Abstract 3304: The mutation landscape of cancers serves as a record of early malignant transformation
Bibliographic record
Abstract
Abstract How the cell lineage influences a tissue's susceptibility to malignant transformation is a fundamental question in cancer biology, which has been barely addressed in cancer genomics thus far. Cell properties are encoded in the cell type-specific chromatin structure and we previously demonstrated that the cell-of-origin (COO) chromatin organization is a key determinant of the landscape of somatic mutations, which accumulated over lifetime serving as a memory of the historical cell lineage (Polak et al, Nature , 2015). We now show that this principle is generalizable to common tumor types and offers insights into the molecular events of cancer initiation. We extended our work to 2,641 genomes from 30 cancer types and epigenetic modifications from 98 normal tissues. In 25 cancer types, the tumor originated from a cell type that was its direct cellular counterpart or a close proxy; in only two, there was no match or a close proxy; and in the remaining three (esophageal, pancreatic ductal and biliary adenocarcinoma) the best matched cell type suggested metaplasia to stomack mucosa like tissue.The cellular context of breast tumor formation was investigated in more detail, showing that the COO, and not the gene inactivation event, determines the subtype. Basal-like tumors appeared to arise from luminal progenitor cells, while all other subtypes arose from mature luminal cells. Furthermore, irrespective of the inactivation mechanism (pathogenic germline, somatic truncating or epigenetic silencing event), all BRCA1/2- and RAD51C-altered basal-like tumors best matched to luminal progenitors while BRCA1/2- and CHEK2 -mutated luminal A/B subtypes best matched mature luminal cells. Finally, we observed that tumor type-specific driver genes reside in genomic regions that are defined by a highly active chromatin environment in their COOs. This highlights their essential role in cell type differentiation and implies the acquisition of somatic mutations early, when the chromatin architecture still reflected the COO. Taken together, our findings shed light on the crucial role of the COO in shaping the mutational landscape and tumor evolution. Citation Format: Paz Polak, Rosa Karlic, Kirsten Kubler, William D. Foulkes, Gad Getz. The mutation landscape of cancers serves as a record of early malignant transformation [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 3304.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".