Deep-learning and MR images to target hypoxic habitats with evofosfamide in preclinical models of sarcoma

Contemporary preclinical mouse models for pediatric rhabdomyosarcoma: from bedside to bench to bedside

Background

Rhabdomyosarcoma (RMS) is the most common pediatric soft-tissue malignancy, characterized by high clinicalopathological and molecular heterogeneity. Preclinical in vivo models are essential for advancing our understanding of RMS oncobiology and developing novel treatment strategies. However, the diversity of scholarly data on preclinical RMS studies may challenge scientists and clinicians. Hence, we performed a systematic literature survey of contemporary RMS mouse models to characterize their phenotypes and assess their translational relevance.

Methods

We identified papers published between 01/07/2018 and 01/07/2023 by searching PubMed and Web of Science databases.

Results

Out of 713 records screened, 118 studies (26.9%) were included in the qualitative synthesis. Cell line-derived xenografts (CDX) were the most commonly utilized (n = 75, 63.6%), followed by patient-derived xenografts (PDX) and syngeneic models, each accounting for 11.9% (n = 14), and genetically engineered mouse models (GEMM) (n = 7, 5.9%). Combinations of different model categories were reported in 5.9% (n = 7) of studies. One study employed a virus-induced RMS model. Overall, 40.0% (n = 30) of the studies utilizing CDX models established alveolar RMS (aRMS), while 38.7% (n = 29) were embryonal phenotypes (eRMS). There were 20.0% (n = 15) of studies that involved a combination of both aRMS and eRMS subtypes. In one study (1.3%), the RMS phenotype was spindle cell/sclerosing. Subcutaneous xenografts (n = 66, 55.9%) were more frequently used compared to orthotopic models (n = 29, 24.6%). Notably, none of the employed cell lines were derived from primary untreated tumors. Only a minority of studies investigated disseminated RMS phenotypes (n = 16, 13.6%). The utilization areas of RMS models included testing drugs (n = 64, 54.2%), studying tumorigenesis (n = 56, 47.5%), tumor modeling (n = 19, 16.1%), imaging (n = 9, 7.6%), radiotherapy (n = 6, 5.1%), long-term effects related to radiotherapy (n = 3, 2.5%), and investigating biomarkers (n = 1, 0.8%). Notably, no preclinical studies focused on surgery.

Conclusions

This up-to-date review highlights the need for mouse models with dissemination phenotypes and cell lines from primary untreated tumors. Furthermore, efforts should be directed towards underexplored areas such as surgery, radiotherapy, and biomarkers.

Artificial intelligence and machine learning in clinical pharmacological research

BACKGROUND

Clinical pharmacology research has always involved computational analysis. With the abundance of drug-related data available, the integration of artificial intelligence (AI) and machine learning (ML) methods has emerged as a promising way to enhance clinical pharmacology research.

METHODS

Based on an accepted definition of clinical pharmacology as a field of research dealing with all aspects of drug-human interactions, the analysis included publications from institutes specializing in clinical pharmacology. Research topics and the most used machine learning methods in clinical pharmacology were retrieved from the PubMed database and summarized.

RESULTS

ML was identified in 674 publications attributed to clinical pharmacology research, with a significant increase in publication activity over the last decade. Notable research topics addressed by ML/AI included Covid-19-related clinical pharmacology research, clinical neuropharmacology, drug safety and risk assessment, clinical pharmacology related to cancer research, and antimicrobial and antiviral research unrelated to Covid-19. In terms of ML methods, neural networks, random forests, and support vector machines were frequently mentioned in the abstracts of the retrieved papers.

CONCLUSIONS

ML, and AI in general, is increasingly being used in various research areas within clinical pharmacology. This report presents specific examples of applications and highlights the most used ML methods.

Multiparametric MRI: From Simultaneous Rapid Acquisition Methods and Analysis Techniques Using Scoring, Machine Learning, Radiomics, and Deep Learning to the Generation of Novel Metrics

ABSTRACT

With the recent advancements in rapid imaging methods, higher numbers of contrasts and quantitative parameters can be acquired in less and less time. Some acquisition models simultaneously obtain multiparametric images and quantitative maps to reduce scan times and avoid potential issues associated with the registration of different images. Multiparametric magnetic resonance imaging (MRI) has the potential to provide complementary information on a target lesion and thus overcome the limitations of individual techniques. In this review, we introduce methods to acquire multiparametric MRI data in a clinically feasible scan time with a particular focus on simultaneous acquisition techniques, and we discuss how multiparametric MRI data can be analyzed as a whole rather than each parameter separately. Such data analysis approaches include clinical scoring systems, machine learning, radiomics, and deep learning. Other techniques combine multiple images to create new quantitative maps associated with meaningful aspects of human biology. They include the magnetic resonance g-ratio, the inner to the outer diameter of a nerve fiber, and the aerobic glycolytic index, which captures the metabolic status of tumor tissues.

