Patient-Derived Xenograft Models of Non-Small Cell Lung Cancer and Their Potential Utility in Personalized Medicine

Patient-Derived Xenograft Models of Breast Cancer and Their Application

Recently, patient-derived xenograft (PDX) models of many types of tumors including breast cancer have emerged as a powerful tool for predicting drug efficacy and for understanding tumor characteristics. PDXs are established by the direct transfer of human tumors into highly immunodeficient mice and then maintained by passaging from mouse to mouse. The ability of PDX models to maintain the original features of patient tumors and to reflect drug sensitivity has greatly improved both basic and clinical study outcomes. However, current PDX models cannot completely predict drug efficacy because they do not recapitulate the tumor microenvironment of origin, a failure which puts emphasis on the necessity for the development of the next generation PDX models. In this article, we summarize the advantages and limitations of current PDX models and discuss the future directions of this field.

Computational Approaches in Theranostics: Mining and Predicting Cancer Data

The ability to understand the complexity of cancer-related data has been prompted by the applications of (1) computer and data sciences, including data mining, predictive analytics, machine learning, and artificial intelligence, and (2) advances in imaging technology and probe development. Computational modelling and simulation are systematic and cost-effective tools able to identify important temporal/spatial patterns (and relationships), characterize distinct molecular features of cancer states, and address other relevant aspects, including tumor detection and heterogeneity, progression and metastasis, and drug resistance. These approaches have provided invaluable insights for improving the experimental design of therapeutic delivery systems and for increasing the translational value of the results obtained from early and preclinical studies. The big question is: Could cancer theranostics be determined and controlled in silico? This review describes the recent progress in the development of computational models and methods used to facilitate research on the molecular basis of cancer and on the respective diagnosis and optimized treatment, with particular emphasis on the design and optimization of theranostic systems. The current role of computational approaches is providing innovative, incremental, and complementary data-driven solutions for the prediction, simplification, and characterization of cancer and intrinsic mechanisms, and to promote new data-intensive, accurate diagnostics and therapeutics.

MiR-223-3p functions as a tumor suppressor in lung squamous cell carcinoma by miR-223-3p-mutant p53 regulatory feedback loop

Background

MicroRNAs have an important role in diverse biological processes including tumorigenesis. MiR-223 has been reported to be deregulated in several human cancer types. However, its biological role has not been functionally characterized in lung squamous cell carcinoma (LSCC). The following study investigates the role of miR-223-3p in LSCC growth and metastasis and its underlying mechanism.

Methods

MicroRNA profiling analyses were conducted to determine differential miRNAs expression levels in LSCC tumor tissues that successfully formed xenografts in immunocompromised mice (XG) and failed tumor tissues (no-XG). RT-PCR and in situ hybridization (ISH) was performed to evaluate the expression of miR-223-3p in 12 paired adjacent normal tissues and LSCC specimens. Cell proliferation and migration were assessed by CCK-8, colony formation and Transwell assay, respectively. The role of miR-223-3p in LSCC tumorigenesis was examined using xenograft nude models. Bioinformatics analysis, Dual-luciferase reporter assays, Chromatin immunoprecipitation (ChIP) assay and Western blot analysis were used to identify the direct target of miR-223-3p and its interactions.

Results

MiR-223-3p was downregulated in LSCC tissues that successfully formed xenografts (XG) compared with tumor tissues that failed (no-XG), which was also significantly reduced in LSCC tissues compared with the adjacent normal tissues. Gain- and loss-of function experiments showed that miR-223-3p inhibited proliferation and migration in vitro. More importantly, miR-223-3p overexpression greatly suppressed tumor growth in vivo. Mechanistically, we found that mutant p53 bound to the promoter region of miR-223 and reduced its transcription. Meanwhile, p53 is a direct target of miR-223-3p. Thus, miR-223-3p regulated mutant p53 expression in a feedback loop that inhibited cell proliferation and migration.

Conclusions

Our study identified miR-223-3p, as a tumor suppressor gene, markedly inhibited cell proliferation and migration via miR-223-3p-mutant p53 feedback loop, which suggested miR-223-3p might be a new therapeutic target in LSCC bearing p53 mutations.

Electronic supplementary material

The online version of this article (10.1186/s13046-019-1079-1) contains supplementary material, which is available to authorized users.

