tumor genomic profiling guides metastatic gastric cancer ......jul 17, 2019 · 5...

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Tumor genomic profiling guides metastatic gastric cancer patients

to targeted treatment: The VIKTORY Umbrella Trial

Jeeyun Lee1*, Seung Tae Kim,1† Kyung Kim,1† Hyuk Lee,2† Iwanka Kozarewa,3† Peter GS Mortimer,4 Justin I. Odegaard,5 Elizabeth A. Harrington,3 Juyoung Lee,1 Taehyang Lee,1 Sung Yong Oh,6 Jung-Hun Kang,7 Jung Hoon Kim,8 Youjin Kim,9 Jun Ho Ji,9 Young Saing Kim,10 Kyoung Eun Lee,11 Jinchul Kim,1 Tae Sung Sohn,12 Ji Yeong An,12 Min-Gew Choi,12 Jun Ho Lee,12 Jae Moon Bae,12 Sung Kim,12 Jae J. Kim,2 Yang Won Min,2 Byung-Hoon Min,2 Nayoung K.D. Kim,134 Sally Luke3, Young Hwa Kim,4 Jung Yong Hong,1 Se Hoon Park,1 Joon Oh Park,1 Young Suk Park,1 Ho Yeong Lim,1 AmirAli Talasaz,5 Simon J Hollingsworth,14 Kyoung-Mee Kim,15* and Won Ki Kang1* 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, UK 4 Clinical, Research and Early Development, Oncology R&D, AstraZeneca, Cambridge, UK 5Guardant Health, USA 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.

10Department 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

Cancer Research. on February 19, 2021. © 2019 American Association forcancerdiscovery.aacrjournals.org Downloaded from

Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on July 17, 2019; DOI: 10.1158/2159-8290.CD-19-0442

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13Samsung Genome Institute, Seoul, Korea 14 Oncology Business Unit, AstraZeneca, Cambridge, UK 15Department of Pathology & Translational Genomics, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea †These authors contributed equally to the work. *Correspondence should be addressed to

Jeeyun Lee, MD Division of Hematology/Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul 06351, Korea. Tel: +82-2-3410-1779; Fax: +82-2-3410-1779; E-mail address: [email protected] Kyoung-Mee Kim, MD Department of Pathology & Translational Genomics, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea Tel: +82-2-3410-2807; Fax: +82-2-3410-1754; E-mail address: [email protected] Won Ki Kang, MD Division of Hematology/Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul 06351, Korea. Tel: +82-2-3410-3451; Fax: +82-2-3410-3451; E-mail address: [email protected]

Running Title: The VIKTORY Umbrella Trial in gastric cancer

Conflicts of interest

I.K, P.M., E.H., S.L., Y.H.K., S.J.H. are employees of Astra Zeneca, U.K.

J.I.O and A.T. are employees of Guardant Health, U.S.A.

The remaining authors have no conflicts of interest to declare.

Grant Support

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

Astra Zeneca.



mailto:[email protected]




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Abstract

The VIKTORY (targeted agent eValuation In gastric cancer basket KORea) trial was designed

to classify metastatic GC patients 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 RICTOR 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),

adavosertib (WEE1 inhibitor), and vistusertib (TORC inhibitor) were tested with or without

chemotherapy. 772 GC patients were enrolled and sequencing was successfully 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 to conventional 2L chemotherapy. 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 impacted response to biomarker-selected

therapies. VIKTORY is the first and largest platform study in GC and supports both the

feasibility of tumor profiling, and its clinical utility.




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INTRODUCTION

Recent advances in molecular analysis have revealed that there are patient subsets

with differing genomic alterations despite the same histologic diagnosis in GC (1-3). It has

been suggested by previous studies that this inter-patient tumor molecular heterogeneity

may affect the outcome from clinical trials, especially with molecularly targeted agents (4, 5).

In order 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

characteristics of the tumor.

GC was the third-leading cause of cancer-related mortality in 2018, causing 783,000 deaths

worldwide(8). The prognosis of patients with metastatic GC remains extremely poor, with a

median overall survival (OS) of less than 12 months with cytotoxic chemotherapy(9, 10). In

addition, GC 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. In order to identify optimal molecular targets and optimal biomarkers, we

designed an umbrella trial for second-line (2L) treatment in metastatic GC 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 candidate drugs(12). VIKTORY

(targeted agent eValuation In gastric cancer basket KORea, trial NCT#02299648) was

designed to classify metastatic GC patients based on clinical sequencing and comprised

eight different biomarker groups (RAS aberration, TP53 mutation, PIK3CA




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mutation/amplification, MET amplification, MET protein overexpression, all negative, TSC2

deficient, or RICTOR amplification) to assign patients to one of the 10 associated phase II

clinical trials in 2L treatment. The study drugs used were capivasertib (AKTi), savolitinib

(METi), selumetinib (MEKi), adavosertib (WEE1i), and vistusertib (TORCi). The umbrella

design was based on the preclinical evidence of known molecular alterations, the

prevalence of molecular alterations, and the availability of the targeted agents for clinical

trials from Astra Zeneca 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 TP53,

PIK3CA, MET, EGFR, FGFR2, RAS and DDR pathway(3). Adavosertib, is one of the most

potent inhibitors 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 anti-tumor efficacy in vivo, especially in combination with other cytotoxic

chemotherapeutic agents(15) including paclitaxel(16). Capivasertib is a selective pan-AKT

inhibitor which inhibits the kinase activity of all three AKT isoforms (AKT1-3) (17) .

