This is a prospective observational clinical study designed to predict the therapeutic efficacy of first-line treatment with tislelizumab combined with standard chemotherapy in patients with ES-SCLC using TCR repertoire technology. The study plans to enroll 40 treatment-naive patients with ES-SCLC.
The advancement of Next-Generation Sequencing (NGS) technology has facilitated the detection of T-cell immune repertoires across various solid tumor types, and a growing body of research indicates that T-cell immune repertoires hold potential as biomarkers for immunotherapy; in the field of non-small cell lung cancer (NSCLC), previous studies have suggested that the characteristics of the baseline T-cell receptor (TCR) repertoire and changes in the TCR repertoire before and after immunotherapy are associated with immunotherapeutic efficacy, while such exploration remains lacking in the field of small cell lung cancer (SCLC). Due to limitations in cost and experimental methods, the currently available TCR databases contain limited information, encompassing only a small fraction of antigen-TCR binding pairs, and furthermore, these binding pair data fail to cover all antigens that any given TCR might potentially bind to; to address this issue, the research community has explored the use of machine learning models to predict the antigen specificity of unknown and experimentally unvalidated TCRs, which has shown feasibility. This study, as a prospective observational clinical study designed to predict the therapeutic efficacy of first-line treatment with tislelizumab combined with standard chemotherapy in patients with extensive-stage small cell lung cancer (ES-SCLC) using TCR repertoire technology, plans to enroll 40 treatment-naive patients with ES-SCLC, and aims to predict the neoantigen-specific TCR repertoire by analyzing tumor neoantigens, integrating T-cell repertoire data and HLA class I detection information, and leveraging the Multimodal-AIR-BERT machine learning model, with the hypothesis that the parameters of this predicted TCR repertoire may exhibit a stronger correlation with immunotherapeutic efficacy.
Study Type
OBSERVATIONAL
Enrollment
40
Henan Cancer Hosipital
Zhengzhou, Henan, China
RECRUITINGProgress-free survival time
Time from enrollment to either radiological progression or death
Time frame: From date of enrollment until the date of first documented progression or date of death from any cause, whichever came first, assessed up to 24 months
Henan Province Cancer Hospital Ethics Committee Henan Province Cancer Hospital Ethics Committee
CONTACT
This platform is for informational purposes only and does not constitute medical advice. Always consult a qualified healthcare professional.