Publications by authors named "G B Mills"

Patients with metastatic pancreatic ductal adenocarcinoma survive longer if disease spreads to the lung but not the liver. Here we generated overlapping, multi-omic datasets to identify molecular and cellular features that distinguish patients whose disease develops liver metastasis (liver cohort) from those whose disease develops lung metastasis without liver metastases (lung cohort). Lung cohort patients survived longer than liver cohort patients, despite sharing the same tumor subtype.

View Article and Find Full Text PDF

Prostate cancer (PC) progresses from benign epithelium through pre-malignant lesions, localized tumors, metastatic dissemination, and castration-resistant stages, with some cases exhibiting phenotype plasticity under therapeutic pressure. However, high-resolution insights into how cell phenotypes evolve across successive stages of PC remain limited. Here, we present the Prostate Cancer Cell Atlas (PCCAT) by integrating ∼710,000 single cells from 197 human samples covering a spectrum of tumor stages.

View Article and Find Full Text PDF

Haematological malignancies affect 12·5 in 100 000 pregnancies. Over the past two decades, the number of haematological malignancies in pregnancy has substantially increased. Life-threatening haematological malignancies in pregnancy, such as acute leukaemia and aggressive lymphomas, pose a unique therapeutic challenge: clinicians must consider both maternal and fetal wellbeing, aiming to deliver optimal curative therapy for the patient and a successful pregnancy outcome.

View Article and Find Full Text PDF

Upregulation of Cyclin E1 and subsequent activation of CDK2 accelerates cell cycle progression from G1 to S phase and is a common oncogenic driver in gynecological malignancies. WEE1 kinase counteracts the effects of Cyclin E1/CDK2 activation by regulating multiple cell cycle checkpoints. Here we characterized the relationship between Cyclin E1/CDK2 activation and sensitivity to the selective WEE1 inhibitor azenosertib.

View Article and Find Full Text PDF

Multiplexed tissue imaging (MTI) technologies enable high-dimensional spatial analysis of tumor microenvironments but face challenges with technical variability in staining intensities. Existing normalization methods, including z-score, ComBat, and MxNorm, often fail to account for the heterogeneous, right-skewed expression patterns of MTI data, compromising signal alignment and downstream analyses. We present UniFORM, a non-parametric, Python-based pipeline for normalizing both feature- and pixel-level MTI data.

View Article and Find Full Text PDF