Engineering complexity in human tissue models of cancer

Vascularized tumor models for the evaluation of drug delivery systems: a paradigm shift

As the conversion rate of preclinical studies for cancer treatment is low, user-friendly models that mimic the pathological microenvironment and drug intake with high throughput are scarce. Animal models are key, but an alternative to reduce their use would be valuable. Vascularized tumor-on-chip models combine great versatility with scalable throughput and are easy to use. Several strategies to integrate both tumor and vascular compartments have been developed, but few have been used to assess drug delivery. Permeability, intra/extravasation, and free drug circulation are often evaluated, but imperfectly recapitulate the processes at stake. Indeed, tumor targeting and chemoresistance bypass must be investigated to design promising cancer therapeutics. In vitro models that would help the development of drug delivery systems (DDS) are thus needed. They would allow selecting good candidates before animal studies based on rational criteria such as drug accumulation, diffusion in the tumor, and potency, as well as absence of side damage. In this review, we focus on vascularized tumor models. First, we detail their fabrication, and especially the materials, cell types, and coculture used. Then, the different strategies of vascularization are described along with their classical applications in intra/extravasation or free drug assessment. Finally, current trends in DDS for cancer are discussed with an overview of the current efforts in the domain.

Bohemian Rhapsody of Future Drug Delivery Systems: Rational Changes Necessary for the Next Revolution

Controlled drug delivery technology has matured for more than 70 years, starting from a twice-a-day oral formulation to 6 month long-acting injectable formulations. Further technological advances require superior formulations to treat various diseases more efficiently. Developing future formulations with practical innovations for treating existing and new diseases necessitates our continued efforts to overcome at least three main hurdles. They include (i) drug delivery with reduced side effects, (ii) long-term treatment of chronic diseases, and (iii) the overcoming of biological barriers. Such efforts start with the improved ability to accurately test drug delivery efficacy using proper controls. Future development can be aided by artificial intelligence if used properly. The next revolution of drug delivery systems will be augmented if implementation is given equal weight as discovery. Such a process can be accelerated with the systemic revamp of the research funding structure and cultivating a new generation of scientists who can think differently.

Bridging systems biology and tissue engineering: Unleashing the full potential of complex 3D in vitro tissue models of disease

Rapid advances in tissue engineering have resulted in more complex and physiologically relevant 3D in vitro tissue models with applications in fundamental biology and therapeutic development. However, the complexity provided by these models is often not leveraged fully due to the reductionist methods used to analyze them. Computational and mathematical models developed in the field of systems biology can address this issue. Yet, traditional systems biology has been mostly applied to simpler in vitro models with little physiological relevance and limited cellular complexity. Therefore, integrating these two inherently interdisciplinary fields can result in new insights and move both disciplines forward. In this review, we provide a systematic overview of how systems biology has been integrated with 3D in vitro tissue models and discuss key application areas where the synergies between both fields have led to important advances with potential translational impact. We then outline key directions for future research and discuss a framework for further integration between fields.

A systematic review on the culture methods and applications of 3D tumoroids for cancer research and personalized medicine

Cancer is a highly heterogeneous disease, and thus treatment responses vary greatly between patients. To improve therapy efficacy and outcome for cancer patients, more representative and patient-specific preclinical models are needed. Organoids and tumoroids are 3D cell culture models that typically retain the genetic and epigenetic characteristics, as well as the morphology, of their tissue of origin. Thus, they can be used to understand the underlying mechanisms of cancer initiation, progression, and metastasis in a more physiological setting. Additionally, co-culture methods of tumoroids and cancer-associated cells can help to understand the interplay between a tumor and its tumor microenvironment. In recent years, tumoroids have already helped to refine treatments and to identify new targets for cancer therapy. Advanced culturing systems such as chip-based fluidic devices and bioprinting methods in combination with tumoroids have been used for high-throughput applications for personalized medicine. Even though organoid and tumoroid models are complex in vitro systems, validation of results in vivo is still the common practice. Here, we describe how both animal- and human-derived tumoroids have helped to identify novel vulnerabilities for cancer treatment in recent years, and how they are currently used for precision medicine.

