Multiple region whole-exome sequencing reveals dramatically evolving intratumor genomic heterogeneity in esophageal squamous cell carcinoma
Bibliographic record
Abstract
Cancer is a disease of genome instability and genomic alterations; now, genomic heterogeneity is rapidly emerging as a defining feature of cancer, both within and between tumors. Motivation for our pilot study of tumor heterogeneity in esophageal squamous cell carcinoma (ESCC) is that it is not well studied, but the highest incidences of esophageal cancers are found in China and ESCC is the most common type. We profiled the mutations and changes in copy number that were identified by whole-exome sequencing and array-based comparative genomic hybridization in multiple regions within an ESCC from two patients. The average mutational heterogeneity rate was 90% in all regions of the individual tumors in each patient; most somatic point mutations were nonsynonymous substitutions, small Indels occurred in untranslated regions of genes, and copy number alterations varied among multiple regions of a tumor. Independent Sanger sequencing technology confirmed selected gene mutations with more than 88% concordance. Phylogenetic analysis of the somatic mutation frequency demonstrated that multiple, genomically heterogeneous divergent clones evolve and co-exist within a primary ESCC and metastatic subclones result from the dispersal and adaptation of an initially non-metastatic parental clone. Therefore, a single-region sampling will not reflect the evolving architecture of a genomically heterogeneous landscape of mutations in ESCC tumors and the divergent complexity of this genomic heterogeneity among patients will complicate any promise of a simple genetic or epigenetic diagnostic signature in ESCC. We conclude that any potential for informative biomarker discovery in ESCC and targeted personalized therapies will require a deeper understanding of the functional biology of the ontogeny and phylogeny of the tumor heterogeneity.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 0.000 |
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".