Article Figures & Data
Figures
- Figure 1.
Proposed preclinical screening and biomarker study in PDX models. This figure graphically illustrates some of the key elements of a preclinical study in PDX models. These studies are likely to be more informative late in preclinical development or in parallel to phase I safety and pharmacology testing. Models can be selected on the basis of tumor types or on predefined molecular subtypes if that information is known and is of interest, or both. We propose a two-step approach. In step 1, a limited number of models can be tested with the agent at doses and schedules known to be effective and pharmacologically active in earlier preclinical studies. Study endpoints need to be carefully selected on the basis of the agent's mechanism of action. Data from step 1 can be used to proceed to step 2 and to redefine model selection based on the molecular understanding of responsive models. In step 2, a larger repertoire of models can be treated. At the conclusion of the study, a decision needs to be made to proceed to clinical development and prioritize biomarkers to be explored in the clinical phase. PD, pharmacodynamic.
- Figure 2.
Co-clinical trial approach with PDX models. A new version of the co-clinical trial concept is presented in which a PDX model is developed from a patient enrolled and treated in a clinical trial with a novel agent. This approach permits models with validated clinical outcome data that can be used to interrogate mechanisms of response and resistance as well as strategies to increase response and overcome resistance, for example, combination strategies. D1, day 1.
- Figure 3.
Personalized medicine strategy. Depicted in this figure is a strategy for individualizing medicine that integrates genomic analysis of a patient tumor with testing in Avatar mouse models. The genomic analysis of a patient tumor is likely to show tens of potential therapeutically targetable mutations. Mining of genomic–drug response databases such as the Cancer Cell Line Encyclopedia (CCLE) or the NCI-60 as well as knowledge of these mutations is likely to result in several potential therapeutic regimens for a given patient. The Avatar model can be used to test and rank these potential treatments to be administered to the patient. A post hoc analysis of this information can be added to existing data to further feed into the existing databases.
Tables
- Table 1.
Key methodologic aspects of selected PDX collections
Reference Tumor type Available models Origin Procurement Processing Mice strain Implantation site Engraftment rate (28) CRC 130 Metastasis Surgery Fresh tumor pieces in Matrigel NOD/SCID s.c. 87% (29) CRC 54 Primary (35) Surgery Fresh tumor pieces Nude s.c. 64% Metastasis (19) (76) CRC 41 Primary Surgery Fresh tumor pieces Nude Orthotopic 89.1% (34) HBC 25 Primary Surgery Fresh tumor pieces Nude s.c. 13% (30) HBC 12 Primary (4) Surgery/fluid drainage Fresh tumor pieces in Matrigel NOD/SCID with estrogen supplementation for ER+ tumors Mammary fat pad 27% Metastasis (8) (16) HBC 24 Primary Biopsies/surgery/fluid drainage Fresh tumor pieces SCID/Beige and NSG w/wo estrogen and immortalized human fibro-blasts Mammary fat pad 3%–21% (21) NSCLC 25 Primary Surgery Fresh tumor pieces NOD/SCID s.c. 25% (20, 22) NSCLC 32 Primary Surgery Fresh tumor pieces NOD/SCID Renal capsule 90% (33) PDAC 42 Primary Surgery Fresh tumor pieces in Matrigel Nude s.c. 61% (26) PDAC 14 Primary Surgery Fresh tumor pieces in Matrigel Nude s.c. NR (77) PDAC 16 Primary (11) Surgery Fresh tumor pieces Nude Orthotopic 62% Metastasis (5) (78) SCCHN 22 Primary Biopsy/surgery Fresh tumor pieces in Matrigel NSG s.c. 85% (25) SCCHN/SCC 21 Primary Surgery Fresh tumor pieces in Matrigel Nude s.c. 54% FOM/FOT (14) Uveal Melanoma 25 Primary (73) Surgery Fresh tumor pieces NOD/SCID s.c. 28% Metastasis (17) NOTE: This table provides a summary of the methodologic approaches used to generate the most comprehensive PDX collections currently available.
Abbreviations: CRC, colorectal cancer; FOM, floor of the mouth; FOT, floor of the tongue; HBC, human breast cancer; NR, not reported; PDAC, pancreatic ductal adenocarcinoma; RCC, renal cell cancer; s.c., subcutaneous implantation; SCC, squamous cell carcinoma; SCCHN, squamous cell carcinoma of the head and neck.
- Table 2.
