Dual targeting of HER2-positive breast cancer with trastuzumab emtansine and pertuzumab: understanding clinical trial results
Bibliographic record
Abstract
// Alberto Ocaña 1 , Eitan Amir 2 and Atanasio Pandiella 3 1 Translational Research Unit, Albacete University Hospital, and CIBERONC, Albacete, Spain 2 Division of Medical Oncology and Hematology, Princess Margaret Cancer Centre, University of Toronto, Toronto, Canada 3 Cancer Research Center, CSIC-IBSAL and CIBERONC, Salamanca, Spain Correspondence to: Alberto Ocaña, email: albertoo@sescam.jccm.es Keywords: breast cancer; HER2; trastuzumab; pertuzumab; TDM1 Received: May 15, 2018 Accepted: June 23, 2018 Published: August 07, 2018 ABSTRACT Targeting of HER2-positive tumors with trastuzumab has shown to improve survival in early stage and advanced breast cancer. The addition of pertuzumab, another anti-HER2 antibody, to trastuzumab-containing regimens has demonstrated a modest increase in disease-free survival in the adjuvant setting. Unexpectedly, when pertuzumab was explored in combination with the antibody-drug conjugate TDM1 in the metastatic setting, no additional benefit was observed compared with dual targeting of HER2 with pertuzumab and trastuzumab, together with chemotherapy. Similar results were observed when exploring pathologic complete response in the neoadjuvant setting. In this article, we discuss basic science and translational data that may explain the limited efficacy observed with the combination of TDM1 and pertuzumab, including tumor heterogeneity, clonal selection, bystander effect or downregulation of the receptor by competitive binding. In addition, we review ongoing studies that could help to understand these findings.
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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 | 0.031 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".