The Role of the TP53 Pathway in Predicting Response to Neoadjuvant Therapy in Esophageal Adenocarcinoma

Abstract

Purpose:

In patients with locally advanced esophageal adenocarcinoma (EAC), response to neoadjuvant therapy strongly predicts survival, but robust molecular predictors of response have been lacking. We therefore sought to discover meaningful predictors of response in these patients.

Experimental Design:

We retrospectively identified all patients with adenocarcinoma of the lower esophagus or gastroesophageal junction who (1) were treated with multimodality therapy with curative intent at our institution from 2014 through 2020 and (2) underwent prospective sequencing by MSK-IMPACT. Clinicopathologic and genomic data were analyzed to identify potential genomic features, somatic alterations, and oncogenic pathways associated with treatment response.

Results:

In total, 237 patients were included. MDM2 amplification was independently associated with poor response to neoadjuvant therapy [OR 0.10 (95% CI 0.01, 0.55); p=0.032], when accounting for significant clinicopathologic variables, including clinical stage, tumor grade, and chemotherapy regimen. Moreover, TP53 pathway alterations, grouped according to inferred severity of TP53 dysfunction, were significantly associated with response to neoadjuvant therapy (p=0.004, q=0.07). Patients with MDM2 amplifications or truncating bi-allelic TP53 mutations had similar outcomes in terms of poor responses to neoadjuvant therapy and, consequently, shorter progression-free survival, compared with patients with TP53 pathway wild-type tumors. Thus, worsening TP53 dysfunction was directly correlated with worse outcomes.

Conclusions:

MDM2 amplification and TP53 status are associated with response to therapy in patients with EAC. Given the dearth of actionable targets in EAC, MDM2 inhibition, in combination with cytotoxic chemotherapy, may represent an important therapeutic strategy to overcome treatment resistance and improve outcomes in these patients.

Introduction

Esophageal adenocarcinoma (EAC) is an aggressive malignancy with poor prognosis owing to its overall resistance to existing therapies (1). According to the National Comprehensive Care Network guidelines, standard treatment for locally advanced EAC involving the lower esophagus and gastroesophageal junction comprises neoadjuvant chemoradiotherapy followed by radical esophagectomy (2,3). The best foundational evidence for this trimodality approach comes from the CROSS (ChemoRadiotherapy for Oesophageal Cancer followed by Surgery Study) trial, conducted with 5 cycles of neoadjuvant carboplatin and paclitaxel concurrent with 40.4 Gy of radiotherapy (4). However, the more recent Cancer and Leukemia Group B (CALGB) 80803 and FLOT3 trials suggest greater efficacy using a combination that includes 5-fluorouracil, leucovorin, and oxaliplatin, especially when administered using a PET-directed approach during induction therapy (5,6).

Several prospective and retrospective studies have established that response to neoadjuvant therapy, particularly with respect to presence or absence of residual nodal disease, is an independent predictor of overall and disease-free survival among patients with locally advanced EAC treated with trimodality therapy (7,8). Our previous work demonstrates that overall survival and disease-free survival are optimized in patients with ≥90% treatment response in the primary tumor bed in the absence of residual pathologic nodal disease (9). A major pathologic response, as defined by this threshold, was seen in approximately 40% of patients. However, the majority of patients were classified as nonresponders with significant residual primary tumor and/or nodal disease after neoadjuvant therapy and 5-year survival likely similar to patients treated with surgery alone (10). At present, robust clinical or molecular predictors of response to neoadjuvant therapy are lacking, and, as a result, clinicians are unable to predict nonresponders upfront. Meanwhile, these patients are in urgent need of alternative therapeutic regimens to enhance the success of surgical resection and long-term outcomes.

Therefore, the current study aims to identify biomarkers of poor pathologic responses to neoadjuvant chemoradiotherapy so that genomic information from their tumors can be leveraged to develop novel therapeutic strategies. We believe that this approach represents, by far, the most significant opportunity to improve outcomes for patients with locally advanced EAC. By merging high-quality clinicopathologic and genomic data from a clinically validated broad-panel next-generation sequencing assay, we investigated genomic features, somatic alterations, and oncogenic pathways associated with treatment response among 237 patients with locally advanced lower esophageal and junctional adenocarcinoma treated with curative intent.

