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Association Between Proportion of Nuclei With High Chromatin Entropy and Prognosis in Gynecological Cancers

Abstract Background Nuclear texture analysis measuring differences in chromatin structure has provided prognostic biomarkers in several cancers. There is a need for improved cell-by-cell chromatin analysis to detect nuclei with highly disorganized chromatin. The purpose of this study was to develop a method for detecting nuclei with high chromatin entropy and to evaluate the association between the presence of such deviating nuclei and prognosis. Methods A new texture-based biomarker that characterizes each cancer based on the proportion of high–chromatin entropy nuclei (<25% vs ≥25%) was developed on a discovery set of 175 uterine sarcomas. The prognostic impact of this biomarker was evaluated on a validation set of 179 uterine sarcomas, as well as on independent validation sets of 246 early-stage ovarian carcinomas and 791 endometrial carcinomas. More than 1 million images of nuclei stained for DNA were included in the study. All statistical tests were two-sided. Results An increased proportion of high–chromatin entropy nuclei was associated with poor clinical outcome. The biomarker predicted five-year overall survival for uterine sarcoma patients with a hazard ratio (HR) of 2.02 (95% confidence interval [CI] = 1.43 to 2.84), time to recurrence for ovarian cancer patients (HR = 2.91, 95% CI = 1.74 to 4.88), and cancer-specific survival for endometrial cancer patients (HR = 3.74, 95% CI = 2.24 to 6.24). Chromatin entropy was an independent prognostic marker in multivariable analyses with clinicopathological parameters (HR = 1.81, 95% CI = 1.21 to 2.70, for sarcoma; HR = 1.71, 95% CI = 1.01 to 2.90, for ovarian cancer; and HR = 2.03, 95% CI = 1.19 to 3.45, for endometrial cancer). Conclusions A novel method detected high–chromatin entropy nuclei, and an increased proportion of such nuclei was associated with poor prognosis. Chromatin entropy supplemented existing prognostic markers in multivariable analyses of three gynecological cancer cohorts.


Sample preparation and imaging
Curettage specimens taken from endometrial tumors and tumor tissue from surgically removed ovarian cancer and uterine sarcomas were formalin-fixed and paraffin-embedded. Haematoxylin and eosin-stained sections from each tumor were evaluated to identify representative regions with tumor tissue. For each tumor, isolated nuclei were prepared from one or more 50 µm sections and stained with Feulgen-Schiff according to an established protocol (1).
A DNA Ploidy System, which consisted of a Zeiss Axioplan microscope equipped with a 546 nm green filter and a monochrome high-resolution digital camera (AxioCam MRm, Zeiss, Jena, Germany or C4742-95, Hamamatsu Photonics, Japan), was used to capture images. The grey level resolution was 10 bits/pixel and the physical resolution was 166 nm/pixel for the uterine and ovarian specimens and 162 nm/pixel for the endometrial specimens.
Cell nuclei were automatically segmented and the cell type of the nuclei was assigned by an automated cell classification system. Trained personnel verified the selected nuclei such that only tumor nuclei, and only epithelial nuclei in the carcinoma data sets, were included in the analyses. Necrotic nuclei, doublets, cut and folded nuclei were discarded. In order to compensate for technical variation in sample preparation and imaging setup across the three materials, the size and grey levels of the nuclear images were normalized based on automatic detection of diploid cells in each sample (2).

Nuclear texture analysis by grey level entropy matrices
A grey level entropy matrix (GLEM) (3,4) was computed from each nuclear image. Such a matrix contains information about the bi-variate distribution of pixel grey level intensities and corresponding grey level entropy values measured locally around the pixels.
The GLEM element value P(i,j) is the normalized frequency of the first order Shannon grey level entropy value j within a window of size w×w centered on a pixel with grey level value i (3). The grey level entropy is defined as where P(i) is the normalized frequency of grey level i within the window and log is the natural logarithm. The entropy measures the grey level heterogeneity within the window. Homogeneous chromatin structures will give low entropy values whereas disorganized chromatin structures will give high entropy values. The number of grey levels in the nuclear images was reduced by uniform re-quantification to 64 before computation of the GLEMs and the window size was set to 15×15 pixels just as in (5).

