Patient-derived xenografts faithfully replicated clinical outcome in a phase II co-clinical trial of arsenic trioxide in relapsed small cell lung cancer

The roles of patient-derived xenograft models and artificial intelligence toward precision medicine

Patient-derived xenografts (PDX) involve transplanting patient cells or tissues into immunodeficient mice, offering superior disease models compared with cell line xenografts and genetically engineered mice. In contrast to traditional cell-line xenografts and genetically engineered mice, PDX models harbor the molecular and biologic features from the original patient tumor and are generationally stable. This high fidelity makes PDX models particularly suitable for preclinical and coclinical drug testing, therefore better predicting therapeutic efficacy. Although PDX models are becoming more useful, the several factors influencing their reliability and predictive power are not well understood. Several existing studies have looked into the possibility that PDX models could be important in enhancing our knowledge with regard to tumor genetics, biomarker discovery, and personalized medicine; however, a number of problems still need to be addressed, such as the high cost and time-consuming processes involved, together with the variability in tumor take rates. This review addresses these gaps by detailing the methodologies to generate PDX models, their application in cancer research, and their advantages over other models. Further, it elaborates on how artificial intelligence and machine learning were incorporated into PDX studies to fast-track therapeutic evaluation. This review is an overview of the progress that has been done so far in using PDX models for cancer research and shows their potential to be further improved in improving our understanding of oncogenesis.

Emerging In Vitro and In Vivo Models: Hope for the Better Understanding of Cancer Progression and Treatment

Various cancer models have been developed to aid the understanding of the underlying mechanisms of tumor development and evaluate the effectiveness of various anticancer drugs in preclinical studies. These models accurately reproduce the critical stages of tumor initiation and development to mimic the tumor microenvironment better. Using these models for target validation, tumor response evaluation, resistance modeling, and toxicity comprehension can significantly enhance the drug development process. Herein, various in vivo or animal models are presented, typically consisting of several mice and in vitro models ranging in complexity from transwell models to spheroids and CRISPR-Cas9 technologies. While in vitro models have been used for decades and dominate the early stages of drug development, they are still limited primary to simplistic tests based on testing on a single cell type cultivated in Petri dishes. Recent advancements in developing new cancer therapies necessitate the generation of complicated animal models that accurately mimic the tumor's complexity and microenvironment. Mice make effective tumor models as they are affordable, have a short reproductive cycle, exhibit rapid tumor growth, and are simple to manipulate genetically. Human cancer mouse models are crucial to understanding the neoplastic process and basic and clinical research improvements. The following review summarizes different in vitro and in vivo metastasis models, their advantages and disadvantages, and their ability to serve as a model for cancer research.

Co-clinical Trial of Novel Bispecific Anti-HER2 Antibody Zanidatamab in Patient-Derived Xenografts

Zanidatamab is a bispecific human epidermal growth factor receptor 2 (HER2)-targeted antibody that has demonstrated antitumor activity in a broad range of HER2-amplified/expressing solid tumors. We determined the antitumor activity of zanidatamab in patient-derived xenograft (PDX) models developed from pretreatment or postprogression biopsies on the first-in-human zanidatamab phase I study (NCT02892123). Of 36 tumors implanted, 19 PDX models were established (52.7% take rate) from 17 patients. Established PDXs represented a broad range of HER2-expressing cancers, and in vivo testing demonstrated an association between antitumor activity in PDXs and matched patients in 7 of 8 co-clinical models tested. We also identified amplification of MET as a potential mechanism of acquired resistance to zanidatamab and demonstrated that MET inhibitors have single-agent activity and can enhance zanidatamab activity in vitro and in vivo. These findings provide evidence that PDXs can be developed from pretreatment biopsies in clinical trials and may provide insight into mechanisms of resistance.

SIGNIFICANCE

We demonstrate that PDXs can be developed from pretreatment and postprogression biopsies in clinical trials and may represent a powerful preclinical tool. We identified amplification of MET as a potential mechanism of acquired resistance to the HER2 inhibitor zanidatamab and MET inhibitors alone and in combination as a therapeutic strategy. This article is featured in Selected Articles from This Issue, p. 695.

Arsenic trioxide: applications, mechanisms of action, toxicity and rescue strategies to date

Arsenical medicine has obtained its status in traditional Chinese medicine for more than 2,000 years. In the 1970s, arsenic trioxide was identified to have high efficacy and potency for the treatment of acute promyelocytic leukemia, which promoted many studies on the therapeutic effects of arsenic trioxide. Currently, arsenic trioxide is widely used to treat acute promyelocytic leukemia and various solid tumors through various mechanisms of action in clinical practice; however, it is accompanied by a series of adverse reactions, especially cardiac toxicity. This review presents a comprehensive overview of arsenic trioxide from preclinical and clinical efficacy, potential mechanisms of action, toxicities, and rescue strategies for toxicities to provide guidance or assistance for the clinical application of arsenic trioxide.

Patient-Derived Xenografts: A Valuable Preclinical Model for Drug Development and Biomarker Discovery

Patient-derived xenografts (PDXs), established by implanting patient tumor cells into immunodeficient mice, offer a platform for faithfully replicating human tumors. They closely mimic the histopathology, genomics, and drug sensitivity of patient tumors. This chapter highlights the versatile applications of PDXs, including studying tumor biology, metastasis, and chemoresistance, as well as their use in biomarker identification, drug screening, and personalized medicine. It also addresses challenges in using PDXs in cancer research, including variations in metastatic potential, lengthy establishment timelines, stromal changes, and limitations in immunocompromised models. Despite these challenges, PDXs remain invaluable tools guiding patient treatment and advancing preclinical drug development.

Orchestrating smart therapeutics to achieve optimal treatment in small cell lung cancer: recent progress and future directions

Small cell lung cancer (SCLC) is a recalcitrant malignancy with elusive mechanism of pathogenesis and dismal prognosis. Over the past decades, platinum-based chemotherapy has been the backbone treatment for SCLC. However, subsequent chemoresistance after initial effectiveness urges researchers to explore novel therapeutic targets of SCLC. Recent years have witnessed significant improvements in targeted therapy in SCLC. New molecular candidates such as Ataxia telangiectasia and RAD3-related protein (ATR), WEE1, checkpoint kinase 1 (CHK1) and poly-ADP-ribose polymerase (PARP) have shown promising therapeutic utility in SCLC. While immune checkpoint inhibitor (ICI) has emerged as an indispensable treatment modality for SCLC, approaches to boost efficacy and reduce toxicity as well as selection of reliable biomarkers for ICI in SCLC have remained elusive and warrants our further investigation. Given the increasing importance of precision medicine in SCLC, optimal subtyping of SCLC using multi-omics have gradually applied into clinical practice, which may identify more drug targets and better tailor treatment strategies to each individual patient. The present review summarizes recent progress and future directions in SCLC. In addition to the emerging new therapeutics, we also focus on the establishment of predictive model for early detection of SCLC. More importantly, we also propose a multi-dimensional model in the prognosis of SCLC to ultimately attain the goal of accurate treatment of SCLC.

Mouse Models to Examine Differentiated Thyroid Cancer Pathogenesis: Recent Updates

Although the overall prognosis of differentiated thyroid cancer (DTC), the most common endocrine malignancy, is favorable, a subset of patients exhibits aggressive features. Therefore, preclinical models that can be utilized to investigate DTC pathogenesis and novel treatments are necessary. Various mouse models have been developed based on advances in thyroid cancer genetics. This review focuses on recent progress in mouse models that have been developed to elucidate the molecular pathogenesis of DTC.

