Purpose: We examined whether the response predicted by a 30-gene pharmacogenomic test correlated with the residual cancer burden (RCB) after preoperative chemotherapy with paclitaxel, 5-fluorouracil, doxorubicin, and cyclophosphamide (T/FAC).

Experimental Design: Gene expression profiling was done at diagnosis in 74 patients with stages I to III breast cancer and was used to calculate a pharmacogenomic score and predict response to chemotherapy [pathologic complete response (pCR) or residual disease (RD)]. All patients received 6 months of preoperative T/FAC. Following pathologic review, a RCB score was calculated based on residual tumor and lymph node features. Four RCB classes were assigned; RCB-0 (pCR), RCB-I (near-PCR), RCB-II (moderate RD), and RCB-III (extensive RD). The correlations between the pharmacogenomic score, predicted pathologic response, RCB score, and RCB class were examined.

Results: Thirty-three patients were predicted to have pCR, and 40 were predicted to have RD. Observed responses were RCB-0: n = 20 (27%); RCB-I: n = 5 (7%); RCB-II: n = 36 (49%); and RCB-III: n = 13 (16%) patients. Pharmacogenomic and RCB scores were correlated (Pearson's R = −0.501, P < 0.0001). There was no difference between the mean genomic predictor scores for RCB-0/I groups (P = 0.94), but these were different from the mean scores of the RCB-II/III groups (P < 0.001). Among the 25 patients with RCB-0/I response, 19 (76%) were predicted to achieve pCR. The pharmacogenomic test correctly predicted RD in 92% of the patients with RCB-III, which corresponds to chemotherapy-resistant disease.

Conclusions: The 30-gene pharmacogenomic test showed good correlation with the extent of residual invasive cancer burden measured as both continuous and categorical variables.

Pathologic complete response (pCR), defined as the complete absence of invasive cancer in the breast and lymph nodes in patients treated with preoperative chemotherapy, represents an early surrogate for excellent long-term, disease-free, and overall survival (16). This good survival is likely due to benefit from chemotherapy because the same clinical and pathologic features that are associated with higher probability of pCR (e.g., high-grade, ER-negative status, high proliferative activity) are associated with poor survival in the absence of systemic chemotherapy (7). However, pathologic response to preoperative chemotherapy represents a continuum of responses. Some clinical tumor shrinkage is seen in the majority of patients who receive preoperative chemotherapy, and the post-chemotherapy surgical resection specimens contain variable amounts of invasive cancer. It has been recognized that the larger the residual invasive cancer, the worse is the prognosis (8). Therefore, considering pathologic response as a dichotomous outcome of either pCR or residual disease (RD) may be overly simplistic. It ignores the possibility that some patients with minimal residual cancer may have the same good outcome as those with pCR, and it also fails to distinguish between those with moderate response (and intermediate survival outcome) and those with extensive residual cancer who experience poor survival despite chemotherapy.

We recently developed a new method to quantify residual invasive cancer after preoperative chemotherapy on a continuous scale (9). This method combines the largest diameter of the invasive cancer, the percent cellularity of the tumor, the number of lymph nodes involved, and the largest diameter of the nodal involvement into a residual cancer burden (RCB) score.6

The RCB score correlates with survival and can also be used to define four distinct pathologic response categories: RCB-0 (pCR), RCB-I (near-pCR), RCB-II (moderate RD), and RCB-III (extensive RD). These RCB categories are also predictive of long-term survival, and more importantly, the subset of patients who achieve RCB-I pathologic response has similar overall and disease-free survival as the patients achieving pCR (i.e., RCB-0; ref. 9).

