Genome Instability Profiles Predict Disease Outcome in a Cohort of 4,003 Patients with Breast Cancer

Breast cancer is the most common cancer among women and, with more than 620,000 deaths worldwide in 2018, also the major cause of cancer-related deaths (1). In addition to surgery and radiotherapy, systemic adjuvant treatment regimens were developed, including chemotherapy and endocrine therapy. Intensive research in the last decades has resulted in the identification of a few biomarkers for therapy stratification, some of which are now included in standard clinical guidelines (2). Estrogen receptor (ER) and progesterone receptor (PR) expression has been routinely measured for more than 30 years and most patients with hormone receptor–positive cancers benefit from antihormonal drugs (3–5). Breast cancers with overexpression of HER2, accounting for around 20% of the cases, can be treated with targeted antibody therapy (6). In addition, Ki-67 expression as a marker of proliferative activity is used as an indication for chemotherapy; however, it is not always part of the diagnostic procedures (7). Therefore, in routine diagnostics, the decision for adjuvant chemotherapy is often based on the estimated prognosis of the patient and additional biomarkers to improve the prognostication or predict therapy response are urgently needed.

Global gene expression analyses were used to calculate the risk for, for example, disease recurrence or therapy failure. Oncotype DX (8) and MammaPrint (9) are two of the most commonly used tests. However, not all patients with breast cancer can benefit: MammaPrint is only valid for early-stage breast cancer (lymph node stage N0-N1), and Oncotype DX is solely applicable for patients with ER-positive and HER2-negative tumors (8–10). In 2009, Swanton and colleagues developed a signature of genes that are linked to chromosomal instability and showed that a high expression of these genes was significantly associated with a poorer disease-free and disease-specific survival (DSS) rate of patients with breast cancer (11). Habermann and colleagues described a gene expression signature of genomic instability in patients with breast cancer (12). The authors showed that both MammaPrint and Oncotype DX expression signatures correlated with the degree of genomic instability (13), determined by quantitative measurements of the nuclear DNA content of the tumor cells using image cytometry. Dozens of studies have been published to finally clarify the prognostic value of the DNA content measurements (14). However, due to (i) methodologic differences between the studies, such as different classification criteria, (ii) the inclusion of different cytometric parameters, and (iii) heterogeneous patient cohorts, contradictory results emerged (15–19).

We now present a comprehensive comparison of the predictive power of routine clinicopathologic prognostic parameters with genome instability profiles established using image cytometry by applying objective, quantitative parameters in 4,003 patients with breast cancer with at least 10 years clinical follow-up.

Our cohort consists of 4,003 female patients with breast cancer diagnosed in the greater Stockholm area (Karolinska University Hospital, Vällingby Clinical Centre as well as other outpatient clinics in Stockholm, Sweden) between 1988 and 2001, for whom clinicopathologic and image cytometry data were available. The mean age of the patients was 59 years (range: 23–95). We retrospectively collected survival data over a postoperative period of at least 10 years (median follow-up 13.2 years). Data acquisition was closed in December 2012. The study was conducted in compliance with the Declaration of Helsinki. The local Swedish ethics committee approved the use of anonymized data for this retrospective study without contacting the patients (Dnr 2013/707-31/3). All clinicopathologic parameters that were included in the analyses are listed in Table 1. Pathologic tumor and lymph node stages were classified according to Union for International Cancer Control guidelines (20). The threshold for ER and PR positivity was set at ≥0.05 fmol/ng DNA, which was the standard procedure at Karolinska Hospital (Stockholm, Sweden) during that time period. Proliferative activity of the tumor cells was assessed by measurement of either Ki-67 or Cyclin A levels by immunocytochemistry. Tumors were classified as highly proliferating if more than 5% and 10% cells were positive for Cyclin A and Ki-67, respectively. As for most patients only one of these parameters had been assessed during routine diagnostics, we created a combined proliferation variable, which indicated high proliferation if at least one of the two parameters had reached the respective threshold.

All patients included in the study were treated rule-based with mastectomy or partial mastectomy, and, if indicated combined with postoperative local radiotherapy. Patients with ER–positive carcinomas received adjuvant endocrine therapy (tamoxifen and/or zoladex). Patients with advanced disease were treated with adjuvant chemotherapy (cyclophosphamide, methotrexate, fluorouracil or anthracycline-based).

