p53, cathepsin D, Bcl-2 are joint prognostic indicators of breast cancer metastatic spreading

A multi-institutional case series of BC patients was collected from the National Cancer Institute of Naples, together with the University of Udine, the district hospital of Venice and Rovigo, Italy. Two hundred and seventeen BC patients were analyzed (Table 1). Clinical data (age, family history, clinical stage, disease follow-up) and conventional prognostic indicators (size, pathological stage, local invasion, margin width, lymph-node invasion, histological type, necrosis, inflammatory infiltration, hormonal receptor status) were recorded [16, 17] (Table 1). Cancer grade was determined as described [18] (Table 1; Additional file 1: Table S1 and Additional file 2: Table S2). Twenty six cases (12 %) showed disease relapse during follow-up (Additional file 1: Table S1). Relapsed cases were matched with a set of control patients by tumor diameter, pathological stage, tumor histotype, age, hormone receptors and grading (Additional file 1: Table S1), and analyzed for expression of tumor progression determinants by immuno-histochemistry (IHC) and DNA sequencing, as indicated. To identify patterns of aggregation of molecular alterations associated to different classes of BC prognosis, stepwise grouping procedures were performed for model structure fitness assessment, as described.

Tissue micro-arrays (TMA) of tumor samples were assembled as described [19, 20]. Briefly, whole-tumor sections of formalin-fixed paraffin-embedded (FFPE) BC samples were stained with hematoxylin-eosin, and used for guiding selection of tumor-containing areas. Three 1 mm diameter cylinders were then obtained from all tumors and transferred to recipient blocks. Filled blocks were heated for 15 min at 37 °C to induce the tumor cores to adhere to the paraffin walls. TMA sections were analysed by IHC for the expression of markers relevant to tumor development and aggressiveness (Figs. 1, 2 and 3, Table 1). Briefly, 5 μm sections of BC TMA were mounted onto Vectabond-coated slides (Vector Laboratory). Before staining, sections were heated at 56 °C and dewaxed in xylene/ethanol. Endogenous peroxidase was blocked with hydrogen peroxide in methanol. Heat-mediated ‘antigen retrieval’ was performed by treatment in pH 6 citrate buffer in a pressure cooker or microwave oven, as required for each specific target. After pre-incubation with appropriate blocking agents, e.g. species-matched normal serum, sections was incubated with the primary antibody (Additional file 3: Table S3). After washing, sections were challenged with fragment antigen-binding (Fab)₂ biotinylated secondary reagents, followed by avidin-peroxidase and 3,3′-diaminobenzidine tetrachlorohydrate, activated with 0.3 % hydrogen peroxide. Nuclei were counterstained with Mayer’s hematoxylin. Appropriate normal mouse/rabbit secondary reagents or unrelated antibodies were used as negative controls. Primary antibodies directed against the chosen targets are listed in Table 2. Immunoreactivity for the various reagents was quantified on an average of 1000 cells in randomly chosen fields (40x objective). Semiquantitative scores were determined by two independent observers (M. P. and R. L.). Percent expression values of ER, PR and HER2 tended to distribute around discrete values (0, 10, 25, 50, 75 and 100 % of tumor cells) and were categorized accordingly. Percentages of Ki-67 and p53 expressing cells were analyzed without discretization, but are reported here as categorical variables for convenience (0, 1–10, 11–75, 76–100) [8]. Intensity scores varied between 0 and 3, where 0 is no reactivity, 1 is borderline detectability, 3 is the maximum observed intensity and 2 corresponds to an intermediate intensity. A combined score was obtained by multiplying percentages of positive cells by intensity. Scores were then categorized for statistical evaluation [21].

