Simeone, D. | Search bioRxiv

bioRxiv2018

Multiplex Enrichment and Detection of Rare KRAS Mutations in Liquid Biopsy Samples using Digital Droplet Pre-Amplification

Oncology research is increasingly incorporating molecular detection of circulating tumor DNA (ctDNA) as a tool for cancer surveillance and early detection. However, non-invasive monitoring of conditions with low tumor burden remains challenging, as the diagnostic sensitivity of most ctDNA assays is inversely correlated with total DNA concentration and ctDNA abundance. Here we present the Multiplex Enrichment using Droplet Pre-Amplification (MED-Amp) method, which com-bines single-molecule emulsification and short-round PCR preamplification with digital droplet PCR (ddPCR) detection of mutant DNA template. The MED-Amp assay increased mutant signal by over 50-fold with minimal distortion in allelic frequency. We demonstrate detection of as few as 3 mutant copies in wild-type DNA concentrations ranging from 5 to 50ng. The MED-Amp assay successfully detected KRAS mutant ctDNA in 86% plasma samples obtained from patients with metastatic pancreatic ductal adenocarcinoma. This assay for high-sensitivity rare variant detection is appropriate for liquid biopsy samples, or other limited clinical biospecimens

bioengineering↗

bioRxiv2018

Building a tumor atlas: integrating single-cell RNA-Seq data with spatial transcriptomics in pancreatic ductal adenocarcinoma

To understand tissue architecture it is necessary to understand both which cell types are present and their physical relationships to one another. Single-cell RNA-Seq (scRNA-Seq) has made significant progress towards the unbiased and systematic characterization of cell populations within a tissue by studying hundreds and thousands of cells in a single experiment. However, the characterization of the spatial organization of individual cells within a tissue has been more elusive. The recently introduced spatial transcriptomics method (ST) reveals the spatial pattern of gene expression within a tissue section at a resolution of a thousand 100 m spots across the tissue, each capturing the transcriptomes of [~]10-20 cells. Here, we present an approach for the integration of scRNA-Seq and ST data generated from the same sample of pancreatic cancer tissue. Using markers for cell types identified by scRNA-Seq, we robustly deconvolved the cell type composition of each ST spot to generate a spatial atlas of cell proportions across the tissue. Studying this atlas, we found that distinct spatial localizations accompany each of the cell populations that we identified. Our results provide a framework for creating a tumor atlas by mapping single-cell populations to their spatial region, as well as the inference of cell architecture in any tissue.

cancer biology↗