Gene expression profiling has successfully yielded new insights into the biologic diversity of breast cancer identifying several distinct molecular subtypes (such as luminal A, luminal B, basal and Her2) differing markedly in prognosis and in the repertoire of therapeutic targets they express [4, 5, 50]. Importantly, these intrinsic subtypes play a key role in prediction of disease recurrence, treatment response, and the provision of new insights into oncogenic pathways and metastatic progression . It is striking that, in the face of what is considered a heterogeneous tumour, molecular signatures of tumour subtypes consistently emerge across independent cohorts with diverse genetic and environmental backgrounds [52–54]. This reproducibility is a crucial primary descriptor of disease phenotype in the early detection of disease, lending key prognostic and predictive information.
Antibody-based proteomics occupies a pivotal space within the cancer biomarker discovery and validation pipeline, facilitating the high-throughput evaluation of candidate markers . In this context, IHC-based high-throughput technology has been demonstrated as an effective platform for identification of protein surrogates of these intrinsic breast cancer subtypes by various groups [23, 53]. For example, a panel of 5 proteins detected by immunohistochemistry was shown to be prognostic for ER-positive breast cancer . The use of validated IHC surrogates should provide more clinically applicable assays in the future, due to ease of accessibility, low technical demand, cost-effectiveness and applicability to FFPE tissue. Despite these advances, the development of IHC-based assays has been globally impaired by the limited availability of high quality antibodies and lack of rigorous validation of emerging biomarkers. However, the development of comprehensive antibody resources and streamlining of reporting standards, promises to help overcome these obstacles [31, 55].
In this study, we sought to determine whether insights from gene expression studies relating to breast cancer progression could be translated into a robust prognostic protein model using a discrete set of IHC markers. This proof-of-concept strategy generated a prognostic panel using high-throughput biomarker screening in combination with a devised panel scoring technique. We confirmed that a high panel score was significantly associated with reduced RFS (p < 0.0001; n = 1,038), using a meta-analysis of publicly available breast cancer transcriptomic datasets. The panel was an independent prognostic marker using multivariate Cox regression analysis (p = 0.018, HR = 1.49, 95% CI = 1.080-2.054, n = 699). This strategy revealed a novel 3-marker prognostic model significantly predictive of RFS based on ANLN, PDZK1 and PBK expression patterns.
Next, we validated this signature on a protein-based platform using TMA technology. The 3-protein panel score correlated with known pathological prognostic variables, including tumour grade and lymph node status, ER, Her2 and Ki67 status. Univariate Cox regression analysis of RFS demonstrated that high panel scores, indicative of poor prognosis, were significantly associated with reduced RFS. However, multivariate analysis demonstrated that the 3-marker panel score was not a significant predictor of either BCSS (HR = 6.38; 95% CI = 0.79-51.26, p = 0.082) or RFS (HR = 1.46; 95% CI = 0.66-3.19, p = 0.348), when adjusted for other well-established variables. We noted that the 3-panel score becomes an independent predictor of BCSS (HR = 11.66; 95% CI = 0.1.50-90.68, p = 0.019), when all variables except for PR status are adjusted for. This may be due to marginal associations of our individual markers with these variables (e.g. PDZK1 and ER status: p = 0.041; PDZK1 and PR status: p = 0.074). Since both PDZK1 and PR are surrogate markers for ER activity, we note that the strength of this panel may be skewed by the presence of PDZK1 protein in the panel. Thus, we hypothesise that additional or alternate biochemical markers, interrogated using this high-throughput platform, may further augment the prognostic accuracy of this algorithm to a point that may allow implementation into routine clinical practice.
Interestingly, the 3 proteins that comprise this panel model are associated with distinct pathways in cancer biology. ANLN, initially characterised as a human homologue of anillin, a Drosophila actin-binding protein, is essential for the organisation of actin cables in the cleavage furrow, and plays a key role in cytokinesis and cell cycle progression [56–59]. ANLN has been demonstrated as a marker of poor prognosis, relating to aggressive cancer phenotypes . In breast cancer, a transcriptomic study of DCIS to IDC breast cancer progression identified ANLN up-regulation in invasive tumour specimens relative to the pre-invasive phenotype . Our study confirms the role of ANLN as a marker of poor prognosis, at the protein level, in an independent breast cancer cohort. PBK phosphorylates p38MAPK during mitosis, is considered a marker for cellular proliferation and is also implicated in DNA damaging sensing and repair [61, 62]. PBK is associated with poorer prognosis in lung cancer , is up-regulated in IDC relative to DCIS at the transcriptomic level , and may be a promising molecular target for treatment of breast cancer . Our findings further support the role of PBK as a marker of poor prognosis in breast cancer, with expression of PBK also associated with the histological markers of proliferation, Ki67 and tumour grade. PDZK1 is a known estrogen response gene in breast cancer, with proposed roles in signal transduction, cell polarity and ion exchange gating [65, 66]. An in-house statistical re-analysis of the genes assessed by van’t Veer and colleagues in the development the 70-gene prognostic signature identified PDZK1 as a marker of good prognosis in breast cancer , which we confirmed at the protein level in this study. The present study successfully validates these gene expression findings at the mRNA level, and also translates them at the protein level.
However, further studies are warranted at the in vitro and in vivo level, to help further interrogate the functional background of each of these markers in breast cancer progression. It will be necessary to further validate these findings with additional independent cohorts of samples to meet accepted international validation guidelines . Although the literature is conflicting with regard to the best way to incorporate histopathology, IHC phenotypes, and gene expression data into an accurate classification system, our findings further support the key role of IHC prognostic models for current breast cancer management.