FFPE BC sections were processed as described [22, 23]. This procedure provided with relatively crude DNA preparations, which, however, could be efficiently used as a template in ≤150 bp-long polymerase chain reaction (PCR) amplifications [24]. Briefly, four 5 μm tumor sections were deparaffinized by two extractions with either xylene or Histoclear (Carlo Erba), followed by two extractions with ethanol. Samples were then digested for 3 h at 50 °C with proteinase K (PK) 2 mg/ml, Tween 20, Tris-Cl 50 mM, EDTA 1 mM, pH 8.5, then overnight at 50 °C after PK replenishing. Samples were then incubated at 95 °C for 15 min to inactivate PK, centrifuged at top speed for 15 min at 4 °C, transferred to a fresh tube and stored at − 20 °C. DNA yields were quantified by ethidium bromide fluorescence in solution [25]. On average 30 μg DNA/sample were obtained. Size distribution of the extracted DNA [26] was profiled by ethidium bromide/agarose gel electrophoresis for sample quality assessment.

After thawing, DNA samples prepared as above were incubated at 95 °C for 25 min (this step was critical for successful amplification). One μl of this crude extract was added to the amplification mix. Primers were designed using Primer3 [27, 28] (Additional file 4: Table S4). TP53 exons (from 2 to 11) were separately amplified using optimized primer sets; exons 4 and 5 were amplified as two overlapping amplicons, using non-overlapping primers, to prevent loss of mutations detection capacity in the primer-annealing region.

In both Ha-RAS (c-Ha-RAS1) and Ki-RAS (c-Ki-RAS2) activating oncogenic mutations are found at hotspots in exon 1 and 2, at codons 12, 13 (exon 1) or 61 (exon 2). Care was taken to differentially amplify the regions of interest of functional genes versus non-expressed pseudogenes, i.e. c-Ha-RAS2 and c-Ki-RAS1. Benchmark PCR amplification of Ha- and Ki-RAS exons 1 and 2 was performed using genomic DNA and cDNA from the T24 cell line, which carries a mutated, oncogenic form of Ha-RAS with a transversion at codon 12 (from GGC to GTC). When using cDNA templates, PCR primers were designed that reside in exonic regions, for simultaneous amplification of both exon 1 and 2 of Ha-and Ki-RAS. Joint amplification of exon 1 and 2 from genomic DNA was only performed for the Ha-RAS gene (the intervening intron is only 267 bp long in the Ha-RAS gene; it is more than 12,500 bp long in the Ki-RAS gene). Additional primers were designed that included intronic regions and were therefore specific for amplification of functional genes from genomic DNA. Amplified fragments were sequenced on both strands. Insertions or deletions (indels) of the TP53 gene (Additional file 5: Table S5) were shown to carry the highest prognostic weight [29]; such mutations were identified and matched against those listed in the IARC database [29].

The independent impacts of individual risk factors on prognosis is commonly evaluated in the framework of uni-or multivariate models [8, 19, 30, 31]. Univariate analyses were performed with GraphPad Prism 6.0 (GraphPad Software Inc., La Jolla, Ca) and XLStat 2009 (Addinsoft, Paris, France). Multivariate analyses and data modeling were performed using MetaboAnalyst 2.0 [32‐34] and SIMCA-P+ 11 (Umetrics, Umea, Sweden) [35] software. However, uni-or multivariate analyses do not effectively quantify interaction effects on the final outcome. To explore such interactions, a priori specified hypotheses have been used in the past as trial models, but at the risk of introducing analytical bias. To overcome these limitations, patterns of aggregation of molecular parameters affecting prognosis were modeled here through logistic regression and partial least squares discriminant analysis (PLS-DA). PLS-DA clustering was performed using relapse as a dichotomic variable. PLS-DA model validation was performed as previously described [36]. Briefly, to define the optimal number of PCs, “7-fold cross-validation” (CV) was applied [37]. Using CV, the predictive power of the model was verified through R² (goodness of fit) and Q² (goodness of prediction). A model with Q² > 0.5 was considered good, Q² > 0.9 excellent [38]. The performance of PLS-DA models was further validated by a permutation test (200 times). To help interpreting results from PLS-DA, we utilized variable importance in the projection (VIP) scores. This allowed to evaluate the parameter influence on the model and to identify the best descriptors of relapsing versus non-relapsing BC. VIP scores are weighted sums of squares of the PLS loading weights, which take into account the amount of explained Y-variation for each dimension [33]. VIP values were cumulatively calculated from all extracted PLS components, usign a threshold of 0.8 [39]. As some variables may exert effects on the whole population (global), while others can be relevant in specific subgroups only (local), procedures were utilized to identify homogeneous subgroups with respect to corresponding parameters subclasses [40‐42]. Spearman’s correlation analysis was performed using MetaboAnalyst 2.0 software [32‐34] and GraphPad Prism.

