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IN THE SPOTLIGHT

Genomic Analyses and Precision Oncology in Gastroesophageal Cancer : forwards or Backwards? Raghav Sundar 1 and Patrick Tan 2 , 3 , 4 , 5

1 Department of Haematology-Oncology, National University Health System, Singapore . 2 Cancer and Stem Cell Biology Program, Duke-NUS Medical School, Singapore. 3 Biomedical Research Council, Agency for Science, Tech-nology and Research, Singapore. 4 Cancer Science Institute of Singapore, National University of Singapore, Singapore. 5 SingHealth/Duke-NUS Insti-tute of Precision Medicine, National Heart Centre Singapore, Singapore. Corresponding Author: Patrick Tan , Duke-NUS Medical School, 8 College Road, Singapore 169857, Singapore. Phone: 65-6516-1783; Fax: 65-6221-2402; E-mail: gmstanp@duke-nus.edu.sg doi: 10.1158/2159-8290.CD-17-1295 ©2018 American Association for Cancer Research.

summary: Gastroesophageal adenocarcinoma (GEA) comprises a myriad of distinct subtypes with signifi cant interpatient, intrapatient, and intratumor heterogeneity. Strategies for tackling molecular heterogeneity will be essential for the success of GEA precision oncology—in this regard, blood-based “liquid biopsies” may provide broader views of the real-time genomic landscape of this disease, identifying actionable biomarkers and monitoring therapy resistance. Cancer Discov; 8(1); 14–6. ©2018 AACR .

See related article by Pectasides et al., p. 37 (1). See related article by Janjigian et al., p. 49 (2).

Cancers of the stomach and esophagus, collectively termed gastroesophageal adenocarcinomas (GEA), are leading causes of global cancer mortality. GEA is recognized as a highly heterogenous disease among individuals, and strategies for subclassifying GEAs into more homogenous subtypes may improve patient outcomes by allowing subtype-specifi c treat-ment regimens, a strategy termed “precision oncology.” Moti-vated by this concept, previous studies have attempted to classify GEAs according to their molecular profi les, including The Cancer Genome Atlas and the Asian Cancer Research Group. However, none of these genomic classifi cations to date have been incorporated into standard treatment algorithms, and biomarkers currently used to guide clinical decisions in metastatic GEA include only HER2 for trastuzumab and microsatellite instability (MSI) or PD-L1 status for pembroli-zumab. Other agents, such as ramucirumab, which targets the VEGF pathway, and nivolumab (anti–PD-1; in Asia), do not require biomarker selection. Currently, biomarker test-ing in GEA has typically involved testing a single biopsy or a few biopsies from the primary tumor (PT). However, recent studies have suggested that signifi cant heterogeneity may exist between PTs and their metastatic lesions, and even within the same PT, which may affect clinical responses. As such, it is criti-cal to explore two questions: First, is genomic characterization of a single PT biopsy suffi cient to capture the majority of targ-etable alterations in a patient with metastatic GEA, and, if not, are there alternative approaches? Second, will treating patients with GEA according to their pretreatment biomarker profi les

result in tangible improvement in clinical outcome, or should other factors be considered, such as co-occurring alterations?

In this issue of Cancer Discovery , two studies are reported addressing these questions ( 1, 2 ). In the fi rst study, Pectasides and colleagues applied various different genomic platforms to distinct patient cohorts to quantify levels of mutational concordance between primary GEA specimens and patient-matched metastatic lesions, including levels of intratumor heterogeneity in the same PT ( 1 ). For some cohorts, circulat-ing cell-free DNA (cfDNA) analysis was also performed to capture the mutational profi les of PTs and metastases. In the second study, Janjigian and colleagues conducted a single-center cohort study of patients with metastatic GEA, whose pretreatment biopsy specimens were analyzed by targeted sequencing (∼340 genes) and correlated with biomarker-defi ned treatments and clinical outcomes ( 2 ). The two stud-ies are highly complementary, and in some cases conceptually similar analyses were performed, providing a platform for contrast and comparison. Taken collectively, these two stud-ies reaffi rm the notion that GEAs are highly heterogeneous and that tackling this heterogeneity will be essential for the success of GEA precision oncology.

To defi ne the extent of GEA heterogeneity in metastatic patients, Pectasides and colleagues ( 1 ) analyzed paired PTs and metastatic lesions (including lymph-node metastasis) from multiple patient cohorts. The results generally suggest a strikingly high level of mutational discordance between PTs and metastatic sites in GEA, from approximately 40% for single-nucleotide variants and insertion–deletion elements (indel), to approximately 60% for amplifi ed genes such as HER2, CDK4/6, EGFR , and KRAS. Importantly, discordant profi les were observed even in patients who had not received prior systemic therapy, suggesting that high GEA heterogene-ity is likely a natural feature of this malignancy, rather than a consequence of clonal selection arising from therapeutic pressure. Interestingly, a conceptually similar assessment of GEA heterogeneity performed by Janjigian and colleagues reported a “high” level of genomic concordance between pre– and post–trastuzumab-treated biopsies, although numerical values were not explicitly provided ( 2 ). This may be due to

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January 2018 CANCER DISCOVERY | 15

cfDNA

Metastases

Primary tumor

Original

LN

A

B

C

DTherapy

differences in technical and study design, such as the use of different sequencing panels (340 cancer genes in Janjigian and colleagues vs. whole-exome sequencing in Pectasides and colleagues), or differences in the methods used to calculate concordance levels or when comparing synchronous versus metachronous lesions.

