The Momentum Microscopic Image Analysis and Machine Learning Research Group, led by Péter Horváth, has combined digital pathology, machine learning, spatial omics, and automated cell isolation to examine tumors at single-cell resolution. Two recent studies report advances in this strategy. One describes a method for connecting the morphology of tumor cells with both their genetic activity and protein profiles. The other investigates how distinct tumor cell populations in a primary melanoma may be related to later metastatic lesions. Together, the findings suggest that microscopic images can serve as a molecular guide to the most consequential regions of a tumor.

Conventional molecular testing commonly analyzes tissue in bulk. A biopsy or surgical specimen is homogenized, and the resulting molecular measurements represent an average across millions of cells. That approach can identify important features of a tumor, but it may conceal the differences between neighboring cell populations. A small group of highly invasive cells can be diluted by less aggressive cells, while spatial relationships between tumor cells and surrounding tissue are lost. The Szeged team’s approach instead preserves the tissue map and uses artificial intelligence to identify specific cells or cell communities before their molecular properties are measured.

A central platform in this work is Deep Visual Proteomics, or DVP. The process begins with high-resolution histological imaging, in which tissue architecture and cellular morphology are recorded. Machine-learning algorithms analyze the images and select cells or populations according to visual characteristics associated with particular biological states. A focused laser then cuts out the selected material with high spatial precision. The isolated cells can subsequently be processed for proteomic analysis, which measures the proteins they contain. Because proteins are the active molecules that regulate cellular functions, their abundance and combinations can reveal how a cancer cell is growing, adapting to stress, communicating with neighboring cells, or resisting therapy.

The researchers extended this strategy by examining AI-selected tumor populations through both proteomics and transcriptomics. Transcriptomics measures RNA molecules and indicates which genes are actively being expressed, while proteomics measures the proteins produced as a result of cellular activity. These two layers provide complementary information: RNA can reveal the instructions being used by a cell, whereas proteins offer a closer view of the functional machinery operating inside it. Studying both may expose cases in which gene activity and protein behavior diverge, as well as biological programs that would remain invisible through morphology or a single molecular assay alone.

The method was applied to clear cell renal cell carcinoma, a kidney cancer known for substantial biological and clinical heterogeneity. The study, published in EMBO Molecular Medicine, is titled “Deep visual multi-omics profiling links morphology and molecular programs in clear cell renal cell carcinoma.” By combining visual analysis with single-cell-level genetic and protein measurements, the researchers sought to determine whether tumor regions that look different also follow distinct molecular programs. The result is a form of multi-omics mapping in which the image does not merely document the tissue; it helps direct the molecular investigation toward the cells most likely to explain disease behavior.

The same technological framework was also used to explore the possible origins of metastasis in a young patient with recurrent metastatic melanoma. Samples from the original tumor and from later lung and brain metastases were examined using AI-guided digital pathology and spatially resolved proteomics. The algorithms identified two visibly distinct tumor cell populations in the primary melanoma. When the molecular profiles were compared, cells in the later metastatic lesions most closely resembled one of those original populations. The observation suggests that a population with features associated with later spread may already have been present in the primary tumor, even before metastases became clinically apparent.

This result does not prove that the identified cells alone caused the metastases, and it does not yet provide a clinical test for predicting which tumors will spread. It does, however, illustrate the type of question that spatial single-cell technologies can address. Instead of asking only whether a tumor contains a particular mutation or protein, researchers can investigate where that feature occurs, which cells carry it, how frequently they appear, and whether the same cellular program is found in distant lesions. Such information could eventually help define the subpopulations that deserve closer monitoring or that may require treatment strategies aimed at more than the tumor’s dominant cell type.

The Szeged group developed its digital pathology and spatial omics work with collaborators in Sweden and Switzerland, including molecular pathologist Holger Moch of University Hospital Zurich and research professor György Marko-Varga of Lund University. Its automated single-cell research center is designed to isolate AI-selected cells without continuous manual intervention, potentially allowing experiments to run with consistent precision over extended periods. For cancer biology, that automation is important because large numbers of individually selected cells may be required to capture the diversity within a tumor and distinguish reproducible patterns from biological noise.