Preclinically, sensitivity to capivasertib has been strongly correlated with the presence of

PIK3CA mutations in GC models (18, 19). Savolitinib is a potent small molecule reversible

MET kinase inhibitor that inhibits MET kinase at an IC50 of 4 nM in MET-amplified cancer

cells and has been shown to demonstrate promising anti-tumor activity in GC patients(20,

21). Selumetinib (AZD6244, ARRY-142886) is a potent, orally active inhibitor of mitogen-

activated protein/extracellular signal-regulated kinase (ERK) kinase (MEK)-1/2 that




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suppresses the pleiotropic output of the RAF/MEK/ERK pathway (22, 23). The tolerability

and anti-tumor efficacy of the combination of selumetinib and docetaxel were

demonstrated in KRAS-mutant NSCLC(24).

Herein we conducted a prospective clinical sequencing master program which was

aligned with 8 pre-specified genomic biomarkers and 10 independent biomarker-associated

clinical trials in metastatic GC patients. We explored if the biomarker-selected platform trial

benefits metastatic GC patients in terms of survival. In addition, we investigated PD-L1 score

and ctDNA change between baseline and post-treatment samples following targeted agents.

RESULTS

Patient characteristics

Between March 2014 and July 2018, 772 metastatic GC patients were enrolled onto the

VIKTORY trial. Targeted sequencing was successfully achieved on tissues from 715 patients

(92.6%) (Figure 1A, 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 (Figure 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 GC, and 43.6% of patients were sequenced during first-line (1L) or at

the time of progression following 1L chemotherapy. The tissue type, site of biopsy for

sequencing, and EBV and mismatch repair (MMR) status of the 715 patients are summarized

in Figure 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




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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), while

317 patients received conventional treatment or treatment via other clinical trials (Figure 1B

and 2A).

Tumor genome profiling

The tumor profiles of the 715 patients are shown in Supplementary Figure 1, and the

detailed sequencing method is provided in supplementary material. The prevalence of the

pre-defined biomarkers was as follows (Figure 2B: Biomarker 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%); Biomarker 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 to E); Biomarker G:

RICTOR amplification (5/715, 0.7%)/TSC2 deficient (7/715, 0.9%). In addition to the pre-

defined biomarkers, we identified other known molecular targets in GC (Supplementary

Figure 1): FGFR2 amplification (30/715, 4.2%), EGFR amplification (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 GC (18/523)

and 4.0% (20/501) were EBV-positive. Concurrent MMR and EBV status are provided in 105




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patients treated according to biomarker status (Figure 2B, left panel). In addition,

concurrent molecular profiling of each patient according to biomarker (i.e. KRAS mutation

and TP53 mutation) and the assigned umbrella arm is summarized in Figure 2B (right panel)

according to the biomarker priority. The incidence of MET overexpression by IHC (defined by

3+) was 8.8% (42/479) in this cohort: 17 (40.5%) of 42 MET overexpressed tumor had MET

amplified tumor by NGS or FISH and 25 (59.5%) patients had no MET amplification which

concurred with our previous finding on co-activation of MET protein without

amplification(25, 26).

Treatment efficacy of the umbrella trial

The cut-off date for treatment outcome analysis was October 1st, 2018. At the time of

analysis, enrollment had been completed in all arms or stopped due to early termination of

drug development (Arms 6, 9, 10) or lack of efficacy at first stage of phase II (Arm 7)

(supplementary Table 1). Currently, enrollment is completed in phase I of Arm 8, and phase

II is being considered. Further patient enrollment was halted in Arm 5 (savolitinib/docetaxel

combination) due to the high efficacy observed with the savolitinib monotherapy arm. The

primary endpoint was ORR; assuming ORR of 20% for 2L paclitaxel, experimental arms were

considered effective if the combination yielded ≥ 50% ORR for Arms 1 – 10 except for Arm 4

(savolitinib monotherapy arm). The ORR for each umbrella arm was as follows – Arm 1

(selumetinib/docetaxel): 28.0% (7/25, 95% CI: 10.4 – 45.6%), Arm 2 (adavosertib/paclitaxel):

24.0% (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): 50.0% (10/20, 95 % CI: 28.0 – 71.9) (supplementary Table 1




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for detailed primary endpoints for each arm). The waterfall plots and swimmer plots are

provided in Figure 3. Seven out of 25 patients who had a partial response (PR) in the

selumetinib/docetaxel arm (Arm 1), had KRAS amplification/MEK-H, KRASwt/MEK-L, KRAS

G12R/MEK-H, KRAS G12D/MEK-L, KRAS G12D/MEK-H, KRAS G13D/MEK-I, KRASwt MEK-H,

and KRAS Q61R/MEK-I, respectively. The longest responder carried a KRAS amp (KRASwt)

with high MEK signature (Arm1-005) (Figure 3A upper panel, right). In terms of KRAS

mutational status, there was no significant difference in ORR between KRAS mutant (4 of 11,

36.4%) and KRAS wild-type (3/14, 21.4%) (P= 0.538, chi-square test). For Biomarker B - Arm

2 (adavosertib/paclitaxel) umbrella, there were six PRs (6/25) and three of these patients

responded longer than 6 months (Figure 3B). For Biomarker C-Arm 3

(capivasertib/paclitaxel), there were 8 responders (8/24) with four patients responding for

more than 6 months (Figure 3C). For Biomarker D – Arm 4 (savolitinib monotherapy), there

were 10 PRs (10 of 20) one of whom (Arm4-010) had the tumor resected after achieving CR

(Figure 3D). This patient was a 65-year old female who was laparoscopically diagnosed with

peritoneal seeding at diagnosis. After failing the first-line capecitabine/oxaliplatin and the

development of rapidly deteriorating malignant ascites, the patient was assigned to

savolitinib due to high MET amplification. After significant tumor reduction following

savolitinib, the patient underwent curative resection and achieved pathologic downstaging

from M1 disease to T3N2M0 disease. The patient remains in CR, now over 1 year at the time

of manuscript preparation.

