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

Pineoblastoma, an enigmatic neoplasm arising in the pineal gland, remains poorly understood due to its rarity, with only a handful of cases treated annually at specialized centers such as St. Jude. The pineal gland itself is a diminutive but vital structure buried deep within the brain, notable for its pinecone shape and critical role in circadian rhythm regulation through melatonin secretion. Scientists hypothesized that the tumor’s origins trace back to aberrations occurring during the rapid developmental expansion of progenitor cells in the gland. To validate this, the research team constructed the first ever single-cell atlas detailing normal human pineal gland development, cataloging the intricate cellular constituents and their gene expression patterns throughout maturation.

The study’s approach involved a meticulous comparison of gene expression profiles between the rare tumor samples—collected from 38 pineoblastoma patients—and the developmental atlas. Using single-cell RNA sequencing, the researchers identified a striking similarity between pineoblastoma cells and a specific population of early pinealocyte progenitors. This association implicated these progenitors as the cellular origin of pineoblastoma. Further validation was achieved through the generation of genetically engineered mouse models harboring perturbations in five distinct pineoblastoma driver genes within these progenitors. These models faithfully recapitulated the human tumor subtypes, providing an unprecedented platform for mechanistic studies.

A particularly remarkable discovery emerged from the transcriptional analysis: regardless of the divergent oncogenic drivers among the tumor subtypes, all shared heightened expression of a set of genes related to light sensitivity. The pineal gland’s evolutionary function as a photoreceptive organ, interfacing with retinal inputs to modulate circadian rhythms, is underpinned by phototransduction machinery. The research revealed that such photoreceptor and phototransduction genes are aberrantly and robustly expressed in pineoblastoma cells, suggesting a molecular addiction to this light-sensing program. This insight propelled the researchers to probe if this signature might represent a broader, exploitable feature in other brain tumors.

Intriguingly, the light-sensing gene signature was not unique to pineoblastoma. Comparative analysis revealed a similar molecular footprint in Group 3 medulloblastoma—a cerebellar tumor subtype—and retinoblastoma, an aggressive ocular cancer arising from retinal progenitor cells. These findings establish a shared developmental state and transcriptomic landscape across anatomically and pathologically distinct central nervous system tumors. The discovery suggests a convergence of tumor initiation pathways despite differing tissue origins, all recapitulating a latent developmental program associated with photoreception.

To assess the functional significance of these genes, CRISPR-Cas9 gene editing was employed to selectively disrupt key components of the light-sensing program in cell cultures derived from pineoblastoma, medulloblastoma, and retinoblastoma tumors. The deletion of these genes led to marked impairment of tumor cell viability across all three cancer types, providing compelling evidence for a shared genetic vulnerability. This dependency underscores a potential therapeutic target whose inhibition could transcend individual tumor classifications, offering hope for broadly effective treatments.

The implications of these findings are vast. By illuminating a molecular vulnerability common to diverse pediatric brain tumors, this research opens avenues for the development of precision medicine strategies centered on disrupting the aberrant light-sensing pathway. Therapeutic interventions aimed at inhibiting phototransduction components—once considered inconsequential outside sensory organs—may prove transformative in combating these malignancies. Moreover, elucidating the developmental origins of these tumors refines our understanding of tumor biology, emphasizing the critical intersection between developmental neurobiology and oncology.

Paul Northcott, PhD, corresponding author and director of the St. Jude Center of Excellence in Neuro-Oncology Sciences, emphasizes the collaborative nature of the work that enabled deep molecular profiling despite the rarity of pineoblastoma cases. The creation of comprehensive single-cell atlases, integration of multi-institutional tumor cohorts, and development of novel mouse models represent a tour de force in cancer research methodology. Such interdisciplinary efforts exemplify the power of combining developmental biology, molecular oncology, and genomics to decode complex disease mechanisms.

The extensive author team includes co-first authors Brian Gudenas, Anthony Liu, and Tanveer Ahmad Sheikh of St. Jude, alongside collaborators from Dana-Farber and Uppsala University. Their combined expertise spans neurobiology, cancer genomics, and pediatric oncology, highlighting the multifaceted approach necessary to tackle rare childhood cancers. The study was enabled by substantial funding from numerous foundations and federal agencies, including the National Cancer Institute, the Mark Foundation, St. Baldrick’s Foundation, and several international research bodies, underscoring the global commitment to eradicating pediatric brain tumors.

This research represents a seminal advancement in cancer biology, emphasizing the critical role of detailed developmental atlasing paired with cutting-edge genetic manipulation to uncover tumor dependencies. The work paves the way for future therapeutic targeting of light-sensing pathways across multiple devastating childhood brain tumors, ultimately aiming to improve survival outcomes and quality of life for afflicted children worldwide. As the oncology community moves forward, this study stands as a beacon illuminating the path toward common vulnerabilities within disparate cancers and their potential convergence into unified treatment paradigms.

Subject of Research: Molecular origins and therapeutic vulnerabilities of pediatric brain tumors, specifically pineoblastoma, medulloblastoma, and retinoblastoma.

Article Title: Shared Origins Illuminate Potential Dependency Across Brain Tumor Types

News Publication Date: March 5, 2026

Web References:

• St. Jude Children’s Research Hospital: https://www.stjude.org/
• Paul Northcott Profile: https://www.stjude.org/people/n/paul-northcott.html
• St. Jude Center of Excellence in Neuro-Oncology Sciences (CENOS): https://www.stjude.org/research/centers-of-excellence/cenos.html
• Cancer Cell DOI link: http://dx.doi.org/10.1016/j.ccell.2026.02.010

References:
Northcott, P.A., Gudenas, B., Liu, A., Sheikh, T.A., et al. (2026). Shared Origins Illuminate Potential Dependency Across Brain Tumor Types. Cancer Cell. DOI: 10.1016/j.ccell.2026.02.010

Image Credits: St. Jude Children’s Research Hospital

Keywords: Pineoblastoma, Medulloblastoma, Retinoblastoma, Pediatric Brain Tumors, Single-cell RNA-sequencing, Phototransduction, Developmental Neurobiology, Tumor Dependencies, CRISPR, Therapeutic Targets