Survival Prolongation Index as a Novel Metric to Assess Anti-Tumor Activity in Xenograft Models

A single efficacy metric quantifying anti-tumor activity in xenograft models is useful in evaluating different tumors' drug sensitivity and dose-response of an anti-tumor agent. Commonly used metrics include the ratio of tumor volume in treated vs. control mice (T/C), tumor growth inhibition (TGI), ratio of area under the curve (AUC), and growth rate inhibition (GRI). However, these metrics have some limitations. In particular, for biologics with long half-lives, tumor volume (TV) of treated xenografts displays a delay in volume reduction (and in some cases, complete regression) followed by a growth rebound. These observed data cannot be described by exponential functions, which is the underlying assumption of TGI and GRI, and the fit depends on how long the tumor volumes are monitored. On the other hand, T/C and TGI only utilizes information from one chosen time point. Here, we propose a new metric called Survival Prolongation Index (SPI), calculated as the time for drug-treated TV to reach a certain size (e.g., 600 mm³) divided by the time for control TV to reach 600mm³ and therefore not dependent on the chosen final time point t_f. Simulations were conducted under different scenarios (i.e., exponential vs. saturable growth, linear vs. nonlinear kill function). For all cases, SPI is the most linear and growth-rate independent metric. Subsequently, a literature analysis was conducted using 11 drugs to evaluate the correlation between pre-clinically obtained SPI and clinical overall response. This retrospective analysis of approved drugs suggests that a predicted SPI of 2 is necessary for clinical response.

The lung microenvironment: an important regulator of tumour growth and metastasis

Lung cancer is a major global health problem, as it is the leading cause of cancer-related deaths worldwide. Major advances in the identification of key mutational alterations have led to the development of molecularly targeted therapies, whose efficacy has been limited by emergence of resistance mechanisms. US Food and Drug Administration (FDA)-approved therapies targeting angiogenesis and more recently immune checkpoints have reinvigorated enthusiasm in elucidating the prognostic and pathophysiological roles of the tumour microenvironment in lung cancer. In this Review, we highlight recent advances and emerging concepts for how the tumour-reprogrammed lung microenvironment promotes both primary lung tumours and lung metastasis from extrapulmonary neoplasms by contributing to inflammation, angiogenesis, immune modulation and response to therapies. We also discuss the potential of understanding tumour microenvironmental processes to identify biomarkers of clinical utility and to develop novel targeted therapies against lung cancer.

Expansion of cancer stem cell pool initiates lung cancer recurrence before angiogenesis

Angiogenesis is essential in the early stage of solid tumor recurrence, but how a suspensive tumor is reactivated before angiogenesis is mostly unknown. Herein, we stumble across an interesting phenomenon that s.c. xenografting human lung cancer tissues can awaken the s.c. suspensive tumor in nude mice. We further found that a high level of insulin-like growth factor 1 (IGF1) was mainly responsible for triggering the transition from suspensive tumor to progressive tumor in this model. The s.c. suspensive tumor is characterized with growth arrest, avascularity, and a steady-state level of proliferating and apoptotic cells. Intriguingly, CD133⁺ lung cancer stem cells (LCSCs) are highly enriched in suspensive tumor compared with progressive tumor. Mechanistically, high IGF1 initiates LCSCs self-renewal from asymmetry to symmetry via the activation of a PI3K/Akt/β-catenin axis. Next, the expansion of LCSC pool promotes angiogenesis by increasing the production of CXCL1 and PlGF in CD133⁺ LCSCs, which results in lung cancer recurrence. Clinically, a high level of serum IGF1 in lung cancer patients after orthotopic lung cancer resection as an unfavorable factor is strongly correlated with the high rate of recurrence and indicates an adverse progression-free survival. Vice versa, blocking IGF1 or CXCL1/PlGF with neutralizing antibodies can prevent the reactivation of a suspensive tumor induced by IGF1 stimulation in the mouse model. Collectively, the expansion of LCSC pool before angiogenesis induced by IGF1 is a key checkpoint during the initiation of cancer relapse, and targeting serum IGF1 may be a promising treatment for preventing recurrence in lung cancer patients.

Utility of Glioblastoma Patient-Derived Orthotopic Xenografts in Drug Discovery and Personalized Therapy

Despite substantial effort and resources dedicated to drug discovery and development, new anticancer agents often fail in clinical trials. Among many reasons, the lack of reliable predictive preclinical cancer models is a fundamental one. For decades, immortalized cancer cell cultures have been used to lay the groundwork for cancer biology and the quest for therapeutic responses. However, cell lines do not usually recapitulate cancer heterogeneity or reveal therapeutic resistance cues. With the rapidly evolving exploration of cancer “omics,” the scientific community is increasingly investigating whether the employment of short-term patient-derived tumor cell cultures (two- and three-dimensional) and/or patient-derived xenograft models might provide a more representative delineation of the cancer core and its therapeutic response. Patient-derived cancer models allow the integration of genomic with drug sensitivity data on a personalized basis and currently represent the ultimate approach for preclinical drug development and biomarker discovery. The proper use of these patient-derived cancer models might soon influence clinical outcomes and allow the implementation of tailored personalized therapy. When assessing drug efficacy for the treatment of glioblastoma multiforme (GBM), currently, the most reliable models are generated through direct injection of patient-derived cells or more frequently the isolation of glioblastoma cells endowed with stem-like features and orthotopically injecting these cells into the cerebrum of immunodeficient mice. Herein, we present the key strengths, weaknesses, and potential applications of cell- and animal-based models of GBM, highlighting our experience with the glioblastoma stem-like patient cell-derived xenograft model and its utility in drug discovery.