Prediction of best clinical response based on genomic variations for individual GC patients




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Genomic variations are increasingly being utilized as reliable biomarkers for predicting

clinical response to cancer therapy for GC(27-29). To identify genomic variants that

significantly correlates with clinical response, we compared the maximal tumor burden

change per Response Evaluation Criteria in Solid Tumors (RECIST) 1.1 against single genomic

alterations (Figure 4A). MET amplifications demonstrated the largest absolute decrease in

tumor burden per RECIST v1.1. In addition, PIK3CA helical domain E542K patients had a

more profound (≥50%) reduction in tumor burden when compared to other point mutations

in PIK3CA mutation – E545G, E545K, E545K, H1047R, C420R, and E453K. Among the TP53

mutations, R273C, R175H, R342X, and Y220C demonstrated the most tumor reduction upon

adavosertib/paclitaxel therapy. Lastly, KRAS G13E and KRAS G12D mutations, KRAS

amplification and MEK-H without KRAS mutation demonstrated the highest tumor burden

reduction by selumetinib/docetaxel. Further focused genomic analysis of Biomarker D (MET

amplification) group and treatment response to savolitinib demonstrated that GC patients

with high MET copy number (>10 MET gene copies by tissue NGS) had high response rates to

savolitinib (Figure 4B). Patient #Arm4-010 who initially had GC with peritoneal seeding had

MET tissue NGS copy number of 25.9 and achieved PR following savolitinib, which eventually

led to curative surgery, as previously mentioned. Although limited by small number of

patients, 5 responders to savolitinib had PD-L1-positive tumors (range, 3 to 80 for CPS score),

including patient #Arm4-010 (Figure 4B). Another focused genomic analysis of Biomarker C

(PIK3CA mutation) group and treatment response to capivasertib/paclitaxel showed that

57.1% (4 of 7 PRs) had E542K mutations. Moreover, PIK3CA E542K mutants demonstrated an

ORR of 50% (4/8), which was higher than non-E542K cohort (3/16, 18.8%) (P=0.063 by chi




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square) (Figure 4C). Toxicity profiles for the four arms are shown in Supplementary Table 2.

Survival analysis

We conducted an overall survival (OS) analysis on biomarker-driven treatment group using

the Kaplan-Meier plot in all patients. In all, GC patients who had biomarker identified and

treated accordingly (N=105) demonstrated better overall survival (median OS, 9.8 months)

when compared with patients who received conventional 2L (N=266; Taxol/Ramucirumab,

N=99; Taxane-based, N=105; Irinotecan-based, N=62) treatment (median OS, 6.9 months)

with statistical significance (P<0.001) (Figure 5A). The biomarker-driven treatment cohort

retained statistical significance in a multivariate analysis and continued to predict better

survival (P<0.0001, hazard ratio=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 (Figure 5B). Concordantly, the VIKTORY biomarker-assigned cohort

(N=105) had significantly prolonged progression-free survival (PFS) when compared with

conventional 2L cohort (N=266) (median PFS, 5.7 months vs 3.8 months, respectively, P <

0.0001; Figure 5C). The multivariate Cox regression analysis for PFS revealed that the

biomarker positive was an independent prognostic factor after adjustment for the several

clinically important factors (Supplementary Figure 2). Hence, when the biomarker is

identified and the patient received a matched treatment with targeted agents at an

appropriate time, patients had prolonged PFS and OS compared to conventional

chemotherapy.

Changes in circulating tumor DNA and PD-L1 expression after treatment




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Based on 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 3) for MET amplification

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 (Figure 6A). The

maximal tumor burden decrease was observed in patients with high adjusted MET copy

number by ctDNA, although statistical significance was not reached (Figure 6B). More

importantly, however, increased adjusted plasma copy number for MET amplification was

significantly associated with prolonged PFS on savolitinib (Figure 6C, P value = 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 (Figure 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, though on 2 of the 6

patients tested retained detectable MET amplification on progression, suggesting additional

off-target mechanisms of acquired resistance (Figure 6E).

We additionally sequenced 55 (from 29 patients) ctDNA samples from Arm 1 (13

patients) and Arm 2 (16 patients) using a 300-gene AZ (AstraZeneca) panel (Supplementary

Table 4 and 5) (Figure 6F, G). Concordance between tumor and ctDNA was observed in 10 of

13 (76.9%) patients for KRAS aberration status (Arm 1) and 75.0 % (12 of 16) for TP53




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mutation status (Arm 2) (Figure 6F, G). Of the 8 baseline/PD paired ctDNA samples in Arm 1,

only 2 (25.0%) 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 disease progression in the 300-gene panel following adavosertib/paclitaxel treatment.

Dynamic changes from baseline to disease progression in ctDNA mutational count using AZ

300-gene panel is shown in Supplementary Figure 3.

Lastly, we analyzed PD-L1 score in 230 patients, which revealed that 30.4 % (70 of

230) had PD-L1 ≥ 1. In this subset, we had 25 paired biopsy specimens (baseline (BL) and at

progression (PD) to one of the VIKTORY regimen) available for PD-L1 analysis

(supplementary Table 6). 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 significant increase in PD-L1 (CPS ≥

10) at progression after 5 to 8 months of selumetinib/docetaxel treatment (Figure 7A).