Generation, evolution, interfering factors, applications, and challenges of patient-derived xenograft models in immunodeficient mice

Establishing appropriate preclinical models is essential for cancer research. Evidence suggests that cancer is a highly heterogeneous disease. This follows the growing use of cancer models in cancer research to avoid these differences between xenograft tumor models and patient tumors. In recent years, a patient-derived xenograft (PDX) tumor model has been actively generated and applied, which preserves both cell-cell interactions and the microenvironment of tumors by directly transplanting cancer tissue from tumors into immunodeficient mice. In addition to this, the advent of alternative hosts, such as zebrafish hosts, or in vitro models (organoids and microfluidics), has also facilitated the advancement of cancer research. However, they still have a long way to go before they become reliable models. The development of immunodeficient mice has enabled PDX to become more mature and radiate new vitality. As one of the most reliable and standard preclinical models, the PDX model in immunodeficient mice (PDX-IM) exerts important effects in drug screening, biomarker development, personalized medicine, co-clinical trials, and immunotherapy. Here, we focus on the development procedures and application of PDX-IM in detail, summarize the implications that the evolution of immunodeficient mice has brought to PDX-IM, and cover the key issues in developing PDX-IM in preclinical studies.

Model-based assessment of combination therapies - ranking of radiosensitizing agents in oncology

BACKGROUND

To increase the chances of finding efficacious anticancer drugs, improve development times and reduce costs, it is of interest to rank test compounds based on their potential for human use as early as possible in the drug development process. In this paper, we present a method for ranking radiosensitizers using preclinical data.

METHODS

We used data from three xenograft mice studies to calibrate a model that accounts for radiation treatment combined with radiosensitizers. A nonlinear mixed effects approach was utilized where between-subject variability and inter-study variability were considered. Using the calibrated model, we ranked three different Ataxia telangiectasia-mutated inhibitors in terms of anticancer activity. The ranking was based on the Tumor Static Exposure (TSE) concept and primarily illustrated through TSE-curves.

RESULTS

The model described data well and the predicted number of eradicated tumors was in good agreement with experimental data. The efficacy of the radiosensitizers was evaluated for the median individual and the 95% population percentile. Simulations predicted that a total dose of 220 Gy (5 radiation sessions a week for 6 weeks) was required for 95% of tumors to be eradicated when radiation was given alone. When radiation was combined with doses that achieved at least 8 [Formula: see text] of each radiosensitizer in mouse blood, it was predicted that the radiation dose could be decreased to 50, 65, and 100 Gy, respectively, while maintaining 95% eradication.

CONCLUSIONS

A simulation-based method for calculating TSE-curves was developed, which provides more accurate predictions of tumor eradication than earlier, analytically derived, TSE-curves. The tool we present can potentially be used for radiosensitizer selection before proceeding to subsequent phases of the drug discovery and development process.

Diverse rescue potencies of p53 mutations to ATO are predetermined by intrinsic mutational properties

Tumor suppressor p53 is inactivated by thousands of heterogeneous mutations in cancer, but their individual druggability remains largely elusive. Here, we evaluated 800 common p53 mutants for their rescue potencies by the representative generic rescue compound arsenic trioxide (ATO) in terms of transactivation activity, cell growth inhibition, and mouse tumor-suppressive activities. The rescue potencies were mainly determined by the solvent accessibility of the mutated residue, a key factor determining whether a mutation is a structural one, and the temperature sensitivity, the ability to reassemble the wild-type DNA binding surface at a low temperature, of the mutant protein. A total of 390 p53 mutants were rescued to varying degrees and thus were termed as type 1, type 2a, and type 2b mutations, depending on the degree to which they were rescued. The 33 type 1 mutations were rescued to amounts comparable to the wild type. In PDX mouse trials, ATO preferentially inhibited growth of tumors harboring type 1 and type 2a mutants. In an ATO clinical trial, we report the first-in-human mutant p53 reactivation in a patient harboring the type 1 V272M mutant. In 47 cell lines derived from 10 cancer types, ATO preferentially and effectively rescued type 1 and type 2a mutants, supporting the broad applicability of ATO in rescuing mutant p53. Our study provides the scientific and clinical communities with a resource of the druggabilities of numerous p53 mutations (www.rescuep53.net) and proposes a conceptual p53-targeting strategy based on individual mutant alleles rather than mutation type.

Arsenic trioxide elicits prophylactic and therapeutic immune responses against solid tumors by inducing necroptosis and ferroptosis

Boosting tumor immunosurveillance with vaccines has been proven to be a feasible and cost-effective strategy to fight cancer. Although major breakthroughs have been achieved in preventative tumor vaccines targeting oncogenic viruses, limited advances have been made in curative vaccines for virus-irrelevant malignancies. Accumulating evidence suggests that preconditioning tumor cells with certain cytotoxic drugs can generate whole-cell tumor vaccines with strong prophylactic activities. However, the immunogenicity of these vaccines is not sufficient to restrain the outgrowth of existing tumors. In this study, we identified arsenic trioxide (ATO) as a wide-spectrum cytotoxic and highly immunogenic drug through multiparameter screening. ATO preconditioning could generate whole-cell tumor vaccines with potent antineoplastic effects in both prophylactic and therapeutic settings. The tumor-preventive or tumor-suppressive benefits of these vaccines relied on CD8+ T cells and type I and II interferon signaling and could be linked to the release of immunostimulatory danger molecules. Unexpectedly, following ATO-induced oxidative stress, multiple cell death pathways were activated, including autophagy, apoptosis, necroptosis, and ferroptosis. CRISPR‒Cas9-mediated knockout of cell death executors revealed that the absence of Rip3, Mlkl, or Acsl4 largely abolished the efficacy of ATO-based prophylactic and therapeutic cancer vaccines. This therapeutic failure could be rescued by coadministration of danger molecule analogs. In addition, PD-1 blockade synergistically improved the therapeutic efficacy of ATO-based cancer vaccines by augmenting local IFN-γ production.

Hedgehog pathway and its inhibitors in chronic obstructive pulmonary disease (COPD)

COPD affects millions of people and is now ranked as the third leading cause of death worldwide. This largely untreatable chronic airway disease results in irreversible destruction of lung architecture. The small lung hypothesis is now supported by epidemiological, physiological and clinical studies. Accordingly, the early and severe COPD phenotype carries the most dreadful prognosis and finds its roots during lung growth. Pathophysiological mechanisms remain poorly understood and implicate individual susceptibility (genetics), a large part of environmental factors (viral infections, tobacco consumption, air pollution) and the combined effects of those triggers on gene expression. Genetic susceptibility is most likely involved as the disease is severe and starts early in life. The latter observation led to the identification of Mendelian inheritance via disease-causing variants of SERPINA1 - known as the basis for alpha-1 anti-trypsin deficiency, and TERT. In the last two decades multiple genome wide association studies (GWAS) identified many single nucleotide polymorphisms (SNPs) associated with COPD. High significance SNPs are located in 4q31 near HHIP which encodes an evolutionarily highly conserved physiological inhibitor of the Hedgehog signaling pathway (HH). HHIP is critical to several in utero developmental lung processes. It is also implicated in homeostasis, injury response, epithelial-mesenchymal transition and tumor resistance to apoptosis. A few studies have reported decreased HHIP RNA and protein levels in human adult COPD lungs. HHIP^+/- murine models led to emphysema. HH pathway inhibitors, such as vismodegib and sonidegib, are already validated in oncology, whereas other drugs have evidenced in vitro effects. Targeting the Hedgehog pathway could lead to a new therapeutic avenue in COPD. In this review, we focused on the early and severe COPD phenotype and the small lung hypothesis by exploring genetic susceptibility traits that are potentially treatable, thus summarizing promising therapeutics for the future.

Harnessing preclinical models for the interrogation of ovarian cancer

Ovarian cancer (OC) is a heterogeneous malignancy with various etiology, histopathology, and biological feature. Despite accumulating understanding of OC in the post-genomic era, the preclinical knowledge still undergoes limited translation from bench to beside, and the prognosis of ovarian cancer has remained dismal over the past 30 years. Henceforth, reliable preclinical model systems are warranted to bridge the gap between laboratory experiments and clinical practice. In this review, we discuss the status quo of ovarian cancer preclinical models which includes conventional cell line models, patient-derived xenografts (PDXs), patient-derived organoids (PDOs), patient-derived explants (PDEs), and genetically engineered mouse models (GEMMs). Each model has its own strengths and drawbacks. We focus on the potentials and challenges of using these valuable tools, either alone or in combination, to interrogate critical issues with OC.