Measuring the compactness and center of mass of a GLEM
In previous studies of early ovarian cancer (5), uterine sarcoma (6) and endometrial cancer (7), we observed that the (grey level, entropy)-values in nuclei from good prognosis patients were (on average) more concentrated to a smaller fraction of the GLEM compared to values in nuclei from poor prognosis cases, and in a study for assessment of dysplasia (8), we found that (grey level, entropy)-values in nuclei from dysplastic cases showed more variation compared to nuclei from non-dysplastic cases. In all these studies we also observed a shift in the mean of the GLEM distributions that was related to the concentration/variation of the (grey level, entropy)values.
Classical texture features extracted from a matrix, such as e.g. a grey level co-occurrence matrix (GLCM) (9) or a GLEM(3), may be seen as a weighted sum of the matrix element values, where the weighting applied to each element is based on a given weighting function. By varying this weighting function, different types of information about the texture can be extracted (4). In previous studies, we have used adaptive weighting functions that are learned from a discovery set and are based on average differences between clinical classes (2,(5)(6)(7)(8)10). In the present study, we propose a new way of extracting features from a GLEM that is not based on computation of a weighted sum. We instead calculate three novel features (defined below) that measure the compactness and the location (mean) of the GLEM probability distribution. The aim of the study was to evaluate if these novel features could be useful for (i) capturing the concentration/variation in a GLEM, (ii) identifying a small subgroup of nuclei with high chromatin entropy and (iii) classifying nuclei and cancer patients according to prognosis.

Center of mass
The center of mass in a GLEM, i.e. the center of mass in the bi-variate distribution of (grey level, entropy)values, was computed as the mean grey level and the mean entropy value.

Relative matrix area
The probability values in the GLEM were sorted in descending order, and then the values were summed until the total sum was greater than or equal to 0.25. The GLEM concentration was measured as the number of matrix elements included in the summation divided by the total number of matrix elements (i.e., the relative matrix area of the summed elements). This measure is a dimensionless relative spread feature, invariant to location, shape and rotation.

Sarcoma discovery and validation sets
The uterine sarcomas were divided into a discovery set and a validation set by sorting the sarcoma patients by date of diagnosis and then assigning every second sarcoma (starting with the first one) to the discovery set and the other sarcomas to the validation set. The two data sets were subsequently balanced for histology type by moving seven leiomyosarcomas from the validation set to the discovery set, and five endometrial stromal sarcomas and two adenosarcomas from the discovery to the validation set (6). To move the leiomyosarcomas, the 129 leiomyosarcomas in the validation set were sorted by date of diagnosis and every eighteenth leiomyosarcoma was moved to the discovery set. Similarly, the 46 endometrial stromal sarcomas in the discovery set were sorted by date of diagnosis and every ninth endometrial stromal sarcoma was moved to the validation set, and the 14 adenosarcomas were sorted by date of diagnosis and every seventh adenosarcoma was moved to the validation set.

