tumor genomic profiling guides patients with metastatic ......sep 13, 2019 · the prognosis of...

Tumor Genomic Profiling Guides Patients with Metastatic Gastric Cancer to Targeted Treatment: The VIKTORY Umbrella Trial Jeeyun Lee1, Seung Tae Kim1, Kyung Kim1, Hyuk Lee2, Iwanka Kozarewa3, Peter G.S. Mortimer4, Justin I. Odegaard5, Elizabeth A. Harrington3, Juyoung Lee1, Taehyang Lee1, Sung Yong Oh6, Jung-Hun Kang7, Jung Hoon Kim8, Youjin Kim9, Jun Ho Ji9, Young Saing Kim10, Kyoung Eun Lee11, Jinchul Kim1, Tae Sung Sohn12, Ji Yeong An12, Min-Gew Choi12, Jun Ho Lee12, Jae Moon Bae12, Sung Kim12, Jae J. Kim2, Yang Won Min2, Byung-Hoon Min2, Nayoung K.D. Kim13,4, Sally Luke3, Young Hwa Kim4, Jung Yong Hong1, Se Hoon Park1, Joon Oh Park1, Young Suk Park1, Ho Yeong Lim1, AmirAli Talasaz5, Simon J. Hollingsworth14, Kyoung-Mee Kim15, and Won Ki Kang1

RESEARCH ARTICLE

Cancer Research. on November 5, 2020. © 2019 American Association forcancerdiscovery.aacrjournals.org Downloaded from

Published OnlineFirst July 17, 2019; DOI: 10.1158/2159-8290.CD-19-0442

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OCTOBER 2019 CANCER DISCOVERY | OF2

ABSTRACT The VIKTORY (targeted agent eValuation In gastric cancer basket KORea) trial was designed to classify patients with metastatic gastric cancer based on clinical

sequencing and focused on eight different biomarker groups (RAS aberration, TP53 mutation, PIK3CA mutation/amplification, MET amplification, MET overexpression, all negative, TSC2 deficient, or RIC-TOR amplification) to assign patients to one of the 10 associated clinical trials in second-line (2L) treatment. Capivasertib (AKT inhibitor), savolitinib (MET inhibitor), selumetinib (MEK inhibitor), ada-vosertib (WEE1 inhibitor), and vistusertib (TORC inhibitor) were tested with or without chemotherapy. Seven hundred seventy-two patients with gastric cancer were enrolled, and sequencing was success-fully achieved in 715 patients (92.6%). When molecular screening was linked to seamless immediate access to parallel matched trials, 14.7% of patients received biomarker-assigned drug treatment. The biomarker-assigned treatment cohort had encouraging response rates and survival when compared with conventional 2L chemotherapy. Circulating tumor (ctDNA) analysis demonstrated good correlation between high MET copy number by ctDNA and response to savolitinib.

SIGNIFICANCE: Prospective clinical sequencing revealed that baseline heterogeneity between tumor samples from different patients affected response to biomarker-selected therapies. VIKTORY is the first and largest platform study in gastric cancer and supports both the feasibility of tumor profiling and its clinical utility.

1Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea. 2Division of Gastroenterology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea. 3Oncology Translational Sciences, IMED Biotech Unit, AstraZeneca, Cambridge, United Kingdom. 4Clinical, Research and Early Development, Oncology R&D, AstraZeneca, Cambridge, United Kingdom. 5Guardant Health, Redwood, California. 6Dong-A University School of Medicine, Busan, Korea. 7Department of Internal Medicine, College of Medicine, Gyeongsang National University, Jinju, Korea. 8Department of Internal Medicine, Gyeongsang National University School of Medicine, Jinju, Korea. 9Division of Hematology-Oncology, Samsung Changwon Hospital, Sungkyunkwan University School of Medicine, Changwon, Korea. 10Depart-ment of Internal Medicine, Gachon University Gil Medical Center, Incheon, Republic of Korea. 11Division of Hematology-Oncology, Department of Internal Medicine, Ewha Womans University, Seoul, Korea. 12Department of Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea. 13Samsung Genome Institute, Seoul, Korea. 14Oncology Business Unit, AstraZeneca, Cambridge, United Kingdom. 15Department of Pathology & Translational Genomics, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.Note: Supplementary data for this article are available at Cancer Discovery Online (http://cancerdiscovery.aacrjournals.org/).S.T. Kim, K. Kim, H. Lee, and I. Kozarewa contributed equally to this article.Corresponding Authors: Jeeyun Lee, Samsung Medical Center, Sungky-unkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul 06351, Korea. Phone: 822-3410-1779; Fax: 822-3410-1754; E-mail: [email protected]; Kyoung-Mee Kim, [email protected]; and Won Ki Kang, [email protected] Discov 2019;9:1–18doi: 10.1158/2159-8290.CD-19-0442©2019 American Association for Cancer Research.

INTRODUCTIONRecent advances in molecular analysis have revealed that

there are patient subsets with differing genomic alterations despite the same histologic diagnosis in gastric cancer (1–3).

It has been suggested by previous studies that this interpa-tient tumor molecular heterogeneity may affect the outcomes of clinical trials, especially with molecularly targeted agents (4, 5). To deliver a more tailored approach for each patient, umbrella or platform clinical trials have been developed (6, 7), which assign treatment arms based on the molecular charac-teristics of the tumor.

Gastric cancer was the third leading cause of cancer-related mortality in 2018, causing 783,000 deaths world-wide (8). The prognosis of patients with metastatic gastric cancer remains extremely poor, with a median overall sur-vival (OS) of less than 12 months with cytotoxic chemo-therapy (9, 10). In addition, gastric cancer is a disease with significant molecular and histologic heterogeneity (1, 3, 11), in which advancements based on “one-size-fits-all” clinical trials have yielded only modest survival benefits. To identify optimal molecular targets and optimal bio-markers, we designed an umbrella trial for second-line (2L) treatment in metastatic gastric cancer based on tumor molecular profiling. We took advantage of an umbrella trial design where patients of a single tumor type are directed toward different arms of the study based on the tumor molecular biomarkers relevant to one or more of the can-didate drugs (12). VIKTORY (targeted agent eValuation In gastric cancer basket KORea, trial NCT#02299648) was designed to classify patients with metastatic gastric cancer based on clinical sequencing and comprised eight different biomarker groups (RAS aberration, TP53 mutation, PIK3CA mutation/amplification, MET amplification, MET protein overexpression, all negative, TSC2 deficient, or RICTOR amplification) to assign patients to one of the 10 associ-ated phase II clinical trials in 2L treatment. The study drugs used were capivasertib (AKT inhibitor), savolitinib (MET inhibitor), selumetinib (MEK inhibitor), adavosertib (WEE1 inhibitor), and vistusertib (TORC inhibitor). The umbrella




Lee et al.RESEARCH ARTICLE

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design was based on the preclinical evidence of known molecular alterations, the prevalence of molecular altera-tions, and the availability of the targeted agents for clinical trials from AstraZeneca at the time of the study design. The candidate molecular alterations for the umbrella trial at the time of clinical trial design were molecular alterations in the p53, PIK3CA, MET, EGFR, FGFR2, RAS, and DDR pathways (3). Adavosertib is one of the most potent inhibi-tors targeting WEE1 (13), which is a tyrosine kinase that phosphorylates cyclin-dependent kinase 1 (CDK1, CDC2) to inactivate the CDC2/cyclin B complex (14). Inhibition of WEE1 activity prevents the phosphorylation of CDC2 and impairs the G2 DNA-damage checkpoint, leading to cancer-cell death. Preclinical studies have demonstrated a very promising antitumor efficacy in vivo, especially in combination with other cytotoxic chemotherapeutic agents (15) including paclitaxel (16). Capivasertib is a selective pan-AKT inhibitor that inhibits the kinase activity of all three AKT isoforms (AKT1–3; ref. 17). Preclinically, sensi-tivity to capivasertib has been strongly correlated with the presence of PIK3CA mutations in gastric cancer models (18, 19). Savolitinib is a potent small-molecule reversible MET kinase inhibitor that inhibits MET kinase at an IC50 of 4 nmol/L in MET-amplified cancer cells and has been shown to demonstrate promising antitumor activity in patients with gastric cancer (20, 21). Selumetinib (AZD6244, ARRY-142886) is a potent, orally active inhibitor of MEK1/2 that suppresses the pleiotropic output of the RAF/MEK/ERK pathway (22, 23). The tolerability and antitumor efficacy of the combination of selumetinib and docetaxel were dem-onstrated in KRAS-mutant non–small cell lung cancer (24).