Optimized scaling of translational factors in oncology: from xenografts to RECIST

Purpose

Tumor growth inhibition (TGI) models are regularly used to quantify the PK–PD relationship between drug concentration and in vivo efficacy in oncology. These models are typically calibrated with data from xenograft mice and before being used for clinical predictions, translational methods have to be applied. Currently, such methods are commonly based on replacing model components or scaling of model parameters. However, difficulties remain in how to accurately account for inter-species differences. Therefore, more research must be done before xenograft data can fully be utilized to predict clinical response.

Method

To contribute to this research, we have calibrated TGI models to xenograft data for three drug combinations using the nonlinear mixed effects framework. The models were translated by replacing mice exposure with human exposure and used to make predictions of clinical response. Furthermore, in search of a better way of translating these models, we estimated an optimal way of scaling model parameters given the available clinical data.

Results

The predictions were compared with clinical data and we found that clinical efficacy was overestimated. The estimated optimal scaling factors were similar to a standard allometric scaling exponent of − 0.25.

Conclusions

We believe that given more data, our methodology could contribute to increasing the translational capabilities of TGI models. More specifically, an appropriate translational method could be developed for drugs with the same mechanism of action, which would allow for all preclinical data to be leveraged for new drugs of the same class. This would ensure that fewer clinically inefficacious drugs are tested in clinical trials.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00280-022-04458-8.

Three-dimensional models of the lung: past, present and future: a mini review

Respiratory diseases are a major reason for death in both men and women worldwide. The development of therapies for these diseases has been slow and the lack of relevant human models to understand lung biology inhibits therapeutic discovery. The lungs are structurally and functionally complex with many different cell types which makes designing relevant lung models particularly challenging. The traditional two-dimensional (2D) cell line cultures are, therefore, not a very accurate representation of the in vivo lung tissue. The recent development of three-dimensional (3D) co-culture systems, popularly known as organoids/spheroids, aims to bridge the gap between 'in-dish' and 'in-tissue' cell behavior. These 3D cultures are modeling systems that are widely divergent in terms of culturing techniques (bottom-up/top-down) that can be developed from stem cells (adult/embryonic/pluripotent stem cells), primary cells or from two or more types of cells, to build a co-culture system. Lung 3D models have diverse applications including the understanding of lung development, lung regeneration, disease modeling, compound screening, and personalized medicine. In this review, we discuss the different techniques currently being used to generate 3D models and their associated cellular and biological materials. We further detail the potential applications of lung 3D cultures for disease modeling and advances in throughput for drug screening.

Molecular Mechanisms Involving the Sonic Hedgehog Pathway in Lung Cancer Therapy: Recent Advances

According to the latest statistics from the International Agency for Research on Cancer (IARC), lung cancer is one of the most lethal malignancies in the world, accounting for approximately 18% of all cancer-associated deaths. Yet, even with aggressive interventions for advanced lung cancer, the five-year survival rate remains low, at around 15%. The hedgehog signaling pathway is highly conserved during embryonic development and is involved in tissue homeostasis as well as organ development. However, studies have documented an increasing prevalence of aberrant activation of HH signaling in lung cancer patients, promoting malignant lung cancer progression with poor prognostic outcomes. Inhibitors targeting the HH pathway have been widely used in tumor therapy, however, they still cannot avoid the occurrence of drug resistance. Interestingly, natural products, either alone or in combination with chemotherapy, have greatly improved overall survival outcomes for lung cancer patients by acting on the HH signaling pathway because of its unique and excellent pharmacological properties. In this review, we elucidate on the underlying molecular mechanisms through which the HH pathway promotes malignant biological behaviors in lung cancer, as well as the potential of inhibitors or natural compounds in targeting HH signaling for clinical applications in lung cancer therapy.

Use of conditional reprogramming cell, patient derived xenograft and organoid for drug screening for individualized prostate cancer therapy: Current and future perspectives (Review)

Prostate cancer mortality is ranked second among all cancer mortalities in men worldwide. There is a great need for a method of efficient drug screening for precision therapy, especially for patients with existing drug‑resistant prostate cancer. Based on the concept of bacterial cell culture and drug sensitivity testing, the traditional approach of cancer drug screening is inadequate. The current and more innovative use of cancer cell culture and in vivo tumor models in drug screening for potential individualization of anti‑cancer therapy is reviewed and discussed in the present review. An ideal screening model would have the ability to identify drug activity for the targeted cells resembling what would have occurred in the in vivo environment. Based on this principle, three available cell culture/tumor screening models for prostate cancer are reviewed and considered. The culture conditions, advantages and disadvantages for each model together with ideas to best utilize these models are discussed. The first screening model uses conditional reprogramed cells derived from patient cancer cells. Although these cells are convenient to grow and use, they are likely to have different markers and characteristics from original tumor cells and thus not likely to be informative. The second model employs patient derived xenograft (PDX) which resembles an in vivo approach, but its main disadvantages are that it cannot be easily genetically modified and it is not suitable for high‑throughput drug screening. Finally, high‑throughput screening is more feasible with tumor organoids grown from patient cancer cells. The last system still needs a large number of tumor cells. It lacks in situ blood vessels, immune cells and the extracellular matrix. Based on these current models, future establishment of an organoid data bank would allow the selection of a specific organoid resembling that of an individual's prostate cancer and used for screening of suitable anticancer drugs. This can be further confirmed using the PDX model. Thus, this combined organoid‑PDX approach is expected to be able to provide the drug sensitivity testing approach for individualization of prostate cancer therapy in the near future.

Cepharanthine hydrochloride inhibits the Wnt/β‑catenin/Hedgehog signaling axis in liver cancer

Cepharanthine, a biscoclaurine alkaloid isolated from the roots of Stephania cephalantha Hayata, has been reported to demonstrate antitumor activity across multiple cancer types; however, the mechanisms are still under investigation. High transcriptional responses by both the Hedgehog and Wnt pathways are frequently associated with specific human cancers, including liver cancer. To investigate whether these signaling pathways are involved in the pharmaceutical action of cepharanthine, we investigated Hedgehog and Wnt signaling in models of liver cancer treated with a semi‑synthetic cepharanthine derivative, cepharanthine hydrochloride (CH), in vitro and in vivo. By using MTT cytotoxic, scratch, Transwell, colony formation and flow cytometry assays, the pharmaceutical effect of CH was assessed. The compound was found to inhibit cellular proliferation and invasion, and promote apoptosis. Subsequent mechanistic investigations revealed that CH suppressed the Hedgehog/Gli1 signaling pathway by inhibiting Gli1 transcription and its transcriptional activity. CH also inhibited Wnt/β‑catenin signaling, and the pathway was found to be an upstream regulator of Hedgehog signaling in CH‑treated liver cancer cells. Finally, the antitumor effects of CH were demonstrated in an in vivo xenograft tumor model. Immunohistochemical analysis indicated that Gli1 protein levels were diminished in CH‑treated xenografts, compared with that noted in the controls. In summary, our results highlight a novel pharmaceutical antitumor mechanism of cepharanthine and provide support for CH as a clinical therapy for refractory liver cancer and other Wnt/Hedgehog‑driven cancers.

