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Supplementary Figures – Developing an immune signature for triple‑negative breast cancer to predict prognosis and immune checkpoint inhibitor

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posted on 02.02.2022, 09:35 by Figshare Future Science GroupFigshare Future Science Group, Guoyou Qin, Ce Wang, Guoshuang Feng, Jingjing Zhu, KeCheng Wei, Chen Huang, Zhenyu Wu, Yongfu Yu

Supplementary Figure S1. Developing an immune signature for triple‑negative breast cancer to predict prognosis and immune checkpoint inhibitor

Flow diagram of the study

Supplementary Figure S2. Developing an immune signature for triple‑negative breast cancer to predict prognosis and immune checkpoint inhibitor

(A) Kaplan-Meier curve of progression-free survival in training set; (B) Kaplan-Meier curve of metastasisfree survival in validation set

Supplementary Figure S3. Developing an immune signature for triple‑negative breast cancer to predict prognosis and immune checkpoint inhibitor

(A) The summary of the overall mutation profile in training set; (B) Mutation profile in training set

Supplementary Figure S4. Developing an immune signature for triple‑negative breast cancer to predict prognosis and immune checkpoint inhibitor

Bar chart of the relative proportion of the 22 immune cells in each patient in training set

Supplementary Figure S5. Developing an immune signature for triple‑negative breast cancer to predict prognosis and immune checkpoint inhibitor

(A) Bar chart of the relative proportion of the 22 immune cells in each patient in validation set; (B) The

difference of proportions of 21 immune infiltration cells in high-risk and low-risk group in validation set

Supplementary Figure S6. Developing an immune signature for triple‑negative breast cancer to predict prognosis and immune checkpoint inhibitor

(A)Immune score between high-risk and low-risk group in validation set; (B) Stromal score between highrisk and low-risk group in validation set;(C) PD-1 expression between high-risk and low-risk group in validation set; (D)

PD-L1 expression between high-risk and low-risk group in validation set; (E) PD-L2 expression between high-risk and

low-risk group in validation set; (F) CTLA-4 expression between high-risk and low-risk group in validation set.

Funding

Beihang University

Capital Medical University Advanced Innovation Center for Big Data-Based Precision Medicine Plan.

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