2023-10-04 08:32:18
by Health Editorial Staff
Developed by the University of Milan-Bicocca, it is based on the analysis of the different types of mutations that can occur in neoplastic cells
Improve the ability to predict how a tumor will evolve, overcoming the limitations of analyzes that only consider single genetic mutations. This is the objective of a new method called Ascetic (Agony-based Cancer EvoluTion InferenCe), developed by the University of Milan-Bicocca, capable of reconstructing models of tumor evolution for each patient and subsequently identifying evolutionary models that are repeated in different patients. The method, illustrated in the article Evolutionary signatures of human cancers revealed via genomic analysis of over 35,000 patients, published in the journal Nature Communications, was developed by a group led by Daniele Ramazzotti, professor of computer science at the Department of Medicine and Surgery of the University of the Milan-Bicocca Studies, in collaboration with Alex Graudenzi (Department of IT), Luca Mologni (Department of Medicine and Surgery) and the researchers Diletta Fontana, Ilaria Crespiatico and Valentina Crippa, for the evaluation and validation activities of the results.
I study
In this study, Ascetic was applied to data derived from over 35,000 tumors, including patients with various blood diseases, patients with early or advanced lung cancer, and many others. Furthermore, a validation of the results obtained on independent datasets was conducted to ensure their reliability and generalization capacity.
Different mutations
Cancer is a complex evolutionary process involving large populations of cells in the human body. These cells undergo genetic mutations and epigenetic modifications, some of which may confer an advantage on tumor cells. This advantage can translate into an increased proliferation and survival capacity of cancer cells, which can ultimately lead to the invasion of surrounding tissues and the formation of metastases. However, not all mutations contribute to the process of disease development. In fact, only a small fraction of them, called driver mutations, play a functional role, while the majority of mutations are neutral, called passenger mutations. Ascetic is based on the observation that, in most cases, the accumulation of passenger mutations during cancer progression follows a random dynamic. However, for driver mutations, which are responsible for tumor progression, evolution can lead to a consistent order observed in different patients.
Strategy
Ascetic tackles this complex problem by breaking it down into three key steps. Initially, he uses evolutionary models to establish an order among the driver genetic mutations in individual patients, allowing us to understand the sequence in which these mutations occurred over the evolutionary history of specific tumors. Then, using artificial intelligence approaches, he identifies the most suitable model to explain all the individual evolutions, offering us a map of how cancer develops globally for a particular type of tumor. Finally, he categorizes patients based on their evolution and checks whether these groups have different survival curves. Thanks to the growing availability of biological data from genetic sequencing experiments on cancer patients and advances in data science and artificial intelligence, we are now able to evaluate the presence of specific evolutionary patterns for different types of cancer. These patterns, which can be defined as evolutionary signatures, represent the preferential paths of acquisition of driver mutations, i.e. functional ones, during the evolution of cancer and can be recurrent in patients with similar prognosis. Although this study is not definitive – Ramazzotti specifies – it represents a significant step towards the creation of a “catalogue” of evolutionary signatures of cancer, which could help to better understand the complex nature of the tumor and improve predictions on its progression and prognosis. In fact, being able to classify cancer patients based on their molecular evolution could allow the prediction of future steps in the progression of the disease and consequently the implementation of optimal and personalized treatments.
October 4, 2023 (modified October 4, 2023 | 08:31)
© ALL RIGHTS RESERVED
#algorithm #reconstruct #tumor #evolution #models #time.news