Group of human colon cancer cells with invasive behavior. Cell nuclei are in yellow and cell bodies in red. The finger-like protrusions of invasive cells are on the upper right region.
Image Credit: © Ariel Ruiz i Altaba, UNIGE
Scientific Frontline: "At a Glance" Summary
- Main Discovery: Researchers at the University of Geneva (UNIGE) have developed an artificial intelligence algorithm capable of predicting the risk of cancer metastasis and recurrence with high reliability.
- Methodology: The team identified specific gene expression signatures in colon cancer cells that drive invasive behavior and trained a predictive model, named MangroveGS, to analyze these genomic patterns across various tumor types to assess metastatic probability.
- Key Data: After training, the AI model achieved a predictive accuracy of nearly 80% in forecasting the occurrence of metastases, transforming complex genomic data into actionable prognostic information.
- Significance: This study fundamentally challenges the concept of cancer as "anarchic" cell growth, instead framing it as a distorted form of orderly biological development where suppressed genetic programs are reactivated.
- Future Application: The algorithm will enable clinicians to stratify patients based on metastatic risk, facilitating personalized treatment strategies and identifying new therapeutic targets to block the spread of tumors.
- Branch of Science: Oncology, Genetics, and Artificial Intelligence.
- Additional Detail: The research highlights that metastatic potential is defined by the reactivation of ancient developmental programs, providing a predictable "logic" to tumor progression that can be decoded by AI.
Why do some tumors spread while others remain localized? The mechanisms governing the metastatic potential of tumor cells remain largely unknown — yet understanding this is crucial for optimizing patient care. Using cells from colon cancers, scientists at the University of Geneva (UNIGE) have pinpointed the criteria that influence the risk of metastasis and identified gene expression signatures that can be used to assess its probability. The team then created an artificial intelligence tool (MangroveGS) capable of transforming these data into predictions for many cancers with unparalleled reliability. These results, published in Cell Reports, pave the way for more precise care and the discovery of new therapeutic targets.
"The origin of cancer is often attributed to 'anarchic cells'," explains Ariel Ruiz i Altaba, professor in the Department of Genetic Medicine and Development at the UNIGE Faculty of Medicine, who led the study. "However, cancer should rather be understood as a distorted form of development." Indeed, under the effect of genetic and epigenetic changes, programs that were suppressed during the development of the organism and tissues are reactivated, giving rise to a tumor.
Thus, far from being an anarchic accident, cancer follows an orderly program. "The challenge is therefore to find the keys to understanding its logic and form. And, in the case of metastases, to identify the characteristics of the cells that will separate from the tumor to create another one elsewhere in the body."
After training, the model achieved an accuracy of nearly 80% in predicting the occurrence of metastases and recurrence of colon cancer, a result far superior to existing tools.
Tracking down metastatic cells
Metastasis remains the leading cause of death in most cancers, particularly colon, breast and lung cancer. Currently, the first detectable sign of the metastatic process is the presence of circulating tumor cells in the blood or in the lymphatic system. By then, it is already too late to prevent them from spreading. Furthermore, while the mutations that lead to the formation of the original tumors are well understood, no single genetic alteration can explain why, in general, some cells migrate, and others do not.
"The difficulty lies in being able to determine the complete molecular identity of a cell – an analysis that destroys it – while observing its function, which requires it to remain alive," explains Professor Ruiz i Altaba. "To this end, we isolated, cloned and cultured tumor cells," adds Arwen Conod, senior lecturer in the Department of Genetic Medicine and Development at the UNIGE Faculty of Medicine and co-first author of the study. "These clones were then evaluated in vitro and in a mouse model to observe their ability to migrate through a real biological filter and generate metastases."
The analysis of the expression of several hundred genes, carried out on about thirty clones from two primary colon tumors, identified gene expression gradients closely linked to their migratory potential. In this context, accurate assessment of metastatic potential does not depend on the profile of a single cell, but on the sum of interactions between related cancer cells that form a group.
A highly reliable prediction algorithm
The gene expression signatures obtained were integrated into an artificial intelligence model developed by the Geneva team. "The great novelty of our tool, called 'Mangrove Gene Signatures (MangroveGS)', is that it exploits dozens, even hundreds, of gene signatures. This makes it particularly resistant to individual variations," explains Aravind Srinivasan, PhD student in the Department of Genetic Medicine and Development at the UNIGE Faculty of Medicine and co-first author of the study. After training, the model achieved an accuracy of nearly 80% in predicting the occurrence of metastases and recurrence of colon cancer, a result far superior to existing tools. In addition, signatures derived from colon cancer can also predict the metastatic potential of other cancers, such as stomach, lung, and breast cancer.
After training, the model achieved an accuracy of nearly 80% in predicting the occurrence of metastases and recurrence of colon cancer, a result far superior to existing tools. In addition, signatures derived from colon cancer can also predict the metastatic potential of other cancers, such as stomach, lung, and breast cancer.
An important step forward for clinical practice and research
Thanks to MangroveGS, tumor samples are sufficient: cells can be analyzed and their RNA sequenced at the hospital, then the metastatic risk score quickly transmitted to oncologists and patients via an encrypted Mangrove portal that has analyzed the anonymized data. "This information will prevent the overtreatment of low-risk patients, thereby limiting side effects and unnecessary costs, while intensifying the monitoring and treatment of those at high risk," adds Ariel Ruiz i Altaba. "It also offers the possibility of optimizing the selection of participants in clinical trials, reducing the number of volunteers required, increasing the statistical power of studies, and providing therapeutic benefits to the patients who need it most."
Funding: This work was carried out with the support of the Swiss National Science Foundation (SNSF), the Swiss Cancer Research Foundation, and the DIP of the State of Geneva, among others.
Published in journal: Cell Reports
Title: Emergence of high-metastatic potentials and prediction of recurrence and metastasis
Authors: Aravind Srinivasan, Arwen Conod, Yann Tapponnier, Marianna Silvano, Luca Dall’Olio, Céline Delucinge-Vivier, Isabel Borges-Grazina, and Ariel Ruiz i Altaba
Source/Credit: Université de Genève
Reference Number: ongy012226_01