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. 2017 Aug 10;16(4):4784–4790. doi: 10.3892/mmr.2017.7213

Individualized drug screening based on next generation sequencing and patient derived xenograft model for pancreatic cancer with bone metastasis

Zhonghai Guan 1,2,*, Huanrong Lan 3,*, Xiangheng Chen 4, Xiaoxia Jiang 2, Xuanwei Wang 5,✉, Ketao Jin 1,✉
PMCID: PMC5647100  PMID: 28849200

Abstract

The efficacy of traditional chemoradiotherapies for pancreatic cancer remains limited, and no effective targeted therapies or screening tests are currently available. Therefore more individualized drug screening is warranted for the clinical treatment of pancreatic cancer. A patient-derived xenograft (PDX) model of pancreatic cancer bone metastasis was established, and next-generation sequencing (NGS) was used to investigate the molecular characteristics of the cancer and screen for potential drugs. Immunohistochemical analysis was performed to validate that the PDX retained the molecular characteristics from the patient. Using NGS technology, 13 pancreatic-cancer-associated polymorphisms/mutations were identified out of 416 genes sequenced. Based on the sequencing results and associated literatures, AZD6244, a highly selective inhibitor against mitogen-activated protein kinase kinase 1 (MEK1), was chosen as a potential therapy. AZD6244, a highly selective MEK1 inhibitor, was evaluated as effective for the pancreatic cancer PDX model, and thus may provide potential efficacy in the clinical treatment of the patient with pancreatic cancer investigated in the present study. The feasibility of the novel NGS-PDX based drug-screening pattern was demonstrated, and has a potential to improve individualized treatment for cancer.

Keywords: next generation sequencing, patient-derived xenograft model, individualized drug screening, pancreatic cancer with bone metastasis

Introduction

Pancreatic cancer is expected to be the second most lethal malignancy in the USA by 2020, and the 5-year survival rate for patients diagnosed with locally advanced or metastatic pancreatic cancer remains <3% (1,2). The efficacy of traditional chemoradiotherapies for pancreatic cancer remains limited (3–5). However, no effective targeted therapies or screening tests for pancreatic cancer are recently available, and no clinically comfirmed biomarkers are available for identifying subsets of patients who might benefit from chemoradiotherapies or targeted theprapies (6–9). Different from frequent liver and peritoneum metastases, the bone metastasis rate of pancreatic cancers is quite low but reaches higher of about 7.3% with the improvement of the diagnosis and treatment level (10,11). For pancreatic cancer patients, especially those in advanced or metastatic disease stages, individualized drug screening is urgently needed for the clinical treatment.

The lack of an appropriate in vivo model for preclinical studies has limited the mechanistic study of tumor resistance to anti-VEGF therapy. Patient-derived xenografts (PDXs), so-called Avatar models (12), have been increasingly widely used in various types of cancers for translational research in recent years, with the greatest advantage of its ability to better predict clinical tumor response (13). Accumulating evidence indicates that PDX is an reliable cancer research tool for drug screening and personalized medicine applications (14).

It is known that somatic genomic alterations alter the function of genes or pathways, thus resulting in tumorigenesis, metastasis, and resistance to therapies (15,16). Therefore, precise molecular profiles of tumors will help to predict drug responses (17). Understanding the genomic landscape of CRC can contribute to drug screening (8,18–20). Large-scale sequencing projects has economically led to the rapid development and clinical popularization of next-generation sequencing (NGS) technologies (21). NGS can be a powerful tool to understand the genomic landscape of patients and mechanism of drug response, which thus might provide a more broad vision for clinically potential drug screening (22–24). Therefore, NGS technologies are being used by pharmaceutical companies throughout the drug discovery process (21).

In our previous studies, we established a series of PDX models of different tumor types and accumulated substantial experiences of drug evaluation, screening and mechanism exploration (25,26). While in the present study, we established a PDX model by pancreatic cancer bone metastasis tumor tissues for evaluation of potential drugs for pancreatic cancer patient. In our study, in order to select the optimal therapy for the patient, the NGS technology was used for investigating of tumor molecular characteristics and searching for potential drugs, which were finally evaluated in the corresponding PDX model. The aim of our study is to demonstrate the feasibility of the novel NGS-PDX based drug screening pattern which has a great potential to improve the cancer individualized treatment.

