A seven-gene risk prediction signature (NCIRPs) model was developed to improve prediction of therapeutic response in locally advanced esophageal squamous cell carcinoma (ESCC).
Higher neutrophil infiltration, enriched TGF-β, and cell cycle pathways were observed in patients with non- (non-pCR).
Infiltration of natural killer cells and activated CD4 T cells, along with signatures of interferon-gamma and antigen processing, were significantly associated with pathological complete response (pCR).
The NCIRPs model demonstrated higher prediction accuracy of pathological response compared to PD-L1 combined positive score (CPS) and other immune signatures in multiple patient cohorts.
No prognostic association or correlation with response to chemoradiotherapy was found in The Cancer Genome Atlas Program ESCC dataset.
Simplified
BACKGROUND: has a promising effect on locally advanced esophageal squamous cell carcinoma (ESCC). However, reliable biomarkers robustly predicting therapeutic response are still lacking.
METHODS: Formalin-fixed and paraffin-embedded pre-neoadjuvant chemoimmunotherapy biopsy samples from locally advanced ESCC patients were collected. Cohort 1 composed of 66 locally advanced ESCC patients from a prospective clinical trial (NCT04506138) received two cycles of camrelizumab in combination with nab-paclitaxel and carboplatin every 3 weeks. Cohort 2 included 48 patients receiving various types of immune checkpoint inhibitors with (nab-)paclitaxel and platinum-based chemotherapy as neoadjuvant therapy. Cohort 3 consisted of 27 ESCC patients receiving neoadjuvant treatment of toripalimab with chemotherapy and was used as the external validation dataset. Targeted RNA sequencing, immunohistochemistry for programmed death ligand 1 (PD-L1), and multiplex immunofluorescence (mIF) imaging were performed.
RESULTS: Integration of targeted RNA sequencing, PD-L1 immunohistochemistry, and mIF revealed a significant immune-suppressive microenvironment with higher neutrophil infiltration, enriched TGF-β, and cell cycle pathways in non- (non-pCR) patients. NK, activated CD4T cell infiltration, interferon-gamma, antigen processing and presentation, and other immune response signatures were significantly associated with pCR. Based on discovered tumor microenvironmental characteristics and their closely related genes were screened. Consequently, a seven-gene neoadjuvant chemoimmunotherapy risk prediction signature (NCIRPs) model, was constructed. In addition to cohort 1, this model alone or with PD-L1-combined positive score (CPS) demonstrated a higher prediction accuracy of pathological response than PD-L1 CPS or other routinely used immune signatures, such as IFN-γ, in cohorts 2 and 3. Neither prognostic association nor correlation with response to chemoradiotherapy was observed in The Cancer Genome Atlas Program ESCC dataset or in ESCC patients in the neoadjuvant chemoradiotherapy cohort (cohort 4). +
CONCLUSION: The NCIRPs model that was developed and validated using treatment-naïve endoscopic samples from the largest ESCC neoadjuvant chemoimmunotherapy dataset represents a robust and clinically meaningful approach to select a putative responder for neoadjuvant chemoimmunotherapy in locally advanced ESCC patients.
Key numbers
61.1%
Rate in Low-Risk Patients
rates in cohort 1 for low-risk patients
28.1%
Rate in PD-L1 CPS ≥1 Group
rates in cohort 1 for PD-L1 CPS ≥1 patients
0.855
AUC for NCIRPs in Cohort 2
Area under the ROC curve for NCIRPs in cohort 2
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