An Unsupervised Learning-Based Regional Deformable Model for Automated Multi-Organ Contour Propagation

The aim of this study is to evaluate a regional deformable model based on a deep unsupervised learning model for automatic contour propagation in breast cone-beam computed tomography-guided adaptive radiation therapy. A deep unsupervised learning model was introduced to map breast's tumor bed, clinical target volume, heart, left lung, right lung, and spinal cord from planning computed tomography to cone-beam CT. To improve the traditional image registration method's performance, we used a regional deformable framework based on the narrow-band mapping, which can mitigate the effect of the image artifacts on the cone-beam CT. We retrospectively selected 373 anonymized cone-beam CT volumes from 111 patients with breast cancer. The cone-beam CTs are divided into three sets. 311 / 20 / 42 cone-beam CT images were used for training, validating, and testing. The manual contour was used as reference for the testing set. We compared the results between the reference and the model prediction for evaluating the performance. The mean Dice between manual reference segmentations and the model predicted segmentations for breast tumor bed, clinical target volume, heart, left lung, right lung, and spinal cord were 0.78 ± 0.09, 0.90 ± 0.03, 0.88 ± 0.04, 0.94 ± 0.03, 0.95 ± 0.02, and 0.77 ± 0.07, respectively. The results demonstrated a good agreement between the reference and the proposed contours. The proposed deep learning-based regional deformable model technique can automatically propagate contours for breast cancer adaptive radiotherapy. Deep learning in contour propagation was promising, but further investigation was warranted.

Patient-derived xenograft models in cancer therapy: technologies and applications

Patient-derived xenograft (PDX) models, in which tumor tissues from patients are implanted into immunocompromised or humanized mice, have shown superiority in recapitulating the characteristics of cancer, such as the spatial structure of cancer and the intratumor heterogeneity of cancer. Moreover, PDX models retain the genomic features of patients across different stages, subtypes, and diversified treatment backgrounds. Optimized PDX engraftment procedures and modern technologies such as multi-omics and deep learning have enabled a more comprehensive depiction of the PDX molecular landscape and boosted the utilization of PDX models. These irreplaceable advantages make PDX models an ideal choice in cancer treatment studies, such as preclinical trials of novel drugs, validating novel drug combinations, screening drug-sensitive patients, and exploring drug resistance mechanisms. In this review, we gave an overview of the history of PDX models and the process of PDX model establishment. Subsequently, the review presents the strengths and weaknesses of PDX models and highlights the integration of novel technologies in PDX model research. Finally, we delineated the broad application of PDX models in chemotherapy, targeted therapy, immunotherapy, and other novel therapies.

Tirapazamine-loaded UiO-66/Cu for ultrasound-mediated promotion of chemodynamic therapy cascade hypoxia-activated anticancer therapy

Chemodynamic therapy (CDT), an emerging oncology treatment, has received considerable attention owing to its high selectivity, less aggressiveness, and endogenous stimulation. However, the complex intra-tumor environment limits the therapeutic effect. In this study, Cu⁺ was directly doped into the structure of the UiO-66 matrix using an in situ one-pot oil bath method. The as-formed UiO-66/Cu possessed a large surface area, making it feasible to modify folic acid (FA) and carry more chemotherapeutic agents like tirapazamine (TPZ), thus forming UiO-66/Cu-FA-TPZ nanoplatforms. For CDT, the nanoplatform catalyzed the cyclic generation of the highly oxidizing hydroxyl radical (·OH) from H₂O₂. Particularly, low-frequency ultrasound enhanced the curative effect. Notably, in a tumor, a severe hypoxic environment and ultrasound can activate more TPZ for safe and efficient chemotherapy, achieving synergistic and hypoxia-activated tumor treatment with a low risk of side effects. Moreover, the nanoplatform exhibits computed tomography imaging functions for combined diagnosis and treatment. Our designed nanoplatform overcomes the dilemma of insufficient efficacy from conventional therapy attributed to a hypoxic environment, expecting to guide the design of future treatment regimens for hypoxic tumors.

