Response Group

ProgressionRegression

Methods Profiling TargetsExome-Seq (Agilent 51 Mb, 120x) DNA mutations, amplifications and insertion/deletions

RNA-Seq (PE 90bp, 50 million reads) RNA expression, fusion, and alternate splicingSNP microarray (Affy SNP 6.0) DNA copy number variations (CNV)

Gene expression microarray (Affy U133 plus 2.0) mRNA expression levelsAdvanced Cell Diagnosis (ACD) RNAscope in situ assays mRNA expression in individual cells

Others (PCR sequencing, western, qPCR, FISH, IHC) Hot spot mutations, gene expression and amplification

Table 1 Molecular Profiling Platform at GenenDesign

Model# Treatment1 Treatment2 Treatment3 Treatment4 Treatment5 Treatment6 Treatment7 Model# Treatment1 Treatment2 Treatment3 Treatment4 Treatment5 Treatment6 Model# Treatment1 Treatment2 Treatment3 Treatment4 Treatment5 Treatment61 Paclitaxel Cisplatin Docetaxel Carboplatin Iressa /Tarceva Selumetinib XL184 61 Paclitaxel Cisplatin Iressa /Tarceva Selumetinib Selumetinib+Paclitaxel 1 Paclitaxel FOLFOX AZD4547 BGJ398 Erbitux

2 Crizotinib Selumetinib+Paclitaxel Selumetinib+Cisplatin 62 Paclitaxel Cisplatin 2 Paclitaxel FOLFOX AZD4547

3 Paclitaxel Cisplatin BGJ398 63 Paclitaxel Cisplatin Docetaxel Selumetinib Selumetinib+Paclitaxel 3 Paclitaxel FOLFOX Crizotinib

4 Paclitaxel 64 Paclitaxel Cisplatin 4 Paclitaxel

5 Paclitaxel Cisplatin Docetaxel Carboplatin Selumetinib XL184 65 Paclitaxel Cisplatin 5 FOLFOX

6 Paclitaxel Cisplatin Docetaxel Carboplatin Erbitux Selumetinib XL184 66 Paclitaxel 6 FOLFOX

7 Crizotinib PF04691502 Palbociclib Selumetinib+Paclitaxel Selumetinib+Cisplatin 67 Paclitaxel Cisplatin Docetaxel Carboplatin 7 Paclitaxel FOLFOX Crizotinib

8 Paclitaxel Cisplatin Docetaxel Carboplatin Selumetinib 68 Paclitaxel Cisplatin Docetaxel 8 Crizotinib Crizotinib +AZD4547

9 Paclitaxel Cisplatin Docetaxel Carboplatin Iressa /Tarceva Erbitux Selumetinib 69 Paclitaxel 9 Paclitaxel FOLFOX Crizotinib Crizotinib +AZD4547

10 XL184 Crizotinib Palbociclib Selumetinib+Paclitaxel Selumetinib+Cisplatin Erbitux + Cisplatin 70 Paclitaxel Cisplatin 10 Paclitaxel Crizotinib

11 Paclitaxel Cisplatin Docetaxel Carboplatin Erbitux XL184 Selumetinib+Paclitaxel 71 Paclitaxel Cisplatin Docetaxel 11 FOLFOX Docetaxel Herceptin

12 Paclitaxel Cisplatin Docetaxel Carboplatin AZD4547 BGJ398 72 Paclitaxel Cisplatin Docetaxel Carboplatin Selumetinib 12 Docetaxel

13 Paclitaxel Cisplatin Docetaxel Carboplatin BGJ398 XL184 Crizotinib 73 Paclitaxel 13 Paclitaxel FOLFOX Docetaxel AZD4547 BGJ398

14 Palbociclib Selumetinib+Paclitaxel Selumetinib+Cisplatin Selumetinib+Erbitux Selumetinib+AZD4547 74 Paclitaxel 14 BGJ398