Herein we conducted a prospective clinical sequencing master program that was aligned with 8 prespecified genomic biomarkers and 10 independent biomarker-associated clin-ical trials in patients with metastatic gastric cancer. We explored whether the biomarker-selected platform trial ben-efits patients with metastatic gastric cancer in terms of sur-vival. In addition, we investigated PD-L1 score and circulating tumor (ctDNA) change between baseline and post-treatment samples following targeted agents.

RESULTSPatient Characteristics

Between March 2014 and July 2018, 772 patients with metastatic gastric cancer were enrolled onto the VIKTORY trial. Targeted sequencing was successfully achieved with tissues from 715 patients (92.6%; Fig. 1A and B). Of the 715 tissues, 150 (21.1%) were from fresh tumors, 564 (78.9%) from formalin-fixed paraffin-embedded (FFPE) specimens, and 1 from ctDNA sequencing using Guardant360 (Fig. 2A). Nearly all samples (96.2%) were from the primary gastric tumor specimen; 56.4% of the patients had their tumor sequenced at the time of diagnosis of metastatic gastric cancer and 43.6% of patients were sequenced during first-line (1L) or at the time of progression following 1L chemo-therapy. The tissue type, site of biopsy for sequencing, and Epstein–Barr virus (EBV) and mismatch-repair (MMR) status of the 715 patients are summarized in Fig. 2A. A total of 75.9% of patients had poorly differentiated adenocarcinoma.

The primary tumor was located in the body (53.2%) or antrum (37.7%) of the stomach in the majority of patients. All patients underwent 1L cytotoxic chemotherapy (>85% with fluoropyrimidine/platinum regimen). In all, 460 of 715 patients (64.3%) were eligible for 2L therapies: 143 of 715 (20.6%) were assigned to one of the umbrella-associated parallel clinical trials in 2L (105 with Biomarker A–E or G; 38 with Biomarker F, unselected), whereas 317 patients received conventional treatment or treatment via other clinical trials (Figs. 1B and 2A).

Tumor Genome ProfilingThe tumor profiles of the 715 patients are shown in Sup-

plementary Fig. S1, and the detailed sequencing method is provided in the Supplementary Material. The prevalence of the predefined biomarkers was as follows (Fig. 2B): Bio-marker A1: RAS mutation/amplification (81/715, 12.2%; KRAS 62/715, 8.7%; HRAS 6/715, 0.8%; NRAS 19/715, 2.7%); Biomarker A2: high or low MEK signature (49/107, 45.8%); Biomarker B: TP53 mutation (321/715, 44.9%); Biomarker C: PIK3CA mutation/amplification (54/715, 7.6%); Bio-marker D: MET amplification (25/715, 3.5%); Biomarker E: MET overexpression by IHC 3+ (42/479, 8.8%); Biomarker F: none of the above (Biomarker A–E); Biomarker G: RICTOR amplification (5/715, 0.7%)/TSC2 deficient (7/715, 0.9%). In addition to the predefined biomarkers, we identified other known molecular targets in gastric cancer (Supplementary Fig. S1): FGFR2 amplification (30/715, 4.2%), EGFR ampli-fication (17/715, 2.4%), MDM2 amplification (8/715, 1.1%), AKT1 amplification (2/715, 0.3%), FGFR1 amplification (10/715, 1.4%), and CCNE1 amplification (14/715, 2.0%). In all, 3.5% were MMR-deficient gastric cancer (18/523) and 4% (20/501) were EBV-positive. Concurrent MMR and EBV status are provided in 105 patients treated according to biomarker status (Fig. 2B, left). In addition, concurrent molecular profiling of each patient according to biomarker (e.g., KRAS mutation and TP53 mutation) and the assigned umbrella arm is summarized in Fig. 2B (right) according to the biomarker priority. The incidence of MET over-expression by IHC (defined by 3+) was 8.8% (42/479) in this cohort: 17 (40.5%) of 42 MET-overexpressed tumors had MET-amplified tumors by next-generation sequencing (NGS) or FISH, and 25 (59.5%) patients had no MET ampli-fication, which concurred with our previous finding on coactivation of MET protein without amplification (25, 26).

Treatment Efficacy of the Umbrella TrialThe cutoff date for treatment outcome analysis was

October 1, 2018. At the time of analysis, enrollment had been completed in all arms or stopped due to early termina-tion of drug development (arms 6, 9, 10) or lack of efficacy at first stage of phase II (arm 7; Supplementary Table S1). Currently, enrollment is completed in phase I of arm 8, and phase II is being considered. Further patient enroll-ment was halted in arm 5 (savolitinib/docetaxel combina-tion) due to the high efficacy observed with the savolitinib monotherapy arm. The primary endpoint was overall response rate (ORR); assuming ORR of 20% for 2L paclitaxel, experi-mental arms were considered effective if the combination yielded ≥50% ORR for arms 1–10 except for arm 4 (savolitinib




The VIKTORY Umbrella Trial in Gastric Cancer RESEARCH ARTICLE


Figure 1. An overview of the VIKTORY trial design. A, The study design of the VIKTORY trial. B, The patient allocation schema of the trial. PII, phase II; PI, phase I; GC, gastric cancer; QC, quality control; PS, performance status; mt, mutation; amp, amplification.

A

B n = 772, Patients with GC were consented to VIKTORY umbrella screening program

n = 57, Excludedn = 48, QC failed for targeted sequencingn = 9, Other reasons (e.g., consent withdrawal)

n = 715, tumor specimens passed QC/sequenced

n = 460, Patients eligible for 2nd-line treatment

n = 143, Assigned to umbrella associated arms

n = 105, Assigned to biomarker-specific trials

n = 255, Not eligible for 2nd-line treatmentn = 157, Poor PS or followup lossn = 48, Not progressed on 1st-line Tx

n = 317, Non–umbrella treatmentn = 99, Taxol/ramucirumabn = 105, Taxane-based chemotherapyn = 62, Irinotecan-based chemotherapyn = 27, Biomarker-specific sponsored trials (non-VIKTORY)n = 24, Immunotherapy trials

Planned biomarker-negative trial (n = 38)1) Planned biomarker-negative PII (biomarker exploratory) (n = 27) (vistusertib + paclitaxel (n = 16), capivasertib + paclitaxel (n = 11)2) Biomarker-negative PI trials (dose finding) (AZD6738 + paclitaxel (n = 9; PI), savolitinib + docetaxel (n = 2; PI)

Arm 1:Selumetinib +

docetaxelKRAS mt oramp/MEKhigh or low

(n = 25)

Arm 2:Adavosertib +

paclitaxel

TP53 mutation(n = 25)

Arm 3:Capivasertib +

paclitaxel

PIK3CA mt oramp (n = 24)

Arm 4:Savolitinib

MET amp(n = 20)

Arm 4-1:Savolitinib +docetaxel

MET amp(n = 4)

Arm 5:Savolitinib +docetaxel

MET 3+ by IHC(n = 4)

Arm 9:Vistusertib +

paclitaxel

TSC2 null(n = 2)

Arm 10:Vistusertib +paclitaxel

RICTOR amp(n = 1)

Patients with metastatic GC

Enrolled for VIKTORY screening56.4% at the time of 1st-line chemotherapy

43.6% during or at the time of failure to 1st-line chemotherapy

Biomarker A1:RAS mtor amp

Biomarker A2:MEK sig

high or low

Biomarker B:TP53

mutation

Biomarker C:PIK3CA

mt or amp

Biomarker D:MET amp

Biomarker E:MET 3+by IHC

Biomarker F:All negative

Biomarker G:TSC2 null/

RICTOR amp

Arm 1: PII Selumetinib +docetaxel

Arm 2: PIIAdavosertib +

paclitaxel

Arm 3: PIICapivasertib +

paclitaxel

Arm 4: PIISavolitinib

Arm 4-1: PIISavolitinib +docetaxel

Arm 5: PI/IISavolitinib +docetaxel

Arm 6: PIIVistusertib +paclitaxel

Arm 7: PIICapivasertib +

paclitaxel

Arm 8: PIAZD6738 +paclitaxel

Arm 9*:Vistusertib +paclitaxel

Arm 10**:Vistusertib +paclitaxel

Tumor pathologic–genomic profiling:1) Targeted tumor sequencing2) NanoString (MEK signature)3) IHC panel: MMR, EBV status, PD-L1, c-MET4) Serial ctDNA sequencing






A

2

EBV status

MMR status

Positive (6) Negative (70)

p-MMR (74)d-MMR (2)

n = 105

N/A (29)

N/A (29)

Assigned umbrella arm

Arm 1 (25) Arm 2 (25)Arm 3 (24)Arm 4 (20)Arm 4-1(4)Arm 5 (4)Arm 9 (2)Arm 10 (1)

