Modelling cancer evolution through the development of artificial intelligence-based techniques on temporal and spatial molecular data

PhD Program in Computer and Control Engineering


Paolo Garza –
Elio Piccolo –
Elisa Ficarra –

Context of the research activity

Cancer is an evolving entity and the evolutionary properties of each tumor are likely to play a critical role in shaping its natural behavior and how it responds to therapy. However, effective tools and metrics to measure and categorize tumors based on their evolutionary characteristics still must be identified. We plan to combine mathematical modelling and AI‐based approaches to develop a new generation of cancer classifiers based on tumor evolutionary properties and proxy data. The project will be developed in collaboration with the Department of Oncology at the University of Torino. The proposed research activity fits in the SmartData@PoliTo interdepartmental centre, that brings together competences from different fields, ranging from modelling to computer programming, from communications to statistics. The candidate will join this interdisciplinary team of experts and collaborate with them.


One of the reasons why cancer evolutionary properties are still largely overlooked in diagnostic predictions is that key evolutionary parameters are hardly measurable in patients due to the impracticability of extensive sampling in space and time, which is needed for accurate estimate of key evolutionary metrics such as diversity and divergence of the cancer cell population. Here we will take advantage of a unique data source, provided by the collaboration with Dr. Andrea Bertotti (University of Torino, Department of Oncology), which allows the extensive molecular characterization of multiple tumor samplings, repeated in time and space, through the exploitation of a dedicated and proprietary ex‐ vivo experimental platform.    This project has three main goals: 1‐ Define appropriate evolutionary metrics to describe and predict the behavior of cancer, by applying (and refining/adapting) classical mathematical models of evolution to the specific context of cancer. 2‐  Identify multidimensional molecular proxies of the cancer evolutionary behavior, based on non‐linear correlation analysis, and artificial intelligence techniques. 3‐ Exploit such non‐linear associations through learning techniques to categorize cancers based on molecular surrogates of their evolutionary properties, as a groundwork for future diagnostic applications.   In detail, the work plan of the project will be the following: 1st year: ‐  Study of high‐throughput genomic data, such as DNA break whole‐genome mapping data, methylation data, sequencing data. ‐  Study of biological data creation and experimental biotech protocols to better understand the biological meaning from the data. ‐  Data framework organization, data formatting, and collection. Definition of new experiments to create complete and coherent data sets. ‐  Development of methodologies for feature extraction based on deep learning approaches. ‐  Study of temporal data analysis techniques and preliminary temporal model development. 2nd year: ‐  Starting from first year results, development of advanced modelling techniques based on machine learning (e.g. regression models, recurrent neural networks, hidden Markov models, etc.), deep learning, and statistical models for the correlation of the evolution of the tumor and the molecular temporal data, and for the identification of more relevant features driving the evolution. 3rd year: ‐ Development of algorithms for the evaluation of spatial/temporal correlations in different tumor clones evolution. ‐  Translation of temporal features into a suitable input for a classifier. ‐  Development of classifiers predicting diagnosis and therapeutic response/resistance based on the most relevant features and paths detected in the previous phases.

The algorithms planned in the proposal rely on different methodologies and then offer to the PhD student very large training opportunities. Moreover, the developed algorithms can be applied on the study of molecular evolution of different diseases, such as different kinds of cancer and neurodegenerative diseases. These diseases are increasingly frequent, and therefore they constitute a major burden to families and health care system. The proposed doctoral research, aiming at the development of algorithms and methods supporting neurodegenerative as well as cancer disease understanding and treatment, could lead to potentially high scientific and social impacts

Skills and competencies for the development of the activity

The project candidate should have good computer programming skills (C, C++, Python, script language) as well as strong inclination to research.


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