Identification of Prognostic Markers of Gynecologic Cancers Utilizing Patient-Derived Xenograft Mouse Models

Patient-derived xenografts (PDXs) are important in vivo models for the development of precision medicine. However, challenges exist regarding genetic alterations and relapse after primary treatment. Thus, PDX models are required as a new approach for preclinical and clinical studies. We established PDX models of gynecologic cancers and analyzed their clinical information. We subcutaneously transplanted 207 tumor tissues from patients with gynecologic cancer into nude mice from 2014 to 2019. The successful engraftment rate of ovarian, cervical, and uterine cancer was 47%, 64%, and 56%, respectively. The subsequent passages (P2 and P3) showed higher success and faster growth rates than the first passage (P1). Using gynecologic cancer PDX models, the tumor grade is a common clinical factor affecting PDX establishment. We found that the PDX success rate correlated with the patient’s prognosis, and also that ovarian cancer patients with a poor prognosis had a faster PDX growth rate (p < 0.0001). Next, the gene sets associated with inflammation and immune responses were shown in high-ranking successful PDX engraftment through gene set enrichment analysis and RNA sequencing. Up-regulated genes in successful engraftment were found to correlate with ovarian clear cell cancer patient outcomes via Gene Expression Omnibus dataset analysis.

Humanized Patient-Derived Xenograft Models of Ovarian Cancer

In vivo modeling of cancer is a critical step in testing novel therapeutic strategies to advance patient care. Here we describe how to develop a humanized patient-derived xenograft (PDX) model of ovarian cancer that uses orthotopically transplanted patient ovarian tumors with autologous transfer of expanded tumor infiltrating T cells (TILs) as a model that can be utilized to test immunomodulating therapeutics in vivo.

Organoids in Lung Cancer Management

Lung cancer is a complex milieu of genomically altered cancer cells, a diverse collection of differentiated cells and nonneoplastic stroma. Lung cancer organoids is a three-dimensional structure grown from patient cancer tissue that could mimic in vivo complex behavior and cellular architecture of the cancer. Furthermore, the genomic alterations of the primary lung tumor is captured ex vivo. Lung cancer organoids have become an important preclinical model for oncology studies in recent years. It could be used to model the development of lung cancer, investigate the process of tumorigenesis, and also study the signaling pathways. The organoids could also be a platform to perform drug screening and biomarker validation of lung cancer, providing a promising prediction of patient-specific drug response. In this review, we described how lung cancer organoids have opened new avenues for translating basic cancer research into clinical therapy and discussed the latest and future developments in organoid technology, which could be further applied in lung cancer organoids research.

Co-Clinical Imaging Resource Program (CIRP): Bridging the Translational Divide to Advance Precision Medicine

The National Institutes of Health’s (National Cancer Institute) precision medicine initiative emphasizes the biological and molecular bases for cancer prevention and treatment. Importantly, it addresses the need for consistency in preclinical and clinical research. To overcome the translational gap in cancer treatment and prevention, the cancer research community has been transitioning toward using animal models that more fatefully recapitulate human tumor biology. There is a growing need to develop best practices in translational research, including imaging research, to better inform therapeutic choices and decision-making. Therefore, the National Cancer Institute has recently launched the Co-Clinical Imaging Research Resource Program (CIRP). Its overarching mission is to advance the practice of precision medicine by establishing consensus-based best practices for co-clinical imaging research by developing optimized state-of-the-art translational quantitative imaging methodologies to enable disease detection, risk stratification, and assessment/prediction of response to therapy. In this communication, we discuss our involvement in the CIRP, detailing key considerations including animal model selection, co-clinical study design, need for standardization of co-clinical instruments, and harmonization of preclinical and clinical quantitative imaging pipelines. An underlying emphasis in the program is to develop best practices toward reproducible, repeatable, and precise quantitative imaging biomarkers for use in translational cancer imaging and therapy. We will conclude with our thoughts on informatics needs to enable collaborative and open science research to advance precision medicine.

Co-clinical FDG-PET radiomic signature in predicting response to neoadjuvant chemotherapy in triple-negative breast cancer

Purpose

We sought to exploit the heterogeneity afforded by patient-derived tumor xenografts (PDX) to first, optimize and identify robust radiomic features to predict response to therapy in subtype-matched triple negative breast cancer (TNBC) PDX, and second, to implement PDX-optimized image features in a TNBC co-clinical study to predict response to therapy using machine learning (ML) algorithms.

Methods

TNBC patients and subtype-matched PDX were recruited into a co-clinical FDG-PET imaging trial to predict response to therapy. One hundred thirty-one imaging features were extracted from PDX and human-segmented tumors. Robust image features were identified based on reproducibility, cross-correlation, and volume independence. A rank importance of predictors using ReliefF was used to identify predictive radiomic features in the preclinical PDX trial in conjunction with ML algorithms: classification and regression tree (CART), Naïve Bayes (NB), and support vector machines (SVM). The top four PDX-optimized image features, defined as radiomic signatures (RadSig), from each task were then used to predict or assess response to therapy. Performance of RadSig in predicting/assessing response was compared to SUV_mean, SUV_max, and lean body mass-normalized SUL_peak measures.

Results

Sixty-four out of 131 preclinical imaging features were identified as robust. NB-RadSig performed highest in predicting and assessing response to therapy in the preclinical PDX trial. In the clinical study, the performance of SVM-RadSig and NB-RadSig to predict and assess response was practically identical and superior to SUV_mean, SUV_max, and SUL_peak measures.

Conclusions

We optimized robust FDG-PET radiomic signatures (RadSig) to predict and assess response to therapy in the context of a co-clinical imaging trial.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00259-021-05489-8.

Deep Learning Segmentation of Triple-Negative Breast Cancer (TNBC) Patient Derived Tumor Xenograft (PDX) and Sensitivity of Radiomic Pipeline to Tumor Probability Boundary

Simple Summary

Co-clinical trials are an emerging area of investigation in which a clinical trial is coupled with a corresponding preclinical trial to inform the corresponding clinical trial. The preclinical arm aids in assessing therapeutic efficacy, patient stratification, and designing optimal imaging strategies. There is much interest in harmonizing preclinical and clinical quantitative imaging pipelines. Radiomics is widely explored in clinical imaging to predict response to therapy. In preclinical imaging, high-throughput radiomic analysis is limited by manual delineation of tumor boundaries, which is labor intensive with poor reproducibility. Our proposed deep-learning-based system was trained to automatically segment tumors from multi-contrast MR images and extract radiomic features. The proposed method is highly reproducible with significant correlation in radiomic features. The deployment of this pipeline in the preclinical arm would provide high throughput and reproducible radiomic analysis.

Abstract

Preclinical magnetic resonance imaging (MRI) is a critical component in a co-clinical research pipeline. Importantly, segmentation of tumors in MRI is a necessary step in tumor phenotyping and assessment of response to therapy. However, manual segmentation is time-intensive and suffers from inter- and intra- observer variability and lack of reproducibility. This study aimed to develop an automated pipeline for accurate localization and delineation of TNBC PDX tumors from preclinical T1w and T2w MR images using a deep learning (DL) algorithm and to assess the sensitivity of radiomic features to tumor boundaries. We tested five network architectures including U-Net, dense U-Net, Res-Net, recurrent residual UNet (R2UNet), and dense R2U-Net (D-R2UNet), which were compared against manual delineation by experts. To mitigate bias among multiple experts, the simultaneous truth and performance level estimation (STAPLE) algorithm was applied to create consensus maps. Performance metrics (F1-Score, recall, precision, and AUC) were used to assess the performance of the networks. Multi-contrast D-R2UNet performed best with F1-score = 0.948; however, all networks scored within 1–3% of each other. Radiomic features extracted from D-R2UNet were highly corelated to STAPLE-derived features with 67.13% of T1w and 53.15% of T2w exhibiting correlation ρ ≥ 0.9 (p ≤ 0.05). D-R2UNet-extracted features exhibited better reproducibility relative to STAPLE with 86.71% of T1w and 69.93% of T2w features found to be highly reproducible (CCC ≥ 0.9, p ≤ 0.05). Finally, 39.16% T1w and 13.9% T2w features were identified as insensitive to tumor boundary perturbations (Spearman correlation (−0.4 ≤ ρ ≤ 0.4). We developed a highly reproducible DL algorithm to circumvent manual segmentation of T1w and T2w MR images and identified sensitivity of radiomic features to tumor boundaries.

