Abstract PD6-2: Identifying genomic signatures in circulating tumour cells from breast cancer
Bibliographic record
Abstract
Abstract Introduction: 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 the primary tumors they originate from. This is indicative of heterogeneity and dynamic evolution of tumor cells as they acquire new functionality. Profiling of CTCs will help identify occult changes occurring in breast cancer cells during progression to metastasis. CTCs could contain genomic alterations that define them as metastatic intermediates, and may be identified in primary tumors at low frequencies, as an aggressive component that responds differentially to chemotherapy. Methodology: CTCs and matched normal white blood cells were isolated from the blood of 17/40 chemonaive breast cancer patients. Copy number analysis was performed with the Affymetrix Genome-Wide Human SNP 6.0 array using a paired tumor-normal approach. Minimal common regions (MCRs) of gain were extrapolated, followed by unsupervised clustering. Associations between MCRs and breast cancer subtypes; as well as metastasis, were identified using PLINK analysis. A TCGA copy number dataset of 787 invasive primary breast tumors was queried for frequency of CTC-like alterations. Results: CTC genomic profiles clustered into 2 groups independent of subtype: a heterogeneous group with 11 MCRs (genes amplified: AKT2, SMAD2), and a more homogeneous group with 400 MCRs, of which 55 were on chromosome 19 (genes amplified: ANGPTL4, BSG). There were 2 MCRs in common, on 19q13 and 21q; containing genes involved in resistance to anoikis, TGFb signaling and metastasis (TFF3, LTBP4, NUMBL). A 1.2Mb region harboring the ERBB2 gene was gained in 15/17 samples. Patients with distant metastases and younger age (<50) clustered together. Region 19q13 was associated with HER2 positivity and triple negative status of matched primary tumors. Regions 20q13 and 15q34 were associated with distant metastases. CTC-like gains were identified at low frequencies of 3-4% in 787 primary tumors (genes amplified: CCNE1, KLK7-14, MIR371-373). Conclusions: The genomic profiles of CTCs clustered into 2 groups: a heterogeneous group with minimal alterations that may be sufficient for dissemination; and a more homogeneous group with extensive alteration, that could define those CTCs that may be en route to the next steps of metastasis. There were only 2 MCRs in common between the groups that highlights an important common point of evolution of CTC genomes. CTCs also appear to be more homogeneous for certain gains, specifically on chromosome 19, which may allow for CTC-like functionality such as invasion, intravasation, survival or chemo-resistance. Furthermore, CTC-like gains were identifiable at low frequencies within a dataset of primary breast tumors. We are currently using multispectral-FISH to examine the most frequent combinations of CTC-like gains in primary breast tumors pre- and post-chemotherapy. It is possible that CTC-like alterations, even if present only focally, could confer a more aggressive course of progression to metastasis. More importantly, these cells could be targeted to stop their spread to distant sites. Citation Information: Cancer Res 2013;73(24 Suppl): Abstract nr PD6-2.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".