27-gene Immuno-Oncology (IO) Score is Associated With Efficacy of Checkpoint Immunotherapy in Advanced NSCLC: A Retrospective BC Cancer Study
Bibliographic record
Abstract
BACKGROUND: Immune checkpoint inhibitors (ICI) are standard of care in advanced non-small cell lung cancer (NSCLC). However, not all patients benefit, even among PD-L1 tumor proportional score (TPS) ≥50%, indicating an unmet need for additional biomarkers such as those assessing the tumor immune microenvironment (TIME). DetermaIO is a 27-gene assay that classifies TIME and has previously demonstrated association with ICI response. METHODS: FFPE samples were selected from BC Cancer and West Clinic Cancer Center patients with performance status (PS) ≤2 who received at least 2 cycles of ICI monotherapy in the first (1L) or second line (2L). IO scores were generated and analyzed for association with PFS and OS. RESULTS: In the entire cohort (N=147), IO score was significantly associated with OS (HR=0.68, 95%CI 0.47-0.99, P = .042) and PFS (HR=0.62, 95%CI 0.43-0.88, P = .0069). In 1L treated patients (PD-L1≥50%, N=78), IO score was significantly associated with PFS (HR=0.55, 95%CI 0.32-0.94, P = .028). In exploratory analyses, IO score was associated with benefit in 1L PS2 patients for OS (HR = 0.26, 95%CI 0.091-0.74, P = .012) and PFS (HR = 0.27, 95%CI 0.098-0.72, P = .0095) which was confirmed in PFS subgroup analysis in the independent West Cancer Center study (N=13 HR=0.14, 95%CI 0.027-0.76, P = .023). CONCLUSION: These data confirm the association of DetermaIO with ICI clinical benefit in NSCLC, and expand on previous studies by demonstrating that first line treated PD-L1≥50% patients can further be stratified by IO score to identify efficacy. Exploratory analysis suggested that the IO score identifies benefit in patients with poor PS.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".