Current status and future prospects of nanomedicine for arsenic trioxide delivery to solid tumors

Despite having a rich history as a poison, arsenic and its compounds have also gained a great reputation as promising anticancer drugs. As a pioneer, arsenic trioxide has been approved for the treatment of acute promyelocytic leukemia. Many in vitro studies suggested that arsenic trioxide could also be used in the treatment of solid tumors. However, the transition from bench to bedside turned out to be challenging, especially in terms of the drug bioavailability and concentration reaching tumor tissues. To address these issues, nanomedicine tools have been proposed. As nanocarriers of arsenic trioxide, various materials have been examined including liposomes, polymer, and inorganic nanoparticles, and many other materials. This review gives an overview of the existing strategies of delivery of arsenic trioxide in cancer treatment with a focus on the drug encapsulation approaches and medicinal impact in the treatment of solid tumors. It focuses on the progress in the last years and gives an outlook and suggestions for further improvements including theragnostic approaches and targeted delivery.

Discovery of Small Molecule Bak Activator for Lung Cancer Therapy

Rationale: Bak is a major proapoptotic Bcl2 family member and a required molecule for apoptotic cell death. High levels of endogenous Bak were observed in both small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC) cell lines. Increased Bak expression was correlated with poor prognosis of NSCLC patients, suggesting that Bak protein is an attractive target for lung cancer therapy. The BH3 domain functions as death domain and is required for Bak to initiate apoptotic cell death. Thus, the BH3 domain is attractive target for discovery of Bak agonist.

Methods: The BH3 death domain binding pocket (aa75-88) of Bak was chosen as a docking site for screening of small molecule Bak activators using the UCSF DOCK 6.1 program suite and the NCI chemical library (300,000 small molecules) database. The top 500 compounds determined to have the highest affinity for the BH3 domain were obtained from the NCI and tested for cytotoxicity for further screening. We identified a small molecule Bak activator BKA-073 as the lead compound. The binding affinity of BKA-073 with Bak protein was analyzed by isothermal titration calorimetry (ITC) assay. BKA-073-mediated Bak activation via oligomerization was analyzed by a cross-linking with Bis (maleimido) hexane (BMH). Sensitivity of BKA-073 to lung cancer cells in vitro was evaluated by dynamic BH3 profiling (DBP) and apoptotic cell death assay. The potency of BKA-073 alone or in combination with radiotherapy or Bcl2 inhibitor was evaluated in animal models.

Results: We found that BKA-073 binds Bak at BH3 domain with high affinity and selectivity. BKA-073/Bak binding promotes Bak oligomerization and mitochondrial priming that activates its proapoptotic function. BKA-073 potently suppresses tumor growth without significant normal tissue toxicity in small cell lung cancer (SCLC) and NSCLC xenografts, patient-derived xenografts, and genetically engineered mouse models of mutant KRAS-driven cancer. Bak accumulates in radioresistant lung cancer cells and BKA-073 reverses radioresistance. Combination of BKA-073 with Bcl-2 inhibitor venetoclax exhibits strong synergy against lung cancer in vivo.

Conclusions: Development of small molecule Bak activator may provide a new class of anticancer agents to treat lung cancer.

GLI1: A Therapeutic Target for Cancer

GLI1 is a transcriptional effector at the terminal end of the Hedgehog signaling (Hh) pathway and is tightly regulated during embryonic development and tissue patterning/differentiation. GLI1 has low-level expression in differentiated tissues, however, in certain cancers, aberrant activation of GLI1 has been linked to the promotion of numerous hallmarks of cancer, such as proliferation, survival, angiogenesis, metastasis, metabolic rewiring, and chemotherapeutic resistance. All of these are driven, in part, by GLI1’s role in regulating cell cycle, DNA replication and DNA damage repair processes. The consequences of GLI1 oncogenic activity, specifically the activity surrounding DNA damage repair proteins, such as NBS1, and cell cycle proteins, such as CDK1, can be linked to tumorigenesis and chemoresistance. Therefore, understanding the underlying mechanisms driving GLI1 dysregulation can provide prognostic and diagnostic biomarkers to identify a patient population that would derive therapeutic benefit from either direct inhibition of GLI1 or targeted therapy towards proteins downstream of GLI1 regulation.

Patient-Derived Xenograft Models in Breast Cancer Research

Patient-derived xenograft (PDX) model can be used as a platform to study the individual patient's sensitivity to targeted agents as well as its ability to guide our understanding in various aspects of tumor biology including the tumor's clonal evolution and interaction with microenvironment. In this chapter, we review the history of PDX models in various tumor types. Additionally, we highlight the key studies that suggested potential value of PDX models in cancer treatment. Specifically, we will briefly introduce several studies on the issue of PDX models for precision medicine. In latter part of this chapter, we focus on the studies that used PDX models to investigate the molecular biology of breast cancer that underlies the process of drug resistance and tumor metastasis. Also, we will address our own experience in developing PDX models using breast cancer tissues from Korean breast cancer patients.

Application of Animal Models in Cancer Research: Recent Progress and Future Prospects

Animal models refers to the animal experimental objects and related materials that can simulate human body established in medical research. As the second-largest disease in terms of morbidity and mortality after cardiovascular disease, cancer has always been the focus of human attention all over the world, which makes it a research hotspot in the medical field. At the same time, more and more animal models have been constructed and used in cancer research. With the deepening of research, the construction methods of cancer animal models are becoming more and more diverse, including chemical induction, xenotransplantation, gene programming, and so on. In recent years, patient-derived xenotransplantation (PDX) model has become a research hotspot because it can retain the microenvironment of the primary tumor and the basic characteristics of cells. Animal models can be used not only to study the biochemical and physiological processes of the occurrence and development of cancer in objects but also for the screening of cancer drugs and the exploration of gene therapy. In this paper, several main tumor animal models and the application progress of animal models in tumor research are systematically reviewed. Finally, combined with the latest progress and development trend in this field, the future research of tumor animal model was prospected.

Plasma-Conditioned Liquids as Anticancer Therapies In Vivo: Current State and Future Directions

Plasma-conditioned liquids (PCL) are gaining increasing attention in the medical field, especially in oncology, and translation to the clinics is advancing on a good path. This emerging technology involving cold plasmas has great potential as a therapeutic approach in cancer diseases, as PCL have been shown to selectively kill cancer cells by triggering apoptotic mechanisms without damaging healthy cells. In this context, PCL can be injected near the tumor or intratumorally, thereby allowing the treatment of malignant tumors located in internal organs that are not accessible for direct cold atmospheric plasma (CAP) treatment. Therefore, PCL constitutes a very interesting and minimally invasive alternative to direct CAP treatment in cancer therapy, avoiding surgeries and allowing multiple local administrations. As the field advances, it is progressively moving to the evaluation of the therapeutic effects of PCL in in vivo scenarios. Exciting developments are pushing forward the clinical translation of this novel therapy. However, there is still room for research, as the quantification and identification of reactive oxygen and nitrogen species (RONS) in in vivo conditions is not yet clarified, dosage regimens are highly variable among studies, and other more relevant in vivo models could be used. In this context, this work aims to present a critical review of the state of the field of PCL as anticancer agents applied in in vivo studies.

Co-Clinical Trials: An Innovative Drug Development Platform for Cholangiocarcinoma

Cholangiocarcinoma (CCA), a group of malignancies that originate from the biliary tract, is associated with a high mortality rate and a concerning increase in worldwide incidence. In Thailand, where the incidence of CCA is the highest, the socioeconomic burden is severe. Yet, treatment options are limited, with surgical resection being the only form of treatment with curative intent. The current standard-of-care remains adjuvant and palliative chemotherapy which is ineffective in most patients. The overall survival rate is dismal, even after surgical resection and the tumor heterogeneity further complicates treatment. Together, this makes CCA a significant burden in Southeast Asia. For effective management of CCA, treatment must be tailored to each patient, individually, for which an assortment of targeted therapies must be available. Despite the increasing numbers of clinical studies in CCA, targeted therapy drugs rarely get approved for clinical use. In this review, we discuss the shortcomings of the conventional clinical trial process and propose the implementation of a novel concept, co-clinical trials to expedite drug development for CCA patients. In co-clinical trials, the preclinical studies and clinical trials are conducted simultaneously, thus enabling real-time data integration to accurately stratify and customize treatment for patients, individually. Hence, co-clinical trials are expected to improve the outcomes of clinical trials and consequently, encourage the approval of targeted therapy drugs. The increased availability of targeted therapy drugs for treatment is expected to facilitate the application of precision medicine in CCA.