Study on Sgc8 Aptamer-mediated Nucleic Acid Nanomaterial-doxorubicin Complex for Tumor Targeted Therapy

Chemotherapy is one of the most important treatments for malignant cancers, but most chemotherapeutic drugs are poorly targeted, highly toxic and expensive, resulting in unsatisfactory treatment results for cancer patients. Therefore, intelligent drug delivery platforms are needed to be explored urgently to enhance drug treatment and reduce toxicity on normal cells. Nucleic acid nanomaterials are a class of nanomaterials developed on the basis of the "base complementary pairing principle", which have the advantages of good programmability, high stability, good biocompatibility, and strong targeting. Herein, we present a simple Sgc8 aptamer-modified nucleic acid nanomaterial (Sgc8NM) for the targeted delivery of Doxorubicin (Dox), a widely used chemotherapy drug in clinic. Studies have shown the Sgc8NM-Dox performed a precise treatment effect on target cells and low toxicity on non-target cells, providing a new strategy for the potential application of nanocomposite drugs in targeted cancer delivery.

Visualising spatial heterogeneity in glioblastoma using imaging habitats

Glioblastoma is a high-grade aggressive neoplasm characterised by significant intra-tumoral spatial heterogeneity. Personalising therapy for this tumour requires non-invasive tools to visualise its heterogeneity to monitor treatment response on a regional level. To date, efforts to characterise glioblastoma's imaging features and heterogeneity have focussed on individual imaging biomarkers, or high-throughput radiomic approaches that consider a vast number of imaging variables across the tumour as a whole. Habitat imaging is a novel approach to cancer imaging that identifies tumour regions or 'habitats' based on shared imaging characteristics, usually defined using multiple imaging biomarkers. Habitat imaging reflects the evolution of imaging biomarkers and offers spatially preserved assessment of tumour physiological processes such perfusion and cellularity. This allows for regional assessment of treatment response to facilitate personalised therapy. In this review, we explore different methodologies to derive imaging habitats in glioblastoma, strategies to overcome its technical challenges, contrast experiences to other cancers, and describe potential clinical applications.

Assessing the therapeutic response of tumors to hypoxia-targeted prodrugs with an in silico approach

Tumor hypoxia is commonly recognized as a condition stimulating the progress of the aggressive phenotype of tumor cells. Hypoxic tumor cells inhibit the delivery of cytotoxic drugs, causing hypoxic areas to receive insufficient amounts of anticancer agents, which results in adverse treatment responses. Being such an obstruction to conventional therapies for cancer, hypoxia might be considered a target to facilitate the efficacy of treatments in the resistive environment of tumor sites. In this regard, benefiting from prodrugs that selectively target hypoxic regions remains an effective approach. Additionally, combining hypoxia-activated prodrugs (HAPs) with conventional chemotherapeutic drugs has been used as a promising strategy to eradicate hypoxic cells. However, determining the appropriate sequencing and scheduling of the combination therapy is also of great importance in obtaining favorable results in anticancer therapy. Here, benefiting from a modeling approach, we study the efficacy of HAPs in combination with chemotherapeutic drugs on tumor growth and the treatment response. Different treatment schedules have been investigated to see the importance of determining the optimal schedule in combination therapy. The effectiveness of HAPs in varying hypoxic conditions has also been explored in the study. The model provides qualitative conclusions about the treatment response, as the maximal benefit is obtained from combination therapy with greater cell death for highly hypoxic tumors. It has also been observed that the antitumor effects of HAPs show a hypoxia-dependent profile.

Mitochondria-targeting multifunctional nanoplatform for cascade phototherapy and hypoxia-activated chemotherapy

Despite considerable progress has been achieved in hypoxia-associated anti-tumor therapy, the efficacy of utilizing hypoxia-activated prodrugs alone is not satisfied owing to the inadequate hypoxia within the tumor regions. In this work, a mitochondrial targeted nanoplatform integrating photodynamic therapy, photothermal therapy and hypoxia-activated chemotherapy has been developed to synergistically treat cancer and maximize the therapeutic window. Polydopamine coated hollow copper sulfide nanoparticles were used as the photothermal nanoagents and thermosensitive drug carriers for loading the hypoxia-activated prodrug, TH302, in our study. Chlorin e6 (Ce6) and triphenyl phosphonium (TPP) were conjugated onto the surface of the nanoplatform. Under the action of TPP, the obtained nanoplatform preferentially accumulated in mitochondria to restore the drug activity and avoid drug resistance. Using 660 nm laser to excite Ce6 can generate ROS and simultaneously exacerbate the cellular hypoxia. While under the irradiation of 808 nm laser, the nanoplatform produced local heat which can increase the release of TH302 in tumor cells, ablate cancer cells as well as intensify the tumor hypoxia levels. The aggravated tumor hypoxia then significantly boosted the anti-tumor efficiency of TH302. Both in vitro and in vivo studies demonstrated the greatly improved anti-cancer activity compared to conventional hypoxia-associated chemotherapy. This work highlights the potential of using a combination of hypoxia-activated prodrugs plus phototherapy for synergistic cancer treatment.