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Exploring the Complementarity of Pancreatic Ductal Adenocarcinoma Preclinical Models.

ABSTRACT: Purpose: Compare pancreatic ductal adenocarcinoma (PDAC), preclinical models, by their transcriptome and drug response landscapes to evaluate their complementarity. Experimental Design: Three paired PDAC preclinical models-patient-derived xenografts (PDX), xenograft-derived pancreatic organoids (XDPO) and xenograft-derived primary cell cultures (XDPCC)-were derived from 20 patients and analyzed at the transcriptomic and chemosensitivity level. Transcriptomic characterization was performed using the basal-like/classical subtyping and the PDAC molecular gradient (PAMG). Chemosensitivity for gemcitabine, irinotecan, 5-fluorouracil and oxaliplatin was established and the associated biological pathways were determined using independent component analysis (ICA) on the transcriptome of each model. The selection criteria used to identify the different components was the chemosensitivity score (CSS) found for each drug in each model. Results: PDX was the most dispersed model whereas XDPO and XDPCC were mainly classical and basal-like, respectively. Chemosensitivity scoring determines that PDX and XDPO display a positive correlation for three out of four drugs tested, whereas PDX and XDPCC did not correlate. No match was observed for each tumor chemosensitivity in the different models. Finally, pathway analysis shows a significant association between PDX and XDPO for the chemosensitivity-associated pathways and PDX and XDPCC for the chemoresistance-associated pathways. Conclusions: Each PDAC preclinical model possesses a unique basal-like/classical transcriptomic phenotype that strongly influences their global chemosensitivity. Each preclinical model is imperfect but complementary, suggesting that a more representative approach of the clinical reality could be obtained by combining them. Translational Relevance: The identification of molecular signatures that underpin drug sensitivity to chemotherapy in PDAC remains clinically challenging. Importantly, the vast majority of studies using preclinical in vivo and in vitro models fail when transferred to patients in a clinical setting despite initially promising results. This study presents for the first time a comparison between three preclinical models directly derived from the same patients. We show that their applicability to preclinical studies should be considered with a complementary focus, avoiding tumor-based direct extrapolations, which might generate misleading conclusions and consequently the overlook of clinically relevant features.

SUBMITTER: Hoare O

PROVIDER: S-EPMC8161239 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal cancer with high incidences of p53 mutations. AZD1775 (adavosertib, previously MK-1775) is a small molecule WEE1 inhibitor that abrogates the G2M checkpoint and can potentially synergize with DNA damaging therapies commonly used in PDAC treatment. The purpose of this study was to identify combination partners for AZD1775, including standard chemotherapy or targeted agents, in PDAC preclinical models. Low powered preliminary screens demonstrated that two of the four PDX models responded better to the combinations of AZD1775 with irinotecan or capecitabine than to either single agent. Following the screens, two full powered PDAC PDX models of differing p53 status were tested with the combinations of AZD1775 and irinotecan or capecitabine. The combinations of AZD1775 and SN38 or 5-FU were also tested on PDAC cell lines. Cellular proliferation was measured using an IncuCyte Live Cell Imager and apoptosis was measured using a Caspase-Glo 3/7 assay. Flow cytometry was conducted to measure alterations in cell cycle distribution. Western blot analysis was used to determine the effects of the drug combinations on downstream effectors. In PDX models with mutated p53 status, there was significant tumor growth inhibition from the combination of AZD1775 with irinotecan or capecitabine (P ≤ 0.03), while PDX models with wild type p53 did not show anti-tumor synergy from the same combinations (P ≥ 0.08). The combination of AZD1775 with SN38 or 5-FU significantly decreased proliferation in all PDAC cell lines, and enhanced apoptosis in multiple cell lines. Cell cycle distribution was disrupted from the combination of AZD1775 with SN38 or 5-FU which was recorded as G2M arrest and decreased G1 phase. AZD1775 inhibited phospho-CDC2 and increased the expression of γH2AX that was either maintained or enhanced after combination with SN38 or 5-FU. The combination of AZD1775 with irinotecan/SN38 or capecitabine/5-FU showed anti-tumor effects in vivo and in vitro in PDAC models. These results support further investigation for these combination strategies to enhance outcomes for PDAC patients.

Project description:IntroductionPancreatic ductal adenocarcinoma (PDAC) has the highest mortality rate among all solid tumors. Tumorigenesis is promoted by the oncogene KRAS, and KRAS mutations are prevalent in patients with PDAC. Therefore, a comprehensive understanding of the interactions between KRAS mutations and PDAC may expediate the development of therapeutic strategies for reversing the progression of malignant tumors. Our study aims at establishing and validating a prediction model of KRAS mutations in patients with PDAC based on survival analysis and mRNA expression.MethodsA total of 184 and 412 patients with PDAC from The Cancer Genome Atlas (TCGA) database and the International Cancer Genome Consortium (ICGC), respectively, were included in the study.ResultsAfter tumor mutation profile and copy number variation (CNV) analyses, we established and validated a prediction model of KRAS mutations, based on survival analysis and mRNA expression, that contained seven genes: CSTF2, FAF2, KIF20B, AKR1A1, APOM, KRT6C, and CD70. We confirmed that the model has a good predictive ability for the prognosis of overall survival (OS) in patients with KRAS-mutated PDAC. Then, we analyzed differential biological pathways, especially the ferroptosis pathway, through principal component analysis, pathway enrichment analysis, Gene Ontology (GO) enrichment analysis, and gene set enrichment analysis (GSEA), with which patients were classified into low- or high-risk groups. Pathway enrichment results revealed enrichment in the cytokine-cytokine receptor interaction, metabolism of xenobiotics by cytochrome P450, and viral protein interaction with cytokine and cytokine receptor pathways. Most of the enriched pathways are metabolic pathways predominantly enriched by downregulated genes, suggesting numerous downregulated metabolic pathways in the high-risk group. Subsequent tumor immune infiltration analysis indicated that neutrophil infiltration, resting CD4 memory T cells, and resting natural killer (NK) cells correlated with the risk score. After verifying that the seven gene expression levels in different KRAS-mutated pancreatic cancer cell lines were similar to that in the model, we screened potential drugs related to the risk score.DiscussionThis study established, analyzed, and validated a model for predicting the prognosis of PDAC based on risk stratification according to KRAS mutations, and identified differential pathways and highly effective drugs.

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Exploring the Complementarity of Pancreatic Ductal Adenocarcinoma Preclinical Models.

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