Abstract 90: Exploring the role of common circulating tumor cell-associated genes in estrogen receptor-positive breast cancer recurrence
Bibliographic record
Abstract
Abstract Despite having the best survival among the molecular subtypes, estrogen receptor-positive (ER+) breast cancer has a tendency for late recurrence. Circulating tumor cells (CTCs) are responsible for disease recurrence by seeding tumors at secondary sites. We have previously developed a CTC signature of 90 common regions of gene copy number gain and demonstrated that this signature is significantly more widespread among cells in metastases than primary tumors.1,2 The objective of our current work is to establish whether the expression data from a 133 gene panel largely derived from the CTC signature can be used to assess the risk of recurrence (ROR) in early ER+ breast cancer patients to help guide treatment decisions following surgery. Formalin-fixed, paraffin-embedded (FFPE) ER+ breast tumor biopsies and linked normal (control) tissues from 192 patients are being analyzed with a custom NanoString nCounter probe set of our 133 CTC genes. From data gathered to date, several genes were shown to be significantly overexpressed in tumors compared to reference normal tissues. This includes MMP11, MKI67, LILRB4, UBE2T, BIRC5, CDC6, MYBL2, and NUF2, which had a log2 fold-change of at least 3.5 (n = 34, p < 0.01). The correlation of our CTC genes to ROR is being evaluated by comparison to the results of the Oncotype DX Breast Recurrence Score Test, a well-established clinical test for ROR in ER+ breast cancer, for each patient. So far, it has been shown that the intratumor heterogeneity (ITH) of CTC genes as established by DEPTH2, an ITH algorithm which evaluates mRNA levels, is significantly positively corelated to Oncotype DX score (n = 23, r = 0.41, p = 0.0495). Moving forward, we will create our own ROR scoring system based on gene expression and copy number data from the CTC gene panel and validate it by comparison to Oncotype DX score and other available clinical data for each of the 192 patient samples. The goal of our work is to gain a deeper understanding of the molecular mechanisms that drive metastasis and recurrence in ER+ breast cancer by focusing on CTCs which play a pivotal role in driving these events. 1. Kanwar N, Hu P, Bedard P, Clemons M, McCready D, Done SJ. Identification of genomic signatures in circulating tumor cells from breast cancer. International Journal of Cancer. 2015;137(2):332-344. doi:10.1002/ijc.29399 2. Kanwar N, Balde Z, Nair R, et al. Heterogeneity of Circulating Tumor Cell-Associated Genomic Gains in Breast Cancer and Its Association with the Host Immune Response. Cancer Research. 2021;81(24):6196-6206. doi:10.1158/0008-5472.can-21-1079 Citation Format: Martin Rotbauer, Melanie Dawe, Philippe Bedard, David Cescon, Susan Done. Exploring the role of common circulating tumor cell-associated genes in estrogen receptor-positive breast cancer recurrence [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 90.
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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) | 0.002 | 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".