samples

Of 2326 ctDNA-NGS tests performed between 9/30/14–7/11/18 on 1780 gastroesophageal patients, 2140 tests from 1630 patients met inclusion criteria for diagnosis with adenocarcinoma of the esophagus, gastroesophageal junction, or stomach (GEA) after filtering out cases with reported non-adenocarcinoma or unknown esophageal carcinoma histologies (Table 1). A large subset of these cases were linked with de-identified patient data from the University of Chicago (UC) (Chicago, IL) (N=273 pts, 601 tests) and Samsung Medical Center (SMC) (Seoul, Korea) (N=96 pts, 97 tests) in institutional review board approved tissue banks. All patient cohorts utilized in this study are described in Table S1. This work was conducted in full concordance with the principles of the Declaration of Helsinki. All patients provided written informed consent, where applicable, or such informed consent was waived by IRB-approved protocols for aggregate de-identified data analysis. Somatic tumor sequencing by Foundation One (Foundation Medicine, Cambridge, MA) was also linked to the UC clinical data using 617 tests from 457 patients, of which 203 patients also had ctDNA-NGS testing performed.

Table 1:

Patient demographics of the Global and the Clinically-annotated cohorts from the University of Chicago and Samsung Medical Center.

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.

Circulating tumor DNA NGS

Plasma circulating tumor DNA sequencing (ctDNA-NGS) results were obtained using the Guardant360 test (G360, Guardant Health; Redwood City; CA).(24) The variant allele fraction of somatic alterations 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. These variant allele fractions can also be artificially inflated due to broader genomic context in a sample, including amplification of the mutated gene or loss of heterozygosity at the locus in question. The assay’s bioinformatics pipeline attempts to filter out alterations of presumed germline origin using a betabinomial model.(25) Absolute plasma copy number was determined utilizing the mode of the normalized number of cell-free DNA fragments covering each gene to estimate the fragment number corresponding to two copies to derive a baseline diploid value. All values of unique fragments for each gene were then normalized by this baseline value. The baseline derivation was informed by molecule counts data from a large set of normal samples from healthy donors’ plasma. Note that the plasma copy number was related to two variables - the copy number in the tissue, and the amount of shedding of tumor DNA into the blood where the tumor DNA - and thus the copy number, was expected to be diluted by abundant leukocyte-derived fragments, the latter having a copy number of 2.0 for each autosomal gene. Centiles of gene copy number reported in the clinical ctDNA-NGS results were denoted by a ‘+’ for absolute plasma copy number greater than 2.1 (<50^th percentile), ‘++’ for copy number greater than 2.4 but less than 4 (<90^th percentile), or ‘+++’ for copy number greater than 4 (≥90^th percentile). In this study, absolute plasma copy number or presence/absence of amplification were used – not these percentiles. Adjusted copy number was calculated from the copy number / (maxVAF+0.01) for each test. Values above the “Global-cohort” median adjusted copy number for a given gene were considered amplified. TMB estimation by ctDNA and tissue NGS were provided by Guardant Health and Foundation Medicine, respectively, according to previously published methodology.(26,27)

Tumor Location

Records from UC and SMC patients who had their initial blood collection for ctDNA prior to stage IV therapy initiation were chart reviewed for disease location at that time and categorized for presence/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 GAs with disease sites was evaluated using Student t-testing, and across multiple categories using ANOVA. Survival analyses were performed as detailed below.

Genetic Landscape

The percent of patients with genomic alterations (GAs identified) in 1627 patients was enumerated amongst the entire cohort using nonsynonymous GAs from each patient (initial test, if serial tests were available). GA distribution was also assessed within the subset of Clinically-annotated samples from UC and SMC, representing ‘Western’ and ‘Eastern’ cohorts. All patients with their initial test available (1627/1630) were included regardless of the presence of detectable GAs. Frequencies were calculated at the gene level per patient, and GA frequencies of ≥5% were reported. This approach calculated a denominator on a gene-by-gene basis accounting for the genes tested/absent in a given assay version (i.e. if only 900/1627 assays included gene X, the denominator would be 900). Synonymous mutations were excluded from analysis, and the number of alterations reported was corrected for removal of these synonymous mutations, unless stated otherwise. Differences between proportion of UC versus SMC alterations was performed using a proportion test. Comparison between TCGA, MSK Impact, and ctDNA-NGS results used TCGA and MSK-Impact data from Cbioportal (accessed on 10/14/18) in combination with this cfDNA cohort.(23,28) Genes reported were filtered to those available in all 3 data sets, and comparisons were made using proportion testing (Figure S3, Table S4).

ctDNA as a biomarker

The Clinically-annotated subset of samples were used for most analyses (Table S1). Cox proportional-hazards models were used for survival analyses and corrected using a likelihood ratio test in the Survival package in R. For gene-by-gene assessment, multiple comparison correction was performed using the Benjamini & Hochberg method. Survival was displayed using Kaplan-Meier curves generated by the SurvMiner R package.

For pre-surgical and minimal residual disease (MRD) analyses (Table S3), patients were classified based upon their diagnosis, peri-operative therapy, and surgical dates. A maxVAF detection cutoff of 0.25% was used based upon reported 100% sensitivity for single nucleotide variants at this level,(24) and patients were stratified into ctDNA “detected” or “not detected”. If ctDNA was sampled on multiple dates in a given interval (Table S1), the first was used.

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

The predictive utility of ctDNA NGS was evaluated in the untreated “Baseline-cohort” by stratifying patients into “amplified” or “non-amplified” using either unadjusted (reported) or adjusted ctDNA-NGS amplification status. Aggregated adjusted ctDNA and/or tissue oncogene amplification was considered positive if either a) amplified adjusted copy number (as above) in the pre-treatment ctDNA assay or b) tissue NGS amplification in any patient sample was identified. Of note, tissue NGS was only available for UC patients.

The majority of immuno-oncologic (IO) treated GEA patients received IO agents (defined as any anti-PD1/PDL1 and/or anti-CTLA4 antibody) in later lines of therapy. Patients were included in this analysis if ctDNA was collected within 60 days prior to IO initiation in stage IV UC patients.