Prognostic grouping of early censored patients
In the present study, the design and evaluation of the chromatin entropy marker were based on a grouping of patients into good and poor prognosis classes (Supplementary Tables 4-5). Due to censoring, this prognostic grouping of patients may cause problems for early censored patients. The 184 uterine sarcoma patients who survived for at least five years were defined as good prognosis, whereas the 170 patients who died within five years were defined as poor prognosis. All patients still alive at the end of the uterine sarcoma study were followed up for more than five years, so in this material early censored patients caused no problems.
In the ovarian cancer material, good prognosis was defined as no recurrence of disease (n=173), whereas poor prognosis was defined as recurrence of disease (n=73). The good prognosis patients had a medium follow-up time of 12.9 years (IQR, 11.3-14.4), whereas the poor prognosis patients had a medium follow-up time of 1.9 years (IQR, 0. 8-4.4). Among the poor prognosis patient, 72 (of 73) patients experienced recurrence within 10 years and only one patient experienced recurrence after 10 years, specifically this recurrence was detected 11.1 years after surgery. Among the good prognosis patients, only 12 (of 173) patients were followed up for less than 10 years. In this cohort, early censoring will thus cause uncertainty about the prognostic grouping in only a very few patients.
In the endometrial cancer material, good prognosis was defined as no endometrial cancer death (n=707), whereas poor prognosis was defined as cancer-specific death (n=84). The good prognosis patients had a median followup time of 37.0 months (IQR, 19.0-55.0), whereas the poor prognosis patients had a median follow-up time of 14.0 months (IQR, 6.0-28.8). In this cohort, the relatively short follow-up time caused uncertainty in the prognostic grouping of the early censored patients that were defined as good prognosis.

Chromatin entropy marker
We have previously shown that nuclear texture varies with nuclear area and that prognostication can be enhanced by grouping the nuclear images according to their area (5,6,11). Analyses in the discovery set of uterine sarcoma showed that nuclei with 2,000-4,999 pixels had sufficiently similar texture with respect to the three described features to be analyzed together, and the new texture biomarker was therefore computed using only these nuclei. After applying the normalization method (2), 2,000-4,999 pixels correspond to a nuclear area between 54 µm 2 and 134 µm 2 .
k-means clustering k-means clustering was used to partition the data points in the three-dimensional feature space (comprised of relative matrix area and center of mass in grey level and entropy) into k=5 clusters. The squared Euclidean distance was used to measure distance between data points. The data points represented all nuclei (with nuclear area 2,000-4,999 pixels) in the discovery set, in total 158,868 nuclei from uterine sarcoma (Supplementary Figure 2A). Prior to clustering, each of the three features was standardized by subtracting the mean and dividing by the standard deviation (SD) of the feature values of the 158,868 nuclei. The five clusters were sorted and named cluster 1 to 5, such that increasing cluster number corresponded to increasing relative matrix area of cluster centroid. Supplementary Figure 2B shows the average GLEM computed from all nuclei in each of the five clusters, while Supplementary Figure 2C shows a boxplot of the relative matrix areas in the clusters.
For each patient in the discovery set, the proportion of nuclei in each of the five clusters was computed. The average proportion of nuclei in each cluster was computed based on the discovery cases in the two prognostic classes. The proportion of nuclei in cluster 5 (corresponding to nuclei with high relative matrix area and defined as nuclei with high chromatin entropy) was statistically significantly higher (P<0.001, Mann-Whitney U test) for patients with a poor prognosis compared to good prognosis patients (16.6% [SD=15.3] versus 9.3% [SD=10.5] high chromatin entropy nuclei). The average proportion of nuclei in clusters 3 and 4 was also higher for poor prognosis patients compared to good prognosis patients, but the difference was not statistically

Patient classification
We chose to focus on the cluster corresponding to the nuclei with highest entropy, i.e. cluster 5, and wanted to select a threshold on the proportion of nuclei with high chromatin entropy (the proportion of nuclei in cluster 5). Tumors with a proportion of high chromatin entropy nuclei less than the threshold value were to be classified as low chromatin entropy whereas tumors with a proportion of high chromatin entropy nuclei more or equal to the threshold were to be classified as high chromatin entropy. Correct classification rate, sensitivity, and specificity on the uterine sarcoma discovery set were computed for all different threshold values for the proportion of high chromatin entropy nuclei (0%-100%, Supplementary Figure 3). The correct classification rate varied between 58% and 62% in a large interval of threshold values (thresholds from 8% to 27%), and then started to decrease with increasing threshold values. In that interval of threshold values, the specificity increased and the sensitivity decreased with increasing threshold value (Supplementary Figure 3B

