Table 1. Patients information.
Patient | Gender | Age | Tumor type | Clinical stage | Tumor size | IHC |
---|---|---|---|---|---|---|
1 | Female | 58 | Invasive breast carcinoma | pT2N0 | 3.0×2.8×2.0cm | CK5&6(−), E-cadherin(1+), EGFR(−), ER(+,95%), HER2(2+), Ki-67(+,30%), P53(−), PR(+, 60%), TOP2A(2+) |
2 | Female | 53 | Invasive breast carcinoma | pT1cN0 | 1.2×1.2×1.0cm | CK5&6(−), E-cadherin(2+), EGFR(−), ER(+, >90%), HER2(1+), Ki-67(+, 25%), PR(+, 10%) |
3 | Female | 54 | Invasive breast carcinoma | pT1cN0 | 1.5×1.2×1.2cm | CK5&6(−), E-cadherin(2+), EGFR(−), ER(+80%), HER2(2+), Ki-67(+20-30%), P53(−), PR(+10%), TOP2A(1+) |
4 | Female | 47 | Invasive lobular carcinoma | pT1cN0 | 1.4×1.2×1.2cm | CK5&6(−), E-cadherin(−), EGFR(−), ER(+, >95%), HER2(1+), Ki-67(+, 10%), P53(−), PR(+, >95%), TOP2A(1+) |
5 | Female | 64 | Invasive breast carcinoma | pT1N0 | 1.8×1.5×1.3cm | CK5&6(−), E-cadherin(1+), EGFR(−), ER(+80%), HER2(1+), Ki-67(+40%), P53(−), PR(+, <10%), TOP2A(2+) |
6 | Female | 38 | Invasive breast carcinoma | pT1cN0 | 1.8×1.5×1.3cm | CK5&6(−), E-cadherin(3+), EGFR(−), ER(+, 80%), HER2(1+), Ki-67(20%+), P53(−), PR(+, 80%), TOP2A(10%+) |
7 | Female | 38 | Invasive breast cancer with focal DCIS | pT1N0 | 2.2×1.8×1.3cm | CK5&6(−), E-cadherin(3+), EGFR(−), ER(+, >90%), HER2(1+), Ki-67(+5%), P53(−), PR(+, 25%) |
8 | Female | 43 | Invasive breast cancer with focal DCIS | pT1bN0 | 1.0×0.5×0.5cm | CK5&6(−), E-cadherin(2+), EGFR(−), ER(+, >90%), HER2(1+), Ki-67(+, 30%), P53(2+), PR(+, 70%), TOP2A(1+) |
9 | Female | 63 | Invasive ductal carcinoma | pT1N0 | 1.8×1.3×1.1cm | CK5/6(−), E-cad(2+), EGFR(1+), ER(+90%), HER2(2+), Ki-67(+, 30%), P53(−), PR(+1-10%), Top2A(1+) |
10 | Female | 57 | Invasive breast carcinoma | pT1N0 | 1.3×1.0×1.0cm | CK5&6(−), E-cadherin(2+), EGFR(−), ER(80%), HER2(1+), Ki-67(30%), P53(+), PR(80%), TOP2A(1+) |
11 | Female | 53 | Invasive breast carcinoma | pT1cN0 | 1.4×1.4×1.2cm | CK5&6(−), E-cadherin(3+), EGFR(−), ER(+, >95%), HER2(−), Ki-67(+, 30%), P53(−), PR(+, 80%), TOP2A(2+) |
To calculate the recurrence score, we converted the relative transcripts expression fold from both RT-MLPSeq method and RT-qPCR method to ΔCt value (−log2) and finally obtained the RS value based on the algorithm presented by Paik, Shak, Tang, et al. in which ΔCt values from real time PCR system were used as input data [3]. As shown in Figure 6, recurrence scores from RT-MLPSeq was highly consistent with RT-qPCR results (R2=0.9309).