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Supplementary Figure 1- Overview of radiation response signature development utilizing
intrinsic radiation sensitivity of human breast cancer cell lines.
Supplementary Figure 2- Unsupervised hierarchical clustering using the gene expression of the
147 differentially expressed genes accurately clusters the radiation resistant cell lines (SF2 Gy
>45%) from the radiation sensitive cell lines (SF 2 Gy <45%).
Supplementary Figure 3- Performance of the radiation sensitivity signature with the previously
published radiation sensitivity signature in the training dataset. Kaplan-Meier local recurrence
survival estimates in our signature (A) and the previously published signature (B). ROC curves
between the radiation sensitivity signature (C) and the previously published signature (D).
Supplementary Figure 4- Performance of the radiation sensitivity signature with the previously
published radiation sensitivity signature in the validation dataset.
Kaplan-Meier local
recurrence, metastasis-free, and overall survival estimates in the radiation sensitivity signature
(A) and the previously published signature (B). ROC curves predicting local recurrence between
the radiation sensitivity signature and the previously published signature. This analysis included
all patients in the validation study without patient censoring (C).
Supplementary Figure 5- Calibration curves with the fitted logistic regression prediction curve
using the out-of-bag cross validation (A) and external validation (B) datasets were generated
with 95% confidence intervals shown. Actual local recurrence event rates are depicted on the y-
axis and recurrence score prediction (RSS score) based on the model (a number between 0 and 1)
is on the x-axis.
Supplementary Table 1- Genes positively and negatively associated with radiation sensitivity in
breast cancer cell lines.
This includes 67 positively correlated genes and 80 negatively
correlated genes.
Supplementary Table 2- Genes positively and negatively associated with radiation sensitivity
in breast cancer cell lines after training in the clinical dataset with associated outcomes. This
includes 23 positively correlated genes and 28 negatively correlated genes.
Supplementary Table 3- Univariate and Multivariable analysis results for locoregional
recurrence in the training and validation datasets for all available clinical and pathologic variable
in these datasets. Additionally, C-indices were calculated and included in the table for the
training and validation dataset.