Fine needle aspirates (FNA) were prospectively collected from all patients and directly processed for diagnostic purposes. FNAs were stained according to Feulgen, following standard procedures to quantify the DNA content of the nuclei (13, 21, 22). Normal lymphocytes served as controls. In every sample, between 100 and 200 tumor cells were analyzed. Examples of different genome instability profiles are shown in Supplementary Fig. S1. The analysis included the measurement of six different parameters (Table 2). In addition, as a seventh parameter, we assessed the “stemline scatter index” (SSI) according to Kronenwett and colleagues as the sum of the percentage of cells in the S-phase fraction, the G₂–M exceeding rate and the coefficient of variation of the tumor cells and considered values ≤8.8 as SSI low and >8.8 as SSI high, as described previously (21).

If not indicated otherwise, statistical analyses were conducted in IBM SPSS Statistics V22.0. The relationship between all clinicopathologic and image cytometric data was assessed by Spearman rank correlation coefficient and visualized by a matrix heatmap.

For survival analyses, DSS was used as endpoint. DSS is defined as the time until death due to breast cancer has occurred. We generated survival curves using the Kaplan–Meier method and tested equivalency of the survival distributions by the log-rank test. Univariable Cox regressions were conducted, estimating the HRs with 95% confidence intervals (CI). For all survival analyses, we categorized continuous variables according to the survival curves taking into account the group size to ensure robust statistical analyses.

Multivariable Cox regression models were used to evaluate the independence of standard clinicopathologic or image cytometric data as predictors for the DSS. We included all variables that had been analyzed in univariable analyses as potential predictors. The variables “recurrences” and “metastases” were excluded because they cannot be used for a priori prognostication. Only cases without any missing values were included in the models. The number of available cases as well as the number of events, is shown for each model separately next to the forest plot in the Results section.

All multivariable Cox regressions were performed using the backward Wald method and variables were retained with P < 0.05 (23, 24). Multivariable analyses were conducted for 16 subgroups of patients separately. On the basis of clinical and pathologic data of patients and tumors, we created nine subgroups (Fig. 1A): patients with tumor stages pT1 and pT2 were differentiated into premenopausal and postmenopausal and further subdivided according to lymph node stage (pN0 and pN1–pN3). This resulted in eight subgroups. Patients with tumor stage pT3 and pT4 were analyzed as one group (N = 113) due to the limited case number. We formed another seven subgroups consisting of patients who had received (1) only endocrine therapy, (2) only radiotherapy, (3) only chemotherapy, or (4)–(7) a combination of these therapies (Fig. 1B). For every subgroup, the initial model as well as the steps to the final model are provided in Supplementary Table S1.

Multivariable analyses including the proliferation data of Ki-67 and Cyclin A (21% of the cases had data for at least one of the markers), were performed within the premenopausal and postmenopausal patient groups, not subgrouped any further.

After Cox regression analyses, for each model, we generated a model excluding the instability parameters that had remained as predictors. We then compared the models with and without genome instability using a likelihood ratio test to ascertain whether the genome instability indeed provides additional predictive power. The test was conducted using the function “anova” in R.

For the subsequent development of the nomograms, two further Cox analyses were performed, including all premenopausal and postmenopausal patients, respectively. The models were internally validated by 1,000 bootstrap resamples and the optimism-corrected concordance index (C-Index) was calculated. The nomograms were developed using the nomogram package in R. We integrated all parameters that remained independent predictors in the Cox models of the subgroups. For the nomograms, age and image cytometric data were included as continuous variables for a better visualization.

The proportional hazards assumption was checked for each variable in all Cox models using the Schoenfeld statistical test in R (package “survival”; ref. 25). The assumption could be supported except for five variables in four different subgroups. Kaplan–Meier curves of these variables and the plots of the Schoenfeld residuals are shown in Supplementary Fig. S2.

The complete available data in this study with more than 4,000 cases had a power above 98% to detect a HR of 1.3 in a Cox regression analysis (two-sided significance level 0.05, group ratio 1:1, assumed survival rate 0.8 vs. 0.9). However, the sample size of the actual conducted subset analyses with a mean of 354 patients (ranging from 96 to 987) achieved the average power of 75% for a presumed HR of 2.11.

All reported P values are two-sided.

We retrospectively evaluated a cohort of 4,003 patients to investigate to which extent genome instability profiles could improve prediction of disease outcome in women with breast cancer. For the entire cohort, the nuclear DNA content had been quantitatively measured to generate genome instability profiles. The clinical follow-up was at least 10 years.

The results of Kaplan–Meier and univariable Cox analyses regarding clinicopathologic parameters were consistent with the literature (26–28; Table 1; Supplementary Figs. S3 and S4).

