Biomedical subjects

Gordon B Mills

Publications and source records attributed to Gordon B Mills.

3 recordsLinked to original sources

PubMed2025

OmicsTweezer: A distribution-independent cell deconvolution model for multi-omics Data.

Cell deconvolution estimates cell type proportions from bulk omics data, enabling insights into tissue microenvironments and disease. However, practical applications are often hindered by batch effects between bulk data and referenced single-cell data, a challenge that is frequently overlooked. To address this discrepancy, we developed OmicsTweezer, a distribution-independent cell deconvolution model. By integrating optimal transport with deep learning, OmicsTweezer aligns simulated and real data in a shared latent space, effectively mitigating data shifts and inter-omics distribution differences. OmicsTweezer is versatile, capable of deconvolving bulk RNA-seq, bulk proteomics, and spatial transcriptomics. Extensive evaluations on simulated and real-world datasets demonstrate its robustness and accuracy. Furthermore, applications in prostate and colon cancer showcase OmicsTweezer's ability to identify biologically meaningful cell types. As a unified deconvolution framework for multi-omics data, OmicsTweezer offers an efficient and powerful tool for studying disease microenvironments.

Humans↗

PubMed2024

Targeted Dynamic Phospho-Proteogenomic Analysis of Gastric Cancer Cells Suggests Host Immunity Provides Survival Benefit.

Despite of massive emergence of molecular targeting drugs, the mainstay of advanced gastric cancer (GC) therapy is DNA-damaging drugs. Using a reverse-phase protein array-based proteogenomic analysis of a panel of 8 GC cell lines, we identified genetic alterations and signaling pathways, potentially associated with resistance to DNA-damaging drugs, including 5-fluorouracil (5FU), cisplatin, and etoposide. Resistance to cisplatin and etoposide, but not 5FU, was negatively associated with global copy number loss, vimentin expression, and caspase activity, which are considered hallmarks of previously established EMT subtype. The segregation of 19,392 protein expression time courses by sensitive and resistant cell lines for the drugs tested revealed that 5FU-resistant cell lines had lower changes in global protein dynamics, suggesting their robust protein level regulation, than their sensitive counterparts, whereas the cell lines that are resistant to other drugs showed increased protein dynamics in response to each drug. Despite faint global protein dynamics, 5FU-resistant cell lines showed increased signal transducer and activator of transcription 1 phosphorylation and PD-L1 expression in response to 5FU. In publicly available cohort data, expression of signal transducer and activator of transcription 1 and NFκB target genes induced by proinflammatory cytokines was associated with prolonged survival in GC. In our validation cohort, total lymphocyte count, rather than PD-L1 positivity, predicted a better relapse-free survival rate in GC patients with 5FU-based adjuvant chemotherapy than those with surgery alone. Moreover, total lymphocyte count+ patients who had no survival benefit from adjuvant chemotherapy were discriminated by expression of IκBα, a potent negative regulator of NFκB. Collectively, our results suggest that 5FU resistance observed in cell lines may be overcome by host immunity or by combination therapy with immune checkpoint blockade.

Stomach Neoplasms↗

PubMed2022

A multi-omic analysis of MCF10A cells provides a resource for integrative assessment of ligand-mediated molecular and phenotypic responses.

The phenotype of a cell and its underlying molecular state is strongly influenced by extracellular signals, including growth factors, hormones, and extracellular matrix proteins. While these signals are normally tightly controlled, their dysregulation leads to phenotypic and molecular states associated with diverse diseases. To develop a detailed understanding of the linkage between molecular and phenotypic changes, we generated a comprehensive dataset that catalogs the transcriptional, proteomic, epigenomic and phenotypic responses of MCF10A mammary epithelial cells after exposure to the ligands EGF, HGF, OSM, IFNG, TGFB and BMP2. Systematic assessment of the molecular and cellular phenotypes induced by these ligands comprise the LINCS Microenvironment (ME) perturbation dataset, which has been curated and made publicly available for community-wide analysis and development of novel computational methods ( synapse.org/LINCS_MCF10A ). In illustrative analyses, we demonstrate how this dataset can be used to discover functionally related molecular features linked to specific cellular phenotypes. Beyond these analyses, this dataset will serve as a resource for the broader scientific community to mine for biological insights, to compare signals carried across distinct molecular modalities, and to develop new computational methods for integrative data analysis.

Epidermal Growth Factor↗