Galvan, Patricia

Abstract 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.

1011 Background: Several trials have shown 25-30% pathologic complete response (pCR) rates in patients with HER2+ BC treated with LT therapy (+ endocrine therapy if ER+), but no chemotherapy (CTX). We hypothesize that a multiparameter classifier, comprised of HER2 gene and protein levels, intratumor heterogeneity (ITH), HER2-enriched (E) subtype, and PIK3CA mutation status can identify patients whose tumors are “addicted” to HER2 signaling and are likely to achieve pCR from a CTX-sparing de-escalation strategy. Methods: Baseline specimens from 2 trials (TBCRC023 [NCT00999804] and PAMELA [NCT01973660]) of neoadjuvant CTX-sparing LT (+ endocrine therapy if ER+) in HER2+ BC were used. HER2 protein and ITH (scored for % 3+ by IHC), and gene amplification (HER2:CEP17 ratio and copy number (CN) by CISH) were measured on the same slide by the dual gene protein assay (GPA). HER2-E and PIK3CA mutation status were assessed by research-based PAM50 and MSK-IMPACT platforms, respectively. A decision tree algorithm was used to determine the GPA cutoffs and to construct the classifier of response (by pCR) in TBCRC023, which was then validated in PAMELA. Results: Of the evaluable patients from TBCRC023 (N = 130) and PAMELA (N = 151), GPA data were available for 121 and 94 cases, respectively. Both cohorts exhibited similar distributions for HER2 ratio, CN, and % 3+, and a strong correlation between HER2 ratio and CN (R > 0.92). In TBCRC023, 73 cases had data from GPA, PAM50, and IMPACT, of which 15 had pCR. Recursive partitioning identified cutoffs of HER2 ratio > 4.6 and % 3+ > 97.5% in both the GPA data cohort (N = 121) and complete data cohort (N = 73). With PAM50 and IMPACT data, the model added HER2-E and PIK3CA wild-type (wt). For practical reasons, the classifier was locked as HER2 ratio ≥ 4.5 AND % 3+ ≥ 90% AND PIK3CA-wt AND HER2-E, which yielded a PPV of 55% and NPV of 94%. Validation in PAMELA using 45 cases with data for all 3 assays yielded PPV of 44% and NPV of 82%. 29 TBCRC023 cases without IMPACT data could be predicted to be non-pCR, of which 26 were correct (NPV = 89%). In PAMELA, 66 additional cases could be predicted to be non-pCR, of which 54 were correct (NPV = 81%). Conclusions: We have constructed a multiparameter classifier that can predict pCR with targeted therapy alone that compare to pCR rates of CTX + dual anti-HER2 in unselected patients. Prospective validation in a clinical trial is warranted. [Table: see text]