Combination therapy: Future directions of immunotherapy in small cell lung cancer

Small cell lung cancer (SCLC), an aggressive and devastating malignancy, is characterized by rapid growth and early metastasis. Although most patients respond to first-line chemotherapy, the majority of patients rapidly relapse and have a relatively poor prognosis. Fortunately, immunotherapy, mainly including antibodies that target the cytotoxic T lymphocyte antigen-4 (CTLA-4), checkpoints programmed death-1 (PD-1), and programmed death-ligand 1 (PD-L1) to block immune regulatory checkpoints on tumor cells, immune cells, fibroblasts cells and endothelial cells, has achieved the milestone in several solid tumors, such as melanoma and non-small-cell lung carcinomas (NSCLC). In recent years, immunotherapy has made progress in the treatment of patients with SCLC, while its response rate is relatively low to monotherapy. Interestingly, the combination of immunotherapy with other therapy, such as chemotherapy, radiotherapy, and targeted therapy, preliminarily achieve greater therapeutic effects for treating SCLC. Combining different immunotherapy drugs may act synergistically because of the complementary effects of the two immune checkpoint pathways (CTLA-4 and PD-1/PD-L1 pathways). The incorporation of chemoradiotherapy in immunotherapy may augment antitumor immune responses because chemoradiotherapy can enhance tumor cell immunogenicity by rapidly inducing tumor lysis and releasing tumor antigens. In addition, since immunotherapy drugs and the molecular targets drugs act on different targets and cells, the combination of these drugs may achieve greater therapeutic effects in the treatment of SCLC. In this review, we focused on the completed and ongoing trials of the combination therapy for immunotherapy of SCLC to find out the rational combination strategies which may improve the outcomes for SCLC.

Preclinical Metrics Correlate With Drug Activity in Phase II Trials of Targeted Therapies for Non-Small Cell Lung Cancer

Novel oncology drugs often fail to progress from preclinical experiments to FDA approval. Therefore, determining which preclinical or clinical factors associate with drug activity could accelerate development of effective therapies. We investigated whether preclinical metrics and patient characteristics are associated with objective response rate (ORR) in phase II clinical trials of targeted therapies for non-small cell lung cancer (NSCLC). We developed a reproducible process to select a single phase II trial and supporting preclinical publication for a given drug-indication pair, which we defined as the pairing of a small molecule inhibitor (e.g., crizotinib) with the specific patient population for which it was designed to work (e.g., patients with an ALK aberration). We demonstrated that robust drug activity in mice, as measured by change in tumor size, is independently associated with improved ORR in phase II clinical trials. The number of mice utilized in experiments, the number of publications referencing the drug for NSCLC before the phase II clinical trial, and whether the drug was approved for a cancer other than NSCLC also significantly correlated with ORR. Among clinical characteristics, sex, race, histology, and smoking history were significantly associated with ORR. Further research into metrics that correlate with drug activity has the potential to optimize selection of novel therapies for clinical trials and enrich the drug development pipeline, particularly for patients with targetable genetic aberrations and rare cancers.

Patient-derived cell line, xenograft and organoid models in lung cancer therapy

Lung cancer accounts for most cancer-related deaths worldwide and has an overall 5-year survival rate of ~15%. Cell lines have played important roles in the study of cancer biology and potential therapeutic targets, as well as pre-clinical testing of novel drugs. However, most experimental therapies that have cleared preclinical testing using established cell lines have failed phase III clinical trials. This suggests that such models may not adequately recapitulate patient tumor biology and clinical outcome predictions. Here, we discuss and compare different pre-clinical lung cancer models, including established cell lines, patient-derived cell lines, xenografts and organoids, summarize the methodology for generating these models, and review their relative advantages and limitations in different oncologic research applications. We further discuss additional gaps in patient-derived pre-clinical models to better recapitulate tumor biology and improve their clinical predictive power.

Optimal co-clinical radiomics: Sensitivity of radiomic features to tumour volume, image noise and resolution in co-clinical T1-weighted and T2-weighted magnetic resonance imaging

Background

Radiomics analyses has been proposed to interrogate the biology of tumour as well as to predict/assess response to therapy in vivo. The objective of this work was to assess the sensitivity of radiomics features to noise, resolution, and tumour volume in the context of a co-clinical trial.

Methods

Triple negative breast cancer (TNBC) patients were recruited into an ongoing co-clinical imaging trial. Sub-typed matched TNBC patient-derived tumour xenografts (PDX) were generated to investigate optimal co-clinical MR radiomic features. The MR imaging protocol included T1-weighed and T2-weighted imaging. To test the sensitivity of radiomics to resolution, PDX were imaged at three different resolutions. Multiple sets of images with varying signal-to-noise ratio (SNR) were generated, and an image independent patch-based method was implemented to measure the noise levels. Forty-eight radiomic features were extracted from manually segmented 2D and 3D segmented tumours and normal tissues of T1- and T2- weighted co-clinical MR images.

Findings

Sixteen radiomics features were identified as volume dependent and corrected for volume-dependency following normalization. Features from grey-level run-length matrix (GLRLM), grey-level size zone matrix (GLSZM) were identified as most sensitive to noise. Radiomic features Kurtosis and Run-length variance (RLV) from GLSZM were most sensitive to changes in resolution in both T1w and T2w MRI. In general, 3D radiomic features were more robust compared to 2D (single slice) measures, although the former exhibited higher variability between subjects.

Interpretation

Tumour volume, noise characteristics, and image resolution significantly impact radiomic analysis in co-clinical studies.

Arsenic trioxide inhibits the growth of cancer stem cells derived from small cell lung cancer by downregulating stem cell-maintenance factors and inducing apoptosis via the Hedgehog signaling blockade

Background

Small cell lung cancer (SCLC) is the most deadly and aggressive type of primary lung cancer, with the 5-year survival rate lower than 5%. The FDA has approved arsenic trioxide (As₂O₃) for acute promyelocytic leukemia (APL) treatment. However, its role in SCLC-derived cancer stem cells (CSCs) remains largely unknown.

Methods

CSCs were enriched from SCLC cell lines by culturing them as spheres in conditioned serum-free medium. Then, qPCR, western blot, serial passage, limiting dilution, Transwell, and tumorigenesis assay were performed to verify the cells' stem phenotypic characteristics. Anticancer efficiency of As₂O₃ was assessed in these cells using CCK8, colony formation, sphere formation, flow cytometry, qPCR, western blot analysis in vitro, and tumor growth curve, immunofluorescence, and TUNEL staining analyses in vivo.

Results

The fifth-passage SCLC spheres showed a potent self-renewal capacity, higher clonal formation efficiency (CFE), SOX2, c-Myc, NANOG, and OCT4 levels, and invasion ability, and stronger tumorigenesis capacity than the parental SCLC cells, indicating that the SCLC sphere cells displayed CSC features. As₂O₃ inhibited the proliferation, clonality and sphere forming ability of SCLC-derived CSCs and suppressed the tumor growth of CSCs-derived xenograft tumors. As₂O₃ induced apoptosis and downregulation of SOX2 and c-Myc in vitro and in xenografts. Besides, SOX2 knockdown suppressed SCLC-derived CSCs to self-renew and induced apoptosis. Mechanistically, expression of GLI1 (a key transcription factor of Hedgehog pathway) and its downstream genes increased in SCLC-derived CSCs, compared to the parental cells. As₂O₃ dramatically downregulated GLI1 and its downstream genes in vitro and in vivo. The GLI inhibitor (GANT-61) recapitulated and enhanced the effects of As₂O₃ on SCLC-derived CSCs, including growth suppression, apoptosis induction, and GLI1, SOX2 and c-Myc downregulation.

