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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".