Materials and Methods

Patients/Samples

We used the Memorial Sloan Kettering (MSK) cBioPortal to identify all patients with adenocarcinoma of the lower esophagus or gastroesophageal junction treated with multimodality therapy with curative intent at our institution from 2014 through 2020 who also underwent prospective sequencing by MSK-IMPACT (MSK–Integrated Mutation Profiling of Actionable Cancer Targets) (11,12). Clinical annotations were then obtained via cross-referencing our manually curated, prospectively maintained institutional database. All patients provided written informed consent for targeted sequencing under protocol NCT01775072, approved by the MSK Institutional Review Board. Tumor tissue for sequencing was obtained from either primary or metastatic sites at the time of biopsy or surgery. Tumor purity was assessed by histopathologic review of specimens by an expert pathologist from the MSK Molecular Diagnostics Service. For patients who had more than one sample sequenced, the sample with the higher tumor purity was selected for inclusion.

Next-Generation Sequencing and Computational Analysis

The MSK-IMPACT next-generation sequencing assay, which is currently FDA-authorized, was performed as part of routine clinical assessment in a CLIA-compliant laboratory, as previously described (13). Briefly, genomic DNA was extracted from tumor tissue and patient-matched blood samples to generate barcoded libraries. After capture of exons and selected introns of the genes included in the sequencing panel, pooled libraries were sequenced on the Illumina HiSeq 2500 system. Samples were sequenced using MSK-IMPACT v1 (341 genes), MSK-IMPACT v2 (410 genes), or MSK-IMPACT v3 (468 genes).

Sequencing files were processed using stringent quality-control criteria and analyzed using an optimized informatics pipeline to identify somatic mutations, copy number alterations, and select structural rearrangements. Full details regarding the performance and validation of the MSK-IMPACT assay have been reported (14). Utilizing the OncoKB database, we excluded variants of unknown significance (15,16). Copy number alterations were identified by comparing targeted regions of the tumor sample to the matched diploid normal sample. The log ratio coverage values for segments were calculated and compared between the tumor and normal samples. A fold change threshold of <−2 and false discovery rate corrected p value < 0.05 was used to determine whole gene loss, or deep deletion/homozygous deletion, while a fold change threshold of >2 was used to determine whole gene amplification. Alterations (oncogenic mutations, copy number alterations, structural rearrangements, or fusions) were considered for analysis only if present in 8% of patients in the cohort after eliminating MSI-high cases. Number of oncogenic drivers was calculated for each patient as the total number of driver alterations present.

MSI status was assessed using the MSI-sensor algorithm, which calculates the percentage of microsatellite loci covered by the MSK-IMPACT assay that are unstable in the tumor as compared to the patient’s matched normal sample (17). Samples with a score ≥10 were classified as MSI-high. To calculate tumor mutation burden, we determined the total number of somatic non-silent protein-coding mutations in the sequenced genes and normalized it to the exonic coverage of the respective MSK-IMPACT panel in megabases. Tumor mutation burden calculations using this panel are strongly associated with those assessed by whole-exome sequencing (18). The fraction of genome altered was defined as the fraction of log₂ copy number variation (gain or loss) >0.2, divided by the size of the genome whose copy number was profiled. Fraction of genome altered was corrected for tumor purity, ploidy, and clonal heterogeneity using the FACETS algorithm (19). Presence or absence of whole-genome doubling was estimated using the probability model as previously described (20). Mutual exclusivity or co-occurrence of genomic alterations was analyzed using a Fisher’s exact test (21). Large-scale transition (LST) scores were inferred based on copy-number data (22).

Bi-allelic inactivation of TP53 was determined to have occurred if no copies of the wild-type allele were present after accounting for the total copy number of TP53, the number of mutant copies present, and whether loss of heterozygosity occurred as assessed by the FACETS algorithm. The number of copies of the mutant allele was determined as described (23).

Pathway Analysis

We evaluated 11 canonical cancer-related signaling pathways as defined by the TCGA PanCancer Atlas Project (24). The pathways analyzed were p53, Cell Cycle, Hippo, Myc, Notch, NRF2, PI3K, RTK (receptor tyrosine kinase)/RAS/MAPK, TGFβ, Wnt, and DDR. A tumor was considered altered in a specific pathway if at least one gene belonging to that pathway was altered, and pathways were considered for analysis if at least 8% of patients harbored an alteration in that pathway. Number of pathways altered (NPA) was calculated for each patient as the total number of altered pathways out of the 11 pathways specified above.