Establishment and evaluation of four different types of patient-derived xenograft models

Background

Patient-derived xenografts (PDX) have a biologically stable in tumor architecture, drug responsiveness, mutational status and global gene-expression patterns. Numerous PDX models have been established to date, however their thorough characterization regarding the tumor formation and rates of tumor growth in the established models remains a challenging task. Our study aimed to provide more detailed information for establishing the PDX models successfully and effectively.

Methods

We transplanted four different types of solid tumors from 108 Chinese patients, including 21 glioblastoma (GBM), 11 lung cancers (LC), 54 gastric cancers (GC) and 21 colorectal cancers (CRC), and took tumor tissues passaged for three successive generations. Here we report the rate of tumor formation, tumor-forming times, tumor growth curves and mortality of mice in PDX model. We also report H&E staining and immunohistochemistry for HLA-A, CD45, Ki67, GFAP, and CEA protein expression between patient cancer tissues and PDX models.

Results

Tumor formation rate increased significantly in subsequent tumor generations. Also, the survival rates of GC and CRC were remarkably higher than GBM and LC. As for the time required for the formation of tumors, which reflects the tumor growth rate, indicated that tumor growth rate always increased as the generation number increased. The tumor growth curves also illustrate this law. Similarly, the survival rate of PDX mice gradually improved with the increased generation number in GC and CRC. And generally, there was more proliferation (Ki67+) in the PDX models than in the patient tumors, which was in accordance with the results of tumor growth rate. The histological findings confirm similar histological architecture and degrees of differentiation between patient cancer tissues and PDX models with statistical analysis by GraphPad Prism 5.0.

Conclusion

We established four different types of PDX models successfully, and our results add to the current understanding of the establishment of PDX models and may contribute to the extension of application of different types of PDX models.

Gamma Secretase Inhibition by BMS-906024 Enhances Efficacy of Paclitaxel in Lung Adenocarcinoma

Notch signaling is aberrantly activated in approximately one third of non-small cell lung cancers (NSCLC). We characterized the interaction between BMS-906024, a clinically relevant Notch gamma secretase inhibitor, and front-line chemotherapy in preclinical models of NSCLC. Chemosensitivity assays were performed on 14 human NSCLC cell lines. There was significantly greater synergy between BMS-906024 and paclitaxel than BMS-906024 and cisplatin [mean combination index (CI) value, 0.54 and 0.85, respectively, P = 0.01]. On an extended panel of 31 NSCLC cell lines, 25 of which were adenocarcinoma, the synergy between BMS-906024 and paclitaxel was significantly greater in KRAS- and BRAF-wildtype than KRAS- or BRAF-mutant cells (mean CI, 0.43 vs. 0.90, respectively; P = 0.003). Paclitaxel-induced Notch1 activation was associated with synergy between BMS-906024 and paclitaxel in the KRAS- or BRAF-mutant group. Knockdown of mutant KRAS increased the synergy between BMS-906024 and paclitaxel in heterozygous KRAS-mutant cell lines. Among KRAS- or BRAF-mutant NSCLC, there was a significant correlation between synergy and mutant or null TP53 status, as well as between synergy and a low H₂O₂ pathway signature. Exogenous overexpression of activated Notch1 or Notch3 had no effect on the enhanced sensitivity of NSCLC to paclitaxel by BMS-906024. In vivo studies with cell line- and patient-derived lung adenocarcinoma xenografts confirmed enhanced antitumor activity for BMS-906024 plus paclitaxel versus either drug alone via decreased cell proliferation and increased apoptosis. These results show that BMS-906024 sensitizes NSCLC to paclitaxel and that wild-type KRAS and BRAF status may predict better patient response to the combination therapy. Mol Cancer Ther; 16(12); 2759-69. ©2017 AACR.

Models of Models: A Translational Route for Cancer Treatment and Drug Development

Every patient and every disease is different. Each patient therefore requires a personalized treatment approach. For technical reasons, a personalized approach is feasible for treatment strategies such as surgery, but not for drug-based therapy or drug development. The development of individual mechanistic models of the disease process in every patient offers the possibility of attaining truly personalized drug-based therapy and prevention. The concept of virtual clinical trials and the integrated use of in silico, in vitro, and in vivo models in preclinical development could lead to significant gains in efficiency and order of magnitude increases in the cost effectiveness of drug development and approval. We have developed mechanistic computational models of large-scale cellular signal transduction networks for prediction of drug effects and functional responses, based on patient-specific multi-level omics profiles. However, a major barrier to the use of such models in a clinical and developmental context is the reliability of predictions. Here we detail how the approach of using “models of models” has the potential to impact cancer treatment and drug development. We describe the iterative refinement process that leverages the flexibility of experimental systems to generate highly dimensional data, which can be used to train and validate computational model parameters and improve model predictions. In this way, highly optimized computational models with robust predictive capacity can be generated. Such models open up a number of opportunities for cancer drug treatment and development, from enhancing the design of experimental studies, reducing costs, and improving animal welfare, to increasing the translational value of results generated.