Leah Officer-Jones

This study identifies mTORC2 as a convergent dependency in lung cancer that stabilizes HIF-1β to drive metabolic adaptation and tumor progression, revealing a therapeutic target in sphingolipid metabolism.

The paper identifies high MYC activity as a targetable feature in lung adenocarcinoma using phenotype-based subtyping and shows sensitivity to a dual inhibitor of MYC co-factors.

This study identifies architectural subtypes of PDAC using morphospatial profiling of CAFs, showing improved prognostication and chemotherapy response prediction.

This study uses spatial transcriptomics to analyze gene expression in lung adenocarcinoma morphologies, revealing plasticity and gene expression trajectories linked to lethal growth patterns.

This paper uses self-supervised AI to identify under-appreciated stromal morphologies in lung adenocarcinoma linked to poor prognosis, independent of current clinical schemas.

This paper introduces a self-supervised learning framework to analyze multiplex immunofluorescence images, revealing novel prognostic spatial patterns in cancer tissues.

This paper uses self-supervised learning on 3446 H&E-stained mesothelioma slides to create a histomorphological atlas, achieving high predictive performance and revealing molecular insights.

This study shows that increased mRNA translation delays lung adenocarcinoma initiation and creates a vulnerability to MEK inhibitors in KRAS-driven tumors.

PixlMap is a deep learning method for cellular phenotyping in multiplex immunofluorescence images that uses nuclear segmentation alone to classify cells without requiring full cell boundaries.