Additional file 2 of Pan-cancer integrative analysis of whole-genome De novo somatic point mutations reveals 17 cancer types
Bibliographic record
Abstract
Additional file 2: Fig. S1. Evaluation plots for finding optimal number of signatures. Fig. S2. Mutational load of feature genes in each cancer type. Fraction of samples that have mutated in each 684 candidate genes for all cancer types separately. The nervous system cancer type samples are less mutated. ALK and PTPN11 are the only significantly mutated genes in nervous system samples. Esophagus and skin cancer type have the most mutated samples. Different patterns of mutation are evident. The X-axis shows cancer types, and Y-axis shows the fraction of samples which had mutation in significant genes. Fig. S3. The fraction of different cancer types samples in each identified 17 subtypes is shown in the heat map. The X-axis shows identified subtypes, and Y-axis shows cancer types. Subtype C4 and C8 consist of head&neck samples primarily (82.8% and 77.8%, respectively). Prostate cancer is the most populated cancer in C1 and C2 (29% and 48.2% respectively), Skin cancer is the most populated cancer in C14 and C17(40.7% and 38.5% respectively), and Blood cancer is the most inhabited in C3 and C11 (68.1% and 37.2% respectively). Fig. S4. a) Mutational load of feature genes in C1 and C2 considering only samples with at least three mutations. Common highly mutated genes for both subtypes are shown. b) Mutational load of feature genes in C1 and C2 considering only samples with at least three mutations. Common highly mutated genes for both subtypes are shown. Fig. S5. Examle of motif rate in feature genes. a) Motif rate for IL1RAPL1 in C1, C2, and C5. b) Motif rate for IL1RAPL1 in C1, C2, C5, and C16. c) Motif rate for MUC16 in C4, C9, and C14. The X-axis shows 96 3-mer motifs, and Y-axis indicates the number of samples mutated in a specific motif divided by all samples. Each color corresponds to a particular class of 3-mer motifs. Fig. S6: Consequence type analysis. Rate of consequence type of mutations. The impact or severity of consequence of mutations are highlighted with different colors. Rate of high impact mutations are higher in some subtypes compared to others. For instance C9 has more high impact mutations among its samples compared to other samples. Some consequqnce types of mutations in ICGC dataset was not available in Ensembl database which is demonstrated as Unavailable impact.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.758 | 0.110 |
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".