Histopathology and molecular biology determinations were performed as indicated [43, 44] (Figs. 1, 2 and 3; Additional file 1: Table S1 and Additional file 2: Table S2). Negative/positive correlations between histopathological and experimental parameters were assessed by Spearman’s correlation analysis (Additional file 3: Table S3). Strongest positive correlations with metastastic relapse were found for grading (rho = 0.454, p = 0.005), local relapse (rho = 0.892, p < 0.001), p53_n (rho = 0.309, p = 0.067) and uPA in the extracellular matrix (rho = 0.387, p = 0.02). Highest negative correlations (Additional file 3: Table S3) were observed between metastastic relapse and intracellular uPA (percent cytoplasmic: rho = -0.369, p = 0.027; expression intensity: rho = -0.435, p = 0.008). Of interest, p53 expression (% positive cells and intensity) was found to be associated with secreted cathepsin D (rho = 0.477, p = 0.003) and was negatively correlated with Bcl-2 (rho = -0.385, p = 0.02). p53 expression was correlated with grading (rho = 0.499, p = 0.002), cyclin E (rho = 0.335, p = 0.046), PAI-1 in the extracellular matrix (rho = 0.444, p = 0.007), Her-2 (rho = 0.368, p = 0.027) and p16 (rho = 0.514, p = 0.001) expression, first suggesting key interactions and potential synergy with other key drivers of tumor malignancy.

Internal benchmarks for additional risk determination procedures were first assessed. Biochemically-determined negativity for estrogen receptors was associated with relapse. Estrogen receptor negativity, by semi-quantitative IHC determination, was associated with twice as high relapse hazard ratio (HR) (HR = 2.0; 95 % C.I. = 0.6–7.4). An increased mitotic index (Ki-67 expression) was associated with increased risk of developing adverse events (HR = 2.6; 95 % C.I. = 0.6–11.2), consistent with previous studies [31].

Correlated analysis of the analyzed tumor determinants revealed marked increase in HR for p53, cathepsin D and Bcl-2 (Figs. 1, 2). Positivity for p53 nuclear expression was found to associate with an eleven-fold increase in relapse risk (HR = 11; 95 % C.I. = 2.5–51.8). Unprecedented increase in risk was found for cathepsin D expression (HR = 20; 95 % C.I. = 2.3–184.3). Notably, expression profiles of p53 and cathepsin D remained significantly different between cases and controls when subgrouping patients by lymph node status, supporting an independent prognostic value of these parameters. Lymph node diffusion correlated with local cancer relapse (rho = 0.405, p = 0.014), but did not with distant metastatic relapse, raising the issue that determinant of local invasion may differ from those required for metastatic diffusion. Hence, we assessed the impact of p53 and cathepsin D in lymph node-negative patients. Remarkably, tumor co-expression of p53 and cathepsin D in this patient subgroup remained associated to a sixteen-fold higher risk of experiencing relapse (HR = 16; 95 % C.I. = 1.5–171.2). Trends for association of positive lymph nodes and tumor size were found: 50 and 78 % of lymph-node-positive women were positive for p53 and cathepsin D, respectively; 63 and 74 % of women with tumors bigger than 2 cm were positive for p53 and cathepsin D, respectively.