Arm1-019 patient developed multiple somatic mutations at the time of progression to

selumetinib/docetaxel treatment by ctDNA analysis (Figure 7B).

DISCUSSION

To our knowledge, this is the first and largest study to use an umbrella platform trial design

with pre-planned genomic biomarker analyses to assign patients to molecularly matched

therapies in advanced gastric cancer. Using a centrally standardized molecular screening

protocol, we enrolled 772 GC patients and successfully performed tissue analysis for more

than 90% (92.6%) of the patients as reported in our previous studies(28, 31). In this study,




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we demonstrated that when comprehensive molecular screening is linked to seamless

immediate access to parallel matched trials nearly 1 in 7 (14.7%) advanced GC patients can

receive biomarker-assigned drug treatment. The proportion 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 monotherapy). Savolitinib is a potent small molecule reversible

MET kinase inhibitor that inhibits MET kinase at an IC50 of 4 nM in MET-amplified cancer cell

lines. Phase II trial of savolitinib monotherapy in 44 patients with MET-altered papillary

renal cell carcinoma (PRCC) showed very promising results, including 8 PRs (32). Our

savolitinib monotherapy arm met the pre-specified 6-week PFS rate and is worthy of phase

III exploration in the MET-amplified subset of GC patients (3-5%)(33, 34). Responders were

enriched for higher MET copy number (7/10 with MET >10 copies), a biologic phenomenon

seen in HER2- and EGFR-amplified GC (35, 36), and adjusted plasma MET copy number was

strongly correlated with duration of PFS. Highlighting the importance of genomic biomarker

context, concurrent RTK (receptor tyrosine kinase) amplifications in addition to MET

amplification resulted in short duration of response or no response to savolitinib. The

importance of understanding the concurrent alteration landscape is highlighted by mixed

results with prior MET-directed therapies in GC, likely owing to incomplete biomarker

selection (37-39). Although lacking functional validation we speculate tumors with higher




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MET copy number without other RTK co-amplifications are more dependent on MET

signaling and may represent the optimal candidates for MET-directed therapies. Of note, GC

patients with 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 anti-tumor

activity with an ORR of 33.3% (95% CI: 14.4 – 52.2%) in 2L GC, especially when compared to

low response rate (<15%) observed in the Arm 7 (PIK3CA wild type-capivasertib).

Capivasertib is a selective pan-AKT inhibitor which inhibits the kinase activity of all three AKT

isoforms (AKT1-3) (17). We and others have previously observed differential distribution of

PIK3CA hotspot mutations (E542K, E545K, H1047R) according to molecular subtypes –

PIK3CA kinase domain H1047R mutations were enriched in MSI-H GC (>80%), whereas

helical domain E542K and E545K mutations were enriched in microsatellite stable tumor

(MSS)(1),(40) . Given that each molecular subtype (MSI-H, MSS, genomically stable or

mesenchymal subtype) have substantially different survival outcome(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 (Figure 4C). In fact, none of the four patients with H1047R PIK3CA

mutations responded to capivasertib. In contrast, four of the eight with E542K mutations

had durable responses to capivasertib/paclitaxel combination, and three of the four patients

were EBV-positive (Figure 3C, green circles). Taken together, capivasertib/paclitaxel

demonstrated the highest anti-tumor activity in MSS GC with PIK3CA E542K mutations.

While this represents the first trial of a pan-AKT inhibitor in PIK3CA-mutated GC, randomized




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data will be important to validate our putative composite biomarker (PIK3CA helical domain

+/MSS) population.

MAPK-pathway alterations are frequent in advanced GC. We attempted to explore

two biomarker selection strategies using selumetinib (AZD6244, ARRY-142886), which is a

potent, orally active inhibitor of mitogen-activated protein/extracellular signal-regulated

kinase (ERK) kinase (MEK)-1/2 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 GC patients supporting the preclinical data with MEK inhibitors (23). Based

on the study showing that RAS pathway was activated in the absence of KRAS mutation and

the RAS pathway signature was superior to KRAS mutation status for the prediction of

response to RAS pathway inhibitor,(41) a 6-gene MEK signature (DUSP4, DUSP6, ETV4, ETV5,

PHLDA1, and SPRY2) was developed and validated in the GC 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 GC. Interestingly, we observed the most durable response in a KRAS

amplification/MEK-H patient without concurrent KRAS mutation, consistent with recent

reports of MEK-inhibition in this genomically defined subset (43).

Recent trials have underscored the importance of anti-PD-1 or PD-L1 therapy in GC

treatment especially in metastatic GC patients with EBV-positive or high mutational load or

MSI-H or PD-L1 combined positive score (CPS) ≥ 1 by immunohistochemistry (27, 44). We

observed substantial induction (increase in >10+) of PD-L1 in 8% (2/25) paired biopsies from




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primary tumors in the selumetinib/docetaxel arm (supplementary Table 6). MAP kinase

inhibition by cobimetinib in preclinical tumor models has shown to promote tumor

infiltrating CD8+ T cells (45). In addition, atezolizumab and cobimetinib combination has

shown to increase intratumoral CD8+ T cell infiltration and MHC I expression in MSS

colorectal cancer (CRC) patients(46). Concordantly, we also observed PD-L1 change with

recruitment of intratumoral CD8+ lymphocytes following selumetinib/docetaxel treatment.

