Turning towards nonimmunoreactive tumors: Evaluation of cancer-associated fibroblasts enables prediction of the immune microenvironment and treatment sensitivity in pancreatic cancer

Siyuan Lu; Jie Hua; Jin Xu; Miaoyan Wei; Chen Liang; Qingcai Meng; Jiang Liu; Bo Zhang; Wei Wang; Xianjun Yu; Si Shi

doi:10.1016/j.csbj.2022.07.029

Turning towards nonimmunoreactive tumors: Evaluation of cancer-associated fibroblasts enables prediction of the immune microenvironment and treatment sensitivity in pancreatic cancer

Comput Struct Biotechnol J. 2022 Jul 20:20:3911-3923. doi: 10.1016/j.csbj.2022.07.029. eCollection 2022.

Authors

Siyuan Lu^{1

2

3

4}, Jie Hua^{1

2

3

4}, Jin Xu^{1

2

3

4}, Miaoyan Wei^{1

2

3

4}, Chen Liang^{1

2

3

4}, Qingcai Meng^{1

2

3

4}, Jiang Liu^{1

2

3

4}, Bo Zhang^{1

2

3

4}, Wei Wang^{1

2

3

4}, Xianjun Yu^{1

2

3

4}, Si Shi^{1

2

3

4}

Affiliations

¹ Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, Shanghai, China.
² Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.
³ Shanghai Pancreatic Cancer Institute, Shanghai, China.
⁴ Pancreatic Cancer Institute, Fudan University, Shanghai, China.

Abstract

Increasing evidence has confirmed that cancer-associated fibroblasts (CAFs) recruit and induce regulatory T cells (Tregs) and macrophages but inhibit cytotoxic T lymphocyte infiltration to a certain extent, indicating that CAFs have a significant influence on the immunosuppressive microenvironment. However, the effect of CAFs on the immune microenvironment and immunotherapy response in pancreatic cancer remains unclear. Our research identified remarkable variation in CAF-associated molecules in multiple cancer types at the genetic and transcriptome levels. Two phenotypes were identified for 476 pancreatic cancer samples, and the different phenotypes exhibited significant variation in immune and inflammatory characteristics. Phenotype 1 exhibited higher levels of immune infiltration and lower expression of tumor-associated gene signatures than phenotype 2. We used a multipart approach to assess the prognostic value of CAF-associated molecules and constructed a CAF score model that could accurately predict patient prognosis. The CAF score accurately predicted infiltrating immune cell abundance, chemosensitivity, and the response to immunotherapy. Additionally, we found that the CAF-associated molecule FGFR4 may promote the proliferation and migration and inhibit the apoptosis of pancreatic cancer cells and is correlated with immune infiltration, suggesting its potential role as an oncogene. CAFs may promote the malignant biological behavior of pancreatic cancer through FGFR4. In summary, our research highlights potential relationships of the dysregulation of CAF-associated molecules with genome alterations and carcinogenesis in multiple malignancies. Our CAF-associated phenotypes and scoring system may enhance the understanding of pancreatic cancer chemotherapy sensitivity and immunotherapy response, providing new insights for personalized chemotherapy and immunotherapy.

Keywords: Bioinformatics analysis; Cancer-associated fibroblasts; Chemotherapy; Immune infiltration; Machine learning; Pancreatic cancer.