Patient-derived xenografts effectively capture responses to oncology therapy in a heterogeneous cohort of patients with solid tumors

Abstract

Background: While patient-derived xenografts (PDXs) offer a powerful modality for translational cancer research, a precise evaluation of how accurately patient responses correlate with matching PDXs in a large, heterogeneous population is needed for assessing the utility of this platform for preclinical drug-testing and personalized patient cancer treatment.

Patients and methods: Tumors obtained from surgical or biopsy procedures from 237 cancer patients with a variety of solid tumors were implanted into immunodeficient mice and whole-exome sequencing was carried out. For 92 patients, responses to anticancer therapies were compared with that of their corresponding PDX models.

Results: We compared whole-exome sequencing of 237 PDX models with equivalent information in The Cancer Genome Atlas database, demonstrating that tumorgrafts faithfully conserve genetic patterns of the primary tumors. We next screened PDXs established for 92 patients with various solid cancers against the same 129 treatments that were administered clinically and correlated patient outcomes with the responses in corresponding models. Our analysis demonstrates that PDXs accurately replicate patients' clinical outcomes, even as patients undergo several additional cycles of therapy over time, indicating the capacity of these models to correctly guide an oncologist to treatments that are most likely to be of clinical benefit.

Conclusions: Integration of PDX models as a preclinical platform for assessment of drug efficacy may allow a higher success-rate in critical end points of clinical benefit.

Keywords: PDX; chemotherapy; patient-derived xenograft; translational model; tumorgraft.

© The Author 2017. Published by Oxford University Press on behalf of the European Society for Medical Oncology. All rights reserved. For permissions, please email: journals.permissions@oup.com.

Figures

Figure 1.

Engraftment rate by tumor type. (A) Graph depicting different tumor types implanted into immunodeficient mice to establish PDX models. Green bars represent the total number of implantations carried out for a particular tumor type, whereas blue bars depict a number of successfully engrafted implants (generating at least one model in immunodeficient mice). (B) Graph depicts the percent of engraftment for PDX models generated from different tumor types. Red line shows the average engraftment rate across all tumors.

Figure 2.

PDX models preserve the histopathology and genetic landscape of the parental tumor. (A) Histology (H&E) of PDX models at early passage (2 or 3) and the parental tumor from which they were established. Four primary lesions and matching PDX models from four different tumor types are shown. ×10 magnification. (B) Histology (H&E) of four PDX models at passage 0 (first transplantation into immunodeficient mice) and passage 4. Four PDX models from 4 different tumor types are shown. ×10 magnification. (C) Background mutation rates from WES analysis of 237 early passage PDX models of different tumor types (109 colorectal, 30 ovarian, 38 lung, 30 head and neck and 30 breast cancers) and samples from the TCGA database (224 colorectal, 316 ovarian, 227 lung, 306 head and neck and 772 breast cancers). (D) Spectrum of specific base-pair substitutions in PDX models and TCGA samples across different cancer types analyzed. (E) Venn diagram summarizes single-nucleotide mutations concurrently detected by WES in four primary tumors and their matched early passage PDX counterparts. (F) Spectrum of specific base-pair substitutions in 4 PDX models and parental tumor from which they were established.

Figure 3.

PDX models accurately replicate both positive and negative patient responses. (A, B) PDX models were screened against the corresponding therapies received by the patient. Graphs show the average tumor volume for three to nine animals ± SD. *Treated groups significantly different from untreated controls at the end point of the experiment (Student's t-test; P < 0.05). (C–E) As per (A) and (B). Graphs show the average tumor volume for three to four animals ± SD. No treated group was significantly different from the corresponding untreated control group at the end point of the experiment (Student's t-test; P < 0.05).

Figure 4.

PDX models retain the capacity to replicate multiple lines of therapy. Fragments of each patient tumor [ovarian for (A), lung for (B), and liposarcoma for (C)] were implanted into immunodeficient mice to establish a PDX model. These models were screened against the therapies that had been used to treat each patient as they cycled through disease stabilization/regression and disease recurrence. Graphs depict the average tumor volume (mm3) for the different treatment groups at each measurement point, with standard deviations plotted (n = 3–6 mice/group). Fractional tumor volume was calculated as the ratio of the tumor volume at time t to the tumor volume at time 0. Treated groups significantly different from untreated controls at the end point of the experiment (Student's t-test; P < 0.05).

Figure 5.

PDX drug responses correlate with patient outcomes. (A) Contingency table for correlations between 129 drug responses across 92 PDX models and clinical outcomes seen in patient from whom they were established. **SD, PR and CR were all considered positive test results (disease control) whereas PD was considered a negative test result. Positive and negative PDX responses were defined from measurements of changes in tumor volume and based on RECIST criteria. Positive and negative patient response information was provided by the consulting oncologist. (B) Associations between drug responses in patients and the corresponding PDXs for seven different types of cancer where more than eight patients were available. (C) Contingency tables for correlations between drug responses in PDXs and matched patients when results were stratified for screens against treatments used immediately following surgery (top) or if additional treatments had been employed before the one against which the PDX was screened (bottom). **SD, PR and CR were all considered positive test results (disease control) whereas PD was considered a negative test result. PDX responses were defined from measurements of changes in tumor volume and based on RECIST criteria. Patient responses were provided by the consulting oncologist.