Additional file 1 of DNA methylation signatures of Prostate Cancer in peripheral T-cells
Bibliographic record
Abstract
Additional file 1: Supplementary Figure 1. (A) The number of T cell DNA samples used in the cohort 1. Ctrl, Negative-biopsy healthy control; LGPCa, Positive-biopsy low Gleason PCa; HGPCa, Positive biopsy high Gleason PCa. (B) Outline of DNA methylation signature determination in DNA of peripheral T cells in buffy coat of PCa patients and healthy controls using Illumina Infinium EPIC methylation array followed by validation with Illumina MiSeq and Pyrosequencing platforms. Supplementary Figure 2. Quality control (QC) of the data from Illumina Infinium EPIC methylation array using QC in ChAMP pipeline. (A) Density plot of raw data showing the beta distributions for each healthy control and PCa sample. None of the samples deviate from each other. (B) Singular value decomposition analysis (SVD) plot shows the significant components of variation in T cell DNA methylation. SVD analysis before (Pre) and after (Post) running the ComBat function in ChAMP pipeline removes the batch effects such as “Slide” variations as shown here. Supplementary Figure 3. Global distribution of the differentially methylated CGs that showed correlation with Gleason score in combined cohort. 1181 significant (p <0.05) differentially methylated CGs that had 10% methylation difference (beta value) in T cell DNA between PCa patients (with Gleason score 9) and in healthy controls were taken and input into IGV browser. Each row represents a Gleason score in a sequence of 6, 7, 8 and 9 while the first row is Chromosome numbers (1-22, X and Y). Hypomethylation (Blue) and hypermethylation (Red). Supplementary Figure 4. Venn diagram showing the overlap of CGs between breast, liver and prostate cancer studies. Methylated CGs in T cell DNA that were correlating with Gleason score in this study were overlapped with CGs that were correlating with breast cancer and liver cancer progression in breast and liver cancer studies, respectively. ns, not significant; ****, p <0.0001. Supplementary Figure 5. Global distribution of the differentially methylated regions (DMRs). DMRs of the T cell DNA between the PCa and healthy control obtained using DMR function in the ChAMP pipeline. The data for the IGV input was obtained from Supplementary Table 3. The first row represents Chromosome numbers (1-22, X and Y), second row shows DMR positions and the last row shows the reference genome H19. Supplementary Figure 6. Correlation analysis of percentage tumor volume and Gleason score. The percentage tumor volume was predicted from core prostate biopsies as described in Methods. Negative-biopsy healthy controls had no tumor and therefore assigned a 0% tumor volume and a Gleason score of 0. (A) Linear regression analysis of the percentage tumor volume of the PCa patients with the Gleason score. The red dotted lines show the 95% confidence band of the line of best fit. (B) Non-linear regression analysis of the percentage tumor volume of the PCa patients with Gleason score. The equation for the graph was an exponential growth curve with least square method as the fitting method. Supplementary Figure 7. Identification and validation of cg02701909 as a DNA methylation marker using Illumina EPIC (850K) and pyrosequencing. (A-B) Percentage methylation of T cell DNA at cg02701909 plotted as; (A) Ctrl (Control, Blue) and PCa (PCa, Red); (B) Ctrl (Control, Blue), LGPCa (Low Gleason PCa, Red) and HGPCa (High Gleason PCa, Green). The number of samples for pyrosequencing were; Ctrl (n = 9), LGPCa (n = 9) and HGPCa (n = 9). Statistical significance was obtained in GraphPad Prism and are represented by asterisks (*P < 0.05; **P < 0.01, ***P < 0.001, ****P < 0.0001). (C) Correlation graph between Illumina EPIC (850K) and pyrosequencing.
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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 | 0.019 |
| 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 | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.874 | 0.176 |
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".