Clinical approval success rates for investigational cancer drugs

A rat-based preclinical platform facilitating transcatheter hepatic arterial infusion in immunodeficient rats with liver xenografts of patient-derived pancreatic ductal adenocarcinoma

Liver metastases from pancreatic ductal adenocarcinoma (PDAC) are highly fatal. A rat-based patient-derived tumor xenograft (PDX) model is available for transcatheter therapy. This study aimed to create an immunodeficient rat model with liver xenografts of patient-derived primary PDAC and evaluate efficacy of hepatic arterial infusion chemotherapy with cisplatin in this model. Three patient-derived PDACs were transplanted into the livers of 21 rats each (totally, 63 rats), randomly assigned into hepatic arterial infusion, systemic venous infusion, and control groups (n = 7 each) four weeks post-implantation. Computed tomography evaluated tumor volumes before and four weeks after treatment. Post-euthanasia, resected tumor specimens underwent histopathological examination. A liver-implanted PDAC PDX rat model was established in all 63 rats, with first CT identifying all tumors. Four weeks post-treatment, arterial infusion groups exhibited significantly smaller tumor volumes than controls for all three tumors on second CT. Xenograft tumors histologically maintained adenocarcinoma features compared to original patient tumors. Ki67 expression was significantly lower in arterial infusion groups than in the other two for the three tumors, indicating reduced tumor growth in PDX rats. A liver-implanted PDAC PDX rat model was established as a rat-based preclinical platform. Arterial cisplatin infusion chemotherapy represents a potential therapy for PDAC liver metastasis.

Engineering peptide drug therapeutics through chemical conjugation and implication in clinics

The development of peptide drugs has made tremendous progress in the past few decades because of the advancements in modification chemistry and analytical technologies. The novel-designed peptide drugs have been modified through various biochemical methods with improved diagnostic, therapeutic, and drug-delivery strategies. Researchers found it a helping hand to overcome the inherent limitations of peptides and bring continued advancements in their applications. Furthermore, the emergence of peptide-drug conjugates (PDCs)-utilizes target-oriented peptide moieties as a vehicle for cytotoxic payloads via conjugation with cleavable chemical agents, resulting in the key foundation of the new era of targeted peptide drugs. This review summarizes the various classifications of peptide drugs, suitable chemical modification strategies to improve the ADME (adsorption, distribution, metabolism, and excretion) features of peptide drugs, and recent (2015-early 2024) progress/achievements in peptide-based drug delivery systems as well as their fruitful implication in preclinical and clinical studies. Furthermore, we also summarized the brief description of other types of PDCs, including peptide-MOF conjugates and peptide-UCNP conjugates. The principal aim is to provide scattered and diversified knowledge in one place and to help researchers understand the pinching knots in the science of PDC development and progress toward a bright future of novel peptide drugs.

Peptide-Drug Conjugates: Design, Chemistry, and Drug Delivery System as a Novel Cancer Theranostic

The emergence of peptide-drug conjugates (PDCs) that utilize target-oriented peptide moieties as carriers of cytotoxic payloads, interconnected with various cleavable/noncleavable linkers, resulted in the key-foundation of the new era of targeted therapeutics. They are capable of retaining the integrity of conjugates in the blood circulatory system as well as releasing the drugs at the tumor microenvironment. Other valuable advantages are specificity and selectivity toward targeted-receptors, higher penetration ability, and drug-loading capacity, making them a suitable candidate to play their vital role as promising carrier agents. In this review, we summarized the types of cell-targeting (CTPs) and cell-penetrating peptides (CPPs) that have broad applications in the advancement of targeted drug-delivery systems (DDS). Moreover, the techniques to overcome the limitations of peptide-chemistry for their extensive implementation to construct the PDCs. Besides this, the diversified breakthrough of linker chemistry, and ample knowledge of various cytotoxic payloads used in PDCs in recent years, as well as the mechanism of action of PDCs was critically discussed. The principal aim is to provide scattered and diversified knowledge in one place and to help researchers understand the pinching knots in the science of PDC development, also their progression toward a bright future for PDCs as novel theranostics in clinical practice.

A multidimensional platform of patient-derived tumors identifies drug susceptibilities for clinical lenvatinib resistance

Lenvatinib, a second-generation multi-receptor tyrosine kinase inhibitor approved by the FDA for first-line treatment of advanced liver cancer, facing limitations due to drug resistance. Here, we applied a multidimensional, high-throughput screening platform comprising patient-derived resistant liver tumor cells (PDCs), organoids (PDOs), and xenografts (PDXs) to identify drug susceptibilities for conquering lenvatinib resistance in clinically relevant settings. Expansion and passaging of PDCs and PDOs from resistant patient liver tumors retained functional fidelity to lenvatinib treatment, expediting drug repurposing screens. Pharmacological screening identified romidepsin, YM155, apitolisib, NVP-TAE684 and dasatinib as potential antitumor agents in lenvatinib-resistant PDC and PDO models. Notably, romidepsin treatment enhanced antitumor response in syngeneic mouse models by triggering immunogenic tumor cell death and blocking the EGFR signaling pathway. A combination of romidepsin and immunotherapy achieved robust and synergistic antitumor effects against lenvatinib resistance in humanized immunocompetent PDX models. Collectively, our findings suggest that patient-derived liver cancer models effectively recapitulate lenvatinib resistance observed in clinical settings and expedite drug discovery for advanced liver cancer, providing a feasible multidimensional platform for personalized medicine.

Tumor Organoids: The Era of Personalized Medicine

The strategies of future medicine are aimed to modernize and integrate quality approaches including early molecular-genetic profiling, identification of new therapeutic targets and adapting design for clinical trials, personalized drug screening (PDS) to help predict and individualize patient treatment regimens. In the past decade, organoid models have emerged as an innovative in vitro platform with the potential to realize the concept of patient-centered medicine. Organoids are spatially restricted three-dimensional clusters of cells ex vivo that self-organize into complex functional structures through genetically programmed determination, which is crucial for reconstructing the architecture of the primary tissue and organs. Currently, there are several strategies to create three-dimensional (3D) tumor systems using (i) surgically resected patient tissue (PDTOs, patient-derived tumor organoids) or (ii) single tumor cells circulating in the patient's blood. Successful application of 3D tumor models obtained by co-culturing autologous tumor organoids (PDTOs) and peripheral blood lymphocytes have been demonstrated in a number of studies. Such models simulate a 3D tumor architecture in vivo and contain all cell types characteristic of this tissue, including immune system cells and stem cells. Components of the tumor microenvironment, such as fibroblasts and immune system cells, affect tumor growth and its drug resistance. In this review, we analyzed the evolution of tumor models from two-dimensional (2D) cell cultures and laboratory animals to 3D tissue-specific tumor organoids, their significance in identifying mechanisms of antitumor response and drug resistance, and use of these models in drug screening and development of precision methods in cancer treatment.

Mathematical modeling predicts pathways to successful implementation of combination TRAIL-producing oncolytic virus and PAC-1 to treat granulosa cell tumors of the ovary

The development of new cancer therapies requires multiple rounds of validation from in vitro and in vivo experiments before they can be considered for clinical trials. Mathematical models assist in this preclinical phase by combining experimental data with human parameters to provide guidance about potential therapeutic regimens to bring forward into trials. However, granulosa cell tumors of the ovary lack a relevant mouse model, complexifying preclinical drug development for this rare tumor. To bridge this gap, we established a mathematical model as a framework to explore the potential of using a tumor necrosis factor-related apoptosis-inducing ligand (TRAIL)-producing oncolytic vaccinia virus in combination with the chemotherapeutic agent first procaspase activating compound (PAC-1). We have previously shown that TRAIL and PAC-1 act synergistically on granulosa tumor cells. In line with our previous results, our current model predicts that, although it is unable to stop the tumor from growing in its current form, combination oral PAC-1 with oncolytic virus (OV) provides the best result compared to monotherapies. Encouragingly, our results suggest that increases to the OV infection rate can lead to the success of this combination therapy within a year. The model developed here can continue to be improved as more data become available, allowing for regimen-tailoring via virtual clinical trials, ultimately shepherding effective regimens into trials.

