Sara J.C. Gosline

Integrated ex vivo screening and transcriptomic profiling to prioritize drug combinations for rare cancers

The paper develops EXACT, a framework combining ex vivo drug screening with transcriptomic analysis to identify effective drug combinations for rare cancers, finding a MEK and HDAC inhibitor combination shows enhanced activity.

Multi-omics integration of malignant peripheral nerve sheath tumors identifies potential targets based on chromosome 8q status

This study integrates multi-omics data from MPNSTs to identify drug targets based on chromosome 8q status, suggesting PLK1 and EGFR inhibitors could be effective in different subtypes.

PU.1 inhibition sensitizes stem-monocytic AML to BCL2 blockade

This study identifies stem-monocytic AML as a distinct subtype resistant to venetoclax and shows that PU.1 inhibition synergizes with BCL2 blockade to enhance therapeutic response.

spammR: an R package designed for analysis and integration of spatial multi-omic measurements

spammR is an R package for analyzing and integrating spatial multi-omic data, focusing on mass-spectrometry datasets and enabling cross-type integration within tissue samples.

A rapid platform for 3D patient-derived cutaneous neurofibroma organoid establishment and screening

This paper introduces a platform for establishing and screening patient-derived cutaneous neurofibroma organoids to support drug discovery for NF1-associated tumors.

Proteome mapping of the human pancreatic islet microenvironment reveals endocrine-exocrine signaling sphere of influence

The paper develops a spatial proteomics method to map the human pancreatic islet microenvironment and identifies spatial gradients in RNA processing proteins.

BayesDeBulk: A Flexible Bayesian Algorithm for the Deconvolution of Bulk Tumor Data

BayesDeBulk estimates immune and stromal cell composition from bulk proteomic and transcriptomic data, outperforming existing tools in synthetic and real data examples.

Utilizing proteomics and phosphoproteomics to predict ex vivo drug sensitivity across genetically diverse AML patients

The paper predicts ex vivo drug sensitivity in AML patients using proteomic and phosphoproteomic signatures, showing better accuracy than mutation or mRNA-based signatures.

Knowledge-based classification of fine-grained immune cell types in single-cell RNA-Seq data with ImmClassifier

ImmClassifier improves fine-grained immune cell type classification in scRNA-Seq data with higher recall and precision than existing tools.

Integrative analysis identifies candidate tumor microenvironment and intracellular signaling pathways that define tumor heterogeneity in NF1

The paper uses statistical and machine learning methods to analyze NF1 tumor genomic data, identifying key signaling pathways and molecular features associated with different tumor types and potential drug targets.