Association between genome‐wide DNA methylation pattern and response to trastuzumab in HER2‐positive breast cancer patients
Bibliographic record
Abstract
Introduction The administration of trastuzumab has led to significant improvement in survival of HER2‐positive breast cancer patients in the adjuvant and metastatic settings. Trastuzumab resistance, however, has been increasingly recognized as a major obstacle. Recent evidence suggests that epigenetic mechanisms might be associated with acquired resistance to cancer therapies. Aim of this study was to explore the association between genome‐wide DNA methylation pattern in breast cancer tissue and the response to trastuzumab. Patients and methods DNA methylation pattern was assessed in breast cancer tissues of trastuzumab‐treated HER2‐positive breast cancer patients who acquired resistance to treatment (case group, n=6) and compared to that of trastuzumab‐treated HER2‐positive breast cancer patients who did not develop resistance (control group, n=6) using the Illumina Infinium HumanMethylation450 BeadChip. Cases were matched to controls for several factors. Bioinformatics analyses were performed using the R statistical environment (robust linear regression method) to identify differentially methylated genes (DMGs) (FDR < 0.05) between case and control groups. Results Compared to the control group, in the matched case group we identified 879 hypermethylated and 293 hypomethylated genes. The differentially methylated set of genes was enriched in molecular and cellular functions associated with cellular movement ( P =8.02E‐07) as well as cell death and survival ( P =4.24E‐09). Pathways associated with ERK/MAPK signaling and regulation of the epithelial‐mesenchymal transition were overrepresented ( P =2.13E‐05, and P=2.43E‐03, respectively). Among the DMGs we observed AGPAT1, a gene related to the PI3K‐mTOR pathway which has been reported to be implicated with trastuzumab resistance in addition to be differentially expressed between HER2‐positive breast cancer patients who responded to trastuzumab and those who acquired resistance. Conclusions Although our sample size was small, we observed DMGs associated with the response to trastuzumab. These data need to be confirmed in a larger prospective cohort of trastuzumab‐treated HER2‐positive breast cancer patients. Support or Funding Information DF received doctoral fellowships from the Fonds de recherché du Québec – Santé (FRQS) and the Laval University Cancer Research Center. CD is a recipient of the Canadian Breast Cancer Foundation‐Canadian Cancer Society Development award (award #703003) and the FRQS Research Scholar. This study was supported by the Fondation des Hôpitaux Enfant Jésus – St‐Sacrement. Clinical specimens were provided by the Fondation du cancer du sein du Québec and the Banque de tissus et de données of the Réseau de recherche sur le cancer of the FRQS, which is affiliated with the Canadian Tumour Repository Network. This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
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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 | 0.000 | 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) | 0.001 | 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".