Although a recent cobimetinib/atezolizumab trial has failed to show survival benefit in MSS

CRC patients(47), selumetinib and anti-PD1 treatment may be explored in MSS GC patients.

Congruently, this highlights the non-static nature of PD-L1 as a selection biomarker and

suggests combination and/or sequential strategies worth exploration.

Although a long way from claiming “VIKTORY” in GC, we have successfully shown that tumor

genomic profiling with matched therapies improves outcomes in 2L treatment; and platform

clinical trials can efficiently identify the optimal biomarker-treatment match (i.e. savolitinib

to MET-amplified GC patients). 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 biomarker-driven treatment cohort in the VIKTORY trial may be

improved with more available targeted agents based on genomic alterations (i.e. FGFR2,

EGFR2 amplification) and inclusion of PD-L1 positivity (especially PD-L1 CPS ≥10) may

interrogate the potential benefit from anti-PD-L1 treatment with or without targeted agents

in future umbrella trials. Finally, although limited by a very small subset of patients, we have

demonstrated that PD-L1 status changes over time in GC following selumetinib/docetaxel




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treatment.

Online methods

Patient selection

Patients with histologically confirmed metastatic and/or recurrent gastric adenocarcinoma,

an Eastern Cooperative Oncology Group (ECOG) performance status of 0 or 1, and at least

one measurable lesion according to the RECIST 1.1 were eligible for enrollment in the

VIKTORY trial, the molecular screening program, and to one of the associated umbrella trial

protocols in GC. Adequate hematologic function, hepatic function, and renal function were

required. Patients with other concurrent uncontrolled medical 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 (ClinicalTrial.gov.Identifier: NCT#02299648).

The trial protocol was approved by the institutional review board of Samsung Medical

Center (Seoul, Korea) and all participating centers, and all patients provided written

informed consent before enrollment.

Study design

The main goal of the VIKTORY trial as a molecular screening program was to identify novel

molecular subsets for assigning patients into one of the associated biomarker-directed arms

(Figure 1A). There were 10 associated independently operated phase II arms (arm 4 and 8

included dose-finding phase I) with eight biomarkers. Each experimental drug protocol was

designed independently from the screening protocol. The eight biomarkers were –




19

Biomarker A1: RAS mutation or RAS amplification; Biomarker A2: high MEK (MEK-H) or low

MEK signature (MEK-L); Biomarker B: TP53 mutation; Biomarker C: PIK3CA mutation or

amplification; Biomarker D: MET amplification; Biomarker E: MET overexpression (3+)

without MET amplification; Biomarker F: all negative (TP53wt/PIK3CAwt/RASwt); and

Biomarker G: TSC2 null or RICTOR amplification. There were 10 phase II trials which were

associated with the VIKTORY screening protocol – Arm 1: selumetinib plus docetaxel

(Biomarker A1/A2, NCT#02448290); Arm 2: adavosertib+paclitaxel (Biomarker B,

NCT#02448329); Arm 3: capivasertib plus 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 (Biomarker 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 conventional

chemotherapy, or non-VIKTORY clinical trials.

Sample collection and Immunohistochemistry (IHC)

FFPE or fresh samples of GC 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, Valencia, CA, USA) according to the

manufacturer’s instructions. The immunohistochemistry (IHC) protocol for MET and HER2




20

used for this trial has been previously reported (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 sequencing

The targeted sequencing method for tissue specimen is provided in the supplementary

material.

PD-L1, CD3, and CD8 Immunohistochemistry (IHC)

Tissue sections were freshly cut to 4 µm-thick sections and mounted on Fisherbrand

Superfrost plus Microscope Slides (Thermo Fisher Scientific, Waltham, MA) and then dried at

60 °C for 1 hour. IHC staining was carried out on Dako Autostainer Link 48 system (Agilent

Technologies, Santa Clara, CA) using Dako PD-L1 IHC 22C3 pharmDx kit (Agilent Technologies)

with EnVision FLEX visualization system and counterstained with hematoxylin according to

the manufacturer's instructions. PD-L1 protein expression was determined by 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 ≥ 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 monoclonal antibodies

for 20 minutes and CD3- and CD8-positive immune cells were visualized using the OptiView

DAB IHC Detection Kit.




21

MMR determination and EBV in situ hybridization

Antibodies used in this study were specific for MLH-1 (M1, Ventana, ready to use) using

Ventana BenchMark XT autostainer (Ventana, Tucson AZ, USA); 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 Biosystem, Melbourne, Australia). In

interpretation, loss of nuclear staining in the tumor cells with positively stained internal

control were counted as abnormal result. In cases with loss or suspected as loss for

mismatch repair (MMR) protein IHC was initially selected and further IHC with entire block

were performed to screen for MMR deficiency. Cases with negative or equivocal nuclear

staining were subsequently tested for microsatellite instability test using polymerase chain

reaction (PCR). EBV status was determined by EBER in situ hybridization using standard

protocols (27).

Circulating tumor DNA (ctDNA) Purification

ctDNA testing using Guardant360 (Guardant Health, Redwood City, USA) was performed as

previously described (49). Briefly, up to 30ng of cfDNA extracted from banked plasma was

used for library preparation and enrichment by hybridization capture. Enriched libraries

were then sequenced on a NextSeq550 (Illumina, San Diego, USA), and the resulting

sequence data was analyzed on a locked, previously-validated custom bioinformatics

pipeline. Plasma copy number was reported as directly observed and adjusted as previously

described(50). Change in total ctDNA levels was calculated as previously described (51) and

reported as proportional fold change truncated at 10% for graphical purposes.




