Benefit From Adjuvant Trastuzumab in NSABP Trial B-31
Benefit From Adjuvant Trastuzumab in NSABP Trial B-31
Perhaps because of the confounding by adjuvant chemo-endocrine therapy, which is quite efficacious for HER2-positive breast cancer, we found that gene-by-trastuzumab interaction was nonlinear, making it difficult to build a predictive model based purely on statistical methods.
Initially we attempted to build a model by selecting genes strictly based on statistical criteria using gene-by-treatment interaction terms in Cox models and identifying genes by 10-fold jack-knifing and other criteria described in the Supplementary Materials, File 3 (available online). However, clustering of these or any other combination of genes selected purely based on statistical significance did not allow us to robustly identify clinically meaningful subsets with differential benefit from trastuzumab. In light of this failure, we decided to attempt a biological approach to identify subsets with differential benefit from trastuzumab.
From among all of the results of gene assessment we had performed, we noticed that the top predictive genes included several ER-associated genes—CA12 (mean Pinteraction = .006), GATA3 (P = .007), PIK3A (P = .04)—as well as genes from the HER2 amplicon—ERBB2 (P = .049) and C17orf37 (P = .04). Using this information and the facts that ER status has been associated with lower rates of complete pathological response in several published studies and that HER2 (ERBB2) is the target for trastuzumab, we decided to select genes whose expression levels were correlated with ESR1 mRNA or with ERBB2 mRNA as the basis to develop a predictive model. The top genes correlated with ESR1 and ERBB2 are shown in Table 2. From this pool, eight genes met the criteria of a Spearman correlation coefficient greater than 0.7 and a minimum interaction P value less than .10. These genes included ESR1, NAT1, GATA3, CA12, IGFR1, ERBB2, c17orf37, and GRB7.
In a principal component analysis, the first two principal components of these genes accounted for 78.6% of the total variance (Supplementary Materials, File 3, available online). To identify subsets with different degree of benefit from trastuzumab while accommodating the nonlinearity of interaction between genes and trastuzumab, we used the first two principal components (PC1 and PC2) obtained from the eight selected predictive genes to create a three-dimensional subset treatment effect pattern plot with spline interpolation to smooth the plot with hazard ratio (HR) for trastuzumab on the Z-axis (Figure 2). Hazard ratios were color coded as green if equal to or less than 0.5 (large benefit from trastuzumab), brown if 0.5 to 1.0 (moderate benefit), or red if equal to or more than1.0 (no benefit). This plot readily identified subsets with differential benefit from trastuzumab. We derived cut points for two principal components (PC1 and PC2), which defined three subsets based on three-dimensional subset treatment effect pattern plot and the event rate in each subgroup. To derive best cut points, we moved cut points back and forth and checked the goodness of fit and discrimination when we applied internal cross-validation to the discovery set. We ignored the small green region in Figure 2 because it seemed that this region showed green because of error in smoothing.
(Enlarge Image)
Figure 2.
Three-dimensional subset treatment effect pattern plot analysis of candidate discovery set (n = 588). Cut points were determined by moving the cut points back and forth and checking the goodness of fit and discrimination by applying internal cross-validation to the discovery set. The cut points for two principal components of the eight predictive genes (PC1 and PC2) that defined the three subsets were determined as follows: no benefit group if PC1 > 0.6 and PC2 > 0.1; large benefit group if −0.12 < PC1 ≤ 0.6 and 0.1 < PC2 ≤ 0.6 and PC2 > PC1 + 0.22, if −0.6 < PC1 ≤ 0.6 and PC2 ≥ 0.6, or if PC1 ≤ −0.12 and −0.55 <PC2 < 0.6. Remaining patients were classified as the moderate benefit group. Red indicates case subjects no benefit from trastuzumab (hazard ratio [HR] ≥1). Brown indicates case subjects with moderate benefit (0.5 < HR < 1). Green indicates case subjects with large benefit (HR ≤ 0.5).
The cut points for two principal components (PC1 and PC2) that defined these three subsets were determined as follows: no benefit group with HR of 1.56 (if PC1 > 0.6 and PC2 > 0.1); large benefit group with hazard ratio of 0.27 (if −0.12 < PC1 ≤ 0.6 and 0.1 < PC2 ≤ 0.6 and PC2 > PC1 + 0.22, if − 0.6 <PC1 ≤ 0.6 and PC2 ≥ 0.6, or if PC1 ≤ −0.12 and −0.55 < PC2 < 0.6). Remaining patients were classified as the moderate benefit group with hazard ratio of 0.56 (see Supplementary Materials, File 3, available online, for Kaplan–Meier plots of the discovery cohort based on these cut points).
