HSE Biologists Identify Factors That Accelerate Breast Cancer Recurrence
Scientists at HSE University have identified a molecular mechanism underlying aggressive breast cancer. They found that the signals supporting tumour growth originate not from the tumour itself but from its microenvironment. The researchers also demonstrated that reduced levels of the IGFBP6 protein in the tumour microenvironment lead to the accumulation of macrophages—immune cells associated with a higher risk of cancer recurrence. These findings already make it possible to assess patient risk more accurately and may, in the future, enable the development of drugs that target cells of the tumour microenvironment. The study has been published in Current Drug Therapy.
Triple-negative breast cancer is an aggressive type of tumour that accounts for about 20% of breast cancer cases. It is named for the absence of three receptors that usually serve as targets for therapy: estrogen, progesterone, and human epidermal growth factor receptors. This type of cancer occurs more frequently in younger women, metastasises faster than other forms, and is associated with a high risk of recurrence.
Because it lacks well-established drug targets, this type of cancer is particularly difficult to treat. As a result, researchers study not only the cancer cells themselves but also their surroundings, including connective tissue, immune cells, and blood vessels. Together, these components form the tumour microenvironment, which can either restrain tumour growth or, conversely, promote it.
A team of researchers from the HSE Faculty of Biology and Biotechnology has identified features of the tumour microenvironment that are associated with a more aggressive course of cancer and a higher risk of early recurrence. To do this, they analysed gene activity in both tumour cells and their surrounding microenvironment and compared this data with patients’ clinical records. The researchers paid particular attention to the IGF2 gene, which encodes insulin-like growth factor 2 (IGF2). Under normal conditions, IGF2 is a signalling protein involved in tissue growth and repair, but in cancer it begins to stimulate tumour cell proliferation.
The researchers found that the main supplier of 'fuel' for tumour growth—the IGF2 protein—is not the cancer cells themselves but fibroblasts, connective tissue cells that are part of the tumour microenvironment. Under normal conditions, fibroblasts support tissue structure, but in disease they can instead promote tumour development. At the same time, the tumour also has a built-in containment system. The regulatory protein IGFBP6 acts as a trap for IGF2, preventing it from uncontrollably stimulating tumour growth. The researchers found that IGFBP6 is produced by both microenvironmental cells and the tumour cells themselves in an attempt to maintain growth in balance.
The link between this mechanism and the clinical course of the disease became apparent during the analysis of patient data. Lower levels of IGFBP6 in tumours were associated with increased infiltration by macrophages—immune cells that normally protect the body but, in established tumours, can 'switch sides' and begin to support cancer cells, leading to earlier disease recurrence.
Even now, these results can be used to more accurately assess risk and guide patient monitoring: those with a higher likelihood of early recurrence may benefit from more intensive monitoring and treatment. In the long term, these findings could inform the development of targeted therapies aimed at the tumour microenvironment.
Maxim Shkurnikov
'Conventional chemotherapy primarily targets rapidly dividing cells, and in triple-negative breast cancer this is often insufficient. We propose shifting the focus to the tumour microenvironment and targeting the cells that support tumour growth,' comments Maxim Shkurnikov, Head of the Laboratory for Research on Molecular Mechanisms of Longevity at the HSE Faculty of Biology and Biotechnology and co-author of the article. 'For example, one could aim to artificially increase IGFBP6 levels or inhibit IGF2 production by fibroblasts. This approach could deprive the tumour of critical support and reduce the risk of rapid recurrence.'
See also:
Neural Network Maps as a Method for Constructing Mathematical Models
Scientists from HSE University–Nizhny Novgorod and the Institute of Physics Belgrade, Serbia, are jointly exploring the application of machine learning techniques and neural networks to the study of nonlinear dynamics. Natalya Stankevich, Leading Research Fellow at the Laboratory of Topological Methods in Dynamics of the Faculty of Informatics, Mathematics, and Computer Science at HSE University–Nizhny Novgorod, spoke to the HSE News Service about this international project.
