Abstract 4011: Prognostic value of transcriptomic analysis in residual post neo-adjuvant triple negative breast cancer tumors
Bibliographic record
Abstract
Abstract Background: Triple negative breast cancer is the most aggressive type of breast cancer. Approximately 50% of TNBC patients respond to pre-operative neo-adjuvant chemotherapy (NAC), however those patients with residual disease (non-pathological complete response or non-pCR) have a very poor prognosis. Recently, the use of capecitabine in the adjuvant setting was shown to increase disease-free survival in non-pCR patients. However, there are no biomarkers to identify patients who may benefit from adjuvant capecitabine. Moreover, novel therapeutic targets are needed to treat patients with non-pCR. Methods: RNA extracted from 32 Formalin Fixed Paraffin Embedded (FFPE) residual post-NAC tumor samples underwent NanoString nCounter gene expression analysis using the BC360 panel as well as RNAseq. Results: Comparison of tumors from poor (RFS<2 yrs) and good prognosis (RFS>2 yrs) patients showed 65 genes differentially expressed (≥1.5 fold change and p<0.05) by nCounter. 40 of these genes had differential expression also validated by RNAseq (R2= 0.96) and were selected for further pathway analysis. Gene Set Enrichment Analysis (GSEA) showed an enrichment for biological pathways involved in cell cycle, with all genes in this set upregulated in the poor prognosis group. GSEA analysis of 310 upregulated genes identified by RNAseq showed an enrichment for biological pathways associated with cell population proliferation and skin development. Interestingly, one of the upregulated genes identified was TYMS, encoding thymidylate synthase, the target of capecitabine, which showed 2-fold increased expression in the poor prognosis group (p≤0.005). Conclusion: Our results suggest that residual tumors from TNBC patients who relapse within two years of surgery show transcriptomic evidence of increased proliferation and that expression levels of TYMS may be a potential biomarker to select patients for capecitabine in the adjuvant setting. Citation Format: Adriana Aguilar-Mahecha, Yasamin Majedi, Oluwadara Elebute, Josiane Lafleur, Andreas Papadakis, Cathy Lan, Manuela Pelmus, Touhidur Rashid, Sarah Jenna, Mark Basik. Prognostic value of transcriptomic analysis in residual post neo-adjuvant triple negative breast cancer tumors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 4011.
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 | 0.001 | 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 | 0.001 |
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".