Heterogeneity Between Disease Sites

Intra-patient heterogeneity was determined by identifying untreated stage IV UC patients with tissue-NGS from a primary and metastatic site within 42 days of their initial ctDNA-NGS (n=34). Common genes to tissue-NGS and ctDNA-NGS panels (n=72) were then compiled, and GAs were tabulated by gene and patient according to where they were identified (primary, metastasis, blood). GAs identified by tissue-NGS as a “VUS” or “equivocal”, or by ctDNA-NGS as “uncertain significance” were only included if the alternate assay identified the alteration as a non-VUS. Filtered germline GAs not clinically reported by ctDNA-NGS were also included if the GA was also called by tissue-NGS. Analysis was repeated excluding GAs that the ctDNA-NGS assay would be unable to detect due to technical limitations, as manually annotated (Figure 4B).

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

A) Amongst untreated stage IV/recurrent untreated patients who underwent baseline Triplet-paired sequencing (NGS) of primary tumor and metastatic (met) tumor and plasma ctDNA (n=34), only 26% of characterized alterations were identified by all 3 methods. Percentages by site name indicate % of total GAs identified across the 34 patient cohort. B) Limiting GAs to those detectable by ctDNA (n=149/183 GAs in these patients), concordance between all 3 approaches increased to 32%, and ctDNA was able to detect 74% of GAs compared with 54% and 57% by tissue testing of either the primary and metastatic site, respectively. C) Comparison between tissue and ctDNA RTK amplification in HER2, EGFR, FGFR2, and MET in baseline untreated metastatic patients, increased sensitivity for detection was observed when using both tissue-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 TP53 mutation along with de novo NF1 mutation in ctDNA after progression on HER2-targeted therapy.

Results

Clinicopathologic Characteristics

All patient cohorts utilized in this study are described in Table S1. The ‘Global-cohort’ of ctDNA-NGS included 2140 tests on 1630 patients (Table 1). In the Global-cohort, the median age was 63, and 71% of patients were male. The primary anatomical tumor location was 53% GC versus 47% EGJ. Patient race, tumor grade, clinical HER2 status by conventional tissue testing (29), and tumor stage was unknown for the majority of patients, although disease was indicated as advanced/metastatic at the time of testing per submitted orders. The ‘Clinically-annotated’ cohort (N=369 patients, 698 tests) included 273 patients from the University of Chicago (UC) and 96 from Samsung Medical Center (SMC). Comparing characteristics between the UC and SMC Clinically-annotated cohorts, UC patients were older (median 62 versus 57.5, p=0.003), predominantly proximal EGJ tumors (67% versus 0%, p<2.2 ×10⁻⁶), and included 5% stage II and 16% stage III patients compared with entirely stage IV patients in the SMC cohort. UC patients were also more frequently HER2-positive by clinical criteria (IHC 3+ or IHC2+/FISH+) with 22% versus 8% of patients positive in at least one tissue sample at any time point in their care (p=2.3 ×10⁻⁵). These large Global and Clinically-annotated cohorts were used for subsequent analyses.

Detection of ctDNA

Plasma cell-free DNA (cfDNA) assays depend on shedding of tumor DNA into the circulation (ctDNA), which then mixes with normal plasma cfDNA that is derived from routine non-malignant cell turnover. The maximal tumor somatic variant allelic frequency (maxVAF) in the plasma reflects the largest mutated ctDNA clone detected among all cfDNA present, and can be used as a proxy to estimate overall ctDNA quantity and to establish degree of subclonality of alterations at lower VAFs. However, gene amplifications must also be taken into account.(26) In early analyses, we observed that patients who had already initiated therapy within 14 days before plasma collection (n=12) had a lower mean maxVAF of 5% versus 11.6% in untreated patients (n=144, p=0.07), and more of these patients demonstrated undetectable GAs. Though not statistically significant, from this finding as well as observations from serial response assessments discussed below, we concluded that prognostic and predictive ctDNA-biomarker evaluations would be best derived from samples obtained in untreated stage IV patients (n=144), referred to as the ‘Baseline-cohort’ (Tables S1–S2).

Using the Baseline-cohort, we then assessed maxVAF as a surrogate marker for disease volume/burden, and confirmed a direct correlation between the number of involved disease sites and maxVAF (Figure 1A, Table S2). Fitting with this, patients with intact primary tumors had a higher mean maxVAF of 10.9% versus 6.5% (p=0.09, 95% CI 0.7–9.9) (Figure 1B). Furthermore, patients with liver involvement (n=39/144) had a higher mean maxVAF, 19.2% versus 6.2% (p=0.001, 95% CI 5.3–20.8), as did those with lung involvement (n=19/144), 23.3% versus 7.6% (p=0.01, 95% CI 3.5–28.0) (Figure 1C). Conversely, those with “peritoneal-only” disease (n=35/144), an aggressive subset of GEA, demonstrated the lowest mean maxVAF of 2.5% versus 11.9% in “non-peritoneal-only” (p=5.1e⁻⁶, 95% CI 5.6–13.6), with many “peritoneal-only” patients having undetectable ctDNA (Figure 1D). These findings demonstrated that both disease site and burden strongly influence tumor DNA shedding and consequent ctDNA-NGS sensitivity.

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 in patients from the Baseline cohort (n=144) directly correlated with maxVAF, suggesting that maxVAF reflected overall disease burden (p=4.9e-8, F=9.8). B) Upon stage IV diagnosis, patients with intact primary tumors (n=101/144) had a generally higher mean maxVAF of 10.9% versus 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 burden, specific disease sites were associated with increased tumor shedding and consequently maxVAF – most notably liver and lymph nodes (p=0.01,F=3.1). D) Conversely, patients with solely peritoneal involvement (n=35/144), had a lower mean maxVAF of 2.5% versus 11.9% in patients with additional/other disease sites (p=5.1e-6, 95% CI 5.6=13.6), and many patients with solely peritoneal involvement had no detectable ctDNA.