Perspectives on Ligand/Protein Binding Kinetics Simulations: Force Fields, Machine Learning, Sampling, and User-Friendliness

Computational techniques applied to drug discovery have gained considerable popularity for their ability to filter potentially active drugs from inactive ones, reducing the time scale and costs of preclinical investigations. The main focus of these studies has historically been the search for compounds endowed with high affinity for a specific molecular target to ensure the formation of stable and long-lasting complexes. Recent evidence has also correlated the in vivo drug efficacy with its binding kinetics, thus opening new fascinating scenarios for ligand/protein binding kinetic simulations in drug discovery. The present article examines the state of the art in the field, providing a brief summary of the most popular and advanced ligand/protein binding kinetics techniques and evaluating their current limitations and the potential solutions to reach more accurate kinetic models. Particular emphasis is put on the need for a paradigm change in the present methodologies toward ligand and protein parametrization, the force field problem, characterization of the transition states, the sampling issue, and algorithms' performance, user-friendliness, and data openness.

The challenge of making the right choice: patient avatars in the era of cancer immunotherapies

Immunotherapies are a key therapeutic strategy to fight cancer. Diverse approaches are used to activate tumor-directed immunity and to overcome tumor immune escape. The dynamic interplay between tumor cells and their tumor(immune)microenvironment (T(I)ME) poses a major challenge to create appropriate model systems. However, those model systems are needed to gain novel insights into tumor (immune) biology and a prerequisite to accurately develop and test immunotherapeutic approaches which can be successfully translated into clinical application. Several model systems have been established and advanced into so-called patient avatars to mimic the patient´s tumor biology. All models have their advantages but also disadvantages underscoring the necessity to pay attention in defining the rationale and requirements for which the patient avatar will be used. Here, we briefly outline the current state of tumor model systems used for tumor (immune)biological analysis as well as evaluation of immunotherapeutic agents. Finally, we provide a recommendation for further development to make patient avatars a complementary tool for testing and predicting immunotherapeutic strategies for personalization of tumor therapies.

Clinical development of new drugs for adults and children with cancer, 2010-2020

BACKGROUND

Many new molecular entities enter clinical development to evaluate potential therapeutic benefits for oncology patients. We characterized adult and pediatric development of the set of new molecular entities that started clinical testing in 2010-2015 worldwide.

METHODS

We extracted data from AdisInsight, an extensive database of global pharmaceutical development, and the FDA.gov website. We followed the cohort of new molecular entities initiating first-in-human phase I clinical trials in 2010-2015 to the end of 2020. For each new molecular entity, we determined whether it was granted US Food and Drug Administration (FDA) approval, studied in a trial open to pediatric enrollment, or stalled during development. We characterized the cumulative incidence of these endpoints using statistical methods for censored data.

RESULTS

The 572 new molecular entities starting first-in-human studies in 2010-2015 were studied in 6142 trials by the end of 2020. Most new molecular entities were small molecules (n = 316, 55.2%), antibodies (n = 148, 25.9%), or antibody-drug conjugates (n = 44, 7.7%). After a mean follow-up of 8.0 years, 173 new molecular entities did not advance beyond first-in-human trials, and 39 were approved by the FDA. New molecular entities had a 10.4% estimated probability (95% confidence interval = 6.6% to 14.1%) of being approved by the FDA within 10 years of first-in-human trials. After a median of 4.6 years since start of first-in-human trials, 67 (11.7%) new molecular entities were tested in trials open to pediatric patients, and 5 (0.9%) were approved for pediatric indications.

CONCLUSIONS

More efficient clinical development strategies are needed to evaluate new cancer therapies, especially for children, and incorporate approaches to ensure knowledge gain from investigational products that stall in development.

A novel ex vivo lung cancer model based on bioengineered rat lungs

Introduction: Two-dimensional cell cultures have contributed substantially to lung cancer research, but 3D cultures are gaining attention as a new, more efficient, and effective research model. A model reproducing the 3D characteristics and tumor microenvironment of the lungs in vivo, including the co-existence of healthy alveolar cells with lung cancer cells, is ideal. Here, we describe the creation of a successful ex vivo lung cancer model based on bioengineered lungs formed by decellularization and recellularization. Methods: Human cancer cells were directly implanted into a bioengineered rat lung, which was created with a decellularized rat lung scaffold reseeded with epithelial cells, endothelial cells and adipose-derived stem cells. Four human lung cancer cell lines (A549, PC-9, H1299, and PC-6) were applied to demonstrate forming cancer nodules on recellularized lungs and histopathological assessment were made among these models. MUC-1 expression analysis, RNA-seq analysis and drug response test were performed to demonstrate the superiority of this cancer model. Results: The morphology and MUC-1 expression of the model were like those of lung cancer in vivo. RNA sequencing revealed an elevated expression of genes related to epithelial-mesenchymal transition, hypoxia, and TNF-α signaling via NF-κB; but suppression of cell cycle-related genes including E2F. Drug response assays showed that gefitinib suppressed PC-9 cell proliferation equally well in the 3D lung cancer model as in 2D culture dishes, albeit over a smaller volume of cells, suggesting that fluctuations in gefitinib resistance genes such as JUN may affect drug sensitivity. Conclusions: A novel ex vivo lung cancer model was closely reproduced the 3D structure and microenvironment of the actual lungs, highlighting its possible use as a platform for lung cancer research and pathophysiological studies.

Experimental mouse models for translational human cancer research

The development and growth of tumors remains an important and ongoing threat to human life around the world. While advanced therapeutic strategies such as immune checkpoint therapy and CAR-T have achieved astonishing progress in the treatment of both solid and hematological malignancies, the malignant initiation and progression of cancer remains a controversial issue, and further research is urgently required. The experimental animal model not only has great advantages in simulating the occurrence, development, and malignant transformation mechanisms of tumors, but also can be used to evaluate the therapeutic effects of a diverse array of clinical interventions, gradually becoming an indispensable method for cancer research. In this paper, we have reviewed recent research progress in relation to mouse and rat models, focusing on spontaneous, induced, transgenic, and transplantable tumor models, to help guide the future study of malignant mechanisms and tumor prevention.

Anti-cancer effect of afatinib, dual inhibitor of HER2 and EGFR, on novel mutation HER2 E401G in models of patient-derived cancer

BACKGROUND

Precision medicine with gene panel testing based on next-generation sequencing for patients with cancer is being used increasingly in clinical practice. HER2, which encodes the human epidermal growth factor receptor 2 (HER2), is a potentially important driver gene. However, therapeutic strategies aimed at mutations in the HER2 extracellular domain have not been clarified. We therefore investigated the effect of EGFR co-targeted therapy with HER2 on patient-derived cancer models with the HER2 extracellular domain mutation E401G, based on our previous findings that this mutation has an epidermal growth factor receptor (EGFR)-mediated activation mechanism.

