Abstract 1229: miRNA and response to trastuzumab
Bibliographic record
Abstract
Abstract The implementation of trastuzumab has revolutionized the clinical management of HER2 positive breast cancers. Unfortunately, 50% of patients are resistant to the treatment. Researchers have already designed alternative anti-HER2 agents, such as pertuzumab and lapatinib. Still, predicting which patients will benefit from the therapy would prevent overtreatment and avoid unnecessary risks of side effects. MiRNAs are small non-coding RNAs involved in post-transcriptional gene regulation, and participate in almost all biological processes, including cancer. Since aberrant miRNA levels can be detected both at tissue level and in the circulation, they are good candidates as predictive and prognostic biomarkers. This study had access to tumor tissue samples from the phase III NeoALTTO trial, aimed at evaluating the efficacy of a HER2 dual blockade with trastuzumab and Lapatinib vs single blocking, in concomitance with chemotherapy, in a pre-operative setting. The primary end-point of the study was pathologic complete response (pCR); the secondary end-point event-free survival (EFS). Focusing on the trastuzumab arm, we identified both a predictive signature (hsa-miR-31-3p, OR 0.70, 95%CI: 0.53-0.92 and hsa-miR-382-3p, OR 1.39, 95%CI: 1.01-1.91) with an AUC value of 0.73 (95%CI: 0.60-0.87), and a prognostic signature (miR-153-3p, HR 1.83, 95%CI: 1.34-2.50 and miR-219a-5p, HR 0.629, 95%CI: 0.51-0.79) leading to a C-statistics of 0.730 (95%CI: 0.63-0.83). Moreover, we identified 2 miRNAs (miR-215-5p and miR-30c-2-3p) associated to EFS with a statistically significant interaction term with pCR (p.interaction: 0.017 and 0.038, respectively). Aiming at deepening the understanding of resistance mechanism, we modulated the expression of miR-31-3p and miR-382-3p in vitro, upon trastuzumab treatment in HER2 positive breast cancer cell lines. Given that miR-31-3p negatively correlates with pCR, we transfected it in HER2-addicted SKBr3 cells to appreciate a possible gain of resistance to trastuzumab; conversely, miR-382-3p positively correlates with pCR and was transfected in HER2 non-addicted HCC1954 cells. Western blot analysis of HER2 signaling pathway highlighted that overexpression of miR-31-3p was able to counteract the reduction of phosphorylated HER2 levels induced by trastuzumab treatment in SKBR3 cells compared to control. Interestingly, miR-31-3p upmodulation increased the proliferation of both treated and non-treated SKBr3 cell in a 3D setting. Conversely, miR-382-3p overexpression in HCC1954 cells only slightly increased responsiveness to trastuzumab in the 3D setting, compared to control. Trastuzumab efficacy also relies on the immune system reaction, thus, in vivo experiments will likely provide further insights into the mechanism of action of these miRNAs. Citation Format: Giulia Cosentino, Sara Pizzamiglio, Chiara M. Ciniselli, Loris De Cecco, Alessandra Cataldo, Ilaria Plantamura, Tiziana Triulzi, Sarra El-abed, Yingbo Wang, Mohammed Bajji, Paolo Nuciforo, Jens Huober, Susan L. Ellard, David L. Rimm, Andrea Gombos, Mariagrazia Daidone, Paolo Verderio, Elda Tagliabue, Serena Di Cosimo, Marilena V. Iorio. miRNA and response to trastuzumab [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 1229.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".