Remarkably, the expression of Bcl-2 was associated with a markedly better prognosis, and a nine-fold reduction of risk (HR = 9.2; 95 % C.I. = 1–87.8). Bcl-2 expression was previously found to correlate with a differentiated cancer phenotype, i.e. with lower grading and lack of p53 [45]. Consistent, Bcl-2 expression was found to correlate with that of ERα and PgR, and was anti-correlated with cancer grading and with the expression of p53, Cyclin E and Her-2 (Table S3). Correspondingly, Bcl-2 expression was shown to have a beneficial influence on prognosis [46, 47], whereas loss of Bcl-2 was found in 70 % of the aggressive triple-negative BC, and was significantly associated with high proliferation, tumor progression, increased risk of death and recurrence [48]. Still, the magnitude of Bcl-2 prognostic impact observed here in metastatic versus non-metastatic BC had not been previously revealed [49], supporting a critical value of correlated evaluation of malignancy determinants (Bcl-2, p53, cathepsin D) for effective use in prognostic assessment.

To verify the strength of this unsupervised analysis, and to further build on it, we performed a supervised PLS-DA [50]. Datasets of pathological/experimental parameters were grouped using a dichotomic classification (metastatic relapse versus no relapse). This model was found to have strong goodness of fit (cumulative R²Y = 0.828) and prediction power (cumulative Q² = 0.548) (Figs. 4, 5 and 6). PLS-DA-identified determinants clusters yielded a clear-cut discrimination between metastatic versus non metastatic tumours (Figs. 4, 5a). A PLS-DA weight plot was generated in order to identify the major discriminants between the groups analyzed (Fig. 4). Next, VIP scores were computed for each parameter. Twenty descriptors, i.e. local relapse, grading, HER-2 (membrane intensity), lymph node status, p53, p16, Bcl-2, Cyclin E, PgR, together with stromal cathepsin D, PAI-1, uPA and MMP-11 were found to markedly contribute to the classification model (VIP score ≥ 0.8) (Fig. 5b) [39]. Permutation tests were carried out in order to validate the PLS-DA model [38, 50]. The original model was found to have higher R² and Q² values than the permuted models, and negative Q² values were obtained for all two permuted groups tested (Fig. 5c).

DNA was extracted from sections of FFPE BC (Additional file 1: Table S1A). Ethidium bromide gel electrophoresis (Fig. 7) and amplification of RAS and TP53 exons benchmarked DNA as viable for downstream analyses. RAS and TP53 sequences were determined on cases and control DNA (Additional file 1: Table S1), as described (Figs. 7 and 8).

Case and control FFPE tumor samples, were systematically analyzed for insertions, deletions and stop codons in the coding region of the TP53 gene by PCR and sequencing of PCR amplification products (Figs. 7 and 8; Additional file 5: Table S5). Structural alterations of the TP53 gene are listed in Additional file 5: Table S5. Three indels were identified, and one stop codon, all of which led to truncation of the corresponding p53 proteins. Remarkably, all truncated p53 (8.7 % of the BC cases) were identified in relapsing cancers, three out of four cases being grade 3, i.e. those with the most malignant phenotype. These findings support models were severely damaged p53 is a strong risk factor for tumor progression in defined subgroups of BC [7, 8, 10, 31]. Notably, though, only one of these cases was a triple-negative tumor, a tumor phenotype traditionally associated with tumor aggressiveness [8], suggesting that the present molecular characterization may lead to novel subgrouping strategies of BC for risk determination. However, larger case series are needed to validate this approach.

Case and control BC samples, were analyzed for mutations at codons 12, 13, 14 and 61 of the Ha-RAS and Ki-RAS genes by PCR amplification and sequencing of the first and second exon. Three cases showed a mutation at codon 12 of Ha-RAS, from GGC to GTC (Gly → Val); one case showed an additional mutation at codon 14 from GTG to ATG (Val → Met), with an overall prevalence of tumors bearing RAS mutations of 6.4 % (Additional file 1: Table S1). Of note, all mutations occurred in the metastatic and locally invasive/relapsing cases. This suggested relevance of mutated Ha-RAS in a small, distinct subset of metastatic BC. Mutations of both Ha-RAS and TP53 were identified in the same cancer, suggesting a possible cooperativity in cell transformation [51].