Histopathological Assessment

All surgical specimens were reviewed by a board-certified expert gastrointestinal pathologist at our center. In post-treatment specimens, if grossly viable tumor (mass, ulcer, or polyp) was present, 5 or more representative sections of the tumor were evaluated; if grossly viable tumor was absent, the scar-like lesions at the primary tumor site were submitted in their entirety for histopathologic evaluation. Pathologic response in the primary tumor bed was quantified as the percentage treatment effect (TE) in terms of residual viable carcinoma in relation to areas of fibrosis or fibro-inflammation within the gross lesion, as well as by use of College of American Pathologists tumor regression grade (CAP-TRG) guidelines.(25) A CAP-TRG score of 0 corresponds to complete response or 100% TE, a score of 1 corresponds to near-complete response or 90%–99% TE, a score of 2 corresponds to partial response or 50%–89% TE, and a score of 3 corresponds to poor or no response or <50% TE. pCR was defined as 100% TR (i.e., CAP-TRG score 0) and pN0. Both clinical and pathologic staging were performed in accordance with the 8^th Edition of the American Joint Committee on Cancer Staging Manual. Patients were considered to be pathologically downstaged if their pathologic stage grouping was lower than their clinical stage grouping (i.e., from clinical stage 3 to pathologic stage 2).

Statistical Analysis

Clinicopathologic characteristics were summarized using frequency and percentage for categorical variables and median and interquartile range (IQR) for continuous variables. Genomic and/or pathway alterations were counted as either present or absent. Association of genomic driver alterations, pathways, features, and clinicopathologic factors with treatment response was evaluated using the Wilcoxon rank-sum test for continuous variables or a Fisher’s exact test for categorical variables. Stage-adjusted ORs were calculated using a multivariable logistic regression model for clinicopathologic variables in conjunction with clinical stage. P-values <0.05 were considered statistically significant; however, false discovery rates (q-values) using the Benjamini-Hochberg procedure were reported for analysis of genomic factors to account for multiple hypothesis testing. Multivariable logistic regression models were then utilized to evaluate the association of treatment response with genomic variables that were found to be significant on univariable analysis at a threshold of q<0.1, controlling for significant clinicopathologic (p<0.05) variables. For analyses of long-term outcomes stratified by a genomic alteration of interest, PFS was estimated using the Kaplan-Meier method and compared using the log-rank test. PFS was measured from the date of completion of neoadjuvant therapy to the date of disease progression, death, or last follow-up. Similarly, the cumulative incidence of progression (1 minus Kaplan-Meier estimate) measured all instances of disease progression, recurrence, or death after completion of neoadjuvant therapy. All statistical analyses were performed using R (version 3.6.3; R Foundation for Statistical Computing, Vienna, Austria).

Data Availability

All study data are freely available on cBioPortal: https://www.cbioportal.org/study/summary?id=egc_msk_tp53_ccr_2022.

Results

Patient Characteristics

In total, 237 patients treated with curative intent were included in our study. Patient characteristics are summarized in Supplemental Table 1. The majority of patients (82%) were male; median age was 61 years (IQR 55, 68). Clinical staging was based on positron-emission tomography (PET)/computed tomography (CT) and either endoscopic ultrasound or endoscopic mucosal resection as indicated, and most patients (186; 78%) were diagnosed with clinical stage III disease. The primary tumor was sequenced in 192 patients (81%), while pre-treatment tissue adequate for sequencing was available in 110 patients (46%).

Neoadjuvant chemotherapy regimens included carboplatin and paclitaxel (Carbo-Taxol) in 115 patients (49%), and either 5-fluorouracil, leucovorin, and oxaliplatin (FOLFOX) or 5-fluoruracil, leucovorin, oxaliplatin, docetaxel (FLOT) in 77 patients (32%). Of note, 151 patients (64%) were treated with induction chemotherapy using a PET-directed strategy, as previously described (26), and as a result 38 patients (16%) received both Carbo-Taxol and FOLFOX due to a post-induction switch in chemotherapy regimen. A total of 213 patients (90%) received radiotherapy concurrent with neoadjuvant chemotherapy. In addition to chemotherapy, 10 patients received neoadjuvant trastuzumab based on clinical Her2 status in the context of either a clinical trial (4 patients) or limited metastatic disease (6 patients) followed by durable response on imaging to warrant surgical resection. Similarly, 33 patients received neoadjuvant immune checkpoint blockade in either the context of a clinical trial (30 patients) or limited metastatic disease (3 patients). In total, 10 patients with clinical stage IVB or limited metastatic disease were included in this study; these patients were treated with curative intent based on multidisciplinary tumor board discussion.

Following neoadjuvant therapy, 210 patients (88%) underwent surgical resection via either esophagectomy and/or gastrectomy. Of the remaining patients, 14 were found at surgical exploration to have unresectable disease, and 13 had progression of disease on re-staging PET that precluded surgery altogether. The rate of pCR in this cohort was only 12%. The median treatment effect in the primary tumor bed was 80% (IQR 30,98), and more than half of patients (126; 53%) had evidence of residual nodal disease. Among the patients who underwent surgical resection, 12 (6%) had positive margins.