To establish genome instability profiles, we measured six parameters of which all were associated with a shorter DSS when increased (Table 1; Supplementary Figs. S3 and S4). These include the S-phase fraction (>5%: HR 2.12; 95% CI, 1.84–2.45), the G₂–M exceeding rate (>1%: HR 1.67; 95% CI, 1.41–1.98), the 2.5c exceeding rate (5–50%: HR 1.85; 95% CI, 1.44–2.39 and >50%: HR 2.66; 95% CI, 2.10–3.37), the 5c exceeding rate (0.1%–10%: HR 1.65; 95% CI, 1.37–1.98 and >10%: HR 2.69; 95% CI, 2.23–3.24), the stemline value (2.21–3.8c: HR 1.94; 95% CI, 1.64–2.30; 3.81–4.2c: HR 1.53; 95% CI, 1.23–1.91 and >4.2c: HR 2.24; 95% CI, 1.73–2.89), all with P < 0.001, and the coefficient of variation of the tumor cells (>5%: HR 1.25; 95% CI, 1.08–1.46; P = 0.003).

Likewise, the SSI (see Materials and Methods) was associated with DSS (>8.8: HR 1.94; 95% CI, 1.68–2.25; P < 0.001).

We assessed possible associations between the parameters that determine the genome instability profiles and clinicopathologic data (Supplementary Table S2). Here, ER and PR positivity showed the strongest correlation coefficient with the following parameters: a low 5c exceeding rate (−0.324 and −0.299), a SSI ≤8.8 (−0.303 and −0.281), a low S-phase fraction (−0.294 and −0.280), a low G₂–M exceeding rate (−0.271 and −0.223), and a low 2.5 exceeding rate (−0.223 and −0.224), that is, receptor-positive tumors show a lower degree of genomic instability.

In premenopausal patients, genome instability profiles were the dominant independent predictors of DSS in multivariable Cox regression models (Fig. 2). In the T1N0 and T2N+ subgroups, the S-phase fraction was the only remaining predictor of DSS (HR 2.02; 95% CI, 1.08–3.79 and HR 1.74; 95% CI, 1.04–2.91, respectively). The SSI was predictive in the T1N+ and T2N0 subgroups (HR 2.05; 95% CI, 1.07–3.92 and HR 4.05; 95% CI, 1.21–13.59, respectively).

Compared with the premenopausal subgroups, genome instability profiles played a less dominant role in postmenopausal patients (Fig. 2). However, a higher S-phase fraction was, in addition to age (>70) and PR negativity, an independent predictor in the T1N+ subgroup (HR 2.43; 95% CI, 1.54–3.83); the 2.5c exceeding rate remained an independent predictor in T1N0 patients (5–50%: HR 4.42; CI, 2.05–9.51 and >50%: HR 3.37; 95% CI, 1.56–7.28).

Due to the limited case number (N = 113), we fitted only one regression model comprising both pT3 and pT4 tumors (Fig. 2). Lymph node positivity showed predictive relevance in the regression model (HR 3.34; 95% CI, 1.48–7.55), and so did age (HR 3.52; 95% CI, 1.31–9.46 for age ≤40 and HR 2.60; 95% CI, 1.27–5.29 for age >70) and ER negativity (HR 2.68; 95% CI, 1.39–5.20). Increased S-phase fraction remained an independent predictor (HR 2.09; 95% CI, 1.13–3.88) for DSS.

We tested whether genome instability parameters could improve prognostication vis-à-vis specific treatments. We therefore computed regression models of seven therapeutic subgroups separately (Fig. 1B). In five of the subgroups (i.e., endocrine, radiotherapy, endocrine + radiotherapy, radiotherapy + chemotherapy, endocrine + radiotherapy + chemotherapy), prognostication could be improved by adding genome instability parameters in the model (Fig. 3; Supplementary Table S3). For patients treated with chemotherapy alone, the genome instability parameters did not show independent prognostic impact. For patients treated with endocrine and chemotherapy, no model could be fitted.

For all Cox analyses in which genome instability parameters remained as predictors in the final model, that is, in seven pathologic and five therapeutic subgroups, we wanted to ascertain if they provide a better prediction compared with the same model without genome instability parameters. The likelihood ratio test showed that for all analyses, the models including genome instability parameters significantly improved the prediction compared with their corresponding models with only standard prognostic parameters (Supplementary Table S3).