We assessed the predefined cut points from the eight-gene prediction model described above in the confirmation cohort (n = 991) B-31 patients not included in the discovery phase for whom specimens were available. Because the predictive model has not yet been developed into a formal clinical test, we did not develop a formal NCI registered date-stamped protocol before proceeding to the cut points assessment. We created Kaplan–Meier plots based on the predefined cut point values for the two principal components created by applying the eigenvector coefficients from the candidate discovery set to the confirmation dataset. As shown in Figure 3, A–C, applying the predefined cut points for the 8-gene prediction model readily identified the following: a subset with no benefit from trastuzumab (Group 1) with hazard ratio of 1.58 (95% confidence interval [CI] = 0.67 to 3.69; P = .29; n = 100) (Figure 3A); a subset with moderate benefit (Group 2) with hazard ratio of 0.60 (95% CI = 0.41 to 0.89; P = .01; n = 449) (Figure 3B); and a subset with large benefit (Group 3) with hazard ratio of 0.28 (95% CI = 0.20 to 0.41; P < .001; n = 442) (Figure 3C). The P value for the interaction between predictive algorithm and trastuzumab was <.001.
(Enlarge Image)
Figure 3.
Confirmation of predictive model and its cut points (n = 991). A) Disease-free survival (DFS) of patients treated with chemo-endocrine therapy (adriamycin cyclophosphamide followed by taxol [ACT]; solid line) vs those treated with trastuzumab added to chemo-endocrine therapy (ACT + herceptin [ACTH]; dashed line) among the no-benefit subgroup (n = 100) identified using the cut point from the candidate discovery set. Hazard ratio for trastuzumab was 1.58 (95% confidence interval [CI] = 0.67 to 3.69; P = .29 by Kaplan–Meier analysis). All statistical tests were two-sided. B) DFS of patients treated with chemo-endocrine therapy (ACT; solid line) vs those treated with trastuzumab added to chemo-endocrine therapy (ACTH; dashed line) among the moderate-benefit subgroup (n = 449) identified using the cut point from the candidate discovery set. Hazard ratio for trastuzumab was 0.60 (95% CI = 0.41 to 0.89; P = .01 by Kaplan–Meier analysis). All statistical tests were two-sided. C) DFS of patients treated with chemo-endocrine therapy (ACT; solid line) vs those treated with trastuzumab added to chemo-endocrine therapy (ACTH; dashed line) among the large-benefit subgroup (n = 442) identified using the cut point from the candidate discovery set. Hazard ratio for trastuzumab was 0.28 (95% CI = 0.20 to 0.41; P < .001 by Kaplan–Meier analysis). All statistical tests were two-sided.
Because HER2 is the target for trastuzumab, it was expected that Group 1 with no benefit should express the lowest levels of ERBB2 mRNA. Figure 4 shows the result of a correlation analysis between ERBB2 and ESR1 mRNA levels in which each subgroup defined by the eight-gene prediction model is color coded. Surprisingly, the subset with no benefit expressed high levels of ESR1 mRNA and intermediate (but overexpressed) levels of ERBB2 mRNA rather than the lowest levels in both candidate discovery and confirmation cohorts (Figure 4).
(Enlarge Image)
Figure 4.
Nonlinear interaction between expression levels of ESR1 and ERBB2 and trastuzumab benefit. Tumors from patients with no benefit expressed moderate levels of ERBB2 mRNA and high levels of ESR1 mRNA. Red circles indicate Group 1, no benefit; brown crosses indicate Group 2, moderate benefit; Green Xs indicate Group 3, large benefit.
Previously we have reported an unexpected finding from the B-31 trial that central HER2 assay–negative patients also derived benefit from trastuzumab. Because the eight-gene prediction model was developed independently of the knowledge of centrally performed HER2 testing results, we tested whether central HER2 assay–negative cases belong to Group 1 defined by the predictive model as having no expected benefit. When central HER2-negative results were overlaid on these subsets, only a few HER2-negative patients belonged to the subgroup with no benefit, whereas a majority belonged to the moderate-benefit subgroup (Figure 5).
(Enlarge Image)
Figure 5.
HER2-negative tumors belonging to the moderate-benefit group rather than no-benefit group. Distribution of HER2 FISH–positive (blank) and –negative (diagonal lines) cases according to trastuzumab benefit group.
These results support the hypothesis that HER2-negative patients may derive benefit from trastuzumab.
