一种预测导管原位癌进展为浸润性乳腺癌风险的基因表达分类器

Background: Ductal carcinoma in situ (DCIS) is a non-obligate precursor to invasive ductal carcinoma. A minority of DCIS cases will ever progress to ipsilateral invasive breast cancer (iIBC), but almost all are treated with breast-conserving surgery and radiotherapy. Reliable biomarkers of progression risk are needed to prevent overtreatment of low-risk lesions. We present a DCIS gene expression classifier to predict the risk of iIBC occurring within the first 5 years of diagnosis. Methods: The model was trained on a dataset of pure primary DCIS RNA-seq samples from a Dutch population-based cohort collected from 1989 to 2005. Samples from 188 patients treated with breast-conserving surgery only were retained for the training data to remove radiotherapy as a confounding factor in iIBC risk. The classifier is a logistic regression model with elastic net penalization, trained in a nested 5x5 cross-validation scheme. The final model was validated on an external, independent dataset of 91 British DCIS samples from the NHS Sloane project. Results: The classifier achieved an overall AUC of 0.642 on the outer loop test sets in the Dutch training set, and 0.694 in the Sloane independent validation set. The classifier risk score is shown in a logistic model to be associated with an increased risk of iIBC within 5 years in the Sloane validation dataset [OR: 1.16; CI: 1.05-1.27; p = 0.0496] where HER2 status, ER status, histopathological grade and age at diagnosis were not. A threshold to categorize risk scores into low- and high-risk categories was chosen on the training set by selecting the cut-point that maximized balanced accuracy. Gene set enrichment analysis showed enrichment of cell cycle and proliferation gene sets in the high-risk category (HALLMARK_E2F_TARGETS, HALLMARK_G2M_CHECKPOINT, GNF2_MKI67). Discussion: The performance of this classifier on an external, independent validation dataset shows that signals of risk of developing iIBC within 5 years of diagnosis can be detected within the gene expression profile of pure primary DCIS lesions. That the classifier was trained and validated on samples from patients who received the least aggressive treatment available without the confounding factors of radiotherapy or mastectomy brings us closer to an understanding of the biology underlying the risk of progression to iIBC in DCIS untreated at diagnosis. Such an understanding could be valuable information for including DCIS patients in active surveillance trials, and a step forward in preventing women having to undergo unnecessary surgery and radiotherapy.