Researchers from the Institute of Personalized Oncology at Sechenov University, a leading center under the Ministry of Health of Russia, have created a computer model that identifies next‑generation tumor markers essential for precise diagnosis and tailored cancer treatment planning. This tool can automatically map thousands of intracellular molecular pathways altered by changes in the activity of around three thousand genes. The team shared insights about this breakthrough with socialbites.ca.
The transformation of a normal cell into a cancer cell involves a dramatic reorganization of the genome. These genomic shifts touch roughly three thousand genes, whose roles can be considered both on their own and within the broader biological processes they control.
Current personalized diagnostic approaches forecast disease progression and treatment response by analyzing the genome and RNA profiles of tumor cells. Today, diagnoses commonly hinge on assessing how genes within specific signaling pathways behave. In this framework, the activation levels of pathways that drive cancer growth serve as critical tumor markers.
Yet such analyses often fall short of delivering a complete picture and carry a notable risk of error. The researchers therefore built a computer model that can describe and study the interactome, which encompasses all the interacting molecules—especially proteins—inside a tumor cell.
“An interactome can be imagined as a mathematical graph—a network of nodes and edges where each node represents a gene product, such as a protein. Our model enables each node to determine its connections to others, uncover hundreds of thousands of intermolecular interactions, and automatically map the molecular pathways associated with each node,” explained Anton Buzdin, principal investigator at the Institute of Personalized Oncology. Acknowledging the model’s capability, he noted that the quantity and quality of tumor markers derived from this new algorithm outperform those provided by current methods .
He emphasized that the rapid operation and the comprehensive reflection of intracellular molecular processes position this tool as a dependable resource for personalized cancer diagnosis and for forecasting patient survival probabilities.
In addition, the researchers highlight the potential for the approach to integrate with existing diagnostic workflows, offering a deeper, systems‑level view of tumor biology that can inform therapy choices and monitoring strategies. The model’s ability to reconstruct complex networks at high fidelity supports a more nuanced understanding of how cancer evolves within a given patient and opens avenues for identifying novel targets for therapy—targets that might be overlooked by conventional analyses. The work underscores the shift toward data‑driven, network‑based oncology that aligns with contemporary efforts to tailor treatment to the molecular portrait of each tumor.
Although the findings are preliminary and further validation in clinical settings is needed, the team is optimistic about translating the model into practical tools for clinicians. By enabling a comprehensive, path‑level view of tumor biology, the approach promises to improve diagnostic accuracy, refine prognosis, and ultimately guide more effective, personalized treatment regimens. This represents a meaningful step toward a future where cancer care is driven by a precise map of molecular interactions rather than by isolated gene signals, bringing new hope to patients across Canada and the United States.
It is important to note that the recent statements also reference the evolving landscape of cancer biomarker discovery and the growing role of systems biology in clinical decision making. Ongoing research will determine how quickly such advanced models can be integrated into routine practice, how clinicians will interpret the wealth of interactome data, and how patients will benefit from more accurate, timely, and individualized care. As the field progresses, collaborative efforts between researchers, clinicians, and health systems will be essential to realizing the full potential of these innovative diagnostic tools.