Conclusions

Altogether, As₂O₃ effectively suppressed SCLC-derived CSCs growth by downregulating stem cell-maintenance factors and inducing apoptosis. These effects are mediated at least partly via the Hedgehog signaling blockade.

Hsp90B enhances MAST1-mediated cisplatin resistance by protecting MAST1 from proteosomal degradation

Microtubule-associated serine/threonine kinase 1 (MAST1) is a central driver of cisplatin resistance in human cancers. However, the molecular mechanism regulating MAST1 levels in cisplatin-resistant tumors is unknown. Through a proteomics screen, we identified the heat shock protein 90 B (hsp90B) chaperone as a direct MAST1 binding partner essential for its stabilization. Targeting hsp90B sensitized cancer cells to cisplatin predominantly through MAST1 destabilization. Mechanistically, interaction of hsp90B with MAST1 blocked ubiquitination of MAST1 at lysines 317 and 545 by the E3 ubiquitin ligase CHIP and prevented proteasomal degradation. The hsp90B-MAST1-CHIP signaling axis and its relationship with cisplatin response were clinically validated in cancer patients. Furthermore, combined treatment with a hsp90 inhibitor and the MAST1 inhibitor lestaurtinib further abrogated MAST1 activity and consequently enhanced cisplatin-induced tumor growth arrest in a patient-derived xenograft model. Our study not only uncovers the regulatory mechanism of MAST1 in tumors but also suggests a promising combinatorial therapy to overcome cisplatin resistance in human cancers.

Preclinical PERCIST and 25% of SUVmax Threshold: Precision Imaging of Response to Therapy in Co-clinical 18F-FDG PET Imaging of Triple-Negative Breast Cancer Patient-Derived Tumor Xenografts

Numerous recent works highlight the limited utility of established tumor cell lines in recapitulating the heterogeneity of tumors in patients. More realistic preclinical cancer models are thought to be provided by transplantable, patient-derived xenografts (PDXs). The inter- and intratumor heterogeneity of PDXs, however, presents several challenges in developing optimal quantitative pipelines to assess response to therapy. The objective of this work was to develop and optimize image metrics for 18F-FDG PET to assess response to combination docetaxel and carboplatin therapy in a co-clinical trial involving triple-negative breast cancer PDXs. We characterized the reproducibility of standardized uptake value (SUV) metrics to assess response to therapy, and we optimized a preclinical PERCIST paradigm to complement clinical standards. Considerations in this effort included variability in tumor growth rate and tumor size, solid tumors versus tumor heterogeneity and a necrotic phenotype, and optimal selection of tumor slices versus whole tumor. Methods: A test-retest protocol was implemented to optimize the reproducibility of 18F-FDG PET SUV thresholds, SUVpeak metrics, and preclinical PERCIST parameters. In assessing response to therapy, 18F-FDG PET imaging was performed at baseline and 4 d after therapy. The reproducibility, accuracy, variability, and performance of imaging metrics to assess response to therapy were determined. We defined an index called the Quantitative Response Assessment Score to integrate parameters of prediction and precision and thus aid in selecting the optimal image metric to assess response to therapy. Results: Our data suggest that a threshold of 25% of SUVmax (SUV25) was highly reproducible (<9% variability). The concordance and reproducibility of preclinical PERCIST were maximized at α = 0.7 and β = 2.8 and exhibited a high correlation with SUV25 measures of tumor uptake, which in turn correlated with the SUV of metabolic tumor. Conclusion: The Quantitative Response Assessment Score favors SUV25 followed by SUVpeak for a sphere with a volume of 14 mm3 (SUVP14) as optimal metrics of response to therapy. Additional studies are warranted to fully characterize the utility of SUV25 and preclinical PERCIST SUVP14 as image metrics for response to therapy across a wide range of therapeutic regimens and PDX models.

Pre-clinical models of small cell lung cancer and the validation of therapeutic targets

Introduction: Small-cell lung cancer (SCLC) is an aggressive form of lung cancer that has a dismal prognosis. One of the factors hindering therapeutic developments for SCLC is that most SCLC is not surgically resected resulting in a paucity of material for analysis. To address this, significant efforts have been made by investigators to develop pre-clinical models of SCLC allowing for downstream target identification in this difficult to treat cancer.Areas covered: In this review, we describe the current pre-clinical models that have been developed to interrogate SCLC, and outline the benefits and limitations associated with each. Using examples we show how each has been used to (i) improve our knowledge of this intractable cancer, and (ii) identify and validate potential therapeutic targets that (iii) are currently under development and testing within the clinic.Expert opinion: The large numbers of preclinical models that have been developed have dramatically improved the ways in which we can examine SCLC and test therapeutic targets/interventions. The newer models are rapidly providing novel avenues for the design and testing of new therapeutics. Despite this many of these models have inherent flaws that limit the possibility of their use for individualized therapy decision-making for SCLC.

Zebrafish Avatars towards Personalized Medicine-A Comparative Review between Avatar Models

Cancer frequency and prevalence have been increasing in the past decades, with devastating impacts on patients and their families. Despite the great advances in targeted approaches, there is still a lack of methods to predict individual patient responses, and therefore treatments are tailored according to average response rates. “Omics” approaches are used for patient stratification and choice of therapeutic options towards a more precise medicine. These methods, however, do not consider all genetic and non-genetic dynamic interactions that occur upon drug treatment. Therefore, the need to directly challenge patient cells in a personalized manner remains. The present review addresses the state of the art of patient-derived in vitro and in vivo models, from organoids to mouse and zebrafish Avatars. The predictive power of each model based on the retrospective correlation with the patient clinical outcome will be considered. Finally, the review is focused on the emerging zebrafish Avatars and their unique characteristics allowing a fast analysis of local and systemic effects of drug treatments at the single-cell level. We also address the technical challenges that the field has yet to overcome.

Scaffold-Assisted Ectopic Transplantation of Internal Organs and Patient-Derived Tumors

Xenotransplantation of human tissues into immunodeficient mice has emerged as an invaluable preclinical model to study human biology and disease progression and predict clinical response. The most common anatomical site for tissue transplantation is the subcutaneous pocket due to simple surgical procedures and accessibility for gross monitoring and advanced imaging modalities. However, subcutaneously implanted tissues initially experience a sharp change in oxygen and nutrient supply and increased mechanical deformation. During this acute phase of tissue integration to the host vasculature, substantial cell death and tissue fibrosis occur limiting engraftment efficiency. Previously, we demonstrated that the implantation of inverted colloidal crystal hydrogel scaffolds triggers proangiogenic and immunomodulatory functions without characteristic foreign body encapsulation. In this study, we examine the use of this unique host response to improve the ectopic transplantation of tissues to the subcutaneous site. Scaffold-assisted tissues preserved morphological features and blood vessel density compared to native tissues, whereas scaffold-free tissues collapsed and were less vascularized. Notably, the supporting biomaterial scaffold modulated the foreign body response to reduce the localization of Ly6G⁺ cells within the transplanted tissues. Cotransplantation of patient-derived gastric cancer with a scaffold resulted in a comparable level of engraftment to conventional methods; however, detailed immunohistological characterization revealed significantly better retention of proliferative cells (Ki67⁺) and human immune cells (CD45⁺) by the end of the study. We envision that leveraging the immunomodulatory properties of biomaterial interfaces can be an attractive strategy to improve the functional engraftment of xenotransplants and accelerate individualized diagnostics and the development of novel therapeutic strategies.

An autologous humanized patient-derived-xenograft platform to evaluate immunotherapy in ovarian cancer

OBJECTIVE

The aim of this study was to "humanize" ovarian cancer patient-derived xenograft (PDX) models by autologous transfer of patient-matched tumor infiltrating lymphocytes (TILs) to evaluate immunotherapies.

