Elevated serum interleukin-8 is associated with enhanced intratumor neutrophils and reduced clinical benefit of immune-checkpoint inhibitors

Schalper, Kurt A.; Carleton, Michael; Zhou, Ming; Tian, Chen; Yang, Feng; Huang, Shu-Pang; Walsh, Alice M.; Baxi, Vipul; Pandya, Dimple; Baradet, Timothy; Locke, Darren; Wu, Qiuyan; Reilly, Timothy P.; Phillips, Penny; Nagineni, Venkata; Gianino, Nicole; Gu, Jianlei; Zhao, Hongyu; Perez-Gracia, José Luis; Sanmamed, Miguel F.; Melero, Ignacio

doi:10.1038/s41591-020-0856-x

Cited by 348 publications

(290 citation statements)

References 33 publications

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“…To explore the relationship between CXCL8 levels and overall survival in patients with tumors, data from two phase 3 clinical studies (CheckMate 067 and CheckMate 025) were downloaded (14). CheckMate 067 included 887 patients with melanoma treated with nivolumab or ipilimumab or nivolumab plus ipilimumab.…”

Section: Patientsmentioning

confidence: 99%

“…Previous studies have revealed an abnormal high expression of CXCL8 in malignant tumors, which is mainly involved in tumor cell growth, apoptosis, invasion, and migration (6)(7)(8)(9)(10)(11)(12). Large-scale cohort studies have suggested that elevated plasma or serum CXCL8 was associated with worse prognosis in melanoma, metastatic urothelial carcinoma, and renal cell carcinoma patients (13,14). In terms of CRC, some studies have demonstrated that a high level of CXCL8 could promote poor overall and disease-free survival.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Model Based on CXCL8-Derived Radiomics for Prognosis Prediction in Colorectal Cancer

Chu

Zeng

et al. 2020

Front. Oncol.

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Introduction: Prognosis prediction is essential to improve therapeutic strategies and to achieve better clinical outcomes in colorectal cancer (CRC) patients. Radiomics based on high-throughput mining of quantitative medical imaging is an emerging field in recent years. However, the relationship among prognosis, radiomics features, and gene expression remains unknown. Methods: We retrospectively analyzed 141 patients (from study 1) diagnosed with CRC from February 2018 to October 2019 and randomly divided them into training (N = 99) and testing (N = 42) cohorts. Radiomics features in venous phase image were extracted from preoperative computed tomography (CT) images. Gene expression was detected by RNA-sequencing on tumor tissues. The least absolute shrinkage and selection operator (LASSO) regression model was used for selecting imaging features and building the radiomics model. A total of 45 CRC patients (study 2) with immunohistochemical (IHC) staining of CXCL8 diagnosed with CRC from January 2014 to October 2018 were included in the independent testing cohort. A clinical model was validated for prognosis prediction in prognostic testing cohort (163 CRC patients from 2014 to 2018, study 3). We performed a combined radiomics model that was composed of radiomics score, tumor stage, and CXCL8-derived radiomics model to make comparison with the clinical model. Results: In our study, we identified the CXCL8 as a hub gene in affecting prognosis, which is mainly through regulating cytokine-cytokine receptor interaction and neutrophil migration pathway. The radiomics model incorporated 12 radiomics features screened by LASSO according to CXCL8 expression in the training cohort and showed good performance in testing and IHC testing cohorts. Finally, the CXCL8-derived radiomics model combined with tumor stage performed high ability in predicting the prognosis of CRC patients in the prognostic testing cohort, with an area under the curve (AUC) of 0.774 [95% confidence interval (CI): 0.674-0.874]. Kaplan-Meier analysis of the overall Chu et al. CXCL8-Derived Radiomics for Prognosis Prediction survival probability in CRC patients stratified by combined model revealed that high-risk patients have a poor prognosis compared with low-risk patients (Log-rank P < 0.0001). Conclusion: We demonstrated that the radiomics model reflected by CXCL8 combined with tumor stage information is a reliable approach to predict the prognosis in CRC patients and has a potential ability in assisting clinical decision-making.

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Section: Patientsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Novel Model Based on CXCL8-Derived Radiomics for Prognosis Prediction in Colorectal Cancer

Chu

Zeng

et al. 2020

Front. Oncol.