It could be helpful if we could predict the extent of pathologic response to a particular chemotherapy regimen before starting treatment. Patients predicted to achieve pCR or RCB-I response could be encouraged to undergo such treatment. Those who are predicted to have extensive residual cancer are not good candidates for preoperative chemotherapy and may be better treated with other regimens or investigational drugs. The benefit from chemotherapy for those with moderate amount of residual cancer (i.e., RCB-II) remains uncertain. Recently, we reported the discovery (n = 82) and initial validation (n = 51) of a 30-gene pharmacogenomic predictor of pCR to preoperative sequential paclitaxel and 5-fluorouracil, doxorubicin, and cyclophosphamide (T/FAC) preoperative chemotherapy (10). This pharmacogenomic test showed significantly higher sensitivity (92% versus 61%) than a multivariable clinical predictor, including age, grade, and ER status. The negative predictive value (96% versus 86%) and area under curve (0.877 versus 0.811) were also slightly better.

The purpose of the present study was to examine the correlation between predictions made by the 30-gene pCR predictor and the extent of residual invasive cancer measured as a continuous variable using the RCB score and the RCB categories. Our hypothesis was that the RCB score and the genomic prediction scores correlate with each other, and that the various RCB categories differ significantly in their mean genomic response prediction scores. We expect that if our pharmacogenomic test truly predicts sensitivity to T/FAC chemotherapy, then patients predicted to achieve pCR will mostly have pCR or RCB-I response, and those predicted to have RD will mostly have extensive (RCB-III) or moderate (RCB-II) amount of RD after treatment.

Patients. As part of an ongoing pharmacogenomic marker validation study, patients treated with preoperative T/FAC chemotherapy were offered fine needle aspiration (FNA) of the primary breast tumor or ipsilateral axillary lymph node metastasis before starting treatment. RNA was extracted from the FNA samples, and gene expression profiling was done on Affymetrix U133A chips as previously described (10, 11). A total of 74 patients were included in the current analysis; 51 from the original validation set and 23 additional new cases. None of these patients were included in the predictor discovery. The goal of the current analysis was to examine how the pharmacogenomic prediction results correlate with pathologic response as a continuous variable. The previous study examined the accuracy of the pharmacogenomic test to predict dichotomous response outcome, pCR versus RD. All patients were treated with preoperative weekly or 3-weekly paclitaxel × 12 weeks, followed by four courses of fluorouracil, doxorubicin, and cyclophosphamide (FAC). When the tumor becomes <2 cm on physical examination or imaging, metallic coils were placed in the tumor bed under ultrasound guidance to identify the primary tumor site at the time of surgery. Definitive breast surgery (mastectomy or breast conserving surgery) followed by axillary dissection or sentinel lymph node biopsy was determined by the surgeon's discretion and patient's preference. This clinical study was approved by the institutional review board of M. D. Anderson Cancer Center, and informed consent was signed by all patients.

Pathologic response assessment. H&E-stained slides from the surgical resection specimen were reviewed microscopically to evaluate RCB. Briefly, RCB in the posttreatment surgical resection specimen is determined from bidimensional diameters of the primary tumor bed in the resection specimen (d1 and d2), the proportion of the primary tumor bed that contains invasive carcinoma expressed as % cellularity (finv), the number of axillary lymph nodes containing carcinoma (LN), and the diameter of the largest metastasis in the involved axillary lymph node (dmet). Largest bidimensional measurements of the residual tumor mass in the breast were recorded from the macroscopic description in the pathology report and were confirmed after review of the corresponding slides. If multiple tumor masses were present, the dimensions of the largest mass were recorded. The bidimensional measurements of the primary tumor bed were combined as follows: dprim =

$$\sqrt{d_{1}d_{2}}$$
⁠. To estimate the proportion of invasive carcinoma (finv) within the cross-sectional area of the primary tumor bed, we first estimated the overall percent area of carcinoma (%CA) from microscopic review of the corresponding slides and then corrected for the component of in situ carcinoma (%CIS): finv = (1 − (%CIS)/100) × (%CA)/100. To estimate the extent of residual cancer in the regional lymph nodes, we reviewed the slides from resected axillary lymph nodes and recorded the number of lymph nodes that contained a metastasis (LN), and the greatest diameter of the largest metastasis (dmet). All diameters were recorded in millimeters. A free, interactive, online RCB calculator is available in a web site6 (9).