METHODS

We generated a xenograft (PDX) and a cancer tissue-originated spheroid (CTOS) from a patient's cancer containing an amplified HER2 E401G mutation. With these platforms, we compared the efficacy of afatinib, a tyrosine kinase inhibitor having anti-HER2 and anti-EGFR activity, with two other therapeutic options: lapatinib, which has similar properties but weaker EGFR inhibition, and trastuzumab plus pertuzumab, for which evidence exists of treatment efficacy against cancers with wild-type HER2 amplification. Similar experiments were also performed with H2170, a cell line with wild-type HER2 amplification, to contrast the characteristics of these drug's efficacies against HER2 E401G.

RESULTS

We confirmed that PDX and CTOS retained morphological and immunohistochemical characteristics and HER2 gene profiles of the original tumor. In both PDX and CTOS, afatinib reduced tumor size more than lapatinib or trastuzumab plus pertuzumab. In addition, afatinib treatment resulted in a statistically significant reduction in HER2 copy number at the end of treatment. On the other hand, in H2170 xenografts with wild-type HER2 amplification, trastuzumab plus pertuzumab was most effective.

CONCLUSIONS

Afatinib, a dual inhibitor of HER2 and EGFR, showed a promising effect on cancers with amplified HER2 E401G, which have an EGFR-mediated activation mechanism. Analysis of the activation mechanisms of mutations and development of therapeutic strategies based on those mechanisms are critical in precision medicine for cancer patients.

Application status and future prospects of the PDX model in lung cancer

Lung cancer is one of the most prevalent, fatal, and highly heterogeneous diseases that, seriously threaten human health. Lung cancer is primarily caused by the aberrant expression of multiple genes in the cells. Lung cancer treatment options include surgery, radiation, chemotherapy, targeted therapy, and immunotherapy. In recent decades, significant progress has been made in developing therapeutic agents for lung cancer as well as a biomarker for its early diagnosis. Nonetheless, the alternative applications of traditional pre-clinical models (cell line models) for diagnosis and prognosis prediction are constrained by several factors, including the lack of microenvironment components necessary to affect cancer biology and drug response, and the differences between laboratory and clinical results. The leading reason is that substantial shifts accrued to cell biological behaviors, such as cell proliferative, metastatic, invasive, and gene expression capabilities of different cancer cells after decades of growing indefinitely in vitro. Moreover, the introduction of individualized treatment has prompted the development of appropriate experimental models. In recent years, preclinical research on lung cancer has primarily relied on the patient-derived tumor xenograft (PDX) model. The PDX provides stable models with recapitulate characteristics of the parental tumor such as the histopathology and genetic blueprint. Additionally, PDXs offer valuable models for efficacy screening of new cancer drugs, thus, advancing the understanding of tumor biology. Concurrently, with the heightened interest in the PDX models, potential shortcomings have gradually emerged. This review summarizes the significant advantages of PDXs over the previous models, their benefits, potential future uses and interrogating open issues.

Cancer organoid applications to investigate chemotherapy resistance

In clinical practice, a large proportion of cancer patients receive chemotherapy, yet tumors persist or acquire resistance; removing this obstacle could help to lower the number of cancer-related fatalities. All areas of cancer research are increasingly using organoid technology, a culture technique that simulates the in vivo environment in vitro, especially in the quickly developing fields of anticancer drug resistance, drug-tolerant persisters, and drug screening. This review provides an overview of organoid technology, the use of organoids in the field of anticancer drug resistance research, their relevance to clinical information and clinical trials, and approaches to automation and high throughput.

Potential use of EGFR-targeted molecular therapies for tumor suppressor CYLD-negative and poor prognosis oral squamous cell carcinoma with chemoresistance

Background

Tumor suppressor CYLD dysfunction by loss of its expression, triggers malignant transformation, especially drug resistance and tumor invasion/metastasis. Although loss of CYLD expression is significantly associated with poor prognosis in a large variety of tumors, no clinically-effective treatment for CYLD-negative cancer patients is available.

Methods

We focused on oral squamous cell carcinoma (OSCC), and sought to develop novel therapeutic agents for CYLD-negative cancer patients with poor prognosis. CYLD-knockdown OSCC cells by using CYLD-specific siRNA, were used to elucidate and determine the efficacy of novel drug candidates by evaluating cell viability and epithelial-mesenchymal transition (EMT)-like change. Therapeutic effects of candidate drug on cell line-derived xenograft (CDX) model and usefulness of CYLD as a novel biomarker using patient-derived xenograft (PDX) model were further investigated.

Results

CYLD-knockdown OSCC cells were resistant for all currently-available cytotoxic chemotherapeutic agents for OSCC, such as, cisplatin, 5-FU, carboplatin, docetaxel, and paclitaxel. By using comprehensive proteome analysis approach, we identified epidermal growth factor receptor (EGFR), a receptor tyrosine kinase, played key roles in CYLD-knockdown OSCC cells. Indeed, cell survival rate in the cisplatin-resistant CYLD-knockdown OSCC cells was markedly inhibited by treatment with clinically available EGFR tyrosine kinase inhibitors (EGFR-TKIs), such as gefitinib. In addition, gefitinib was significantly effective for not only cell survival, but also EMT-like changes through inhibiting transforming growth factor-β (TGF-β) signaling in CYLD-knockdown OSCC cells. Thereby, overall survival of CYLD-knockdown CDX models was significantly prolonged by gefitinib treatment. Moreover, we found that CYLD expression was significantly associated with gefitinib response by using PDX models.

Conclusions

Our results first revealed that EGFR-targeted molecular therapies, such as EGFR-TKIs, could have potential to be novel therapeutic agents for the CYLD-negative OSCC patients with poor prognosis.

Recent advances in the development of transplanted colorectal cancer mouse models

Despite progress in prevention and treatment, colorectal cancer (CRC) remains the third most common malignancy worldwide and the second most common cause of cancer death in 2020. To evaluate various characteristics of human CRC, a variety of mouse models have been established. Transplant mouse models have distinct advantages in studying the clinical behavior and therapeutic progress of CRC. Host, xenograft, and transplantation routes are the basis of transplant mouse models. As the effects of the tumor microenvironment and the systemic environment on cancer cells are gradually revealed, 3 key elements of transplanted CRC mouse models have been revolutionized. This has led to the development of humanized mice, patient-derived xenografts, and orthotopic transplants that reflect the human systemic environment, patient's tumor of origin, and tumor growth microenvironments in immunodeficient mice, respectively. These milestone events have allowed for great progress in tumor biology and the treatment of CRC. This article reviews the evolution of these events and points out their strengths and weaknesses as innovative and useful preclinical tools to study CRC progression and metastasis and to exploit novel treatment schedules by establishing a testing platform. This review article depicts the optimal transplanted CRC mouse models and emphasizes the significance of surgical models in the study of CRC behavior and treatment response.

The potential of a data centred approach & knowledge graph data representation in chemical safety and drug design

Big Data pervades nearly all areas of life sciences, yet the analysis of large integrated data sets remains a major challenge. Moreover, the field of life sciences is highly fragmented and, consequently, so is its data, knowledge, and standards. This, in turn, makes integrated data analysis and knowledge gathering across sub-fields a demanding task. At the same time, the integration of various research angles and data types is crucial for modelling the complexity of organisms and biological processes in a holistic manner. This is especially valid in the context of drug development and chemical safety assessment where computational methods can provide solutions for the urgent need of fast, effective, and sustainable approaches. At the same time, such computational methods require the development of methodologies suitable for an integrated and data centred Big Data view. Here we discuss Knowledge Graphs (KG) as a solution to a data centred analysis approach for drug and chemical development and safety assessment. KGs are knowledge bases, data analysis engines, and knowledge discovery systems all in one, allowing them to be used from simple data retrieval, over meta-analysis to complex predictive and knowledge discovery systems. Therefore, KGs have immense potential to advance the data centred approach, the re-usability, and informativity of data. Furthermore, they can improve the power of analysis, and the complexity of modelled processes, all while providing knowledge in a natively human understandable network data model.

