Somatic mutation profiling and associations with prognosis and trastuzumab benefit in early breast cancer

Sherene Loi, Stefan Michiels, Diether Lambrechts, Debora Fumagalli, Bart Claes, Pirkko-Liisa Kellokumpu-Lehtinen, Petri Bono, Vesa Kataja, Martine J Piccart, Heikki Joensuu, Christos Sotiriou, Sherene Loi, Stefan Michiels, Diether Lambrechts, Debora Fumagalli, Bart Claes, Pirkko-Liisa Kellokumpu-Lehtinen, Petri Bono, Vesa Kataja, Martine J Piccart, Heikki Joensuu, Christos Sotiriou

Abstract

Background: Certain somatic alterations in breast cancer can define prognosis and response to therapy. This study investigated the frequencies, prognostic effects, and predictive effects of known cancer somatic mutations using a randomized, adjuvant, phase III clinical trial dataset.

Methods: The FinHER trial was a phase III, randomized adjuvant breast cancer trial involving 1010 women. Patients with human epidermal growth factor receptor 2 (HER2)-positive breast cancer were further randomized to 9 weeks of trastuzumab or no trastuzumab. Seven hundred five of 1010 tumors had sufficient DNA for genotyping of 70 somatic hotspot mutations in 20 genes using mass spectrometry. Distant disease-free survival (DDFS), overall survival (OS), and interactions with trastuzumab were explored with Kaplan-Meier and Cox regression analyses. All statistical tests were two-sided.

Results: Median follow-up was 62 months. Of 705 tumors, 687 were successfully genotyped. PIK3CA mutations (exons 1, 2, 4, 9, 13, 18, and 20) were present in 25.3% (174 of 687) and TP53 mutations in 10.2% (70 of 687). Few other mutations were found: three ERBB2 and single cases of KRAS, ALK, STK11/LKB1, and AKT2. PIK3CA mutations were associated with estrogen receptor positivity (P < .001) and the luminal-A phenotype (P = .04) but were not statistically significantly associated with prognosis (DDFS: hazard ratio [HR] = 0.88, 95% confidence [CI] = 0.58 to 1.34, P = .56; OS: HR = 0.603, 95% CI = .32 to 1.13, P = .11), although a statistically significant nonproportional prognostic effect was observed for DDFS (P = .002). PIK3CA mutations were not statistically significantly associated with trastuzumab benefit (P(interaction): DDFS P = .14; OS P = .24).

Conclusions: In this dataset, targeted genotyping revealed only two alterations at a frequency greater than 10%, with other mutations observed infrequently. PIK3CA mutations were associated with a better outcome, however this effect disappeared after 3 years. There were no statistically significant associations with trastuzumab benefit.

Figures

Figure 1.
Figure 1.
Frequency and associations between mutations. Absolute numbers are shown of PIK3CA mutant, PIK3CA wild type, ERBB2 mutant, and TP53 mutant, as well as those tumors with coexisting mutations. PIK3CA exon 9 and 20 mutations (and other locations) are also shown.
Figure 2.
Figure 2.
Prognostic associations between patients who had a PIK3CA mutation (mt) vs wild type (WT) and clinical outcome. A–C) Kaplan-Meier plots of the cumulative proportion of patients surviving with the time in years. Various clinical end points are shown: distant disease-free survival (A), recurrence-free survival (B), and overall survival (C). Cox regression hazard ratios (HRs) and 95% confidence intervals (CIs) are shown, stratified by chemotherapy type given. All statistical tests are two-sided. The number of patients at risk in each group is given below the graphs.
Figure 3.
Figure 3.
Prognostic associations between PIK3CA genotype and clinical outcome according to mutation location on the gene (helical/exon 9 vs kinase/exon 20 domain). The number of patients at risk in each group is given below the graphs. A–C) Kaplan-Meier plots of the cumulative proportion of patients surviving with the time in years. Various clinical end points are shown: distant disease-free survival (A), recurrence-free survival (B), and overall survival (C). The two patients with dual mutations were excluded and all treatment arms were pooled. P values correspond to log-rank tests; mt = mutant; WT = wild type. All statistical tests are two-sided.
Figure 4.
Figure 4.
Interaction between PIK3CA genotype and trastuzumab efficacy. A) Kaplan-Meier plots comparing trastuzumab vs no trastuzumab treatment arms for PIK3CA mutated (mt), HER2-positive cohorts. Cumulative proportions of patients surviving distant disease free are shown. B) Kaplan-Meier plots comparing trastuzumab vs no trastuzumab for PIK3CA wild-type (WT), HER2-positive cohorts. Cumulative proportions of patients surviving distant disease free are shown. C) Interaction forest plots indicate Cox regression hazard ratios (HRs) and 95% confidence intervals (CIs) stratified by chemotherapy type given for trastuzumab benefit for distant disease-free survival (DDFS). according to PIK3CA genotype and by overall series. D) Kaplan-Meier plots comparing trastuzumab vs no trastuzumab treatment arms for PIK3CA mt, HER2-positive cohorts. Cumulative proportions of patients surviving relapse free are shown. E) Kaplan-Meier plots comparing trastuzumab vs no trastuzumab treatment arms for PIK3CA WT, HER2-positive cohorts. Cumulative proportions of patients surviving relapse free are shown. F) Interaction forest plots indicate Cox regression HRs and 95% CIs stratified by chemotherapy type given for trastuzumab benefit for recurrence-free survival (RFS) according to PIK3CA genotype and by overall series. G) Kaplan-Meier plots comparing trastuzumab vs no trastuzumab treatment arms for PIK3CA mt, HER2-positive cohorts. Cumulative proportions of patients alive are shown. H) Kaplan-Meier plots comparing trastuzumab vs no trastuzumab treatment arms for PIK3CA WT, HER2-positive cohorts. Cumulative proportions of patients alive are shown. I) Interaction forest plots indicate Cox regression HRs and 95% CIs stratified by chemotherapy type given for trastuzumab benefit for overall survival. according to PIK3CA genotype and by overall series. All statistical tests are two-sided. The number of patients at risk in each group is given below the graphs.

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Source: PubMed

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