Clinical Utility of maxVAF

Clinical ctDNA-NGS is generally performed in order to identify actionable GAs, but the amount of ctDNA being shed into circulation could itself potentially serve as a prognostic biomarker both in early and late stage disease. We tested this hypothesis first in the locally-advanced ‘Pre-Neoadjuvant’ cohort of patients at first diagnosis prior to therapy/surgery, and found that those with detectable ctDNA (defined as maxVAF≥0.25%, n=17/29) had shorter disease-free survival (mDFS) of 15.2 months versus unreached, though this did not reach significance (p=0.1, HR=0.2, 95% CI 0.03–2.1) (Figure 2A, Table S3). Importantly, patients with detectable ctDNA (n=7/22) in samples drawn after curative-intent resection (median=50 days, range=20–135 days after surgery) had significantly diminished mDFS of 12.5 months versus unreached (p=0.03, HR=0.1, 95% CI 0.01–1.1) (Figure 2B, Table S3–S4). Resolution or persistence of detectable ctDNA helped predict non-recurrent and recurrent disease, respectively, in representative cases (Figure 2C–D). Sample size was inadequate to formally assess association of ctDNA clearance by neoadjuvant and/or adjuvant therapy. Despite these small numbers, presence and quantity of ctDNA was clearly prognostic in locally advanced disease, and should be validated in future large prospective studies with ctDNA-NGS assays optimized for this purpose.

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

A) Detection of >0.25% maxVAF prior to neoadjuvant therapy was associated with a 15.2 month mDFS (n=17/29), versus not reached mDFS in patients with lower or undetectable maxVAF (p=0.1, HR=0.2, 95% CI 0.03–2.1). B) Patients with maxVAF >0.25% (n=7/22) within 180 post-operative days and before adjuvant therapy, if applicable, had a 12.5 month mDFS versus unreached mDFS in patients with lower or undetectable maxVAF (p=0.03, HR=0.1, 95% CI 0.01–1.1). C) Representative ‘tumor-response map’ of an individual demonstrating detectable pre-therapy ctDNA, with post-operative clearance of ctDNA; in a patient with no evidence of recurrence on follow up examination ~24 months from surgery. D) Representative ‘tumor-response map’ of an individual demonstrating persistent ctDNA post-operatively (maxVAF 2.3%), with recurrence within 6 months of surgery. E) Newly diagnosed metastatic patients (104/144) with below-mean (‘low’) maxVAF (<9.7%) had a mOS of 14.8 versus 9.4 months for above-mean (‘high’) maxVAF (p=0.1, HR 0.7, 95% CI 0.4–1.1). F) Patients with detectable ctDNA upon stage IV diagnosis (maxVAF>0.5%) and upon repeat testing within 150 days demonstrating a decline by ≥50% (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) Representative ‘tumor-response map’ revealing ctDNA decline (“response”) in a patient on first line therapy who remains alive beyond 24 months with stage IV GEA. H) Representative ‘tumor-response map’ demonstrating ctDNA non-responding patient who died of from disease progression ~3 months from diagnosis of stage IV GEA, despite receiving standard therapy. I) Patients who had ctDNA tested within 60 days prior to IO initiation and were 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 with above median maxVAF, from the time of IO initiation to death (p=0.04, HR 0.4, 95% CI 0.1–0.96).

Following this, since we observed that maxVAF correlated with burden/volume of disease, we hypothesized that higher maxVAF would portend a worse prognosis in the advanced setting. Within the Baseline-cohort, those (n=104) having below-mean (‘low’) maxVAF (<9.7%) had a mOS of 14.8 versus 9.4 months for patients (n=40) with above-mean (‘high’) maxVAF (p=0.1, HR 0.7, 95% CI 0.4–1.1) (Figure 2E). We next assessed whether serial ctDNA-NGS analysis could assist with prognostication. In the Baseline-cohort, those on first line therapy who underwent serial ctDNA-NGS (Table S1) within their first 150 days from stage IV diagnosis who had a ≥50% decline in maxVAF (n=23/35) survived a median of 13.7 versus 8.6 months for those that did not (p=0.02, HR 0.3 95% CI 0.1–0.8) (time between serial-collections: median=68 days, range=28–108 days) (Figure 2F); individual representative patient cases are shown (Figure 2G–H). Taken together, the maxVAF dynamics observed suggest that ctDNA-NGS could be used as an early prognostic biomarker, and studies assessing whether altering therapy earlier in ‘non-responders’ may be warranted, akin to PET-directed therapy,(30) in an attempt to improve outcomes.

Finally, we assessed whether maxVAF could assist in prognostication of patients treated with immune checkpoint inhibitors (IO) in the IO cohort (Table S1). Twenty-seven patients in this IO cohort (any line of therapy: nivolumab n=12, pembrolizumab n=13, durvalumab+tremelimumab n=1, tremelimumab n=1) underwent ctDNA-NGS within 60 days prior to IO initiation. Patients with less than the median maxVAF of 3.5% (n=14/27) had a mOS of 8.8 versus 2.5 months for those higher than the median, from IO initiation to death (p=0.04, HR 0.4, 95% CI 0.1–0.96), (Figure 2I). This suggests that among IO treated patients, those with higher disease burden have worse outcomes; IO-specific benefit within the low/high disease burden subsets should be confirmed with prospective controlled analyses to account for the recognized improved prognosis with low burden disease generally.

Genomic Landscape of GEA

After 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 the molecular level. Of the 2140 assays in the Global-cohort, a median of 3 GAs were identified per test (range 0–80 GAs), and at least 1 non-synonymous GA was identified in 1756 (82%) cases (Table 1, Table S1). GAs were more commonly identified with proximal primary EGJ versus distal GC tumors (85% versus 79%, p=0.0009). Fifteen patients (0.9%) had ≥20 GAs identified in an individual test, and 10 (0.6%) had ≥20 GAs identified once excluding synonymous mutations. These cases included 4 known MSI-High patients and 1 POLD1 mutation. The mean number of detected GAs between EGJ and GC primary sites was significantly skewed by the presence of these MSI-High or POLD1 mutated GC patients. Excluding these few special cases, significantly more GAs were found in EGJ than GC cases (mean 3.7 versus 3.3, p=0.005). Within the Locally-advanced cohort, 81% of tests identified ≥1 GA at diagnosis. Overall, these findings demonstrate that at diagnosis most GEA patients, even in earlier stages, have identifiable ctDNA-GAs.

