Extensive-stage Small-cell Lung Cancer
Conditions
Brief summary
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.
Detailed description
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.
Interventions
None listed
Sponsors
Henan Cancer Hospital
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
Inclusion criteria
* Histologically proven ES-SCLC (American Joint Cancer Commission (7th Edition) Stage IV SCLC \[any T, any N and M1a/b\]), or T3-4 patients who are unable to be included in a tolerable radiotherapy program due to wide multiple incidences or excessive tumor volume. * Patients with brain metastases must have asymptomatic or stable steroid and anticonvulsant treatment for at least 1 month before study treatment. Patients with suspected brain metastasis during screening should undergo brain CT/MRI examination before enrollment of the study. * Have at least one measurable tumour lesion according to RECIST v1.1. * aged ≥18 years * Eastern Cooperative Oncology Group (ECOG) physical status score of 0-1
Exclusion criteria
* Have a history of chest radiotherapy or plan to undergo intensive chest radiotherapy before systemic treatment. Radiotherapy outside the chest (i.e., bone metastasis) is allowed for palliative care purposes, however, must be done before the first medication of the study drug * Previous non-infectious pneumonia requiring systemic glucocorticoid therapy or current non-infectious pneumonia combined with mild to moderate interstitial pneumonia, inactive interstitial pneumonia. * Presence of unmitigated toxicity from prior antineoplastic therapy, with unmitigated defined as failure to recover to NCI CTCAE version 5.0 grade 0 or 1 (except alopecia areata) or failure to recover to levels specified in the inclusion/
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Progress-free survival time | 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 | Time from enrollment to either radiological progression or death |
Countries
China
Contacts
Primary ContactHenan Province Cancer Hospital Ethics Committee Henan Province Cancer Hospital Ethics Committee
Outcome results
None listed