METHODS

Orthotopic high-grade serous ovarian cancer (HGSOC) PDX models were established from three patient donors. Models were molecularly and histologically validated by immunohistochemistry. TILs were expanded from donor tumors using a rapid expansion protocol. Ex vivo TIL and tumor co-cultures were performed to validate TIL reactivity against patient-matched autologous tumor cells. Expression of TIL activation markers and cytokine secretion was quantitated by flow cytometry and ELISA. As proof of concept, the efficacy of anti-PD-1 monotherapy was tested in autologous TIL/tumor HGSOC PDX models.

RESULTS

Evaluation of T-cell activation in autologous TIL/tumor co-cultures resulted in an increase in HLA-dependent IFNγ production and T-cell activation. In response to increased IFNγ production, tumor cell expression of PD-L1 was increased. Addition of anti-PD-1 antibody to TIL/tumor co-cultures increased autologous tumor lysis in a CCNE1 amplified model. Orthotopic HGSOC PDX models from parallel patient-matched tumors maintained their original morphology and molecular marker profile. Autologous tumor-reactive TIL administration in patient-matched PDX models resulted in reduced tumor burden and increased survival, in groups that also received anti-PD-1 therapy.

CONCLUSIONS

This study validates a novel, clinically relevant model system for in vivo testing of immunomodulating therapeutic strategies for ovarian cancer, and provides a unique platform for assessing patient-specific T-cell response to immunotherapy.

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.

Crossroads of Cancer and HIV-1: Pathways to a Cure for HIV

Recently, a second individual (the “London patient”) with HIV-1 infection and concomitant leukemia was cured of both diseases by a conditioning myeloablative regimen followed by transplantation of stem cells bearing the homozygous CCR5 Δ32 mutation. The substantial risks and cost associated with this procedure render it unfeasible on a large scale. This strategy also indicates that a common pathway toward a cure for both HIV and cancer may exist. Successful approaches to curing both diseases should ideally possess three components, i.e., (1) direct targeting of pathological cells (neoplastic cells in cancer and the HIV-infected reservoir cells), (2) subsequent impediment to reconstitution of the pool of pathological cells and (3) sustained, immunologic control of the disease (both diseases are characterized by detrimental immune hyper-activation that hinders successful establishment of immunity). In this review, we explore medications that are either investigational or FDA-approved anticancer treatments that may be employed to achieve the goal of curing HIV-1. These include: thioredoxin reductase inhibitors (phases 1–3), immune checkpoint inhibitors (phases 1, 3), Jak inhibitors (FDA approved for arthritis and multiple cancer indications, summarized in Table 1). Of note, some of these medications such as arsenic trioxide and Jak inhibitors may also reversibly down regulate CCR5 expression on CD4⁺ T-cells, thus escaping the ethical issues of inducing or transferring mutations in CCR5 that are presently the subject of interest as it relates to HIV-1 cure strategies.

Mouse models of thyroid cancer: Bridging pathogenesis and novel therapeutics

Due to a global increase in the incidence of thyroid cancer, numerous novel mouse models were established to reveal thyroid cancer pathogenesis and test promising therapeutic strategies, necessitating a comprehensive review of translational medicine that covers (i) the role of mouse models in the research of thyroid cancer pathogenesis, and (ii) preclinical testing of potential anti-thyroid cancer therapeutics. The present review article aims to: (i) describe the current approaches for mouse modeling of thyroid cancer, (ii) provide insight into the biology and genetics of thyroid cancers, and (iii) offer guidance on the use of mouse models for testing potential therapeutics in preclinical settings. Based on research with mouse models of thyroid cancer pathogenesis involving the RTK, RAS/RAF/MEK/ERK, PI3K/AKT/mTOR, SRC, and JAK-STAT signaling pathways, inhibitors of VEGFR, MEK, mTOR, SRC, and STAT3 have been developed as anti-thyroid cancer drugs for "bench-to-bedside" translation. In the future, mouse models of thyroid cancer will be designed to be ''humanized" and "patient-like," offering opportunities to: (i) investigate the pathogenesis of thyroid cancer through target screening based on the CRISPR/Cas system, (ii) test drugs based on new mouse models, and (iii) explore the underlying mechanisms based on multi-omics.

Current and Future Horizons of Patient-Derived Xenograft Models in Colorectal Cancer Translational Research

Our poor understanding of the intricate biology of cancer and the limited availability of preclinical models that faithfully recapitulate the complexity of tumors are primary contributors to the high failure rate of novel therapeutics in oncology clinical studies. To address this need, patient-derived xenograft (PDX) platforms have been widely deployed and have reached a point of development where we can critically review their utility to model and interrogate relevant clinical scenarios, including tumor heterogeneity and clonal evolution, contributions of the tumor microenvironment, identification of novel drugs and biomarkers, and mechanisms of drug resistance. Colorectal cancer (CRC) constitutes a unique case to illustrate clinical perspectives revealed by PDX studies, as they overcome limitations intrinsic to conventional ex vivo models. Furthermore, the success of molecularly annotated "Avatar" models for co-clinical trials in other diseases suggests that this approach may provide an additional opportunity to improve clinical decisions, including opportunities for precision targeted therapeutics, for patients with CRC in real time. Although critical weaknesses have been identified with regard to the ability of PDX models to predict clinical outcomes, for now, they are certainly the model of choice for preclinical studies in CRC. Ongoing multi-institutional efforts to develop and share large-scale, well-annotated PDX resources aim to maximize their translational potential. This review comprehensively surveys the current status of PDX models in translational CRC research and discusses the opportunities and considerations for future PDX development.

Precision Medicine Tools to Guide Therapy and Monitor Response to Treatment in a HER-2+ Gastric Cancer Patient: Case Report

Trastuzumab, has played a major role in improving treatment outcomes in HER-2 positive gastric cancer. However, once there is disease progression there is a paucity of evidence for second line therapy. Patient-derived xenografts (PDXs) in combination with liquid biopsies can help guide individual therapeutic decisions and have now started to be studied. In the present case we established a PDX model from a metastatic HER-2+ gastric cancer patient and after the first engraftment passage we performed a mouse clinical trial to test T-DM1 as an alternative therapy for the patient. The PDX tumor response served as a guide to administer T-DM1 therapy to the patient who responded to treatment before relapsing 6 months later. Throughout out the clinical follow up of the patient, ctDNA levels of HER-2 copy number and a PIK3CA mutation were monitored and we found their correlation with drug response and disease progression to outperform that of CEA levels. This study highlights the utility of applying precision medicine tools combining PDX models to guide therapy with circulating tumor DNA (ctDNA) to monitor treatment response and disease progression.

Development of Personalized Therapeutic Strategies by Targeting Actionable Vulnerabilities in Metastatic and Chemotherapy-Resistant Breast Cancer PDXs

Human breast cancer is characterized by a high degree of inter-patients heterogeneity in terms of histology, genomic alterations, gene expression patterns, and metastatic behavior, which deeply influences individual prognosis and treatment response. The main cause of mortality in breast cancer is the therapy-resistant metastatic disease, which sets the priority for novel treatment strategies for these patients. In the present study, we demonstrate that Patient Derived Xenografts (PDXs) that were obtained from metastatic and therapy-resistant breast cancer samples recapitulate the wide spectrum of the disease in terms of histologic subtypes and mutational profiles, as evaluated by whole exome sequencing. We have integrated genomic and transcriptomic data to identify oncogenic and actionable pathways in each PDX. By taking advantage of primary short-term in vitro cultures from PDX tumors, we showed their resistance to standard chemotherapy (Paclitaxel), as seen in the patients. Moreover, we selected targeting drugs and analyzed PDX sensitivity to single agents or to combination of targeted and standard therapy on the basis of PDX-specific genomic or transcriptomic alterations. Our data demonstrate that PDXs represent a suitable model to test new targeting drugs or drug combinations and to prioritize personalized therapeutic regimens for pre-clinal and clinical tests.