Defining Responders vs. Non-Responders to Neoadjuvant Therapy

The primary outcome of interest in this study was response to neoadjuvant therapy. Due to previously observed differences in both clinical and genomic behavior (27), we eliminated MSI-high patients (n=14; 6%) from analyses of treatment response and focused only on microsatellite stable patients with CIN characteristics. Patients were classified as either neoadjuvant therapy responders (nT-Rs) or non-responders (nT-NRs) based on our definition of major pathologic response (9). Specifically, nT-Rs were patients who had met both of the following criteria: 1) absence of residual nodal disease (pN0), 2) treatment effect ≥ 90% (TRG score 0 or 1). On the other hand, nT-NRs were patients with residual nodal disease, those with treatment effect <90% (TRG score >1), or those with progression of disease either on re-staging PET or surgical exploration. Figure 1A provides a flow chart of how patients were classified as either nT-R (n=64) or nT-NR (n=159) after elimination of MSI-high patients from the cohort.

Figure 1.

Classification and Survival of Responders (nT-Rs) vs. Non-responders (nT-NRs). (A) Flow chart demonstrating criteria used to classify nT-Rs vs. nT-NRs. (B) Comparison of progression-free survival between nT-Rs vs. nT-NRs. (C) Relationship between nT-R group and 2 other metrics of treatment response: PET response and pathologic downstaging.

Patients classified as nT-R had significantly longer progression-free survival than those classified as nT-NR (Figure 1B). Furthermore, our definition of nT-R heavily overlapped with other clinically relevant metrics of treatment response, such as pathologic downstaging and metabolic response on PET (defined as ≥35% decrease in standard uptake value [SUV] measured in the primary tumor between pre- and post-treatment PET scans) (Figure 1C).

Clinicopathologic Predictors of Treatment Response

First, we evaluated relevant clinicopathologic factors for their association with pathologic treatment response by comparing the nT-R and nT-NR groups (Table 1). Because higher clinical stage was associated with lack of treatment response, we report clinical stage-adjusted odds ratios (ORs) for the remainder of variables. In addition to clinical stage, both tumor grade and neoadjuvant chemotherapy regimen were significantly associated with treatment response. In particular, high grade or poorly differentiated tumors were associated with worse treatment response (OR 0.52, 95% CI 0.28,0.94, p=0.03). In comparison to Carbo-Taxol, FOLFOX was associated with a significantly better treatment response (OR 2.38, 95% CI 1.20, 4.76, p=0.013). Notably, metabolic response on PET was not significantly associated with pathologic treatment response in our cohort.

Table 1.

Comparison of relevant clinicopathologic characteristics between nT-R and nT-NR groups

			Unadjusted	Adjusted for Clinical Stage
Characteristic	nT-R (N=64)	nT-NR (N=159)	p value	OR	95% CI	p value
Age at Diagnosis	60 (55, 67)	62 (54, 68)	0.677	0.99	0.97, 1.02	0.7
Sex			0.845	0.98	0.46, 2.24	>0.9
Female	11 (17%)	26 (16%)
Male	53 (83%)	133 (84%)
Clinical Stage			0.005
II	4 (6.3%)	8 (5.0%)
III	57 (89%)	117 (74%)
IV	3 (4.7%)	34 (21%)
Pre-Treatment PET mSUV	12 (9, 18)	12 (7, 16)	0.072	1.03	1.00, 1.07	0.061
Neoadjuvant Chemotherapy			0.043
Carbo-Taxol Only	27 (42%)	81 (51%)		ref	-	-
FOLFOX Only	26 (41%)	35 (22%)		2.38	1.20, 4.76	0.013
FLOT	3 (4.7%)	8 (5.0%)		1.54	0.30, 6.39	0.6
Carbo-Taxol + FOLFOX	8 (13%)	28 (18%)		0.93	0.35, 2.25	0.9
Other	0 (0%)	7 (4.4%)
Neoadjuvant Radiation Therapy	60 (94%)	140 (88%)	0.235
Tumor Grade			0.052
Well/Moderate	34 (53%)	61 (38%)		ref	-	-
Poor	30 (47%)	98 (62%)		0.52	0.28, 0.94	0.032
PET Responder			0.014
Yes	52 (81%)	106 (67%)		2.28	1.01, 5.70	0.059
No	8 (13%)	33 (21%)		ref	-	-
Unknown or N/A	4 (6%)	20 (12%)

nT-R, neoadjuvant therapy responders; nT-NR, neoadjuvant therapy non-responders; mSUV, maximum standard uptake value; N/A, not applicable; ICI, immune checkpoint inhibitor