15 Paclitaxel Cisplatin Docetaxel Carboplatin Selumetinib+Paclitaxel 75 Paclitaxel Cisplatin Docetaxel 15 AZD4547 BGJ398

16 Paclitaxel Cisplatin Docetaxel Carboplatin Erbitux Selumetinib XL184 16 BGJ398

17 Crizotinib Selumetinib+Paclitaxel Selumetinib+Cisplatin 17 Paclitaxel FOLFOX Docetaxel Herceptin Lapatinib

18 Paclitaxel Cisplatin Docetaxel AZD4547 BGJ398 18 Paclitaxel FOLFOX Herceptin Lapatinib Crizotinib

19 Paclitaxel Cisplatin BGJ398 19 Herceptin Lapatinib Crizotinib +Herceptin

20 Paclitaxel Cisplatin Erbitux + Crizotinib 20 Herceptin

21 Paclitaxel 21 Paclitaxel FOLFOX Herceptin Lapatinib Crizotinib Crizotinib +Herceptin

22 Paclitaxel Cisplatin Docetaxel Carboplatin Selumetinib+Paclitaxel 22 Herceptin

23 Paclitaxel Erbitux 23 Paclitaxel FOLFOX Herceptin

24 Paclitaxel Cisplatin Docetaxel Carboplatin Selumetinib Selumetinib+Paclitaxel Selumetinib+Cisplatin 24 Paclitaxel FOLFOX Herceptin Lapatinib AZD4547 BGJ398

25 Paclitaxel Cisplatin Docetaxel Carboplatin Selumetinib PF04691502 25 Herceptin+BGJ398 Lapatinib+BGJ398 Lapatinib+FOLFOX AZD4547+FOLFOX BGJ398+FOLFOX Herceptin+FOLFOX

26 Paclitaxel Cisplatin Docetaxel AZD4547 BGJ398 26 AZD4547+Paclitaxel BGJ398+Paclitaxel Herceptin+Paclitaxel Lapatinib+Paclitaxel

27 Paclitaxel Cisplatin Docetaxel Carboplatin Selumetinib 27 Paclitaxel FOLFOX Crizotinib XL184

28 Paclitaxel Cisplatin Carboplatin Selumetinib 28 Crizotinib

29 Paclitaxel Cisplatin Docetaxel Carboplatin Erbitux Selumetinib XL184 29 Crizotinib

30 Palbociclib Selumetinib+Paclitaxel 30 Paclitaxel FOLFOX Herceptin

31 Paclitaxel Cisplatin Docetaxel AZD4547 BGJ398 31 FOLFOX

32 Paclitaxel Cisplatin 32 Paclitaxel FOLFOX Herceptin Lapatinib

33 Paclitaxel Cisplatin AZD4547 BGJ398 33 FOLFOX

34 Paclitaxel Cisplatin Docetaxel Carboplatin Selumetinib XL184 34 FOLFOX

35 Selumetinib+Paclitaxel Selumetinib+Cisplatin 35 FOLFOX

36 Paclitaxel Cisplatin Erbitux 36 FOLFOX

37 Paclitaxel Cisplatin Docetaxel Carboplatin Erbitux Selumetinib XL184 37 FOLFOX

38 Crizotinib Palbociclib 38 Paclitaxel FOLFOX Herceptin Lapatinib

39 Paclitaxel Cisplatin Docetaxel Carboplatin Selumetinib XL184 Crizotinib 39 Paclitaxel FOLFOX Crizotinib XL184

40 Paclitaxel Cisplatin Docetaxel Carboplatin Selumetinib 40 Paclitaxel

41 Paclitaxel Cisplatin Iressa /Tarceva Erbitux Selumetinib 41 Paclitaxel FOLFOX Herceptin Lapatinib AZD4547 BGJ398

42 Paclitaxel Cisplatin Docetaxel 42 Herceptin +AZD4547

43 Paclitaxel Cisplatin Docetaxel Carboplatin Selumetinib Selumetinib+Paclitaxel Selumetinib+Erbitux 43 Lapatinib

44 Paclitaxel Cisplatin Docetaxel Carboplatin Selumetinib XL184 44 Paclitaxel FOLFOX AZD4547 BGJ398

45 Selumetinib+Paclitaxel Selumetinib+Cisplatin Selumetinib+Erbitux 45 AZD4547 BGJ398

46 Paclitaxel Cisplatin 46 BGJ398

47 Paclitaxel Cisplatin Docetaxel Selumetinib Selumetinib+Paclitaxel 47 Paclitaxel FOLFOX AZD4547 BGJ398 Crizotinib XL184