B

Targeted seq

FF vs. FFPE

Site of biopsyEBV status

MMR statusDisease status

Pathology category

Treatment assigned

VIKTORY umbrella trial (n = 715)

Yes (715, 100%)Targeted sequencing

Tissue typeFF (150, 21.0%) FFPE (564, 78.9%)

Site of biopsy for sequencing

EBV status

MMR status

Disease statusMetastatic at diagnosis (577, 80.7%)

Metastatic lesion (15, 2.1%)Primary lesion (688, 96.2%)

Positive (20/501, 4%)

Negative (481/501, 96%)

p-MMR (505/523, 96.5%)d-MMR (18/523, 3.5%)

Recurrent after surgery (138, 19.3%)

Pathology category


w/d adeno (12, 1.7%)m/d adeno (150, 21%)

Others (10, 1.4%)

Arm 1, RAS: Selumetinib/docetaxel (25, 3.5%) Arm 2, TP53 mutation: Adavosertib/paclitaxel (25, 3.5%) Arm 3, PIK3CA mt/amp: Capivasertib/paclitaxel (24, 3.4%)

Arm 4, MET amp: Savolitinib (20, 2.8%)

Arm 9, TSC1/2 null: Vistusertib/paclitaxel (2, 0.3%)

Arm 10, RICTOR amp: Vistusertib/paclitaxel (1, 0.1%)

N/A (n = 214)

N/A (n = 192)

N/A (n = 12)

Conventional (317, 44.3%)

Arm 4-1, MET amp: Savolitinib/docetaxel (4, 0.6%)

Savolitinib/docetaxel phase I (2, 0.3%)


PIK3CA mutation (n = 25)

RAS mt/amplification (n = 22)

MET amplification (n = 23)

TP53 mutation (n = 48)

MET overexpression (n = 21)

MEK high or low (n = 17)

n = 102

p/d adeno ~ signet ring (543, 75.9%)

Arm 5, MET overexp: Savolitinib/docetaxel (4, 0.6%) Arm 6/7, Biomarker-negative: Vistusertib or capivasertib (27, 3.8%) Arm 8, AZD6738 phase I (9, 1.3%)

PD-L1≥1 (25)<1 (22)N/A (58)






monotherapy arm). The ORR for each umbrella arm was as follows: arm 1 (selumetinib/docetaxel): 28% (7/25, 95% CI: 10.4–45.6), arm 2 (adavosertib/paclitaxel): 24% (6/25, 95% CI: 7.3–40.7), arm 3 (capivasertib/paclitaxel): 33.3% (8/24; 95% CI: 14.4–52.2), and arm 4 (savolitinib): 50% (10/20, 95% CI: 28.0–71.9; Supplementary Table S1 for detailed primary end-points for each arm). The waterfall plots and swimmer plots are provided in Fig. 3. Seven of 25 patients who had a partial response (PR) in the selumetinib/docetaxel arm (arm 1) had KRAS amplification/high MEK (MEK-H), KRAS wild-type (WT)/low MEK (MEK-L), KRAS G12R/MEK-H, KRAS G12D/MEK-L, KRAS G12D/MEK-H, KRAS G13D/MEK-I, KRAS WT MEK-H, and KRAS Q61R/MEK-I, respectively. The longest responder carried a KRAS amp (KRAS WT) with high MEK signature (arm1-005; Fig. 3A, top right). In terms of KRAS mutational status, there was no significant difference in ORR between KRAS-mutant (4 of 11, 36.4%) and KRAS WT (3/14, 21.4%; P = 0.538, χ2 test). For the Biomarker B–arm 2 (ada-vosertib/paclitaxel) umbrella, there were six PRs (6/25) and 3 of these patients responded longer than 6 months (Fig. 3B). For Biomarker C–arm 3 (capivasertib/paclitaxel), there were 8 responders (8/24) with 4 patients responding for more than 6 months (Fig. 3C). For Biomarker D–arm 4 (savolitinib mono-therapy), there were 10 PRs (10 of 20) one of whom (arm4-010) had the tumor resected after achieving complete response (CR; Fig. 3D). This patient was a 65-year-old female who was laparoscopically diagnosed with peritoneal seeding at diag-nosis. After failing the 1L capecitabine/oxaliplatin treatment and developing rapidly deteriorating malignant ascites, the patient was assigned to savolitinib due to high copy-number MET amplification. After significant tumor reduction fol-lowing savolitinib, the patient underwent curative resection and achieved pathologic downstaging from M1 disease to T3N2M0 disease. The patient remains in CR, now more than 1 year at the time of manuscript preparation.

Prediction of Best Clinical Response Based on Genomic Variations for Individual Patients with Gastric Cancer

Genomic variations are increasingly being utilized as reli-able biomarkers for predicting clinical response to therapy for gastric cancer (27–29). To identify genomic variants that significantly correlate with clinical response, we compared the maximal tumor burden change per RECIST 1.1 against single genomic alterations (Fig. 4A). Patients with MET amplifications demonstrated the largest absolute decrease in tumor burden per RECIST 1.1. In addition, PIK3CA helical domain E542K patients had a more profound (≥50%) reduc-tion in tumor burden when compared with patients with other point mutations in PIK3CA—E545G, E545K, E545K, H1047R, C420R, or E453K. Among patients with TP53 muta-tions, R273C, R175H, R342X, and Y220C demonstrated the

most tumor reduction upon adavosertib/paclitaxel therapy. Finally, patients with KRAS G13E and KRAS G12D muta-tions, KRAS amplification, or MEK-H without KRAS muta-tion demonstrated the highest tumor burden reduction by selumetinib/docetaxel. Further focused genomic analysis of the Biomarker D (MET amplification) group and treatment response to savolitinib demonstrated that patients with gas-tric cancer with high MET copy number (>10 MET gene copies by tissue NGS) had high response rates to savolitinib (Fig. 4B). Patient arm4-010 who initially had gastric cancer with peritoneal seeding had a MET tissue NGS copy num-ber of 25.9 and achieved PR following savolitinib, which eventually led to curative surgery, as mentioned previously. Although limited by the small number of patients, 5 respond-ers to savolitinib had PD-L1–positive tumors [range, 3 to 80 for combined positive score (CPS)], including patient arm4-010 (Fig. 4B). Another focused genomic analysis of the Bio-marker C (PIK3CA mutation) group and treatment response to capivasertib/paclitaxel showed that 57.1% (4 of 7 PRs) had E542K mutations. Moreover, patients with PIK3CA E542K mutations demonstrated an ORR of 50% (4/8), which was higher than the non-E542K cohort (3/16, 18.8%; P = 0.063 by χ2; Fig. 4C). Toxicity profiles for the four arms are shown in Supplementary Table S2.

Survival AnalysisWe conducted an OS analysis on the biomarker-driven

treatment group using the Kaplan–Meier plot in all patients. In all, patients with gastric cancer who had biomarkers iden-tified and were treated accordingly (N = 105) demonstrated better OS (median OS, 9.8 months) when compared with patients who received conventional 2L therapy (N = 266; taxol/ ramucirumab, N = 99; taxane-based, N = 105; irinotecan-based, N = 62) treatment (median OS, 6.9 months) with statistical sig-nificance (P < 0.001; Fig. 5A). The results from the biomarker-driven treatment cohort retained statistical significance in a multivariate analysis, and this treatment continued to predict better survival (P < 0.0001, HR = 0.58; 95% CI: 0.45–0.76) after correcting for potential prognostic factors such as age, gender, number of involved organs, EBV status, MMR status, and performance status (Fig. 5B). Concordantly, the VIKTORY biomarker-assigned cohort (N = 105) had significantly pro-longed progression-free survival (PFS) when compared with the conventional 2L cohort (N = 266; median PFS, 5.7 months vs. 3.8 months, respectively, P < 0.0001; Fig. 5C). The mul-tivariate Cox regression analysis for PFS revealed that being biomarker positive was an independent prognostic factor after adjustment for the several clinically important factors (Sup-plementary Fig. S2). Hence, when the biomarker was identified and the patient received a matched treatment with targeted agents at an appropriate time, patients had prolonged PFS and OS compared with conventional chemotherapy.

