Establishment of patient-derived xenografts from patients with gastrointestinal stromal tumors: analysis of clinicopathological characteristics related to engraftment success

Na, Young Soon; Ryu, Min Hee; Park, Young Soo; Lee, Chae Won; Lee, Ju Kyung; Park, Yangsoon; Park, Jung Min; Ma, Jungeun; Kang, Yoon Koo

doi:10.1038/s41598-020-64552-w

Cited by 4 publications

(2 citation statements)

References 33 publications

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“…One possibility for the higher engraftment rate is that when tumor tissue samples are implanted into immunodeficient mice, cells with strongly positive Ki-67 own high proliferation capacity. Similar results were also reported in PDX models of other types of cancer (31)(32)(33)(34). Besides, our results showed that a higher engraftment rate was observed in dMMR tumors, while no significant association was observed between other important gene mutations (e.g., KRAS and BRAF mutations) and engraftment rate.…”

Section: Discussionsupporting

confidence: 91%

Patient-derived xenograft models for gastrointestinal tumors: A single-center retrospective study

Chen²,

Lü³

et al. 2022

Front. Oncol.

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BackgroundPatient-derived xenograft (PDX) models have shown a great efficiency in preclinical and translational applications. Gastrointestinal (GI) tumors have a strong heterogeneity, and the engraftment rate of PDX models remarkably vary. However, the clinicopathological and molecular characteristics affecting the engraftment rate still remain elusive.MethodsA total of 312 fresh tumor tissue samples from patients with GI cancer were implanted into immunodeficient mice. The median follow-up time of patients was 37 months. Patients’ characteristics were compared in terms of PDX growth and overall survival. PDX models of 3-6 generations were used for drug evaluation.ResultsIn total, 171 (54.8%, 171/312) PDX models were established, including 85 PDX models of colorectal cancer, 21 PDX models of esophageal cancer, and 65 PDX models of gastric cancer. Other than tumor site, histology, differentiation degree, and serum alpha-fetoprotein (AFP) level, no significant differences were found between transplantation of xenografts and patients’ characteristics. For patients who had undergone neoadjuvant therapy, the incidence of tumor formation was higher in those with progressive disease (PD) or stable disease (SD). In gastric cancer, the results showed a higher transplantation rate in deficient mismatch repair (dMMR) tumors, and Ki-67 could be an important factor affecting the engraftment rate. The gene mutation status of RAS and BRAF, two important molecular markers in colorectal cancer, showed a high degree of consistency between patients’ tumors and PDXs. However, no significant effects of these two mutations on PDX engraftment rate were observed. More importantly, in this study although KRAS mutations were detected in two clinical cases, evident tumor inhibition was still observed after cetuximab treatment in both PDX models and patients.ConclusionA large-scale PDX model including 171 cases was successfully established for GI tumors in our center. The relationship between clinicopathological and molecular features and engraftment rates were clarified. Furthermore, this resource provides us with profound insights into tumor heterogeneity, making these models valuable for PDX-guided treatment decisions, and offering the PDX model as a great tool for personalized treatment and translation research.

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Section: Discussionsupporting

confidence: 91%

Patient-derived xenograft models for gastrointestinal tumors: A single-center retrospective study

Chen²,

Lü³

et al. 2022

Front. Oncol.

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“…Modeling TKI-resistant SDH-deficient GIST is needed to find effective therapies for this patient population. Existing GIST models are primarily for KIT-mutant GISTs, while SDH-deficient GISTs have been difficult to model (6)(7)(8)(9). Models for SDH-deficient tumors are limited and the downstream effects of SDH-loss and succinate accumulation is primarily studied by knockdown or genetic depletion of SDH subunits in already established cell lines, murine or hamster cells (10)(11)(12)(13)(14)(15)(16)(17)(18).…”

Section: Introductionmentioning

confidence: 99%

Establishment of Patient-Derived Succinate Dehydrogenase–Deficient Gastrointestinal Stromal Tumor Models for Predicting Therapeutic Response

Yebra

Bhargava

Kumar

et al. 2022

Clinical Cancer Research

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Purpose: Gastrointestinal stromal tumor (GIST) is the most common sarcoma of the gastrointestinal tract, with mutant succinate dehydrogenase (SDH) subunits (A–D) comprising less than 7.5% (i.e., 150–200/year) of new cases annually in the United States. Contrary to GISTs harboring KIT or PDGFRA mutations, SDH-mutant GISTs affect adolescents/young adults, often metastasize, and are frequently resistant to tyrosine kinase inhibitors (TKI). Lack of human models for any SDH-mutant tumors, including GIST, has limited molecular characterization and drug discovery. Experimental Design: We describe methods for establishing novel patient-derived SDH-mutant (mSDH) GIST models and interrogated the efficacy of temozolomide on these tumor models in vitro and in clinical trials of patients with mSDH GIST. Results: Molecular and metabolic characterization of our patient-derived mSDH GIST models revealed that these models recapitulate the transcriptional and metabolic hallmarks of parent tumors and SDH deficiency. We further demonstrate that temozolomide elicits DNA damage and apoptosis in our mSDH GIST models. Translating our in vitro discovery to the clinic, a cohort of patients with SDH-mutant GIST treated with temozolomide (n = 5) demonstrated a 40% objective response rate and 100% disease control rate, suggesting that temozolomide represents a promising therapy for this subset of GIST. Conclusions: We report the first methods to establish patient-derived mSDH tumor models, which can be readily employed for understanding patient-specific tumor biology and treatment strategies. We also demonstrate that temozolomide is effective in patients with mSDH GIST who are refractory to existing chemotherapeutic drugs (namely, TKIs) in clinic for GISTs, bringing a promising treatment option for these patients to clinic. See related commentary by Blakely et al., p. 3

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A Transcriptome-Based Precision Oncology Platform for Patient–Therapy Alignment in a Diverse Set of Treatment-Resistant Malignancies

et al. 2023

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Predicting in vivo response to antineoplastics remains an elusive challenge. We performed first-of-kind evaluation of two transcriptome-based precision cancer medicine methodologies to predict tumor sensitivity to a comprehensive repertoire of clinically relevant oncology drugs, whose mechanism-of-action we experimentally assessed in cognate cell lines. We enrolled patients with histologically distinct, poor prognosis malignancies who had progressed on multiple therapies, and developed low-passage, patient-derived xenograft models that were used to validate 35 patient-specific drug predictions. Both OncoTarget, which identifies high-affinity inhibitors of individual Master Regulator (MR) proteins, and OncoTreat, which identifies drugs that invert the transcriptional activity of hyper-connected MR modules, produced highly significant 30-day disease control rates (68% and 91%, respectively). Moreover, of 18 OncoTreat-predicted drugs, 15 induced the predicted MR-module activity inversion in vivo. Predicted drugs significantly outperformed antineoplastic drugs selected as unpredicted controls, suggesting these methods may substantively complement existing PCM approaches, as also illustrated by a case study.

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Establishment of patient-derived xenografts from patients with gastrointestinal stromal tumors: analysis of clinicopathological characteristics related to engraftment success

Cited by 4 publications

References 33 publications

Patient-derived xenograft models for gastrointestinal tumors: A single-center retrospective study

Patient-derived xenograft models for gastrointestinal tumors: A single-center retrospective study

Establishment of Patient-Derived Succinate Dehydrogenase–Deficient Gastrointestinal Stromal Tumor Models for Predicting Therapeutic Response

A Transcriptome-Based Precision Oncology Platform for Patient–Therapy Alignment in a Diverse Set of Treatment-Resistant Malignancies

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