We previously defined two cutoff points for the RCB score that resulted in four pathologic response categories that correlated with survival in 241 T/FAC-treated patients (9). None of the current 74 patients were included in the previous analysis that defined the RCB cutoff values. To determine the first cutoff point, we fitted a multivariate Cox regression model that included all the clinical covariates and a dichotomized (above or below the cutoff) RCB score. The optimal RCB cutoff point was selected that maximized the profile log-likelihood of the Cox model. This occurred at the value of 3.28 that corresponds to the 87.5th quantile of the RCB distribution. A second cutoff point was determined similarly by maximizing the log-likelihood of a Cox model that included all clinical covariates and the second dichotomized RCB factor (above or below the second cutoff). This cutoff point occurred at the value of 1.36 or the 40th quantile of the RCB distribution. These two cutoffs define four groups: (a) RCB-0 (RCB score 0), corresponding to pCR defined as no residual invasive carcinoma in the breast and axillary lymph nodes; (b) RCB-I (RCB scores 0 < RCB ≤ 1.338), corresponding to minimal residual invasive disease; (c) RCB-II (RCB scores 1.338 < RCB ≤ 3.278), corresponding to moderate RD; and (d) RCB-III (RCB scores >3.278), corresponding to extensive RD. These four different categories of residual cancer had very different survival (9). The rates of the 5-year relapse-free–survival were 95% in the RCB-0 and 98% in the RCB-I groups. In contrast, these were 84% in the RCB-II and 46% in the RCB-III groups (P < 0.001 in the log-rank test) The RCB categories I, II, and III were all lumped together as RD in our previous analysis (10).

Statistical analysis. The previously reported and fully defined pharmacogenomic response predictor was applied to the normalized gene expression data from each case. This predictor takes the expression values of 30-probe sets and applies diagonal linear discriminant analysis prediction rules to generate a prediction score. Prediction scores >0 predict for pCR, and scores <0 (i.e., negative scores) predict for RD. Pearson's correlation was done to determine the correlation between the 30-gene predictor scores and the RCB scores and the RCB categories. The one-way ANOVA test was used for the comparison of means of the 30-gene predictor scores between various RCB categories. Paired two-way comparison t tests were used to calculate differences among these groups. All tests were two tailed, with a P value of <0.05 considered significant. The SPSS 12.0 software package (SPSS Inc.) was used for statistical analyses.

Correlation between the pharmacogenomic prediction scores and the RCB scores. Gene expression results and pCR versus RD outcome information were available on all 74 cases. RCB categories (0, I, II, or III) could be assigned to 73 patients and RCB scores to 72 patients due to missing slides for review. One of the two patients with missing RCB scores had progression on preoperative chemotherapy and was categorized as RCB III. Patient and tumor characteristics are shown in Table 1. The mean 30-gene predictor score was −0.60 (range, 54.35 to −42.14) and the mean RCB score was 1.80 (range, 0 to 4.45). The correlation between the 30-gene predictor scores and the RCB scores showed a Pearson's R = −0.501 (P < 0.0001; Fig. 1A). Similar correlation was found between the 30-gene predictor scores and the RCB categories (Pearson's R = −0.506, P < 0.0001; Fig. 1B). One-way ANOVA analysis showed that the mean 30-gene predictor scores were significantly different between the four RCB categories (P < 0.0001; Fig. 2A and B; Table 2). In the two-way comparison t tests, the mean 30-gene predictor score in RCB-0 cases was significantly different from the mean values in RCB-II and RCB-III (P = 0.0005 and P = 0.0002, respectively). However, there was no significant difference between the mean 30-gene predictor scores for RCB-0 and RCB-I response categories (P = 0.94), and there was also no significant difference between the predictor scores for RCB-II and RCB-III response (P = 0.14). These results indicate that some of the “false-positive” pCR prediction results from the pharmacogenomic test include patients who have minimal RD and, therefore, may benefit from chemotherapy to the same extent as patients with pCR.

