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Record W2953462291 · doi:10.1158/1538-7445.am2019-2727

Abstract 2727: The premalignant state captured in the landscape of somatic mutations can reveal the cancer cell-of-origin

2019· article· en· W2953462291 on OpenAlexaff
Kirsten Kübler, Rosa Karlić, 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, Hwa-Jin Lee, Kristian Vlahoviček, Charles L. Sawyers, Katherine A. Hoadley, Edwin Cuppen, Amnon Koren, Peter F. Arndt, David N. Louis, Lincoln Stein, William D. Foulkes, Paz Polak, Gad Getz

Bibliographic record

VenueCancer Research · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Genomics and Diagnostics
Canadian institutionsMcGill UniversityCanada's Michael Smith Genome Sciences CentreOntario Institute for Cancer Research
Fundersnot available
KeywordsBiologySomatic cellCarcinogenesisCancerChromatinGeneticsCell of originCell typeEpigeneticsCancer cellGenomeOvarian cancerBreast cancerCellCancer researchDNAGene

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.033
GPT teacher head0.350
Teacher spread0.318 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2019
Admission routes1
Has abstractyes

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