Patient-derived tumour xenografts for breast cancer drug discovery

Application and prospect of organoid technology in breast cancer

Breast cancer is the most common malignant tumor in women. Due to the high heterogeneity of breast cancer cells, traditional in vitro research models still have major limitations. Therefore, it is urgent to establish an experimental model that can accurately simulate the characteristics of human breast cancer. Breast cancer organoid technology emerged as the times required, that is, to construct tissue analogs with organ characteristics by using a patient's tumor tissue through 3D culture in vitro. Since the breast cancer organoid can fully preserve the histology and genetic characteristics of the original tumor, it provides a reliable model for preclinical drug screening, establishment of breast cancer organoid biobanks, research into the mechanisms of tumor development, and determination of cancer targets. It has promoted personalized treatment for clinical breast cancer patients. This article mainly focuses on recent research progress and applications of organoid technology in breast cancer, discussing the current limitations and prospects of breast cancer organoid technology.

Application of Artificial Intelligence in Discovery and Development of Anticancer and Antidiabetic Therapeutic Agents

Spectacular developments in molecular and cellular biology have led to important discoveries in cancer research. Despite cancer is one of the major causes of morbidity and mortality globally, diabetes is one of the most leading sources of group of disorders. Artificial intelligence (AI) has been considered the fourth industrial revolution machine. The most major hurdles in drug discovery and development are the time and expenditures required to sustain the drug research pipeline. Large amounts of data can be explored and generated by AI, which can then be converted into useful knowledge. Because of this, the world's largest drug companies have already begun to use AI in their drug development research. In the present era, AI has a huge amount of potential for the rapid discovery and development of new anticancer drugs. Clinical studies, electronic medical records, high-resolution medical imaging, and genomic assessments are just a few of the tools that could aid drug development. Large data sets are available to researchers in the pharmaceutical and medical fields, which can be analyzed by advanced AI systems. This review looked at how computational biology and AI technologies may be utilized in cancer precision drug development by combining knowledge of cancer medicines, drug resistance, and structural biology. This review also highlighted a realistic assessment of the potential for AI in understanding and managing diabetes.

The Progress and Clinical Application of Breast Cancer Organoids

Breast cancer is the malignant tumor with the highest incidence in women. Nowadays, the objects in vitro of models of this disease are mainly from breast cancer cell lines and patient-derived patient-derived xenograft (PDX). However, there is a significant gap between traditional cell lines and breast cancer solid tumors, meanwhiles, PDX is not highly consistent with patients due to different species. As a techonlogy, obtaining patient-derived tumor cells, combined with three-dimensional culture technology, adding cytokines that promotes the proliferation of breast cancer stem cells and inhibit their apoptosis, breast cancer organoids form a structure in vitro which is similar to tumor in the body. This model can not only study the occurrence and envolution of breast cancer, but is more prominent in clinical application. screening drugs by high-throughput, personalized treatment, textingtoxicity and immunotherapy. This article will review the breast cancer organoids, in evolution, source, culture system and clinical applications.

Genetic heterogeneity and clonal evolution during metastasis in breast cancer patient-derived tumor xenograft models

Genetic heterogeneity within a tumor arises by clonal evolution, and patients with highly heterogeneous tumors are more likely to be resistant to therapy and have reduced survival. Clonal evolution also occurs when a subset of cells leave the primary tumor to form metastases, which leads to reduced genetic heterogeneity at the metastatic site. Although this process has been observed in human cancer, experimental models which recapitulate this process are lacking. Patient-derived tumor xenografts (PDX) have been shown to recapitulate the patient's original tumor's intra-tumor genetic heterogeneity, as well as its genomics and response to treatment, but whether they can be used to model clonal evolution in the metastatic process is currently unknown. Here, we address this question by following genetic changes in two breast cancer PDX models during metastasis. First, we discovered that mouse stroma can be a confounding factor in assessing intra-tumor heterogeneity by whole exome sequencing, thus we developed a new bioinformatic approach to correct for this. Finally, in a spontaneous, but not experimental (tail-vein) metastasis model we observed a loss of heterogeneity in PDX metastases compared to their orthotopic "primary" tumors, confirming that PDX models can faithfully mimic the clonal evolution process undergone in human patients during metastatic spreading.

Nanomaterials for Cancer Precision Medicine

Medical science has recently advanced to the point where diagnosis and therapeutics can be carried out with high precision, even at the molecular level. A new field of "precision medicine" has consequently emerged with specific clinical implications and challenges that can be well-addressed by newly developed nanomaterials. Here, a nanoscience approach to precision medicine is provided, with a focus on cancer therapy, based on a new concept of "molecularly-defined cancers." "Next-generation sequencing" is introduced to identify the oncogene that is responsible for a class of cancers. This new approach is fundamentally different from all conventional cancer therapies that rely on diagnosis of the anatomic origins where the tumors are found. To treat cancers at molecular level, a recently developed "microRNA replacement therapy" is applied, utilizing nanocarriers, in order to regulate the driver oncogene, which is the core of cancer precision therapeutics. Furthermore, the outcome of the nanomediated oncogenic regulation has to be accurately assessed by the genetically characterized, patient-derived xenograft models. Cancer therapy in this fashion is a quintessential example of precision medicine, presenting many challenges to the materials communities with new issues in structural design, surface functionalization, gene/drug storage and delivery, cell targeting, and medical imaging.

Computational approach to discriminate human and mouse sequences in patient-derived tumour xenografts

BACKGROUND

Patient-Derived Tumour Xenografts (PDTXs) have emerged as the pre-clinical models that best represent clinical tumour diversity and intra-tumour heterogeneity. The molecular characterization of PDTXs using High-Throughput Sequencing (HTS) is essential; however, the presence of mouse stroma is challenging for HTS data analysis. Indeed, the high homology between the two genomes results in a proportion of mouse reads being mapped as human.

RESULTS

In this study we generated Whole Exome Sequencing (WES), Reduced Representation Bisulfite Sequencing (RRBS) and RNA sequencing (RNA-seq) data from samples with known mixtures of mouse and human DNA or RNA and from a cohort of human breast cancers and their derived PDTXs. We show that using an In silico Combined human-mouse Reference Genome (ICRG) for alignment discriminates between human and mouse reads with up to 99.9% accuracy and decreases the number of false positive somatic mutations caused by misalignment by >99.9%. We also derived a model to estimate the human DNA content in independent PDTX samples. For RNA-seq and RRBS data analysis, the use of the ICRG allows dissecting computationally the transcriptome and methylome of human tumour cells and mouse stroma. In a direct comparison with previously reported approaches, our method showed similar or higher accuracy while requiring significantly less computing time.

CONCLUSIONS

The computational pipeline we describe here is a valuable tool for the molecular analysis of PDTXs as well as any other mixture of DNA or RNA species.

Mechanisms governing metastatic dormancy in breast cancer

Breast cancer is a systemic disease characterized by early dissemination of tumor cells to distant organs. In this foreign environment, tumor cells may stay in a dormant state as single cells or as micrometastases for many years before growing out into a macrometastatic lesion. As metastasis is the primary cause for breast cancer-related death, it is important to understand the mechanisms underlying the maintenance of dormancy and dormancy escape to find druggable targets to eradicate metastatic tumor cells. Metastatic dormancy is regulated by complex interactions between tumor cells and the local microenvironment. In addition, cancer-directed immunity and systemic instigation play a crucial role.