Figure 2. Pathologic–genomic landscape of the VIKTORY trial patients. A, A total of 715 patients with gastric cancer were enrolled in the screening program of the VIKTORY trial. Tumor characteristics for each patient are summarized. B, 105 patients were assigned to one of the ongoing biomarker-driven arms. Tumor characteristics for the 105 patients are shown in right panel (MMR status, EBV status, PD-L1 status); concurrently occurring molecu-lar alterations relevant for the clinical trial allocation of each the 105 enrolled patients are shown in right panel. adeno, adenocarcinoma; FF, fresh-frozen tissue; FFPE, formalin-fixed paraffin-embedded; d-MMR, mismatch repair–deficient; p-MMR, mismatch repair–proficient; N/A, not available; w/d, well differentiated; m/d, moderately differentiated; p/d, poorly differentiated; mt, mutation.






0 8 16 24 32 40 48 56

Arm4-008

Arm4-D0004

Arm4-S0001

Arm4-015

Arm4-D0002

Arm4-014

Arm4-002

Arm4-D0003

Arm4-006

Arm4-013

Arm4-009

Arm4-007

Arm4-004

Arm4-001

Arm4-011

Arm4-003

Arm4-012

Arm4-005

Arm4-010D

Time on study treatment (weeks)

Ongoing

5≤ copy <10 MET

Not evaluable

≥10 copy MET

−100

−75

−50

−25

0

25

50

75

100

* *

PRSDPD

0 8 16 24 32 40 48 56 64

Arm3-S001Arm3-018Arm3-006Arm3-021Arm3-016Arm3-007Arm3-014Arm3-020Arm3-002Arm3-015Arm3-001Arm3-008Arm3-009Arm3-011Arm3-019

Arm3-DE0001Arm3-004Arm3-005Arm3-012Arm3-003Arm3-013Arm3-010Arm3-017

Arm3-KE0001C


Ongoing

Stopped due to adverse event

EBV-positive

−100

−75

−50

−25

0

25

50

75

* *

*

PRSDPD

Arm3-018

Arm4-D0002

Arm4-D0004

Arm4-D0003

Arm4-008

Arm4-014

Arm4-007

Arm4-006

Arm4-004

Arm4-005

Arm4-015

Arm4-003

Arm4-013

Arm4-011

Arm4-012

Arm4-002

Arm4-009

Arm4-001

Arm4-010

Arm3-015

Arm3-011

Arm3-006

Arm3-007

Arm3-013

Arm3-020

Arm3-014

Arm3-008

Arm3-005

Arm3-KE0001

Arm3-002

Arm3-009

Arm3-001

Arm3-021

Arm3-019

Arm3-004

Arm3-010

Arm3-003

Arm3-017

Arm3-016

Arm3-012

Arm3-DE0001

*

−100

−75

−50

−25

0

Arm2-020

Arm2-022

Arm2-018

Arm2-001

Arm2-008

Arm2-003

Arm2-023

Arm2-006

Arm2-007

Arm2-002

Arm2-012

Arm2-009

Arm2-021

Arm2-010

Arm2-017

Arm2-016

Arm2-019

Arm2-011

Arm2-015

Arm2-004

Arm2-005

Arm2-014

Arm2-K0001

Arm2-D0001

25

50

75

100

125B


* **

0 5 10 15 20 25 30 35 40 45 50

Arm2-008Arm2-022Arm2-001Arm2-020Arm2-006Arm2-018Arm2-009Arm2-002Arm2-010Arm2-021Arm2-023Arm2-012

Arm2-D0001Arm2-K0001

Arm2-007Arm2-016Arm2-003Arm2-017Arm2-005Arm2-011Arm2-019Arm2-015Arm2-014Arm2-004

* *

PRSDPD

−100 Arm1-017

Arm1-024

Arm1-002

Arm1-001

Arm1-004

Arm1-026

Arm1-006

Arm1-015

Arm1-011

Arm1-019

Arm1-016

Arm1-010

Arm1-009

Arm1-008

Arm1-022

Arm1-003

Arm1-027

Arm1-012

Arm1-005

Arm1-021

Arm1-007

Arm1-025

−75

−50

−25

0

25

50

75

* *

* *

PRSDPD

0 8 16 24 32 40 48 56Arm1-020Arm1-014Arm1-018Arm1-011Arm1-010Arm1-017Arm1-001Arm1-025Arm1-015Arm1-022Arm1-026Arm1-027Arm1-024Arm1-021Arm1-002Arm1-012Arm1-004Arm1-009Arm1-003Arm1-016Arm1-007Arm1-019Arm1-006Arm1-008Arm1-005

Not evaluable


KRAS WT/MEK-L

KRAS amp/MEK-HKRAS Q61H/MEK-I

KRAS WT//MEK-L

KRAS WT/MEK-LKRAS WT/MEK-L

KRAS G12R/MEK-HKRAS G12R, G12D/MEK-I

KRAS G12D/MEK-IKRAS G12D/MEK-LKRAS G13D/MEK-L

KRAS G13D/MEK-IKRAS WT/MEK-H

KRAS WT/MEK-HKRAS Q61R/MEK-I

KRAS WT/MEK-HKRAS WT/MEK-H

KRAS WT/MEK-HKRAS Q61H/MEK-IKRAS amp/MEK-I

KRAS WT/MEK-LKRAS G12C/MEK-I

KRAS WT/MEK-LKRAS WT/MEK-H

KRAS K117N/MEK-I

A

Figure 3. The drug efficacy data. The left panel shows the waterfall plot and the right panel demonstrates the swimmer plot. The y-axis represents % of maximum tumor reduction assessed according to RECIST 1.1 criteria. A, Arm 1: selumetinib (MEK inhibitor)/docetaxel arm for patients with RAS- aberrant gastric cancer. B, Arm 2: adovasertib (WEE1 inhibitor)/paclitaxel arm for patients with TP53-mutant gastric cancer. C, Arm 3: capivasertib (AKT inhibitor)/paclitaxel arm for patients with PIK3CA-mutant gastric cancer. D, Arm 4: savolitinib (MET inhibitor) monotherapy arm for patients with MET-amplified gastric cancer. * indicates newly developed lesion per RECIST 1.1. SD, stable disease.






Figure 4. Molecular alterations and drug efficacy for each patient. A, Demonstration of each patient’s tumor profile and the maximal tumor size with each drug. (continued on next page)

A Arm2-004Arm2-010Arm2-020Arm2-015Arm2-011Arm2-014Arm2-007Arm2-008Arm2-017Arm2-006Arm2-021Arm2-018

Arm2-012

Arm2-009Arm2-001Arm2-002Arm2-003Arm2-019Arm2-023Arm2-005Arm2-016Arm2-022Arm3-019Arm3-010

Arm3-007Arm3-011Arm3-015Arm3-020Arm3-004Arm3-001Arm3-005Arm3-018

Arm3-012Arm3-016Arm3-017Arm3-003Arm3-002Arm3-014Arm3-013Arm3-006Arm3-008Arm3-009Arm4-010Arm4-001Arm4-009Arm4-002Arm4-012Arm4-011Arm4-013Arm4-003Arm4-004Arm4-006Arm4-007Arm4-014Arm4-005

