Characterization of drug responses of mini patient-derived xenografts in mice for predicting cancer patient clinical therapeutic response

Genome-wide CRISPR screen identifies ELP5 as a determinant of gemcitabine sensitivity in gallbladder cancer

Gemcitabine is the first-line treatment for locally advanced and metastatic gallbladder cancer (GBC), but poor gemcitabine response is universal. Here, we utilize a genome-wide CRISPR screen to identify that loss of ELP5 reduces the gemcitabine-induced apoptosis in GBC cells in a P53-dependent manner through the Elongator complex and other uridine 34 (U₃₄) tRNA-modifying enzymes. Mechanistically, loss of ELP5 impairs the integrity and stability of the Elongator complex to abrogate wobble U₃₄ tRNA modification, and directly impedes the wobble U₃₄ modification-dependent translation of hnRNPQ mRNA, a validated P53 internal ribosomal entry site (IRES) trans-acting factor. Downregulated hnRNPQ is unable to drive P53 IRES-dependent translation, but rescuing a U₃₄ modification-independent hnRNPQ mutant could restore P53 translation and gemcitabine sensitivity in ELP5-depleted GBC cells. GBC patients with lower ELP5, hnRNPQ, or P53 expression have poor survival outcomes after gemcitabine chemotherapy. These results indicate that the Elongator/hnRNPQ/P53 axis controls gemcitabine sensitivity in GBC cells.

Gemcitabine is used to treat gallbaldder cancer but patient responses are variable. Here, the authors use a genome-wide CRISPR screen and identify the translational elongator protein ELP5 as a protein that is important for mediating gemcitabine-induced apoptosis.

Generation and application of patient-derived xenograft models in pancreatic cancer research

OBJECTIVE

Pancreatic ductal adenocarcinoma cancer (PDAC) is one of the leading causes of cancer-related death worldwide. Hence, the development of effective anti-PDAC therapies is urgently required. Patient-derived xenograft (PDX) models are useful models for developing anti-cancer therapies and screening drugs for precision medicine. This review aimed to provide an updated summary of using PDX models in PDAC.

DATA SOURCES

The author retrieved information from the PubMed database up to June 2019 using various combinations of search terms, including PDAC, pancreatic carcinoma, pancreatic cancer, patient-derived xenografts or PDX, and patient-derived tumor xenografts or PDTX.

STUDY SELECTION

Original articles and review articles relevant to the review's theme were selected.

RESULTS

PDX models are better than cell line-derived xenograft and other models. PDX models consistently demonstrate retained tumor morphology and genetic stability, are beneficial in cancer research, could enhance drug discovery and oncologic mechanism development of PDAC, allow an improved understanding of human cancer cell biology, and help guide personalized treatment.

CONCLUSIONS

In this review, we outline the status and application of PDX models in both basic and pre-clinical pancreatic cancer researches. PDX model is one of the most appropriate pre-clinical tools that can improve the prognosis of patients with pancreatic cancer in the future.

Addressing Patient Specificity in the Engineering of Tumor Models

Cancer treatment is challenged by the heterogeneous nature of cancer, where prognosis depends on tumor type and disease stage, as well as previous treatments. Optimal patient stratification is critical for the development and validation of effective treatments, yet pre-clinical model systems are lacking in the delivery of effective individualized platforms that reflect distinct patient-specific clinical situations. Advances in cancer cell biology, biofabrication, and microengineering technologies have led to the development of more complex in vitro three-dimensional (3D) models to act as drug testing platforms and to elucidate novel cancer mechanisms. Mostly, these strategies have enabled researchers to account for the tumor microenvironment context including tumor-stroma interactions, a key factor of heterogeneity that affects both progression and therapeutic resistance. This is aided by state-of-the-art biomaterials and tissue engineering technologies, coupled with reproducible and high-throughput platforms that enable modeling of relevant physical and chemical factors. Yet, the translation of these models and technologies has been impaired by neglecting to incorporate patient-derived cells or tissues, and largely focusing on immortalized cell lines instead, contributing to drug failure rates. While this is a necessary step to establish and validate new models, a paradigm shift is needed to enable the systematic inclusion of patient-derived materials in the design and use of such models. In this review, we first present an overview of the components responsible for heterogeneity in different tumor microenvironments. Next, we introduce the state-of-the-art of current in vitro 3D cancer models employing patient-derived materials in traditional scaffold-free approaches, followed by novel bioengineered scaffold-based approaches, and further supported by dynamic systems such as bioreactors, microfluidics, and tumor-on-a-chip devices. We critically discuss the challenges and clinical prospects of models that have succeeded in providing clinical relevance and impact, and present emerging concepts of novel cancer model systems that are addressing patient specificity, the next frontier to be tackled by the field.

Developing a Quantitative Ultrasound Image Feature Analysis Scheme to Assess Tumor Treatment Efficacy Using a Mouse Model

The aim of this study is to investigate the feasibility of identifying and applying quantitative imaging features computed from ultrasound images of athymic nude mice to predict tumor response to treatment at an early stage. A computer-aided detection (CAD) scheme with a graphic user interface was developed to conduct tumor segmentation and image feature analysis. A dataset involving ultrasound images of 23 athymic nude mice bearing C26 mouse adenocarcinomas was assembled. These mice were divided into 7 treatment groups utilizing a combination of thermal and nanoparticle-controlled drug delivery. Longitudinal ultrasound images of mice were taken prior and post-treatment in day 3 and day 6. After tumor segmentation, CAD scheme computed image features and created four feature pools including features computed from (1) prior treatment images only and (2) difference between prior and post-treatment images of day 3 and day 6, respectively. To predict tumor treatment efficacy, data analysis was performed to identify top image features and an optimal feature fusion method, which have a higher correlation to tumor size increase ratio (TSIR) determined at Day 10. Using image features computed from day 3, the highest Pearson Correlation coefficients between the top two features selected from two feature pools versus TSIR were 0.373 and 0.552, respectively. Using an equally weighted fusion method of two features computed from prior and post-treatment images, the correlation coefficient increased to 0.679. Meanwhile, using image features computed from day 6, the highest correlation coefficient was 0.680. Study demonstrated the feasibility of extracting quantitative image features from the ultrasound images taken at an early treatment stage to predict tumor response to therapies.