Predicting breast cancer metastasis from whole-blood transcriptomic measurements

Abstract

Objective: In this exploratory work we investigate whether blood gene expression measurements predict breast cancer metastasis. Early detection of increased metastatic risk could potentially be life-saving. Our data comes from the Norwegian Women and Cancer epidemiological cohort study. The women who contributed to these data provided a blood sample up to a year before receiving a breast cancer diagnosis. We estimate a penalized maximum likelihood logistic regression. We evaluate this in terms of calibration, concordance probability, and stability, all of which we estimate by the bootstrap. Results: We identify a set of 108 candidate predictor genes that exhibit a fold change in average metastasized observation where there is none for the average non-metastasized observation.

Metastatic Breast Cancer and Pre-Diagnostic Blood Gene Expression Profiles—The Norwegian Women and Cancer (NOWAC) Post-Genome Cohort

“…We made some of these choices for convenience, such as the use of single-gene regressions and the use of hard-coded prior parameters rather than a hierarchical model. We have explored other approaches to these data not reported here, notably ( 8 , 30 ).…”

A novel machine learning prediction model for metastasis in breast cancer

“…Moreover, the AUROCs were 0.932 and 0.905 in the training and validation sets, respectively, which means that this model had a good predictive effect on BC metastasis. Furthermore, the AUROCs of previous developed predictive models ranged from 0.58 to 0.90, which means that this model was more accurate when compared to previous models 44–46 . Furthermore, this model had good Brier scores of 0.113 and 0.097 for the training and external validation sets, respectively, showing good calibration.…”

“…Furthermore, the AUROCs of previous developed predictive models ranged from 0.58 to 0.90, which means that this model was more accurate when compared to previous models. [44][45][46] Further-…”