Arm4-008

Arm1-007Arm1-016Arm1-019Arm1-006Arm1-002Arm1-003Arm1-025Arm1-027Arm1-022

Arm1-024Arm1-026Arm1-001Arm1-021Arm1-008Arm1-009Arm1-012Arm1-004Arm1-011

Arm1-017Arm1-010

–100 –50 0 50

Maximum change from baseline per RECIST 1.1

100

Arm1-005

Arm1-015

Arm4-D0002

Arm4-D0003Arm4-D0004

Arm3-DE0001

Arm3-KE0001

Arm2-K0001

Arm2-D0001

TP53 Y220CTP53 Y220C

TP53 R342XTP53 R342X

TP53 R273CTP53 R273C

TP53 R273CTP53 R248W

TP53 R248WTP53 R248Q

TP53 R248QTP53 R213X

TP53 R175H

TP53 Y163C

TP53 R175HTP53 R174X

TP53 P152fsTP53 L252P

TP53 G245S

TP53 C135Y

TP53 I63S

TP53 G244STP53 D281H

PIK3CA P471LPIK3CA M820V

PIK3CA H1047RPIK3CA H1047R

PIK3CA H1047RPIK3CA H1047R

PIK3CA E545K

PIK3CA G364RPIK3CA E545K

PIK3CA E545KPIK3CA E545K

PIK3CA E545GPIK3CA E542K





PIK3CA C420RMET Amp

MET AmpMET Amp

MET AmpMET AmpMET Amp

MET AmpMET Amp

MET AmpMET AmpMET Amp

MET AmpMET Amp

MET Amp

MET AmpMET Amp

MEK LowMEK LowMEK Low

MEK LowMEK Low

MEK HighMEK High

MEK HighMEK High

KRAS Q61RKRAS Q61H

KRAS G13DKRAS G12VKRAS G12D, G12R

KRAS G12DKRAS G12D

KRAS G12CKRAS Amp

KRAS A146PKRAS Amp

MEK High

MEK High; KRAS G12R

MET Amp

TP53 R175H; R248Q; Y163N






Biomarker D-Arm 4: Savolitinib

Biomarker C-Arm 3: Capivasertib + Paclitaxel

PR

SD/PD

Mutation

Amplification

Deletion

B

C

EBV status

MMR status

PositiveNegative

p-MMRd-MMR

N/A

N/A

PD-L1

R R R R R R R R R NR NR NR NR NR NR NR NR

EBV

0

10

MMR

PD-L1MET copynumber

48.6 26.7 25.9 11.6 11.3 10.2 8.5 6.3 5.4 27.4 10.9 10.9 8.9 8.6 8.0 5.2 2.1

TP53

FGFR2

KIT

PIK3CA

KRAS

GNAS

PDGFRA

EGFR

IDH2

PTEN

TSHR

MDM2

CCND1

ERBB2

Arm4-002

Arm4-013

Arm4-010

Arm4-001

Arm4-003

Arm4-011

Arm4-005

Arm4-012

Arm4-009

Arm4-D0003

Arm4-D0002

Arm4-D0004

Arm4-008

Arm4-007

Arm4-014

Arm4-004

Arm4-006

R R R R R R R NR NR NR NR NR NR NR NR NR NR NR NR NR NR NR

EBV

MMR

PD-L1

PIK3CAE542

KE542

KE542

KE542

KP471

LE545

KM820

VE542

KE542

KE542

KE542

KE545

KE545

KE545

KH104

7RH104

7RH104

7RH104

7RE453

KG364

RC420

RE545

G

MET

TP53

KRAS

CTNNB1

EGFR

KIT

FGFR2

IDH1

IDH2

NRAS

PTEN

RET

STK11

MDM2

Arm3-016

Arm3-003

Arm3-017

Arm3-012

Arm3-019

Arm3-004

Arm3-010

Arm3-002

Arm3-006

Arm3-014

Arm3-013

Arm3-001

Arm3-005

Arm3-018

Arm3-015

Arm3-DE0001

Arm3-007

Arm3-011

Arm3-008

Arm3-020

Arm3-009

Arm3-KE0001

≥10N/A

Mut

atio

n co

unt

0

10

Mut

atio

n co

unt

Figure 4. (Continued) B, Molecular landscape of the patients enrolled in savolitinib monotherapy (arm 4). C, Molecular landscape of the patients enrolled in capivasertib/paclitaxel (arm 3). Heat map showing the mutational landscape of patients. Bar plot showing mutation counts. R, responder; NR, nonresponder; N/A, not available.






Figure 5. Survival outcome. Survival analysis of the patients with gastric cancer who were treated according to biomarker as 2L treatment (N = 105) versus conventional 2L treatment (N = 266; taxol/ramucirumab, N = 99; taxane-based, N = 105; irinotecan-based, N = 62). A, OS subgroup analysis for 371 patients with gastric cancer who underwent any 2L treatment; B, HRs for OS. C, PFS subgroup analysis for 371 patients with gastric cancer who underwent any 2L treatment.

AP < 0.0001

0.00

PS 1,2 vs. 0

Metastasis ≥ 2 vs. < 2

Biomarker-positive

0.4 0.6 0.8 1 2

1.53

1.11

1.11

0.93

0.81

0.77

0.55

0.52EBV-positive

Recurrent vs. metastatic

p-MMR/N/D vs. d-MMR

Male vs. female

Age ≥ 65 vs. < 65

0.25

0.50

0.75

1.00

0Time

Sur

viva

l pro

babi

lity

266105Biomarker-driven treatment

Conventional chemotherapy

Numbers at risk

5 10 15 20 25

18585

4346

8

9

06

02

Overall survival

Conventional chemotherapy (n = 266)

Biomarker-driven treatment (n = 105)

P < 0.0001

0.00

0.25

0.50

0.75

1.00

0Time

Sur

viva

l pro

babi

lity

Progression-free survival

266

105

10 15 20 255

78

57

8

19

0

3

0

1

0

1

Conventional chemotherapy (n = 266)

Biomarker-driven treatment (n = 105)

Biomarker-driven treatment

Conventional chemotherapy

Number at risk

C

B






Changes in ctDNA and PD-L1 Expression after Treatment

On the basis of the tumor heterogeneity and genomic changes we observed in our previous studies (11, 27, 30), we collected plasma for ctDNA analysis at baseline and every CT evaluation until progression to address tumor evolution. The concordance rate between tumor and ctDNA (tested by Guardant360; Supplementary Table S3) for MET amplifica-tion was 89.5%, with 100% specificity and 83.3% sensitivity relative to tissue testing, which increased to 100% if patients without detectable ctDNA were excluded (Fig. 6A). The maxi-mal tumor burden decrease was observed in patients with high adjusted MET copy number by ctDNA, although statisti-cal significance was not reached (Fig. 6B). More importantly, however, increased adjusted plasma copy number for MET amplification was significantly associated with prolonged PFS on savolitinib (Fig. 6C; P = 0.0216) to a significantly greater degree than tissue NGS MET copy number, which may reflect plasma’s ability to synthesize the entire tumor cell population. Savolitinib therapy markedly decreased total ctDNA levels in all patients for which baseline and 4-week plasma results were available (Fig. 6D), demonstrating clear biological activity before most radiographic evidence of response. Congruently, adjusted plasma MET copy number was markedly suppressed at 4 weeks in all patients for whom results were available, although 2 of the 6 patients tested retained detectable MET amplification on progression, suggesting additional off-target mechanisms of acquired resistance (Fig. 6E).

We additionally sequenced 55 (from 29 patients) ctDNA samples from arm 1 (13 patients) and arm 2 (16 patients) using a 300-gene AstraZeneca (AZ) panel (Supplementary Table S4 and S5; Fig. 6F and G). Concordance between tumor DNA and ctDNA was observed in 10 of 13 (76.9%) patients for KRAS aberration status (arm 1) and 75% (12 of 16) for TP53 mutation status (arm 2; Fig. 6F and G). Of the 8 baseline/progressive disease (PD) paired ctDNA samples in arm 1, only 2 (25%) had retained baseline genomic alterations at disease progression. Of the 11 baseline/PD paired ctDNA samples in arm 2, 5 (45.5%) patients showed no major alterations at dis-ease progression in the 300-gene panel following adavosertib/paclitaxel treatment. Dynamic changes from baseline to dis-ease progression in ctDNA mutational count using the AZ 300-gene panel are shown in Supplementary Fig. S3.

Finally, we analyzed PD-L1 score in 230 patients, which revealed that 30.4% (70 of 230) had PD-L1 CPS ≥1. In this subset, we had 25 paired biopsy specimens (baseline and at PD to one of the VIKTORY regimens) available for PD-L1 analysis (Supplementary Table S6). All baseline and post-treatment biopsies were obtained from the same primary stomach lesion. Of the 25 paired samples analyzed, there were 2 patients (both treated with selumetinib/docetaxel) who showed a significant increase in PD-L1 (CPS ≥ 10) at progression after

5 to 8 months of selumetinib/docetaxel treatment (Fig. 7A). Arm1-019 patient developed multiple somatic mutations at the time of progression on selumetinib/docetaxel treatment by ctDNA analysis (Fig. 7B).

DISCUSSIONTo our knowledge, this is the first and largest study to use

an umbrella platform trial design with preplanned genomic biomarker analyses to assign patients with advanced gastric cancer to molecularly matched therapies. Using a centrally standardized molecular screening protocol, we enrolled 772 patients with gastric cancer and successfully performed tis-sue analysis for more than 90% (92.6%) of the patients as reported in our previous studies (28, 31). In this study, we demonstrated that when comprehensive molecular screening is linked to seamless immediate access to parallel matched tri-als, nearly 1 in 7 (14.7%) patients with advanced gastric cancer can receive biomarker-assigned drug treatment. The propor-tion of biomarker-driven treatment (14.7%) can be increased if the availability of seamless parallel trials is increased (i.e., FGFR2 amplification, EGFR amplification). Importantly, we showed that the biomarker-assigned cohort had encouraging response rates, underscoring the importance of genomically characterizing every patient’s tumor for precision therapy.

