Use of genomic grade index (GGI) to predict pathologic response to preoperative chemotherapy in breast cancer

Abstract

541 Background: The genomic grade index (GGI) is a 97-gene measure of histologic tumor grade. High GGI is associated with decreased relapse-free survival without and with systemic adjuvant endocrine- or chemotherapies. In the current study we examined whether GGI predicts pathologic response to neoadjuvant chemotherapy in 229 patients with HER2-normal breast cancer. We hypothesized that high GGI will predict for greater sensitivity to chemotherapy. Methods: Gene expression data was generated with Affymetrix U133A gene chips from fine-needle aspirations samples prospectively collected before starting therapy. All patients received sequential paclitaxel, 5-fluorouracil, doxorubicin, cyclophosphamide (T/FAC) neoadjuvant chemotherapy. Pathologic response was quantified using the residual cancer burden (RCB) method. The association between the GGI and pathologic response was assessed in univariate and multivariate logistic regression analyses including age, estrogen receptor (ER) and node status, nuclear grade, baseline T size and the GGI. The performance of a multivariate (clinical + GGI) predictor was evaluated in 3-fold cross-validation with 1,000 iterations. Results: GGI correlated with tumor grade, 79% of grade 1 tumors had low GGI whereas 88% of grade 3 tumors had high GGI. Sixty-one percent of grade 2 tumors were reclassified as low GGI. GGI scores were significantly (p<0.04) higher in patients with pathologic complete (pCR) or excellent response (RCB 0/I = pCR or near-pCR) compared to lesser response in both ER + and ER- cancers. In multivariate analysis, ER-status (OR:0.43, 95%CI:0.2–0.91), node status (OR:0.35, 95%CI:0.16–0.8) and GGI (OR:1.86, 95%CI:1.16–3.0) were significant (p<0.03) independent predictors of RCB 0/I response. A multivariate prediction model had mean AUC of 0.73, PPV=0.53, NPV=0.72, sensitivity=38% and specificity=86% in cross validation. Conclusions: High GGI is predictive of increased sensitivity to neoadjuvant T/FAC chemotherapy in both ER- and ER+ patients. GGI can be combined with clinical variables to produce a reasonably accurate multivariate prediction model of pathologic response to chemotherapy. Author Disclosure Employment or Leadership Consultant or Advisory Role Stock Ownership Honoraria Research Expert Testimony Other Remuneration Nuvera Biosciences Ipsogen Nuvera Biosciences Inc