Results
Results of the nCounter Assay in the Candidate Discovery Cohort (n=588) and Development of a Prediction Model
Perhaps because of the confounding by adjuvant chemo-endocrine therapy, which is quite efficacious for HER2-positive breast cancer, we found that gene-by-trastuzumab interaction was nonlinear, making it difficult to build a predictive model based purely on statistical methods.
Initially we attempted to build a model by selecting genes strictly based on statistical criteria using gene-by-treatment interaction terms in Cox models and identifying genes by 10-fold jack-knifing and other criteria described in the Supplementary Materials, File 3 (available online). However, clustering of these or any other combination of genes selected purely based on statistical significance did not allow us to robustly identify clinically meaningful subsets with differential benefit from trastuzumab. In light of this failure, we decided to attempt a biological approach to identify subsets with differential benefit from trastuzumab.
From among all of the results of gene assessment we had performed, we noticed that the top predictive genes included several ER-associated genes—CA12 (mean Pinteraction = .006), GATA3 (P = .007), PIK3A (P = .04)—as well as genes from the HER2 amplicon—ERBB2 (P = .049) and C17orf37 (P = .04). Using this information and the facts that ER status has been associated with lower rates of complete pathological response in several published studies and that HER2 (ERBB2) is the target for trastuzumab, we decided to select genes whose expression levels were correlated with ESR1 mRNA or with ERBB2 mRNA as the basis to develop a predictive model. The top genes correlated with ESR1 and ERBB2 are shown in Table 2. From this pool, eight genes met the criteria of a Spearman correlation coefficient greater than 0.7 and a minimum interaction P value less than .10. These genes included ESR1, NAT1, GATA3, CA12, IGFR1, ERBB2, c17orf37, and GRB7.
In a principal component analysis, the first two principal components of these genes accounted for 78.6% of the total variance (Supplementary Materials, File 3, available online). To identify subsets with different degree of benefit from trastuzumab while accommodating the nonlinearity of interaction between genes and trastuzumab, we used the first two principal components (PC1 and PC2) obtained from the eight selected predictive genes to create a three-dimensional subset treatment effect pattern plot with spline interpolation to smooth the plot with hazard ratio (HR) for trastuzumab on the Z-axis (Figure 2). Hazard ratios were color coded as green if equal to or less than 0.5 (large benefit from trastuzumab), brown if 0.5 to 1.0 (moderate benefit), or red if equal to or more than1.0 (no benefit). This plot readily identified subsets with differential benefit from trastuzumab. We derived cut points for two principal components (PC1 and PC2), which defined three subsets based on three-dimensional subset treatment effect pattern plot and the event rate in each subgroup. To derive best cut points, we moved cut points back and forth and checked the goodness of fit and discrimination when we applied internal cross-validation to the discovery set. We ignored the small green region in Figure 2 because it seemed that this region showed green because of error in smoothing.
(Enlarge Image)
Figure 2.
Three-dimensional subset treatment effect pattern plot analysis of candidate discovery set (n = 588). Cut points were determined by moving the cut points back and forth and checking the goodness of fit and discrimination by applying internal cross-validation to the discovery set. The cut points for two principal components of the eight predictive genes (PC1 and PC2) that defined the three subsets were determined as follows: no benefit group if PC1 > 0.6 and PC2 > 0.1; large benefit group if −0.12 < PC1 ≤ 0.6 and 0.1 < PC2 ≤ 0.6 and PC2 > PC1 + 0.22, if −0.6 < PC1 ≤ 0.6 and PC2 ≥ 0.6, or if PC1 ≤ −0.12 and −0.55 <PC2 < 0.6. Remaining patients were classified as the moderate benefit group. Red indicates case subjects no benefit from trastuzumab (hazard ratio [HR] ≥1). Brown indicates case subjects with moderate benefit (0.5 < HR < 1). Green indicates case subjects with large benefit (HR ≤ 0.5).
The cut points for two principal components (PC1 and PC2) that defined these three subsets were determined as follows: no benefit group with HR of 1.56 (if PC1 > 0.6 and PC2 > 0.1); large benefit group with hazard ratio of 0.27 (if −0.12 < PC1 ≤ 0.6 and 0.1 < PC2 ≤ 0.6 and PC2 > PC1 + 0.22, if − 0.6 <PC1 ≤ 0.6 and PC2 ≥ 0.6, or if PC1 ≤ −0.12 and −0.55 < PC2 < 0.6). Remaining patients were classified as the moderate benefit group with hazard ratio of 0.56 (see Supplementary Materials, File 3, available online, for Kaplan–Meier plots of the discovery cohort based on these cut points).