48 Paclitaxel Cisplatin 48 Paclitaxel FOLFOX AZD4547 BGJ398 Crizotinib

49 Paclitaxel Cisplatin Docetaxel Carboplatin Selumetinib XL184 Crizotinib 49 Paclitaxel FOLFOX AZD4547

50 Paclitaxel Cisplatin Docetaxel Carboplatin Selumetinib XL184 Crizotinib 50 FOLFOX Docetaxel Lapatinib

51 Paclitaxel Cisplatin 51 Paclitaxel FOLFOX Herceptin Lapatinib Crizotinib XL184

52 Paclitaxel Cisplatin Docetaxel Carboplatin AZD4547 BGJ398 52 Herceptin +XL184

53 Paclitaxel Cisplatin 53 Paclitaxel

54 Paclitaxel Cisplatin 54 Paclitaxel FOLFOX Docetaxel Herceptin Lapatinib AZD4547

55 Paclitaxel Cisplatin Docetaxel Carboplatin Selumetinib XL184 55 BGJ398 Palbociclib

56 Crizotinib PF04691502 56 Paclitaxel FOLFOX Crizotinib

57 Paclitaxel Cisplatin 57 Paclitaxel Crizotinib

58 Paclitaxel Cisplatin Docetaxel Carboplatin Erbitux Selumetinib 58 Crizotinib

59 Selumetinib+Paclitaxel Erbitux + Cisplatin 59 Paclitaxel Crizotinib

60 Paclitaxel Cisplatin Docetaxel Carboplatin Iressa /Tarceva Selumetinib XL184 60 Paclitaxel FOLFOX AZD4547 BGJ398

Lung Model Gastric ModelLung Model

Establishment and Characterization of PDX Tumor Models

GenenDesign PDX Platform

All in vivo studies are performed in accordance with IACUC guidelines and the Guide for Laboratory Animal Care and Use. GenenDesign animal facility is certified by AAALAC.

GenenDesign PDX tumor models are established by serial passage of surgically removed human tumors in immunodeficient mice (BALB/c Nude mice) and display a diverse range of intertumor complexity and heterogeneity in histopathology. All models are being characterized to assess their growth rates, molecular profiles and responses to various anticancer therapies (Table 1).

Figure 1 : GenenDesign Master Mouse Trial Scheme. The Master Mouse Trial scheme mimics multiple clinical trial practices and has the advantage in reproducibility and flexibility. It is a matrix of all the four mouse trial types described in Table 4.

Biomarker discovery through bioinformatics analysis of genomic profiles of PDX models with different responses to cancer therapies AACR 2015

Abstract#3222Jingjing Jiang, Tengfei Yu, Ying Yan, Wei Du, Tingting Tan, Xuqin Yang, Jiali Gu, Liang Hua, Xin K. Ye, Pan Du and Zhenyu Gu

GenenDesign, 590 Ruiqing Road, Bldg 7, 5F, Pudong Shanghai, P. R. China 201201

GenenDesign Master Mouse Trial Scheme and Drug Response Data Sets

Through our in-house efforts, PDX models of different tumor types were tested with related SOCs and clinical candidates in a biomarker-driven multi-drug multi-arm clinical trial setting (Figure 1). So far, more than 1200 data sets have been generated, including responses to targeted inhibitors against HER2, EGFR, FGFRs, c-Met/ALK, cell cycle regulators, Ras/Raf pathway, PI3K/Akt pathway, epigenetic targets, as well as chemotherapy drugs. The representative drug response data generated from selected Lung, Esophagus and Gastric models are shown in Figure 2.

Regression tumor shrink > 20%

Partial responseTGI > 40%, tumor shrink <20%

Progression TGI <40% Untested

Figure 1

Figure 2 GenenDesign Lung, Esophagus and Gastric Drug Response Panels

The majority of GenenDesign PDX tumor models represent cancer types that are prevalent in Asian patients, including gastric cancers, lung cancers, liver cancers, esophageal cancers, and colorectal cancers (Table 2). Efforts are also made to derive acquired resistance PDX models under continuous drug treatment over the past few years (Table 3).

