Local in Time Statistics for detecting weak gene expression signals in blood – illustrated for prediction of metastases in breast cancer in the NOWAC Post-genome Cohort
Received 12 December 2016
Accepted for publication 18 May 2017
Published 10 July 2017 Volume 2017:7 Pages 11—28
Checked for plagiarism Yes
Review by Single-blind
Peer reviewers approved by Dr Akshita Wason
Peer reviewer comments 3
Editor who approved publication: Dr John Martignetti
Marit Holden,1 Lars Holden,1 Karina Standahl Olsen,2 Eiliv Lund2
1Norwegian Computing Center, Oslo, Norway; 2Department of Community Medicine, UiT The Arctic University of Norway, Tromsø, Norway
Background: Functional genomics in a processual analysis cover the time-dependent changes in transcriptomics and epigenetics before diagnosis of a disease, reflecting the changes in both life style and disease processes. The aim of this paper is to explore the dynamic, time-dependent mechanisms of the metastatic processes, using blood transcriptomics and including time in a continuous manner. For achieving this goal, we have developed new statistical methods based on statistics that are local in time.
Methods: The new statistical method, Local in Time Statistics (LITS), is based on calculating statistics in moving windows and randomization. The method has been tested for the analysis of a dataset that collectively provides information on the blood transcriptome up to 8 years before breast cancer diagnosis. The dataset from the Norwegian Women and Cancer (NOWAC) Post-genome Cohort consists of 467 case-control pairs matched on birth year and time of blood sampling. The data for a pair are the difference in log2 gene expression between the case and control. The stratified analyses are based on important biological differences like metastatic versus non-metastatic cancer, and the mode of cancer detection, ie, screening-detected cancers versus clinically detected cancers. The dataset was used for examining whether the gene expression profile varies between cases and controls, with time, or between cases with and without metastases.
Results: The null hypotheses of no differences between cases and controls, no time-dependent changes, and no differences between different strata were all rejected. For screening-detected cancers, the probability of correct prediction of metastasis status was best in year 1 before diagnosis compared to year 3 and 4 before diagnosis for clinically detected cancers. The predictor was not very sensitive to the number of genes included.
Conclusion: Using a new statistical method, LITS, we have demonstrated time-dependent changes of the blood transcriptome up to 8 years before breast cancer diagnosis.
Keywords: processual analysis, transcriptomics, prediction, breast cancer, blood, Local in Time Statistics
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