Materials and methods

Reagents and drugs

AZD6244 (cat. no. S1008) and Capecitabine (cat. no. S1156) were purchased from Selleck Chemicals (Shanghai, China). The antibodies against ki-67, CK19, CK7, PCNA, Caspase-3, ERK, p-ERK, and β-actin were purchased from Abcam (Cambridge, UK).

Patient and tumor tissues

Pancreatic cancer bone metastasis (diagnosed as adenocarcinoma) tissues were obtained at surgery from a 67-year-old female patient. A single bone metastasis was imageologically found at the right pedicle of L2 vertebral arch, which means a high risk of fracture and paraplegia. In addition, the patient urged for operation treatment. The study was done in accordance with the principles of the Declaration of Helsinki and the International Conference on Harmonization and Good Clinical Practice guidelines. The Institutional Ethical Committee approved the current study.

Establishment of PDX model

BALB/c nude mice (3-to-4-week-old, female) were purchased from Shanghai Slaccas Laboratory Animal and housed in SPF laboratory animal rooms at laboratory animal center of Zhejiang University. Mice were acclimated to new environments for at least 3 days before use. Surgical tumor tissues were cut into pieces of 3 to 4 mm and transplanted within 30 min s.c. to mice. Additional tissues were snap-frozen and stored at −80°C until use. Animals were monitored periodically for their weight with an electronic balance and tumor growth with a Vernier caliper twice every week. The tumor volume was calculated as formula V=LD × (SD)2/2, where V represents the tumor volume, LD and SD are the longest and the shortest tumor diameter, respectively. Tumors were then harvested, minced and re-implanted as described above for passaging. At each generation, tumors were harvested and stored in liquid nitrogen for further use. The usage of experimental animals was according to the Principles of Laboratory Animal Care (NIH #85-23, 1985 version). All animal studies were according to the Institutional Animal Care and Use Committee of Zhejiang University, and the approval ID was SYXK (ZHE) 2005–0072.

Multiple gene mutation analysis by next generation sequencing

The sequencing including 416 gene exons was conducted by Geneseeq Technology Inc. (Nanjing, China). ctDNA was extracted from patient's tumor. The purified ctDNA is quantified by a Picogreen fluorescence assay using the provided lambda DNA standards (Invitrogen Life Technologies, Carlsbad, CA, USA). Then, library construction with the KAPA Hyper DNA Library Prep Kit, containing mixes for end repair, dA addition and ligation, were performed in 96-well plates (Eppendorf). Dual-indexed sequencing libraries are PCR amplified for 4–7 cycles. The 5′-biotinylated probe solution is provided as capture probes, the baits target 416 cancer-related genes. 1 µg of each ctDNA-fragment sequencing library is mixed with 5 µg of human Cot-1 DNA, 5 µg of salmon sperm DNA, and 1 unit adaptor-specific blocker DNA in hybridization buffer, heated for 10 min at 95°C, and held for 5 min at 65°C in the thermocycler. Within 5 min, the capture probes are added to the mixture, and the solution hybridization is performed for 16–18 h at 65°C. After hybridization is complete, the captured targets are selected by pulling down the biotinylated probe/target hybrids using streptavidin-coated magnetic beads, and off-target library is removed by washing with wash buffer. The PCR master mix is added to directly amplify (6–8 cycles) the captured library from the washed beads. After amplification, the samples are purified by AMPure XP beads, quantified by qPCR (Kapa Biosystems, Inc., Wilmington, MA, USA) and sized on bioanalyzer 2100 (Agilent Technologies, Inc., Santa Clara, CA, USA). Libraries are normalized to 2.5 nM and pooled. Deep Sequencing is performed on Illumina HiSeq 4000 using PE75 V1 kit. Cluster generation and sequencing is performed according to manufacturer's protocol. Base calling was performed using bcl2fastq v2.16.0.10 (Illumina, Inc., San Diego, CA, USA) to generate sequence reads in FASTQ format (Illumina 1.8+ encoding). Quality control (QC) was applied with Trimmomatic (27). High quality reads were mapped to the human genome (hg19, GRCh37 Genome Reference Consortium Human Reference 37) using modified BWA aligner 0.7.12 (28) with BWA-MEM algorithm and default parameters to create SAM files. Picard 1.119 (http://picard.sourceforge.net/) was used to convert SAM files to compressed BAM files which were then sorted according to chromosome coordinates. The Genome Analysis Toolkit (29) (GATK, version 3.4–0) was modified and used to locally realign the BAMs files at intervals with indel mismatches and recalibrate base quality scores of reads in BAM files (30). Single nucleotide variants (SNVs) and short insertions/deletions (indels) were identified using VarScan2 2.3.9 (31) with minimum variant allele frequency threshold set at 0.01 and P-value threshold for calling variants set at 0.05 to generate Variant Call Format (VCF) files. All SNVs/indels were annotated with ANNOVAR, and each SNV/indel was manually checked with the Integrative Genomics Viewer (32) (IGV). Copy number variations (CNVs) were identified using ADTEx 1.0.4 (33). The 416 gene exons sequencing report from Geneseeq Technology Inc also provided the drug treatment suggestions.

