Additional file 1 of Synergistic effects of type I PRMT and PARP inhibitors against non-small cell lung cancer cells
Bibliographic record
Abstract
Additional file 1. Figure S1. Validation of the drug screen. Graphic showing original data from the screen (plain circle) highest (20) and lowest (20) hits. Results from the validation are labelled with (+) if validation was confirmed or (-) if validation failed. Pie chart summarizes result of validation process; 28 of 40 compounds matched the primary output, leading to a validation rate for the screen of 70%. Figure S2. MS023 and BMN-673 synergy dependency on MTAP in NSCLC cell lines. A) Immunoblotting of SK-LU-1 and HCC4006 cell lines infected with the empty lentivector (pLoc) or pLoc-MTAP. Clones #1 and #2 show the re-expression of MTAP using anti-MTAP antibodies. Antibodies against β-actin were used to show equivalent loading. The molecular mass markers are shown in kDa. B) Same as panel A except the cellular lysates were immunoblotted with anti-SDMA and β-actin antibodies as indicated. C-D) Cell death curves as determined by MTT assay of the SK-LU-1 and HC4006 clones treated with a range of MS023 concentrations. Dotted vertical lines represent IC50 values for each cell line (SK-LU-1: n=5; HCC4006: n=4). Stars (*: p <0.05; **: p <0.01; ***: p <0.001, ****: p <0.0001; two-way ANOVA). E-F) Cell death curves as determined by MTT assay of the SK-LU-1 and HCC4006 clones treated with a range of BMN-673 in combination 10 µM MS023 (SK-LU-1, n=4) or 0.2 µM MS023 (HCC4006, n=6). G-H) Bliss synergy scores calculated for BMN-673 and MS023 combination treatment in SK-LU-1 and HCC4006 cells, respectively. Table 1. Drug screening results. Synergy indexes are shown for the 181 compounds treated in combination with MS023 in A549 cells. Table 2. Drug validation results. Synergy indexes are shown for the 40 compounds (20 highest and 20 lowest hits) treated in combination with MS023 in A549 cells. Validation and screening synergy indexes are shown. Cells colored in grey highlight opposing results between screen and validation results. Overall, the validation rate was at 70% (28/40).
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.893 | 0.177 |
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".