Genomic Predictors of Treatment Response

Targeted sequence analysis of our study group identified 8 genes harboring recurrent oncogenic driver alterations (cross-validated using OncoKB and TCGA databases) at ≥8% prevalence: TP53 (79%), CDKN2A (22%), SMAD4 (10%), ARID1A (14%), ERBB2 (16%), KRAS (19%), MDM2 (9%), and CCNE1 (8%) (Figure 2A). In particular, ERBB2, KRAS, MDM2, and CCNE1 were predominantly affected by oncogenic amplifications. Similarly, 8 oncogenic pathways were identified with a ≥8% prevalence of alteration in our cohort: TP53, Cell Cycle, RTK-RAS, WNT, TGF-Beta, PI3K, Notch, and DNA damage response (DDR). We then assessed whether any genomic alterations, pathways, or features were significantly associated with response to neoadjuvant therapy by comparing the nT-R and nT-NR groups (Supplemental Table 2). Of note, neither tumor purity, location of sample sequenced, nor whether any treatment was received prior to sampling varied significantly by treatment response group.

Figure 2.

Clinicopathologic and Genomic Predictors of Treatment Response. (A) Oncoprint of the most prevalent oncogenic driver alterations and their distribution between nT-R and nT-NR groups. *, P<0.05 by Fisher’s exact test. (B) Breakdown of neoadjuvant treatment regimens by nT-R and nT-NR groups. (C) Multivariable logistic regression model demonstrates that clinical stage, tumor grade, neoadjuvant chemotherapy regimen, and MDM2 amplification are each independent predictors of treatment response.

Among individual genomic alterations, only MDM2 amplification was significantly associated with the nT-NR group (p=0.01, q=0.09). We then constructed a multivariable logistic regression model including both MDM2 and significant clinicopathologic variables: clinical stage, tumor grade, and neoadjuvant chemotherapy regimen. All variables except for clinical stage retained statistical significance, and thus MDM2 amplification was found to be independently associated with poor treatment response in our cohort (OR 0.10, 95% CI 0.01, 0.55, p=0.032).

Among oncogenic pathways, alterations in the TP53 pathway were enriched in the nT-NR group, but this association did not meet the threshold for statistical significance after multiple hypothesis testing (p=0.05, q=0.27) (Supplemental Table 2). Interestingly, no other genomic alterations, pathways, or features such as tumor mutation burden, whole-genome doubling, fraction of genome altered, or large-scale transition scores were associated with response to neoadjuvant therapy in our cohort.

The Role of the TP53 Pathway in Predicting Treatment Response

Given the results above with respect to the TP53 pathway, we next investigated whether the specific type of TP53 mutation was associated with treatment response, or if MDM2 amplification was the lone driver of TP53 pathway–level effects. We hypothesized that bi-allelic truncating mutations, which lead to abolished TP53 function, may be associated with worse treatment response and outcome in comparison to other types of TP53 mutations, such as mono-allelic truncating mutations or missense or splice mutations, which often lead to incomplete TP53 dysfunction. As shown in Figure 3A, TP53 and MDM2 were, by far, the most frequently altered members of the TP53 pathway altered in this dataset, while ATM and CHEK2 mutations were rare. Therefore, we instead subdivided the TP53 pathway into 4 categories reflecting the severity of alteration in terms of TP53 dysfunction: 1) MDM2 amplification, 2) TP53 truncating bi-allelic mutation (TP53 TruncBi), 3) other TP53 pathway mutation (TP53 Other), and 4) TP53 pathway wild-type (WT). On univariable analysis, this modified TP53 pathway was significantly associated with treatment response (p=0.004, q=0.07). Furthermore, on multivariable analysis, MDM2 amplification was associated with worse treatment response in comparison to WT (OR 0.06, 95% CI 0.00, 0.39, p=0.01), while TP53 TruncBi mutation trended similarly (OR 0.31, 95% CI 0.08, 1.03, p=0.06) (Figure 3B). Tumor grade and neoadjuvant chemotherapy regimen were also significantly associated with treatment response in this model, while clinical stage was not, as TP53 status was also correlated with nodal status.

Figure 3.