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.

Patient-derived cancer models: Valuable platforms for anticancer drug testing

Molecularly targeted treatments and immunotherapy are cornerstones in oncology, with demonstrated efficacy across different tumor types. Nevertheless, the overwhelming majority metastatic disease is incurable due to the onset of drug resistance. Preclinical models including genetically engineered mouse models, patient-derived xenografts and two- and three-dimensional cell cultures have emerged as a useful resource to study mechanisms of cancer progression and predict efficacy of anticancer drugs. However, variables including tumor heterogeneity and the complexities of the microenvironment can impair the faithfulness of these platforms. Here, we will discuss advantages and limitations of these preclinical models, their applicability for drug testing and in co-clinical trials and potential strategies to increase their reliability in predicting responsiveness to anticancer medications.

Drosophila melanogaster: A platform for anticancer drug discovery and personalized therapies

Cancer is a complex disease whereby multiple genetic aberrations, epigenetic modifications, metabolic reprogramming, and the microenvironment contribute to the development of a tumor. In the traditional anticancer drug discovery pipeline, drug candidates are usually screened in vitro using two-dimensional or three-dimensional cell culture. However, these methods fail to accurately mimic the human disease state. This has led to the poor success rate of anticancer drugs in the preclinical stages since many drugs are abandoned due to inefficacy or toxicity when transitioned to whole-organism models. The common fruit fly, Drosophila melanogaster, has emerged as a beneficial system for modeling human cancers. Decades of fundamental research have shown the evolutionary conservation of key genes and signaling pathways between flies and humans. Moreover, Drosophila has a lower genetic redundancy in comparison to mammals. These factors, in addition to the advancement of genetic toolkits for manipulating gene expression, allow for the generation of complex Drosophila genotypes and phenotypes. Numerous studies have successfully created Drosophila models for colorectal, lung, thyroid, and brain cancers. These models were utilized in the high-throughput screening of FDA-approved drugs which led to the identification of several compounds capable of reducing proliferation and rescuing phenotypes. More noteworthy, Drosophila has also unlocked the potential for personalized therapies. Drosophila ‘avatars’ presenting the same mutations as a patient are used to screen multiple therapeutic agents targeting multiple pathways to find the most appropriate combination of drugs. The outcomes of these studies have translated to significant responses in patients with adenoid cystic carcinoma and metastatic colorectal cancers. Despite not being widely utilized, the concept of in vivo screening of drugs in Drosophila is making significant contributions to the current drug discovery pipeline. In this review, we discuss the application of Drosophila as a platform in anticancer drug discovery; with special focus on the cancer models that have been generated, drug libraries that have been screened and the status of personalized therapies. In addition, we elaborate on the biological and technical limitations of this system.

Eventual success rate and predictors of success for oncology drugs tested in phase I trials

Previous estimates of the likelihood of a drug tested in phase I trials obtaining FDA clearance are out of date. In the intervening years, newer pharmaceuticals have been developed, resulting in new delivery systems and lines of therapies. We sought to explore and update these estimates by comprehensively searching drugs tested in phase I trials and to determine the factors associated with later receiving FDA approval. In a cross-sectional analysis, we searched for anti-tumor drugs tested in phase I trials and published in scientific journals or presented at hematology/oncology conferences. For each drug, we searched PubMed for phase II and phase III studies testing the drug for the same indication tested in phase I studies. We found 51 drug approvals; 4 were withdrawn. The probability of a drug tested in 2015 being approved by 2021 was 6.2%. Drugs tested as monotherapy were more likely to receive approval than drugs tested in combination, and monoclonal antibodies were more likely to receive approval than drugs of other mechanisms. In adjusted models, response rates higher than 40% in phase I studies, demonstrating an improvement in overall survival (OS) in phase III studies, and drugs tested as monotherapy were associated with receiving FDA approval. When looking at all drugs tested during a single year, most drugs were not approved, and among those that are approved, almost 8% are withdrawn. Response rates higher than 40%, testing a drug as monotherapy, and demonstrating an improvement in OS were associated with receiving FDA approval. This article is protected by copyright. All rights reserved.

Validation of a Patient-Derived Xenograft Model for Cervical Cancer Based on Genomic and Phenotypic Characterization

Simple Summary

The rate of total tumor engraftment of patient-derived xenografts is 50% in cervical cancer. These cancers retain their histopathological characteristics. The gene mutations and expression patterns associated with carcinogenesis and infiltration and the expression levels of genes in extracellular vesicles released from the tumors are similar between patient-derived xenograft models and primary tumors. Patient-derived xenograft models of cervical cancer could be potentially useful tools for translational research.

Abstract

Patient-derived xenograft (PDX) models are useful tools for preclinical drug evaluation, biomarker identification, and personalized medicine strategies, and can be developed by the heterotopic or orthotopic grafting of surgically resected tumors into immunodeficient mice. We report the PDX models of cervical cancer and demonstrate the similarities among original and different generations of PDX tumors. Fresh tumor tissues collected from 22 patients with primary cervical cancer were engrafted subcutaneously into NOD.CB17-PrkdcSCID/J mice. Histological and immunohistochemical analyses were performed to compare primary and different generations of PDX tumors. DNA and RNA sequencing were performed to verify the similarity between the genetic profiles of primary and PDX tumors. Total RNA in extracellular vesicles (EVs) released from primary and PDX tumors was also quantified to evaluate gene expression. The total tumor engraftment rate was 50%. Histologically, no major differences were observed between the original and PDX tumors. Most of the gene mutations and expression patterns related to carcinogenesis and infiltration were similar between the primary tumor and xenograft. Most genes associated with carcinogenesis and infiltration showed similar expression levels in the primary tumor and xenograft EVs. Therefore, compared with primary tumors, PDX models could be potentially more useful for translational research.

Patient-Derived Xenograft: A More Standard "Avatar" Model in Preclinical Studies of Gastric Cancer

Despite advances in diagnosis and treatment, gastric cancer remains the third most common cause of cancer-related death in humans. The establishment of relevant animal models of gastric cancer is critical for further research. Due to the complexity of the tumor microenvironment and the genetic heterogeneity of gastric cancer, the commonly used preclinical animal models fail to adequately represent clinically relevant models of gastric cancer. However, patient-derived models are able to replicate as much of the original inter-tumoral and intra-tumoral heterogeneity of gastric cancer as possible, reflecting the cellular interactions of the tumor microenvironment. In addition to implanting patient tissues or primary cells into immunodeficient mouse hosts for culture, the advent of alternative hosts such as humanized mouse hosts, zebrafish hosts, and in vitro culture modalities has also facilitated the advancement of gastric cancer research. This review highlights the current status, characteristics, interfering factors, and applications of patient-derived models that have emerged as more valuable preclinical tools for studying the progression and metastasis of gastric cancer.

The Cell-Isolation Capsules with Rod-Like Channels Ensure the Survival and Response of Cancer Cells to Their Microenvironment

Current macrocapsules with semipermeable but immunoprotective polymeric membranes are attractive devices to achieve the purpose of immunoisolation, however, their ability to allow diffusion of essential nutrients and oxygen is limited, which leads to a low survival rate of encapsulated cells. Here, a novel method is reported by taking advantage of thermotropic liquid crystals, sodium laurylsulfonate (SDS) liquid crystals (LCs), and rod-like crystal fragments (LCFs) to develop engineered alginate hydrogels with rod-like channels. This cell-isolation capsule with an engineered alginate hydrogel-wall allows small molecules, large molecules, and bacteria to diffuse out from the capsules freely but immobilizes the encapsulated cells inside and prevents cells in the microenvironment from moving in. The encapsulated cells show a high survival rate with isolation of host immune cells and long-term growth with adequate nutrients and oxygen supply. In addition, by sharing and responding to the normal molecular and vesicular microenvironment (NMV microenvironment), encapsulated cancer cells display a transition from tumorous phenotypes to ductal features of normal epithelial cells. Thus, this device will be potentially useful for clinical application in cell therapy by secreting molecules and for establishment of patient-derived xenograft (PDX) models that are often difficult to achieve for certain types of tumors, such as prostate cancer.