22

Treatment allocation procedure

The molecular tumor board (MTB) was composed of medical oncologists, pathologists,

bioinformaticians, and the small molecule experts from AstraZeneca. The MTB had the

responsibilities of scientific validation, prioritization of identified molecular aberrations, and

providing guidance on the suitable biomarker-driven experimental arm under the umbrella

trial. The process time between biopsy and molecular results was set up as 21 – 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

– 1) PIK3CA mutation/amplification; 2) RAS mutation/amplification or MEK signature; 3)

MET amplification; 4) TP53 mutation; 5) RICTOR amplification; 6) TSC2 null; 7) MET

overexpression by IHC 3+ and 8) 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 trials or conventional treatment. The

status of enrollment for 10 associated clinical trials (10 phase II studies) is shown in

Supplementary Table 1. Currently, patient enrollment has been completed in Arms 1, 2, 3, 4,

6, 7. Further patient enrollment was stopped in Arms 4-1 and 5 and Arms 9/10 have been

closed early due to early termination of the drug for further clinical development.

Statistical consideration

This trial was designed as two parts: 1) VIKTORY screening protocol for molecular profiling; 2)

parallel phase I/II study with independent statistical assumptions for each arm. For each arm,

the primary endpoint was ORR. We adopted Simon’s Optimal design with assuming ORR of




23

20% for second-line 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 renders ≥ 50% of ORR. Each arm was designed as a two-

stage design allowing ineffective drugs to be terminated early at stage 1. Secondary

endpoints were PFS, OS, and correlative biomarker analysis using ctDNA, PD-L1 score, and

genomic aberration.

Statistical analyses were performed using the software environment 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, and 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’s

proportional hazard models were conducted. 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 hazard ratios according to the OS was

generated using an in-house code. We used the lollipop chart to visualize the maximum change in

the tumor size per RECIST 1.1.

Author contributions

J.L., S.T.K., K.K., H.L., I.K., P.M., K.M.K., W.K. wrote the manuscript.




24

J.L., S.T.K., K.M.K., Y.H.K., S.J.H. initiated the study concept. K.K., I.K., S.L., E.A.H. analyzed the genomic data. J.I.O and A.T. analyzed Guardant360 ctDNA assay in correlation to clinical response. Juyoung Lee, N.K.D.K, T.L. collected specimens and handled genomic analysis. J.K. have performed survival analysis. S.Y.H, J.H.K, J.H.K, Y.K., J.H.J., J.M.B., S.K., J.J.KIM, Y.W.M., B.H.M., J.Y.H., S.H.P., J.O.P., Y.S.P., H.Y.L supervised the patient enrollment and participated and handled study participants. All authors approved the final manuscript.

Acknowledgments

We 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.




25

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Figure Legends

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

Figure 2. Pathologic-genomic landscape of the VIKTORY trial patients. A) A total of 715 GC patients were enrolled on the screening program of the VIKTORY trial. Tumor characteristics for each patient is 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 molecular alterations relevant for the clinical trial allocation of each the 105 enrolled patients are shown in right panel. 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.

Figure 3. The drug efficacy data. The left panel shows waterfall plot and the right panel




30

demonstrates the swimmer plot. Y-axis represents % of maximum tumor reduction assessed according to RECIST 1.1 criteria. A) Arm 1: Selumetinib(MEKi) /docetaxel arm for RAS aberrant GC patients; B) Arm 2: Adovasertib (WEE1i)/Paclitaxel arm for TP53 mutant GC patients; C) Arm 3: Capivasertib(AKTi)/paclitaxel arm for PIK3CA mutant GC patients; and D) Arm 4: Savolitinib (METi) monotherapy arm for MET amplified GC patients. * indicates newly developed lesion per RECIST 1.1.

Figure 4. Molecular alterations and the drug efficacy for each patient. A) Demonstration of each patient’s tumor profile and the maximal tumor size with each drug; B) Molecular landscape of the enrolled patients in Savolitinib monotherapy (Arm 4); C) Molecular landscape of the enrolled patients in Capivasertib/paclitaxel (Arm 3). Heatmap diagram showing the mutational landscape of patients. Bar plot showing mutation counts. R, responder; NR, non-responder, N/A, not available.

Figure 5. Survival outcome. Survival analysis of the GC patients 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 GC patients who underwent any 2L treatment; B) Hazard ratios for OS; C) PFS subgroup analysis for371 GC patients who underwent any 2L treatment.

Figure 6. ctDNA genomic analysis. A) The concordance rate between tumor and ctDNA (tested by Guardant360) for MET amplification. 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);

Figure 7. Changes in PD-L1 after docetaxel/selumetinib.

A) Changes in PD-L1 score between baseline and at disease progression following 8 months of selumetinib/docetaxel treatment from Arm1-019 patient. Immunohistochemistry for T-cell markers (CD3 and CD8) showed dramatic increase in T cells after treatment (left two columns). Likewise, the patient Arm1-012 demonstrated a dramatic increase in PD-L1 CPS score which 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.




Figure 1A.

