Veeraraghavan, JamunaraniGutierrez, CarolinaDe Angelis, CarmineDavis, RobertWang, TaoPascual, TomasSelenica, PierSanchez, KatherineNitta, HiroakiKapadia, MoneshPavlick, Anne C.Galvan, PatriciaRexer, BrentForero-Torres, AndresNanda, RitaStorniolo, Anna M.Krop, Ian E.Goetz, Matthew P.Nangia, Julie R.Wolff, Antonio C.Weigelt, BrittaReis-Filho, Jorge S.Hilsenbeck, Susan G.Prat, AleixOsborne, C. KentSchiff, RachelRimawi, Mothaffar F.2024-06-272024-06-272023Veeraraghavan J, Gutierrez C, De Angelis C, et al. A Multiparameter Molecular Classifier to Predict Response to Neoadjuvant Lapatinib plus Trastuzumab without Chemotherapy in HER2+ Breast Cancer. Clin Cancer Res. 2023;29(16):3101-3109. doi:10.1158/1078-0432.CCR-22-3753https://hdl.handle.net/1805/41941Purpose: Clinical trials reported 25% to 30% pathologic complete response (pCR) rates in HER2+ patients with breast cancer treated with anti-HER2 therapies without chemotherapy. We hypothesize that a multiparameter classifier can identify patients with HER2-"addicted" tumors who may benefit from a chemotherapy-sparing strategy. Experimental design: Baseline HER2+ breast cancer specimens from the TBCRC023 and PAMELA trials, which included neoadjuvant treatment with lapatinib and trastuzumab, were used. In the case of estrogen receptor-positive (ER+) tumors, endocrine therapy was also administered. HER2 protein and gene amplification (ratio), HER2-enriched (HER2-E), and PIK3CA mutation status were assessed by dual gene protein assay (GPA), research-based PAM50, and targeted DNA-sequencing. GPA cutoffs and classifier of response were constructed in TBCRC023 using a decision tree algorithm, then validated in PAMELA. Results: In TBCRC023, 72 breast cancer specimens had GPA, PAM50, and sequencing data, of which 15 had pCR. Recursive partitioning identified cutoffs of HER2 ratio ≥ 4.6 and %3+ IHC staining ≥ 97.5%. With PAM50 and sequencing data, the model added HER2-E and PIK3CA wild-type (WT). For clinical implementation, the classifier was locked as HER2 ratio ≥ 4.5, %3+ IHC staining ≥ 90%, and PIK3CA-WT and HER2-E, yielding 55% and 94% positive (PPV) and negative (NPV) predictive values, respectively. Independent validation using 44 PAMELA cases with all three biomarkers yielded 47% PPV and 82% NPV. Importantly, our classifier's high NPV signifies its strength in accurately identifying patients who may not be good candidates for treatment deescalation. Conclusions: Our multiparameter classifier differentially identifies patients who may benefit from HER2-targeted therapy alone from those who need chemotherapy and predicts pCR to anti-HER2 therapy alone comparable with chemotherapy plus dual anti-HER2 therapy in unselected patients.en-USPublisher PolicyHER2-positive breast cancerHER2 levels and heterogeneityPIK3CA mutationsPAM50 subtypeTreatment de-escalationMolecular predictorArticle