Recent studies have shown that circulating tumor cfDNA can harbor genetic abnormalities reflective of both the original PT and also metastatic lesions, released in the blood stream through cancer cell necrosis or apoptosis (3). Com-paring cfDNAs, PTs, and metastatic lesions from the same patients, Pectasides and colleagues observed concordances but also discordances in clinically relevant genomic aberra-tions (e.g., HER2, FGFR2). Demonstrating that cfDNA can identify new targetable alterations absent in solid-tissue biopsies, patients with GEA were identified exhibiting CDK6 amplifications in cfDNAs not observed in PTs. Reciprocally, however, in other cases, alterations such as FGFR2 and HER2 were observed to be present in PTs but not matched cfDNAs, suggesting that not every alteration in a patient with GEA can be detected by cfDNA. These results suggest that cfDNAs likely provide a “summarized” snapshot of the majority tumor profile, rather than an exhaustive comprehensive catalog. The observation of better concordance between cfDNA and metastasis profiles may have therapeutic implications, as in a preliminary clinical trial cohort (PANGEA) in Pectasides and colleagues, and knowledge of metastatic profiles led to a reassignment in treatment selection in 32% of patients, with anecdotal clinical responses.

Besides tumor heterogeneity, another important question is the extent to which outcomes of patients with GEA are indeed improved by treating patients according to their underly-ing tumor molecular profiles. In this regard, Janjigian and colleagues found that approximately 50% of patients with GEA exhibit a potentially targetable alteration as defined

by the OncoKB database, and they explored GEA responses to three broad therapeutic classes: chemotherapy, targeted therapies (trastuzumab), and immune checkpoint inhibition. In the Janjigian and colleagues cohort, patients with MSI-high (MSI-H) GEAs progressed significantly more rapidly on cytotoxic therapy compared with non–MSI-H GEAs, a finding that has also been independently observed (4). However, the team was unable to identify specific genetic abnormalities predictive of GEA responses to first-line platinum/fluoropy-rimidine combination therapy. Specifically, in other tumor types, homologous recombination defects (HRD) have been proposed to predict responses to platinum therapy, and in an analysis of over 10,000 cancer genomes across 36 tumor types, about 10% of GEAs were identified with possible HRD based on the presence of a mutational signature (Signature 3; ref. 5). However, Janjigian and colleagues were unable to correlate biomarkers of HRD, including mutations in DNA repair genes, with treatment response, putting into question the contribution of HRD in GEA therapeutic response. It should be noted, however, that Janjigian and colleagues used large-scale transitions (LST) to detect HRD status, which as a single modality may not fully capture HRD levels compared with combination scores integrating LST with other measure-ments (telomeric allelic imbalance and loss of heterozygosity), or using methods such as HRDetect (6, 7).

Janjigian and colleagues also found strong correlations in HER2 copy-number gains inferred by next-generation sequencing (NGS) or clinical methods such as IHC or FISH. Reaffirming the role of HER2 copy-number levels as a criti-cal predictor of therapeutic sensitivity, the few discordant cases (8%) labeled HER2-negative by NGS had significantly poorer outcomes on trastuzumab. Importantly, Janjigian and colleagues demonstrated that besides HER2 levels, co-occur-ring genomic alterations, particularly in the MAPK or PI3K pathway, also modulated primary or acquired resistance to

Figure 1.  Spatial and temporal heterogeneity in GEA. A, Intratumoral heterogeneity of primary tumor and lymph-nodal (LN) metastatic spread (red, white, and black cells). B, Tumoral hetero-geneity in distant metastatic sites, including subclonal evolution (green cells). C, cfDNA may capture a broader scope of tumoral heteroge-neity of both primary and metastatic disease. D, Selection pressure imposed by therapies may lead to depletion of sensitive clones (jagged black cells depict cell death), but resistant clones may appear (blue cells). These could also poten-tially be detected using cfDNA or serial biopsies.

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anti-HER2 therapy. Such findings thus highlight the impor-tance of measuring multiple and not just single biomark-ers in one individual. Clonal selection with loss of HER2 amplification or secondary alterations in the HER2 gene were also identified as mechanisms of acquired resistance; drawing knowledge from Pectasides and colleagues, it remains possible that studies of cfDNA in these patients may identify markers of resistance to HER2 therapy earlier in the course of treat-ment, allowing for timely switches in treatment modalities.

