Stemness Indices - New Concept may be Helpful to Progress Cancer Therapies Development

Researchers from institutions such as the University of São Paulo have developed a new indexing model that can provide relevant information for the prognosis of cancer patients, and can also professionally guide cancer patients to apply more appropriate therapies for treatment. Furthermore, it can identify the target for the development of new drugs. The researchers used artificial intelligence algorithms to analyze the genomic data of 12,000 samples from 33 different types of tumors, clarifying the molecular mechanisms of cancer progression. Related results being published on the journal of CELL.

Houtan Noushmehr, one of the researchers said that the methodology used in this study is part of a new trend in biomedical research that aims to take advantage of a large amount of molecular biology data to help scientists conduct in-depth research. However, the current challenge researchers have to face is how to manage, interpret, and analyze different types of data, which will require effectively integrate biology, computer science, and statistics.

In previous studies, researchers identified important genomic features of brain tumors through research. The researchers believe that the development of this new indexing model is expected to be used as an assistive method in clinical research to help researchers target different patients and tumor types in order to choose the most appropriate therapy. At present, it is generally believed that the growth tumors that are transformed by healthy cells include the following two characteristics: (1) the cells will lack the characteristics they possess, and breeding in a disorderly manner; (2) this process can also be considered as a specialized deficiency, and tumor cells will gradually differentiate. In general, cancer stem cell subpopulations will drive the growth of tumors, the "stemness indices" proposed by the researchers can provide a measurement method to indicate how many tumor cells will be the same as stem cells.

Tumor cells have some similarities with stem cells. In this study, researchers utilized machine learning algorithms to detect and classify the molecular characteristics of healthy stem cells and derived cells. This software can analyze thousands of cells at different stages of cell differentiation to identify the typical molecular characteristics of stem cells. Furthermore, the researchers developed two independent indices with stem cell similarity based on gene expression and DNA methylation characteristics. These indices range from 0 to 1, when it is 0 means that the similarity with the stem cells is low, while 1 means that the similarity with the stem cells is higher.

In the past 10 years, scientists engaged in The Cancer Genome Atlas (TCGA) have discovered and stored multiple genetic and epigenetic changes in tumors. Now researchers may be able to detect the degree of stem cell index that the sample has in TCGA with stemness indices, which described by the investigators can help them elucidate the path of tumor dedifferentiation. The higher indices seems to be directly related to the degree of progression of many types of cancer.

Researchers have found that metastatic tumors often have a high degree of similarity with stem cells. In addition, this index can help researchers effectively identify new targets of anticancer drugs, which will help researchers develop new therapies to inhibit tumor progression.

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