Stratified time-course gene preselection shows a pre-diagnostic transcriptomic signal for metastasis in blood cells: a proof of concept from the NOWAC study

Abstract

We investigate whether there is information in gene expression levels in blood that predicts breast cancer metastasis. Our data comes from the NOWAC epidemiological cohort study where blood samples were provided at enrollment. This could be anywhere from years to weeks before any cancer diagnosis. When and if a cancer is diagnosed, it could be so in different ways: at a screening, between screenings, or in the clinic, outside of the screening program. To build predictive models we propose that variable selection should include followup time and stratify by detection method. We show by simulations that this improves the probability of selecting relevant predictor genes. We also demonstrate that it leads to improved predictions and more stable gene signatures in our data. There is some indication that blood gene expression levels hold predictive information about metastasis. With further development such information could be used for early detection of metastatic potential and as such aid in cancer treatment.