Of the multiple arms, the highest response rate was observed in arm 4 (MET amplification–savolitinib mono-therapy). Savolitinib is a potent small-molecule reversible MET kinase inhibitor that inhibits MET kinase with an IC50 of 4 nmol/L in MET-amplified cancer cell lines. A phase II trial of savolitinib monotherapy in 44 patients with MET-altered papillary renal cell carcinoma showed very promising results, including 8 PRs (32). Our savolitinib monotherapy arm met the prespecified 6-week PFS rate, indicating that this treatment is worthy of phase III exploration in the MET-amplified subset of patients with gastric cancer (3%–5%; refs. 33, 34). Responders were enriched for higher MET copy num-ber (7/10 with MET >10 copies), a biological phenomenon seen in HER2- and EGFR-amplified gastric cancer (35, 36), and adjusted plasma MET copy number was strongly cor-related with duration of PFS. Highlighting the importance of genomic biomarker context, concurrent receptor tyrosine kinase (RTK) amplifications in addition to MET amplifica-tion resulted in short duration of response or no response to savolitinib. The importance of understanding the concur-rent alteration landscape is highlighted by mixed results with prior MET-directed therapies in gastric cancer, likely owing to incomplete biomarker selection (37–39). Although this analysis is lacking functional validation, we speculate tumors with higher MET copy number without other RTK coamplifications are more dependent on MET signaling and may represent the optimal candidates for MET-directed

Figure 6. ctDNA genomic analysis. A, The concordance rate between tumor and ctDNA (tested by Guardant360) for MET amplification. PPA, positive percent agreement; NPA, negative percent agreement; PPV, positive predictive value; TND, target not detected. B, The maximal tumor burden decrease was observed in patients with high adjusted MET copy number by ctDNA, although statistical significance was not reached. C, Correlation between plasma copy number for MET amplification and PFS on savolitinib. D, Baseline and 4-week plasma ctDNA MET amplification change during savolitinib treatment. E, Adjusted plasma MET copy number at baseline (before savolitinib treatment), at 4 weeks and at progression. F, ctDNA landscape for arm 1 (RAS–selumetinib/docetaxel). G, Arm 2 (TP53 mutation–adavosertib/paclitaxel). cfDNA, cell-free DNA.






A

PlasmaTND

PPANPA

83.3%100%100%89.5%

PPVConcordance

Tissue

10 052

02

−

−+

+

200

150

50

20 0 −20 −40 −60 −80

Greatest change in lesion size (%)

R2

Observed plasma

Tissue

0.0012 0.9294

0.7010.3687

0.032030.1165Adjusted plasma

P

100

ME

T c

opy

num

ber

ME

T c

opy

num

ber

B

0 10 20 30 40

PFS (weeks)

0.01464

0.1088 0.425

0.73920.02160.5031

Observed plasma

TissueAdjusted plasma

R2 P

200

150

50

0

100

C

SD

PR10

1

≤0.1

ctD

NA

am

plifi

catio

n ch

ange

(pro

port

ion

initi

al)

D

SDPR B5-005

1.5

1.0

0.5

0.0Baseline

ctD

NA

ME

T a

mpl

ifica

tion

(pro

port

ion

adju

sted

cop

y nu

mbe

r)

C3D1 Progression

B5-013

B5-006, B5-007B5-009, B5-014

E

Tumor

ctD

NA

KRAS amp

KRAS mtKRAS WT

50%

KRAS

KRAS

CTNNB1

USP9X

B1-

001_

PD

B1-

019_

PD

B1-

011_

PD

B1-

009_

PD

B1-

008_

PD

B1-

006_

PD

B1-

005_

PD

B1-

004_

PD

B1-

003_

PD

B1-

002_

PD

B1-

011_

B

B1-

010_

B

B1-

009_

B

B1-

008_

B

B1-

006_

B

B1-

004_

B

B1-

003_

B

B1-

002_

B

B1-

017_

B

B1-

019_

B

TBL1XR1SMAD4RUNX1ROS1RELNRBM10PIK3CGPIK3C3PBRM1

MET

CDKN2ACDH1BRD3BRAF

ARID1AARSMC3SMAD2NF1KITERBB2PIK3CATP5335%

20%10%10%10%10%10%

5%5%5%5%5%5%5%5%5%5%5%5%5%5%5%5%5%5%

2 1084 60

Selumetinib arm: paired baseline/PD cfDNA

F

Tumor TP53

TP53

TP53NF1ARID1A

PIK3C2GER882ESR1RP56KB2

MAP3K1

AR

RHOA

MAP3K13

STK11MSH3FBXW7PIK3R1ATMRB1KEAP1ER884MET

DCUN1D1KMT2CATRVEZF1TSC1

LRRK2ZNF217TERTSMARCA4RPTORRP56RETRASA1POLEPIK3CGPALB2KRASJAK1FGFR4FGF8FGF7EPPK1CTNNB1CTCFARID2AMER1ACVR2AGAS6RPL22MDM4EPPK_1BCL9TOP2ASOX17MAP2K1ABL1BRCA2

PTENPIK3CA

EPHA3

NOTCH1EGFRREUNAPC

ctD

NA

B2-

004-

B

B2-

023-

PD

B2-

022-

PD

B2-

021-

PD

B2-

020-

PD

B2-

017-

PD

B2-

016-

PD

B2-

015-

PD

B2-

012-

PD

B2-

010-

B

B2-

008-

PD

B2-

007-

PD

B2-

004-

PD

B2-

023-

B

B2-

022-

B

B2-

021-

B

B2-

020-

BB

2-01

9-B

B2-

017-

B

B2-

016-

B

B2-

015-

B

B2-

012-

B

B2-

008-

B

B2-

009-

B

B2-

007-

B

84%28%20%24%24%12%

12%12%12%12%12%

16%16%16%16%16%16%8%

8%8%8%8%8%8%8%8%8%8%8%8%8%8%8%8%8%8%8%8%8%8%8%8%8%8%8%8%8%8%8%8%8%8%8%8%8%8%4%4%4%4%4%4%4%4%

0 5 10 15 20

Adavosertib arm: paired baseline/PD cfDNA

G

Key to alterationsAmplification

Splice site mutationTruncating mutationDeletionMissense mutationFrameshift mutation






Progression (stomach) Arm1-019 baseline (stomach)A

PD-L1 CPS 0 PD-L1 CPS 80

CD8

Arm1-012 baseline (stomach)

CD3CD3

CD8

PD-L1 CPS 0 PD-L1 CPS 41

CD8

CD3CD3

CD8

BArm1-019

150SMC3_c.343A>G_M115V

SMAD4_c.1497C>A_C499*

ROS1_c.3391T>A_F1131I

PIK3CG_c.2936A>G_N979S

PBRM1_c.829A>T_K277*

DNMT3A_c.1600C>T_Q534*

CTNNB1_c.100G>C_G34R

ARID1A_c.3296_3297delGT_C1099fs

100

Cum

ulat

ive

alle

lic fr

eque

ncy

(%)

50

0Baseline

Average VAF 3.67On-treatment

follow-upAverage VAF 5.43

ProgressionAverage VAF 20.57

Progression (stomach)

Figure 7. Changes in PD-L1 after docetaxel/selumetinib treatment. A, Changes in PD-L1 score between baseline and at disease progression following 8 months of selumetinib/docetaxel treatment from patient arm1-019. IHC for T-cell markers (CD3 and CD8) showed a dramatic increase in T cells after treatment (left two columns). Likewise, patient arm1-012 demonstrated a dramatic increase in PD-L1 CPS that was accompanied by increase in CD3+ and CD8+ lymphocyte infiltration following 5 months of selumtinib/docetaxel treatment. All biopsies were obtained from primary stomach cancer tissue. B, For patient arm1-019, the genomic landscape of ctDNA changed during selumetinib/docetaxel treatment with newly emerged mutations at disease progression. VAF, variant allele frequency.






therapies. Of note, patients with gastric cancer with a high level of ctDNA MET amplification (by Guardant360 assay in our study) may benefit more substantially from MET-targeted therapy.

In arm 3 (PIK3CA mutation–capivasertib), we observed moderate antitumor activity with an ORR of 33.3% (95% CI: 14.4–52.2) in 2L gastric cancer, especially when com-pared with the low response rate (<15%) observed in arm 7 (PIK3CA WT–capivasertib). Capivasertib is a selective pan-AKT inhibitor that inhibits the kinase activity of all three AKT isoforms (AKT1–3; ref. 17). We and others have previ-ously observed differential distribution of PIK3CA hotspot mutations (E542K, E545K, H1047R) according to molecu-lar subtypes—PIK3CA kinase domain H1047R mutations were enriched in microsatellite instability–high (MSI-H) gastric cancer (>80%), whereas helical domain E542K and E545K mutations were enriched in microsatellite-stable (MSS) tumors (1, 40). Given that each molecular subtype (MSI-H, MSS, genomically stable, or mesenchymal sub-type) has substantially different survival outcomes (1), we have hypothesized that specific point mutations may show different drug sensitivity to capivasertib. Among arm 3 patients, we observed strikingly different efficacy based on PIK3CA genotype (Fig. 4C). In fact, none of the 4 patients with H1047R PIK3CA mutations responded to capivasertib. In contrast, 4 of the 8 with E542K mutations had durable responses to the capivasertib/paclitaxel combination, and 3 of the 4 patients were EBV-positive (Fig. 3C, green cir-cles). Taken together, capivasertib/paclitaxel demonstrated the highest antitumor activity in MSS gastric cancer with PIK3CA E542K mutations. While this represents the first trial of a pan-AKT inhibitor in PIK3CA-mutated gastric can-cer, randomized data will be important to validate our puta-tive composite biomarker (PIK3CA helical domain +/MSS) population.