In addition to providing a survey of GA frequencies per sample, one can also infer tumor mutational burden (TMB) from the number of identified GAs, which may have therapeutic implications.(31,32) However, this is challenging using ctDNA due to more limited gene coverage potentially affecting precision, and also ctDNA quantity (directly related to cancer burden and tumor shed at the time of sample collection) influencing the raw number of detected GAs (r²=0.82, p<2.2e-16) (Figure S1A). Therefore, we corrected this by calculating TMB relative to sequencing coverage and VAF (Figure S1B–C), previously described.(26) We then compared paired tissue-NGS and ctDNA-NGS TMB estimates (n=86), which correlated relatively poorly with one-another (r²=0.15, p<0.24), though both were now adequately independent of maxVAF after correction (Figure S1D–F). Significantly, all 6 patients with known MSI-high tumors demonstrated ctDNA-TMB scores ≥90^th percentile of all tested samples, suggesting that for MSI-High tumors, very high ctDNA-TMB is readily detectable. Most importantly, by directly sequencing microsatellite regions, ctDNA-NGS identified 6/6 (100%) patients known to be MSI-High (via IHC and tissue-NGS), at a plasma maxVAF range of 0.09% – 47.7%, with obvious clinical implications.(7)

We next assessed the detailed genomic landscape of the cohorts, including mutations, amplifications, indels, and splice variants. In the Global-cohort, GAs were frequently observed in TP53 (53%), HER2 (17%), EGFR (17%), KRAS (15%), MYC (13%), PIK3CA (13%), and MET (11%) (Figure 3A, Table S5A). GAs were further stratified into non-synonymous mutations (Figure 3B) and amplifications (Figure 3C), where events in TP53, ARID1A, APC, and SMAD4 were typically mutations, while MYC, HER2, KRAS, EGFR, MET, and FGFR2 events were more often amplifications.

Figure 3. Relative frequency of common (>5%) non-synonymous ctDNA alterations between Western and Eastern populations and various ctDNA-NGS and tissue-NGS cohorts.

A) Non-synonymous GA frequency by Global versus UChicago versus Samsung ctDNA-NGS cohorts revealed a higher rate of TP53, KRAS, ARID1A, and CDKN2A alterations (including SNVs, copy number alterations, fusions, splice variants, and indels) in the Western (UChicago) than Eastern (Samsung) cohorts. B) Mutation frequencies (SNV+indel+splice variants) by cohort highlight that mutations in KRAS and ARID1A account for the increased alteration frequency differences between the UC and SMC cohorts. C) Oncogene amplification frequency between the UChicago and Samsung cohorts demonstrating higher amplification frequencies in global and UC cohorts than SMC patients, potentially reflecting more proximal CIN patients in Western cases. D) GA frequency between resected GEA primary tumors stages I-III (TCGA), baseline primary tumor stage IV GEA (MSK Impact), and ctDNA (ctDNA-NGS) revealed similar but not identical incidences of GAs using tissue-NGS compared with ctDNA-NGS, a reflection of different tumor stages, treatment time points, tumor sites and biologic compartments.

We next compared the UC and SMC cohort GA landscapes, reflecting representative Western and Eastern populations (Figure 3A–C, Tables S5B–C). More frequent ARID1A mutations and KRAS, EGFR, and PIK3CA amplifications were observed in the UC cohort. Specifically comparing GC cases (excluding EGJ) amongst UC and SMC cohorts, a higher incidence of mutations in ARID1A and KRAS was still observed in the UC cohort, while mutations in PIK3CA were more common in the SMC cohort.

Finally, we evaluated whether there were significant GA rate differences between early and late stage disease, or between tissue-NGS versus ctDNA-NGS testing. Despite having comparatively few early stage disease samples, within the “Clinically-Annotated” cohort a direct correlation was observed between disease stage and number of alterations (Figure S2), and likely confounded by disease burden, as elucidated above. For further comparison, we compared tissue-NGS GA incidences from the previously reported Cancer Genome Atlas (TCGA) cohorts representing early stage primary tumors (Stages I-III) (N=265),(20,21) the MSK IMPACT cohort (N=305) representing predominantly primary tumor biopsies from newly diagnosed stage IV patients,(23) and with ctDNA-NGS from the present Global-cohort (N=1627), reflecting ‘whole-disease’ burden and predominantly pre-treated settings of advanced disease (Figure 3D, Table S5D). TP53 mutations were significantly more common in MSK and TCGA patient samples (p=8.4×10⁻¹⁵). Amplifications of MYC (p=2×10⁻⁶), CDK6 (p=0.003), and CCNE1 (p=0.0006) were more common in TCGA than in the MSK and Global-cohorts (Figure 1D). HER2 amplification was seen in only 11% of Global-cohort patient samples versus 29% in MSK and 25% in TCGA (p=8.6×10⁻¹⁸). Most differences across the three cohorts likely reflected a combination of sample acquisition timing, intra-patient heterogeneity, and/or tumor shed limitations. Specifically, ‘HER2 conversion’ is now well recognized after treatment with anti-HER2 therapy,(33–35) and potentially accounts for lower incidence of HER2 amplification in the Global-cohort, given that this cohort presumably reflects patients in later lines of therapy after already failing anti-HER2 therapy. This was addressed in more detail in HER2-analyses below. Moreover, acknowledging that some Global-cohort cases would have low tumor DNA shed (eg. Peritoneal-only GC) and others collected at inopportune time points (e.g. shortly after effective therapy), the analysis was repeated by a) including only Global-cohort cases with GAs detected and b) including only patients with a maxVAF ≥ 0.5%, to limit underestimation of ctDNA-NGS GA frequencies relative to tissue-NGS testing (Figure S3A–D, Table S6A–D). Using this approach, TP53 mutation frequency differences lost statistical significance (therefore likely driven by the DNA shedding limitation), though they remained significant for HER2 amplifications (potentially driven by post-treatment HER2 amplification loss in later-line settings). Overall, the GA profiles from these cohorts using tissue-NGS or ctDNA-NGS highlight and contrast the incidences of GAs across tumor stages, treatment time points, tumor sites, and biologic compartments. Notably, there were generally higher incidences of targetable GAs, particularly RTK amplifications (e.g. MET, FGFR2, and EGFR), in the Global-cohort than seen with tissue-NGS.