SSI and S-phase fraction were the dominant prognostic factors in multivariable models, both being associated with cell proliferation. Therefore, we wanted to determine if they remained independent predictors within the patients for whom independent proliferation data (Ki-67/Cyclin A) were also available. In premenopausal patients (N = 225), Cox regression determined two variables as independent prognostic factors: pathologic tumor stage (HR 0.73; 95% CI, 0.34–1.57 for pT2 and HR 15.11; 95% CI, 5.27–43.32 for pT3-pT4) and the SSI >8.8 (HR 2.96; 95% CI, 1.42–6.15). The Ki-67/Cyclin A proliferation variable could not improve prognostication in premenopausal patients. In contrast, in postmenopausal patients (N = 617), high Ki-67/Cyclin A levels remained an independent prognostic factor (HR 1.92; 95% CI, 1.30–2.83), while none of the genome instability profile parameters remained independent predictors (Supplementary Fig. S5A). Excluding the proliferation variable from this analysis, we determined that the S-phase fraction remained an independent predictor (>5%: HR 1.73; 95% CI, 1.14–2.62), now substituting the Ki-67/Cyclin A variable (Supplementary Fig. S5B).

On the basis of the independent predictors found in the Cox regression analyses, we developed two nomograms separately for premenopausal and postmenopausal patients (Fig. 4). Nomograms estimate the survival probability for a patient after 5 and 10 years based on a total score which is calculated by addition of zero to 100 points for every individual predictor. In the nomogram for premenopausal patients (Fig. 4A), the S-phase fraction showed the highest impact of all parameters because a high value of the S-phase fraction can add up to 100 points to the final score. In contrast, in postmenopausal patients, the instability parameters (S-phase fraction and 2.5c exceeding rate) only play a minor role for prediction (Fig. 4B). The internal validation of the underlying regression models showed optimism-corrected C-index values of 0.69 and 0.74 for premenopausal and postmenopausal patients, respectively.

Our analyses were conducted on a cohort of 4,003 patients with breast cancer that had been analyzed by image cytometry. We first analyzed routine diagnostic parameters by Kaplan–Meier survival analyses using DSS. Our results confirmed the current state of the literature and therefore the integrity of our patient cohort (26–28).

Image cytometric measurements of the nuclear DNA content have been discussed as additional prognostic parameters in several cancer entities, particularly in breast cancer, for more than 30 years (14, 29). In addition, image cytometry can provide information about the proliferation rate or intratumor heterogeneity (30, 31). The degree of aneuploidy and intratumor heterogeneity influences disease outcome (14, 32).

We now analyzed genome instability profiles using image cytometry in a coherent cohort of 4,003 patients with breast cancer to assess its prognostic potential. In Kaplan–Meier analyses and univariable Cox regressions, all cytometric parameters showed prognostic significance, and the results were consistent with the underlying biology.

Except for hormone receptor status and age at diagnosis, none of the clinicopathologic parameters showed strong associations with parameters of the genome instability profiles. Therefore, we surmised that the genome instability profiles could have independent prognostic value when analyzed together with clinicopathologic parameters, which we validated in multivariable analyses.

The S-phase fraction and the SSI emerged as the most important image cytometric parameters for prognosis and, particularly in premenopausal patients, even more meaningful than standard clinicopathologic parameters. The S-phase fraction was already subject of dozens of studies, analyzing its prognostic value in breast cancer. Assessed by means of image cytometry in breast cancer, it was shown to correlate with disease-specific survival as well as with larger tumor sizes and younger age at diagnosis (19, 33). However, to our knowledge so far, no study analyzing the S-phase fraction by image cytometry on a comparably large and coherent cohort has been published. In contrast to the S-phase fraction, the SSI, first proposed by Kronenwett and colleagues (21), is less commonly studied. Nevertheless, in our analyses, it showed clear potential as an independent prognostic factor in premenopausal patients.

Because the assessment of the SSI includes the S-phase fraction, both of these parameters are associated with the proliferative activity of the tumor cells. The current approach for diagnosis and therapy decision in breast cancer includes IHC staining of Ki-67 as a proliferation marker. Studies already showed that both the image cytometric and flow cytometric S-phase fraction are significantly associated with the Ki-67 expression of breast tumors (33–35). In our analyses of the subgroup of patients for whom data of the standard proliferation markers Ki-67 or Cyclin A were available, the SSI remained an independent predictor for the prognosis of premenopausal patients, outperforming the standard proliferation variable.

