During the last decade, chemotherapy plus dual anti-HER2 therapy (trastuzumab+pertuzumab) became the standard systemic therapy in HER2+ breast cancer. Recently, the T-DM1 and T-DXd antibody-drug conjugates further extended the therapeutic armament. Biomarkers predictive for benefit of these drugs are warranted. Through analysis of pretreatment tumor samples, Braso-Maristany et al. (1) show that ERBB2 mRNA expression according to HER2DX standardized assay (2) provides prognostic and predictive information in patients with HER2+ metastatic breast cancer treated with T-DM1, extending their previous results (3). In the neoadjuvant setting, the same team reported the predictive value of ERBB2 mRNA expression in univariate analysis for pathological response after T-DM1 (3,4). To our knowledge, no study assessed its potential to predict the benefit from T-DM1 vs trastuzumab-paclitaxel.
Here, we analyzed the data from 127 HER2+ patients treated in the I-SPY2 randomized trial (5) with neoadjuvant chemotherapy (NACT) plus anti-HER2 therapy either standard (control arm [n = 75]: sequential weekly paclitaxel combined with trastuzumab+pertuzumab [n = 44] or with trastuzumab [n = 31], followed by doxorubicin and cyclophosphamide) or experimental (investigational arm: sequential T-DM1+pertuzumab, followed by doxorubicin or cyclophosphamide, [n = 52]). The pre-NACT tumor samples were profiled using DNA microarrays and reverse-phase protein array. We examined ERBB2 mRNA expression as a continuous value as well as 2 continuous variables associated with HER2 activation: Gatza’s mRNA score (6) and phosphorylated ERBB2 expression (ERBB2.Y1248) (7). The pathological complete response (pCR) to NACT was defined as absence of invasive cancer in both breast and lymph nodes on the operative specimen (ypT0/Tis ypN0): the pCR rates were 45% and 58% in the control and investigational arms, respectively.
In the investigational arm (T-DM1+pertuzumab), ERBB2 mRNA expression was associated with pCR achievement (odds ratio = 7.09, 95% confidence interval = 2.55 to 19.68, P = 1.70E-04; logit function) and remained significant in multivariable analysis including tumor grade, hormone receptor status, and HER2 activation markers (Gatza’s score, ERBB2.Y1248 expression; Figure 1, A). This independent favorable predictive value of ERBB2 mRNA expression reinforces the one reported in univariate analysis in 41 patients treated with T-DM1± endocrine therapy in the WSG-ADAPT trial (3) and 217 patients treated with T-DM1+pertuzumab in the KRISTINE trial (4).
Figure 1.
Univariate and multivariable analyses for pathological complete response (pCR) to neoadjuvant chemotherapy and anti-HER2 drugs. A) T-DM1+pertuzumab investigational arm: univariate and multivariable analysis for pCR. The odds ratios scale was log10-transformed. B) Univariate and multivariable interaction analyses in the whole series. Each variable was tested in the multivariable analysis, including all other variables and the type of HER2 blockade (single or dual). CI = confidence interval; HR = hormone receptor; OR = odds ratio.
The control and investigational arms allowed to test, in an interaction analysis, the predictive value of ERBB2 mRNA expression for T-DM1 benefit vs trastuzumab+paclitaxel. In the control arm, ERBB2 mRNA expression was associated with pCR in univariate (odds ratio = 1.75, 95% confidence interval = 1.10 to 2.78, P = 1.82E-02) and multivariable analyses. However, the odds ratio for pCR achievement was lower than in the investigational arm, and the marker–treatment interaction was significant in multivariable analysis (P = 3.15E-02; Figure 1, B), with stronger benefit from T-DM1 associated with higher ERBB2 mRNA expression.
Our results reinforce Braso-Maristany’s results and call for assessment of ERBB2 mRNA expression according to the HER2DX standardized assay as well as the promising HER2DX assay (2) in the neoadjuvant setting. To our knowledge, this is the first analysis suggesting a biomarker predictive for benefit from T-DM1 vs trastuzumab+paclitaxel. Our results also suggest that targeted chemotherapy (T-DM1) is more efficient than untargeted chemotherapy (trastuzumab–paclitaxel) when expression of the target is high and that an ERBB2 mRNA-high class may be clinically relevant in the neoadjuvant setting.
Acknowledgements
The funders did not play a role in the design of the study; the collection, analysis, and interpretation of the data; the writing of the manuscript; and the decision to submit the manuscript for publication.
Contributor Information
François Bertucci, Aix Marseille Univ, INSERM U1068, CNRS UMR725, Institut Paoli-Calmettes, Laboratory of Predictive Oncology, CRCM, Label “Ligue Contre le Cancer”, Marseille, France.
Alexandre de Nonneville, Aix Marseille Univ, INSERM U1068, CNRS UMR725, Institut Paoli-Calmettes, Laboratory of Predictive Oncology, CRCM, Label “Ligue Contre le Cancer”, Marseille, France.
Emilie Mamessier, Aix Marseille Univ, INSERM U1068, CNRS UMR725, Institut Paoli-Calmettes, Laboratory of Predictive Oncology, CRCM, Label “Ligue Contre le Cancer”, Marseille, France.
Data availability
The data underlying this article are publicly available in the Gene Expression Omnibus database at [https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE194040] and can be accessed with accession number GSE194040.
Author contributions
François Bertucci, MD, PhD (Conceptualization; Formal analysis; Supervision; Writing – original draft; Writing – review & editing); Alexandre de Nonneville, MD, PhD (Conceptualization; Writing – review & editing); Pascal Finetti, MSc (Conceptualization; Formal analysis; Writing – review & editing); Emilie Mamessier, PhD (Conceptualization; Writing – review & editing).
Funding
This work has been supported by grants from the Ligue Nationale Contre Le Cancer (EL2022 to FB) and Association Ruban Rose (2020 to FB).
Conflict of interest
The authors have no conflict of interest to disclose.
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Associated Data
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Data Availability Statement
The data underlying this article are publicly available in the Gene Expression Omnibus database at [https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE194040] and can be accessed with accession number GSE194040.