The Role of Natural Products as Sources of Therapeutic Agents for Innovative Drug Discovery

Emerging threats to human health require a concerted effort in search of both preventive and treatment strategies, placing natural products at the center of efforts to obtain new therapies and reduce disease spread and associated mortality. The therapeutic value of compounds found in plants has been known for ages, resulting in their utilization in homes and in clinics for the treatment of many ailments ranging from common headache to serious conditions such as wounds. Despite the advancement observed in the world, plant based medicines are still being used to treat many pathological conditions or are used as alternatives to modern medicines. In most cases, these natural products or plant-based medicines are used in an un-purified state as extracts. A lot of research is underway to identify and purify the active compounds responsible for the healing process. Some of the current drugs used in clinics have their origins as natural products or came from plant extracts. In addition, several synthetic analogues are natural product-based or plant-based. With the emergence of novel infectious agents such as the SARS-CoV-2 in addition to already burdensome diseases such as diabetes, cancer, tuberculosis and HIV/AIDS, there is need to come up with new drugs that can cure these conditions. Natural products offer an opportunity to discover new compounds that can be converted into drugs given their chemical structure diversity. Advances in analytical processes make drug discovery a multi-dimensional process involving computational designing and testing and eventual laboratory screening of potential drug candidates. Lead compounds will then be evaluated for safety, pharmacokinetics and efficacy. New technologies including Artificial Intelligence, better organ and tissue models such as organoids allow virtual screening, automation and high-throughput screening to be part of drug discovery. The use of bioinformatics and computation means that drug discovery can be a fast and efficient process and enable the use of natural products structures to obtain novel drugs. The removal of potential bottlenecks resulting in minimal false positive leads in drug development has enabled an efficient system of drug discovery. This review describes the biosynthesis and screening of natural products during drug discovery as well as methods used in studying natural products.

Patient-Derived Xenografts of High-Grade Serous Ovarian Cancer Subtype as a Powerful Tool in Pre-Clinical Research

(1) Background. PDX models have become the preferred tool in research laboratories seeking to improve development and pre-clinical testing of new drugs. PDXs have been shown to capture the cellular and molecular characteristics of human tumors better than simpler cell line-based models. More recently, however, hints that PDXs may change their characteristics over time have begun to emerge, emphasizing the need for comprehensive analysis of PDX evolution. (2) Methods. We established a panel of high-grade serous ovarian carcinoma (HGSOC) PDXs and developed and validated a 300-SNP signature that can be successfully utilized to assess genetic drift across PDX passages and detect PDX contamination with lymphoproliferative tissues. In addition, we performed a detailed histological characterization and functional assessment of multiple PDX passages. (3) Results. Our data show that the PDXs remain largely stable throughout propagation, with marginal genetic drift at the time of PDX initiation and adaptation to mouse host. Importantly, our PDX lines retained the major histological characteristics of the original patients' tumors even after multiple passages in mice, demonstrating a strong concordance with the clinical responses of their corresponding patients. (4) Conclusions. Our data underline the value of defined HGSOC PDXs as a pre-clinical tumor model.

Factors associated with successful phase III trials for solid tumors: A systematic review

Background

It is known that the success rates of phase III trials for solid cancers are low. The aim of this study was to investigate factors related to trial design and operation that were associated with the probability of the success of phase III trials for solid cancers based on the latest comprehensive data.

Methods

Relevant clinical trials, started between September 2007 and December 2017, were retrieved from ClinicalTrials.gov. Then, variables related to the selected trials such as types of primary endpoint and duration of trial enrollment were collected from the literature and ClinicalTrials.gov. Based on the collected data, a multivariate logistic regression analysis was conducted to find factors associated with the successful results.

Results

Four hundred phase III trials were found eligible for the study. Unsuccessful trials were 207 and successful trials were 193. As a result of multivariate logistic regression analysis, factors that presented a statistically significant relationship were primary endpoint (Odds ratio [OR]: 2.79 [95% CI: 1.59–4.89]), control arm (OR: 3.06 [95% CI: 1.39–6.73]), start year of trial (OR: 3.28 [95% CI: 1.87–5.77]), and duration of trial enrollment (OR: 0.77 [95% CI: 0.60–0.99]).

Conclusion

Type of primary endpoints (time-to-event endpoints other than overall survival), control arm (treatments with lower evidence level, placebo or best supportive care), and duration of trial enrollment (faster enrollment speed) were associated with phase III trial success.

Nanolipoprotein-Mediated Her2 Protein Transfection Induces Malignant Transformation in Human Breast Acinar Cultures

Her2 overexpression is associated with an aggressive form of breast cancer and malignant transformation. We demonstrate in this work that nanolipoprotein particles (NLPs) synthesized in a cell-free manner can be used to transfer Her2 protein into the membrane of nonmalignant cells in 3D culture in a nontoxic and facile manner. With NLP-mediated Her2 protein delivery, we observed an increased probability of nonmalignant cells forming apolar nongrowth-arrested tumor-like structures. The NLP delivery system alone or Her2-NLPs plus the Her2 inhibitor trastuzumab showed no effect on the acinar organization rate, indicating that Her2 signaling is key to this process. Transcriptomics revealed essentially no effect of empty NLPs compared to untreated cells, whereas Her2-NLPs versus either untreated or empty-NLP-treated cells revealed upregulation of several factors associated with breast cancer. Pathway analysis also suggested that known nodes downstream of Her2 were activated in response to Her2-NLP treatment. This demonstrates that Her2 protein delivery with NLPs is sufficient for the malignant transformation of nonmalignant cells. Thus, this system offers a new model for studying cell surface receptor signaling without genomic modification or transformation techniques.

Brain Cancer Drug Discovery: Clinical Trials, Drug Classes, Targets, and Combinatorial Therapies

Brain cancer is a formidable challenge for drug development, and drugs derived from many cutting-edge technologies are being tested in clinical trials. We manually characterized 981 clinical trials on brain tumors that were registered in ClinicalTrials.gov from 2010 to 2020. We identified 582 unique therapeutic entities targeting 581 unique drug targets and 557 unique treatment combinations involving drugs. We performed the classification of both the drugs and drug targets based on pharmacological and structural classifications. Our analysis demonstrates a large diversity of agents and targets. Currently, we identified 32 different pharmacological directions for therapies that are based on 42 structural classes of agents. Our analysis shows that kinase inhibitors, chemotherapeutic agents, and cancer vaccines are the three most common classes of agents identified in trials. Agents in clinical trials demonstrated uneven distribution in combination approaches; chemotherapy agents, proteasome inhibitors, and immune modulators frequently appeared in combinations, whereas kinase inhibitors, modified immune effector cells did not as was shown by combination networks and descriptive statistics. This analysis provides an extensive overview of the drug discovery field in brain cancer, shifts that have been happening in recent years, and challenges that are likely to come. SIGNIFICANCE STATEMENT: This review provides comprehensive quantitative analysis and discussion of the brain cancer drug discovery field, including classification of drug, targets, and therapies.