Treatment protocol

From the 3rd generation, PDX tumors were permitted to grow to a volume of 150–200 mm3, then mice were randomized (6 mice with tumors per group and housed in per rearing cage) and dosing was administrated (AZD6244, 50 mg/kg p.o. qd; Capecitabine, 1.0 mM/kg p.o. qd) for 4 weeks. Mice were weighed for signs of toxicity and tumor size was evaluated once per week. TGI (Relative tumor growth inhibition) was calculated using the following formula: (1-T/C)%, where T means the relative tumor volume of the treated mice, and C means the relative tumor volume of the control mice.

Immunohistochemistry

Specimen were fixed by 10 neutral formalin, then embedded in paraffin, sectioned (5 µm thick) and placed on slides for marker analysis. Sections were incubated with the primary antibodies overnight at 4°C, after blocking nonspecific antibody bindings. The streptavidin-biotin peroxidase complex method (Lab Vision, Nairobi, Kenya) was used for immunohistochemistry. The slides were photographed using an Olympus BX60 (Olympus, Hamburg, Germany).

Statistical analysis

Results were presented as mean ± SD. Calculation and statistics were performed with Excel 2010 (Microsoft Corporation, Redmond, WA, USA) and GraphPad Prism 5 (GraphPad Software Inc., La Jolla, CA, USA). One-way ANOVA were used to analyze the significance of differences among groups. P<0.05 was considered statistically significant.

Results

Patient characteristics and PDX model establishment

Pancreatic cancer bone metastasis (diagnosed as adenocarcinoma) tissues obtained at surgery from a 67-year-old female patient were subcutaneously implanted into BALB/c nude mice for the PDX model establishment. Tumors were re-implanted in new mice after reaching a volume of 1,000 mm3 as model passaging. The PDX model was serially passaged in animals 3 times. In order to further evaluate the PDX xenograft, immunohistochemical test was performed to identify if the patient's characteristics were retained in the PDX. Immunohistochemical expressions of CK19, CK7, and ki67 as well as the H&E staining showed that the pathological characteristics of the third passage xenograft was in accordance with the original patient sample (Fig. 1).

Figure 1.

Figure 1.

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Immunohistochemical expressions compaired with PDX and patient tumor. The pathological characteristics of the third passage PDX xenograft was in accordance with the original patient sample. PDX, patient-derived xenograft; H&E, hematoxylin and eosin.

Next generation sequencing for drug efficacy prediction

The sequencing of pancreatic cancer bone metastasis tissues of the patient tumor was conducted by Geneseeq Technology Inc. Totally, 13 pancreatic cancer-associated gene polymorphisms/mutations were found out of the 416 genes sequenced (Tables I and II). Based on the sequencing results and associated literatures, there were no under-clinical-trial targeted therapies of pancreatic cancer directly suitable for the genes detected. Therefore AZD6244 (AZD for short, also named as Selumetinib), a highly selective inhibitor against MEK1, was chosen as a potential therapy whose antitumor efficacy would then be evaluated in our PDX model.

