Adjusting for systematic technical biases in risk assessment of gene signatures in transcriptomic cancer cohorts

In recent years, many efforts in clinical and basic research have focused on finding molecular features of tumor samples with prognostic or classification potential. Among these, the association of the expression of gene signatures with survival probability is of special interest given its relatively direct applicability in the clinic and its power to shed insights into the molecular basis of cancer. Although great efforts have been invested in data processing to control for unknown sources of variability in a gene-wise manner, little is known about the behaviour of gene signatures with respect to the effect of technical variables. Here we show that the association of signatures with survival may be biased due to technical reasons and propose a simple and low intensive methodology based on correction by expectation under gene randomization. The resulting estimates are centred around zero and ensure correct asymptotic inference. Moreover, our methodology is robust against spurious correlations between global dataset tendencies and clinical outcome. All tools (will be soon) available in the "HRunbiased" R package as well as processed datasets for colorectal and breast cancer.