Genetic mutation profile of Chinese HER2-positive breast cancers and genetic predictors of responses to Neoadjuvant anti-HER2 therapy

HER2 positivity accounts for about 15–20% of breast cancers and the development of HER2-targeted therapies has profoundly changed the course of these patients [1]. More and more HER2-targeted drugs, such as trastuzumab, pertuzumab, T-DM1 and neratinib, have become available for treatment of HER2-positive breast cancer. Despite this progress, however, many patients still die of HER2-positive breast cancer, calling for the identification and investigation of genetic profiles of HER2-positive breast cancer and predictors of responses to HER2-targeted therapies.

Neoadjuvant chemotherapy (NACT) is used commonly to downstage locally advanced cancer to allow breast-conserving surgery and to predict responses to systemic therapy based on pathological assessment. Based on the recent progress, NACT has gained momentum as an ideal setting in which to investigate predictive biomarkers of treatment responses. The Collaborative Trials in Neoadjuvant Breast Cancer (CTNeoBC) pooled analysis has confirmed NACT to be a good way to discriminate patients who have different clinical outcomes by their responses to the therapies [2]. Patients who attain complete eradication of tumor after NACT in both breast and lymph nodes have improved survival, which is defined as pathological complete response (pCR) [2, 3]. The NeoSphere and NeoALTTO trials both tried to seek higher pCR rates by different anti-HER2 combinations [4, 5] and new treatments in the past decade have significantly improved the prognosis of HER2-positive breast cancer with a pCR rate as high as 75% [6]. Despite these achievements, however, HER2-positive breast cancer patients still have a high death rate [6]. According to the latest interim analysis of the landmark KATHERINE trial, patients with HER2-positive early breast cancer who had residual invasive disease after NACT have improved survival when they receive adjuvant T-DM1 therapy after surgery [7]. As these great trials have provided effective regimens for patients with different responses, it is important to investigate the response predictors.

Large-scale, next-generation sequencing studies have provided large amounts of genetic information and produced valuable insights into the genomic landscape of primary breast cancers [8‐12]. These studies have highlighted that TP53 and PIK3CA were the two most prevalent mutated genes in HER2-positive breast cancers [8] and enriched in residual tissues after HER2-targeted therapies. PIK3CA mutation rate in HER2-positive breast cancers is about 23%, and patients with PIK3CA mutations have a lower pCR rate after HER2-targeted NACT [13].

In this study, we investigated the mutation profiles of HER2-positive breast cancer patients in China and analyzed the mutation differences between primary HER2-positive breast cancers with pCR and non-pCR after HER2-targeted NACT. We hypothesized that there would be mutations predictive of the anti-HER2 therapy responses and could be used as biomarkers for guiding treatment decisions.

Our findings indicate significant associations (p = 0.020 and p = 0.036) of TP53 LOF mutations and lower initial Ki67 status (< 40%) with a high probability of pCR in HER2-positive breast cancer patients receiving NACT.

TP53 has been reported to have heterogenous types of mutations which include attenuation of function, separation of function or neomorphic function [15‐17]. There are gain-of-function mutations mostly TP53 missense mutations and LOF mutations which have a common characteristic of losing functions of wild-type TP53 and composed of splice site, frame shift and nonsense mutations [16, 18].

HER2-positive breast cancer has a high frequency of TP53 mutations (up to 72%) [8], and in our previous study, the mutation frequency of TP53 in the triple-positive HR + /HER2 + group is 66.1% and up to 89.3% in the HR-/HER2 + group in Chinese breast cancer patients [19]. Interestingly, the TP53 mutation rate was similar in those of HER2-positive patients who achieved a pCR or not (88.9% v 81.8%), and there was no statistical difference of TP53 mutation rate between two groups (p = 0.859). However, as far as specific types of TP53 mutations were considered, such as missense mutations, LOF mutations and so on, quite significant mutational differences were manifested. TP53 missense mutation rates in pCR and non-pCR groups were quite different (22.2% vs 59.1%, p = 0.019, Table 2 and Fig. 3). Similarly, patients in pCR group were detected to have much higher LOF mutation rate (66.7% vs 22.7%, p = 0.005, Table 2 and Fig. 3). Univariate logistic regression showed TP53 missense mutation and TP53 LOF mutation were significantly associated with pCR status (OR 0.198, 95% CI 0.049–0.801, p = 0.023 and OR 6.800, 95% CI 1.680–27.522, p = 0.007). However, when multivariate logistic regression was applied, only TP53 LOF mutation was retained to be predictive of pCR status.

