Biology subjects

Iacobuzio-Donahue, C. A.

Publications and source records attributed to Iacobuzio-Donahue, C. A..

2 recordsLinked to original sources

bioRxiv2017

Endoplasmic reticulum stress engenders immune-resistant, latent pancreatic cancer metastases

Patients who have had their primary pancreatic ductal adenocarcinoma (PDA) surgically resected often develop metastatic disease, exemplifying the problem of latent metastases. Livers from patients and mice with PDA contained single, disseminated cancer cells (DCCs) with an unusual phenotype of being cytokeratin-19 (CK19)- and MHC class I (MHCI)-. We created a mouse model to determine how DCCs develop, their relationship to metastatic latency, and the role of immunity. Intra-portal injection of immunogenic PDA cells into pre-immunized mice seeded livers only with single, non-replicating DCCs lacking MHCI and CK19; naive recipients had macro-metastases. Transcriptomic analysis of PDA cells with the DCC phenotype demonstrated an endoplasmic reticulum (ER) stress response. Relieving ER stress with a chemical chaperone, in combination with T cell-depletion, stimulated outgrowth of macro-metastatic lesions containing PDA cells expressing MHCI and CK19. The ER stress response is the cell-autonomous reaction that enables DCCs to escape immunity and establish latent metastases.\n\nOne sentence summaryLatent pancreatic cancer metastases are created when T cells select disseminated cancer cells in which immune resistance and quiescence have been imposed by endoplasmic stress.

immunology↗

bioRxiv2017

Enabling cross-study analysis of RNA-Sequencing data

Driven by the recent advances of next generation sequencing (NGS) technologies and an urgent need to decode complex human diseases, a multitude of large-scale studies were conducted recently that have resulted in an unprecedented volume of whole transcriptome sequencing (RNA-seq) data. While these data offer new opportunities to identify the mechanisms underlying disease, the comparison of data from different sources poses a great challenge, due to differences in sample and data processing. Here, we present a pipeline that processes and unifies RNA-seq data from different studies, which includes uniform realignment and gene expression quantification as well as batch effect removal. We find that uniform alignment and quantification is not sufficient when combining RNA-seq data from different sources and that the removal of other batch effects is essential to facilitate data comparison. We have processed data from the Genotype Tissue Expression project (GTEx) and The Cancer Genome Atlas (TCGA) and have successfully corrected for study-specific biases, enabling comparative analysis across studies. The normalized data are available for download via GitHub (at https://github.com/mskcc/RNAseqDB).

bioinformatics↗