Gene amplification is clinically relevant in GEA due to the predominance of chromosomally unstable disease (CIN).(20,21) Thus, we specifically assessed the incidence of amplifications across the Global-cohort and found 4136 amplifications in 813 tests from 648 patients (39.8% of Global-cohort cases). Focusing on the most immediately therapeutically relevant RTKs, both EGFR and MET demonstrated predominantly low-level ctDNA amplifications, while HER2 and FGFR2 included a subset of patients with extremely high-level ctDNA amplifications (Figure S4A). Generally, higher gene copy number in tissue samples has correlated with more clinical benefit from respective targeted therapies.(36–38) By ctDNA-NGS, the plasma absolute gene copy number level could reflect either homogenous amplification throughout all disease sites (in the context of the amount of ctDNA shed or maxVAF), or it could represent 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 between tissue-NGS on primary and metastatic biopsies, which was most pronounced in RTK amplifications.(10) As noted, the absolute level of ctDNA gene amplification is dependent on the plasma maxVAF (point mutations/indels). For instance, we noted that a low level ctDNA amplification observed in the context of a very low/non-detectable ctDNA maxVAF usually represented very high tissue gene amplification in order for it to be observed in plasma. Reciprocally, low level gene amplification in the context of very high maxVAF (i.e. high tumor burden), typically did not reflect clinically relevant high level and homogenously distributed gene amplification. Therefore, to address the limitation of tumor shed, plasma gene copy number was normalized by dividing by maxVAF+0.01. This “adjusted” copy number method increased the ability to discern between high- and low-level tissue-NGS gene amplification in the settings of low or high ctDNA shed (Figure S4B). Overall, ctDNA analysis effectively detected cases with gene amplification, and when accounting for maxVAF, identified patients with RTK amplifications most likely to benefit from matched targeted therapy.

HER2 amplification is the only GA routinely assessed in newly diagnosed advanced GEA patients to-date, thus we sought to investigate 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 serially tested). The distribution of amplification level across these ctDNA samples was 33/55/96 patients having ‘<50^th / 50^th-90^th / or >90^th’ percentile amplification (see methods), respectively, (gene plasma copy number range 2.1–84.1, median 4.2 copies). To further assess HER2 amplification incidence and concordance with tissue-based analyses, while considering clinical characteristics like treatment timing, we focused on the Clinically-annotated-cohort. Among the 305 stage IV UC/SMC patients, 18.4% were HER2 amplified by ctDNA-NGS (range 2.1–68.2 copies, median 6 copies), and of these 305 patients, 35/158 (22.2%) with available tissue-NGS were HER2 amplified. When evaluating only clinically HER2-positive stage IV patients (Table S1), only 36/58 (62%) of patients had detectable HER2 amplification by ctDNA-NGS (Table S7). This was recapitulated in the Baseline-cohort where 17/28 (61%) of untreated clinically HER2-positive patients also harbored HER2 ctDNA amplification. The discordance between tissue versus ctDNA-NGS HER2 status could be due to tumor shedding limitations but also intrapatient molecular heterogeneity. Thus, we further investigated the degree that each of these factors contributed towards the observed HER2 discordance between tissue-NGS and ctDNA-NGS.

Extensive Spatial and Temporal Molecular Heterogeneity in GEA

At initial diagnosis, spatial heterogeneity of HER2, along with other GAs, has been recently detailed.(10) Here we sought to further expand on this finding with additional cases, and identified 34 newly diagnosed untreated stage IV GEA patients who had undergone ctDNA-NGS along with tissue-NGS of both baseline primary tumor and a metastatic site (‘triplet-pairs’) (Table S1). When limiting to genes present in both ctDNA and tissue panels (n=72), any GA was identified in 57%, 58%, and 62% of cases within the primary tumor, metastatic tumor, and ctDNA, respectively (Figure 4A). However, of the 183 characterized GAs identified, only 48 (26%) GAs were universally concordant within triplet-pairs. Of these, 21 (44%) were mutations in TP53, which represented 81% of the TP53 GAs and were likely ‘truncal’ in the evolutionary phylogenetic tree. Only 2/7 triplet-pairs were universally concordant for HER2 amplification. Notably, 14%, 11%, and 22% of GAs were uniquely found in the primary, metastasis, and ctDNA, respectively. Importantly, this analysis did not account for technical limitations of ctDNA-NGS due to the recognized inability to detect large-scale deletion or regions not sequenced. Excluding these tissue-based GAs, 149 GAs 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% of GAs uniquely detected in the primary, metastasis, and ctDNA, respectively (Figure 4B). Combining tissue-NGS and ctDNA-NGS increased sensitivity for detection of HER2, EGFR, FGFR2, and MET alterations (Figure 4C). This highlights the complementary benefit of using ctDNA-NGS together with tissue-NGS to overcome the inherent false negative rates of either test, either due to spatial heterogeneity (tissue) or technical shedding limitations (ctDNA).

In addition to baseline spatial molecular heterogeneity, ctDNA-NGS may detect acquired resistance over time (temporal heterogeneity). First, we focused on the incidence of persistent HER2 amplification versus conversion to HER2 non-amplified status after failed first line anti-HER2 therapy using paired pre/post therapy tissue and plasma samples. In this ‘Serial-HER2’ cohort, upon disease progression, only 4/15 (27%) patients demonstrated persistent HER2 amplification by ctDNA-NGS (Figure 4D). Two of these ctDNA amplified patients also demonstrated persistent HER2 IHC 3+ expression. However, another patient retained tissue HER2 amplification, but lacked HER2 ctDNA amplification upon progression – likely a result of low tumor shed in this case. Those with persistent HER2-amplification by either ctDNA-NGS and/or tissue, post-therapy ctDNA-NGS identified additional acquired mutations in KRAS (G12D and T35A), NF1 (N1503S), and PIK3CA (E542K and S1008T), and co-amplifications of BRAF, KRAS, PIK3CA, and FGFR1 as likely mechanisms of resistance (Figure 4E, Tables S7–S8).

