Biology subjects

Rakovic, K.

Publications and source records attributed to Rakovic, K..

7 recordsLinked to original sources

bioRxiv2026

Glandular architecture and malignant behaviour in colorectal cancer is regulated by the sialomucin Podocalyxin.

Glandular architecture-the coordination of lumen-containing structures by an apical-basal polarised epithelium-is frequently maintained in colorectal cancer (CRC), yet whether it actively contributes to tumour progression or metastatic competence remains unclear. Here, we identify Podocalyxin (PODXL), a developmental regulator of epithelial lumen formation, as a key determinant of glandular tumour architecture in CRC. PODXL is upregulated in CRC, particularly in poor-prognosis Consensus Molecular Subtype 4 (CMS4) tumours, where high expression predicts reduced survival. Using genetically engineered mouse models, matched organoids, human cell lines and xenografts, we show that PODXL promotes organisation of CRC cells into gland-like, lumen-containing structures. Loss of PODXL disrupts glandular architecture in both primary tumours and liver metastases, reducing tumour growth and metastatic colonisation. Mechanistically, TGF-{beta} signalling drives PODXL upregulation. Together, these findings establish glandular architecture as an active determinant of CRC progression and identify PODXL as a functional contributor rather than merely a prognostic biomarker.

cancer biology↗

bioRxiv2026

Morphospatial profiling of cancer-associated fibroblasts reveals architectural subtypes of pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is a lethal malignancy with an urgent need for biomarkers to predict prognosis and guide treatment. Understanding the complex spatial biology of pancreatic cancer-associated fibroblasts (CAFs) and the broader architecture of the PDAC tumour microenvironment is central to this challenge. Using a multi-omics approach across multiple spatial resolutions in a large human PDAC cohort, we integrate geometry and shape to define discrete morphological CAF subtypes, expanding CAF phenotyping beyond conventional proteomics. We then reveal an architectural and molecular axis of PDAC at tissue level, suggestive of epithelial-stromal co-evolution, with translational implications and prioritisation of stromal targets. Finally, we recapitulate this axis by introducing four unique, internally validated architectural subtypes of PDAC, each characterised by a common microenvironment and CAF enrichment profile. These archetypes outperform conventional pathology in prognostication, and predict response to adjuvant chemotherapy. Collectively, this study establishes a novel morphological paradigm for spatial biology, illuminates the architectural landscape of PDAC, and provides a framework for spatial biomarker discovery to close the translational gap in this devastating disease.

cancer biology↗

bioRxiv2025

Self-supervised AI reveals a lethal discohesive phenotype in lung adenocarcinoma

Applications of artificial intelligence (AI) to histopathology are now common, but most require supervision which inherently limits their scope. By using self-supervised learning (SSL), we discover and quantify the full range of histopathological appearances in a disease, and associate them with clinicopathological ground truths such as prognosis. We used this approach to discover under-appreciated morphologies of lung adenocarcinoma (LUAD), using a highly characterised resected tumour cohort of over 4000 slides from over 1000 patients. By constructing an authoritative lexicon of recurrent LUAD appearances, we ab initio discovered several stromal morphologies strongly predictive of outcome. With multimodal data integration and external dataset validation, we propose that epithelial discohesion is lethal, but only in the context of immunologically cold stroma. Both these morphological features are independent of current prognostic schema. Crucially, we describe these features in the context of real-world diagnostic histopathology, giving them immediate clinical translatability. Statement of SignificanceHistopathology relies heavily on epithelial morphology, often neglecting the stroma. We use self-supervised AI to identify under-appreciated morphologies in LUAD linked to poor outcome and validate these observations in an external cohort, demonstrating the utility of self-supervised AI as a powerful biological discovery tool.

cancer biology↗

bioRxiv2025

Self-Supervised AI Reveals a Hidden Landscape of Prognostic Spatial Patterns in Multiplex Immunofluorescence Images

