Abstract 811: Identifying genomic signatures within circulating breast cancer cells.
Bibliographic record
Abstract
Abstract Introduction: Breast cancer is not a single disease but encompasses several subtypes, each with a distinct molecular profile and response to treatment. The proportion of cells that express prognostic markers (ER/PR/HER2) can vary greatly within tumors, affecting sensitivity or resistance to therapies. Levels of circulating tumor cells (CTCs) in blood have been found to be related to disease progression. They show genetic evolution from the primary tumors they originate from, especially with acquired HER2 expression. CTCs could contain genomic alterations that define them as metastatic intermediates, and may be identified in primary tumors as an aggressive component that responds differentially to chemotherapy. Array-CGH likely fails to detect alterations present at low frequencies within primary tumours. Profiling of CTCs will help identify occult changes occurring in breast cancer cells during progression to metastasis. Methodology: CTCs and matched normal white blood cells were isolated from the blood of 17/38 breast cancer patients, prior to commencement of treatment. Copy number analysis was performed with the Affymetrix Genome-Wide Human SNP 6.0 array using a paired tumor-normal approach (Partek Genomics Suite). Minimal common regions (MCRs) of genomic gain were extrapolated, followed by unsupervised clustering. Associations between MCRs and luminal/HER2/triple negative subtypes; as well as metastasis, were identified using the Fisher's Exact test. Results: CTC genomic profiles clustered into 2 groups independent of subtype. A 1.2Mb amplicon containing the ERBB2 gene was gained in 15/17 patients, irrespective of HER2 status of the primary tumor. Cluster A had significantly fewer gains with 13 MCRs (AKT2, COL18A1), and was heterogeneous with no overlap in Cluster B. Cluster B showed higher genomic alteration and homogeneity between samples with 665 MCRs. Chromosome 19 was most frequently altered across all patients with 54/113 (48%) MCRs. Analysis of 787 primary breast carcinomas showed similar MCRs in 3-4% of samples. Patients with distant metastases and younger age (<50) clustered together. Region 19q13 (genes involved in proliferation, adhesion and cell projection organization) was associated with HER2 positivity and triple negative status of primary tumors. Regions 20q13 and 15q24 (genes involved in survival and progression) were associated with distant metastasis. Conclusions: Genomic profiles of CTCs clustered into two groups: a heterogeneous group with minimal alterations that may be sufficient for dissemination; and a more homogenous group with extensive alteration (particularly on chromosome 19) that could define those CTCs that are capable of survival and metastasis. The dissimilarity between groups is suggestive of cancer cells transitioning through stages that may be reflected in their genomes. We are currently using FISH to detect cells within primary tumors with CTC-like signatures. Citation Format: Nisha Kanwar, Pingzhao Hu, Mark Clemons, Philippe Bedard, David McCready, Susan J. Done. Identifying genomic signatures within circulating breast cancer cells. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 811. doi:10.1158/1538-7445.AM2013-811
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 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) | 0.003 | 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".