PhD Project: RNA Sequencing-based Tools for Breast Cancer Diagnostics

Insights into the molecular markup of tumors have transformed cancer research in recent years. Many people worldwide work on translating these insights into the clinic to improve diagnostic areas such as patient risk stratification and treatment selection. Most of these efforts focus on DNA-based short-read high-throughput sequencing (DNA-seq).

RNA sequencing (RNA-seq) is a sister technology to DNA-seq that probes the current state of the cell’s transcriptional machinery (the transcriptome) and is commonly used for research, but is underused as a diagnostic tool. RNA-seq is inexpensive and can provide a wealth of information that is not accessible from DNA (gene/isoform expression), in addition to being able, to some degree, to detect features commonly determined from DNA (e.g., point mutations, short insertions and deletions, copy number, structural variants). As such it is possible to develop a variety of diagnostics that can be derived from a single sequencing dataset, making RNA-seq a potentially powerful diagnostic tool.

The topic of my PhD thesis was exploring the diagnostic power of RNA-seq by developing and evaluating computational resources for the Sweden Cancerome Analysis Network–Breast (SCAN-B) breast cancer project and by developing diagnostic tools from the generated data. Within SCAN-B the vast majority of breast cancer patients from the participating hospital regions are being enrolled since 2010, and RNA-seq data is being generated from their tumors within one week from surgery.

See below for a list of results generated from this work.

Christian Brueffer
Christian Brueffer
Bioinformatician and Data Scientist

Freelance Bioinformatician and Data Scientist with interests including disease biology and diagnostics, particularly in cancer, and open source bioinformatics.