The broader significance of these studies lies in a shift in how tumors are understood. Artificial intelligence is not replacing pathologists or independently diagnosing patients in this approach. Rather, it is acting as a high-resolution instrument that connects tissue appearance with molecular function. By revealing the cellular geography of a tumor, the technology may help researchers understand why some regions become invasive, why others evade treatment, and how metastatic potential emerges. The findings are not an immediate new therapy, but they point toward a future in which cancer samples are analyzed as dynamic cellular landscapes, enabling more precise risk assessment and, ultimately, treatment decisions directed at the most dangerous populations within a tumor.

Subject of Research: AI-guided digital pathology, Deep Visual Proteomics, spatial omics, single-cell tumor analysis, cancer heterogeneity, and metastasis.

Article Title: Deep visual multi-omics profiling links morphology and molecular programs in clear cell renal cell carcinoma

Web References: https://www.brc.hu/en/research/institute-of-biochemistry/synthetic-and-systems-biology-unit/lenduelet-laboratory-of-microscopic-image-analysis-and-machine-learning; https://doi.org/10.1038/s44321-026-00484-8; https://doi.org/10.1038/s41698-026-01569-w

References: EMBO Molecular Medicine, DOI: 10.1038/s44321-026-00484-8; npj Precision Oncology, DOI: 10.1038/s41698-026-01569-w

Image Credits: András Kriston

Keywords: artificial intelligence, digital pathology, Deep Visual Proteomics, spatial omics, single-cell analysis, cancer research, clear cell renal cell carcinoma, melanoma, metastasis, precision oncology

Cancer’s multifaceted nature manifests in significant genetic variability both within and between tumors, challenging traditional methodologies that rely on single-analyte assessments. The advent of multi-omics technologies, paired with sophisticated machine learning algorithms, enables exhaustive characterization of the cancer landscape. These datasets, however, are inherently high-dimensional and complex, encompassing millions of features that demand robust computational frameworks for effective analysis. AI, with its capacity to model nonlinear interactions and uncover latent patterns, has emerged as the essential technology for managing and interpreting these datasets at scale, enabling researchers to transcend the limitations imposed by classical statistical methods.

The integration of multimodal data sources is not merely a technical advancement but a conceptual leap in understanding tumorigenesis. By uniting genetic alterations with proteomic signatures and clinical endpoints such as survival and treatment response, AI-powered models dissect cancer’s heterogeneity across multiple biological hierarchies. This systems biology approach facilitates the identification of novel biomarkers and therapeutic targets, while also supporting the stratification of patients into subgroups with distinct prognostic and predictive profiles. Consequently, clinical decision-making becomes more agile and tailored, improving patient outcomes and minimizing adverse effects by aligning interventions with precise tumor characteristics.

Central to these advancements is the development of explainable AI (XAI), which counteracts the traditional “black-box” nature of machine learning models. For clinical implementation, transparency and interpretability are non-negotiable, as physicians must understand the rationale behind AI-driven recommendations before adopting them into practice. Explainable models provide intuitive visualizations and mechanistic insights that bolster clinician confidence, enhance patient trust, and facilitate regulatory approval. Moreover, XAI fosters hypothesis generation by revealing previously unrecognized biological relationships, thereby accelerating translational research and innovation.

Despite remarkable progress, the integration of multi-omics and clinical data via AI encounters several significant challenges. Data accessibility remains a bottleneck, as the heterogeneity and proprietary nature of biomedical datasets limit comprehensive model training. Furthermore, variability in data quality, missing values, and batch effects introduce noise that can undermine model robustness and reproducibility. Additionally, ensuring that AI models generalize well across diverse patient populations and clinical settings is critical to avoid biased outcomes and health disparities. Solutions such as federated learning, data harmonization protocols, and enhanced standardization initiatives are actively being explored to overcome these obstacles.

The complexity of cancer demands that analytical frameworks accommodate dynamic changes in tumor biology over time. AI models capable of integrating longitudinal multi-omics and clinical data are being developed to capture temporal tumor evolution and therapeutic trajectories. Such dynamic models have the potential to anticipate disease progression and resistance mechanisms, enabling timely intervention adjustments. This temporal dimension enhances the predictive power of AI frameworks and supports proactive patient management, which is essential in combatting adaptive resistance that hampers durable remissions.

