A chemotherapy response prediction model derived from tumor-promoting B and Tregs and proinflammatory macrophages in HGSOC

Yue Xi; Yingchun Zhang; Kun Zheng; Jiawei Zou; Lv Gui; Xin Zou; Liang Chen; Jie Hao; Yiming Zhang

doi:10.3389/fonc.2023.1171582

A chemotherapy response prediction model derived from tumor-promoting B and Tregs and proinflammatory macrophages in HGSOC

Front Oncol. 2023 Jul 14:13:1171582. doi: 10.3389/fonc.2023.1171582. eCollection 2023.

Authors

Yue Xi¹, Yingchun Zhang¹, Kun Zheng², Jiawei Zou³, Lv Gui⁴, Xin Zou⁵, Liang Chen⁶, Jie Hao³, Yiming Zhang¹

Affiliations

¹ Department of Reproductive Medicine, Central Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China.
² Department of Urology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.
³ Institute of Clinical Science, Zhongshan Hospital, Fudan University, Shanghai, China.
⁴ Department of Pathology, Jinshan Hospital, Fudan University, Shanghai, China.
⁵ Jinshan Hospital Center for Tumor Diagnosis & Therapy, Jinshan Hospital, Fudan University, Shanghai, China.
⁶ Department of Gynecological Oncology, Shandong Cancer Hospital Affiliated to Shandong University, Shandong Academy of Medical Sciences, Jinan, China.

Abstract

Background: Most patients with high-grade serous ovarian cancer (HGSOC) experienced disease recurrence with cumulative chemoresistance, leading to treatment failure. However, few biomarkers are currently available in clinical practice that can accurately predict chemotherapy response. The tumor immune microenvironment is critical for cancer development, and its transcriptomic profile may be associated with treatment response and differential outcomes. The aim of this study was to develop a new predictive signature for chemotherapy in patients with HGSOC.

Methods: Two HGSOC single-cell RNA sequencing datasets from patients receiving chemotherapy were reinvestigated. The subtypes of endoplasmic reticulum stress-related XBP1⁺ B cells, invasive metastasis-related ACTB⁺ Tregs, and proinflammatory-related macrophage subtypes with good predictive power and associated with chemotherapy response were identified. These results were verified in an independent HGSOC bulk RNA-seq dataset for chemotherapy. Further validation in clinical cohorts used quantitative real-time PCR (qRT-PCR).

Results: By combining cluster-specific genes for the aforementioned cell subtypes, we constructed a chemotherapy response prediction model containing 43 signature genes that achieved an area under the receiver operator curve (AUC) of 0.97 (p = 2.1e-07) for the GSE156699 cohort (88 samples). A huge improvement was achieved compared to existing prediction models with a maximum AUC of 0.74. In addition, its predictive capability was validated in multiple independent bulk RNA-seq datasets. The qRT-PCR results demonstrate that the expression of the six genes has the highest diagnostic value, consistent with the trend observed in the analysis of public data.

Conclusions: The developed chemotherapy response prediction model can be used as a valuable clinical decision tool to guide chemotherapy in HGSOC patients.

Keywords: bioinformatics; chemotherapy; high-grade serous ovarian cancer (HGSOC); response prediction model; single-cell RNA-seq.

Grants and funding

This work was supported in part by the National Natural Science Foundation of China [82170045 to JH] and the Innovative Research Team of High-level Local Universities in Shanghai [SHSMU-ZLCX20212301 to JH].