Regarding therapeutic approaches, we could show that genome instability profiles help to detect high-risk patients in five of seven therapeutic subgroups and therefore might be a tool to tailor adjuvant therapy. Patients who were treated with endocrine therapy only had a worse prognosis if they showed high 5c and/or G₂–M exceeding rates. In these cases, adjuvant radiotherapy or chemotherapy might have been suitable. This applies, in particular, to young (≤40 years) and old (>70 years) patients with positive lymph nodes and a negative PR status. Lymph node positivity and an S-phase fraction above 5% indicated a worse prognosis in patients who received single radiotherapy; these patients might have benefitted from additional chemotherapy. Adjuvant chemotherapy might also be suitable in patients who are treated with a combination of endocrine therapy and radiotherapy if an S-phase fraction above 5% and a G₂–M exceeding rate above 1% are measured, especially in lymph node positive, PR-negative patients with high tumor stages.

Several nomograms for the prediction of overall or disease-free survival of patients with breast cancer have been published (36–39). Different types of parameters were used for their development, but most of them were based on clinicopathologic features only. To our knowledge so far, no nomogram has been developed combining clinicopathologic and genome instability parameters.

As described in Materials and Methods, for the regression model categorized variables were used instead of the continuous measures. We are aware that categorization of those variables means a certain loss of information which possibly decreased the power of the models. Nevertheless, as our main goal was to qualitatively describe which variables can be prognostically useful, we chose to use the categorical approach for an easier interpretation and visualization of the results. Furthermore, not all variables showed a linear relation with the survival time, for example, age. Including those variables as continuous measures would not have provided this information. The cut-off values were chosen on the basis of the hazards of the categories as well as the group size so that robust results could be ensured. Our results provide a basic understanding about which parameters of the genome instability profiles can add prognostic information to standard clinicopathologic factors and should be further evaluated in prospective studies.

To our knowledge, this is one of the largest patient cohort that has ever been analyzed by image cytometry. The high case number allowed us to form clinically relevant subgroups of at least 100 patients to be analyzed separately. All patients were from the greater Stockholm area, which assured standard diagnostics and treatments, and were observed over a postoperative time period of at least 10 years. Nevertheless, besides the strengths of our study, there are weaknesses that have to be mentioned. The patients were diagnosed between 1988 and 2001, therefore data on the HER2 status were not available. Due to the lack of standardization of the image cytometry, the analyzed parameters vary between different studies. For this reason, an independent patient cohort to validate our results was not available and we focused on the explorative data analysis of the subgroups.

In summary, our study showed that in the majority of clinicopathologic and therapeutic breast cancer subgroups, the genome instability profiles can add prognostic information to standard clinicopathologic parameters or even substitute them and serve as single independent predictors. S-phase fraction and SSI were the most meaningful predictors with the highest impact in premenopausal patients. Our results reveal a new possibility for a more precise risk group stratification in patients with breast cancer based on genome instability profiles, which is a further step to individualized medicine. The value of including genome instability data in breast cancer prognostication, especially the assessment of S-phase fraction and SSI, should now be prospectively validated in multicenter studies.

A. Lischka reports personal fees from Ad Infinitum Foundation (PhD scholarship) during the conduct of the study. N. Doberstein reports personal fees from Ad Infinitum Foundation (PhD scholarship) during the conduct of the study. No potential conflicts of interest were disclosed by the other authors.

A. Lischka: Conceptualization, data curation, formal analysis, investigation, visualization, methodology, writing-original draft, writing-review and editing. N. Doberstein: Conceptualization, data curation, formal analysis, investigation, visualization, writing-review and editing. S. Freitag-Wolf: Data curation, formal analysis, investigation, visualization, methodology, writing-original draft, writing-review and editing. A. Koçak: Investigation, writing-review and editing. T. Gemoll: Writing-review and editing. K. Heselmeyer-Haddad: Writing-review and editing. T. Ried: Conceptualization, writing-original draft, project administration, writing-review and editing. G. Auer: Conceptualization, project administration, writing-review and editing. J.K. Habermann: Conceptualization, resources, writing-original draft, project administration, writing-review and editing.

This work was supported in part by the Intramural Research Program of the NCI at the NIH, intramural funding of the University of Lübeck as well as by the Interdisciplinary Center for Biobanking-Lübeck (ICB-L), University of Lübeck. A. Lischka and N. Doberstein obtained a PhD fellowship by the Ad Infinitum Foundation. A. Koçak was supported by the “Deutsche Stiftung für junge Erwachsene mit Krebs”. The authors are grateful to Dr. Stanley Lipkowitz and Dr. Reinhard Ebner for critical comments on the manuscript.

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