The Role of the TP53 Pathway in Predicting Treatment Response. (A) Oncoprint of TP53 pathway alterations and their distribution between nT-R and nT-NR groups. TP53 truncating bi-allelic status is annotated. (B) Multivariable logistic regression model of treatment response, including clinicopathologic variables and the modified TP53 pathway. (C) Mutual exclusivity between TP53 pathway members. (D) Comparison of pathologic treatment effect between tumors with MDM2 amplification, TP53 truncating bi-allelic (TruncBi) mutation, TP53 Other mutation, or TP53 Pathway WT status. (E) Comparison of progression-free survival between tumors with MDM2 amplification, TP53 TruncBi mutation, TP53 Other mutation, or TP53 pathway WT status.

Mutual exclusivity between TP53 mutation and MDM2 amplification has been reported (28), and in our dataset TP53 TruncBi mutations and MDM2 amplification were almost entirely mutually exclusive (q<0.05) (Figure 3B). Moreover, tumors with MDM2 amplification and those with TP53 TruncBi mutations displayed remarkably similar clinical behavior and outcomes with respect to treatment responses (Table 2), cumulative incidence of progression, and progression-free survival (PFS). Compared with the WT group, both MDM2 amplification and TP53 TruncBi groups had a significantly lower pathologic treatment effect seen in the primary tumor (Figure 3D), and consequently a significantly higher cumulative incidence of progression and shorter PFS (Figure 3E). In particular, the 24-month cumulative incidence of progression was 61% (95% CI 28, 79%) among patients with MDM2 amplification and 67% (95% CI 44, 80%) among patients with a TP53 TruncBi mutation. By comparison, TP53 pathway WT patients had a 24-month cumulative incidence of progression of 42% (95% CI 19, 58%). Overall, the median length of follow-up among survivors was 20.9 months (range 1.6, 98.4) and the median PFS was 15.9 months (95% CI 13.8, 20.9).

Table 2.

Clinicopathologic characteristics of patients according to modified TP53 pathway

Characteristic	MDM2 Amp (N=21)	TP53 TruncBi (N=37)	TP53 Other (N=136)	TP53 Pathway WT (N=29)
Age at Diagnosis	61 (54, 65)	61 (52, 68)	62 (55, 68)	61 (55, 65)
Sex
Female	7 (33%)	6 (16%)	19 (14%)	5 (17%)
Male	14 (67%)	31 (84%)	117 (86%)	24 (83%)
Clinical Stage
II	0 (0%)	2 (5.4%)	7 (5.1%)	3 (10%)
III	15 (71%)	25 (68%)	112 (82%)	22 (76%)
IV	6 (29%)	10 (27%)	17 (13%)	4 (14%)
Neoadjuvant Chemotherapy
Carbo-Taxol Only	6 (29%)	15 (41%)	74 (54%)	13 (45%)
FOLFOX Only	4 (19%)	9 (24%)	37 (27%)	11 (38%)
FLOT	3 (14%)	1 (2.7%)	5 (3.7%)	2 (6.9%)
Carbo-Taxol + FOLFOX	8 (38%)	8 (22%)	17 (13%)	3 (10%)
Other	0 (0%)	4 (11%)	3 (2.2%)	0 (0%)
Neoadjuvant Radiation Therapy	18 (86%)	33 (89%)	122 (90%)	27 (93%)
Surgery
Esophagectomy/Gastrectomy	17 (81%)	27 (73%)	124 (91%)	28 (97%)
Exploration	3 (14%)	6 (16%)	4 (2.9%)	1 (3.4%)
No Surgery	1 (4.8%)	4 (11%)	8 (5.9%)	0 (0%)
Pathologic Stage
I	4 (19%)	9 (24%)	47 (35%)	15 (52%)
II	2 (9.5%)	2 (5.4%)	21 (15%)	2 (6.9%)
III	10 (48%)	14 (38%)	48 (35%)	8 (28%)
IV	5 (24%)	12 (32%)	20 (15%)	4 (14%)
Tumor Grade
Well/Moderate	9 (43%)	14 (38%)	60 (44%)	12 (41%)
Poor	12 (57%)	23 (62%)	76 (56%)	17 (59%)
Treatment Effect (%)	50 (5, 70)	60 (0, 98)	80 (40, 95)	95 (65, 100)
Residual Nodal Disease
Yes	16 (76%)	25 (68%)	68 (50%)	12 (41%)
No	5 (24%)	12 (32%)	68 (50%)	17 (59%)
Responder Category
nT-R	1 (4.8%)	7 (19%)	43 (32%)	13 (45%)
nT-NR	20 (95%)	30 (81%)	93 (68%)	16 (55%)
Pathologic Downstaging
Yes	8 (38%)	14 (38%)	75 (55%)	18 (62%)
No	13 (62%)	23 (62%)	61 (45%)	11 (38%)
PET Responder
Yes	12 (57%)	25 (68%)	104 (76%)	17 (59%)
No	8 (38%)	9 (24%)	17 (13%)	7 (24%)
Unknown or N/A	1 (4.8%)	3 (8.1%)	15 (11%)	5 (17%)

