Browsing by Author "Kapadia, Monesh"

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    A Multiparameter Molecular Classifier to Predict Response to Neoadjuvant Lapatinib plus Trastuzumab without Chemotherapy in HER2+ Breast Cancer
    (American Association for Cancer Research, 2023) Veeraraghavan, Jamunarani; Gutierrez, Carolina; De Angelis, Carmine; Davis, Robert; Wang, Tao; Pascual, Tomas; Selenica, Pier; Sanchez, Katherine; Nitta, Hiroaki; Kapadia, Monesh; Pavlick, Anne C.; Galvan, Patricia; Rexer, Brent; Forero-Torres, Andres; Nanda, Rita; Storniolo, Anna M.; Krop, Ian E.; Goetz, Matthew P.; Nangia, Julie R.; Wolff, Antonio C.; Weigelt, Britta; Reis-Filho, Jorge S.; Hilsenbeck, Susan G.; Prat, Aleix; Osborne, C. Kent; Schiff, Rachel; Rimawi, Mothaffar F.; Medicine, School of Medicine
    Purpose: 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.