Study

Pancreatic cancer RNA sequencing

Study ID Alternative Stable ID Type
EGAS00001004706 Other

Study Description

For optimal pancreatic cancer treatment, early and accurate diagnosis is vital. Blood-derived biomarkers and genetic predispositions can contribute to early diagnosis, but they often have limited accuracy or applicability. Here, we seek to exploit the synergy between both approaches by combining the standard clinical blood biomarker CA19-9 with novel genetic variants. Concretely, we aim to use deep sequencing and deep learning to improve pancreatic cancer diagnosis, to differentiate between resectable pancreatic ductal adenocarcinoma (rPDAC) from chronic pancreatitis (CP), and to estimate survival. We obtained samples of nucleated cells found in peripheral blood from over 300 patients suffering from resectable pancreatic ductal adenocarcinoma, non-resectable pancreatic cancer (nrPC), chronic pancreatitis or none of these. We sequenced RNA with high coverage and reduced millions of raw to hundreds of high-quality, significant genetic variants. Together with CA19-9 levels, these served as input to deep learning to separate cancer from non-cancer, resectable PDAC from chronic ... (Show More)

Study Datasets 1 dataset.

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Dataset ID Description Technology Samples
EGAD00001006915
Samples of nucleated cells found in peripheral blood from over 300 patients suffering from resectable pancreatic ductal adenocarcinoma, non-resectable pancreatic cancer, chronic pancreatitis, or none of these.
Illumina HiSeq 2500 311

Who archives the data?

There are no publications available