Fidelity and stability of PDX models
Reference Tumor type Original tumor-first passage Subsequent passages (28) CRC Conserved histopathology characteristics between donor and PDX models. Stable CNA across passages. Similarities in CNA between donor and PDX models. Consistent KRAS, NRAS, BRAF, and PI3K mutation status. (29) CRC Unsupervised clustering analysis using aCGH and GE shows that the donor tumors and PDX clustered together. Stable aCGH and GE profile across passages. 203 differentially expressed annotated genes correspond to stroma-related genes and pathways. (31, 34) HBC Conserved IHC expression of ER, PR, and HER2. Stable CNA and GE profile across passages. Analysis of CNA showed 14/18 paired tumors–PDX shared more than 56% CNA. Variations in stromal related genes. 16/18 paired tumors–PDX clustered together in unsupervised hierarchical analysis. PDX showed losses in 176 and gains in 202 chromosome regions compared with primary tumors. Stable GE profile with less than 5% variations. (30) HBC Conserved histopathology characteristics between donor and PDX models. Stable IHC profile over time. Conserved IHC expression for CK, E-cadherin, β-catenin, vimentin, ER, PR, and HER2. Unsupervised clustering analysis using GE shows that donor tumors and PDX clustered together. Maintenance in the pattern of CNA. Intrinsic breast cancer subtypes concordant between the donor tumors and PDX. (16) HBC Conserved histopathology characteristics between donor and PDX models. Stable histopathologic and IHC expression. Conserved IHC expression for CK, p53, Ki67, ER, PR, HER2, and EGFR. Stable GE, RPPA, and SNP across passages. Intrinsic breast cancer subtypes represented in PDX models. (21) NSCLC Conserved histopathologic characteristics between donor and PDX models. Conserved IHC expression of Ki67 and EpCAM. Unsupervised clustering analysis using GE shows the donor tumors and PDX clustered together with correlation coefficient ranging from 0.78 to 0.94. 134 differentially expressed genes correspond to cell adhesion and immune response genes and pathways. (26, 33) PDAC Concordance in mutations in KRAS and DPC4. Concordance in gemcitabine response between F3 and F6. Conserved GE profile (R2 = 0.69). Enrichment in angiogenesis gene signature in F5. (25) SCC/SCCHN Conserved histopathologic characteristics between donor and PDX models. High correlation (R2 ∼ 0.94) in GE from F2-F4. High correlation (R2 = 0.91) in EGFR expression. Concordance in cetuximab response between F2 and F4. High correlation (R2 ∼ 0.8) in GE. Variation in immune-related pathways. (15) RCC Conserved histopathologic characteristics. Conserved histopathologic characteristics. Donor and PDX models cluster together in unsupervised hierarchical clustering analysis using GE. Serial passages clustered together in unsupervised hierarchical clustering analysis. PDX retained CNA from the donor tumor. Maintains CNA of the donor tumor. Similar mutation landscape in NGS studies. NOTE: This table summarizes the data from different studies in which PDX models have been compared with donor tumors using a variety of methods.
Abbreviations: aCGH, comparative genomic hybridization array; CRC, colorectal cancer; GE, gene expression; HBC, human breast cancer; IHC, immunohistochemistry; NGS, next-generation sequencing, PR, progesterone receptor; RCC, renal cell cancer; RPPA, reverse phase protein array; SNP, single-nucleotide polymorphism; SCC, squamous cell carcinoma.
- Table 3.
Studies correlating PDX treatment results with clinical data
Tumor type (reference) Definition activity Standard agent n RR (%) Clinical correlates CRC TR > 50% Cetuximab 47 10 N/A CRC KRAS WT (28) Cetuximab 38 17 CRC (29) T/C < 10% 5-Fluorouracil 52 13 N/A Oxaliplatin 52 0 Irinotecan 49 38 Cetuximab 52 26 HBC (34) Complete response AC 17 76 Response to treatment in the PDX model was concordant with clinical data in 5/7 patients. TGI > 50% or T/C GD > 2-fold Docetaxel 17 47 Trastuzumab 2 50 GnRH antagonist 1 100 HBC (16) RR > 30% Docetaxel 7 14 92% correlation between clinical responses and responses in PDX Doxorubicin 4 0 Trastuzumab–lapatinib 1 100 NSCLC (20) Statistically significant decrement in tumor area in treated vs. control tumors Cisplatin–vinorelbine 32 28 PDX models from 6/7 patients with early recurrent disease were resistant to the clinically used regimen. Cisplatin–docetaxel 19 42 Cisplatin–gemcitabine 16 44 NSCLC (21) T/C < 5% Etoposide 25 4 N/A Carboplatin 25 12 Gemcitabine 25 12 Paclitaxel 25 16 Vinorelbine 11 0 Cetuximab 25 12 Erlotinib 25 1 SCCHN (25) T/C < 20% Cetuximab 11 9% N/A RCC (15) Statistically significant differences in TGI Sunitinib 8 Active N/A Sirolimus Active Erlotinib Inactive PDAC (26) T/C < 20% Gemcitabine 14 36 N/A Erlotinib 0 Temsirolimus 0 PDAC (33) TGI > 85% Gemcitabine 23 17% Response to gemcitabine in the PDX model predicted longer time to progression in patients. NOTE: This table provides a summary of studies in which PDX models from different cancer types have been treated with agents used in the clinical care of these patients.
Abbreviations: AC, adriamycin–cyclophosphamide; CRC, colorectal cancer; GD, growth delay; GnRH, gonadotrophin-releasing hormone; HBC, human breast cancer; N/A, not available; RR, response rate; TGI, tumor growth inhibition; TR, tumor regression; T/C, treated divided by control; WT, wild-type.
Supplementary Figure 1
Files in this Data Supplement:
- Supplementary Figure 1 - Genomics Mimicry for Personalized Cancer Treatment.
- Supplementary Tables 1 - 2 - Supplementary Table 1: Mouse Strains Used to Develop PDX Models. Supplementary Table 2: Collection of PDX Models Available by the EuroPDX Consortium.
- Supplementary Figure and Table Legends - Supplementary Figure and Tables Legends.
Volume 4, Issue 9
