Biology subjects

Ficken, C.

Publications and source records attributed to Ficken, C..

4 recordsLinked to original sources

bioRxiv2026

Phenotype-dependent subtyping exposes high MYC activity as a targetable dependency in LuAd

C-MYC (MYC) occupies a critical nexus of oncogenic signalling and deregulated expression of MYC is widespread across most human cancer types, suggesting that MYC should be an attractive target for therapeutic intervention. Although 30-40% of human Non-Small Cell lung cancers show low level amplification of c-MYC and genetic evidence has shown that c-Myc is a key downstream effector of KRas-driven lung tumourigenesis in mouse models, the functional contribution of MYC to human lung cancer remains unclear. We applied a phenotype-based classifier to the TCGA Lung Adenocarcinoma (LuAd) cohort and found that high MYC transcriptional activity identifies a subset of LuAd with significantly reduced survival. Application of the same methodology to a panel of genetically engineered mouse models identified multiple genotypes that give rise to the high MYC activity phenotype, disease positioning such models as reflective of a distinct subset of human LuAd. We show that high MYC activity predicts sensitivity to a small molecule dual-inhibitor of the MYC co-factors, EZH2 and G9A, HKMTi-1-005, and that treatment with HKMTi-1-005 strongly reduced MYC protein expression, induced B cell-mediated immune surveillance and suppressed growth of autochthonous KRasG12D-driven lung tumours. Statement of significanceThis work establishes the principle of indirectly targeting MYC in LuAd, via inhibition of associated enzymatic cofactors, EZH2 and G9A, and identifies a large subset of aggressive human LuAd with a high MYC activity signature that may benefit from this approach.

cancer biology↗

bioRxiv2026

YBX3 overexpression in mesothelioma drives aberrant cell proliferation

Malignant pleural mesothelioma (MpM) is a lethal tumour closely linked to asbestos exposure and is a cancer of unmet clinical need with no known oncogenic drivers. Recent advancements in technologies to identify RNA-binding proteins (RBPs) has uncovered an emerging role for RBP-RNA interactions in cancer progression and we therefore assessed changes in the RBPome of patient-derived MpM cell lines. We identify over 350 RBPs showing altered RNA binding, with functions consistent with key cancer hallmarks, and discovered YBX3 as a potential oncoprotein driving cell proliferation in MpM. Mechanistically we show the impact of YBX3 on cell growth is achieved through its control of the expression of the amino acid transporter SLC7A5/LAT1, with increased amino acid uptake increasing protein synthesis rates. Notably, we show the inhibition of cell growth by YBX3 deletion is recapitulated by the clinically-relevant SLC7A5/LAT1 inhibitor JPH203. Finally, we demonstrate that JPH203 sensitizes MpM cells to radiotherapy, which could provide a promising therapeutic strategy for MpM. TeaserHigher levels of YBX3 expression in mesothelioma increases cell proliferation and protein synthesis rates by upregulation of amino acid uptake.

molecular 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↗

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↗