Laura DeLong Wood

This study identifies SPINK1 as a regulator of epigenomic states and stemness in PDAC through its interaction with COL18A1 and endostatin release.

CODAvision provides a user-friendly interface for rapid, customizable medical image segmentation using deep learning, enabling non-experts to train and evaluate models across diverse imaging modalities.

PICTographPlus infers clone-resolved transcriptional programs from bulk RNA-seq by integrating clonal phylogenies and proportions, achieving high accuracy in recovering clone-level expression and localizing pathway changes.

This paper introduces SMILE, a diffusion model for converting H&E stains to multiplex IHC images, showing superior performance in pancreatic islet analysis and breast cancer stain conversion.

STAPLE automates spatial transcriptomics analysis by integrating multiple tools into a modular framework and provides AI-driven interpretation of results.

The paper develops a 3D multi-omic pipeline using deep learning to analyze chemotherapy response in pancreatic cancer, identifying molecular signatures of resistance and fibroblast-cancer interactions.

The paper identifies multi-omic phenotypes in pancreatic precursor lesions to distinguish progressive from indolent precancers using integrative transcriptomic, genomic, and spatial data.

This study profiles proteins and glycoproteins in pancreatic neoplasms to identify expression patterns and potential progression markers.

The paper introduces a framework for mathematical modeling of multicellular systems using interpretable cell behavior rules derived from biological knowledge and multi-omics data.

This study develops a novel pancreatic cancer mouse model by expressing Myc and mutant Kras in adult acinar cells, showing metastasis and therapeutic resistance similar to human PDAc.

The paper introduces FunCN, a method to analyze cellular neighborhoods in PDAC, revealing distinct immune interactions in primary and metastatic sites.

PIVOT is an open-source tool for nonlinear registration of multi-omic spatial data, demonstrating improved throughput compared to existing workflows.

This study shows that 3D assessments are necessary to accurately determine tissue composition and neoplastic content, as 2D methods under-sample spatial heterogeneity.

SVCFit estimates SV cellular fractions from bulk sequencing to reconstruct clonal evolution in mCRPC under therapy, showing treatment-driven clonal reconfiguration.

The paper profiles the immune microenvironment of PanINs in 3D at single-cell resolution to understand inflammation's role in cancer progression and finds immune heterogeneity and correlations with PanIN grade.

The paper uses AI-based 3D tissue mapping to show PanIN lesion sizes follow a power-law distribution and identifies growth mechanisms driving pancreatic cancer precursor lesions.

This paper introduces SpaceMarkers, a method to infer molecular changes from cell-cell interactions in spatial transcriptomics data, applied to tumor-immune interactions in metastasis and immunotherapy.

This paper introduces PICTograph, a Bayesian model for estimating cancer cell fractions and clone trees from multi-region sequencing data, showing improved accuracy in subclone reconstruction and evolutionary inference.

The paper identifies tissue morphology biomarkers in long-term PDAC survivors using digital pathology and machine learning, finding reduced heterogeneity and extra-tumoral space features correlate with better survival.