Table 2.

Summary table of the six external datasets for the clinical association of the malignancy-risk gene signature.

Dataset	Sample size (n)	Endpoint	Statistics method	Test statistics	p value
Cancer risk
Turashvili et al.’s IDC study	10	IDC versus normal	random effect model		p=0.029
Cancer relapse/progression
Chanrion et al’s Tamoxifen-Treated Primary Breast Cancer	155	relapse of primary breast cancer	Continuous risk score:
			1. logistic regression	coefficient=0.137	p<0.0001
			2. ROC	AUC=0.81	p<0.0001
			3. SVM	Accuracy rate=74%
			4. two-sample t-test		p<0.0001
			Binary risk score:
			logistic regression	OR=8.16	<0.0001
Ma et al’s breast cancer study	61	histological status (ADH, DCIS, IDC)	correlation analysis	r=0.50 (Pearson or Spearman)	<0.0001
			logistic regression	OR (DCIS)=2.28 (compared to ADH)	p=0.016
			logistic regression	OR (IDC)=3.31 (compared to ADH)	p=0.008
Prognosis
van ‘t Veer et al’s breast metastasis dataset	training=78 test=263	time to metastasis	Continuous risk score
			log-rank test	X2=11.8 (training set); X2=20.4 (test set)	p=0.0006 (training);p<0.0001 (test)
			Binary risk score:
			log-rank test	X2=12.2 (training set); X2=22.4 (test set)	p=0.0005 (training);p<0.0001 (test)
Wang et al’s breast cancer relapse free survival study	286	metastasis-free survival	Continuous risk score:
			log-rank test	X2=12.8	p=0.0004
			Binary risk score:
			log-rank test	X2=12.6	p=0.0004
Huang et al’s breast lymph node study	37	lynph node (pos vs. neg)	Continuous risk score:
			1. logistic regression	coefficient=0.2	p=0.0107
			2. ROC	AUC=0.75	p=0.0041
			3. SVM	Accuracy rate=73%
			4. two-sample t-test		p=0.004
			Binary risk score:
			logistic regression	OR=7.29	p=0.007