Table 2 PDX models summary

Tumor Origin Established model (>P3)

Early passage (P1-P2) Total

Stomach 217 1 218Liver 53 9 62

Pancreas 68 1 69Esophagus 185 38 223

Colon 87 8 95Lung 115 42 157

Ovarian 13 5 18Gall Bladder 8 0 8

GIST 10 0 10Others 13 4 17TOTAL 769 108 877

Table 3 Acquired Resistance PDX models summary

Introduction

Current issues in precision medicine and diagnosis• Lack of predictive biomarkers

- Lack of sufficient clinical information for biomarkerdiscovery

- Lack of high-quality drug response and genomicinformation

• Lack of biomarker validation methods- lack of capability for repetitive and combination test in

clinical settings- lack of adequate preclinical models to validate biomarkers

Biomarker discovery with PDX models as solutions• PDX models better resemble human cancer tissues than tumor cell

line xenograft models• Large number of PDX models provide better coverage of cancer

complexity • Simultaneous testing of multiple drugs and combinations• Reproducibility and flexibility• Convenient tissue collection for high sample quality and reliable

analysis• Quick data acquisition and significant cost reduction

1. Patient-Derived Tumor Xenografts: Transforming Clinical Samples into Mouse Models (2013) Siolas D. and Hannon G. J. Cancer Res; 73(17); 1–5. 2. Drug response of PDX tumor models in a biomarker-driven multi-arm clinical trial setting (2014) Jiang J.J., Yu T.F. and Gu Z.Y. et al., ENA Meeting Abstract #154.3. A Patient Derived Xenograft Tumor Model Platform for “Mouse Trials” (2014) Yan Y. Ye. K. X. and Gu Z.Y. et al., Cancer Res 2014; 74(19 Suppl).4. Basket Trials and the Evolution of Clinical Trial Design in an Era of Genomic Medicine (2015) Jänne. P.A., J. Clinical Oncology; 33(9); 975-977.5. Prognostic and predictive biomarkers in lung cancer. A review (2014) Thunnissen E., Oord K. and Bakker M. Virchows Archiv; 464(3); 347-358.

1. Large panels of PDX models can be used for multiple types of tests to mimic cancer patient treatments.2. Matched genomic profiling and drug response information from mouse trials provide important resource for biomarker discovery.3. The master scheme drug response data could be easily translated into various clinical trial formats, helping new indication search

and companion biomarker identification.4. Preliminary bioinformatics analysis has identified potential biomarkers to predict chemotherapy responses in lung PDX models.

Mouse Trials in Biomarker-driven Multi-arm clinical trial settingsThe mouse trials at GenenDesign are classified into four categories (Table 4), including Signal search mouse trials, Proof-of-concept mouse trials, Efficacy evaluation mouse trials and Combination selection mouse trials. The figures below are the representative mouse trial examples from each category.

Table 4 Design and Purpose of Mouse Trials at GenenDesign

Mouse trials at GenenDesign

Signal search mouse trial

Proof-of-concept mouse trial

Efficacy evaluation mouse trial

Combination selection mouse trial

Stud

y de

sign

● Multiple treatments vs Vehiclein one cancer type

● Single treatment vs Vehiclein multiple cancer types

New treatment A vs vehicle

Treatment A+B vs Treatment B

(B=chemo or targeted drug)

Treatment A+B vs Treatment A+C

(B/C=chemo)

Stud

ypu

rpos

e

Screen multiple test articles in a specific cancer type (disease focused) Drug response survey Compare and combine with

SOCIdentify additive or

synergic effects

Screen one test article in multiple cancer types (drug project focused)

Biomarkeranalysis Dose evaluation Evaluate potential

toxicity

Summary and Discussion

References

GenenDesign精迪生物医药技术（上海）有限公司

GenenDesign精迪生物医药技术（上海）有限公司

Drug Target Model Type PDX Models with Acquired Resistance

Herceptin Her2 amplification Stomach 6Lapatinib Stomach 5AZD4547 FGFR2 amplification Stomach 3BGJ398 Stomach 5

Crizotinib c-Met amplification Stomach 5Liver 2

ALK fusion Stomach 2XL184 c-Met amplification Liver 2

Iressa/Tarceva EGFR mutation Lung 3Selumetinib N/A Lung 1

Paclitaxel N/AStomach 4

Lung 12Esophagus 1

Docetaxel N/A Stomach 1Lung 3

FOLFOX N/A Stomach 7Cisplatin N/A Lung 3

Model# Treatment1 Treatment2 Treatment3 Treatment4 Treatment5 Treatment61 Paclitaxel Cisplatin FOLFOX Erbitux Selumetinib AZD4547

