一一一一一一一一一一一一一一一一一一一一一一一一一一一一一一一一一一一一一一一一一一一一一一一一一一一一一一一一一一一一一一一一一一一一一一一一一一一一一一一一一一Usually, the patient's tumor is sequenced, and targeted treatment is carried out after identification of driver mutations. For example, BCL---ABL mutant leukemia patients with imatinib (gleev) , cancer overexpressing HER2, trastuzumab (hersetin) , etc. However, only a small number of cancer-dependent oncogenic genes can be targeted, which leads to only a few cancer patients who can truly benefit from genome-oriented cancer therapies. Therefore, it is very important to integrate more biological aspects such as metabolism, appearance, microenvironment , etc. to add therapy decisions. The resulting biobank of patient model has been developed, but its application depends largely on the ability to reshape clinical responses.
2D cell cultures have been widely used in the past few decades. They usually represent the fastest growing cells and cannot reflect the diversity of tumors . Patient-derived xenograft (PDX) model can capture the genetic diversity of tumor and physiological characteristics, but it takes a long time and is costly, and immune remodeling is required to study immuno-oncology. Transplanted tissue in vitro captures the 3D structure of the tumor, but the need for tissue quantity, low throughput and low repetition limits its clinically-scale use. In recent years, a new model system, 3D patient tumor replacement (3D patient tumor avatars, 3D-PTAs) , has emerged, including patient-derived organoids (PDOs) , organ-type tumor spheroids (PDOTs) , 3D bioprinting, chip organoids and microorgan spheroids (MOS) , they can simulate cell behavior, capture the real characteristics of the tissues they originate, and they are faster and have higher throughput. Among them, PDOs have been proven to predict the response of patient tumors to chemoradiotherapy. Nevertheless, 3D-PTAs need further standardization.
Recently, Terasaki Institute for Biomedical Innovation Shen Xiling , The Jackson Laboratory for Genomic Medicine Jeffrey H. Chuang, National Cancer Institute Konstantin Salnikow jointly published a comment article on Cancer CellA path to translation: How 3D patient tumor avatars enable next generation precision oncology.
Standardized operation and experimental protocol
3D-PTAs are largely dependent on the technology of experimental operators. The current success rate can only be at most 70-95%. The establishment of standardized processes and the confirmation of internal/inter-heterogeneity of tissue tumors need to be solved urgently. The batch effect of matrix gel, the potential interference of synthetic gel, and the metabolic effect of growth factor have led to the most suitable extracellular matrix scaffolds, culture medium and growth factor combinations for different types of 3D-PTAs. In addition, the difference in ischemic changes caused by the time and number of patients' tissues is difficult to measure, and there are many differences in the steps of processing tissues. The above reasons make comparisons between methods very difficult. Standardized operating manuals should be shared among different research teams, and the software analysis process of processing should also be verified, which will provide a solid foundation for the use of 3D-PTAs.
Standardized patient data collection
As the number and complexity of tumor samples in the 3D-PTAs biobank increases, clinical information such as ethnic race, body mass index, socioeconomic factors, gender and age, sample collection date and processing process, cancer type and treatment history, etc. should be standardized to obtain meaningful conclusions.The key to achieving this is the adoption of public rules and regulations for data exchange and acquisition worldwide. Based on the PDX minimum information standard (PDX-MI) , 3D-PTAs research standards around clinical information, patient metadata collection and patient informed consent should be developed in the next few years.
clinical trial design reform
is similar to genome-guided therapy, 3D-PTAs-guided therapy must also undergo rigorous prospective clinical trials before it can be promoted and used, but the specific verification path is still uncertain. Current clinical trials are also evaluating the potential of 3D-PTAs to promote the management of patients with different tumor types, mostly based on observational non-interference. Next, we need to evaluate its potential for predicting therapeutic response and progression-free survival, which can be achieved by monitoring immune, stromal, tumor cell types, and finally scaled and randomized until a progress-free endpoint with a clinical significance is obtained.
Based on tissue biopsy, 3D-PTAs can be tested for drug before standard treatment plans and test the efficacy of the test drugs through secondary biopsy (PET or circulating tumor DNA) , which helps accelerate the development of new drugs. 3D-PTAs are also an effective tool for patient selection for specific experimental therapies. They can predict patient responses, distinguish patient populations, thereby improving overall benefits, and achieving effective utilization of medical resources. In addition, 3D-PTAs, as a personalized platform, can distinguish between racial and socio-economic diversity, benefit more from ethnic minorities and vulnerable groups. Comparison of
Multiomics and 3D-PTAs-guided therapy
The combination of genome and functional experiments has developed rapidly, but the accurate prediction of clinical patient responses is still unknown whether it depends more on univariate factors or multiple factors. A patient can only receive one therapy at a time, and 3D-PTAs can not only test multiple drugs and their combinations in parallel, realize high-throughput screening, but also meet low-throughput in vivo studies. After being combined with the molecular map, a large number of biopsy tissues can be mapped. The data are used for computational training to obtain more accurate patient response predictions. This integrated method will also have a profound impact on therapy resistance. In terms of analysis, it can also integrate metabolism, proteomics, immunity, morphology, and genetics data, which helps to discover previously unknown links. This integrated approach will work together and develop as clinical specimens and data grow.
combination of diagnosis and diagnosis and treatment
3D-PTAs are multi-fold for drug testing and development. First, for patients who cannot benefit from existing therapies, the development of new drugs requires higher specificity and targeting, which requires very high patient selection in clinical trials. 3D-PTAs reduce costs, shorten time, and provide correction opportunities for drug strategies, improving the success rate of experiments. Second, 3D-PTAs can guide patients to efficiently choose treatment plans (standardized/new plan) , thereby reducing waste of medical resources, reducing the toxicity of inefficient therapies, improving patients' quality of life, and prolonging survival time. Third, 3D-PTAs can be passed down and preserved in biobanks, and are an important resource for marker research and deep learning algorithm development. They also help the development of diagnosis and treatment, and maximize their utilization in precision oncology.
In summary, this article lists the benefits, challenges and future efforts of 3D-PTAs technology. The standardization of technology, the involvement of patient collection, analysis tools and clinical trial design will bring more accurate predictive effects, accelerate the diagnosis and treatment of refractory diseases, and thus promote the true personalization of patient care.
original link:
https://www.cell.com/cancer-cell/fulltext/S1535-6108(22)00475-5#%20
Platformer: Eleven
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