A particularly exciting frontier in this domain is the conceptualization and realization of patient-specific digital twins. These computational avatars simulate individual disease courses by integrating personalized molecular profiles, imaging data, and treatment histories. Digital twins provide a virtual platform to test therapeutic strategies in silico, optimizing treatment regimens before clinical application. This approach exemplifies the convergence of AI, systems biology, and precision medicine, promising to transform oncology by enabling highly individualized, data-driven treatment plans that reflect each patient’s unique tumor ecology and response kinetics.

The convergence of AI and multi-omics also accelerates drug discovery and development. Machine learning models trained on integrated datasets can identify drug resistance pathways and predict patient subsets likely to benefit from novel agents. This accelerates the translation of molecular insights into clinical interventions and informs the design of adaptive clinical trials. Integrative AI frameworks thus serve not only as diagnostic or prognostic tools but also as engines of therapeutic innovation, fostering a virtuous cycle between bench research and bedside application.

Imaging modalities such as radiomics further enrich this integrative framework by providing spatial and morphological context to molecular data. AI-driven image analysis extracts quantitative features that relate to tumor heterogeneity, microenvironmental interactions, and phenotypic plasticity. When coupled with multi-omics profiles, these imaging biomarkers enhance the granularity and dimensionality of datasets, allowing for a more nuanced understanding of cancer biology. This multi-layered data synergy underscores the crucial role of AI as a mediator between disparate data types, synthesizing heterogeneous information into cohesive, clinically actionable insights.

Emerging AI techniques such as deep learning offer unparalleled feature extraction capabilities, automatically learning representations from raw data without explicit feature engineering. These algorithms excel at modeling complex biological phenomena but necessitate extensive training data to avoid overfitting. Strategies incorporating transfer learning and multimodal architecture designs are currently being refined to leverage pre-existing knowledge and optimize model performance across different cancer types and data modalities. The goal is to build robust, scalable AI systems capable of continuous learning and adaptation in clinical environments.

The ethical and regulatory landscape surrounding AI-driven multi-omics integration is rapidly evolving. Ensuring patient privacy, data security, and algorithmic fairness are central to responsible AI deployment. Transparent reporting standards and validation practices must accompany model development to ensure replicability and clinical reliability. Additionally, equitable access to these cutting-edge technologies is paramount to avoid exacerbating healthcare disparities. Collaborative efforts among researchers, clinicians, policymakers, and patient advocates are crucial to shaping frameworks that balance innovation with ethical oversight.

Training the next generation of researchers and clinicians in AI and multi-omics integration is essential to translate technological promise into real-world impact. Interdisciplinary education programs that blend computational sciences with molecular biology and clinical oncology foster a workforce proficient in leveraging complex datasets for precision medicine. This cross-pollination of expertise accelerates adoption and ensures that emerging AI tools address clinically relevant challenges while remaining grounded in biological reality.

Looking ahead, the synergy between AI and multi-omics integration is poised to redefine oncological paradigms, fostering a shift from reactive to predictive and preventative medicine. Continuous refinement of algorithms, expansion of diverse and interoperable datasets, and alignment with clinical workflows will consolidate AI’s role as an indispensable assistant in cancer care. The prospect of personalized digital twins and dynamic models heralds an era where data-driven decisions improve survival rates, quality of life, and cost-effectiveness of treatments, bringing precision oncology from aspirational concept to routine clinical reality.

In summary, the integration of multi-omics data with clinical and imaging modalities powered by artificial intelligence represents a transformative leap in cancer research and treatment. This holistic approach captures the multifactorial nature of tumorigenesis, enabling early diagnosis, accurate patient stratification, personalized therapeutic interventions, and elucidation of complex resistance mechanisms. While challenges in data quality, accessibility, and model generalizability remain, ongoing advancements signal a future where precision oncology is underpinned by comprehensive, interpretable, and dynamic AI systems. Such innovations promise to elevate cancer care to new levels of efficacy and individualization, reshaping the trajectory of oncological science and patient outcomes alike.

Subject of Research: Integration of multi-omics and clinical data using artificial intelligence to advance cancer research and precision oncology.

Article Title: Advancing AI for multi-omics and clinical data integration in basic and translational cancer research.

Article References:
Liu, F., Beck, S., Yang, L. et al. Advancing AI for multi-omics and clinical data integration in basic and translational cancer research. Nat Rev Cancer (2026). https://doi.org/10.1038/s41568-026-00922-2

Image Credits: AI Generated