Identification of genomic signatures in circulating tumor cells from breast cancer
Bibliographic record
Abstract
Levels of circulating tumor cells (CTCs) in blood have prognostic value in early and metastatic breast cancer. CTCs also show varying degrees of concordance with molecular markers of primary tumors they originate from. It is expected that individual cells reflect the heterogeneity and evolution of tumor cells as they acquire new functions and differential responses to chemotherapy. However, a degree of commonality is also plausible, highlighting alterations that allow tumor cells to perform CTC-defining activities such as invasion and intravasation. Using a matched tumor-normal approach, we performed high-resolution copy number profiling of CTCs from breast cancer to identify occult changes occurring during progression to metastasis. We identified a signature of recurrent gain in CTCs, consisting of 90 minimal common regions (MCRs) of copy number gain. These were predominantly found across chromosome 19 and were identified at low frequencies (3-4%) in 787 primary breast carcinomas examined. CTC genomic signatures clustered into two groups independent of subtype: a dormancy-related signature with 16 MCRs (AKT2, PTEN, CADM2); and a tumor-aggressiveness related signature with 358 MCRs (ANGPTL4, BSG, MIR-373). There were two MCRs in common between the groups on 19q13 and 21q21, containing genes involved in resistance to anoikis, TGFβ-signaling and metastasis (TFF3, LTBP4, NUMBL). Furthermore, a region harboring the ERBB2 gene was gained in a majority of patients. Regions 20q13 and 15q24 were associated with distant metastasis. The distinctiveness of CTC signatures highlights cell populations with different functional or metastatic potential. Such novel targets could help to specifically identify and block dissemination.
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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) | 0.001 | 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".