Table 1.

Patient and tumor characteristics (n = 74)

CharacteristicNumber of patients, n (%)
Median age, y (range) 49 (27-73)
Ethnicity
White 46 (62)
Black 8 (11)
Hispanic 17 (23)
Asian 3 (4)
Histologic type
Invasive ductal 66 (90)
Invasive lobular 4 (5)
Mixed ductal and lobular 4 (5)
T classification at diagnosis
T1 10 (14)
T2 37 (50)
T3 12 (16)
T4 15 (20)
N classification at diagnosis
N0 16 (22)
N1 33 (45)
N2 12 (16)
N3 13 (17)
1 or 2 36 (49)
3 38 (51)
Estrogen receptor status
Positive 46 (62)
Negative 28 (38)
HER2 status
Positive 11 (15)
Negative 63 (85)
T weekly × 4 + FAC × 4 65 (88)
T thrice/week × 4 + FAC × 4 9 (12)
CharacteristicNumber of patients, n (%)
Median age, y (range) 49 (27-73)
Ethnicity
White 46 (62)
Black 8 (11)
Hispanic 17 (23)
Asian 3 (4)
Histologic type
Invasive ductal 66 (90)
Invasive lobular 4 (5)
Mixed ductal and lobular 4 (5)
T classification at diagnosis
T1 10 (14)
T2 37 (50)
T3 12 (16)
T4 15 (20)
N classification at diagnosis
N0 16 (22)
N1 33 (45)
N2 12 (16)
N3 13 (17)
1 or 2 36 (49)
3 38 (51)
Estrogen receptor status
Positive 46 (62)
Negative 28 (38)
HER2 status
Positive 11 (15)
Negative 63 (85)
T weekly × 4 + FAC × 4 65 (88)
T thrice/week × 4 + FAC × 4 9 (12)

Abbreviation: FAC, 5-fluorouracil, doxorubicin, and cyclophosphamide, given in a 28-d cycle.

Fig. 1.

A, Correlation between the 30-gene predictor scores and the RCB scores. B, correlation between the 30-gene predictor scores and the RCB categories.

Fig. 1.

A, Correlation between the 30-gene predictor scores and the RCB scores. B, correlation between the 30-gene predictor scores and the RCB categories.

Close modal
Fig. 2.

A, 30-gene predictor scores corresponding to RCB categories. B, distribution of the 30-gene predictor scores (means).

Fig. 2.

A, 30-gene predictor scores corresponding to RCB categories. B, distribution of the 30-gene predictor scores (means).

Close modal
Table 2.

30-gene predictors scores corresponding to RCB categories

NMinimumMaximumMean (SD)
RCB-0 20 −24.08 54.35 15.12 (20.94)
RCB-I −20.11 50.39 15.93 (30.43)
RCB-II 36 −42.13 27.15 −6.25 (17.54)
RCB-III 13 −37.62 30.51 −14.59 (17.14)
NMinimumMaximumMean (SD)
RCB-0 20 −24.08 54.35 15.12 (20.94)
RCB-I −20.11 50.39 15.93 (30.43)
RCB-II 36 −42.13 27.15 −6.25 (17.54)
RCB-III 13 −37.62 30.51 −14.59 (17.14)

Correlation between pharmacogenomically predicted response and RCB categories. RCB-0 response (pCR) was observed in 20 (27.4%) cases, RCB-I was observed in 5 (6.9%), RCB-II was observed in 36 (49.3%), and RCB-III was observed in 13 (16.4%) patients. A total of 33 patients were predicted to have pCR, and 40 were predicted to have RD by the pharmacogenomic test. The overall agreement between the 30-gene predictor class (pCR versus RD) and the RCB classes (RCB-0 versus RCB-I, RCB-II, and RCB-III) was 70.2% (Table 3). The 30-gene predictor predicted RCB-0 outcome as a pCR in 80%, RCB-II and RCB-III were predicted as RD in 64% and 92%, respectively. RCB-I (near-pCR) was predicted more frequently (60%) as pCR than as RD (40%). Among the 25 patients with complete (RCB-0) or near-complete (RCB-I) pathologic response who are projected to have excellent survival, 19 (76%) were predicted to achieve pCR by the pharmacogenomic test.

