Activin as a biomarker for platinum resistance in non-small cell lung cancer.
Bibliographic record
Abstract
e21737 Background: Lung cancer is the leading cause of cancer death in Australia with 13,000 new cases per year. Although targeted therapy and immunotherapy have drastically changed the treatment landscape, the majority of patients will receive platinum-based chemotherapy for which the response rate is approximately 30% (Reck et al, 2016). An immunohistochemistry-based, predictive biomarker would be beneficial for patients and help avoid toxicity for patients unlikely to respond. Marini et al (2018) identified 3 biomarkers associated with in-vitro platinum resistance – activin A, growth differentiation factor-11 and transforming growth factor-b – which were investigated in a real-world retrospective cohort to determine their relation to objective radiological response and overall survival. Methods: We identified 101 patients with advanced non-small cell lung cancer who received platinum chemotherapy at 2 cancer centres between 2014-2015. Archival formalin-fixed paraffin embedded tissue samples were stained with activin A. Slides were manually scored by 2 independent clinicians using the multiplicative quickscore method (Detre et al, 1995). Kaplan Meier analysis for overall survival, a Cox-proportional hazards model for confounding variables and a chi-square analysis was performed to analyse the relationship between high immunohistochemistry scores (greater or less than 6) and radiological response. Results: We performed statistical analysis around the median cytoplasmic score (6). The overall median survival was 15.3 months. No significant difference in survival was detected between the two populations (p value = 0.97). The immunohistochemistry score was also not associated with rates of partial response (p value = 0.98) or progressive disease (p value 0.22). Conclusions: Despite an association with lower progression-free survival in a retrospective cohort in a previous study, high expression of activin does not appear to be a useful biomarker for platinum response in the setting of non-small cell lung cancer. Further research into associated antibodies including GDF-11 and TGF-b is in progress.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".