Abstract 2727: The premalignant state captured in the landscape of somatic mutations can reveal the cancer cell-of-origin
Bibliographic record
Abstract
Abstract Despite increasing knowledge of tumorigenesis, the identity of the cancer cell-of-origin, i.e. the normal cell type that acquired the cancer-initiating event, remains largely unknown. Our approach of identifying the cell-of-origin is based on two observations: (1) the chromatin structure is cell-specific; and (2) the density of somatic mutations along the genome is associated with the regional profile of chromatin modifications. We have previously developed a method that quantifies the ability to predict the mutational distribution along the cancer genome from the profile of epigenetic modifications in different normal cell types. Here we present the largest application of our method using 2,550 whole genomes representing 32 distinct cancer types. To identify the cell-of-origin, we determined the correlation between the observed density of mutations along the genome and the predicted values based on chromatin modifications from 104 different normal tissue types. The normal cell type that showed the strongest correlation with a specific cancer mutational landscape was the candidate cell-of-origin. We found that in almost all cancer types the cell-of-origin can be characterized solely from DNA sequences. Interestingly, we found that the fallopian tube was the best match for high-grade serous ovarian cancer, providing independent evidence that this is the cancer’s site of origin. For breast cancer we found that the four distinct subtypes best-matched cells from the luminal cell lineage: basal-like breast cancer likely originates from luminal progenitors, whereas all other subtypes from luminal mature cells. This association holds true even when accounting for different alterations in the homologous recombination repair pathway, suggesting that subtypes are more determined by the cell-of-origin than the specific DNA repair defect. In addition, we found that we could identify the cell-of-origin using metastatic samples – a finding that may help in difficult clinical diagnoses. Moreover, we demonstrate that cancer drivers, both germline risk alleles and somatically mutated drivers, reside in active chromatin regions in the respective cell-of-origin. Taken together, our findings indicate that many of the somatic mutations accumulated while the cells maintained a chromatin structure similar to the cell-of-origin (likely occurring prior to transformation). Therefore, this historical record, captured in the DNA, can be used to identify, the often elusive, cancer cell-of-origin. Our approach can ultimately help better understand the potential of particular normal cell types to transform and initiate cancer, as well as the association of the cell-of-origin with tumor subtypes and sensitivity to treatment. Citation Format: Kirsten Kubler, Rosa Karlic, Nicholas J. Haradhvala, Kyungsik Ha, Jaegil Kim, Maja Kuzman, Wei Jiao, Sitanshu Gakkhar, Kent W. Mouw, Lior Z. Braunstein, Olivier Elemento, Andrew V. Biankin, Ilse Rooman, Mendy Miller, Christopher D. Nogiec, Edward Curry, Mari Mino-Kenudson, Leif W. Ellisen, Robert Brown, Alexander Gusev, Cristian Tomasetti, Hong-Gee Kim, Hwajin Lee, Kristian Vlahovicek, Charles Sawyers, Katherine A. Hoadley, Edwin Cuppen, Amnon Koren, Peter F. Arndt, David N. Louis, Lincoln Stein, William D. Foulkes, Paz Polak, Gad Getz. The premalignant state captured in the landscape of somatic mutations can reveal the cancer cell-of-origin [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2727.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".