HSE Scientists Develop Method to Compress Large Language Models Without Losing Quality
Researchers from the AI and Digital Science Institute at the HSE Faculty of Computer Science have developed a new compression method for large language models such as GPT and LLaMA that reduces their size by 25–36% without additional training or significant loss of accuracy. This is the first approach to use mathematical transformations—specifically, rotations of model weights—to make models more amenable to compression with structured matrices. The study results have been published in ACL Findings 2025. The code is available on GitHub.
Machine Learning Models Can Help Reduce Volatility and Boost Stock Market Returns
The use of machine learning models makes it possible to achieve greater accuracy in predicting risks in the Russian stock market compared to classical econometric approaches. The predictive power of these models increases by 23%, while the average investor’s return can reach up to 13% per annum. These conclusions were drawn by Nikita Lysenok from the Department of Financial Market Infrastructure at the HSE Faculty of Economic Sciences. The paper has been published in Fundamental and Applied Mathematics.
Pocket Money, Personal Interest, and Family Practices: What Shapes Students’ Economic Literacy?
University students' economic literacy depends not only on their field of study but also on their interest in economics, the learning environment, and family financial practices. For example, students who received pocket money irregularly tend to perform better on economic literacy tests than their peers who received financial support on a regular basis. These findings come from a study conducted by HSE University involving more than 1,100 students from five Russian universities. The findings have been published in Cakrawala Pendidikan.
HSE Study Reveals Imbalance in the Generative AI Market
Researchers at HSE University analysed how effectively the global generative artificial intelligence market converts investment into real revenue, concluding that AI is currently developing faster than it is paying off. The results have been published in the journal Foresight and STI Governance.
‘Entering Robotics Now Means Growing with the Area’
Unmanned vehicles, courier robots, and smart speakers are rapidly becoming a part of our lives. In 2026, the HSE Faculty of Computer Science opens its new Bachelor’s Programme ‘Design of Intelligent Robotic Systems’ (DIRS). It will train specialists at the intersection of IT, artificial intelligence, and robotics. Academic Supervisor of DIRS Vadim Morgachev explains how studies are organised and why graduates of the programme ‘will definitely be accepted into the future.’
HSE Scientists Train Neural Network to 'Hear' Faults in Electric Motors
Researchers at the AI and Digital Science Institute of the HSE Faculty of Computer Science have developed a new method—the Signature-Guided Data Augmentation (SGDA) framework—that achieves 99% accuracy in motor fault detection and 86% accuracy in fault classification. The application of this approach can reduce industrial equipment repair costs, minimise downtime, and improve production safety. The study results have been published in Engineering Applications of Artificial Intelligence.
MIEM Tech Day at Pokrovka: Exploring HSE’s Engineering DNA Together
On May 26, 2026, the central atrium of the building at 11 Pokrovsky Bulvar will host the annual large-scale festival of engineering developments created by project teams from the HSE Tikhonov Moscow Institute of Electronics and Mathematics (HSE MIEM). The programme includes presentations of the best student technological projects, stands from partner companies and joint workshops, a lecture series featuring practising engineers, a round table on the development of engineering education, and presentations of MIEM master’s degree programmes.
The 'Second Shift' Is Not Why Women Avoid News
Women are more likely than men to avoid political and economic news, but the reasons for this behaviour are linked less to structural inequality or family-related stress than to personal attitudes and the emotional perception of news content. This conclusion was reached by HSE researchers after analysing data from a large-scale survey of more than 10,000 residents across 61 regions of Russia. The study findings have been published in Woman in Russian Society.
Resource Race and Green Transition: Three Unexpected Conclusions from Foresight Centre’s Research on Climate and Poverty
Beneath the surface of green energy—which most people associate with solar panels, electric vehicles, and reduced CO2 emissions—lies a complex web of geopolitical interests, international inequality, and resource constraints. Researchers from the Laboratory for Science and Technology Studies (LST) at the HSE ISSEK Foresight Centre have published a series of articles in leading international journals on hidden and overt conflicts surrounding critically important metals and minerals, as well as related processes in the energy sector.