Immunohistochemical validation study of 15-gene biomarker panel predictive of benefit from adjuvant chemotherapy in resected non-small-cell lung cancer: analysis of JBR.10
Bibliographic record
Abstract
OBJECTIVE: shown to be prognostic and predictive of benefit, into a readily applicable immunohistochemistry (IHC) panel. METHODS: For seven of the genes in the gene expression profile (GEP) for which suitable commercial antibodies were available, we semiquantitatively assessed the IHC expression and prognostic significance for 173 patients treated at the Saint John Regional Hospital (SJRH). Cut-offs for high and low expression were defined for each marker and applied to IHC scores from 291 of the 482 patients in JBR.10, including patients on both the adjuvant chemotherapy and observation arms. The prognostic and predictive value of these markers on overall survival (OS) or recurrence-free survival (RFS) was assessed by Cox regression models. RESULTS: In the SJRH cohort, in 62 patients with resected stage II-III NSCLC, the prognostic significance of IHC assays for four proteins were concordant with Zhu's GEP results. Low FOSL2 (OS, HR=0.15; p=0.0001; RFS, HR=0.14; p<0.0001) and high STMN2 (RFS, HR=2.501; p=0.0197) were adverse prognostic factors. Low ATP1B1 and low TRIM14 expression trended toward worse OS and RFS. Validation of these markers with JBR.10 patients failed to show prognostic significance either individually or in combined risk classifications. Additionally, the interaction between these markers and chemotherapy treatment in predicting OS (FOSL2, p=0.52; STMN2 p=0.14; ATP1B1, p=0.33; TRIM14, p=0.81) or RFS (FOSL2, p=0.63; STMN2, p=0.12; ATP1B1, p=0.66; TRIM14, p=0.57) did not reach significance, individually or in combination panels. CONCLUSIONS: Zhu's GEP could not be translated into an IHC panel predictive of benefit from adjuvant chemotherapy. Future predictive biomarker analysis in the adjuvant NSCLC setting may need to focus on novel therapies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".