Abstract P5-13-26: The future of HER2-positive breast cancer patients might be written in miRNAs: An exploratory analysis from the NeoALTTO study
Bibliographic record
Abstract
Abstract Importance. Neoadjuvant therapy with dual HER2 blockade improved pathological complete response (pCR) rate. Nevertheless, it would be desirable to identify patients exquisitely responsive to single agent trastuzumab to minimize or avoid overtreatment. Objective. To evaluate the predictive and prognostic value of basal primary tumor miRNA expression profile within the trastuzumab arm of the NeoALTTO study.Design, Setting and Participants. RNA samples from baseline biopsies were randomized into training (n =45) and testing (n =47) sets. After normalization, miRNAs associated with Event-free survival (EFS) and pCR were identified by univariate analysis. Multivariate models were implemented to generate specific signatures which were first confirmed, and then analyzed according to other clinical and pathological variables.Main outcomes and measures. Association between miRNA expression and pCR and/or EFS.Results. We identified a prognostic signature including hsa-miR-153-3p (HR 1.831, 95%CI: 1.34-2.50) and hsa-miR-219a-5p (HR 0.629, 95%CI: 0.50 - 0.78). For two additional miRNAs (miR-215-5p and miR-30c-2-3p), we found a statistically significant interaction term with pCR (p.interaction: 0.017 and 0.038, respectively). Besides, a two-miRNA signature was predictive of pCR (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). Notably, the performance of this predictive miRNA signature resembled that of the genomic classifiers PAM50 and TRAR, and did not improve when the extended models were fitted.Conclusions and relevance. Analysis of primary tumor tissue miRNAs holds the potential of a parsimonious tool to identify patients with differential clinical outcomes after trastuzumab based neoadjuvant therapy. Trial registration. ClinicalTrials.gov Identifier: NCT00553358. Table 1.MiRNAs associated with EFS. Results of the univariate Cox regression model- training set.miRNAsHR95% CIhsa-miR-12000.671(0.512; 0.879)ahsa-miR-1238-3p0.669(0.543; 0.826)ahsa-miR-12650.839(0.705; 0.997)ahsa-miR-129-5p0.785(0.628; 0.980)ahsa-miR-153-3p1.412(1.026; 1.943)ahsa-miR-15390.826(0.697; 0.979)ahsa-miR-1908-5p0.796(0.648; 0.978)ahsa-miR-205-5p0.737(0.553; 0.982)hsa-miR-219a-5p0.787(0.627; 0.989)ahsa-miR-25-5p0.566(0.391; 0.819)ahsa-miR-3000.581(0.399; 0.845)ahsa-miR-382-3p0.791(0.636; 0.985)ahsa-miR-4920.774(0.635; 0.944)ahsa-miR-519a-3p0.834(0.696; 0.998)ahsa-miR-551b-5p0.666(0.452; 0.981)ahsa-miR-5830.727(0.549; 0.963)ahsa-miR-6001.373(1.03; 1.829)ahsa-miR-6260.819(0.673; 0.998)ahsa-miR-651-5p0.611(0.382; 0.977)hsa-miR-7610.845(0.721; 0.991)ahsa-miR-891b0.658(0.479; 0.904)ahsa-miR-92a-2-5p0.787(0.637; 0.971)ahsa-miR-937-3p0.748(0.578; 0.967)aHR, Hazard Ratio; CI, Confidence Intervala microRNAs retained its statistically significance (at 10%) also when normalized for the overall mean. Table 2.MiRNAs associated with pCR. Results of the univariate logistic model - training set.miRNAsOR95% CIhsa-miR-132-3p0.410(0.169; 0.991)ahsa-miR-23b-5p0.520(0.316; 0.856)ahsa-miR-31-3p0.566(0.351; 0.912)ahsa-miR-31-5p0.508(0.291; 0.887)ahsa-miR-330-3p0.501(0.251; 0.999)hsa-miR-34b-3p0.673(0.474; 0.956)ahsa-miR-382-3p1.392(1.006; 1.925)ahsa-miR-548j-5p1.702(1.090; 2.658)aOR, Odds Ratio; CI, Confidence Interval. a microRNAs retained its statistically significance (at 10%) also when normalized for the overall mean. 2 Citation Format: Marilena Valeria Iorio, Sara Pizzamiglio, Giulia Cosentino, Chiara M Ciniselli, Loris De Cecco, Alessandra Cataldo, Ilaria Plantamura, Tiziana Triulzi, Sarra El-abed, Yingbo Wang, Mohammed Bajji, Paolo Nuciforo, Jens Huober, Susan Ellard, David Rimm, Andrea Gombos, Mariagrazia Daidone, Paolo Verderio, Elda Tagliabue, Serena Di Cosimo. The future of HER2-positive breast cancer patients might be written in miRNAs: An exploratory analysis from the NeoALTTO study [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P5-13-26.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".