Application of Ovarian Cancer Organoids in Precision Medicine: Key Challenges and Current Opportunities

Ovarian cancer (OC) is the leading cause of death among gynecologic malignances. Over the past decades, human-derived models have advanced from monolayer cell cultures to three-dimensional (3D) organoids that could faithfully recapitulate biological characteristics and tumor heterogeneity of primary tissues. As a complement of previous studies based on cell lines or xenografts, organoids provide a 3D platform for mutation–carcinogenesis modeling, high-throughput drug screening, genetic engineering, and biobanking, which might fulfill the gap between basic research and clinical practice. Stepwise, cutting-edge bioengineering techniques of organoid-on-a-chip and 3D bioprinting might converge current challenges and contribute to personalized therapy. We comprehensively reviewed the advantages, challenges, and translational potential of OC organoids. Undeniably, organoids represent an excellent near-physiological platform for OC, paving the way for precision medicine implementation. Future efforts will doubtlessly bring this innovative technique from bench to bedside.

Establishment and Characterization of Feline Mammary Tumor Patient-Derived Xenograft Model

Simple Summary

Feline mammary tumor (FMT) is a relatively commonly diagnosed neoplasm in the cat. Development of new veterinary cancer therapies is limited by the shortage of in vivo models that reproduce tumor microenvironment and metastatic progression. In this study, FMT patient-derived xenografts (PDX) model were established and primary cell line isolated from orthotopic PDX. The tumor grafts conserve original tumor essential features, including distant metastasis. FMT-PDX represents an available resource for bridging the biology of FMT with preclinical studies of FMT in cats.

Abstract

Feline mammary tumor is a relatively commonly diagnosed neoplasm in the cat. Development of new veterinary cancer therapies is limited by the shortage of in vivo models that reproduce tumor microenvironment and metastatic progression. Four feline mammary tumor orthotopic patient-derived xenograft model (PDX) successfully established in NOD-SCID gamma (NSG) mice. The overall success rate of PDX establishment was 36% (4/11). Histological, immunohistochemical, and short tandem repeat analysis showed a remarkable similarity between patient’s tumor and xenograft. The tumor grafts conserve original tumor essential features, including distant metastasis. Primary FMT-1807 cell line isolated from FMT-1807PDX tumor tissue. Tumorigenicity of FMT-1807 cells expanded from PDX was assessed by orthotopic injection into NSG mice. Mice yielded tumors which preserve the lung and liver metastasis ability. This work provides a platform for FMT translational investigation.

A Personalized Therapeutics Approach Using an In Silico Drosophila Patient Model Reveals Optimal Chemo- and Targeted Therapy Combinations for Colorectal Cancer

In silico models of biomolecular regulation in cancer, annotated with patient-specific gene expression data, can aid in the development of novel personalized cancer therapeutic strategies. Drosophila melanogaster is a well-established animal model that is increasingly being employed to evaluate such preclinical personalized cancer therapies. Here, we report five Boolean network models of biomolecular regulation in cells lining the Drosophila midgut epithelium and annotate them with colorectal cancer patient-specific mutation data to develop an in silico Drosophila Patient Model (DPM). We employed cell-type-specific RNA-seq gene expression data from the FlyGut-seq database to annotate and then validate these networks. Next, we developed three literature-based colorectal cancer case studies to evaluate cell fate outcomes from the model. Results obtained from analyses of the proposed DPM help: (i) elucidate cell fate evolution in colorectal tumorigenesis, (ii) validate cytotoxicity of nine FDA-approved CRC drugs, and (iii) devise optimal personalized treatment combinations. The personalized network models helped identify synergistic combinations of paclitaxel-regorafenib, paclitaxel-bortezomib, docetaxel-bortezomib, and paclitaxel-imatinib for treating different colorectal cancer patients. Follow-on therapeutic screening of six colorectal cancer patients from cBioPortal using this drug combination demonstrated a 100% increase in apoptosis and a 100% decrease in proliferation. In conclusion, this work outlines a novel roadmap for decoding colorectal tumorigenesis along with the development of personalized combinatorial therapeutics for preclinical translational studies.

Patient-derived tumor models: a more suitable tool for pre-clinical studies in colorectal cancer

Colorectal cancer (CRC), despite the advances in screening and surveillance, remains the second most common cause of cancer death worldwide. The biological inadequacy of pre-clinical models to fully recapitulate the multifactorial etiology and the complexity of tumor microenvironment and human CRC's genetic heterogeneity has limited cancer treatment development. This has led to the development of Patient-derived models able to phenocopy as much as possible the original inter- and intra-tumor heterogeneity of CRC, reflecting the tumor microenvironment's cellular interactions. Implantation of patient tissue into immunodeficient mice hosts and the culture of tumor organoids have allowed advances in cancer biology and metastasis. This review highlights the advantages and limits of Patient-derived models as innovative and valuable pre-clinical tools to study progression and metastasis of CRC, develop novel therapeutic strategies by creating a drug screening platform, and predict the efficacy of clinical response to therapy.

Deep Learning Enables Individual Xenograft Cell Classification in Histological Images by Analysis of Contextual Features

Patient-Derived Xenografts (PDXs) are the preclinical models which best recapitulate inter- and intra-patient complexity of human breast malignancies, and are also emerging as useful tools to study the normal breast epithelium. However, data analysis generated with such models is often confounded by the presence of host cells and can give rise to data misinterpretation. For instance, it is important to discriminate between xenografted and host cells in histological sections prior to performing immunostainings. We developed Single Cell Classifier (SCC), a data-driven deep learning-based computational tool that provides an innovative approach for automated cell species discrimination based on a multi-step process entailing nuclei segmentation and single cell classification. We show that human and murine cell contextual features, more than cell-intrinsic ones, can be exploited to discriminate between cell species in both normal and malignant tissues, yielding up to 96% classification accuracy. SCC will facilitate the interpretation of H&E- and DAPI-stained histological sections of xenografted human-in-mouse tissues and it is open to new in-house built models for further applications. SCC is released as an open-source plugin in ImageJ/Fiji available at the following link: https://github.com/Biomedical-Imaging-Group/SingleCellClassifier .

Parkinson's Disease Drug Development Since 1999: A Story of Repurposing and Relative Success

Background:

A global overview of drug development programs in Parkinson’s disease over the last few decades is lacking, while such programs are challenging given the multifaceted and heterogeneous nature of the disease.

Objective:

To indirectly assess drug development programs in Parkinson’s disease, exploring some factors associated with compound attrition at different trial phases.

Methods:

We assessed all Parkinson’s disease trials in the WHO trials portal, from inception (1999) to September 2019. Independent authors selected trials and extracted data. The success rate was the number of compounds that progressed to the next drug development phase divided by the number of compounds in that phase.

Results:

Overall, 357 trials (studying 152 compounds) fulfilled our inclusion criteria, with 62 (17.3%) phase 1 trials, 135 (37.8%) phase 2 trials, 85 (23.8%) phase 3 trials, and 53 (14.8%) phase 4 trials. The success rate was 42.4% from phase 2 to 3. Original compounds received regulatory approval by the FDA in 21.4% of cases, compared with 6.7% of repurposed compounds, representing an overall success rate of 14.9%. We found 172 trials (48.2%) conducted for repurposing previously licensed compounds. These figures were approximately the same regarding approval by the EMA. Most compounds were approved to treat parkinsonism and motor fluctuations.

Conclusion:

We found a moderate-to-high success rate in all phases of drug development. This was largely based on the success of original compounds, despite almost half of the identified trials attempting compound repurposing.

