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Record W4407694297 · doi:10.1186/s12885-025-13712-0

Optimal early endpoint for second-line or subsequent immune checkpoint inhibitors in previously treated advanced solid cancers: a systematic review

2025· review· en· W4407694297 on OpenAlexaboutno aff
Jingqiu Li, Xiaoding Zhou, Lei Wu, Jiabao Ma, Yan Tan, San‐Gang Wu, Jie Zhu, Qifeng Wang, Qiuling Shi

Bibliographic record

VenueBMC Cancer · 2025
Typereview
Languageen
FieldMedicine
TopicCancer Immunotherapy and Biomarkers
Canadian institutionsnot available
FundersUniversity of Electronic Science and Technology of ChinaDepartment of Science and Technology of Sichuan Province
KeywordsSurgical oncologyMedicineOncologySolid tumorImmune checkpointInternal medicineImmune systemCancer researchIntensive care medicineImmunologyImmunotherapyCancer

Abstract

fetched live from OpenAlex

BACKGROUND: The administration of second-line or subsequent immune checkpoint inhibitors (ICIs) in previously treated patients with advanced solid cancers has been clinically investigated. However, previous clinical trials lacked an appropriate primary endpoint for efficacy assessment. This systematic review aimed to explore the most optimal early efficacy endpoint for such trials. METHODS: Phase 2 or 3 clinical trials involving patients with advanced solid cancers with disease progression following standard first-line therapy receiving second-line or subsequent ICI administration, with adequate survival outcome data, were included from PubMed, Embase, Web of Science, and Cochrane Library databases before February 2023. Quality assessment was conducted using the Cochrane tool and Newcastle-Ottawa Quality Assessment Scale for Cohort Studies for randomized controlled trials (RCTs) and non-randomized trials, respectively. Objective response rate (ORR) and progression-free survival (PFS) at 3, 6, and 9 months were investigated as potential early efficacy endpoint candidates for 12-month overall survival (OS), with a strong correlation defined as Pearson's correlation coefficient r ≥ 0.8. RESULTS: A total of 64 RCTs comprising 22,725 patients and 106 non-randomized prospective trials involving 10,608 participants were eligible for modeling and external validation, respectively. RCTs examined 15 different cancer types, predominantly non-small-cell lung cancer (NSCLC) (17, 28%), melanoma (9, 14%), and esophageal squamous cell carcinoma (5, 8%). The median sample size of RCTs was 124 patients, and the median follow-up time was 3.2-57.7 months. The ORR (r = 0.38; 95% confidence interval [CI], 0.18-0.54) and PFS (r = 0.42; 95% CI, 0.14-0.64) exhibited weak trial-level correlations with OS. Within ICI treatment arms, the r values of ORR and 3-, 6-, and 9-month PFS with 12-month OS were 0.61 (95% CI, 0.37-0.79), 0.78 (95% CI, 0.62-0.88), 0.84 (95% CI, 0.77-0.90), and 0.86 (95% CI, 0.79-0.90), respectively. External validation of 6-month PFS indicated an acceptable discrepancy between actual and predicted 12-month OS. CONCLUSIONS: In non-randomized phase 2 trials on second-line or subsequent ICI therapy in patients with advanced solid cancers, 6-month PFS could serve as an early efficacy endpoint. However, early efficacy endpoints are not recommended in RCTs to replace OS.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.017
metaresearch head score (Gemma)0.047
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (broad)
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.984
Threshold uncertainty score0.092

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.047
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0160.015
Bibliometrics0.0080.007
Science and technology studies0.0010.001
Scholarly communication0.0040.003
Open science0.0020.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0040.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.049
GPT teacher head0.377
Teacher spread0.329 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designSystematic review
DomainMethods
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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