MAPK pathway alterations are frequent in advanced gas-tric cancer. We attempted to explore two biomarker selec-tion strategies using selumetinib (AZD6244, ARRY-142886), which is a potent, orally active inhibitor of MEK1/2 that suppresses the pleiotropic output of the RAF/MEK/ERK pathway (22, 23). First, we confirmed that KRAS mutational status did not predict response to selumetinib in patients with gastric cancer, supporting the preclinical data with MEK inhibitors (23). On the basis of a study showing that the RAS pathway can be activated in the absence of KRAS mutation and that the RAS-pathway signature was superior to KRAS mutation status for the prediction of response to RAS-pathway inhibitors (41), a 6-gene MEK signature (DUSP4, DUSP6, ETV4, ETV5, PHLDA1, and SPRY2) was devel-oped and validated in the gastric cancer cohort (42). Given that the prevalence of high MEK signature was only 6.9%, the predictive power of high MEK signature should be tested in a subsequent enriched clinical trial with high MEK signature as a selection biomarker in gastric cancer. Interestingly, we observed the most durable response in a KRAS amplification/MEK-H patient without concurrent KRAS mutation, consist-ent with recent reports of MEK inhibition in this genomically defined subset (43).

Recent trials have underscored the importance of anti–PD-1 or anti–PD-L1 therapy in gastric cancer treatment

especially in patients with metastatic gastric cancer with EBV-positive or high mutational load or MSI-H or PD-L1 combined CPS ≥ 1 by IHC (27, 44). We observed substan-tial induction (increase of ≥10) of PD-L1 in 8% (2/25) of paired biopsies from primary tumors in the selumetinib/docetaxel arm (Supplementary Table S6). MAPK inhibition by cobimetinib in preclinical tumor models has been shown to promote tumor-infiltrating CD8+ T-cell activity (45). In addition, atezolizumab and cobimetinib combination treat-ment has been shown to increase intratumoral CD8+ T-cell infiltration and MHC I expression in patients with MSS colorectal cancer (46). Concordantly, we also observed PD-L1 change with recruitment of intratumoral CD8+ lympho-cytes following selumetinib/docetaxel treatment. Although a recent cobimetinib/atezolizumab trial has failed to show survival benefit in patients with MSS colorectal cancer (47), selumetinib and anti–PD-1 treatment may be explored in patients with MSS gastric cancer. Congruently, this high-lights the nonstatic nature of PD-L1 as a selection biomarker and suggests combination and/or sequential strategies are worth exploration.

Although we are a long way from claiming “VIKTORY” in gastric cancer, we have successfully shown that tumor genomic profiling with matched therapies improves out-comes in 2L treatment, and that platform clinical trials can efficiently identify the optimal biomarker treatment match (e.g., savolitinib to patients with MET-amplified gastric cancer). Nevertheless, this signal needs to be confirmed in an expansion or randomized trial. Exploratory analyses demonstrated that biomarkers such as genomic alterations and/or PD-L1 may not be static, especially during or after treatment. The proportion (14.7%) of patients receiving biomarker-driven treatment in the VIKTORY trial may be improved with more available targeted agents based on genomic alterations (e.g., FGFR2 or EGFR2 amplification) and inclusion of PD-L1 positivity (especially PD-L1 CPS ≥10) may enable interrogation of the potential benefit of anti–PD-L1 treatment with or without targeted agents in future umbrella trials. Finally, although limited by a very small number of patients, we have demonstrated that PD-L1 status changes over time in gastric cancer following selumetinib/docetaxel treatment.

METHODSPatient Selection

Patients with histologically confirmed metastatic and/or recur-rent gastric adenocarcinoma, an Eastern Cooperative Oncology Group (ECOG) performance status of 0 or 1, and at least one meas-urable lesion according to the RECIST 1.1 were eligible for enroll-ment in the VIKTORY trial, the molecular screening program, and one of the associated umbrella trial protocols in gastric cancer. Ade-quate hematologic function, hepatic function, and renal function were required. Patients with other concurrent uncontrolled medi-cal diseases and/or other tumors were also excluded. The trial was conducted in accordance with the Declaration of Helsinki and the Guidelines for Good Clinical Practice (ClinicalTrials.gov Identifier: NCT#02299648). The trial protocol was approved by the institu-tional review board of Samsung Medical Center (Seoul, Korea) and all participating centers, and all patients provided written informed consent before enrollment.






Study DesignThe main goal of the VIKTORY trial as a molecular screening pro-

gram was to identify novel molecular subsets for assigning patients into one of the associated biomarker-directed arms (Fig. 1A). There were 10 associated independently operated phase II arms (arms 4 and 8 included a dose-finding phase I trial) with eight biomark-ers. Each experimental drug protocol was designed independently from the screening protocol. The eight biomarkers were: Biomarker A1: RAS mutation or RAS amplification; Biomarker A2: high MEK (MEK-H) or low MEK (MEK-L) signature; Biomarker B: TP53 muta-tion; Biomarker C: PIK3CA mutation or amplification; Biomarker D: MET amplification; Biomarker E: MET overexpression (3+) without MET amplification; Biomarker F: all negative (TP53 WT/PIK3CA WT/RAS WT); and Biomarker G: TSC2 null or RICTOR amplifica-tion. There were 10 phase II trials that were associated with the VIK-TORY screening protocol: arm 1: selumetinib + docetaxel (Biomarker A1/A2, NCT#02448290); arm 2: adavosertib + paclitaxel (Biomarker B, NCT#02448329); arm 3: capivasertib + paclitaxel (Biomarker C, NCT#02451956); arm 4-1: savolitinib monotherapy (Biomarker D, #02449551); arm 4-2: savolitinib + docetaxel (Biomarker D, NCT#02447406), arm 5: savolitinib + docetaxel (Biomarker E, NCT#02447380); arm 6/7/8: vistusertib + paclitaxel or capivasertib + paclitaxel (Biomarker F, NCT#02449655) or AZD6738 + paclitaxel (NCT#02630199); and arm 9–10: vistusertib + paclitaxel (Biomarker G, NCT#03082833, NCT#02449655), vistusertib + paclitaxel (Bio-marker G, NCT#03061708). If patients initially enrolled in the VIKTORY trial were not eligible or refused to participate in one of the associated trials, they were allowed to be treated with conven-tional chemotherapy, or in non-VIKTORY clinical trials.

Sample Collection and IHCFFPE or fresh samples of gastric cancer containing >40% tumor

cellularity were used for targeted sequencing. Genomic DNA was extracted using the Qiagen DNA kit for FFPE tissue or the QIAamp DNA Mini Kit for fresh tumor tissues (Qiagen) according to the manufacturer’s instructions. The IHC protocol for MET and HER2 used for this trial has been reported previously (48). The remaining tissue samples were reused in case of insufficient DNA amount/quality for molecular analysis, or otherwise stored for further study.

Tissue DNA Targeted SequencingThe targeted sequencing method for tissue specimens is provided

in the Supplementary Material.

PD-L1, CD3, and CD8 IHCTissue sections were freshly cut to 4 μm–thick sections and

mounted on Fisherbrand Superfrost Plus Microscope Slides (Thermo Fisher Scientific) and then dried at 60°C for 1 hour. IHC stain-ing was carried out on Dako Autostainer Link 48 system (Agilent Technologies) using Dako PD-L1 IHC 22C3 pharmDx kit (Agilent Technologies) with EnVision FLEX visualization system and coun-terstained with hematoxylin according to the manufacturer’s instruc-tions. PD-L1 protein expression was determined using CPS, which was the number of PD-L1–staining cells (tumor cells, lymphocytes, macrophages) divided by the total number of viable tumor cells, multiplied by 100. The specimen was considered to have PD-L1 expression if CPS was ≥1. For CD3 and CD8, IHC staining was performed on tissue sections from FFPE-embedded specimens with VENTANA BenchMark automated staining instrument (Ventana Medical Systems, Inc.). Specimens were incubated with CONFIRM anti-CD3 (2GV6) and CONFIRM anti-CD8 (SP57) rabbit mAbs for 20 minutes and CD3+ and CD8+ immune cells were visualized using the OptiView DAB IHC Detection Kit.

