Pennie, R. L. | Search bioRxiv

bioRxiv2025

Increased mRNA translation delays lung adenocarcinoma initiation and exposes a therapeutic vulnerability to MEK inhibitors

Although protein synthesis inhibitors are being evaluated as anti-cancer agents, the dynamics of mRNA translation in early tumorigenesis are still poorly understood. We report that deletion of the mRNA-translation repressor, eIF4A2 in early KRAS-driven lung adenocarcinoma leads to a dysregulated protein synthesis landscape characterised by a strongly upregulated secretome, enlarged secretory compartments, increased oxidative metabolism and acquisition of senescence-like characteristics. Paradoxically, this overdriven protein synthesis landscape delays tumorigenesis and leads to appearance of clusters of non-proliferative, p21-positive KRAS-expressing cells in the lung. Administration of rapamycin to reduce mRNA translation suppresses senescence and restores tumorigenesis following eIF4A2 deletion. Importantly, some eIF4A2 knockout cells overcome senescence to form tumours that exhibit enhanced MAP-kinase signalling and, in contrast to eIF4A2+/+ lesions, these may be eradicated by administration of a MEK inhibitor. Thus, dysregulated mRNA translation exposes a potential therapeutic vulnerability in KRAS-driven lung adenocarcinoma by forcing cancer cells to rely on MEK signalling. Statement of significanceThe requirement for anabolism in cancers has led to the search for inhibitors of mRNA translation as anti-cancer agents. However, we report that increased rates of mRNA translation promote a senescence-like phenotype in KRAS-driven lung cancer which delays tumorigenesis and renders the resulting tumours sensitive to MEK inhibition.

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↗