Next, we assessed resistance mechanisms to targeted therapy towards other pertinent RTK amplifications, including EGFR, MET and FGFR2. Resistance mechanisms to anti-EGFR therapy were previously reported, and included loss of EGFR amplified clones and/or GAs rendering upregulation of various bypass pathways including RTKs and MAPK/PI3K (10,38) Patients harboring MET and FGFR2 amplified samples treated with matched TKIs or monoclonal antibodies also revealed upregulation of similar bypass pathways in RTKs and MAPK/PI3K pathway GAs, and redirecting therapy based on observed ctDNA-NGS changes yielded promising results. Based on our findings, exemplified in five cases (File S1, Figure S5), it is apparent that baseline spatial and temporal heterogeneity are inter-related, since pre-existing spatially distributed resistant clones were repeatedly selected under targeted therapeutic pressure, yet in some instances these were not identified at baseline, and only became apparent over time. ctDNA-NGS identified resistance mechanisms to targeted therapy in evaluated patients upon progression and may direct optimal next-line therapy.

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

In the context of its role in measuring tumor burden/maxVAF and accounting for inter-patient and intra-patient molecular heterogeneity, ctDNA-NGS may identify prognostic and/or predictive GAs. To assess this, the Baseline-cohort (n=144) was again analyzed for key genes (PIK3CA, BRAF, KRAS, HER2, FGFR2, MET, and EGFR) previously reported to have prognostic and/or predictive significance in GEA or other cancers.

Presence of PIK3CA mutation corresponded with shorter survival of 3.8 versus 13.6 months (p=0.006, HR 3.4, 95% CI 1.6–7.2) (Figure 5A). Similarly, BRAF GAs corresponded with a mOS of 5.6 months versus 13.7 months in BRAF wildtype patients (p=0.02, HR 3.0, 95% CI 1.4–6.7) (Figure 5B). However, none of these nor others evaluated remained statistically significant after multiple comparison correction and multivariate analyses (Supplemental Table S9). Within the 144 patient cohort, only 2/11 FGFR2 amplified patients and 2/11 MET amplified patients received RTK inhibitors, therefore survival analysis could not be robustly performed. These data suggest that mutations in PIK3CA and GAs in BRAF portend generally poor prognoses, but should be confirmed in larger clinically-annotated homogenously-treated studies.

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

A) Presence of a PIK3CA mutation corresponded with shorter survival of 3.8 versus 13.6 months (p=0.006, HR 3.4, 95% CI 1.6–7.2). B) BRAF alterations corresponded with a mOS 5.6 months versus 13.7 months in BRAF wildtype 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 available, 24 were either HER2 clinically positive or HER2 amplified by tissue-NGS or ctDNA-NGS at any time during their disease – with 54% universal concordance. D) Amongst all 23 patients considered clinically HER2 positive who underwent ctDNA-NGS at the time of stage IV diagnosis and then received HER2-directed therapy, mOS was 12.7 versus 8.7 months in ctDNA HER2-amplified patients (n=15/23) versus those without ctDNA HER2 amplification (p=0.4, HR 0.6, 95% CI 0.2–1.7). E) Amongst all 23 patients considered clinically HER2 positive, using an adjusted copy number, i.e. copy number/ (maxVAF+0.01), patients with a greater than median HER2 copy 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, 95% CI 0.1–1.1). F) evaluating patients with proven tissue amplification and/or greater than median ctDNA amplification (n=16/23) in complementary fashion, the mOS benefit increased to 26.3 versus 7.4 months (p=0.004, HR 0.2, 95% CI 0.05–0.6). G) EGFR amplification was not prognostic, as the median overall survival of EGFR amplified, non-targeted patients (n=12/130) was similar to that of non-EGFR amplified patients – 14.4 months 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-NGS in the Baseline cohort who received EGFR inhibitors (n=9/27) in any line had a mOS of 21.1 versus 14.4 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 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).

HER2

Given that <50% of HER2-positive patients demonstrate response to first line anti-HER2 therapy, we asked whether incorporating ctDNA-NGS could improve the predictive utility over standard single-lesion tissue-based HER2-testing. Across all “Baseline” stage IV patients having both ctDNA- and tissue-NGS at any time point, 21/86 (24.4%) harbored HER2 amplification by at least one approach, but only 13/86 (15.1%) were amplified by both (6/8 of discordant patients were identified by tissue-NGS only) (Figure 5C), and an additional 3/86 patients were considered clinically HER2 positive, but lacked amplification by tissue- or ctDNA-NGS. Among HER2-targeted patients in the Baseline-cohort, 23 patients had received first line HER2-directed therapy – either lapatinib (n=7), lapatinib + trastuzumab (n=1), trastuzumab (n=14), or trastuzumab and pertuzumab (n=1). An additional patient was excluded from survival analysis as they had received HER2-directed peri-operative therapy before recurrence. Amongst the HER2-targeted patients, 19/23 were clinically HER2-positive by routine tissue analyses, and only 15 were ctDNA-NGS HER2 amplified. Relying solely on reported ctDNA-NGS HER2 amplification, in this small series there was no difference in survival compared to those with or without ctDNA-NGS HER2 amplification - 12.7 versus 8.7 months (p=0.4, HR 0.6, 95% CI 0.2–1.7) (Figure 5D). However, this failed to consider the relationship between copy number and maxVAF as noted earlier. After adjustment (copy number / maxVAF+0.01), patients with plasma copy numbers greater than the median (n=10/23), demonstrated improved mOS of 15.9 versus 9.4 months (p=0.07, HR 0.4, 95% CI 0.1–1.1) (Figure 5E). Further, we identified patients with low tumor DNA shed and therefore HER2 amplification not detected by ctDNA-NGS, as well as molecular heterogeneity missed by single site tumor profiling by comparing those with HER2 amplification present in tissue (n=11/23) or adjusted ctDNA-NGS (n=10/23 – only 5 both tissue and ctDNA amplified). With this approach, patients with HER2 amplification had a 26.3 versus 7.4 month mOS in this ‘Complementary-amplified’ group (n=16/23, p=0.004, HR 0.2, 95% CI 0.05–0.6) (Figure 5F). ctDNA identified actionable GAs in cases that would have been missed with tissue-testing alone. These findings focused on HER2 further delineate baseline and temporal molecular heterogeneity of GEA and demonstrate the importance of complementary tissue/plasma-NGS testing to best identify biomarkers of therapeutic relevance.