Although nearly a third of the patients (28%) treated with immune checkpoint inhibition in the study by Janjigian and colleagues responded, only 13% (n = 5) had a prolonged response. This is disappointing considering that patients in this cohort were preferentially directed to this therapy based on NGS results or MSI status, and is similar to large phase III studies in heavily treated gastric cancer, without biomarker selection (8). Notably, 2 patients (one EBV-positive) with low tumor mutational burden and non–MSI-H had long and dura-ble responses to immunotherapy, whereas not all MSI-H tumors responded. Tumor mutational burden appeared to be a good predictor of response to immunotherapy, with patients with tumors exhibiting more than approximately 10 mutations/Mb having superior outcomes. In one anti–PD-1 responder, a new B2M mutation was detected in the posttreatment sample. B2M alterations have been described as resistance mechanisms to immunotherapy in melanoma (9), suggesting that mecha-nisms associated with immunotherapy resistance may be con-served across different tumor types.

In summary, both teams should be congratulated for remarkable studies that deepen our understanding of GEA. Pectasides and colleagues’ study has revealed the humbling truth of roadblocks faced by GEA precision oncology in the face of vast genomic tumoral heterogeneity (Fig. 1), whereas the Janjigian and colleagues study gives us hope that with a deeper understanding of the disease and the addition of immunotherapy and other novel therapeutic agents, steady gains will be eked out in the battle against this dreadful disease. Finally, the importance of genomic drivers notwith-standing, future work should also undoubtedly focus on understanding tumor heterogeneity at other levels, such as those related to epigenetics, the tumor microenvironment, and the tumor microbiome, that collectively interact to drive gastroesophageal malignancy (10).

Disclosure of Potential Conflicts of InterestNo potential conflicts of interest were disclosed.

AcknowledgmentsR. Sundar is supported by a National Medical Research Council

(NMRC) MOH Healthcare Research Scholarship, Singapore. P. Tan is supported by Duke-NUS Medical School and the Biomedical Research Council, Agency for Science, Technology and Research.

Published online January 8, 2018.

RefeRenCes 1. Pectasides E, Stachler MD, Derks S, Liu Y, Maron S, Islam M, et al. Genomic

heterogeneity as a barrier to precision medicine in gastroesophageal adenocarcinoma. Cancer Discov 2018;8:37–48.

2. Janjigian YY, Sanchez-Vega F, Jonsson P, Chatila WK, Hechtman JF, Ku GY, et al. Genetic predictors of response to systemic therapy in esophagogastric cancer. Cancer Discov 2018;8:49–58.

3. Wan JCM, Massie C, Garcia-Corbacho J, Mouliere F, Brenton JD, Caldas C, et al. Liquid biopsies come of age: towards implementation of circulating tumour DNA. Nat Rev Cancer 2017;17:223–38.

4. Smyth EC, Wotherspoon A, Peckitt C, Gonzalez D, Hulkki-Wilson S, Eltahir Z, et al. Mismatch repair deficiency, microsatellite instability, and survival: an exploratory analysis of the medical research coun-cil adjuvant gastric infusional chemotherapy (MAGIC) trial. JAMA Oncol 2017;3:1197–203.

5. Alexandrov LB, Nik-Zainal S, Siu HC, Leung SY, Stratton MR. A mutational signature in gastric cancer suggests therapeutic strate-gies. Nat Commun 2015;6:8683.

6. Telli ML, Timms KM, Reid J, Hennessy B, Mills GB, Jensen KC, et al. Homologous recombination deficiency (HRD) score predicts response to platinum-containing neoadjuvant chemotherapy in patients with triple-negative breast cancer. Clin Cancer Res 2016;22:3764–73.

7. Davies H, Glodzik D, Morganella S, Yates LR, Staaf J, Zou X, et al. HRDetect is a predictor of BRCA1 and BRCA2 deficiency based on mutational signatures. Nat Med 2017;23:517–25.

8. Kang YK, Boku N, Satoh T, Ryu MH, Chao Y, Kato K, et al. Nivolumab in patients with advanced gastric or gastro-oesophageal junction can-cer refractory to, or intolerant of, at least two previous chemotherapy regimens (ONO-4538-12, ATTRACTION-2): a randomised, double-blind, placebo-controlled, phase 3 trial. Lancet 2017;390:2461–71.

9. Zaretsky JM, Garcia-Diaz A, Shin DS, Escuin-Ordinas H, Hugo W, Hu-Lieskovan S, et al. Mutations Associated with Acquired Resist-ance to PD-1 Blockade in Melanoma. N Engl J Med 2016;375:819–29.

10. Padmanabhan N, Ushijima T, Tan P. How to stomach an epigenetic insult: the gastric cancer epigenome. Nat Rev Gastroenterol Hepatol 2017;14:467–78.

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