Printing of 3D biomimetic structures for the study of bone metastasis: A review

Bone metastasis primarily occurs when breast, prostate, or lung cancers disseminate tumoral cells into bone tissue, leading to a range of complications in skeletal tissues and, in severe cases, paralysis resulting from spinal cord compression. Unfortunately, our understanding of pathophysiological mechanisms is incomplete and the translation of bone metastasis research into the clinic has been slow, mainly due to the lack of credible ex vivo and in vivo models to study the disease progression. Development of reliable and rational models to study how tumor cells become circulating cells and then invade and sequentially colonize the bone are in great need. Advances in tissue engineering technologies offers reliable 3D tissue alternatives which answer relevant research questions towards the understanding of cancer evolution and key functional properties of metastasis progression as well as prognosis of therapeutic approach. Here we performed an overview of cellular mechanisms involved in bone metastasis including a short summary of normal bone physiology and metastasis initiation and progression. Also, we comprehensively summarized current advances and methodologies in fabrication of reliable bone tumor models based on state-of-the-art printing technologies which recapitulate structural and biological features of native tissue. STATEMENT OF SIGNIFICANCE: This review provides a comprehensive summary of the collective findings in relation to various printed bone metastasis models utilized for investigating specific bone metastasis diseases, related characteristic functions and chemotherapeutic drug screening. These tumoral models are comprehensively evaluated and compared, in terms of their ability to recapitulate physiological metastasis microenvironment. Various biomaterials (natural and synthetic polymers and ceramic based substrates) and printing strategies and design architecture of models used for printing of 3D bone metastasis models are discussed here. This review clearly out-lines current challenges and prospects for 3D printing technologies in bone metastasis research by focusing on the required perspective models for clinical application of these technologies in chemotherapeutic drug screening.

Using machine learning approach for screening metastatic biomarkers in colorectal cancer and predictive modeling with experimental validation

Colorectal cancer (CRC) liver metastasis accounts for the majority of fatalities associated with CRC. Early detection of metastasis is crucial for improving patient outcomes but can be delayed due to a lack of symptoms. In this research, we aimed to investigate CRC metastasis-related biomarkers by employing a machine learning (ML) approach and experimental validation. The gene expression profile of CRC patients with liver metastasis was obtained using the GSE41568 dataset, and the differentially expressed genes between primary and metastatic samples were screened. Subsequently, we carried out feature selection to identify the most relevant DEGs using LASSO and Penalized-SVM methods. DEGs commonly selected by these methods were selected for further analysis. Finally, the experimental validation was done through qRT-PCR. 11 genes were commonly selected by LASSO and P-SVM algorithms, among which seven had prognostic value in colorectal cancer. It was found that the expression of the MMP3 gene decreases in stage IV of colorectal cancer compared to other stages (P value < 0.01). Also, the expression level of the WNT11 gene was observed to increase significantly in this stage (P value < 0.001). It was also found that the expression of WNT5a, TNFSF11, and MMP3 is significantly lower, and the expression level of WNT11 is significantly higher in liver metastasis samples compared to primary tumors. In summary, this study has identified a set of potential biomarkers for CRC metastasis using ML algorithms. The findings of this research may provide new insights into identifying biomarkers for CRC metastasis and may potentially lay the groundwork for innovative therapeutic strategies for treatment of this disease.

A new tissue-agnostic microfluidic device to model physiology and disease: the lattice platform

To accurately phenocopy human biology in vitro, researchers have been reducing their dependence on standard, static two-dimensional (2D) cultures and instead are moving towards three-dimensional (3D) and/or multicellular culture techniques. While these culture innovations are becoming more commonplace, there is a growing body of research that illustrates the benefits and even necessity of recapitulating the dynamic flow of nutrients, gas, waste exchange and tissue interactions that occur in vivo. However, cost and engineering complexity are two main factors that hinder the adoption of these technologies and incorporation into standard laboratory workflows. We developed LATTICE, a plug-and-play microfluidic platform able to house up to eight large tissue or organ models that can be cultured individually or in an interconnected fashion. The functionality of the platform to model both healthy and diseased tissue states was demonstrated using 3D cultures of reproductive tissues including murine ovarian tissues and human fallopian tube explants (hFTE). When exogenously exposed to pathological doses of gonadotropins and androgens to mimic the endocrinology of polycystic ovarian syndrome (PCOS), subsequent ovarian follicle development, hormone production and ovulation copied key features of this endocrinopathy. Further, hFTE cilia beating decreased significantly only when experiencing continuous media exchanges. We were then able to endogenously recreate this phenotype on the platform by dynamically co-culturing the PCOS ovary and hFTE. LATTICE was designed to be customizable with flexibility in 3D culture formats and can serve as a powerful automated tool to enable the study of tissue and cellular dynamics in health and disease in all fields of research.