nT-R, neoadjuvant therapy responders; nT-NR, neoadjuvant therapy non-responders

Among the 21 patients with MDM2-amplified tumors, 1 patient showed disease progression on imaging and was no longer a candidate for surgery, 3 patients underwent surgical exploration and were found to be unresectable, and 1 patient was resected with a positive proximal margin despite the fact that the cervical esophagus was divided as high as possible to still allow for reconstruction with a gastric pull up (Table 2). Eight patients underwent a switch in chemotherapy post-induction due to lack of response on post-induction PET CT, and only 3 of these ultimately went on to have an improvement in PET response, though all but one underwent surgical resection with negative margins. Most patients with MDM2-amplified tumors (16/21; 76%) were noted to have either clinical or pathologic nodal disease. Five of the patients with MDM2 amplification had a PFS greater than 20 months, and 4 of these 5 had concurrent TP53 mutations. While 8 patients were pathologically downstaged, only a single patient with an MDM2-amplified tumor was classified in the nT-R group. This patient had a 95% treatment effect (TRG score 1) in the absence of residual nodal disease and remains free of recurrence at 9 months of follow-up. Notably, this patient presented with a clinical stage III junctional tumor and was treated with neoadjuvant FLOT chemotherapy alone followed by complete resection.

Among the 37 patients with TP53 TruncBi-mutant tumors, 4 had disease progression on post-treatment imaging, and 6 underwent surgical exploration and were deemed unresectable. Furthermore, while only 15 TruncBi patients (41%) were clinical node positive, 25 patients (68%) had pathologic evidence of residual nodal disease.

Discussion

Prospectively applying broad-panel clinical next-generation sequencing to patients with EAC treated with curative intent, we discovered that MDM2 amplification is independently associated with response to neoadjuvant therapy, irrespective of important clinicopathologic factors including chemotherapy regimen, clinical stage, or tumor grade. Moreover, patients with MDM2 amplifications or with truncating mutations that lead to bi-allelic TP53 loss appear to face similar outcomes: poor response to neoadjuvant chemotherapy, and consequently, a higher cumulative incidence of progression and shorter PFS in comparison to patients with TP53 pathway WT tumors.

EAC is widely known to be resistant to systemic therapy. This trait is attributed primarily to chromosomal instability with consequent increased intra-tumoral heterogeneity and rapid tumor evolution (29,30). Furthermore, loss of TP53 tumor suppressor function is known to be a primary driver event in EAC tumorigenesis. To our knowledge, this is the first study to identify individual driver alterations within the TP53 pathway that are strongly associated with poor treatment response and outcome in EAC. More specifically, a greater degree of TP53 loss and dysfunction in EAC appears to correspond to worse treatment response and shorter PFS. A total of 57 patients, or 26%, in our cohort harbored either an MDM2 amplification or TP53 TruncBi mutation, accounting for a substantial proportion of non-responders. Conversely, TP53 pathway WT patients consistently had the best treatment responses and long-term outcomes in our cohort.

Interestingly, we did not detect any relationship between genomic features known to be correlated with higher levels of chromosomal instability, such as whole-genome doubling or fraction of copy-number genome altered, and treatment response (Supplemental Figure 1A). However, rates of whole-genome doubling were higher in tumors with MDM2 amplification and significantly higher in tumors with TP53 TruncBi mutation in comparison to those with TP53 pathway WT status (Supplemental Figure 1B). Intra-tumoral heterogeneity has been a proposed driver of treatment resistance, but unfortunately this characteristic is difficult to measure using panel-based sequencing. Because of this limitation of our methodology, we cannot draw any definitive conclusions regarding the contribution of intra-tumoral heterogeneity to treatment resistance. Therefore, the mechanism by which either MDM2 amplification or TP53 TruncBi mutation confers treatment resistance remains unclear. Their strong mutual exclusivity, but closely related clinical behavior, suggests functional overlap in that both alterations lead to critical, if not absolute, TP53 dysfunction.

