Researchers from the School of Medicine in Ribeirão Preto (FMRP), at the University of São Paulo (USP), in collaboration with international groups, have developed indices that provide information about the prognosis of cancers, aid in the choice of the most appropriate therapy to be used and identify potential targets for the development of new drugs. The article reporting these results - Machine Learning Identity Stemness Features Associated with Oncogenic Dedifferentiation - will be published on April 5 in Cell.
To perform the study, researchers at the Omics laboratory from the Department of Genetics of the FMRP combined the use of artificial intelligence algorithms, genomic data from 12,000 samples from 33 different types of tumors, and an understanding of how progression of cancer occurs.
According to Houtan Noushmehr, senior author of the study, the methodologies used in this work are part of a new trend in biomedical sciences research, consequence of the large amount of molecular data currently available. "The present challenge is to manage, interpret and analyze different categories of data," says Noushmehr, "which requires researchers to integrate knowledge in biology, computer science and statistics." He considers the training of young scientists to manage coherently these massive data amounts as his main task, both as a teacher and a researcher.
These results build on the group's previous studies, including one also published in Cell, that identified important genomic features of brain tumors. "The goal is that our index can be used one day in the clinical routine," explains Tathiane Malta, first author of the study, "as additional information for the clinician to choose the most appropriate treatment for each patient and tumor." In addition to the Ribeirão Preto team, researchers from Harvard University, in the United States, and the University of Poznan, in Poland, also authored the study.
Cancer stem cells
According to a currently accepted understanding, transformations that healthy cells undergo when growing tumors include mainly two characteristics: The loss of their specific features and the acquisition of the ability to multiply in a disorderly fashion. This process can also be considered as a loss of specialization, with tumor cells become progressively undifferentiated. Typically, the sub-population of cancer stem cells "drives" tumor growth. The stemness indices developed by the researchers provide a measure of how much the tumor cells resemble stem cells.
Based on the idea that there is a similarity between tumor cells and stem cells, the USP researchers used a machine-learning algorithm to detect and systematize molecular characteristics of healthy stem cells and differentiated cells derived from them. The software analyzed thousands of cells at different stages of differentiation, to identify typical molecular signatures of stem cells. With this information, they created two independent "stemness-like" indices, based on gene expression and DNA methylation. The indices range from zero to one, with zero meaning low similarity to stem cells, and one high similarity.
The Cancer Genome Atlas program
The database from The Cancer Genome Atlas (TCGA) program includes samples from primary tumors of 12,000 people, covering 33 different types of cancers. Over the last 10 years, scientists involved in the program have generated and stored data on genetic and epigenetic changes in tumors. Using the stemness indices, the researchers detected the tumoral degree of stemness in the TCGA samples.
The main finding of the study is that stemness indices provide a measure of the path of tumor cells towards "de-differentiation", with higher indices correlating with tumor aggressiveness in many types of cancer. Accordingly, the researchers found that metastatic tumors have high rates of similarity to stem cells. In addition, the stemness indices could allow the identification of new targets for anticancer drugs, aimed at halting the progression of the cells towards de-differentiation. "If we can identify the point at which the tumor cells start to have characteristics of stem cells, we can prevent this trajectory and avoid its aggravation," Noushmehr adds.
Universidade de São Paulo
Universidade de São Paulo is a state-funded university in the Brazilian state of São Paulo. It is the largest Brazilian university and the country's most prestigious educational institution, the best university in Ibero-America,[and holds a high reputation among world universities, being ranked 100 worldwide in reputation by the Times Higher Education World University Rankings. USP is involved in teaching, research and university extension in all areas of knowledge, offering a broad range of courses.
The university was founded in 1934, regrouping already existing schools in the state of São Paulo. The university has subsequently created new departments, becoming one of the largest institutions of higher education in Latin America, with approximately 90,000 enrolled students. Currently, it has eleven campuses, four of them in the city of São Paulo. The remaining campuses are in the cities of Bauru, Lorena, Piracicaba, Pirassununga, Ribeirão Preto and two in São Carlos.
Ribeirao Preto Medical School (FMRP) at University of São Paulo (USP) provides excellence in patient care and in higher education, by training health professionals with a high level of qualification and qualified researchers in its graduate programs, which produce high-quality innovative research with international insertion. In this context, OMICs Lab aims to identify and understand the epigenomic signatures that define normal and disease states. The ultimate goal is to help develop better therapies and cures for complex diseases such as diabetes and cancer. To this end, we generate (by our own and through our collaborators), next generation sequencing data across a multitude of -omics type data, genomics, epigenomics, proteomics and transcriptomics and perform mechanistic studies. A main focus of the lab is to train and educate the next generation of scientists skills in analyzing and interpreting these data types and by doing so we introduce important skills in the scientific method, bioinformatics and leadership. For more information, visit http://www.