Table I.

Next generation sequencing of the patient tumor.

Gene AA Change Type Allele call Abundance
BRCA2 N372H SNP Homozygous
BRIP1 R439X SNP Homozygous 48%
CYP2D6 P34S SNP Homozygous
CYP3A5 CYP3A5*3 SNP Homozygous
EGFR R521K SNP Homozygous
ERBB2 I655V SNP Homozygous
ERBB2 P1170A SNP Heterozygous
GSTM1 Deletion Homozygous
GSTT1 Deletion Homozygous
KRAS G12D SNP 5%
NQO1 P187S SNP Homozygous
PTEN R173C SNP 37%
UGT1A1 6/7TA SNP Heterozygous
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Table II.

416 genes for analysis.

ABCC2 DMNT3A KDR RAF1
ACTB DNM2 KIF1B RARA
ADH1B DOCK1 KIT RASGEF1A
AIP DOT1L KMT2B RB1
AKT1 DPYD KMT2C RECQL4
AKT2 DUSP2 KRAS RELN
AKT3 EBF1 LEF1 RET
ALDH2 ECT2L LMO1 RHBDF2
ALK EED LSP1 RHOA
AMER1 EGFR LYN RICTOR
AP3B1 EGR1 LYST RNF146
APC EP300 LZTR1 RNF43
AR EPCAM MAP2K1 ROS1
ARAF EPHA3 MAP2K2 ROS1
ARID1A ERBB2 MAP2K4 RPTOR
ARID2 ERBB3 MAP3K1 RRM1
ARID5B ERBB4 MCL1 RUNX1
ASXL1 ERCC1 MDM2 SBDS
ATM ERCC2 MDM4 SDHA
ATR ERCC3 MECOM SDHAF2
ATRX ERCC4 MED12 SDHB
AURKA ERCC5 MEF2B SDHC
AURKB ESR1 MEN1 SDHD
AXIN1 ETV1 MET SERP2
AXL ETV4 MGMT SETBP1
B2M EWSR1 MITF SETD2
BAP1 EXT1 MLH1 SF3B1
BARD1 EXT2 MLL SGK1
BAT3 EZH2 MLLT10 SH2D1A
BCL2 FANCA MLPH SLX4
BCL2L1 FANCB MPL SMAD2
BCL2L2 FANCC MRE11A SMAD3
BCORL1 FANCD2 MSH2 SMAD4
BIM(BCL2L11) FANCE MSH3 SMAD7
BLM FANCF MSH6 SMARCA4
BMPR1A FANCG MTHFR SMARCB1
BRAF FANCI MTOR SMC1A
BRCA1 FANCL MUTYH SMC3
BRCA2 FANCM MYC SMO
BRD4 FAT1 MYCL1 SOX2
BRIP1 FBXO11 MYCN SPOP
BTG2 FCGR2B MYD88 SRC
BTK FGF19 MYNN SRSF2
BTLA FGFR1 NBN STAG2
BUB1B FGFR2 NCSTN STAT3
c11orf30 FGFR3 NF1 STAT5A
CALR FGFR4 NF2 STAT5B
CBL FH NFKBIA STIL
CCND1 FIP1L1 NKX2-1 STK11
CCNE1 FLCN NOTCH1 STMN1
CCT6B FLT1 NOTCH2 STX11
CD22 FLT3 NPM1 STXBP2
CD274 FLT4 NQO1 SUFU
CD58 GADD45B NRAS SUZ12
CD70 GATA1 NRG1 SYN3
CDA GATA2 NSD1 TCN2
CDC73 GATA3 NT5C2 TEK
CDH1 GATA4 NTRK1 TEKT4
CDK10 GATA6 PAG1 TERC
CDK12 GNA11 PAK3 TERT
CDK4 GNA13 PALB2 TET2
CDK6 GNAQ PARK2 TGFBR2
CDK8 GNAS PAX5 TLE1
CDKN1B GPC3 PBRM1 TLE4
CDKN1C GRIN2A PC TMEM127
CDKN2A GRM3 PDCD1 TMPRSS2
CDKN2B GSTM1 PDCD1LG2 TNFAIP3
CDKN2C GSTP1 PDGFRA TNFRSF14
CEBPA GSTT1 PDGFRB TNFRSF17
CEP57 HBA1 PDK1 TNFRSF19
CHD4 HBA2 PHF6 TOP1
CHEK1 HBB PHOX2B TOP2A
CHEK2 HDAC1 PICK3R1 TP53
CKS1B HDAC2 PIK3C3 TP63
CREBBP HDAC4 PIK3CA TPMT
CRKL HDAC7 PIK3CD TRAF2
CROT HGF PIK3R1 TRAF3
CSF1R HNF1A PIK3R2 TRAF5
CSF3R HNF1B PLCE1 TSC1
CTCF HRAS PLK1 TSC2
CTLA4 ID3 PMS1 TSHR
CTNNB1 IDH1 PMS2 TTF1
CUX1 IDH2 POLD1 TUBB3
CXCR4 IGF1R POLD3 TYMS
CYLD IGF2 POLE TYR
CYP2B6*6 IKBKE POT1 U2AF1
CYP2B6*6 IKZF1 PPP2R1A UGT1A1
CYP2C19*2 IKZF2 PRDM1 UNC13D
CYP2C9*3 IKZF3 PRF1 VEGFA
CYP2D6 IL13 PRKAR1A VHL
CYP2D6*3 IL7R PRKCI WISP3
CYP2D6*4 INPP4B PTCH1 WRN
CYP2D6*6 INPP5D PTEN WT1
CYP3A4*4 IRF1 PTPN11 XIAP
CYP3A5*3 IRF2 PTPN2 XPA
DAB2 IRF4 PTPN6 XPC
DAXX IRF8 PTPRO XPO1
DDB2 JAK1 QKI XRCC1
DDR2 JAK2 RAC1 YAP1
DDX1 JAK3 RAD21 ZAP70
DHFR JARID2 RAD50 ZBTB20
DICER1 JUN RAD51 ZNF217
DIS3L2 KDM2B RAD51C ZNF703
DLG2 KDM5A RAD51D ZRSR2
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Efficacy evaluation of AZD6244 based on PDX model

