ArticleClinical cancer research : an official journal of the American Association for Cancer Research2023
A Multiparameter Molecular Classifier to Predict Response to Neoadjuvant Lapatinib plus Trastuzumab without Chemotherapy in HER2+ Breast Cancer.
Article in Clinical cancer research : an official journal of the American Association for Cancer Research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 16 citations in OpenAlex.
- Trastuzumab plus lapatinib or chemotherapy in patients with HER2-overexpressed advanced breast cancer: a randomized, phase II trial (GIM12-TYPHER).The oncologist · 2025Trial
- Addition of Ipatasertib to Dual Anti-HER2 Maintenance Therapy in HER2-Positive Metastatic Breast Tumors with PIK3CA Mutations: The Phase Ib SOLTI-1507 IPATHER Trial.Clinical cancer research : an official journal of the American Association for Cancer Research · 2026Article
- Improving HER2 Diagnostics with Digital Real-Time PCR for Ultrafast, Precise Prediction of Anti-HER2 Therapy Response in Patients with Breast Cancer.Small methods · 2026Article
- HER2-Positive Breast Cancer Treatment and Resistance.Advances in experimental medicine and biology · 2025Review
- iGenSig-Rx: an integral genomic signature based white-box tool for modeling cancer therapeutic responses using multi-omics data.BMC bioinformatics · 2024Article
- iGenSig-Rx: an integral genomic signature based white-box tool for modeling cancer therapeutic responses using multi-omics data.Research square · 2023Article
- Neratinib plus trastuzumab is superior to pertuzumab plus trastuzumab in HER2-positive breast cancer xenograft models.NPJ breast cancer · 2021Article
Corrections and comments
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Authors and funding
27 authors at 13 institutions in 2 countries.
Funding
Abstract
purposeClinical 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
designBaseline 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.
resultsIn 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.
conclusionsOur 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.
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