EGFR

We recently assessed the prognostic and predictive nature of EGFR amplification in GEA.(38) We sought to further evaluate this biomarker, and focus on the utility of ctDNA-NGS. There was no difference in survival between the ctDNA-NGS amplified (n=12/130) and non-amplified untreated stage IV patients who did not receive EGFR-directed therapy (14.4 versus 13.3 months, p=0.6, HR 1.3, 95% CI 0.5–3.0) (Figure 5G), suggesting that EGFR amplification does not have specific prognostic implication in this cohort. In the Baseline cohort, 22/144 patients had ctDNA-NGS EGFR amplification, and an additional 5 patients by tissue-NGS. Of these, 14 received EGFR-directed therapy – ABT806 (n=12) or cetuximab (n=2). Amongst EGFR amplified patients, those who received EGFR inhibitors (n=9/27) in any line of therapy showed a mOS of 21.1 versus 14.4 months versus patients who did not (p=0.01, HR 0.2, 0.06–0.8) (Figure 5H). This survival benefit was accentuated to 21.1 versus 6.2 months when comparing patients with either an adjusted copy number greater than median or tissue-based amplification (n=9/14, p=0.001, HR 0.05, 95% CI 0.006–0.4), despite limitations by small sample size (Figure 5I). These data support that EGFR amplification, again optimally captured in complementary fashion by ctDNA and/or tissue NGS, is not prognostic but potentially predictive of benefit to anti-EGFR therapy, consistent with previous reports.(38,39)

Discussion

Herein, we present the largest comprehensive analysis evaluating the utility of ctDNA-NGS from a large commercial database with 2140 individual tests on 1630 GEA patients, and a substantial subset (698 tests from 369 patients) having clinical annotation for detailed clinicopathologic and outcomes analyses.

Using these cohorts, we first established an understanding of the detection limit of ctDNA-NGS as it relates to disease burden, disease site, and treatment timing. Though the median maxVAF was quite low, as seen in other studies, there was a long tail of patients with a high maxVAF. Biologically, this may be due to patients with very high maxVAF upon stage IV diagnosis, but technically, can reflect difficulty in filtering germline alterations in patients with high tumor shed and/or genomic instability. However, finding several genes at high level suggests biologic origin, rather than technical.(26,40) For patients with low disease burden (few organ sites involved), peritoneal-only disease, and samples obtained shortly after therapy, each demonstrated lower ctDNA yield and in many cases non-detectable ctDNA. The biologic reason for lower plasma ctDNA in peritoneal-only disease is uncertain, but may be attributed to less shed into the peripheral vascular system, which was recently noted in patients with peritoneal carcinomatosis in other cancer types,(41) and/or different GAs which are not assessed by the ctDNA-NGS panel used. However, patients with peritoneal-only disease often have diffuse or mixed-type histology, and mutations in genes such as CDH1 and RHOA associated with this subtype (the TCGA ‘genomically stable’ molecular subtype) are indeed part of the 73-gene ctDNA-NGS panel.(20) It is noteworthy, however, that peritoneal-only disease often has insufficient DNA even for tissue-NGS, likely due to the low viable tumor content within dense desmoplastic tumors from both primary and metastatic biopsies. Therefore, future studies addressing these apparent molecular profiling limitations from both tissue and plasma of this difficult-to-treat subset of GEA patients are needed, as well as peritoneal fluid or lavage as potential sample types.

Regardless, from these limit-of-detection observations, we next determined that residual ctDNA detection after curative-intent resection reliably heralded eventual recurrence and worse prognosis in early stage disease. This is consistent with reports from other tumor types,(42) and suggests that post-operative ctDNA-detection in GEA could be an important stratification factor within prospective adjuvant therapy studies. Moreover, via prospective studies, this biomarker may help to select those patients that should and should not receive further adjuvant therapy. However, we must be mindful of false positives in older patients resulting from clonal hematopoiesis. Three patients (all elderly) with detectable mutations after surgery, each at similar low maxVAFs prior to treatment/surgery, have not recurred to date, and none of these mutations were identified by tissue sequencing, which suggests that they may not be tumor-derived at all. Future strategies need to be mindful of both germline and hematopoietic confounding. Similarly, in advanced disease, we observed that baseline ctDNA quantity and early serial changes correlated with clinical characteristics and outcomes. It is possible that ctDNA-NGS may also prove useful here to assess whether patients benefit from changing therapy earlier in these ‘ctDNA non-responders’, prior to initial restaging CT scans. This hypothesis would be particularly interesting to investigate prospectively – especially when 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 would need to be validated. Our findings are corroborated by others, who also recently noted that changes in maxVAF for GAs reflected response to treatment, with an early spike in the first 1–3 days of effective chemo- or targeted therapy followed by order of magnitude drops in maxVAF, reflecting molecular response,(43) but differ from that found when trending total cfDNA.(44) Finally, as it pertained to levels of ctDNA in the plasma, we developed a framework to optimally identify and understand gene amplifications by adjusting for maxVAF in order to take into account tumor burden, spatial molecular heterogeneity, and DNA shed.