2 XL184 Crizotinib

3 Paclitaxel Cisplatin FOLFOX Selumetinib XL184

4 Paclitaxel Cisplatin FOLFOX Selumetinib XL184 Crizotinib

5 Palbociclib Erbitux + Cisplatin PF04691502

6 Cisplatin FOLFOX

7 Paclitaxel Cisplatin FOLFOX Selumetinib

8 Paclitaxel FOLFOX

9 Paclitaxel Cisplatin FOLFOX Selumetinib XL184 Crizotinib

10 Paclitaxel Cisplatin FOLFOX Selumetinib Palbociclib

11 XL184 Crizotinib

12 FOLFOX AZD4547

13 Paclitaxel Cisplatin FOLFOX

14 Paclitaxel

15 Paclitaxel Cisplatin FOLFOX Selumetinib XL184

16 Crizotinib Palbociclib

17 Paclitaxel Cisplatin FOLFOX

18 Paclitaxel Cisplatin FOLFOX Selumetinib XL184

19 Paclitaxel Cisplatin FOLFOX Selumetinib XL184

20 Cisplatin FOLFOX

21 FOLFOX

22 FOLFOX

23 FOLFOX

24 Cisplatin FOLFOX

25 Paclitaxel Cisplatin FOLFOX Selumetinib XL184

Esophagus Model

Translation of Master Scheme Drug Response Data into a Basket Trial Format

Figure 3 : Basket trial with FGFR inhibitors. Here we use FGFRibasket trial format as an example to illustrate how our drug response database could help scientists and clinicians to find new indication and design clinical trials.

FGFR inhibitors

Gastric PDX Models

Lung PDX Models

Esophagus PDX Models

Liver PDX Models

Screen for FGFR1-3 amp/over-exp models across various cancer types

Analyze the responses of each type of PDX models

Regression tumor shrink > 20%

Partial responseTGI > 40%, tumor shrink <20%

Progression TGI <40%

Model TypeModel# 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5

AZD4547BGJ398

Model TypeModel# 1 2 3 4 5 6 7 8 9 1 2 3 4 5 6 7 8

AZD4547BGJ398

Gastric

Esophagus

Lung

Liver

Figure 3

Drug Response Data Packages for Biomarker Analysis

PDX Model Type Content # of Data Sets

Lung 600Liver 200

Gastric 150Esophagus 100

CRC 50

Drug response data sets fromtreatment with chemotherapies

and targeted drugs

Oncology Target Cancer Type # of Data Sets

c-Met Lung, Liver, Gastric, Esophagus 200K-Ras Lung, Esophagus, CRC 100FGFR Lung, Liver, Gastric, Esophagus 80HER2 Gastric 50EGFR Lung, CRC 30

Cell cycle regulators Lung, Liver, Esophagus, CRC 50MAPK pathway Lung, Liver, Esophagus, CRC 50All data presented here are generated from GenenDesign internal studies. The

number of data sets were calculated as of Mar. 2015.

Biomarkers Associated with Chemotherapy Responses in Lung PDX ModelsGene signatures associated with chemotherapy responses in Lung PDX models were identified through bioinformatics analysis on RNA-Seq, Exome-Seq and SNP6.0 array data.

Figure 4 : Heatmaps of differential genes associated with either Paclitaxel (Figure 4a) or Cisplatin (Figure 4b) between binary and continuous analyses on RNA expression data. The lists of differentially expressed genes were selected based on binary model adjusted by cancer subtypes in Figure 4a, while separated by cancer subtypes in Figure 4b.

Cisplatin on Adeno NSCLC Cisplatin on SCC NSCLC Paclitaxel on NSCLC

Figure 5 : Waterfall plot of selected gene signatures and the responses to Paclitaxel. SNV and INDEL mutation information from Exome-Seq data of all lung samples were analyzed for their association with chemotherapy responses. The gene signatures associated with paclitaxel responses in lung PDX models contain genes in cytoskeleton regulation, stem cell function, tyrosine kinase-coupled signaling, and DNA repair pathways etc.

Figure 4a Figure 4b

Figure 5

131.5 32.4

−3

−2

−1

0

1

2

3

182.832.4

Progression Partial Inhibition / Stasis RegressionProgression Partial Inhibition / Stasis Regression

SubtypeAdeno SCC

−4

−2

0

2

4

PaclitaxelIndex

178.6

22

Progression Partial Inhibition / Stasis Regression

Table 5 Drug response data package by cancer type Table 6 Drug response data package by oncology targets