Assessment of the Predefined Cut Points for the Prediction Model in the Confirmation Cohort
We assessed the predefined cut points from the eight-gene prediction model described above in the confirmation cohort (n = 991) B-31 patients not included in the discovery phase for whom specimens were available. Because the predictive model has not yet been developed into a formal clinical test, we did not develop a formal NCI registered date-stamped protocol before proceeding to the cut points assessment. We created Kaplan–Meier plots based on the predefined cut point values for the two principal components created by applying the eigenvector coefficients from the candidate discovery set to the confirmation dataset. As shown in Figure 3, A–C, applying the predefined cut points for the 8-gene prediction model readily identified the following: a subset with no benefit from trastuzumab (Group 1) with hazard ratio of 1.58 (95% confidence interval [CI] = 0.67 to 3.69; P = .29; n = 100) (Figure 3A); a subset with moderate benefit (Group 2) with hazard ratio of 0.60 (95% CI = 0.41 to 0.89; P = .01; n = 449) (Figure 3B); and a subset with large benefit (Group 3) with hazard ratio of 0.28 (95% CI = 0.20 to 0.41; P < .001; n = 442) (Figure 3C). The P value for the interaction between predictive algorithm and trastuzumab was <.001.
(Enlarge Image)
Figure 3.
Confirmation of predictive model and its cut points (n = 991). A) Disease-free survival (DFS) of patients treated with chemo-endocrine therapy (adriamycin cyclophosphamide followed by taxol [ACT]; solid line) vs those treated with trastuzumab added to chemo-endocrine therapy (ACT + herceptin [ACTH]; dashed line) among the no-benefit subgroup (n = 100) identified using the cut point from the candidate discovery set. Hazard ratio for trastuzumab was 1.58 (95% confidence interval [CI] = 0.67 to 3.69; P = .29 by Kaplan–Meier analysis). All statistical tests were two-sided. B) DFS of patients treated with chemo-endocrine therapy (ACT; solid line) vs those treated with trastuzumab added to chemo-endocrine therapy (ACTH; dashed line) among the moderate-benefit subgroup (n = 449) identified using the cut point from the candidate discovery set. Hazard ratio for trastuzumab was 0.60 (95% CI = 0.41 to 0.89; P = .01 by Kaplan–Meier analysis). All statistical tests were two-sided. C) DFS of patients treated with chemo-endocrine therapy (ACT; solid line) vs those treated with trastuzumab added to chemo-endocrine therapy (ACTH; dashed line) among the large-benefit subgroup (n = 442) identified using the cut point from the candidate discovery set. Hazard ratio for trastuzumab was 0.28 (95% CI = 0.20 to 0.41; P < .001 by Kaplan–Meier analysis). All statistical tests were two-sided.
Distribution of Central HER2 Assay Negative Cases Among Categories Defined by the Prediction Model
Because HER2 is the target for trastuzumab, it was expected that Group 1 with no benefit should express the lowest levels of ERBB2 mRNA. Figure 4 shows the result of a correlation analysis between ERBB2 and ESR1 mRNA levels in which each subgroup defined by the eight-gene prediction model is color coded. Surprisingly, the subset with no benefit expressed high levels of ESR1 mRNA and intermediate (but overexpressed) levels of ERBB2 mRNA rather than the lowest levels in both candidate discovery and confirmation cohorts (Figure 4).
(Enlarge Image)
Figure 4.
Nonlinear interaction between expression levels of ESR1 and ERBB2 and trastuzumab benefit. Tumors from patients with no benefit expressed moderate levels of ERBB2 mRNA and high levels of ESR1 mRNA. Red circles indicate Group 1, no benefit; brown crosses indicate Group 2, moderate benefit; Green Xs indicate Group 3, large benefit.
Previously we have reported an unexpected finding from the B-31 trial that central HER2 assay–negative patients also derived benefit from trastuzumab. Because the eight-gene prediction model was developed independently of the knowledge of centrally performed HER2 testing results, we tested whether central HER2 assay–negative cases belong to Group 1 defined by the predictive model as having no expected benefit. When central HER2-negative results were overlaid on these subsets, only a few HER2-negative patients belonged to the subgroup with no benefit, whereas a majority belonged to the moderate-benefit subgroup (Figure 5).
(Enlarge Image)
Figure 5.
HER2-negative tumors belonging to the moderate-benefit group rather than no-benefit group. Distribution of HER2 FISH–positive (blank) and –negative (diagonal lines) cases according to trastuzumab benefit group.
These results support the hypothesis that HER2-negative patients may derive benefit from trastuzumab.
Source...