MMR Determination and EBV In Situ HybridizationAntibodies used in this study were specific for MLH-1 (M1,

Ventana, ready to use) using Ventana BenchMark XT autostainer (Ventana); MSH2 (G219-1129, 1:500, Cell Marque), PMS2 (MRQ-28, 1:20, Cell Marque), and MSH6 (44/MSH6, 1:500, BD Biosciences) using BOND-MAX autoimmunostainer (Leica Biosystems). In inter-pretation, loss of nuclear staining in the tumor cells with positively stained internal control was counted as an abnormal result. In cases with loss or suspected as loss of MMR protein, IHC was initially selected and further IHC with the entire block was performed to screen for MMR deficiency. Cases with negative or equivocal nuclear staining were subsequently tested for microsatellite instability using PCR. EBV status was determined by EBER in situ hybridization using standard protocols (27).

ctDNA PurificationctDNA testing using Guardant360 (Guardant Health) was per-

formed as described previously (49). Briefly, up to 30 ng of cell-free DNA extracted from banked plasma was used for library prepara-tion and enrichment by hybridization capture. Enriched libraries were then sequenced on a NextSeq 550 (Illumina), and the resulting sequence data was analyzed using a locked, previously validated cus-tom bioinformatics pipeline. Plasma copy number was reported as directly observed and adjusted as described previously (50). Change in total ctDNA levels was calculated as described previously (51) and reported as proportional fold change truncated at 10% for graphical purposes.

Treatment Allocation ProcedureThe molecular tumor board (MTB) was composed of medical

oncologists, pathologists, bioinformaticians, and the small-molecule experts from AstraZeneca. The MTB had the responsibilities of sci-entific validation, prioritization of identified molecular aberrations, and providing guidance on the suitable biomarker-driven experimen-tal arm under the umbrella trial. The process time between biopsy and molecular results was set as 21 to 30 days from our previous study (28, 31). If multiple targets were simultaneously detected in a single patient, the following prioritization was used for patient assignment based on known drivers: (i) PIK3CA mutation/amplifica-tion; (ii) RAS mutation/amplification or MEK signature; (iii) MET amplification; (iv) TP53 mutation; (v) RICTOR amplification; (vi) TSC2 null; (vii) MET overexpression by IHC 3+; and (viii) if none of the above biomarkers were present, patients were allocated to the biomarker-negative arms AZD6738/paclitaxel, capivasertib/ paclitaxel, phase I portion of docetaxel/savolitinib, other clinical tri-als, or conventional treatment. The status of enrollment for 10 asso-ciated clinical trials (10 phase II studies) is shown in Supplementary Table S1. Currently, patient enrollment has been completed in arms 1, 2, 3, 4, 6, and 7. Further patient enrollment was stopped in arms 4-1 and 5, and arms 9/10 have been closed early due to early termina-tion of the drug for further clinical development.

Statistical AnalysisThis trial was designed as two parts: (i) VIKTORY screening pro-

tocol for molecular profiling; (ii) parallel phase I/II study with inde-pendent statistical assumptions for each arm. For each arm, the primary endpoint was ORR. We adopted Simon’s optimal design assuming ORR of 20% for 2L weekly paclitaxel regimen based on robust data from previous studies; the experimental arm (paclitaxel + targeted agents) was considered effective for further development if the combination rendered an ORR of ≥50%. Each arm was designed as a two-stage design, allowing ineffective drugs to be terminated early at stage I. Secondary endpoints were PFS, OS, and correlative biomarker analysis using ctDNA, PD-L1 score, and genomic aberration.






Statistical analyses were performed using the software environ-ment R v3.4.0. The clinical information distribution plots were created using Circos (52). Survival analyses were performed to explore the influences of age, gender, pathology, disease status, the number of metastatic organs, EBV status, MMR status, PD-L1 status, and VIKTORY biomarker status. Survival function curves were visualized using the library and the differences between the levels of each factor were assessed using a log-rank test. Likewise, to model hazard functions and determine the effects of these factors on a patient’s survival, Cox proportional hazard models were used. The proportional hazard assumption of Cox models was tested using the R library survival (53). The significance of multiple predictors of survival was assessed by the Cox regression analysis. P < 0.01 was considered to indicate a statistically significant difference. The forest plot of the HRs according to the OS was generated using in-house code. We used the lollipop chart to visualize the maximum change in tumor size per RECIST 1.1.

Disclosure of Potential Conflicts of InterestI. Kozarewa has ownership interest (including stock, patents, etc.)

in AstraZeneca. P.G.S. Mortimer is a senior principal medical scien-tist at AstraZeneca plc and has ownership interest (including stock, patents, etc.) in the same. J.I. Odegaard is a VP, clinical development at Guardant Health. E.A. Harrington has ownership interest (includ-ing stock, patents, etc.) in AstraZeneca. S. Luke has ownership inter-est (including stock, patents, etc.) in AstraZeneca and Illumina. Y.H. Kim has ownership interest in AstraZeneca stock and is a consultant/advisory board member at Orum Therapeutics. A.A. Talasaz is Presi-dent and COO at Guardant Health, Inc. and has ownership interest (including stock, patents, etc.) in the same. S.J. Hollingsworth has ownership interest in AstraZeneca stock. No potential conflicts of interest were disclosed by the other authors.

Authors’ ContributionsConception and design: J. Lee, S.T. Kim, E.A. Harrington, Y.H. Kim, J.O. Park, H.Y. Lim, A.A. Talasaz, S.J. Hollingsworth, K.-M. Kim, W.K. KangDevelopment of methodology: J. Lee, S.T. Kim, J.I. Odegaard, E.A. Harrington, H.Y. Lim, K.-M. Kim, W.K. KangAcquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): J. Lee, S.T. Kim, H. Lee, I. Kozarewa, J.I. Odegaard, J. Lee, T. Lee, S.Y. Oh, J.-H. Kang, J.H. Kim, Y. Kim, J.H. Ji, Y.S. Kim, K.E. Lee, T.S. Sohn, J.Y. An, M.-G. Choi, J.H. Lee, J.M. Bae, S. Kim, J.J. Kim, Y.W. Min, B.-H. Min, J.Y. Hong, S.H. Park, J.O. Park, Y.S. Park, H.Y. Lim, K.-M. Kim, W.K. KangAnalysis and interpretation of data (e.g., statistical analysis, biosta-tistics, computational analysis): J. Lee, S.T. Kim, K. Kim, I. Kozarewa, J.I. Odegaard, E.A. Harrington, J.H. Ji, J. Kim, T.S. Sohn, N.K.D. Kim, S. Luke, Y.S. Park, H.Y. Lim, A.A. Talasaz, K.-M. Kim, W.K. KangWriting, review, and/or revision of the manuscript: J. Lee, S.T. Kim, K. Kim, I. Kozarewa, P.G.S. Mortimer, J.I. Odegaard, E.A. Harrington, J.-H. Kang, J.H. Ji, J.Y. An, J.M. Bae, S. Kim, S. Luke, Y.H. Kim, H.Y. Lim, S.J. Hollingsworth, K.-M. Kim, W.K. KangAdministrative, technical, or material support (i.e., reporting or organizing data, constructing databases): J.-H. Kang, Y. Kim, B.-H. MinStudy supervision: J. Lee, E.A. Harrington, J.-H. Kang, J.H. Ji, A.A. Talasaz, K.-M. Kim, W.K. Kang

AcknowledgmentsWe would like to thank Drs. Adam J. Bass, Dr. Joseph Chao, and

Dr. Samuel J. Klempner for scientific discussion and critical review of our manuscript. On behalf of the VIKTORY team, we would like thank our patients and their families for their participation. This

work was supported by funding from the Korean Health Technology R&D Project, Ministry of Health & Welfare, Republic of Korea (HI14C3418). Support was also provided by a grant from the 20 by 20 Project of Samsung Medical Center (GF01140111). This investigator-initiated trial was also funded by a study-drug donation and partial fund from AstraZeneca.

Received April 14, 2019; revised July 2, 2019; accepted July 12, 2019; published first July 17, 2019.

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Published OnlineFirst July 17, 2019.Cancer Discov Jeeyun Lee, Seung Tae Kim, Kyung Kim, et al. TrialGastric Cancer to Targeted Treatment: The VIKTORY Umbrella Tumor Genomic Profiling Guides Patients with Metastatic

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