To test whether the PDX model of pancreatic cancer bone metastasis was sensitive to the suggested therapy, antitumor-growth ability of AZD6244 were evaluated (Capecitabine for positive control). Since tumors volume reached 150–200 mm3, orally administration of AZD6244 (50 mg/kg), Capecitabine (1.0 mM/kg) or saline were then given once a day for 28 days. The mice were killed and excised tumors were measured. Then, relative tumor growth inhibition (TGI) was calculated as per the following formula: (1-T/C) %, where T is relative tumor volume of treated group mice, and C is relative tumor volume of control group mice. We found that single AZD6244 exhibited better efficacy (TGI, 33.03%) than Capecitabine (TGI, 26.93%), although without statistical significance. While the combination of both shown a significant synergistic effect, with TGI of 54.82% (Fig. 2). By western blotting, we evaluated the changes of ERK and p-ERK expressions in all groups, to find that p-ERK expressions were significantly suppressed in both single and combined AZD6244 groups (Fig. 3). By immunohistochemical staining, we found that PCNA (proliferating cell nuclear antigen) expressions in the AZD6244-treated groups were significantly suppressed, while caspase-3 (one of apoptosis associated antigens) expressions were significantly upregulated (Figs. 4 and 5). Therefore, AZD6244 was evaluated effective for the pancreatic cancer PDX model, thus might provide potential efficay in the clinical treatment of the very pancreatic cancer patient.

Figure 2.

Figure 2.

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(A) Antitumor-growth ability of AZD6244. (B) The single AZD6244 exhibited better efficacy than Capecitabine, while the combination of both shown a significant synergistic effect.