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“…Recent studies showed that high serum levels of the c-MET ligand HGF correlated with increasing neutrophil counts, which was associated with poor responses to checkpoint blockade therapies (17)(18)(19)(20). IL-8 is secreted by malignant tumor and stroma cells across many different types of tumor, and is a member of the CXC chemokine family identi ed as a chemotactic factor for neutrophils (21)(22)(23). A previous study also reported that decreases in serum IL-8 level 2-4 weeks after anti-PD-1 blockade therapies were strongly associated with response to immune checkpoint blockade in metastatic melanoma and NSCLC patients (24).…”

Section: Discussionmentioning

confidence: 99%

Biomarkers of Response to Combinational Use of PD-1 Blockade and Anti-angiogenesis: An Analysis from a Phase II Trial in Advanced Triple-negative Breast Cancer Patients

Liu

et al. 2020

Preprint

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Background: We recently reported the results of a phase II clinical trial that the combinational use of camrelizumab plus apatinib induced an objective response rate (ORR) at 43.3% in advanced triple-negative breast cancer (TNBC) patients. This study presents the analysis of potential tumor and blood biomarkers for the patients responded to the combinational therapy. Methods: Stromal tumor-infiltrating lymphocytes (TILs), CD8+ T cells and the protein expression of programmed death protein 1 (PD-1) and PD-L1 were evaluated in tumor samples collected before and after the treatment. Peripheral blood samples were collected before treatment, 2-weeks and 8-weeks after treatment. 59 cytokines/chemokines, growth factors, or checkpoint-related proteins, blood immune cell subpopulations were analyzed in the blood samples. The correlation between biomarkers and clinical outcomes including ORR, progression-free survival (PFS), and overall survival (OS) was analyzed. Results: Upon database lock, the ORR of 28 evaluable patients was 46.4%. An increase of tumor-infiltrating CD8+ T cells more than 15% during therapy was significantly associated with higher ORR (P=0.040). Patients with lower baseline plasma levels of HGF or IL-8 were more likely to respond to the combinational treatment (P=0.005 or 0.001, respectively), and showed a longer PFS and OS (HGF: PPFS<0.0001, POS<0.0001; IL-8: PPFS<0.0001, POS=0.009). Patients with a decrease of IL-8, or an increase of TIM-3 or CD152 during treatment responded more to the treatment (P=0.008, 0.040, or 0.014, respectively). Responders had a higher baseline CD4+ T cells and B cell proportions in blood than non-responders (P=0.002 and 0.030 respectively). Moreover, patients with higher baseline CD4+ T cells or B cells proportions in blood showed a longer PFS (P<0.001) or a longer OS (P=0.030) respectively. Conclusion: Higher baseline TILs or a greater increase of tumor-infiltrating CD8+ T cells during therapy, lower baseline plasma HGF/IL-8, a decrease of plasma IL-8, an increase of plasma TIM-3/CD152 during therapy, higher baseline CD4+ T cells or B cells proportion in blood are potential biomarkers for the combinational anti-angiogenesis and immunotherapy in advanced TNBC patients.

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“…Anti–PD-1 monoclonal antibody therapy (atezolizumab) showed poor therapeutic effect with high levels of IL-8 in plasma, peripheral blood mononuclear cells, and tumors. A large-scale retrospective analysis reported that the poor prognoses of advanced cancer patients treated with nivolumab and/or ipilimumab, everolimus, or docetaxel in phase III clinical trials were related to upregulated baseline serum IL-8 ( 167 ). The effect of IL-8 in ovarian cancer remains unclear, and further investigation is warranted.…”

Section: Challenges and Future Developmentsmentioning

confidence: 99%

Immunotherapy for Ovarian Cancer: Adjuvant, Combination, and Neoadjuvant

et al. 2020

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Ovarian cancer is the most lethal gynecologic malignancy. Surgery and chemotherapy are the primary treatments for ovarian cancer; however, patients often succumb to recurrence with chemotherapeutic resistance within several years after the initial treatment. In the past two decades, immunotherapy has rapidly developed, and has revolutionized the treatment of various types of cancer. Despite the fact that immunotherapy response rates among ovarian cancer patients remain modest, treatment with immune checkpoint inhibitors (ICIs), chimeric antigen receptor (CAR)- and TCR-engineered T cells is rapidly developing. Therapeutic efficiency could be improved significantly if immunotherapy is included as an adjuvant therapy, in combination with chemotherapy, radiation therapy, and the use of anti-angiogenesis drugs, and poly ADP ribose polymerase inhibitors (PARPi). Newly developed technologies that identify therapeutic targets, predict treatment efficacy, rapidly screen potential immunotherapy drugs, provide neoadjuvant immunotherapy, and utilize nanomedicine technology provide new opportunities for the treatment of ovarian cancer, and have the potential to prolong patient survival. However, important issues that may hinder the efficacy of such approaches, including hyperprogressive disease (HPD), immunotherapy-resistance, and toxicity of the treatments, including neurotoxicity, must be taken into account and addressed for these therapies to be effective.

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Elevated serum interleukin-8 is associated with enhanced intratumor neutrophils and reduced clinical benefit of immune-checkpoint inhibitors

Cited by 348 publications

References 33 publications

A Novel Model Based on CXCL8-Derived Radiomics for Prognosis Prediction in Colorectal Cancer

A Novel Model Based on CXCL8-Derived Radiomics for Prognosis Prediction in Colorectal Cancer

Biomarkers of Response to Combinational Use of PD-1 Blockade and Anti-angiogenesis: An Analysis from a Phase II Trial in Advanced Triple-negative Breast Cancer Patients

Immunotherapy for Ovarian Cancer: Adjuvant, Combination, and Neoadjuvant

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