Release date: 2017-07-27
The creation of in vitro and in vivo tumor model processes using a personalized approach can be used to help determine the choice of cancer treatments and increase our understanding of tumor response.
Accurate Cancer Therapy, along with the latest research in cancer biology and cutting-edge technology, can be combined with anticancer drugs to identify genetic changes. Regarding cancer research, Pauli et al. published their online paper on DNA sequencing of tumor samples and testing of patient cell models, opening another chapter in this work. The study used high-throughput drug screening to assess treatment response patterns and expand the range of options to personalize cancer treatment.
Pauli and his colleagues embarked on a clinical research program that compares the tumor tissue of each patient to the healthy tissue by sequencing the DNA in the protein-coding region of the genome to determine the tumor specificity that may be caused by the drug target. Variety. However, the authors quickly realized that information based solely on DNA sequences was not sufficient to effectively guide treatment decisions in most cases. Most of the 501 cancer patients tested were in advanced cancer, and only about 10% had genetic alterations that could be directly matched to the US Food and Drug Administration (FDA) approved targeting agents. This proportion includes those on the use of non-standard drugs, in which a drug approved for the treatment of one type of tumor can also be used to treat another.
In contrast to the findings of Pauli and his colleagues, the cancer treatment programs that researchers are investigating have found that approximately 40% of tumors use less stringent but widely accepted criteria that are commonly used in clinical trials. Even so, for the majority of patients tested in both studies, due to the limited number of available medications, DNA sequencing results did not have enough information for personalized care.
This prompted Pauli et al to try another way to identify potential effective matches between tumors and drugs. They use organisms that grow in in vitro 3D cell culture systems that maintain cell-cell interactions and the interaction of surrounding extracellular matrices. These substances, extracted from the patient's tumor cells, better preserve the biological characteristics of the tissue, rather than the universal monolayer cell culture. Organisms provide a large-scale platform for drug-sensitive screening, providing rapid turnaround time for early application to the clinic.
Using this organism, Pauli et al. constructed a library of nearly 160 compounds and tested them to identify effective drug and drug combinations that limit cancer cell growth. They found that organ cells were transplanted into immunodeficient mice in vitro to produce a personalized therapeutic model, "patient-derived xenografts" (PDXs), the gold standard for pre-treatment evaluation of cancer (Figure 1). The study by Pauli and his colleagues provides an organelle-based organism-based approach that uses high-throughput drug screening and drug validation of PDXs from the same organ.
From 145 samples including 18 different tumor types, the researchers performed 56 organ cell cultures and 19 PDX model studies from biopsies of primary tumors or from cancer cell tissue samples from other locations. Tumor DNA sequencing, high-throughput drug dose response testing, and PDX validation results were described in detail in four patients. The identification of effective targeted drugs was carried out in vitro. Based on the secondary drug screening test, the optimal combination scheme was selected for verification. However, it is worth noting that the individual's proposed treatment is not tested on the patient, so the researchers cannot show that the drug they selected is clinically active. The authors also recognize that the “non-standardized†use of approved or experimental drugs remains a significant challenge that limits clinical testing based on data obtained from such precision drug platforms.
Analysis of high-throughput drug-sensitive tumor patient samples and their association with the genomic map of drug use revealed that they were not sensitive to chemotherapy response in studies targeting leukemia. Under non-standardized use of drugs (outside clinical trials, people at risk can be exposed to unlicensed drugs), leukemia patients can receive the expected combination of drugs through effective drug sensitivity screening and can promise A short-term response report.
However, the researchers also found evidence that with the development of leukemia in patients, tumors are resistant to in vitro tested agents and may have potential effects on other drugs, while DNA sequencing reveals A series of genetic changes associated with disease progression.
The use of a tumor-based drug screening platform accurately predicts clinical response, which relies on cultured cells to retain the complex molecular and biological properties of the tumor. Although cancer DNA and transcriptional characteristics are reasonable in preclinical cell models compared to the patient's original tumor, there are differences in the immune and vascular microenvironments. Drugs from inhibitors of the immune system checkpoint, and interactions between tumor cells and surrounding cells (stromal cells), cannot be recognized by organ cell compounds from cancer cells.
In addition, organisms are often involved in the high expression of foreign material response genes and metabolic process genes that may affect the growth and survival of cancer cells. In addition, the drug response observed in the organism may not guarantee the patient's response due to differences in the process of activating the drug molecule in the preclinical model and in the human body. In addition, the half-life of the compound and its highest concentration at the tumor site vary widely in both in vitro and in vivo experiments.
However, despite these changes, these changes are critical in selecting the optimal drug screening model system, but in Pauli et al., the observed drug response pattern is relative between the organism and the PDX system. Consistent. Future technological advances may increase our ability to predict clinical outcomes. These advances may include body experiments, including various cell types, such as stromal cells, mimicking the microenvironment in vivo, and more PDX models of humanized immunodeficient mice with human immune cells, which will facilitate the promotion. Immunotherapy research.
We believe that the true potential from patient cell banks is used by researchers to generate large shared databases that allow for the study of the correlation between complex tumor genomics and drug susceptibility, as well as the identification of combinations of target drugs for the use of standard chemistry for certain tumors. Therapy can be performed better. In the clinical treatment of a single drug of choice, since specific genetic changes are usually transient, and in most cases, the most effective combination of drugs cannot be selected based on DNA sequencing results and published cancer studies.
There are two types of correlation analysis that may prove to be particularly insightful, and once many cases are submitted, these combined “big data†can be used for modeling. The first is a cross-comparison of drug response indicators for different behavioral mechanisms. Despite the potential genomic and transcriptional variations in any of the tumors, a combination of drugs with highly relevant sensitivity patterns in the sample can anchor the combination therapy. The second method is to link drug sensitivity to tumor genomics. Even if only genomic data is available, a combination of drugs combined with synergistic effects can be selected. The precise treatments described by Pauli et al. may help bridge the gap between the understanding of cancer genomics and the development of personalized treatment designs. Although such methods have only been tried in a few research institutions, unexpected drug targeting has been discovered using an organic high-throughput screening platform.
This approach allows the identification of tumor drug susceptibility in clinical trials. In order to facilitate the broader clinical application of this approach, it is necessary to improve the success rate of organ establishment from tumor samples and to investigate alternative sources of tumor cells (such as blood samples) to avoid relying on individuals with solid tumors during biopsy A transfer occurred. Over time, combined with genomic analysis to understand the evolutionary process of drug-sensitive and drug-resistant individuals, organic drug screening techniques may pave the way for optimal true personalization, adaptive, and dynamic treatment of advanced cancer.
Source: Bio Geek
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