Impact and correlation of mutational load (ML) and specific mutations (mts) assessed by limited targeted profiling (LTP) with PD-L1 tumour expression (exp) in resected non-small cell lung carcinoma (NSCLC).
Bibliographic record
Abstract
11587 Background: The advent of immunotherapy represents a paradigm shift in the treatment of NSCLC compared to conventional chemotherapy. Recent studies have shown higher mts burden assessed by exome sequencing are associated with improved objective response and clinical benefit. We performed this study to evaluate the impact of ML assessment by LTP, correlating with PD-L1 exp and clinicopathological variables in resected NSCLC. Methods: NSCLC patients(pts) who underwent curative resection between 1998 and 2006 at our institution were included. PD-L1 status was assessed using Ventana SP124 antibody on archival FFPE surgical tumour specimens cores. PD-L1 was scored positive if membranous staining was present in >1% of tumour cells aggregated across the replicate cores to address heterogeneity. In collaboration with the Lung Cancer Genomics Ireland Study a targeted panel of 49 genes were assessed by Sequenom MassArray including genes in MAPK and PI3K pathways. Clinical data was obtained from hospital electronic database. Results: Ninety-one pts were included, of which 51 (56.0%) were males, with a median age of 65 years (range: 42 – 82). 51.6%, n=47 with squamous histological subtypes, 46.2%, n=42 were ex-smoker and 49.5%, n=45 had Stage I disease. 23.1%, n=21 had PD-L1 positivity. 149 mts were identified of which, 32(21.5%) with PHLPP2, 31(20.9%) with PIK3R1 and 21(14.1%) with TP53. The presence of PI3K and TP53 mts are associated with positive PD-L1 status (see table). An inverse correlation of PD-L1 positivity with ML of (1 vs 2 vs 3: 53.8% vs 30.8% vs 15.4%) was noted. Conclusions: We did not identify higher PD-L1 exp with higher ML assessed by a LTP widely used in clincial practice. However, positive PD-L1 exp was correlated with PIK3R1 and TP53 mts , warranting further investigation as potential modulators or surrogates of positve PD-L1 expression. [Table: see text]
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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 | 0.000 |
| 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".