Validation of the chromatin entropy marker
For each nuclear image with 2,000-4,999 pixels in the uterine sarcoma validation set, in total 167,381 nuclear images, we calculated the squared Euclidean distance between the three-dimensional data point (comprised of the relative matrix area and the center of mass feature values) and each of the five cluster centroids. The nucleus was assigned to the cluster with the nearest centroid.
In the same way, each data point in the ovarian cancer set (corresponding to 53,027 nuclei) and in the endometrial cancer set (623,595 nuclei) was assigned to one of the five clusters. The feature values computed from all three validation sets were normalized using the mean and standard deviation computed from the uterine sarcoma discovery set. Statistics on the proportion of nuclei/patient in cluster 5 for good and poor prognosis patients in the three data sets are shown in Supplementary Table 5.  Table 1], where the marker of high chromatin entropy nuclei is not included in the model) plotted versus the proportion of high chromatin entropy nuclei. Abbreviation: LOWESS, locally weighted scatterplot smoothing.

Supplementary Figure 5. Hazard ratios (blue points) and 95% confidence intervals (grey regions) in A)
univariate and B) multivariate 5-year overall survival analysis obtained from evaluating the uterine sarcoma discovery set (175 patients) for different classification thresholds for the proportion of high chromatin entropy nuclei (percent nuclei in cluster 5) versus the proportion of high chromatin entropy nuclei. The biomarker classified each patient as low chromatin entropy or high chromatin entropy based on the proportion of high chromatin entropy nuclei (< classification threshold versus ≥ classification threshold). In addition to chromatin entropy, the following variables were included in the multivariate analysis: histological subtype, mitotic index, tumor extent, tumor size, tumor margins, cellular atypia, tumor necrosis, hyaline necrosis, and vascular invasion (see Table 1). Figure 6. Forest plot of the high chromatin entropy marker for the uterine sarcoma patients in analysis of 5-year overall survival. The P values were calculated using the two-sided Mantel-Cox log-rank test. Other sarcomas include ten sarcoma not otherwise specified, four rhabdomyosarcoma, two giant cell tumors with/without leiomyosarcoma and one PEComa (12). Abbreviation: HR, hazard ratio; CI, confidence interval.  (13). Histological grade was assessed using hysterectomy specimens. The P values were calculated using the two-sided Mantel-Cox log-rank test. Abbreviation: HR, hazard ratio; CI, confidence interval; n/a, not applicable because all patients with poor prognosis had the same classification. *The uterine sarcoma patients who survived for at least five years were defined as good prognosis, whereas the patients who died within five years were defined as poor prognosis. In the ovarian cancer material, good prognosis was defined as no recurrence of disease, whereas poor prognosis was defined as recurrence of disease.

Supplementary
In the endometrial cancer material, good prognosis was defined as no endometrial cancer death, whereas poor prognosis was defined as cancer-specific death. Abbreviation: CCR, correct classification rate; PPV, positive predictive value; NPV, negative predictive value; CI, confidence interval. *The average, standard deviation (SD), median and interquartile range (IQR) of the proportion of high chromatin entropy nuclei (i.e., nuclei in cluster 5) for a patient computed in different prognostic groups of patients with uterine sarcoma, ovarian cancer and endometrial cancer. †The uterine sarcoma patients who survived for at least five years were defined as good prognosis, whereas the patients who died within five years were defined as poor prognosis. In the ovarian cancer material, good prognosis was defined as no recurrence of disease, whereas poor prognosis was defined as recurrence of disease. In the endometrial cancer material, good prognosis was defined as no endometrial cancer death, whereas poor prognosis was defined as cancer-specific death.

Supplementary
‡The P values were calculated using the two-sided Mann-Whitney U test.  Table 1), backward selection was used in stepwise Cox regression analysis (exclusion criterion P>0.05) and all excluded variables were eventually tested for model inclusion.

Supplementary
†The P values were calculated using the two-sided Wald χ 2 test. Abbreviation: HR, hazard ratio; CI, confidence interval.