Microvascularized tumor assembloids model for drug delivery evaluation in colorectal cancer-derived peritoneal metastasis

Peritoneal metastasis (PM) is a fatal state of colorectal cancer, and only a few patients may benefit from systemic chemotherapy. Although hyperthermic intraperitoneal chemotherapy (HIPEC) brings hope for affected patients, the drug development and preclinical evaluation of HIPEC are seriously lagging behind, mainly due to the lack of an ideal in vitro PM model that makes drug development over-reliant on expensive and inefficient animal experiments. This study developed an in vitro colorectal cancer PM model [microvascularized tumor assembloids (vTA)] based on an assembly strategy of endothelialized microvessels and tumor spheroids. Our data showed that the in vitro perfusion cultured vTA could maintain a similar gene expression pattern to their parental xenografts. Also, the drug penetration pattern of the in vitro HIPEC in vTA could mimic the drug delivery behavior in tumor nodules during in vivo HIPEC. More importantly, we further confirmed the feasibility of constructing a tumor burden-controlled PM animal model using vTA. In conclusion, we propose a simple and effective strategy to construct physiologically simulated PM models in vitro, thus providing a basis for PM-related drug development and preclinical evaluation of locoregional therapies. STATEMENT OF SIGNIFICANCE: This study developed an in vitro colorectal cancer peritoneal metastasis (PM) model based on microvascularized tumor assembloids (vTA) for drug evaluation. With perfusion culture, vTA could maintain a similar gene expression pattern and tumor heterogeneity to their parental xenografts. And the drug penetration pattern in vTA was similar to the drug delivery behavior in tumor nodules under in vivo treatment. Moreover, vTA was more conducive to construct PM animal models with controllable tumor burden. In conclusion, the construction of vTA could provide a new strategy for the PM-related drug development and preclinical evaluation of locoregional therapies.

Recent Advancements in Deep Learning Using Whole Slide Imaging for Cancer Prognosis

This review furnishes an exhaustive analysis of the latest advancements in deep learning techniques applied to whole slide images (WSIs) in the context of cancer prognosis, focusing specifically on publications from 2019 through 2023. The swiftly maturing field of deep learning, in combination with the burgeoning availability of WSIs, manifests significant potential in revolutionizing the predictive modeling of cancer prognosis. In light of the swift evolution and profound complexity of the field, it is essential to systematically review contemporary methodologies and critically appraise their ramifications. This review elucidates the prevailing landscape of this intersection, cataloging major developments, evaluating their strengths and weaknesses, and providing discerning insights into prospective directions. In this paper, a comprehensive overview of the field aims to be presented, which can serve as a critical resource for researchers and clinicians, ultimately enhancing the quality of cancer care outcomes. This review's findings accentuate the need for ongoing scrutiny of recent studies in this rapidly progressing field to discern patterns, understand breakthroughs, and navigate future research trajectories.

A Human Ovarian Tumor & Liver Organ-on-Chip for Simultaneous and More Predictive Toxo-Efficacy Assays

In oncology, the poor success rate of clinical trials is becoming increasingly evident due to the weak predictability of preclinical assays, which either do not recapitulate the complexity of human tissues (i.e., in vitro tests) or reveal species-specific outcomes (i.e., animal testing). Therefore, the development of novel approaches is fundamental for better evaluating novel anti-cancer treatments. Here, a multicompartmental organ-on-chip (OOC) platform was adopted to fluidically connect 3D ovarian cancer tissues to hepatic cellular models and resemble the systemic cisplatin administration for contemporarily investigating drug efficacy and hepatotoxic effects in a physiological context. Computational fluid dynamics was performed to impose capillary-like blood flows and predict cisplatin diffusion. After a cisplatin concentration screening using 2D/3D tissue models, cytotoxicity assays were conducted in the multicompartmental OOC and compared with static co-cultures and dynamic single-organ models. A linear decay of SKOV-3 ovarian cancer and HepG2 liver cell viability was observed with increasing cisplatin concentration. Furthermore, 3D ovarian cancer models showed higher drug resistance than the 2D model in static conditions. Most importantly, when compared to clinical therapy, the experimental approach combining 3D culture, fluid-dynamic conditions, and multi-organ connection displayed the most predictive toxicity and efficacy results, demonstrating that OOC-based approaches are reliable 3Rs alternatives in preclinic.