Focusing on the landscape of GAs in GEA as determined by ctDNA-NGS, we demonstrated that at first diagnosis, the vast majority of GEA patients, even in earlier stages, had identifiable ctDNA-GAs, especially after excluding those with peritoneal-only disease and recent therapy. Very importantly, we showed that all known MSI-High cases in our cohort having ctDNA-NGS performed were accurately identified – including a patient with a maxVAF as low as 0.09%. This is the highest sensitivity for plasma-detected MSI-H reported by any method to-date and will be a useful tool to identify this relatively infrequent but highly targetable GA where traditionally tissue-based MSI testing is less routinely performed or insufficient tissue is available.(7,32) When comparing the ctDNA-GA landscape between the ‘Western’ UC and ‘Eastern’ SMC cohorts, we noted similar GA incidences, but there were also some interesting differences, even after considering only the GC UC subset with the GC SMC cohort. These differences included a higher incidence of KRAS and ARID1A GAs in the UC subset, which was consistent with prior literature,(45) while the SMC cohort was enriched for PIK3CA mutations. The latter is remarkable since it has been reported that PIK3CA mutation is associated with EBV-positive GC,(20,46) which may also be more common in Asian countries,(47) although the literature is conflicting.(45,48)

Comparisons of the ctDNA-GA landscape to cohorts published during manuscript preparation(49,50) and previously reported large-scale tissue-based analyses of GEA patients both revealed similar but not identical incidences of various GAs. When dissecting this further, we noted that incidence differences from these 3 large cohorts were mostly attributable to differences in disease stage, sample acquisition time points, along with differing disease sites and tissue compartments assessed. There were generally higher incidences of targetable GAs, particularly RTK amplifications, in the Global-cohort. In this regard, ctDNA accounted for increasing intra-patient molecular heterogeneity, including at baseline and secondary to treatment pressure and evolving resistance. This served to survey the metastatic burden of patients best, in order to determine optimal targeted therapeutic regimens.

Along these lines, molecular heterogeneity both between and within patients has become a formidable hurdle to successful implementation of targeted therapies in GEA.(10) Herein we evaluated the largest ‘triplet-pairs’ cohort reported to date for GEA, and again we uncovered significant discordance, including in routine known and potentially targetable RTKs such as HER2, EGFR, MET, and FGFR2. Together, these 4 RTKs account for approximately 30–40% of GEA patients, which make up a large subset of CIN tumors, and therefore a very significant consideration for ensuing targeted therapeutic decisions. Of note, the high frequency of EGFR amplifications in our cohort likely reflects a Western predominance of EGJ CIN tumors at our center. We also demonstrated numerous temporal resistance mechanisms, particularly after specific targeted therapy towards RTK amplifications, which included loss of the RTK amplification itself and/or GAs rendering upregulation of various known pathways to circumvent this inhibition strategy. Serial molecular profiling led to changes in treatment decision for these cases at disease progression points. Although EGFR amplification is not recommended to be routinely assessed by current guidelines, our work builds upon our previous and others’ work suggesting benefit for these patients.(38,39,51) It should be also noted that many patients in our annotated cohorts could not be assessed as ‘triplet pairs’ due to insufficient tissue in either the primary tumor and/or the metastatic site at baseline and also at disease progression. This points towards the practicality of ctDNA-NGS to best assess baseline and temporal heterogeneity due to convenience, expediency, and less-invasive nature of a ‘liquid-biopsy’ in the clinic. Our observations of vast interpatient and intrapatient molecular heterogeneity, spatially at baseline and temporally after therapy, are very much connected. A personalized treatment strategy that incorporates molecular profiling from both the tissue and the plasma at baseline and subsequently over time will likely be necessary in order to successfully improve outcomes of this disease with targeted therapeutics. In fact, we observed that incorporating tissue-NGS and ctDNA-NGS profiling in aggregate identified patients most likely to benefit from anti-HER2 and other targeted therapies. These findings mirror those 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 redundant testing, with reflex tissue testing only if ctDNA-NGS is unrevealing.(53) This may also be applicable for GEA and warrants attention. Ultimately, incorporating ctDNA-NGS may be a strategy to overcome recognized molecular heterogeneity, both at baseline and over time, and prospective innovative trials designs are ongoing to test this hypothesis.(54,55)

This study has some limitations. The Global-cohort, albeit large, was relatively limited in clinical utility without the granular clinicopathologic characteristics to contextualize the GA distribution landscape. To address this, we combined two clinically-annotated cohorts which provided robust understanding of GA events with clinicopathologic perspective, and subsequent analyses were restricted to samples drawn prior to any therapy to avoid underestimating ctDNA-NGS GAs and to perform tissue-plasma concordance studies more precisely. Another inherent limitation when comparing the 73-gene cfDNA-NGS versus 315-gene tissue-NGS panel is the expected discordance resulting from technical and biological differences between these different tests of distinct biological compartments. Technical limitations leading to discordance between tissue and plasma obviously included non-overlapping genes, but also some regions of overlapping genes not sequenced on the ctDNA-NGS panel. Another technical limitation is the recognized inability of ctDNA-NGS to discern large-scale deletions amongst the vast sea of wildtype cfDNA. To account for these limitations and to focus on only those GAs that overlapped, we compared only those regions covered by both panels and excluded large deletions identified by tissue-NGS. This admittedly underestimates the level of ‘real-life’ discordance that the clinical oncologist will observe. However, by doing so, we were able to focus on and identify specific biologic reasons for discordance, including disease burden and tumor site, which was directly related to tumor shed, as well as intrapatient spatial molecular heterogeneity. Finally, despite the relatively large size of the Clinically-annotated cohort, inherent to low-frequency GAs, was our inability to definitively evaluate the prognostic importance of individual GAs nor the predictive impact of targeting these infrequent events.

Conclusions

In summary, clinical ctDNA-NGS testing holds promise for GEA – both in the detection of minimal residual disease in early stage disease and as a serial tumor marker. ctDNA-NGS used in conjunction with tissue-NGS may be an approach to best identify actionable GAs and resistance mechanisms in order to overcome intrapatient heterogeneity. However, prospective validation of these findings in future studies is necessary for integration into clinical care.

Statement of Translational Relevance:

This is the largest and most comprehensive evaluation of ctDNA-NGS for GEA, and demonstrates a comparable but not identical incidence rate of common GAs as seen in recent large-scale tissue-based analyses. Using clinically-linked samples from nearly 400 patients, this study initially evaluates determinants of ctDNA detection including disease sites, tumor burden, and collection timing relative to treatment that can aide in timing clinical collection. It also highlights the high degree of intra-patient molecular heterogeneity in GEA through space and time, which is optimally characterized by ctDNA-NGS in conjunction with tissue-NGS, and explains why so many targeted therapy trials fail in GEA. Finally, the predictive nature of specific ctDNA GAs including MSI-High and ERBB2 (HER2) and EGFR amplifications are described – including strategies with which we can better identify targeted therapy populations in a heterogeneous cancer by using ctDNA-NGS.