Figure 1B. N=772, GC patients were consented to VIKTORY umbrella screening program

N=57, Excluded

N=48, QC failed for targeted sequencing

N=9, Other reasons (i.e. 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 treatment

N=157, Poor PS or follow-up loss

N=48, Not progressed on 1st line tx

N=317, Non-umbrella treatment

N=99, Taxol/Ramucirumab

N=105, Taxane based chemotherapy

N=62, Irinotecan based chemotherapy

N=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 P I trials (dose finding)

(AZD6738 + paclitaxel (N=9, PI), savolitinib+docetaxel (N=2, PI)

Arm 1:

Selumetinib +

docetaxel

KRAS mt or

amp/ MEK

High or low

(N=25)

Arm 2:

Adavosertib +

paclitaxel

TP53 mutation

(N=25)

Arm 3:

Capivasertib +

Paclitaxel

PIK3CA mt or

amp (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

TSC null

(N=2)

Arm 10:

Vistusertib +

paclitaxel

RICTOR amp

(N=1)

Metastatic GC patients

Enrolled for VIKTORY screening

56.4% : at the time of 1st line chemotherapy

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

Tumor pathologic-genomic profiling:

1) Targeted tumor sequencing

2) Nanostring (MEK signature)

3) IHC panel : MMR, EBV status, PDL1, c-MET

4) Serial ctDNA sequencing

Biomarker A1:

RAS mt

or 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: PII

Adavosertib+

paclitaxel

Arm 3: PII

Capivasertib+

paclitaxel

Arm 4: PII

Savolitinib

Arm 4-1: PII

Savolitinib +

docetaxel

Arm 5 : PI/II

Savolitinib+

docetaxel

Arm 6: PII

Vistusertib +

paclitaxel

Arm 7: PII

Capivasertib+

paclitaxel

Arm 8: PI

AZD6738 +

paclitaxel

Arm 9*:

Vistusertib +

paclitaxel

Arm 10**:

Vistusertib +

paclitaxel




Figure 2A.

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)

Figure 2B.

Targeted seq

FF vs FFPE

Site of biopsy

EBV status

MMR status

Disease status

Pathology category

Treatment assigned

VIKTORY Umbrella Trial (N=715)

Yes (715, 100%)

Targeted seq

Tissue Type

FF (150, 21.0%)

FFPE (564, 78.9%)

Site of biopsy for sequencing

EBV status

MMR status

Disease status

Metastatic at diagnosis (577, 80.7%)

Metastatic lesion (15, 2.1%)

Primary lesion (688, 96.2%)

Positive (20/501, 4.0%)

Negative (481/501, 96.0%)

p-MMR (505/523, 96.5%)

d-MMR (18/523, 3.5%)

Recurrent after surgery (138, 19.3%)

Pathology category

Assigned Umbrella Arm

w/d adeno (12, 1.7%)

m/d adeno (150, 21.0%)

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 1 (2, 0.3%)

Assigned Umbrella Arm

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 1 (9, 1.3%)

PD-L1

≥1 (25) <1 (22) N/A (58)




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

Arm4-010

0 8 16 24 32 40 48 56 64

Arm3-S001

Arm3-018

Arm3-006

Arm3-021

Arm3-016

Arm3-007

Arm3-014

Arm3-020

Arm3-002

Arm3-015

Arm3-001

Arm3-008

Arm3-009

Arm3-011

Arm3-019

Arm3-DE0001

Arm3-004

Arm3-005

Arm3-012

Arm3-003

Arm3-013

Arm3-010

Arm3-017

Arm3-KE0001

-100

-75

-50

-25

0

25

50

75

100

125

-100

-75

-50

-25

0

25

50

75

0 8 16 24 32 40 48 56

Arm1-020

Arm1-014

Arm1-018

Arm1-011

Arm1-010

Arm1-017

Arm1-001

Arm1-025

Arm1-015

Arm1-022

Arm1-026

Arm1-027

Arm1-024

Arm1-021

Arm1-002

Arm1-012

Arm1-004

Arm1-009

Arm1-003

Arm1-016

Arm1-007

Arm1-019

Arm1-006

Arm1-008

Arm1-005

Figure 3.

KRASwt/MEK-L

KRASamp/MEK-H

KRAS Q61H/MEK-I KRASwt//MEK-L

KRASwt/MEK-L

KRASwt/MEK-L

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

KRAS G12D/MEK-I

KRAS G12D/MEK-L KRAS G13D/MEK-L

KRAS G13D/MEK-I KRASwt/MEK-H

KRASwt/MEK-H KRAS Q61R/MEK-I

KRASwt/MEK-H

KRASwt/MEK-H

KRASwt/MEK-H

KRAS Q61H/MEK-I KRAS amp/MEK-I

KRASwt/MEK-L

KRAS G12C/MEK-I KRASwt/MEK-L

KRASwt/MEK-H

KRAS K117N/MEK-I

A.

B.

C.

D.

Time on study treatment (weeks)

Not Evaluable


Ongoing

5 ≤ copy <10 MET


Not Evaluable

≥10 copy MET

PR SD

PD


Ongoing

Stop due to adverse event

EBV-positive

* *

* *

* * *

0 5 10 15 20 25 30 35 40 45 50

Arm2-008

Arm2-022

Arm2-001

Arm2-020

Arm2-006

Arm2-018

Arm2-009

Arm2-002

Arm2-010

Arm2-021

Arm2-023

Arm2-012

Arm2-D0001

Arm2-K0001

Arm2-007

Arm2-016

Arm2-003

Arm2-017

Arm2-005

Arm2-011

Arm2-019

Arm2-015

Arm2-014

Arm2-004

* *

-100

-75

-50

-25

0

25

50

75

100

* *

-100

-75

-50

-25

0

25

50

75

* *

* *

PR SD

PD

PR SD

PD

PR SD

PD

PR SD

PD




Figure 4.