Modern spatial proteomic methods, such as multiplex immunofluorescence (mIF) imaging, offer a data-rich view of spatial biology in intact tissues. However, interpreting its complexity is a major bottleneck, limiting its potential for biological discovery and clinical translation. Current computational methods often rely on segmentation-based approaches that discard crucial morphological information and are limited to testing pre-defined hypotheses. Here, we introduce a self-supervised learning (SSL) framework that enables hypothesis-agnostic, context-aware discovery of biomarkers directly from mIF images. Our approach extracts rich feature representations that capture holistic architectural patterns, which integrate cellular morphology, marker interactions, and microenvironmental context without human supervision. Applying this framework to over 7,000 mIF tissue images from over 1,800 patients in two distinct cancer types, we demonstrate superior prognostic performance over conventional segmentation analyses. The method autonomously identified previously unknown and potentially clinically actionable biological patterns. In lung adenocarcinoma, these include a Ki67-mediated immune evasion phenotype, a sub-cellular pattern of GLB1 expression which aligns with low-grade EGFR-driven tumours, and distinct modes of tumour-immune interaction in PD-L1+ patients. We also find a regulatory T-cell-mediated immunosupressive environment promoting tumour budding in colorectal carcinoma. Our work establishes SSL as a powerful, scalable, and unbiased platform to decode tissue ecosystems while being fully explainable without pre-defined hypotheses. This paradigm shift transforms high-plex imaging from a hypothesistesting tool into a hypothesis-generating engine that can accelerate the discovery of next-generation spatial biomarkers.

cancer biology↗

bioRxiv2025

PixlMap: A generalisable pixel classifier for cellular phenotyping in multiplex immunofluorescence images

Multiplexed methods for the detection of protein expression generate extremely data-rich images of intact tissue sections. These images are invaluable for the quantification and analysis of complex biology and biomarker development. However, their interpretation presents a considerable analytical challenge. Cell segmentation from images is a key bottleneck and a major focus of research activity in artificial intelligence. Most current methods depend initially on the use of a nuclear counterstain to identify nuclear boundaries, which is a relatively straightforward task. The cellular boundary is then assigned either by expansion of the nuclear outline, or by the use of membrane or cytoplasm-specific stains to delineate cell boundaries, or by some combination of the two. The task is critical, as inaccurate segmentation leads to information loss and data contamination from neighbouring cells. Increasingly sophisticated methods are being developed to address these issues, but each has its own shortcomings. We present an alternative method which is inspired by the fact that the assignation of a cellular phenotype by eye does not depend upon the accurate identification of cell boundaries. We present an easy-to-use deep learning-based cellular phenotyping method which leverages this human capacity to assign phenotypes without segmenting the entire cell, and which can accurately phenotype cells based on nuclear segmentation alone. Using human ground truth annotations of entire cellular regions, we developed a classifier leveraging the U-Net architecture within a commercially available deep learning image analysis platform, but the principle is transferrable to any deep-learning framework. Crucially, training requires only a single example of each compartmental stain (nuclear/cytoplasmic/membranous). The resulting algorithm assigns class identities to cells with nuclear labelling alone, without the need for whole cell expansion. The method is highly novel, broadly generalisable, and comparable in accuracy to intensity-based phenotyping methods, bridging the gap between inaccurate cellular segmentation and accurate phenotype generation.

pathology↗

bioRxiv2024

A histomorphological atlas of resected mesothelioma from 3446 whole-slide images discovered by self-supervised learning

1Mesothelioma is a highly lethal and poorly biologically understood disease which presents diagnostic challenges due to its morphological complexity. This study uses self-supervised AI (Artificial Intelligence) to map the histomorphological landscape of the disease. The resulting atlas consists of recurrent patterns identified from 3446 Hematoxylin and Eosin (H&E) stained images scanned from resected tumour slides. These patterns generate highly interpretable predictions, achieving state-of-the-art performance with 0.65 concordance index (c-index) for outcomes and 88% AUC in subtyping. Their clinical relevance is endorsed by comprehensive human pathological assessment. Furthermore, we characterise the molecular underpinnings of these diverse, meaningful, predictive patterns. Our approach both improves diagnosis and deepens our understanding of mesothelioma biology, highlighting the power of this self-learning method in clinical applications and scientific discovery.

pathology↗

bioRxiv2022

Spatial regulation of the glycocalyx component Podocalyxin is a switch for pro-metastatic function

The glycocalyx component and sialomucin Podocalyxin (PODXL) is required for normal tissue development by promoting apical membranes to form between cells, triggering lumen formation. Elevated PODXL expression is also associated with metastasis and poor clinical outcome in multiple tumour types. How PODXL presents this duality in effect remains unknown. We identify an unexpected function of PODXL as a decoy receptor for Galectin-3 (GAL3), whereby the PODXL-GAL3 interaction releases GAL3 repression of integrin-based invasion. Differential cortical targeting of PODXL, regulated by ubiquitination, is the molecular mechanism controlling alternate fates. Both PODXL high versus low surface levels occur in parallel subpopulations within cancer cells. Orthotopic intraprostatic xenograft of PODXL-manipulated cells or those with different surface levels of PODXL define that this axis controls metastasis in vivo. Clinically, interplay between PODXL-GAL3 stratifies prostate cancer patients with poor outcome. Our studies define the molecular mechanisms and context in which PODXL promotes invasion and metastasis.

cell biology↗