And why is that? How TP53 LOF mutation mediates a good response to NACT is complicated. TP53 has been reported to be activated in response to mitotic stress caused by agents such as taxanes that disrupt microtubules [20]. Evidences from breast cancer models and tumors have shown p53 directs cells to undergo cell cycle arrest and senescence [21, 22]. One mechanism that contributes to senescent cell survival and persistence in the residual disease after chemotherapy treatment is the engulfing and cannibalizing of neighboring cells [23]. We believe that TP53 LOF mutations lose capacity to activate cell cycle arrest and senescence to escape apoptosis induced by therapy, and as a result mediate a good response to NACT.

Some TP53 missense mutations are associated with enhanced characteristics of invasion and metastasis when they acquire a gain-of-function effect [24]. More than 80% of TP53 alterations are missense mutations that will produce a stable but transcriptionally deficient protein. These mutant-TP53-expressing tumors are aggressive and associated to poor prognosis [25, 26]. Compelling evidences have proved that TP53 missense mutations promote cell migration and metastasis and dramatically influence tumor progression [27‐29]. In our study, TP53 missense mutations showed significant difference between pCR and non-pCR groups, and also a predictor for non-pCR by univariate regression. We speculated that TP53 missense may affect therapy response depending on other variables.

TP53 mutations were not predictive of neoadjuvant chemotherapy response in the EORTC 10,994/BIG 1-00 trial [30]. However, in that study, patients with HER2 + breast cancers were randomly assigned to different NACT groups without HER2-targeted therapy, and the yeast assay was used to assess TP53 mutations which does not distinguish between pure loss-of-function mutations compared to mutations with simultaneous gain and loss-of-function [31, 32]. Several molecular alterations are thought to contribute to trastuzumab resistance, including TP53 mutation [33, 34] and PIK3CA alteration [13, 35, 36], but results evaluating these biomarkers as response predictors have been inconsistent.

Two retrospective studies [37, 38] reported TP53 mutations were significantly predictive of HER2 + patient treatment response (pCR) to neoadjuvant chemotherapies. Soley et al. report that for patient samples with concordant BluePrint/MammaPrint and PAM50 data, the pCR plus non-pCR rate among patients whose tumors were TP53 mutant was 17/39 (44%), whereas in patients whose tumors were TP53 wild type, it was 5/31 (16%), p = 0.020 [37]. And Stefan et al. report that the response rate among TP53-mutated patients was 30%, significantly higher than TP53 wild-type patients (10%; p = 0.0032) [38]. However, both studies used the AmpliChip TP53 assay (Roche Molecular Systems, Pleasanton, CA), a DNA microarray-based resequencing assay designed to detect single-base substitutions and single-base deletions in all coding regions of the TP53 gene, which needs a reference sequence and is unable to detect all the possible mutations like a NGS assay [39, 40]. Two other studies found no associations of TP53 mutations with HER2 + NACT treatment response [41, 42]. However, all these studies only examined associations of TP53 mutations as a whole with treatment response, despite the fact that TP53 missense mutations and loss-of-function mutations have quite different functions during breast cancer progression [16, 24, 26]. In our study, we assessed the associations of TP53 missense mutations and LOF mutations separately with treatment response and found quite different predictive characteristics.

From a tumor biological point of view, Ki67 should be viewed as a continuous variable, as it reflects the percentage of proliferating cells in the tumor, which can reach any value between 0 and 100%. The fact of defining our cut points should not be seen as a limitation of the marker but point to a strength of Ki67, as studies have shown a wide range of cut points was significant for various endpoints and subgroups [43]. Therefore in our study, Ki67 is still a predictive biomarker for HER2-positive breast cancer subgroup receiving chemotherapy and HER2-targeted therapy.

Tumor size was analyzed to be a predictor of pCR status by univariate regression and not by multivariate regression. It is easy to understand that tumor size is a factor susceptible to other pathological and mutational factors.

In conclusion, our study reports TP53 LOF mutations and initial Ki67 status predict pCR status for HER2-positive breast cancer patients receiving NACT. As this study is an exploratory retrospective study of small size, further prospective clinical research with large sample is still needed.