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Biology subjects

Baird, R. L.

Publications and source records attributed to Baird, R. L..

3 recordsLinked to original sources

mTORC2 stabilizes HIF-1β to coordinate metabolic adaptation in lung cancer

Despite extensive genetic heterogeneity, lung tumors frequently converge on shared signaling dependencies that remain therapeutically underexploited. Here, we identify mTORC2 signaling as a convergent dependency across genetically distinct lung cancer subtypes and uncover HIF-1{beta} as a selective metabolic effector downstream of mTORC2 that promotes lung tumor progression. Elevated mTORC2 signaling in lung adenocarcinoma was associated with poor overall survival, metastatic dissemination and metabolic rewiring. Using complementary genetically engineered mouse models of Rictor deletion or overexpression in Kras-driven lung tumors, we show that mTORC2 activity is dispensable for normal lung homeostasis but required for tumor progression and metabolic adaptation in vivo. Mechanistically, mTORC2 stabilized HIF-1{beta} by preventing its ubiquitin-independent proteasomal degradation through a non-canonical PKC-CK2 signaling axis, independently of AKT. Integrated multi-omics analyses identified extensive metabolic rewiring downstream of the mTORC2-HIF-1{beta} axis, with sphingolipid metabolism emerging as a prominent and therapeutically exploitable vulnerability. Accordingly, pharmacological targeting of sphingolipid metabolism markedly impaired the growth of mTORC2-driven lung tumors in vivo. Together, our findings establish a non-canonical mTORC2-HIF-1{beta} signaling axis that couples oncogenic signaling to metabolic adaptation and defines therapeutically actionable metabolic vulnerabilities in lung cancer.

cancer biology↗

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↗

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↗