While this is the first study to elucidate the role of MDM2 amplification in predicting treatment response in EAC, MDM2 amplification has been previously shown to be associated with poor response to both chemotherapy and radiation in other cancer types. Kondo et al. were among the first to recognize this and suggested that MDM2 overexpression inhibits cisplatin-induced apoptosis in human glioblastoma cell lines via TP53 downregulation (31). Similar effects have been seen with other chemotherapeutic agents as well, such as doxorubicin and 5-fluorouracil (32). MDM2 is known to tightly regulate TP53 via a negative feedback loop that involves TP53 directly binding MDM2, followed by ubiquitination and degradation of TP53. These effects of MDM2 are likely to be more potent on wild-type TP53 than on mutant TP53 (33). This idea may, in part, explain why the few patients with concurrent MDM2 amplification and TP53 mutation had marginally better long-term outcomes. Though the mechanism of MDM2-related treatment resistance in EAC is likely to be dependent on this TP53 negative feedback loop, additional studies are needed to test this hypothesis.

Reduced or abolished TP53 function has been linked with resistance to a variety of chemotherapeutic and targeted agents (alkylating agents, anthracyclines, antimetabolites, antiestrogens, and tyrosine kinase inhibitors). Therefore, discovery and testing of small molecules that can restore wild-type TP53 conformation and function represent important areas of future investigation in efforts to overcome treatment resistance (34). While a few agents, i.e. PRIMA-1 and related thiosemicarbazones, have demonstrated early promise in preclinical studies, their efficacy in tumors with truncating TP53 mutations may be diminished, given that their mechanism of restoring TP53 function is by correcting misfolded TP53 proteins. Therefore, we anticipate that TP53 truncating bi-allelic mutations may be challenging to target.

On the other hand, several MDM2 inhibitors, predominantly derivatives of Nutlins, are currently under either preclinical or clinical investigation. In the setting of wild-type or functional TP53, MDM2 antagonists lead to reactivation of TP53, which suggests that if they are used in combination with other chemotherapeutic agents, treatment response may be greatly enhanced. For example, in preclinical studies, idasanutlin administered in combination with chemotherapy exhibited potent anticancer activity against xenograft models of fibrosarcoma and acute myeloid leukemia expressing functional TP53 (35). Furthermore, early clinical trials in patients with acute myeloid leukemia demonstrated reasonable safety and tolerability of idasanutlin combined with cytotoxic chemotherapy (36). Therefore, MDM2 inhibitors in combination with chemotherapy may represent an important therapeutic breakthrough in the approximately 9% of EAC patients harboring MDM2 amplifications, most of whom currently exhibit poor responses to systemic therapy.

Consistent with initial results of the CALGB 80803 trial (5), we also observed better responses to neoadjuvant FOLFOX in comparison to Carbo-Taxol, which was used in the CROSS trial. We also did not discern any alterations differentiating responders vs. non-responders to either regimen, though interestingly, a higher proportion of patients with MDM2 amplified and TP53 TruncBi-mutated tumors were treated with Carbo-Taxol in comparison to FOLFOX. Moreover, as previously shown (37), metabolic response on PET did not correlate well with response to neoadjuvant therapy. The pCR rate in this study was low at 12%, which is likely related to bias in patient selection, e.g., from the availability or purity of tissue for sequencing. Patients in our cohort tended to have more advanced clinical stages of disease and poor response to treatment, and these patients also tend to have increased availability of adequate tumor tissue for next-generation sequencing. Conversely, inclusion of patients with pCR depended on adequate pre-treatment endoscopic biopsy tissue; if this was unavailable, their tumors could not be sequenced.

In conclusion, MDM2 amplification was found to be independently associated with poor response to neoadjuvant therapy in patients with EAC treated with curative intent. Given the dearth of actionable targets in EAC, MDM2 inhibition, in combination with cytotoxic chemotherapy, may represent an important therapeutic strategy to overcome treatment resistance and improve outcomes in these patients. Furthermore, TP53 status was strongly associated with response to neoadjuvant therapy in EAC, with worsening TP53 dysfunction corresponding to worse outcomes.

Statement of Translational Relevance.

Esophageal adenocarcinoma (EAC) is an aggressive malignancy with a rapidly increasing incidence in the U.S. and a 5-year survival of <20%. Neoadjuvant chemoradiotherapy followed by surgery is the standard of care for patients with locally advanced EAC, but the majority of patients exhibit poor responses to trimodality therapy and derive negligible survival benefit from this approach. A priori identification of this patient population and development of novel therapeutic strategies are paramount to improving outcomes. Integrative analysis of genomic and clinicopathologic data from 237 patients with EAC treated with curative intent demonstrated that a greater degree of TP53 pathway dysfunction predicts poor responses to neoadjuvant therapy and, consequently, worse progression-free survival. Specifically, MDM2 amplification, which leads to p53 inactivation, was independently associated with chemoradiotherapy resistance. Thus, MDM2 inhibitors, possibly in combination with chemoradiotherapy, may offer an avenue to improve therapeutic responses in this patient population.