Table 3.

Agreement between predicted and observed pathologic tumor response

Observed responsePredicted response (N = 73)
pCRRD
RCB category
RCB-0 16
RCB-I
RCB-II 13 23
RCB-III 11
Dichotomous response
pCR 19
RD 14 34
Observed responsePredicted response (N = 73)
pCRRD
RCB category
RCB-0 16
RCB-I
RCB-II 13 23
RCB-III 11
Dichotomous response
pCR 19
RD 14 34

NOTE: Percentage agreement, 70.2% for predicted response and RCB category and 64.8% for predicted response and dichotomous pathologic response (i.e., pCR versus RD).

Abbreviation: RCB, residual cancer burden.

In this study, we examined the correlation between a 30-gene pCR predictor score determined before treatment and the extent of residual invasive cancer measured after completion of 6 months of preoperative chemotherapy. Residual invasive cancer was measured as a continuous variable using a recently developed RCB score. Our findings showed a correlation between the pharmacogenomic predictor scores that were determined at diagnosis and the RCB scores determined after definitive surgery (Pearson's R = −0.501, P < 0.0001). The RCB scores can be used to define four categories of pathologic response (RCB-0, RCB-I, RCB-II, and RCB-III) with distinct long-term survival outcome (9). The 30-gene pharmacogenomic predictor correctly predicted 80% (n = 16/20) of the patients with RCB-0 (pCR) response. About 60% of cases (3/5) with RCB-I (minimal RD) were also predicted as pCR. These patients are considered as false-positive cases using the dichotomous pCR versus RD classification; however, their long-term survival is projected to be very similar to those with pCR. There was no significant difference in the mean pharmacogenomic prediction scores of the RCB-0 (mean, 15.12; SD, 20.9) and RCB-I (mean, 15.93; SD, 30.4) response categories. About 48% of cases with moderate amount of RD, RCB-II (mean prediction score, −6.2; SD, 17.5) were misclassified as pCR by the pharmacogenomic test, but only 1 of 12 (8%) patients with extensive residual cancer, RCB-III (mean prediction score −14.6, SD 17.1), was misclassified as pCR. These results suggest that the genomic test correctly identifies ∼76% (n = 19/25) of patients who will achieve either pCR (RCB-0) or near-pCR (RCB-I) to preoperative T/FAC chemotherapy. Equally importantly, 92% of those who will end up with extensive RD (RCB-III) are correctly identified as RD by the test.

These results corroborate our previous finding that the 30-gene pharmacogenomic test predicts chemotherapy sensitivity (12). The pharmacogenomic prediction scores inversely correlate with the extent of residual invasive cancer (i.e., RCB score). The lower the prediction score, the higher is the residual invasive cancer burden (Fig. 2).

Other investigators also reported that it is possible to identify gene signatures that are associated with pathologic response to chemotherapy (1320). However, no other proposed response predictors were assessed in independent validation, and no detailed analysis of response prediction scores and extent of residual cancer was previously reported. The observation that a 30-gene pharmacogenomic prediction score, which was optimized to predict dichotomous outcome (i.e., pCR versus RD), correlated with the extent of residual invasive cancer when cancer was present increases our confidence in this genomic test. Our data add to the growing body of literature that indicates that there are large-scale gene expression differences between highly chemotherapy-sensitive and less sensitive cancers. Whether these emerging pharmacogenomic predictive tests can improve medical decision making and lead to improved patient outcome needs further investigation.

Grant support: National Cancer Institute (RO1-CA106290), the Breast Cancer Research Foundation, and the Goodwin Foundation (L. Pusztai) and the Nellie B. Connally Breast Cancer Research Fund (G.N. Hortobagyi).

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

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