Approval success rates of drug candidates based on target, action, modality, application, and their combinations

The current success rate of a drug candidate, from the beginning of the clinical trial to receiving marketing approval, is about 10%-20%, and it has not changed during the past few decades. Therefore, pharmaceutical companies are under pressure to select one compound, among many others, with a high probability of success. The differences in drug features affect their probabilities of approval success. In this study, we examined the approval success rates of drug candidates, developed in the United States, the European Union, or Japan, by focusing on four parameters ("drug target," "drug action," "drug modality," and "drug application") and their combinations, and identified factors that conditioned the outcome of the drug development process. We obtained a total success rate of 12.8%, after evaluating 3999 compounds. Moreover, after analyzing the combinations of these parameters, the approval success rates of drugs that corresponded to the following categories-a stimulant in drug action or an enzyme in drug target and biologics (excluding monoclonal antibody) in drug modality-were high (34.1% and 31.3%, respectively). Univariate and multivariate logistic regression analyses revealed that stimulant in drug action, and "B" (blood and blood forming organs), "G" (genito-urinary system and sex), and "J" (anti-infectives for systemic use) in drug application were statistically associated with high approval success rates. We found several parameters and their combinations that affected drug approval success rates. Our results could assist pharmaceutical companies in evaluating the probability of success of their drug candidates and, thus, in efficiently conducting the clinical development process.

Modeling colorectal cancers using multidimensional organoids

Organoids have revolutionized cancer research as highly adaptable models that enable an array of experimental techniques to interrogate tissue morphology and function. Because they preserve the genetic, phenotypic, and behavioral traits of their source tissue, organoids have gained traction as the most relevant models for drug discovery, tracking therapeutic response and for personalized medicine. As organoids are indisputably becoming a mainstay of cancer research, this review specifically addresses how colon-derived organoids can be perfected as multidimensional, scalable, reproducible models of healthy, pre-neoplastic and neoplastic conditions of the colon and for use in high-throughput "Phase-0" human clinical trials-in-a-dish.

Deep learning assisted multi-omics integration for survival and drug-response prediction in breast cancer

Background

Survival and drug response are two highly emphasized clinical outcomes in cancer research that directs the prognosis of a cancer patient. Here, we have proposed a late multi omics integrative framework that robustly quantifies survival and drug response for breast cancer patients with a focus on the relative predictive ability of available omics datatypes. Neighborhood component analysis (NCA), a supervised feature selection algorithm selected relevant features from multi-omics datasets retrieved from The Cancer Genome Atlas (TCGA) and Genomics of Drug Sensitivity in Cancer (GDSC) databases. A Neural network framework, fed with NCA selected features, was used to develop survival and drug response prediction models for breast cancer patients. The drug response framework used regression and unsupervised clustering (K-means) to segregate samples into responders and non-responders based on their predicted IC50 values (Z-score).

Results

The survival prediction framework was highly effective in categorizing patients into risk subtypes with an accuracy of 94%. Compared to single-omics and early integration approaches, our drug response prediction models performed significantly better and were able to predict IC50 values (Z-score) with a mean square error (MSE) of 1.154 and an overall regression value of 0.92, showing a linear relationship between predicted and actual IC50 values.

Conclusion

The proposed omics integration strategy provides an effective way of extracting critical information from diverse omics data types enabling estimation of prognostic indicators. Such integrative models with high predictive power would have a significant impact and utility in precision oncology.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-021-07524-2.

Construction of in vitro patient-derived tumor models to evaluate anticancer agents and cancer immunotherapy

An in vitro assay system using patient-derived tumor models represents a promising preclinical cancer model that replicates the disease better than traditional cell culture models. Patient-derived tumor organoid (PDO) and patient-derived tumor xenograft (PDX) models have been previously established from different types of human tumors to recapitulate accurately and efficiently their tissue architecture and function. However, these models have low throughput and are challenging to construct. Thus, the present study aimed to establish a simple in vitro high-throughput assay system using PDO and PDX models. Furthermore, the current study aimed to evaluate different classes of anticancer drugs, including chemotherapeutic, molecular targeted and antibody drugs, using PDO and PDX models. First, an in vitro high-throughput assay system was constructed using PDO and PDX established from solid and hematopoietic tumors cultured in 384-well plates to evaluate anticancer agents. In addition, an in vitro evaluation system of the immune response was developed using PDO and PDX. Novel cancer immunotherapeutic agents with marked efficacy have been used against various types of tumor. Thus, there is an urgent need for in vitro functional potency assays that can simulate the complex interaction of immune cells with tumor cells and can rapidly test the efficacy of different immunotherapies or antibody drugs. An evaluation system for the antibody-dependent cellular cytotoxic activity of anti-epidermal growth factor receptor antibody and the cytotoxic activity of activated lymphocytes, such as cytotoxic T lymphocytes and natural killer cells, was constructed. Moreover, immune response assay systems with bispecific T-cell engagers were developed using effector cells. The present results demonstrated that in vitro assay systems using PDO and PDX may be suitable for evaluating anticancer agents and immunotherapy potency with high reproducibility and simplicity.

Xenografts derived from patients with head and neck cancer recapitulate patient tumour properties

Rodent models mimic the heterogeneity of head and neck cancer (HNC) malignancies and are used to investigate HNC-associated biomarkers and evaluate drug responses. To assess the utility of patient-derived xenografts (PDXs) as an HNC model, 18 tumour samples were obtained from surgical specimens of patients with HNC and implanted into non-obese diabetic severe combined immunodeficient mice. The histological features of PDXs and corresponding patient samples were compared. Furthermore, the present study investigated how PDX responses to anticancer drugs mimic patient clinical responses, as well as the expression of adenosine triphosphate-binding cassette transporters through chemotherapy in an HNC-PDX model. A total of five PDXs from patients with HNC exhibiting high correspondence with histopathological features of the original patient samples were established (establishment rate, 28%). The responses of three PDXs to cisplatin were associated with clinical responses of the patients. ABC transporter expression was augmented in one PDX model after anticancer drug treatment, but not in PBS-treated passaged PDXs. PDX models exhibited similar biological and chemosensitive characteristics to those of the primary tumours. PDXs could be a useful preclinical tool to test novel therapeutic agents and identify novel targets and biomarkers in HNC.

Cancer Clinical Trials: What Every Radiologist Wants to Know but Is Afraid to Ask

OBJECTIVE. The purpose of this article is to provide radiologists with a guide to the fundamental principles of oncology clinical trials. The review summarizes the evolution and structure of modern clinical trials with an emphasis on the relevance of clinical trials in the field of oncologic imaging. CONCLUSION. Understanding the structure and clinical relevance of modern clinical trials is beneficial for radiologists in the field of oncologic imaging.

Differences in the Central Energy Metabolism of Cancer Cells between Conventional 2D and Novel 3D Culture Systems

The conventional two-dimensional (2D) culture is available as an in vitro experimental model. However, the culture system reportedly does not recapitulate the in vivo cancer microenvironment. We recently developed a tissueoid cell culture system using Cellbed, which resembles the loose connective tissue in living organisms. The present study performed 2D and three-dimensional (3D) culture using prostate and bladder cancer cell lines and a comprehensive metabolome analysis. Compared to 3D, the 2D culture had significantly lower levels of most metabolites. The 3D culture system did not impair mitochondrial function in the cancer cells and produce energy through the mitochondria simultaneously with aerobic glycolysis. Conversely, ATP production, biomass (nucleotides, amino acids, lipids and NADPH) synthesis and redox balance maintenance were conducted in 3D culture. In contrast, in 2D culture, biomass production was delayed due to the suppression of metabolic activity. The 3D metabolome analysis using the tissueoid cell culture system capable of in vivo cancer cell culture yielded results consistent with previously reported cancer metabolism theories. This system is expected to be an essential experimental tool in a wide range of cancer research fields, especially in preclinical stages while transitioning from in vitro to in vivo.