Biomarker D-Arm 4: Savolitinib Biomarker C-Arm 3: Capivasertib+Paclitaxel

Mu

tati

on

co

un

t

R PR

NR SD/PD

MUTATION

AMPLICATION

DELETION

Mu

tati

on

co

un

t

A.

B. C.

EBV status

MMR status

Positive

Negative

p-MMR

d-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 negati

ve

negati

ve

negati

ve

negati

ve

negati

ve

negati

ve

negati

ve

Positiv

e

negati

ve

negati

ve

MMR MSS MSS MSS MSS MSS MSS MSS MSS MSS MSS MSS MSS

PD-L1

MET copy

# 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 1 1 1 1

FGFR2 1 1 2

KIT 1 1 2

PIK3CA 1 1

KRAS 1 1

GNAS 1 1

PDGFRA 1 1

EGFR 1 1

IDH2 1

PTEN 1

TSHR 3

MDM2 3

CCND1 2

ERBB2 2

Arm4

-002

Arm4

-013

Arm4

-010

Arm4

-001

Arm4

-003

Arm4

-011

Arm4

-005

Arm4

-012

Arm4

-009

Arm4

-D00

03

Arm4

-D00

02

Arm4

-D00

04

Arm4

-008

Arm4

-007

Arm4

-014

Arm4

-004

Arm4

-006

0

10

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

EBV Positive negative Positive negative negative negative Positive Positive negative negative negative negative negative negative negative negative negative

MMR MSS MSS MSS MSS MSS MSS MSI-H MSS MSS MSS MSS MSS MSS MSS

PD-L1

PIK3CA E542

K

E542

K

E542

K

E542

K

P471

L

E545

K

M820

V

E542

K

E542

K

E542

K

E542

K

E545

K

E545

K

E545

K

H104

7R

H104

7R

H104

7R

H104

7R

E453

K

G364

R

C420

R

E545

G

MET 2 2 2 2 2 2 2

TP53 1 1 1

KRAS 1 1 2

CTNNB1 1 1

EGFR 2 1

KIT 2 1

FGFR2 1

IDH1 1

IDH2 1

NRAS 1

PTEN 1

RET 1

STK11 2

MDM2 2

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

-DE0

001

Arm3

-007

Arm3

-011

Arm3

-008

Arm3

-020

Arm3

-009

Arm3

-KE0

001

0

10

≥1

0

N/A

TP53 Y220C TP53 Y220C

TP53 Y163C TP53 R342X

TP53 R342X TP53 R273C

TP53 R273C TP53 R273C

TP53 R248W TP53 R248W

TP53 R248Q TP53 R248Q

TP53 R213X TP53 R175H; R248Q; Y163N

TP53 R175H TP53 R175H

TP53 R174X TP53 P152fs

TP53 L252P TP53 I63S

TP53 G245S TP53 G244S

TP53 D281H TP53 C135Y

PIK3CA P471L PIK3CA M820V

PIK3CA H1047R PIK3CA H1047R

PIK3CA H1047R PIK3CA H1047R

PIK3CA G364R PIK3CA E545K

PIK3CA E545K PIK3CA E545K

PIK3CA E545K PIK3CA E545G





PIK3CA E453K PIK3CA C420R

MET Amp MET Amp

MET Amp MET Amp MET Amp

MET Amp MET Amp

MET Amp MET Amp MET Amp

MET Amp MET Amp

MET Amp

MET Amp

MET Amp MET Amp

MET Amp MEK Low

MEK Low MEK Low

MEK Low MEK Low

MEK High; KRAS G12R MEK High

MEK High MEK High

MEK High MEK High

KRAS Q61R KRAS Q61H

KRAS G13D KRAS G12V KRAS G12D, G12R

KRAS G12D KRAS G12D

KRAS G12C KRAS Amp

KRAS A146P KRAS Amp




Figure 5.

A. p < 0.0001

0.00

0.25

0.50

0.75

1.00

0

Time

Surv

ival P

robabili

ty

266

105 Biomarker driven treatment

Conventional chemotherapy

Numbers at risk

5 10 15 20 25

185

85

43

46

8

9

0

6

0

2

Overall Survival

Conventional chemotherapy (N=266)

Biomarker driven treatment (N=105)

p < 0.0001

0.00

0.25

0.50

0.75

1.00

0

Time

Surv

ival P

robabili

ty

Progression-free Survival

266

105

10 15 20 25 5

78

57

8

19

0

3

0

1

0

1

Conventional chemotherapy (N=266)

Biomarker driven treatment (N=105)

Biomarker driven treatment

Conventional chemotherapy

Numbers at risk

C.

B.




Figure 6.

A. B. C.

E.

Selumetinib arm: paired baseline/PD cfDNA

Tumor

ctDNA

Adavosertib arm: paired baseline/PD cfDNA TP53

Tumor

ctDNA

F. G.

D.




Arm1-019 baseline (stomach) Progression (stomach)

Figure 7.

A.

PD-L1 CPS 0 PD-L1 CPS 80

CD8

Arm1-012 baseline (stomach) Progression (stomach)

CD3

CD3

CD8

PD-L1 CPS 0 PD-L1 CPS 41

CD8

CD3

CD3

CD8

B. Arm1-019




Published OnlineFirst July 17, 2019.Cancer Discov Jeeyun Lee, Seung Tae Kim, Kyung Kim, et al. patients to targeted treatment: The VIKTORY Umbrella TrialTumor genomic profiling guides metastatic gastric cancer

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tumor genomic profiling guides metastatic gastric cancer ......jul 17, 2019 · 5...

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