Multifunctional profiling of triple-negative breast cancer patient-derived tumoroids for disease modeling

Deep neural network model for enhancing disease prediction using auto encoder based broad learning

Bioinformatics and Healthcare Integration Disease prediction models have been revolutionized by Big Data. These models, which make use of extensive medical data, predict illnesses before symptoms appear. Deep neural networks are well-known for their ability to increase accuracy by extending the network's depth and modifying weights through gradient descent. Traditional approaches, however, are hindered by issues such as gradient instability and delayed training. As a substitute, the Broad Learning (BL) system is introduced, which avoids gradient descent in favor of quick reconstruction by incremental learning. However, BL has trouble extracting complicated features from medical data, which makes it perform poorly in cases involving complex healthcare. We suggest ABL, which combines the effectiveness of BL with the noise reduction of Denoising Auto Encoder (AE), to address this. Robust feature extraction is an area in which the hybrid model shines, especially in intricate medical environments. Accuracy of up to 98.50 % is achieved by remarkable results from validation using a variety of datasets. The ability of ABL to quickly adapt through incremental learning suggests that it may be used to forecast diseases in complicated healthcare contexts with agility and accuracy.

Emerging roles of 3D-culture systems in tackling tumor drug resistance

Drug resistance that affects patients universally is a major challenge in cancer therapy. The development of drug resistance in cancer cells is a multifactor event, and its process involves numerous mechanisms that allow these cells to evade the effect of treatments. As a result, the need to understand the molecular mechanisms underlying cancer drug sensitivity is imperative. Traditional 2D cell culture systems have been utilized to study drug resistance, but they often fail to mimic the 3D milieu and the architecture of real tissues and cell-cell interactions. As a result of this, 3D cell culture systems are now considered a comprehensive model to study drug resistance in vitro. Cancer cells exhibit an in vivo behavior when grown in a three-dimensional environment and react to therapy more physiologically. In this review, we discuss the relevance of main 3D culture systems in the study of potential approaches to overcome drug resistance and in the identification of personalized drug targets with the aim of developing patient-specific treatment strategies that can be put in place when resistance emerges.

Intratumoral injection of melatonin enhances tumor regression in cell line-derived and patient-derived xenografts of head and neck cancer by increasing mitochondrial oxidative stress

Head and neck squamous cell carcinoma present a high mortality rate. Melatonin has been shown to have oncostatic effects in different types of cancers. However, inconsistent results have been reported for in vivo applications. Consequently, an alternative administration route is needed to improve bioavailability and establish the optimal dosage of melatonin for cancer treatment. On the other hand, the use of patient-derived tumor models has transformed the field of drug research because they reflect the heterogeneity of patient tumor tissues. In the present study, we explore mechanisms for increasing melatonin bioavailability in tumors and investigate its potential as an adjuvant to improve the therapeutic efficacy of cisplatin in the setting of both xenotransplanted cell lines and primary human HNSCC. We analyzed the effect of two different formulations of melatonin administered subcutaneously or intratumorally in Cal-27 and SCC-9 xenografts and in patient-derived xenografts. Melatonin effects on tumor mitochondrial metabolism was also evaluated as well as melatonin actions on tumor cell migration. In contrast to the results obtained with the subcutaneous melatonin, intratumoral injection of melatonin drastically inhibited tumor progression in HNSCC-derived xenografts, as well as in patient-derived xenografts. Interestingly, intratumoral injection of melatonin potentiated CDDP effects, decreasing Cal-27 tumor growth. We demonstrated that melatonin increases ROS production and apoptosis in tumors, targeting mitochondria. Melatonin also reduces migration capacities and metastasis markers. These results illustrate the great clinical potential of intratumoral melatonin treatment and encourage a future clinical trial in cancer patients to establish a proper clinical melatonin treatment.

The Role of Patient-Derived Organoids in Triple-Negative Breast Cancer Drug Screening

Triple-negative breast cancer (TNBC) is one of the most aggressive breast cancer subtypes, with a grave prognosis and few effective treatment options. Organoids represent revolutionary three-dimensional cell culture models, derived from stem or differentiated cells and preserving the capacity to differentiate into the cell types of their tissue of origin. The current review aims at studying the potential of patient-derived TNBC organoids for drug sensitivity testing as well as highlighting the advantages of the organoid technology in terms of drug screening. In order to identify relevant studies, a literature review was conducted using the MEDLINE and LIVIVO databases. The search terms “organoid” and “triple-negative breast cancer” were employed, and we were able to identify 25 studies published between 2018 and 2022. The current manuscript represents the first comprehensive review of the literature focusing on the use of patient-derived organoids for drug sensitivity testing in TNBC. Patient-derived organoids are excellent in vitro study models capable of promoting personalized TNBC therapy by reflecting the treatment responses of the corresponding patients and exhibiting high predictive value in the context of patient survival evaluation.

Organoid factory: The recent role of the human induced pluripotent stem cells (hiPSCs) in precision medicine

During the last decades, hiPSC-derived organoids have been extensively studied and used as in vitro models for several applications among which research studies. They can be considered as organ and tissue prototypes, especially for those difficult to obtain. Moreover, several diseases can be accurately modeled and studied. Hence, patient-derived organoids (PDOs) can be used to predict individual drug responses, thus paving the way toward personalized medicine. Lastly, by applying tissue engineering and 3D printing techniques, organoids could be used in the future to replace or regenerate damaged tissue. In this review, we will focus on hiPSC-derived 3D cultures and their ability to model human diseases with an in-depth analysis of gene editing applications, as well as tumor models. Furthermore, we will highlight the state-of-the-art of organoid facilities that around the world offer know-how and services. This is an increasing trend that shed the light on the need of bridging the publicand the private sector. Hence, in the context of drug discovery, Organoid Factories can offer biobanks of validated 3D organoid models that can be used in collaboration with pharmaceutical companies to speed up the drug screening process. Finally, we will discuss the limitations and the future development that will lead hiPSC-derived technology from bench to bedside, toward personalized medicine, such as maturity, organoid interconnections, costs, reproducibility and standardization, and ethics. hiPSC-derived organoid technology is now passing from a proof-of-principle to real applications in the clinic, also thanks to the applicability of techniques, such as CRISPR/Cas9 genome editing system, material engineering for the scaffolds, or microfluidic systems. The benefits will have a crucial role in the advance of both basic biological and translational research, particularly in the pharmacological field and drug development. In fact, in the near future, 3D organoids will guide the clinical decision-making process, having validated patient-specific drug screening platforms. This is particularly important in the context of rare genetic diseases or when testing cancer treatments that could in principle have severe side effects. Therefore, this technology has enabled the advancement of personalized medicine in a way never seen before.