Bioinformatic Approaches to Validation and Functional Analysis of 3D Lung Cancer Models

3D models of cancer have the potential to improve basic, translational, and clinical studies. Patient-derived xenografts, spheroids, and organoids are broad categories of 3D models of cancer, and to date, these 3D models of cancer have been established for a variety of cancer types. In lung cancer, for example, 3D models offer a promising new avenue to gain novel insights into lung tumor biology and improve outcomes for patients afflicted with the number one cancer killer worldwide. However, the adoption and utility of these 3D models of cancer vary, and demonstrating the fidelity of these models is a critical first step before seeking meaningful applications. Here, we review use cases of current 3D lung cancer models and bioinformatic approaches to assessing model fidelity. Bioinformatics approaches play a key role in both validating 3D lung cancer models and high dimensional functional analyses to support downstream applications.

Gene expression profiles of liver cancer cell lines reveal two hepatocyte-like and fibroblast-like clusters

Cancer cell lines are widely used in basic research to study cancer development, growth, invasion, or metastasis. They are also used for the development and screening of anticancer drugs. However, there are no clear criteria for choosing the most suitable cell lines among the wide variety of cancer cell lines commercially available for research, and the choice is often based on previously published reports. Here, we investigated the characteristics of liver cancer cell lines by analyzing the gene expression data available in the Cancer Cell Line Encyclopedia. Unsupervised clustering analysis of 28 liver cancer cell lines yielded two main clusters. One cluster showed a gene expression pattern similar to that of hepatocytes, and the other showed a pattern similar to that of fibroblasts. Analysis of hepatocellular carcinoma gene expression profiles available in The Cancer Genome Atlas showed that the gene expression patterns in most hepatoma tissues were similar to those in the hepatocyte-like cluster. With respect to liver cancer research, our findings may be useful for selecting an appropriate cell line for a specific study objective. Furthermore, our approach of utilizing a public database for comparing the properties of cell lines could be an attractive cell line selection strategy that can be applied to other fields of research.

Microengineered 3D Tumor Models for Anti-Cancer Drug Discovery in Female-Related Cancers

The burden of cancer continues to increase in society and negatively impacts the lives of numerous patients. Due to the high cost of current treatment strategies, there is a crucial unmet need to develop inexpensive preclinical platforms to accelerate the process of anti-cancer drug discovery to improve outcomes in cancer patients, most especially in female patients. Many current methods employ expensive animal models which not only present ethical concerns but also do not often accurately predict human physiology and the outcomes of anti-cancer drug responsiveness. Conventional treatment approaches for cancer generally include systemic therapy after a surgical procedure. Although this treatment technique is effective, the outcome is not always positive due to various complex factors such as intratumor heterogeneity and confounding factors within the tumor microenvironment (TME). Patients who develop metastatic disease still have poor prognosis. To that end, recent efforts have attempted to use 3D microengineered platforms to enhance the predictive power and efficacy of anti-cancer drug screening, ultimately to develop personalized therapies. Fascinating features of microengineered assays, such as microfluidics, have led to the advancement in the development of the tumor-on-chip technology platforms, which have shown tremendous potential for meaningful and physiologically relevant anti-cancer drug discovery and screening. Three dimensional microscale models provide unprecedented ability to unveil the biological complexities of cancer and shed light into the mechanism of anti-cancer drug resistance in a timely and resource efficient manner. In this review, we discuss recent advances in the development of microengineered tumor models for anti-cancer drug discovery and screening in female-related cancers. We specifically focus on female-related cancers to draw attention to the various approaches being taken to improve the survival rate of women diagnosed with cancers caused by sex disparities. We also briefly discuss other cancer types like colon adenocarcinomas and glioblastoma due to their high rate of occurrence in females, as well as the high likelihood of sex-biased mutations which complicate current treatment strategies for women. We highlight recent advances in the development of 3D microscale platforms including 3D tumor spheroids, microfluidic platforms as well as bioprinted models, and discuss how they have been utilized to address major challenges in the process of drug discovery, such as chemoresistance, intratumor heterogeneity, drug toxicity, etc. We also present the potential of these platform technologies for use in high-throughput drug screening approaches as a replacements of conventional assays. Within each section, we will provide our perspectives on advantages of the discussed platform technologies.

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 Tumor Xenograft Models: Toward the Establishment of Precision Cancer Medicine

Patient-derived xenografts (PDXs) describe models involving the implantation of patient-derived tumor tissue into immunodeficient mice. Compared with conventional preclinical models involving the implantation of cancer cell lines into mice, PDXs can be characterized by the preservation of tumor heterogeneity, and the tumor microenvironment (including stroma/vasculature) more closely resembles that in patients. Consequently, the use of PDX models has improved the predictability of clinical therapeutic responses to 80% or greater, compared with approximately 5% for existing models. In the future, molecular biological analyses, omics analyses, and other experiments will be conducted using recently prepared PDX models under the strong expectation that the analysis of cancer pathophysiology, stem cells, and novel treatment targets and biomarkers will be improved, thereby promoting drug development. This review outlines the methods for preparing PDX models, advances in cancer research using PDX mice, and perspectives for the establishment of precision cancer medicine within the framework of personalized cancer medicine.

Maltotriose-modified poly(propylene imine) Glycodendrimers as a potential novel platform in the treatment of chronic lymphocytic Leukemia. A proof-of-concept pilot study in the animal model of CLL

Cancer nanotherapeutics have shown promise in resolving some of the limitations of conventional drug delivery systems such as nonspecific biodistribution and targeting, lack of water solubility, and low therapeutic indices, Among the various nanoparticles that are available, dendrimers, highly branched macromolecules with a specific size and shape, are one of the most promising ones. In this preliminary study, we tested the anti-tumor activity of maltotriose-modified fourth-generation poly(propylene imine) glycodendrimers (PPI-G4-M3) in vivo in the subcutaneous MEC-1 xenograft model of human chronic lymphocytic leukemia (CLL) in NOD scid gamma mice. Fludarabine was used for model validation and as a positive treatment control. The anti-tumor response was calculated as tumor volume, tumor control ratio, and tumor growth inhibition. The study showed that PPI-G4-M3 inhibited subcutaneous tumor growth more efficiently than fludarabine. The anti-tumor response was dose-dependent. Cationic PPI-G4-M3 showed the highest anti-tumor activity but also higher toxicity than the neutral dendrimers and fludarabine. These first promising results warrant further studies in the optimization of dendrimers charge, dose, route and schedule of administration to combat CLL.

Position paper: new insights into the immunobiology and dynamics of tumor-host interactions require adaptations of clinical studies

INTRODUCTION

Prospective double-blind placebo-controlled randomized clinical trials (RCTs) are considered standard for the proof of the efficacy of oncologic therapies. Molecular methods have provided new insights into tumor biology and led to the development of targeted therapies. Due to the increasing complexity of molecular tumor characteristics and of the individuality of specific anti-tumor immune reactivity, RCTs are unfortunately only of limited use.

AREAS COVERED

The historical methods of drug research and approval and the related practices of reimbursement by statutory and private health insurance companies are being questioned. New, innovative methods for the documentation of evidence in personalized medicine will be addressed. Possible perspectives and new approaches are discussed, in particular with regard to glioblastoma.

EXPERT OPINION

Highly specialized translational oncology groups like the IOZK can contribute to medical progress and quick transfer 'from bench to bedside.' Their contribution should be acknowledged and taken into account more strongly in the development of guidelines and the reimbursement of therapy costs. Methodological plurality should be encouraged.