Figure 3.

Figure 3.

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Western blot analysis for changes of ERK and p-ERK expressions in all groups. The p-ERK expressions were significantly suppressed in both single and combined AZD6244 groups. **P<0.01, *P<0.05.

Figure 4.

Figure 4.

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Immunohistochemical staining shown that PCNA expressions in the AZD6244-treated groups were significantly suppressed.

Figure 5.

Figure 5.

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Immunohistochemical staining shown that caspase-3 expressions in the AZD6244-treated groups were significantly upregulated.

Disscussion

Novel technologies contribute to the progress of the drug screening of pancreatic cancer during recent years. PDX models are being used for pancreatic cancer research in a series of studies (2,7,34,35), while NGS technologies contribute to the translational research of pancreatic cancer (36–38). Multiple clinical studies have showed NGS and PDX will ameliorate personalized medicine and will be necessary for discovering novel therapeutic targets and biomarkers (39). With the progress of these technologies, both are getting economically availble for patients. In our study, we combined PDX and NGS as an promising pattern of individualized drug screening to improve the clinical treatment of pancreatic cancer patients.

The PDX model of pancreatic cancer bone metastasis we established was comfirmed as highly molecularly stable with clinical patients in our study. Immunohistochemical expressions of CK19, CK7, and ki67 as well as the H&E staining showed that the pathological characteristics of the third passage xenograft was in accordance with the original patient tumor. Therefore, our PDX model could be considered as an ‘Avatar’ or a ‘stand-in’ of our pancreatic cancer patient, which would be a quite promising platform for drug screeing and evaluation.

In order to select the potential therapies customized for the pancreatic cancer patient, the bone metastasis tumor tissues were used for NGS detection (Geneseeq Technology Inc). However, based on the sequencing results and associated literatures, we found no under-clinical-trial targeted therapies of pancreatic cancer directly suitable for the genes detected. The sequencing report from Geneseeq Technology Inc also provided the alternative drug treatment suggestions, and MEK1 inhibitor was one of the most promising targeted therapies suggested. Then we concentrated on MEK1, a downstream gene of KRAS, which might be a potential target for treatment. Therefore we chose AZD6244, a MEK1 inhibitor, as a potential therapy which would then be evaluated in our PDX model.

In our study, we found that single AZD6244 exhibited better efficacy than Capecitabine, although without statistical significance. While the combination of both shown a significant synergistic effect, with TGI of 54.82%. AZD6244 significantly suppressed p-ERK expressions of the pancreatic cancer PDX model. AZD6244 significantly suppressed tumor cell proliferation and upregulated tumor cell apoptosis. Several studies have evaluated the effect of AZD6244 in pancreatic cancer in preclinical and clinical phase, and AZD6244 was shown to be effective in combination with EGFR/PIK3CA/STAT3 inhibitors in patients with pancreatic cancer (40–42). While we have shown that AZD6244 also has a synergistic effect in combination with Capecitabine. In addition, it was suggested that AZD6244 alone was mainly cytostatic, and apoptosis was mainly induced by combination therapies targeting multiple pathways (43). While here in the present study, we shown that AZD6244 also suppressed tumor cell proliferation as a sinlge agent. Therefore, AZD6244 was evaluated as effective for the pancreatic cancer PDX model, thus might provide potential efficay in the clinical treatment of this pancreatic cancer patient.

In our study, AZD6244, a highly selective MEK1 inhibitor, was evaluated as effective for the pancreatic cancer PDX model, and thus might provide potential efficay in the clinical treatment of this pancreatic cancer patient. Although only one targeted agent was evaluated, we have successfully shown PDX-NGS based drug screening as a novel promising pattern of individualized drug screening to improve the clinical treatment of pancreatic cancer patients.

Acknowledgements

The present study was supported by the National Natural Science Foundation of China (grant no. 81374014), Zhejiang Provincial Science and Technology Projects (grant nos. 2015C33264, 2017C33212 and 2017C33213), and Zhejiang Provincial Medical and Healthy Science and Technology Projects (grant nos. 2013KYA228 and 2016KYA180).

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